diff --git a/lama_cleaner/__init__.py b/lama_cleaner/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1ce1a122db142dc55afcdd72ef356331be046d7
--- /dev/null
+++ b/lama_cleaner/__init__.py
@@ -0,0 +1,18 @@
+import os
+
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
+
+import warnings
+
+warnings.simplefilter("ignore", UserWarning)
+
+from lama_cleaner.parse_args import parse_args
+
+
+def entry_point():
+    args = parse_args()
+    # To make os.environ["XDG_CACHE_HOME"] = args.model_cache_dir works for diffusers
+    # https://github.com/huggingface/diffusers/blob/be99201a567c1ccd841dc16fb24e88f7f239c187/src/diffusers/utils/constants.py#L18
+    from lama_cleaner.server import main
+
+    main(args)
diff --git a/lama_cleaner/__pycache__/__init__.cpython-38.pyc b/lama_cleaner/__pycache__/__init__.cpython-38.pyc
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diff --git a/lama_cleaner/__pycache__/parse_args.cpython-38.pyc b/lama_cleaner/__pycache__/parse_args.cpython-38.pyc
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diff --git a/lama_cleaner/__pycache__/runtime.cpython-38.pyc b/lama_cleaner/__pycache__/runtime.cpython-38.pyc
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diff --git a/lama_cleaner/__pycache__/schema.cpython-38.pyc b/lama_cleaner/__pycache__/schema.cpython-38.pyc
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diff --git a/lama_cleaner/__pycache__/server.cpython-38.pyc b/lama_cleaner/__pycache__/server.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e92d1e93916cc7272ad255b9b772fd1a7bbac321
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diff --git a/lama_cleaner/__pycache__/web_config.cpython-38.pyc b/lama_cleaner/__pycache__/web_config.cpython-38.pyc
new file mode 100644
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diff --git a/lama_cleaner/app/build/.DS_Store b/lama_cleaner/app/build/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6
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diff --git a/lama_cleaner/app/build/asset-manifest.json b/lama_cleaner/app/build/asset-manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..0f8168c07a94839041014c04248b2818691a795a
--- /dev/null
+++ b/lama_cleaner/app/build/asset-manifest.json
@@ -0,0 +1,51 @@
+{
+  "files": {
+    "main.css": "/static/css/main.ce986cc8.css",
+    "main.js": "/static/js/main.1fda6320.js",
+    "static/media/coffee-machine-lineal.gif": "/static/media/coffee-machine-lineal.ee32631219cc3986f861.gif",
+    "static/media/Inter.var.woff2?v=3.19": "/static/media/Inter.var.c2fe3cb2b7c746f7966a.woff2",
+    "static/media/Inter-italic.var.woff2?v=3.19": "/static/media/Inter-italic.var.30807be7abc48ba8c73c.woff2",
+    "static/media/Inter-roman.var.woff2?v=3.19": "/static/media/Inter-roman.var.ba4caefcdf5b36b438db.woff2",
+    "static/media/Inter-SemiBoldItalic.woff?v=3.19": "/static/media/Inter-SemiBoldItalic.463bdbfb28abad0fa6df.woff",
+    "static/media/Inter-BoldItalic.woff?v=3.19": "/static/media/Inter-BoldItalic.b376885042f6c961a541.woff",
+    "static/media/Inter-MediumItalic.woff?v=3.19": "/static/media/Inter-MediumItalic.3d0107dd43d0101274d3.woff",
+    "static/media/Inter-ExtraBoldItalic.woff?v=3.19": "/static/media/Inter-ExtraBoldItalic.6ab17abedc4d3f140953.woff",
+    "static/media/Inter-LightItalic.woff?v=3.19": "/static/media/Inter-LightItalic.ef9f65d91d2b0ba9b2e4.woff",
+    "static/media/Inter-ExtraLightItalic.woff?v=3.19": "/static/media/Inter-ExtraLightItalic.170dddfca278d3c2ad4a.woff",
+    "static/media/Inter-BlackItalic.woff?v=3.19": "/static/media/Inter-BlackItalic.ca1e738e4f349f27514d.woff",
+    "static/media/Inter-ThinItalic.woff?v=3.19": "/static/media/Inter-ThinItalic.bae95eb2f889c797e435.woff",
+    "static/media/Inter-Italic.woff?v=3.19": "/static/media/Inter-Italic.890025e726861dba417f.woff",
+    "static/media/Inter-Bold.woff?v=3.19": "/static/media/Inter-Bold.93c1301bd9f486c573b3.woff",
+    "static/media/Inter-SemiBold.woff?v=3.19": "/static/media/Inter-SemiBold.cca62d21c8c555c392e5.woff",
+    "static/media/Inter-ExtraBold.woff?v=3.19": "/static/media/Inter-ExtraBold.d0fa3bb2b7c9063dc594.woff",
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+    "static/media/Inter-ExtraLight.woff?v=3.19": "/static/media/Inter-ExtraLight.c4248615291a9e8f1fb7.woff",
+    "static/media/Inter-Light.woff?v=3.19": "/static/media/Inter-Light.994e34451cc19ede31d3.woff",
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+    "static/media/Inter-Thin.woff?v=3.19": "/static/media/Inter-Thin.29b9c616a95a912abf73.woff",
+    "static/media/Inter-Regular.woff?v=3.19": "/static/media/Inter-Regular.8c206db99195777c6769.woff",
+    "static/media/Inter-MediumItalic.woff2?v=3.19": "/static/media/Inter-MediumItalic.085cb93e613ba3d40d2b.woff2",
+    "static/media/Inter-SemiBoldItalic.woff2?v=3.19": "/static/media/Inter-SemiBoldItalic.d9467ee321a8f38aefff.woff2",
+    "static/media/Inter-BoldItalic.woff2?v=3.19": "/static/media/Inter-BoldItalic.2d26c56a606662486796.woff2",
+    "static/media/Inter-ExtraBoldItalic.woff2?v=3.19": "/static/media/Inter-ExtraBoldItalic.535a6cf662596b3bd6a6.woff2",
+    "static/media/Inter-ExtraLightItalic.woff2?v=3.19": "/static/media/Inter-ExtraLightItalic.5c7d7d6deb1d2ec8d48c.woff2",
+    "static/media/Inter-LightItalic.woff2?v=3.19": "/static/media/Inter-LightItalic.f86952265d7b0f02c921.woff2",
+    "static/media/Inter-BlackItalic.woff2?v=3.19": "/static/media/Inter-BlackItalic.cb2a7335650c690077fe.woff2",
+    "static/media/Inter-Italic.woff2?v=3.19": "/static/media/Inter-Italic.cb10ffd7684cd9836a05.woff2",
+    "static/media/Inter-ThinItalic.woff2?v=3.19": "/static/media/Inter-ThinItalic.bf213704dce6b437ede4.woff2",
+    "static/media/Inter-Bold.woff2?v=3.19": "/static/media/Inter-Bold.ec64ea577b0349e055ad.woff2",
+    "static/media/Inter-ExtraBold.woff2?v=3.19": "/static/media/Inter-ExtraBold.cbe0ae49c52c920fd563.woff2",
+    "static/media/Inter-Medium.woff2?v=3.19": "/static/media/Inter-Medium.293fd13dbca5a3e450ef.woff2",
+    "static/media/Inter-SemiBold.woff2?v=3.19": "/static/media/Inter-SemiBold.b5f0f109bc88052d4000.woff2",
+    "static/media/Inter-Light.woff2?v=3.19": "/static/media/Inter-Light.2d5198822ab091ce4305.woff2",
+    "static/media/Inter-ExtraLight.woff2?v=3.19": "/static/media/Inter-ExtraLight.72505e6a122c6acd5471.woff2",
+    "static/media/Inter-Black.woff2?v=3.19": "/static/media/Inter-Black.15ca31c0a2a68f76d2d1.woff2",
+    "static/media/Inter-Thin.woff2?v=3.19": "/static/media/Inter-Thin.fff2a096db014f6239d4.woff2",
+    "static/media/Inter-Regular.woff2?v=3.19": "/static/media/Inter-Regular.c8ba52b05a9ef10f4758.woff2",
+    "index.html": "/index.html"
+  },
+  "entrypoints": [
+    "static/css/main.ce986cc8.css",
+    "static/js/main.1fda6320.js"
+  ]
+}
\ No newline at end of file
diff --git a/lama_cleaner/app/build/favicon.ico b/lama_cleaner/app/build/favicon.ico
new file mode 100644
index 0000000000000000000000000000000000000000..f5607e2dd979b9685d5471fb0bd85fe6233ed605
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diff --git a/lama_cleaner/app/build/index.html b/lama_cleaner/app/build/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..704b475c357f89558184e25d046b333c9cbce0b3
--- /dev/null
+++ b/lama_cleaner/app/build/index.html
@@ -0,0 +1 @@
+<!doctype html><html lang="en"><head><meta http-equiv="Cache-Control" content="no-cache, no-store, must-revalidate"/><meta http-equiv="Pragma" content="no-cache"/><meta http-equiv="Expires" content="0"/><meta charset="utf-8"/><link rel="icon" href="/favicon.ico"/><meta name="viewport" content="width=device-width,initial-scale=1,maximum-scale=1,user-scalable=0"/><meta name="theme-color" content="#ffffff"/><title>lama-cleaner - Image inpainting powered by SOTA AI model</title><script defer="defer" src="/static/js/main.1fda6320.js"></script><link href="/static/css/main.ce986cc8.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div></body></html>
\ No newline at end of file
diff --git a/lama_cleaner/app/build/static/css/main.6ca672e8.css b/lama_cleaner/app/build/static/css/main.6ca672e8.css
new file mode 100644
index 0000000000000000000000000000000000000000..ef499f08c6fa4cd09d79cc4f27e29db5506cfdad
--- /dev/null
+++ b/lama_cleaner/app/build/static/css/main.6ca672e8.css
@@ -0,0 +1 @@
+:root{--blackA1:rgba(0,0,0,.012);--blackA2:rgba(0,0,0,.027);--blackA3:rgba(0,0,0,.047);--blackA4:rgba(0,0,0,.071);--blackA5:rgba(0,0,0,.09);--blackA6:rgba(0,0,0,.114);--blackA7:rgba(0,0,0,.141);--blackA8:rgba(0,0,0,.22);--blackA9:rgba(0,0,0,.439);--blackA10:rgba(0,0,0,.478);--blackA11:rgba(0,0,0,.565);--blackA12:rgba(0,0,0,.91);--mauve1:#fdfcfd;--mauve2:#f9f8f9;--mauve3:#f4f2f4;--mauve4:#eeedef;--mauve5:#e9e8ea;--mauve6:#e4e2e4;--mauve7:#dcdbdd;--mauve8:#c8c7cb;--mauve9:#908e96;--mauve10:#86848d;--mauve11:#6f6e77;--mauve12:#1a1523;--violet1:#fdfcfe;--violet2:#fbfaff;--violet3:#f5f2ff;--violet4:#ede9fe;--violet5:#e4defc;--violet6:#d7cff9;--violet7:#c4b8f3;--violet8:#aa99ec;--violet9:#6e56cf;--violet10:#644fc1;--violet11:#5746af;--violet12:#20134b;--page-bg:#fff;--page-bg-light:hsla(0,0%,100%,.5);--page-text-color:#040404;--yellow-accent:#fc0;--yellow-accent-light:#ffcc0055;--link-color:#000;--border-color:#eff1f4;--border-color-light:hsla(240,9%,43%,.5);--tooltip-bg:#e6e6ea;--tooltip-text-color:#000;--error-color:#ef4444;--success-color:#10b981;--editor-toolkit-bg:hsla(0,0%,100%,.5);--editor-options-bg:#e6e6ea;--options-text-color:var(--page-text-color);--editor-size-border-color:var(--border-color);--editor-toolkit-panel-border:0;--modal-bg:var(--page-bg);--modal-text-color:#000;--modal-hotkey-border-color:#000;--model-mask-bg:rgba(209,213,219,.4);--text-color:#040404;--text-color-gray:#6b6f76;--text-color-disabled:#6b6f76;--btn-text-color:var(--text-color);--btn-text-hover-color:#040404;--btn-border-color:#646478;--btn-primary-hover-bg:var(--yellow-accent);--animation-pulsing-bg:hsla(0,0%,100%,.5);--switch-root-background-color:#dfe1e4;--switch-thumb-color:var(--page-bg);--switch-thumb-checked-color:var(--page-bg);--slider-background-color:var(--switch-root-background-color);--tooltip-bg:var(--page-bg);--badge-background-color:#f1f3f5;--badge-color:#687076;--box-shadow:inset 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pulsing{0%{opacity:1}50%{background-color:hsla(0,0%,100%,.5);background-color:var(--animation-pulsing-bg);opacity:.75}to{opacity:1}}@keyframes pulsing{0%{opacity:1}50%{background-color:hsla(0,0%,100%,.5);background-color:var(--animation-pulsing-bg);opacity:.75}to{opacity:1}}@-webkit-keyframes opacityReveal{0%{opacity:0}to{opacity:1}}@keyframes opacityReveal{0%{opacity:0}to{opacity:1}}@-webkit-keyframes slideDown{0%{-webkit-transform:translateY(-100%);transform:translateY(-100%)}to{-webkit-transform:translateY(0);transform:translateY(0)}}@keyframes slideDown{0%{-webkit-transform:translateY(-100%);transform:translateY(-100%)}to{-webkit-transform:translateY(0);transform:translateY(0)}}@-webkit-keyframes slideUp{0%{-webkit-transform:translateY(100%);transform:translateY(100%)}to{-webkit-transform:translateY(0);transform:translateY(0)}}@keyframes slideUp{0%{-webkit-transform:translateY(100%);transform:translateY(100%)}to{-webkit-transform:translateY(0);transform:translateY(0)}}@-webkit-keyframes slideIn{0%{-webkit-transform:translateX(calc(100% + 25px));transform:translateX(calc(100% + 25px))}to{-webkit-transform:translateX(0);transform:translateX(0)}}@keyframes slideIn{0%{-webkit-transform:translateX(calc(100% + 25px));transform:translateX(calc(100% + 25px))}to{-webkit-transform:translateX(0);transform:translateX(0)}}@-webkit-keyframes spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}to{-webkit-transform:rotate(1turn);transform:rotate(1turn)}}@keyframes spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}to{-webkit-transform:rotate(1turn);transform:rotate(1turn)}}@-webkit-keyframes slideUpAndFade{0%{opacity:0;-webkit-transform:translateY(2px);transform:translateY(2px)}to{opacity:1;-webkit-transform:translateY(0);transform:translateY(0)}}@-webkit-keyframes 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\ No newline at end of file
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@@ -0,0 +1 @@
+:root{--blackA1:rgba(0,0,0,.012);--blackA2:rgba(0,0,0,.027);--blackA3:rgba(0,0,0,.047);--blackA4:rgba(0,0,0,.071);--blackA5:rgba(0,0,0,.09);--blackA6:rgba(0,0,0,.114);--blackA7:rgba(0,0,0,.141);--blackA8:rgba(0,0,0,.22);--blackA9:rgba(0,0,0,.439);--blackA10:rgba(0,0,0,.478);--blackA11:rgba(0,0,0,.565);--blackA12:rgba(0,0,0,.91);--mauve1:#fdfcfd;--mauve2:#f9f8f9;--mauve3:#f4f2f4;--mauve4:#eeedef;--mauve5:#e9e8ea;--mauve6:#e4e2e4;--mauve7:#dcdbdd;--mauve8:#c8c7cb;--mauve9:#908e96;--mauve10:#86848d;--mauve11:#6f6e77;--mauve12:#1a1523;--violet1:#fdfcfe;--violet2:#fbfaff;--violet3:#f5f2ff;--violet4:#ede9fe;--violet5:#e4defc;--violet6:#d7cff9;--violet7:#c4b8f3;--violet8:#aa99ec;--violet9:#6e56cf;--violet10:#644fc1;--violet11:#5746af;--violet12:#20134b;--page-bg:#fff;--page-bg-light:hsla(0,0%,100%,.5);--page-text-color:#040404;--yellow-accent:#fc0;--yellow-accent-light:#ffcc0055;--link-color:#000;--border-color:#eff1f4;--border-color-light:hsla(240,9%,43%,.5);--tooltip-bg:#e6e6ea;--tooltip-text-color:#000;--error-color:#ef4444;--success-color:#10b981;--editor-toolkit-bg:hsla(0,0%,100%,.5);--editor-options-bg:#e6e6ea;--options-text-color:var(--page-text-color);--editor-size-border-color:var(--border-color);--editor-toolkit-panel-border:0;--modal-bg:var(--page-bg);--modal-text-color:#000;--modal-hotkey-border-color:#000;--model-mask-bg:rgba(209,213,219,.4);--text-color:#040404;--text-color-gray:#6b6f76;--text-color-disabled:#6b6f76;--btn-text-color:var(--text-color);--btn-text-hover-color:#040404;--btn-border-color:#646478;--btn-primary-hover-bg:var(--yellow-accent);--animation-pulsing-bg:hsla(0,0%,100%,.5);--switch-root-background-color:#dfe1e4;--switch-thumb-color:var(--page-bg);--switch-thumb-checked-color:var(--page-bg);--slider-background-color:var(--switch-root-background-color);--tooltip-bg:var(--page-bg);--badge-background-color:#f1f3f5;--badge-color:#687076;--box-shadow:inset 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\ No newline at end of file
diff --git a/lama_cleaner/app/build/static/css/main.ce986cc8.css b/lama_cleaner/app/build/static/css/main.ce986cc8.css
new file mode 100644
index 0000000000000000000000000000000000000000..aeb198a075b7caba58f1200013b49b3f8e7be99a
--- /dev/null
+++ b/lama_cleaner/app/build/static/css/main.ce986cc8.css
@@ -0,0 +1 @@
+:root{--blackA1:rgba(0,0,0,.012);--blackA2:rgba(0,0,0,.027);--blackA3:rgba(0,0,0,.047);--blackA4:rgba(0,0,0,.071);--blackA5:rgba(0,0,0,.09);--blackA6:rgba(0,0,0,.114);--blackA7:rgba(0,0,0,.141);--blackA8:rgba(0,0,0,.22);--blackA9:rgba(0,0,0,.439);--blackA10:rgba(0,0,0,.478);--blackA11:rgba(0,0,0,.565);--blackA12:rgba(0,0,0,.91);--mauve1:#fdfcfd;--mauve2:#f9f8f9;--mauve3:#f4f2f4;--mauve4:#eeedef;--mauve5:#e9e8ea;--mauve6:#e4e2e4;--mauve7:#dcdbdd;--mauve8:#c8c7cb;--mauve9:#908e96;--mauve10:#86848d;--mauve11:#6f6e77;--mauve12:#1a1523;--violet1:#fdfcfe;--violet2:#fbfaff;--violet3:#f5f2ff;--violet4:#ede9fe;--violet5:#e4defc;--violet6:#d7cff9;--violet7:#c4b8f3;--violet8:#aa99ec;--violet9:#6e56cf;--violet10:#644fc1;--violet11:#5746af;--violet12:#20134b;--page-bg:#fff;--page-bg-light:hsla(0,0%,100%,.5);--page-text-color:#040404;--yellow-accent:#fc0;--yellow-accent-light:#ffcc0055;--link-color:#000;--border-color:#eff1f4;--border-color-light:hsla(240,9%,43%,.5);--tooltip-bg:#e6e6ea;--tooltip-text-color:#000;--error-color:#ef4444;--success-color:#10b981;--editor-toolkit-bg:hsla(0,0%,100%,.5);--editor-options-bg:#e6e6ea;--options-text-color:var(--page-text-color);--editor-size-border-color:var(--border-color);--editor-toolkit-panel-border:0;--modal-bg:var(--page-bg);--modal-text-color:#000;--modal-hotkey-border-color:#000;--model-mask-bg:rgba(209,213,219,.4);--text-color:#040404;--text-color-gray:#6b6f76;--text-color-disabled:#6b6f76;--btn-text-color:var(--text-color);--btn-text-hover-color:#040404;--btn-border-color:#646478;--btn-primary-hover-bg:var(--yellow-accent);--animation-pulsing-bg:hsla(0,0%,100%,.5);--switch-root-background-color:#dfe1e4;--switch-thumb-color:var(--page-bg);--switch-thumb-checked-color:var(--page-bg);--slider-background-color:var(--switch-root-background-color);--tooltip-bg:var(--page-bg);--badge-background-color:#f1f3f5;--badge-color:#687076;--box-shadow:inset 0 0.5px hsla(0,0%,100%,.1),inset 0 1px 5px #f8f9fa,0px 0px 0px 0.5px #c1c8cd,0px 2px 1px -1px #c1c8cd,0 1px #c1c8cd;--croper-bg:rgba(0,0,0,.5);--tabs-active-color:#f0f3f9}[data-theme=dark]{--page-bg:#040404;--page-bg-light:#04040488;--page-text-color:#f9f9f9;--yellow-accent:#fc0;--yellow-accent-light:#ffcc0055;--link-color:var(--yellow-accent);--border-color:#1e1e1e;--border-color-light:#666;--tooltip-bg:#212121;--tooltip-text-color:#d2d2d2;--editor-toolkit-bg:rgba(0,0,0,.5);--editor-options-bg:#212121;--options-text-color:var(--page-text-color);--editor-size-border-color:var(--yellow-accent);--editor-toolkit-panel-border:1px solid 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.shortcut-description{font-size:.95rem;justify-self:start;text-align:left}@media screen and (max-width:767px){.shortcut-options .shortcut-description{justify-self:start;text-align:left;width:auto}}.shortcut-options-column{gap:12px;width:320px}.setting-block,.setting-block .option-desc,.shortcut-options-column{display:flex;flex-direction:column}.setting-block .option-desc{border:1px solid #eff1f4;border:1px solid var(--border-color);border-radius:.3rem;color:#6b6f76;color:var(--text-color-gray);gap:8px;margin-top:12px;padding:1rem}.setting-block .option-desc .sub-setting-block{color:#040404;color:var(--text-color)}.setting-block .option-desc 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.3rem;position:absolute;right:1.5rem;top:68px;z-index:4}.side-panel-trigger{border:0;font-size:16px}.side-panel-content{background-color:#fff;background-color:var(--page-bg);border-color:#eff1f4;border-color:var(--border-color);border-radius:.8rem;border-style:solid;border-width:1px;color:#040404;color:var(--text-color);display:flex;flex-direction:column;font-size:14px;gap:12px;outline:none;padding:1rem;position:relative;right:1.5rem;top:8px;z-index:9}.side-panel-content .setting-block-content{gap:1rem}.negative-prompt{all:unset;border-radius:.5rem;border-width:0;max-width:200px;min-height:150px;outline:1px solid #eff1f4;outline:1px solid var(--border-color);padding:12px .8rem;width:100%}.negative-prompt:focus-visible{border-width:0;outline:1px solid #fc0;outline:1px solid var(--yellow-accent)}.negative-prompt:-webkit-input-placeholder{padding-top:10px}.negative-prompt:-moz-input-placeholder{padding-top:10px}.negative-prompt:-ms-input-placeholder{padding-top:10px}.resize-title-tile{color:#6b6f76;color:var(--text-color-gray);font-size:.5rem;width:86px}.plugins{background-color:#fff;background-color:var(--page-bg);border:1px solid #eff1f4;border:1px solid var(--border-color);border-radius:8px;z-index:4}.DropdownMenuContent,.DropdownMenuSubContent{-webkit-animation-duration:.4s;animation-duration:.4s;-webkit-animation-timing-function:cubic-bezier(.16,1,.3,1);animation-timing-function:cubic-bezier(.16,1,.3,1);background-color:#fff;background-color:var(--page-bg);border:1px solid #eff1f4;border:1px solid var(--border-color);border-radius:6px;box-shadow:0 10px 38px -10px rgba(22,23,24,.35),0 10px 20px -15px 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diff --git a/lama_cleaner/app/build/static/js/main.06abf3fb.js b/lama_cleaner/app/build/static/js/main.06abf3fb.js
new file mode 100644
index 0000000000000000000000000000000000000000..87c2abf22e9e2a33d5746024631c6d7911d990d4
--- /dev/null
+++ b/lama_cleaner/app/build/static/js/main.06abf3fb.js
@@ -0,0 +1,2 @@
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t,n,r,o;return{previousScale:null!==(t=e.initialScale)&&void 0!==t?t:Ol.scale,scale:null!==(n=e.initialScale)&&void 0!==n?n:Ol.scale,positionX:null!==(r=e.initialPositionX)&&void 0!==r?r:Ol.positionX,positionY:null!==(o=e.initialPositionY)&&void 0!==o?o:Ol.positionY}},Il=function(e){var t=ul({},Ll);return Object.keys(e).forEach((function(n){var r="undefined"!==typeof e[n];if("undefined"!==typeof Ll[n]&&r){var o=Object.prototype.toString.call(Ll[n]),i="[object Object]"===o,a="[object Array]"===o;t[n]=i?ul(ul({},Ll[n]),e[n]):a?ll(ll([],Ll[n]),e[n]):e[n]}})),t},jl=function(e,t,n){var r=e.transformState.scale,o=e.wrapperComponent,i=e.setup,a=i.maxScale,u=i.minScale,l=i.zoomAnimation.size;if(!o)throw new Error("Wrapper is not mounted");var c=r*Math.exp(t*n);return El(cl(c,3),u,a,l,!1)};function Bl(e,t,n,r,o){var i=e.wrapperComponent,a=e.transformState,u=a.scale,l=a.positionX,c=a.positionY;if(!i)return console.error("No WrapperComponent found");var 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0===o||o.removeEventListener("keyup",e.setKeyUnPressed,n),null===o||void 0===o||o.removeEventListener("keydown",e.setKeyPressed,n),document.removeEventListener("mouseleave",e.clearPanning,n),pl(e)},e.handleInitializeWrapperEvents=function(t){var n=Xl();t.addEventListener("wheel",e.onWheelZoom,n),t.addEventListener("dblclick",e.onDoubleClick,n),t.addEventListener("touchstart",e.onTouchPanningStart,n),t.addEventListener("touchmove",e.onTouchPanning,n),t.addEventListener("touchend",e.onTouchPanningStop,n)},e.handleInitialize=function(){var t=e.setup.centerOnInit;e.applyTransformation(),e.forceUpdate(),t&&(setTimeout((function(){e.mounted&&e.setCenter()}),50),setTimeout((function(){e.mounted&&e.setCenter()}),100),setTimeout((function(){e.mounted&&e.setCenter()}),200))},e.onWheelZoom=function(t){e.setup.disabled||ec(e,t)&&e.isPressingKeys(e.setup.wheel.activationKeys)&&(ic(e,t),ac(e,t),uc(e,t))},e.onPanningStart=function(t){var n=e.setup.disabled,r=e.props.onPanningStart;n||xl(e,t)&&e.isPressingKeys(e.setup.panning.activationKeys)&&(t.preventDefault(),t.stopPropagation(),pl(e),Dl(e,t),sl(ql(e),t,r))},e.onPanning=function(t){var n=e.setup.disabled,r=e.props.onPanning;n||Cl(e)&&e.isPressingKeys(e.setup.panning.activationKeys)&&(t.preventDefault(),t.stopPropagation(),Fl(e,t.clientX,t.clientY),sl(ql(e),t,r))},e.onPanningStop=function(t){var n=e.props.onPanningStop;e.isPanning&&(Al(e),sl(ql(e),t,n))},e.onPinchStart=function(t){var n=e.setup.disabled,r=e.props,o=r.onPinchingStart,i=r.onZoomStart;n||nc(e,t)&&(lc(e,t),pl(e),sl(ql(e),t,o),sl(ql(e),t,i))},e.onPinch=function(t){var n=e.setup.disabled,r=e.props,o=r.onPinching,i=r.onZoom;n||rc(e)&&(t.preventDefault(),t.stopPropagation(),cc(e,t),sl(ql(e),t,o),sl(ql(e),t,i))},e.onPinchStop=function(t){var n=e.props,r=n.onPinchingStop,o=n.onZoomStop;e.pinchStartScale&&(sc(e),sl(ql(e),t,r),sl(ql(e),t,o))},e.onTouchPanningStart=function(t){var n=e.setup.disabled,r=e.props.onPanningStart;if(!n&&xl(e,t))if(e.lastTouch&&+new Date-e.lastTouch<200&&1===t.touches.length)e.onDoubleClick(t);else{e.lastTouch=+new Date,pl(e);var o=t.touches,i=1===o.length,a=2===o.length;i&&(pl(e),Dl(e,t),sl(ql(e),t,r)),a&&e.onPinchStart(t)}},e.onTouchPanning=function(t){var n=e.setup.disabled,r=e.props.onPanning;if(e.isPanning&&1===t.touches.length){if(n)return;if(!Cl(e))return;t.preventDefault(),t.stopPropagation();var o=t.touches[0];Fl(e,o.clientX,o.clientY),sl(ql(e),t,r)}else t.touches.length>1&&e.onPinch(t)},e.onTouchPanningStop=function(t){e.onPanningStop(t),e.onPinchStop(t)},e.onDoubleClick=function(t){e.setup.disabled||dc(e,t)&&fc(e,t)},e.clearPanning=function(t){e.isPanning&&e.onPanningStop(t)},e.setKeyPressed=function(t){e.pressedKeys[t.key]=!0},e.setKeyUnPressed=function(t){e.pressedKeys[t.key]=!1},e.isPressingKeys=function(t){return!t.length||Boolean(t.find((function(t){return e.pressedKeys[t]})))},e.setComponents=function(t,n){e.cleanupWindowEvents(),e.wrapperComponent=t,e.contentComponent=n,gl(e,e.transformState.scale),e.handleInitializeWrapperEvents(t),e.initializeWindowEvents(),e.handleInitialize(),e.handleRef(),e.isInitialized=!0,sl(ql(e),void 0,e.props.onInit)},e.setTransformState=function(t,n,r){var o=e.props.onTransformed;isNaN(t)||isNaN(n)||isNaN(r)?console.error("Detected NaN set state values"):(t!==e.transformState.scale&&(e.transformState.previousScale=e.transformState.scale,e.transformState.scale=t),e.transformState.positionX=n,e.transformState.positionY=r,sl(ql(e),{scale:t,positionX:n,positionY:r},o),e.applyTransformation())},e.setCenter=function(){if(e.wrapperComponent&&e.contentComponent){var t=Jl(e.transformState.scale,e.wrapperComponent,e.contentComponent);e.setTransformState(t.scale,t.positionX,t.positionY)}},e.applyTransformation=function(){if(e.mounted&&e.contentComponent){var t=e.transformState,n=t.scale,r=t.positionX,o=t.positionY,i=Ql(r,o,n);e.contentComponent.style.transform=i,e.handleRef()}},e.handleRef=function(){e.props.setRef(ql(e))},e}return function(e,t){if("function"!==typeof t&&null!==t)throw new TypeError("Class extends value "+String(t)+" is not a constructor or null");function n(){this.constructor=e}al(e,t),e.prototype=null===t?Object.create(t):(n.prototype=t.prototype,new n)}(n,t),n.prototype.componentDidMount=function(){this.initializeWindowEvents()},n.prototype.componentWillUnmount=function(){this.cleanupWindowEvents()},n.prototype.componentDidUpdate=function(e){e!==this.props&&(gl(this,this.transformState.scale),this.setup=Il(this.props))},n.prototype.render=function(){var t=ql(this),n=this.props.children,r="function"===typeof n?n(t):n;return e.createElement(pc.Provider,{value:ul(ul({},this.transformState),{setComponents:this.setComponents,contextInstance:this})},r)},n}(e.Component),hc=e.forwardRef((function(t,n){var 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diff --git a/lama_cleaner/app/build/static/js/main.06abf3fb.js.LICENSE.txt b/lama_cleaner/app/build/static/js/main.06abf3fb.js.LICENSE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..441c3c00f2ec908e7deb0bd72378cc9ec53162f9
--- /dev/null
+++ b/lama_cleaner/app/build/static/js/main.06abf3fb.js.LICENSE.txt
@@ -0,0 +1,69 @@
+/*
+object-assign
+(c) Sindre Sorhus
+@license MIT
+*/
+
+/*! regenerator-runtime -- Copyright (c) 2014-present, Facebook, Inc. -- license (MIT): https://github.com/facebook/regenerator/blob/main/LICENSE */
+
+/**
+ * @license
+ * Lodash <https://lodash.com/>
+ * Copyright OpenJS Foundation and other contributors <https://openjsf.org/>
+ * Released under MIT license <https://lodash.com/license>
+ * Based on Underscore.js 1.8.3 <http://underscorejs.org/LICENSE>
+ * Copyright Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors
+ */
+
+/** @license React v0.20.2
+ * scheduler.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/** @license React v17.0.2
+ * react-dom.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/** @license React v17.0.2
+ * react-jsx-runtime.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/** @license React v17.0.2
+ * react.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/**!
+ * FlexSearch.js v0.7.21 (Bundle)
+ * Copyright 2018-2021 Nextapps GmbH
+ * Author: Thomas Wilkerling
+ * Licence: Apache-2.0
+ * https://github.com/nextapps-de/flexsearch
+ */
+
+/**! 
+ * hotkeys-js v3.9.4 
+ * A simple micro-library for defining and dispatching keyboard shortcuts. It has no dependencies. 
+ * 
+ * Copyright (c) 2022 kenny wong <wowohoo@qq.com> 
+ * http://jaywcjlove.github.io/hotkeys 
+ * Licensed under the MIT license 
+ */
diff --git a/lama_cleaner/app/build/static/js/main.1fda6320.js b/lama_cleaner/app/build/static/js/main.1fda6320.js
new file mode 100644
index 0000000000000000000000000000000000000000..c1af85be13477810a831e56fc4ebe4f3ce3d7e21
--- /dev/null
+++ b/lama_cleaner/app/build/static/js/main.1fda6320.js
@@ -0,0 +1,2 @@
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closed",context:t})},this.ws.onmessage=function(t){return e.onData(t.data)},this.ws.onerror=function(t){return e.onError("websocket error",t)}}},{key:"write",value:function(e){var t=this;this.writable=!1;for(var n=function(){var n=e[r],o=r===e.length-1;ZI(n,t.supportsBinary,(function(e){try{t.ws.send(e)}catch(n){}o&&RB((function(){t.writable=!0,t.emitReserved("drain")}),t.setTimeoutFn)}))},r=0;r<e.length;r++)n()}},{key:"doClose",value:function(){"undefined"!==typeof this.ws&&(this.ws.close(),this.ws=null)}},{key:"uri",value:function(){var e=this.query||{},t=this.opts.secure?"wss":"ws",n="";this.opts.port&&("wss"===t&&443!==Number(this.opts.port)||"ws"===t&&80!==Number(this.opts.port))&&(n=":"+this.opts.port),this.opts.timestampRequests&&(e[this.opts.timestampParam]=bB()),this.supportsBinary||(e.b64=1);var r=wB(e);return t+"://"+(-1!==this.opts.hostname.indexOf(":")?"["+this.opts.hostname+"]":this.opts.hostname)+n+this.opts.path+(r.length?"?"+r:"")}},{key:"check",value:function(){return!!FB}}]),n}(pB),NB={websocket:OB,polling:DB},LB=/^(?:(?![^:@]+:[^:@\/]*@)(http|https|ws|wss):\/\/)?((?:(([^:@]*)(?::([^:@]*))?)?@)?((?:[a-f0-9]{0,4}:){2,7}[a-f0-9]{0,4}|[^:\/?#]*)(?::(\d*))?)(((\/(?:[^?#](?![^?#\/]*\.[^?#\/.]+(?:[?#]|$)))*\/?)?([^?#\/]*))(?:\?([^#]*))?(?:#(.*))?)/,MB=["source","protocol","authority","userInfo","user","password","host","port","relative","path","directory","file","query","anchor"];function jB(e){var t=e,n=e.indexOf("["),r=e.indexOf("]");-1!=n&&-1!=r&&(e=e.substring(0,n)+e.substring(n,r).replace(/:/g,";")+e.substring(r,e.length));for(var o=LB.exec(e||""),i={},a=14;a--;)i[MB[a]]=o[a]||"";return-1!=n&&-1!=r&&(i.source=t,i.host=i.host.substring(1,i.host.length-1).replace(/;/g,":"),i.authority=i.authority.replace("[","").replace("]","").replace(/;/g,":"),i.ipv6uri=!0),i.pathNames=function(e,t){var n=/\/{2,9}/g,r=t.replace(n,"/").split("/");"/"!=t.slice(0,1)&&0!==t.length||r.splice(0,1);"/"==t.slice(-1)&&r.splice(r.length-1,1);return r}(0,i.path),i.queryKey=function(e,t){var n={};return t.replace(/(?:^|&)([^&=]*)=?([^&]*)/g,(function(e,t,r){t&&(n[t]=r)})),n}(0,i.query),i}var IB=function(e){A(n,e);var t=R(n);function n(e){var r,o=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return F(this,n),r=t.call(this),e&&"object"===typeof e&&(o=e,e=null),e?(e=jB(e),o.hostname=e.host,o.secure="https"===e.protocol||"wss"===e.protocol,o.port=e.port,e.query&&(o.query=e.query)):o.host&&(o.hostname=jB(o.host).host),sB(D(r),o),r.secure=null!=o.secure?o.secure:"undefined"!==typeof location&&"https:"===location.protocol,o.hostname&&!o.port&&(o.port=r.secure?"443":"80"),r.hostname=o.hostname||("undefined"!==typeof location?location.hostname:"localhost"),r.port=o.port||("undefined"!==typeof location&&location.port?location.port:r.secure?"443":"80"),r.transports=o.transports||["polling","websocket"],r.readyState="",r.writeBuffer=[],r.prevBufferLen=0,r.opts=Object.assign({path:"/engine.io",agent:!1,withCredentials:!1,upgrade:!0,timestampParam:"t",rememberUpgrade:!1,rejectUnauthorized:!0,perMessageDeflate:{threshold:1024},transportOptions:{},closeOnBeforeunload:!0},o),r.opts.path=r.opts.path.replace(/\/$/,"")+"/","string"===typeof r.opts.query&&(r.opts.query=EB(r.opts.query)),r.id=null,r.upgrades=null,r.pingInterval=null,r.pingTimeout=null,r.pingTimeoutTimer=null,"function"===typeof addEventListener&&(r.opts.closeOnBeforeunload&&(r.beforeunloadEventListener=function(){r.transport&&(r.transport.removeAllListeners(),r.transport.close())},addEventListener("beforeunload",r.beforeunloadEventListener,!1)),"localhost"!==r.hostname&&(r.offlineEventListener=function(){r.onClose("transport close",{description:"network connection lost"})},addEventListener("offline",r.offlineEventListener,!1))),r.open(),r}return O(n,[{key:"createTransport",value:function(e){var t=Object.assign({},this.opts.query);t.EIO=4,t.transport=e,this.id&&(t.sid=this.id);var n=Object.assign({},this.opts.transportOptions[e],this.opts,{query:t,socket:this,hostname:this.hostname,secure:this.secure,port:this.port});return new NB[e](n)}},{key:"open",value:function(){var e,t=this;if(this.opts.rememberUpgrade&&n.priorWebsocketSuccess&&-1!==this.transports.indexOf("websocket"))e="websocket";else{if(0===this.transports.length)return void this.setTimeoutFn((function(){t.emitReserved("error","No transports available")}),0);e=this.transports[0]}this.readyState="opening";try{e=this.createTransport(e)}catch(r){return this.transports.shift(),void this.open()}e.open(),this.setTransport(e)}},{key:"setTransport",value:function(e){var t=this;this.transport&&this.transport.removeAllListeners(),this.transport=e,e.on("drain",this.onDrain.bind(this)).on("packet",this.onPacket.bind(this)).on("error",this.onError.bind(this)).on("close",(function(e){return t.onClose("transport close",e)}))}},{key:"probe",value:function(e){var t=this,r=this.createTransport(e),o=!1;n.priorWebsocketSuccess=!1;var i=function(){o||(r.send([{type:"ping",data:"probe"}]),r.once("packet",(function(e){if(!o)if("pong"===e.type&&"probe"===e.data){if(t.upgrading=!0,t.emitReserved("upgrading",r),!r)return;n.priorWebsocketSuccess="websocket"===r.name,t.transport.pause((function(){o||"closed"!==t.readyState&&(f(),t.setTransport(r),r.send([{type:"upgrade"}]),t.emitReserved("upgrade",r),r=null,t.upgrading=!1,t.flush())}))}else{var i=new Error("probe error");i.transport=r.name,t.emitReserved("upgradeError",i)}})))};function a(){o||(o=!0,f(),r.close(),r=null)}var u=function(e){var n=new Error("probe error: "+e);n.transport=r.name,a(),t.emitReserved("upgradeError",n)};function l(){u("transport closed")}function c(){u("socket closed")}function s(e){r&&e.name!==r.name&&a()}var f=function(){r.removeListener("open",i),r.removeListener("error",u),r.removeListener("close",l),t.off("close",c),t.off("upgrading",s)};r.once("open",i),r.once("error",u),r.once("close",l),this.once("close",c),this.once("upgrading",s),r.open()}},{key:"onOpen",value:function(){if(this.readyState="open",n.priorWebsocketSuccess="websocket"===this.transport.name,this.emitReserved("open"),this.flush(),"open"===this.readyState&&this.opts.upgrade&&this.transport.pause)for(var e=0,t=this.upgrades.length;e<t;e++)this.probe(this.upgrades[e])}},{key:"onPacket",value:function(e){if("opening"===this.readyState||"open"===this.readyState||"closing"===this.readyState)switch(this.emitReserved("packet",e),this.emitReserved("heartbeat"),e.type){case"open":this.onHandshake(JSON.parse(e.data));break;case"ping":this.resetPingTimeout(),this.sendPacket("pong"),this.emitReserved("ping"),this.emitReserved("pong");break;case"error":var t=new Error("server error");t.code=e.data,this.onError(t);break;case"message":this.emitReserved("data",e.data),this.emitReserved("message",e.data)}}},{key:"onHandshake",value:function(e){this.emitReserved("handshake",e),this.id=e.sid,this.transport.query.sid=e.sid,this.upgrades=this.filterUpgrades(e.upgrades),this.pingInterval=e.pingInterval,this.pingTimeout=e.pingTimeout,this.maxPayload=e.maxPayload,this.onOpen(),"closed"!==this.readyState&&this.resetPingTimeout()}},{key:"resetPingTimeout",value:function(){var e=this;this.clearTimeoutFn(this.pingTimeoutTimer),this.pingTimeoutTimer=this.setTimeoutFn((function(){e.onClose("ping timeout")}),this.pingInterval+this.pingTimeout),this.opts.autoUnref&&this.pingTimeoutTimer.unref()}},{key:"onDrain",value:function(){this.writeBuffer.splice(0,this.prevBufferLen),this.prevBufferLen=0,0===this.writeBuffer.length?this.emitReserved("drain"):this.flush()}},{key:"flush",value:function(){if("closed"!==this.readyState&&this.transport.writable&&!this.upgrading&&this.writeBuffer.length){var e=this.getWritablePackets();this.transport.send(e),this.prevBufferLen=e.length,this.emitReserved("flush")}}},{key:"getWritablePackets",value:function(){if(!(this.maxPayload&&"polling"===this.transport.name&&this.writeBuffer.length>1))return this.writeBuffer;for(var e,t=1,n=0;n<this.writeBuffer.length;n++){var r=this.writeBuffer[n].data;if(r&&(t+="string"===typeof(e=r)?function(e){for(var t=0,n=0,r=0,o=e.length;r<o;r++)(t=e.charCodeAt(r))<128?n+=1:t<2048?n+=2:t<55296||t>=57344?n+=3:(r++,n+=4);return n}(e):Math.ceil(1.33*(e.byteLength||e.size))),n>0&&t>this.maxPayload)return this.writeBuffer.slice(0,n);t+=2}return this.writeBuffer}},{key:"write",value:function(e,t,n){return this.sendPacket("message",e,t,n),this}},{key:"send",value:function(e,t,n){return this.sendPacket("message",e,t,n),this}},{key:"sendPacket",value:function(e,t,n,r){if("function"===typeof t&&(r=t,t=void 0),"function"===typeof n&&(r=n,n=null),"closing"!==this.readyState&&"closed"!==this.readyState){(n=n||{}).compress=!1!==n.compress;var o={type:e,data:t,options:n};this.emitReserved("packetCreate",o),this.writeBuffer.push(o),r&&this.once("flush",r),this.flush()}}},{key:"close",value:function(){var e=this,t=function(){e.onClose("forced close"),e.transport.close()},n=function n(){e.off("upgrade",n),e.off("upgradeError",n),t()},r=function(){e.once("upgrade",n),e.once("upgradeError",n)};return"opening"!==this.readyState&&"open"!==this.readyState||(this.readyState="closing",this.writeBuffer.length?this.once("drain",(function(){e.upgrading?r():t()})):this.upgrading?r():t()),this}},{key:"onError",value:function(e){n.priorWebsocketSuccess=!1,this.emitReserved("error",e),this.onClose("transport error",e)}},{key:"onClose",value:function(e,t){"opening"!==this.readyState&&"open"!==this.readyState&&"closing"!==this.readyState||(this.clearTimeoutFn(this.pingTimeoutTimer),this.transport.removeAllListeners("close"),this.transport.close(),this.transport.removeAllListeners(),"function"===typeof removeEventListener&&(removeEventListener("beforeunload",this.beforeunloadEventListener,!1),removeEventListener("offline",this.offlineEventListener,!1)),this.readyState="closed",this.id=null,this.emitReserved("close",e,t),this.writeBuffer=[],this.prevBufferLen=0)}},{key:"filterUpgrades",value:function(e){for(var t=[],n=0,r=e.length;n<r;n++)~this.transports.indexOf(e[n])&&t.push(e[n]);return t}}]),n}(iB);IB.protocol=4;IB.protocol;var BB="function"===typeof ArrayBuffer,zB=Object.prototype.toString,VB="function"===typeof Blob||"undefined"!==typeof Blob&&"[object BlobConstructor]"===zB.call(Blob),UB="function"===typeof File||"undefined"!==typeof File&&"[object FileConstructor]"===zB.call(File);function WB(e){return BB&&(e instanceof ArrayBuffer||function(e){return"function"===typeof ArrayBuffer.isView?ArrayBuffer.isView(e):e.buffer instanceof ArrayBuffer}(e))||VB&&e instanceof Blob||UB&&e instanceof File}function HB(e,t){if(!e||"object"!==typeof e)return!1;if(Array.isArray(e)){for(var n=0,r=e.length;n<r;n++)if(HB(e[n]))return!0;return!1}if(WB(e))return!0;if(e.toJSON&&"function"===typeof e.toJSON&&1===arguments.length)return HB(e.toJSON(),!0);for(var o in e)if(Object.prototype.hasOwnProperty.call(e,o)&&HB(e[o]))return!0;return!1}function KB(e){var t=[],n=e.data,r=e;return r.data=qB(n,t),r.attachments=t.length,{packet:r,buffers:t}}function qB(e,t){if(!e)return e;if(WB(e)){var n={_placeholder:!0,num:t.length};return t.push(e),n}if(Array.isArray(e)){for(var r=new Array(e.length),o=0;o<e.length;o++)r[o]=qB(e[o],t);return r}if("object"===typeof e&&!(e instanceof Date)){var i={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&(i[a]=qB(e[a],t));return i}return e}function GB(e,t){return e.data=YB(e.data,t),delete e.attachments,e}function YB(e,t){if(!e)return e;if(e&&!0===e._placeholder){if("number"===typeof e.num&&e.num>=0&&e.num<t.length)return t[e.num];throw new Error("illegal attachments")}if(Array.isArray(e))for(var n=0;n<e.length;n++)e[n]=YB(e[n],t);else if("object"===typeof e)for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(e[r]=YB(e[r],t));return e}var XB,ZB=5;!function(e){e[e.CONNECT=0]="CONNECT",e[e.DISCONNECT=1]="DISCONNECT",e[e.EVENT=2]="EVENT",e[e.ACK=3]="ACK",e[e.CONNECT_ERROR=4]="CONNECT_ERROR",e[e.BINARY_EVENT=5]="BINARY_EVENT",e[e.BINARY_ACK=6]="BINARY_ACK"}(XB||(XB={}));var $B=function(){function e(t){F(this,e),this.replacer=t}return O(e,[{key:"encode",value:function(e){return e.type!==XB.EVENT&&e.type!==XB.ACK||!HB(e)?[this.encodeAsString(e)]:this.encodeAsBinary({type:e.type===XB.EVENT?XB.BINARY_EVENT:XB.BINARY_ACK,nsp:e.nsp,data:e.data,id:e.id})}},{key:"encodeAsString",value:function(e){var t=""+e.type;return e.type!==XB.BINARY_EVENT&&e.type!==XB.BINARY_ACK||(t+=e.attachments+"-"),e.nsp&&"/"!==e.nsp&&(t+=e.nsp+","),null!=e.id&&(t+=e.id),null!=e.data&&(t+=JSON.stringify(e.data,this.replacer)),t}},{key:"encodeAsBinary",value:function(e){var t=KB(e),n=this.encodeAsString(t.packet),r=t.buffers;return r.unshift(n),r}}]),e}(),QB=function(e){A(n,e);var t=R(n);function n(e){var r;return F(this,n),(r=t.call(this)).reviver=e,r}return O(n,[{key:"add",value:function(e){var t;if("string"===typeof e){if(this.reconstructor)throw new Error("got plaintext data when reconstructing a packet");var r=(t=this.decodeString(e)).type===XB.BINARY_EVENT;r||t.type===XB.BINARY_ACK?(t.type=r?XB.EVENT:XB.ACK,this.reconstructor=new JB(t),0===t.attachments&&WI(h(n.prototype),"emitReserved",this).call(this,"decoded",t)):WI(h(n.prototype),"emitReserved",this).call(this,"decoded",t)}else{if(!WB(e)&&!e.base64)throw new Error("Unknown type: "+e);if(!this.reconstructor)throw new Error("got binary data when not reconstructing a packet");(t=this.reconstructor.takeBinaryData(e))&&(this.reconstructor=null,WI(h(n.prototype),"emitReserved",this).call(this,"decoded",t))}}},{key:"decodeString",value:function(e){var t=0,r={type:Number(e.charAt(0))};if(void 0===XB[r.type])throw new Error("unknown packet type "+r.type);if(r.type===XB.BINARY_EVENT||r.type===XB.BINARY_ACK){for(var o=t+1;"-"!==e.charAt(++t)&&t!=e.length;);var i=e.substring(o,t);if(i!=Number(i)||"-"!==e.charAt(t))throw new Error("Illegal attachments");r.attachments=Number(i)}if("/"===e.charAt(t+1)){for(var a=t+1;++t;){if(","===e.charAt(t))break;if(t===e.length)break}r.nsp=e.substring(a,t)}else r.nsp="/";var u=e.charAt(t+1);if(""!==u&&Number(u)==u){for(var l=t+1;++t;){var c=e.charAt(t);if(null==c||Number(c)!=c){--t;break}if(t===e.length)break}r.id=Number(e.substring(l,t+1))}if(e.charAt(++t)){var s=this.tryParse(e.substr(t));if(!n.isPayloadValid(r.type,s))throw new Error("invalid payload");r.data=s}return r}},{key:"tryParse",value:function(e){try{return JSON.parse(e,this.reviver)}catch(t){return!1}}},{key:"destroy",value:function(){this.reconstructor&&(this.reconstructor.finishedReconstruction(),this.reconstructor=null)}}],[{key:"isPayloadValid",value:function(e,t){switch(e){case XB.CONNECT:return"object"===typeof t;case XB.DISCONNECT:return void 0===t;case XB.CONNECT_ERROR:return"string"===typeof t||"object"===typeof t;case XB.EVENT:case XB.BINARY_EVENT:return Array.isArray(t)&&t.length>0;case XB.ACK:case XB.BINARY_ACK:return Array.isArray(t)}}}]),n}(iB),JB=function(){function e(t){F(this,e),this.packet=t,this.buffers=[],this.reconPack=t}return O(e,[{key:"takeBinaryData",value:function(e){if(this.buffers.push(e),this.buffers.length===this.reconPack.attachments){var t=GB(this.reconPack,this.buffers);return this.finishedReconstruction(),t}return null}},{key:"finishedReconstruction",value:function(){this.reconPack=null,this.buffers=[]}}]),e}();function ez(e,t,n){return e.on(t,n),function(){e.off(t,n)}}var tz=Object.freeze({connect:1,connect_error:1,disconnect:1,disconnecting:1,newListener:1,removeListener:1}),nz=function(e){A(n,e);var t=R(n);function n(e,r,o){var i;return F(this,n),(i=t.call(this)).connected=!1,i.receiveBuffer=[],i.sendBuffer=[],i.ids=0,i.acks={},i.flags={},i.io=e,i.nsp=r,o&&o.auth&&(i.auth=o.auth),i.io._autoConnect&&i.open(),i}return O(n,[{key:"disconnected",get:function(){return!this.connected}},{key:"subEvents",value:function(){if(!this.subs){var e=this.io;this.subs=[ez(e,"open",this.onopen.bind(this)),ez(e,"packet",this.onpacket.bind(this)),ez(e,"error",this.onerror.bind(this)),ez(e,"close",this.onclose.bind(this))]}}},{key:"active",get:function(){return!!this.subs}},{key:"connect",value:function(){return this.connected||(this.subEvents(),this.io._reconnecting||this.io.open(),"open"===this.io._readyState&&this.onopen()),this}},{key:"open",value:function(){return this.connect()}},{key:"send",value:function(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];return t.unshift("message"),this.emit.apply(this,t),this}},{key:"emit",value:function(e){if(tz.hasOwnProperty(e))throw new Error('"'+e.toString()+'" is a reserved event name');for(var t=arguments.length,n=new Array(t>1?t-1:0),r=1;r<t;r++)n[r-1]=arguments[r];n.unshift(e);var o={type:XB.EVENT,data:n,options:{}};if(o.options.compress=!1!==this.flags.compress,"function"===typeof n[n.length-1]){var i=this.ids++,a=n.pop();this._registerAckCallback(i,a),o.id=i}var u=this.io.engine&&this.io.engine.transport&&this.io.engine.transport.writable,l=this.flags.volatile&&(!u||!this.connected);return l||(this.connected?(this.notifyOutgoingListeners(o),this.packet(o)):this.sendBuffer.push(o)),this.flags={},this}},{key:"_registerAckCallback",value:function(e,t){var n=this,r=this.flags.timeout;if(void 0!==r){var o=this.io.setTimeoutFn((function(){delete n.acks[e];for(var r=0;r<n.sendBuffer.length;r++)n.sendBuffer[r].id===e&&n.sendBuffer.splice(r,1);t.call(n,new Error("operation has timed out"))}),r);this.acks[e]=function(){n.io.clearTimeoutFn(o);for(var e=arguments.length,r=new Array(e),i=0;i<e;i++)r[i]=arguments[i];t.apply(n,[null].concat(r))}}else this.acks[e]=t}},{key:"packet",value:function(e){e.nsp=this.nsp,this.io._packet(e)}},{key:"onopen",value:function(){var e=this;"function"==typeof this.auth?this.auth((function(t){e.packet({type:XB.CONNECT,data:t})})):this.packet({type:XB.CONNECT,data:this.auth})}},{key:"onerror",value:function(e){this.connected||this.emitReserved("connect_error",e)}},{key:"onclose",value:function(e,t){this.connected=!1,delete this.id,this.emitReserved("disconnect",e,t)}},{key:"onpacket",value:function(e){if(e.nsp===this.nsp)switch(e.type){case XB.CONNECT:if(e.data&&e.data.sid){var t=e.data.sid;this.onconnect(t)}else this.emitReserved("connect_error",new Error("It seems you are trying to reach a Socket.IO server in v2.x with a v3.x client, but they are not compatible (more information here: https://socket.io/docs/v3/migrating-from-2-x-to-3-0/)"));break;case XB.EVENT:case XB.BINARY_EVENT:this.onevent(e);break;case XB.ACK:case XB.BINARY_ACK:this.onack(e);break;case XB.DISCONNECT:this.ondisconnect();break;case XB.CONNECT_ERROR:this.destroy();var n=new Error(e.data.message);n.data=e.data.data,this.emitReserved("connect_error",n)}}},{key:"onevent",value:function(e){var t=e.data||[];null!=e.id&&t.push(this.ack(e.id)),this.connected?this.emitEvent(t):this.receiveBuffer.push(Object.freeze(t))}},{key:"emitEvent",value:function(e){if(this._anyListeners&&this._anyListeners.length){var t,r=x(this._anyListeners.slice());try{for(r.s();!(t=r.n()).done;){t.value.apply(this,e)}}catch(fz){r.e(fz)}finally{r.f()}}WI(h(n.prototype),"emit",this).apply(this,e)}},{key:"ack",value:function(e){var t=this,n=!1;return function(){if(!n){n=!0;for(var r=arguments.length,o=new Array(r),i=0;i<r;i++)o[i]=arguments[i];t.packet({type:XB.ACK,id:e,data:o})}}}},{key:"onack",value:function(e){var t=this.acks[e.id];"function"===typeof t&&(t.apply(this,e.data),delete this.acks[e.id])}},{key:"onconnect",value:function(e){this.id=e,this.connected=!0,this.emitBuffered(),this.emitReserved("connect")}},{key:"emitBuffered",value:function(){var e=this;this.receiveBuffer.forEach((function(t){return e.emitEvent(t)})),this.receiveBuffer=[],this.sendBuffer.forEach((function(t){e.notifyOutgoingListeners(t),e.packet(t)})),this.sendBuffer=[]}},{key:"ondisconnect",value:function(){this.destroy(),this.onclose("io server disconnect")}},{key:"destroy",value:function(){this.subs&&(this.subs.forEach((function(e){return e()})),this.subs=void 0),this.io._destroy(this)}},{key:"disconnect",value:function(){return this.connected&&this.packet({type:XB.DISCONNECT}),this.destroy(),this.connected&&this.onclose("io client disconnect"),this}},{key:"close",value:function(){return this.disconnect()}},{key:"compress",value:function(e){return this.flags.compress=e,this}},{key:"volatile",get:function(){return this.flags.volatile=!0,this}},{key:"timeout",value:function(e){return this.flags.timeout=e,this}},{key:"onAny",value:function(e){return this._anyListeners=this._anyListeners||[],this._anyListeners.push(e),this}},{key:"prependAny",value:function(e){return this._anyListeners=this._anyListeners||[],this._anyListeners.unshift(e),this}},{key:"offAny",value:function(e){if(!this._anyListeners)return this;if(e){for(var t=this._anyListeners,n=0;n<t.length;n++)if(e===t[n])return t.splice(n,1),this}else this._anyListeners=[];return this}},{key:"listenersAny",value:function(){return this._anyListeners||[]}},{key:"onAnyOutgoing",value:function(e){return this._anyOutgoingListeners=this._anyOutgoingListeners||[],this._anyOutgoingListeners.push(e),this}},{key:"prependAnyOutgoing",value:function(e){return this._anyOutgoingListeners=this._anyOutgoingListeners||[],this._anyOutgoingListeners.unshift(e),this}},{key:"offAnyOutgoing",value:function(e){if(!this._anyOutgoingListeners)return this;if(e){for(var t=this._anyOutgoingListeners,n=0;n<t.length;n++)if(e===t[n])return t.splice(n,1),this}else this._anyOutgoingListeners=[];return this}},{key:"listenersAnyOutgoing",value:function(){return this._anyOutgoingListeners||[]}},{key:"notifyOutgoingListeners",value:function(e){if(this._anyOutgoingListeners&&this._anyOutgoingListeners.length){var t,n=x(this._anyOutgoingListeners.slice());try{for(n.s();!(t=n.n()).done;){t.value.apply(this,e.data)}}catch(fz){n.e(fz)}finally{n.f()}}}}]),n}(iB);function rz(e){e=e||{},this.ms=e.min||100,this.max=e.max||1e4,this.factor=e.factor||2,this.jitter=e.jitter>0&&e.jitter<=1?e.jitter:0,this.attempts=0}rz.prototype.duration=function(){var e=this.ms*Math.pow(this.factor,this.attempts++);if(this.jitter){var t=Math.random(),n=Math.floor(t*this.jitter*e);e=0==(1&Math.floor(10*t))?e-n:e+n}return 0|Math.min(e,this.max)},rz.prototype.reset=function(){this.attempts=0},rz.prototype.setMin=function(e){this.ms=e},rz.prototype.setMax=function(e){this.max=e},rz.prototype.setJitter=function(e){this.jitter=e};var oz=function(t){A(r,t);var n=R(r);function r(t,o){var i,a;F(this,r),(i=n.call(this)).nsps={},i.subs=[],t&&"object"===typeof t&&(o=t,t=void 0),(o=o||{}).path=o.path||"/socket.io",i.opts=o,sB(D(i),o),i.reconnection(!1!==o.reconnection),i.reconnectionAttempts(o.reconnectionAttempts||1/0),i.reconnectionDelay(o.reconnectionDelay||1e3),i.reconnectionDelayMax(o.reconnectionDelayMax||5e3),i.randomizationFactor(null!==(a=o.randomizationFactor)&&void 0!==a?a:.5),i.backoff=new rz({min:i.reconnectionDelay(),max:i.reconnectionDelayMax(),jitter:i.randomizationFactor()}),i.timeout(null==o.timeout?2e4:o.timeout),i._readyState="closed",i.uri=t;var u=o.parser||e;return i.encoder=new u.Encoder,i.decoder=new u.Decoder,i._autoConnect=!1!==o.autoConnect,i._autoConnect&&i.open(),i}return O(r,[{key:"reconnection",value:function(e){return arguments.length?(this._reconnection=!!e,this):this._reconnection}},{key:"reconnectionAttempts",value:function(e){return void 0===e?this._reconnectionAttempts:(this._reconnectionAttempts=e,this)}},{key:"reconnectionDelay",value:function(e){var t;return void 0===e?this._reconnectionDelay:(this._reconnectionDelay=e,null===(t=this.backoff)||void 0===t||t.setMin(e),this)}},{key:"randomizationFactor",value:function(e){var t;return void 0===e?this._randomizationFactor:(this._randomizationFactor=e,null===(t=this.backoff)||void 0===t||t.setJitter(e),this)}},{key:"reconnectionDelayMax",value:function(e){var t;return void 0===e?this._reconnectionDelayMax:(this._reconnectionDelayMax=e,null===(t=this.backoff)||void 0===t||t.setMax(e),this)}},{key:"timeout",value:function(e){return arguments.length?(this._timeout=e,this):this._timeout}},{key:"maybeReconnectOnOpen",value:function(){!this._reconnecting&&this._reconnection&&0===this.backoff.attempts&&this.reconnect()}},{key:"open",value:function(e){var t=this;if(~this._readyState.indexOf("open"))return this;this.engine=new IB(this.uri,this.opts);var n=this.engine,r=this;this._readyState="opening",this.skipReconnect=!1;var o=ez(n,"open",(function(){r.onopen(),e&&e()})),i=ez(n,"error",(function(n){r.cleanup(),r._readyState="closed",t.emitReserved("error",n),e?e(n):r.maybeReconnectOnOpen()}));if(!1!==this._timeout){var a=this._timeout;0===a&&o();var u=this.setTimeoutFn((function(){o(),n.close(),n.emit("error",new Error("timeout"))}),a);this.opts.autoUnref&&u.unref(),this.subs.push((function(){clearTimeout(u)}))}return this.subs.push(o),this.subs.push(i),this}},{key:"connect",value:function(e){return this.open(e)}},{key:"onopen",value:function(){this.cleanup(),this._readyState="open",this.emitReserved("open");var e=this.engine;this.subs.push(ez(e,"ping",this.onping.bind(this)),ez(e,"data",this.ondata.bind(this)),ez(e,"error",this.onerror.bind(this)),ez(e,"close",this.onclose.bind(this)),ez(this.decoder,"decoded",this.ondecoded.bind(this)))}},{key:"onping",value:function(){this.emitReserved("ping")}},{key:"ondata",value:function(e){try{this.decoder.add(e)}catch(t){this.onclose("parse error",t)}}},{key:"ondecoded",value:function(e){var t=this;RB((function(){t.emitReserved("packet",e)}),this.setTimeoutFn)}},{key:"onerror",value:function(e){this.emitReserved("error",e)}},{key:"socket",value:function(e,t){var n=this.nsps[e];return n||(n=new nz(this,e,t),this.nsps[e]=n),n}},{key:"_destroy",value:function(e){for(var t=0,n=Object.keys(this.nsps);t<n.length;t++){var r=n[t];if(this.nsps[r].active)return}this._close()}},{key:"_packet",value:function(e){for(var t=this.encoder.encode(e),n=0;n<t.length;n++)this.engine.write(t[n],e.options)}},{key:"cleanup",value:function(){this.subs.forEach((function(e){return e()})),this.subs.length=0,this.decoder.destroy()}},{key:"_close",value:function(){this.skipReconnect=!0,this._reconnecting=!1,this.onclose("forced close"),this.engine&&this.engine.close()}},{key:"disconnect",value:function(){return this._close()}},{key:"onclose",value:function(e,t){this.cleanup(),this.backoff.reset(),this._readyState="closed",this.emitReserved("close",e,t),this._reconnection&&!this.skipReconnect&&this.reconnect()}},{key:"reconnect",value:function(){var e=this;if(this._reconnecting||this.skipReconnect)return this;var t=this;if(this.backoff.attempts>=this._reconnectionAttempts)this.backoff.reset(),this.emitReserved("reconnect_failed"),this._reconnecting=!1;else{var n=this.backoff.duration();this._reconnecting=!0;var r=this.setTimeoutFn((function(){t.skipReconnect||(e.emitReserved("reconnect_attempt",t.backoff.attempts),t.skipReconnect||t.open((function(n){n?(t._reconnecting=!1,t.reconnect(),e.emitReserved("reconnect_error",n)):t.onreconnect()})))}),n);this.opts.autoUnref&&r.unref(),this.subs.push((function(){clearTimeout(r)}))}}},{key:"onreconnect",value:function(){var e=this.backoff.attempts;this._reconnecting=!1,this.backoff.reset(),this.emitReserved("reconnect",e)}}]),r}(iB),iz={};function az(e,t){"object"===typeof e&&(t=e,e=void 0);var n,r=function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"",n=arguments.length>2?arguments[2]:void 0,r=e;n=n||"undefined"!==typeof location&&location,null==e&&(e=n.protocol+"//"+n.host),"string"===typeof e&&("/"===e.charAt(0)&&(e="/"===e.charAt(1)?n.protocol+e:n.host+e),/^(https?|wss?):\/\//.test(e)||(e="undefined"!==typeof n?n.protocol+"//"+e:"https://"+e),r=jB(e)),r.port||(/^(http|ws)$/.test(r.protocol)?r.port="80":/^(http|ws)s$/.test(r.protocol)&&(r.port="443")),r.path=r.path||"/";var o=-1!==r.host.indexOf(":")?"["+r.host+"]":r.host;return r.id=r.protocol+"://"+o+":"+r.port+t,r.href=r.protocol+"://"+o+(n&&n.port===r.port?"":":"+r.port),r}(e,(t=t||{}).path||"/socket.io"),o=r.source,i=r.id,a=r.path,u=iz[i]&&a in iz[i].nsps;return t.forceNew||t["force new connection"]||!1===t.multiplex||u?n=new oz(o,t):(iz[i]||(iz[i]=new oz(o,t)),n=iz[i]),r.query&&!t.query&&(t.query=r.queryKey),n.socket(r.path,t)}Object.assign(az,{Manager:oz,Socket:nz,io:az,connect:az});var uz=az(),lz=function(){var e=Wu(wS),n=Wu(B_),r=v(t.useState(!1),2),o=r[0],i=r[1],a=v(t.useState(0),2),u=a[0],l=a[1],c=Wu(gS),s=Math.max(c.sdSteps,1),f=Math.min(Math.round(u/s*100),100);return t.useEffect((function(){if(e)return uz.on("connect",(function(){i(!0)})),uz.on("disconnect",(function(){i(!1)})),uz.on("diffusion_progress",(function(e){console.log("step: ".concat(e.step+1)),e&&l(e.step+1)})),uz.on("diffusion_finish",(function(e){l(0)})),function(){uz.off("connect"),uz.off("disconnect"),uz.off("diffusion_progress"),uz.off("diffusion_finish")}}),[e]),(0,Zu.jsxs)("div",{className:"ProgressWrapper",style:{visibility:n&&o&&e?"visible":"hidden"},children:[(0,Zu.jsx)(zI,{value:f,className:"ProgressRoot",children:(0,Zu.jsx)(VI,{className:"ProgressIndicator",style:{transform:"translateX(-".concat(100-f,"%)")}})}),(0,Zu.jsxs)("div",{style:{width:45,display:"flex",justifyContent:"center",fontVariantNumeric:"tabular-nums"},children:[f,"%"]})]})},cz=["image/jpeg","image/png","image/webp","image/bmp","image/tiff"];var sz=function(){var e=v(Hu(Y_),2),n=e[0],r=e[1],o=v(Hu($u),2),i=o[0],a=o[1],u=Ku(uS),l=function(){var e=v((0,t.useState)(),2),n=e[0],r=e[1],o=(0,t.useCallback)((function(){var e=new Headers;e.append("pragma","no-cache"),e.append("cache-control","no-cache"),fetch("/inputimage",{headers:e}).then(function(){var e=p(E().mark((function e(t){var n,o,i,a;return E().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return o=null===(n=t.headers.get("content-disposition"))||void 0===n?void 0:n.split("filename=")[1].split(";")[0],e.next=3,t.blob();case 3:(i=e.sent)&&i.type.startsWith("image")&&(a=new File([i],void 0!==o?o:"inputImage"),r(a));case 5:case"end":return e.stop()}}),e)})));return function(t){return e.apply(this,arguments)}}())}),[r]);return(0,t.useEffect)((function(){o()}),[o]),n}(),c=Ku(V_);(0,t.useEffect)((function(){r(l)}),[l,r]),(0,t.useEffect)((function(){var e=function(){var e=p(E().mark((function e(){return E().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,fetch("".concat(ik,"/is_desktop"),{method:"GET"}).then((function(e){return e.text()}));case 2:"True"===e.sent&&XS();case 4:case"end":return e.stop()}}),e)})));return function(){return e.apply(this,arguments)}}();e()}),[]),(0,t.useEffect)((function(){var e=function(){var e=p(E().mark((function e(){var t;return E().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,fetch("".concat(ik,"/server_config"),{method:"GET"}).then((function(e){return e.json()}));case 2:t=e.sent,console.log(t),c(t);case 5:case"end":return e.stop()}}),e)})));return function(){return e.apply(this,arguments)}}();e()}),[]),qk("shift+d",(function(){a("light"===i?"dark":"light")}),{},[i]),(0,t.useEffect)((function(){document.body.setAttribute("data-theme",i)}),[i]);var s=(0,t.useMemo)((function(){return function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:21;return crypto.getRandomValues(new Uint8Array(e)).reduce((function(e,t){return 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t=e.clipboardData.items,n=[].slice.call(t).filter((function(e){return-1!==e.type.indexOf("image")}));if(0!==n.length){e.preventDefault(),e.stopPropagation();var o=n[0].getAsFile();o&&r(o)}}}),[]);return t.useEffect((function(){return window.addEventListener("dragenter",g),window.addEventListener("dragleave",y),window.addEventListener("dragover",m),window.addEventListener("drop",b),window.addEventListener("paste",w),function(){window.removeEventListener("dragenter",g),window.removeEventListener("dragleave",y),window.removeEventListener("dragover",m),window.removeEventListener("drop",b),window.removeEventListener("paste",w)}})),(0,Zu.jsxs)("div",{className:"lama-cleaner",children:[(0,Zu.jsx)(EI,{}),(0,Zu.jsx)(lz,{}),(0,Zu.jsx)(rI,{},s)]})};r.render((0,Zu.jsx)(t.StrictMode,{children:(0,Zu.jsx)(zu,{children:(0,Zu.jsx)(sz,{})})}),document.getElementById("root"))}()})();
\ No newline at end of file
diff --git a/lama_cleaner/app/build/static/js/main.1fda6320.js.LICENSE.txt b/lama_cleaner/app/build/static/js/main.1fda6320.js.LICENSE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..441c3c00f2ec908e7deb0bd72378cc9ec53162f9
--- /dev/null
+++ b/lama_cleaner/app/build/static/js/main.1fda6320.js.LICENSE.txt
@@ -0,0 +1,69 @@
+/*
+object-assign
+(c) Sindre Sorhus
+@license MIT
+*/
+
+/*! regenerator-runtime -- Copyright (c) 2014-present, Facebook, Inc. -- license (MIT): https://github.com/facebook/regenerator/blob/main/LICENSE */
+
+/**
+ * @license
+ * Lodash <https://lodash.com/>
+ * Copyright OpenJS Foundation and other contributors <https://openjsf.org/>
+ * Released under MIT license <https://lodash.com/license>
+ * Based on Underscore.js 1.8.3 <http://underscorejs.org/LICENSE>
+ * Copyright Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors
+ */
+
+/** @license React v0.20.2
+ * scheduler.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/** @license React v17.0.2
+ * react-dom.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/** @license React v17.0.2
+ * react-jsx-runtime.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/** @license React v17.0.2
+ * react.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/**!
+ * FlexSearch.js v0.7.21 (Bundle)
+ * Copyright 2018-2021 Nextapps GmbH
+ * Author: Thomas Wilkerling
+ * Licence: Apache-2.0
+ * https://github.com/nextapps-de/flexsearch
+ */
+
+/**! 
+ * hotkeys-js v3.9.4 
+ * A simple micro-library for defining and dispatching keyboard shortcuts. It has no dependencies. 
+ * 
+ * Copyright (c) 2022 kenny wong <wowohoo@qq.com> 
+ * http://jaywcjlove.github.io/hotkeys 
+ * Licensed under the MIT license 
+ */
diff --git a/lama_cleaner/app/build/static/js/main.44aac645.js b/lama_cleaner/app/build/static/js/main.44aac645.js
new file mode 100644
index 0000000000000000000000000000000000000000..8ad95f7fecb6ca81491cd812221b79770585d999
--- /dev/null
+++ b/lama_cleaner/app/build/static/js/main.44aac645.js
@@ -0,0 +1,2 @@
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t,n,r,o;return{previousScale:null!==(t=e.initialScale)&&void 0!==t?t:Ol.scale,scale:null!==(n=e.initialScale)&&void 0!==n?n:Ol.scale,positionX:null!==(r=e.initialPositionX)&&void 0!==r?r:Ol.positionX,positionY:null!==(o=e.initialPositionY)&&void 0!==o?o:Ol.positionY}},Il=function(e){var t=ul({},Ll);return Object.keys(e).forEach((function(n){var r="undefined"!==typeof e[n];if("undefined"!==typeof Ll[n]&&r){var o=Object.prototype.toString.call(Ll[n]),i="[object Object]"===o,a="[object Array]"===o;t[n]=i?ul(ul({},Ll[n]),e[n]):a?ll(ll([],Ll[n]),e[n]):e[n]}})),t},jl=function(e,t,n){var r=e.transformState.scale,o=e.wrapperComponent,i=e.setup,a=i.maxScale,u=i.minScale,l=i.zoomAnimation.size;if(!o)throw new Error("Wrapper is not mounted");var c=r*Math.exp(t*n);return El(cl(c,3),u,a,l,!1)};function Bl(e,t,n,r,o){var i=e.wrapperComponent,a=e.transformState,u=a.scale,l=a.positionX,c=a.positionY;if(!i)return console.error("No WrapperComponent found");var 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0===o||o.removeEventListener("keyup",e.setKeyUnPressed,n),null===o||void 0===o||o.removeEventListener("keydown",e.setKeyPressed,n),document.removeEventListener("mouseleave",e.clearPanning,n),pl(e)},e.handleInitializeWrapperEvents=function(t){var n=Xl();t.addEventListener("wheel",e.onWheelZoom,n),t.addEventListener("dblclick",e.onDoubleClick,n),t.addEventListener("touchstart",e.onTouchPanningStart,n),t.addEventListener("touchmove",e.onTouchPanning,n),t.addEventListener("touchend",e.onTouchPanningStop,n)},e.handleInitialize=function(){var t=e.setup.centerOnInit;e.applyTransformation(),e.forceUpdate(),t&&(setTimeout((function(){e.mounted&&e.setCenter()}),50),setTimeout((function(){e.mounted&&e.setCenter()}),100),setTimeout((function(){e.mounted&&e.setCenter()}),200))},e.onWheelZoom=function(t){e.setup.disabled||ec(e,t)&&e.isPressingKeys(e.setup.wheel.activationKeys)&&(ic(e,t),ac(e,t),uc(e,t))},e.onPanningStart=function(t){var n=e.setup.disabled,r=e.props.onPanningStart;n||xl(e,t)&&e.isPressingKeys(e.setup.panning.activationKeys)&&(t.preventDefault(),t.stopPropagation(),pl(e),Dl(e,t),sl(ql(e),t,r))},e.onPanning=function(t){var n=e.setup.disabled,r=e.props.onPanning;n||Cl(e)&&e.isPressingKeys(e.setup.panning.activationKeys)&&(t.preventDefault(),t.stopPropagation(),Fl(e,t.clientX,t.clientY),sl(ql(e),t,r))},e.onPanningStop=function(t){var n=e.props.onPanningStop;e.isPanning&&(Al(e),sl(ql(e),t,n))},e.onPinchStart=function(t){var n=e.setup.disabled,r=e.props,o=r.onPinchingStart,i=r.onZoomStart;n||nc(e,t)&&(lc(e,t),pl(e),sl(ql(e),t,o),sl(ql(e),t,i))},e.onPinch=function(t){var n=e.setup.disabled,r=e.props,o=r.onPinching,i=r.onZoom;n||rc(e)&&(t.preventDefault(),t.stopPropagation(),cc(e,t),sl(ql(e),t,o),sl(ql(e),t,i))},e.onPinchStop=function(t){var n=e.props,r=n.onPinchingStop,o=n.onZoomStop;e.pinchStartScale&&(sc(e),sl(ql(e),t,r),sl(ql(e),t,o))},e.onTouchPanningStart=function(t){var n=e.setup.disabled,r=e.props.onPanningStart;if(!n&&xl(e,t))if(e.lastTouch&&+new Date-e.lastTouch<200&&1===t.touches.length)e.onDoubleClick(t);else{e.lastTouch=+new Date,pl(e);var o=t.touches,i=1===o.length,a=2===o.length;i&&(pl(e),Dl(e,t),sl(ql(e),t,r)),a&&e.onPinchStart(t)}},e.onTouchPanning=function(t){var n=e.setup.disabled,r=e.props.onPanning;if(e.isPanning&&1===t.touches.length){if(n)return;if(!Cl(e))return;t.preventDefault(),t.stopPropagation();var o=t.touches[0];Fl(e,o.clientX,o.clientY),sl(ql(e),t,r)}else t.touches.length>1&&e.onPinch(t)},e.onTouchPanningStop=function(t){e.onPanningStop(t),e.onPinchStop(t)},e.onDoubleClick=function(t){e.setup.disabled||dc(e,t)&&fc(e,t)},e.clearPanning=function(t){e.isPanning&&e.onPanningStop(t)},e.setKeyPressed=function(t){e.pressedKeys[t.key]=!0},e.setKeyUnPressed=function(t){e.pressedKeys[t.key]=!1},e.isPressingKeys=function(t){return!t.length||Boolean(t.find((function(t){return e.pressedKeys[t]})))},e.setComponents=function(t,n){e.cleanupWindowEvents(),e.wrapperComponent=t,e.contentComponent=n,gl(e,e.transformState.scale),e.handleInitializeWrapperEvents(t),e.initializeWindowEvents(),e.handleInitialize(),e.handleRef(),e.isInitialized=!0,sl(ql(e),void 0,e.props.onInit)},e.setTransformState=function(t,n,r){var o=e.props.onTransformed;isNaN(t)||isNaN(n)||isNaN(r)?console.error("Detected NaN set state values"):(t!==e.transformState.scale&&(e.transformState.previousScale=e.transformState.scale,e.transformState.scale=t),e.transformState.positionX=n,e.transformState.positionY=r,sl(ql(e),{scale:t,positionX:n,positionY:r},o),e.applyTransformation())},e.setCenter=function(){if(e.wrapperComponent&&e.contentComponent){var t=Jl(e.transformState.scale,e.wrapperComponent,e.contentComponent);e.setTransformState(t.scale,t.positionX,t.positionY)}},e.applyTransformation=function(){if(e.mounted&&e.contentComponent){var t=e.transformState,n=t.scale,r=t.positionX,o=t.positionY,i=Ql(r,o,n);e.contentComponent.style.transform=i,e.handleRef()}},e.handleRef=function(){e.props.setRef(ql(e))},e}return function(e,t){if("function"!==typeof t&&null!==t)throw new TypeError("Class extends value "+String(t)+" is not a constructor or null");function n(){this.constructor=e}al(e,t),e.prototype=null===t?Object.create(t):(n.prototype=t.prototype,new n)}(n,t),n.prototype.componentDidMount=function(){this.initializeWindowEvents()},n.prototype.componentWillUnmount=function(){this.cleanupWindowEvents()},n.prototype.componentDidUpdate=function(e){e!==this.props&&(gl(this,this.transformState.scale),this.setup=Il(this.props))},n.prototype.render=function(){var t=ql(this),n=this.props.children,r="function"===typeof n?n(t):n;return e.createElement(pc.Provider,{value:ul(ul({},this.transformState),{setComponents:this.setComponents,contextInstance:this})},r)},n}(e.Component),hc=e.forwardRef((function(t,n){var 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+++ b/lama_cleaner/app/build/static/js/main.44aac645.js.LICENSE.txt
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+/*
+object-assign
+(c) Sindre Sorhus
+@license MIT
+*/
+
+/*! regenerator-runtime -- Copyright (c) 2014-present, Facebook, Inc. -- license (MIT): https://github.com/facebook/regenerator/blob/main/LICENSE */
+
+/**
+ * @license
+ * Lodash <https://lodash.com/>
+ * Copyright OpenJS Foundation and other contributors <https://openjsf.org/>
+ * Released under MIT license <https://lodash.com/license>
+ * Based on Underscore.js 1.8.3 <http://underscorejs.org/LICENSE>
+ * Copyright Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors
+ */
+
+/** @license React v0.20.2
+ * scheduler.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/** @license React v17.0.2
+ * react-dom.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/** @license React v17.0.2
+ * react-jsx-runtime.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/** @license React v17.0.2
+ * react.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/**!
+ * FlexSearch.js v0.7.21 (Bundle)
+ * Copyright 2018-2021 Nextapps GmbH
+ * Author: Thomas Wilkerling
+ * Licence: Apache-2.0
+ * https://github.com/nextapps-de/flexsearch
+ */
+
+/**! 
+ * hotkeys-js v3.9.4 
+ * A simple micro-library for defining and dispatching keyboard shortcuts. It has no dependencies. 
+ * 
+ * Copyright (c) 2022 kenny wong <wowohoo@qq.com> 
+ * http://jaywcjlove.github.io/hotkeys 
+ * Licensed under the MIT license 
+ */
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diff --git a/lama_cleaner/benchmark.py b/lama_cleaner/benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0a170ec872475c4dfec397c42057ad53376cf81
--- /dev/null
+++ b/lama_cleaner/benchmark.py
@@ -0,0 +1,109 @@
+#!/usr/bin/env python3
+
+import argparse
+import os
+import time
+
+import numpy as np
+import nvidia_smi
+import psutil
+import torch
+
+from lama_cleaner.model_manager import ModelManager
+from lama_cleaner.schema import Config, HDStrategy, SDSampler
+
+try:
+    torch._C._jit_override_can_fuse_on_cpu(False)
+    torch._C._jit_override_can_fuse_on_gpu(False)
+    torch._C._jit_set_texpr_fuser_enabled(False)
+    torch._C._jit_set_nvfuser_enabled(False)
+except:
+    pass
+
+NUM_THREADS = str(4)
+
+os.environ["OMP_NUM_THREADS"] = NUM_THREADS
+os.environ["OPENBLAS_NUM_THREADS"] = NUM_THREADS
+os.environ["MKL_NUM_THREADS"] = NUM_THREADS
+os.environ["VECLIB_MAXIMUM_THREADS"] = NUM_THREADS
+os.environ["NUMEXPR_NUM_THREADS"] = NUM_THREADS
+if os.environ.get("CACHE_DIR"):
+    os.environ["TORCH_HOME"] = os.environ["CACHE_DIR"]
+
+
+def run_model(model, size):
+    # RGB
+    image = np.random.randint(0, 256, (size[0], size[1], 3)).astype(np.uint8)
+    mask = np.random.randint(0, 255, size).astype(np.uint8)
+
+    config = Config(
+        ldm_steps=2,
+        hd_strategy=HDStrategy.ORIGINAL,
+        hd_strategy_crop_margin=128,
+        hd_strategy_crop_trigger_size=128,
+        hd_strategy_resize_limit=128,
+        prompt="a fox is sitting on a bench",
+        sd_steps=5,
+        sd_sampler=SDSampler.ddim
+    )
+    model(image, mask, config)
+
+
+def benchmark(model, times: int, empty_cache: bool):
+    sizes = [(512, 512)]
+
+    nvidia_smi.nvmlInit()
+    device_id = 0
+    handle = nvidia_smi.nvmlDeviceGetHandleByIndex(device_id)
+
+    def format(metrics):
+        return f"{np.mean(metrics):.2f} ± {np.std(metrics):.2f}"
+
+    process = psutil.Process(os.getpid())
+    # 每个 size 给出显存和内存占用的指标
+    for size in sizes:
+        torch.cuda.empty_cache()
+        time_metrics = []
+        cpu_metrics = []
+        memory_metrics = []
+        gpu_memory_metrics = []
+        for _ in range(times):
+            start = time.time()
+            run_model(model, size)
+            torch.cuda.synchronize()
+
+            # cpu_metrics.append(process.cpu_percent())
+            time_metrics.append((time.time() - start) * 1000)
+            memory_metrics.append(process.memory_info().rss / 1024 / 1024)
+            gpu_memory_metrics.append(nvidia_smi.nvmlDeviceGetMemoryInfo(handle).used / 1024 / 1024)
+
+        print(f"size: {size}".center(80, "-"))
+        # print(f"cpu: {format(cpu_metrics)}")
+        print(f"latency: {format(time_metrics)}ms")
+        print(f"memory: {format(memory_metrics)} MB")
+        print(f"gpu memory: {format(gpu_memory_metrics)} MB")
+
+    nvidia_smi.nvmlShutdown()
+
+
+def get_args_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--name")
+    parser.add_argument("--device", default="cuda", type=str)
+    parser.add_argument("--times", default=10, type=int)
+    parser.add_argument("--empty-cache", action="store_true")
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = get_args_parser()
+    device = torch.device(args.device)
+    model = ModelManager(
+        name=args.name,
+        device=device,
+        sd_run_local=True,
+        disable_nsfw=True,
+        sd_cpu_textencoder=True,
+        hf_access_token="123"
+    )
+    benchmark(model, args.times, args.empty_cache)
diff --git a/lama_cleaner/const.py b/lama_cleaner/const.py
new file mode 100644
index 0000000000000000000000000000000000000000..a54c8476fb2e886c7d21728ba71d310f8a603cee
--- /dev/null
+++ b/lama_cleaner/const.py
@@ -0,0 +1,173 @@
+import json
+import os
+from enum import Enum
+from pydantic import BaseModel
+
+
+MPS_SUPPORT_MODELS = [
+    "instruct_pix2pix",
+    "sd1.5",
+    "anything4",
+    "realisticVision1.4",
+    "sd2",
+    "paint_by_example",
+    "controlnet",
+]
+
+DEFAULT_MODEL = "lama"
+AVAILABLE_MODELS = [
+    "lama",
+    "ldm",
+    "zits",
+    "mat",
+    "fcf",
+    "sd1.5",
+    "anything4",
+    "realisticVision1.4",
+    "cv2",
+    "manga",
+    "sd2",
+    "paint_by_example",
+    "instruct_pix2pix",
+]
+SD15_MODELS = ["sd1.5", "anything4", "realisticVision1.4"]
+
+AVAILABLE_DEVICES = ["cuda", "cpu", "mps"]
+DEFAULT_DEVICE = "cuda"
+
+NO_HALF_HELP = """
+Using full precision model.
+If your generate result is always black or green, use this argument. (sd/paint_by_exmaple)
+"""
+
+CPU_OFFLOAD_HELP = """
+Offloads all models to CPU, significantly reducing vRAM usage. (sd/paint_by_example)
+"""
+
+DISABLE_NSFW_HELP = """
+Disable NSFW checker. (sd/paint_by_example)
+"""
+
+SD_CPU_TEXTENCODER_HELP = """
+Run Stable Diffusion text encoder model on CPU to save GPU memory.
+"""
+
+SD_CONTROLNET_HELP = """
+Run Stable Diffusion inpainting model with ControlNet. You can switch control method in webui.
+"""
+DEFAULT_CONTROLNET_METHOD = "control_v11p_sd15_canny"
+SD_CONTROLNET_CHOICES = [
+    "control_v11p_sd15_canny",
+    "control_v11p_sd15_openpose",
+    "control_v11p_sd15_inpaint",
+    "control_v11f1p_sd15_depth"
+]
+
+SD_LOCAL_MODEL_HELP = """
+Load Stable Diffusion 1.5 model(ckpt/safetensors) from local path.
+"""
+
+LOCAL_FILES_ONLY_HELP = """
+Use local files only, not connect to Hugging Face server. (sd/paint_by_example)
+"""
+
+ENABLE_XFORMERS_HELP = """
+Enable xFormers optimizations. Requires xformers package has been installed. See: https://github.com/facebookresearch/xformers (sd/paint_by_example)
+"""
+
+DEFAULT_MODEL_DIR = os.getenv(
+    "XDG_CACHE_HOME", os.path.join(os.path.expanduser("~"), ".cache")
+)
+MODEL_DIR_HELP = """
+Model download directory (by setting XDG_CACHE_HOME environment variable), by default model downloaded to ~/.cache
+"""
+
+OUTPUT_DIR_HELP = """
+Result images will be saved to output directory automatically without confirmation.
+"""
+
+INPUT_HELP = """
+If input is image, it will be loaded by default.
+If input is directory, you can browse and select image in file manager.
+"""
+
+GUI_HELP = """
+Launch Lama Cleaner as desktop app
+"""
+
+NO_GUI_AUTO_CLOSE_HELP = """
+Prevent backend auto close after the GUI window closed.
+"""
+
+QUALITY_HELP = """
+Quality of image encoding, 0-100. Default is 95, higher quality will generate larger file size.
+"""
+
+
+class RealESRGANModelName(str, Enum):
+    realesr_general_x4v3 = "realesr-general-x4v3"
+    RealESRGAN_x4plus = "RealESRGAN_x4plus"
+    RealESRGAN_x4plus_anime_6B = "RealESRGAN_x4plus_anime_6B"
+
+
+RealESRGANModelNameList = [e.value for e in RealESRGANModelName]
+
+INTERACTIVE_SEG_HELP = "Enable interactive segmentation using Segment Anything."
+INTERACTIVE_SEG_MODEL_HELP = "Model size: vit_b < vit_l < vit_h. Bigger model size means better segmentation but slower speed."
+AVAILABLE_INTERACTIVE_SEG_MODELS = ["vit_b", "vit_l", "vit_h"]
+AVAILABLE_INTERACTIVE_SEG_DEVICES = ["cuda", "cpu", "mps"]
+REMOVE_BG_HELP = "Enable remove background. Always run on CPU"
+ANIMESEG_HELP = "Enable anime segmentation. Always run on CPU"
+REALESRGAN_HELP = "Enable realesrgan super resolution"
+REALESRGAN_AVAILABLE_DEVICES = ["cpu", "cuda", "mps"]
+GFPGAN_HELP = (
+    "Enable GFPGAN face restore. To enhance background, use with --enable-realesrgan"
+)
+GFPGAN_AVAILABLE_DEVICES = ["cpu", "cuda", "mps"]
+RESTOREFORMER_HELP = "Enable RestoreFormer face restore. To enhance background, use with --enable-realesrgan"
+RESTOREFORMER_AVAILABLE_DEVICES = ["cpu", "cuda", "mps"]
+GIF_HELP = "Enable GIF plugin. Make GIF to compare original and cleaned image"
+
+
+class Config(BaseModel):
+    host: str = "127.0.0.1"
+    port: int = 8080
+    model: str = DEFAULT_MODEL
+    sd_local_model_path: str = None
+    sd_controlnet: bool = False
+    sd_controlnet_method: str = DEFAULT_CONTROLNET_METHOD
+    device: str = DEFAULT_DEVICE
+    gui: bool = False
+    no_gui_auto_close: bool = False
+    no_half: bool = False
+    cpu_offload: bool = False
+    disable_nsfw: bool = False
+    sd_cpu_textencoder: bool = False
+    enable_xformers: bool = False
+    local_files_only: bool = False
+    model_dir: str = DEFAULT_MODEL_DIR
+    input: str = None
+    output_dir: str = None
+    # plugins
+    enable_interactive_seg: bool = False
+    interactive_seg_model: str = "vit_l"
+    interactive_seg_device: str = "cpu"
+    enable_remove_bg: bool = False
+    enable_anime_seg: bool = False
+    enable_realesrgan: bool = False
+    realesrgan_device: str = "cpu"
+    realesrgan_model: str = RealESRGANModelName.realesr_general_x4v3.value
+    realesrgan_no_half: bool = False
+    enable_gfpgan: bool = False
+    gfpgan_device: str = "cpu"
+    enable_restoreformer: bool = False
+    restoreformer_device: str = "cpu"
+    enable_gif: bool = False
+
+
+def load_config(installer_config: str):
+    if os.path.exists(installer_config):
+        with open(installer_config, "r", encoding="utf-8") as f:
+            return Config(**json.load(f))
+    else:
+        return Config()
diff --git a/lama_cleaner/file_manager/__init__.py b/lama_cleaner/file_manager/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a2499873fc1536ac1077241f2cd361a8d394156
--- /dev/null
+++ b/lama_cleaner/file_manager/__init__.py
@@ -0,0 +1 @@
+from .file_manager import FileManager
diff --git a/lama_cleaner/file_manager/__pycache__/__init__.cpython-38.pyc b/lama_cleaner/file_manager/__pycache__/__init__.cpython-38.pyc
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diff --git a/lama_cleaner/file_manager/__pycache__/utils.cpython-38.pyc b/lama_cleaner/file_manager/__pycache__/utils.cpython-38.pyc
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diff --git a/lama_cleaner/file_manager/file_manager.py b/lama_cleaner/file_manager/file_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..73d389702d3c3f406b4b2a61652646a27a4e1b05
--- /dev/null
+++ b/lama_cleaner/file_manager/file_manager.py
@@ -0,0 +1,265 @@
+# Copy from https://github.com/silentsokolov/flask-thumbnails/blob/master/flask_thumbnails/thumbnail.py
+import os
+from datetime import datetime
+
+import cv2
+import time
+from io import BytesIO
+from pathlib import Path
+import numpy as np
+# from watchdog.events import FileSystemEventHandler
+# from watchdog.observers import Observer
+
+from PIL import Image, ImageOps, PngImagePlugin
+from loguru import logger
+
+LARGE_ENOUGH_NUMBER = 100
+PngImagePlugin.MAX_TEXT_CHUNK = LARGE_ENOUGH_NUMBER * (1024**2)
+from .storage_backends import FilesystemStorageBackend
+from .utils import aspect_to_string, generate_filename, glob_img
+
+
+class FileManager:
+    def __init__(self, app=None):
+        self.app = app
+        self._default_root_directory = "media"
+        self._default_thumbnail_directory = "media"
+        self._default_root_url = "/"
+        self._default_thumbnail_root_url = "/"
+        self._default_format = "JPEG"
+        self.output_dir: Path = None
+
+        if app is not None:
+            self.init_app(app)
+
+        self.image_dir_filenames = []
+        self.output_dir_filenames = []
+
+        self.image_dir_observer = None
+        self.output_dir_observer = None
+
+        self.modified_time = {
+            "image": datetime.utcnow(),
+            "output": datetime.utcnow(),
+        }
+
+    # def start(self):
+    #     self.image_dir_filenames = self._media_names(self.root_directory)
+    #     self.output_dir_filenames = self._media_names(self.output_dir)
+    #
+    #     logger.info(f"Start watching image directory: {self.root_directory}")
+    #     self.image_dir_observer = Observer()
+    #     self.image_dir_observer.schedule(self, self.root_directory, recursive=False)
+    #     self.image_dir_observer.start()
+    #
+    #     logger.info(f"Start watching output directory: {self.output_dir}")
+    #     self.output_dir_observer = Observer()
+    #     self.output_dir_observer.schedule(self, self.output_dir, recursive=False)
+    #     self.output_dir_observer.start()
+
+    def on_modified(self, event):
+        if not os.path.isdir(event.src_path):
+            return
+        if event.src_path == str(self.root_directory):
+            logger.info(f"Image directory {event.src_path} modified")
+            self.image_dir_filenames = self._media_names(self.root_directory)
+            self.modified_time["image"] = datetime.utcnow()
+        elif event.src_path == str(self.output_dir):
+            logger.info(f"Output directory {event.src_path} modified")
+            self.output_dir_filenames = self._media_names(self.output_dir)
+            self.modified_time["output"] = datetime.utcnow()
+
+    def init_app(self, app):
+        if self.app is None:
+            self.app = app
+        app.thumbnail_instance = self
+
+        if not hasattr(app, "extensions"):
+            app.extensions = {}
+
+        if "thumbnail" in app.extensions:
+            raise RuntimeError("Flask-thumbnail extension already initialized")
+
+        app.extensions["thumbnail"] = self
+
+        app.config.setdefault("THUMBNAIL_MEDIA_ROOT", self._default_root_directory)
+        app.config.setdefault(
+            "THUMBNAIL_MEDIA_THUMBNAIL_ROOT", self._default_thumbnail_directory
+        )
+        app.config.setdefault("THUMBNAIL_MEDIA_URL", self._default_root_url)
+        app.config.setdefault(
+            "THUMBNAIL_MEDIA_THUMBNAIL_URL", self._default_thumbnail_root_url
+        )
+        app.config.setdefault("THUMBNAIL_DEFAULT_FORMAT", self._default_format)
+
+    @property
+    def root_directory(self):
+        path = self.app.config["THUMBNAIL_MEDIA_ROOT"]
+
+        if os.path.isabs(path):
+            return path
+        else:
+            return os.path.join(self.app.root_path, path)
+
+    @property
+    def thumbnail_directory(self):
+        path = self.app.config["THUMBNAIL_MEDIA_THUMBNAIL_ROOT"]
+
+        if os.path.isabs(path):
+            return path
+        else:
+            return os.path.join(self.app.root_path, path)
+
+    @property
+    def root_url(self):
+        return self.app.config["THUMBNAIL_MEDIA_URL"]
+
+    @property
+    def media_names(self):
+        # return self.image_dir_filenames
+        return self._media_names(self.root_directory)
+
+    @property
+    def output_media_names(self):
+        return self._media_names(self.output_dir)
+        # return self.output_dir_filenames
+
+    @staticmethod
+    def _media_names(directory: Path):
+        names = sorted([it.name for it in glob_img(directory)])
+        res = []
+        for name in names:
+            path = os.path.join(directory, name)
+            img = Image.open(path)
+            res.append(
+                {
+                    "name": name,
+                    "height": img.height,
+                    "width": img.width,
+                    "ctime": os.path.getctime(path),
+                    "mtime": os.path.getmtime(path),
+                }
+            )
+        return res
+
+    @property
+    def thumbnail_url(self):
+        return self.app.config["THUMBNAIL_MEDIA_THUMBNAIL_URL"]
+
+    def get_thumbnail(
+        self, directory: Path, original_filename: str, width, height, **options
+    ):
+        storage = FilesystemStorageBackend(self.app)
+        crop = options.get("crop", "fit")
+        background = options.get("background")
+        quality = options.get("quality", 90)
+
+        original_path, original_filename = os.path.split(original_filename)
+        original_filepath = os.path.join(directory, original_path, original_filename)
+        image = Image.open(BytesIO(storage.read(original_filepath)))
+
+        # keep ratio resize
+        if width is not None:
+            height = int(image.height * width / image.width)
+        else:
+            width = int(image.width * height / image.height)
+
+        thumbnail_size = (width, height)
+
+        thumbnail_filename = generate_filename(
+            original_filename,
+            aspect_to_string(thumbnail_size),
+            crop,
+            background,
+            quality,
+        )
+
+        thumbnail_filepath = os.path.join(
+            self.thumbnail_directory, original_path, thumbnail_filename
+        )
+        thumbnail_url = os.path.join(
+            self.thumbnail_url, original_path, thumbnail_filename
+        )
+
+        if storage.exists(thumbnail_filepath):
+            return thumbnail_url, (width, height)
+
+        try:
+            image.load()
+        except (IOError, OSError):
+            self.app.logger.warning("Thumbnail not load image: %s", original_filepath)
+            return thumbnail_url, (width, height)
+
+        # get original image format
+        options["format"] = options.get("format", image.format)
+
+        image = self._create_thumbnail(
+            image, thumbnail_size, crop, background=background
+        )
+
+        raw_data = self.get_raw_data(image, **options)
+        storage.save(thumbnail_filepath, raw_data)
+
+        return thumbnail_url, (width, height)
+
+    def get_raw_data(self, image, **options):
+        data = {
+            "format": self._get_format(image, **options),
+            "quality": options.get("quality", 90),
+        }
+
+        _file = BytesIO()
+        image.save(_file, **data)
+        return _file.getvalue()
+
+    @staticmethod
+    def colormode(image, colormode="RGB"):
+        if colormode == "RGB" or colormode == "RGBA":
+            if image.mode == "RGBA":
+                return image
+            if image.mode == "LA":
+                return image.convert("RGBA")
+            return image.convert(colormode)
+
+        if colormode == "GRAY":
+            return image.convert("L")
+
+        return image.convert(colormode)
+
+    @staticmethod
+    def background(original_image, color=0xFF):
+        size = (max(original_image.size),) * 2
+        image = Image.new("L", size, color)
+        image.paste(
+            original_image,
+            tuple(map(lambda x: (x[0] - x[1]) / 2, zip(size, original_image.size))),
+        )
+
+        return image
+
+    def _get_format(self, image, **options):
+        if options.get("format"):
+            return options.get("format")
+        if image.format:
+            return image.format
+
+        return self.app.config["THUMBNAIL_DEFAULT_FORMAT"]
+
+    def _create_thumbnail(self, image, size, crop="fit", background=None):
+        try:
+            resample = Image.Resampling.LANCZOS
+        except AttributeError:  # pylint: disable=raise-missing-from
+            resample = Image.ANTIALIAS
+
+        if crop == "fit":
+            image = ImageOps.fit(image, size, resample)
+        else:
+            image = image.copy()
+            image.thumbnail(size, resample=resample)
+
+        if background is not None:
+            image = self.background(image)
+
+        image = self.colormode(image)
+
+        return image
diff --git a/lama_cleaner/file_manager/storage_backends.py b/lama_cleaner/file_manager/storage_backends.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f453ade4dcd167856efe74f638e11dbc6145462
--- /dev/null
+++ b/lama_cleaner/file_manager/storage_backends.py
@@ -0,0 +1,46 @@
+# Copy from https://github.com/silentsokolov/flask-thumbnails/blob/master/flask_thumbnails/storage_backends.py
+import errno
+import os
+from abc import ABC, abstractmethod
+
+
+class BaseStorageBackend(ABC):
+    def __init__(self, app=None):
+        self.app = app
+
+    @abstractmethod
+    def read(self, filepath, mode="rb", **kwargs):
+        raise NotImplementedError
+
+    @abstractmethod
+    def exists(self, filepath):
+        raise NotImplementedError
+
+    @abstractmethod
+    def save(self, filepath, data):
+        raise NotImplementedError
+
+
+class FilesystemStorageBackend(BaseStorageBackend):
+    def read(self, filepath, mode="rb", **kwargs):
+        with open(filepath, mode) as f:  # pylint: disable=unspecified-encoding
+            return f.read()
+
+    def exists(self, filepath):
+        return os.path.exists(filepath)
+
+    def save(self, filepath, data):
+        directory = os.path.dirname(filepath)
+
+        if not os.path.exists(directory):
+            try:
+                os.makedirs(directory)
+            except OSError as e:
+                if e.errno != errno.EEXIST:
+                    raise
+
+        if not os.path.isdir(directory):
+            raise IOError("{} is not a directory".format(directory))
+
+        with open(filepath, "wb") as f:
+            f.write(data)
diff --git a/lama_cleaner/file_manager/utils.py b/lama_cleaner/file_manager/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a0567142d26bcf790d160f4aec2fef01f339e62
--- /dev/null
+++ b/lama_cleaner/file_manager/utils.py
@@ -0,0 +1,67 @@
+# Copy from: https://github.com/silentsokolov/flask-thumbnails/blob/master/flask_thumbnails/utils.py
+import importlib
+import os
+from pathlib import Path
+
+from typing import Union
+
+
+def generate_filename(original_filename, *options):
+    name, ext = os.path.splitext(original_filename)
+    for v in options:
+        if v:
+            name += "_%s" % v
+    name += ext
+
+    return name
+
+
+def parse_size(size):
+    if isinstance(size, int):
+        # If the size parameter is a single number, assume square aspect.
+        return [size, size]
+
+    if isinstance(size, (tuple, list)):
+        if len(size) == 1:
+            # If single value tuple/list is provided, exand it to two elements
+            return size + type(size)(size)
+        return size
+
+    try:
+        thumbnail_size = [int(x) for x in size.lower().split("x", 1)]
+    except ValueError:
+        raise ValueError(  # pylint: disable=raise-missing-from
+            "Bad thumbnail size format. Valid format is INTxINT."
+        )
+
+    if len(thumbnail_size) == 1:
+        # If the size parameter only contains a single integer, assume square aspect.
+        thumbnail_size.append(thumbnail_size[0])
+
+    return thumbnail_size
+
+
+def aspect_to_string(size):
+    if isinstance(size, str):
+        return size
+
+    return "x".join(map(str, size))
+
+
+IMG_SUFFIX = {'.jpg', '.jpeg', '.png', '.JPG', '.JPEG', '.PNG'}
+
+
+def glob_img(p: Union[Path, str], recursive: bool = False):
+    p = Path(p)
+    if p.is_file() and p.suffix in IMG_SUFFIX:
+        yield p
+    else:
+        if recursive:
+            files = Path(p).glob("**/*.*")
+        else:
+            files = Path(p).glob("*.*")
+
+        for it in files:
+            if it.suffix not in IMG_SUFFIX:
+                continue
+            yield it
diff --git a/lama_cleaner/helper.py b/lama_cleaner/helper.py
new file mode 100644
index 0000000000000000000000000000000000000000..babbeac622fdb59b1dce6b3cb581d3c9a1357fd9
--- /dev/null
+++ b/lama_cleaner/helper.py
@@ -0,0 +1,292 @@
+import io
+import os
+import sys
+from typing import List, Optional
+
+from urllib.parse import urlparse
+import cv2
+from PIL import Image, ImageOps, PngImagePlugin
+import numpy as np
+import torch
+from lama_cleaner.const import MPS_SUPPORT_MODELS
+from loguru import logger
+from torch.hub import download_url_to_file, get_dir
+import hashlib
+
+
+def md5sum(filename):
+    md5 = hashlib.md5()
+    with open(filename, "rb") as f:
+        for chunk in iter(lambda: f.read(128 * md5.block_size), b""):
+            md5.update(chunk)
+    return md5.hexdigest()
+
+
+def switch_mps_device(model_name, device):
+    if model_name not in MPS_SUPPORT_MODELS and str(device) == "mps":
+        logger.info(f"{model_name} not support mps, switch to cpu")
+        return torch.device("cpu")
+    return device
+
+
+def get_cache_path_by_url(url):
+    parts = urlparse(url)
+    hub_dir = get_dir()
+    model_dir = os.path.join(hub_dir, "checkpoints")
+    if not os.path.isdir(model_dir):
+        os.makedirs(model_dir)
+    filename = os.path.basename(parts.path)
+    cached_file = os.path.join(model_dir, filename)
+    return cached_file
+
+
+def download_model(url, model_md5: str = None):
+    cached_file = get_cache_path_by_url(url)
+    if not os.path.exists(cached_file):
+        sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
+        hash_prefix = None
+        download_url_to_file(url, cached_file, hash_prefix, progress=True)
+        if model_md5:
+            _md5 = md5sum(cached_file)
+            if model_md5 == _md5:
+                logger.info(f"Download model success, md5: {_md5}")
+            else:
+                try:
+                    os.remove(cached_file)
+                    logger.error(
+                        f"Model md5: {_md5}, expected md5: {model_md5}, wrong model deleted. Please restart lama-cleaner."
+                        f"If you still have errors, please try download model manually first https://lama-cleaner-docs.vercel.app/install/download_model_manually.\n"
+                    )
+                except:
+                    logger.error(
+                        f"Model md5: {_md5}, expected md5: {model_md5}, please delete {cached_file} and restart lama-cleaner."
+                    )
+                exit(-1)
+
+    return cached_file
+
+
+def ceil_modulo(x, mod):
+    if x % mod == 0:
+        return x
+    return (x // mod + 1) * mod
+
+
+def handle_error(model_path, model_md5, e):
+    _md5 = md5sum(model_path)
+    if _md5 != model_md5:
+        try:
+            os.remove(model_path)
+            logger.error(
+                f"Model md5: {_md5}, expected md5: {model_md5}, wrong model deleted. Please restart lama-cleaner."
+                f"If you still have errors, please try download model manually first https://lama-cleaner-docs.vercel.app/install/download_model_manually.\n"
+            )
+        except:
+            logger.error(
+                f"Model md5: {_md5}, expected md5: {model_md5}, please delete {model_path} and restart lama-cleaner."
+            )
+    else:
+        logger.error(
+            f"Failed to load model {model_path},"
+            f"please submit an issue at https://github.com/Sanster/lama-cleaner/issues and include a screenshot of the error:\n{e}"
+        )
+    exit(-1)
+
+
+def load_jit_model(url_or_path, device, model_md5: str):
+    if os.path.exists(url_or_path):
+        model_path = url_or_path
+    else:
+        model_path = download_model(url_or_path, model_md5)
+
+    logger.info(f"Loading model from: {model_path}")
+    try:
+        model = torch.jit.load(model_path, map_location="cpu").to(device)
+    except Exception as e:
+        handle_error(model_path, model_md5, e)
+    model.eval()
+    return model
+
+
+def load_model(model: torch.nn.Module, url_or_path, device, model_md5):
+    if os.path.exists(url_or_path):
+        model_path = url_or_path
+    else:
+        model_path = download_model(url_or_path, model_md5)
+
+    try:
+        logger.info(f"Loading model from: {model_path}")
+        state_dict = torch.load(model_path, map_location="cpu")
+        model.load_state_dict(state_dict, strict=True)
+        model.to(device)
+    except Exception as e:
+        handle_error(model_path, model_md5, e)
+    model.eval()
+    return model
+
+
+def numpy_to_bytes(image_numpy: np.ndarray, ext: str) -> bytes:
+    data = cv2.imencode(
+        f".{ext}",
+        image_numpy,
+        [int(cv2.IMWRITE_JPEG_QUALITY), 100, int(cv2.IMWRITE_PNG_COMPRESSION), 0],
+    )[1]
+    image_bytes = data.tobytes()
+    return image_bytes
+
+
+def pil_to_bytes(pil_img, ext: str, quality: int = 95, exif_infos={}) -> bytes:
+    with io.BytesIO() as output:
+        kwargs = {k: v for k, v in exif_infos.items() if v is not None}
+        if ext == "png" and "parameters" in kwargs:
+            pnginfo_data = PngImagePlugin.PngInfo()
+            pnginfo_data.add_text("parameters", kwargs["parameters"])
+            kwargs["pnginfo"] = pnginfo_data
+
+        pil_img.save(
+            output,
+            format=ext,
+            quality=quality,
+            **kwargs,
+        )
+        image_bytes = output.getvalue()
+    return image_bytes
+
+
+def load_img(img_bytes, gray: bool = False, return_exif: bool = False):
+    alpha_channel = None
+    image = Image.open(io.BytesIO(img_bytes))
+
+    if return_exif:
+        info = image.info or {}
+        exif_infos = {"exif": image.getexif(), "parameters": info.get("parameters")}
+
+    try:
+        image = ImageOps.exif_transpose(image)
+    except:
+        pass
+
+    if gray:
+        image = image.convert("L")
+        np_img = np.array(image)
+    else:
+        if image.mode == "RGBA":
+            np_img = np.array(image)
+            alpha_channel = np_img[:, :, -1]
+            np_img = cv2.cvtColor(np_img, cv2.COLOR_RGBA2RGB)
+        else:
+            image = image.convert("RGB")
+            np_img = np.array(image)
+
+    if return_exif:
+        return np_img, alpha_channel, exif_infos
+    return np_img, alpha_channel
+
+
+def norm_img(np_img):
+    if len(np_img.shape) == 2:
+        np_img = np_img[:, :, np.newaxis]
+    np_img = np.transpose(np_img, (2, 0, 1))
+    np_img = np_img.astype("float32") / 255
+    return np_img
+
+
+def resize_max_size(
+    np_img, size_limit: int, interpolation=cv2.INTER_CUBIC
+) -> np.ndarray:
+    # Resize image's longer size to size_limit if longer size larger than size_limit
+    h, w = np_img.shape[:2]
+    if max(h, w) > size_limit:
+        ratio = size_limit / max(h, w)
+        new_w = int(w * ratio + 0.5)
+        new_h = int(h * ratio + 0.5)
+        return cv2.resize(np_img, dsize=(new_w, new_h), interpolation=interpolation)
+    else:
+        return np_img
+
+
+def pad_img_to_modulo(
+    img: np.ndarray, mod: int, square: bool = False, min_size: Optional[int] = None
+):
+    """
+
+    Args:
+        img: [H, W, C]
+        mod:
+        square: 是否为正方形
+        min_size:
+
+    Returns:
+
+    """
+    if len(img.shape) == 2:
+        img = img[:, :, np.newaxis]
+    height, width = img.shape[:2]
+    out_height = ceil_modulo(height, mod)
+    out_width = ceil_modulo(width, mod)
+
+    if min_size is not None:
+        assert min_size % mod == 0
+        out_width = max(min_size, out_width)
+        out_height = max(min_size, out_height)
+
+    if square:
+        max_size = max(out_height, out_width)
+        out_height = max_size
+        out_width = max_size
+
+    return np.pad(
+        img,
+        ((0, out_height - height), (0, out_width - width), (0, 0)),
+        mode="symmetric",
+    )
+
+
+def boxes_from_mask(mask: np.ndarray) -> List[np.ndarray]:
+    """
+    Args:
+        mask: (h, w, 1)  0~255
+
+    Returns:
+
+    """
+    height, width = mask.shape[:2]
+    _, thresh = cv2.threshold(mask, 127, 255, 0)
+    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    boxes = []
+    for cnt in contours:
+        x, y, w, h = cv2.boundingRect(cnt)
+        box = np.array([x, y, x + w, y + h]).astype(int)
+
+        box[::2] = np.clip(box[::2], 0, width)
+        box[1::2] = np.clip(box[1::2], 0, height)
+        boxes.append(box)
+
+    return boxes
+
+
+def only_keep_largest_contour(mask: np.ndarray) -> List[np.ndarray]:
+    """
+    Args:
+        mask: (h, w)  0~255
+
+    Returns:
+
+    """
+    _, thresh = cv2.threshold(mask, 127, 255, 0)
+    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    max_area = 0
+    max_index = -1
+    for i, cnt in enumerate(contours):
+        area = cv2.contourArea(cnt)
+        if area > max_area:
+            max_area = area
+            max_index = i
+
+    if max_index != -1:
+        new_mask = np.zeros_like(mask)
+        return cv2.drawContours(new_mask, contours, max_index, 255, -1)
+    else:
+        return mask
diff --git a/lama_cleaner/installer.py b/lama_cleaner/installer.py
new file mode 100644
index 0000000000000000000000000000000000000000..f255e33548d4fb561e753fb96961d6e2b2a9e446
--- /dev/null
+++ b/lama_cleaner/installer.py
@@ -0,0 +1,12 @@
+import subprocess
+import sys
+
+
+def install(package):
+    subprocess.check_call([sys.executable, "-m", "pip", "install", package])
+
+
+def install_plugins_package():
+    install("rembg")
+    install("realesrgan")
+    install("gfpgan")
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index 0000000000000000000000000000000000000000..08c27b1c7218308ac77735d050e30e625464d1d5
--- /dev/null
+++ b/lama_cleaner/model/base.py
@@ -0,0 +1,298 @@
+import abc
+from typing import Optional
+
+import cv2
+import torch
+import numpy as np
+from loguru import logger
+
+from lama_cleaner.helper import (
+    boxes_from_mask,
+    resize_max_size,
+    pad_img_to_modulo,
+    switch_mps_device,
+)
+from lama_cleaner.schema import Config, HDStrategy
+
+
+class InpaintModel:
+    name = "base"
+    min_size: Optional[int] = None
+    pad_mod = 8
+    pad_to_square = False
+
+    def __init__(self, device, **kwargs):
+        """
+
+        Args:
+            device:
+        """
+        device = switch_mps_device(self.name, device)
+        self.device = device
+        self.init_model(device, **kwargs)
+
+    @abc.abstractmethod
+    def init_model(self, device, **kwargs):
+        ...
+
+    @staticmethod
+    @abc.abstractmethod
+    def is_downloaded() -> bool:
+        ...
+
+    @abc.abstractmethod
+    def forward(self, image, mask, config: Config):
+        """Input images and output images have same size
+        images: [H, W, C] RGB
+        masks: [H, W, 1] 255 为 masks 区域
+        return: BGR IMAGE
+        """
+        ...
+
+    def _pad_forward(self, image, mask, config: Config):
+        origin_height, origin_width = image.shape[:2]
+        pad_image = pad_img_to_modulo(
+            image, mod=self.pad_mod, square=self.pad_to_square, min_size=self.min_size
+        )
+        pad_mask = pad_img_to_modulo(
+            mask, mod=self.pad_mod, square=self.pad_to_square, min_size=self.min_size
+        )
+
+        logger.info(f"final forward pad size: {pad_image.shape}")
+
+        result = self.forward(pad_image, pad_mask, config)
+        result = result[0:origin_height, 0:origin_width, :]
+
+        result, image, mask = self.forward_post_process(result, image, mask, config)
+
+        mask = mask[:, :, np.newaxis]
+        result = result * (mask / 255) + image[:, :, ::-1] * (1 - (mask / 255))
+        return result
+
+    def forward_post_process(self, result, image, mask, config):
+        return result, image, mask
+
+    @torch.no_grad()
+    def __call__(self, image, mask, config: Config):
+        """
+        images: [H, W, C] RGB, not normalized
+        masks: [H, W]
+        return: BGR IMAGE
+        """
+        inpaint_result = None
+        logger.info(f"hd_strategy: {config.hd_strategy}")
+        if config.hd_strategy == HDStrategy.CROP:
+            if max(image.shape) > config.hd_strategy_crop_trigger_size:
+                logger.info(f"Run crop strategy")
+                boxes = boxes_from_mask(mask)
+                crop_result = []
+                for box in boxes:
+                    crop_image, crop_box = self._run_box(image, mask, box, config)
+                    crop_result.append((crop_image, crop_box))
+
+                inpaint_result = image[:, :, ::-1]
+                for crop_image, crop_box in crop_result:
+                    x1, y1, x2, y2 = crop_box
+                    inpaint_result[y1:y2, x1:x2, :] = crop_image
+
+        elif config.hd_strategy == HDStrategy.RESIZE:
+            if max(image.shape) > config.hd_strategy_resize_limit:
+                origin_size = image.shape[:2]
+                downsize_image = resize_max_size(
+                    image, size_limit=config.hd_strategy_resize_limit
+                )
+                downsize_mask = resize_max_size(
+                    mask, size_limit=config.hd_strategy_resize_limit
+                )
+
+                logger.info(
+                    f"Run resize strategy, origin size: {image.shape} forward size: {downsize_image.shape}"
+                )
+                inpaint_result = self._pad_forward(
+                    downsize_image, downsize_mask, config
+                )
+
+                # only paste masked area result
+                inpaint_result = cv2.resize(
+                    inpaint_result,
+                    (origin_size[1], origin_size[0]),
+                    interpolation=cv2.INTER_CUBIC,
+                )
+                original_pixel_indices = mask < 127
+                inpaint_result[original_pixel_indices] = image[:, :, ::-1][
+                    original_pixel_indices
+                ]
+
+        if inpaint_result is None:
+            inpaint_result = self._pad_forward(image, mask, config)
+
+        return inpaint_result
+
+    def _crop_box(self, image, mask, box, config: Config):
+        """
+
+        Args:
+            image: [H, W, C] RGB
+            mask: [H, W, 1]
+            box: [left,top,right,bottom]
+
+        Returns:
+            BGR IMAGE, (l, r, r, b)
+        """
+        box_h = box[3] - box[1]
+        box_w = box[2] - box[0]
+        cx = (box[0] + box[2]) // 2
+        cy = (box[1] + box[3]) // 2
+        img_h, img_w = image.shape[:2]
+
+        w = box_w + config.hd_strategy_crop_margin * 2
+        h = box_h + config.hd_strategy_crop_margin * 2
+
+        _l = cx - w // 2
+        _r = cx + w // 2
+        _t = cy - h // 2
+        _b = cy + h // 2
+
+        l = max(_l, 0)
+        r = min(_r, img_w)
+        t = max(_t, 0)
+        b = min(_b, img_h)
+
+        # try to get more context when crop around image edge
+        if _l < 0:
+            r += abs(_l)
+        if _r > img_w:
+            l -= _r - img_w
+        if _t < 0:
+            b += abs(_t)
+        if _b > img_h:
+            t -= _b - img_h
+
+        l = max(l, 0)
+        r = min(r, img_w)
+        t = max(t, 0)
+        b = min(b, img_h)
+
+        crop_img = image[t:b, l:r, :]
+        crop_mask = mask[t:b, l:r]
+
+        logger.info(f"box size: ({box_h},{box_w}) crop size: {crop_img.shape}")
+
+        return crop_img, crop_mask, [l, t, r, b]
+
+    def _calculate_cdf(self, histogram):
+        cdf = histogram.cumsum()
+        normalized_cdf = cdf / float(cdf.max())
+        return normalized_cdf
+
+    def _calculate_lookup(self, source_cdf, reference_cdf):
+        lookup_table = np.zeros(256)
+        lookup_val = 0
+        for source_index, source_val in enumerate(source_cdf):
+            for reference_index, reference_val in enumerate(reference_cdf):
+                if reference_val >= source_val:
+                    lookup_val = reference_index
+                    break
+            lookup_table[source_index] = lookup_val
+        return lookup_table
+
+    def _match_histograms(self, source, reference, mask):
+        transformed_channels = []
+        for channel in range(source.shape[-1]):
+            source_channel = source[:, :, channel]
+            reference_channel = reference[:, :, channel]
+
+            # only calculate histograms for non-masked parts
+            source_histogram, _ = np.histogram(source_channel[mask == 0], 256, [0, 256])
+            reference_histogram, _ = np.histogram(
+                reference_channel[mask == 0], 256, [0, 256]
+            )
+
+            source_cdf = self._calculate_cdf(source_histogram)
+            reference_cdf = self._calculate_cdf(reference_histogram)
+
+            lookup = self._calculate_lookup(source_cdf, reference_cdf)
+
+            transformed_channels.append(cv2.LUT(source_channel, lookup))
+
+        result = cv2.merge(transformed_channels)
+        result = cv2.convertScaleAbs(result)
+
+        return result
+
+    def _apply_cropper(self, image, mask, config: Config):
+        img_h, img_w = image.shape[:2]
+        l, t, w, h = (
+            config.croper_x,
+            config.croper_y,
+            config.croper_width,
+            config.croper_height,
+        )
+        r = l + w
+        b = t + h
+
+        l = max(l, 0)
+        r = min(r, img_w)
+        t = max(t, 0)
+        b = min(b, img_h)
+
+        crop_img = image[t:b, l:r, :]
+        crop_mask = mask[t:b, l:r]
+        return crop_img, crop_mask, (l, t, r, b)
+
+    def _run_box(self, image, mask, box, config: Config):
+        """
+
+        Args:
+            image: [H, W, C] RGB
+            mask: [H, W, 1]
+            box: [left,top,right,bottom]
+
+        Returns:
+            BGR IMAGE
+        """
+        crop_img, crop_mask, [l, t, r, b] = self._crop_box(image, mask, box, config)
+
+        return self._pad_forward(crop_img, crop_mask, config), [l, t, r, b]
+
+
+class DiffusionInpaintModel(InpaintModel):
+    @torch.no_grad()
+    def __call__(self, image, mask, config: Config):
+        """
+        images: [H, W, C] RGB, not normalized
+        masks: [H, W]
+        return: BGR IMAGE
+        """
+        # boxes = boxes_from_mask(mask)
+        if config.use_croper:
+            crop_img, crop_mask, (l, t, r, b) = self._apply_cropper(image, mask, config)
+            crop_image = self._scaled_pad_forward(crop_img, crop_mask, config)
+            inpaint_result = image[:, :, ::-1]
+            inpaint_result[t:b, l:r, :] = crop_image
+        else:
+            inpaint_result = self._scaled_pad_forward(image, mask, config)
+
+        return inpaint_result
+
+    def _scaled_pad_forward(self, image, mask, config: Config):
+        longer_side_length = int(config.sd_scale * max(image.shape[:2]))
+        origin_size = image.shape[:2]
+        downsize_image = resize_max_size(image, size_limit=longer_side_length)
+        downsize_mask = resize_max_size(mask, size_limit=longer_side_length)
+        if config.sd_scale != 1:
+            logger.info(
+                f"Resize image to do sd inpainting: {image.shape} -> {downsize_image.shape}"
+            )
+        inpaint_result = self._pad_forward(downsize_image, downsize_mask, config)
+        # only paste masked area result
+        inpaint_result = cv2.resize(
+            inpaint_result,
+            (origin_size[1], origin_size[0]),
+            interpolation=cv2.INTER_CUBIC,
+        )
+        original_pixel_indices = mask < 127
+        inpaint_result[original_pixel_indices] = image[:, :, ::-1][
+            original_pixel_indices
+        ]
+        return inpaint_result
diff --git a/lama_cleaner/model/controlnet.py b/lama_cleaner/model/controlnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d6347e885b0b384d4e7d93ea0e045c8683d90f6
--- /dev/null
+++ b/lama_cleaner/model/controlnet.py
@@ -0,0 +1,289 @@
+import gc
+
+import PIL.Image
+import cv2
+import numpy as np
+import torch
+from diffusers import ControlNetModel
+from loguru import logger
+
+from lama_cleaner.model.base import DiffusionInpaintModel
+from lama_cleaner.model.utils import torch_gc, get_scheduler
+from lama_cleaner.schema import Config
+
+
+class CPUTextEncoderWrapper:
+    def __init__(self, text_encoder, torch_dtype):
+        self.config = text_encoder.config
+        self.text_encoder = text_encoder.to(torch.device("cpu"), non_blocking=True)
+        self.text_encoder = self.text_encoder.to(torch.float32, non_blocking=True)
+        self.torch_dtype = torch_dtype
+        del text_encoder
+        torch_gc()
+
+    def __call__(self, x, **kwargs):
+        input_device = x.device
+        return [
+            self.text_encoder(x.to(self.text_encoder.device), **kwargs)[0]
+            .to(input_device)
+            .to(self.torch_dtype)
+        ]
+
+    @property
+    def dtype(self):
+        return self.torch_dtype
+
+
+NAMES_MAP = {
+    "sd1.5": "runwayml/stable-diffusion-inpainting",
+    "anything4": "Sanster/anything-4.0-inpainting",
+    "realisticVision1.4": "Sanster/Realistic_Vision_V1.4-inpainting",
+}
+
+NATIVE_NAMES_MAP = {
+    "sd1.5": "runwayml/stable-diffusion-v1-5",
+    "anything4": "andite/anything-v4.0",
+    "realisticVision1.4": "SG161222/Realistic_Vision_V1.4",
+}
+
+
+def make_inpaint_condition(image, image_mask):
+    """
+    image: [H, W, C] RGB
+    mask: [H, W, 1] 255 means area to repaint
+    """
+    image = image.astype(np.float32) / 255.0
+    image[image_mask[:, :, -1] > 128] = -1.0  # set as masked pixel
+    image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
+    image = torch.from_numpy(image)
+    return image
+
+
+def load_from_local_model(
+    local_model_path, torch_dtype, controlnet, pipe_class, is_native_control_inpaint
+):
+    from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
+        download_from_original_stable_diffusion_ckpt,
+    )
+
+    logger.info(f"Converting {local_model_path} to diffusers controlnet pipeline")
+
+    try:
+        pipe = download_from_original_stable_diffusion_ckpt(
+            local_model_path,
+            num_in_channels=4 if is_native_control_inpaint else 9,
+            from_safetensors=local_model_path.endswith("safetensors"),
+            device="cpu",
+            load_safety_checker=False,
+        )
+    except Exception as e:
+        err_msg = str(e)
+        logger.exception(e)
+        if is_native_control_inpaint and "[320, 9, 3, 3]" in err_msg:
+            logger.error(
+                "control_v11p_sd15_inpaint method requires normal SD model, not inpainting SD model"
+            )
+        if not is_native_control_inpaint and "[320, 4, 3, 3]" in err_msg:
+            logger.error(
+                f"{controlnet.config['_name_or_path']} method requires inpainting SD model, "
+                f"you can convert any SD model to inpainting model in AUTO1111: \n"
+                f"https://www.reddit.com/r/StableDiffusion/comments/zyi24j/how_to_turn_any_model_into_an_inpainting_model/"
+            )
+        exit(-1)
+
+    inpaint_pipe = pipe_class(
+        vae=pipe.vae,
+        text_encoder=pipe.text_encoder,
+        tokenizer=pipe.tokenizer,
+        unet=pipe.unet,
+        controlnet=controlnet,
+        scheduler=pipe.scheduler,
+        safety_checker=None,
+        feature_extractor=None,
+        requires_safety_checker=False,
+    )
+
+    del pipe
+    gc.collect()
+    return inpaint_pipe.to(torch_dtype=torch_dtype)
+
+
+class ControlNet(DiffusionInpaintModel):
+    name = "controlnet"
+    pad_mod = 8
+    min_size = 512
+
+    def init_model(self, device: torch.device, **kwargs):
+        fp16 = not kwargs.get("no_half", False)
+
+        model_kwargs = {
+            "local_files_only": kwargs.get("local_files_only", kwargs["sd_run_local"])
+        }
+        if kwargs["disable_nsfw"] or kwargs.get("cpu_offload", False):
+            logger.info("Disable Stable Diffusion Model NSFW checker")
+            model_kwargs.update(
+                dict(
+                    safety_checker=None,
+                    feature_extractor=None,
+                    requires_safety_checker=False,
+                )
+            )
+
+        use_gpu = device == torch.device("cuda") and torch.cuda.is_available()
+        torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
+
+        sd_controlnet_method = kwargs["sd_controlnet_method"]
+        self.sd_controlnet_method = sd_controlnet_method
+
+        if sd_controlnet_method == "control_v11p_sd15_inpaint":
+            from diffusers import StableDiffusionControlNetPipeline as PipeClass
+
+            self.is_native_control_inpaint = True
+        else:
+            from .pipeline import StableDiffusionControlNetInpaintPipeline as PipeClass
+
+            self.is_native_control_inpaint = False
+
+        if self.is_native_control_inpaint:
+            model_id = NATIVE_NAMES_MAP[kwargs["name"]]
+        else:
+            model_id = NAMES_MAP[kwargs["name"]]
+
+        controlnet = ControlNetModel.from_pretrained(
+            f"lllyasviel/{sd_controlnet_method}", torch_dtype=torch_dtype
+        )
+        self.is_local_sd_model = False
+        if kwargs.get("sd_local_model_path", None):
+            self.is_local_sd_model = True
+            self.model = load_from_local_model(
+                kwargs["sd_local_model_path"],
+                torch_dtype=torch_dtype,
+                controlnet=controlnet,
+                pipe_class=PipeClass,
+                is_native_control_inpaint=self.is_native_control_inpaint,
+            )
+        else:
+            self.model = PipeClass.from_pretrained(
+                model_id,
+                controlnet=controlnet,
+                revision="fp16" if use_gpu and fp16 else "main",
+                torch_dtype=torch_dtype,
+                **model_kwargs,
+            )
+
+        # https://huggingface.co/docs/diffusers/v0.7.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing
+        self.model.enable_attention_slicing()
+        # https://huggingface.co/docs/diffusers/v0.7.0/en/optimization/fp16#memory-efficient-attention
+        if kwargs.get("enable_xformers", False):
+            self.model.enable_xformers_memory_efficient_attention()
+
+        if kwargs.get("cpu_offload", False) and use_gpu:
+            logger.info("Enable sequential cpu offload")
+            self.model.enable_sequential_cpu_offload(gpu_id=0)
+        else:
+            self.model = self.model.to(device)
+            if kwargs["sd_cpu_textencoder"]:
+                logger.info("Run Stable Diffusion TextEncoder on CPU")
+                self.model.text_encoder = CPUTextEncoderWrapper(
+                    self.model.text_encoder, torch_dtype
+                )
+
+        self.callback = kwargs.pop("callback", None)
+
+    def forward(self, image, mask, config: Config):
+        """Input image and output image have same size
+        image: [H, W, C] RGB
+        mask: [H, W, 1] 255 means area to repaint
+        return: BGR IMAGE
+        """
+        scheduler_config = self.model.scheduler.config
+        scheduler = get_scheduler(config.sd_sampler, scheduler_config)
+        self.model.scheduler = scheduler
+
+        if config.sd_mask_blur != 0:
+            k = 2 * config.sd_mask_blur + 1
+            mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
+
+        img_h, img_w = image.shape[:2]
+
+        if self.is_native_control_inpaint:
+            control_image = make_inpaint_condition(image, mask)
+            output = self.model(
+                prompt=config.prompt,
+                image=control_image,
+                height=img_h,
+                width=img_w,
+                num_inference_steps=config.sd_steps,
+                guidance_scale=config.sd_guidance_scale,
+                controlnet_conditioning_scale=config.controlnet_conditioning_scale,
+                negative_prompt=config.negative_prompt,
+                generator=torch.manual_seed(config.sd_seed),
+                output_type="np.array",
+                callback=self.callback,
+            ).images[0]
+        else:
+            if "canny" in self.sd_controlnet_method:
+                canny_image = cv2.Canny(image, 100, 200)
+                canny_image = canny_image[:, :, None]
+                canny_image = np.concatenate(
+                    [canny_image, canny_image, canny_image], axis=2
+                )
+                canny_image = PIL.Image.fromarray(canny_image)
+                control_image = canny_image
+            elif "openpose" in self.sd_controlnet_method:
+                from controlnet_aux import OpenposeDetector
+
+                processor = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
+                control_image = processor(image, hand_and_face=True)
+            elif "depth" in self.sd_controlnet_method:
+                from transformers import pipeline
+
+                depth_estimator = pipeline("depth-estimation")
+                depth_image = depth_estimator(PIL.Image.fromarray(image))["depth"]
+                depth_image = np.array(depth_image)
+                depth_image = depth_image[:, :, None]
+                depth_image = np.concatenate(
+                    [depth_image, depth_image, depth_image], axis=2
+                )
+                control_image = PIL.Image.fromarray(depth_image)
+            else:
+                raise NotImplementedError(
+                    f"{self.sd_controlnet_method} not implemented"
+                )
+
+            mask_image = PIL.Image.fromarray(mask[:, :, -1], mode="L")
+            image = PIL.Image.fromarray(image)
+
+            output = self.model(
+                image=image,
+                control_image=control_image,
+                prompt=config.prompt,
+                negative_prompt=config.negative_prompt,
+                mask_image=mask_image,
+                num_inference_steps=config.sd_steps,
+                guidance_scale=config.sd_guidance_scale,
+                output_type="np.array",
+                callback=self.callback,
+                height=img_h,
+                width=img_w,
+                generator=torch.manual_seed(config.sd_seed),
+                controlnet_conditioning_scale=config.controlnet_conditioning_scale,
+            ).images[0]
+
+        output = (output * 255).round().astype("uint8")
+        output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
+        return output
+
+    def forward_post_process(self, result, image, mask, config):
+        if config.sd_match_histograms:
+            result = self._match_histograms(result, image[:, :, ::-1], mask)
+
+        if config.sd_mask_blur != 0:
+            k = 2 * config.sd_mask_blur + 1
+            mask = cv2.GaussianBlur(mask, (k, k), 0)
+        return result, image, mask
+
+    @staticmethod
+    def is_downloaded() -> bool:
+        # model will be downloaded when app start, and can't switch in frontend settings
+        return True
diff --git a/lama_cleaner/model/ddim_sampler.py b/lama_cleaner/model/ddim_sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1e440098a7d8dba3a9c0b04011dc9262ffc87f0
--- /dev/null
+++ b/lama_cleaner/model/ddim_sampler.py
@@ -0,0 +1,193 @@
+import torch
+import numpy as np
+from tqdm import tqdm
+
+from lama_cleaner.model.utils import make_ddim_timesteps, make_ddim_sampling_parameters, noise_like
+
+from loguru import logger
+
+
+class DDIMSampler(object):
+    def __init__(self, model, schedule="linear"):
+        super().__init__()
+        self.model = model
+        self.ddpm_num_timesteps = model.num_timesteps
+        self.schedule = schedule
+
+    def register_buffer(self, name, attr):
+        setattr(self, name, attr)
+
+    def make_schedule(
+        self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0, verbose=True
+    ):
+        self.ddim_timesteps = make_ddim_timesteps(
+            ddim_discr_method=ddim_discretize,
+            num_ddim_timesteps=ddim_num_steps,
+            # array([1])
+            num_ddpm_timesteps=self.ddpm_num_timesteps,
+            verbose=verbose,
+        )
+        alphas_cumprod = self.model.alphas_cumprod  # torch.Size([1000])
+        assert (
+                alphas_cumprod.shape[0] == self.ddpm_num_timesteps
+        ), "alphas have to be defined for each timestep"
+        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+        self.register_buffer("betas", to_torch(self.model.betas))
+        self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
+        self.register_buffer(
+            "alphas_cumprod_prev", to_torch(self.model.alphas_cumprod_prev)
+        )
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer(
+            "sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod.cpu()))
+        )
+        self.register_buffer(
+            "sqrt_one_minus_alphas_cumprod",
+            to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())),
+        )
+        self.register_buffer(
+            "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod.cpu()))
+        )
+        self.register_buffer(
+            "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu()))
+        )
+        self.register_buffer(
+            "sqrt_recipm1_alphas_cumprod",
+            to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)),
+        )
+
+        # ddim sampling parameters
+        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(
+            alphacums=alphas_cumprod.cpu(),
+            ddim_timesteps=self.ddim_timesteps,
+            eta=ddim_eta,
+            verbose=verbose,
+        )
+        self.register_buffer("ddim_sigmas", ddim_sigmas)
+        self.register_buffer("ddim_alphas", ddim_alphas)
+        self.register_buffer("ddim_alphas_prev", ddim_alphas_prev)
+        self.register_buffer("ddim_sqrt_one_minus_alphas", np.sqrt(1.0 - ddim_alphas))
+        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
+            (1 - self.alphas_cumprod_prev)
+            / (1 - self.alphas_cumprod)
+            * (1 - self.alphas_cumprod / self.alphas_cumprod_prev)
+        )
+        self.register_buffer(
+            "ddim_sigmas_for_original_num_steps", sigmas_for_original_sampling_steps
+        )
+
+    @torch.no_grad()
+    def sample(self, steps, conditioning, batch_size, shape):
+        self.make_schedule(ddim_num_steps=steps, ddim_eta=0, verbose=False)
+        # sampling
+        C, H, W = shape
+        size = (batch_size, C, H, W)
+
+        # samples: 1,3,128,128
+        return self.ddim_sampling(
+            conditioning,
+            size,
+            quantize_denoised=False,
+            ddim_use_original_steps=False,
+            noise_dropout=0,
+            temperature=1.0,
+        )
+
+    @torch.no_grad()
+    def ddim_sampling(
+        self,
+        cond,
+        shape,
+        ddim_use_original_steps=False,
+        quantize_denoised=False,
+        temperature=1.0,
+        noise_dropout=0.0,
+    ):
+        device = self.model.betas.device
+        b = shape[0]
+        img = torch.randn(shape, device=device, dtype=cond.dtype)
+        timesteps = (
+            self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+        )
+
+        time_range = (
+            reversed(range(0, timesteps))
+            if ddim_use_original_steps
+            else np.flip(timesteps)
+        )
+        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
+        logger.info(f"Running DDIM Sampling with {total_steps} timesteps")
+
+        iterator = tqdm(time_range, desc="DDIM Sampler", total=total_steps)
+
+        for i, step in enumerate(iterator):
+            index = total_steps - i - 1
+            ts = torch.full((b,), step, device=device, dtype=torch.long)
+
+            outs = self.p_sample_ddim(
+                img,
+                cond,
+                ts,
+                index=index,
+                use_original_steps=ddim_use_original_steps,
+                quantize_denoised=quantize_denoised,
+                temperature=temperature,
+                noise_dropout=noise_dropout,
+            )
+            img, _ = outs
+
+        return img
+
+    @torch.no_grad()
+    def p_sample_ddim(
+        self,
+        x,
+        c,
+        t,
+        index,
+        repeat_noise=False,
+        use_original_steps=False,
+        quantize_denoised=False,
+        temperature=1.0,
+        noise_dropout=0.0,
+    ):
+        b, *_, device = *x.shape, x.device
+        e_t = self.model.apply_model(x, t, c)
+
+        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+        alphas_prev = (
+            self.model.alphas_cumprod_prev
+            if use_original_steps
+            else self.ddim_alphas_prev
+        )
+        sqrt_one_minus_alphas = (
+            self.model.sqrt_one_minus_alphas_cumprod
+            if use_original_steps
+            else self.ddim_sqrt_one_minus_alphas
+        )
+        sigmas = (
+            self.model.ddim_sigmas_for_original_num_steps
+            if use_original_steps
+            else self.ddim_sigmas
+        )
+        # select parameters corresponding to the currently considered timestep
+        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+        sqrt_one_minus_at = torch.full(
+            (b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
+        )
+
+        # current prediction for x_0
+        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+        if quantize_denoised:  # 没用
+            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+        # direction pointing to x_t
+        dir_xt = (1.0 - a_prev - sigma_t ** 2).sqrt() * e_t
+        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+        if noise_dropout > 0.0:  # 没用
+            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+        return x_prev, pred_x0
diff --git a/lama_cleaner/model/fcf.py b/lama_cleaner/model/fcf.py
new file mode 100644
index 0000000000000000000000000000000000000000..07292c6908d535d2a66920715856467cfb1989fd
--- /dev/null
+++ b/lama_cleaner/model/fcf.py
@@ -0,0 +1,1733 @@
+import os
+import random
+
+import cv2
+import torch
+import numpy as np
+import torch.fft as fft
+
+from lama_cleaner.schema import Config
+
+from lama_cleaner.helper import (
+    load_model,
+    get_cache_path_by_url,
+    norm_img,
+    boxes_from_mask,
+    resize_max_size,
+)
+from lama_cleaner.model.base import InpaintModel
+from torch import conv2d, nn
+import torch.nn.functional as F
+
+from lama_cleaner.model.utils import (
+    setup_filter,
+    _parse_scaling,
+    _parse_padding,
+    Conv2dLayer,
+    FullyConnectedLayer,
+    MinibatchStdLayer,
+    activation_funcs,
+    conv2d_resample,
+    bias_act,
+    upsample2d,
+    normalize_2nd_moment,
+    downsample2d,
+)
+
+
+def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl="cuda"):
+    assert isinstance(x, torch.Tensor)
+    return _upfirdn2d_ref(
+        x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain
+    )
+
+
+def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1):
+    """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops."""
+    # Validate arguments.
+    assert isinstance(x, torch.Tensor) and x.ndim == 4
+    if f is None:
+        f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
+    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
+    assert f.dtype == torch.float32 and not f.requires_grad
+    batch_size, num_channels, in_height, in_width = x.shape
+    upx, upy = _parse_scaling(up)
+    downx, downy = _parse_scaling(down)
+    padx0, padx1, pady0, pady1 = _parse_padding(padding)
+
+    # Upsample by inserting zeros.
+    x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1])
+    x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1])
+    x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx])
+
+    # Pad or crop.
+    x = torch.nn.functional.pad(
+        x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)]
+    )
+    x = x[
+        :,
+        :,
+        max(-pady0, 0) : x.shape[2] - max(-pady1, 0),
+        max(-padx0, 0) : x.shape[3] - max(-padx1, 0),
+    ]
+
+    # Setup filter.
+    f = f * (gain ** (f.ndim / 2))
+    f = f.to(x.dtype)
+    if not flip_filter:
+        f = f.flip(list(range(f.ndim)))
+
+    # Convolve with the filter.
+    f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim)
+    if f.ndim == 4:
+        x = conv2d(input=x, weight=f, groups=num_channels)
+    else:
+        x = conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels)
+        x = conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels)
+
+    # Downsample by throwing away pixels.
+    x = x[:, :, ::downy, ::downx]
+    return x
+
+
+class EncoderEpilogue(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels,  # Number of input channels.
+        cmap_dim,  # Dimensionality of mapped conditioning label, 0 = no label.
+        z_dim,  # Output Latent (Z) dimensionality.
+        resolution,  # Resolution of this block.
+        img_channels,  # Number of input color channels.
+        architecture="resnet",  # Architecture: 'orig', 'skip', 'resnet'.
+        mbstd_group_size=4,  # Group size for the minibatch standard deviation layer, None = entire minibatch.
+        mbstd_num_channels=1,  # Number of features for the minibatch standard deviation layer, 0 = disable.
+        activation="lrelu",  # Activation function: 'relu', 'lrelu', etc.
+        conv_clamp=None,  # Clamp the output of convolution layers to +-X, None = disable clamping.
+    ):
+        assert architecture in ["orig", "skip", "resnet"]
+        super().__init__()
+        self.in_channels = in_channels
+        self.cmap_dim = cmap_dim
+        self.resolution = resolution
+        self.img_channels = img_channels
+        self.architecture = architecture
+
+        if architecture == "skip":
+            self.fromrgb = Conv2dLayer(
+                self.img_channels, in_channels, kernel_size=1, activation=activation
+            )
+        self.mbstd = (
+            MinibatchStdLayer(
+                group_size=mbstd_group_size, num_channels=mbstd_num_channels
+            )
+            if mbstd_num_channels > 0
+            else None
+        )
+        self.conv = Conv2dLayer(
+            in_channels + mbstd_num_channels,
+            in_channels,
+            kernel_size=3,
+            activation=activation,
+            conv_clamp=conv_clamp,
+        )
+        self.fc = FullyConnectedLayer(
+            in_channels * (resolution**2), z_dim, activation=activation
+        )
+        self.dropout = torch.nn.Dropout(p=0.5)
+
+    def forward(self, x, cmap, force_fp32=False):
+        _ = force_fp32  # unused
+        dtype = torch.float32
+        memory_format = torch.contiguous_format
+
+        # FromRGB.
+        x = x.to(dtype=dtype, memory_format=memory_format)
+
+        # Main layers.
+        if self.mbstd is not None:
+            x = self.mbstd(x)
+        const_e = self.conv(x)
+        x = self.fc(const_e.flatten(1))
+        x = self.dropout(x)
+
+        # Conditioning.
+        if self.cmap_dim > 0:
+            x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))
+
+        assert x.dtype == dtype
+        return x, const_e
+
+
+class EncoderBlock(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels,  # Number of input channels, 0 = first block.
+        tmp_channels,  # Number of intermediate channels.
+        out_channels,  # Number of output channels.
+        resolution,  # Resolution of this block.
+        img_channels,  # Number of input color channels.
+        first_layer_idx,  # Index of the first layer.
+        architecture="skip",  # Architecture: 'orig', 'skip', 'resnet'.
+        activation="lrelu",  # Activation function: 'relu', 'lrelu', etc.
+        resample_filter=[
+            1,
+            3,
+            3,
+            1,
+        ],  # Low-pass filter to apply when resampling activations.
+        conv_clamp=None,  # Clamp the output of convolution layers to +-X, None = disable clamping.
+        use_fp16=False,  # Use FP16 for this block?
+        fp16_channels_last=False,  # Use channels-last memory format with FP16?
+        freeze_layers=0,  # Freeze-D: Number of layers to freeze.
+    ):
+        assert in_channels in [0, tmp_channels]
+        assert architecture in ["orig", "skip", "resnet"]
+        super().__init__()
+        self.in_channels = in_channels
+        self.resolution = resolution
+        self.img_channels = img_channels + 1
+        self.first_layer_idx = first_layer_idx
+        self.architecture = architecture
+        self.use_fp16 = use_fp16
+        self.channels_last = use_fp16 and fp16_channels_last
+        self.register_buffer("resample_filter", setup_filter(resample_filter))
+
+        self.num_layers = 0
+
+        def trainable_gen():
+            while True:
+                layer_idx = self.first_layer_idx + self.num_layers
+                trainable = layer_idx >= freeze_layers
+                self.num_layers += 1
+                yield trainable
+
+        trainable_iter = trainable_gen()
+
+        if in_channels == 0:
+            self.fromrgb = Conv2dLayer(
+                self.img_channels,
+                tmp_channels,
+                kernel_size=1,
+                activation=activation,
+                trainable=next(trainable_iter),
+                conv_clamp=conv_clamp,
+                channels_last=self.channels_last,
+            )
+
+        self.conv0 = Conv2dLayer(
+            tmp_channels,
+            tmp_channels,
+            kernel_size=3,
+            activation=activation,
+            trainable=next(trainable_iter),
+            conv_clamp=conv_clamp,
+            channels_last=self.channels_last,
+        )
+
+        self.conv1 = Conv2dLayer(
+            tmp_channels,
+            out_channels,
+            kernel_size=3,
+            activation=activation,
+            down=2,
+            trainable=next(trainable_iter),
+            resample_filter=resample_filter,
+            conv_clamp=conv_clamp,
+            channels_last=self.channels_last,
+        )
+
+        if architecture == "resnet":
+            self.skip = Conv2dLayer(
+                tmp_channels,
+                out_channels,
+                kernel_size=1,
+                bias=False,
+                down=2,
+                trainable=next(trainable_iter),
+                resample_filter=resample_filter,
+                channels_last=self.channels_last,
+            )
+
+    def forward(self, x, img, force_fp32=False):
+        # dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
+        dtype = torch.float32
+        memory_format = (
+            torch.channels_last
+            if self.channels_last and not force_fp32
+            else torch.contiguous_format
+        )
+
+        # Input.
+        if x is not None:
+            x = x.to(dtype=dtype, memory_format=memory_format)
+
+        # FromRGB.
+        if self.in_channels == 0:
+            img = img.to(dtype=dtype, memory_format=memory_format)
+            y = self.fromrgb(img)
+            x = x + y if x is not None else y
+            img = (
+                downsample2d(img, self.resample_filter)
+                if self.architecture == "skip"
+                else None
+            )
+
+        # Main layers.
+        if self.architecture == "resnet":
+            y = self.skip(x, gain=np.sqrt(0.5))
+            x = self.conv0(x)
+            feat = x.clone()
+            x = self.conv1(x, gain=np.sqrt(0.5))
+            x = y.add_(x)
+        else:
+            x = self.conv0(x)
+            feat = x.clone()
+            x = self.conv1(x)
+
+        assert x.dtype == dtype
+        return x, img, feat
+
+
+class EncoderNetwork(torch.nn.Module):
+    def __init__(
+        self,
+        c_dim,  # Conditioning label (C) dimensionality.
+        z_dim,  # Input latent (Z) dimensionality.
+        img_resolution,  # Input resolution.
+        img_channels,  # Number of input color channels.
+        architecture="orig",  # Architecture: 'orig', 'skip', 'resnet'.
+        channel_base=16384,  # Overall multiplier for the number of channels.
+        channel_max=512,  # Maximum number of channels in any layer.
+        num_fp16_res=0,  # Use FP16 for the N highest resolutions.
+        conv_clamp=None,  # Clamp the output of convolution layers to +-X, None = disable clamping.
+        cmap_dim=None,  # Dimensionality of mapped conditioning label, None = default.
+        block_kwargs={},  # Arguments for DiscriminatorBlock.
+        mapping_kwargs={},  # Arguments for MappingNetwork.
+        epilogue_kwargs={},  # Arguments for EncoderEpilogue.
+    ):
+        super().__init__()
+        self.c_dim = c_dim
+        self.z_dim = z_dim
+        self.img_resolution = img_resolution
+        self.img_resolution_log2 = int(np.log2(img_resolution))
+        self.img_channels = img_channels
+        self.block_resolutions = [
+            2**i for i in range(self.img_resolution_log2, 2, -1)
+        ]
+        channels_dict = {
+            res: min(channel_base // res, channel_max)
+            for res in self.block_resolutions + [4]
+        }
+        fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
+
+        if cmap_dim is None:
+            cmap_dim = channels_dict[4]
+        if c_dim == 0:
+            cmap_dim = 0
+
+        common_kwargs = dict(
+            img_channels=img_channels, architecture=architecture, conv_clamp=conv_clamp
+        )
+        cur_layer_idx = 0
+        for res in self.block_resolutions:
+            in_channels = channels_dict[res] if res < img_resolution else 0
+            tmp_channels = channels_dict[res]
+            out_channels = channels_dict[res // 2]
+            use_fp16 = res >= fp16_resolution
+            use_fp16 = False
+            block = EncoderBlock(
+                in_channels,
+                tmp_channels,
+                out_channels,
+                resolution=res,
+                first_layer_idx=cur_layer_idx,
+                use_fp16=use_fp16,
+                **block_kwargs,
+                **common_kwargs,
+            )
+            setattr(self, f"b{res}", block)
+            cur_layer_idx += block.num_layers
+        if c_dim > 0:
+            self.mapping = MappingNetwork(
+                z_dim=0,
+                c_dim=c_dim,
+                w_dim=cmap_dim,
+                num_ws=None,
+                w_avg_beta=None,
+                **mapping_kwargs,
+            )
+        self.b4 = EncoderEpilogue(
+            channels_dict[4],
+            cmap_dim=cmap_dim,
+            z_dim=z_dim * 2,
+            resolution=4,
+            **epilogue_kwargs,
+            **common_kwargs,
+        )
+
+    def forward(self, img, c, **block_kwargs):
+        x = None
+        feats = {}
+        for res in self.block_resolutions:
+            block = getattr(self, f"b{res}")
+            x, img, feat = block(x, img, **block_kwargs)
+            feats[res] = feat
+
+        cmap = None
+        if self.c_dim > 0:
+            cmap = self.mapping(None, c)
+        x, const_e = self.b4(x, cmap)
+        feats[4] = const_e
+
+        B, _ = x.shape
+        z = torch.zeros(
+            (B, self.z_dim), requires_grad=False, dtype=x.dtype, device=x.device
+        )  ## Noise for Co-Modulation
+        return x, z, feats
+
+
+def fma(a, b, c):  # => a * b + c
+    return _FusedMultiplyAdd.apply(a, b, c)
+
+
+class _FusedMultiplyAdd(torch.autograd.Function):  # a * b + c
+    @staticmethod
+    def forward(ctx, a, b, c):  # pylint: disable=arguments-differ
+        out = torch.addcmul(c, a, b)
+        ctx.save_for_backward(a, b)
+        ctx.c_shape = c.shape
+        return out
+
+    @staticmethod
+    def backward(ctx, dout):  # pylint: disable=arguments-differ
+        a, b = ctx.saved_tensors
+        c_shape = ctx.c_shape
+        da = None
+        db = None
+        dc = None
+
+        if ctx.needs_input_grad[0]:
+            da = _unbroadcast(dout * b, a.shape)
+
+        if ctx.needs_input_grad[1]:
+            db = _unbroadcast(dout * a, b.shape)
+
+        if ctx.needs_input_grad[2]:
+            dc = _unbroadcast(dout, c_shape)
+
+        return da, db, dc
+
+
+def _unbroadcast(x, shape):
+    extra_dims = x.ndim - len(shape)
+    assert extra_dims >= 0
+    dim = [
+        i
+        for i in range(x.ndim)
+        if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1)
+    ]
+    if len(dim):
+        x = x.sum(dim=dim, keepdim=True)
+    if extra_dims:
+        x = x.reshape(-1, *x.shape[extra_dims + 1 :])
+    assert x.shape == shape
+    return x
+
+
+def modulated_conv2d(
+    x,  # Input tensor of shape [batch_size, in_channels, in_height, in_width].
+    weight,  # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width].
+    styles,  # Modulation coefficients of shape [batch_size, in_channels].
+    noise=None,  # Optional noise tensor to add to the output activations.
+    up=1,  # Integer upsampling factor.
+    down=1,  # Integer downsampling factor.
+    padding=0,  # Padding with respect to the upsampled image.
+    resample_filter=None,
+    # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter().
+    demodulate=True,  # Apply weight demodulation?
+    flip_weight=True,  # False = convolution, True = correlation (matches torch.nn.functional.conv2d).
+    fused_modconv=True,  # Perform modulation, convolution, and demodulation as a single fused operation?
+):
+    batch_size = x.shape[0]
+    out_channels, in_channels, kh, kw = weight.shape
+
+    # Pre-normalize inputs to avoid FP16 overflow.
+    if x.dtype == torch.float16 and demodulate:
+        weight = weight * (
+            1
+            / np.sqrt(in_channels * kh * kw)
+            / weight.norm(float("inf"), dim=[1, 2, 3], keepdim=True)
+        )  # max_Ikk
+        styles = styles / styles.norm(float("inf"), dim=1, keepdim=True)  # max_I
+
+    # Calculate per-sample weights and demodulation coefficients.
+    w = None
+    dcoefs = None
+    if demodulate or fused_modconv:
+        w = weight.unsqueeze(0)  # [NOIkk]
+        w = w * styles.reshape(batch_size, 1, -1, 1, 1)  # [NOIkk]
+    if demodulate:
+        dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt()  # [NO]
+    if demodulate and fused_modconv:
+        w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1)  # [NOIkk]
+    # Execute by scaling the activations before and after the convolution.
+    if not fused_modconv:
+        x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1)
+        x = conv2d_resample.conv2d_resample(
+            x=x,
+            w=weight.to(x.dtype),
+            f=resample_filter,
+            up=up,
+            down=down,
+            padding=padding,
+            flip_weight=flip_weight,
+        )
+        if demodulate and noise is not None:
+            x = fma(
+                x, dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1), noise.to(x.dtype)
+            )
+        elif demodulate:
+            x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1)
+        elif noise is not None:
+            x = x.add_(noise.to(x.dtype))
+        return x
+
+    # Execute as one fused op using grouped convolution.
+    batch_size = int(batch_size)
+    x = x.reshape(1, -1, *x.shape[2:])
+    w = w.reshape(-1, in_channels, kh, kw)
+    x = conv2d_resample(
+        x=x,
+        w=w.to(x.dtype),
+        f=resample_filter,
+        up=up,
+        down=down,
+        padding=padding,
+        groups=batch_size,
+        flip_weight=flip_weight,
+    )
+    x = x.reshape(batch_size, -1, *x.shape[2:])
+    if noise is not None:
+        x = x.add_(noise)
+    return x
+
+
+class SynthesisLayer(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels,  # Number of input channels.
+        out_channels,  # Number of output channels.
+        w_dim,  # Intermediate latent (W) dimensionality.
+        resolution,  # Resolution of this layer.
+        kernel_size=3,  # Convolution kernel size.
+        up=1,  # Integer upsampling factor.
+        use_noise=True,  # Enable noise input?
+        activation="lrelu",  # Activation function: 'relu', 'lrelu', etc.
+        resample_filter=[
+            1,
+            3,
+            3,
+            1,
+        ],  # Low-pass filter to apply when resampling activations.
+        conv_clamp=None,  # Clamp the output of convolution layers to +-X, None = disable clamping.
+        channels_last=False,  # Use channels_last format for the weights?
+    ):
+        super().__init__()
+        self.resolution = resolution
+        self.up = up
+        self.use_noise = use_noise
+        self.activation = activation
+        self.conv_clamp = conv_clamp
+        self.register_buffer("resample_filter", setup_filter(resample_filter))
+        self.padding = kernel_size // 2
+        self.act_gain = activation_funcs[activation].def_gain
+
+        self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
+        memory_format = (
+            torch.channels_last if channels_last else torch.contiguous_format
+        )
+        self.weight = torch.nn.Parameter(
+            torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(
+                memory_format=memory_format
+            )
+        )
+        if use_noise:
+            self.register_buffer("noise_const", torch.randn([resolution, resolution]))
+            self.noise_strength = torch.nn.Parameter(torch.zeros([]))
+        self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
+
+    def forward(self, x, w, noise_mode="none", fused_modconv=True, gain=1):
+        assert noise_mode in ["random", "const", "none"]
+        in_resolution = self.resolution // self.up
+        styles = self.affine(w)
+
+        noise = None
+        if self.use_noise and noise_mode == "random":
+            noise = (
+                torch.randn(
+                    [x.shape[0], 1, self.resolution, self.resolution], device=x.device
+                )
+                * self.noise_strength
+            )
+        if self.use_noise and noise_mode == "const":
+            noise = self.noise_const * self.noise_strength
+
+        flip_weight = self.up == 1  # slightly faster
+        x = modulated_conv2d(
+            x=x,
+            weight=self.weight,
+            styles=styles,
+            noise=noise,
+            up=self.up,
+            padding=self.padding,
+            resample_filter=self.resample_filter,
+            flip_weight=flip_weight,
+            fused_modconv=fused_modconv,
+        )
+
+        act_gain = self.act_gain * gain
+        act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
+        x = F.leaky_relu(x, negative_slope=0.2, inplace=False)
+        if act_gain != 1:
+            x = x * act_gain
+        if act_clamp is not None:
+            x = x.clamp(-act_clamp, act_clamp)
+        return x
+
+
+class ToRGBLayer(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        w_dim,
+        kernel_size=1,
+        conv_clamp=None,
+        channels_last=False,
+    ):
+        super().__init__()
+        self.conv_clamp = conv_clamp
+        self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
+        memory_format = (
+            torch.channels_last if channels_last else torch.contiguous_format
+        )
+        self.weight = torch.nn.Parameter(
+            torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(
+                memory_format=memory_format
+            )
+        )
+        self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
+        self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size**2))
+
+    def forward(self, x, w, fused_modconv=True):
+        styles = self.affine(w) * self.weight_gain
+        x = modulated_conv2d(
+            x=x,
+            weight=self.weight,
+            styles=styles,
+            demodulate=False,
+            fused_modconv=fused_modconv,
+        )
+        x = bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp)
+        return x
+
+
+class SynthesisForeword(torch.nn.Module):
+    def __init__(
+        self,
+        z_dim,  # Output Latent (Z) dimensionality.
+        resolution,  # Resolution of this block.
+        in_channels,
+        img_channels,  # Number of input color channels.
+        architecture="skip",  # Architecture: 'orig', 'skip', 'resnet'.
+        activation="lrelu",  # Activation function: 'relu', 'lrelu', etc.
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.z_dim = z_dim
+        self.resolution = resolution
+        self.img_channels = img_channels
+        self.architecture = architecture
+
+        self.fc = FullyConnectedLayer(
+            self.z_dim, (self.z_dim // 2) * 4 * 4, activation=activation
+        )
+        self.conv = SynthesisLayer(
+            self.in_channels, self.in_channels, w_dim=(z_dim // 2) * 3, resolution=4
+        )
+
+        if architecture == "skip":
+            self.torgb = ToRGBLayer(
+                self.in_channels,
+                self.img_channels,
+                kernel_size=1,
+                w_dim=(z_dim // 2) * 3,
+            )
+
+    def forward(self, x, ws, feats, img, force_fp32=False):
+        _ = force_fp32  # unused
+        dtype = torch.float32
+        memory_format = torch.contiguous_format
+
+        x_global = x.clone()
+        # ToRGB.
+        x = self.fc(x)
+        x = x.view(-1, self.z_dim // 2, 4, 4)
+        x = x.to(dtype=dtype, memory_format=memory_format)
+
+        # Main layers.
+        x_skip = feats[4].clone()
+        x = x + x_skip
+
+        mod_vector = []
+        mod_vector.append(ws[:, 0])
+        mod_vector.append(x_global.clone())
+        mod_vector = torch.cat(mod_vector, dim=1)
+
+        x = self.conv(x, mod_vector)
+
+        mod_vector = []
+        mod_vector.append(ws[:, 2 * 2 - 3])
+        mod_vector.append(x_global.clone())
+        mod_vector = torch.cat(mod_vector, dim=1)
+
+        if self.architecture == "skip":
+            img = self.torgb(x, mod_vector)
+            img = img.to(dtype=torch.float32, memory_format=torch.contiguous_format)
+
+        assert x.dtype == dtype
+        return x, img
+
+
+class SELayer(nn.Module):
+    def __init__(self, channel, reduction=16):
+        super(SELayer, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Sequential(
+            nn.Linear(channel, channel // reduction, bias=False),
+            nn.ReLU(inplace=False),
+            nn.Linear(channel // reduction, channel, bias=False),
+            nn.Sigmoid(),
+        )
+
+    def forward(self, x):
+        b, c, _, _ = x.size()
+        y = self.avg_pool(x).view(b, c)
+        y = self.fc(y).view(b, c, 1, 1)
+        res = x * y.expand_as(x)
+        return res
+
+
+class FourierUnit(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        groups=1,
+        spatial_scale_factor=None,
+        spatial_scale_mode="bilinear",
+        spectral_pos_encoding=False,
+        use_se=False,
+        se_kwargs=None,
+        ffc3d=False,
+        fft_norm="ortho",
+    ):
+        # bn_layer not used
+        super(FourierUnit, self).__init__()
+        self.groups = groups
+
+        self.conv_layer = torch.nn.Conv2d(
+            in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0),
+            out_channels=out_channels * 2,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            groups=self.groups,
+            bias=False,
+        )
+        self.relu = torch.nn.ReLU(inplace=False)
+
+        # squeeze and excitation block
+        self.use_se = use_se
+        if use_se:
+            if se_kwargs is None:
+                se_kwargs = {}
+            self.se = SELayer(self.conv_layer.in_channels, **se_kwargs)
+
+        self.spatial_scale_factor = spatial_scale_factor
+        self.spatial_scale_mode = spatial_scale_mode
+        self.spectral_pos_encoding = spectral_pos_encoding
+        self.ffc3d = ffc3d
+        self.fft_norm = fft_norm
+
+    def forward(self, x):
+        batch = x.shape[0]
+
+        if self.spatial_scale_factor is not None:
+            orig_size = x.shape[-2:]
+            x = F.interpolate(
+                x,
+                scale_factor=self.spatial_scale_factor,
+                mode=self.spatial_scale_mode,
+                align_corners=False,
+            )
+
+        r_size = x.size()
+        # (batch, c, h, w/2+1, 2)
+        fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1)
+        ffted = fft.rfftn(x, dim=fft_dim, norm=self.fft_norm)
+        ffted = torch.stack((ffted.real, ffted.imag), dim=-1)
+        ffted = ffted.permute(0, 1, 4, 2, 3).contiguous()  # (batch, c, 2, h, w/2+1)
+        ffted = ffted.view(
+            (
+                batch,
+                -1,
+            )
+            + ffted.size()[3:]
+        )
+
+        if self.spectral_pos_encoding:
+            height, width = ffted.shape[-2:]
+            coords_vert = (
+                torch.linspace(0, 1, height)[None, None, :, None]
+                .expand(batch, 1, height, width)
+                .to(ffted)
+            )
+            coords_hor = (
+                torch.linspace(0, 1, width)[None, None, None, :]
+                .expand(batch, 1, height, width)
+                .to(ffted)
+            )
+            ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1)
+
+        if self.use_se:
+            ffted = self.se(ffted)
+
+        ffted = self.conv_layer(ffted)  # (batch, c*2, h, w/2+1)
+        ffted = self.relu(ffted)
+
+        ffted = (
+            ffted.view(
+                (
+                    batch,
+                    -1,
+                    2,
+                )
+                + ffted.size()[2:]
+            )
+            .permute(0, 1, 3, 4, 2)
+            .contiguous()
+        )  # (batch,c, t, h, w/2+1, 2)
+        ffted = torch.complex(ffted[..., 0], ffted[..., 1])
+
+        ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:]
+        output = torch.fft.irfftn(
+            ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm
+        )
+
+        if self.spatial_scale_factor is not None:
+            output = F.interpolate(
+                output,
+                size=orig_size,
+                mode=self.spatial_scale_mode,
+                align_corners=False,
+            )
+
+        return output
+
+
+class SpectralTransform(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        stride=1,
+        groups=1,
+        enable_lfu=True,
+        **fu_kwargs,
+    ):
+        # bn_layer not used
+        super(SpectralTransform, self).__init__()
+        self.enable_lfu = enable_lfu
+        if stride == 2:
+            self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
+        else:
+            self.downsample = nn.Identity()
+
+        self.stride = stride
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(
+                in_channels, out_channels // 2, kernel_size=1, groups=groups, bias=False
+            ),
+            # nn.BatchNorm2d(out_channels // 2),
+            nn.ReLU(inplace=True),
+        )
+        self.fu = FourierUnit(out_channels // 2, out_channels // 2, groups, **fu_kwargs)
+        if self.enable_lfu:
+            self.lfu = FourierUnit(out_channels // 2, out_channels // 2, groups)
+        self.conv2 = torch.nn.Conv2d(
+            out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False
+        )
+
+    def forward(self, x):
+
+        x = self.downsample(x)
+        x = self.conv1(x)
+        output = self.fu(x)
+
+        if self.enable_lfu:
+            n, c, h, w = x.shape
+            split_no = 2
+            split_s = h // split_no
+            xs = torch.cat(
+                torch.split(x[:, : c // 4], split_s, dim=-2), dim=1
+            ).contiguous()
+            xs = torch.cat(torch.split(xs, split_s, dim=-1), dim=1).contiguous()
+            xs = self.lfu(xs)
+            xs = xs.repeat(1, 1, split_no, split_no).contiguous()
+        else:
+            xs = 0
+
+        output = self.conv2(x + output + xs)
+
+        return output
+
+
+class FFC(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        ratio_gin,
+        ratio_gout,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=False,
+        enable_lfu=True,
+        padding_type="reflect",
+        gated=False,
+        **spectral_kwargs,
+    ):
+        super(FFC, self).__init__()
+
+        assert stride == 1 or stride == 2, "Stride should be 1 or 2."
+        self.stride = stride
+
+        in_cg = int(in_channels * ratio_gin)
+        in_cl = in_channels - in_cg
+        out_cg = int(out_channels * ratio_gout)
+        out_cl = out_channels - out_cg
+        # groups_g = 1 if groups == 1 else int(groups * ratio_gout)
+        # groups_l = 1 if groups == 1 else groups - groups_g
+
+        self.ratio_gin = ratio_gin
+        self.ratio_gout = ratio_gout
+        self.global_in_num = in_cg
+
+        module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d
+        self.convl2l = module(
+            in_cl,
+            out_cl,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            padding_mode=padding_type,
+        )
+        module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d
+        self.convl2g = module(
+            in_cl,
+            out_cg,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            padding_mode=padding_type,
+        )
+        module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d
+        self.convg2l = module(
+            in_cg,
+            out_cl,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            padding_mode=padding_type,
+        )
+        module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform
+        self.convg2g = module(
+            in_cg,
+            out_cg,
+            stride,
+            1 if groups == 1 else groups // 2,
+            enable_lfu,
+            **spectral_kwargs,
+        )
+
+        self.gated = gated
+        module = (
+            nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d
+        )
+        self.gate = module(in_channels, 2, 1)
+
+    def forward(self, x, fname=None):
+        x_l, x_g = x if type(x) is tuple else (x, 0)
+        out_xl, out_xg = 0, 0
+
+        if self.gated:
+            total_input_parts = [x_l]
+            if torch.is_tensor(x_g):
+                total_input_parts.append(x_g)
+            total_input = torch.cat(total_input_parts, dim=1)
+
+            gates = torch.sigmoid(self.gate(total_input))
+            g2l_gate, l2g_gate = gates.chunk(2, dim=1)
+        else:
+            g2l_gate, l2g_gate = 1, 1
+
+        spec_x = self.convg2g(x_g)
+
+        if self.ratio_gout != 1:
+            out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate
+        if self.ratio_gout != 0:
+            out_xg = self.convl2g(x_l) * l2g_gate + spec_x
+
+        return out_xl, out_xg
+
+
+class FFC_BN_ACT(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        ratio_gin,
+        ratio_gout,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=False,
+        norm_layer=nn.SyncBatchNorm,
+        activation_layer=nn.Identity,
+        padding_type="reflect",
+        enable_lfu=True,
+        **kwargs,
+    ):
+        super(FFC_BN_ACT, self).__init__()
+        self.ffc = FFC(
+            in_channels,
+            out_channels,
+            kernel_size,
+            ratio_gin,
+            ratio_gout,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            enable_lfu,
+            padding_type=padding_type,
+            **kwargs,
+        )
+        lnorm = nn.Identity if ratio_gout == 1 else norm_layer
+        gnorm = nn.Identity if ratio_gout == 0 else norm_layer
+        global_channels = int(out_channels * ratio_gout)
+        # self.bn_l = lnorm(out_channels - global_channels)
+        # self.bn_g = gnorm(global_channels)
+
+        lact = nn.Identity if ratio_gout == 1 else activation_layer
+        gact = nn.Identity if ratio_gout == 0 else activation_layer
+        self.act_l = lact(inplace=True)
+        self.act_g = gact(inplace=True)
+
+    def forward(self, x, fname=None):
+        x_l, x_g = self.ffc(
+            x,
+            fname=fname,
+        )
+        x_l = self.act_l(x_l)
+        x_g = self.act_g(x_g)
+        return x_l, x_g
+
+
+class FFCResnetBlock(nn.Module):
+    def __init__(
+        self,
+        dim,
+        padding_type,
+        norm_layer,
+        activation_layer=nn.ReLU,
+        dilation=1,
+        spatial_transform_kwargs=None,
+        inline=False,
+        ratio_gin=0.75,
+        ratio_gout=0.75,
+    ):
+        super().__init__()
+        self.conv1 = FFC_BN_ACT(
+            dim,
+            dim,
+            kernel_size=3,
+            padding=dilation,
+            dilation=dilation,
+            norm_layer=norm_layer,
+            activation_layer=activation_layer,
+            padding_type=padding_type,
+            ratio_gin=ratio_gin,
+            ratio_gout=ratio_gout,
+        )
+        self.conv2 = FFC_BN_ACT(
+            dim,
+            dim,
+            kernel_size=3,
+            padding=dilation,
+            dilation=dilation,
+            norm_layer=norm_layer,
+            activation_layer=activation_layer,
+            padding_type=padding_type,
+            ratio_gin=ratio_gin,
+            ratio_gout=ratio_gout,
+        )
+        self.inline = inline
+
+    def forward(self, x, fname=None):
+        if self.inline:
+            x_l, x_g = (
+                x[:, : -self.conv1.ffc.global_in_num],
+                x[:, -self.conv1.ffc.global_in_num :],
+            )
+        else:
+            x_l, x_g = x if type(x) is tuple else (x, 0)
+
+        id_l, id_g = x_l, x_g
+
+        x_l, x_g = self.conv1((x_l, x_g), fname=fname)
+        x_l, x_g = self.conv2((x_l, x_g), fname=fname)
+
+        x_l, x_g = id_l + x_l, id_g + x_g
+        out = x_l, x_g
+        if self.inline:
+            out = torch.cat(out, dim=1)
+        return out
+
+
+class ConcatTupleLayer(nn.Module):
+    def forward(self, x):
+        assert isinstance(x, tuple)
+        x_l, x_g = x
+        assert torch.is_tensor(x_l) or torch.is_tensor(x_g)
+        if not torch.is_tensor(x_g):
+            return x_l
+        return torch.cat(x, dim=1)
+
+
+class FFCBlock(torch.nn.Module):
+    def __init__(
+        self,
+        dim,  # Number of output/input channels.
+        kernel_size,  # Width and height of the convolution kernel.
+        padding,
+        ratio_gin=0.75,
+        ratio_gout=0.75,
+        activation="linear",  # Activation function: 'relu', 'lrelu', etc.
+    ):
+        super().__init__()
+        if activation == "linear":
+            self.activation = nn.Identity
+        else:
+            self.activation = nn.ReLU
+        self.padding = padding
+        self.kernel_size = kernel_size
+        self.ffc_block = FFCResnetBlock(
+            dim=dim,
+            padding_type="reflect",
+            norm_layer=nn.SyncBatchNorm,
+            activation_layer=self.activation,
+            dilation=1,
+            ratio_gin=ratio_gin,
+            ratio_gout=ratio_gout,
+        )
+
+        self.concat_layer = ConcatTupleLayer()
+
+    def forward(self, gen_ft, mask, fname=None):
+        x = gen_ft.float()
+
+        x_l, x_g = (
+            x[:, : -self.ffc_block.conv1.ffc.global_in_num],
+            x[:, -self.ffc_block.conv1.ffc.global_in_num :],
+        )
+        id_l, id_g = x_l, x_g
+
+        x_l, x_g = self.ffc_block((x_l, x_g), fname=fname)
+        x_l, x_g = id_l + x_l, id_g + x_g
+        x = self.concat_layer((x_l, x_g))
+
+        return x + gen_ft.float()
+
+
+class FFCSkipLayer(torch.nn.Module):
+    def __init__(
+        self,
+        dim,  # Number of input/output channels.
+        kernel_size=3,  # Convolution kernel size.
+        ratio_gin=0.75,
+        ratio_gout=0.75,
+    ):
+        super().__init__()
+        self.padding = kernel_size // 2
+
+        self.ffc_act = FFCBlock(
+            dim=dim,
+            kernel_size=kernel_size,
+            activation=nn.ReLU,
+            padding=self.padding,
+            ratio_gin=ratio_gin,
+            ratio_gout=ratio_gout,
+        )
+
+    def forward(self, gen_ft, mask, fname=None):
+        x = self.ffc_act(gen_ft, mask, fname=fname)
+        return x
+
+
+class SynthesisBlock(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels,  # Number of input channels, 0 = first block.
+        out_channels,  # Number of output channels.
+        w_dim,  # Intermediate latent (W) dimensionality.
+        resolution,  # Resolution of this block.
+        img_channels,  # Number of output color channels.
+        is_last,  # Is this the last block?
+        architecture="skip",  # Architecture: 'orig', 'skip', 'resnet'.
+        resample_filter=[
+            1,
+            3,
+            3,
+            1,
+        ],  # Low-pass filter to apply when resampling activations.
+        conv_clamp=None,  # Clamp the output of convolution layers to +-X, None = disable clamping.
+        use_fp16=False,  # Use FP16 for this block?
+        fp16_channels_last=False,  # Use channels-last memory format with FP16?
+        **layer_kwargs,  # Arguments for SynthesisLayer.
+    ):
+        assert architecture in ["orig", "skip", "resnet"]
+        super().__init__()
+        self.in_channels = in_channels
+        self.w_dim = w_dim
+        self.resolution = resolution
+        self.img_channels = img_channels
+        self.is_last = is_last
+        self.architecture = architecture
+        self.use_fp16 = use_fp16
+        self.channels_last = use_fp16 and fp16_channels_last
+        self.register_buffer("resample_filter", setup_filter(resample_filter))
+        self.num_conv = 0
+        self.num_torgb = 0
+        self.res_ffc = {4: 0, 8: 0, 16: 0, 32: 1, 64: 1, 128: 1, 256: 1, 512: 1}
+
+        if in_channels != 0 and resolution >= 8:
+            self.ffc_skip = nn.ModuleList()
+            for _ in range(self.res_ffc[resolution]):
+                self.ffc_skip.append(FFCSkipLayer(dim=out_channels))
+
+        if in_channels == 0:
+            self.const = torch.nn.Parameter(
+                torch.randn([out_channels, resolution, resolution])
+            )
+
+        if in_channels != 0:
+            self.conv0 = SynthesisLayer(
+                in_channels,
+                out_channels,
+                w_dim=w_dim * 3,
+                resolution=resolution,
+                up=2,
+                resample_filter=resample_filter,
+                conv_clamp=conv_clamp,
+                channels_last=self.channels_last,
+                **layer_kwargs,
+            )
+            self.num_conv += 1
+
+        self.conv1 = SynthesisLayer(
+            out_channels,
+            out_channels,
+            w_dim=w_dim * 3,
+            resolution=resolution,
+            conv_clamp=conv_clamp,
+            channels_last=self.channels_last,
+            **layer_kwargs,
+        )
+        self.num_conv += 1
+
+        if is_last or architecture == "skip":
+            self.torgb = ToRGBLayer(
+                out_channels,
+                img_channels,
+                w_dim=w_dim * 3,
+                conv_clamp=conv_clamp,
+                channels_last=self.channels_last,
+            )
+            self.num_torgb += 1
+
+        if in_channels != 0 and architecture == "resnet":
+            self.skip = Conv2dLayer(
+                in_channels,
+                out_channels,
+                kernel_size=1,
+                bias=False,
+                up=2,
+                resample_filter=resample_filter,
+                channels_last=self.channels_last,
+            )
+
+    def forward(
+        self,
+        x,
+        mask,
+        feats,
+        img,
+        ws,
+        fname=None,
+        force_fp32=False,
+        fused_modconv=None,
+        **layer_kwargs,
+    ):
+        dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
+        dtype = torch.float32
+        memory_format = (
+            torch.channels_last
+            if self.channels_last and not force_fp32
+            else torch.contiguous_format
+        )
+        if fused_modconv is None:
+            fused_modconv = (not self.training) and (
+                dtype == torch.float32 or int(x.shape[0]) == 1
+            )
+
+        x = x.to(dtype=dtype, memory_format=memory_format)
+        x_skip = (
+            feats[self.resolution].clone().to(dtype=dtype, memory_format=memory_format)
+        )
+
+        # Main layers.
+        if self.in_channels == 0:
+            x = self.conv1(x, ws[1], fused_modconv=fused_modconv, **layer_kwargs)
+        elif self.architecture == "resnet":
+            y = self.skip(x, gain=np.sqrt(0.5))
+            x = self.conv0(
+                x, ws[0].clone(), fused_modconv=fused_modconv, **layer_kwargs
+            )
+            if len(self.ffc_skip) > 0:
+                mask = F.interpolate(
+                    mask,
+                    size=x_skip.shape[2:],
+                )
+                z = x + x_skip
+                for fres in self.ffc_skip:
+                    z = fres(z, mask)
+                x = x + z
+            else:
+                x = x + x_skip
+            x = self.conv1(
+                x,
+                ws[1].clone(),
+                fused_modconv=fused_modconv,
+                gain=np.sqrt(0.5),
+                **layer_kwargs,
+            )
+            x = y.add_(x)
+        else:
+            x = self.conv0(
+                x, ws[0].clone(), fused_modconv=fused_modconv, **layer_kwargs
+            )
+            if len(self.ffc_skip) > 0:
+                mask = F.interpolate(
+                    mask,
+                    size=x_skip.shape[2:],
+                )
+                z = x + x_skip
+                for fres in self.ffc_skip:
+                    z = fres(z, mask)
+                x = x + z
+            else:
+                x = x + x_skip
+            x = self.conv1(
+                x, ws[1].clone(), fused_modconv=fused_modconv, **layer_kwargs
+            )
+        # ToRGB.
+        if img is not None:
+            img = upsample2d(img, self.resample_filter)
+        if self.is_last or self.architecture == "skip":
+            y = self.torgb(x, ws[2].clone(), fused_modconv=fused_modconv)
+            y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format)
+            img = img.add_(y) if img is not None else y
+
+        x = x.to(dtype=dtype)
+        assert x.dtype == dtype
+        assert img is None or img.dtype == torch.float32
+        return x, img
+
+
+class SynthesisNetwork(torch.nn.Module):
+    def __init__(
+        self,
+        w_dim,  # Intermediate latent (W) dimensionality.
+        z_dim,  # Output Latent (Z) dimensionality.
+        img_resolution,  # Output image resolution.
+        img_channels,  # Number of color channels.
+        channel_base=16384,  # Overall multiplier for the number of channels.
+        channel_max=512,  # Maximum number of channels in any layer.
+        num_fp16_res=0,  # Use FP16 for the N highest resolutions.
+        **block_kwargs,  # Arguments for SynthesisBlock.
+    ):
+        assert img_resolution >= 4 and img_resolution & (img_resolution - 1) == 0
+        super().__init__()
+        self.w_dim = w_dim
+        self.img_resolution = img_resolution
+        self.img_resolution_log2 = int(np.log2(img_resolution))
+        self.img_channels = img_channels
+        self.block_resolutions = [
+            2**i for i in range(3, self.img_resolution_log2 + 1)
+        ]
+        channels_dict = {
+            res: min(channel_base // res, channel_max) for res in self.block_resolutions
+        }
+        fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
+
+        self.foreword = SynthesisForeword(
+            img_channels=img_channels,
+            in_channels=min(channel_base // 4, channel_max),
+            z_dim=z_dim * 2,
+            resolution=4,
+        )
+
+        self.num_ws = self.img_resolution_log2 * 2 - 2
+        for res in self.block_resolutions:
+            if res // 2 in channels_dict.keys():
+                in_channels = channels_dict[res // 2] if res > 4 else 0
+            else:
+                in_channels = min(channel_base // (res // 2), channel_max)
+            out_channels = channels_dict[res]
+            use_fp16 = res >= fp16_resolution
+            use_fp16 = False
+            is_last = res == self.img_resolution
+            block = SynthesisBlock(
+                in_channels,
+                out_channels,
+                w_dim=w_dim,
+                resolution=res,
+                img_channels=img_channels,
+                is_last=is_last,
+                use_fp16=use_fp16,
+                **block_kwargs,
+            )
+            setattr(self, f"b{res}", block)
+
+    def forward(self, x_global, mask, feats, ws, fname=None, **block_kwargs):
+
+        img = None
+
+        x, img = self.foreword(x_global, ws, feats, img)
+
+        for res in self.block_resolutions:
+            block = getattr(self, f"b{res}")
+            mod_vector0 = []
+            mod_vector0.append(ws[:, int(np.log2(res)) * 2 - 5])
+            mod_vector0.append(x_global.clone())
+            mod_vector0 = torch.cat(mod_vector0, dim=1)
+
+            mod_vector1 = []
+            mod_vector1.append(ws[:, int(np.log2(res)) * 2 - 4])
+            mod_vector1.append(x_global.clone())
+            mod_vector1 = torch.cat(mod_vector1, dim=1)
+
+            mod_vector_rgb = []
+            mod_vector_rgb.append(ws[:, int(np.log2(res)) * 2 - 3])
+            mod_vector_rgb.append(x_global.clone())
+            mod_vector_rgb = torch.cat(mod_vector_rgb, dim=1)
+            x, img = block(
+                x,
+                mask,
+                feats,
+                img,
+                (mod_vector0, mod_vector1, mod_vector_rgb),
+                fname=fname,
+                **block_kwargs,
+            )
+        return img
+
+
+class MappingNetwork(torch.nn.Module):
+    def __init__(
+        self,
+        z_dim,  # Input latent (Z) dimensionality, 0 = no latent.
+        c_dim,  # Conditioning label (C) dimensionality, 0 = no label.
+        w_dim,  # Intermediate latent (W) dimensionality.
+        num_ws,  # Number of intermediate latents to output, None = do not broadcast.
+        num_layers=8,  # Number of mapping layers.
+        embed_features=None,  # Label embedding dimensionality, None = same as w_dim.
+        layer_features=None,  # Number of intermediate features in the mapping layers, None = same as w_dim.
+        activation="lrelu",  # Activation function: 'relu', 'lrelu', etc.
+        lr_multiplier=0.01,  # Learning rate multiplier for the mapping layers.
+        w_avg_beta=0.995,  # Decay for tracking the moving average of W during training, None = do not track.
+    ):
+        super().__init__()
+        self.z_dim = z_dim
+        self.c_dim = c_dim
+        self.w_dim = w_dim
+        self.num_ws = num_ws
+        self.num_layers = num_layers
+        self.w_avg_beta = w_avg_beta
+
+        if embed_features is None:
+            embed_features = w_dim
+        if c_dim == 0:
+            embed_features = 0
+        if layer_features is None:
+            layer_features = w_dim
+        features_list = (
+            [z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim]
+        )
+
+        if c_dim > 0:
+            self.embed = FullyConnectedLayer(c_dim, embed_features)
+        for idx in range(num_layers):
+            in_features = features_list[idx]
+            out_features = features_list[idx + 1]
+            layer = FullyConnectedLayer(
+                in_features,
+                out_features,
+                activation=activation,
+                lr_multiplier=lr_multiplier,
+            )
+            setattr(self, f"fc{idx}", layer)
+
+        if num_ws is not None and w_avg_beta is not None:
+            self.register_buffer("w_avg", torch.zeros([w_dim]))
+
+    def forward(
+        self, z, c, truncation_psi=1, truncation_cutoff=None, skip_w_avg_update=False
+    ):
+        # Embed, normalize, and concat inputs.
+        x = None
+        with torch.autograd.profiler.record_function("input"):
+            if self.z_dim > 0:
+                x = normalize_2nd_moment(z.to(torch.float32))
+            if self.c_dim > 0:
+                y = normalize_2nd_moment(self.embed(c.to(torch.float32)))
+                x = torch.cat([x, y], dim=1) if x is not None else y
+
+        # Main layers.
+        for idx in range(self.num_layers):
+            layer = getattr(self, f"fc{idx}")
+            x = layer(x)
+
+        # Update moving average of W.
+        if self.w_avg_beta is not None and self.training and not skip_w_avg_update:
+            with torch.autograd.profiler.record_function("update_w_avg"):
+                self.w_avg.copy_(
+                    x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta)
+                )
+
+        # Broadcast.
+        if self.num_ws is not None:
+            with torch.autograd.profiler.record_function("broadcast"):
+                x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
+
+        # Apply truncation.
+        if truncation_psi != 1:
+            with torch.autograd.profiler.record_function("truncate"):
+                assert self.w_avg_beta is not None
+                if self.num_ws is None or truncation_cutoff is None:
+                    x = self.w_avg.lerp(x, truncation_psi)
+                else:
+                    x[:, :truncation_cutoff] = self.w_avg.lerp(
+                        x[:, :truncation_cutoff], truncation_psi
+                    )
+        return x
+
+
+class Generator(torch.nn.Module):
+    def __init__(
+        self,
+        z_dim,  # Input latent (Z) dimensionality.
+        c_dim,  # Conditioning label (C) dimensionality.
+        w_dim,  # Intermediate latent (W) dimensionality.
+        img_resolution,  # Output resolution.
+        img_channels,  # Number of output color channels.
+        encoder_kwargs={},  # Arguments for EncoderNetwork.
+        mapping_kwargs={},  # Arguments for MappingNetwork.
+        synthesis_kwargs={},  # Arguments for SynthesisNetwork.
+    ):
+        super().__init__()
+        self.z_dim = z_dim
+        self.c_dim = c_dim
+        self.w_dim = w_dim
+        self.img_resolution = img_resolution
+        self.img_channels = img_channels
+        self.encoder = EncoderNetwork(
+            c_dim=c_dim,
+            z_dim=z_dim,
+            img_resolution=img_resolution,
+            img_channels=img_channels,
+            **encoder_kwargs,
+        )
+        self.synthesis = SynthesisNetwork(
+            z_dim=z_dim,
+            w_dim=w_dim,
+            img_resolution=img_resolution,
+            img_channels=img_channels,
+            **synthesis_kwargs,
+        )
+        self.num_ws = self.synthesis.num_ws
+        self.mapping = MappingNetwork(
+            z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs
+        )
+
+    def forward(
+        self,
+        img,
+        c,
+        fname=None,
+        truncation_psi=1,
+        truncation_cutoff=None,
+        **synthesis_kwargs,
+    ):
+        mask = img[:, -1].unsqueeze(1)
+        x_global, z, feats = self.encoder(img, c)
+        ws = self.mapping(
+            z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff
+        )
+        img = self.synthesis(x_global, mask, feats, ws, fname=fname, **synthesis_kwargs)
+        return img
+
+
+FCF_MODEL_URL = os.environ.get(
+    "FCF_MODEL_URL",
+    "https://github.com/Sanster/models/releases/download/add_fcf/places_512_G.pth",
+)
+FCF_MODEL_MD5 = os.environ.get("FCF_MODEL_MD5", "3323152bc01bf1c56fd8aba74435a211")
+
+
+class FcF(InpaintModel):
+    name = "fcf"
+    min_size = 512
+    pad_mod = 512
+    pad_to_square = True
+
+    def init_model(self, device, **kwargs):
+        seed = 0
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+        torch.backends.cudnn.deterministic = True
+        torch.backends.cudnn.benchmark = False
+
+        kwargs = {
+            "channel_base": 1 * 32768,
+            "channel_max": 512,
+            "num_fp16_res": 4,
+            "conv_clamp": 256,
+        }
+        G = Generator(
+            z_dim=512,
+            c_dim=0,
+            w_dim=512,
+            img_resolution=512,
+            img_channels=3,
+            synthesis_kwargs=kwargs,
+            encoder_kwargs=kwargs,
+            mapping_kwargs={"num_layers": 2},
+        )
+        self.model = load_model(G, FCF_MODEL_URL, device, FCF_MODEL_MD5)
+        self.label = torch.zeros([1, self.model.c_dim], device=device)
+
+    @staticmethod
+    def is_downloaded() -> bool:
+        return os.path.exists(get_cache_path_by_url(FCF_MODEL_URL))
+
+    @torch.no_grad()
+    def __call__(self, image, mask, config: Config):
+        """
+        images: [H, W, C] RGB, not normalized
+        masks: [H, W]
+        return: BGR IMAGE
+        """
+        if image.shape[0] == 512 and image.shape[1] == 512:
+            return self._pad_forward(image, mask, config)
+
+        boxes = boxes_from_mask(mask)
+        crop_result = []
+        config.hd_strategy_crop_margin = 128
+        for box in boxes:
+            crop_image, crop_mask, crop_box = self._crop_box(image, mask, box, config)
+            origin_size = crop_image.shape[:2]
+            resize_image = resize_max_size(crop_image, size_limit=512)
+            resize_mask = resize_max_size(crop_mask, size_limit=512)
+            inpaint_result = self._pad_forward(resize_image, resize_mask, config)
+
+            # only paste masked area result
+            inpaint_result = cv2.resize(
+                inpaint_result,
+                (origin_size[1], origin_size[0]),
+                interpolation=cv2.INTER_CUBIC,
+            )
+
+            original_pixel_indices = crop_mask < 127
+            inpaint_result[original_pixel_indices] = crop_image[:, :, ::-1][
+                original_pixel_indices
+            ]
+
+            crop_result.append((inpaint_result, crop_box))
+
+        inpaint_result = image[:, :, ::-1]
+        for crop_image, crop_box in crop_result:
+            x1, y1, x2, y2 = crop_box
+            inpaint_result[y1:y2, x1:x2, :] = crop_image
+
+        return inpaint_result
+
+    def forward(self, image, mask, config: Config):
+        """Input images and output images have same size
+        images: [H, W, C] RGB
+        masks: [H, W] mask area == 255
+        return: BGR IMAGE
+        """
+
+        image = norm_img(image)  # [0, 1]
+        image = image * 2 - 1  # [0, 1] -> [-1, 1]
+        mask = (mask > 120) * 255
+        mask = norm_img(mask)
+
+        image = torch.from_numpy(image).unsqueeze(0).to(self.device)
+        mask = torch.from_numpy(mask).unsqueeze(0).to(self.device)
+
+        erased_img = image * (1 - mask)
+        input_image = torch.cat([0.5 - mask, erased_img], dim=1)
+
+        output = self.model(
+            input_image, self.label, truncation_psi=0.1, noise_mode="none"
+        )
+        output = (
+            (output.permute(0, 2, 3, 1) * 127.5 + 127.5)
+            .round()
+            .clamp(0, 255)
+            .to(torch.uint8)
+        )
+        output = output[0].cpu().numpy()
+        cur_res = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
+        return cur_res
diff --git a/lama_cleaner/model/instruct_pix2pix.py b/lama_cleaner/model/instruct_pix2pix.py
new file mode 100644
index 0000000000000000000000000000000000000000..20e24f3fc0ccd4885bf66ea9a8e7f92ee29e362b
--- /dev/null
+++ b/lama_cleaner/model/instruct_pix2pix.py
@@ -0,0 +1,83 @@
+import PIL.Image
+import cv2
+import torch
+from loguru import logger
+
+from lama_cleaner.model.base import DiffusionInpaintModel
+from lama_cleaner.model.utils import set_seed
+from lama_cleaner.schema import Config
+
+
+class InstructPix2Pix(DiffusionInpaintModel):
+    name = "instruct_pix2pix"
+    pad_mod = 8
+    min_size = 512
+
+    def init_model(self, device: torch.device, **kwargs):
+        from diffusers import StableDiffusionInstructPix2PixPipeline
+        fp16 = not kwargs.get('no_half', False)
+
+        model_kwargs = {"local_files_only": kwargs.get('local_files_only', False)}
+        if kwargs['disable_nsfw'] or kwargs.get('cpu_offload', False):
+            logger.info("Disable Stable Diffusion Model NSFW checker")
+            model_kwargs.update(dict(
+                safety_checker=None,
+                feature_extractor=None,
+                requires_safety_checker=False
+            ))
+
+        use_gpu = device == torch.device('cuda') and torch.cuda.is_available()
+        torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
+        self.model = StableDiffusionInstructPix2PixPipeline.from_pretrained(
+            "timbrooks/instruct-pix2pix",
+            revision="fp16" if use_gpu and fp16 else "main",
+            torch_dtype=torch_dtype,
+            **model_kwargs
+        )
+
+        self.model.enable_attention_slicing()
+        if kwargs.get('enable_xformers', False):
+            self.model.enable_xformers_memory_efficient_attention()
+
+        if kwargs.get('cpu_offload', False) and use_gpu:
+            logger.info("Enable sequential cpu offload")
+            self.model.enable_sequential_cpu_offload(gpu_id=0)
+        else:
+            self.model = self.model.to(device)
+
+    def forward(self, image, mask, config: Config):
+        """Input image and output image have same size
+        image: [H, W, C] RGB
+        mask: [H, W, 1] 255 means area to repaint
+        return: BGR IMAGE
+        edit = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.5, guidance_scale=7).images[0]
+        """
+        output = self.model(
+            image=PIL.Image.fromarray(image),
+            prompt=config.prompt,
+            negative_prompt=config.negative_prompt,
+            num_inference_steps=config.p2p_steps,
+            image_guidance_scale=config.p2p_image_guidance_scale,
+            guidance_scale=config.p2p_guidance_scale,
+            output_type="np.array",
+            generator=torch.manual_seed(config.sd_seed)
+        ).images[0]
+
+        output = (output * 255).round().astype("uint8")
+        output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
+        return output
+
+    #
+    # def forward_post_process(self, result, image, mask, config):
+    #     if config.sd_match_histograms:
+    #         result = self._match_histograms(result, image[:, :, ::-1], mask)
+    #
+    #     if config.sd_mask_blur != 0:
+    #         k = 2 * config.sd_mask_blur + 1
+    #         mask = cv2.GaussianBlur(mask, (k, k), 0)
+    #     return result, image, mask
+
+    @staticmethod
+    def is_downloaded() -> bool:
+        # model will be downloaded when app start, and can't switch in frontend settings
+        return True
diff --git a/lama_cleaner/model/lama.py b/lama_cleaner/model/lama.py
new file mode 100644
index 0000000000000000000000000000000000000000..bdcdf0d2c13bb58dd24f78c79a4b2210093d348e
--- /dev/null
+++ b/lama_cleaner/model/lama.py
@@ -0,0 +1,51 @@
+import os
+
+import cv2
+import numpy as np
+import torch
+
+from lama_cleaner.helper import (
+    norm_img,
+    get_cache_path_by_url,
+    load_jit_model,
+)
+from lama_cleaner.model.base import InpaintModel
+from lama_cleaner.schema import Config
+
+LAMA_MODEL_URL = os.environ.get(
+    "LAMA_MODEL_URL",
+    "https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
+)
+LAMA_MODEL_MD5 = os.environ.get("LAMA_MODEL_MD5", "e3aa4aaa15225a33ec84f9f4bc47e500")
+
+
+class LaMa(InpaintModel):
+    name = "lama"
+    pad_mod = 8
+
+    def init_model(self, device, **kwargs):
+        self.model = load_jit_model(LAMA_MODEL_URL, device, LAMA_MODEL_MD5).eval()
+
+    @staticmethod
+    def is_downloaded() -> bool:
+        return os.path.exists(get_cache_path_by_url(LAMA_MODEL_URL))
+
+    def forward(self, image, mask, config: Config):
+        """Input image and output image have same size
+        image: [H, W, C] RGB
+        mask: [H, W]
+        return: BGR IMAGE
+        """
+        image = norm_img(image)
+        mask = norm_img(mask)
+
+        mask = (mask > 0) * 1
+        image = torch.from_numpy(image).unsqueeze(0).to(self.device)
+        mask = torch.from_numpy(mask).unsqueeze(0).to(self.device)
+
+        inpainted_image = self.model(image, mask)
+
+        cur_res = inpainted_image[0].permute(1, 2, 0).detach().cpu().numpy()
+        cur_res = np.clip(cur_res * 255, 0, 255).astype("uint8")
+        cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)
+        return cur_res
diff --git a/lama_cleaner/model/ldm.py b/lama_cleaner/model/ldm.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5b6d12f3987562b38df25ba415360e348407ba0
--- /dev/null
+++ b/lama_cleaner/model/ldm.py
@@ -0,0 +1,329 @@
+import os
+
+import numpy as np
+import torch
+from loguru import logger
+
+from lama_cleaner.model.base import InpaintModel
+from lama_cleaner.model.ddim_sampler import DDIMSampler
+from lama_cleaner.model.plms_sampler import PLMSSampler
+from lama_cleaner.schema import Config, LDMSampler
+
+torch.manual_seed(42)
+import torch.nn as nn
+from lama_cleaner.helper import (
+    download_model,
+    norm_img,
+    get_cache_path_by_url,
+    load_jit_model,
+)
+from lama_cleaner.model.utils import (
+    make_beta_schedule,
+    timestep_embedding,
+)
+
+LDM_ENCODE_MODEL_URL = os.environ.get(
+    "LDM_ENCODE_MODEL_URL",
+    "https://github.com/Sanster/models/releases/download/add_ldm/cond_stage_model_encode.pt",
+)
+LDM_ENCODE_MODEL_MD5 = os.environ.get(
+    "LDM_ENCODE_MODEL_MD5", "23239fc9081956a3e70de56472b3f296"
+)
+
+LDM_DECODE_MODEL_URL = os.environ.get(
+    "LDM_DECODE_MODEL_URL",
+    "https://github.com/Sanster/models/releases/download/add_ldm/cond_stage_model_decode.pt",
+)
+LDM_DECODE_MODEL_MD5 = os.environ.get(
+    "LDM_DECODE_MODEL_MD5", "fe419cd15a750d37a4733589d0d3585c"
+)
+
+LDM_DIFFUSION_MODEL_URL = os.environ.get(
+    "LDM_DIFFUSION_MODEL_URL",
+    "https://github.com/Sanster/models/releases/download/add_ldm/diffusion.pt",
+)
+
+LDM_DIFFUSION_MODEL_MD5 = os.environ.get(
+    "LDM_DIFFUSION_MODEL_MD5", "b0afda12bf790c03aba2a7431f11d22d"
+)
+
+
+class DDPM(nn.Module):
+    # classic DDPM with Gaussian diffusion, in image space
+    def __init__(
+        self,
+        device,
+        timesteps=1000,
+        beta_schedule="linear",
+        linear_start=0.0015,
+        linear_end=0.0205,
+        cosine_s=0.008,
+        original_elbo_weight=0.0,
+        v_posterior=0.0,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
+        l_simple_weight=1.0,
+        parameterization="eps",  # all assuming fixed variance schedules
+        use_positional_encodings=False,
+    ):
+        super().__init__()
+        self.device = device
+        self.parameterization = parameterization
+        self.use_positional_encodings = use_positional_encodings
+
+        self.v_posterior = v_posterior
+        self.original_elbo_weight = original_elbo_weight
+        self.l_simple_weight = l_simple_weight
+
+        self.register_schedule(
+            beta_schedule=beta_schedule,
+            timesteps=timesteps,
+            linear_start=linear_start,
+            linear_end=linear_end,
+            cosine_s=cosine_s,
+        )
+
+    def register_schedule(
+        self,
+        given_betas=None,
+        beta_schedule="linear",
+        timesteps=1000,
+        linear_start=1e-4,
+        linear_end=2e-2,
+        cosine_s=8e-3,
+    ):
+        betas = make_beta_schedule(
+            self.device,
+            beta_schedule,
+            timesteps,
+            linear_start=linear_start,
+            linear_end=linear_end,
+            cosine_s=cosine_s,
+        )
+        alphas = 1.0 - betas
+        alphas_cumprod = np.cumprod(alphas, axis=0)
+        alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])
+
+        (timesteps,) = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.linear_start = linear_start
+        self.linear_end = linear_end
+        assert (
+            alphas_cumprod.shape[0] == self.num_timesteps
+        ), "alphas have to be defined for each timestep"
+
+        to_torch = lambda x: torch.tensor(x, dtype=torch.float32).to(self.device)
+
+        self.register_buffer("betas", to_torch(betas))
+        self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
+        self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod)))
+        self.register_buffer(
+            "sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
+        )
+        self.register_buffer(
+            "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
+        )
+        self.register_buffer(
+            "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
+        )
+        self.register_buffer(
+            "sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
+        )
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = (1 - self.v_posterior) * betas * (
+            1.0 - alphas_cumprod_prev
+        ) / (1.0 - alphas_cumprod) + self.v_posterior * betas
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.register_buffer("posterior_variance", to_torch(posterior_variance))
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.register_buffer(
+            "posterior_log_variance_clipped",
+            to_torch(np.log(np.maximum(posterior_variance, 1e-20))),
+        )
+        self.register_buffer(
+            "posterior_mean_coef1",
+            to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)),
+        )
+        self.register_buffer(
+            "posterior_mean_coef2",
+            to_torch(
+                (1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)
+            ),
+        )
+
+        if self.parameterization == "eps":
+            lvlb_weights = self.betas**2 / (
+                2
+                * self.posterior_variance
+                * to_torch(alphas)
+                * (1 - self.alphas_cumprod)
+            )
+        elif self.parameterization == "x0":
+            lvlb_weights = (
+                0.5
+                * np.sqrt(torch.Tensor(alphas_cumprod))
+                / (2.0 * 1 - torch.Tensor(alphas_cumprod))
+            )
+        else:
+            raise NotImplementedError("mu not supported")
+        # TODO how to choose this term
+        lvlb_weights[0] = lvlb_weights[1]
+        self.register_buffer("lvlb_weights", lvlb_weights, persistent=False)
+        assert not torch.isnan(self.lvlb_weights).all()
+
+
+class LatentDiffusion(DDPM):
+    def __init__(
+        self,
+        diffusion_model,
+        device,
+        cond_stage_key="image",
+        cond_stage_trainable=False,
+        concat_mode=True,
+        scale_factor=1.0,
+        scale_by_std=False,
+        *args,
+        **kwargs,
+    ):
+        self.num_timesteps_cond = 1
+        self.scale_by_std = scale_by_std
+        super().__init__(device, *args, **kwargs)
+        self.diffusion_model = diffusion_model
+        self.concat_mode = concat_mode
+        self.cond_stage_trainable = cond_stage_trainable
+        self.cond_stage_key = cond_stage_key
+        self.num_downs = 2
+        self.scale_factor = scale_factor
+
+    def make_cond_schedule(
+        self,
+    ):
+        self.cond_ids = torch.full(
+            size=(self.num_timesteps,),
+            fill_value=self.num_timesteps - 1,
+            dtype=torch.long,
+        )
+        ids = torch.round(
+            torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)
+        ).long()
+        self.cond_ids[: self.num_timesteps_cond] = ids
+
+    def register_schedule(
+        self,
+        given_betas=None,
+        beta_schedule="linear",
+        timesteps=1000,
+        linear_start=1e-4,
+        linear_end=2e-2,
+        cosine_s=8e-3,
+    ):
+        super().register_schedule(
+            given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s
+        )
+
+        self.shorten_cond_schedule = self.num_timesteps_cond > 1
+        if self.shorten_cond_schedule:
+            self.make_cond_schedule()
+
+    def apply_model(self, x_noisy, t, cond):
+        # x_recon = self.model(x_noisy, t, cond['c_concat'][0])  # cond['c_concat'][0].shape 1,4,128,128
+        t_emb = timestep_embedding(x_noisy.device, t, 256, repeat_only=False)
+        x_recon = self.diffusion_model(x_noisy, t_emb, cond)
+        return x_recon
+
+
+class LDM(InpaintModel):
+    name = "ldm"
+    pad_mod = 32
+
+    def __init__(self, device, fp16: bool = True, **kwargs):
+        self.fp16 = fp16
+        super().__init__(device)
+        self.device = device
+
+    def init_model(self, device, **kwargs):
+        self.diffusion_model = load_jit_model(
+            LDM_DIFFUSION_MODEL_URL, device, LDM_DIFFUSION_MODEL_MD5
+        )
+        self.cond_stage_model_decode = load_jit_model(
+            LDM_DECODE_MODEL_URL, device, LDM_DECODE_MODEL_MD5
+        )
+        self.cond_stage_model_encode = load_jit_model(
+            LDM_ENCODE_MODEL_URL, device, LDM_ENCODE_MODEL_MD5
+        )
+        if self.fp16 and "cuda" in str(device):
+            self.diffusion_model = self.diffusion_model.half()
+            self.cond_stage_model_decode = self.cond_stage_model_decode.half()
+            self.cond_stage_model_encode = self.cond_stage_model_encode.half()
+
+        self.model = LatentDiffusion(self.diffusion_model, device)
+
+    @staticmethod
+    def is_downloaded() -> bool:
+        model_paths = [
+            get_cache_path_by_url(LDM_DIFFUSION_MODEL_URL),
+            get_cache_path_by_url(LDM_DECODE_MODEL_URL),
+            get_cache_path_by_url(LDM_ENCODE_MODEL_URL),
+        ]
+        return all([os.path.exists(it) for it in model_paths])
+
+    @torch.cuda.amp.autocast()
+    def forward(self, image, mask, config: Config):
+        """
+        image: [H, W, C] RGB
+        mask: [H, W, 1]
+        return: BGR IMAGE
+        """
+        # image [1,3,512,512] float32
+        # mask: [1,1,512,512] float32
+        # masked_image: [1,3,512,512] float32
+        if config.ldm_sampler == LDMSampler.ddim:
+            sampler = DDIMSampler(self.model)
+        elif config.ldm_sampler == LDMSampler.plms:
+            sampler = PLMSSampler(self.model)
+        else:
+            raise ValueError()
+
+        steps = config.ldm_steps
+        image = norm_img(image)
+        mask = norm_img(mask)
+
+        mask[mask < 0.5] = 0
+        mask[mask >= 0.5] = 1
+
+        image = torch.from_numpy(image).unsqueeze(0).to(self.device)
+        mask = torch.from_numpy(mask).unsqueeze(0).to(self.device)
+        masked_image = (1 - mask) * image
+
+        mask = self._norm(mask)
+        masked_image = self._norm(masked_image)
+
+        c = self.cond_stage_model_encode(masked_image)
+        torch.cuda.empty_cache()
+
+        cc = torch.nn.functional.interpolate(mask, size=c.shape[-2:])  # 1,1,128,128
+        c = torch.cat((c, cc), dim=1)  # 1,4,128,128
+
+        shape = (c.shape[1] - 1,) + c.shape[2:]
+        samples_ddim = sampler.sample(
+            steps=steps, conditioning=c, batch_size=c.shape[0], shape=shape
+        )
+        torch.cuda.empty_cache()
+        x_samples_ddim = self.cond_stage_model_decode(
+            samples_ddim
+        )  # samples_ddim: 1, 3, 128, 128 float32
+        torch.cuda.empty_cache()
+
+        # image = torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
+        # mask = torch.clamp((mask + 1.0) / 2.0, min=0.0, max=1.0)
+        inpainted_image = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
+
+        # inpainted = (1 - mask) * image + mask * predicted_image
+        inpainted_image = inpainted_image.cpu().numpy().transpose(0, 2, 3, 1)[0] * 255
+        inpainted_image = inpainted_image.astype(np.uint8)[:, :, ::-1]
+        return inpainted_image
+
+    def _norm(self, tensor):
+        return tensor * 2.0 - 1.0
diff --git a/lama_cleaner/model/manga.py b/lama_cleaner/model/manga.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6e27e508a9bc0dec48e3f05177abd968646cac4
--- /dev/null
+++ b/lama_cleaner/model/manga.py
@@ -0,0 +1,91 @@
+import os
+import random
+
+import cv2
+import numpy as np
+import torch
+import time
+from loguru import logger
+
+from lama_cleaner.helper import get_cache_path_by_url, load_jit_model
+from lama_cleaner.model.base import InpaintModel
+from lama_cleaner.schema import Config
+
+
+MANGA_INPAINTOR_MODEL_URL = os.environ.get(
+    "MANGA_INPAINTOR_MODEL_URL",
+    "https://github.com/Sanster/models/releases/download/manga/manga_inpaintor.jit",
+)
+MANGA_INPAINTOR_MODEL_MD5 = os.environ.get(
+    "MANGA_INPAINTOR_MODEL_MD5", "7d8b269c4613b6b3768af714610da86c"
+)
+
+MANGA_LINE_MODEL_URL = os.environ.get(
+    "MANGA_LINE_MODEL_URL",
+    "https://github.com/Sanster/models/releases/download/manga/erika.jit",
+)
+MANGA_LINE_MODEL_MD5 = os.environ.get(
+    "MANGA_LINE_MODEL_MD5", "0c926d5a4af8450b0d00bc5b9a095644"
+)
+
+
+class Manga(InpaintModel):
+    name = "manga"
+    pad_mod = 16
+
+    def init_model(self, device, **kwargs):
+        self.inpaintor_model = load_jit_model(
+            MANGA_INPAINTOR_MODEL_URL, device, MANGA_INPAINTOR_MODEL_MD5
+        )
+        self.line_model = load_jit_model(
+            MANGA_LINE_MODEL_URL, device, MANGA_LINE_MODEL_MD5
+        )
+        self.seed = 42
+
+    @staticmethod
+    def is_downloaded() -> bool:
+        model_paths = [
+            get_cache_path_by_url(MANGA_INPAINTOR_MODEL_URL),
+            get_cache_path_by_url(MANGA_LINE_MODEL_URL),
+        ]
+        return all([os.path.exists(it) for it in model_paths])
+
+    def forward(self, image, mask, config: Config):
+        """
+        image: [H, W, C] RGB
+        mask: [H, W, 1]
+        return: BGR IMAGE
+        """
+        seed = self.seed
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+
+        gray_img = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+        gray_img = torch.from_numpy(
+            gray_img[np.newaxis, np.newaxis, :, :].astype(np.float32)
+        ).to(self.device)
+        start = time.time()
+        lines = self.line_model(gray_img)
+        torch.cuda.empty_cache()
+        lines = torch.clamp(lines, 0, 255)
+        logger.info(f"erika_model time: {time.time() - start}")
+
+        mask = torch.from_numpy(mask[np.newaxis, :, :, :]).to(self.device)
+        mask = mask.permute(0, 3, 1, 2)
+        mask = torch.where(mask > 0.5, 1.0, 0.0)
+        noise = torch.randn_like(mask)
+        ones = torch.ones_like(mask)
+
+        gray_img = gray_img / 255 * 2 - 1.0
+        lines = lines / 255 * 2 - 1.0
+
+        start = time.time()
+        inpainted_image = self.inpaintor_model(gray_img, lines, mask, noise, ones)
+        logger.info(f"image_inpaintor_model time: {time.time() - start}")
+
+        cur_res = inpainted_image[0].permute(1, 2, 0).detach().cpu().numpy()
+        cur_res = (cur_res * 127.5 + 127.5).astype(np.uint8)
+        cur_res = cv2.cvtColor(cur_res, cv2.COLOR_GRAY2BGR)
+        return cur_res
diff --git a/lama_cleaner/model/mat.py b/lama_cleaner/model/mat.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e09bf4e6305310bcbad4cc10cefe37fa5ae6a3b
--- /dev/null
+++ b/lama_cleaner/model/mat.py
@@ -0,0 +1,1935 @@
+import os
+import random
+
+import cv2
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+
+from lama_cleaner.helper import load_model, get_cache_path_by_url, norm_img
+from lama_cleaner.model.base import InpaintModel
+from lama_cleaner.model.utils import (
+    setup_filter,
+    Conv2dLayer,
+    FullyConnectedLayer,
+    conv2d_resample,
+    bias_act,
+    upsample2d,
+    activation_funcs,
+    MinibatchStdLayer,
+    to_2tuple,
+    normalize_2nd_moment,
+    set_seed,
+)
+from lama_cleaner.schema import Config
+
+
+class ModulatedConv2d(nn.Module):
+    def __init__(
+        self,
+        in_channels,  # Number of input channels.
+        out_channels,  # Number of output channels.
+        kernel_size,  # Width and height of the convolution kernel.
+        style_dim,  # dimension of the style code
+        demodulate=True,  # perfrom demodulation
+        up=1,  # Integer upsampling factor.
+        down=1,  # Integer downsampling factor.
+        resample_filter=[
+            1,
+            3,
+            3,
+            1,
+        ],  # Low-pass filter to apply when resampling activations.
+        conv_clamp=None,  # Clamp the output to +-X, None = disable clamping.
+    ):
+        super().__init__()
+        self.demodulate = demodulate
+
+        self.weight = torch.nn.Parameter(
+            torch.randn([1, out_channels, in_channels, kernel_size, kernel_size])
+        )
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
+        self.padding = self.kernel_size // 2
+        self.up = up
+        self.down = down
+        self.register_buffer("resample_filter", setup_filter(resample_filter))
+        self.conv_clamp = conv_clamp
+
+        self.affine = FullyConnectedLayer(style_dim, in_channels, bias_init=1)
+
+    def forward(self, x, style):
+        batch, in_channels, height, width = x.shape
+        style = self.affine(style).view(batch, 1, in_channels, 1, 1)
+        weight = self.weight * self.weight_gain * style
+
+        if self.demodulate:
+            decoefs = (weight.pow(2).sum(dim=[2, 3, 4]) + 1e-8).rsqrt()
+            weight = weight * decoefs.view(batch, self.out_channels, 1, 1, 1)
+
+        weight = weight.view(
+            batch * self.out_channels, in_channels, self.kernel_size, self.kernel_size
+        )
+        x = x.view(1, batch * in_channels, height, width)
+        x = conv2d_resample(
+            x=x,
+            w=weight,
+            f=self.resample_filter,
+            up=self.up,
+            down=self.down,
+            padding=self.padding,
+            groups=batch,
+        )
+        out = x.view(batch, self.out_channels, *x.shape[2:])
+
+        return out
+
+
+class StyleConv(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels,  # Number of input channels.
+        out_channels,  # Number of output channels.
+        style_dim,  # Intermediate latent (W) dimensionality.
+        resolution,  # Resolution of this layer.
+        kernel_size=3,  # Convolution kernel size.
+        up=1,  # Integer upsampling factor.
+        use_noise=False,  # Enable noise input?
+        activation="lrelu",  # Activation function: 'relu', 'lrelu', etc.
+        resample_filter=[
+            1,
+            3,
+            3,
+            1,
+        ],  # Low-pass filter to apply when resampling activations.
+        conv_clamp=None,  # Clamp the output of convolution layers to +-X, None = disable clamping.
+        demodulate=True,  # perform demodulation
+    ):
+        super().__init__()
+
+        self.conv = ModulatedConv2d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            style_dim=style_dim,
+            demodulate=demodulate,
+            up=up,
+            resample_filter=resample_filter,
+            conv_clamp=conv_clamp,
+        )
+
+        self.use_noise = use_noise
+        self.resolution = resolution
+        if use_noise:
+            self.register_buffer("noise_const", torch.randn([resolution, resolution]))
+            self.noise_strength = torch.nn.Parameter(torch.zeros([]))
+
+        self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
+        self.activation = activation
+        self.act_gain = activation_funcs[activation].def_gain
+        self.conv_clamp = conv_clamp
+
+    def forward(self, x, style, noise_mode="random", gain=1):
+        x = self.conv(x, style)
+
+        assert noise_mode in ["random", "const", "none"]
+
+        if self.use_noise:
+            if noise_mode == "random":
+                xh, xw = x.size()[-2:]
+                noise = (
+                    torch.randn([x.shape[0], 1, xh, xw], device=x.device)
+                    * self.noise_strength
+                )
+            if noise_mode == "const":
+                noise = self.noise_const * self.noise_strength
+            x = x + noise
+
+        act_gain = self.act_gain * gain
+        act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
+        out = bias_act(
+            x, self.bias, act=self.activation, gain=act_gain, clamp=act_clamp
+        )
+
+        return out
+
+
+class ToRGB(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        style_dim,
+        kernel_size=1,
+        resample_filter=[1, 3, 3, 1],
+        conv_clamp=None,
+        demodulate=False,
+    ):
+        super().__init__()
+
+        self.conv = ModulatedConv2d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            style_dim=style_dim,
+            demodulate=demodulate,
+            resample_filter=resample_filter,
+            conv_clamp=conv_clamp,
+        )
+        self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
+        self.register_buffer("resample_filter", setup_filter(resample_filter))
+        self.conv_clamp = conv_clamp
+
+    def forward(self, x, style, skip=None):
+        x = self.conv(x, style)
+        out = bias_act(x, self.bias, clamp=self.conv_clamp)
+
+        if skip is not None:
+            if skip.shape != out.shape:
+                skip = upsample2d(skip, self.resample_filter)
+            out = out + skip
+
+        return out
+
+
+def get_style_code(a, b):
+    return torch.cat([a, b], dim=1)
+
+
+class DecBlockFirst(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        activation,
+        style_dim,
+        use_noise,
+        demodulate,
+        img_channels,
+    ):
+        super().__init__()
+        self.fc = FullyConnectedLayer(
+            in_features=in_channels * 2,
+            out_features=in_channels * 4 ** 2,
+            activation=activation,
+        )
+        self.conv = StyleConv(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            style_dim=style_dim,
+            resolution=4,
+            kernel_size=3,
+            use_noise=use_noise,
+            activation=activation,
+            demodulate=demodulate,
+        )
+        self.toRGB = ToRGB(
+            in_channels=out_channels,
+            out_channels=img_channels,
+            style_dim=style_dim,
+            kernel_size=1,
+            demodulate=False,
+        )
+
+    def forward(self, x, ws, gs, E_features, noise_mode="random"):
+        x = self.fc(x).view(x.shape[0], -1, 4, 4)
+        x = x + E_features[2]
+        style = get_style_code(ws[:, 0], gs)
+        x = self.conv(x, style, noise_mode=noise_mode)
+        style = get_style_code(ws[:, 1], gs)
+        img = self.toRGB(x, style, skip=None)
+
+        return x, img
+
+
+class DecBlockFirstV2(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        activation,
+        style_dim,
+        use_noise,
+        demodulate,
+        img_channels,
+    ):
+        super().__init__()
+        self.conv0 = Conv2dLayer(
+            in_channels=in_channels,
+            out_channels=in_channels,
+            kernel_size=3,
+            activation=activation,
+        )
+        self.conv1 = StyleConv(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            style_dim=style_dim,
+            resolution=4,
+            kernel_size=3,
+            use_noise=use_noise,
+            activation=activation,
+            demodulate=demodulate,
+        )
+        self.toRGB = ToRGB(
+            in_channels=out_channels,
+            out_channels=img_channels,
+            style_dim=style_dim,
+            kernel_size=1,
+            demodulate=False,
+        )
+
+    def forward(self, x, ws, gs, E_features, noise_mode="random"):
+        # x = self.fc(x).view(x.shape[0], -1, 4, 4)
+        x = self.conv0(x)
+        x = x + E_features[2]
+        style = get_style_code(ws[:, 0], gs)
+        x = self.conv1(x, style, noise_mode=noise_mode)
+        style = get_style_code(ws[:, 1], gs)
+        img = self.toRGB(x, style, skip=None)
+
+        return x, img
+
+
+class DecBlock(nn.Module):
+    def __init__(
+        self,
+        res,
+        in_channels,
+        out_channels,
+        activation,
+        style_dim,
+        use_noise,
+        demodulate,
+        img_channels,
+    ):  # res = 2, ..., resolution_log2
+        super().__init__()
+        self.res = res
+
+        self.conv0 = StyleConv(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            style_dim=style_dim,
+            resolution=2 ** res,
+            kernel_size=3,
+            up=2,
+            use_noise=use_noise,
+            activation=activation,
+            demodulate=demodulate,
+        )
+        self.conv1 = StyleConv(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            style_dim=style_dim,
+            resolution=2 ** res,
+            kernel_size=3,
+            use_noise=use_noise,
+            activation=activation,
+            demodulate=demodulate,
+        )
+        self.toRGB = ToRGB(
+            in_channels=out_channels,
+            out_channels=img_channels,
+            style_dim=style_dim,
+            kernel_size=1,
+            demodulate=False,
+        )
+
+    def forward(self, x, img, ws, gs, E_features, noise_mode="random"):
+        style = get_style_code(ws[:, self.res * 2 - 5], gs)
+        x = self.conv0(x, style, noise_mode=noise_mode)
+        x = x + E_features[self.res]
+        style = get_style_code(ws[:, self.res * 2 - 4], gs)
+        x = self.conv1(x, style, noise_mode=noise_mode)
+        style = get_style_code(ws[:, self.res * 2 - 3], gs)
+        img = self.toRGB(x, style, skip=img)
+
+        return x, img
+
+
+class MappingNet(torch.nn.Module):
+    def __init__(
+        self,
+        z_dim,  # Input latent (Z) dimensionality, 0 = no latent.
+        c_dim,  # Conditioning label (C) dimensionality, 0 = no label.
+        w_dim,  # Intermediate latent (W) dimensionality.
+        num_ws,  # Number of intermediate latents to output, None = do not broadcast.
+        num_layers=8,  # Number of mapping layers.
+        embed_features=None,  # Label embedding dimensionality, None = same as w_dim.
+        layer_features=None,  # Number of intermediate features in the mapping layers, None = same as w_dim.
+        activation="lrelu",  # Activation function: 'relu', 'lrelu', etc.
+        lr_multiplier=0.01,  # Learning rate multiplier for the mapping layers.
+        w_avg_beta=0.995,  # Decay for tracking the moving average of W during training, None = do not track.
+        torch_dtype=torch.float32,
+    ):
+        super().__init__()
+        self.z_dim = z_dim
+        self.c_dim = c_dim
+        self.w_dim = w_dim
+        self.num_ws = num_ws
+        self.num_layers = num_layers
+        self.w_avg_beta = w_avg_beta
+        self.torch_dtype = torch_dtype
+
+        if embed_features is None:
+            embed_features = w_dim
+        if c_dim == 0:
+            embed_features = 0
+        if layer_features is None:
+            layer_features = w_dim
+        features_list = (
+            [z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim]
+        )
+
+        if c_dim > 0:
+            self.embed = FullyConnectedLayer(c_dim, embed_features)
+        for idx in range(num_layers):
+            in_features = features_list[idx]
+            out_features = features_list[idx + 1]
+            layer = FullyConnectedLayer(
+                in_features,
+                out_features,
+                activation=activation,
+                lr_multiplier=lr_multiplier,
+            )
+            setattr(self, f"fc{idx}", layer)
+
+        if num_ws is not None and w_avg_beta is not None:
+            self.register_buffer("w_avg", torch.zeros([w_dim]))
+
+    def forward(
+        self, z, c, truncation_psi=1, truncation_cutoff=None, skip_w_avg_update=False
+    ):
+        # Embed, normalize, and concat inputs.
+        x = None
+        if self.z_dim > 0:
+            x = normalize_2nd_moment(z)
+        if self.c_dim > 0:
+            y = normalize_2nd_moment(self.embed(c))
+            x = torch.cat([x, y], dim=1) if x is not None else y
+
+        # Main layers.
+        for idx in range(self.num_layers):
+            layer = getattr(self, f"fc{idx}")
+            x = layer(x)
+
+        # Update moving average of W.
+        if self.w_avg_beta is not None and self.training and not skip_w_avg_update:
+            self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta))
+
+        # Broadcast.
+        if self.num_ws is not None:
+            x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
+
+        # Apply truncation.
+        if truncation_psi != 1:
+            assert self.w_avg_beta is not None
+            if self.num_ws is None or truncation_cutoff is None:
+                x = self.w_avg.lerp(x, truncation_psi)
+            else:
+                x[:, :truncation_cutoff] = self.w_avg.lerp(
+                    x[:, :truncation_cutoff], truncation_psi
+                )
+
+        return x
+
+
+class DisFromRGB(nn.Module):
+    def __init__(
+        self, in_channels, out_channels, activation
+    ):  # res = 2, ..., resolution_log2
+        super().__init__()
+        self.conv = Conv2dLayer(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=1,
+            activation=activation,
+        )
+
+    def forward(self, x):
+        return self.conv(x)
+
+
+class DisBlock(nn.Module):
+    def __init__(
+        self, in_channels, out_channels, activation
+    ):  # res = 2, ..., resolution_log2
+        super().__init__()
+        self.conv0 = Conv2dLayer(
+            in_channels=in_channels,
+            out_channels=in_channels,
+            kernel_size=3,
+            activation=activation,
+        )
+        self.conv1 = Conv2dLayer(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            down=2,
+            activation=activation,
+        )
+        self.skip = Conv2dLayer(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=1,
+            down=2,
+            bias=False,
+        )
+
+    def forward(self, x):
+        skip = self.skip(x, gain=np.sqrt(0.5))
+        x = self.conv0(x)
+        x = self.conv1(x, gain=np.sqrt(0.5))
+        out = skip + x
+
+        return out
+
+
+class Discriminator(torch.nn.Module):
+    def __init__(
+        self,
+        c_dim,  # Conditioning label (C) dimensionality.
+        img_resolution,  # Input resolution.
+        img_channels,  # Number of input color channels.
+        channel_base=32768,  # Overall multiplier for the number of channels.
+        channel_max=512,  # Maximum number of channels in any layer.
+        channel_decay=1,
+        cmap_dim=None,  # Dimensionality of mapped conditioning label, None = default.
+        activation="lrelu",
+        mbstd_group_size=4,  # Group size for the minibatch standard deviation layer, None = entire minibatch.
+        mbstd_num_channels=1,  # Number of features for the minibatch standard deviation layer, 0 = disable.
+    ):
+        super().__init__()
+        self.c_dim = c_dim
+        self.img_resolution = img_resolution
+        self.img_channels = img_channels
+
+        resolution_log2 = int(np.log2(img_resolution))
+        assert img_resolution == 2 ** resolution_log2 and img_resolution >= 4
+        self.resolution_log2 = resolution_log2
+
+        def nf(stage):
+            return np.clip(
+                int(channel_base / 2 ** (stage * channel_decay)), 1, channel_max
+            )
+
+        if cmap_dim == None:
+            cmap_dim = nf(2)
+        if c_dim == 0:
+            cmap_dim = 0
+        self.cmap_dim = cmap_dim
+
+        if c_dim > 0:
+            self.mapping = MappingNet(
+                z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None
+            )
+
+        Dis = [DisFromRGB(img_channels + 1, nf(resolution_log2), activation)]
+        for res in range(resolution_log2, 2, -1):
+            Dis.append(DisBlock(nf(res), nf(res - 1), activation))
+
+        if mbstd_num_channels > 0:
+            Dis.append(
+                MinibatchStdLayer(
+                    group_size=mbstd_group_size, num_channels=mbstd_num_channels
+                )
+            )
+        Dis.append(
+            Conv2dLayer(
+                nf(2) + mbstd_num_channels, nf(2), kernel_size=3, activation=activation
+            )
+        )
+        self.Dis = nn.Sequential(*Dis)
+
+        self.fc0 = FullyConnectedLayer(nf(2) * 4 ** 2, nf(2), activation=activation)
+        self.fc1 = FullyConnectedLayer(nf(2), 1 if cmap_dim == 0 else cmap_dim)
+
+    def forward(self, images_in, masks_in, c):
+        x = torch.cat([masks_in - 0.5, images_in], dim=1)
+        x = self.Dis(x)
+        x = self.fc1(self.fc0(x.flatten(start_dim=1)))
+
+        if self.c_dim > 0:
+            cmap = self.mapping(None, c)
+
+        if self.cmap_dim > 0:
+            x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))
+
+        return x
+
+
+def nf(stage, channel_base=32768, channel_decay=1.0, channel_max=512):
+    NF = {512: 64, 256: 128, 128: 256, 64: 512, 32: 512, 16: 512, 8: 512, 4: 512}
+    return NF[2 ** stage]
+
+
+class Mlp(nn.Module):
+    def __init__(
+        self,
+        in_features,
+        hidden_features=None,
+        out_features=None,
+        act_layer=nn.GELU,
+        drop=0.0,
+    ):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = FullyConnectedLayer(
+            in_features=in_features, out_features=hidden_features, activation="lrelu"
+        )
+        self.fc2 = FullyConnectedLayer(
+            in_features=hidden_features, out_features=out_features
+        )
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.fc2(x)
+        return x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = (
+        x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    )
+    return windows
+
+
+def window_reverse(windows, window_size: int, H: int, W: int):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    # B = windows.shape[0] / (H * W / window_size / window_size)
+    x = windows.view(
+        B, H // window_size, W // window_size, window_size, window_size, -1
+    )
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class Conv2dLayerPartial(nn.Module):
+    def __init__(
+        self,
+        in_channels,  # Number of input channels.
+        out_channels,  # Number of output channels.
+        kernel_size,  # Width and height of the convolution kernel.
+        bias=True,  # Apply additive bias before the activation function?
+        activation="linear",  # Activation function: 'relu', 'lrelu', etc.
+        up=1,  # Integer upsampling factor.
+        down=1,  # Integer downsampling factor.
+        resample_filter=[
+            1,
+            3,
+            3,
+            1,
+        ],  # Low-pass filter to apply when resampling activations.
+        conv_clamp=None,  # Clamp the output to +-X, None = disable clamping.
+        trainable=True,  # Update the weights of this layer during training?
+    ):
+        super().__init__()
+        self.conv = Conv2dLayer(
+            in_channels,
+            out_channels,
+            kernel_size,
+            bias,
+            activation,
+            up,
+            down,
+            resample_filter,
+            conv_clamp,
+            trainable,
+        )
+
+        self.weight_maskUpdater = torch.ones(1, 1, kernel_size, kernel_size)
+        self.slide_winsize = kernel_size ** 2
+        self.stride = down
+        self.padding = kernel_size // 2 if kernel_size % 2 == 1 else 0
+
+    def forward(self, x, mask=None):
+        if mask is not None:
+            with torch.no_grad():
+                if self.weight_maskUpdater.type() != x.type():
+                    self.weight_maskUpdater = self.weight_maskUpdater.to(x)
+                update_mask = F.conv2d(
+                    mask,
+                    self.weight_maskUpdater,
+                    bias=None,
+                    stride=self.stride,
+                    padding=self.padding,
+                )
+                mask_ratio = self.slide_winsize / (update_mask.to(torch.float32) + 1e-8)
+                update_mask = torch.clamp(update_mask, 0, 1)  # 0 or 1
+                mask_ratio = torch.mul(mask_ratio, update_mask).to(x.dtype)
+            x = self.conv(x)
+            x = torch.mul(x, mask_ratio)
+            return x, update_mask
+        else:
+            x = self.conv(x)
+            return x, None
+
+
+class WindowAttention(nn.Module):
+    r"""Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(
+        self,
+        dim,
+        window_size,
+        num_heads,
+        down_ratio=1,
+        qkv_bias=True,
+        qk_scale=None,
+        attn_drop=0.0,
+        proj_drop=0.0,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.q = FullyConnectedLayer(in_features=dim, out_features=dim)
+        self.k = FullyConnectedLayer(in_features=dim, out_features=dim)
+        self.v = FullyConnectedLayer(in_features=dim, out_features=dim)
+        self.proj = FullyConnectedLayer(in_features=dim, out_features=dim)
+
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask_windows=None, mask=None):
+        """
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        norm_x = F.normalize(x, p=2.0, dim=-1, eps=torch.finfo(x.dtype).eps)
+        q = (
+            self.q(norm_x)
+            .reshape(B_, N, self.num_heads, C // self.num_heads)
+            .permute(0, 2, 1, 3)
+        )
+        k = (
+            self.k(norm_x)
+            .view(B_, -1, self.num_heads, C // self.num_heads)
+            .permute(0, 2, 3, 1)
+        )
+        v = (
+            self.v(x)
+            .view(B_, -1, self.num_heads, C // self.num_heads)
+            .permute(0, 2, 1, 3)
+        )
+
+        attn = (q @ k) * self.scale
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(
+                1
+            ).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+
+        if mask_windows is not None:
+            attn_mask_windows = mask_windows.squeeze(-1).unsqueeze(1).unsqueeze(1)
+            attn = attn + attn_mask_windows.masked_fill(
+                attn_mask_windows == 0, float(-100.0)
+            ).masked_fill(attn_mask_windows == 1, float(0.0))
+            with torch.no_grad():
+                mask_windows = torch.clamp(
+                    torch.sum(mask_windows, dim=1, keepdim=True), 0, 1
+                ).repeat(1, N, 1)
+
+        attn = self.softmax(attn)
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        return x, mask_windows
+
+
+class SwinTransformerBlock(nn.Module):
+    r"""Swin Transformer Block.
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resulotion.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(
+        self,
+        dim,
+        input_resolution,
+        num_heads,
+        down_ratio=1,
+        window_size=7,
+        shift_size=0,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop=0.0,
+        attn_drop=0.0,
+        drop_path=0.0,
+        act_layer=nn.GELU,
+        norm_layer=nn.LayerNorm,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        if min(self.input_resolution) <= self.window_size:
+            # if window size is larger than input resolution, we don't partition windows
+            self.shift_size = 0
+            self.window_size = min(self.input_resolution)
+        assert (
+            0 <= self.shift_size < self.window_size
+        ), "shift_size must in 0-window_size"
+
+        if self.shift_size > 0:
+            down_ratio = 1
+        self.attn = WindowAttention(
+            dim,
+            window_size=to_2tuple(self.window_size),
+            num_heads=num_heads,
+            down_ratio=down_ratio,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+        )
+
+        self.fuse = FullyConnectedLayer(
+            in_features=dim * 2, out_features=dim, activation="lrelu"
+        )
+
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(
+            in_features=dim,
+            hidden_features=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=drop,
+        )
+
+        if self.shift_size > 0:
+            attn_mask = self.calculate_mask(self.input_resolution)
+        else:
+            attn_mask = None
+
+        self.register_buffer("attn_mask", attn_mask)
+
+    def calculate_mask(self, x_size):
+        # calculate attention mask for SW-MSA
+        H, W = x_size
+        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
+        h_slices = (
+            slice(0, -self.window_size),
+            slice(-self.window_size, -self.shift_size),
+            slice(-self.shift_size, None),
+        )
+        w_slices = (
+            slice(0, -self.window_size),
+            slice(-self.window_size, -self.shift_size),
+            slice(-self.shift_size, None),
+        )
+        cnt = 0
+        for h in h_slices:
+            for w in w_slices:
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+        mask_windows = window_partition(
+            img_mask, self.window_size
+        )  # nW, window_size, window_size, 1
+        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
+            attn_mask == 0, float(0.0)
+        )
+
+        return attn_mask
+
+    def forward(self, x, x_size, mask=None):
+        # H, W = self.input_resolution
+        H, W = x_size
+        B, L, C = x.shape
+        # assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = x.view(B, H, W, C)
+        if mask is not None:
+            mask = mask.view(B, H, W, 1)
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(
+                x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)
+            )
+            if mask is not None:
+                shifted_mask = torch.roll(
+                    mask, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)
+                )
+        else:
+            shifted_x = x
+            if mask is not None:
+                shifted_mask = mask
+
+        # partition windows
+        x_windows = window_partition(
+            shifted_x, self.window_size
+        )  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(
+            -1, self.window_size * self.window_size, C
+        )  # nW*B, window_size*window_size, C
+        if mask is not None:
+            mask_windows = window_partition(shifted_mask, self.window_size)
+            mask_windows = mask_windows.view(-1, self.window_size * self.window_size, 1)
+        else:
+            mask_windows = None
+
+        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
+        if self.input_resolution == x_size:
+            attn_windows, mask_windows = self.attn(
+                x_windows, mask_windows, mask=self.attn_mask
+            )  # nW*B, window_size*window_size, C
+        else:
+            attn_windows, mask_windows = self.attn(
+                x_windows,
+                mask_windows,
+                mask=self.calculate_mask(x_size).to(x.dtype).to(x.device),
+            )  # nW*B, window_size*window_size, C
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
+        if mask is not None:
+            mask_windows = mask_windows.view(-1, self.window_size, self.window_size, 1)
+            shifted_mask = window_reverse(mask_windows, self.window_size, H, W)
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(
+                shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)
+            )
+            if mask is not None:
+                mask = torch.roll(
+                    shifted_mask, shifts=(self.shift_size, self.shift_size), dims=(1, 2)
+                )
+        else:
+            x = shifted_x
+            if mask is not None:
+                mask = shifted_mask
+        x = x.view(B, H * W, C)
+        if mask is not None:
+            mask = mask.view(B, H * W, 1)
+
+        # FFN
+        x = self.fuse(torch.cat([shortcut, x], dim=-1))
+        x = self.mlp(x)
+
+        return x, mask
+
+
+class PatchMerging(nn.Module):
+    def __init__(self, in_channels, out_channels, down=2):
+        super().__init__()
+        self.conv = Conv2dLayerPartial(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            activation="lrelu",
+            down=down,
+        )
+        self.down = down
+
+    def forward(self, x, x_size, mask=None):
+        x = token2feature(x, x_size)
+        if mask is not None:
+            mask = token2feature(mask, x_size)
+        x, mask = self.conv(x, mask)
+        if self.down != 1:
+            ratio = 1 / self.down
+            x_size = (int(x_size[0] * ratio), int(x_size[1] * ratio))
+        x = feature2token(x)
+        if mask is not None:
+            mask = feature2token(mask)
+        return x, x_size, mask
+
+
+class PatchUpsampling(nn.Module):
+    def __init__(self, in_channels, out_channels, up=2):
+        super().__init__()
+        self.conv = Conv2dLayerPartial(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            activation="lrelu",
+            up=up,
+        )
+        self.up = up
+
+    def forward(self, x, x_size, mask=None):
+        x = token2feature(x, x_size)
+        if mask is not None:
+            mask = token2feature(mask, x_size)
+        x, mask = self.conv(x, mask)
+        if self.up != 1:
+            x_size = (int(x_size[0] * self.up), int(x_size[1] * self.up))
+        x = feature2token(x)
+        if mask is not None:
+            mask = feature2token(mask)
+        return x, x_size, mask
+
+
+class BasicLayer(nn.Module):
+    """A basic Swin Transformer layer for one stage.
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(
+        self,
+        dim,
+        input_resolution,
+        depth,
+        num_heads,
+        window_size,
+        down_ratio=1,
+        mlp_ratio=2.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop=0.0,
+        attn_drop=0.0,
+        drop_path=0.0,
+        norm_layer=nn.LayerNorm,
+        downsample=None,
+        use_checkpoint=False,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # patch merging layer
+        if downsample is not None:
+            # self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+            self.downsample = downsample
+        else:
+            self.downsample = None
+
+        # build blocks
+        self.blocks = nn.ModuleList(
+            [
+                SwinTransformerBlock(
+                    dim=dim,
+                    input_resolution=input_resolution,
+                    num_heads=num_heads,
+                    down_ratio=down_ratio,
+                    window_size=window_size,
+                    shift_size=0 if (i % 2 == 0) else window_size // 2,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    qk_scale=qk_scale,
+                    drop=drop,
+                    attn_drop=attn_drop,
+                    drop_path=drop_path[i]
+                    if isinstance(drop_path, list)
+                    else drop_path,
+                    norm_layer=norm_layer,
+                )
+                for i in range(depth)
+            ]
+        )
+
+        self.conv = Conv2dLayerPartial(
+            in_channels=dim, out_channels=dim, kernel_size=3, activation="lrelu"
+        )
+
+    def forward(self, x, x_size, mask=None):
+        if self.downsample is not None:
+            x, x_size, mask = self.downsample(x, x_size, mask)
+        identity = x
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x, mask = checkpoint.checkpoint(blk, x, x_size, mask)
+            else:
+                x, mask = blk(x, x_size, mask)
+        if mask is not None:
+            mask = token2feature(mask, x_size)
+        x, mask = self.conv(token2feature(x, x_size), mask)
+        x = feature2token(x) + identity
+        if mask is not None:
+            mask = feature2token(mask)
+        return x, x_size, mask
+
+
+class ToToken(nn.Module):
+    def __init__(self, in_channels=3, dim=128, kernel_size=5, stride=1):
+        super().__init__()
+
+        self.proj = Conv2dLayerPartial(
+            in_channels=in_channels,
+            out_channels=dim,
+            kernel_size=kernel_size,
+            activation="lrelu",
+        )
+
+    def forward(self, x, mask):
+        x, mask = self.proj(x, mask)
+
+        return x, mask
+
+
+class EncFromRGB(nn.Module):
+    def __init__(
+        self, in_channels, out_channels, activation
+    ):  # res = 2, ..., resolution_log2
+        super().__init__()
+        self.conv0 = Conv2dLayer(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=1,
+            activation=activation,
+        )
+        self.conv1 = Conv2dLayer(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            activation=activation,
+        )
+
+    def forward(self, x):
+        x = self.conv0(x)
+        x = self.conv1(x)
+
+        return x
+
+
+class ConvBlockDown(nn.Module):
+    def __init__(
+        self, in_channels, out_channels, activation
+    ):  # res = 2, ..., resolution_log
+        super().__init__()
+
+        self.conv0 = Conv2dLayer(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            activation=activation,
+            down=2,
+        )
+        self.conv1 = Conv2dLayer(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            activation=activation,
+        )
+
+    def forward(self, x):
+        x = self.conv0(x)
+        x = self.conv1(x)
+
+        return x
+
+
+def token2feature(x, x_size):
+    B, N, C = x.shape
+    h, w = x_size
+    x = x.permute(0, 2, 1).reshape(B, C, h, w)
+    return x
+
+
+def feature2token(x):
+    B, C, H, W = x.shape
+    x = x.view(B, C, -1).transpose(1, 2)
+    return x
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        res_log2,
+        img_channels,
+        activation,
+        patch_size=5,
+        channels=16,
+        drop_path_rate=0.1,
+    ):
+        super().__init__()
+
+        self.resolution = []
+
+        for idx, i in enumerate(range(res_log2, 3, -1)):  # from input size to 16x16
+            res = 2 ** i
+            self.resolution.append(res)
+            if i == res_log2:
+                block = EncFromRGB(img_channels * 2 + 1, nf(i), activation)
+            else:
+                block = ConvBlockDown(nf(i + 1), nf(i), activation)
+            setattr(self, "EncConv_Block_%dx%d" % (res, res), block)
+
+    def forward(self, x):
+        out = {}
+        for res in self.resolution:
+            res_log2 = int(np.log2(res))
+            x = getattr(self, "EncConv_Block_%dx%d" % (res, res))(x)
+            out[res_log2] = x
+
+        return out
+
+
+class ToStyle(nn.Module):
+    def __init__(self, in_channels, out_channels, activation, drop_rate):
+        super().__init__()
+        self.conv = nn.Sequential(
+            Conv2dLayer(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                kernel_size=3,
+                activation=activation,
+                down=2,
+            ),
+            Conv2dLayer(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                kernel_size=3,
+                activation=activation,
+                down=2,
+            ),
+            Conv2dLayer(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                kernel_size=3,
+                activation=activation,
+                down=2,
+            ),
+        )
+
+        self.pool = nn.AdaptiveAvgPool2d(1)
+        self.fc = FullyConnectedLayer(
+            in_features=in_channels, out_features=out_channels, activation=activation
+        )
+        # self.dropout = nn.Dropout(drop_rate)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.pool(x)
+        x = self.fc(x.flatten(start_dim=1))
+        # x = self.dropout(x)
+
+        return x
+
+
+class DecBlockFirstV2(nn.Module):
+    def __init__(
+        self,
+        res,
+        in_channels,
+        out_channels,
+        activation,
+        style_dim,
+        use_noise,
+        demodulate,
+        img_channels,
+    ):
+        super().__init__()
+        self.res = res
+
+        self.conv0 = Conv2dLayer(
+            in_channels=in_channels,
+            out_channels=in_channels,
+            kernel_size=3,
+            activation=activation,
+        )
+        self.conv1 = StyleConv(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            style_dim=style_dim,
+            resolution=2 ** res,
+            kernel_size=3,
+            use_noise=use_noise,
+            activation=activation,
+            demodulate=demodulate,
+        )
+        self.toRGB = ToRGB(
+            in_channels=out_channels,
+            out_channels=img_channels,
+            style_dim=style_dim,
+            kernel_size=1,
+            demodulate=False,
+        )
+
+    def forward(self, x, ws, gs, E_features, noise_mode="random"):
+        # x = self.fc(x).view(x.shape[0], -1, 4, 4)
+        x = self.conv0(x)
+        x = x + E_features[self.res]
+        style = get_style_code(ws[:, 0], gs)
+        x = self.conv1(x, style, noise_mode=noise_mode)
+        style = get_style_code(ws[:, 1], gs)
+        img = self.toRGB(x, style, skip=None)
+
+        return x, img
+
+
+class DecBlock(nn.Module):
+    def __init__(
+        self,
+        res,
+        in_channels,
+        out_channels,
+        activation,
+        style_dim,
+        use_noise,
+        demodulate,
+        img_channels,
+    ):  # res = 4, ..., resolution_log2
+        super().__init__()
+        self.res = res
+
+        self.conv0 = StyleConv(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            style_dim=style_dim,
+            resolution=2 ** res,
+            kernel_size=3,
+            up=2,
+            use_noise=use_noise,
+            activation=activation,
+            demodulate=demodulate,
+        )
+        self.conv1 = StyleConv(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            style_dim=style_dim,
+            resolution=2 ** res,
+            kernel_size=3,
+            use_noise=use_noise,
+            activation=activation,
+            demodulate=demodulate,
+        )
+        self.toRGB = ToRGB(
+            in_channels=out_channels,
+            out_channels=img_channels,
+            style_dim=style_dim,
+            kernel_size=1,
+            demodulate=False,
+        )
+
+    def forward(self, x, img, ws, gs, E_features, noise_mode="random"):
+        style = get_style_code(ws[:, self.res * 2 - 9], gs)
+        x = self.conv0(x, style, noise_mode=noise_mode)
+        x = x + E_features[self.res]
+        style = get_style_code(ws[:, self.res * 2 - 8], gs)
+        x = self.conv1(x, style, noise_mode=noise_mode)
+        style = get_style_code(ws[:, self.res * 2 - 7], gs)
+        img = self.toRGB(x, style, skip=img)
+
+        return x, img
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self, res_log2, activation, style_dim, use_noise, demodulate, img_channels
+    ):
+        super().__init__()
+        self.Dec_16x16 = DecBlockFirstV2(
+            4, nf(4), nf(4), activation, style_dim, use_noise, demodulate, img_channels
+        )
+        for res in range(5, res_log2 + 1):
+            setattr(
+                self,
+                "Dec_%dx%d" % (2 ** res, 2 ** res),
+                DecBlock(
+                    res,
+                    nf(res - 1),
+                    nf(res),
+                    activation,
+                    style_dim,
+                    use_noise,
+                    demodulate,
+                    img_channels,
+                ),
+            )
+        self.res_log2 = res_log2
+
+    def forward(self, x, ws, gs, E_features, noise_mode="random"):
+        x, img = self.Dec_16x16(x, ws, gs, E_features, noise_mode=noise_mode)
+        for res in range(5, self.res_log2 + 1):
+            block = getattr(self, "Dec_%dx%d" % (2 ** res, 2 ** res))
+            x, img = block(x, img, ws, gs, E_features, noise_mode=noise_mode)
+
+        return img
+
+
+class DecStyleBlock(nn.Module):
+    def __init__(
+        self,
+        res,
+        in_channels,
+        out_channels,
+        activation,
+        style_dim,
+        use_noise,
+        demodulate,
+        img_channels,
+    ):
+        super().__init__()
+        self.res = res
+
+        self.conv0 = StyleConv(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            style_dim=style_dim,
+            resolution=2 ** res,
+            kernel_size=3,
+            up=2,
+            use_noise=use_noise,
+            activation=activation,
+            demodulate=demodulate,
+        )
+        self.conv1 = StyleConv(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            style_dim=style_dim,
+            resolution=2 ** res,
+            kernel_size=3,
+            use_noise=use_noise,
+            activation=activation,
+            demodulate=demodulate,
+        )
+        self.toRGB = ToRGB(
+            in_channels=out_channels,
+            out_channels=img_channels,
+            style_dim=style_dim,
+            kernel_size=1,
+            demodulate=False,
+        )
+
+    def forward(self, x, img, style, skip, noise_mode="random"):
+        x = self.conv0(x, style, noise_mode=noise_mode)
+        x = x + skip
+        x = self.conv1(x, style, noise_mode=noise_mode)
+        img = self.toRGB(x, style, skip=img)
+
+        return x, img
+
+
+class FirstStage(nn.Module):
+    def __init__(
+        self,
+        img_channels,
+        img_resolution=256,
+        dim=180,
+        w_dim=512,
+        use_noise=False,
+        demodulate=True,
+        activation="lrelu",
+    ):
+        super().__init__()
+        res = 64
+
+        self.conv_first = Conv2dLayerPartial(
+            in_channels=img_channels + 1,
+            out_channels=dim,
+            kernel_size=3,
+            activation=activation,
+        )
+        self.enc_conv = nn.ModuleList()
+        down_time = int(np.log2(img_resolution // res))
+        # 根据图片尺寸构建 swim transformer 的层数
+        for i in range(down_time):  # from input size to 64
+            self.enc_conv.append(
+                Conv2dLayerPartial(
+                    in_channels=dim,
+                    out_channels=dim,
+                    kernel_size=3,
+                    down=2,
+                    activation=activation,
+                )
+            )
+
+        # from 64 -> 16 -> 64
+        depths = [2, 3, 4, 3, 2]
+        ratios = [1, 1 / 2, 1 / 2, 2, 2]
+        num_heads = 6
+        window_sizes = [8, 16, 16, 16, 8]
+        drop_path_rate = 0.1
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
+
+        self.tran = nn.ModuleList()
+        for i, depth in enumerate(depths):
+            res = int(res * ratios[i])
+            if ratios[i] < 1:
+                merge = PatchMerging(dim, dim, down=int(1 / ratios[i]))
+            elif ratios[i] > 1:
+                merge = PatchUpsampling(dim, dim, up=ratios[i])
+            else:
+                merge = None
+            self.tran.append(
+                BasicLayer(
+                    dim=dim,
+                    input_resolution=[res, res],
+                    depth=depth,
+                    num_heads=num_heads,
+                    window_size=window_sizes[i],
+                    drop_path=dpr[sum(depths[:i]) : sum(depths[: i + 1])],
+                    downsample=merge,
+                )
+            )
+
+        # global style
+        down_conv = []
+        for i in range(int(np.log2(16))):
+            down_conv.append(
+                Conv2dLayer(
+                    in_channels=dim,
+                    out_channels=dim,
+                    kernel_size=3,
+                    down=2,
+                    activation=activation,
+                )
+            )
+        down_conv.append(nn.AdaptiveAvgPool2d((1, 1)))
+        self.down_conv = nn.Sequential(*down_conv)
+        self.to_style = FullyConnectedLayer(
+            in_features=dim, out_features=dim * 2, activation=activation
+        )
+        self.ws_style = FullyConnectedLayer(
+            in_features=w_dim, out_features=dim, activation=activation
+        )
+        self.to_square = FullyConnectedLayer(
+            in_features=dim, out_features=16 * 16, activation=activation
+        )
+
+        style_dim = dim * 3
+        self.dec_conv = nn.ModuleList()
+        for i in range(down_time):  # from 64 to input size
+            res = res * 2
+            self.dec_conv.append(
+                DecStyleBlock(
+                    res,
+                    dim,
+                    dim,
+                    activation,
+                    style_dim,
+                    use_noise,
+                    demodulate,
+                    img_channels,
+                )
+            )
+
+    def forward(self, images_in, masks_in, ws, noise_mode="random"):
+        x = torch.cat([masks_in - 0.5, images_in * masks_in], dim=1)
+
+        skips = []
+        x, mask = self.conv_first(x, masks_in)  # input size
+        skips.append(x)
+        for i, block in enumerate(self.enc_conv):  # input size to 64
+            x, mask = block(x, mask)
+            if i != len(self.enc_conv) - 1:
+                skips.append(x)
+
+        x_size = x.size()[-2:]
+        x = feature2token(x)
+        mask = feature2token(mask)
+        mid = len(self.tran) // 2
+        for i, block in enumerate(self.tran):  # 64 to 16
+            if i < mid:
+                x, x_size, mask = block(x, x_size, mask)
+                skips.append(x)
+            elif i > mid:
+                x, x_size, mask = block(x, x_size, None)
+                x = x + skips[mid - i]
+            else:
+                x, x_size, mask = block(x, x_size, None)
+
+                mul_map = torch.ones_like(x) * 0.5
+                mul_map = F.dropout(mul_map, training=True)
+                ws = self.ws_style(ws[:, -1])
+                add_n = self.to_square(ws).unsqueeze(1)
+                add_n = (
+                    F.interpolate(
+                        add_n, size=x.size(1), mode="linear", align_corners=False
+                    )
+                    .squeeze(1)
+                    .unsqueeze(-1)
+                )
+                x = x * mul_map + add_n * (1 - mul_map)
+                gs = self.to_style(
+                    self.down_conv(token2feature(x, x_size)).flatten(start_dim=1)
+                )
+                style = torch.cat([gs, ws], dim=1)
+
+        x = token2feature(x, x_size).contiguous()
+        img = None
+        for i, block in enumerate(self.dec_conv):
+            x, img = block(
+                x, img, style, skips[len(self.dec_conv) - i - 1], noise_mode=noise_mode
+            )
+
+        # ensemble
+        img = img * (1 - masks_in) + images_in * masks_in
+
+        return img
+
+
+class SynthesisNet(nn.Module):
+    def __init__(
+        self,
+        w_dim,  # Intermediate latent (W) dimensionality.
+        img_resolution,  # Output image resolution.
+        img_channels=3,  # Number of color channels.
+        channel_base=32768,  # Overall multiplier for the number of channels.
+        channel_decay=1.0,
+        channel_max=512,  # Maximum number of channels in any layer.
+        activation="lrelu",  # Activation function: 'relu', 'lrelu', etc.
+        drop_rate=0.5,
+        use_noise=False,
+        demodulate=True,
+    ):
+        super().__init__()
+        resolution_log2 = int(np.log2(img_resolution))
+        assert img_resolution == 2 ** resolution_log2 and img_resolution >= 4
+
+        self.num_layers = resolution_log2 * 2 - 3 * 2
+        self.img_resolution = img_resolution
+        self.resolution_log2 = resolution_log2
+
+        # first stage
+        self.first_stage = FirstStage(
+            img_channels,
+            img_resolution=img_resolution,
+            w_dim=w_dim,
+            use_noise=False,
+            demodulate=demodulate,
+        )
+
+        # second stage
+        self.enc = Encoder(
+            resolution_log2, img_channels, activation, patch_size=5, channels=16
+        )
+        self.to_square = FullyConnectedLayer(
+            in_features=w_dim, out_features=16 * 16, activation=activation
+        )
+        self.to_style = ToStyle(
+            in_channels=nf(4),
+            out_channels=nf(2) * 2,
+            activation=activation,
+            drop_rate=drop_rate,
+        )
+        style_dim = w_dim + nf(2) * 2
+        self.dec = Decoder(
+            resolution_log2, activation, style_dim, use_noise, demodulate, img_channels
+        )
+
+    def forward(self, images_in, masks_in, ws, noise_mode="random", return_stg1=False):
+        out_stg1 = self.first_stage(images_in, masks_in, ws, noise_mode=noise_mode)
+
+        # encoder
+        x = images_in * masks_in + out_stg1 * (1 - masks_in)
+        x = torch.cat([masks_in - 0.5, x, images_in * masks_in], dim=1)
+        E_features = self.enc(x)
+
+        fea_16 = E_features[4]
+        mul_map = torch.ones_like(fea_16) * 0.5
+        mul_map = F.dropout(mul_map, training=True)
+        add_n = self.to_square(ws[:, 0]).view(-1, 16, 16).unsqueeze(1)
+        add_n = F.interpolate(
+            add_n, size=fea_16.size()[-2:], mode="bilinear", align_corners=False
+        )
+        fea_16 = fea_16 * mul_map + add_n * (1 - mul_map)
+        E_features[4] = fea_16
+
+        # style
+        gs = self.to_style(fea_16)
+
+        # decoder
+        img = self.dec(fea_16, ws, gs, E_features, noise_mode=noise_mode)
+
+        # ensemble
+        img = img * (1 - masks_in) + images_in * masks_in
+
+        if not return_stg1:
+            return img
+        else:
+            return img, out_stg1
+
+
+class Generator(nn.Module):
+    def __init__(
+        self,
+        z_dim,  # Input latent (Z) dimensionality, 0 = no latent.
+        c_dim,  # Conditioning label (C) dimensionality, 0 = no label.
+        w_dim,  # Intermediate latent (W) dimensionality.
+        img_resolution,  # resolution of generated image
+        img_channels,  # Number of input color channels.
+        synthesis_kwargs={},  # Arguments for SynthesisNetwork.
+        mapping_kwargs={},  # Arguments for MappingNetwork.
+    ):
+        super().__init__()
+        self.z_dim = z_dim
+        self.c_dim = c_dim
+        self.w_dim = w_dim
+        self.img_resolution = img_resolution
+        self.img_channels = img_channels
+
+        self.synthesis = SynthesisNet(
+            w_dim=w_dim,
+            img_resolution=img_resolution,
+            img_channels=img_channels,
+            **synthesis_kwargs,
+        )
+        self.mapping = MappingNet(
+            z_dim=z_dim,
+            c_dim=c_dim,
+            w_dim=w_dim,
+            num_ws=self.synthesis.num_layers,
+            **mapping_kwargs,
+        )
+
+    def forward(
+        self,
+        images_in,
+        masks_in,
+        z,
+        c,
+        truncation_psi=1,
+        truncation_cutoff=None,
+        skip_w_avg_update=False,
+        noise_mode="none",
+        return_stg1=False,
+    ):
+        ws = self.mapping(
+            z,
+            c,
+            truncation_psi=truncation_psi,
+            truncation_cutoff=truncation_cutoff,
+            skip_w_avg_update=skip_w_avg_update,
+        )
+        img = self.synthesis(images_in, masks_in, ws, noise_mode=noise_mode)
+        return img
+
+
+class Discriminator(torch.nn.Module):
+    def __init__(
+        self,
+        c_dim,  # Conditioning label (C) dimensionality.
+        img_resolution,  # Input resolution.
+        img_channels,  # Number of input color channels.
+        channel_base=32768,  # Overall multiplier for the number of channels.
+        channel_max=512,  # Maximum number of channels in any layer.
+        channel_decay=1,
+        cmap_dim=None,  # Dimensionality of mapped conditioning label, None = default.
+        activation="lrelu",
+        mbstd_group_size=4,  # Group size for the minibatch standard deviation layer, None = entire minibatch.
+        mbstd_num_channels=1,  # Number of features for the minibatch standard deviation layer, 0 = disable.
+    ):
+        super().__init__()
+        self.c_dim = c_dim
+        self.img_resolution = img_resolution
+        self.img_channels = img_channels
+
+        resolution_log2 = int(np.log2(img_resolution))
+        assert img_resolution == 2 ** resolution_log2 and img_resolution >= 4
+        self.resolution_log2 = resolution_log2
+
+        if cmap_dim == None:
+            cmap_dim = nf(2)
+        if c_dim == 0:
+            cmap_dim = 0
+        self.cmap_dim = cmap_dim
+
+        if c_dim > 0:
+            self.mapping = MappingNet(
+                z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None
+            )
+
+        Dis = [DisFromRGB(img_channels + 1, nf(resolution_log2), activation)]
+        for res in range(resolution_log2, 2, -1):
+            Dis.append(DisBlock(nf(res), nf(res - 1), activation))
+
+        if mbstd_num_channels > 0:
+            Dis.append(
+                MinibatchStdLayer(
+                    group_size=mbstd_group_size, num_channels=mbstd_num_channels
+                )
+            )
+        Dis.append(
+            Conv2dLayer(
+                nf(2) + mbstd_num_channels, nf(2), kernel_size=3, activation=activation
+            )
+        )
+        self.Dis = nn.Sequential(*Dis)
+
+        self.fc0 = FullyConnectedLayer(nf(2) * 4 ** 2, nf(2), activation=activation)
+        self.fc1 = FullyConnectedLayer(nf(2), 1 if cmap_dim == 0 else cmap_dim)
+
+        # for 64x64
+        Dis_stg1 = [DisFromRGB(img_channels + 1, nf(resolution_log2) // 2, activation)]
+        for res in range(resolution_log2, 2, -1):
+            Dis_stg1.append(DisBlock(nf(res) // 2, nf(res - 1) // 2, activation))
+
+        if mbstd_num_channels > 0:
+            Dis_stg1.append(
+                MinibatchStdLayer(
+                    group_size=mbstd_group_size, num_channels=mbstd_num_channels
+                )
+            )
+        Dis_stg1.append(
+            Conv2dLayer(
+                nf(2) // 2 + mbstd_num_channels,
+                nf(2) // 2,
+                kernel_size=3,
+                activation=activation,
+            )
+        )
+        self.Dis_stg1 = nn.Sequential(*Dis_stg1)
+
+        self.fc0_stg1 = FullyConnectedLayer(
+            nf(2) // 2 * 4 ** 2, nf(2) // 2, activation=activation
+        )
+        self.fc1_stg1 = FullyConnectedLayer(
+            nf(2) // 2, 1 if cmap_dim == 0 else cmap_dim
+        )
+
+    def forward(self, images_in, masks_in, images_stg1, c):
+        x = self.Dis(torch.cat([masks_in - 0.5, images_in], dim=1))
+        x = self.fc1(self.fc0(x.flatten(start_dim=1)))
+
+        x_stg1 = self.Dis_stg1(torch.cat([masks_in - 0.5, images_stg1], dim=1))
+        x_stg1 = self.fc1_stg1(self.fc0_stg1(x_stg1.flatten(start_dim=1)))
+
+        if self.c_dim > 0:
+            cmap = self.mapping(None, c)
+
+        if self.cmap_dim > 0:
+            x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))
+            x_stg1 = (x_stg1 * cmap).sum(dim=1, keepdim=True) * (
+                1 / np.sqrt(self.cmap_dim)
+            )
+
+        return x, x_stg1
+
+
+MAT_MODEL_URL = os.environ.get(
+    "MAT_MODEL_URL",
+    "https://github.com/Sanster/models/releases/download/add_mat/Places_512_FullData_G.pth",
+)
+
+MAT_MODEL_MD5 = os.environ.get("MAT_MODEL_MD5", "8ca927835fa3f5e21d65ffcb165377ed")
+
+
+class MAT(InpaintModel):
+    name = "mat"
+    min_size = 512
+    pad_mod = 512
+    pad_to_square = True
+
+    def init_model(self, device, **kwargs):
+        seed = 240  # pick up a random number
+        set_seed(seed)
+
+        fp16 = not kwargs.get("no_half", False)
+        use_gpu = "cuda" in str(device) and torch.cuda.is_available()
+        self.torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
+
+        G = Generator(
+            z_dim=512,
+            c_dim=0,
+            w_dim=512,
+            img_resolution=512,
+            img_channels=3,
+            mapping_kwargs={"torch_dtype": self.torch_dtype},
+        ).to(self.torch_dtype)
+        # fmt: off
+        self.model = load_model(G, MAT_MODEL_URL, device, MAT_MODEL_MD5)
+        self.z = torch.from_numpy(np.random.randn(1, G.z_dim)).to(self.torch_dtype).to(device)
+        self.label = torch.zeros([1, self.model.c_dim], device=device).to(self.torch_dtype)
+        # fmt: on
+
+    @staticmethod
+    def is_downloaded() -> bool:
+        return os.path.exists(get_cache_path_by_url(MAT_MODEL_URL))
+
+    def forward(self, image, mask, config: Config):
+        """Input images and output images have same size
+        images: [H, W, C] RGB
+        masks: [H, W] mask area == 255
+        return: BGR IMAGE
+        """
+
+        image = norm_img(image)  # [0, 1]
+        image = image * 2 - 1  # [0, 1] -> [-1, 1]
+
+        mask = (mask > 127) * 255
+        mask = 255 - mask
+        mask = norm_img(mask)
+
+        image = (
+            torch.from_numpy(image).unsqueeze(0).to(self.torch_dtype).to(self.device)
+        )
+        mask = torch.from_numpy(mask).unsqueeze(0).to(self.torch_dtype).to(self.device)
+
+        output = self.model(
+            image, mask, self.z, self.label, truncation_psi=1, noise_mode="none"
+        )
+        output = (
+            (output.permute(0, 2, 3, 1) * 127.5 + 127.5)
+            .round()
+            .clamp(0, 255)
+            .to(torch.uint8)
+        )
+        output = output[0].cpu().numpy()
+        cur_res = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
+        return cur_res
diff --git a/lama_cleaner/model/opencv2.py b/lama_cleaner/model/opencv2.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0618dd2c26d33842826ec3ada2012ef4deddabc
--- /dev/null
+++ b/lama_cleaner/model/opencv2.py
@@ -0,0 +1,28 @@
+import cv2
+from lama_cleaner.model.base import InpaintModel
+from lama_cleaner.schema import Config
+
+flag_map = {"INPAINT_NS": cv2.INPAINT_NS, "INPAINT_TELEA": cv2.INPAINT_TELEA}
+
+
+class OpenCV2(InpaintModel):
+    name = "cv2"
+    pad_mod = 1
+
+    @staticmethod
+    def is_downloaded() -> bool:
+        return True
+
+    def forward(self, image, mask, config: Config):
+        """Input image and output image have same size
+        image: [H, W, C] RGB
+        mask: [H, W, 1]
+        return: BGR IMAGE
+        """
+        cur_res = cv2.inpaint(
+            image[:, :, ::-1],
+            mask,
+            inpaintRadius=config.cv2_radius,
+            flags=flag_map[config.cv2_flag],
+        )
+        return cur_res
diff --git a/lama_cleaner/model/paint_by_example.py b/lama_cleaner/model/paint_by_example.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b5ae749b5ac7be7ee0d6a3569ee65ebc72af4ee
--- /dev/null
+++ b/lama_cleaner/model/paint_by_example.py
@@ -0,0 +1,79 @@
+import PIL
+import PIL.Image
+import cv2
+import torch
+from diffusers import DiffusionPipeline
+from loguru import logger
+
+from lama_cleaner.model.base import DiffusionInpaintModel
+from lama_cleaner.model.utils import set_seed
+from lama_cleaner.schema import Config
+
+
+class PaintByExample(DiffusionInpaintModel):
+    name = "paint_by_example"
+    pad_mod = 8
+    min_size = 512
+
+    def init_model(self, device: torch.device, **kwargs):
+        fp16 = not kwargs.get('no_half', False)
+        use_gpu = device == torch.device('cuda') and torch.cuda.is_available()
+        torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
+        model_kwargs = {"local_files_only": kwargs.get('local_files_only', False)}
+
+        if kwargs['disable_nsfw'] or kwargs.get('cpu_offload', False):
+            logger.info("Disable Paint By Example Model NSFW checker")
+            model_kwargs.update(dict(
+                safety_checker=None,
+                requires_safety_checker=False
+            ))
+
+        self.model = DiffusionPipeline.from_pretrained(
+            "Fantasy-Studio/Paint-by-Example",
+            torch_dtype=torch_dtype,
+            **model_kwargs
+        )
+
+        self.model.enable_attention_slicing()
+        if kwargs.get('enable_xformers', False):
+            self.model.enable_xformers_memory_efficient_attention()
+
+        # TODO: gpu_id
+        if kwargs.get('cpu_offload', False) and use_gpu:
+            self.model.image_encoder = self.model.image_encoder.to(device)
+            self.model.enable_sequential_cpu_offload(gpu_id=0)
+        else:
+            self.model = self.model.to(device)
+
+    def forward(self, image, mask, config: Config):
+        """Input image and output image have same size
+        image: [H, W, C] RGB
+        mask: [H, W, 1] 255 means area to repaint
+        return: BGR IMAGE
+        """
+        output = self.model(
+            image=PIL.Image.fromarray(image),
+            mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
+            example_image=config.paint_by_example_example_image,
+            num_inference_steps=config.paint_by_example_steps,
+            output_type='np.array',
+            generator=torch.manual_seed(config.paint_by_example_seed)
+        ).images[0]
+
+        output = (output * 255).round().astype("uint8")
+        output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
+        return output
+
+    def forward_post_process(self, result, image, mask, config):
+        if config.paint_by_example_match_histograms:
+            result = self._match_histograms(result, image[:, :, ::-1], mask)
+
+        if config.paint_by_example_mask_blur != 0:
+            k = 2 * config.paint_by_example_mask_blur + 1
+            mask = cv2.GaussianBlur(mask, (k, k), 0)
+        return result, image, mask
+
+    @staticmethod
+    def is_downloaded() -> bool:
+        # model will be downloaded when app start, and can't switch in frontend settings
+        return True
diff --git a/lama_cleaner/model/pipeline/__init__.py b/lama_cleaner/model/pipeline/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9056bc6cd88b6f11be2f7e7de3fad68eca77aa08
--- /dev/null
+++ b/lama_cleaner/model/pipeline/__init__.py
@@ -0,0 +1,3 @@
+from .pipeline_stable_diffusion_controlnet_inpaint import (
+    StableDiffusionControlNetInpaintPipeline,
+)
diff --git a/lama_cleaner/model/pipeline/__pycache__/__init__.cpython-38.pyc b/lama_cleaner/model/pipeline/__pycache__/__init__.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..fbc2b7eaffe9e64e08bbc6fd3e5bddfad54dfe07
Binary files /dev/null and b/lama_cleaner/model/pipeline/__pycache__/__init__.cpython-38.pyc differ
diff --git a/lama_cleaner/model/pipeline/__pycache__/pipeline_stable_diffusion_controlnet_inpaint.cpython-38.pyc b/lama_cleaner/model/pipeline/__pycache__/pipeline_stable_diffusion_controlnet_inpaint.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c573444128574f4487b38dda515ab1eda037d586
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diff --git a/lama_cleaner/model/pipeline/pipeline_stable_diffusion_controlnet_inpaint.py b/lama_cleaner/model/pipeline/pipeline_stable_diffusion_controlnet_inpaint.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5883b85715e9bab6af456b10691c874ff913566
--- /dev/null
+++ b/lama_cleaner/model/pipeline/pipeline_stable_diffusion_controlnet_inpaint.py
@@ -0,0 +1,585 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Copy from https://github.com/mikonvergence/ControlNetInpaint/blob/main/src/pipeline_stable_diffusion_controlnet_inpaint.py
+
+import torch
+import PIL.Image
+import numpy as np
+
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import *
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> # !pip install opencv-python transformers accelerate
+        >>> from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, UniPCMultistepScheduler
+        >>> from diffusers.utils import load_image
+        >>> import numpy as np
+        >>> import torch
+
+        >>> import cv2
+        >>> from PIL import Image
+        >>> # download an image
+        >>> image = load_image(
+        ...     "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
+        ... )
+        >>> image = np.array(image)
+        >>> mask_image = load_image(
+        ...     "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
+        ... )
+        >>> mask_image = np.array(mask_image)
+        >>> # get canny image
+        >>> canny_image = cv2.Canny(image, 100, 200)
+        >>> canny_image = canny_image[:, :, None]
+        >>> canny_image = np.concatenate([canny_image, canny_image, canny_image], axis=2)
+        >>> canny_image = Image.fromarray(canny_image)
+
+        >>> # load control net and stable diffusion v1-5
+        >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
+        >>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
+        ...     "runwayml/stable-diffusion-inpainting", controlnet=controlnet, torch_dtype=torch.float16
+        ... )
+
+        >>> # speed up diffusion process with faster scheduler and memory optimization
+        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+        >>> # remove following line if xformers is not installed
+        >>> pipe.enable_xformers_memory_efficient_attention()
+
+        >>> pipe.enable_model_cpu_offload()
+
+        >>> # generate image
+        >>> generator = torch.manual_seed(0)
+        >>> image = pipe(
+        ...     "futuristic-looking doggo",
+        ...     num_inference_steps=20,
+        ...     generator=generator,
+        ...     image=image,
+        ...     control_image=canny_image,
+        ...     mask_image=mask_image
+        ... ).images[0]
+        ```
+"""
+
+
+def prepare_mask_and_masked_image(image, mask):
+    """
+    Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
+    converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
+    ``image`` and ``1`` for the ``mask``.
+    The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
+    binarized (``mask > 0.5``) and cast to ``torch.float32`` too.
+    Args:
+        image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
+            It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
+            ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.
+        mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
+            It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
+            ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.
+    Raises:
+        ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask
+        should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
+        TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
+            (ot the other way around).
+    Returns:
+        tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
+            dimensions: ``batch x channels x height x width``.
+    """
+    if isinstance(image, torch.Tensor):
+        if not isinstance(mask, torch.Tensor):
+            raise TypeError(
+                f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not"
+            )
+
+        # Batch single image
+        if image.ndim == 3:
+            assert (
+                image.shape[0] == 3
+            ), "Image outside a batch should be of shape (3, H, W)"
+            image = image.unsqueeze(0)
+
+        # Batch and add channel dim for single mask
+        if mask.ndim == 2:
+            mask = mask.unsqueeze(0).unsqueeze(0)
+
+        # Batch single mask or add channel dim
+        if mask.ndim == 3:
+            # Single batched mask, no channel dim or single mask not batched but channel dim
+            if mask.shape[0] == 1:
+                mask = mask.unsqueeze(0)
+
+            # Batched masks no channel dim
+            else:
+                mask = mask.unsqueeze(1)
+
+        assert (
+            image.ndim == 4 and mask.ndim == 4
+        ), "Image and Mask must have 4 dimensions"
+        assert (
+            image.shape[-2:] == mask.shape[-2:]
+        ), "Image and Mask must have the same spatial dimensions"
+        assert (
+            image.shape[0] == mask.shape[0]
+        ), "Image and Mask must have the same batch size"
+
+        # Check image is in [-1, 1]
+        if image.min() < -1 or image.max() > 1:
+            raise ValueError("Image should be in [-1, 1] range")
+
+        # Check mask is in [0, 1]
+        if mask.min() < 0 or mask.max() > 1:
+            raise ValueError("Mask should be in [0, 1] range")
+
+        # Binarize mask
+        mask[mask < 0.5] = 0
+        mask[mask >= 0.5] = 1
+
+        # Image as float32
+        image = image.to(dtype=torch.float32)
+    elif isinstance(mask, torch.Tensor):
+        raise TypeError(
+            f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not"
+        )
+    else:
+        # preprocess image
+        if isinstance(image, (PIL.Image.Image, np.ndarray)):
+            image = [image]
+
+        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
+            image = [np.array(i.convert("RGB"))[None, :] for i in image]
+            image = np.concatenate(image, axis=0)
+        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
+            image = np.concatenate([i[None, :] for i in image], axis=0)
+
+        image = image.transpose(0, 3, 1, 2)
+        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
+
+        # preprocess mask
+        if isinstance(mask, (PIL.Image.Image, np.ndarray)):
+            mask = [mask]
+
+        if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
+            mask = np.concatenate(
+                [np.array(m.convert("L"))[None, None, :] for m in mask], axis=0
+            )
+            mask = mask.astype(np.float32) / 255.0
+        elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
+            mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
+
+        mask[mask < 0.5] = 0
+        mask[mask >= 0.5] = 1
+        mask = torch.from_numpy(mask)
+
+    masked_image = image * (mask < 0.5)
+
+    return mask, masked_image
+
+
+class StableDiffusionControlNetInpaintPipeline(StableDiffusionControlNetPipeline):
+    r"""
+    Pipeline for text-guided image inpainting using Stable Diffusion with ControlNet guidance.
+
+    This model inherits from [`StableDiffusionControlNetPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        controlnet ([`ControlNetModel`]):
+            Provides additional conditioning to the unet during the denoising process
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    def prepare_mask_latents(
+        self,
+        mask,
+        masked_image,
+        batch_size,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        do_classifier_free_guidance,
+    ):
+        # resize the mask to latents shape as we concatenate the mask to the latents
+        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
+        # and half precision
+        mask = torch.nn.functional.interpolate(
+            mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
+        )
+        mask = mask.to(device=device, dtype=dtype)
+
+        masked_image = masked_image.to(device=device, dtype=dtype)
+
+        # encode the mask image into latents space so we can concatenate it to the latents
+        if isinstance(generator, list):
+            masked_image_latents = [
+                self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(
+                    generator=generator[i]
+                )
+                for i in range(batch_size)
+            ]
+            masked_image_latents = torch.cat(masked_image_latents, dim=0)
+        else:
+            masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(
+                generator=generator
+            )
+        masked_image_latents = self.vae.config.scaling_factor * masked_image_latents
+
+        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
+        if mask.shape[0] < batch_size:
+            if not batch_size % mask.shape[0] == 0:
+                raise ValueError(
+                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
+                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
+                    " of masks that you pass is divisible by the total requested batch size."
+                )
+            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
+        if masked_image_latents.shape[0] < batch_size:
+            if not batch_size % masked_image_latents.shape[0] == 0:
+                raise ValueError(
+                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
+                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
+                    " Make sure the number of images that you pass is divisible by the total requested batch size."
+                )
+            masked_image_latents = masked_image_latents.repeat(
+                batch_size // masked_image_latents.shape[0], 1, 1, 1
+            )
+
+        mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
+        masked_image_latents = (
+            torch.cat([masked_image_latents] * 2)
+            if do_classifier_free_guidance
+            else masked_image_latents
+        )
+
+        # aligning device to prevent device errors when concating it with the latent model input
+        masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
+        return mask, masked_image_latents
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        image: Union[torch.FloatTensor, PIL.Image.Image] = None,
+        control_image: Union[
+            torch.FloatTensor,
+            PIL.Image.Image,
+            List[torch.FloatTensor],
+            List[PIL.Image.Image],
+        ] = None,
+        mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        controlnet_conditioning_scale: float = 1.0,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            image (`PIL.Image.Image`):
+                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
+                be masked out with `mask_image` and repainted according to `prompt`.
+            control_image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]` or `List[PIL.Image.Image]`):
+                The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
+                the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. PIL.Image.Image` can
+                also be accepted as an image. The control image is automatically resized to fit the output image.
+            mask_image (`PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
+                repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
+                to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
+                instead of 3, so the expected shape would be `(B, H, W, 1)`.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
+                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttnProcessor` as defined under
+                `self.processor` in
+                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
+            controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0):
+                The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
+                to the residual in the original unet.
+        Examples:
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        # 0. Default height and width to unet
+        height, width = self._default_height_width(height, width, control_image)
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            control_image,
+            height,
+            width,
+            callback_steps,
+            negative_prompt,
+            prompt_embeds,
+            negative_prompt_embeds,
+        )
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        prompt_embeds = self._encode_prompt(
+            prompt,
+            device,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+        )
+
+        # 4. Prepare image
+        control_image = self.prepare_image(
+            control_image,
+            width,
+            height,
+            batch_size * num_images_per_prompt,
+            num_images_per_prompt,
+            device,
+            self.controlnet.dtype,
+        )
+
+        if do_classifier_free_guidance:
+            control_image = torch.cat([control_image] * 2)
+
+        # 5. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 6. Prepare latent variables
+        num_channels_latents = self.controlnet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # EXTRA: prepare mask latents
+        mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
+        mask, masked_image_latents = self.prepare_mask_latents(
+            mask,
+            masked_image,
+            batch_size * num_images_per_prompt,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            do_classifier_free_guidance,
+        )
+
+        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 8. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = (
+                    torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                )
+                latent_model_input = self.scheduler.scale_model_input(
+                    latent_model_input, t
+                )
+
+                down_block_res_samples, mid_block_res_sample = self.controlnet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    controlnet_cond=control_image,
+                    return_dict=False,
+                )
+
+                down_block_res_samples = [
+                    down_block_res_sample * controlnet_conditioning_scale
+                    for down_block_res_sample in down_block_res_samples
+                ]
+                mid_block_res_sample *= controlnet_conditioning_scale
+
+                # predict the noise residual
+                latent_model_input = torch.cat(
+                    [latent_model_input, mask, masked_image_latents], dim=1
+                )
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    down_block_additional_residuals=down_block_res_samples,
+                    mid_block_additional_residual=mid_block_res_sample,
+                ).sample
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (
+                        noise_pred_text - noise_pred_uncond
+                    )
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(
+                    noise_pred, t, latents, **extra_step_kwargs
+                ).prev_sample
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or (
+                    (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
+                ):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        callback(i, t, latents)
+
+        # If we do sequential model offloading, let's offload unet and controlnet
+        # manually for max memory savings
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.unet.to("cpu")
+            self.controlnet.to("cpu")
+            torch.cuda.empty_cache()
+
+        if output_type == "latent":
+            image = latents
+            has_nsfw_concept = None
+        elif output_type == "pil":
+            # 8. Post-processing
+            image = self.decode_latents(latents)
+
+            # 9. Run safety checker
+            image, has_nsfw_concept = self.run_safety_checker(
+                image, device, prompt_embeds.dtype
+            )
+
+            # 10. Convert to PIL
+            image = self.numpy_to_pil(image)
+        else:
+            # 8. Post-processing
+            image = self.decode_latents(latents)
+
+            # 9. Run safety checker
+            image, has_nsfw_concept = self.run_safety_checker(
+                image, device, prompt_embeds.dtype
+            )
+
+        # Offload last model to CPU
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.final_offload_hook.offload()
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(
+            images=image, nsfw_content_detected=has_nsfw_concept
+        )
diff --git a/lama_cleaner/model/plms_sampler.py b/lama_cleaner/model/plms_sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9c0416740f58eb71e83bdb8898645669b407906
--- /dev/null
+++ b/lama_cleaner/model/plms_sampler.py
@@ -0,0 +1,225 @@
+# From: https://github.com/CompVis/latent-diffusion/blob/main/ldm/models/diffusion/plms.py
+import torch
+import numpy as np
+from lama_cleaner.model.utils import make_ddim_timesteps, make_ddim_sampling_parameters, noise_like
+from tqdm import tqdm
+
+
+class PLMSSampler(object):
+    def __init__(self, model, schedule="linear", **kwargs):
+        super().__init__()
+        self.model = model
+        self.ddpm_num_timesteps = model.num_timesteps
+        self.schedule = schedule
+
+    def register_buffer(self, name, attr):
+        setattr(self, name, attr)
+
+    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+        if ddim_eta != 0:
+            raise ValueError('ddim_eta must be 0 for PLMS')
+        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
+                                                  num_ddpm_timesteps=self.ddpm_num_timesteps, verbose=verbose)
+        alphas_cumprod = self.model.alphas_cumprod
+        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
+        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+        self.register_buffer('betas', to_torch(self.model.betas))
+        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
+        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+
+        # ddim sampling parameters
+        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
+                                                                                   ddim_timesteps=self.ddim_timesteps,
+                                                                                   eta=ddim_eta, verbose=verbose)
+        self.register_buffer('ddim_sigmas', ddim_sigmas)
+        self.register_buffer('ddim_alphas', ddim_alphas)
+        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
+        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
+            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
+                    1 - self.alphas_cumprod / self.alphas_cumprod_prev))
+        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+
+    @torch.no_grad()
+    def sample(self,
+               steps,
+               batch_size,
+               shape,
+               conditioning=None,
+               callback=None,
+               normals_sequence=None,
+               img_callback=None,
+               quantize_x0=False,
+               eta=0.,
+               mask=None,
+               x0=None,
+               temperature=1.,
+               noise_dropout=0.,
+               score_corrector=None,
+               corrector_kwargs=None,
+               verbose=False,
+               x_T=None,
+               log_every_t=100,
+               unconditional_guidance_scale=1.,
+               unconditional_conditioning=None,
+               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+               **kwargs
+               ):
+        if conditioning is not None:
+            if isinstance(conditioning, dict):
+                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
+                if cbs != batch_size:
+                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+            else:
+                if conditioning.shape[0] != batch_size:
+                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+        self.make_schedule(ddim_num_steps=steps, ddim_eta=eta, verbose=verbose)
+        # sampling
+        C, H, W = shape
+        size = (batch_size, C, H, W)
+        print(f'Data shape for PLMS sampling is {size}')
+
+        samples = self.plms_sampling(conditioning, size,
+                                     callback=callback,
+                                     img_callback=img_callback,
+                                     quantize_denoised=quantize_x0,
+                                     mask=mask, x0=x0,
+                                     ddim_use_original_steps=False,
+                                     noise_dropout=noise_dropout,
+                                     temperature=temperature,
+                                     score_corrector=score_corrector,
+                                     corrector_kwargs=corrector_kwargs,
+                                     x_T=x_T,
+                                     log_every_t=log_every_t,
+                                     unconditional_guidance_scale=unconditional_guidance_scale,
+                                     unconditional_conditioning=unconditional_conditioning,
+                                     )
+        return samples
+
+    @torch.no_grad()
+    def plms_sampling(self, cond, shape,
+                      x_T=None, ddim_use_original_steps=False,
+                      callback=None, timesteps=None, quantize_denoised=False,
+                      mask=None, x0=None, img_callback=None, log_every_t=100,
+                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                      unconditional_guidance_scale=1., unconditional_conditioning=None, ):
+        device = self.model.betas.device
+        b = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=device)
+        else:
+            img = x_T
+
+        if timesteps is None:
+            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+        elif timesteps is not None and not ddim_use_original_steps:
+            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+            timesteps = self.ddim_timesteps[:subset_end]
+
+        time_range = list(reversed(range(0, timesteps))) if ddim_use_original_steps else np.flip(timesteps)
+        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
+        print(f"Running PLMS Sampling with {total_steps} timesteps")
+
+        iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
+        old_eps = []
+
+        for i, step in enumerate(iterator):
+            index = total_steps - i - 1
+            ts = torch.full((b,), step, device=device, dtype=torch.long)
+            ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
+
+            if mask is not None:
+                assert x0 is not None
+                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
+                img = img_orig * mask + (1. - mask) * img
+
+            outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
+                                      quantize_denoised=quantize_denoised, temperature=temperature,
+                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
+                                      corrector_kwargs=corrector_kwargs,
+                                      unconditional_guidance_scale=unconditional_guidance_scale,
+                                      unconditional_conditioning=unconditional_conditioning,
+                                      old_eps=old_eps, t_next=ts_next)
+            img, pred_x0, e_t = outs
+            old_eps.append(e_t)
+            if len(old_eps) >= 4:
+                old_eps.pop(0)
+            if callback: callback(i)
+            if img_callback: img_callback(pred_x0, i)
+
+        return img
+
+    @torch.no_grad()
+    def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                      unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
+        b, *_, device = *x.shape, x.device
+
+        def get_model_output(x, t):
+            if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+                e_t = self.model.apply_model(x, t, c)
+            else:
+                x_in = torch.cat([x] * 2)
+                t_in = torch.cat([t] * 2)
+                c_in = torch.cat([unconditional_conditioning, c])
+                e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
+                e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+
+            if score_corrector is not None:
+                assert self.model.parameterization == "eps"
+                e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+            return e_t
+
+        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+
+        def get_x_prev_and_pred_x0(e_t, index):
+            # select parameters corresponding to the currently considered timestep
+            a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+            a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+            sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+            sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device)
+
+            # current prediction for x_0
+            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+            if quantize_denoised:
+                pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+            # direction pointing to x_t
+            dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t
+            noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+            if noise_dropout > 0.:
+                noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+            x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+            return x_prev, pred_x0
+
+        e_t = get_model_output(x, t)
+        if len(old_eps) == 0:
+            # Pseudo Improved Euler (2nd order)
+            x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
+            e_t_next = get_model_output(x_prev, t_next)
+            e_t_prime = (e_t + e_t_next) / 2
+        elif len(old_eps) == 1:
+            # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
+            e_t_prime = (3 * e_t - old_eps[-1]) / 2
+        elif len(old_eps) == 2:
+            # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
+            e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
+        elif len(old_eps) >= 3:
+            # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
+            e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
+
+        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
+
+        return x_prev, pred_x0, e_t
diff --git a/lama_cleaner/model/sd.py b/lama_cleaner/model/sd.py
new file mode 100644
index 0000000000000000000000000000000000000000..945c5061b4f1852cd35131626e77d390e3eaf064
--- /dev/null
+++ b/lama_cleaner/model/sd.py
@@ -0,0 +1,193 @@
+import gc
+
+import PIL.Image
+import cv2
+import numpy as np
+import torch
+from loguru import logger
+
+from lama_cleaner.model.base import DiffusionInpaintModel
+from lama_cleaner.model.utils import torch_gc, get_scheduler
+from lama_cleaner.schema import Config
+
+
+class CPUTextEncoderWrapper:
+    def __init__(self, text_encoder, torch_dtype):
+        self.config = text_encoder.config
+        self.text_encoder = text_encoder.to(torch.device("cpu"), non_blocking=True)
+        self.text_encoder = self.text_encoder.to(torch.float32, non_blocking=True)
+        self.torch_dtype = torch_dtype
+        del text_encoder
+        torch_gc()
+
+    def __call__(self, x, **kwargs):
+        input_device = x.device
+        return [
+            self.text_encoder(x.to(self.text_encoder.device), **kwargs)[0]
+            .to(input_device)
+            .to(self.torch_dtype)
+        ]
+
+    @property
+    def dtype(self):
+        return self.torch_dtype
+
+
+def load_from_local_model(local_model_path, torch_dtype, disable_nsfw=True):
+    from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
+        download_from_original_stable_diffusion_ckpt,
+    )
+    from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
+
+    logger.info(f"Converting {local_model_path} to diffusers pipeline")
+
+    pipe = download_from_original_stable_diffusion_ckpt(
+        local_model_path,
+        num_in_channels=9,
+        from_safetensors=local_model_path.endswith("safetensors"),
+        device="cpu",
+    )
+
+    inpaint_pipe = StableDiffusionInpaintPipeline(
+        vae=pipe.vae,
+        text_encoder=pipe.text_encoder,
+        tokenizer=pipe.tokenizer,
+        unet=pipe.unet,
+        scheduler=pipe.scheduler,
+        safety_checker=None if disable_nsfw else pipe.safety_checker,
+        feature_extractor=None if disable_nsfw else pipe.safety_checker,
+        requires_safety_checker=not disable_nsfw,
+    )
+
+    del pipe
+    gc.collect()
+    return inpaint_pipe.to(torch_dtype=torch_dtype)
+
+
+class SD(DiffusionInpaintModel):
+    pad_mod = 8
+    min_size = 512
+
+    def init_model(self, device: torch.device, **kwargs):
+        from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
+
+        fp16 = not kwargs.get("no_half", False)
+
+        model_kwargs = {
+            "local_files_only": kwargs.get("local_files_only", kwargs["sd_run_local"])
+        }
+        if kwargs["disable_nsfw"] or kwargs.get("cpu_offload", False):
+            logger.info("Disable Stable Diffusion Model NSFW checker")
+            model_kwargs.update(
+                dict(
+                    safety_checker=None,
+                    feature_extractor=None,
+                    requires_safety_checker=False,
+                )
+            )
+
+        use_gpu = device == torch.device("cuda") and torch.cuda.is_available()
+        torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
+
+        if kwargs.get("sd_local_model_path", None):
+            self.model = load_from_local_model(
+                kwargs["sd_local_model_path"],
+                torch_dtype=torch_dtype,
+            )
+        else:
+            self.model = StableDiffusionInpaintPipeline.from_pretrained(
+                self.model_id_or_path,
+                revision="fp16" if use_gpu and fp16 else "main",
+                torch_dtype=torch_dtype,
+                use_auth_token=kwargs["hf_access_token"],
+                **model_kwargs,
+            )
+
+        # https://huggingface.co/docs/diffusers/v0.7.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing
+        self.model.enable_attention_slicing()
+        # https://huggingface.co/docs/diffusers/v0.7.0/en/optimization/fp16#memory-efficient-attention
+        if kwargs.get("enable_xformers", False):
+            self.model.enable_xformers_memory_efficient_attention()
+
+        if kwargs.get("cpu_offload", False) and use_gpu:
+            # TODO: gpu_id
+            logger.info("Enable sequential cpu offload")
+            self.model.enable_sequential_cpu_offload(gpu_id=0)
+        else:
+            self.model = self.model.to(device)
+            if kwargs["sd_cpu_textencoder"]:
+                logger.info("Run Stable Diffusion TextEncoder on CPU")
+                self.model.text_encoder = CPUTextEncoderWrapper(
+                    self.model.text_encoder, torch_dtype
+                )
+
+        self.callback = kwargs.pop("callback", None)
+
+    def forward(self, image, mask, config: Config):
+        """Input image and output image have same size
+        image: [H, W, C] RGB
+        mask: [H, W, 1] 255 means area to repaint
+        return: BGR IMAGE
+        """
+
+        scheduler_config = self.model.scheduler.config
+        scheduler = get_scheduler(config.sd_sampler, scheduler_config)
+        self.model.scheduler = scheduler
+
+        if config.sd_mask_blur != 0:
+            k = 2 * config.sd_mask_blur + 1
+            mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
+
+        img_h, img_w = image.shape[:2]
+
+        output = self.model(
+            image=PIL.Image.fromarray(image),
+            prompt=config.prompt,
+            negative_prompt=config.negative_prompt,
+            mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
+            num_inference_steps=config.sd_steps,
+            guidance_scale=config.sd_guidance_scale,
+            output_type="np.array",
+            callback=self.callback,
+            height=img_h,
+            width=img_w,
+            generator=torch.manual_seed(config.sd_seed),
+        ).images[0]
+
+        output = (output * 255).round().astype("uint8")
+        output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
+        return output
+
+    def forward_post_process(self, result, image, mask, config):
+        if config.sd_match_histograms:
+            result = self._match_histograms(result, image[:, :, ::-1], mask)
+
+        if config.sd_mask_blur != 0:
+            k = 2 * config.sd_mask_blur + 1
+            mask = cv2.GaussianBlur(mask, (k, k), 0)
+        return result, image, mask
+
+    @staticmethod
+    def is_downloaded() -> bool:
+        # model will be downloaded when app start, and can't switch in frontend settings
+        return True
+
+
+class SD15(SD):
+    name = "sd1.5"
+    model_id_or_path = "runwayml/stable-diffusion-inpainting"
+
+
+class Anything4(SD):
+    name = "anything4"
+    model_id_or_path = "Sanster/anything-4.0-inpainting"
+
+
+class RealisticVision14(SD):
+    name = "realisticVision1.4"
+    model_id_or_path = "Sanster/Realistic_Vision_V1.4-inpainting"
+
+
+class SD2(SD):
+    name = "sd2"
+    model_id_or_path = "stabilityai/stable-diffusion-2-inpainting"
diff --git a/lama_cleaner/model/utils.py b/lama_cleaner/model/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..998db43c05c61eb0f7d1e4b9c7b159acefed7138
--- /dev/null
+++ b/lama_cleaner/model/utils.py
@@ -0,0 +1,941 @@
+import math
+import random
+from typing import Any
+
+import torch
+import numpy as np
+import collections
+from itertools import repeat
+
+from diffusers import (
+    DDIMScheduler,
+    PNDMScheduler,
+    LMSDiscreteScheduler,
+    EulerDiscreteScheduler,
+    EulerAncestralDiscreteScheduler,
+    DPMSolverMultistepScheduler,
+    UniPCMultistepScheduler,
+)
+
+from lama_cleaner.schema import SDSampler
+from torch import conv2d, conv_transpose2d
+
+
+def make_beta_schedule(
+    device, schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3
+):
+    if schedule == "linear":
+        betas = (
+            torch.linspace(
+                linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64
+            )
+            ** 2
+        )
+
+    elif schedule == "cosine":
+        timesteps = (
+            torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
+        ).to(device)
+        alphas = timesteps / (1 + cosine_s) * np.pi / 2
+        alphas = torch.cos(alphas).pow(2).to(device)
+        alphas = alphas / alphas[0]
+        betas = 1 - alphas[1:] / alphas[:-1]
+        betas = np.clip(betas, a_min=0, a_max=0.999)
+
+    elif schedule == "sqrt_linear":
+        betas = torch.linspace(
+            linear_start, linear_end, n_timestep, dtype=torch.float64
+        )
+    elif schedule == "sqrt":
+        betas = (
+            torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
+            ** 0.5
+        )
+    else:
+        raise ValueError(f"schedule '{schedule}' unknown.")
+    return betas.numpy()
+
+
+def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
+    # select alphas for computing the variance schedule
+    alphas = alphacums[ddim_timesteps]
+    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
+
+    # according the the formula provided in https://arxiv.org/abs/2010.02502
+    sigmas = eta * np.sqrt(
+        (1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)
+    )
+    if verbose:
+        print(
+            f"Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}"
+        )
+        print(
+            f"For the chosen value of eta, which is {eta}, "
+            f"this results in the following sigma_t schedule for ddim sampler {sigmas}"
+        )
+    return sigmas, alphas, alphas_prev
+
+
+def make_ddim_timesteps(
+    ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True
+):
+    if ddim_discr_method == "uniform":
+        c = num_ddpm_timesteps // num_ddim_timesteps
+        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
+    elif ddim_discr_method == "quad":
+        ddim_timesteps = (
+            (np.linspace(0, np.sqrt(num_ddpm_timesteps * 0.8), num_ddim_timesteps)) ** 2
+        ).astype(int)
+    else:
+        raise NotImplementedError(
+            f'There is no ddim discretization method called "{ddim_discr_method}"'
+        )
+
+    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
+    # add one to get the final alpha values right (the ones from first scale to data during sampling)
+    steps_out = ddim_timesteps + 1
+    if verbose:
+        print(f"Selected timesteps for ddim sampler: {steps_out}")
+    return steps_out
+
+
+def noise_like(shape, device, repeat=False):
+    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(
+        shape[0], *((1,) * (len(shape) - 1))
+    )
+    noise = lambda: torch.randn(shape, device=device)
+    return repeat_noise() if repeat else noise()
+
+
+def timestep_embedding(device, timesteps, dim, max_period=10000, repeat_only=False):
+    """
+    Create sinusoidal timestep embeddings.
+    :param timesteps: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an [N x dim] Tensor of positional embeddings.
+    """
+    half = dim // 2
+    freqs = torch.exp(
+        -math.log(max_period)
+        * torch.arange(start=0, end=half, dtype=torch.float32)
+        / half
+    ).to(device=device)
+
+    args = timesteps[:, None].float() * freqs[None]
+
+    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+    if dim % 2:
+        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+    return embedding
+
+
+###### MAT and FcF #######
+
+
+def normalize_2nd_moment(x, dim=1):
+    return (
+        x * (x.square().mean(dim=dim, keepdim=True) + torch.finfo(x.dtype).eps).rsqrt()
+    )
+
+
+class EasyDict(dict):
+    """Convenience class that behaves like a dict but allows access with the attribute syntax."""
+
+    def __getattr__(self, name: str) -> Any:
+        try:
+            return self[name]
+        except KeyError:
+            raise AttributeError(name)
+
+    def __setattr__(self, name: str, value: Any) -> None:
+        self[name] = value
+
+    def __delattr__(self, name: str) -> None:
+        del self[name]
+
+
+def _bias_act_ref(x, b=None, dim=1, act="linear", alpha=None, gain=None, clamp=None):
+    """Slow reference implementation of `bias_act()` using standard TensorFlow ops."""
+    assert isinstance(x, torch.Tensor)
+    assert clamp is None or clamp >= 0
+    spec = activation_funcs[act]
+    alpha = float(alpha if alpha is not None else spec.def_alpha)
+    gain = float(gain if gain is not None else spec.def_gain)
+    clamp = float(clamp if clamp is not None else -1)
+
+    # Add bias.
+    if b is not None:
+        assert isinstance(b, torch.Tensor) and b.ndim == 1
+        assert 0 <= dim < x.ndim
+        assert b.shape[0] == x.shape[dim]
+        x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)])
+
+    # Evaluate activation function.
+    alpha = float(alpha)
+    x = spec.func(x, alpha=alpha)
+
+    # Scale by gain.
+    gain = float(gain)
+    if gain != 1:
+        x = x * gain
+
+    # Clamp.
+    if clamp >= 0:
+        x = x.clamp(-clamp, clamp)  # pylint: disable=invalid-unary-operand-type
+    return x
+
+
+def bias_act(
+    x, b=None, dim=1, act="linear", alpha=None, gain=None, clamp=None, impl="ref"
+):
+    r"""Fused bias and activation function.
+
+    Adds bias `b` to activation tensor `x`, evaluates activation function `act`,
+    and scales the result by `gain`. Each of the steps is optional. In most cases,
+    the fused op is considerably more efficient than performing the same calculation
+    using standard PyTorch ops. It supports first and second order gradients,
+    but not third order gradients.
+
+    Args:
+        x:      Input activation tensor. Can be of any shape.
+        b:      Bias vector, or `None` to disable. Must be a 1D tensor of the same type
+                as `x`. The shape must be known, and it must match the dimension of `x`
+                corresponding to `dim`.
+        dim:    The dimension in `x` corresponding to the elements of `b`.
+                The value of `dim` is ignored if `b` is not specified.
+        act:    Name of the activation function to evaluate, or `"linear"` to disable.
+                Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc.
+                See `activation_funcs` for a full list. `None` is not allowed.
+        alpha:  Shape parameter for the activation function, or `None` to use the default.
+        gain:   Scaling factor for the output tensor, or `None` to use default.
+                See `activation_funcs` for the default scaling of each activation function.
+                If unsure, consider specifying 1.
+        clamp:  Clamp the output values to `[-clamp, +clamp]`, or `None` to disable
+                the clamping (default).
+        impl:   Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
+
+    Returns:
+        Tensor of the same shape and datatype as `x`.
+    """
+    assert isinstance(x, torch.Tensor)
+    assert impl in ["ref", "cuda"]
+    return _bias_act_ref(
+        x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp
+    )
+
+
+def _get_filter_size(f):
+    if f is None:
+        return 1, 1
+
+    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
+    fw = f.shape[-1]
+    fh = f.shape[0]
+
+    fw = int(fw)
+    fh = int(fh)
+    assert fw >= 1 and fh >= 1
+    return fw, fh
+
+
+def _get_weight_shape(w):
+    shape = [int(sz) for sz in w.shape]
+    return shape
+
+
+def _parse_scaling(scaling):
+    if isinstance(scaling, int):
+        scaling = [scaling, scaling]
+    assert isinstance(scaling, (list, tuple))
+    assert all(isinstance(x, int) for x in scaling)
+    sx, sy = scaling
+    assert sx >= 1 and sy >= 1
+    return sx, sy
+
+
+def _parse_padding(padding):
+    if isinstance(padding, int):
+        padding = [padding, padding]
+    assert isinstance(padding, (list, tuple))
+    assert all(isinstance(x, int) for x in padding)
+    if len(padding) == 2:
+        padx, pady = padding
+        padding = [padx, padx, pady, pady]
+    padx0, padx1, pady0, pady1 = padding
+    return padx0, padx1, pady0, pady1
+
+
+def setup_filter(
+    f,
+    device=torch.device("cpu"),
+    normalize=True,
+    flip_filter=False,
+    gain=1,
+    separable=None,
+):
+    r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`.
+
+    Args:
+        f:           Torch tensor, numpy array, or python list of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable),
+                     `[]` (impulse), or
+                     `None` (identity).
+        device:      Result device (default: cpu).
+        normalize:   Normalize the filter so that it retains the magnitude
+                     for constant input signal (DC)? (default: True).
+        flip_filter: Flip the filter? (default: False).
+        gain:        Overall scaling factor for signal magnitude (default: 1).
+        separable:   Return a separable filter? (default: select automatically).
+
+    Returns:
+        Float32 tensor of the shape
+        `[filter_height, filter_width]` (non-separable) or
+        `[filter_taps]` (separable).
+    """
+    # Validate.
+    if f is None:
+        f = 1
+    f = torch.as_tensor(f, dtype=torch.float32)
+    assert f.ndim in [0, 1, 2]
+    assert f.numel() > 0
+    if f.ndim == 0:
+        f = f[np.newaxis]
+
+    # Separable?
+    if separable is None:
+        separable = f.ndim == 1 and f.numel() >= 8
+    if f.ndim == 1 and not separable:
+        f = f.ger(f)
+    assert f.ndim == (1 if separable else 2)
+
+    # Apply normalize, flip, gain, and device.
+    if normalize:
+        f /= f.sum()
+    if flip_filter:
+        f = f.flip(list(range(f.ndim)))
+    f = f * (gain ** (f.ndim / 2))
+    f = f.to(device=device)
+    return f
+
+
+def _ntuple(n):
+    def parse(x):
+        if isinstance(x, collections.abc.Iterable):
+            return x
+        return tuple(repeat(x, n))
+
+    return parse
+
+
+to_2tuple = _ntuple(2)
+
+activation_funcs = {
+    "linear": EasyDict(
+        func=lambda x, **_: x,
+        def_alpha=0,
+        def_gain=1,
+        cuda_idx=1,
+        ref="",
+        has_2nd_grad=False,
+    ),
+    "relu": EasyDict(
+        func=lambda x, **_: torch.nn.functional.relu(x),
+        def_alpha=0,
+        def_gain=np.sqrt(2),
+        cuda_idx=2,
+        ref="y",
+        has_2nd_grad=False,
+    ),
+    "lrelu": EasyDict(
+        func=lambda x, alpha, **_: torch.nn.functional.leaky_relu(x, alpha),
+        def_alpha=0.2,
+        def_gain=np.sqrt(2),
+        cuda_idx=3,
+        ref="y",
+        has_2nd_grad=False,
+    ),
+    "tanh": EasyDict(
+        func=lambda x, **_: torch.tanh(x),
+        def_alpha=0,
+        def_gain=1,
+        cuda_idx=4,
+        ref="y",
+        has_2nd_grad=True,
+    ),
+    "sigmoid": EasyDict(
+        func=lambda x, **_: torch.sigmoid(x),
+        def_alpha=0,
+        def_gain=1,
+        cuda_idx=5,
+        ref="y",
+        has_2nd_grad=True,
+    ),
+    "elu": EasyDict(
+        func=lambda x, **_: torch.nn.functional.elu(x),
+        def_alpha=0,
+        def_gain=1,
+        cuda_idx=6,
+        ref="y",
+        has_2nd_grad=True,
+    ),
+    "selu": EasyDict(
+        func=lambda x, **_: torch.nn.functional.selu(x),
+        def_alpha=0,
+        def_gain=1,
+        cuda_idx=7,
+        ref="y",
+        has_2nd_grad=True,
+    ),
+    "softplus": EasyDict(
+        func=lambda x, **_: torch.nn.functional.softplus(x),
+        def_alpha=0,
+        def_gain=1,
+        cuda_idx=8,
+        ref="y",
+        has_2nd_grad=True,
+    ),
+    "swish": EasyDict(
+        func=lambda x, **_: torch.sigmoid(x) * x,
+        def_alpha=0,
+        def_gain=np.sqrt(2),
+        cuda_idx=9,
+        ref="x",
+        has_2nd_grad=True,
+    ),
+}
+
+
+def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl="cuda"):
+    r"""Pad, upsample, filter, and downsample a batch of 2D images.
+
+    Performs the following sequence of operations for each channel:
+
+    1. Upsample the image by inserting N-1 zeros after each pixel (`up`).
+
+    2. Pad the image with the specified number of zeros on each side (`padding`).
+       Negative padding corresponds to cropping the image.
+
+    3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it
+       so that the footprint of all output pixels lies within the input image.
+
+    4. Downsample the image by keeping every Nth pixel (`down`).
+
+    This sequence of operations bears close resemblance to scipy.signal.upfirdn().
+    The fused op is considerably more efficient than performing the same calculation
+    using standard PyTorch ops. It supports gradients of arbitrary order.
+
+    Args:
+        x:           Float32/float64/float16 input tensor of the shape
+                     `[batch_size, num_channels, in_height, in_width]`.
+        f:           Float32 FIR filter of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable), or
+                     `None` (identity).
+        up:          Integer upsampling factor. Can be a single int or a list/tuple
+                     `[x, y]` (default: 1).
+        down:        Integer downsampling factor. Can be a single int or a list/tuple
+                     `[x, y]` (default: 1).
+        padding:     Padding with respect to the upsampled image. Can be a single number
+                     or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                     (default: 0).
+        flip_filter: False = convolution, True = correlation (default: False).
+        gain:        Overall scaling factor for signal magnitude (default: 1).
+        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    # assert isinstance(x, torch.Tensor)
+    # assert impl in ['ref', 'cuda']
+    return _upfirdn2d_ref(
+        x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain
+    )
+
+
+def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1):
+    """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops."""
+    # Validate arguments.
+    assert isinstance(x, torch.Tensor) and x.ndim == 4
+    if f is None:
+        f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
+    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
+    assert not f.requires_grad
+    batch_size, num_channels, in_height, in_width = x.shape
+    # upx, upy = _parse_scaling(up)
+    # downx, downy = _parse_scaling(down)
+
+    upx, upy = up, up
+    downx, downy = down, down
+
+    # padx0, padx1, pady0, pady1 = _parse_padding(padding)
+    padx0, padx1, pady0, pady1 = padding[0], padding[1], padding[2], padding[3]
+
+    # Upsample by inserting zeros.
+    x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1])
+    x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1])
+    x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx])
+
+    # Pad or crop.
+    x = torch.nn.functional.pad(
+        x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)]
+    )
+    x = x[
+        :,
+        :,
+        max(-pady0, 0) : x.shape[2] - max(-pady1, 0),
+        max(-padx0, 0) : x.shape[3] - max(-padx1, 0),
+    ]
+
+    # Setup filter.
+    f = f * (gain ** (f.ndim / 2))
+    f = f.to(x.dtype)
+    if not flip_filter:
+        f = f.flip(list(range(f.ndim)))
+
+    # Convolve with the filter.
+    f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim)
+    if f.ndim == 4:
+        x = conv2d(input=x, weight=f, groups=num_channels)
+    else:
+        x = conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels)
+        x = conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels)
+
+    # Downsample by throwing away pixels.
+    x = x[:, :, ::downy, ::downx]
+    return x
+
+
+def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl="cuda"):
+    r"""Downsample a batch of 2D images using the given 2D FIR filter.
+
+    By default, the result is padded so that its shape is a fraction of the input.
+    User-specified padding is applied on top of that, with negative values
+    indicating cropping. Pixels outside the image are assumed to be zero.
+
+    Args:
+        x:           Float32/float64/float16 input tensor of the shape
+                     `[batch_size, num_channels, in_height, in_width]`.
+        f:           Float32 FIR filter of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable), or
+                     `None` (identity).
+        down:        Integer downsampling factor. Can be a single int or a list/tuple
+                     `[x, y]` (default: 1).
+        padding:     Padding with respect to the input. Can be a single number or a
+                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                     (default: 0).
+        flip_filter: False = convolution, True = correlation (default: False).
+        gain:        Overall scaling factor for signal magnitude (default: 1).
+        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    downx, downy = _parse_scaling(down)
+    # padx0, padx1, pady0, pady1 = _parse_padding(padding)
+    padx0, padx1, pady0, pady1 = padding, padding, padding, padding
+
+    fw, fh = _get_filter_size(f)
+    p = [
+        padx0 + (fw - downx + 1) // 2,
+        padx1 + (fw - downx) // 2,
+        pady0 + (fh - downy + 1) // 2,
+        pady1 + (fh - downy) // 2,
+    ]
+    return upfirdn2d(
+        x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl
+    )
+
+
+def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl="cuda"):
+    r"""Upsample a batch of 2D images using the given 2D FIR filter.
+
+    By default, the result is padded so that its shape is a multiple of the input.
+    User-specified padding is applied on top of that, with negative values
+    indicating cropping. Pixels outside the image are assumed to be zero.
+
+    Args:
+        x:           Float32/float64/float16 input tensor of the shape
+                     `[batch_size, num_channels, in_height, in_width]`.
+        f:           Float32 FIR filter of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable), or
+                     `None` (identity).
+        up:          Integer upsampling factor. Can be a single int or a list/tuple
+                     `[x, y]` (default: 1).
+        padding:     Padding with respect to the output. Can be a single number or a
+                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                     (default: 0).
+        flip_filter: False = convolution, True = correlation (default: False).
+        gain:        Overall scaling factor for signal magnitude (default: 1).
+        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    upx, upy = _parse_scaling(up)
+    # upx, upy = up, up
+    padx0, padx1, pady0, pady1 = _parse_padding(padding)
+    # padx0, padx1, pady0, pady1 = padding, padding, padding, padding
+    fw, fh = _get_filter_size(f)
+    p = [
+        padx0 + (fw + upx - 1) // 2,
+        padx1 + (fw - upx) // 2,
+        pady0 + (fh + upy - 1) // 2,
+        pady1 + (fh - upy) // 2,
+    ]
+    return upfirdn2d(
+        x,
+        f,
+        up=up,
+        padding=p,
+        flip_filter=flip_filter,
+        gain=gain * upx * upy,
+        impl=impl,
+    )
+
+
+class MinibatchStdLayer(torch.nn.Module):
+    def __init__(self, group_size, num_channels=1):
+        super().__init__()
+        self.group_size = group_size
+        self.num_channels = num_channels
+
+    def forward(self, x):
+        N, C, H, W = x.shape
+        G = (
+            torch.min(torch.as_tensor(self.group_size), torch.as_tensor(N))
+            if self.group_size is not None
+            else N
+        )
+        F = self.num_channels
+        c = C // F
+
+        y = x.reshape(
+            G, -1, F, c, H, W
+        )  # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c.
+        y = y - y.mean(dim=0)  # [GnFcHW] Subtract mean over group.
+        y = y.square().mean(dim=0)  # [nFcHW]  Calc variance over group.
+        y = (y + 1e-8).sqrt()  # [nFcHW]  Calc stddev over group.
+        y = y.mean(dim=[2, 3, 4])  # [nF]     Take average over channels and pixels.
+        y = y.reshape(-1, F, 1, 1)  # [nF11]   Add missing dimensions.
+        y = y.repeat(G, 1, H, W)  # [NFHW]   Replicate over group and pixels.
+        x = torch.cat([x, y], dim=1)  # [NCHW]   Append to input as new channels.
+        return x
+
+
+class FullyConnectedLayer(torch.nn.Module):
+    def __init__(
+        self,
+        in_features,  # Number of input features.
+        out_features,  # Number of output features.
+        bias=True,  # Apply additive bias before the activation function?
+        activation="linear",  # Activation function: 'relu', 'lrelu', etc.
+        lr_multiplier=1,  # Learning rate multiplier.
+        bias_init=0,  # Initial value for the additive bias.
+    ):
+        super().__init__()
+        self.weight = torch.nn.Parameter(
+            torch.randn([out_features, in_features]) / lr_multiplier
+        )
+        self.bias = (
+            torch.nn.Parameter(torch.full([out_features], np.float32(bias_init)))
+            if bias
+            else None
+        )
+        self.activation = activation
+
+        self.weight_gain = lr_multiplier / np.sqrt(in_features)
+        self.bias_gain = lr_multiplier
+
+    def forward(self, x):
+        w = self.weight * self.weight_gain
+        b = self.bias
+        if b is not None and self.bias_gain != 1:
+            b = b * self.bias_gain
+
+        if self.activation == "linear" and b is not None:
+            # out = torch.addmm(b.unsqueeze(0), x, w.t())
+            x = x.matmul(w.t())
+            out = x + b.reshape([-1 if i == x.ndim - 1 else 1 for i in range(x.ndim)])
+        else:
+            x = x.matmul(w.t())
+            out = bias_act(x, b, act=self.activation, dim=x.ndim - 1)
+        return out
+
+
+def _conv2d_wrapper(
+    x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True
+):
+    """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations."""
+    out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
+
+    # Flip weight if requested.
+    if (
+        not flip_weight
+    ):  # conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False).
+        w = w.flip([2, 3])
+
+    # Workaround performance pitfall in cuDNN 8.0.5, triggered when using
+    # 1x1 kernel + memory_format=channels_last + less than 64 channels.
+    if (
+        kw == 1
+        and kh == 1
+        and stride == 1
+        and padding in [0, [0, 0], (0, 0)]
+        and not transpose
+    ):
+        if x.stride()[1] == 1 and min(out_channels, in_channels_per_group) < 64:
+            if out_channels <= 4 and groups == 1:
+                in_shape = x.shape
+                x = w.squeeze(3).squeeze(2) @ x.reshape(
+                    [in_shape[0], in_channels_per_group, -1]
+                )
+                x = x.reshape([in_shape[0], out_channels, in_shape[2], in_shape[3]])
+            else:
+                x = x.to(memory_format=torch.contiguous_format)
+                w = w.to(memory_format=torch.contiguous_format)
+                x = conv2d(x, w, groups=groups)
+            return x.to(memory_format=torch.channels_last)
+
+    # Otherwise => execute using conv2d_gradfix.
+    op = conv_transpose2d if transpose else conv2d
+    return op(x, w, stride=stride, padding=padding, groups=groups)
+
+
+def conv2d_resample(
+    x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False
+):
+    r"""2D convolution with optional up/downsampling.
+
+    Padding is performed only once at the beginning, not between the operations.
+
+    Args:
+        x:              Input tensor of shape
+                        `[batch_size, in_channels, in_height, in_width]`.
+        w:              Weight tensor of shape
+                        `[out_channels, in_channels//groups, kernel_height, kernel_width]`.
+        f:              Low-pass filter for up/downsampling. Must be prepared beforehand by
+                        calling setup_filter(). None = identity (default).
+        up:             Integer upsampling factor (default: 1).
+        down:           Integer downsampling factor (default: 1).
+        padding:        Padding with respect to the upsampled image. Can be a single number
+                        or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                        (default: 0).
+        groups:         Split input channels into N groups (default: 1).
+        flip_weight:    False = convolution, True = correlation (default: True).
+        flip_filter:    False = convolution, True = correlation (default: False).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    # Validate arguments.
+    assert isinstance(x, torch.Tensor) and (x.ndim == 4)
+    assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype)
+    assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2])
+    assert isinstance(up, int) and (up >= 1)
+    assert isinstance(down, int) and (down >= 1)
+    # assert isinstance(groups, int) and (groups >= 1), f"!!!!!! groups: {groups} isinstance(groups, int)  {isinstance(groups, int)} {type(groups)}"
+    out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
+    fw, fh = _get_filter_size(f)
+    # px0, px1, py0, py1 = _parse_padding(padding)
+    px0, px1, py0, py1 = padding, padding, padding, padding
+
+    # Adjust padding to account for up/downsampling.
+    if up > 1:
+        px0 += (fw + up - 1) // 2
+        px1 += (fw - up) // 2
+        py0 += (fh + up - 1) // 2
+        py1 += (fh - up) // 2
+    if down > 1:
+        px0 += (fw - down + 1) // 2
+        px1 += (fw - down) // 2
+        py0 += (fh - down + 1) // 2
+        py1 += (fh - down) // 2
+
+    # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve.
+    if kw == 1 and kh == 1 and (down > 1 and up == 1):
+        x = upfirdn2d(
+            x=x, f=f, down=down, padding=[px0, px1, py0, py1], flip_filter=flip_filter
+        )
+        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
+        return x
+
+    # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample.
+    if kw == 1 and kh == 1 and (up > 1 and down == 1):
+        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
+        x = upfirdn2d(
+            x=x,
+            f=f,
+            up=up,
+            padding=[px0, px1, py0, py1],
+            gain=up ** 2,
+            flip_filter=flip_filter,
+        )
+        return x
+
+    # Fast path: downsampling only => use strided convolution.
+    if down > 1 and up == 1:
+        x = upfirdn2d(x=x, f=f, padding=[px0, px1, py0, py1], flip_filter=flip_filter)
+        x = _conv2d_wrapper(
+            x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight
+        )
+        return x
+
+    # Fast path: upsampling with optional downsampling => use transpose strided convolution.
+    if up > 1:
+        if groups == 1:
+            w = w.transpose(0, 1)
+        else:
+            w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw)
+            w = w.transpose(1, 2)
+            w = w.reshape(
+                groups * in_channels_per_group, out_channels // groups, kh, kw
+            )
+        px0 -= kw - 1
+        px1 -= kw - up
+        py0 -= kh - 1
+        py1 -= kh - up
+        pxt = max(min(-px0, -px1), 0)
+        pyt = max(min(-py0, -py1), 0)
+        x = _conv2d_wrapper(
+            x=x,
+            w=w,
+            stride=up,
+            padding=[pyt, pxt],
+            groups=groups,
+            transpose=True,
+            flip_weight=(not flip_weight),
+        )
+        x = upfirdn2d(
+            x=x,
+            f=f,
+            padding=[px0 + pxt, px1 + pxt, py0 + pyt, py1 + pyt],
+            gain=up ** 2,
+            flip_filter=flip_filter,
+        )
+        if down > 1:
+            x = upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
+        return x
+
+    # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d.
+    if up == 1 and down == 1:
+        if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0:
+            return _conv2d_wrapper(
+                x=x, w=w, padding=[py0, px0], groups=groups, flip_weight=flip_weight
+            )
+
+    # Fallback: Generic reference implementation.
+    x = upfirdn2d(
+        x=x,
+        f=(f if up > 1 else None),
+        up=up,
+        padding=[px0, px1, py0, py1],
+        gain=up ** 2,
+        flip_filter=flip_filter,
+    )
+    x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
+    if down > 1:
+        x = upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
+    return x
+
+
+class Conv2dLayer(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels,  # Number of input channels.
+        out_channels,  # Number of output channels.
+        kernel_size,  # Width and height of the convolution kernel.
+        bias=True,  # Apply additive bias before the activation function?
+        activation="linear",  # Activation function: 'relu', 'lrelu', etc.
+        up=1,  # Integer upsampling factor.
+        down=1,  # Integer downsampling factor.
+        resample_filter=[
+            1,
+            3,
+            3,
+            1,
+        ],  # Low-pass filter to apply when resampling activations.
+        conv_clamp=None,  # Clamp the output to +-X, None = disable clamping.
+        channels_last=False,  # Expect the input to have memory_format=channels_last?
+        trainable=True,  # Update the weights of this layer during training?
+    ):
+        super().__init__()
+        self.activation = activation
+        self.up = up
+        self.down = down
+        self.register_buffer("resample_filter", setup_filter(resample_filter))
+        self.conv_clamp = conv_clamp
+        self.padding = kernel_size // 2
+        self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
+        self.act_gain = activation_funcs[activation].def_gain
+
+        memory_format = (
+            torch.channels_last if channels_last else torch.contiguous_format
+        )
+        weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(
+            memory_format=memory_format
+        )
+        bias = torch.zeros([out_channels]) if bias else None
+        if trainable:
+            self.weight = torch.nn.Parameter(weight)
+            self.bias = torch.nn.Parameter(bias) if bias is not None else None
+        else:
+            self.register_buffer("weight", weight)
+            if bias is not None:
+                self.register_buffer("bias", bias)
+            else:
+                self.bias = None
+
+    def forward(self, x, gain=1):
+        w = self.weight * self.weight_gain
+        x = conv2d_resample(
+            x=x,
+            w=w,
+            f=self.resample_filter,
+            up=self.up,
+            down=self.down,
+            padding=self.padding,
+        )
+
+        act_gain = self.act_gain * gain
+        act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
+        out = bias_act(
+            x, self.bias, act=self.activation, gain=act_gain, clamp=act_clamp
+        )
+        return out
+
+
+def torch_gc():
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+        torch.cuda.ipc_collect()
+
+
+def set_seed(seed: int):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+
+def get_scheduler(sd_sampler, scheduler_config):
+    if sd_sampler == SDSampler.ddim:
+        return DDIMScheduler.from_config(scheduler_config)
+    elif sd_sampler == SDSampler.pndm:
+        return PNDMScheduler.from_config(scheduler_config)
+    elif sd_sampler == SDSampler.k_lms:
+        return LMSDiscreteScheduler.from_config(scheduler_config)
+    elif sd_sampler == SDSampler.k_euler:
+        return EulerDiscreteScheduler.from_config(scheduler_config)
+    elif sd_sampler == SDSampler.k_euler_a:
+        return EulerAncestralDiscreteScheduler.from_config(scheduler_config)
+    elif sd_sampler == SDSampler.dpm_plus_plus:
+        return DPMSolverMultistepScheduler.from_config(scheduler_config)
+    elif sd_sampler == SDSampler.uni_pc:
+        return UniPCMultistepScheduler.from_config(scheduler_config)
+    else:
+        raise ValueError(sd_sampler)
diff --git a/lama_cleaner/model/zits.py b/lama_cleaner/model/zits.py
new file mode 100644
index 0000000000000000000000000000000000000000..664ca1562c1058e0517ffc66588e537d22527fd3
--- /dev/null
+++ b/lama_cleaner/model/zits.py
@@ -0,0 +1,447 @@
+import os
+import time
+
+import cv2
+import torch
+import torch.nn.functional as F
+
+from lama_cleaner.helper import get_cache_path_by_url, load_jit_model
+from lama_cleaner.schema import Config
+import numpy as np
+
+from lama_cleaner.model.base import InpaintModel
+
+ZITS_INPAINT_MODEL_URL = os.environ.get(
+    "ZITS_INPAINT_MODEL_URL",
+    "https://github.com/Sanster/models/releases/download/add_zits/zits-inpaint-0717.pt",
+)
+ZITS_INPAINT_MODEL_MD5 = os.environ.get(
+    "ZITS_INPAINT_MODEL_MD5", "9978cc7157dc29699e42308d675b2154"
+)
+
+ZITS_EDGE_LINE_MODEL_URL = os.environ.get(
+    "ZITS_EDGE_LINE_MODEL_URL",
+    "https://github.com/Sanster/models/releases/download/add_zits/zits-edge-line-0717.pt",
+)
+ZITS_EDGE_LINE_MODEL_MD5 = os.environ.get(
+    "ZITS_EDGE_LINE_MODEL_MD5", "55e31af21ba96bbf0c80603c76ea8c5f"
+)
+
+ZITS_STRUCTURE_UPSAMPLE_MODEL_URL = os.environ.get(
+    "ZITS_STRUCTURE_UPSAMPLE_MODEL_URL",
+    "https://github.com/Sanster/models/releases/download/add_zits/zits-structure-upsample-0717.pt",
+)
+ZITS_STRUCTURE_UPSAMPLE_MODEL_MD5 = os.environ.get(
+    "ZITS_STRUCTURE_UPSAMPLE_MODEL_MD5", "3d88a07211bd41b2ec8cc0d999f29927"
+)
+
+ZITS_WIRE_FRAME_MODEL_URL = os.environ.get(
+    "ZITS_WIRE_FRAME_MODEL_URL",
+    "https://github.com/Sanster/models/releases/download/add_zits/zits-wireframe-0717.pt",
+)
+ZITS_WIRE_FRAME_MODEL_MD5 = os.environ.get(
+    "ZITS_WIRE_FRAME_MODEL_MD5", "a9727c63a8b48b65c905d351b21ce46b"
+)
+
+
+def resize(img, height, width, center_crop=False):
+    imgh, imgw = img.shape[0:2]
+
+    if center_crop and imgh != imgw:
+        # center crop
+        side = np.minimum(imgh, imgw)
+        j = (imgh - side) // 2
+        i = (imgw - side) // 2
+        img = img[j : j + side, i : i + side, ...]
+
+    if imgh > height and imgw > width:
+        inter = cv2.INTER_AREA
+    else:
+        inter = cv2.INTER_LINEAR
+    img = cv2.resize(img, (height, width), interpolation=inter)
+
+    return img
+
+
+def to_tensor(img, scale=True, norm=False):
+    if img.ndim == 2:
+        img = img[:, :, np.newaxis]
+    c = img.shape[-1]
+
+    if scale:
+        img_t = torch.from_numpy(img).permute(2, 0, 1).float().div(255)
+    else:
+        img_t = torch.from_numpy(img).permute(2, 0, 1).float()
+
+    if norm:
+        mean = torch.tensor([0.5, 0.5, 0.5]).reshape(c, 1, 1)
+        std = torch.tensor([0.5, 0.5, 0.5]).reshape(c, 1, 1)
+        img_t = (img_t - mean) / std
+    return img_t
+
+
+def load_masked_position_encoding(mask):
+    ones_filter = np.ones((3, 3), dtype=np.float32)
+    d_filter1 = np.array([[1, 1, 0], [1, 1, 0], [0, 0, 0]], dtype=np.float32)
+    d_filter2 = np.array([[0, 0, 0], [1, 1, 0], [1, 1, 0]], dtype=np.float32)
+    d_filter3 = np.array([[0, 1, 1], [0, 1, 1], [0, 0, 0]], dtype=np.float32)
+    d_filter4 = np.array([[0, 0, 0], [0, 1, 1], [0, 1, 1]], dtype=np.float32)
+    str_size = 256
+    pos_num = 128
+
+    ori_mask = mask.copy()
+    ori_h, ori_w = ori_mask.shape[0:2]
+    ori_mask = ori_mask / 255
+    mask = cv2.resize(mask, (str_size, str_size), interpolation=cv2.INTER_AREA)
+    mask[mask > 0] = 255
+    h, w = mask.shape[0:2]
+    mask3 = mask.copy()
+    mask3 = 1.0 - (mask3 / 255.0)
+    pos = np.zeros((h, w), dtype=np.int32)
+    direct = np.zeros((h, w, 4), dtype=np.int32)
+    i = 0
+    while np.sum(1 - mask3) > 0:
+        i += 1
+        mask3_ = cv2.filter2D(mask3, -1, ones_filter)
+        mask3_[mask3_ > 0] = 1
+        sub_mask = mask3_ - mask3
+        pos[sub_mask == 1] = i
+
+        m = cv2.filter2D(mask3, -1, d_filter1)
+        m[m > 0] = 1
+        m = m - mask3
+        direct[m == 1, 0] = 1
+
+        m = cv2.filter2D(mask3, -1, d_filter2)
+        m[m > 0] = 1
+        m = m - mask3
+        direct[m == 1, 1] = 1
+
+        m = cv2.filter2D(mask3, -1, d_filter3)
+        m[m > 0] = 1
+        m = m - mask3
+        direct[m == 1, 2] = 1
+
+        m = cv2.filter2D(mask3, -1, d_filter4)
+        m[m > 0] = 1
+        m = m - mask3
+        direct[m == 1, 3] = 1
+
+        mask3 = mask3_
+
+    abs_pos = pos.copy()
+    rel_pos = pos / (str_size / 2)  # to 0~1 maybe larger than 1
+    rel_pos = (rel_pos * pos_num).astype(np.int32)
+    rel_pos = np.clip(rel_pos, 0, pos_num - 1)
+
+    if ori_w != w or ori_h != h:
+        rel_pos = cv2.resize(rel_pos, (ori_w, ori_h), interpolation=cv2.INTER_NEAREST)
+        rel_pos[ori_mask == 0] = 0
+        direct = cv2.resize(direct, (ori_w, ori_h), interpolation=cv2.INTER_NEAREST)
+        direct[ori_mask == 0, :] = 0
+
+    return rel_pos, abs_pos, direct
+
+
+def load_image(img, mask, device, sigma256=3.0):
+    """
+    Args:
+        img: [H, W, C] RGB
+        mask: [H, W] 255 为 masks 区域
+        sigma256:
+
+    Returns:
+
+    """
+    h, w, _ = img.shape
+    imgh, imgw = img.shape[0:2]
+    img_256 = resize(img, 256, 256)
+
+    mask = (mask > 127).astype(np.uint8) * 255
+    mask_256 = cv2.resize(mask, (256, 256), interpolation=cv2.INTER_AREA)
+    mask_256[mask_256 > 0] = 255
+
+    mask_512 = cv2.resize(mask, (512, 512), interpolation=cv2.INTER_AREA)
+    mask_512[mask_512 > 0] = 255
+
+    # original skimage implemention
+    # https://scikit-image.org/docs/stable/api/skimage.feature.html#skimage.feature.canny
+    # low_threshold: Lower bound for hysteresis thresholding (linking edges). If None, low_threshold is set to 10% of dtype’s max.
+    # high_threshold: Upper bound for hysteresis thresholding (linking edges). If None, high_threshold is set to 20% of dtype’s max.
+
+    try:
+        import skimage
+        gray_256 = skimage.color.rgb2gray(img_256)
+        edge_256 = skimage.feature.canny(gray_256, sigma=3.0, mask=None).astype(float)
+        # cv2.imwrite("skimage_gray.jpg", (gray_256*255).astype(np.uint8))
+        # cv2.imwrite("skimage_edge.jpg", (edge_256*255).astype(np.uint8))
+    except:
+        gray_256 = cv2.cvtColor(img_256, cv2.COLOR_RGB2GRAY)
+        gray_256_blured = cv2.GaussianBlur(gray_256, ksize=(7, 7), sigmaX=sigma256, sigmaY=sigma256)
+        edge_256 = cv2.Canny(gray_256_blured, threshold1=int(255*0.1), threshold2=int(255*0.2))
+
+    # cv2.imwrite("opencv_edge.jpg", edge_256)
+
+    # line
+    img_512 = resize(img, 512, 512)
+
+    rel_pos, abs_pos, direct = load_masked_position_encoding(mask)
+
+    batch = dict()
+    batch["images"] = to_tensor(img.copy()).unsqueeze(0).to(device)
+    batch["img_256"] = to_tensor(img_256, norm=True).unsqueeze(0).to(device)
+    batch["masks"] = to_tensor(mask).unsqueeze(0).to(device)
+    batch["mask_256"] = to_tensor(mask_256).unsqueeze(0).to(device)
+    batch["mask_512"] = to_tensor(mask_512).unsqueeze(0).to(device)
+    batch["edge_256"] = to_tensor(edge_256, scale=False).unsqueeze(0).to(device)
+    batch["img_512"] = to_tensor(img_512).unsqueeze(0).to(device)
+    batch["rel_pos"] = torch.LongTensor(rel_pos).unsqueeze(0).to(device)
+    batch["abs_pos"] = torch.LongTensor(abs_pos).unsqueeze(0).to(device)
+    batch["direct"] = torch.LongTensor(direct).unsqueeze(0).to(device)
+    batch["h"] = imgh
+    batch["w"] = imgw
+
+    return batch
+
+
+def to_device(data, device):
+    if isinstance(data, torch.Tensor):
+        return data.to(device)
+    if isinstance(data, dict):
+        for key in data:
+            if isinstance(data[key], torch.Tensor):
+                data[key] = data[key].to(device)
+        return data
+    if isinstance(data, list):
+        return [to_device(d, device) for d in data]
+
+
+class ZITS(InpaintModel):
+    name = "zits"
+    min_size = 256
+    pad_mod = 32
+    pad_to_square = True
+
+    def __init__(self, device, **kwargs):
+        """
+
+        Args:
+            device:
+        """
+        super().__init__(device)
+        self.device = device
+        self.sample_edge_line_iterations = 1
+
+    def init_model(self, device, **kwargs):
+        self.wireframe = load_jit_model(ZITS_WIRE_FRAME_MODEL_URL, device, ZITS_WIRE_FRAME_MODEL_MD5)
+        self.edge_line = load_jit_model(ZITS_EDGE_LINE_MODEL_URL, device, ZITS_EDGE_LINE_MODEL_MD5)
+        self.structure_upsample = load_jit_model(
+            ZITS_STRUCTURE_UPSAMPLE_MODEL_URL, device, ZITS_STRUCTURE_UPSAMPLE_MODEL_MD5
+        )
+        self.inpaint = load_jit_model(ZITS_INPAINT_MODEL_URL, device, ZITS_INPAINT_MODEL_MD5)
+
+    @staticmethod
+    def is_downloaded() -> bool:
+        model_paths = [
+            get_cache_path_by_url(ZITS_WIRE_FRAME_MODEL_URL),
+            get_cache_path_by_url(ZITS_EDGE_LINE_MODEL_URL),
+            get_cache_path_by_url(ZITS_STRUCTURE_UPSAMPLE_MODEL_URL),
+            get_cache_path_by_url(ZITS_INPAINT_MODEL_URL),
+        ]
+        return all([os.path.exists(it) for it in model_paths])
+
+    def wireframe_edge_and_line(self, items, enable: bool):
+        # 最终向 items 中添加 edge 和 line key
+        if not enable:
+            items["edge"] = torch.zeros_like(items["masks"])
+            items["line"] = torch.zeros_like(items["masks"])
+            return
+
+        start = time.time()
+        try:
+            line_256 = self.wireframe_forward(
+                items["img_512"],
+                h=256,
+                w=256,
+                masks=items["mask_512"],
+                mask_th=0.85,
+            )
+        except:
+            line_256 = torch.zeros_like(items["mask_256"])
+
+        print(f"wireframe_forward time: {(time.time() - start) * 1000:.2f}ms")
+
+        # np_line = (line[0][0].numpy() * 255).astype(np.uint8)
+        # cv2.imwrite("line.jpg", np_line)
+
+        start = time.time()
+        edge_pred, line_pred = self.sample_edge_line_logits(
+            context=[items["img_256"], items["edge_256"], line_256],
+            mask=items["mask_256"].clone(),
+            iterations=self.sample_edge_line_iterations,
+            add_v=0.05,
+            mul_v=4,
+        )
+        print(f"sample_edge_line_logits time: {(time.time() - start) * 1000:.2f}ms")
+
+        # np_edge_pred = (edge_pred[0][0].numpy() * 255).astype(np.uint8)
+        # cv2.imwrite("edge_pred.jpg", np_edge_pred)
+        # np_line_pred = (line_pred[0][0].numpy() * 255).astype(np.uint8)
+        # cv2.imwrite("line_pred.jpg", np_line_pred)
+        # exit()
+
+        input_size = min(items["h"], items["w"])
+        if input_size != 256 and input_size > 256:
+            while edge_pred.shape[2] < input_size:
+                edge_pred = self.structure_upsample(edge_pred)
+                edge_pred = torch.sigmoid((edge_pred + 2) * 2)
+
+                line_pred = self.structure_upsample(line_pred)
+                line_pred = torch.sigmoid((line_pred + 2) * 2)
+
+            edge_pred = F.interpolate(
+                edge_pred,
+                size=(input_size, input_size),
+                mode="bilinear",
+                align_corners=False,
+            )
+            line_pred = F.interpolate(
+                line_pred,
+                size=(input_size, input_size),
+                mode="bilinear",
+                align_corners=False,
+            )
+
+        # np_edge_pred = (edge_pred[0][0].numpy() * 255).astype(np.uint8)
+        # cv2.imwrite("edge_pred_upsample.jpg", np_edge_pred)
+        # np_line_pred = (line_pred[0][0].numpy() * 255).astype(np.uint8)
+        # cv2.imwrite("line_pred_upsample.jpg", np_line_pred)
+        # exit()
+
+        items["edge"] = edge_pred.detach()
+        items["line"] = line_pred.detach()
+
+    @torch.no_grad()
+    def forward(self, image, mask, config: Config):
+        """Input images and output images have same size
+        images: [H, W, C] RGB
+        masks: [H, W]
+        return: BGR IMAGE
+        """
+        mask = mask[:, :, 0]
+        items = load_image(image, mask, device=self.device)
+
+        self.wireframe_edge_and_line(items, config.zits_wireframe)
+
+        inpainted_image = self.inpaint(
+            items["images"],
+            items["masks"],
+            items["edge"],
+            items["line"],
+            items["rel_pos"],
+            items["direct"],
+        )
+
+        inpainted_image = inpainted_image * 255.0
+        inpainted_image = (
+            inpainted_image.cpu().permute(0, 2, 3, 1)[0].numpy().astype(np.uint8)
+        )
+        inpainted_image = inpainted_image[:, :, ::-1]
+
+        # cv2.imwrite("inpainted.jpg", inpainted_image)
+        # exit()
+
+        return inpainted_image
+
+    def wireframe_forward(self, images, h, w, masks, mask_th=0.925):
+        lcnn_mean = torch.tensor([109.730, 103.832, 98.681]).reshape(1, 3, 1, 1)
+        lcnn_std = torch.tensor([22.275, 22.124, 23.229]).reshape(1, 3, 1, 1)
+        images = images * 255.0
+        # the masks value of lcnn is 127.5
+        masked_images = images * (1 - masks) + torch.ones_like(images) * masks * 127.5
+        masked_images = (masked_images - lcnn_mean) / lcnn_std
+
+        def to_int(x):
+            return tuple(map(int, x))
+
+        lines_tensor = []
+        lmap = np.zeros((h, w))
+
+        output_masked = self.wireframe(masked_images)
+
+        output_masked = to_device(output_masked, "cpu")
+        if output_masked["num_proposals"] == 0:
+            lines_masked = []
+            scores_masked = []
+        else:
+            lines_masked = output_masked["lines_pred"].numpy()
+            lines_masked = [
+                [line[1] * h, line[0] * w, line[3] * h, line[2] * w]
+                for line in lines_masked
+            ]
+            scores_masked = output_masked["lines_score"].numpy()
+
+        for line, score in zip(lines_masked, scores_masked):
+            if score > mask_th:
+                try:
+                    import skimage
+                    rr, cc, value = skimage.draw.line_aa(
+                        *to_int(line[0:2]), *to_int(line[2:4])
+                    )
+                    lmap[rr, cc] = np.maximum(lmap[rr, cc], value)
+                except:
+                    cv2.line(lmap, to_int(line[0:2][::-1]), to_int(line[2:4][::-1]), (1, 1, 1), 1, cv2.LINE_AA)
+
+        lmap = np.clip(lmap * 255, 0, 255).astype(np.uint8)
+        lines_tensor.append(to_tensor(lmap).unsqueeze(0))
+
+        lines_tensor = torch.cat(lines_tensor, dim=0)
+        return lines_tensor.detach().to(self.device)
+
+    def sample_edge_line_logits(
+        self, context, mask=None, iterations=1, add_v=0, mul_v=4
+    ):
+        [img, edge, line] = context
+
+        img = img * (1 - mask)
+        edge = edge * (1 - mask)
+        line = line * (1 - mask)
+
+        for i in range(iterations):
+            edge_logits, line_logits = self.edge_line(img, edge, line, masks=mask)
+
+            edge_pred = torch.sigmoid(edge_logits)
+            line_pred = torch.sigmoid((line_logits + add_v) * mul_v)
+            edge = edge + edge_pred * mask
+            edge[edge >= 0.25] = 1
+            edge[edge < 0.25] = 0
+            line = line + line_pred * mask
+
+            b, _, h, w = edge_pred.shape
+            edge_pred = edge_pred.reshape(b, -1, 1)
+            line_pred = line_pred.reshape(b, -1, 1)
+            mask = mask.reshape(b, -1)
+
+            edge_probs = torch.cat([1 - edge_pred, edge_pred], dim=-1)
+            line_probs = torch.cat([1 - line_pred, line_pred], dim=-1)
+            edge_probs[:, :, 1] += 0.5
+            line_probs[:, :, 1] += 0.5
+            edge_max_probs = edge_probs.max(dim=-1)[0] + (1 - mask) * (-100)
+            line_max_probs = line_probs.max(dim=-1)[0] + (1 - mask) * (-100)
+
+            indices = torch.sort(
+                edge_max_probs + line_max_probs, dim=-1, descending=True
+            )[1]
+
+            for ii in range(b):
+                keep = int((i + 1) / iterations * torch.sum(mask[ii, ...]))
+
+                assert torch.sum(mask[ii][indices[ii, :keep]]) == keep, "Error!!!"
+                mask[ii][indices[ii, :keep]] = 0
+
+            mask = mask.reshape(b, 1, h, w)
+            edge = edge * (1 - mask)
+            line = line * (1 - mask)
+
+        edge, line = edge.to(torch.float32), line.to(torch.float32)
+        return edge, line
diff --git a/lama_cleaner/model_manager.py b/lama_cleaner/model_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f29dd3cdb4f402fb665cda36fd83600cc6cd7b4
--- /dev/null
+++ b/lama_cleaner/model_manager.py
@@ -0,0 +1,118 @@
+import torch
+import gc
+
+from loguru import logger
+
+from lama_cleaner.const import SD15_MODELS
+from lama_cleaner.helper import switch_mps_device
+from lama_cleaner.model.controlnet import ControlNet
+from lama_cleaner.model.fcf import FcF
+from lama_cleaner.model.lama import LaMa
+from lama_cleaner.model.ldm import LDM
+from lama_cleaner.model.manga import Manga
+from lama_cleaner.model.mat import MAT
+from lama_cleaner.model.paint_by_example import PaintByExample
+from lama_cleaner.model.instruct_pix2pix import InstructPix2Pix
+from lama_cleaner.model.sd import SD15, SD2, Anything4, RealisticVision14
+from lama_cleaner.model.utils import torch_gc
+from lama_cleaner.model.zits import ZITS
+from lama_cleaner.model.opencv2 import OpenCV2
+from lama_cleaner.schema import Config
+
+models = {
+    "lama": LaMa,
+    "ldm": LDM,
+    "zits": ZITS,
+    "mat": MAT,
+    "fcf": FcF,
+    SD15.name: SD15,
+    Anything4.name: Anything4,
+    RealisticVision14.name: RealisticVision14,
+    "cv2": OpenCV2,
+    "manga": Manga,
+    "sd2": SD2,
+    "paint_by_example": PaintByExample,
+    "instruct_pix2pix": InstructPix2Pix,
+}
+
+
+class ModelManager:
+    def __init__(self, name: str, device: torch.device, **kwargs):
+        self.name = name
+        self.device = device
+        self.kwargs = kwargs
+        self.model = self.init_model(name, device, **kwargs)
+
+    def init_model(self, name: str, device, **kwargs):
+        if name in SD15_MODELS and kwargs.get("sd_controlnet", False):
+            return ControlNet(device, **{**kwargs, "name": name})
+
+        if name in models:
+            model = models[name](device, **kwargs)
+        else:
+            raise NotImplementedError(f"Not supported model: {name}")
+        return model
+
+    def is_downloaded(self, name: str) -> bool:
+        if name in models:
+            return models[name].is_downloaded()
+        else:
+            raise NotImplementedError(f"Not supported model: {name}")
+
+    def __call__(self, image, mask, config: Config):
+        self.switch_controlnet_method(control_method=config.controlnet_method)
+        return self.model(image, mask, config)
+
+    def switch(self, new_name: str, **kwargs):
+        if new_name == self.name:
+            return
+        try:
+            if torch.cuda.memory_allocated() > 0:
+                # Clear current loaded model from memory
+                torch.cuda.empty_cache()
+                del self.model
+                gc.collect()
+
+            self.model = self.init_model(
+                new_name, switch_mps_device(new_name, self.device), **self.kwargs
+            )
+            self.name = new_name
+        except NotImplementedError as e:
+            raise e
+
+    def switch_controlnet_method(self, control_method: str):
+        if not self.kwargs.get("sd_controlnet"):
+            return
+        if self.kwargs["sd_controlnet_method"] == control_method:
+            return
+        if not hasattr(self.model, "is_local_sd_model"):
+            return
+
+        if self.model.is_local_sd_model:
+            # is_native_control_inpaint 表示加载了普通 SD 模型
+            if (
+                self.model.is_native_control_inpaint
+                and control_method != "control_v11p_sd15_inpaint"
+            ):
+                raise RuntimeError(
+                    f"--sd-local-model-path load a normal SD model, "
+                    f"to use {control_method} you should load an inpainting SD model"
+                )
+            elif (
+                not self.model.is_native_control_inpaint
+                and control_method == "control_v11p_sd15_inpaint"
+            ):
+                raise RuntimeError(
+                    f"--sd-local-model-path load an inpainting SD model, "
+                    f"to use {control_method} you should load a norml SD model"
+                )
+
+        del self.model
+        torch_gc()
+
+        old_method = self.kwargs["sd_controlnet_method"]
+        self.kwargs["sd_controlnet_method"] = control_method
+        self.model = self.init_model(
+            self.name, switch_mps_device(self.name, self.device), **self.kwargs
+        )
+        logger.info(f"Switch ControlNet method from {old_method} to {control_method}")
diff --git a/lama_cleaner/parse_args.py b/lama_cleaner/parse_args.py
new file mode 100644
index 0000000000000000000000000000000000000000..e27d8ea547e4643726055475f850547caf7bbfc5
--- /dev/null
+++ b/lama_cleaner/parse_args.py
@@ -0,0 +1,256 @@
+import os
+import imghdr
+import argparse
+from pathlib import Path
+
+from loguru import logger
+
+from lama_cleaner.const import *
+from lama_cleaner.runtime import dump_environment_info
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument("--host", default="127.0.0.1")
+    parser.add_argument("--port", default=8080, type=int)
+
+    parser.add_argument(
+        "--config-installer",
+        action="store_true",
+        help="Open config web page, mainly for windows installer",
+    )
+    parser.add_argument(
+        "--load-installer-config",
+        action="store_true",
+        help="Load all cmd args from installer config file",
+    )
+    parser.add_argument(
+        "--installer-config", default=None, help="Config file for windows installer"
+    )
+
+    parser.add_argument("--model", default=DEFAULT_MODEL, choices=AVAILABLE_MODELS)
+    parser.add_argument("--no-half", action="store_true", help=NO_HALF_HELP)
+    parser.add_argument("--cpu-offload", action="store_true", help=CPU_OFFLOAD_HELP)
+    parser.add_argument("--disable-nsfw", action="store_true", help=DISABLE_NSFW_HELP)
+    parser.add_argument(
+        "--sd-cpu-textencoder", action="store_true", help=SD_CPU_TEXTENCODER_HELP
+    )
+    parser.add_argument("--sd-controlnet", action="store_true", help=SD_CONTROLNET_HELP)
+    parser.add_argument(
+        "--sd-controlnet-method",
+        default=DEFAULT_CONTROLNET_METHOD,
+        choices=SD_CONTROLNET_CHOICES,
+    )
+    parser.add_argument("--sd-local-model-path", default=None, help=SD_LOCAL_MODEL_HELP)
+    parser.add_argument(
+        "--local-files-only", action="store_true", help=LOCAL_FILES_ONLY_HELP
+    )
+    parser.add_argument(
+        "--enable-xformers", action="store_true", help=ENABLE_XFORMERS_HELP
+    )
+    parser.add_argument(
+        "--device", default=DEFAULT_DEVICE, type=str, choices=AVAILABLE_DEVICES
+    )
+    parser.add_argument("--gui", action="store_true", help=GUI_HELP)
+    parser.add_argument(
+        "--no-gui-auto-close", action="store_true", help=NO_GUI_AUTO_CLOSE_HELP
+    )
+    parser.add_argument(
+        "--gui-size",
+        default=[1600, 1000],
+        nargs=2,
+        type=int,
+        help="Set window size for GUI",
+    )
+    parser.add_argument("--input", type=str, default=None, help=INPUT_HELP)
+    parser.add_argument("--output-dir", type=str, default=None, help=OUTPUT_DIR_HELP)
+    parser.add_argument(
+        "--model-dir", type=str, default=DEFAULT_MODEL_DIR, help=MODEL_DIR_HELP
+    )
+    parser.add_argument(
+        "--disable-model-switch",
+        action="store_true",
+        help="Disable model switch in frontend",
+    )
+    parser.add_argument(
+        "--quality",
+        default=95,
+        type=int,
+        help=QUALITY_HELP,
+    )
+
+    # Plugins
+    parser.add_argument(
+        "--enable-interactive-seg",
+        action="store_true",
+        help=INTERACTIVE_SEG_HELP,
+    )
+    parser.add_argument(
+        "--interactive-seg-model",
+        default="vit_l",
+        choices=AVAILABLE_INTERACTIVE_SEG_MODELS,
+        help=INTERACTIVE_SEG_MODEL_HELP,
+    )
+    parser.add_argument(
+        "--interactive-seg-device",
+        default="cpu",
+        choices=AVAILABLE_INTERACTIVE_SEG_DEVICES,
+    )
+    parser.add_argument(
+        "--enable-remove-bg",
+        action="store_true",
+        help=REMOVE_BG_HELP,
+    )
+    parser.add_argument(
+        "--enable-anime-seg",
+        action="store_true",
+        help=ANIMESEG_HELP,
+    )
+    parser.add_argument(
+        "--enable-realesrgan",
+        action="store_true",
+        help=REALESRGAN_HELP,
+    )
+    parser.add_argument(
+        "--realesrgan-device",
+        default="cpu",
+        type=str,
+        choices=REALESRGAN_AVAILABLE_DEVICES,
+    )
+    parser.add_argument(
+        "--realesrgan-model",
+        default=RealESRGANModelName.realesr_general_x4v3.value,
+        type=str,
+        choices=RealESRGANModelNameList,
+    )
+    parser.add_argument(
+        "--realesrgan-no-half",
+        action="store_true",
+        help="Disable half precision for RealESRGAN",
+    )
+    parser.add_argument("--enable-gfpgan", action="store_true", help=GFPGAN_HELP)
+    parser.add_argument(
+        "--gfpgan-device", default="cpu", type=str, choices=GFPGAN_AVAILABLE_DEVICES
+    )
+    parser.add_argument(
+        "--enable-restoreformer", action="store_true", help=RESTOREFORMER_HELP
+    )
+    parser.add_argument(
+        "--restoreformer-device",
+        default="cpu",
+        type=str,
+        choices=RESTOREFORMER_AVAILABLE_DEVICES,
+    )
+    parser.add_argument(
+        "--enable-gif",
+        action="store_true",
+        help=GIF_HELP,
+    )
+    parser.add_argument(
+        "--install-plugins-package",
+        action="store_true",
+    )
+    #########
+
+    # useless args
+    parser.add_argument("--debug", action="store_true", help=argparse.SUPPRESS)
+    parser.add_argument("--hf_access_token", default="", help=argparse.SUPPRESS)
+    parser.add_argument(
+        "--sd-disable-nsfw", action="store_true", help=argparse.SUPPRESS
+    )
+    parser.add_argument("--sd-run-local", action="store_true", help=argparse.SUPPRESS)
+    parser.add_argument(
+        "--sd-enable-xformers", action="store_true", help=argparse.SUPPRESS
+    )
+
+    args = parser.parse_args()
+
+    # collect system info to help debug
+    dump_environment_info()
+    if args.install_plugins_package:
+        from lama_cleaner.installer import install_plugins_package
+
+        install_plugins_package()
+        exit()
+
+    if args.config_installer:
+        if args.installer_config is None:
+            parser.error(
+                "args.config_installer==True, must set args.installer_config to store config file"
+            )
+        from lama_cleaner.web_config import main
+
+        logger.info("Launching installer web config page")
+        main(args.installer_config)
+        exit()
+
+    if args.load_installer_config:
+        if args.installer_config and not os.path.exists(args.installer_config):
+            parser.error(f"args.installer_config={args.installer_config} not exists")
+
+        logger.info(f"Loading installer config from {args.installer_config}")
+        _args = load_config(args.installer_config)
+        for k, v in vars(_args).items():
+            if k in vars(args):
+                setattr(args, k, v)
+
+    if args.device == "cuda":
+        import platform
+
+        if platform.system() == "Darwin":
+            logger.info("MacOS does not support cuda, use cpu instead")
+            setattr(args, "device", "cpu")
+        else:
+            import torch
+
+            if torch.cuda.is_available() is False:
+                parser.error(
+                    "torch.cuda.is_available() is False, please use --device cpu or check your pytorch installation"
+                )
+
+    if args.sd_local_model_path and args.model == "sd1.5":
+        if not os.path.exists(args.sd_local_model_path):
+            parser.error(
+                f"invalid --sd-local-model-path: {args.sd_local_model_path} not exists"
+            )
+        if not os.path.isfile(args.sd_local_model_path):
+            parser.error(
+                f"invalid --sd-local-model-path: {args.sd_local_model_path} is a directory"
+            )
+
+    os.environ["U2NET_HOME"] = DEFAULT_MODEL_DIR
+    if args.model_dir and args.model_dir is not None:
+        if os.path.isfile(args.model_dir):
+            parser.error(f"invalid --model-dir: {args.model_dir} is a file")
+
+        if not os.path.exists(args.model_dir):
+            logger.info(f"Create model cache directory: {args.model_dir}")
+            Path(args.model_dir).mkdir(exist_ok=True, parents=True)
+
+        os.environ["XDG_CACHE_HOME"] = args.model_dir
+        os.environ["U2NET_HOME"] = args.model_dir
+
+    if args.input and args.input is not None:
+        if not os.path.exists(args.input):
+            parser.error(f"invalid --input: {args.input} not exists")
+        if os.path.isfile(args.input):
+            if imghdr.what(args.input) is None:
+                parser.error(f"invalid --input: {args.input} is not a valid image file")
+        else:
+            if args.output_dir is None:
+                parser.error(
+                    f"invalid --input: {args.input} is a directory, --output-dir is required"
+                )
+
+    if args.output_dir is not None:
+        output_dir = Path(args.output_dir)
+        if not output_dir.exists():
+            logger.info(f"Creating output directory: {output_dir}")
+            output_dir.mkdir(parents=True)
+        else:
+            if not output_dir.is_dir():
+                parser.error(f"invalid --output-dir: {output_dir} is not a directory")
+
+    return args
diff --git a/lama_cleaner/plugins/__init__.py b/lama_cleaner/plugins/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a75ea36949daeb1467d7aaed6ad670c978dcd3a
--- /dev/null
+++ b/lama_cleaner/plugins/__init__.py
@@ -0,0 +1,7 @@
+from .interactive_seg import InteractiveSeg
+from .remove_bg import RemoveBG
+from .realesrgan import RealESRGANUpscaler
+from .gfpgan_plugin import GFPGANPlugin
+from .restoreformer import RestoreFormerPlugin
+from .gif import MakeGIF
+from .anime_seg import AnimeSeg
diff --git a/lama_cleaner/plugins/__pycache__/__init__.cpython-38.pyc b/lama_cleaner/plugins/__pycache__/__init__.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..bd85c4d8e579e12094aae9814c4523ac243ec6aa
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diff --git a/lama_cleaner/plugins/__pycache__/anime_seg.cpython-38.pyc b/lama_cleaner/plugins/__pycache__/anime_seg.cpython-38.pyc
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index 0000000000000000000000000000000000000000..2e999b53de4e6d8281682b4ef046ac6477e6f8de
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diff --git a/lama_cleaner/plugins/__pycache__/base_plugin.cpython-38.pyc b/lama_cleaner/plugins/__pycache__/base_plugin.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..33b1bd198c1b1612cc087210f4b5dd827532e234
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diff --git a/lama_cleaner/plugins/__pycache__/gfpgan_plugin.cpython-38.pyc b/lama_cleaner/plugins/__pycache__/gfpgan_plugin.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..918075bb3a3d795d4f9a10043138a728898d4bbe
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diff --git a/lama_cleaner/plugins/__pycache__/gfpganer.cpython-38.pyc b/lama_cleaner/plugins/__pycache__/gfpganer.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1944e57b7639eb63d0bcb69b82e67f9e0938d4c9
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diff --git a/lama_cleaner/plugins/__pycache__/gif.cpython-38.pyc b/lama_cleaner/plugins/__pycache__/gif.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..36a7a6f8e4f6e6669c62d4081f3816cdec6ac955
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diff --git a/lama_cleaner/plugins/__pycache__/interactive_seg.cpython-38.pyc b/lama_cleaner/plugins/__pycache__/interactive_seg.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0ef9547485cc11aef77074cc28130bba6cc08bd8
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diff --git a/lama_cleaner/plugins/__pycache__/realesrgan.cpython-38.pyc b/lama_cleaner/plugins/__pycache__/realesrgan.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e9bb8c8491d6d457fca7241fd4f08a606c829299
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diff --git a/lama_cleaner/plugins/__pycache__/remove_bg.cpython-38.pyc b/lama_cleaner/plugins/__pycache__/remove_bg.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..37a985b3bf13ceb1f2ec53f0a0ea7738d9d89991
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diff --git a/lama_cleaner/plugins/__pycache__/restoreformer.cpython-38.pyc b/lama_cleaner/plugins/__pycache__/restoreformer.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2a588690aa04520559a92c63ed1cf9bd69b9248b
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diff --git a/lama_cleaner/plugins/anime_seg.py b/lama_cleaner/plugins/anime_seg.py
new file mode 100644
index 0000000000000000000000000000000000000000..ecfc7d139360bd37dd057e29e2707cce58edcc31
--- /dev/null
+++ b/lama_cleaner/plugins/anime_seg.py
@@ -0,0 +1,455 @@
+import cv2
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from PIL import Image
+
+from lama_cleaner.helper import load_model
+from lama_cleaner.plugins.base_plugin import BasePlugin
+
+
+class REBNCONV(nn.Module):
+    def __init__(self, in_ch=3, out_ch=3, dirate=1, stride=1):
+        super(REBNCONV, self).__init__()
+
+        self.conv_s1 = nn.Conv2d(
+            in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate, stride=stride
+        )
+        self.bn_s1 = nn.BatchNorm2d(out_ch)
+        self.relu_s1 = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        hx = x
+        xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
+
+        return xout
+
+
+## upsample tensor 'src' to have the same spatial size with tensor 'tar'
+def _upsample_like(src, tar):
+    src = F.interpolate(src, size=tar.shape[2:], mode="bilinear", align_corners=False)
+
+    return src
+
+
+### RSU-7 ###
+class RSU7(nn.Module):
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512):
+        super(RSU7, self).__init__()
+
+        self.in_ch = in_ch
+        self.mid_ch = mid_ch
+        self.out_ch = out_ch
+
+        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)  ## 1 -> 1/2
+
+        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
+        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
+        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
+        self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
+        self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
+        self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1)
+
+        self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2)
+
+        self.rebnconv6d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
+
+    def forward(self, x):
+        b, c, h, w = x.shape
+
+        hx = x
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+        hx = self.pool3(hx3)
+
+        hx4 = self.rebnconv4(hx)
+        hx = self.pool4(hx4)
+
+        hx5 = self.rebnconv5(hx)
+        hx = self.pool5(hx5)
+
+        hx6 = self.rebnconv6(hx)
+
+        hx7 = self.rebnconv7(hx6)
+
+        hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1))
+        hx6dup = _upsample_like(hx6d, hx5)
+
+        hx5d = self.rebnconv5d(torch.cat((hx6dup, hx5), 1))
+        hx5dup = _upsample_like(hx5d, hx4)
+
+        hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
+        hx4dup = _upsample_like(hx4d, hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
+        hx3dup = _upsample_like(hx3d, hx2)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
+        hx2dup = _upsample_like(hx2d, hx1)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
+
+        return hx1d + hxin
+
+
+### RSU-6 ###
+class RSU6(nn.Module):
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU6, self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
+        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
+        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
+        self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
+        self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
+
+        self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2)
+
+        self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
+
+    def forward(self, x):
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+        hx = self.pool3(hx3)
+
+        hx4 = self.rebnconv4(hx)
+        hx = self.pool4(hx4)
+
+        hx5 = self.rebnconv5(hx)
+
+        hx6 = self.rebnconv6(hx5)
+
+        hx5d = self.rebnconv5d(torch.cat((hx6, hx5), 1))
+        hx5dup = _upsample_like(hx5d, hx4)
+
+        hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
+        hx4dup = _upsample_like(hx4d, hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
+        hx3dup = _upsample_like(hx3d, hx2)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
+        hx2dup = _upsample_like(hx2d, hx1)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
+
+        return hx1d + hxin
+
+
+### RSU-5 ###
+class RSU5(nn.Module):
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU5, self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
+        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
+        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
+        self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
+
+        self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2)
+
+        self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
+
+    def forward(self, x):
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+        hx = self.pool3(hx3)
+
+        hx4 = self.rebnconv4(hx)
+
+        hx5 = self.rebnconv5(hx4)
+
+        hx4d = self.rebnconv4d(torch.cat((hx5, hx4), 1))
+        hx4dup = _upsample_like(hx4d, hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
+        hx3dup = _upsample_like(hx3d, hx2)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
+        hx2dup = _upsample_like(hx2d, hx1)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
+
+        return hx1d + hxin
+
+
+### RSU-4 ###
+class RSU4(nn.Module):
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU4, self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
+        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
+        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
+
+        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2)
+
+        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
+
+    def forward(self, x):
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+
+        hx4 = self.rebnconv4(hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
+        hx3dup = _upsample_like(hx3d, hx2)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
+        hx2dup = _upsample_like(hx2d, hx1)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
+
+        return hx1d + hxin
+
+
+### RSU-4F ###
+class RSU4F(nn.Module):
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU4F, self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
+        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=2)
+        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=4)
+
+        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8)
+
+        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=4)
+        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=2)
+        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
+
+    def forward(self, x):
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx2 = self.rebnconv2(hx1)
+        hx3 = self.rebnconv3(hx2)
+
+        hx4 = self.rebnconv4(hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
+        hx2d = self.rebnconv2d(torch.cat((hx3d, hx2), 1))
+        hx1d = self.rebnconv1d(torch.cat((hx2d, hx1), 1))
+
+        return hx1d + hxin
+
+
+class ISNetDIS(nn.Module):
+    def __init__(self, in_ch=3, out_ch=1):
+        super(ISNetDIS, self).__init__()
+
+        self.conv_in = nn.Conv2d(in_ch, 64, 3, stride=2, padding=1)
+        self.pool_in = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.stage1 = RSU7(64, 32, 64)
+        self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.stage2 = RSU6(64, 32, 128)
+        self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.stage3 = RSU5(128, 64, 256)
+        self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.stage4 = RSU4(256, 128, 512)
+        self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.stage5 = RSU4F(512, 256, 512)
+        self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        self.stage6 = RSU4F(512, 256, 512)
+
+        # decoder
+        self.stage5d = RSU4F(1024, 256, 512)
+        self.stage4d = RSU4(1024, 128, 256)
+        self.stage3d = RSU5(512, 64, 128)
+        self.stage2d = RSU6(256, 32, 64)
+        self.stage1d = RSU7(128, 16, 64)
+
+        self.side1 = nn.Conv2d(64, out_ch, 3, padding=1)
+
+    def forward(self, x):
+        hx = x
+
+        hxin = self.conv_in(hx)
+        hx = self.pool_in(hxin)
+
+        # stage 1
+        hx1 = self.stage1(hxin)
+        hx = self.pool12(hx1)
+
+        # stage 2
+        hx2 = self.stage2(hx)
+        hx = self.pool23(hx2)
+
+        # stage 3
+        hx3 = self.stage3(hx)
+        hx = self.pool34(hx3)
+
+        # stage 4
+        hx4 = self.stage4(hx)
+        hx = self.pool45(hx4)
+
+        # stage 5
+        hx5 = self.stage5(hx)
+        hx = self.pool56(hx5)
+
+        # stage 6
+        hx6 = self.stage6(hx)
+        hx6up = _upsample_like(hx6, hx5)
+
+        # -------------------- decoder --------------------
+        hx5d = self.stage5d(torch.cat((hx6up, hx5), 1))
+        hx5dup = _upsample_like(hx5d, hx4)
+
+        hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1))
+        hx4dup = _upsample_like(hx4d, hx3)
+
+        hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1))
+        hx3dup = _upsample_like(hx3d, hx2)
+
+        hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1))
+        hx2dup = _upsample_like(hx2d, hx1)
+
+        hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1))
+
+        # side output
+        d1 = self.side1(hx1d)
+        d1 = _upsample_like(d1, x)
+        return d1.sigmoid()
+
+
+# 从小到大
+ANIME_SEG_MODELS = {
+    "url": "https://github.com/Sanster/models/releases/download/isnetis/isnetis.pth",
+    "md5": "5f25479076b73074730ab8de9e8f2051",
+}
+
+
+class AnimeSeg(BasePlugin):
+    # Model from: https://github.com/SkyTNT/anime-segmentation
+    name = "AnimeSeg"
+
+    def __init__(self):
+        super().__init__()
+        self.model = load_model(
+            ISNetDIS(),
+            ANIME_SEG_MODELS["url"],
+            "cpu",
+            ANIME_SEG_MODELS["md5"],
+        )
+
+    def __call__(self, rgb_np_img, files, form):
+        return self.forward(rgb_np_img)
+
+    @torch.no_grad()
+    def forward(self, rgb_np_img):
+        s = 1024
+
+        h0, w0 = h, w = rgb_np_img.shape[0], rgb_np_img.shape[1]
+        if h > w:
+            h, w = s, int(s * w / h)
+        else:
+            h, w = int(s * h / w), s
+        ph, pw = s - h, s - w
+        tmpImg = np.zeros([s, s, 3], dtype=np.float32)
+        tmpImg[ph // 2 : ph // 2 + h, pw // 2 : pw // 2 + w] = (
+            cv2.resize(rgb_np_img, (w, h)) / 255
+        )
+        tmpImg = tmpImg.transpose((2, 0, 1))
+        tmpImg = torch.from_numpy(tmpImg).unsqueeze(0).type(torch.FloatTensor)
+        mask = self.model(tmpImg)
+        mask = mask[0, :, ph // 2 : ph // 2 + h, pw // 2 : pw // 2 + w]
+        mask = cv2.resize(mask.cpu().numpy().transpose((1, 2, 0)), (w0, h0))
+        mask = Image.fromarray((mask * 255).astype("uint8"), mode="L")
+
+        empty = Image.new("RGBA", (w0, h0), 0)
+        img = Image.fromarray(rgb_np_img)
+        cutout = Image.composite(img, empty, mask)
+        return np.asarray(cutout)
diff --git a/lama_cleaner/plugins/base_plugin.py b/lama_cleaner/plugins/base_plugin.py
new file mode 100644
index 0000000000000000000000000000000000000000..39d7491f92a3990667a28a927da20a1bcf70198a
--- /dev/null
+++ b/lama_cleaner/plugins/base_plugin.py
@@ -0,0 +1,15 @@
+from loguru import logger
+
+
+class BasePlugin:
+    def __init__(self):
+        err_msg = self.check_dep()
+        if err_msg:
+            logger.error(err_msg)
+            exit(-1)
+
+    def __call__(self, rgb_np_img, files, form):
+        ...
+
+    def check_dep(self):
+        ...
diff --git a/lama_cleaner/plugins/gfpgan_plugin.py b/lama_cleaner/plugins/gfpgan_plugin.py
new file mode 100644
index 0000000000000000000000000000000000000000..2422094aa4950ecc6156a3ee97cc3246ffbf3363
--- /dev/null
+++ b/lama_cleaner/plugins/gfpgan_plugin.py
@@ -0,0 +1,71 @@
+import cv2
+from loguru import logger
+
+from lama_cleaner.helper import download_model
+from lama_cleaner.plugins.base_plugin import BasePlugin
+
+
+class GFPGANPlugin(BasePlugin):
+    name = "GFPGAN"
+
+    def __init__(self, device, upscaler=None):
+        super().__init__()
+        from .gfpganer import MyGFPGANer
+
+        url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
+        model_md5 = "94d735072630ab734561130a47bc44f8"
+        model_path = download_model(url, model_md5)
+        logger.info(f"GFPGAN model path: {model_path}")
+
+        import facexlib
+
+        if hasattr(facexlib.detection.retinaface, "device"):
+            facexlib.detection.retinaface.device = device
+
+        # Use GFPGAN for face enhancement
+        self.face_enhancer = MyGFPGANer(
+            model_path=model_path,
+            upscale=1,
+            arch="clean",
+            channel_multiplier=2,
+            device=device,
+            bg_upsampler=upscaler.model if upscaler is not None else None,
+        )
+        self.face_enhancer.face_helper.face_det.mean_tensor.to(device)
+        self.face_enhancer.face_helper.face_det = (
+            self.face_enhancer.face_helper.face_det.to(device)
+        )
+
+    def __call__(self, rgb_np_img, files, form):
+        weight = 0.5
+        bgr_np_img = cv2.cvtColor(rgb_np_img, cv2.COLOR_RGB2BGR)
+        logger.info(f"GFPGAN input shape: {bgr_np_img.shape}")
+        _, _, bgr_output = self.face_enhancer.enhance(
+            bgr_np_img,
+            has_aligned=False,
+            only_center_face=False,
+            paste_back=True,
+            weight=weight,
+        )
+        logger.info(f"GFPGAN output shape: {bgr_output.shape}")
+
+        # try:
+        #     if scale != 2:
+        #         interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
+        #         h, w = img.shape[0:2]
+        #         output = cv2.resize(
+        #             output,
+        #             (int(w * scale / 2), int(h * scale / 2)),
+        #             interpolation=interpolation,
+        #         )
+        # except Exception as error:
+        #     print("wrong scale input.", error)
+        return bgr_output
+
+    def check_dep(self):
+        try:
+            import gfpgan
+        except ImportError:
+            return (
+                "gfpgan is not installed, please install it first. pip install gfpgan"
+            )
diff --git a/lama_cleaner/plugins/gfpganer.py b/lama_cleaner/plugins/gfpganer.py
new file mode 100644
index 0000000000000000000000000000000000000000..75a575dbd182193f7a62badae139762322fb55a5
--- /dev/null
+++ b/lama_cleaner/plugins/gfpganer.py
@@ -0,0 +1,84 @@
+import os
+
+import torch
+from facexlib.utils.face_restoration_helper import FaceRestoreHelper
+from gfpgan import GFPGANv1Clean, GFPGANer
+from torch.hub import get_dir
+
+
+class MyGFPGANer(GFPGANer):
+    """Helper for restoration with GFPGAN.
+
+    It will detect and crop faces, and then resize the faces to 512x512.
+    GFPGAN is used to restored the resized faces.
+    The background is upsampled with the bg_upsampler.
+    Finally, the faces will be pasted back to the upsample background image.
+
+    Args:
+        model_path (str): The path to the GFPGAN model. It can be urls (will first download it automatically).
+        upscale (float): The upscale of the final output. Default: 2.
+        arch (str): The GFPGAN architecture. Option: clean | original. Default: clean.
+        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
+        bg_upsampler (nn.Module): The upsampler for the background. Default: None.
+    """
+
+    def __init__(
+        self,
+        model_path,
+        upscale=2,
+        arch="clean",
+        channel_multiplier=2,
+        bg_upsampler=None,
+        device=None,
+    ):
+        self.upscale = upscale
+        self.bg_upsampler = bg_upsampler
+
+        # initialize model
+        self.device = (
+            torch.device("cuda" if torch.cuda.is_available() else "cpu")
+            if device is None
+            else device
+        )
+        # initialize the GFP-GAN
+        if arch == "clean":
+            self.gfpgan = GFPGANv1Clean(
+                out_size=512,
+                num_style_feat=512,
+                channel_multiplier=channel_multiplier,
+                decoder_load_path=None,
+                fix_decoder=False,
+                num_mlp=8,
+                input_is_latent=True,
+                different_w=True,
+                narrow=1,
+                sft_half=True,
+            )
+        elif arch == "RestoreFormer":
+            from gfpgan.archs.restoreformer_arch import RestoreFormer
+
+            self.gfpgan = RestoreFormer()
+
+        hub_dir = get_dir()
+        model_dir = os.path.join(hub_dir, "checkpoints")
+
+        # initialize face helper
+        self.face_helper = FaceRestoreHelper(
+            upscale,
+            face_size=512,
+            crop_ratio=(1, 1),
+            det_model="retinaface_resnet50",
+            save_ext="png",
+            use_parse=True,
+            device=self.device,
+            model_rootpath=model_dir,
+        )
+
+        loadnet = torch.load(model_path)
+        if "params_ema" in loadnet:
+            keyname = "params_ema"
+        else:
+            keyname = "params"
+        self.gfpgan.load_state_dict(loadnet[keyname], strict=True)
+        self.gfpgan.eval()
+        self.gfpgan = self.gfpgan.to(self.device)
diff --git a/lama_cleaner/plugins/gif.py b/lama_cleaner/plugins/gif.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9921c33af84ffd520db33ff9c67ca5c23c052de
--- /dev/null
+++ b/lama_cleaner/plugins/gif.py
@@ -0,0 +1,149 @@
+import io
+import math
+
+from PIL import Image, ImageDraw
+
+from lama_cleaner.helper import load_img
+from lama_cleaner.plugins.base_plugin import BasePlugin
+
+
+def keep_ratio_resize(img, size, resample=Image.BILINEAR):
+    if img.width > img.height:
+        w = size
+        h = int(img.height * size / img.width)
+    else:
+        h = size
+        w = int(img.width * size / img.height)
+    return img.resize((w, h), resample)
+
+
+def cubic_bezier(p1, p2, duration: int, frames: int):
+    """
+
+    Args:
+        p1:
+        p2:
+        duration: Total duration of the curve
+        frames:
+
+    Returns:
+
+    """
+    x0, y0 = (0, 0)
+    x1, y1 = p1
+    x2, y2 = p2
+    x3, y3 = (1, 1)
+
+    def cal_y(t):
+        return (
+            math.pow(1 - t, 3) * y0
+            + 3 * math.pow(1 - t, 2) * t * y1
+            + 3 * (1 - t) * math.pow(t, 2) * y2
+            + math.pow(t, 3) * y3
+        )
+
+    def cal_x(t):
+        return (
+            math.pow(1 - t, 3) * x0
+            + 3 * math.pow(1 - t, 2) * t * x1
+            + 3 * (1 - t) * math.pow(t, 2) * x2
+            + math.pow(t, 3) * x3
+        )
+
+    res = []
+    for t in range(0, 1 * frames, duration):
+        t = t / frames
+        res.append((cal_x(t), cal_y(t)))
+
+    res.append((1, 0))
+    return res
+
+
+def make_compare_gif(
+    clean_img: Image.Image,
+    src_img: Image.Image,
+    max_side_length: int = 600,
+    splitter_width: int = 5,
+    splitter_color=(255, 203, 0, int(255 * 0.73)),
+):
+    if clean_img.size != src_img.size:
+        clean_img = clean_img.resize(src_img.size, Image.BILINEAR)
+
+    duration_per_frame = 20
+    num_frames = 50
+    # erase-in-out
+    cubic_bezier_points = cubic_bezier((0.33, 0), (0.66, 1), 1, num_frames)
+    cubic_bezier_points.reverse()
+
+    max_side_length = min(max_side_length, max(clean_img.size))
+
+    src_img = keep_ratio_resize(src_img, max_side_length)
+    clean_img = keep_ratio_resize(clean_img, max_side_length)
+    width, height = src_img.size
+
+    # Generate images to make Gif from right to left
+    images = []
+
+    for i in range(num_frames):
+        new_frame = Image.new("RGB", (width, height))
+        new_frame.paste(clean_img, (0, 0))
+
+        left = int(cubic_bezier_points[i][0] * width)
+        cropped_src_img = src_img.crop((left, 0, width, height))
+        new_frame.paste(cropped_src_img, (left, 0, width, height))
+        if i != num_frames - 1:
+            # draw a yellow splitter on the edge of the cropped image
+            draw = ImageDraw.Draw(new_frame)
+            draw.line(
+                [(left, 0), (left, height)], width=splitter_width, fill=splitter_color
+            )
+        images.append(new_frame)
+
+    for i in range(30):
+        images.append(src_img)
+
+    cubic_bezier_points.reverse()
+    # Generate images to make Gif from left to right
+    for i in range(num_frames):
+        new_frame = Image.new("RGB", (width, height))
+        new_frame.paste(src_img, (0, 0))
+
+        right = int(cubic_bezier_points[i][0] * width)
+        cropped_src_img = clean_img.crop((0, 0, right, height))
+        new_frame.paste(cropped_src_img, (0, 0, right, height))
+        if i != num_frames - 1:
+            # draw a yellow splitter on the edge of the cropped image
+            draw = ImageDraw.Draw(new_frame)
+            draw.line(
+                [(right, 0), (right, height)], width=splitter_width, fill=splitter_color
+            )
+        images.append(new_frame)
+
+    for _ in range(30):
+        images.append(clean_img)
+
+    img_byte_arr = io.BytesIO()
+    clean_img.save(
+        img_byte_arr,
+        format="GIF",
+        save_all=True,
+        include_color_table=True,
+        append_images=images,
+        optimize=False,
+        duration=duration_per_frame,
+        loop=0,
+    )
+    return img_byte_arr.getvalue()
+
+
+class MakeGIF(BasePlugin):
+    name = "MakeGIF"
+
+    def __call__(self, rgb_np_img, files, form):
+        origin_image = rgb_np_img
+        clean_image_bytes = files["clean_img"].read()
+        clean_image, _ = load_img(clean_image_bytes)
+        gif_bytes = make_compare_gif(
+            Image.fromarray(origin_image), Image.fromarray(clean_image)
+        )
+        return gif_bytes
diff --git a/lama_cleaner/plugins/interactive_seg.py b/lama_cleaner/plugins/interactive_seg.py
new file mode 100644
index 0000000000000000000000000000000000000000..9cb537161d4f1d033779ec523c02e8507abe2663
--- /dev/null
+++ b/lama_cleaner/plugins/interactive_seg.py
@@ -0,0 +1,75 @@
+import json
+
+import cv2
+import numpy as np
+from loguru import logger
+
+from lama_cleaner.helper import download_model
+from lama_cleaner.plugins.base_plugin import BasePlugin
+from lama_cleaner.plugins.segment_anything import SamPredictor, sam_model_registry
+
+# 从小到大
+SEGMENT_ANYTHING_MODELS = {
+    "vit_b": {
+        "url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth",
+        "md5": "01ec64d29a2fca3f0661936605ae66f8",
+    },
+    "vit_l": {
+        "url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth",
+        "md5": "0b3195507c641ddb6910d2bb5adee89c",
+    },
+    "vit_h": {
+        "url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth",
+        "md5": "4b8939a88964f0f4ff5f5b2642c598a6",
+    },
+}
+
+
+class InteractiveSeg(BasePlugin):
+    name = "InteractiveSeg"
+
+    def __init__(self, model_name, device):
+        super().__init__()
+        model_path = download_model(
+            SEGMENT_ANYTHING_MODELS[model_name]["url"],
+            SEGMENT_ANYTHING_MODELS[model_name]["md5"],
+        )
+        logger.info(f"SegmentAnything model path: {model_path}")
+        self.predictor = SamPredictor(
+            sam_model_registry[model_name](checkpoint=model_path).to(device)
+        )
+        self.prev_img_md5 = None
+
+    def __call__(self, rgb_np_img, files, form):
+        clicks = json.loads(form["clicks"])
+        return self.forward(rgb_np_img, clicks, form["img_md5"])
+
+    def forward(self, rgb_np_img, clicks, img_md5):
+        input_point = []
+        input_label = []
+        for click in clicks:
+            x = click[0]
+            y = click[1]
+            input_point.append([x, y])
+            input_label.append(click[2])
+
+        if img_md5 and img_md5 != self.prev_img_md5:
+            self.prev_img_md5 = img_md5
+            self.predictor.set_image(rgb_np_img)
+
+        masks, scores, _ = self.predictor.predict(
+            point_coords=np.array(input_point),
+            point_labels=np.array(input_label),
+            multimask_output=False,
+        )
+        mask = masks[0].astype(np.uint8) * 255
+        # TODO: how to set kernel size?
+        kernel_size = 9
+        mask = cv2.dilate(
+            mask, np.ones((kernel_size, kernel_size), np.uint8), iterations=1
+        )
+        # fronted brush color "ffcc00bb"
+        res_mask = np.zeros((mask.shape[0], mask.shape[1], 4), dtype=np.uint8)
+        res_mask[mask == 255] = [255, 203, 0, int(255 * 0.73)]
+        res_mask = cv2.cvtColor(res_mask, cv2.COLOR_BGRA2RGBA)
+        return res_mask
diff --git a/lama_cleaner/plugins/realesrgan.py b/lama_cleaner/plugins/realesrgan.py
new file mode 100644
index 0000000000000000000000000000000000000000..6dcf761fcaa5e4948970575a65dc7d7a10cb0d07
--- /dev/null
+++ b/lama_cleaner/plugins/realesrgan.py
@@ -0,0 +1,96 @@
+from enum import Enum
+
+import cv2
+from loguru import logger
+
+from lama_cleaner.const import RealESRGANModelName
+from lama_cleaner.helper import download_model
+from lama_cleaner.plugins.base_plugin import BasePlugin
+
+
+class RealESRGANUpscaler(BasePlugin):
+    name = "RealESRGAN"
+
+    def __init__(self, name, device, no_half=False):
+        super().__init__()
+        from basicsr.archs.rrdbnet_arch import RRDBNet
+        from realesrgan import RealESRGANer
+        from realesrgan.archs.srvgg_arch import SRVGGNetCompact
+
+        REAL_ESRGAN_MODELS = {
+            RealESRGANModelName.realesr_general_x4v3: {
+                "url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
+                "scale": 4,
+                "model": lambda: SRVGGNetCompact(
+                    num_in_ch=3,
+                    num_out_ch=3,
+                    num_feat=64,
+                    num_conv=32,
+                    upscale=4,
+                    act_type="prelu",
+                ),
+                "model_md5": "91a7644643c884ee00737db24e478156",
+            },
+            RealESRGANModelName.RealESRGAN_x4plus: {
+                "url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
+                "scale": 4,
+                "model": lambda: RRDBNet(
+                    num_in_ch=3,
+                    num_out_ch=3,
+                    num_feat=64,
+                    num_block=23,
+                    num_grow_ch=32,
+                    scale=4,
+                ),
+                "model_md5": "99ec365d4afad750833258a1a24f44ca",
+            },
+            RealESRGANModelName.RealESRGAN_x4plus_anime_6B: {
+                "url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
+                "scale": 4,
+                "model": lambda: RRDBNet(
+                    num_in_ch=3,
+                    num_out_ch=3,
+                    num_feat=64,
+                    num_block=6,
+                    num_grow_ch=32,
+                    scale=4,
+                ),
+                "model_md5": "d58ce384064ec1591c2ea7b79dbf47ba",
+            },
+        }
+        if name not in REAL_ESRGAN_MODELS:
+            raise ValueError(f"Unknown RealESRGAN model name: {name}")
+        model_info = REAL_ESRGAN_MODELS[name]
+
+        model_path = download_model(model_info["url"], model_info["model_md5"])
+        logger.info(f"RealESRGAN model path: {model_path}")
+
+        self.model = RealESRGANer(
+            scale=model_info["scale"],
+            model_path=model_path,
+            model=model_info["model"](),
+            half=True if "cuda" in str(device) and not no_half else False,
+            tile=512,
+            tile_pad=10,
+            pre_pad=10,
+            device=device,
+        )
+
+    def __call__(self, rgb_np_img, files, form):
+        bgr_np_img = cv2.cvtColor(rgb_np_img, cv2.COLOR_RGB2BGR)
+        scale = float(form["upscale"])
+        logger.info(f"RealESRGAN input shape: {bgr_np_img.shape}, scale: {scale}")
+        result = self.forward(bgr_np_img, scale)
+        logger.info(f"RealESRGAN output shape: {result.shape}")
+        return result
+
+    def forward(self, bgr_np_img, scale: float):
+        # 输出是 BGR
+        upsampled = self.model.enhance(bgr_np_img, outscale=scale)[0]
+        return upsampled
+
+    def check_dep(self):
+        try:
+            import realesrgan
+        except ImportError:
+            return "RealESRGAN is not installed, please install it first. pip install realesrgan"
diff --git a/lama_cleaner/plugins/remove_bg.py b/lama_cleaner/plugins/remove_bg.py
new file mode 100644
index 0000000000000000000000000000000000000000..402519825787c76bef3dcbddacd2b9164f357c42
--- /dev/null
+++ b/lama_cleaner/plugins/remove_bg.py
@@ -0,0 +1,39 @@
+import os
+import cv2
+import numpy as np
+from torch.hub import get_dir
+
+from lama_cleaner.plugins.base_plugin import BasePlugin
+
+
+class RemoveBG(BasePlugin):
+    name = "RemoveBG"
+
+    def __init__(self):
+        super().__init__()
+        from rembg import new_session
+
+        hub_dir = get_dir()
+        model_dir = os.path.join(hub_dir, "checkpoints")
+        os.environ["U2NET_HOME"] = model_dir
+
+        self.session = new_session(model_name="u2net")
+
+    def __call__(self, rgb_np_img, files, form):
+        bgr_np_img = cv2.cvtColor(rgb_np_img, cv2.COLOR_RGB2BGR)
+        return self.forward(bgr_np_img)
+
+    def forward(self, bgr_np_img) -> np.ndarray:
+        from rembg import remove
+
+        # return BGRA image
+        output = remove(bgr_np_img, session=self.session)
+        return cv2.cvtColor(output, cv2.COLOR_BGRA2RGBA)
+
+    def check_dep(self):
+        try:
+            import rembg
+        except ImportError:
+            return (
+                "RemoveBG is not installed, please install it first. pip install rembg"
+            )
diff --git a/lama_cleaner/plugins/restoreformer.py b/lama_cleaner/plugins/restoreformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..0cd8b10fb066e483eceb192e11c33797d2c88669
--- /dev/null
+++ b/lama_cleaner/plugins/restoreformer.py
@@ -0,0 +1,54 @@
+import cv2
+from loguru import logger
+
+from lama_cleaner.helper import download_model
+from lama_cleaner.plugins.base_plugin import BasePlugin
+
+
+class RestoreFormerPlugin(BasePlugin):
+    name = "RestoreFormer"
+
+    def __init__(self, device, upscaler=None):
+        super().__init__()
+        from .gfpganer import MyGFPGANer
+
+        url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth"
+        model_md5 = "eaeeff6c4a1caa1673977cb374e6f699"
+        model_path = download_model(url, model_md5)
+        logger.info(f"RestoreFormer model path: {model_path}")
+
+        import facexlib
+
+        if hasattr(facexlib.detection.retinaface, "device"):
+            facexlib.detection.retinaface.device = device
+
+        self.face_enhancer = MyGFPGANer(
+            model_path=model_path,
+            upscale=1,
+            arch="RestoreFormer",
+            channel_multiplier=2,
+            device=device,
+            bg_upsampler=upscaler.model if upscaler is not None else None,
+        )
+
+    def __call__(self, rgb_np_img, files, form):
+        weight = 0.5
+        bgr_np_img = cv2.cvtColor(rgb_np_img, cv2.COLOR_RGB2BGR)
+        logger.info(f"RestoreFormer input shape: {bgr_np_img.shape}")
+        _, _, bgr_output = self.face_enhancer.enhance(
+            bgr_np_img,
+            has_aligned=False,
+            only_center_face=False,
+            paste_back=True,
+            weight=weight,
+        )
+        logger.info(f"RestoreFormer output shape: {bgr_output.shape}")
+        return bgr_output
+
+    def check_dep(self):
+        try:
+            import gfpgan
+        except ImportError:
+            return (
+                "gfpgan is not installed, please install it first. pip install gfpgan"
+            )
diff --git a/lama_cleaner/plugins/segment_anything/__init__.py b/lama_cleaner/plugins/segment_anything/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..66b540cd466ad61e2e5ed80307d4c5b2c88f77ed
--- /dev/null
+++ b/lama_cleaner/plugins/segment_anything/__init__.py
@@ -0,0 +1,14 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .build_sam import (
+    build_sam,
+    build_sam_vit_h,
+    build_sam_vit_l,
+    build_sam_vit_b,
+    sam_model_registry,
+)
+from .predictor import SamPredictor
diff --git a/lama_cleaner/plugins/segment_anything/__pycache__/__init__.cpython-38.pyc b/lama_cleaner/plugins/segment_anything/__pycache__/__init__.cpython-38.pyc
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new file mode 100644
index 0000000000000000000000000000000000000000..adbe402cd928cdcfdecbe8aa49d57f929c9f42b6
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diff --git a/lama_cleaner/plugins/segment_anything/__pycache__/predictor.cpython-38.pyc b/lama_cleaner/plugins/segment_anything/__pycache__/predictor.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..403c707cdb03566485dd30f02500c3b8c006588e
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diff --git a/lama_cleaner/plugins/segment_anything/build_sam.py b/lama_cleaner/plugins/segment_anything/build_sam.py
new file mode 100644
index 0000000000000000000000000000000000000000..07abfca24e96eced7f13bdefd3212ce1b77b8999
--- /dev/null
+++ b/lama_cleaner/plugins/segment_anything/build_sam.py
@@ -0,0 +1,107 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+
+from functools import partial
+
+from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer
+
+
+def build_sam_vit_h(checkpoint=None):
+    return _build_sam(
+        encoder_embed_dim=1280,
+        encoder_depth=32,
+        encoder_num_heads=16,
+        encoder_global_attn_indexes=[7, 15, 23, 31],
+        checkpoint=checkpoint,
+    )
+
+
+build_sam = build_sam_vit_h
+
+
+def build_sam_vit_l(checkpoint=None):
+    return _build_sam(
+        encoder_embed_dim=1024,
+        encoder_depth=24,
+        encoder_num_heads=16,
+        encoder_global_attn_indexes=[5, 11, 17, 23],
+        checkpoint=checkpoint,
+    )
+
+
+def build_sam_vit_b(checkpoint=None):
+    return _build_sam(
+        encoder_embed_dim=768,
+        encoder_depth=12,
+        encoder_num_heads=12,
+        encoder_global_attn_indexes=[2, 5, 8, 11],
+        checkpoint=checkpoint,
+    )
+
+
+sam_model_registry = {
+    "default": build_sam,
+    "vit_h": build_sam,
+    "vit_l": build_sam_vit_l,
+    "vit_b": build_sam_vit_b,
+}
+
+
+def _build_sam(
+    encoder_embed_dim,
+    encoder_depth,
+    encoder_num_heads,
+    encoder_global_attn_indexes,
+    checkpoint=None,
+):
+    prompt_embed_dim = 256
+    image_size = 1024
+    vit_patch_size = 16
+    image_embedding_size = image_size // vit_patch_size
+    sam = Sam(
+        image_encoder=ImageEncoderViT(
+            depth=encoder_depth,
+            embed_dim=encoder_embed_dim,
+            img_size=image_size,
+            mlp_ratio=4,
+            norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
+            num_heads=encoder_num_heads,
+            patch_size=vit_patch_size,
+            qkv_bias=True,
+            use_rel_pos=True,
+            global_attn_indexes=encoder_global_attn_indexes,
+            window_size=14,
+            out_chans=prompt_embed_dim,
+        ),
+        prompt_encoder=PromptEncoder(
+            embed_dim=prompt_embed_dim,
+            image_embedding_size=(image_embedding_size, image_embedding_size),
+            input_image_size=(image_size, image_size),
+            mask_in_chans=16,
+        ),
+        mask_decoder=MaskDecoder(
+            num_multimask_outputs=3,
+            transformer=TwoWayTransformer(
+                depth=2,
+                embedding_dim=prompt_embed_dim,
+                mlp_dim=2048,
+                num_heads=8,
+            ),
+            transformer_dim=prompt_embed_dim,
+            iou_head_depth=3,
+            iou_head_hidden_dim=256,
+        ),
+        pixel_mean=[123.675, 116.28, 103.53],
+        pixel_std=[58.395, 57.12, 57.375],
+    )
+    sam.eval()
+    if checkpoint is not None:
+        with open(checkpoint, "rb") as f:
+            state_dict = torch.load(f)
+        sam.load_state_dict(state_dict)
+    return sam
diff --git a/lama_cleaner/plugins/segment_anything/modeling/__init__.py b/lama_cleaner/plugins/segment_anything/modeling/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..38e906243d898d7fc071c0fe218338c5cace3ea1
--- /dev/null
+++ b/lama_cleaner/plugins/segment_anything/modeling/__init__.py
@@ -0,0 +1,11 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .sam import Sam
+from .image_encoder import ImageEncoderViT
+from .mask_decoder import MaskDecoder
+from .prompt_encoder import PromptEncoder
+from .transformer import TwoWayTransformer
diff --git a/lama_cleaner/plugins/segment_anything/modeling/__pycache__/__init__.cpython-38.pyc b/lama_cleaner/plugins/segment_anything/modeling/__pycache__/__init__.cpython-38.pyc
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diff --git a/lama_cleaner/plugins/segment_anything/modeling/__pycache__/transformer.cpython-38.pyc b/lama_cleaner/plugins/segment_anything/modeling/__pycache__/transformer.cpython-38.pyc
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diff --git a/lama_cleaner/plugins/segment_anything/modeling/common.py b/lama_cleaner/plugins/segment_anything/modeling/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bf15236a3eb24d8526073bc4fa2b274cccb3f96
--- /dev/null
+++ b/lama_cleaner/plugins/segment_anything/modeling/common.py
@@ -0,0 +1,43 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+
+from typing import Type
+
+
+class MLPBlock(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        mlp_dim: int,
+        act: Type[nn.Module] = nn.GELU,
+    ) -> None:
+        super().__init__()
+        self.lin1 = nn.Linear(embedding_dim, mlp_dim)
+        self.lin2 = nn.Linear(mlp_dim, embedding_dim)
+        self.act = act()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.lin2(self.act(self.lin1(x)))
+
+
+# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
+# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119  # noqa
+class LayerNorm2d(nn.Module):
+    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(num_channels))
+        self.bias = nn.Parameter(torch.zeros(num_channels))
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        u = x.mean(1, keepdim=True)
+        s = (x - u).pow(2).mean(1, keepdim=True)
+        x = (x - u) / torch.sqrt(s + self.eps)
+        x = self.weight[:, None, None] * x + self.bias[:, None, None]
+        return x
diff --git a/lama_cleaner/plugins/segment_anything/modeling/image_encoder.py b/lama_cleaner/plugins/segment_anything/modeling/image_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6ad9ad2938842308e482a05c9d35ab08db9b2c3
--- /dev/null
+++ b/lama_cleaner/plugins/segment_anything/modeling/image_encoder.py
@@ -0,0 +1,395 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from typing import Optional, Tuple, Type
+
+from .common import LayerNorm2d, MLPBlock
+
+
+# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
+class ImageEncoderViT(nn.Module):
+    def __init__(
+        self,
+        img_size: int = 1024,
+        patch_size: int = 16,
+        in_chans: int = 3,
+        embed_dim: int = 768,
+        depth: int = 12,
+        num_heads: int = 12,
+        mlp_ratio: float = 4.0,
+        out_chans: int = 256,
+        qkv_bias: bool = True,
+        norm_layer: Type[nn.Module] = nn.LayerNorm,
+        act_layer: Type[nn.Module] = nn.GELU,
+        use_abs_pos: bool = True,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        window_size: int = 0,
+        global_attn_indexes: Tuple[int, ...] = (),
+    ) -> None:
+        """
+        Args:
+            img_size (int): Input image size.
+            patch_size (int): Patch size.
+            in_chans (int): Number of input image channels.
+            embed_dim (int): Patch embedding dimension.
+            depth (int): Depth of ViT.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_abs_pos (bool): If True, use absolute positional embeddings.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks.
+            global_attn_indexes (list): Indexes for blocks using global attention.
+        """
+        super().__init__()
+        self.img_size = img_size
+
+        self.patch_embed = PatchEmbed(
+            kernel_size=(patch_size, patch_size),
+            stride=(patch_size, patch_size),
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+        )
+
+        self.pos_embed: Optional[nn.Parameter] = None
+        if use_abs_pos:
+            # Initialize absolute positional embedding with pretrain image size.
+            self.pos_embed = nn.Parameter(
+                torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim)
+            )
+
+        self.blocks = nn.ModuleList()
+        for i in range(depth):
+            block = Block(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                use_rel_pos=use_rel_pos,
+                rel_pos_zero_init=rel_pos_zero_init,
+                window_size=window_size if i not in global_attn_indexes else 0,
+                input_size=(img_size // patch_size, img_size // patch_size),
+            )
+            self.blocks.append(block)
+
+        self.neck = nn.Sequential(
+            nn.Conv2d(
+                embed_dim,
+                out_chans,
+                kernel_size=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+            nn.Conv2d(
+                out_chans,
+                out_chans,
+                kernel_size=3,
+                padding=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.patch_embed(x)
+        if self.pos_embed is not None:
+            x = x + self.pos_embed
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x = self.neck(x.permute(0, 3, 1, 2))
+
+        return x
+
+
+class Block(nn.Module):
+    """Transformer blocks with support of window attention and residual propagation blocks"""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        norm_layer: Type[nn.Module] = nn.LayerNorm,
+        act_layer: Type[nn.Module] = nn.GELU,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        window_size: int = 0,
+        input_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks. If it equals 0, then
+                use global attention.
+            input_size (int or None): Input resolution for calculating the relative positional
+                parameter size.
+        """
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            use_rel_pos=use_rel_pos,
+            rel_pos_zero_init=rel_pos_zero_init,
+            input_size=input_size if window_size == 0 else (window_size, window_size),
+        )
+
+        self.norm2 = norm_layer(dim)
+        self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
+
+        self.window_size = window_size
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        shortcut = x
+        x = self.norm1(x)
+        # Window partition
+        if self.window_size > 0:
+            H, W = x.shape[1], x.shape[2]
+            x, pad_hw = window_partition(x, self.window_size)
+
+        x = self.attn(x)
+        # Reverse window partition
+        if self.window_size > 0:
+            x = window_unpartition(x, self.window_size, pad_hw, (H, W))
+
+        x = shortcut + x
+        x = x + self.mlp(self.norm2(x))
+
+        return x
+
+
+class Attention(nn.Module):
+    """Multi-head Attention block with relative position embeddings."""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = True,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        input_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads.
+            qkv_bias (bool:  If True, add a learnable bias to query, key, value.
+            rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            input_size (int or None): Input resolution for calculating the relative positional
+                parameter size.
+        """
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.proj = nn.Linear(dim, dim)
+
+        self.use_rel_pos = use_rel_pos
+        if self.use_rel_pos:
+            assert (
+                input_size is not None
+            ), "Input size must be provided if using relative positional encoding."
+            # initialize relative positional embeddings
+            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
+            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, H, W, _ = x.shape
+        # qkv with shape (3, B, nHead, H * W, C)
+        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        # q, k, v with shape (B * nHead, H * W, C)
+        q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
+
+        attn = (q * self.scale) @ k.transpose(-2, -1)
+
+        if self.use_rel_pos:
+            attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
+
+        attn = attn.softmax(dim=-1)
+        x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
+        x = self.proj(x)
+
+        return x
+
+
+def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
+    """
+    Partition into non-overlapping windows with padding if needed.
+    Args:
+        x (tensor): input tokens with [B, H, W, C].
+        window_size (int): window size.
+
+    Returns:
+        windows: windows after partition with [B * num_windows, window_size, window_size, C].
+        (Hp, Wp): padded height and width before partition
+    """
+    B, H, W, C = x.shape
+
+    pad_h = (window_size - H % window_size) % window_size
+    pad_w = (window_size - W % window_size) % window_size
+    if pad_h > 0 or pad_w > 0:
+        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
+    Hp, Wp = H + pad_h, W + pad_w
+
+    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows, (Hp, Wp)
+
+
+def window_unpartition(
+    windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
+) -> torch.Tensor:
+    """
+    Window unpartition into original sequences and removing padding.
+    Args:
+        x (tensor): input tokens with [B * num_windows, window_size, window_size, C].
+        window_size (int): window size.
+        pad_hw (Tuple): padded height and width (Hp, Wp).
+        hw (Tuple): original height and width (H, W) before padding.
+
+    Returns:
+        x: unpartitioned sequences with [B, H, W, C].
+    """
+    Hp, Wp = pad_hw
+    H, W = hw
+    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
+    x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
+
+    if Hp > H or Wp > W:
+        x = x[:, :H, :W, :].contiguous()
+    return x
+
+
+def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
+    """
+    Get relative positional embeddings according to the relative positions of
+        query and key sizes.
+    Args:
+        q_size (int): size of query q.
+        k_size (int): size of key k.
+        rel_pos (Tensor): relative position embeddings (L, C).
+
+    Returns:
+        Extracted positional embeddings according to relative positions.
+    """
+    max_rel_dist = int(2 * max(q_size, k_size) - 1)
+    # Interpolate rel pos if needed.
+    if rel_pos.shape[0] != max_rel_dist:
+        # Interpolate rel pos.
+        rel_pos_resized = F.interpolate(
+            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
+            size=max_rel_dist,
+            mode="linear",
+        )
+        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
+    else:
+        rel_pos_resized = rel_pos
+
+    # Scale the coords with short length if shapes for q and k are different.
+    q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
+    k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
+    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
+
+    return rel_pos_resized[relative_coords.long()]
+
+
+def add_decomposed_rel_pos(
+    attn: torch.Tensor,
+    q: torch.Tensor,
+    rel_pos_h: torch.Tensor,
+    rel_pos_w: torch.Tensor,
+    q_size: Tuple[int, int],
+    k_size: Tuple[int, int],
+) -> torch.Tensor:
+    """
+    Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
+    https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py   # noqa B950
+    Args:
+        attn (Tensor): attention map.
+        q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
+        rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
+        rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
+        q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
+        k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
+
+    Returns:
+        attn (Tensor): attention map with added relative positional embeddings.
+    """
+    q_h, q_w = q_size
+    k_h, k_w = k_size
+    Rh = get_rel_pos(q_h, k_h, rel_pos_h)
+    Rw = get_rel_pos(q_w, k_w, rel_pos_w)
+
+    B, _, dim = q.shape
+    r_q = q.reshape(B, q_h, q_w, dim)
+    rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
+    rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
+
+    attn = (
+        attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
+    ).view(B, q_h * q_w, k_h * k_w)
+
+    return attn
+
+
+class PatchEmbed(nn.Module):
+    """
+    Image to Patch Embedding.
+    """
+
+    def __init__(
+        self,
+        kernel_size: Tuple[int, int] = (16, 16),
+        stride: Tuple[int, int] = (16, 16),
+        padding: Tuple[int, int] = (0, 0),
+        in_chans: int = 3,
+        embed_dim: int = 768,
+    ) -> None:
+        """
+        Args:
+            kernel_size (Tuple): kernel size of the projection layer.
+            stride (Tuple): stride of the projection layer.
+            padding (Tuple): padding size of the projection layer.
+            in_chans (int): Number of input image channels.
+            embed_dim (int):  embed_dim (int): Patch embedding dimension.
+        """
+        super().__init__()
+
+        self.proj = nn.Conv2d(
+            in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x)
+        # B C H W -> B H W C
+        x = x.permute(0, 2, 3, 1)
+        return x
diff --git a/lama_cleaner/plugins/segment_anything/modeling/mask_decoder.py b/lama_cleaner/plugins/segment_anything/modeling/mask_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e86f7cc9ad95582a08ef2531c68d03fa4af8d99
--- /dev/null
+++ b/lama_cleaner/plugins/segment_anything/modeling/mask_decoder.py
@@ -0,0 +1,176 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from typing import List, Tuple, Type
+
+from .common import LayerNorm2d
+
+
+class MaskDecoder(nn.Module):
+    def __init__(
+        self,
+        *,
+        transformer_dim: int,
+        transformer: nn.Module,
+        num_multimask_outputs: int = 3,
+        activation: Type[nn.Module] = nn.GELU,
+        iou_head_depth: int = 3,
+        iou_head_hidden_dim: int = 256,
+    ) -> None:
+        """
+        Predicts masks given an image and prompt embeddings, using a
+        tranformer architecture.
+
+        Arguments:
+          transformer_dim (int): the channel dimension of the transformer
+          transformer (nn.Module): the transformer used to predict masks
+          num_multimask_outputs (int): the number of masks to predict
+            when disambiguating masks
+          activation (nn.Module): the type of activation to use when
+            upscaling masks
+          iou_head_depth (int): the depth of the MLP used to predict
+            mask quality
+          iou_head_hidden_dim (int): the hidden dimension of the MLP
+            used to predict mask quality
+        """
+        super().__init__()
+        self.transformer_dim = transformer_dim
+        self.transformer = transformer
+
+        self.num_multimask_outputs = num_multimask_outputs
+
+        self.iou_token = nn.Embedding(1, transformer_dim)
+        self.num_mask_tokens = num_multimask_outputs + 1
+        self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
+
+        self.output_upscaling = nn.Sequential(
+            nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
+            LayerNorm2d(transformer_dim // 4),
+            activation(),
+            nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
+            activation(),
+        )
+        self.output_hypernetworks_mlps = nn.ModuleList(
+            [
+                MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)
+                for i in range(self.num_mask_tokens)
+            ]
+        )
+
+        self.iou_prediction_head = MLP(
+            transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth
+        )
+
+    def forward(
+        self,
+        image_embeddings: torch.Tensor,
+        image_pe: torch.Tensor,
+        sparse_prompt_embeddings: torch.Tensor,
+        dense_prompt_embeddings: torch.Tensor,
+        multimask_output: bool,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Predict masks given image and prompt embeddings.
+
+        Arguments:
+          image_embeddings (torch.Tensor): the embeddings from the image encoder
+          image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
+          sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
+          dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
+          multimask_output (bool): Whether to return multiple masks or a single
+            mask.
+
+        Returns:
+          torch.Tensor: batched predicted masks
+          torch.Tensor: batched predictions of mask quality
+        """
+        masks, iou_pred = self.predict_masks(
+            image_embeddings=image_embeddings,
+            image_pe=image_pe,
+            sparse_prompt_embeddings=sparse_prompt_embeddings,
+            dense_prompt_embeddings=dense_prompt_embeddings,
+        )
+
+        # Select the correct mask or masks for outptu
+        if multimask_output:
+            mask_slice = slice(1, None)
+        else:
+            mask_slice = slice(0, 1)
+        masks = masks[:, mask_slice, :, :]
+        iou_pred = iou_pred[:, mask_slice]
+
+        # Prepare output
+        return masks, iou_pred
+
+    def predict_masks(
+        self,
+        image_embeddings: torch.Tensor,
+        image_pe: torch.Tensor,
+        sparse_prompt_embeddings: torch.Tensor,
+        dense_prompt_embeddings: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Predicts masks. See 'forward' for more details."""
+        # Concatenate output tokens
+        output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
+        output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
+        tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
+
+        # Expand per-image data in batch direction to be per-mask
+        src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
+        src = src + dense_prompt_embeddings
+        pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
+        b, c, h, w = src.shape
+
+        # Run the transformer
+        hs, src = self.transformer(src, pos_src, tokens)
+        iou_token_out = hs[:, 0, :]
+        mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]
+
+        # Upscale mask embeddings and predict masks using the mask tokens
+        src = src.transpose(1, 2).view(b, c, h, w)
+        upscaled_embedding = self.output_upscaling(src)
+        hyper_in_list: List[torch.Tensor] = []
+        for i in range(self.num_mask_tokens):
+            hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
+        hyper_in = torch.stack(hyper_in_list, dim=1)
+        b, c, h, w = upscaled_embedding.shape
+        masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
+
+        # Generate mask quality predictions
+        iou_pred = self.iou_prediction_head(iou_token_out)
+
+        return masks, iou_pred
+
+
+# Lightly adapted from
+# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
+class MLP(nn.Module):
+    def __init__(
+        self,
+        input_dim: int,
+        hidden_dim: int,
+        output_dim: int,
+        num_layers: int,
+        sigmoid_output: bool = False,
+    ) -> None:
+        super().__init__()
+        self.num_layers = num_layers
+        h = [hidden_dim] * (num_layers - 1)
+        self.layers = nn.ModuleList(
+            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
+        )
+        self.sigmoid_output = sigmoid_output
+
+    def forward(self, x):
+        for i, layer in enumerate(self.layers):
+            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+        if self.sigmoid_output:
+            x = F.sigmoid(x)
+        return x
diff --git a/lama_cleaner/plugins/segment_anything/modeling/prompt_encoder.py b/lama_cleaner/plugins/segment_anything/modeling/prompt_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3143f4f8e02ddd7ca8587b40ff5d47c3a6b7ef3
--- /dev/null
+++ b/lama_cleaner/plugins/segment_anything/modeling/prompt_encoder.py
@@ -0,0 +1,214 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+from torch import nn
+
+from typing import Any, Optional, Tuple, Type
+
+from .common import LayerNorm2d
+
+
+class PromptEncoder(nn.Module):
+    def __init__(
+        self,
+        embed_dim: int,
+        image_embedding_size: Tuple[int, int],
+        input_image_size: Tuple[int, int],
+        mask_in_chans: int,
+        activation: Type[nn.Module] = nn.GELU,
+    ) -> None:
+        """
+        Encodes prompts for input to SAM's mask decoder.
+
+        Arguments:
+          embed_dim (int): The prompts' embedding dimension
+          image_embedding_size (tuple(int, int)): The spatial size of the
+            image embedding, as (H, W).
+          input_image_size (int): The padded size of the image as input
+            to the image encoder, as (H, W).
+          mask_in_chans (int): The number of hidden channels used for
+            encoding input masks.
+          activation (nn.Module): The activation to use when encoding
+            input masks.
+        """
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.input_image_size = input_image_size
+        self.image_embedding_size = image_embedding_size
+        self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
+
+        self.num_point_embeddings: int = 4  # pos/neg point + 2 box corners
+        point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]
+        self.point_embeddings = nn.ModuleList(point_embeddings)
+        self.not_a_point_embed = nn.Embedding(1, embed_dim)
+
+        self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])
+        self.mask_downscaling = nn.Sequential(
+            nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
+            LayerNorm2d(mask_in_chans // 4),
+            activation(),
+            nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
+            LayerNorm2d(mask_in_chans),
+            activation(),
+            nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
+        )
+        self.no_mask_embed = nn.Embedding(1, embed_dim)
+
+    def get_dense_pe(self) -> torch.Tensor:
+        """
+        Returns the positional encoding used to encode point prompts,
+        applied to a dense set of points the shape of the image encoding.
+
+        Returns:
+          torch.Tensor: Positional encoding with shape
+            1x(embed_dim)x(embedding_h)x(embedding_w)
+        """
+        return self.pe_layer(self.image_embedding_size).unsqueeze(0)
+
+    def _embed_points(
+        self,
+        points: torch.Tensor,
+        labels: torch.Tensor,
+        pad: bool,
+    ) -> torch.Tensor:
+        """Embeds point prompts."""
+        points = points + 0.5  # Shift to center of pixel
+        if pad:
+            padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)
+            padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
+            points = torch.cat([points, padding_point], dim=1)
+            labels = torch.cat([labels, padding_label], dim=1)
+        point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
+        point_embedding[labels == -1] = 0.0
+        point_embedding[labels == -1] += self.not_a_point_embed.weight
+        point_embedding[labels == 0] += self.point_embeddings[0].weight
+        point_embedding[labels == 1] += self.point_embeddings[1].weight
+        return point_embedding
+
+    def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
+        """Embeds box prompts."""
+        boxes = boxes + 0.5  # Shift to center of pixel
+        coords = boxes.reshape(-1, 2, 2)
+        corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
+        corner_embedding[:, 0, :] += self.point_embeddings[2].weight
+        corner_embedding[:, 1, :] += self.point_embeddings[3].weight
+        return corner_embedding
+
+    def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
+        """Embeds mask inputs."""
+        mask_embedding = self.mask_downscaling(masks)
+        return mask_embedding
+
+    def _get_batch_size(
+        self,
+        points: Optional[Tuple[torch.Tensor, torch.Tensor]],
+        boxes: Optional[torch.Tensor],
+        masks: Optional[torch.Tensor],
+    ) -> int:
+        """
+        Gets the batch size of the output given the batch size of the input prompts.
+        """
+        if points is not None:
+            return points[0].shape[0]
+        elif boxes is not None:
+            return boxes.shape[0]
+        elif masks is not None:
+            return masks.shape[0]
+        else:
+            return 1
+
+    def _get_device(self) -> torch.device:
+        return self.point_embeddings[0].weight.device
+
+    def forward(
+        self,
+        points: Optional[Tuple[torch.Tensor, torch.Tensor]],
+        boxes: Optional[torch.Tensor],
+        masks: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Embeds different types of prompts, returning both sparse and dense
+        embeddings.
+
+        Arguments:
+          points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
+            and labels to embed.
+          boxes (torch.Tensor or none): boxes to embed
+          masks (torch.Tensor or none): masks to embed
+
+        Returns:
+          torch.Tensor: sparse embeddings for the points and boxes, with shape
+            BxNx(embed_dim), where N is determined by the number of input points
+            and boxes.
+          torch.Tensor: dense embeddings for the masks, in the shape
+            Bx(embed_dim)x(embed_H)x(embed_W)
+        """
+        bs = self._get_batch_size(points, boxes, masks)
+        sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
+        if points is not None:
+            coords, labels = points
+            point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
+            sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
+        if boxes is not None:
+            box_embeddings = self._embed_boxes(boxes)
+            sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
+
+        if masks is not None:
+            dense_embeddings = self._embed_masks(masks)
+        else:
+            dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
+                bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]
+            )
+
+        return sparse_embeddings, dense_embeddings
+
+
+class PositionEmbeddingRandom(nn.Module):
+    """
+    Positional encoding using random spatial frequencies.
+    """
+
+    def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
+        super().__init__()
+        if scale is None or scale <= 0.0:
+            scale = 1.0
+        self.register_buffer(
+            "positional_encoding_gaussian_matrix",
+            scale * torch.randn((2, num_pos_feats)),
+        )
+
+    def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
+        """Positionally encode points that are normalized to [0,1]."""
+        # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
+        coords = 2 * coords - 1
+        coords = coords @ self.positional_encoding_gaussian_matrix
+        coords = 2 * np.pi * coords
+        # outputs d_1 x ... x d_n x C shape
+        return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
+
+    def forward(self, size: Tuple[int, int]) -> torch.Tensor:
+        """Generate positional encoding for a grid of the specified size."""
+        h, w = size
+        device: Any = self.positional_encoding_gaussian_matrix.device
+        grid = torch.ones((h, w), device=device, dtype=torch.float32)
+        y_embed = grid.cumsum(dim=0) - 0.5
+        x_embed = grid.cumsum(dim=1) - 0.5
+        y_embed = y_embed / h
+        x_embed = x_embed / w
+
+        pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
+        return pe.permute(2, 0, 1)  # C x H x W
+
+    def forward_with_coords(
+        self, coords_input: torch.Tensor, image_size: Tuple[int, int]
+    ) -> torch.Tensor:
+        """Positionally encode points that are not normalized to [0,1]."""
+        coords = coords_input.clone()
+        coords[:, :, 0] = coords[:, :, 0] / image_size[1]
+        coords[:, :, 1] = coords[:, :, 1] / image_size[0]
+        return self._pe_encoding(coords.to(torch.float))  # B x N x C
diff --git a/lama_cleaner/plugins/segment_anything/modeling/sam.py b/lama_cleaner/plugins/segment_anything/modeling/sam.py
new file mode 100644
index 0000000000000000000000000000000000000000..303bc2f40c3dbc84f5d4286bb73336e075a86589
--- /dev/null
+++ b/lama_cleaner/plugins/segment_anything/modeling/sam.py
@@ -0,0 +1,174 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from typing import Any, Dict, List, Tuple
+
+from .image_encoder import ImageEncoderViT
+from .mask_decoder import MaskDecoder
+from .prompt_encoder import PromptEncoder
+
+
+class Sam(nn.Module):
+    mask_threshold: float = 0.0
+    image_format: str = "RGB"
+
+    def __init__(
+        self,
+        image_encoder: ImageEncoderViT,
+        prompt_encoder: PromptEncoder,
+        mask_decoder: MaskDecoder,
+        pixel_mean: List[float] = [123.675, 116.28, 103.53],
+        pixel_std: List[float] = [58.395, 57.12, 57.375],
+    ) -> None:
+        """
+        SAM predicts object masks from an image and input prompts.
+
+        Arguments:
+          image_encoder (ImageEncoderViT): The backbone used to encode the
+            image into image embeddings that allow for efficient mask prediction.
+          prompt_encoder (PromptEncoder): Encodes various types of input prompts.
+          mask_decoder (MaskDecoder): Predicts masks from the image embeddings
+            and encoded prompts.
+          pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
+          pixel_std (list(float)): Std values for normalizing pixels in the input image.
+        """
+        super().__init__()
+        self.image_encoder = image_encoder
+        self.prompt_encoder = prompt_encoder
+        self.mask_decoder = mask_decoder
+        self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
+        self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
+
+    @property
+    def device(self) -> Any:
+        return self.pixel_mean.device
+
+    @torch.no_grad()
+    def forward(
+        self,
+        batched_input: List[Dict[str, Any]],
+        multimask_output: bool,
+    ) -> List[Dict[str, torch.Tensor]]:
+        """
+        Predicts masks end-to-end from provided images and prompts.
+        If prompts are not known in advance, using SamPredictor is
+        recommended over calling the model directly.
+
+        Arguments:
+          batched_input (list(dict)): A list over input images, each a
+            dictionary with the following keys. A prompt key can be
+            excluded if it is not present.
+              'image': The image as a torch tensor in 3xHxW format,
+                already transformed for input to the model.
+              'original_size': (tuple(int, int)) The original size of
+                the image before transformation, as (H, W).
+              'point_coords': (torch.Tensor) Batched point prompts for
+                this image, with shape BxNx2. Already transformed to the
+                input frame of the model.
+              'point_labels': (torch.Tensor) Batched labels for point prompts,
+                with shape BxN.
+              'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.
+                Already transformed to the input frame of the model.
+              'mask_inputs': (torch.Tensor) Batched mask inputs to the model,
+                in the form Bx1xHxW.
+          multimask_output (bool): Whether the model should predict multiple
+            disambiguating masks, or return a single mask.
+
+        Returns:
+          (list(dict)): A list over input images, where each element is
+            as dictionary with the following keys.
+              'masks': (torch.Tensor) Batched binary mask predictions,
+                with shape BxCxHxW, where B is the number of input promts,
+                C is determiend by multimask_output, and (H, W) is the
+                original size of the image.
+              'iou_predictions': (torch.Tensor) The model's predictions
+                of mask quality, in shape BxC.
+              'low_res_logits': (torch.Tensor) Low resolution logits with
+                shape BxCxHxW, where H=W=256. Can be passed as mask input
+                to subsequent iterations of prediction.
+        """
+        input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0)
+        image_embeddings = self.image_encoder(input_images)
+
+        outputs = []
+        for image_record, curr_embedding in zip(batched_input, image_embeddings):
+            if "point_coords" in image_record:
+                points = (image_record["point_coords"], image_record["point_labels"])
+            else:
+                points = None
+            sparse_embeddings, dense_embeddings = self.prompt_encoder(
+                points=points,
+                boxes=image_record.get("boxes", None),
+                masks=image_record.get("mask_inputs", None),
+            )
+            low_res_masks, iou_predictions = self.mask_decoder(
+                image_embeddings=curr_embedding.unsqueeze(0),
+                image_pe=self.prompt_encoder.get_dense_pe(),
+                sparse_prompt_embeddings=sparse_embeddings,
+                dense_prompt_embeddings=dense_embeddings,
+                multimask_output=multimask_output,
+            )
+            masks = self.postprocess_masks(
+                low_res_masks,
+                input_size=image_record["image"].shape[-2:],
+                original_size=image_record["original_size"],
+            )
+            masks = masks > self.mask_threshold
+            outputs.append(
+                {
+                    "masks": masks,
+                    "iou_predictions": iou_predictions,
+                    "low_res_logits": low_res_masks,
+                }
+            )
+        return outputs
+
+    def postprocess_masks(
+        self,
+        masks: torch.Tensor,
+        input_size: Tuple[int, ...],
+        original_size: Tuple[int, ...],
+    ) -> torch.Tensor:
+        """
+        Remove padding and upscale masks to the original image size.
+
+        Arguments:
+          masks (torch.Tensor): Batched masks from the mask_decoder,
+            in BxCxHxW format.
+          input_size (tuple(int, int)): The size of the image input to the
+            model, in (H, W) format. Used to remove padding.
+          original_size (tuple(int, int)): The original size of the image
+            before resizing for input to the model, in (H, W) format.
+
+        Returns:
+          (torch.Tensor): Batched masks in BxCxHxW format, where (H, W)
+            is given by original_size.
+        """
+        masks = F.interpolate(
+            masks,
+            (self.image_encoder.img_size, self.image_encoder.img_size),
+            mode="bilinear",
+            align_corners=False,
+        )
+        masks = masks[..., : input_size[0], : input_size[1]]
+        masks = F.interpolate(masks, original_size, mode="bilinear", align_corners=False)
+        return masks
+
+    def preprocess(self, x: torch.Tensor) -> torch.Tensor:
+        """Normalize pixel values and pad to a square input."""
+        # Normalize colors
+        x = (x - self.pixel_mean) / self.pixel_std
+
+        # Pad
+        h, w = x.shape[-2:]
+        padh = self.image_encoder.img_size - h
+        padw = self.image_encoder.img_size - w
+        x = F.pad(x, (0, padw, 0, padh))
+        return x
diff --git a/lama_cleaner/plugins/segment_anything/modeling/transformer.py b/lama_cleaner/plugins/segment_anything/modeling/transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1a2812f613cc55b1d0b3e3e1d0c84a760d1fb87
--- /dev/null
+++ b/lama_cleaner/plugins/segment_anything/modeling/transformer.py
@@ -0,0 +1,240 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from torch import Tensor, nn
+
+import math
+from typing import Tuple, Type
+
+from .common import MLPBlock
+
+
+class TwoWayTransformer(nn.Module):
+    def __init__(
+        self,
+        depth: int,
+        embedding_dim: int,
+        num_heads: int,
+        mlp_dim: int,
+        activation: Type[nn.Module] = nn.ReLU,
+        attention_downsample_rate: int = 2,
+    ) -> None:
+        """
+        A transformer decoder that attends to an input image using
+        queries whose positional embedding is supplied.
+
+        Args:
+          depth (int): number of layers in the transformer
+          embedding_dim (int): the channel dimension for the input embeddings
+          num_heads (int): the number of heads for multihead attention. Must
+            divide embedding_dim
+          mlp_dim (int): the channel dimension internal to the MLP block
+          activation (nn.Module): the activation to use in the MLP block
+        """
+        super().__init__()
+        self.depth = depth
+        self.embedding_dim = embedding_dim
+        self.num_heads = num_heads
+        self.mlp_dim = mlp_dim
+        self.layers = nn.ModuleList()
+
+        for i in range(depth):
+            self.layers.append(
+                TwoWayAttentionBlock(
+                    embedding_dim=embedding_dim,
+                    num_heads=num_heads,
+                    mlp_dim=mlp_dim,
+                    activation=activation,
+                    attention_downsample_rate=attention_downsample_rate,
+                    skip_first_layer_pe=(i == 0),
+                )
+            )
+
+        self.final_attn_token_to_image = Attention(
+            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+        )
+        self.norm_final_attn = nn.LayerNorm(embedding_dim)
+
+    def forward(
+        self,
+        image_embedding: Tensor,
+        image_pe: Tensor,
+        point_embedding: Tensor,
+    ) -> Tuple[Tensor, Tensor]:
+        """
+        Args:
+          image_embedding (torch.Tensor): image to attend to. Should be shape
+            B x embedding_dim x h x w for any h and w.
+          image_pe (torch.Tensor): the positional encoding to add to the image. Must
+            have the same shape as image_embedding.
+          point_embedding (torch.Tensor): the embedding to add to the query points.
+            Must have shape B x N_points x embedding_dim for any N_points.
+
+        Returns:
+          torch.Tensor: the processed point_embedding
+          torch.Tensor: the processed image_embedding
+        """
+        # BxCxHxW -> BxHWxC == B x N_image_tokens x C
+        bs, c, h, w = image_embedding.shape
+        image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
+        image_pe = image_pe.flatten(2).permute(0, 2, 1)
+
+        # Prepare queries
+        queries = point_embedding
+        keys = image_embedding
+
+        # Apply transformer blocks and final layernorm
+        for layer in self.layers:
+            queries, keys = layer(
+                queries=queries,
+                keys=keys,
+                query_pe=point_embedding,
+                key_pe=image_pe,
+            )
+
+        # Apply the final attenion layer from the points to the image
+        q = queries + point_embedding
+        k = keys + image_pe
+        attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
+        queries = queries + attn_out
+        queries = self.norm_final_attn(queries)
+
+        return queries, keys
+
+
+class TwoWayAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_heads: int,
+        mlp_dim: int = 2048,
+        activation: Type[nn.Module] = nn.ReLU,
+        attention_downsample_rate: int = 2,
+        skip_first_layer_pe: bool = False,
+    ) -> None:
+        """
+        A transformer block with four layers: (1) self-attention of sparse
+        inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp
+        block on sparse inputs, and (4) cross attention of dense inputs to sparse
+        inputs.
+
+        Arguments:
+          embedding_dim (int): the channel dimension of the embeddings
+          num_heads (int): the number of heads in the attention layers
+          mlp_dim (int): the hidden dimension of the mlp block
+          activation (nn.Module): the activation of the mlp block
+          skip_first_layer_pe (bool): skip the PE on the first layer
+        """
+        super().__init__()
+        self.self_attn = Attention(embedding_dim, num_heads)
+        self.norm1 = nn.LayerNorm(embedding_dim)
+
+        self.cross_attn_token_to_image = Attention(
+            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+        )
+        self.norm2 = nn.LayerNorm(embedding_dim)
+
+        self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
+        self.norm3 = nn.LayerNorm(embedding_dim)
+
+        self.norm4 = nn.LayerNorm(embedding_dim)
+        self.cross_attn_image_to_token = Attention(
+            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+        )
+
+        self.skip_first_layer_pe = skip_first_layer_pe
+
+    def forward(
+        self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor
+    ) -> Tuple[Tensor, Tensor]:
+        # Self attention block
+        if self.skip_first_layer_pe:
+            queries = self.self_attn(q=queries, k=queries, v=queries)
+        else:
+            q = queries + query_pe
+            attn_out = self.self_attn(q=q, k=q, v=queries)
+            queries = queries + attn_out
+        queries = self.norm1(queries)
+
+        # Cross attention block, tokens attending to image embedding
+        q = queries + query_pe
+        k = keys + key_pe
+        attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
+        queries = queries + attn_out
+        queries = self.norm2(queries)
+
+        # MLP block
+        mlp_out = self.mlp(queries)
+        queries = queries + mlp_out
+        queries = self.norm3(queries)
+
+        # Cross attention block, image embedding attending to tokens
+        q = queries + query_pe
+        k = keys + key_pe
+        attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
+        keys = keys + attn_out
+        keys = self.norm4(keys)
+
+        return queries, keys
+
+
+class Attention(nn.Module):
+    """
+    An attention layer that allows for downscaling the size of the embedding
+    after projection to queries, keys, and values.
+    """
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_heads: int,
+        downsample_rate: int = 1,
+    ) -> None:
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.internal_dim = embedding_dim // downsample_rate
+        self.num_heads = num_heads
+        assert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim."
+
+        self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
+        self.k_proj = nn.Linear(embedding_dim, self.internal_dim)
+        self.v_proj = nn.Linear(embedding_dim, self.internal_dim)
+        self.out_proj = nn.Linear(self.internal_dim, embedding_dim)
+
+    def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
+        b, n, c = x.shape
+        x = x.reshape(b, n, num_heads, c // num_heads)
+        return x.transpose(1, 2)  # B x N_heads x N_tokens x C_per_head
+
+    def _recombine_heads(self, x: Tensor) -> Tensor:
+        b, n_heads, n_tokens, c_per_head = x.shape
+        x = x.transpose(1, 2)
+        return x.reshape(b, n_tokens, n_heads * c_per_head)  # B x N_tokens x C
+
+    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
+        # Input projections
+        q = self.q_proj(q)
+        k = self.k_proj(k)
+        v = self.v_proj(v)
+
+        # Separate into heads
+        q = self._separate_heads(q, self.num_heads)
+        k = self._separate_heads(k, self.num_heads)
+        v = self._separate_heads(v, self.num_heads)
+
+        # Attention
+        _, _, _, c_per_head = q.shape
+        attn = q @ k.permute(0, 1, 3, 2)  # B x N_heads x N_tokens x N_tokens
+        attn = attn / math.sqrt(c_per_head)
+        attn = torch.softmax(attn, dim=-1)
+
+        # Get output
+        out = attn @ v
+        out = self._recombine_heads(out)
+        out = self.out_proj(out)
+
+        return out
diff --git a/lama_cleaner/plugins/segment_anything/predictor.py b/lama_cleaner/plugins/segment_anything/predictor.py
new file mode 100644
index 0000000000000000000000000000000000000000..23d0649a8f27a1d2121aaccb54e6154be69940ed
--- /dev/null
+++ b/lama_cleaner/plugins/segment_anything/predictor.py
@@ -0,0 +1,285 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+
+from .modeling import Sam
+
+from typing import Optional, Tuple
+
+
+class SamPredictor:
+    def __init__(
+        self,
+        sam_model: Sam,
+    ) -> None:
+        """
+        Uses SAM to calculate the image embedding for an image, and then
+        allow repeated, efficient mask prediction given prompts.
+
+        Arguments:
+          sam_model (Sam): The model to use for mask prediction.
+        """
+        super().__init__()
+        self.model = sam_model
+        from .utils.transforms import ResizeLongestSide
+
+        self.transform = ResizeLongestSide(sam_model.image_encoder.img_size)
+        self.reset_image()
+
+    def set_image(
+        self,
+        image: np.ndarray,
+        image_format: str = "RGB",
+    ) -> None:
+        """
+        Calculates the image embeddings for the provided image, allowing
+        masks to be predicted with the 'predict' method.
+
+        Arguments:
+          image (np.ndarray): The image for calculating masks. Expects an
+            image in HWC uint8 format, with pixel values in [0, 255].
+          image_format (str): The color format of the image, in ['RGB', 'BGR'].
+        """
+        assert image_format in [
+            "RGB",
+            "BGR",
+        ], f"image_format must be in ['RGB', 'BGR'], is {image_format}."
+        if image_format != self.model.image_format:
+            image = image[..., ::-1]
+
+        # Transform the image to the form expected by the model
+        input_image = self.transform.apply_image(image)
+        input_image_torch = torch.as_tensor(input_image, device=self.device)
+        input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[
+            None, :, :, :
+        ]
+
+        self.set_torch_image(input_image_torch, image.shape[:2])
+
+    @torch.no_grad()
+    def set_torch_image(
+        self,
+        transformed_image: torch.Tensor,
+        original_image_size: Tuple[int, ...],
+    ) -> None:
+        """
+        Calculates the image embeddings for the provided image, allowing
+        masks to be predicted with the 'predict' method. Expects the input
+        image to be already transformed to the format expected by the model.
+
+        Arguments:
+          transformed_image (torch.Tensor): The input image, with shape
+            1x3xHxW, which has been transformed with ResizeLongestSide.
+          original_image_size (tuple(int, int)): The size of the image
+            before transformation, in (H, W) format.
+        """
+        assert (
+            len(transformed_image.shape) == 4
+            and transformed_image.shape[1] == 3
+            and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size
+        ), f"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}."
+        self.reset_image()
+
+        self.original_size = original_image_size
+        self.input_size = tuple(transformed_image.shape[-2:])
+        input_image = self.model.preprocess(transformed_image)
+        self.features = self.model.image_encoder(input_image)
+        self.is_image_set = True
+
+    def predict(
+        self,
+        point_coords: Optional[np.ndarray] = None,
+        point_labels: Optional[np.ndarray] = None,
+        box: Optional[np.ndarray] = None,
+        mask_input: Optional[np.ndarray] = None,
+        multimask_output: bool = True,
+        return_logits: bool = False,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Predict masks for the given input prompts, using the currently set image.
+
+        Arguments:
+          point_coords (np.ndarray or None): A Nx2 array of point prompts to the
+            model. Each point is in (X,Y) in pixels.
+          point_labels (np.ndarray or None): A length N array of labels for the
+            point prompts. 1 indicates a foreground point and 0 indicates a
+            background point.
+          box (np.ndarray or None): A length 4 array given a box prompt to the
+            model, in XYXY format.
+          mask_input (np.ndarray): A low resolution mask input to the model, typically
+            coming from a previous prediction iteration. Has form 1xHxW, where
+            for SAM, H=W=256.
+          multimask_output (bool): If true, the model will return three masks.
+            For ambiguous input prompts (such as a single click), this will often
+            produce better masks than a single prediction. If only a single
+            mask is needed, the model's predicted quality score can be used
+            to select the best mask. For non-ambiguous prompts, such as multiple
+            input prompts, multimask_output=False can give better results.
+          return_logits (bool): If true, returns un-thresholded masks logits
+            instead of a binary mask.
+
+        Returns:
+          (np.ndarray): The output masks in CxHxW format, where C is the
+            number of masks, and (H, W) is the original image size.
+          (np.ndarray): An array of length C containing the model's
+            predictions for the quality of each mask.
+          (np.ndarray): An array of shape CxHxW, where C is the number
+            of masks and H=W=256. These low resolution logits can be passed to
+            a subsequent iteration as mask input.
+        """
+        if not self.is_image_set:
+            raise RuntimeError(
+                "An image must be set with .set_image(...) before mask prediction."
+            )
+
+        # Transform input prompts
+        coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None
+        if point_coords is not None:
+            assert (
+                point_labels is not None
+            ), "point_labels must be supplied if point_coords is supplied."
+            point_coords = self.transform.apply_coords(point_coords, self.original_size)
+            coords_torch = torch.as_tensor(
+                point_coords, dtype=torch.float, device=self.device
+            )
+            labels_torch = torch.as_tensor(
+                point_labels, dtype=torch.int, device=self.device
+            )
+            coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :]
+        if box is not None:
+            box = self.transform.apply_boxes(box, self.original_size)
+            box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device)
+            box_torch = box_torch[None, :]
+        if mask_input is not None:
+            mask_input_torch = torch.as_tensor(
+                mask_input, dtype=torch.float, device=self.device
+            )
+            mask_input_torch = mask_input_torch[None, :, :, :]
+
+        masks, iou_predictions, low_res_masks = self.predict_torch(
+            coords_torch,
+            labels_torch,
+            box_torch,
+            mask_input_torch,
+            multimask_output,
+            return_logits=return_logits,
+        )
+
+        masks = masks[0].detach().cpu().numpy()
+        iou_predictions = iou_predictions[0].detach().cpu().numpy()
+        low_res_masks = low_res_masks[0].detach().cpu().numpy()
+        return masks, iou_predictions, low_res_masks
+
+    @torch.no_grad()
+    def predict_torch(
+        self,
+        point_coords: Optional[torch.Tensor],
+        point_labels: Optional[torch.Tensor],
+        boxes: Optional[torch.Tensor] = None,
+        mask_input: Optional[torch.Tensor] = None,
+        multimask_output: bool = True,
+        return_logits: bool = False,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Predict masks for the given input prompts, using the currently set image.
+        Input prompts are batched torch tensors and are expected to already be
+        transformed to the input frame using ResizeLongestSide.
+
+        Arguments:
+          point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the
+            model. Each point is in (X,Y) in pixels.
+          point_labels (torch.Tensor or None): A BxN array of labels for the
+            point prompts. 1 indicates a foreground point and 0 indicates a
+            background point.
+          box (np.ndarray or None): A Bx4 array given a box prompt to the
+            model, in XYXY format.
+          mask_input (np.ndarray): A low resolution mask input to the model, typically
+            coming from a previous prediction iteration. Has form Bx1xHxW, where
+            for SAM, H=W=256. Masks returned by a previous iteration of the
+            predict method do not need further transformation.
+          multimask_output (bool): If true, the model will return three masks.
+            For ambiguous input prompts (such as a single click), this will often
+            produce better masks than a single prediction. If only a single
+            mask is needed, the model's predicted quality score can be used
+            to select the best mask. For non-ambiguous prompts, such as multiple
+            input prompts, multimask_output=False can give better results.
+          return_logits (bool): If true, returns un-thresholded masks logits
+            instead of a binary mask.
+
+        Returns:
+          (torch.Tensor): The output masks in BxCxHxW format, where C is the
+            number of masks, and (H, W) is the original image size.
+          (torch.Tensor): An array of shape BxC containing the model's
+            predictions for the quality of each mask.
+          (torch.Tensor): An array of shape BxCxHxW, where C is the number
+            of masks and H=W=256. These low res logits can be passed to
+            a subsequent iteration as mask input.
+        """
+        if not self.is_image_set:
+            raise RuntimeError(
+                "An image must be set with .set_image(...) before mask prediction."
+            )
+
+        if point_coords is not None:
+            points = (point_coords, point_labels)
+        else:
+            points = None
+
+        # Embed prompts
+        sparse_embeddings, dense_embeddings = self.model.prompt_encoder(
+            points=points,
+            boxes=boxes,
+            masks=mask_input,
+        )
+
+        # Predict masks
+        low_res_masks, iou_predictions = self.model.mask_decoder(
+            image_embeddings=self.features,
+            image_pe=self.model.prompt_encoder.get_dense_pe(),
+            sparse_prompt_embeddings=sparse_embeddings,
+            dense_prompt_embeddings=dense_embeddings,
+            multimask_output=multimask_output,
+        )
+
+        # Upscale the masks to the original image resolution
+        masks = self.model.postprocess_masks(
+            low_res_masks, self.input_size, self.original_size
+        )
+
+        if not return_logits:
+            masks = masks > self.model.mask_threshold
+
+        return masks, iou_predictions, low_res_masks
+
+    def get_image_embedding(self) -> torch.Tensor:
+        """
+        Returns the image embeddings for the currently set image, with
+        shape 1xCxHxW, where C is the embedding dimension and (H,W) are
+        the embedding spatial dimension of SAM (typically C=256, H=W=64).
+        """
+        if not self.is_image_set:
+            raise RuntimeError(
+                "An image must be set with .set_image(...) to generate an embedding."
+            )
+        assert (
+            self.features is not None
+        ), "Features must exist if an image has been set."
+        return self.features
+
+    @property
+    def device(self) -> torch.device:
+        return self.model.device
+
+    def reset_image(self) -> None:
+        """Resets the currently set image."""
+        self.is_image_set = False
+        self.features = None
+        self.orig_h = None
+        self.orig_w = None
+        self.input_h = None
+        self.input_w = None
diff --git a/lama_cleaner/plugins/segment_anything/utils/__init__.py b/lama_cleaner/plugins/segment_anything/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae
--- /dev/null
+++ b/lama_cleaner/plugins/segment_anything/utils/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
diff --git a/lama_cleaner/plugins/segment_anything/utils/__pycache__/__init__.cpython-38.pyc b/lama_cleaner/plugins/segment_anything/utils/__pycache__/__init__.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..879e60dea6875b7e6ba44ff294fe7f66b6c2c66e
Binary files /dev/null and b/lama_cleaner/plugins/segment_anything/utils/__pycache__/__init__.cpython-38.pyc differ
diff --git a/lama_cleaner/plugins/segment_anything/utils/__pycache__/transforms.cpython-38.pyc b/lama_cleaner/plugins/segment_anything/utils/__pycache__/transforms.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a1fb598d58e370872d5d9014d61c9514cc9676d4
Binary files /dev/null and b/lama_cleaner/plugins/segment_anything/utils/__pycache__/transforms.cpython-38.pyc differ
diff --git a/lama_cleaner/plugins/segment_anything/utils/transforms.py b/lama_cleaner/plugins/segment_anything/utils/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..90f50edd731ee0e9aa950614c6918b7ea3b330f7
--- /dev/null
+++ b/lama_cleaner/plugins/segment_anything/utils/transforms.py
@@ -0,0 +1,112 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+from torch.nn import functional as F
+from torchvision.transforms.functional import resize, to_pil_image  # type: ignore
+
+from copy import deepcopy
+from typing import Tuple
+
+
+class ResizeLongestSide:
+    """
+    Resizes images to longest side 'target_length', as well as provides
+    methods for resizing coordinates and boxes. Provides methods for
+    transforming both numpy array and batched torch tensors.
+    """
+
+    def __init__(self, target_length: int) -> None:
+        self.target_length = target_length
+
+    def apply_image(self, image: np.ndarray) -> np.ndarray:
+        """
+        Expects a numpy array with shape HxWxC in uint8 format.
+        """
+        target_size = self.get_preprocess_shape(
+            image.shape[0], image.shape[1], self.target_length
+        )
+        return np.array(resize(to_pil_image(image), target_size))
+
+    def apply_coords(
+        self, coords: np.ndarray, original_size: Tuple[int, ...]
+    ) -> np.ndarray:
+        """
+        Expects a numpy array of length 2 in the final dimension. Requires the
+        original image size in (H, W) format.
+        """
+        old_h, old_w = original_size
+        new_h, new_w = self.get_preprocess_shape(
+            original_size[0], original_size[1], self.target_length
+        )
+        coords = deepcopy(coords).astype(float)
+        coords[..., 0] = coords[..., 0] * (new_w / old_w)
+        coords[..., 1] = coords[..., 1] * (new_h / old_h)
+        return coords
+
+    def apply_boxes(
+        self, boxes: np.ndarray, original_size: Tuple[int, ...]
+    ) -> np.ndarray:
+        """
+        Expects a numpy array shape Bx4. Requires the original image size
+        in (H, W) format.
+        """
+        boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)
+        return boxes.reshape(-1, 4)
+
+    def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:
+        """
+        Expects batched images with shape BxCxHxW and float format. This
+        transformation may not exactly match apply_image. apply_image is
+        the transformation expected by the model.
+        """
+        # Expects an image in BCHW format. May not exactly match apply_image.
+        target_size = self.get_preprocess_shape(
+            image.shape[0], image.shape[1], self.target_length
+        )
+        return F.interpolate(
+            image, target_size, mode="bilinear", align_corners=False, antialias=True
+        )
+
+    def apply_coords_torch(
+        self, coords: torch.Tensor, original_size: Tuple[int, ...]
+    ) -> torch.Tensor:
+        """
+        Expects a torch tensor with length 2 in the last dimension. Requires the
+        original image size in (H, W) format.
+        """
+        old_h, old_w = original_size
+        new_h, new_w = self.get_preprocess_shape(
+            original_size[0], original_size[1], self.target_length
+        )
+        coords = deepcopy(coords).to(torch.float)
+        coords[..., 0] = coords[..., 0] * (new_w / old_w)
+        coords[..., 1] = coords[..., 1] * (new_h / old_h)
+        return coords
+
+    def apply_boxes_torch(
+        self, boxes: torch.Tensor, original_size: Tuple[int, ...]
+    ) -> torch.Tensor:
+        """
+        Expects a torch tensor with shape Bx4. Requires the original image
+        size in (H, W) format.
+        """
+        boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size)
+        return boxes.reshape(-1, 4)
+
+    @staticmethod
+    def get_preprocess_shape(
+        oldh: int, oldw: int, long_side_length: int
+    ) -> Tuple[int, int]:
+        """
+        Compute the output size given input size and target long side length.
+        """
+        scale = long_side_length * 1.0 / max(oldh, oldw)
+        newh, neww = oldh * scale, oldw * scale
+        neww = int(neww + 0.5)
+        newh = int(newh + 0.5)
+        return (newh, neww)
diff --git a/lama_cleaner/runtime.py b/lama_cleaner/runtime.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8cc2e073145a0ead4dda986903bb452de967b73
--- /dev/null
+++ b/lama_cleaner/runtime.py
@@ -0,0 +1,50 @@
+# https://github.com/huggingface/huggingface_hub/blob/5a12851f54bf614be39614034ed3a9031922d297/src/huggingface_hub/utils/_runtime.py
+import platform
+import sys
+import packaging.version
+from rich import print
+from typing import Dict, Any
+
+_PY_VERSION: str = sys.version.split()[0].rstrip("+")
+
+if packaging.version.Version(_PY_VERSION) < packaging.version.Version("3.8.0"):
+    import importlib_metadata  # type: ignore
+else:
+    import importlib.metadata as importlib_metadata  # type: ignore
+
+_package_versions = {}
+
+_CANDIDATES = [
+    "torch",
+    "torchvision",
+    "Pillow",
+    "diffusers",
+    "transformers",
+    "opencv-python",
+    "xformers",
+    "accelerate",
+    "lama-cleaner",
+    "rembg",
+    "realesrgan",
+    "gfpgan",
+]
+# Check once at runtime
+for name in _CANDIDATES:
+    _package_versions[name] = "N/A"
+    try:
+        _package_versions[name] = importlib_metadata.version(name)
+    except importlib_metadata.PackageNotFoundError:
+        pass
+
+
+def dump_environment_info() -> Dict[str, str]:
+    """Dump information about the machine to help debugging issues. """
+
+    # Generic machine info
+    info: Dict[str, Any] = {
+        "Platform": platform.platform(),
+        "Python version": platform.python_version(),
+    }
+    info.update(_package_versions)
+    print("\n".join([f"- {prop}: {val}" for prop, val in info.items()]) + "\n")
+    return info
diff --git a/lama_cleaner/schema.py b/lama_cleaner/schema.py
new file mode 100644
index 0000000000000000000000000000000000000000..9eba2f5b624249e8bd1330df3ca11bd83938f2b3
--- /dev/null
+++ b/lama_cleaner/schema.py
@@ -0,0 +1,101 @@
+from typing import Optional
+from enum import Enum
+
+from PIL.Image import Image
+from pydantic import BaseModel
+
+
+class HDStrategy(str, Enum):
+    # Use original image size
+    ORIGINAL = "Original"
+    # Resize the longer side of the image to a specific size(hd_strategy_resize_limit),
+    # then do inpainting on the resized image. Finally, resize the inpainting result to the original size.
+    # The area outside the mask will not lose quality.
+    RESIZE = "Resize"
+    # Crop masking area(with a margin controlled by hd_strategy_crop_margin) from the original image to do inpainting
+    CROP = "Crop"
+
+
+class LDMSampler(str, Enum):
+    ddim = "ddim"
+    plms = "plms"
+
+
+class SDSampler(str, Enum):
+    ddim = "ddim"
+    pndm = "pndm"
+    k_lms = "k_lms"
+    k_euler = "k_euler"
+    k_euler_a = "k_euler_a"
+    dpm_plus_plus = "dpm++"
+    uni_pc = "uni_pc"
+
+
+class Config(BaseModel):
+    class Config:
+        arbitrary_types_allowed = True
+
+    # Configs for ldm model
+    ldm_steps: int
+    ldm_sampler: str = LDMSampler.plms
+
+    # Configs for zits model
+    zits_wireframe: bool = True
+
+    # Configs for High Resolution Strategy(different way to preprocess image)
+    hd_strategy: str  # See HDStrategy Enum
+    hd_strategy_crop_margin: int
+    # If the longer side of the image is larger than this value, use crop strategy
+    hd_strategy_crop_trigger_size: int
+    hd_strategy_resize_limit: int
+
+    # Configs for Stable Diffusion 1.5
+    prompt: str = ""
+    negative_prompt: str = ""
+    # Crop image to this size before doing sd inpainting
+    # The value is always on the original image scale
+    use_croper: bool = False
+    croper_x: int = None
+    croper_y: int = None
+    croper_height: int = None
+    croper_width: int = None
+
+    # Resize the image before doing sd inpainting, the area outside the mask will not lose quality.
+    # Used by sd models and paint_by_example model
+    sd_scale: float = 1.0
+    # Blur the edge of mask area. The higher the number the smoother blend with the original image
+    sd_mask_blur: int = 0
+    # Ignore this value, it's useless for inpainting
+    sd_strength: float = 0.75
+    # The number of denoising steps. More denoising steps usually lead to a
+    # higher quality image at the expense of slower inference.
+    sd_steps: int = 50
+    # Higher guidance scale encourages to generate images that are closely linked
+    # to the text prompt, usually at the expense of lower image quality.
+    sd_guidance_scale: float = 7.5
+    sd_sampler: str = SDSampler.uni_pc
+    # -1 mean random seed
+    sd_seed: int = 42
+    sd_match_histograms: bool = False
+
+    # Configs for opencv inpainting
+    # opencv document https://docs.opencv.org/4.6.0/d7/d8b/group__photo__inpaint.html#gga8002a65f5a3328fbf15df81b842d3c3ca05e763003a805e6c11c673a9f4ba7d07
+    cv2_flag: str = "INPAINT_NS"
+    cv2_radius: int = 4
+
+    # Paint by Example
+    paint_by_example_steps: int = 50
+    paint_by_example_guidance_scale: float = 7.5
+    paint_by_example_mask_blur: int = 0
+    paint_by_example_seed: int = 42
+    paint_by_example_match_histograms: bool = False
+    paint_by_example_example_image: Optional[Image] = None
+
+    # InstructPix2Pix
+    p2p_steps: int = 50
+    p2p_image_guidance_scale: float = 1.5
+    p2p_guidance_scale: float = 7.5
+
+    # ControlNet
+    controlnet_conditioning_scale: float = 0.4
+    controlnet_method: str = "control_v11p_sd15_canny"
diff --git a/lama_cleaner/server.py b/lama_cleaner/server.py
new file mode 100644
index 0000000000000000000000000000000000000000..a354b415af916911a3d66fac27eed18d4a976fbb
--- /dev/null
+++ b/lama_cleaner/server.py
@@ -0,0 +1,615 @@
+#!/usr/bin/env python3
+import os
+import hashlib
+
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
+
+import imghdr
+import io
+import logging
+import multiprocessing
+import random
+import time
+from pathlib import Path
+
+import cv2
+import numpy as np
+import torch
+from PIL import Image
+from loguru import logger
+
+from lama_cleaner.const import SD15_MODELS
+from lama_cleaner.file_manager import FileManager
+from lama_cleaner.model.utils import torch_gc
+from lama_cleaner.model_manager import ModelManager
+from lama_cleaner.plugins import (
+    InteractiveSeg,
+    RemoveBG,
+    RealESRGANUpscaler,
+    MakeGIF,
+    GFPGANPlugin,
+    RestoreFormerPlugin,
+    AnimeSeg,
+)
+from lama_cleaner.schema import Config
+
+try:
+    torch._C._jit_override_can_fuse_on_cpu(False)
+    torch._C._jit_override_can_fuse_on_gpu(False)
+    torch._C._jit_set_texpr_fuser_enabled(False)
+    torch._C._jit_set_nvfuser_enabled(False)
+except:
+    pass
+
+from flask import (
+    Flask,
+    request,
+    send_file,
+    cli,
+    make_response,
+    send_from_directory,
+    jsonify,
+)
+from flask_socketio import SocketIO
+
+# Disable ability for Flask to display warning about using a development server in a production environment.
+# https://gist.github.com/jerblack/735b9953ba1ab6234abb43174210d356
+cli.show_server_banner = lambda *_: None
+from flask_cors import CORS
+
+from lama_cleaner.helper import (
+    load_img,
+    numpy_to_bytes,
+    resize_max_size,
+    pil_to_bytes,
+)
+
+NUM_THREADS = str(multiprocessing.cpu_count())
+
+# fix libomp problem on windows https://github.com/Sanster/lama-cleaner/issues/56
+os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
+
+os.environ["OMP_NUM_THREADS"] = NUM_THREADS
+os.environ["OPENBLAS_NUM_THREADS"] = NUM_THREADS
+os.environ["MKL_NUM_THREADS"] = NUM_THREADS
+os.environ["VECLIB_MAXIMUM_THREADS"] = NUM_THREADS
+os.environ["NUMEXPR_NUM_THREADS"] = NUM_THREADS
+if os.environ.get("CACHE_DIR"):
+    os.environ["TORCH_HOME"] = os.environ["CACHE_DIR"]
+
+BUILD_DIR = os.environ.get("LAMA_CLEANER_BUILD_DIR", "app/build")
+
+
+class NoFlaskwebgui(logging.Filter):
+    def filter(self, record):
+        msg = record.getMessage()
+        if "Running on http:" in msg:
+            print(msg[msg.index("Running on http:") :])
+
+        return (
+            "flaskwebgui-keep-server-alive" not in msg
+            and "socket.io" not in msg
+            and "This is a development server." not in msg
+        )
+
+
+logging.getLogger("werkzeug").addFilter(NoFlaskwebgui())
+
+app = Flask(__name__, static_folder=os.path.join(BUILD_DIR, "static"))
+app.config["JSON_AS_ASCII"] = False
+CORS(app, expose_headers=["Content-Disposition"])
+
+sio_logger = logging.getLogger("sio-logger")
+sio_logger.setLevel(logging.ERROR)
+socketio = SocketIO(app, cors_allowed_origins="*", async_mode="threading")
+
+model: ModelManager = None
+thumb: FileManager = None
+output_dir: str = None
+device = None
+input_image_path: str = None
+is_disable_model_switch: bool = False
+is_controlnet: bool = False
+controlnet_method: str = "control_v11p_sd15_canny"
+is_enable_file_manager: bool = False
+is_enable_auto_saving: bool = False
+is_desktop: bool = False
+image_quality: int = 95
+plugins = {}
+
+
+def get_image_ext(img_bytes):
+    w = imghdr.what("", img_bytes)
+    if w is None:
+        w = "jpeg"
+    return w
+
+
+def diffuser_callback(i, t, latents):
+    socketio.emit("diffusion_progress", {"step": i})
+
+
+@app.route("/save_image", methods=["POST"])
+def save_image():
+    if output_dir is None:
+        return "--output-dir is None", 500
+
+    input = request.files
+    filename = request.form["filename"]
+    origin_image_bytes = input["image"].read()  # RGB
+    ext = get_image_ext(origin_image_bytes)
+    image, alpha_channel, exif_infos = load_img(origin_image_bytes, return_exif=True)
+    save_path = os.path.join(output_dir, filename)
+
+    if alpha_channel is not None:
+        if alpha_channel.shape[:2] != image.shape[:2]:
+            alpha_channel = cv2.resize(
+                alpha_channel, dsize=(image.shape[1], image.shape[0])
+            )
+        image = np.concatenate((image, alpha_channel[:, :, np.newaxis]), axis=-1)
+
+    pil_image = Image.fromarray(image)
+
+    img_bytes = pil_to_bytes(
+        pil_image,
+        ext,
+        quality=image_quality,
+        exif_infos=exif_infos,
+    )
+    with open(save_path, "wb") as fw:
+        fw.write(img_bytes)
+
+    return "ok", 200
+
+
+@app.route("/medias/<tab>")
+def medias(tab):
+    if tab == "image":
+        response = make_response(jsonify(thumb.media_names), 200)
+    else:
+        response = make_response(jsonify(thumb.output_media_names), 200)
+    # response.last_modified = thumb.modified_time[tab]
+    # response.cache_control.no_cache = True
+    # response.cache_control.max_age = 0
+    # response.make_conditional(request)
+    return response
+
+
+@app.route("/media/<tab>/<filename>")
+def media_file(tab, filename):
+    if tab == "image":
+        return send_from_directory(thumb.root_directory, filename)
+    return send_from_directory(thumb.output_dir, filename)
+
+
+@app.route("/media_thumbnail/<tab>/<filename>")
+def media_thumbnail_file(tab, filename):
+    args = request.args
+    width = args.get("width")
+    height = args.get("height")
+    if width is None and height is None:
+        width = 256
+    if width:
+        width = int(float(width))
+    if height:
+        height = int(float(height))
+
+    directory = thumb.root_directory
+    if tab == "output":
+        directory = thumb.output_dir
+    thumb_filename, (width, height) = thumb.get_thumbnail(
+        directory, filename, width, height
+    )
+    thumb_filepath = f"{app.config['THUMBNAIL_MEDIA_THUMBNAIL_ROOT']}{thumb_filename}"
+
+    response = make_response(send_file(thumb_filepath))
+    response.headers["X-Width"] = str(width)
+    response.headers["X-Height"] = str(height)
+    return response
+
+
+#不使用接口,而是直接使用
+# @app.route("/inpaint", methods=["POST"])
+#每次上传一个图片和遮罩 =>files ,然后返回bytes数据
+def process(files:dict,payload:dict):
+    input = files
+    # RGB
+    origin_image_bytes = input["image"].read()
+    image, alpha_channel, exif_infos = load_img(origin_image_bytes, return_exif=True)
+
+    mask, _ = load_img(input["mask"].read(), gray=True)
+    mask = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)[1]
+
+    if image.shape[:2] != mask.shape[:2]:
+        return (
+            f"Mask shape{mask.shape[:2]} not queal to Image shape{image.shape[:2]}",
+            400,
+        )
+
+    original_shape = image.shape
+    interpolation = cv2.INTER_CUBIC
+
+    form = payload
+    size_limit = max(image.shape)
+
+    if "paintByExampleImage" in input:
+        paint_by_example_example_image, _ = load_img(
+            input["paintByExampleImage"].read()
+        )
+        paint_by_example_example_image = Image.fromarray(paint_by_example_example_image)
+    else:
+        paint_by_example_example_image = None
+
+    config = Config(
+        ldm_steps=form["ldmSteps"],
+        ldm_sampler=form["ldmSampler"],
+        hd_strategy=form["hdStrategy"],
+        zits_wireframe=form["zitsWireframe"],
+        hd_strategy_crop_margin=form["hdStrategyCropMargin"],
+        hd_strategy_crop_trigger_size=form["hdStrategyCropTrigerSize"],
+        hd_strategy_resize_limit=form["hdStrategyResizeLimit"],
+        prompt=form["prompt"],
+        negative_prompt=form["negativePrompt"],
+        use_croper=form["useCroper"],
+        croper_x=form["croperX"],
+        croper_y=form["croperY"],
+        croper_height=form["croperHeight"],
+        croper_width=form["croperWidth"],
+        sd_scale=form["sdScale"],
+        sd_mask_blur=form["sdMaskBlur"],
+        sd_strength=form["sdStrength"],
+        sd_steps=form["sdSteps"],
+        sd_guidance_scale=form["sdGuidanceScale"],
+        sd_sampler=form["sdSampler"],
+        sd_seed=form["sdSeed"],
+        sd_match_histograms=form["sdMatchHistograms"],
+        cv2_flag=form["cv2Flag"],
+        cv2_radius=form["cv2Radius"],
+        paint_by_example_steps=form["paintByExampleSteps"],
+        paint_by_example_guidance_scale=form["paintByExampleGuidanceScale"],
+        paint_by_example_mask_blur=form["paintByExampleMaskBlur"],
+        paint_by_example_seed=form["paintByExampleSeed"],
+        paint_by_example_match_histograms=form["paintByExampleMatchHistograms"],
+        paint_by_example_example_image=paint_by_example_example_image,
+        p2p_steps=form["p2pSteps"],
+        p2p_image_guidance_scale=form["p2pImageGuidanceScale"],
+        p2p_guidance_scale=form["p2pGuidanceScale"],
+        controlnet_conditioning_scale=form["controlnet_conditioning_scale"],
+        controlnet_method=form["controlnet_method"],
+    )
+
+    if config.sd_seed == -1:
+        config.sd_seed = random.randint(1, 999999999)
+    if config.paint_by_example_seed == -1:
+        config.paint_by_example_seed = random.randint(1, 999999999)
+
+    logger.info(f"Origin image shape: {original_shape}")
+    image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
+
+    mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation)
+
+    start = time.time()
+    try:
+        res_np_img = model(image, mask, config)
+    except RuntimeError as e:
+        if "CUDA out of memory. " in str(e):
+            # NOTE: the string may change?
+            return "CUDA out of memory", 500
+        else:
+            logger.exception(e)
+            return f"{str(e)}", 500
+    finally:
+        logger.info(f"process time: {(time.time() - start) * 1000}ms")
+        torch_gc()
+
+    res_np_img = cv2.cvtColor(res_np_img.astype(np.uint8), cv2.COLOR_BGR2RGB)
+    if alpha_channel is not None:
+        if alpha_channel.shape[:2] != res_np_img.shape[:2]:
+            alpha_channel = cv2.resize(
+                alpha_channel, dsize=(res_np_img.shape[1], res_np_img.shape[0])
+            )
+        res_np_img = np.concatenate(
+            (res_np_img, alpha_channel[:, :, np.newaxis]), axis=-1
+        )
+
+    ext = get_image_ext(origin_image_bytes)
+
+    bytes_io = io.BytesIO(
+        pil_to_bytes(
+            Image.fromarray(res_np_img),
+            ext,
+            quality=image_quality,
+            exif_infos=exif_infos,
+        )
+    )
+
+    return bytes_io
+
+
+@app.route("/run_plugin", methods=["POST"])
+def run_plugin():
+    form = request.form
+    files = request.files
+    name = form["name"]
+    if name not in plugins:
+        return "Plugin not found", 500
+
+    origin_image_bytes = files["image"].read()  # RGB
+    rgb_np_img, alpha_channel, exif_infos = load_img(
+        origin_image_bytes, return_exif=True
+    )
+
+    start = time.time()
+    try:
+        form = dict(form)
+        if name == InteractiveSeg.name:
+            img_md5 = hashlib.md5(origin_image_bytes).hexdigest()
+            form["img_md5"] = img_md5
+        bgr_res = plugins[name](rgb_np_img, files, form)
+    except RuntimeError as e:
+        torch.cuda.empty_cache()
+        if "CUDA out of memory. " in str(e):
+            # NOTE: the string may change?
+            return "CUDA out of memory", 500
+        else:
+            logger.exception(e)
+            return "Internal Server Error", 500
+
+    logger.info(f"{name} process time: {(time.time() - start) * 1000}ms")
+    torch_gc()
+
+    if name == MakeGIF.name:
+        return send_file(
+            io.BytesIO(bgr_res),
+            mimetype="image/gif",
+            as_attachment=True,
+            download_name=form["filename"],
+        )
+    if name == InteractiveSeg.name:
+        return make_response(
+            send_file(
+                io.BytesIO(numpy_to_bytes(bgr_res, "png")),
+                mimetype="image/png",
+            )
+        )
+
+    if name in [RemoveBG.name, AnimeSeg.name]:
+        rgb_res = bgr_res
+        ext = "png"
+    else:
+        rgb_res = cv2.cvtColor(bgr_res, cv2.COLOR_BGR2RGB)
+        ext = get_image_ext(origin_image_bytes)
+        if alpha_channel is not None:
+            if alpha_channel.shape[:2] != rgb_res.shape[:2]:
+                alpha_channel = cv2.resize(
+                    alpha_channel, dsize=(rgb_res.shape[1], rgb_res.shape[0])
+                )
+            rgb_res = np.concatenate(
+                (rgb_res, alpha_channel[:, :, np.newaxis]), axis=-1
+            )
+
+    response = make_response(
+        send_file(
+            io.BytesIO(
+                pil_to_bytes(
+                    Image.fromarray(rgb_res),
+                    ext,
+                    quality=image_quality,
+                    exif_infos=exif_infos,
+                )
+            ),
+            mimetype=f"image/{ext}",
+        )
+    )
+    return response
+
+
+@app.route("/server_config", methods=["GET"])
+def get_server_config():
+    return {
+        "isControlNet": is_controlnet,
+        "controlNetMethod": controlnet_method,
+        "isDisableModelSwitchState": is_disable_model_switch,
+        "isEnableAutoSaving": is_enable_auto_saving,
+        "enableFileManager": is_enable_file_manager,
+        "plugins": list(plugins.keys()),
+    }, 200
+
+
+@app.route("/model")
+def current_model():
+    return model.name, 200
+
+
+@app.route("/model_downloaded/<name>")
+def model_downloaded(name):
+    return str(model.is_downloaded(name)), 200
+
+
+@app.route("/is_desktop")
+def get_is_desktop():
+    return str(is_desktop), 200
+
+
+@app.route("/model", methods=["POST"])
+def switch_model():
+    if is_disable_model_switch:
+        return "Switch model is disabled", 400
+
+    new_name = request.form.get("name")
+    if new_name == model.name:
+        return "Same model", 200
+
+    try:
+        model.switch(new_name)
+    except NotImplementedError:
+        return f"{new_name} not implemented", 403
+    return f"ok, switch to {new_name}", 200
+
+
+@app.route("/")
+def index():
+    return send_file(os.path.join(BUILD_DIR, "index.html"))
+
+
+@app.route("/inputimage")
+def set_input_photo():
+    if input_image_path:
+        with open(input_image_path, "rb") as f:
+            image_in_bytes = f.read()
+        return send_file(
+            input_image_path,
+            as_attachment=True,
+            download_name=Path(input_image_path).name,
+            mimetype=f"image/{get_image_ext(image_in_bytes)}",
+        )
+    else:
+        return "No Input Image"
+
+
+def build_plugins(args):
+    global plugins
+    if args.enable_interactive_seg:
+        logger.info(f"Initialize {InteractiveSeg.name} plugin")
+        plugins[InteractiveSeg.name] = InteractiveSeg(
+            args.interactive_seg_model, args.interactive_seg_device
+        )
+
+    if args.enable_remove_bg:
+        logger.info(f"Initialize {RemoveBG.name} plugin")
+        plugins[RemoveBG.name] = RemoveBG()
+
+    if args.enable_anime_seg:
+        logger.info(f"Initialize {AnimeSeg.name} plugin")
+        plugins[AnimeSeg.name] = AnimeSeg()
+
+    if args.enable_realesrgan:
+        logger.info(
+            f"Initialize {RealESRGANUpscaler.name} plugin: {args.realesrgan_model}, {args.realesrgan_device}"
+        )
+        plugins[RealESRGANUpscaler.name] = RealESRGANUpscaler(
+            args.realesrgan_model,
+            args.realesrgan_device,
+            no_half=args.realesrgan_no_half,
+        )
+
+    if args.enable_gfpgan:
+        logger.info(f"Initialize {GFPGANPlugin.name} plugin")
+        if args.enable_realesrgan:
+            logger.info("Use realesrgan as GFPGAN background upscaler")
+        else:
+            logger.info(
+                f"GFPGAN no background upscaler, use --enable-realesrgan to enable it"
+            )
+        plugins[GFPGANPlugin.name] = GFPGANPlugin(
+            args.gfpgan_device, upscaler=plugins.get(RealESRGANUpscaler.name, None)
+        )
+
+    if args.enable_restoreformer:
+        logger.info(f"Initialize {RestoreFormerPlugin.name} plugin")
+        plugins[RestoreFormerPlugin.name] = RestoreFormerPlugin(
+            args.restoreformer_device,
+            upscaler=plugins.get(RealESRGANUpscaler.name, None),
+        )
+
+    if args.enable_gif:
+        logger.info(f"Initialize GIF plugin")
+        plugins[MakeGIF.name] = MakeGIF()
+
+
+def main(args):
+    global model
+    global device
+    global input_image_path
+    global is_disable_model_switch
+    global is_enable_file_manager
+    global is_desktop
+    global thumb
+    global output_dir
+    global is_enable_auto_saving
+    global is_controlnet
+    global controlnet_method
+    global image_quality
+
+    build_plugins(args)
+
+    image_quality = args.quality
+
+    if args.sd_controlnet and args.model in SD15_MODELS:
+        is_controlnet = True
+        controlnet_method = args.sd_controlnet_method
+
+    output_dir = args.output_dir
+    if output_dir:
+        is_enable_auto_saving = True
+
+    device = torch.device(args.device)
+    is_disable_model_switch = args.disable_model_switch
+    is_desktop = args.gui
+    if is_disable_model_switch:
+        logger.info(
+            f"Start with --disable-model-switch, model switch on frontend is disable"
+        )
+
+    if args.input and os.path.isdir(args.input):
+        logger.info(f"Initialize file manager")
+        thumb = FileManager(app)
+        is_enable_file_manager = True
+        app.config["THUMBNAIL_MEDIA_ROOT"] = args.input
+        app.config["THUMBNAIL_MEDIA_THUMBNAIL_ROOT"] = os.path.join(
+            args.output_dir, "lama_cleaner_thumbnails"
+        )
+        thumb.output_dir = Path(args.output_dir)
+        # thumb.start()
+        # try:
+        #     while True:
+        #         time.sleep(1)
+        # finally:
+        #     thumb.image_dir_observer.stop()
+        #     thumb.image_dir_observer.join()
+        #     thumb.output_dir_observer.stop()
+        #     thumb.output_dir_observer.join()
+
+    else:
+        input_image_path = args.input
+
+    model = ModelManager(
+        name=args.model,
+        sd_controlnet=args.sd_controlnet,
+        sd_controlnet_method=args.sd_controlnet_method,
+        device=device,
+        no_half=args.no_half,
+        hf_access_token=args.hf_access_token,
+        disable_nsfw=args.sd_disable_nsfw or args.disable_nsfw,
+        sd_cpu_textencoder=args.sd_cpu_textencoder,
+        sd_run_local=args.sd_run_local,
+        sd_local_model_path=args.sd_local_model_path,
+        local_files_only=args.local_files_only,
+        cpu_offload=args.cpu_offload,
+        enable_xformers=args.sd_enable_xformers or args.enable_xformers,
+        callback=diffuser_callback,
+    )
+
+    #只初始化,不构建flask ,方便使用process函数
+    # if args.gui:
+    #     app_width, app_height = args.gui_size
+    #     from flaskwebgui import FlaskUI
+    #
+    #     ui = FlaskUI(
+    #         app,
+    #         socketio=socketio,
+    #         width=app_width,
+    #         height=app_height,
+    #         host=args.host,
+    #         port=args.port,
+    #         close_server_on_exit=not args.no_gui_auto_close,
+    #     )
+    #     ui.run()
+    # else:
+    #     socketio.run(
+    #         app,
+    #         host=args.host,
+    #         port=args.port,
+    #         debug=args.debug,
+    #         allow_unsafe_werkzeug=True,
+    #     )
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--- /dev/null
+++ b/lama_cleaner/tests/test_controlnet.py
@@ -0,0 +1,195 @@
+import os
+
+from lama_cleaner.const import SD_CONTROLNET_CHOICES
+
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
+from pathlib import Path
+
+import pytest
+import torch
+
+from lama_cleaner.model_manager import ModelManager
+from lama_cleaner.schema import HDStrategy, SDSampler
+from lama_cleaner.tests.test_model import get_config, assert_equal
+
+current_dir = Path(__file__).parent.absolute().resolve()
+save_dir = current_dir / "result"
+save_dir.mkdir(exist_ok=True, parents=True)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+device = torch.device(device)
+
+
+@pytest.mark.parametrize("sd_device", ["cuda", "mps"])
+@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
+@pytest.mark.parametrize("sampler", [SDSampler.uni_pc])
+@pytest.mark.parametrize("cpu_textencoder", [True])
+@pytest.mark.parametrize("disable_nsfw", [True])
+@pytest.mark.parametrize("sd_controlnet_method", SD_CONTROLNET_CHOICES)
+def test_runway_sd_1_5(
+    sd_device, strategy, sampler, cpu_textencoder, disable_nsfw, sd_controlnet_method
+):
+    if sd_device == "cuda" and not torch.cuda.is_available():
+        return
+    if device == "mps" and not torch.backends.mps.is_available():
+        return
+
+    sd_steps = 1 if sd_device == "cpu" else 30
+    model = ModelManager(
+        name="sd1.5",
+        sd_controlnet=True,
+        device=torch.device(sd_device),
+        hf_access_token="",
+        sd_run_local=False,
+        disable_nsfw=disable_nsfw,
+        sd_cpu_textencoder=cpu_textencoder,
+        sd_controlnet_method=sd_controlnet_method,
+    )
+
+    controlnet_conditioning_scale = {
+        "control_v11p_sd15_canny": 0.4,
+        "control_v11p_sd15_openpose": 0.4,
+        "control_v11p_sd15_inpaint": 1.0,
+        "control_v11f1p_sd15_depth": 1.0,
+    }[sd_controlnet_method]
+
+    cfg = get_config(
+        strategy,
+        prompt="a fox sitting on a bench",
+        sd_steps=sd_steps,
+        controlnet_conditioning_scale=controlnet_conditioning_scale,
+        controlnet_method=sd_controlnet_method,
+    )
+    cfg.sd_sampler = sampler
+
+    name = f"device_{sd_device}_{sampler}_cpu_textencoder_disable_nsfw"
+
+    assert_equal(
+        model,
+        cfg,
+        f"sd_controlnet_{sd_controlnet_method}_{name}.png",
+        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
+        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
+        fx=1.2,
+        fy=1.2,
+    )
+
+
+@pytest.mark.parametrize("sd_device", ["cuda", "mps"])
+@pytest.mark.parametrize("sampler", [SDSampler.uni_pc])
+def test_local_file_path(sd_device, sampler):
+    if sd_device == "cuda" and not torch.cuda.is_available():
+        return
+    if device == "mps" and not torch.backends.mps.is_available():
+        return
+
+    sd_steps = 1 if sd_device == "cpu" else 30
+    model = ModelManager(
+        name="sd1.5",
+        sd_controlnet=True,
+        device=torch.device(sd_device),
+        hf_access_token="",
+        sd_run_local=False,
+        disable_nsfw=True,
+        sd_cpu_textencoder=False,
+        cpu_offload=True,
+        sd_local_model_path="/Users/cwq/data/models/sd-v1-5-inpainting.ckpt",
+        sd_controlnet_method="control_v11p_sd15_canny",
+    )
+    cfg = get_config(
+        HDStrategy.ORIGINAL,
+        prompt="a fox sitting on a bench",
+        sd_steps=sd_steps,
+        controlnet_method="control_v11p_sd15_canny",
+    )
+    cfg.sd_sampler = sampler
+
+    name = f"device_{sd_device}_{sampler}"
+
+    assert_equal(
+        model,
+        cfg,
+        f"sd_controlnet_canny_local_model_{name}.png",
+        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
+        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
+    )
+
+
+@pytest.mark.parametrize("sd_device", ["cuda", "mps"])
+@pytest.mark.parametrize("sampler", [SDSampler.uni_pc])
+def test_local_file_path_controlnet_native_inpainting(sd_device, sampler):
+    if sd_device == "cuda" and not torch.cuda.is_available():
+        return
+    if device == "mps" and not torch.backends.mps.is_available():
+        return
+
+    sd_steps = 1 if sd_device == "cpu" else 30
+    model = ModelManager(
+        name="sd1.5",
+        sd_controlnet=True,
+        device=torch.device(sd_device),
+        hf_access_token="",
+        sd_run_local=False,
+        disable_nsfw=True,
+        sd_cpu_textencoder=False,
+        cpu_offload=True,
+        sd_local_model_path="/Users/cwq/data/models/v1-5-pruned-emaonly.safetensors",
+        sd_controlnet_method="control_v11p_sd15_inpaint",
+    )
+    cfg = get_config(
+        HDStrategy.ORIGINAL,
+        prompt="a fox sitting on a bench",
+        sd_steps=sd_steps,
+        controlnet_conditioning_scale=1.0,
+        sd_strength=1.0,
+        controlnet_method="control_v11p_sd15_inpaint",
+    )
+    cfg.sd_sampler = sampler
+
+    name = f"device_{sd_device}_{sampler}"
+
+    assert_equal(
+        model,
+        cfg,
+        f"sd_controlnet_local_native_{name}.png",
+        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
+        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
+    )
+
+
+@pytest.mark.parametrize("sd_device", ["cuda", "mps"])
+@pytest.mark.parametrize("sampler", [SDSampler.uni_pc])
+def test_controlnet_switch(sd_device, sampler):
+    if sd_device == "cuda" and not torch.cuda.is_available():
+        return
+    if device == "mps" and not torch.backends.mps.is_available():
+        return
+
+    sd_steps = 1 if sd_device == "cpu" else 30
+    model = ModelManager(
+        name="sd1.5",
+        sd_controlnet=True,
+        device=torch.device(sd_device),
+        hf_access_token="",
+        sd_run_local=False,
+        disable_nsfw=True,
+        sd_cpu_textencoder=False,
+        cpu_offload=True,
+        sd_controlnet_method="control_v11p_sd15_canny",
+    )
+    cfg = get_config(
+        HDStrategy.ORIGINAL,
+        prompt="a fox sitting on a bench",
+        sd_steps=sd_steps,
+        controlnet_method="control_v11p_sd15_inpaint",
+    )
+    cfg.sd_sampler = sampler
+
+    name = f"device_{sd_device}_{sampler}"
+
+    assert_equal(
+        model,
+        cfg,
+        f"sd_controlnet_switch_to_inpaint_local_model_{name}.png",
+        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
+        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
+    )
diff --git a/lama_cleaner/tests/test_instruct_pix2pix.py b/lama_cleaner/tests/test_instruct_pix2pix.py
new file mode 100644
index 0000000000000000000000000000000000000000..9778160fff3dbf6e647b9159c100af2f6ad1cc2d
--- /dev/null
+++ b/lama_cleaner/tests/test_instruct_pix2pix.py
@@ -0,0 +1,62 @@
+from pathlib import Path
+
+import pytest
+import torch
+
+from lama_cleaner.model_manager import ModelManager
+from lama_cleaner.tests.test_model import get_config, assert_equal
+from lama_cleaner.schema import HDStrategy
+
+current_dir = Path(__file__).parent.absolute().resolve()
+save_dir = current_dir / 'result'
+save_dir.mkdir(exist_ok=True, parents=True)
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+
+@pytest.mark.parametrize("disable_nsfw", [True, False])
+@pytest.mark.parametrize("cpu_offload", [False, True])
+def test_instruct_pix2pix(disable_nsfw, cpu_offload):
+    sd_steps = 50 if device == 'cuda' else 1
+    model = ModelManager(name="instruct_pix2pix",
+                         device=torch.device(device),
+                         hf_access_token="",
+                         sd_run_local=False,
+                         disable_nsfw=disable_nsfw,
+                         sd_cpu_textencoder=False,
+                         cpu_offload=cpu_offload)
+    cfg = get_config(strategy=HDStrategy.ORIGINAL, prompt='What if it were snowing?', p2p_steps=sd_steps, sd_scale=1.1)
+
+    name = f"device_{device}_disnsfw_{disable_nsfw}_cpu_offload_{cpu_offload}"
+
+    assert_equal(
+        model,
+        cfg,
+        f"instruct_pix2pix_{name}.png",
+        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
+        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
+        fx=1.3
+    )
+
+
+@pytest.mark.parametrize("disable_nsfw", [False])
+@pytest.mark.parametrize("cpu_offload", [False])
+def test_instruct_pix2pix_snow(disable_nsfw, cpu_offload):
+    sd_steps = 50 if device == 'cuda' else 1
+    model = ModelManager(name="instruct_pix2pix",
+                         device=torch.device(device),
+                         hf_access_token="",
+                         sd_run_local=False,
+                         disable_nsfw=disable_nsfw,
+                         sd_cpu_textencoder=False,
+                         cpu_offload=cpu_offload)
+    cfg = get_config(strategy=HDStrategy.ORIGINAL, prompt='What if it were snowing?', p2p_steps=sd_steps)
+
+    name = f"snow"
+
+    assert_equal(
+        model,
+        cfg,
+        f"instruct_pix2pix_{name}.png",
+        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
+        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
+    )
diff --git a/lama_cleaner/tests/test_interactive_seg.py b/lama_cleaner/tests/test_interactive_seg.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8829d1c33eb09cfd782a4819286aeac1eba73ce
--- /dev/null
+++ b/lama_cleaner/tests/test_interactive_seg.py
@@ -0,0 +1,43 @@
+from pathlib import Path
+
+import cv2
+import numpy as np
+
+from lama_cleaner.plugins import InteractiveSeg, Click
+
+current_dir = Path(__file__).parent.absolute().resolve()
+save_dir = current_dir / "result"
+save_dir.mkdir(exist_ok=True, parents=True)
+img_p = current_dir / "overture-creations-5sI6fQgYIuo.png"
+
+
+def test_interactive_seg():
+    interactive_seg_model = InteractiveSeg()
+    img = cv2.imread(str(img_p))
+    pred = interactive_seg_model.forward(
+        img, clicks=[Click(coords=(256, 256), indx=0, is_positive=True)]
+    )
+    cv2.imwrite(str(save_dir / "test_interactive_seg.png"), pred)
+
+
+def test_interactive_seg_with_negative_click():
+    interactive_seg_model = InteractiveSeg()
+    img = cv2.imread(str(img_p))
+    pred = interactive_seg_model.forward(
+        img,
+        clicks=[
+            Click(coords=(256, 256), indx=0, is_positive=True),
+            Click(coords=(384, 256), indx=1, is_positive=False),
+        ],
+    )
+    cv2.imwrite(str(save_dir / "test_interactive_seg_negative.png"), pred)
+
+
+def test_interactive_seg_with_prev_mask():
+    interactive_seg_model = InteractiveSeg()
+    img = cv2.imread(str(img_p))
+    mask = np.zeros_like(img)[:, :, 0]
+    pred = interactive_seg_model.forward(
+        img, clicks=[Click(coords=(256, 256), indx=0, is_positive=True)], prev_mask=mask
+    )
+    cv2.imwrite(str(save_dir / "test_interactive_seg_with_mask.png"), pred)
diff --git a/lama_cleaner/tests/test_load_img.py b/lama_cleaner/tests/test_load_img.py
new file mode 100644
index 0000000000000000000000000000000000000000..6028a60d8f3cb8c27959c68041b621d7819c4635
--- /dev/null
+++ b/lama_cleaner/tests/test_load_img.py
@@ -0,0 +1,21 @@
+from pathlib import Path
+
+from lama_cleaner.helper import load_img
+
+current_dir = Path(__file__).parent.absolute().resolve()
+png_img_p = current_dir / "image.png"
+jpg_img_p = current_dir / "bunny.jpeg"
+
+
+def test_load_png_image():
+    with open(png_img_p, "rb") as f:
+        np_img, alpha_channel = load_img(f.read())
+    assert np_img.shape == (256, 256, 3)
+    assert alpha_channel.shape == (256, 256)
+
+
+def test_load_jpg_image():
+    with open(jpg_img_p, "rb") as f:
+        np_img, alpha_channel = load_img(f.read())
+    assert np_img.shape == (394, 448, 3)
+    assert alpha_channel is None
diff --git a/lama_cleaner/tests/test_model.py b/lama_cleaner/tests/test_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..be253b596966410d7a74b4fcbfd84eda47a60951
--- /dev/null
+++ b/lama_cleaner/tests/test_model.py
@@ -0,0 +1,194 @@
+from pathlib import Path
+
+import cv2
+import pytest
+import torch
+
+from lama_cleaner.model_manager import ModelManager
+from lama_cleaner.schema import Config, HDStrategy, LDMSampler, SDSampler
+
+current_dir = Path(__file__).parent.absolute().resolve()
+save_dir = current_dir / "result"
+save_dir.mkdir(exist_ok=True, parents=True)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+device = torch.device(device)
+
+
+def get_data(
+    fx: float = 1,
+    fy: float = 1.0,
+    img_p=current_dir / "image.png",
+    mask_p=current_dir / "mask.png",
+):
+    img = cv2.imread(str(img_p))
+    img = cv2.cvtColor(img, cv2.COLOR_BGRA2RGB)
+    mask = cv2.imread(str(mask_p), cv2.IMREAD_GRAYSCALE)
+    img = cv2.resize(img, None, fx=fx, fy=fy, interpolation=cv2.INTER_AREA)
+    mask = cv2.resize(mask, None, fx=fx, fy=fy, interpolation=cv2.INTER_NEAREST)
+    return img, mask
+
+
+def get_config(strategy, **kwargs):
+    data = dict(
+        ldm_steps=1,
+        ldm_sampler=LDMSampler.plms,
+        hd_strategy=strategy,
+        hd_strategy_crop_margin=32,
+        hd_strategy_crop_trigger_size=200,
+        hd_strategy_resize_limit=200,
+    )
+    data.update(**kwargs)
+    return Config(**data)
+
+
+def assert_equal(
+    model,
+    config,
+    gt_name,
+    fx: float = 1,
+    fy: float = 1,
+    img_p=current_dir / "image.png",
+    mask_p=current_dir / "mask.png",
+):
+    img, mask = get_data(fx=fx, fy=fy, img_p=img_p, mask_p=mask_p)
+    print(f"Input image shape: {img.shape}")
+    res = model(img, mask, config)
+    cv2.imwrite(
+        str(save_dir / gt_name),
+        res,
+        [int(cv2.IMWRITE_JPEG_QUALITY), 100, int(cv2.IMWRITE_PNG_COMPRESSION), 0],
+    )
+
+    """
+    Note that JPEG is lossy compression, so even if it is the highest quality 100, 
+    when the saved images is reloaded, a difference occurs with the original pixel value. 
+    If you want to save the original images as it is, save it as PNG or BMP.
+    """
+    # gt = cv2.imread(str(current_dir / gt_name), cv2.IMREAD_UNCHANGED)
+    # assert np.array_equal(res, gt)
+
+
+@pytest.mark.parametrize(
+    "strategy", [HDStrategy.ORIGINAL, HDStrategy.RESIZE, HDStrategy.CROP]
+)
+def test_lama(strategy):
+    model = ModelManager(name="lama", device=device)
+    assert_equal(
+        model,
+        get_config(strategy),
+        f"lama_{strategy[0].upper() + strategy[1:]}_result.png",
+    )
+
+    fx = 1.3
+    assert_equal(
+        model,
+        get_config(strategy),
+        f"lama_{strategy[0].upper() + strategy[1:]}_fx_{fx}_result.png",
+        fx=1.3,
+    )
+
+
+@pytest.mark.parametrize(
+    "strategy", [HDStrategy.ORIGINAL, HDStrategy.RESIZE, HDStrategy.CROP]
+)
+@pytest.mark.parametrize("ldm_sampler", [LDMSampler.ddim, LDMSampler.plms])
+def test_ldm(strategy, ldm_sampler):
+    model = ModelManager(name="ldm", device=device)
+    cfg = get_config(strategy, ldm_sampler=ldm_sampler)
+    assert_equal(
+        model, cfg, f"ldm_{strategy[0].upper() + strategy[1:]}_{ldm_sampler}_result.png"
+    )
+
+    fx = 1.3
+    assert_equal(
+        model,
+        cfg,
+        f"ldm_{strategy[0].upper() + strategy[1:]}_{ldm_sampler}_fx_{fx}_result.png",
+        fx=fx,
+    )
+
+
+@pytest.mark.parametrize(
+    "strategy", [HDStrategy.ORIGINAL, HDStrategy.RESIZE, HDStrategy.CROP]
+)
+@pytest.mark.parametrize("zits_wireframe", [False, True])
+def test_zits(strategy, zits_wireframe):
+    model = ModelManager(name="zits", device=device)
+    cfg = get_config(strategy, zits_wireframe=zits_wireframe)
+    # os.environ['ZITS_DEBUG_LINE_PATH'] = str(current_dir / 'zits_debug_line.jpg')
+    # os.environ['ZITS_DEBUG_EDGE_PATH'] = str(current_dir / 'zits_debug_edge.jpg')
+    assert_equal(
+        model,
+        cfg,
+        f"zits_{strategy[0].upper() + strategy[1:]}_wireframe_{zits_wireframe}_result.png",
+    )
+
+    fx = 1.3
+    assert_equal(
+        model,
+        cfg,
+        f"zits_{strategy.capitalize()}_wireframe_{zits_wireframe}_fx_{fx}_result.png",
+        fx=fx,
+    )
+
+
+@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
+@pytest.mark.parametrize("no_half", [True, False])
+def test_mat(strategy, no_half):
+    model = ModelManager(name="mat", device=device, no_half=no_half)
+    cfg = get_config(strategy)
+
+    for _ in range(10):
+        assert_equal(
+            model,
+            cfg,
+            f"mat_{strategy.capitalize()}_result.png",
+        )
+
+
+@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
+def test_fcf(strategy):
+    model = ModelManager(name="fcf", device=device)
+    cfg = get_config(strategy)
+
+    assert_equal(model, cfg, f"fcf_{strategy.capitalize()}_result.png", fx=2, fy=2)
+
+    assert_equal(model, cfg, f"fcf_{strategy.capitalize()}_result.png", fx=3.8, fy=2)
+
+
+@pytest.mark.parametrize(
+    "strategy", [HDStrategy.ORIGINAL, HDStrategy.RESIZE, HDStrategy.CROP]
+)
+@pytest.mark.parametrize("cv2_flag", ["INPAINT_NS", "INPAINT_TELEA"])
+@pytest.mark.parametrize("cv2_radius", [3, 15])
+def test_cv2(strategy, cv2_flag, cv2_radius):
+    model = ModelManager(
+        name="cv2",
+        device=torch.device(device),
+    )
+    cfg = get_config(strategy, cv2_flag=cv2_flag, cv2_radius=cv2_radius)
+    assert_equal(
+        model,
+        cfg,
+        f"sd_{strategy.capitalize()}_{cv2_flag}_{cv2_radius}.png",
+        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
+        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
+    )
+
+
+@pytest.mark.parametrize(
+    "strategy", [HDStrategy.ORIGINAL, HDStrategy.RESIZE, HDStrategy.CROP]
+)
+def test_manga(strategy):
+    model = ModelManager(
+        name="manga",
+        device=torch.device(device),
+    )
+    cfg = get_config(strategy)
+    assert_equal(
+        model,
+        cfg,
+        f"sd_{strategy.capitalize()}.png",
+        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
+        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
+    )
diff --git a/lama_cleaner/tests/test_model_md5.py b/lama_cleaner/tests/test_model_md5.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e50e46f5c61fbccfc2ce034d31f079e3ec34764
--- /dev/null
+++ b/lama_cleaner/tests/test_model_md5.py
@@ -0,0 +1,49 @@
+def test_load_model():
+    from lama_cleaner.plugins import InteractiveSeg
+    from lama_cleaner.model_manager import ModelManager
+
+    interactive_seg_model = InteractiveSeg('vit_l', 'cpu')
+
+    models = [
+        "lama",
+        "ldm",
+        "zits",
+        "mat",
+        "fcf",
+        "manga",
+    ]
+    for m in models:
+        ModelManager(
+            name=m,
+            device="cpu",
+            no_half=False,
+            hf_access_token="",
+            disable_nsfw=False,
+            sd_cpu_textencoder=True,
+            sd_run_local=True,
+            local_files_only=True,
+            cpu_offload=True,
+            enable_xformers=False,
+        )
+
+
+# def create_empty_file(tmp_dir, name):
+#     tmp_model_dir = os.path.join(tmp_dir, "torch", "hub", "checkpoints")
+#     Path(tmp_model_dir).mkdir(exist_ok=True, parents=True)
+#     path = os.path.join(tmp_model_dir, name)
+#     with open(path, "w") as f:
+#         f.write("1")
+#
+#
+# def test_load_model_error():
+#     MODELS = [
+#         ("big-lama.pt", "e3aa4aaa15225a33ec84f9f4bc47e500"),
+#         ("cond_stage_model_encode.pt", "23239fc9081956a3e70de56472b3f296"),
+#         ("cond_stage_model_decode.pt", "fe419cd15a750d37a4733589d0d3585c"),
+#         ("diffusion.pt", "b0afda12bf790c03aba2a7431f11d22d"),
+#     ]
+#     with tempfile.TemporaryDirectory() as tmp_dir:
+#         os.environ["XDG_CACHE_HOME"] = tmp_dir
+#         for name, md5 in MODELS:
+#             create_empty_file(tmp_dir, name)
+#             test_load_model()
diff --git a/lama_cleaner/tests/test_paint_by_example.py b/lama_cleaner/tests/test_paint_by_example.py
new file mode 100644
index 0000000000000000000000000000000000000000..c495690c9f3a22ac79b97177e6be26a2c68114a1
--- /dev/null
+++ b/lama_cleaner/tests/test_paint_by_example.py
@@ -0,0 +1,106 @@
+from pathlib import Path
+
+import cv2
+import pytest
+import torch
+from PIL import Image
+
+from lama_cleaner.model_manager import ModelManager
+from lama_cleaner.schema import HDStrategy
+from lama_cleaner.tests.test_model import get_config, get_data
+
+current_dir = Path(__file__).parent.absolute().resolve()
+save_dir = current_dir / 'result'
+save_dir.mkdir(exist_ok=True, parents=True)
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+device = torch.device(device)
+
+
+def assert_equal(
+    model, config, gt_name,
+    fx: float = 1, fy: float = 1,
+    img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
+    mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
+    example_p=current_dir / "bunny.jpeg",
+):
+    img, mask = get_data(fx=fx, fy=fy, img_p=img_p, mask_p=mask_p)
+
+    example_image = cv2.imread(str(example_p))
+    example_image = cv2.cvtColor(example_image, cv2.COLOR_BGRA2RGB)
+    example_image = cv2.resize(example_image, None, fx=fx, fy=fy, interpolation=cv2.INTER_AREA)
+
+    print(f"Input image shape: {img.shape}, example_image: {example_image.shape}")
+    config.paint_by_example_example_image = Image.fromarray(example_image)
+    res = model(img, mask, config)
+    cv2.imwrite(str(save_dir / gt_name), res)
+
+
+@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
+def test_paint_by_example(strategy):
+    model = ModelManager(name="paint_by_example", device=device, disable_nsfw=True)
+    cfg = get_config(strategy, paint_by_example_steps=30)
+    assert_equal(
+        model,
+        cfg,
+        f"paint_by_example_{strategy.capitalize()}.png",
+        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
+        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
+        fy=0.9,
+        fx=1.3,
+    )
+
+
+@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
+def test_paint_by_example_disable_nsfw(strategy):
+    model = ModelManager(name="paint_by_example", device=device, disable_nsfw=False)
+    cfg = get_config(strategy, paint_by_example_steps=30)
+    assert_equal(
+        model,
+        cfg,
+        f"paint_by_example_{strategy.capitalize()}_disable_nsfw.png",
+        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
+        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
+    )
+
+
+@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
+def test_paint_by_example_sd_scale(strategy):
+    model = ModelManager(name="paint_by_example", device=device, disable_nsfw=True)
+    cfg = get_config(strategy, paint_by_example_steps=30, sd_scale=0.85)
+    assert_equal(
+        model,
+        cfg,
+        f"paint_by_example_{strategy.capitalize()}_sdscale.png",
+        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
+        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
+        fy=0.9,
+        fx=1.3
+    )
+
+
+@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
+def test_paint_by_example_cpu_offload(strategy):
+    model = ModelManager(name="paint_by_example", device=device, cpu_offload=True, disable_nsfw=False)
+    cfg = get_config(strategy, paint_by_example_steps=30, sd_scale=0.85)
+    assert_equal(
+        model,
+        cfg,
+        f"paint_by_example_{strategy.capitalize()}_cpu_offload.png",
+        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
+        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
+    )
+
+
+@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
+def test_paint_by_example_cpu_offload_cpu_device(strategy):
+    model = ModelManager(name="paint_by_example", device=torch.device('cpu'), cpu_offload=True, disable_nsfw=True)
+    cfg = get_config(strategy, paint_by_example_steps=1, sd_scale=0.85)
+    assert_equal(
+        model,
+        cfg,
+        f"paint_by_example_{strategy.capitalize()}_cpu_offload_cpu_device.png",
+        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
+        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
+        fy=0.9,
+        fx=1.3
+    )
diff --git a/lama_cleaner/tests/test_plugins.py b/lama_cleaner/tests/test_plugins.py
new file mode 100644
index 0000000000000000000000000000000000000000..834c71955f61f23154a9ef49347a6b04eee09281
--- /dev/null
+++ b/lama_cleaner/tests/test_plugins.py
@@ -0,0 +1,103 @@
+import hashlib
+import os
+import time
+
+from lama_cleaner.plugins.anime_seg import AnimeSeg
+
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
+from pathlib import Path
+
+import cv2
+import pytest
+import torch.cuda
+
+from lama_cleaner.plugins import (
+    RemoveBG,
+    RealESRGANUpscaler,
+    GFPGANPlugin,
+    RestoreFormerPlugin,
+    InteractiveSeg,
+)
+
+current_dir = Path(__file__).parent.absolute().resolve()
+save_dir = current_dir / "result"
+save_dir.mkdir(exist_ok=True, parents=True)
+img_p = current_dir / "bunny.jpeg"
+img_bytes = open(img_p, "rb").read()
+bgr_img = cv2.imread(str(img_p))
+rgb_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB)
+
+
+def _save(img, name):
+    cv2.imwrite(str(save_dir / name), img)
+
+
+def test_remove_bg():
+    model = RemoveBG()
+    res = model.forward(bgr_img)
+    res = cv2.cvtColor(res, cv2.COLOR_RGBA2BGRA)
+    _save(res, "test_remove_bg.png")
+
+
+def test_anime_seg():
+    model = AnimeSeg()
+    img = cv2.imread(str(current_dir / "anime_test.png"))
+    res = model.forward(img)
+    assert len(res.shape) == 3
+    assert res.shape[-1] == 4
+    _save(res, "test_anime_seg.png")
+
+
+@pytest.mark.parametrize("device", ["cuda", "cpu", "mps"])
+def test_upscale(device):
+    if device == "cuda" and not torch.cuda.is_available():
+        return
+    if device == "mps" and not torch.backends.mps.is_available():
+        return
+
+    model = RealESRGANUpscaler("realesr-general-x4v3", device)
+    res = model.forward(bgr_img, 2)
+    _save(res, f"test_upscale_x2_{device}.png")
+
+    res = model.forward(bgr_img, 4)
+    _save(res, f"test_upscale_x4_{device}.png")
+
+
+@pytest.mark.parametrize("device", ["cuda", "cpu", "mps"])
+def test_gfpgan(device):
+    if device == "cuda" and not torch.cuda.is_available():
+        return
+    if device == "mps" and not torch.backends.mps.is_available():
+        return
+    model = GFPGANPlugin(device)
+    res = model(rgb_img, None, None)
+    _save(res, f"test_gfpgan_{device}.png")
+
+
+@pytest.mark.parametrize("device", ["cuda", "cpu", "mps"])
+def test_restoreformer(device):
+    if device == "cuda" and not torch.cuda.is_available():
+        return
+    if device == "mps" and not torch.backends.mps.is_available():
+        return
+    model = RestoreFormerPlugin(device)
+    res = model(rgb_img, None, None)
+    _save(res, f"test_restoreformer_{device}.png")
+
+
+@pytest.mark.parametrize("device", ["cuda", "cpu", "mps"])
+def test_segment_anything(device):
+    if device == "cuda" and not torch.cuda.is_available():
+        return
+    if device == "mps" and not torch.backends.mps.is_available():
+        return
+    img_md5 = hashlib.md5(img_bytes).hexdigest()
+    model = InteractiveSeg("vit_l", device)
+    new_mask = model.forward(rgb_img, [[448 // 2, 394 // 2, 1]], img_md5)
+
+    save_name = f"test_segment_anything_{device}.png"
+    _save(new_mask, save_name)
+
+    start = time.time()
+    model.forward(rgb_img, [[448 // 2, 394 // 2, 1]], img_md5)
+    print(f"Time for {save_name}: {time.time() - start:.2f}s")
diff --git a/lama_cleaner/tests/test_save_exif.py b/lama_cleaner/tests/test_save_exif.py
new file mode 100644
index 0000000000000000000000000000000000000000..592125698e967ecadfd30c504d5bf6b863d9db76
--- /dev/null
+++ b/lama_cleaner/tests/test_save_exif.py
@@ -0,0 +1,43 @@
+import io
+from pathlib import Path
+
+from PIL import Image
+
+from lama_cleaner.helper import pil_to_bytes, load_img
+
+current_dir = Path(__file__).parent.absolute().resolve()
+
+
+def print_exif(exif):
+    for k, v in exif.items():
+        print(f"{k}: {v}")
+
+
+def run_test(img_p: Path):
+    print(img_p)
+    ext = img_p.suffix.strip(".")
+    img_bytes = img_p.read_bytes()
+    np_img, _, exif_infos = load_img(img_bytes, False, True)
+    print(exif_infos)
+    print("Original exif_infos")
+    print_exif(exif_infos["exif"])
+
+    pil_to_bytes(Image.fromarray(np_img), ext=ext, exif_infos={})
+
+    pil_bytes = pil_to_bytes(Image.fromarray(np_img), ext=ext, exif_infos=exif_infos)
+    res_img = Image.open(io.BytesIO(pil_bytes))
+    print(f"Result img info: {res_img.info}")
+    res_exif = res_img.getexif()
+    print_exif(res_exif)
+    assert res_exif == exif_infos["exif"]
+    assert exif_infos["parameters"] == res_img.info.get("parameters")
+
+
+def test_png():
+    run_test(current_dir / "image.png")
+    run_test(current_dir / "pnginfo_test.png")
+
+
+def test_jpeg():
+    jpg_img_p = current_dir / "bunny.jpeg"
+    run_test(jpg_img_p)
diff --git a/lama_cleaner/tests/test_sd_model.py b/lama_cleaner/tests/test_sd_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b80886b24c93547d0c6a9998168583e2f2aa25f
--- /dev/null
+++ b/lama_cleaner/tests/test_sd_model.py
@@ -0,0 +1,241 @@
+import os
+
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
+from pathlib import Path
+
+import pytest
+import torch
+
+from lama_cleaner.model_manager import ModelManager
+from lama_cleaner.schema import HDStrategy, SDSampler
+from lama_cleaner.tests.test_model import get_config, assert_equal
+
+current_dir = Path(__file__).parent.absolute().resolve()
+save_dir = current_dir / "result"
+save_dir.mkdir(exist_ok=True, parents=True)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+device = torch.device(device)
+
+
+@pytest.mark.parametrize("sd_device", ["cuda"])
+@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
+@pytest.mark.parametrize("sampler", [SDSampler.ddim])
+@pytest.mark.parametrize("cpu_textencoder", [True, False])
+@pytest.mark.parametrize("disable_nsfw", [True, False])
+def test_runway_sd_1_5_ddim(
+    sd_device, strategy, sampler, cpu_textencoder, disable_nsfw
+):
+    def callback(i, t, latents):
+        pass
+
+    if sd_device == "cuda" and not torch.cuda.is_available():
+        return
+
+    sd_steps = 50 if sd_device == "cuda" else 1
+    model = ModelManager(
+        name="sd1.5",
+        device=torch.device(sd_device),
+        hf_access_token="",
+        sd_run_local=True,
+        disable_nsfw=disable_nsfw,
+        sd_cpu_textencoder=cpu_textencoder,
+        callback=callback,
+    )
+    cfg = get_config(strategy, prompt="a fox sitting on a bench", sd_steps=sd_steps)
+    cfg.sd_sampler = sampler
+
+    name = f"device_{sd_device}_{sampler}_cpu_textencoder_{cpu_textencoder}_disnsfw_{disable_nsfw}"
+
+    assert_equal(
+        model,
+        cfg,
+        f"runway_sd_{strategy.capitalize()}_{name}.png",
+        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
+        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
+        fx=1.3,
+    )
+
+
+@pytest.mark.parametrize("sd_device", ["cuda"])
+@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
+@pytest.mark.parametrize(
+    "sampler", [SDSampler.pndm, SDSampler.k_lms, SDSampler.k_euler, SDSampler.k_euler_a]
+)
+@pytest.mark.parametrize("cpu_textencoder", [False])
+@pytest.mark.parametrize("disable_nsfw", [True])
+def test_runway_sd_1_5(sd_device, strategy, sampler, cpu_textencoder, disable_nsfw):
+    def callback(i, t, latents):
+        print(f"sd_step_{i}")
+
+    if sd_device == "cuda" and not torch.cuda.is_available():
+        return
+
+    sd_steps = 50 if sd_device == "cuda" else 1
+    model = ModelManager(
+        name="sd1.5",
+        device=torch.device(sd_device),
+        hf_access_token="",
+        sd_run_local=True,
+        disable_nsfw=disable_nsfw,
+        sd_cpu_textencoder=cpu_textencoder,
+        callback=callback,
+    )
+    cfg = get_config(strategy, prompt="a fox sitting on a bench", sd_steps=sd_steps)
+    cfg.sd_sampler = sampler
+
+    name = f"device_{sd_device}_{sampler}_cpu_textencoder_{cpu_textencoder}_disnsfw_{disable_nsfw}"
+
+    assert_equal(
+        model,
+        cfg,
+        f"runway_sd_{strategy.capitalize()}_{name}.png",
+        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
+        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
+        fx=1.3,
+    )
+
+
+@pytest.mark.parametrize("sd_device", ["cuda"])
+@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
+@pytest.mark.parametrize("sampler", [SDSampler.ddim])
+def test_runway_sd_1_5_negative_prompt(sd_device, strategy, sampler):
+    def callback(i, t, latents):
+        pass
+
+    if sd_device == "cuda" and not torch.cuda.is_available():
+        return
+
+    sd_steps = 50 if sd_device == "cuda" else 1
+    model = ModelManager(
+        name="sd1.5",
+        device=torch.device(sd_device),
+        hf_access_token="",
+        sd_run_local=True,
+        disable_nsfw=False,
+        sd_cpu_textencoder=False,
+        callback=callback,
+    )
+    cfg = get_config(
+        strategy,
+        sd_steps=sd_steps,
+        prompt="Face of a fox, high resolution, sitting on a park bench",
+        negative_prompt="orange, yellow, small",
+        sd_sampler=sampler,
+        sd_match_histograms=True,
+    )
+
+    name = f"{sampler}_negative_prompt"
+
+    assert_equal(
+        model,
+        cfg,
+        f"runway_sd_{strategy.capitalize()}_{name}.png",
+        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
+        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
+        fx=1,
+    )
+
+
+@pytest.mark.parametrize("sd_device", ["cuda"])
+@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
+@pytest.mark.parametrize("sampler", [SDSampler.k_euler_a])
+@pytest.mark.parametrize("cpu_textencoder", [False])
+@pytest.mark.parametrize("disable_nsfw", [False])
+def test_runway_sd_1_5_sd_scale(
+    sd_device, strategy, sampler, cpu_textencoder, disable_nsfw
+):
+    if sd_device == "cuda" and not torch.cuda.is_available():
+        return
+
+    sd_steps = 50 if sd_device == "cuda" else 1
+    model = ModelManager(
+        name="sd1.5",
+        device=torch.device(sd_device),
+        hf_access_token="",
+        sd_run_local=True,
+        disable_nsfw=disable_nsfw,
+        sd_cpu_textencoder=cpu_textencoder,
+    )
+    cfg = get_config(
+        strategy, prompt="a fox sitting on a bench", sd_steps=sd_steps, sd_scale=0.85
+    )
+    cfg.sd_sampler = sampler
+
+    name = f"device_{sd_device}_{sampler}_cpu_textencoder_{cpu_textencoder}_disnsfw_{disable_nsfw}"
+
+    assert_equal(
+        model,
+        cfg,
+        f"runway_sd_{strategy.capitalize()}_{name}_sdscale.png",
+        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
+        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
+        fx=1.3,
+    )
+
+
+@pytest.mark.parametrize("sd_device", ["cuda"])
+@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
+@pytest.mark.parametrize("sampler", [SDSampler.k_euler_a])
+def test_runway_sd_1_5_cpu_offload(sd_device, strategy, sampler):
+    if sd_device == "cuda" and not torch.cuda.is_available():
+        return
+
+    sd_steps = 50 if sd_device == "cuda" else 1
+    model = ModelManager(
+        name="sd1.5",
+        device=torch.device(sd_device),
+        hf_access_token="",
+        sd_run_local=True,
+        disable_nsfw=True,
+        sd_cpu_textencoder=False,
+        cpu_offload=True,
+    )
+    cfg = get_config(
+        strategy, prompt="a fox sitting on a bench", sd_steps=sd_steps, sd_scale=0.85
+    )
+    cfg.sd_sampler = sampler
+
+    name = f"device_{sd_device}_{sampler}"
+
+    assert_equal(
+        model,
+        cfg,
+        f"runway_sd_{strategy.capitalize()}_{name}_cpu_offload.png",
+        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
+        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
+    )
+
+
+@pytest.mark.parametrize("sd_device", ["cuda", "mps"])
+@pytest.mark.parametrize("sampler", [SDSampler.uni_pc])
+def test_local_file_path(sd_device, sampler):
+    if sd_device == "cuda" and not torch.cuda.is_available():
+        return
+
+    sd_steps = 1 if sd_device == "cpu" else 50
+    model = ModelManager(
+        name="sd1.5",
+        device=torch.device(sd_device),
+        hf_access_token="",
+        sd_run_local=True,
+        disable_nsfw=True,
+        sd_cpu_textencoder=False,
+        cpu_offload=True,
+        sd_local_model_path="/Users/cwq/data/models/sd-v1-5-inpainting.ckpt",
+    )
+    cfg = get_config(
+        HDStrategy.ORIGINAL,
+        prompt="a fox sitting on a bench",
+        sd_steps=sd_steps,
+    )
+    cfg.sd_sampler = sampler
+
+    name = f"device_{sd_device}_{sampler}"
+
+    assert_equal(
+        model,
+        cfg,
+        f"sd_local_model_{name}.png",
+        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
+        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
+    )
diff --git a/lama_cleaner/web_config.py b/lama_cleaner/web_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..61efdcca47fe757321ec7eaa3cf4ab39101bc470
--- /dev/null
+++ b/lama_cleaner/web_config.py
@@ -0,0 +1,246 @@
+import json
+import os
+from datetime import datetime
+
+import gradio as gr
+from loguru import logger
+
+from lama_cleaner.const import *
+
+_config_file = None
+
+
+def save_config(
+    host,
+    port,
+    model,
+    sd_local_model_path,
+    sd_controlnet,
+    sd_controlnet_method,
+    device,
+    gui,
+    no_gui_auto_close,
+    no_half,
+    cpu_offload,
+    disable_nsfw,
+    sd_cpu_textencoder,
+    enable_xformers,
+    local_files_only,
+    model_dir,
+    input,
+    output_dir,
+    quality,
+    enable_interactive_seg,
+    interactive_seg_model,
+    interactive_seg_device,
+    enable_remove_bg,
+    enable_anime_seg,
+    enable_realesrgan,
+    realesrgan_device,
+    realesrgan_model,
+    enable_gfpgan,
+    gfpgan_device,
+    enable_restoreformer,
+    restoreformer_device,
+    enable_gif,
+):
+    config = Config(**locals())
+    print(config)
+    if config.input and not os.path.exists(config.input):
+        return "[Error] Input file or directory does not exist"
+
+    current_time = datetime.now().strftime("%H:%M:%S")
+    msg = f"[{current_time}] Successful save config to: {os.path.abspath(_config_file)}"
+    logger.info(msg)
+    try:
+        with open(_config_file, "w", encoding="utf-8") as f:
+            json.dump(config.dict(), f, indent=4, ensure_ascii=False)
+    except Exception as e:
+        return f"Save failed: {str(e)}"
+    return msg
+
+
+def close_server(*args):
+    # TODO: make close both browser and server works
+    import os, signal
+
+    pid = os.getpid()
+    os.kill(pid, signal.SIGUSR1)
+
+
+def main(config_file: str):
+    global _config_file
+    _config_file = config_file
+
+    init_config = load_config(config_file)
+
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column(scale=1):
+                save_btn = gr.Button(value="Save configurations")
+                message = gr.HTML()
+
+        with gr.Tabs():
+            with gr.Tab("Common"):
+                with gr.Row():
+                    host = gr.Textbox(init_config.host, label="Host")
+                    port = gr.Number(init_config.port, label="Port", precision=0)
+
+                model = gr.Radio(
+                    AVAILABLE_MODELS, label="Model", value=init_config.model
+                )
+                device = gr.Radio(
+                    AVAILABLE_DEVICES, label="Device", value=init_config.device
+                )
+                quality = gr.Slider(
+                    value=95,
+                    label=f"Image Quality ({QUALITY_HELP})",
+                    minimum=75,
+                    maximum=100,
+                    step=1,
+                )
+
+                with gr.Column():
+                    gui = gr.Checkbox(init_config.gui, label=f"{GUI_HELP}")
+                    no_gui_auto_close = gr.Checkbox(
+                        init_config.no_gui_auto_close, label=f"{NO_GUI_AUTO_CLOSE_HELP}"
+                    )
+
+                with gr.Column():
+                    model_dir = gr.Textbox(
+                        init_config.model_dir, label=f"{MODEL_DIR_HELP}"
+                    )
+                    input = gr.Textbox(
+                        init_config.input,
+                        label=f"Input file or directory. {INPUT_HELP}",
+                    )
+                    output_dir = gr.Textbox(
+                        init_config.output_dir,
+                        label=f"Output directory. {OUTPUT_DIR_HELP}",
+                    )
+
+            with gr.Tab("Plugins"):
+                enable_interactive_seg = gr.Checkbox(
+                    init_config.enable_interactive_seg, label=INTERACTIVE_SEG_HELP
+                )
+                interactive_seg_model = gr.Radio(
+                    AVAILABLE_INTERACTIVE_SEG_MODELS,
+                    label=f"Segment Anything models. {INTERACTIVE_SEG_MODEL_HELP}",
+                    value=init_config.interactive_seg_model,
+                )
+                interactive_seg_device = gr.Radio(
+                    AVAILABLE_INTERACTIVE_SEG_DEVICES,
+                    label="Segment Anything Device",
+                    value=init_config.interactive_seg_device,
+                )
+                with gr.Row():
+                    enable_remove_bg = gr.Checkbox(
+                        init_config.enable_remove_bg, label=REMOVE_BG_HELP
+                    )
+                with gr.Row():
+                    enable_anime_seg = gr.Checkbox(
+                        init_config.enable_anime_seg, label=ANIMESEG_HELP
+                    )
+
+                with gr.Row():
+                    enable_realesrgan = gr.Checkbox(
+                        init_config.enable_realesrgan, label=REALESRGAN_HELP
+                    )
+                    realesrgan_device = gr.Radio(
+                        REALESRGAN_AVAILABLE_DEVICES,
+                        label="RealESRGAN Device",
+                        value=init_config.realesrgan_device,
+                    )
+                    realesrgan_model = gr.Radio(
+                        RealESRGANModelNameList,
+                        label="RealESRGAN model",
+                        value=init_config.realesrgan_model,
+                    )
+                with gr.Row():
+                    enable_gfpgan = gr.Checkbox(
+                        init_config.enable_gfpgan, label=GFPGAN_HELP
+                    )
+                    gfpgan_device = gr.Radio(
+                        GFPGAN_AVAILABLE_DEVICES,
+                        label="GFPGAN Device",
+                        value=init_config.gfpgan_device,
+                    )
+                with gr.Row():
+                    enable_restoreformer = gr.Checkbox(
+                        init_config.enable_restoreformer, label=RESTOREFORMER_HELP
+                    )
+                    restoreformer_device = gr.Radio(
+                        RESTOREFORMER_AVAILABLE_DEVICES,
+                        label="RestoreFormer Device",
+                        value=init_config.restoreformer_device,
+                    )
+                enable_gif = gr.Checkbox(init_config.enable_gif, label=GIF_HELP)
+
+            with gr.Tab("Diffusion Model"):
+                sd_local_model_path = gr.Textbox(
+                    init_config.sd_local_model_path, label=f"{SD_LOCAL_MODEL_HELP}"
+                )
+                sd_controlnet = gr.Checkbox(
+                    init_config.sd_controlnet, label=f"{SD_CONTROLNET_HELP}"
+                )
+                sd_controlnet_method = gr.Radio(
+                    SD_CONTROLNET_CHOICES,
+                    lable="ControlNet method",
+                    value=init_config.sd_controlnet_method,
+                )
+                no_half = gr.Checkbox(init_config.no_half, label=f"{NO_HALF_HELP}")
+                cpu_offload = gr.Checkbox(
+                    init_config.cpu_offload, label=f"{CPU_OFFLOAD_HELP}"
+                )
+                sd_cpu_textencoder = gr.Checkbox(
+                    init_config.sd_cpu_textencoder, label=f"{SD_CPU_TEXTENCODER_HELP}"
+                )
+                disable_nsfw = gr.Checkbox(
+                    init_config.disable_nsfw, label=f"{DISABLE_NSFW_HELP}"
+                )
+                enable_xformers = gr.Checkbox(
+                    init_config.enable_xformers, label=f"{ENABLE_XFORMERS_HELP}"
+                )
+                local_files_only = gr.Checkbox(
+                    init_config.local_files_only, label=f"{LOCAL_FILES_ONLY_HELP}"
+                )
+
+        save_btn.click(
+            save_config,
+            [
+                host,
+                port,
+                model,
+                sd_local_model_path,
+                sd_controlnet,
+                sd_controlnet_method,
+                device,
+                gui,
+                no_gui_auto_close,
+                no_half,
+                cpu_offload,
+                disable_nsfw,
+                sd_cpu_textencoder,
+                enable_xformers,
+                local_files_only,
+                model_dir,
+                input,
+                output_dir,
+                quality,
+                enable_interactive_seg,
+                interactive_seg_model,
+                interactive_seg_device,
+                enable_remove_bg,
+                enable_anime_seg,
+                enable_realesrgan,
+                realesrgan_device,
+                realesrgan_model,
+                enable_gfpgan,
+                gfpgan_device,
+                enable_restoreformer,
+                restoreformer_device,
+                enable_gif,
+            ],
+            message,
+        )
+    demo.launch(inbrowser=True, show_api=False)