setup diffusion playground

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,6 +1,6 @@
 ---
 title: Playground Diffusion
-emoji: 🏃
+emoji: 🎮
 colorFrom: gray
 colorTo: green
 sdk: gradio
@@ -10,4 +10,4 @@ pinned: false
 license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+This Space is based on [anzorq/finetuned_diffusion](https://huggingface.co/spaces/anzorq/finetuned_diffusion), go and support them and thank them for their open source work!

app.py

@@ -0,0 +1,246 @@
+from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
+import gradio as gr
+import torch
+from PIL import Image
+import utils
+
+is_colab = utils.is_google_colab()
+
+class Model:
+    def __init__(self, name, path, prefix):
+        self.name = name
+        self.path = path
+        self.prefix = prefix
+        self.pipe_t2i = None
+        self.pipe_i2i = None
+
+models = [
+     Model("Beeple", "riccardogiorato/beeple-diffusion", "beeple style "),
+]
+
+last_mode = "txt2img"
+current_model = models[1]
+current_model_path = current_model.path
+
+if is_colab:
+  pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16)
+
+else: # download all models
+  vae = AutoencoderKL.from_pretrained(current_model.path, subfolder="vae", torch_dtype=torch.float16)
+  for model in models[1:]:
+    try:
+        unet = UNet2DConditionModel.from_pretrained(model.path, subfolder="unet", torch_dtype=torch.float16)
+        model.pipe_t2i = StableDiffusionPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16)
+        model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16)
+    except:
+        models.remove(model)
+  pipe = models[1].pipe_t2i
+  
+if torch.cuda.is_available():
+  pipe = pipe.to("cuda")
+
+device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
+
+def custom_model_changed(path):
+  models[0].path = path
+  global current_model
+  current_model = models[0]
+
+def inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""):
+
+  global current_model
+  for model in models:
+    if model.name == model_name:
+      current_model = model
+      model_path = current_model.path
+
+  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
+
+  if img is not None:
+    return img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator)
+  else:
+    return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator)
+
+def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator=None):
+
+    global last_mode
+    global pipe
+    global current_model_path
+    if model_path != current_model_path or last_mode != "txt2img":
+        current_model_path = model_path
+
+        if is_colab or current_model == models[0]:
+          pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16)
+        else:
+          pipe.to("cpu")
+          pipe = current_model.pipe_t2i
+
+        if torch.cuda.is_available():
+          pipe = pipe.to("cuda")
+        last_mode = "txt2img"
+
+    prompt = current_model.prefix + prompt
+    result = pipe(
+      prompt,
+      negative_prompt = neg_prompt,
+      # num_images_per_prompt=n_images,
+      num_inference_steps = int(steps),
+      guidance_scale = guidance,
+      width = width,
+      height = height,
+      generator = generator)
+    
+    return replace_nsfw_images(result)
+
+def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator=None):
+
+    global last_mode
+    global pipe
+    global current_model_path
+    if model_path != current_model_path or last_mode != "img2img":
+        current_model_path = model_path
+
+        if is_colab or current_model == models[0]:
+          pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16)
+        else:
+          pipe.to("cpu")
+          pipe = current_model.pipe_i2i
+        
+        if torch.cuda.is_available():
+          pipe = pipe.to("cuda")
+        last_mode = "img2img"
+
+    prompt = current_model.prefix + prompt
+    ratio = min(height / img.height, width / img.width)
+    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
+    result = pipe(
+        prompt,
+        negative_prompt = neg_prompt,
+        # num_images_per_prompt=n_images,
+        init_image = img,
+        num_inference_steps = int(steps),
+        strength = strength,
+        guidance_scale = guidance,
+        width = width,
+        height = height,
+        generator = generator)
+        
+    return replace_nsfw_images(result)
+
+def replace_nsfw_images(results):
+    for i in range(len(results.images)):
+      if results.nsfw_content_detected[i]:
+        results.images[i] = Image.open("nsfw.png")
+    return results.images[0]
+
+css = """
+  <style>
+  .finetuned-diffusion-div {
+      text-align: center;
+      max-width: 700px;
+      margin: 0 auto;
+    }
+    .finetuned-diffusion-div div {
+      display: inline-flex;
+      align-items: center;
+      gap: 0.8rem;
+      font-size: 1.75rem;
+    }
+    .finetuned-diffusion-div div h1 {
+      font-weight: 900;
+      margin-bottom: 7px;
+    }
+    .finetuned-diffusion-div p {
+      margin-bottom: 10px;
+      font-size: 94%;
+    }
+    .finetuned-diffusion-div p a {
+      text-decoration: underline;
+    }
+    .tabs {
+      margin-top: 0px;
+      margin-bottom: 0px;
+    }
+    #gallery {
+      min-height: 20rem;
+    }
+  </style>
+"""
+with gr.Blocks(css=css) as demo:
+    gr.HTML(
+        f"""
+            <div class="finetuned-diffusion-div">
+              <div>
+                <h1>Finetuned Diffusion</h1>
+              </div>
+              <p>
+               Demo for multiple fine-tuned Stable Diffusion models, trained on different styles: <br>
+               <a href="https://huggingface.co/nitrosocke/Arcane-Diffusion">Arcane</a>, <a href="https://huggingface.co/nitrosocke/archer-diffusion">Archer</a>, <a href="https://huggingface.co/nitrosocke/elden-ring-diffusion">Elden Ring</a>, <a href="https://huggingface.co/nitrosocke/spider-verse-diffusion">Spider-Verse</a>, <a href="https://huggingface.co/nitrosocke/modern-disney-diffusion">Modern Disney</a>, <a href="https://huggingface.co/nitrosocke/classic-anim-diffusion">Classic Disney</a>, <a href="https://huggingface.co/hakurei/waifu-diffusion">Waifu</a>, <a href="https://huggingface.co/lambdalabs/sd-pokemon-diffusers">Pokémon</a>, <a href="https://huggingface.co/AstraliteHeart/pony-diffusion">Pony Diffusion</a>, <a href="https://huggingface.co/nousr/robo-diffusion">Robo Diffusion</a>, <a href="https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion">Cyberpunk Anime</a>, <a href="https://huggingface.co/dallinmackay/Tron-Legacy-diffusion">Tron Legacy</a> + any other custom Diffusers 🧨 SD model hosted on HuggingFace 🤗.
+              </p>
+              <p>Don't want to wait in queue? <a href="https://colab.research.google.com/gist/qunash/42112fb104509c24fd3aa6d1c11dd6e0/copy-of-fine-tuned-diffusion-gradio.ipynb"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667"></a></p>
+               Running on <b>{device}</b>{(" in a <b>Google Colab</b>." if is_colab else "")}
+              </p>
+            </div>
+        """
+    )
+    with gr.Row():
+        
+        with gr.Column(scale=55):
+          with gr.Group():
+              model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
+              with gr.Box(visible=False) as custom_model_group:
+                custom_model_path = gr.Textbox(label="Custom model path", placeholder="Path to model, e.g. nitrosocke/Arcane-Diffusion", interactive=True)
+                gr.HTML("<div><font size='2'>Custom models have to be downloaded first, so give it some time.</font></div>")
+              
+              with gr.Row():
+                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="Enter prompt. Style applied automatically").style(container=False)
+                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
+
+
+              image_out = gr.Image(height=512)
+              # gallery = gr.Gallery(
+              #     label="Generated images", show_label=False, elem_id="gallery"
+              # ).style(grid=[1], height="auto")
+
+        with gr.Column(scale=45):
+          with gr.Tab("Options"):
+            with gr.Group():
+              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
+
+              # n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=4, step=1)
+
+              with gr.Row():
+                guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
+                steps = gr.Slider(label="Steps", value=50, minimum=2, maximum=100, step=1)
+
+              with gr.Row():
+                width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
+                height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
+
+              seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
+
+          with gr.Tab("Image to image"):
+              with gr.Group():
+                image = gr.Image(label="Image", height=256, tool="editor", type="pil")
+                strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
+
+    model_name.change(lambda x: gr.update(visible = x == models[0].name), inputs=model_name, outputs=custom_model_group)
+    custom_model_path.change(custom_model_changed, inputs=custom_model_path, outputs=None)
+    # n_images.change(lambda n: gr.Gallery().style(grid=[2 if n > 1 else 1], height="auto"), inputs=n_images, outputs=gallery)
+
+    inputs = [model_name, prompt, guidance, steps, width, height, seed, image, strength, neg_prompt]
+    prompt.submit(inference, inputs=inputs, outputs=image_out)
+    generate.click(inference, inputs=inputs, outputs=image_out)
+
+    ex = gr.Examples([
+        [models[1].name, "Neon techno-magic robot with spear pierces an ancient beast, hyperrealism, no blur, 4k resolution, ultra detailed", 7.5, 50],
+    ], [model_name, prompt, guidance, steps, seed], image_out, inference, cache_examples=False)
+
+    gr.Markdown('''
+      Models by [@riccardogiorato](https://huggingface.co/riccardogiorato) <br>
+      Space by: [![Twitter Follow](https://img.shields.io/twitter/follow/riccardogiorato?style=social)](https://twitter.com/riccardogiorato)
+    ''')
+
+if not is_colab:
+  demo.queue(concurrency_count=1)
+demo.launch(debug=is_colab, share=is_colab)

requirements.txt

@@ -0,0 +1,6 @@
+--extra-index-url https://download.pytorch.org/whl/cu113
+torch
+diffusers
+transformers
+scipy
+ftfy

utils.py

@@ -0,0 +1,6 @@
+def is_google_colab():
+    try:
+        import google.colab
+        return True
+    except:
+        return False