Spaces:
Running
on
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Running
on
Zero
gokaygokay
commited on
Commit
•
ece05f2
1
Parent(s):
df8e598
pano
Browse files- app.py +146 -0
- i2p-mask.jpg +0 -0
- img2panoimg/__init__.py +3 -0
- img2panoimg/image_to_360panorama_image_pipeline.py +248 -0
- img2panoimg/pipeline_i2p.py +1740 -0
- img2panoimg/pipeline_sr.py +1202 -0
- requirements.txt +11 -0
- txt2panoimg/__init__.py +3 -0
- txt2panoimg/pipeline_base.py +849 -0
- txt2panoimg/pipeline_sr.py +1202 -0
- txt2panoimg/text_to_360panorama_image_pipeline.py +212 -0
app.py
ADDED
@@ -0,0 +1,146 @@
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import spaces
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import streamlit as st
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import torch
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from huggingface_hub import snapshot_download
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from txt2panoimg import Text2360PanoramaImagePipeline
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from img2panoimg import Image2360PanoramaImagePipeline
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from PIL import Image
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from streamlit_pannellum import streamlit_pannellum
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# Custom CSS to make the UI more attractive
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st.markdown("""
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<style>
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.stApp {
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max-width: 1200px;
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margin: 0 auto;
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}
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.main {
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background-color: #f0f2f6;
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}
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h1 {
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color: #1E3A8A;
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text-align: center;
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padding: 20px 0;
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font-size: 2.5rem;
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}
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.stTabs {
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background-color: white;
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padding: 20px;
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border-radius: 10px;
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box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
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}
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.stButton>button {
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background-color: #1E3A8A;
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color: white;
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font-weight: bold;
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}
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.viewer-column {
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background-color: white;
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padding: 20px;
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border-radius: 10px;
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box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
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}
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</style>
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""", unsafe_allow_html=True)
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# Download the model
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model_path = snapshot_download("archerfmy0831/sd-t2i-360panoimage")
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# Initialize pipelines
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txt2panoimg = Text2360PanoramaImagePipeline(model_path, torch_dtype=torch.float16)
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img2panoimg = Image2360PanoramaImagePipeline(model_path, torch_dtype=torch.float16)
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# Load the default mask image
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default_mask = Image.open("i2p-mask.jpg").convert("RGB")
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@spaces.GPU(duration=200)
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def text_to_pano(prompt, upscale):
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input_data = {'prompt': prompt, 'upscale': upscale}
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output = txt2panoimg(input_data)
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return output
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@spaces.GPU(duration=200)
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def image_to_pano(image, mask, prompt, upscale):
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image = image.resize((512, 512))
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if mask is None:
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mask = default_mask.resize((512, 512))
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else:
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mask = mask.resize((512, 512))
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input_data = {
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'prompt': prompt,
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'image': image,
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'mask': mask,
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'upscale': upscale
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}
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output = img2panoimg(input_data)
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return output
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st.title("360° Panorama Image Generation")
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tab1, tab2 = st.tabs(["Text to 360° Panorama", "Image to 360° Panorama"])
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# Function to display the panorama viewer
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def display_panorama(image):
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streamlit_pannellum(
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config={
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"default": {
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"firstScene": "generated",
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},
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"scenes": {
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"generated": {
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"title": "Generated Panorama",
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"type": "equirectangular",
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"panorama": image,
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"autoLoad": True,
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}
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}
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}
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)
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with tab1:
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col1, col2 = st.columns([1, 1])
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with col1:
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st.subheader("Input")
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t2p_input = st.text_area("Enter your prompt", height=100)
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t2p_upscale = st.checkbox("Upscale (requires >16GB GPU)")
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generate_button = st.button("Generate Panorama")
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with col2:
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st.subheader("Output")
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output_placeholder = st.empty()
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viewer_placeholder = st.empty()
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if generate_button:
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with st.spinner("Generating your 360° panorama..."):
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output = text_to_pano(t2p_input, t2p_upscale)
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output_placeholder.image(output, caption="Generated 360° Panorama", use_column_width=True)
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with viewer_placeholder.container():
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display_panorama(output)
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with tab2:
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col1, col2 = st.columns([1, 1])
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with col1:
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st.subheader("Input")
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i2p_image = st.file_uploader("Upload Input Image", type=["png", "jpg", "jpeg"])
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i2p_mask = st.file_uploader("Upload Mask Image (Optional)", type=["png", "jpg", "jpeg"])
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i2p_prompt = st.text_area("Enter your prompt", height=100)
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i2p_upscale = st.checkbox("Upscale (requires >16GB GPU)", key="i2p_upscale")
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generate_button = st.button("Generate Panorama", key="i2p_generate")
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with col2:
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st.subheader("Output")
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output_placeholder = st.empty()
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viewer_placeholder = st.empty()
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if generate_button and i2p_image is not None:
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with st.spinner("Generating your 360° panorama..."):
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image = Image.open(i2p_image)
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mask = Image.open(i2p_mask) if i2p_mask is not None else None
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output = image_to_pano(image, mask, i2p_prompt, i2p_upscale)
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output_placeholder.image(output, caption="Generated 360° Panorama", use_column_width=True)
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with viewer_placeholder.container():
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display_panorama(output)
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elif generate_button and i2p_image is None:
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st.error("Please upload an input image.")
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i2p-mask.jpg
ADDED
img2panoimg/__init__.py
ADDED
@@ -0,0 +1,3 @@
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from .pipeline_i2p import StableDiffusionImage2PanoPipeline
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from .pipeline_sr import StableDiffusionControlNetImg2ImgPanoPipeline
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from .image_to_360panorama_image_pipeline import Image2360PanoramaImagePipeline
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img2panoimg/image_to_360panorama_image_pipeline.py
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# Copyright © Alibaba, Inc. and its affiliates.
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import random
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from typing import Any, Dict
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4 |
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import numpy as np
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import torch
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from diffusers import (ControlNetModel, DiffusionPipeline,
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EulerAncestralDiscreteScheduler,
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UniPCMultistepScheduler)
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from PIL import Image
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from RealESRGAN import RealESRGAN
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from .pipeline_i2p import StableDiffusionImage2PanoPipeline
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from .pipeline_sr import StableDiffusionControlNetImg2ImgPanoPipeline
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import py360convert
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class LazyRealESRGAN:
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def __init__(self, device, scale):
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self.device = device
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self.scale = scale
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self.model = None
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self.model_path = None
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def load_model(self):
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if self.model is None:
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self.model = RealESRGAN(self.device, scale=self.scale)
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self.model.load_weights(self.model_path, download=False)
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def predict(self, img):
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self.load_model()
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return self.model.predict(img)
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class Image2360PanoramaImagePipeline(DiffusionPipeline):
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""" Stable Diffusion for 360 Panorama Image Generation Pipeline.
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Example:
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>>> import torch
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>>> from txt2panoimg import Text2360PanoramaImagePipeline
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>>> prompt = 'The mountains'
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>>> input = {'prompt': prompt, 'upscale': True}
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>>> model_id = 'models/'
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>>> txt2panoimg = Text2360PanoramaImagePipeline(model_id, torch_dtype=torch.float16)
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>>> output = txt2panoimg(input)
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>>> output.save('result.png')
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"""
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def __init__(self, model: str, device: str = 'cuda', **kwargs):
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"""
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Use `model` to create a stable diffusion pipeline for 360 panorama image generation.
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Args:
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model: model id on modelscope hub.
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device: str = 'cuda'
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"""
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super().__init__()
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu'
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) if device is None else device
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if device == 'gpu':
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device = torch.device('cuda')
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torch_dtype = kwargs.get('torch_dtype', torch.float16)
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enable_xformers_memory_efficient_attention = kwargs.get(
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'enable_xformers_memory_efficient_attention', True)
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model_id = model + '/sr-base/'
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# init i2p model
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controlnet = ControlNetModel.from_pretrained(model + '/sd-i2p', torch_dtype=torch.float16)
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self.pipe = StableDiffusionImage2PanoPipeline.from_pretrained(
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model_id, controlnet=controlnet, torch_dtype=torch_dtype).to(device)
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self.pipe.vae.enable_tiling()
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self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(
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self.pipe.scheduler.config)
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# remove following line if xformers is not installed
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try:
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if enable_xformers_memory_efficient_attention:
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self.pipe.enable_xformers_memory_efficient_attention()
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except Exception as e:
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print(e)
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self.pipe.enable_model_cpu_offload()
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+
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# init controlnet-sr model
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base_model_path = model + '/sr-base'
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controlnet_path = model + '/sr-control'
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controlnet = ControlNetModel.from_pretrained(
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controlnet_path, torch_dtype=torch_dtype)
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self.pipe_sr = StableDiffusionControlNetImg2ImgPanoPipeline.from_pretrained(
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base_model_path, controlnet=controlnet,
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torch_dtype=torch_dtype).to(device)
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self.pipe_sr.scheduler = UniPCMultistepScheduler.from_config(
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self.pipe.scheduler.config)
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self.pipe_sr.vae.enable_tiling()
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# remove following line if xformers is not installed
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try:
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if enable_xformers_memory_efficient_attention:
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96 |
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self.pipe_sr.enable_xformers_memory_efficient_attention()
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except Exception as e:
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print(e)
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self.pipe_sr.enable_model_cpu_offload()
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device = torch.device("cuda")
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101 |
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model_path = model + '/RealESRGAN_x2plus.pth'
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self.upsampler = LazyRealESRGAN(device=device, scale=2)
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103 |
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self.upsampler.model_path = model_path
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104 |
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105 |
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@staticmethod
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106 |
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def process_control_image(image, mask):
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107 |
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def to_tensor(img: Image, batch_size: int, width=1024, height=512):
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108 |
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img = img.resize((width, height), resample=Image.BICUBIC)
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109 |
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img = np.array(img).astype(np.float32) / 255.0
|
110 |
+
img = np.vstack([img[None].transpose(0, 3, 1, 2)] * batch_size)
|
111 |
+
img = torch.from_numpy(img)
|
112 |
+
return img
|
113 |
+
|
114 |
+
zeros = np.zeros_like(np.array(image))
|
115 |
+
dice_np = [np.array(image) if x == 0 else zeros for x in range(6)]
|
116 |
+
output_image = py360convert.c2e(dice_np, 512, 1024, cube_format='list')
|
117 |
+
bk_image = to_tensor(image, batch_size=1)
|
118 |
+
|
119 |
+
control_image = Image.fromarray(output_image.astype(np.uint8))
|
120 |
+
control_image = to_tensor(control_image, batch_size=1)
|
121 |
+
mask_image = to_tensor(mask, batch_size=1)
|
122 |
+
|
123 |
+
control_image = (1 - mask_image) * bk_image + mask_image * control_image
|
124 |
+
|
125 |
+
control_image = torch.cat([mask_image[:, :1, :, :], control_image], dim=1)
|
126 |
+
|
127 |
+
return control_image
|
128 |
+
|
129 |
+
@staticmethod
|
130 |
+
def blend_h(a, b, blend_extent):
|
131 |
+
a = np.array(a)
|
132 |
+
b = np.array(b)
|
133 |
+
blend_extent = min(a.shape[1], b.shape[1], blend_extent)
|
134 |
+
for x in range(blend_extent):
|
135 |
+
b[:, x, :] = a[:, -blend_extent
|
136 |
+
+ x, :] * (1 - x / blend_extent) + b[:, x, :] * (
|
137 |
+
x / blend_extent)
|
138 |
+
return b
|
139 |
+
|
140 |
+
def __call__(self, inputs: Dict[str, Any],
|
141 |
+
**forward_params) -> Dict[str, Any]:
|
142 |
+
if not isinstance(inputs, dict):
|
143 |
+
raise ValueError(
|
144 |
+
f'Expected the input to be a dictionary, but got {type(input)}'
|
145 |
+
)
|
146 |
+
num_inference_steps = inputs.get('num_inference_steps', 20)
|
147 |
+
guidance_scale = inputs.get('guidance_scale', 7.0)
|
148 |
+
preset_a_prompt = 'photorealistic, trend on artstation, ((best quality)), ((ultra high res))'
|
149 |
+
add_prompt = inputs.get('add_prompt', preset_a_prompt)
|
150 |
+
preset_n_prompt = 'persons, complex texture, small objects, sheltered, blur, worst quality, '\
|
151 |
+
'low quality, zombie, logo, text, watermark, username, monochrome, '\
|
152 |
+
'complex lighting'
|
153 |
+
negative_prompt = inputs.get('negative_prompt', preset_n_prompt)
|
154 |
+
seed = inputs.get('seed', -1)
|
155 |
+
upscale = inputs.get('upscale', True)
|
156 |
+
refinement = inputs.get('refinement', True)
|
157 |
+
|
158 |
+
guidance_scale_sr_step1 = inputs.get('guidance_scale_sr_step1', 15)
|
159 |
+
guidance_scale_sr_step2 = inputs.get('guidance_scale_sr_step1', 17)
|
160 |
+
|
161 |
+
image = inputs['image']
|
162 |
+
mask = inputs['mask']
|
163 |
+
|
164 |
+
control_image = self.process_control_image(image, mask)
|
165 |
+
|
166 |
+
if 'prompt' in inputs.keys():
|
167 |
+
prompt = inputs['prompt']
|
168 |
+
else:
|
169 |
+
# for demo_service
|
170 |
+
prompt = forward_params.get('prompt', 'the living room')
|
171 |
+
|
172 |
+
print(f'Test with prompt: {prompt}')
|
173 |
+
|
174 |
+
if seed == -1:
|
175 |
+
seed = random.randint(0, 65535)
|
176 |
+
print(f'global seed: {seed}')
|
177 |
+
|
178 |
+
generator = torch.manual_seed(seed)
|
179 |
+
|
180 |
+
prompt = '<360panorama>, ' + prompt + ', ' + add_prompt
|
181 |
+
output_img = self.pipe(
|
182 |
+
prompt,
|
183 |
+
image=(control_image[:, 1:, :, :] / 0.5 - 1.0),
|
184 |
+
control_image=control_image,
|
185 |
+
controlnet_conditioning_scale=1.0,
|
186 |
+
strength=1.0,
|
187 |
+
negative_prompt=negative_prompt,
|
188 |
+
num_inference_steps=num_inference_steps,
|
189 |
+
height=512,
|
190 |
+
width=1024,
|
191 |
+
guidance_scale=guidance_scale,
|
192 |
+
generator=generator).images[0]
|
193 |
+
|
194 |
+
if not upscale:
|
195 |
+
print('finished')
|
196 |
+
else:
|
197 |
+
print('inputs: upscale=True, running upscaler.')
|
198 |
+
print('running upscaler step1. Initial super-resolution')
|
199 |
+
sr_scale = 2.0
|
200 |
+
output_img = self.pipe_sr(
|
201 |
+
prompt.replace('<360panorama>, ', ''),
|
202 |
+
negative_prompt=negative_prompt,
|
203 |
+
image=output_img.resize(
|
204 |
+
(int(1536 * sr_scale), int(768 * sr_scale))),
|
205 |
+
num_inference_steps=7,
|
206 |
+
generator=generator,
|
207 |
+
control_image=output_img.resize(
|
208 |
+
(int(1536 * sr_scale), int(768 * sr_scale))),
|
209 |
+
strength=0.8,
|
210 |
+
controlnet_conditioning_scale=1.0,
|
211 |
+
guidance_scale=guidance_scale_sr_step1,
|
212 |
+
).images[0]
|
213 |
+
|
214 |
+
print('running upscaler step2. Super-resolution with Real-ESRGAN')
|
215 |
+
output_img = output_img.resize((1536 * 2, 768 * 2))
|
216 |
+
w = output_img.size[0]
|
217 |
+
blend_extend = 10
|
218 |
+
outscale = 2
|
219 |
+
output_img = np.array(output_img)
|
220 |
+
output_img = np.concatenate(
|
221 |
+
[output_img, output_img[:, :blend_extend, :]], axis=1)
|
222 |
+
output_img = self.upsampler.predict(
|
223 |
+
output_img)
|
224 |
+
output_img = self.blend_h(output_img, output_img,
|
225 |
+
blend_extend * outscale)
|
226 |
+
output_img = Image.fromarray(output_img[:, :w * outscale, :])
|
227 |
+
|
228 |
+
if refinement:
|
229 |
+
print(
|
230 |
+
'inputs: refinement=True, running refinement. This is a bit time-consuming.'
|
231 |
+
)
|
232 |
+
sr_scale = 4
|
233 |
+
output_img = self.pipe_sr(
|
234 |
+
prompt.replace('<360panorama>, ', ''),
|
235 |
+
negative_prompt=negative_prompt,
|
236 |
+
image=output_img.resize(
|
237 |
+
(int(1536 * sr_scale), int(768 * sr_scale))),
|
238 |
+
num_inference_steps=7,
|
239 |
+
generator=generator,
|
240 |
+
control_image=output_img.resize(
|
241 |
+
(int(1536 * sr_scale), int(768 * sr_scale))),
|
242 |
+
strength=0.8,
|
243 |
+
controlnet_conditioning_scale=1.0,
|
244 |
+
guidance_scale=guidance_scale_sr_step2,
|
245 |
+
).images[0]
|
246 |
+
print('finished')
|
247 |
+
|
248 |
+
return output_img
|
img2panoimg/pipeline_i2p.py
ADDED
@@ -0,0 +1,1740 @@
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|
1 |
+
# Copyright © Alibaba, Inc. and its affiliates.
|
2 |
+
# The implementation here is modifed based on diffusers.StableDiffusionPipeline,
|
3 |
+
# originally Apache 2.0 License and public available at
|
4 |
+
# https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
|
5 |
+
|
6 |
+
import copy
|
7 |
+
import inspect
|
8 |
+
import re
|
9 |
+
import warnings
|
10 |
+
from typing import Any, Callable, Dict, List, Optional, Union, Tuple
|
11 |
+
|
12 |
+
import os
|
13 |
+
import torch
|
14 |
+
import torch.nn.functional as F
|
15 |
+
from diffusers import (AutoencoderKL, DiffusionPipeline, ControlNetModel, UNet2DConditionModel)
|
16 |
+
from diffusers.image_processor import VaeImageProcessor
|
17 |
+
from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
|
18 |
+
try:
|
19 |
+
from diffusers.models.autoencoders.vae import DecoderOutput
|
20 |
+
except:
|
21 |
+
from diffusers.models.vae import DecoderOutput
|
22 |
+
from diffusers.models.controlnet import ControlNetOutput
|
23 |
+
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
|
24 |
+
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
|
25 |
+
from diffusers.pipelines.stable_diffusion.safety_checker import \
|
26 |
+
StableDiffusionSafetyChecker
|
27 |
+
from diffusers.schedulers import KarrasDiffusionSchedulers
|
28 |
+
from diffusers.utils import (PIL_INTERPOLATION, deprecate, is_accelerate_available,
|
29 |
+
is_accelerate_version, logging,
|
30 |
+
replace_example_docstring)
|
31 |
+
from diffusers.utils.torch_utils import is_compiled_module, randn_tensor
|
32 |
+
|
33 |
+
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
|
34 |
+
import PIL
|
35 |
+
import numpy as np
|
36 |
+
|
37 |
+
|
38 |
+
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
39 |
+
|
40 |
+
EXAMPLE_DOC_STRING = """
|
41 |
+
Examples:
|
42 |
+
```py
|
43 |
+
>>> import torch
|
44 |
+
>>> from diffusers import EulerAncestralDiscreteScheduler
|
45 |
+
>>> from txt2panoimage.pipeline_base import StableDiffusionBlendExtendPipeline
|
46 |
+
>>> model_id = "models/sd-base"
|
47 |
+
>>> pipe = StableDiffusionBlendExtendPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
|
48 |
+
>>> pipe = pipe.to("cuda")
|
49 |
+
>>> pipe.vae.enable_tiling()
|
50 |
+
>>> pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
|
51 |
+
>>> # remove following line if xformers is not installed
|
52 |
+
>>> pipe.enable_xformers_memory_efficient_attention()
|
53 |
+
>>> pipe.enable_model_cpu_offload()
|
54 |
+
>>> prompt = "a living room"
|
55 |
+
>>> image = pipe(prompt).images[0]
|
56 |
+
```
|
57 |
+
"""
|
58 |
+
|
59 |
+
re_attention = re.compile(
|
60 |
+
r"""
|
61 |
+
\\\(|
|
62 |
+
\\\)|
|
63 |
+
\\\[|
|
64 |
+
\\]|
|
65 |
+
\\\\|
|
66 |
+
\\|
|
67 |
+
\(|
|
68 |
+
\[|
|
69 |
+
:([+-]?[.\d]+)\)|
|
70 |
+
\)|
|
71 |
+
]|
|
72 |
+
[^\\()\[\]:]+|
|
73 |
+
:
|
74 |
+
""",
|
75 |
+
re.X,
|
76 |
+
)
|
77 |
+
|
78 |
+
|
79 |
+
def parse_prompt_attention(text):
|
80 |
+
"""
|
81 |
+
Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
|
82 |
+
Accepted tokens are:
|
83 |
+
(abc) - increases attention to abc by a multiplier of 1.1
|
84 |
+
(abc:3.12) - increases attention to abc by a multiplier of 3.12
|
85 |
+
[abc] - decreases attention to abc by a multiplier of 1.1
|
86 |
+
"""
|
87 |
+
|
88 |
+
res = []
|
89 |
+
round_brackets = []
|
90 |
+
square_brackets = []
|
91 |
+
|
92 |
+
round_bracket_multiplier = 1.1
|
93 |
+
square_bracket_multiplier = 1 / 1.1
|
94 |
+
|
95 |
+
def multiply_range(start_position, multiplier):
|
96 |
+
for p in range(start_position, len(res)):
|
97 |
+
res[p][1] *= multiplier
|
98 |
+
|
99 |
+
for m in re_attention.finditer(text):
|
100 |
+
text = m.group(0)
|
101 |
+
weight = m.group(1)
|
102 |
+
|
103 |
+
if text.startswith('\\'):
|
104 |
+
res.append([text[1:], 1.0])
|
105 |
+
elif text == '(':
|
106 |
+
round_brackets.append(len(res))
|
107 |
+
elif text == '[':
|
108 |
+
square_brackets.append(len(res))
|
109 |
+
elif weight is not None and len(round_brackets) > 0:
|
110 |
+
multiply_range(round_brackets.pop(), float(weight))
|
111 |
+
elif text == ')' and len(round_brackets) > 0:
|
112 |
+
multiply_range(round_brackets.pop(), round_bracket_multiplier)
|
113 |
+
elif text == ']' and len(square_brackets) > 0:
|
114 |
+
multiply_range(square_brackets.pop(), square_bracket_multiplier)
|
115 |
+
else:
|
116 |
+
res.append([text, 1.0])
|
117 |
+
|
118 |
+
for pos in round_brackets:
|
119 |
+
multiply_range(pos, round_bracket_multiplier)
|
120 |
+
|
121 |
+
for pos in square_brackets:
|
122 |
+
multiply_range(pos, square_bracket_multiplier)
|
123 |
+
|
124 |
+
if len(res) == 0:
|
125 |
+
res = [['', 1.0]]
|
126 |
+
|
127 |
+
# merge runs of identical weights
|
128 |
+
i = 0
|
129 |
+
while i + 1 < len(res):
|
130 |
+
if res[i][1] == res[i + 1][1]:
|
131 |
+
res[i][0] += res[i + 1][0]
|
132 |
+
res.pop(i + 1)
|
133 |
+
else:
|
134 |
+
i += 1
|
135 |
+
|
136 |
+
return res
|
137 |
+
|
138 |
+
|
139 |
+
def get_prompts_with_weights(pipe: DiffusionPipeline, prompt: List[str],
|
140 |
+
max_length: int):
|
141 |
+
r"""
|
142 |
+
Tokenize a list of prompts and return its tokens with weights of each token.
|
143 |
+
|
144 |
+
No padding, starting or ending token is included.
|
145 |
+
"""
|
146 |
+
tokens = []
|
147 |
+
weights = []
|
148 |
+
truncated = False
|
149 |
+
for text in prompt:
|
150 |
+
texts_and_weights = parse_prompt_attention(text)
|
151 |
+
text_token = []
|
152 |
+
text_weight = []
|
153 |
+
for word, weight in texts_and_weights:
|
154 |
+
# tokenize and discard the starting and the ending token
|
155 |
+
token = pipe.tokenizer(word).input_ids[1:-1]
|
156 |
+
text_token += token
|
157 |
+
# copy the weight by length of token
|
158 |
+
text_weight += [weight] * len(token)
|
159 |
+
# stop if the text is too long (longer than truncation limit)
|
160 |
+
if len(text_token) > max_length:
|
161 |
+
truncated = True
|
162 |
+
break
|
163 |
+
# truncate
|
164 |
+
if len(text_token) > max_length:
|
165 |
+
truncated = True
|
166 |
+
text_token = text_token[:max_length]
|
167 |
+
text_weight = text_weight[:max_length]
|
168 |
+
tokens.append(text_token)
|
169 |
+
weights.append(text_weight)
|
170 |
+
if truncated:
|
171 |
+
logger.warning(
|
172 |
+
'Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples'
|
173 |
+
)
|
174 |
+
return tokens, weights
|
175 |
+
|
176 |
+
|
177 |
+
def pad_tokens_and_weights(tokens,
|
178 |
+
weights,
|
179 |
+
max_length,
|
180 |
+
bos,
|
181 |
+
eos,
|
182 |
+
pad,
|
183 |
+
no_boseos_middle=True,
|
184 |
+
chunk_length=77):
|
185 |
+
r"""
|
186 |
+
Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
|
187 |
+
"""
|
188 |
+
max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
|
189 |
+
weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
|
190 |
+
for i in range(len(tokens)):
|
191 |
+
tokens[i] = [
|
192 |
+
bos
|
193 |
+
] + tokens[i] + [pad] * (max_length - 1 - len(tokens[i]) - 1) + [eos]
|
194 |
+
if no_boseos_middle:
|
195 |
+
weights[i] = [1.0] + weights[i] + [1.0] * (
|
196 |
+
max_length - 1 - len(weights[i]))
|
197 |
+
else:
|
198 |
+
w = []
|
199 |
+
if len(weights[i]) == 0:
|
200 |
+
w = [1.0] * weights_length
|
201 |
+
else:
|
202 |
+
for j in range(max_embeddings_multiples):
|
203 |
+
w.append(1.0) # weight for starting token in this chunk
|
204 |
+
w += weights[i][j * (chunk_length - 2):min(
|
205 |
+
len(weights[i]), (j + 1) * (chunk_length - 2))]
|
206 |
+
w.append(1.0) # weight for ending token in this chunk
|
207 |
+
w += [1.0] * (weights_length - len(w))
|
208 |
+
weights[i] = w[:]
|
209 |
+
|
210 |
+
return tokens, weights
|
211 |
+
|
212 |
+
|
213 |
+
def get_unweighted_text_embeddings(
|
214 |
+
pipe: DiffusionPipeline,
|
215 |
+
text_input: torch.Tensor,
|
216 |
+
chunk_length: int,
|
217 |
+
no_boseos_middle: Optional[bool] = True,
|
218 |
+
):
|
219 |
+
"""
|
220 |
+
When the length of tokens is a multiple of the capacity of the text encoder,
|
221 |
+
it should be split into chunks and sent to the text encoder individually.
|
222 |
+
"""
|
223 |
+
max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)
|
224 |
+
if max_embeddings_multiples > 1:
|
225 |
+
text_embeddings = []
|
226 |
+
for i in range(max_embeddings_multiples):
|
227 |
+
# extract the i-th chunk
|
228 |
+
text_input_chunk = text_input[:, i * (chunk_length - 2):(i + 1)
|
229 |
+
* (chunk_length - 2) + 2].clone()
|
230 |
+
|
231 |
+
# cover the head and the tail by the starting and the ending tokens
|
232 |
+
text_input_chunk[:, 0] = text_input[0, 0]
|
233 |
+
text_input_chunk[:, -1] = text_input[0, -1]
|
234 |
+
text_embedding = pipe.text_encoder(text_input_chunk)[0]
|
235 |
+
|
236 |
+
if no_boseos_middle:
|
237 |
+
if i == 0:
|
238 |
+
# discard the ending token
|
239 |
+
text_embedding = text_embedding[:, :-1]
|
240 |
+
elif i == max_embeddings_multiples - 1:
|
241 |
+
# discard the starting token
|
242 |
+
text_embedding = text_embedding[:, 1:]
|
243 |
+
else:
|
244 |
+
# discard both starting and ending tokens
|
245 |
+
text_embedding = text_embedding[:, 1:-1]
|
246 |
+
|
247 |
+
text_embeddings.append(text_embedding)
|
248 |
+
text_embeddings = torch.concat(text_embeddings, axis=1)
|
249 |
+
else:
|
250 |
+
text_embeddings = pipe.text_encoder(text_input)[0]
|
251 |
+
return text_embeddings
|
252 |
+
|
253 |
+
|
254 |
+
def get_weighted_text_embeddings(
|
255 |
+
pipe: DiffusionPipeline,
|
256 |
+
prompt: Union[str, List[str]],
|
257 |
+
uncond_prompt: Optional[Union[str, List[str]]] = None,
|
258 |
+
max_embeddings_multiples: Optional[int] = 3,
|
259 |
+
no_boseos_middle: Optional[bool] = False,
|
260 |
+
skip_parsing: Optional[bool] = False,
|
261 |
+
skip_weighting: Optional[bool] = False,
|
262 |
+
):
|
263 |
+
r"""
|
264 |
+
Prompts can be assigned with local weights using brackets. For example,
|
265 |
+
prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
|
266 |
+
and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.
|
267 |
+
|
268 |
+
Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.
|
269 |
+
|
270 |
+
Args:
|
271 |
+
pipe (`DiffusionPipeline`):
|
272 |
+
Pipe to provide access to the tokenizer and the text encoder.
|
273 |
+
prompt (`str` or `List[str]`):
|
274 |
+
The prompt or prompts to guide the image generation.
|
275 |
+
uncond_prompt (`str` or `List[str]`):
|
276 |
+
The unconditional prompt or prompts for guide the image generation. If unconditional prompt
|
277 |
+
is provided, the embeddings of prompt and uncond_prompt are concatenated.
|
278 |
+
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
|
279 |
+
The max multiple length of prompt embeddings compared to the max output length of text encoder.
|
280 |
+
no_boseos_middle (`bool`, *optional*, defaults to `False`):
|
281 |
+
If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and
|
282 |
+
ending token in each of the chunk in the middle.
|
283 |
+
skip_parsing (`bool`, *optional*, defaults to `False`):
|
284 |
+
Skip the parsing of brackets.
|
285 |
+
skip_weighting (`bool`, *optional*, defaults to `False`):
|
286 |
+
Skip the weighting. When the parsing is skipped, it is forced True.
|
287 |
+
"""
|
288 |
+
max_length = (pipe.tokenizer.model_max_length
|
289 |
+
- 2) * max_embeddings_multiples + 2
|
290 |
+
if isinstance(prompt, str):
|
291 |
+
prompt = [prompt]
|
292 |
+
|
293 |
+
if not skip_parsing:
|
294 |
+
prompt_tokens, prompt_weights = get_prompts_with_weights(
|
295 |
+
pipe, prompt, max_length - 2)
|
296 |
+
if uncond_prompt is not None:
|
297 |
+
if isinstance(uncond_prompt, str):
|
298 |
+
uncond_prompt = [uncond_prompt]
|
299 |
+
uncond_tokens, uncond_weights = get_prompts_with_weights(
|
300 |
+
pipe, uncond_prompt, max_length - 2)
|
301 |
+
else:
|
302 |
+
prompt_tokens = [
|
303 |
+
token[1:-1] for token in pipe.tokenizer(
|
304 |
+
prompt, max_length=max_length, truncation=True).input_ids
|
305 |
+
]
|
306 |
+
prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
|
307 |
+
if uncond_prompt is not None:
|
308 |
+
if isinstance(uncond_prompt, str):
|
309 |
+
uncond_prompt = [uncond_prompt]
|
310 |
+
uncond_tokens = [
|
311 |
+
token[1:-1] for token in pipe.tokenizer(
|
312 |
+
uncond_prompt, max_length=max_length,
|
313 |
+
truncation=True).input_ids
|
314 |
+
]
|
315 |
+
uncond_weights = [[1.0] * len(token) for token in uncond_tokens]
|
316 |
+
|
317 |
+
# round up the longest length of tokens to a multiple of (model_max_length - 2)
|
318 |
+
max_length = max([len(token) for token in prompt_tokens])
|
319 |
+
if uncond_prompt is not None:
|
320 |
+
max_length = max(max_length,
|
321 |
+
max([len(token) for token in uncond_tokens]))
|
322 |
+
|
323 |
+
max_embeddings_multiples = min(
|
324 |
+
max_embeddings_multiples,
|
325 |
+
(max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1,
|
326 |
+
)
|
327 |
+
max_embeddings_multiples = max(1, max_embeddings_multiples)
|
328 |
+
max_length = (pipe.tokenizer.model_max_length
|
329 |
+
- 2) * max_embeddings_multiples + 2
|
330 |
+
|
331 |
+
# pad the length of tokens and weights
|
332 |
+
bos = pipe.tokenizer.bos_token_id
|
333 |
+
eos = pipe.tokenizer.eos_token_id
|
334 |
+
pad = getattr(pipe.tokenizer, 'pad_token_id', eos)
|
335 |
+
prompt_tokens, prompt_weights = pad_tokens_and_weights(
|
336 |
+
prompt_tokens,
|
337 |
+
prompt_weights,
|
338 |
+
max_length,
|
339 |
+
bos,
|
340 |
+
eos,
|
341 |
+
pad,
|
342 |
+
no_boseos_middle=no_boseos_middle,
|
343 |
+
chunk_length=pipe.tokenizer.model_max_length,
|
344 |
+
)
|
345 |
+
prompt_tokens = torch.tensor(
|
346 |
+
prompt_tokens, dtype=torch.long, device=pipe.device)
|
347 |
+
if uncond_prompt is not None:
|
348 |
+
uncond_tokens, uncond_weights = pad_tokens_and_weights(
|
349 |
+
uncond_tokens,
|
350 |
+
uncond_weights,
|
351 |
+
max_length,
|
352 |
+
bos,
|
353 |
+
eos,
|
354 |
+
pad,
|
355 |
+
no_boseos_middle=no_boseos_middle,
|
356 |
+
chunk_length=pipe.tokenizer.model_max_length,
|
357 |
+
)
|
358 |
+
uncond_tokens = torch.tensor(
|
359 |
+
uncond_tokens, dtype=torch.long, device=pipe.device)
|
360 |
+
|
361 |
+
# get the embeddings
|
362 |
+
text_embeddings = get_unweighted_text_embeddings(
|
363 |
+
pipe,
|
364 |
+
prompt_tokens,
|
365 |
+
pipe.tokenizer.model_max_length,
|
366 |
+
no_boseos_middle=no_boseos_middle,
|
367 |
+
)
|
368 |
+
prompt_weights = torch.tensor(
|
369 |
+
prompt_weights,
|
370 |
+
dtype=text_embeddings.dtype,
|
371 |
+
device=text_embeddings.device)
|
372 |
+
if uncond_prompt is not None:
|
373 |
+
uncond_embeddings = get_unweighted_text_embeddings(
|
374 |
+
pipe,
|
375 |
+
uncond_tokens,
|
376 |
+
pipe.tokenizer.model_max_length,
|
377 |
+
no_boseos_middle=no_boseos_middle,
|
378 |
+
)
|
379 |
+
uncond_weights = torch.tensor(
|
380 |
+
uncond_weights,
|
381 |
+
dtype=uncond_embeddings.dtype,
|
382 |
+
device=uncond_embeddings.device)
|
383 |
+
|
384 |
+
# assign weights to the prompts and normalize in the sense of mean
|
385 |
+
# TODO: should we normalize by chunk or in a whole (current implementation)?
|
386 |
+
if (not skip_parsing) and (not skip_weighting):
|
387 |
+
previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(
|
388 |
+
text_embeddings.dtype)
|
389 |
+
text_embeddings *= prompt_weights.unsqueeze(-1)
|
390 |
+
current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(
|
391 |
+
text_embeddings.dtype)
|
392 |
+
text_embeddings *= (previous_mean
|
393 |
+
/ current_mean).unsqueeze(-1).unsqueeze(-1)
|
394 |
+
if uncond_prompt is not None:
|
395 |
+
previous_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(
|
396 |
+
uncond_embeddings.dtype)
|
397 |
+
uncond_embeddings *= uncond_weights.unsqueeze(-1)
|
398 |
+
current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(
|
399 |
+
uncond_embeddings.dtype)
|
400 |
+
uncond_embeddings *= (previous_mean
|
401 |
+
/ current_mean).unsqueeze(-1).unsqueeze(-1)
|
402 |
+
|
403 |
+
if uncond_prompt is not None:
|
404 |
+
return text_embeddings, uncond_embeddings
|
405 |
+
return text_embeddings, None
|
406 |
+
|
407 |
+
|
408 |
+
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
|
409 |
+
"""
|
410 |
+
Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
|
411 |
+
Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
|
412 |
+
"""
|
413 |
+
std_text = noise_pred_text.std(
|
414 |
+
dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
|
415 |
+
std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
|
416 |
+
# rescale the results from guidance (fixes overexposure)
|
417 |
+
noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
|
418 |
+
# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
|
419 |
+
noise_cfg = guidance_rescale * noise_pred_rescaled + (
|
420 |
+
1 - guidance_rescale) * noise_cfg
|
421 |
+
return noise_cfg
|
422 |
+
|
423 |
+
|
424 |
+
def prepare_image(image):
|
425 |
+
if isinstance(image, torch.Tensor):
|
426 |
+
# Batch single image
|
427 |
+
if image.ndim == 3:
|
428 |
+
image = image.unsqueeze(0)
|
429 |
+
|
430 |
+
image = image.to(dtype=torch.float32)
|
431 |
+
else:
|
432 |
+
# preprocess image
|
433 |
+
if isinstance(image, (PIL.Image.Image, np.ndarray)):
|
434 |
+
image = [image]
|
435 |
+
|
436 |
+
if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
|
437 |
+
image = [np.array(i.convert("RGB"))[None, :] for i in image]
|
438 |
+
image = np.concatenate(image, axis=0)
|
439 |
+
elif isinstance(image, list) and isinstance(image[0], np.ndarray):
|
440 |
+
image = np.concatenate([i[None, :] for i in image], axis=0)
|
441 |
+
|
442 |
+
image = image.transpose(0, 3, 1, 2)
|
443 |
+
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
|
444 |
+
|
445 |
+
return image
|
446 |
+
|
447 |
+
class StableDiffusionImage2PanoPipeline(DiffusionPipeline, TextualInversionLoaderMixin):
|
448 |
+
r"""
|
449 |
+
Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.
|
450 |
+
|
451 |
+
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
|
452 |
+
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
|
453 |
+
|
454 |
+
In addition the pipeline inherits the following loading methods:
|
455 |
+
- *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
|
456 |
+
|
457 |
+
Args:
|
458 |
+
vae ([`AutoencoderKL`]):
|
459 |
+
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
|
460 |
+
text_encoder ([`CLIPTextModel`]):
|
461 |
+
Frozen text-encoder. Stable Diffusion uses the text portion of
|
462 |
+
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
|
463 |
+
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
|
464 |
+
tokenizer (`CLIPTokenizer`):
|
465 |
+
Tokenizer of class
|
466 |
+
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
|
467 |
+
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
|
468 |
+
controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
|
469 |
+
Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets
|
470 |
+
as a list, the outputs from each ControlNet are added together to create one combined additional
|
471 |
+
conditioning.
|
472 |
+
scheduler ([`SchedulerMixin`]):
|
473 |
+
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
|
474 |
+
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
|
475 |
+
safety_checker ([`StableDiffusionSafetyChecker`]):
|
476 |
+
Classification module that estimates whether generated images could be considered offensive or harmful.
|
477 |
+
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
|
478 |
+
feature_extractor ([`CLIPImageProcessor`]):
|
479 |
+
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
|
480 |
+
"""
|
481 |
+
_optional_components = ["safety_checker", "feature_extractor"]
|
482 |
+
|
483 |
+
def __init__(
|
484 |
+
self,
|
485 |
+
vae: AutoencoderKL,
|
486 |
+
text_encoder: CLIPTextModel,
|
487 |
+
tokenizer: CLIPTokenizer,
|
488 |
+
unet: UNet2DConditionModel,
|
489 |
+
controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],
|
490 |
+
scheduler: KarrasDiffusionSchedulers,
|
491 |
+
safety_checker: StableDiffusionSafetyChecker,
|
492 |
+
feature_extractor: CLIPImageProcessor,
|
493 |
+
requires_safety_checker: bool = True,
|
494 |
+
):
|
495 |
+
super().__init__()
|
496 |
+
|
497 |
+
if safety_checker is None and requires_safety_checker:
|
498 |
+
logger.warning(
|
499 |
+
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
|
500 |
+
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
|
501 |
+
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
|
502 |
+
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
|
503 |
+
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
|
504 |
+
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
|
505 |
+
)
|
506 |
+
|
507 |
+
if safety_checker is not None and feature_extractor is None:
|
508 |
+
raise ValueError(
|
509 |
+
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
|
510 |
+
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
|
511 |
+
)
|
512 |
+
|
513 |
+
if isinstance(controlnet, (list, tuple)):
|
514 |
+
controlnet = MultiControlNetModel(controlnet)
|
515 |
+
|
516 |
+
self.register_modules(
|
517 |
+
vae=vae,
|
518 |
+
text_encoder=text_encoder,
|
519 |
+
tokenizer=tokenizer,
|
520 |
+
unet=unet,
|
521 |
+
controlnet=controlnet,
|
522 |
+
scheduler=scheduler,
|
523 |
+
safety_checker=safety_checker,
|
524 |
+
feature_extractor=feature_extractor,
|
525 |
+
)
|
526 |
+
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
|
527 |
+
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
|
528 |
+
self.register_to_config(requires_safety_checker=requires_safety_checker)
|
529 |
+
|
530 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
|
531 |
+
def enable_vae_slicing(self):
|
532 |
+
r"""
|
533 |
+
Enable sliced VAE decoding.
|
534 |
+
|
535 |
+
When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
|
536 |
+
steps. This is useful to save some memory and allow larger batch sizes.
|
537 |
+
"""
|
538 |
+
self.vae.enable_slicing()
|
539 |
+
|
540 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
|
541 |
+
def disable_vae_slicing(self):
|
542 |
+
r"""
|
543 |
+
Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
|
544 |
+
computing decoding in one step.
|
545 |
+
"""
|
546 |
+
self.vae.disable_slicing()
|
547 |
+
|
548 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
|
549 |
+
def enable_vae_tiling(self):
|
550 |
+
r"""
|
551 |
+
Enable tiled VAE decoding.
|
552 |
+
|
553 |
+
When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in
|
554 |
+
several steps. This is useful to save a large amount of memory and to allow the processing of larger images.
|
555 |
+
"""
|
556 |
+
self.vae.enable_tiling()
|
557 |
+
|
558 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
|
559 |
+
def disable_vae_tiling(self):
|
560 |
+
r"""
|
561 |
+
Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to
|
562 |
+
computing decoding in one step.
|
563 |
+
"""
|
564 |
+
self.vae.disable_tiling()
|
565 |
+
|
566 |
+
def enable_sequential_cpu_offload(self, gpu_id=0):
|
567 |
+
r"""
|
568 |
+
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
|
569 |
+
text_encoder, vae, controlnet, and safety checker have their state dicts saved to CPU and then are moved to a
|
570 |
+
`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
|
571 |
+
Note that offloading happens on a submodule basis. Memory savings are higher than with
|
572 |
+
`enable_model_cpu_offload`, but performance is lower.
|
573 |
+
"""
|
574 |
+
if is_accelerate_available():
|
575 |
+
from accelerate import cpu_offload
|
576 |
+
else:
|
577 |
+
raise ImportError("Please install accelerate via `pip install accelerate`")
|
578 |
+
|
579 |
+
device = torch.device(f"cuda:{gpu_id}")
|
580 |
+
|
581 |
+
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.controlnet]:
|
582 |
+
cpu_offload(cpu_offloaded_model, device)
|
583 |
+
|
584 |
+
if self.safety_checker is not None:
|
585 |
+
cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)
|
586 |
+
|
587 |
+
def enable_model_cpu_offload(self, gpu_id=0):
|
588 |
+
r"""
|
589 |
+
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
|
590 |
+
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
|
591 |
+
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
|
592 |
+
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
|
593 |
+
"""
|
594 |
+
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
|
595 |
+
from accelerate import cpu_offload_with_hook
|
596 |
+
else:
|
597 |
+
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
|
598 |
+
|
599 |
+
device = torch.device(f"cuda:{gpu_id}")
|
600 |
+
|
601 |
+
hook = None
|
602 |
+
for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
|
603 |
+
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
|
604 |
+
|
605 |
+
if self.safety_checker is not None:
|
606 |
+
# the safety checker can offload the vae again
|
607 |
+
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
|
608 |
+
|
609 |
+
# control net hook has be manually offloaded as it alternates with unet
|
610 |
+
cpu_offload_with_hook(self.controlnet, device)
|
611 |
+
|
612 |
+
# We'll offload the last model manually.
|
613 |
+
self.final_offload_hook = hook
|
614 |
+
|
615 |
+
@property
|
616 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
|
617 |
+
def _execution_device(self):
|
618 |
+
r"""
|
619 |
+
Returns the device on which the pipeline's models will be executed. After calling
|
620 |
+
`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
|
621 |
+
hooks.
|
622 |
+
"""
|
623 |
+
if not hasattr(self.unet, "_hf_hook"):
|
624 |
+
return self.device
|
625 |
+
for module in self.unet.modules():
|
626 |
+
if (
|
627 |
+
hasattr(module, "_hf_hook")
|
628 |
+
and hasattr(module._hf_hook, "execution_device")
|
629 |
+
and module._hf_hook.execution_device is not None
|
630 |
+
):
|
631 |
+
return torch.device(module._hf_hook.execution_device)
|
632 |
+
return self.device
|
633 |
+
|
634 |
+
def _encode_prompt(
|
635 |
+
self,
|
636 |
+
prompt,
|
637 |
+
device,
|
638 |
+
num_images_per_prompt,
|
639 |
+
do_classifier_free_guidance,
|
640 |
+
negative_prompt=None,
|
641 |
+
max_embeddings_multiples=3,
|
642 |
+
prompt_embeds: Optional[torch.FloatTensor] = None,
|
643 |
+
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
644 |
+
lora_scale: Optional[float] = None,
|
645 |
+
):
|
646 |
+
r"""
|
647 |
+
Encodes the prompt into text encoder hidden states.
|
648 |
+
|
649 |
+
Args:
|
650 |
+
prompt (`str` or `list(int)`):
|
651 |
+
prompt to be encoded
|
652 |
+
device: (`torch.device`):
|
653 |
+
torch device
|
654 |
+
num_images_per_prompt (`int`):
|
655 |
+
number of images that should be generated per prompt
|
656 |
+
do_classifier_free_guidance (`bool`):
|
657 |
+
whether to use classifier free guidance or not
|
658 |
+
negative_prompt (`str` or `List[str]`):
|
659 |
+
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
|
660 |
+
if `guidance_scale` is less than `1`).
|
661 |
+
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
|
662 |
+
The max multiple length of prompt embeddings compared to the max output length of text encoder.
|
663 |
+
"""
|
664 |
+
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
|
665 |
+
self._lora_scale = lora_scale
|
666 |
+
|
667 |
+
if prompt is not None and isinstance(prompt, str):
|
668 |
+
batch_size = 1
|
669 |
+
elif prompt is not None and isinstance(prompt, list):
|
670 |
+
batch_size = len(prompt)
|
671 |
+
else:
|
672 |
+
batch_size = prompt_embeds.shape[0]
|
673 |
+
|
674 |
+
if negative_prompt_embeds is None:
|
675 |
+
if negative_prompt is None:
|
676 |
+
negative_prompt = [""] * batch_size
|
677 |
+
elif isinstance(negative_prompt, str):
|
678 |
+
negative_prompt = [negative_prompt] * batch_size
|
679 |
+
if batch_size != len(negative_prompt):
|
680 |
+
raise ValueError(
|
681 |
+
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
682 |
+
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
683 |
+
" the batch size of `prompt`."
|
684 |
+
)
|
685 |
+
if prompt_embeds is None or negative_prompt_embeds is None:
|
686 |
+
if isinstance(self, TextualInversionLoaderMixin):
|
687 |
+
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
|
688 |
+
if do_classifier_free_guidance and negative_prompt_embeds is None:
|
689 |
+
negative_prompt = self.maybe_convert_prompt(negative_prompt, self.tokenizer)
|
690 |
+
|
691 |
+
prompt_embeds1, negative_prompt_embeds1 = get_weighted_text_embeddings(
|
692 |
+
pipe=self,
|
693 |
+
prompt=prompt,
|
694 |
+
uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
|
695 |
+
max_embeddings_multiples=max_embeddings_multiples,
|
696 |
+
)
|
697 |
+
if prompt_embeds is None:
|
698 |
+
prompt_embeds = prompt_embeds1
|
699 |
+
if negative_prompt_embeds is None:
|
700 |
+
negative_prompt_embeds = negative_prompt_embeds1
|
701 |
+
|
702 |
+
bs_embed, seq_len, _ = prompt_embeds.shape
|
703 |
+
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
704 |
+
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
705 |
+
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
|
706 |
+
|
707 |
+
if do_classifier_free_guidance:
|
708 |
+
bs_embed, seq_len, _ = negative_prompt_embeds.shape
|
709 |
+
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
710 |
+
negative_prompt_embeds = negative_prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
|
711 |
+
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
712 |
+
|
713 |
+
return prompt_embeds
|
714 |
+
|
715 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
|
716 |
+
def run_safety_checker(self, image, device, dtype):
|
717 |
+
if self.safety_checker is None:
|
718 |
+
has_nsfw_concept = None
|
719 |
+
else:
|
720 |
+
if torch.is_tensor(image):
|
721 |
+
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
|
722 |
+
else:
|
723 |
+
feature_extractor_input = self.image_processor.numpy_to_pil(image)
|
724 |
+
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
|
725 |
+
image, has_nsfw_concept = self.safety_checker(
|
726 |
+
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
|
727 |
+
)
|
728 |
+
return image, has_nsfw_concept
|
729 |
+
|
730 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
|
731 |
+
def decode_latents(self, latents):
|
732 |
+
warnings.warn(
|
733 |
+
"The decode_latents method is deprecated and will be removed in a future version. Please"
|
734 |
+
" use VaeImageProcessor instead",
|
735 |
+
FutureWarning,
|
736 |
+
)
|
737 |
+
latents = 1 / self.vae.config.scaling_factor * latents
|
738 |
+
image = self.vae.decode(latents, return_dict=False)[0]
|
739 |
+
image = (image / 2 + 0.5).clamp(0, 1)
|
740 |
+
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
|
741 |
+
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
|
742 |
+
return image
|
743 |
+
|
744 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
|
745 |
+
def prepare_extra_step_kwargs(self, generator, eta):
|
746 |
+
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
747 |
+
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
748 |
+
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
749 |
+
# and should be between [0, 1]
|
750 |
+
|
751 |
+
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
752 |
+
extra_step_kwargs = {}
|
753 |
+
if accepts_eta:
|
754 |
+
extra_step_kwargs["eta"] = eta
|
755 |
+
|
756 |
+
# check if the scheduler accepts generator
|
757 |
+
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
758 |
+
if accepts_generator:
|
759 |
+
extra_step_kwargs["generator"] = generator
|
760 |
+
return extra_step_kwargs
|
761 |
+
|
762 |
+
def check_inputs(
|
763 |
+
self,
|
764 |
+
prompt,
|
765 |
+
image,
|
766 |
+
height,
|
767 |
+
width,
|
768 |
+
callback_steps,
|
769 |
+
negative_prompt=None,
|
770 |
+
prompt_embeds=None,
|
771 |
+
negative_prompt_embeds=None,
|
772 |
+
controlnet_conditioning_scale=1.0,
|
773 |
+
):
|
774 |
+
if height % 8 != 0 or width % 8 != 0:
|
775 |
+
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
|
776 |
+
|
777 |
+
if (callback_steps is None) or (
|
778 |
+
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
|
779 |
+
):
|
780 |
+
raise ValueError(
|
781 |
+
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
|
782 |
+
f" {type(callback_steps)}."
|
783 |
+
)
|
784 |
+
|
785 |
+
if prompt is not None and prompt_embeds is not None:
|
786 |
+
raise ValueError(
|
787 |
+
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
|
788 |
+
" only forward one of the two."
|
789 |
+
)
|
790 |
+
elif prompt is None and prompt_embeds is None:
|
791 |
+
raise ValueError(
|
792 |
+
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
|
793 |
+
)
|
794 |
+
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
|
795 |
+
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
796 |
+
|
797 |
+
if negative_prompt is not None and negative_prompt_embeds is not None:
|
798 |
+
raise ValueError(
|
799 |
+
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
|
800 |
+
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
801 |
+
)
|
802 |
+
|
803 |
+
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
804 |
+
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
805 |
+
raise ValueError(
|
806 |
+
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
|
807 |
+
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
|
808 |
+
f" {negative_prompt_embeds.shape}."
|
809 |
+
)
|
810 |
+
|
811 |
+
# `prompt` needs more sophisticated handling when there are multiple
|
812 |
+
# conditionings.
|
813 |
+
if isinstance(self.controlnet, MultiControlNetModel):
|
814 |
+
if isinstance(prompt, list):
|
815 |
+
logger.warning(
|
816 |
+
f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}"
|
817 |
+
" prompts. The conditionings will be fixed across the prompts."
|
818 |
+
)
|
819 |
+
|
820 |
+
# Check `image`
|
821 |
+
is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
|
822 |
+
self.controlnet, torch._dynamo.eval_frame.OptimizedModule
|
823 |
+
)
|
824 |
+
if (
|
825 |
+
isinstance(self.controlnet, ControlNetModel)
|
826 |
+
or is_compiled
|
827 |
+
and isinstance(self.controlnet._orig_mod, ControlNetModel)
|
828 |
+
):
|
829 |
+
self.check_image(image, prompt, prompt_embeds)
|
830 |
+
elif (
|
831 |
+
isinstance(self.controlnet, MultiControlNetModel)
|
832 |
+
or is_compiled
|
833 |
+
and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
|
834 |
+
):
|
835 |
+
if not isinstance(image, list):
|
836 |
+
raise TypeError("For multiple controlnets: `image` must be type `list`")
|
837 |
+
|
838 |
+
# When `image` is a nested list:
|
839 |
+
# (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])
|
840 |
+
elif any(isinstance(i, list) for i in image):
|
841 |
+
raise ValueError("A single batch of multiple conditionings are supported at the moment.")
|
842 |
+
elif len(image) != len(self.controlnet.nets):
|
843 |
+
raise ValueError(
|
844 |
+
"For multiple controlnets: `image` must have the same length as the number of controlnets."
|
845 |
+
)
|
846 |
+
|
847 |
+
for image_ in image:
|
848 |
+
self.check_image(image_, prompt, prompt_embeds)
|
849 |
+
else:
|
850 |
+
assert False
|
851 |
+
|
852 |
+
# Check `controlnet_conditioning_scale`
|
853 |
+
if (
|
854 |
+
isinstance(self.controlnet, ControlNetModel)
|
855 |
+
or is_compiled
|
856 |
+
and isinstance(self.controlnet._orig_mod, ControlNetModel)
|
857 |
+
):
|
858 |
+
if not isinstance(controlnet_conditioning_scale, float):
|
859 |
+
raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
|
860 |
+
elif (
|
861 |
+
isinstance(self.controlnet, MultiControlNetModel)
|
862 |
+
or is_compiled
|
863 |
+
and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
|
864 |
+
):
|
865 |
+
if isinstance(controlnet_conditioning_scale, list):
|
866 |
+
if any(isinstance(i, list) for i in controlnet_conditioning_scale):
|
867 |
+
raise ValueError("A single batch of multiple conditionings are supported at the moment.")
|
868 |
+
elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
|
869 |
+
self.controlnet.nets
|
870 |
+
):
|
871 |
+
raise ValueError(
|
872 |
+
"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
|
873 |
+
" the same length as the number of controlnets"
|
874 |
+
)
|
875 |
+
else:
|
876 |
+
assert False
|
877 |
+
|
878 |
+
def check_image(self, image, prompt, prompt_embeds):
|
879 |
+
image_is_pil = isinstance(image, PIL.Image.Image)
|
880 |
+
image_is_tensor = isinstance(image, torch.Tensor)
|
881 |
+
image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
|
882 |
+
image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)
|
883 |
+
|
884 |
+
if not image_is_pil and not image_is_tensor and not image_is_pil_list and not image_is_tensor_list:
|
885 |
+
raise TypeError(
|
886 |
+
"image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors"
|
887 |
+
)
|
888 |
+
|
889 |
+
if image_is_pil:
|
890 |
+
image_batch_size = 1
|
891 |
+
elif image_is_tensor:
|
892 |
+
image_batch_size = image.shape[0]
|
893 |
+
elif image_is_pil_list:
|
894 |
+
image_batch_size = len(image)
|
895 |
+
elif image_is_tensor_list:
|
896 |
+
image_batch_size = len(image)
|
897 |
+
|
898 |
+
if prompt is not None and isinstance(prompt, str):
|
899 |
+
prompt_batch_size = 1
|
900 |
+
elif prompt is not None and isinstance(prompt, list):
|
901 |
+
prompt_batch_size = len(prompt)
|
902 |
+
elif prompt_embeds is not None:
|
903 |
+
prompt_batch_size = prompt_embeds.shape[0]
|
904 |
+
|
905 |
+
if image_batch_size != 1 and image_batch_size != prompt_batch_size:
|
906 |
+
raise ValueError(
|
907 |
+
f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
|
908 |
+
)
|
909 |
+
|
910 |
+
# Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image
|
911 |
+
def prepare_control_image(
|
912 |
+
self,
|
913 |
+
image,
|
914 |
+
width,
|
915 |
+
height,
|
916 |
+
batch_size,
|
917 |
+
num_images_per_prompt,
|
918 |
+
device,
|
919 |
+
dtype,
|
920 |
+
do_classifier_free_guidance=False,
|
921 |
+
guess_mode=False,
|
922 |
+
):
|
923 |
+
if not isinstance(image, torch.Tensor):
|
924 |
+
if isinstance(image, PIL.Image.Image):
|
925 |
+
image = [image]
|
926 |
+
|
927 |
+
if isinstance(image[0], PIL.Image.Image):
|
928 |
+
images = []
|
929 |
+
|
930 |
+
for image_ in image:
|
931 |
+
image_ = image_.convert("RGB")
|
932 |
+
image_ = image_.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
|
933 |
+
image_ = np.array(image_)
|
934 |
+
image_ = image_[None, :]
|
935 |
+
images.append(image_)
|
936 |
+
|
937 |
+
image = images
|
938 |
+
|
939 |
+
image = np.concatenate(image, axis=0)
|
940 |
+
image = np.array(image).astype(np.float32) / 255.0
|
941 |
+
image = image.transpose(0, 3, 1, 2)
|
942 |
+
image = torch.from_numpy(image)
|
943 |
+
elif isinstance(image[0], torch.Tensor):
|
944 |
+
image = torch.cat(image, dim=0)
|
945 |
+
|
946 |
+
image_batch_size = image.shape[0]
|
947 |
+
|
948 |
+
if image_batch_size == 1:
|
949 |
+
repeat_by = batch_size
|
950 |
+
else:
|
951 |
+
# image batch size is the same as prompt batch size
|
952 |
+
repeat_by = num_images_per_prompt
|
953 |
+
|
954 |
+
image = image.repeat_interleave(repeat_by, dim=0)
|
955 |
+
|
956 |
+
image = image.to(device=device, dtype=dtype)
|
957 |
+
|
958 |
+
if do_classifier_free_guidance and not guess_mode:
|
959 |
+
image = torch.cat([image] * 2)
|
960 |
+
|
961 |
+
return image
|
962 |
+
|
963 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
|
964 |
+
def get_timesteps(self, num_inference_steps, strength, device):
|
965 |
+
# get the original timestep using init_timestep
|
966 |
+
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
|
967 |
+
|
968 |
+
t_start = max(num_inference_steps - init_timestep, 0)
|
969 |
+
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
|
970 |
+
|
971 |
+
return timesteps, num_inference_steps - t_start
|
972 |
+
|
973 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents
|
974 |
+
def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
|
975 |
+
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
|
976 |
+
raise ValueError(
|
977 |
+
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
|
978 |
+
)
|
979 |
+
|
980 |
+
image = image.to(device=device, dtype=dtype)
|
981 |
+
|
982 |
+
batch_size = batch_size * num_images_per_prompt
|
983 |
+
if isinstance(generator, list) and len(generator) != batch_size:
|
984 |
+
raise ValueError(
|
985 |
+
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
|
986 |
+
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
|
987 |
+
)
|
988 |
+
|
989 |
+
if isinstance(generator, list):
|
990 |
+
init_latents = [
|
991 |
+
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
|
992 |
+
]
|
993 |
+
init_latents = torch.cat(init_latents, dim=0)
|
994 |
+
else:
|
995 |
+
init_latents = self.vae.encode(image).latent_dist.sample(generator)
|
996 |
+
|
997 |
+
init_latents = self.vae.config.scaling_factor * init_latents
|
998 |
+
|
999 |
+
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
|
1000 |
+
# expand init_latents for batch_size
|
1001 |
+
deprecation_message = (
|
1002 |
+
f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
|
1003 |
+
" images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
|
1004 |
+
" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
|
1005 |
+
" your script to pass as many initial images as text prompts to suppress this warning."
|
1006 |
+
)
|
1007 |
+
deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
|
1008 |
+
additional_image_per_prompt = batch_size // init_latents.shape[0]
|
1009 |
+
init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
|
1010 |
+
elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
|
1011 |
+
raise ValueError(
|
1012 |
+
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
|
1013 |
+
)
|
1014 |
+
else:
|
1015 |
+
init_latents = torch.cat([init_latents], dim=0)
|
1016 |
+
|
1017 |
+
shape = init_latents.shape
|
1018 |
+
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
1019 |
+
|
1020 |
+
# get latents
|
1021 |
+
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
|
1022 |
+
latents = init_latents
|
1023 |
+
|
1024 |
+
return latents
|
1025 |
+
|
1026 |
+
def _default_height_width(self, height, width, image):
|
1027 |
+
# NOTE: It is possible that a list of images have different
|
1028 |
+
# dimensions for each image, so just checking the first image
|
1029 |
+
# is not _exactly_ correct, but it is simple.
|
1030 |
+
while isinstance(image, list):
|
1031 |
+
image = image[0]
|
1032 |
+
|
1033 |
+
if height is None:
|
1034 |
+
if isinstance(image, PIL.Image.Image):
|
1035 |
+
height = image.height
|
1036 |
+
elif isinstance(image, torch.Tensor):
|
1037 |
+
height = image.shape[2]
|
1038 |
+
|
1039 |
+
height = (height // 8) * 8 # round down to nearest multiple of 8
|
1040 |
+
|
1041 |
+
if width is None:
|
1042 |
+
if isinstance(image, PIL.Image.Image):
|
1043 |
+
width = image.width
|
1044 |
+
elif isinstance(image, torch.Tensor):
|
1045 |
+
width = image.shape[3]
|
1046 |
+
|
1047 |
+
width = (width // 8) * 8 # round down to nearest multiple of 8
|
1048 |
+
|
1049 |
+
return height, width
|
1050 |
+
|
1051 |
+
# override DiffusionPipeline
|
1052 |
+
def save_pretrained(
|
1053 |
+
self,
|
1054 |
+
save_directory: Union[str, os.PathLike],
|
1055 |
+
safe_serialization: bool = False,
|
1056 |
+
variant: Optional[str] = None,
|
1057 |
+
):
|
1058 |
+
if isinstance(self.controlnet, ControlNetModel):
|
1059 |
+
super().save_pretrained(save_directory, safe_serialization, variant)
|
1060 |
+
else:
|
1061 |
+
raise NotImplementedError("Currently, the `save_pretrained()` is not implemented for Multi-ControlNet.")
|
1062 |
+
|
1063 |
+
def denoise_latents(self, latents, t, prompt_embeds, control_image, controlnet_conditioning_scale, guess_mode, cross_attention_kwargs, do_classifier_free_guidance, guidance_scale, extra_step_kwargs, views_scheduler_status):
|
1064 |
+
# expand the latents if we are doing classifier free guidance
|
1065 |
+
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
1066 |
+
self.scheduler.__dict__.update(views_scheduler_status[0])
|
1067 |
+
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
1068 |
+
|
1069 |
+
# controlnet(s) inference
|
1070 |
+
if guess_mode and do_classifier_free_guidance:
|
1071 |
+
# Infer ControlNet only for the conditional batch.
|
1072 |
+
controlnet_latent_model_input = latents
|
1073 |
+
controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
|
1074 |
+
else:
|
1075 |
+
controlnet_latent_model_input = latent_model_input
|
1076 |
+
controlnet_prompt_embeds = prompt_embeds
|
1077 |
+
|
1078 |
+
down_block_res_samples, mid_block_res_sample = self.controlnet(
|
1079 |
+
controlnet_latent_model_input,
|
1080 |
+
t,
|
1081 |
+
encoder_hidden_states=controlnet_prompt_embeds,
|
1082 |
+
controlnet_cond=control_image,
|
1083 |
+
conditioning_scale=controlnet_conditioning_scale,
|
1084 |
+
guess_mode=guess_mode,
|
1085 |
+
return_dict=False,
|
1086 |
+
)
|
1087 |
+
|
1088 |
+
if guess_mode and do_classifier_free_guidance:
|
1089 |
+
# Infered ControlNet only for the conditional batch.
|
1090 |
+
# To apply the output of ControlNet to both the unconditional and conditional batches,
|
1091 |
+
# add 0 to the unconditional batch to keep it unchanged.
|
1092 |
+
down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
|
1093 |
+
mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
|
1094 |
+
|
1095 |
+
# predict the noise residual
|
1096 |
+
noise_pred = self.unet(
|
1097 |
+
latent_model_input,
|
1098 |
+
t,
|
1099 |
+
encoder_hidden_states=prompt_embeds,
|
1100 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
1101 |
+
down_block_additional_residuals=down_block_res_samples,
|
1102 |
+
mid_block_additional_residual=mid_block_res_sample,
|
1103 |
+
return_dict=False,
|
1104 |
+
)[0]
|
1105 |
+
|
1106 |
+
# perform guidance
|
1107 |
+
if do_classifier_free_guidance:
|
1108 |
+
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
1109 |
+
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
1110 |
+
|
1111 |
+
# compute the previous noisy sample x_t -> x_t-1
|
1112 |
+
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
1113 |
+
return latents
|
1114 |
+
|
1115 |
+
def blend_v(self, a, b, blend_extent):
|
1116 |
+
blend_extent = min(a.shape[2], b.shape[2], blend_extent)
|
1117 |
+
for y in range(blend_extent):
|
1118 |
+
b[:, :, y, :] = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
|
1119 |
+
return b
|
1120 |
+
|
1121 |
+
def blend_h(self, a, b, blend_extent):
|
1122 |
+
blend_extent = min(a.shape[3], b.shape[3], blend_extent)
|
1123 |
+
for x in range(blend_extent):
|
1124 |
+
b[:, :, :, x] = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
|
1125 |
+
return b
|
1126 |
+
|
1127 |
+
def get_blocks(self, latents, control_image, tile_latent_min_size, overlap_size):
|
1128 |
+
rows_latents = []
|
1129 |
+
rows_control_images = []
|
1130 |
+
for i in range(0, latents.shape[2] - overlap_size, overlap_size):
|
1131 |
+
row_latents = []
|
1132 |
+
row_control_images = []
|
1133 |
+
for j in range(0, latents.shape[3] - overlap_size, overlap_size):
|
1134 |
+
latents_input = latents[:, :, i: i + tile_latent_min_size, j: j + tile_latent_min_size]
|
1135 |
+
control_image_input = control_image[:, :,
|
1136 |
+
self.vae_scale_factor * i: self.vae_scale_factor * (i + tile_latent_min_size),
|
1137 |
+
self.vae_scale_factor * j: self.vae_scale_factor * (j + tile_latent_min_size)]
|
1138 |
+
row_latents.append(latents_input)
|
1139 |
+
row_control_images.append(control_image_input)
|
1140 |
+
rows_latents.append(row_latents)
|
1141 |
+
rows_control_images.append(row_control_images)
|
1142 |
+
return rows_latents, rows_control_images
|
1143 |
+
|
1144 |
+
@torch.no_grad()
|
1145 |
+
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
1146 |
+
def __call__(
|
1147 |
+
self,
|
1148 |
+
prompt: Union[str, List[str]] = None,
|
1149 |
+
image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] = None,
|
1150 |
+
control_image: Union[
|
1151 |
+
torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]
|
1152 |
+
] = None,
|
1153 |
+
height: Optional[int] = None,
|
1154 |
+
width: Optional[int] = None,
|
1155 |
+
strength: float = 0.8,
|
1156 |
+
num_inference_steps: int = 50,
|
1157 |
+
guidance_scale: float = 7.5,
|
1158 |
+
negative_prompt: Optional[Union[str, List[str]]] = None,
|
1159 |
+
num_images_per_prompt: Optional[int] = 1,
|
1160 |
+
eta: float = 0.0,
|
1161 |
+
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
1162 |
+
latents: Optional[torch.FloatTensor] = None,
|
1163 |
+
prompt_embeds: Optional[torch.FloatTensor] = None,
|
1164 |
+
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
1165 |
+
output_type: Optional[str] = "pil",
|
1166 |
+
return_dict: bool = True,
|
1167 |
+
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
1168 |
+
callback_steps: int = 1,
|
1169 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
1170 |
+
controlnet_conditioning_scale: Union[float, List[float]] = 0.8,
|
1171 |
+
guess_mode: bool = False,
|
1172 |
+
mask: Optional[torch.FloatTensor] = None,
|
1173 |
+
):
|
1174 |
+
r"""
|
1175 |
+
Function invoked when calling the pipeline for generation.
|
1176 |
+
|
1177 |
+
Args:
|
1178 |
+
prompt (`str` or `List[str]`, *optional*):
|
1179 |
+
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
|
1180 |
+
instead.
|
1181 |
+
image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`,
|
1182 |
+
`List[List[torch.FloatTensor]]`, or `List[List[PIL.Image.Image]]`):
|
1183 |
+
The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
|
1184 |
+
the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can
|
1185 |
+
also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If
|
1186 |
+
height and/or width are passed, `image` is resized according to them. If multiple ControlNets are
|
1187 |
+
specified in init, images must be passed as a list such that each element of the list can be correctly
|
1188 |
+
batched for input to a single controlnet.
|
1189 |
+
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
1190 |
+
The height in pixels of the generated image.
|
1191 |
+
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
1192 |
+
The width in pixels of the generated image.
|
1193 |
+
num_inference_steps (`int`, *optional*, defaults to 50):
|
1194 |
+
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
1195 |
+
expense of slower inference.
|
1196 |
+
guidance_scale (`float`, *optional*, defaults to 7.5):
|
1197 |
+
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
1198 |
+
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
1199 |
+
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
1200 |
+
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
1201 |
+
usually at the expense of lower image quality.
|
1202 |
+
negative_prompt (`str` or `List[str]`, *optional*):
|
1203 |
+
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
1204 |
+
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
1205 |
+
less than `1`).
|
1206 |
+
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
1207 |
+
The number of images to generate per prompt.
|
1208 |
+
eta (`float`, *optional*, defaults to 0.0):
|
1209 |
+
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
1210 |
+
[`schedulers.DDIMScheduler`], will be ignored for others.
|
1211 |
+
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
1212 |
+
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
|
1213 |
+
to make generation deterministic.
|
1214 |
+
latents (`torch.FloatTensor`, *optional*):
|
1215 |
+
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
|
1216 |
+
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
1217 |
+
tensor will ge generated by sampling using the supplied random `generator`.
|
1218 |
+
prompt_embeds (`torch.FloatTensor`, *optional*):
|
1219 |
+
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
1220 |
+
provided, text embeddings will be generated from `prompt` input argument.
|
1221 |
+
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
1222 |
+
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
1223 |
+
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
1224 |
+
argument.
|
1225 |
+
output_type (`str`, *optional*, defaults to `"pil"`):
|
1226 |
+
The output format of the generate image. Choose between
|
1227 |
+
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
1228 |
+
return_dict (`bool`, *optional*, defaults to `True`):
|
1229 |
+
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
1230 |
+
plain tuple.
|
1231 |
+
callback (`Callable`, *optional*):
|
1232 |
+
A function that will be called every `callback_steps` steps during inference. The function will be
|
1233 |
+
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
1234 |
+
callback_steps (`int`, *optional*, defaults to 1):
|
1235 |
+
The frequency at which the `callback` function will be called. If not specified, the callback will be
|
1236 |
+
called at every step.
|
1237 |
+
cross_attention_kwargs (`dict`, *optional*):
|
1238 |
+
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
1239 |
+
`self.processor` in
|
1240 |
+
[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
|
1241 |
+
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
|
1242 |
+
The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
|
1243 |
+
to the residual in the original unet. If multiple ControlNets are specified in init, you can set the
|
1244 |
+
corresponding scale as a list. Note that by default, we use a smaller conditioning scale for inpainting
|
1245 |
+
than for [`~StableDiffusionControlNetPipeline.__call__`].
|
1246 |
+
guess_mode (`bool`, *optional*, defaults to `False`):
|
1247 |
+
In this mode, the ControlNet encoder will try best to recognize the content of the input image even if
|
1248 |
+
you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.
|
1249 |
+
|
1250 |
+
Examples:
|
1251 |
+
|
1252 |
+
Returns:
|
1253 |
+
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
1254 |
+
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
|
1255 |
+
When returning a tuple, the first element is a list with the generated images, and the second element is a
|
1256 |
+
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
|
1257 |
+
(nsfw) content, according to the `safety_checker`.
|
1258 |
+
"""
|
1259 |
+
|
1260 |
+
def controlnet_forward(
|
1261 |
+
self,
|
1262 |
+
sample: torch.FloatTensor,
|
1263 |
+
timestep: Union[torch.Tensor, float, int],
|
1264 |
+
encoder_hidden_states: torch.Tensor,
|
1265 |
+
controlnet_cond: torch.FloatTensor,
|
1266 |
+
conditioning_scale: float = 1.0,
|
1267 |
+
class_labels: Optional[torch.Tensor] = None,
|
1268 |
+
timestep_cond: Optional[torch.Tensor] = None,
|
1269 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1270 |
+
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
|
1271 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
1272 |
+
guess_mode: bool = False,
|
1273 |
+
return_dict: bool = True,
|
1274 |
+
mask: Optional[torch.FloatTensor] = None,
|
1275 |
+
) -> Union[ControlNetOutput, Tuple]:
|
1276 |
+
"""
|
1277 |
+
The [`ControlNetModel`] forward method.
|
1278 |
+
|
1279 |
+
Args:
|
1280 |
+
sample (`torch.FloatTensor`):
|
1281 |
+
The noisy input tensor.
|
1282 |
+
timestep (`Union[torch.Tensor, float, int]`):
|
1283 |
+
The number of timesteps to denoise an input.
|
1284 |
+
encoder_hidden_states (`torch.Tensor`):
|
1285 |
+
The encoder hidden states.
|
1286 |
+
controlnet_cond (`torch.FloatTensor`):
|
1287 |
+
The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
|
1288 |
+
conditioning_scale (`float`, defaults to `1.0`):
|
1289 |
+
The scale factor for ControlNet outputs.
|
1290 |
+
class_labels (`torch.Tensor`, *optional*, defaults to `None`):
|
1291 |
+
Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
|
1292 |
+
timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):
|
1293 |
+
attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
|
1294 |
+
added_cond_kwargs (`dict`):
|
1295 |
+
Additional conditions for the Stable Diffusion XL UNet.
|
1296 |
+
cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):
|
1297 |
+
A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
|
1298 |
+
guess_mode (`bool`, defaults to `False`):
|
1299 |
+
In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if
|
1300 |
+
you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
|
1301 |
+
return_dict (`bool`, defaults to `True`):
|
1302 |
+
Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.
|
1303 |
+
|
1304 |
+
Returns:
|
1305 |
+
[`~models.controlnet.ControlNetOutput`] **or** `tuple`:
|
1306 |
+
If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is
|
1307 |
+
returned where the first element is the sample tensor.
|
1308 |
+
"""
|
1309 |
+
# check channel order
|
1310 |
+
channel_order = self.config.controlnet_conditioning_channel_order
|
1311 |
+
|
1312 |
+
if channel_order == "rgb":
|
1313 |
+
# in rgb order by default
|
1314 |
+
...
|
1315 |
+
elif channel_order == "bgr":
|
1316 |
+
controlnet_cond = torch.flip(controlnet_cond, dims=[1])
|
1317 |
+
else:
|
1318 |
+
raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")
|
1319 |
+
|
1320 |
+
# prepare attention_mask
|
1321 |
+
if attention_mask is not None:
|
1322 |
+
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
|
1323 |
+
attention_mask = attention_mask.unsqueeze(1)
|
1324 |
+
|
1325 |
+
# 1. time
|
1326 |
+
timesteps = timestep
|
1327 |
+
if not torch.is_tensor(timesteps):
|
1328 |
+
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
|
1329 |
+
# This would be a good case for the `match` statement (Python 3.10+)
|
1330 |
+
is_mps = sample.device.type == "mps"
|
1331 |
+
if isinstance(timestep, float):
|
1332 |
+
dtype = torch.float32 if is_mps else torch.float64
|
1333 |
+
else:
|
1334 |
+
dtype = torch.int32 if is_mps else torch.int64
|
1335 |
+
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
|
1336 |
+
elif len(timesteps.shape) == 0:
|
1337 |
+
timesteps = timesteps[None].to(sample.device)
|
1338 |
+
|
1339 |
+
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
1340 |
+
timesteps = timesteps.expand(sample.shape[0])
|
1341 |
+
|
1342 |
+
t_emb = self.time_proj(timesteps)
|
1343 |
+
|
1344 |
+
# timesteps does not contain any weights and will always return f32 tensors
|
1345 |
+
# but time_embedding might actually be running in fp16. so we need to cast here.
|
1346 |
+
# there might be better ways to encapsulate this.
|
1347 |
+
t_emb = t_emb.to(dtype=sample.dtype)
|
1348 |
+
|
1349 |
+
emb = self.time_embedding(t_emb, timestep_cond)
|
1350 |
+
aug_emb = None
|
1351 |
+
|
1352 |
+
if self.class_embedding is not None:
|
1353 |
+
if class_labels is None:
|
1354 |
+
raise ValueError("class_labels should be provided when num_class_embeds > 0")
|
1355 |
+
|
1356 |
+
if self.config.class_embed_type == "timestep":
|
1357 |
+
class_labels = self.time_proj(class_labels)
|
1358 |
+
|
1359 |
+
class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
|
1360 |
+
emb = emb + class_emb
|
1361 |
+
|
1362 |
+
if self.config.addition_embed_type is not None:
|
1363 |
+
if self.config.addition_embed_type == "text":
|
1364 |
+
aug_emb = self.add_embedding(encoder_hidden_states)
|
1365 |
+
|
1366 |
+
elif self.config.addition_embed_type == "text_time":
|
1367 |
+
if "text_embeds" not in added_cond_kwargs:
|
1368 |
+
raise ValueError(
|
1369 |
+
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
|
1370 |
+
)
|
1371 |
+
text_embeds = added_cond_kwargs.get("text_embeds")
|
1372 |
+
if "time_ids" not in added_cond_kwargs:
|
1373 |
+
raise ValueError(
|
1374 |
+
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
|
1375 |
+
)
|
1376 |
+
time_ids = added_cond_kwargs.get("time_ids")
|
1377 |
+
time_embeds = self.add_time_proj(time_ids.flatten())
|
1378 |
+
time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
|
1379 |
+
|
1380 |
+
add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
|
1381 |
+
add_embeds = add_embeds.to(emb.dtype)
|
1382 |
+
aug_emb = self.add_embedding(add_embeds)
|
1383 |
+
|
1384 |
+
emb = emb + aug_emb if aug_emb is not None else emb
|
1385 |
+
|
1386 |
+
# 2. pre-process
|
1387 |
+
sample = self.conv_in(sample)
|
1388 |
+
|
1389 |
+
controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
|
1390 |
+
|
1391 |
+
if mask is not None:
|
1392 |
+
sample = (1 - mask.to(sample.dtype)) * sample + mask.to(sample.dtype) * controlnet_cond
|
1393 |
+
else:
|
1394 |
+
sample = sample + controlnet_cond
|
1395 |
+
|
1396 |
+
# 3. down
|
1397 |
+
down_block_res_samples = (sample,)
|
1398 |
+
for downsample_block in self.down_blocks:
|
1399 |
+
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
|
1400 |
+
sample, res_samples = downsample_block(
|
1401 |
+
hidden_states=sample,
|
1402 |
+
temb=emb,
|
1403 |
+
encoder_hidden_states=encoder_hidden_states,
|
1404 |
+
attention_mask=attention_mask,
|
1405 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
1406 |
+
)
|
1407 |
+
else:
|
1408 |
+
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
|
1409 |
+
|
1410 |
+
down_block_res_samples += res_samples
|
1411 |
+
|
1412 |
+
# 4. mid
|
1413 |
+
if self.mid_block is not None:
|
1414 |
+
sample = self.mid_block(
|
1415 |
+
sample,
|
1416 |
+
emb,
|
1417 |
+
encoder_hidden_states=encoder_hidden_states,
|
1418 |
+
attention_mask=attention_mask,
|
1419 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
1420 |
+
)
|
1421 |
+
|
1422 |
+
# 5. Control net blocks
|
1423 |
+
|
1424 |
+
controlnet_down_block_res_samples = ()
|
1425 |
+
|
1426 |
+
for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
|
1427 |
+
down_block_res_sample = controlnet_block(down_block_res_sample)
|
1428 |
+
controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,)
|
1429 |
+
|
1430 |
+
down_block_res_samples = controlnet_down_block_res_samples
|
1431 |
+
|
1432 |
+
mid_block_res_sample = self.controlnet_mid_block(sample)
|
1433 |
+
|
1434 |
+
# 6. scaling
|
1435 |
+
if guess_mode and not self.config.global_pool_conditions:
|
1436 |
+
scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device) # 0.1 to 1.0
|
1437 |
+
|
1438 |
+
scales = scales * conditioning_scale
|
1439 |
+
down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]
|
1440 |
+
mid_block_res_sample = mid_block_res_sample * scales[-1] # last one
|
1441 |
+
else:
|
1442 |
+
down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
|
1443 |
+
mid_block_res_sample = mid_block_res_sample * conditioning_scale
|
1444 |
+
|
1445 |
+
if self.config.global_pool_conditions:
|
1446 |
+
down_block_res_samples = [
|
1447 |
+
torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples
|
1448 |
+
]
|
1449 |
+
mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)
|
1450 |
+
|
1451 |
+
if not return_dict:
|
1452 |
+
return (down_block_res_samples, mid_block_res_sample)
|
1453 |
+
|
1454 |
+
return ControlNetOutput(
|
1455 |
+
down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
|
1456 |
+
)
|
1457 |
+
self.controlnet.forward = controlnet_forward.__get__(self.controlnet, ControlNetModel)
|
1458 |
+
|
1459 |
+
def tiled_decode(
|
1460 |
+
self,
|
1461 |
+
z: torch.FloatTensor,
|
1462 |
+
return_dict: bool = True
|
1463 |
+
) -> Union[DecoderOutput, torch.FloatTensor]:
|
1464 |
+
r"""Decode a batch of images using a tiled decoder.
|
1465 |
+
|
1466 |
+
Args:
|
1467 |
+
When this option is enabled, the VAE will split the input tensor into tiles to compute decoding in several
|
1468 |
+
steps. This is useful to keep memory use constant regardless of image size. The end result of tiled
|
1469 |
+
decoding is: different from non-tiled decoding due to each tile using a different decoder.
|
1470 |
+
To avoid tiling artifacts, the tiles overlap and are blended together to form a smooth output.
|
1471 |
+
You may still see tile-sized changes in the look of the output, but they should be much less noticeable.
|
1472 |
+
z (`torch.FloatTensor`): Input batch of latent vectors. return_dict (`bool`, *optional*, defaults to
|
1473 |
+
`True`):
|
1474 |
+
Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
|
1475 |
+
"""
|
1476 |
+
_tile_overlap_factor = 1 - self.tile_overlap_factor
|
1477 |
+
overlap_size = int(self.tile_latent_min_size
|
1478 |
+
* _tile_overlap_factor)
|
1479 |
+
blend_extent = int(self.tile_sample_min_size
|
1480 |
+
* self.tile_overlap_factor)
|
1481 |
+
row_limit = self.tile_sample_min_size - blend_extent
|
1482 |
+
w = z.shape[3]
|
1483 |
+
z = torch.cat([z, z[:, :, :, :w // 4]], dim=-1)
|
1484 |
+
# Split z into overlapping 64x64 tiles and decode them separately.
|
1485 |
+
# The tiles have an overlap to avoid seams between tiles.
|
1486 |
+
|
1487 |
+
rows = []
|
1488 |
+
for i in range(0, z.shape[2], overlap_size):
|
1489 |
+
row = []
|
1490 |
+
tile = z[:, :, i:i + self.tile_latent_min_size, :]
|
1491 |
+
tile = self.post_quant_conv(tile)
|
1492 |
+
decoded = self.decoder(tile)
|
1493 |
+
vae_scale_factor = decoded.shape[-1] // tile.shape[-1]
|
1494 |
+
row.append(decoded)
|
1495 |
+
rows.append(row)
|
1496 |
+
result_rows = []
|
1497 |
+
for i, row in enumerate(rows):
|
1498 |
+
result_row = []
|
1499 |
+
for j, tile in enumerate(row):
|
1500 |
+
# blend the above tile and the left tile
|
1501 |
+
# to the current tile and add the current tile to the result row
|
1502 |
+
if i > 0:
|
1503 |
+
tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
|
1504 |
+
if j > 0:
|
1505 |
+
tile = self.blend_h(row[j - 1], tile, blend_extent)
|
1506 |
+
result_row.append(
|
1507 |
+
self.blend_h(
|
1508 |
+
tile[:, :, :row_limit, w * vae_scale_factor:],
|
1509 |
+
tile[:, :, :row_limit, :w * vae_scale_factor],
|
1510 |
+
tile.shape[-1] - w * vae_scale_factor))
|
1511 |
+
result_rows.append(torch.cat(result_row, dim=3))
|
1512 |
+
|
1513 |
+
dec = torch.cat(result_rows, dim=2)
|
1514 |
+
if not return_dict:
|
1515 |
+
return (dec, )
|
1516 |
+
|
1517 |
+
return DecoderOutput(sample=dec)
|
1518 |
+
|
1519 |
+
self.vae.tiled_decode = tiled_decode.__get__(self.vae, AutoencoderKL)
|
1520 |
+
|
1521 |
+
# 0. Default height and width to unet
|
1522 |
+
height, width = self._default_height_width(height, width, image)
|
1523 |
+
self.blend_extend = width // self.vae_scale_factor // 32
|
1524 |
+
|
1525 |
+
# 1. Check inputs. Raise error if not correct
|
1526 |
+
self.check_inputs(
|
1527 |
+
prompt,
|
1528 |
+
control_image,
|
1529 |
+
height,
|
1530 |
+
width,
|
1531 |
+
callback_steps,
|
1532 |
+
negative_prompt,
|
1533 |
+
prompt_embeds,
|
1534 |
+
negative_prompt_embeds,
|
1535 |
+
controlnet_conditioning_scale,
|
1536 |
+
)
|
1537 |
+
|
1538 |
+
# 2. Define call parameters
|
1539 |
+
if prompt is not None and isinstance(prompt, str):
|
1540 |
+
batch_size = 1
|
1541 |
+
elif prompt is not None and isinstance(prompt, list):
|
1542 |
+
batch_size = len(prompt)
|
1543 |
+
else:
|
1544 |
+
batch_size = prompt_embeds.shape[0]
|
1545 |
+
|
1546 |
+
device = self._execution_device
|
1547 |
+
self.controlnet.to(device)
|
1548 |
+
|
1549 |
+
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
1550 |
+
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
1551 |
+
# corresponds to doing no classifier free guidance.
|
1552 |
+
do_classifier_free_guidance = guidance_scale > 1.0
|
1553 |
+
|
1554 |
+
controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
|
1555 |
+
|
1556 |
+
if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
|
1557 |
+
controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
|
1558 |
+
|
1559 |
+
global_pool_conditions = (
|
1560 |
+
controlnet.config.global_pool_conditions
|
1561 |
+
if isinstance(controlnet, ControlNetModel)
|
1562 |
+
else controlnet.nets[0].config.global_pool_conditions
|
1563 |
+
)
|
1564 |
+
guess_mode = guess_mode or global_pool_conditions
|
1565 |
+
|
1566 |
+
# 3. Encode input prompt
|
1567 |
+
prompt_embeds = self._encode_prompt(
|
1568 |
+
prompt,
|
1569 |
+
device,
|
1570 |
+
num_images_per_prompt,
|
1571 |
+
do_classifier_free_guidance,
|
1572 |
+
negative_prompt,
|
1573 |
+
prompt_embeds=prompt_embeds,
|
1574 |
+
negative_prompt_embeds=negative_prompt_embeds,
|
1575 |
+
)
|
1576 |
+
# 4. Prepare image, and controlnet_conditioning_image
|
1577 |
+
image = prepare_image(image)
|
1578 |
+
|
1579 |
+
# 5. Prepare image
|
1580 |
+
if isinstance(controlnet, ControlNetModel):
|
1581 |
+
control_image = self.prepare_control_image(
|
1582 |
+
image=control_image,
|
1583 |
+
width=width,
|
1584 |
+
height=height,
|
1585 |
+
batch_size=batch_size * num_images_per_prompt,
|
1586 |
+
num_images_per_prompt=num_images_per_prompt,
|
1587 |
+
device=device,
|
1588 |
+
dtype=controlnet.dtype,
|
1589 |
+
do_classifier_free_guidance=do_classifier_free_guidance,
|
1590 |
+
guess_mode=guess_mode,
|
1591 |
+
)
|
1592 |
+
elif isinstance(controlnet, MultiControlNetModel):
|
1593 |
+
control_images = []
|
1594 |
+
|
1595 |
+
for control_image_ in control_image:
|
1596 |
+
control_image_ = self.prepare_control_image(
|
1597 |
+
image=control_image_,
|
1598 |
+
width=width,
|
1599 |
+
height=height,
|
1600 |
+
batch_size=batch_size * num_images_per_prompt,
|
1601 |
+
num_images_per_prompt=num_images_per_prompt,
|
1602 |
+
device=device,
|
1603 |
+
dtype=controlnet.dtype,
|
1604 |
+
do_classifier_free_guidance=do_classifier_free_guidance,
|
1605 |
+
guess_mode=guess_mode,
|
1606 |
+
)
|
1607 |
+
|
1608 |
+
control_images.append(control_image_)
|
1609 |
+
|
1610 |
+
control_image = control_images
|
1611 |
+
else:
|
1612 |
+
assert False
|
1613 |
+
|
1614 |
+
# 5. Prepare timesteps
|
1615 |
+
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
1616 |
+
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
|
1617 |
+
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
|
1618 |
+
|
1619 |
+
# 6. Prepare latent variables
|
1620 |
+
latents = self.prepare_latents(
|
1621 |
+
image,
|
1622 |
+
latent_timestep,
|
1623 |
+
batch_size,
|
1624 |
+
num_images_per_prompt,
|
1625 |
+
prompt_embeds.dtype,
|
1626 |
+
device,
|
1627 |
+
generator,
|
1628 |
+
)
|
1629 |
+
if mask is not None:
|
1630 |
+
mask = torch.cat([mask] * batch_size, dim=0)
|
1631 |
+
|
1632 |
+
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
1633 |
+
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
1634 |
+
|
1635 |
+
views_scheduler_status = [copy.deepcopy(self.scheduler.__dict__)]
|
1636 |
+
# value = torch.zeros_like(latents)
|
1637 |
+
latents = torch.cat([latents, latents[:, :, :, :self.blend_extend]], dim=-1)
|
1638 |
+
control_image = torch.cat([control_image, control_image[:, :, :, :self.blend_extend * self.vae_scale_factor]], dim=-1)
|
1639 |
+
if mask is not None:
|
1640 |
+
mask = torch.cat([mask] * batch_size, dim=0)
|
1641 |
+
mask = torch.cat([mask, mask[:, :, :, :self.blend_extend]], dim=-1)
|
1642 |
+
|
1643 |
+
|
1644 |
+
# 8. Denoising loop
|
1645 |
+
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
1646 |
+
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
1647 |
+
for i, t in enumerate(timesteps):
|
1648 |
+
# expand the latents if we are doing classifier free guidance
|
1649 |
+
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
1650 |
+
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
1651 |
+
if mask is not None:
|
1652 |
+
mask_input = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
|
1653 |
+
else:
|
1654 |
+
mask_input = None
|
1655 |
+
|
1656 |
+
# controlnet(s) inference
|
1657 |
+
if guess_mode and do_classifier_free_guidance:
|
1658 |
+
# Infer ControlNet only for the conditional batch.
|
1659 |
+
controlnet_latent_model_input = latents
|
1660 |
+
controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
|
1661 |
+
else:
|
1662 |
+
controlnet_latent_model_input = latent_model_input
|
1663 |
+
controlnet_prompt_embeds = prompt_embeds
|
1664 |
+
|
1665 |
+
down_block_res_samples, mid_block_res_sample = self.controlnet(
|
1666 |
+
controlnet_latent_model_input,
|
1667 |
+
t,
|
1668 |
+
encoder_hidden_states=controlnet_prompt_embeds,
|
1669 |
+
controlnet_cond=control_image,
|
1670 |
+
conditioning_scale=controlnet_conditioning_scale,
|
1671 |
+
guess_mode=guess_mode,
|
1672 |
+
return_dict=False,
|
1673 |
+
mask=mask_input,
|
1674 |
+
)
|
1675 |
+
|
1676 |
+
if guess_mode and do_classifier_free_guidance:
|
1677 |
+
# Infered ControlNet only for the conditional batch.
|
1678 |
+
# To apply the output of ControlNet to both the unconditional and conditional batches,
|
1679 |
+
# add 0 to the unconditional batch to keep it unchanged.
|
1680 |
+
down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
|
1681 |
+
mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
|
1682 |
+
|
1683 |
+
# predict the noise residual
|
1684 |
+
noise_pred = self.unet(
|
1685 |
+
latent_model_input,
|
1686 |
+
t,
|
1687 |
+
encoder_hidden_states=prompt_embeds,
|
1688 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
1689 |
+
down_block_additional_residuals=down_block_res_samples,
|
1690 |
+
mid_block_additional_residual=mid_block_res_sample,
|
1691 |
+
return_dict=False,
|
1692 |
+
)[0]
|
1693 |
+
|
1694 |
+
# perform guidance
|
1695 |
+
if do_classifier_free_guidance:
|
1696 |
+
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
1697 |
+
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
1698 |
+
|
1699 |
+
# compute the previous noisy sample x_t -> x_t-1
|
1700 |
+
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
1701 |
+
|
1702 |
+
# call the callback, if provided
|
1703 |
+
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
1704 |
+
progress_bar.update()
|
1705 |
+
if callback is not None and i % callback_steps == 0:
|
1706 |
+
callback(i, t, latents)
|
1707 |
+
# latents = value + 0.0
|
1708 |
+
latents = self.blend_h(latents, latents, self.blend_extend)
|
1709 |
+
latents = self.blend_h(latents, latents, self.blend_extend)
|
1710 |
+
latents = latents[:, :, :, :width // self.vae_scale_factor]
|
1711 |
+
|
1712 |
+
# If we do sequential model offloading, let's offload unet and controlnet
|
1713 |
+
# manually for max memory savings
|
1714 |
+
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
1715 |
+
self.unet.to("cpu")
|
1716 |
+
self.controlnet.to("cpu")
|
1717 |
+
torch.cuda.empty_cache()
|
1718 |
+
|
1719 |
+
if not output_type == "latent":
|
1720 |
+
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
|
1721 |
+
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
|
1722 |
+
else:
|
1723 |
+
image = latents
|
1724 |
+
has_nsfw_concept = None
|
1725 |
+
|
1726 |
+
if has_nsfw_concept is None:
|
1727 |
+
do_denormalize = [True] * image.shape[0]
|
1728 |
+
else:
|
1729 |
+
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
|
1730 |
+
|
1731 |
+
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
|
1732 |
+
|
1733 |
+
# Offload last model to CPU
|
1734 |
+
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
1735 |
+
self.final_offload_hook.offload()
|
1736 |
+
|
1737 |
+
if not return_dict:
|
1738 |
+
return (image, has_nsfw_concept)
|
1739 |
+
|
1740 |
+
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|
img2panoimg/pipeline_sr.py
ADDED
@@ -0,0 +1,1202 @@
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|
1 |
+
# Copyright © Alibaba, Inc. and its affiliates.
|
2 |
+
# The implementation here is modifed based on diffusers.StableDiffusionControlNetImg2ImgPipeline,
|
3 |
+
# originally Apache 2.0 License and public available at
|
4 |
+
# https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py
|
5 |
+
|
6 |
+
import copy
|
7 |
+
import re
|
8 |
+
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
|
9 |
+
|
10 |
+
import numpy as np
|
11 |
+
import PIL.Image
|
12 |
+
import torch
|
13 |
+
import torch.nn.functional as F
|
14 |
+
from diffusers import (AutoencoderKL, DiffusionPipeline,
|
15 |
+
StableDiffusionControlNetImg2ImgPipeline)
|
16 |
+
from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
|
17 |
+
from diffusers.models import ControlNetModel
|
18 |
+
try:
|
19 |
+
from diffusers.models.autoencoders.vae import DecoderOutput
|
20 |
+
except:
|
21 |
+
from diffusers.models.vae import DecoderOutput
|
22 |
+
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
|
23 |
+
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
|
24 |
+
from diffusers.utils import logging, replace_example_docstring
|
25 |
+
from diffusers.utils.torch_utils import is_compiled_module
|
26 |
+
|
27 |
+
from transformers import CLIPTokenizer
|
28 |
+
|
29 |
+
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
30 |
+
|
31 |
+
EXAMPLE_DOC_STRING = """
|
32 |
+
Examples:
|
33 |
+
```py
|
34 |
+
>>> import torch
|
35 |
+
>>> from PIL import Image
|
36 |
+
>>> from txt2panoimage.pipeline_sr import StableDiffusionControlNetImg2ImgPanoPipeline
|
37 |
+
>>> base_model_path = "models/sr-base"
|
38 |
+
>>> controlnet_path = "models/sr-control"
|
39 |
+
>>> controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
|
40 |
+
>>> pipe = StableDiffusionControlNetImg2ImgPanoPipeline.from_pretrained(base_model_path, controlnet=controlnet,
|
41 |
+
... torch_dtype=torch.float16)
|
42 |
+
>>> pipe.vae.enable_tiling()
|
43 |
+
>>> # remove following line if xformers is not installed
|
44 |
+
>>> pipe.enable_xformers_memory_efficient_attention()
|
45 |
+
>>> pipe.enable_model_cpu_offload()
|
46 |
+
>>> input_image_path = 'data/test.png'
|
47 |
+
>>> image = Image.open(input_image_path)
|
48 |
+
>>> image = pipe(
|
49 |
+
... "futuristic-looking woman",
|
50 |
+
... num_inference_steps=20,
|
51 |
+
... image=image,
|
52 |
+
... height=768,
|
53 |
+
... width=1536,
|
54 |
+
... control_image=image,
|
55 |
+
... ).images[0]
|
56 |
+
|
57 |
+
```
|
58 |
+
"""
|
59 |
+
|
60 |
+
re_attention = re.compile(
|
61 |
+
r"""
|
62 |
+
\\\(|
|
63 |
+
\\\)|
|
64 |
+
\\\[|
|
65 |
+
\\]|
|
66 |
+
\\\\|
|
67 |
+
\\|
|
68 |
+
\(|
|
69 |
+
\[|
|
70 |
+
:([+-]?[.\d]+)\)|
|
71 |
+
\)|
|
72 |
+
]|
|
73 |
+
[^\\()\[\]:]+|
|
74 |
+
:
|
75 |
+
""",
|
76 |
+
re.X,
|
77 |
+
)
|
78 |
+
|
79 |
+
|
80 |
+
def parse_prompt_attention(text):
|
81 |
+
"""
|
82 |
+
Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
|
83 |
+
Accepted tokens are:
|
84 |
+
(abc) - increases attention to abc by a multiplier of 1.1
|
85 |
+
(abc:3.12) - increases attention to abc by a multiplier of 3.12
|
86 |
+
[abc] - decreases attention to abc by a multiplier of 1.1
|
87 |
+
"""
|
88 |
+
|
89 |
+
res = []
|
90 |
+
round_brackets = []
|
91 |
+
square_brackets = []
|
92 |
+
|
93 |
+
round_bracket_multiplier = 1.1
|
94 |
+
square_bracket_multiplier = 1 / 1.1
|
95 |
+
|
96 |
+
def multiply_range(start_position, multiplier):
|
97 |
+
for p in range(start_position, len(res)):
|
98 |
+
res[p][1] *= multiplier
|
99 |
+
|
100 |
+
for m in re_attention.finditer(text):
|
101 |
+
text = m.group(0)
|
102 |
+
weight = m.group(1)
|
103 |
+
|
104 |
+
if text.startswith('\\'):
|
105 |
+
res.append([text[1:], 1.0])
|
106 |
+
elif text == '(':
|
107 |
+
round_brackets.append(len(res))
|
108 |
+
elif text == '[':
|
109 |
+
square_brackets.append(len(res))
|
110 |
+
elif weight is not None and len(round_brackets) > 0:
|
111 |
+
multiply_range(round_brackets.pop(), float(weight))
|
112 |
+
elif text == ')' and len(round_brackets) > 0:
|
113 |
+
multiply_range(round_brackets.pop(), round_bracket_multiplier)
|
114 |
+
elif text == ']' and len(square_brackets) > 0:
|
115 |
+
multiply_range(square_brackets.pop(), square_bracket_multiplier)
|
116 |
+
else:
|
117 |
+
res.append([text, 1.0])
|
118 |
+
|
119 |
+
for pos in round_brackets:
|
120 |
+
multiply_range(pos, round_bracket_multiplier)
|
121 |
+
|
122 |
+
for pos in square_brackets:
|
123 |
+
multiply_range(pos, square_bracket_multiplier)
|
124 |
+
|
125 |
+
if len(res) == 0:
|
126 |
+
res = [['', 1.0]]
|
127 |
+
|
128 |
+
# merge runs of identical weights
|
129 |
+
i = 0
|
130 |
+
while i + 1 < len(res):
|
131 |
+
if res[i][1] == res[i + 1][1]:
|
132 |
+
res[i][0] += res[i + 1][0]
|
133 |
+
res.pop(i + 1)
|
134 |
+
else:
|
135 |
+
i += 1
|
136 |
+
|
137 |
+
return res
|
138 |
+
|
139 |
+
|
140 |
+
def get_prompts_with_weights(pipe: DiffusionPipeline, prompt: List[str],
|
141 |
+
max_length: int):
|
142 |
+
r"""
|
143 |
+
Tokenize a list of prompts and return its tokens with weights of each token.
|
144 |
+
|
145 |
+
No padding, starting or ending token is included.
|
146 |
+
"""
|
147 |
+
tokens = []
|
148 |
+
weights = []
|
149 |
+
truncated = False
|
150 |
+
for text in prompt:
|
151 |
+
texts_and_weights = parse_prompt_attention(text)
|
152 |
+
text_token = []
|
153 |
+
text_weight = []
|
154 |
+
for word, weight in texts_and_weights:
|
155 |
+
# tokenize and discard the starting and the ending token
|
156 |
+
token = pipe.tokenizer(word).input_ids[1:-1]
|
157 |
+
text_token += token
|
158 |
+
# copy the weight by length of token
|
159 |
+
text_weight += [weight] * len(token)
|
160 |
+
# stop if the text is too long (longer than truncation limit)
|
161 |
+
if len(text_token) > max_length:
|
162 |
+
truncated = True
|
163 |
+
break
|
164 |
+
# truncate
|
165 |
+
if len(text_token) > max_length:
|
166 |
+
truncated = True
|
167 |
+
text_token = text_token[:max_length]
|
168 |
+
text_weight = text_weight[:max_length]
|
169 |
+
tokens.append(text_token)
|
170 |
+
weights.append(text_weight)
|
171 |
+
if truncated:
|
172 |
+
logger.warning(
|
173 |
+
'Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples'
|
174 |
+
)
|
175 |
+
return tokens, weights
|
176 |
+
|
177 |
+
|
178 |
+
def pad_tokens_and_weights(tokens,
|
179 |
+
weights,
|
180 |
+
max_length,
|
181 |
+
bos,
|
182 |
+
eos,
|
183 |
+
pad,
|
184 |
+
no_boseos_middle=True,
|
185 |
+
chunk_length=77):
|
186 |
+
r"""
|
187 |
+
Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
|
188 |
+
"""
|
189 |
+
max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
|
190 |
+
weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
|
191 |
+
for i in range(len(tokens)):
|
192 |
+
tokens[i] = [
|
193 |
+
bos
|
194 |
+
] + tokens[i] + [pad] * (max_length - 1 - len(tokens[i]) - 1) + [eos]
|
195 |
+
if no_boseos_middle:
|
196 |
+
weights[i] = [1.0] + weights[i] + [1.0] * (
|
197 |
+
max_length - 1 - len(weights[i]))
|
198 |
+
else:
|
199 |
+
w = []
|
200 |
+
if len(weights[i]) == 0:
|
201 |
+
w = [1.0] * weights_length
|
202 |
+
else:
|
203 |
+
for j in range(max_embeddings_multiples):
|
204 |
+
w.append(1.0) # weight for starting token in this chunk
|
205 |
+
w += weights[i][j * (chunk_length - 2):min(
|
206 |
+
len(weights[i]), (j + 1) * (chunk_length - 2))]
|
207 |
+
w.append(1.0) # weight for ending token in this chunk
|
208 |
+
w += [1.0] * (weights_length - len(w))
|
209 |
+
weights[i] = w[:]
|
210 |
+
|
211 |
+
return tokens, weights
|
212 |
+
|
213 |
+
|
214 |
+
def get_unweighted_text_embeddings(
|
215 |
+
pipe: DiffusionPipeline,
|
216 |
+
text_input: torch.Tensor,
|
217 |
+
chunk_length: int,
|
218 |
+
no_boseos_middle: Optional[bool] = True,
|
219 |
+
):
|
220 |
+
"""
|
221 |
+
When the length of tokens is a multiple of the capacity of the text encoder,
|
222 |
+
it should be split into chunks and sent to the text encoder individually.
|
223 |
+
"""
|
224 |
+
max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)
|
225 |
+
if max_embeddings_multiples > 1:
|
226 |
+
text_embeddings = []
|
227 |
+
for i in range(max_embeddings_multiples):
|
228 |
+
# extract the i-th chunk
|
229 |
+
text_input_chunk = text_input[:, i * (chunk_length - 2):(i + 1)
|
230 |
+
* (chunk_length - 2) + 2].clone()
|
231 |
+
|
232 |
+
# cover the head and the tail by the starting and the ending tokens
|
233 |
+
text_input_chunk[:, 0] = text_input[0, 0]
|
234 |
+
text_input_chunk[:, -1] = text_input[0, -1]
|
235 |
+
text_embedding = pipe.text_encoder(text_input_chunk)[0]
|
236 |
+
|
237 |
+
if no_boseos_middle:
|
238 |
+
if i == 0:
|
239 |
+
# discard the ending token
|
240 |
+
text_embedding = text_embedding[:, :-1]
|
241 |
+
elif i == max_embeddings_multiples - 1:
|
242 |
+
# discard the starting token
|
243 |
+
text_embedding = text_embedding[:, 1:]
|
244 |
+
else:
|
245 |
+
# discard both starting and ending tokens
|
246 |
+
text_embedding = text_embedding[:, 1:-1]
|
247 |
+
|
248 |
+
text_embeddings.append(text_embedding)
|
249 |
+
text_embeddings = torch.concat(text_embeddings, axis=1)
|
250 |
+
else:
|
251 |
+
text_embeddings = pipe.text_encoder(text_input)[0]
|
252 |
+
return text_embeddings
|
253 |
+
|
254 |
+
|
255 |
+
def get_weighted_text_embeddings(
|
256 |
+
pipe: DiffusionPipeline,
|
257 |
+
prompt: Union[str, List[str]],
|
258 |
+
uncond_prompt: Optional[Union[str, List[str]]] = None,
|
259 |
+
max_embeddings_multiples: Optional[int] = 3,
|
260 |
+
no_boseos_middle: Optional[bool] = False,
|
261 |
+
skip_parsing: Optional[bool] = False,
|
262 |
+
skip_weighting: Optional[bool] = False,
|
263 |
+
):
|
264 |
+
r"""
|
265 |
+
Prompts can be assigned with local weights using brackets. For example,
|
266 |
+
prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
|
267 |
+
and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.
|
268 |
+
|
269 |
+
Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.
|
270 |
+
|
271 |
+
Args:
|
272 |
+
pipe (`DiffusionPipeline`):
|
273 |
+
Pipe to provide access to the tokenizer and the text encoder.
|
274 |
+
prompt (`str` or `List[str]`):
|
275 |
+
The prompt or prompts to guide the image generation.
|
276 |
+
uncond_prompt (`str` or `List[str]`):
|
277 |
+
The unconditional prompt or prompts for guide the image generation. If unconditional prompt
|
278 |
+
is provided, the embeddings of prompt and uncond_prompt are concatenated.
|
279 |
+
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
|
280 |
+
The max multiple length of prompt embeddings compared to the max output length of text encoder.
|
281 |
+
no_boseos_middle (`bool`, *optional*, defaults to `False`):
|
282 |
+
If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and
|
283 |
+
ending token in each of the chunk in the middle.
|
284 |
+
skip_parsing (`bool`, *optional*, defaults to `False`):
|
285 |
+
Skip the parsing of brackets.
|
286 |
+
skip_weighting (`bool`, *optional*, defaults to `False`):
|
287 |
+
Skip the weighting. When the parsing is skipped, it is forced True.
|
288 |
+
"""
|
289 |
+
max_length = (pipe.tokenizer.model_max_length
|
290 |
+
- 2) * max_embeddings_multiples + 2
|
291 |
+
if isinstance(prompt, str):
|
292 |
+
prompt = [prompt]
|
293 |
+
|
294 |
+
if not skip_parsing:
|
295 |
+
prompt_tokens, prompt_weights = get_prompts_with_weights(
|
296 |
+
pipe, prompt, max_length - 2)
|
297 |
+
if uncond_prompt is not None:
|
298 |
+
if isinstance(uncond_prompt, str):
|
299 |
+
uncond_prompt = [uncond_prompt]
|
300 |
+
uncond_tokens, uncond_weights = get_prompts_with_weights(
|
301 |
+
pipe, uncond_prompt, max_length - 2)
|
302 |
+
else:
|
303 |
+
prompt_tokens = [
|
304 |
+
token[1:-1] for token in pipe.tokenizer(
|
305 |
+
prompt, max_length=max_length, truncation=True).input_ids
|
306 |
+
]
|
307 |
+
prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
|
308 |
+
if uncond_prompt is not None:
|
309 |
+
if isinstance(uncond_prompt, str):
|
310 |
+
uncond_prompt = [uncond_prompt]
|
311 |
+
uncond_tokens = [
|
312 |
+
token[1:-1] for token in pipe.tokenizer(
|
313 |
+
uncond_prompt, max_length=max_length,
|
314 |
+
truncation=True).input_ids
|
315 |
+
]
|
316 |
+
uncond_weights = [[1.0] * len(token) for token in uncond_tokens]
|
317 |
+
|
318 |
+
# round up the longest length of tokens to a multiple of (model_max_length - 2)
|
319 |
+
max_length = max([len(token) for token in prompt_tokens])
|
320 |
+
if uncond_prompt is not None:
|
321 |
+
max_length = max(max_length,
|
322 |
+
max([len(token) for token in uncond_tokens]))
|
323 |
+
|
324 |
+
max_embeddings_multiples = min(
|
325 |
+
max_embeddings_multiples,
|
326 |
+
(max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1,
|
327 |
+
)
|
328 |
+
max_embeddings_multiples = max(1, max_embeddings_multiples)
|
329 |
+
max_length = (pipe.tokenizer.model_max_length
|
330 |
+
- 2) * max_embeddings_multiples + 2
|
331 |
+
|
332 |
+
# pad the length of tokens and weights
|
333 |
+
bos = pipe.tokenizer.bos_token_id
|
334 |
+
eos = pipe.tokenizer.eos_token_id
|
335 |
+
pad = getattr(pipe.tokenizer, 'pad_token_id', eos)
|
336 |
+
prompt_tokens, prompt_weights = pad_tokens_and_weights(
|
337 |
+
prompt_tokens,
|
338 |
+
prompt_weights,
|
339 |
+
max_length,
|
340 |
+
bos,
|
341 |
+
eos,
|
342 |
+
pad,
|
343 |
+
no_boseos_middle=no_boseos_middle,
|
344 |
+
chunk_length=pipe.tokenizer.model_max_length,
|
345 |
+
)
|
346 |
+
prompt_tokens = torch.tensor(
|
347 |
+
prompt_tokens, dtype=torch.long, device=pipe.device)
|
348 |
+
if uncond_prompt is not None:
|
349 |
+
uncond_tokens, uncond_weights = pad_tokens_and_weights(
|
350 |
+
uncond_tokens,
|
351 |
+
uncond_weights,
|
352 |
+
max_length,
|
353 |
+
bos,
|
354 |
+
eos,
|
355 |
+
pad,
|
356 |
+
no_boseos_middle=no_boseos_middle,
|
357 |
+
chunk_length=pipe.tokenizer.model_max_length,
|
358 |
+
)
|
359 |
+
uncond_tokens = torch.tensor(
|
360 |
+
uncond_tokens, dtype=torch.long, device=pipe.device)
|
361 |
+
|
362 |
+
# get the embeddings
|
363 |
+
text_embeddings = get_unweighted_text_embeddings(
|
364 |
+
pipe,
|
365 |
+
prompt_tokens,
|
366 |
+
pipe.tokenizer.model_max_length,
|
367 |
+
no_boseos_middle=no_boseos_middle,
|
368 |
+
)
|
369 |
+
prompt_weights = torch.tensor(
|
370 |
+
prompt_weights,
|
371 |
+
dtype=text_embeddings.dtype,
|
372 |
+
device=text_embeddings.device)
|
373 |
+
if uncond_prompt is not None:
|
374 |
+
uncond_embeddings = get_unweighted_text_embeddings(
|
375 |
+
pipe,
|
376 |
+
uncond_tokens,
|
377 |
+
pipe.tokenizer.model_max_length,
|
378 |
+
no_boseos_middle=no_boseos_middle,
|
379 |
+
)
|
380 |
+
uncond_weights = torch.tensor(
|
381 |
+
uncond_weights,
|
382 |
+
dtype=uncond_embeddings.dtype,
|
383 |
+
device=uncond_embeddings.device)
|
384 |
+
|
385 |
+
# assign weights to the prompts and normalize in the sense of mean
|
386 |
+
# TODO: should we normalize by chunk or in a whole (current implementation)?
|
387 |
+
if (not skip_parsing) and (not skip_weighting):
|
388 |
+
previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(
|
389 |
+
text_embeddings.dtype)
|
390 |
+
text_embeddings *= prompt_weights.unsqueeze(-1)
|
391 |
+
current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(
|
392 |
+
text_embeddings.dtype)
|
393 |
+
text_embeddings *= (previous_mean
|
394 |
+
/ current_mean).unsqueeze(-1).unsqueeze(-1)
|
395 |
+
if uncond_prompt is not None:
|
396 |
+
previous_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(
|
397 |
+
uncond_embeddings.dtype)
|
398 |
+
uncond_embeddings *= uncond_weights.unsqueeze(-1)
|
399 |
+
current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(
|
400 |
+
uncond_embeddings.dtype)
|
401 |
+
uncond_embeddings *= (previous_mean
|
402 |
+
/ current_mean).unsqueeze(-1).unsqueeze(-1)
|
403 |
+
|
404 |
+
if uncond_prompt is not None:
|
405 |
+
return text_embeddings, uncond_embeddings
|
406 |
+
return text_embeddings, None
|
407 |
+
|
408 |
+
|
409 |
+
def prepare_image(image):
|
410 |
+
if isinstance(image, torch.Tensor):
|
411 |
+
# Batch single image
|
412 |
+
if image.ndim == 3:
|
413 |
+
image = image.unsqueeze(0)
|
414 |
+
|
415 |
+
image = image.to(dtype=torch.float32)
|
416 |
+
else:
|
417 |
+
# preprocess image
|
418 |
+
if isinstance(image, (PIL.Image.Image, np.ndarray)):
|
419 |
+
image = [image]
|
420 |
+
|
421 |
+
if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
|
422 |
+
image = [np.array(i.convert('RGB'))[None, :] for i in image]
|
423 |
+
image = np.concatenate(image, axis=0)
|
424 |
+
elif isinstance(image, list) and isinstance(image[0], np.ndarray):
|
425 |
+
image = np.concatenate([i[None, :] for i in image], axis=0)
|
426 |
+
|
427 |
+
image = image.transpose(0, 3, 1, 2)
|
428 |
+
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
|
429 |
+
|
430 |
+
return image
|
431 |
+
|
432 |
+
|
433 |
+
class StableDiffusionControlNetImg2ImgPanoPipeline(
|
434 |
+
StableDiffusionControlNetImg2ImgPipeline):
|
435 |
+
r"""
|
436 |
+
Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.
|
437 |
+
|
438 |
+
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
|
439 |
+
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
|
440 |
+
|
441 |
+
In addition the pipeline inherits the following loading methods:
|
442 |
+
- *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
|
443 |
+
|
444 |
+
Args:
|
445 |
+
vae ([`AutoencoderKL`]):
|
446 |
+
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
|
447 |
+
text_encoder ([`CLIPTextModel`]):
|
448 |
+
Frozen text-encoder. Stable Diffusion uses the text portion of
|
449 |
+
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
|
450 |
+
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
|
451 |
+
tokenizer (`CLIPTokenizer`):
|
452 |
+
Tokenizer of class
|
453 |
+
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/
|
454 |
+
model_doc/clip#transformers.CLIPTokenizer).
|
455 |
+
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
|
456 |
+
controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
|
457 |
+
Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets
|
458 |
+
as a list, the outputs from each ControlNet are added together to create one combined additional
|
459 |
+
conditioning.
|
460 |
+
scheduler ([`SchedulerMixin`]):
|
461 |
+
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
|
462 |
+
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
|
463 |
+
safety_checker ([`StableDiffusionSafetyChecker`]):
|
464 |
+
Classification module that estimates whether generated images could be considered offensive or harmful.
|
465 |
+
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
|
466 |
+
feature_extractor ([`CLIPImageProcessor`]):
|
467 |
+
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
|
468 |
+
"""
|
469 |
+
_optional_components = ['safety_checker', 'feature_extractor']
|
470 |
+
|
471 |
+
def check_inputs(
|
472 |
+
self,
|
473 |
+
prompt,
|
474 |
+
image,
|
475 |
+
height,
|
476 |
+
width,
|
477 |
+
callback_steps,
|
478 |
+
negative_prompt=None,
|
479 |
+
prompt_embeds=None,
|
480 |
+
negative_prompt_embeds=None,
|
481 |
+
controlnet_conditioning_scale=1.0,
|
482 |
+
):
|
483 |
+
if height % 8 != 0 or width % 8 != 0:
|
484 |
+
raise ValueError(
|
485 |
+
f'`height` and `width` have to be divisible by 8 but are {height} and {width}.'
|
486 |
+
)
|
487 |
+
condition_1 = callback_steps is not None
|
488 |
+
condition_2 = not isinstance(callback_steps,
|
489 |
+
int) or callback_steps <= 0
|
490 |
+
if (callback_steps is None) or (condition_1 and condition_2):
|
491 |
+
raise ValueError(
|
492 |
+
f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
|
493 |
+
f' {type(callback_steps)}.')
|
494 |
+
if prompt is not None and prompt_embeds is not None:
|
495 |
+
raise ValueError(
|
496 |
+
f'Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to'
|
497 |
+
' only forward one of the two.')
|
498 |
+
elif prompt is None and prompt_embeds is None:
|
499 |
+
raise ValueError(
|
500 |
+
'Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.'
|
501 |
+
)
|
502 |
+
elif prompt is not None and (not isinstance(prompt, str)
|
503 |
+
and not isinstance(prompt, list)):
|
504 |
+
raise ValueError(
|
505 |
+
f'`prompt` has to be of type `str` or `list` but is {type(prompt)}'
|
506 |
+
)
|
507 |
+
if negative_prompt is not None and negative_prompt_embeds is not None:
|
508 |
+
raise ValueError(
|
509 |
+
f'Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:'
|
510 |
+
f' {negative_prompt_embeds}. Please make sure to only forward one of the two.'
|
511 |
+
)
|
512 |
+
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
513 |
+
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
514 |
+
raise ValueError(
|
515 |
+
'`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but'
|
516 |
+
f' got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`'
|
517 |
+
f' {negative_prompt_embeds.shape}.')
|
518 |
+
# `prompt` needs more sophisticated handling when there are multiple
|
519 |
+
# conditionings.
|
520 |
+
if isinstance(self.controlnet, MultiControlNetModel):
|
521 |
+
if isinstance(prompt, list):
|
522 |
+
logger.warning(
|
523 |
+
f'You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}'
|
524 |
+
' prompts. The conditionings will be fixed across the prompts.'
|
525 |
+
)
|
526 |
+
# Check `image`
|
527 |
+
is_compiled = hasattr(
|
528 |
+
F, 'scaled_dot_product_attention') and isinstance(
|
529 |
+
self.controlnet, torch._dynamo.eval_frame.OptimizedModule)
|
530 |
+
if (isinstance(self.controlnet, ControlNetModel) or is_compiled
|
531 |
+
and isinstance(self.controlnet._orig_mod, ControlNetModel)):
|
532 |
+
self.check_image(image, prompt, prompt_embeds)
|
533 |
+
elif (isinstance(self.controlnet, MultiControlNetModel) or is_compiled
|
534 |
+
and isinstance(self.controlnet._orig_mod, MultiControlNetModel)):
|
535 |
+
if not isinstance(image, list):
|
536 |
+
raise TypeError(
|
537 |
+
'For multiple controlnets: `image` must be type `list`')
|
538 |
+
# When `image` is a nested list:
|
539 |
+
# (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])
|
540 |
+
elif any(isinstance(i, list) for i in image):
|
541 |
+
raise ValueError(
|
542 |
+
'A single batch of multiple conditionings are supported at the moment.'
|
543 |
+
)
|
544 |
+
elif len(image) != len(self.controlnet.nets):
|
545 |
+
raise ValueError(
|
546 |
+
'For multiple controlnets: `image` must have the same length as the number of controlnets.'
|
547 |
+
)
|
548 |
+
for image_ in image:
|
549 |
+
self.check_image(image_, prompt, prompt_embeds)
|
550 |
+
else:
|
551 |
+
assert False
|
552 |
+
# Check `controlnet_conditioning_scale`
|
553 |
+
if (isinstance(self.controlnet, ControlNetModel) or is_compiled
|
554 |
+
and isinstance(self.controlnet._orig_mod, ControlNetModel)):
|
555 |
+
if not isinstance(controlnet_conditioning_scale, float):
|
556 |
+
raise TypeError(
|
557 |
+
'For single controlnet: `controlnet_conditioning_scale` must be type `float`.'
|
558 |
+
)
|
559 |
+
elif (isinstance(self.controlnet, MultiControlNetModel) or is_compiled
|
560 |
+
and isinstance(self.controlnet._orig_mod, MultiControlNetModel)):
|
561 |
+
if isinstance(controlnet_conditioning_scale, list):
|
562 |
+
if any(
|
563 |
+
isinstance(i, list)
|
564 |
+
for i in controlnet_conditioning_scale):
|
565 |
+
raise ValueError(
|
566 |
+
'A single batch of multiple conditionings are supported at the moment.'
|
567 |
+
)
|
568 |
+
elif isinstance(
|
569 |
+
controlnet_conditioning_scale,
|
570 |
+
list) and len(controlnet_conditioning_scale) != len(
|
571 |
+
self.controlnet.nets):
|
572 |
+
raise ValueError(
|
573 |
+
'For multiple controlnets: When `controlnet_conditioning_scale` '
|
574 |
+
'is specified as `list`, it must have'
|
575 |
+
' the same length as the number of controlnets')
|
576 |
+
else:
|
577 |
+
assert False
|
578 |
+
|
579 |
+
def _default_height_width(self, height, width, image):
|
580 |
+
# NOTE: It is possible that a list of images have different
|
581 |
+
# dimensions for each image, so just checking the first image
|
582 |
+
# is not _exactly_ correct, but it is simple.
|
583 |
+
while isinstance(image, list):
|
584 |
+
image = image[0]
|
585 |
+
if height is None:
|
586 |
+
if isinstance(image, PIL.Image.Image):
|
587 |
+
height = image.height
|
588 |
+
elif isinstance(image, torch.Tensor):
|
589 |
+
height = image.shape[2]
|
590 |
+
height = (height // 8) * 8 # round down to nearest multiple of 8
|
591 |
+
if width is None:
|
592 |
+
if isinstance(image, PIL.Image.Image):
|
593 |
+
width = image.width
|
594 |
+
elif isinstance(image, torch.Tensor):
|
595 |
+
width = image.shape[3]
|
596 |
+
width = (width // 8) * 8 # round down to nearest multiple of 8
|
597 |
+
return height, width
|
598 |
+
|
599 |
+
def _encode_prompt(
|
600 |
+
self,
|
601 |
+
prompt,
|
602 |
+
device,
|
603 |
+
num_images_per_prompt,
|
604 |
+
do_classifier_free_guidance,
|
605 |
+
negative_prompt=None,
|
606 |
+
max_embeddings_multiples=3,
|
607 |
+
prompt_embeds: Optional[torch.FloatTensor] = None,
|
608 |
+
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
609 |
+
lora_scale: Optional[float] = None,
|
610 |
+
):
|
611 |
+
r"""
|
612 |
+
Encodes the prompt into text encoder hidden states.
|
613 |
+
|
614 |
+
Args:
|
615 |
+
prompt (`str` or `list(int)`):
|
616 |
+
prompt to be encoded
|
617 |
+
device: (`torch.device`):
|
618 |
+
torch device
|
619 |
+
num_images_per_prompt (`int`):
|
620 |
+
number of images that should be generated per prompt
|
621 |
+
do_classifier_free_guidance (`bool`):
|
622 |
+
whether to use classifier free guidance or not
|
623 |
+
negative_prompt (`str` or `List[str]`):
|
624 |
+
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
|
625 |
+
if `guidance_scale` is less than `1`).
|
626 |
+
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
|
627 |
+
The max multiple length of prompt embeddings compared to the max output length of text encoder.
|
628 |
+
"""
|
629 |
+
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
|
630 |
+
self._lora_scale = lora_scale
|
631 |
+
|
632 |
+
if prompt is not None and isinstance(prompt, str):
|
633 |
+
batch_size = 1
|
634 |
+
elif prompt is not None and isinstance(prompt, list):
|
635 |
+
batch_size = len(prompt)
|
636 |
+
else:
|
637 |
+
batch_size = prompt_embeds.shape[0]
|
638 |
+
|
639 |
+
if negative_prompt_embeds is None:
|
640 |
+
if negative_prompt is None:
|
641 |
+
negative_prompt = [''] * batch_size
|
642 |
+
elif isinstance(negative_prompt, str):
|
643 |
+
negative_prompt = [negative_prompt] * batch_size
|
644 |
+
if batch_size != len(negative_prompt):
|
645 |
+
raise ValueError(
|
646 |
+
f'`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:'
|
647 |
+
f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
|
648 |
+
' the batch size of `prompt`.')
|
649 |
+
if prompt_embeds is None or negative_prompt_embeds is None:
|
650 |
+
if isinstance(self, TextualInversionLoaderMixin):
|
651 |
+
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
|
652 |
+
if do_classifier_free_guidance and negative_prompt_embeds is None:
|
653 |
+
negative_prompt = self.maybe_convert_prompt(
|
654 |
+
negative_prompt, self.tokenizer)
|
655 |
+
|
656 |
+
prompt_embeds1, negative_prompt_embeds1 = get_weighted_text_embeddings(
|
657 |
+
pipe=self,
|
658 |
+
prompt=prompt,
|
659 |
+
uncond_prompt=negative_prompt
|
660 |
+
if do_classifier_free_guidance else None,
|
661 |
+
max_embeddings_multiples=max_embeddings_multiples,
|
662 |
+
)
|
663 |
+
if prompt_embeds is None:
|
664 |
+
prompt_embeds = prompt_embeds1
|
665 |
+
if negative_prompt_embeds is None:
|
666 |
+
negative_prompt_embeds = negative_prompt_embeds1
|
667 |
+
|
668 |
+
bs_embed, seq_len, _ = prompt_embeds.shape
|
669 |
+
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
670 |
+
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
671 |
+
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt,
|
672 |
+
seq_len, -1)
|
673 |
+
|
674 |
+
if do_classifier_free_guidance:
|
675 |
+
bs_embed, seq_len, _ = negative_prompt_embeds.shape
|
676 |
+
negative_prompt_embeds = negative_prompt_embeds.repeat(
|
677 |
+
1, num_images_per_prompt, 1)
|
678 |
+
negative_prompt_embeds = negative_prompt_embeds.view(
|
679 |
+
bs_embed * num_images_per_prompt, seq_len, -1)
|
680 |
+
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
681 |
+
|
682 |
+
return prompt_embeds
|
683 |
+
|
684 |
+
def denoise_latents(self, latents, t, prompt_embeds, control_image,
|
685 |
+
controlnet_conditioning_scale, guess_mode,
|
686 |
+
cross_attention_kwargs, do_classifier_free_guidance,
|
687 |
+
guidance_scale, extra_step_kwargs,
|
688 |
+
views_scheduler_status):
|
689 |
+
# expand the latents if we are doing classifier free guidance
|
690 |
+
latent_model_input = torch.cat(
|
691 |
+
[latents] * 2) if do_classifier_free_guidance else latents
|
692 |
+
self.scheduler.__dict__.update(views_scheduler_status[0])
|
693 |
+
latent_model_input = self.scheduler.scale_model_input(
|
694 |
+
latent_model_input, t)
|
695 |
+
# controlnet(s) inference
|
696 |
+
if guess_mode and do_classifier_free_guidance:
|
697 |
+
# Infer ControlNet only for the conditional batch.
|
698 |
+
controlnet_latent_model_input = latents
|
699 |
+
controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
|
700 |
+
else:
|
701 |
+
controlnet_latent_model_input = latent_model_input
|
702 |
+
controlnet_prompt_embeds = prompt_embeds
|
703 |
+
down_block_res_samples, mid_block_res_sample = self.controlnet(
|
704 |
+
controlnet_latent_model_input,
|
705 |
+
t,
|
706 |
+
encoder_hidden_states=controlnet_prompt_embeds,
|
707 |
+
controlnet_cond=control_image,
|
708 |
+
conditioning_scale=controlnet_conditioning_scale,
|
709 |
+
guess_mode=guess_mode,
|
710 |
+
return_dict=False,
|
711 |
+
)
|
712 |
+
if guess_mode and do_classifier_free_guidance:
|
713 |
+
# Infered ControlNet only for the conditional batch.
|
714 |
+
# To apply the output of ControlNet to both the unconditional and conditional batches,
|
715 |
+
# add 0 to the unconditional batch to keep it unchanged.
|
716 |
+
down_block_res_samples = [
|
717 |
+
torch.cat([torch.zeros_like(d), d])
|
718 |
+
for d in down_block_res_samples
|
719 |
+
]
|
720 |
+
mid_block_res_sample = torch.cat(
|
721 |
+
[torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
|
722 |
+
# predict the noise residual
|
723 |
+
noise_pred = self.unet(
|
724 |
+
latent_model_input,
|
725 |
+
t,
|
726 |
+
encoder_hidden_states=prompt_embeds,
|
727 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
728 |
+
down_block_additional_residuals=down_block_res_samples,
|
729 |
+
mid_block_additional_residual=mid_block_res_sample,
|
730 |
+
return_dict=False,
|
731 |
+
)[0]
|
732 |
+
# perform guidance
|
733 |
+
if do_classifier_free_guidance:
|
734 |
+
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
735 |
+
noise_pred = noise_pred_uncond + guidance_scale * (
|
736 |
+
noise_pred_text - noise_pred_uncond)
|
737 |
+
# compute the previous noisy sample x_t -> x_t-1
|
738 |
+
latents = self.scheduler.step(
|
739 |
+
noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
740 |
+
return latents
|
741 |
+
|
742 |
+
def blend_v(self, a, b, blend_extent):
|
743 |
+
blend_extent = min(a.shape[2], b.shape[2], blend_extent)
|
744 |
+
for y in range(blend_extent):
|
745 |
+
b[:, :,
|
746 |
+
y, :] = a[:, :, -blend_extent
|
747 |
+
+ y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (
|
748 |
+
y / blend_extent)
|
749 |
+
return b
|
750 |
+
|
751 |
+
def blend_h(self, a, b, blend_extent):
|
752 |
+
blend_extent = min(a.shape[3], b.shape[3], blend_extent)
|
753 |
+
for x in range(blend_extent):
|
754 |
+
b[:, :, :, x] = a[:, :, :, -blend_extent
|
755 |
+
+ x] * (1 - x / blend_extent) + b[:, :, :, x] * (
|
756 |
+
x / blend_extent)
|
757 |
+
return b
|
758 |
+
|
759 |
+
def get_blocks(self, latents, control_image, tile_latent_min_size,
|
760 |
+
overlap_size):
|
761 |
+
rows_latents = []
|
762 |
+
rows_control_images = []
|
763 |
+
for i in range(0, latents.shape[2] - overlap_size, overlap_size):
|
764 |
+
row_latents = []
|
765 |
+
row_control_images = []
|
766 |
+
for j in range(0, latents.shape[3] - overlap_size, overlap_size):
|
767 |
+
latents_input = latents[:, :, i:i + tile_latent_min_size,
|
768 |
+
j:j + tile_latent_min_size]
|
769 |
+
c_start_i = self.vae_scale_factor * i
|
770 |
+
c_end_i = self.vae_scale_factor * (i + tile_latent_min_size)
|
771 |
+
c_start_j = self.vae_scale_factor * j
|
772 |
+
c_end_j = self.vae_scale_factor * (j + tile_latent_min_size)
|
773 |
+
control_image_input = control_image[:, :, c_start_i:c_end_i,
|
774 |
+
c_start_j:c_end_j]
|
775 |
+
row_latents.append(latents_input)
|
776 |
+
row_control_images.append(control_image_input)
|
777 |
+
rows_latents.append(row_latents)
|
778 |
+
rows_control_images.append(row_control_images)
|
779 |
+
return rows_latents, rows_control_images
|
780 |
+
|
781 |
+
@torch.no_grad()
|
782 |
+
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
783 |
+
def __call__(
|
784 |
+
self,
|
785 |
+
prompt: Union[str, List[str]] = None,
|
786 |
+
image: Union[torch.FloatTensor, PIL.Image.Image,
|
787 |
+
List[torch.FloatTensor], List[PIL.Image.Image]] = None,
|
788 |
+
control_image: Union[torch.FloatTensor, PIL.Image.Image,
|
789 |
+
List[torch.FloatTensor],
|
790 |
+
List[PIL.Image.Image]] = None,
|
791 |
+
height: Optional[int] = None,
|
792 |
+
width: Optional[int] = None,
|
793 |
+
strength: float = 0.8,
|
794 |
+
num_inference_steps: int = 50,
|
795 |
+
guidance_scale: float = 7.5,
|
796 |
+
negative_prompt: Optional[Union[str, List[str]]] = None,
|
797 |
+
num_images_per_prompt: Optional[int] = 1,
|
798 |
+
eta: float = 0.0,
|
799 |
+
generator: Optional[Union[torch.Generator,
|
800 |
+
List[torch.Generator]]] = None,
|
801 |
+
latents: Optional[torch.FloatTensor] = None,
|
802 |
+
prompt_embeds: Optional[torch.FloatTensor] = None,
|
803 |
+
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
804 |
+
output_type: Optional[str] = 'pil',
|
805 |
+
return_dict: bool = True,
|
806 |
+
callback: Optional[Callable[[int, int, torch.FloatTensor],
|
807 |
+
None]] = None,
|
808 |
+
callback_steps: int = 1,
|
809 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
810 |
+
controlnet_conditioning_scale: Union[float, List[float]] = 0.8,
|
811 |
+
guess_mode: bool = False,
|
812 |
+
context_size: int = 768,
|
813 |
+
):
|
814 |
+
r"""
|
815 |
+
Function invoked when calling the pipeline for generation.
|
816 |
+
|
817 |
+
Args:
|
818 |
+
prompt (`str` or `List[str]`, *optional*):
|
819 |
+
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
|
820 |
+
instead.
|
821 |
+
image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`,
|
822 |
+
`List[List[torch.FloatTensor]]`, or `List[List[PIL.Image.Image]]`):
|
823 |
+
The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
|
824 |
+
the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can
|
825 |
+
also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If
|
826 |
+
height and/or width are passed, `image` is resized according to them. If multiple ControlNets are
|
827 |
+
specified in init, images must be passed as a list such that each element of the list can be correctly
|
828 |
+
batched for input to a single controlnet.
|
829 |
+
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
830 |
+
The height in pixels of the generated image.
|
831 |
+
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
832 |
+
The width in pixels of the generated image.
|
833 |
+
num_inference_steps (`int`, *optional*, defaults to 50):
|
834 |
+
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
835 |
+
expense of slower inference.
|
836 |
+
guidance_scale (`float`, *optional*, defaults to 7.5):
|
837 |
+
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
838 |
+
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
839 |
+
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
840 |
+
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
841 |
+
usually at the expense of lower image quality.
|
842 |
+
negative_prompt (`str` or `List[str]`, *optional*):
|
843 |
+
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
844 |
+
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
845 |
+
less than `1`).
|
846 |
+
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
847 |
+
The number of images to generate per prompt.
|
848 |
+
eta (`float`, *optional*, defaults to 0.0):
|
849 |
+
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
850 |
+
[`schedulers.DDIMScheduler`], will be ignored for others.
|
851 |
+
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
852 |
+
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
|
853 |
+
to make generation deterministic.
|
854 |
+
latents (`torch.FloatTensor`, *optional*):
|
855 |
+
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
|
856 |
+
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
857 |
+
tensor will ge generated by sampling using the supplied random `generator`.
|
858 |
+
prompt_embeds (`torch.FloatTensor`, *optional*):
|
859 |
+
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
860 |
+
provided, text embeddings will be generated from `prompt` input argument.
|
861 |
+
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
862 |
+
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
863 |
+
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
864 |
+
argument.
|
865 |
+
output_type (`str`, *optional*, defaults to `"pil"`):
|
866 |
+
The output format of the generate image. Choose between
|
867 |
+
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
868 |
+
return_dict (`bool`, *optional*, defaults to `True`):
|
869 |
+
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
870 |
+
plain tuple.
|
871 |
+
callback (`Callable`, *optional*):
|
872 |
+
A function that will be called every `callback_steps` steps during inference. The function will be
|
873 |
+
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
874 |
+
callback_steps (`int`, *optional*, defaults to 1):
|
875 |
+
The frequency at which the `callback` function will be called. If not specified, the callback will be
|
876 |
+
called at every step.
|
877 |
+
cross_attention_kwargs (`dict`, *optional*):
|
878 |
+
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
879 |
+
`self.processor` in
|
880 |
+
[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/
|
881 |
+
src/diffusers/models/cross_attention.py).
|
882 |
+
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
|
883 |
+
The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
|
884 |
+
to the residual in the original unet. If multiple ControlNets are specified in init, you can set the
|
885 |
+
corresponding scale as a list. Note that by default, we use a smaller conditioning scale for inpainting
|
886 |
+
than for [`~StableDiffusionControlNetPipeline.__call__`].
|
887 |
+
guess_mode (`bool`, *optional*, defaults to `False`):
|
888 |
+
In this mode, the ControlNet encoder will try best to recognize the content of the input image even if
|
889 |
+
you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.
|
890 |
+
context_size ('int', *optional*, defaults to '768'):
|
891 |
+
tiled size when denoise the latents.
|
892 |
+
|
893 |
+
Examples:
|
894 |
+
|
895 |
+
Returns:
|
896 |
+
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
897 |
+
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
|
898 |
+
When returning a tuple, the first element is a list with the generated images, and the second element is a
|
899 |
+
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
|
900 |
+
(nsfw) content, according to the `safety_checker`.
|
901 |
+
"""
|
902 |
+
|
903 |
+
def tiled_decode(
|
904 |
+
self,
|
905 |
+
z: torch.FloatTensor,
|
906 |
+
return_dict: bool = True
|
907 |
+
) -> Union[DecoderOutput, torch.FloatTensor]:
|
908 |
+
r"""Decode a batch of images using a tiled decoder.
|
909 |
+
|
910 |
+
Args:
|
911 |
+
When this option is enabled, the VAE will split the input tensor into tiles to compute decoding in several
|
912 |
+
steps. This is useful to keep memory use constant regardless of image size. The end result of tiled
|
913 |
+
decoding is: different from non-tiled decoding due to each tile using a different decoder.
|
914 |
+
To avoid tiling artifacts, the tiles overlap and are blended together to form a smooth output.
|
915 |
+
You may still see tile-sized changes in the look of the output, but they should be much less noticeable.
|
916 |
+
z (`torch.FloatTensor`): Input batch of latent vectors. return_dict (`bool`, *optional*, defaults to
|
917 |
+
`True`):
|
918 |
+
Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
|
919 |
+
"""
|
920 |
+
_tile_overlap_factor = 1 - self.tile_overlap_factor
|
921 |
+
overlap_size = int(self.tile_latent_min_size
|
922 |
+
* _tile_overlap_factor)
|
923 |
+
blend_extent = int(self.tile_sample_min_size
|
924 |
+
* self.tile_overlap_factor)
|
925 |
+
row_limit = self.tile_sample_min_size - blend_extent
|
926 |
+
w = z.shape[3]
|
927 |
+
z = torch.cat([z, z[:, :, :, :w // 4]], dim=-1)
|
928 |
+
# Split z into overlapping 64x64 tiles and decode them separately.
|
929 |
+
# The tiles have an overlap to avoid seams between tiles.
|
930 |
+
|
931 |
+
rows = []
|
932 |
+
for i in range(0, z.shape[2], overlap_size):
|
933 |
+
row = []
|
934 |
+
tile = z[:, :, i:i + self.tile_latent_min_size, :]
|
935 |
+
tile = self.post_quant_conv(tile)
|
936 |
+
decoded = self.decoder(tile)
|
937 |
+
vae_scale_factor = decoded.shape[-1] // tile.shape[-1]
|
938 |
+
row.append(decoded)
|
939 |
+
rows.append(row)
|
940 |
+
result_rows = []
|
941 |
+
for i, row in enumerate(rows):
|
942 |
+
result_row = []
|
943 |
+
for j, tile in enumerate(row):
|
944 |
+
# blend the above tile and the left tile
|
945 |
+
# to the current tile and add the current tile to the result row
|
946 |
+
if i > 0:
|
947 |
+
tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
|
948 |
+
if j > 0:
|
949 |
+
tile = self.blend_h(row[j - 1], tile, blend_extent)
|
950 |
+
result_row.append(
|
951 |
+
self.blend_h(
|
952 |
+
tile[:, :, :row_limit, w * vae_scale_factor:],
|
953 |
+
tile[:, :, :row_limit, :w * vae_scale_factor],
|
954 |
+
tile.shape[-1] - w * vae_scale_factor))
|
955 |
+
result_rows.append(torch.cat(result_row, dim=3))
|
956 |
+
|
957 |
+
dec = torch.cat(result_rows, dim=2)
|
958 |
+
if not return_dict:
|
959 |
+
return (dec, )
|
960 |
+
|
961 |
+
return DecoderOutput(sample=dec)
|
962 |
+
|
963 |
+
self.vae.tiled_decode = tiled_decode.__get__(self.vae, AutoencoderKL)
|
964 |
+
|
965 |
+
# 0. Default height and width to unet
|
966 |
+
height, width = self._default_height_width(height, width, image)
|
967 |
+
|
968 |
+
# 1. Check inputs. Raise error if not correct
|
969 |
+
self.check_inputs(
|
970 |
+
prompt,
|
971 |
+
control_image,
|
972 |
+
height,
|
973 |
+
width,
|
974 |
+
callback_steps,
|
975 |
+
negative_prompt,
|
976 |
+
prompt_embeds,
|
977 |
+
negative_prompt_embeds,
|
978 |
+
controlnet_conditioning_scale,
|
979 |
+
)
|
980 |
+
|
981 |
+
# 2. Define call parameters
|
982 |
+
if prompt is not None and isinstance(prompt, str):
|
983 |
+
batch_size = 1
|
984 |
+
elif prompt is not None and isinstance(prompt, list):
|
985 |
+
batch_size = len(prompt)
|
986 |
+
else:
|
987 |
+
batch_size = prompt_embeds.shape[0]
|
988 |
+
|
989 |
+
device = self._execution_device
|
990 |
+
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
991 |
+
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
992 |
+
# corresponds to doing no classifier free guidance.
|
993 |
+
do_classifier_free_guidance = guidance_scale > 1.0
|
994 |
+
|
995 |
+
controlnet = self.controlnet._orig_mod if is_compiled_module(
|
996 |
+
self.controlnet) else self.controlnet
|
997 |
+
|
998 |
+
if isinstance(controlnet, MultiControlNetModel) and isinstance(
|
999 |
+
controlnet_conditioning_scale, float):
|
1000 |
+
controlnet_conditioning_scale = [controlnet_conditioning_scale
|
1001 |
+
] * len(controlnet.nets)
|
1002 |
+
|
1003 |
+
global_pool_conditions = (
|
1004 |
+
controlnet.config.global_pool_conditions if isinstance(
|
1005 |
+
controlnet, ControlNetModel) else
|
1006 |
+
controlnet.nets[0].config.global_pool_conditions)
|
1007 |
+
guess_mode = guess_mode or global_pool_conditions
|
1008 |
+
|
1009 |
+
# 3. Encode input prompt
|
1010 |
+
prompt_embeds = self._encode_prompt(
|
1011 |
+
prompt,
|
1012 |
+
device,
|
1013 |
+
num_images_per_prompt,
|
1014 |
+
do_classifier_free_guidance,
|
1015 |
+
negative_prompt,
|
1016 |
+
prompt_embeds=prompt_embeds,
|
1017 |
+
negative_prompt_embeds=negative_prompt_embeds,
|
1018 |
+
)
|
1019 |
+
# 4. Prepare image, and controlnet_conditioning_image
|
1020 |
+
image = prepare_image(image)
|
1021 |
+
|
1022 |
+
# 5. Prepare image
|
1023 |
+
if isinstance(controlnet, ControlNetModel):
|
1024 |
+
control_image = self.prepare_control_image(
|
1025 |
+
image=control_image,
|
1026 |
+
width=width,
|
1027 |
+
height=height,
|
1028 |
+
batch_size=batch_size * num_images_per_prompt,
|
1029 |
+
num_images_per_prompt=num_images_per_prompt,
|
1030 |
+
device=device,
|
1031 |
+
dtype=controlnet.dtype,
|
1032 |
+
do_classifier_free_guidance=do_classifier_free_guidance,
|
1033 |
+
guess_mode=guess_mode,
|
1034 |
+
)
|
1035 |
+
elif isinstance(controlnet, MultiControlNetModel):
|
1036 |
+
control_images = []
|
1037 |
+
|
1038 |
+
for control_image_ in control_image:
|
1039 |
+
control_image_ = self.prepare_control_image(
|
1040 |
+
image=control_image_,
|
1041 |
+
width=width,
|
1042 |
+
height=height,
|
1043 |
+
batch_size=batch_size * num_images_per_prompt,
|
1044 |
+
num_images_per_prompt=num_images_per_prompt,
|
1045 |
+
device=device,
|
1046 |
+
dtype=controlnet.dtype,
|
1047 |
+
do_classifier_free_guidance=do_classifier_free_guidance,
|
1048 |
+
guess_mode=guess_mode,
|
1049 |
+
)
|
1050 |
+
|
1051 |
+
control_images.append(control_image_)
|
1052 |
+
|
1053 |
+
control_image = control_images
|
1054 |
+
else:
|
1055 |
+
assert False
|
1056 |
+
|
1057 |
+
# 5. Prepare timesteps
|
1058 |
+
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
1059 |
+
timesteps, num_inference_steps = self.get_timesteps(
|
1060 |
+
num_inference_steps, strength, device)
|
1061 |
+
latent_timestep = timesteps[:1].repeat(batch_size
|
1062 |
+
* num_images_per_prompt)
|
1063 |
+
|
1064 |
+
# 6. Prepare latent variables
|
1065 |
+
latents = self.prepare_latents(
|
1066 |
+
image,
|
1067 |
+
latent_timestep,
|
1068 |
+
batch_size,
|
1069 |
+
num_images_per_prompt,
|
1070 |
+
prompt_embeds.dtype,
|
1071 |
+
device,
|
1072 |
+
generator,
|
1073 |
+
)
|
1074 |
+
|
1075 |
+
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
1076 |
+
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
1077 |
+
|
1078 |
+
views_scheduler_status = [copy.deepcopy(self.scheduler.__dict__)]
|
1079 |
+
# value = torch.zeros_like(latents)
|
1080 |
+
_, _, height, width = control_image.size()
|
1081 |
+
tile_latent_min_size = context_size // self.vae_scale_factor
|
1082 |
+
tile_overlap_factor = 0.5
|
1083 |
+
overlap_size = int(tile_latent_min_size * (1 - tile_overlap_factor))
|
1084 |
+
blend_extent = int(tile_latent_min_size * tile_overlap_factor)
|
1085 |
+
row_limit = tile_latent_min_size - blend_extent
|
1086 |
+
w = latents.shape[3]
|
1087 |
+
latents = torch.cat([latents, latents[:, :, :, :overlap_size]], dim=-1)
|
1088 |
+
control_image_extend = control_image[:, :, :, :overlap_size
|
1089 |
+
* self.vae_scale_factor]
|
1090 |
+
control_image = torch.cat([control_image, control_image_extend],
|
1091 |
+
dim=-1)
|
1092 |
+
|
1093 |
+
# 8. Denoising loop
|
1094 |
+
num_warmup_steps = len(
|
1095 |
+
timesteps) - num_inference_steps * self.scheduler.order
|
1096 |
+
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
1097 |
+
for i, t in enumerate(timesteps):
|
1098 |
+
latents_input, control_image_input = self.get_blocks(
|
1099 |
+
latents, control_image, tile_latent_min_size, overlap_size)
|
1100 |
+
rows = []
|
1101 |
+
for latents_input_, control_image_input_ in zip(
|
1102 |
+
latents_input, control_image_input):
|
1103 |
+
num_block = len(latents_input_)
|
1104 |
+
# get batched latents_input
|
1105 |
+
latents_input_ = torch.cat(
|
1106 |
+
latents_input_[:num_block], dim=0)
|
1107 |
+
# get batched prompt_embeds
|
1108 |
+
prompt_embeds_ = torch.cat(
|
1109 |
+
[prompt_embeds.chunk(2)[0]] * num_block
|
1110 |
+
+ [prompt_embeds.chunk(2)[1]] * num_block,
|
1111 |
+
dim=0)
|
1112 |
+
# get batched control_image_input
|
1113 |
+
control_image_input_ = torch.cat(
|
1114 |
+
[
|
1115 |
+
x[0, :, :, ][None, :, :, :]
|
1116 |
+
for x in control_image_input_[:num_block]
|
1117 |
+
] + [
|
1118 |
+
x[1, :, :, ][None, :, :, :]
|
1119 |
+
for x in control_image_input_[:num_block]
|
1120 |
+
],
|
1121 |
+
dim=0)
|
1122 |
+
latents_output = self.denoise_latents(
|
1123 |
+
latents_input_, t, prompt_embeds_,
|
1124 |
+
control_image_input_, controlnet_conditioning_scale,
|
1125 |
+
guess_mode, cross_attention_kwargs,
|
1126 |
+
do_classifier_free_guidance, guidance_scale,
|
1127 |
+
extra_step_kwargs, views_scheduler_status)
|
1128 |
+
rows.append(list(latents_output.chunk(num_block)))
|
1129 |
+
result_rows = []
|
1130 |
+
for i, row in enumerate(rows):
|
1131 |
+
result_row = []
|
1132 |
+
for j, tile in enumerate(row):
|
1133 |
+
# blend the above tile and the left tile
|
1134 |
+
# to the current tile and add the current tile to the result row
|
1135 |
+
if i > 0:
|
1136 |
+
tile = self.blend_v(rows[i - 1][j], tile,
|
1137 |
+
blend_extent)
|
1138 |
+
if j > 0:
|
1139 |
+
tile = self.blend_h(row[j - 1], tile, blend_extent)
|
1140 |
+
if j == 0:
|
1141 |
+
tile = self.blend_h(row[-1], tile, blend_extent)
|
1142 |
+
if i != len(rows) - 1:
|
1143 |
+
if j == len(row) - 1:
|
1144 |
+
result_row.append(tile[:, :, :row_limit, :])
|
1145 |
+
else:
|
1146 |
+
result_row.append(
|
1147 |
+
tile[:, :, :row_limit, :row_limit])
|
1148 |
+
else:
|
1149 |
+
if j == len(row) - 1:
|
1150 |
+
result_row.append(tile[:, :, :, :])
|
1151 |
+
else:
|
1152 |
+
result_row.append(tile[:, :, :, :row_limit])
|
1153 |
+
result_rows.append(torch.cat(result_row, dim=3))
|
1154 |
+
latents = torch.cat(result_rows, dim=2)
|
1155 |
+
|
1156 |
+
# call the callback, if provided
|
1157 |
+
condition_i = i == len(timesteps) - 1
|
1158 |
+
condition_warm = (i + 1) > num_warmup_steps and (
|
1159 |
+
i + 1) % self.scheduler.order == 0
|
1160 |
+
if condition_i or condition_warm:
|
1161 |
+
progress_bar.update()
|
1162 |
+
if callback is not None and i % callback_steps == 0:
|
1163 |
+
callback(i, t, latents)
|
1164 |
+
latents = latents[:, :, :, :w]
|
1165 |
+
|
1166 |
+
# If we do sequential model offloading, let's offload unet and controlnet
|
1167 |
+
# manually for max memory savings
|
1168 |
+
if hasattr(
|
1169 |
+
self,
|
1170 |
+
'final_offload_hook') and self.final_offload_hook is not None:
|
1171 |
+
self.unet.to('cpu')
|
1172 |
+
self.controlnet.to('cpu')
|
1173 |
+
torch.cuda.empty_cache()
|
1174 |
+
|
1175 |
+
if not output_type == 'latent':
|
1176 |
+
image = self.vae.decode(
|
1177 |
+
latents / self.vae.config.scaling_factor, return_dict=False)[0]
|
1178 |
+
image, has_nsfw_concept = self.run_safety_checker(
|
1179 |
+
image, device, prompt_embeds.dtype)
|
1180 |
+
else:
|
1181 |
+
image = latents
|
1182 |
+
has_nsfw_concept = None
|
1183 |
+
|
1184 |
+
if has_nsfw_concept is None:
|
1185 |
+
do_denormalize = [True] * image.shape[0]
|
1186 |
+
else:
|
1187 |
+
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
|
1188 |
+
|
1189 |
+
image = self.image_processor.postprocess(
|
1190 |
+
image, output_type=output_type, do_denormalize=do_denormalize)
|
1191 |
+
|
1192 |
+
# Offload last model to CPU
|
1193 |
+
if hasattr(
|
1194 |
+
self,
|
1195 |
+
'final_offload_hook') and self.final_offload_hook is not None:
|
1196 |
+
self.final_offload_hook.offload()
|
1197 |
+
|
1198 |
+
if not return_dict:
|
1199 |
+
return (image, has_nsfw_concept)
|
1200 |
+
|
1201 |
+
return StableDiffusionPipelineOutput(
|
1202 |
+
images=image, nsfw_content_detected=has_nsfw_concept)
|
requirements.txt
ADDED
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
diffusers==0.26.0
|
2 |
+
accelerate
|
3 |
+
xformers
|
4 |
+
triton
|
5 |
+
transformers
|
6 |
+
git+https://github.com/doevent/Real-ESRGAN.git
|
7 |
+
py360convert
|
8 |
+
numpy==1.23.5
|
9 |
+
basicsr
|
10 |
+
streamlit
|
11 |
+
streamlit_pannellum
|
txt2panoimg/__init__.py
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
from .pipeline_base import StableDiffusionBlendExtendPipeline
|
2 |
+
from .pipeline_sr import StableDiffusionControlNetImg2ImgPanoPipeline
|
3 |
+
from .text_to_360panorama_image_pipeline import Text2360PanoramaImagePipeline
|
txt2panoimg/pipeline_base.py
ADDED
@@ -0,0 +1,849 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
1 |
+
# Copyright © Alibaba, Inc. and its affiliates.
|
2 |
+
# The implementation here is modifed based on diffusers.StableDiffusionPipeline,
|
3 |
+
# originally Apache 2.0 License and public available at
|
4 |
+
# https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
|
5 |
+
|
6 |
+
import re
|
7 |
+
from typing import Any, Callable, Dict, List, Optional, Union
|
8 |
+
|
9 |
+
import torch
|
10 |
+
from diffusers import (AutoencoderKL, DiffusionPipeline,
|
11 |
+
StableDiffusionPipeline)
|
12 |
+
|
13 |
+
from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
|
14 |
+
try:
|
15 |
+
from diffusers.models.autoencoders.vae import DecoderOutput
|
16 |
+
except:
|
17 |
+
from diffusers.models.vae import DecoderOutput
|
18 |
+
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
|
19 |
+
from diffusers.utils import logging, replace_example_docstring
|
20 |
+
from transformers import CLIPTokenizer
|
21 |
+
|
22 |
+
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
23 |
+
|
24 |
+
EXAMPLE_DOC_STRING = """
|
25 |
+
Examples:
|
26 |
+
```py
|
27 |
+
>>> import torch
|
28 |
+
>>> from diffusers import EulerAncestralDiscreteScheduler
|
29 |
+
>>> from txt2panoimage.pipeline_base import StableDiffusionBlendExtendPipeline
|
30 |
+
>>> model_id = "models/sd-base"
|
31 |
+
>>> pipe = StableDiffusionBlendExtendPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
|
32 |
+
>>> pipe = pipe.to("cuda")
|
33 |
+
>>> pipe.vae.enable_tiling()
|
34 |
+
>>> pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
|
35 |
+
>>> # remove following line if xformers is not installed
|
36 |
+
>>> pipe.enable_xformers_memory_efficient_attention()
|
37 |
+
>>> pipe.enable_model_cpu_offload()
|
38 |
+
>>> prompt = "a living room"
|
39 |
+
>>> image = pipe(prompt).images[0]
|
40 |
+
```
|
41 |
+
"""
|
42 |
+
|
43 |
+
re_attention = re.compile(
|
44 |
+
r"""
|
45 |
+
\\\(|
|
46 |
+
\\\)|
|
47 |
+
\\\[|
|
48 |
+
\\]|
|
49 |
+
\\\\|
|
50 |
+
\\|
|
51 |
+
\(|
|
52 |
+
\[|
|
53 |
+
:([+-]?[.\d]+)\)|
|
54 |
+
\)|
|
55 |
+
]|
|
56 |
+
[^\\()\[\]:]+|
|
57 |
+
:
|
58 |
+
""",
|
59 |
+
re.X,
|
60 |
+
)
|
61 |
+
|
62 |
+
|
63 |
+
def parse_prompt_attention(text):
|
64 |
+
"""
|
65 |
+
Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
|
66 |
+
Accepted tokens are:
|
67 |
+
(abc) - increases attention to abc by a multiplier of 1.1
|
68 |
+
(abc:3.12) - increases attention to abc by a multiplier of 3.12
|
69 |
+
[abc] - decreases attention to abc by a multiplier of 1.1
|
70 |
+
"""
|
71 |
+
|
72 |
+
res = []
|
73 |
+
round_brackets = []
|
74 |
+
square_brackets = []
|
75 |
+
|
76 |
+
round_bracket_multiplier = 1.1
|
77 |
+
square_bracket_multiplier = 1 / 1.1
|
78 |
+
|
79 |
+
def multiply_range(start_position, multiplier):
|
80 |
+
for p in range(start_position, len(res)):
|
81 |
+
res[p][1] *= multiplier
|
82 |
+
|
83 |
+
for m in re_attention.finditer(text):
|
84 |
+
text = m.group(0)
|
85 |
+
weight = m.group(1)
|
86 |
+
|
87 |
+
if text.startswith('\\'):
|
88 |
+
res.append([text[1:], 1.0])
|
89 |
+
elif text == '(':
|
90 |
+
round_brackets.append(len(res))
|
91 |
+
elif text == '[':
|
92 |
+
square_brackets.append(len(res))
|
93 |
+
elif weight is not None and len(round_brackets) > 0:
|
94 |
+
multiply_range(round_brackets.pop(), float(weight))
|
95 |
+
elif text == ')' and len(round_brackets) > 0:
|
96 |
+
multiply_range(round_brackets.pop(), round_bracket_multiplier)
|
97 |
+
elif text == ']' and len(square_brackets) > 0:
|
98 |
+
multiply_range(square_brackets.pop(), square_bracket_multiplier)
|
99 |
+
else:
|
100 |
+
res.append([text, 1.0])
|
101 |
+
|
102 |
+
for pos in round_brackets:
|
103 |
+
multiply_range(pos, round_bracket_multiplier)
|
104 |
+
|
105 |
+
for pos in square_brackets:
|
106 |
+
multiply_range(pos, square_bracket_multiplier)
|
107 |
+
|
108 |
+
if len(res) == 0:
|
109 |
+
res = [['', 1.0]]
|
110 |
+
|
111 |
+
# merge runs of identical weights
|
112 |
+
i = 0
|
113 |
+
while i + 1 < len(res):
|
114 |
+
if res[i][1] == res[i + 1][1]:
|
115 |
+
res[i][0] += res[i + 1][0]
|
116 |
+
res.pop(i + 1)
|
117 |
+
else:
|
118 |
+
i += 1
|
119 |
+
|
120 |
+
return res
|
121 |
+
|
122 |
+
|
123 |
+
def get_prompts_with_weights(pipe: DiffusionPipeline, prompt: List[str],
|
124 |
+
max_length: int):
|
125 |
+
r"""
|
126 |
+
Tokenize a list of prompts and return its tokens with weights of each token.
|
127 |
+
|
128 |
+
No padding, starting or ending token is included.
|
129 |
+
"""
|
130 |
+
tokens = []
|
131 |
+
weights = []
|
132 |
+
truncated = False
|
133 |
+
for text in prompt:
|
134 |
+
texts_and_weights = parse_prompt_attention(text)
|
135 |
+
text_token = []
|
136 |
+
text_weight = []
|
137 |
+
for word, weight in texts_and_weights:
|
138 |
+
# tokenize and discard the starting and the ending token
|
139 |
+
token = pipe.tokenizer(word).input_ids[1:-1]
|
140 |
+
text_token += token
|
141 |
+
# copy the weight by length of token
|
142 |
+
text_weight += [weight] * len(token)
|
143 |
+
# stop if the text is too long (longer than truncation limit)
|
144 |
+
if len(text_token) > max_length:
|
145 |
+
truncated = True
|
146 |
+
break
|
147 |
+
# truncate
|
148 |
+
if len(text_token) > max_length:
|
149 |
+
truncated = True
|
150 |
+
text_token = text_token[:max_length]
|
151 |
+
text_weight = text_weight[:max_length]
|
152 |
+
tokens.append(text_token)
|
153 |
+
weights.append(text_weight)
|
154 |
+
if truncated:
|
155 |
+
logger.warning(
|
156 |
+
'Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples'
|
157 |
+
)
|
158 |
+
return tokens, weights
|
159 |
+
|
160 |
+
|
161 |
+
def pad_tokens_and_weights(tokens,
|
162 |
+
weights,
|
163 |
+
max_length,
|
164 |
+
bos,
|
165 |
+
eos,
|
166 |
+
pad,
|
167 |
+
no_boseos_middle=True,
|
168 |
+
chunk_length=77):
|
169 |
+
r"""
|
170 |
+
Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
|
171 |
+
"""
|
172 |
+
max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
|
173 |
+
weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
|
174 |
+
for i in range(len(tokens)):
|
175 |
+
tokens[i] = [
|
176 |
+
bos
|
177 |
+
] + tokens[i] + [pad] * (max_length - 1 - len(tokens[i]) - 1) + [eos]
|
178 |
+
if no_boseos_middle:
|
179 |
+
weights[i] = [1.0] + weights[i] + [1.0] * (
|
180 |
+
max_length - 1 - len(weights[i]))
|
181 |
+
else:
|
182 |
+
w = []
|
183 |
+
if len(weights[i]) == 0:
|
184 |
+
w = [1.0] * weights_length
|
185 |
+
else:
|
186 |
+
for j in range(max_embeddings_multiples):
|
187 |
+
w.append(1.0) # weight for starting token in this chunk
|
188 |
+
w += weights[i][j * (chunk_length - 2):min(
|
189 |
+
len(weights[i]), (j + 1) * (chunk_length - 2))]
|
190 |
+
w.append(1.0) # weight for ending token in this chunk
|
191 |
+
w += [1.0] * (weights_length - len(w))
|
192 |
+
weights[i] = w[:]
|
193 |
+
|
194 |
+
return tokens, weights
|
195 |
+
|
196 |
+
|
197 |
+
def get_unweighted_text_embeddings(
|
198 |
+
pipe: DiffusionPipeline,
|
199 |
+
text_input: torch.Tensor,
|
200 |
+
chunk_length: int,
|
201 |
+
no_boseos_middle: Optional[bool] = True,
|
202 |
+
):
|
203 |
+
"""
|
204 |
+
When the length of tokens is a multiple of the capacity of the text encoder,
|
205 |
+
it should be split into chunks and sent to the text encoder individually.
|
206 |
+
"""
|
207 |
+
max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)
|
208 |
+
if max_embeddings_multiples > 1:
|
209 |
+
text_embeddings = []
|
210 |
+
for i in range(max_embeddings_multiples):
|
211 |
+
# extract the i-th chunk
|
212 |
+
text_input_chunk = text_input[:, i * (chunk_length - 2):(i + 1)
|
213 |
+
* (chunk_length - 2) + 2].clone()
|
214 |
+
|
215 |
+
# cover the head and the tail by the starting and the ending tokens
|
216 |
+
text_input_chunk[:, 0] = text_input[0, 0]
|
217 |
+
text_input_chunk[:, -1] = text_input[0, -1]
|
218 |
+
text_embedding = pipe.text_encoder(text_input_chunk)[0]
|
219 |
+
|
220 |
+
if no_boseos_middle:
|
221 |
+
if i == 0:
|
222 |
+
# discard the ending token
|
223 |
+
text_embedding = text_embedding[:, :-1]
|
224 |
+
elif i == max_embeddings_multiples - 1:
|
225 |
+
# discard the starting token
|
226 |
+
text_embedding = text_embedding[:, 1:]
|
227 |
+
else:
|
228 |
+
# discard both starting and ending tokens
|
229 |
+
text_embedding = text_embedding[:, 1:-1]
|
230 |
+
|
231 |
+
text_embeddings.append(text_embedding)
|
232 |
+
text_embeddings = torch.concat(text_embeddings, axis=1)
|
233 |
+
else:
|
234 |
+
text_embeddings = pipe.text_encoder(text_input)[0]
|
235 |
+
return text_embeddings
|
236 |
+
|
237 |
+
|
238 |
+
def get_weighted_text_embeddings(
|
239 |
+
pipe: DiffusionPipeline,
|
240 |
+
prompt: Union[str, List[str]],
|
241 |
+
uncond_prompt: Optional[Union[str, List[str]]] = None,
|
242 |
+
max_embeddings_multiples: Optional[int] = 3,
|
243 |
+
no_boseos_middle: Optional[bool] = False,
|
244 |
+
skip_parsing: Optional[bool] = False,
|
245 |
+
skip_weighting: Optional[bool] = False,
|
246 |
+
):
|
247 |
+
r"""
|
248 |
+
Prompts can be assigned with local weights using brackets. For example,
|
249 |
+
prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
|
250 |
+
and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.
|
251 |
+
|
252 |
+
Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.
|
253 |
+
|
254 |
+
Args:
|
255 |
+
pipe (`DiffusionPipeline`):
|
256 |
+
Pipe to provide access to the tokenizer and the text encoder.
|
257 |
+
prompt (`str` or `List[str]`):
|
258 |
+
The prompt or prompts to guide the image generation.
|
259 |
+
uncond_prompt (`str` or `List[str]`):
|
260 |
+
The unconditional prompt or prompts for guide the image generation. If unconditional prompt
|
261 |
+
is provided, the embeddings of prompt and uncond_prompt are concatenated.
|
262 |
+
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
|
263 |
+
The max multiple length of prompt embeddings compared to the max output length of text encoder.
|
264 |
+
no_boseos_middle (`bool`, *optional*, defaults to `False`):
|
265 |
+
If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and
|
266 |
+
ending token in each of the chunk in the middle.
|
267 |
+
skip_parsing (`bool`, *optional*, defaults to `False`):
|
268 |
+
Skip the parsing of brackets.
|
269 |
+
skip_weighting (`bool`, *optional*, defaults to `False`):
|
270 |
+
Skip the weighting. When the parsing is skipped, it is forced True.
|
271 |
+
"""
|
272 |
+
max_length = (pipe.tokenizer.model_max_length
|
273 |
+
- 2) * max_embeddings_multiples + 2
|
274 |
+
if isinstance(prompt, str):
|
275 |
+
prompt = [prompt]
|
276 |
+
|
277 |
+
if not skip_parsing:
|
278 |
+
prompt_tokens, prompt_weights = get_prompts_with_weights(
|
279 |
+
pipe, prompt, max_length - 2)
|
280 |
+
if uncond_prompt is not None:
|
281 |
+
if isinstance(uncond_prompt, str):
|
282 |
+
uncond_prompt = [uncond_prompt]
|
283 |
+
uncond_tokens, uncond_weights = get_prompts_with_weights(
|
284 |
+
pipe, uncond_prompt, max_length - 2)
|
285 |
+
else:
|
286 |
+
prompt_tokens = [
|
287 |
+
token[1:-1] for token in pipe.tokenizer(
|
288 |
+
prompt, max_length=max_length, truncation=True).input_ids
|
289 |
+
]
|
290 |
+
prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
|
291 |
+
if uncond_prompt is not None:
|
292 |
+
if isinstance(uncond_prompt, str):
|
293 |
+
uncond_prompt = [uncond_prompt]
|
294 |
+
uncond_tokens = [
|
295 |
+
token[1:-1] for token in pipe.tokenizer(
|
296 |
+
uncond_prompt, max_length=max_length,
|
297 |
+
truncation=True).input_ids
|
298 |
+
]
|
299 |
+
uncond_weights = [[1.0] * len(token) for token in uncond_tokens]
|
300 |
+
|
301 |
+
# round up the longest length of tokens to a multiple of (model_max_length - 2)
|
302 |
+
max_length = max([len(token) for token in prompt_tokens])
|
303 |
+
if uncond_prompt is not None:
|
304 |
+
max_length = max(max_length,
|
305 |
+
max([len(token) for token in uncond_tokens]))
|
306 |
+
|
307 |
+
max_embeddings_multiples = min(
|
308 |
+
max_embeddings_multiples,
|
309 |
+
(max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1,
|
310 |
+
)
|
311 |
+
max_embeddings_multiples = max(1, max_embeddings_multiples)
|
312 |
+
max_length = (pipe.tokenizer.model_max_length
|
313 |
+
- 2) * max_embeddings_multiples + 2
|
314 |
+
|
315 |
+
# pad the length of tokens and weights
|
316 |
+
bos = pipe.tokenizer.bos_token_id
|
317 |
+
eos = pipe.tokenizer.eos_token_id
|
318 |
+
pad = getattr(pipe.tokenizer, 'pad_token_id', eos)
|
319 |
+
prompt_tokens, prompt_weights = pad_tokens_and_weights(
|
320 |
+
prompt_tokens,
|
321 |
+
prompt_weights,
|
322 |
+
max_length,
|
323 |
+
bos,
|
324 |
+
eos,
|
325 |
+
pad,
|
326 |
+
no_boseos_middle=no_boseos_middle,
|
327 |
+
chunk_length=pipe.tokenizer.model_max_length,
|
328 |
+
)
|
329 |
+
prompt_tokens = torch.tensor(
|
330 |
+
prompt_tokens, dtype=torch.long, device=pipe.device)
|
331 |
+
if uncond_prompt is not None:
|
332 |
+
uncond_tokens, uncond_weights = pad_tokens_and_weights(
|
333 |
+
uncond_tokens,
|
334 |
+
uncond_weights,
|
335 |
+
max_length,
|
336 |
+
bos,
|
337 |
+
eos,
|
338 |
+
pad,
|
339 |
+
no_boseos_middle=no_boseos_middle,
|
340 |
+
chunk_length=pipe.tokenizer.model_max_length,
|
341 |
+
)
|
342 |
+
uncond_tokens = torch.tensor(
|
343 |
+
uncond_tokens, dtype=torch.long, device=pipe.device)
|
344 |
+
|
345 |
+
# get the embeddings
|
346 |
+
text_embeddings = get_unweighted_text_embeddings(
|
347 |
+
pipe,
|
348 |
+
prompt_tokens,
|
349 |
+
pipe.tokenizer.model_max_length,
|
350 |
+
no_boseos_middle=no_boseos_middle,
|
351 |
+
)
|
352 |
+
prompt_weights = torch.tensor(
|
353 |
+
prompt_weights,
|
354 |
+
dtype=text_embeddings.dtype,
|
355 |
+
device=text_embeddings.device)
|
356 |
+
if uncond_prompt is not None:
|
357 |
+
uncond_embeddings = get_unweighted_text_embeddings(
|
358 |
+
pipe,
|
359 |
+
uncond_tokens,
|
360 |
+
pipe.tokenizer.model_max_length,
|
361 |
+
no_boseos_middle=no_boseos_middle,
|
362 |
+
)
|
363 |
+
uncond_weights = torch.tensor(
|
364 |
+
uncond_weights,
|
365 |
+
dtype=uncond_embeddings.dtype,
|
366 |
+
device=uncond_embeddings.device)
|
367 |
+
|
368 |
+
# assign weights to the prompts and normalize in the sense of mean
|
369 |
+
# TODO: should we normalize by chunk or in a whole (current implementation)?
|
370 |
+
if (not skip_parsing) and (not skip_weighting):
|
371 |
+
previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(
|
372 |
+
text_embeddings.dtype)
|
373 |
+
text_embeddings *= prompt_weights.unsqueeze(-1)
|
374 |
+
current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(
|
375 |
+
text_embeddings.dtype)
|
376 |
+
text_embeddings *= (previous_mean
|
377 |
+
/ current_mean).unsqueeze(-1).unsqueeze(-1)
|
378 |
+
if uncond_prompt is not None:
|
379 |
+
previous_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(
|
380 |
+
uncond_embeddings.dtype)
|
381 |
+
uncond_embeddings *= uncond_weights.unsqueeze(-1)
|
382 |
+
current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(
|
383 |
+
uncond_embeddings.dtype)
|
384 |
+
uncond_embeddings *= (previous_mean
|
385 |
+
/ current_mean).unsqueeze(-1).unsqueeze(-1)
|
386 |
+
|
387 |
+
if uncond_prompt is not None:
|
388 |
+
return text_embeddings, uncond_embeddings
|
389 |
+
return text_embeddings, None
|
390 |
+
|
391 |
+
|
392 |
+
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
|
393 |
+
"""
|
394 |
+
Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
|
395 |
+
Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
|
396 |
+
"""
|
397 |
+
std_text = noise_pred_text.std(
|
398 |
+
dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
|
399 |
+
std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
|
400 |
+
# rescale the results from guidance (fixes overexposure)
|
401 |
+
noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
|
402 |
+
# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
|
403 |
+
noise_cfg = guidance_rescale * noise_pred_rescaled + (
|
404 |
+
1 - guidance_rescale) * noise_cfg
|
405 |
+
return noise_cfg
|
406 |
+
|
407 |
+
|
408 |
+
class StableDiffusionBlendExtendPipeline(StableDiffusionPipeline):
|
409 |
+
r"""
|
410 |
+
Pipeline for text-to-image generation using Stable Diffusion.
|
411 |
+
|
412 |
+
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
|
413 |
+
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
|
414 |
+
|
415 |
+
In addition the pipeline inherits the following loading methods:
|
416 |
+
- *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
|
417 |
+
- *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`]
|
418 |
+
- *Ckpt*: [`loaders.FromCkptMixin.from_ckpt`]
|
419 |
+
|
420 |
+
as well as the following saving methods:
|
421 |
+
- *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`]
|
422 |
+
|
423 |
+
Args:
|
424 |
+
vae ([`AutoencoderKL`]):
|
425 |
+
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
|
426 |
+
text_encoder ([`CLIPTextModel`]):
|
427 |
+
Frozen text-encoder. Stable Diffusion uses the text portion of
|
428 |
+
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
|
429 |
+
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
|
430 |
+
tokenizer (`CLIPTokenizer`):
|
431 |
+
Tokenizer of class
|
432 |
+
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/
|
433 |
+
en/model_doc/clip#transformers.CLIPTokenizer).
|
434 |
+
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
|
435 |
+
scheduler ([`SchedulerMixin`]):
|
436 |
+
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
|
437 |
+
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
|
438 |
+
safety_checker ([`StableDiffusionSafetyChecker`]):
|
439 |
+
Classification module that estimates whether generated images could be considered offensive or harmful.
|
440 |
+
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
|
441 |
+
feature_extractor ([`CLIPImageProcessor`]):
|
442 |
+
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
|
443 |
+
"""
|
444 |
+
_optional_components = ['safety_checker', 'feature_extractor']
|
445 |
+
|
446 |
+
def _encode_prompt(
|
447 |
+
self,
|
448 |
+
prompt,
|
449 |
+
device,
|
450 |
+
num_images_per_prompt,
|
451 |
+
do_classifier_free_guidance,
|
452 |
+
negative_prompt=None,
|
453 |
+
max_embeddings_multiples=3,
|
454 |
+
prompt_embeds: Optional[torch.FloatTensor] = None,
|
455 |
+
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
456 |
+
lora_scale: Optional[float] = None,
|
457 |
+
):
|
458 |
+
r"""
|
459 |
+
Encodes the prompt into text encoder hidden states.
|
460 |
+
|
461 |
+
Args:
|
462 |
+
prompt (`str` or `list(int)`):
|
463 |
+
prompt to be encoded
|
464 |
+
device: (`torch.device`):
|
465 |
+
torch device
|
466 |
+
num_images_per_prompt (`int`):
|
467 |
+
number of images that should be generated per prompt
|
468 |
+
do_classifier_free_guidance (`bool`):
|
469 |
+
whether to use classifier free guidance or not
|
470 |
+
negative_prompt (`str` or `List[str]`):
|
471 |
+
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
|
472 |
+
if `guidance_scale` is less than `1`).
|
473 |
+
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
|
474 |
+
The max multiple length of prompt embeddings compared to the max output length of text encoder.
|
475 |
+
"""
|
476 |
+
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
|
477 |
+
self._lora_scale = lora_scale
|
478 |
+
|
479 |
+
if prompt is not None and isinstance(prompt, str):
|
480 |
+
batch_size = 1
|
481 |
+
elif prompt is not None and isinstance(prompt, list):
|
482 |
+
batch_size = len(prompt)
|
483 |
+
else:
|
484 |
+
batch_size = prompt_embeds.shape[0]
|
485 |
+
|
486 |
+
if negative_prompt_embeds is None:
|
487 |
+
if negative_prompt is None:
|
488 |
+
negative_prompt = [''] * batch_size
|
489 |
+
elif isinstance(negative_prompt, str):
|
490 |
+
negative_prompt = [negative_prompt] * batch_size
|
491 |
+
if batch_size != len(negative_prompt):
|
492 |
+
raise ValueError(
|
493 |
+
f'`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:'
|
494 |
+
f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
|
495 |
+
' the batch size of `prompt`.')
|
496 |
+
if prompt_embeds is None or negative_prompt_embeds is None:
|
497 |
+
if isinstance(self, TextualInversionLoaderMixin):
|
498 |
+
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
|
499 |
+
if do_classifier_free_guidance and negative_prompt_embeds is None:
|
500 |
+
negative_prompt = self.maybe_convert_prompt(
|
501 |
+
negative_prompt, self.tokenizer)
|
502 |
+
|
503 |
+
prompt_embeds1, negative_prompt_embeds1 = get_weighted_text_embeddings(
|
504 |
+
pipe=self,
|
505 |
+
prompt=prompt,
|
506 |
+
uncond_prompt=negative_prompt
|
507 |
+
if do_classifier_free_guidance else None,
|
508 |
+
max_embeddings_multiples=max_embeddings_multiples,
|
509 |
+
)
|
510 |
+
if prompt_embeds is None:
|
511 |
+
prompt_embeds = prompt_embeds1
|
512 |
+
if negative_prompt_embeds is None:
|
513 |
+
negative_prompt_embeds = negative_prompt_embeds1
|
514 |
+
|
515 |
+
bs_embed, seq_len, _ = prompt_embeds.shape
|
516 |
+
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
517 |
+
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
518 |
+
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt,
|
519 |
+
seq_len, -1)
|
520 |
+
|
521 |
+
if do_classifier_free_guidance:
|
522 |
+
bs_embed, seq_len, _ = negative_prompt_embeds.shape
|
523 |
+
negative_prompt_embeds = negative_prompt_embeds.repeat(
|
524 |
+
1, num_images_per_prompt, 1)
|
525 |
+
negative_prompt_embeds = negative_prompt_embeds.view(
|
526 |
+
bs_embed * num_images_per_prompt, seq_len, -1)
|
527 |
+
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
528 |
+
|
529 |
+
return prompt_embeds
|
530 |
+
|
531 |
+
def blend_v(self, a, b, blend_extent):
|
532 |
+
blend_extent = min(a.shape[2], b.shape[2], blend_extent)
|
533 |
+
for y in range(blend_extent):
|
534 |
+
b[:, :,
|
535 |
+
y, :] = a[:, :, -blend_extent
|
536 |
+
+ y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (
|
537 |
+
y / blend_extent)
|
538 |
+
return b
|
539 |
+
|
540 |
+
def blend_h(self, a, b, blend_extent):
|
541 |
+
blend_extent = min(a.shape[3], b.shape[3], blend_extent)
|
542 |
+
for x in range(blend_extent):
|
543 |
+
b[:, :, :, x] = a[:, :, :, -blend_extent
|
544 |
+
+ x] * (1 - x / blend_extent) + b[:, :, :, x] * (
|
545 |
+
x / blend_extent)
|
546 |
+
return b
|
547 |
+
|
548 |
+
@torch.no_grad()
|
549 |
+
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
550 |
+
def __call__(
|
551 |
+
self,
|
552 |
+
prompt: Union[str, List[str]] = None,
|
553 |
+
height: Optional[int] = None,
|
554 |
+
width: Optional[int] = None,
|
555 |
+
num_inference_steps: int = 50,
|
556 |
+
guidance_scale: float = 7.5,
|
557 |
+
negative_prompt: Optional[Union[str, List[str]]] = None,
|
558 |
+
num_images_per_prompt: Optional[int] = 1,
|
559 |
+
eta: float = 0.0,
|
560 |
+
generator: Optional[Union[torch.Generator,
|
561 |
+
List[torch.Generator]]] = None,
|
562 |
+
latents: Optional[torch.FloatTensor] = None,
|
563 |
+
prompt_embeds: Optional[torch.FloatTensor] = None,
|
564 |
+
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
565 |
+
output_type: Optional[str] = 'pil',
|
566 |
+
return_dict: bool = True,
|
567 |
+
callback: Optional[Callable[[int, int, torch.FloatTensor],
|
568 |
+
None]] = None,
|
569 |
+
callback_steps: int = 1,
|
570 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
571 |
+
guidance_rescale: float = 0.0,
|
572 |
+
):
|
573 |
+
r"""
|
574 |
+
Function invoked when calling the pipeline for generation.
|
575 |
+
|
576 |
+
Args:
|
577 |
+
prompt (`str` or `List[str]`, *optional*):
|
578 |
+
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
|
579 |
+
instead.
|
580 |
+
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
581 |
+
The height in pixels of the generated image.
|
582 |
+
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
583 |
+
The width in pixels of the generated image.
|
584 |
+
num_inference_steps (`int`, *optional*, defaults to 50):
|
585 |
+
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
586 |
+
expense of slower inference.
|
587 |
+
guidance_scale (`float`, *optional*, defaults to 7.5):
|
588 |
+
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
589 |
+
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
590 |
+
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
591 |
+
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
592 |
+
usually at the expense of lower image quality.
|
593 |
+
negative_prompt (`str` or `List[str]`, *optional*):
|
594 |
+
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
595 |
+
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
596 |
+
less than `1`).
|
597 |
+
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
598 |
+
The number of images to generate per prompt.
|
599 |
+
eta (`float`, *optional*, defaults to 0.0):
|
600 |
+
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
601 |
+
[`schedulers.DDIMScheduler`], will be ignored for others.
|
602 |
+
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
603 |
+
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
|
604 |
+
to make generation deterministic.
|
605 |
+
latents (`torch.FloatTensor`, *optional*):
|
606 |
+
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
|
607 |
+
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
608 |
+
tensor will ge generated by sampling using the supplied random `generator`.
|
609 |
+
prompt_embeds (`torch.FloatTensor`, *optional*):
|
610 |
+
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
611 |
+
provided, text embeddings will be generated from `prompt` input argument.
|
612 |
+
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
613 |
+
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
614 |
+
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
615 |
+
argument.
|
616 |
+
output_type (`str`, *optional*, defaults to `"pil"`):
|
617 |
+
The output format of the generate image. Choose between
|
618 |
+
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
619 |
+
return_dict (`bool`, *optional*, defaults to `True`):
|
620 |
+
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
621 |
+
plain tuple.
|
622 |
+
callback (`Callable`, *optional*):
|
623 |
+
A function that will be called every `callback_steps` steps during inference. The function will be
|
624 |
+
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
625 |
+
callback_steps (`int`, *optional*, defaults to 1):
|
626 |
+
The frequency at which the `callback` function will be called. If not specified, the callback will be
|
627 |
+
called at every step.
|
628 |
+
cross_attention_kwargs (`dict`, *optional*):
|
629 |
+
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
630 |
+
`self.processor` in
|
631 |
+
[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
|
632 |
+
guidance_rescale (`float`, *optional*, defaults to 0.7):
|
633 |
+
Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
|
634 |
+
Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
|
635 |
+
[Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
|
636 |
+
Guidance rescale factor should fix overexposure when using zero terminal SNR.
|
637 |
+
|
638 |
+
Examples:
|
639 |
+
|
640 |
+
Returns:
|
641 |
+
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
642 |
+
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
|
643 |
+
When returning a tuple, the first element is a list with the generated images, and the second element is a
|
644 |
+
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
|
645 |
+
(nsfw) content, according to the `safety_checker`.
|
646 |
+
"""
|
647 |
+
|
648 |
+
def tiled_decode(
|
649 |
+
self,
|
650 |
+
z: torch.FloatTensor,
|
651 |
+
return_dict: bool = True
|
652 |
+
) -> Union[DecoderOutput, torch.FloatTensor]:
|
653 |
+
r"""Decode a batch of images using a tiled decoder.
|
654 |
+
|
655 |
+
Args:
|
656 |
+
When this option is enabled, the VAE will split the input tensor into tiles to compute decoding in several
|
657 |
+
steps. This is useful to keep memory use constant regardless of image size.
|
658 |
+
The end result of tiled decoding is: different from non-tiled decoding due to each tile using a different
|
659 |
+
decoder. To avoid tiling artifacts, the tiles overlap and are blended together to form a smooth output.
|
660 |
+
You may still see tile-sized changes in the look of the output, but they should be much less noticeable.
|
661 |
+
z (`torch.FloatTensor`): Input batch of latent vectors. return_dict (`bool`, *optional*, defaults to
|
662 |
+
`True`):
|
663 |
+
Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
|
664 |
+
"""
|
665 |
+
_tile_overlap_factor = 1 - self.tile_overlap_factor
|
666 |
+
overlap_size = int(self.tile_latent_min_size
|
667 |
+
* _tile_overlap_factor)
|
668 |
+
blend_extent = int(self.tile_sample_min_size
|
669 |
+
* self.tile_overlap_factor)
|
670 |
+
row_limit = self.tile_sample_min_size - blend_extent
|
671 |
+
w = z.shape[3]
|
672 |
+
z = torch.cat([z, z[:, :, :, :w // 4]], dim=-1)
|
673 |
+
# Split z into overlapping 64x64 tiles and decode them separately.
|
674 |
+
# The tiles have an overlap to avoid seams between tiles.
|
675 |
+
|
676 |
+
rows = []
|
677 |
+
for i in range(0, z.shape[2], overlap_size):
|
678 |
+
row = []
|
679 |
+
tile = z[:, :, i:i + self.tile_latent_min_size, :]
|
680 |
+
tile = self.post_quant_conv(tile)
|
681 |
+
decoded = self.decoder(tile)
|
682 |
+
vae_scale_factor = decoded.shape[-1] // tile.shape[-1]
|
683 |
+
row.append(decoded)
|
684 |
+
rows.append(row)
|
685 |
+
result_rows = []
|
686 |
+
for i, row in enumerate(rows):
|
687 |
+
result_row = []
|
688 |
+
for j, tile in enumerate(row):
|
689 |
+
# blend the above tile and the left tile
|
690 |
+
# to the current tile and add the current tile to the result row
|
691 |
+
if i > 0:
|
692 |
+
tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
|
693 |
+
if j > 0:
|
694 |
+
tile = self.blend_h(row[j - 1], tile, blend_extent)
|
695 |
+
result_row.append(
|
696 |
+
self.blend_h(
|
697 |
+
tile[:, :, :row_limit, w * vae_scale_factor:],
|
698 |
+
tile[:, :, :row_limit, :w * vae_scale_factor],
|
699 |
+
tile.shape[-1] - w * vae_scale_factor))
|
700 |
+
result_rows.append(torch.cat(result_row, dim=3))
|
701 |
+
|
702 |
+
dec = torch.cat(result_rows, dim=2)
|
703 |
+
if not return_dict:
|
704 |
+
return (dec, )
|
705 |
+
|
706 |
+
return DecoderOutput(sample=dec)
|
707 |
+
|
708 |
+
self.vae.tiled_decode = tiled_decode.__get__(self.vae, AutoencoderKL)
|
709 |
+
|
710 |
+
# 0. Default height and width to unet
|
711 |
+
height = height or self.unet.config.sample_size * self.vae_scale_factor
|
712 |
+
width = width or self.unet.config.sample_size * self.vae_scale_factor
|
713 |
+
|
714 |
+
# 1. Check inputs. Raise error if not correct
|
715 |
+
self.check_inputs(prompt, height, width, callback_steps,
|
716 |
+
negative_prompt, prompt_embeds,
|
717 |
+
negative_prompt_embeds)
|
718 |
+
self.blend_extend = width // self.vae_scale_factor // 32
|
719 |
+
|
720 |
+
# 2. Define call parameters
|
721 |
+
if prompt is not None and isinstance(prompt, str):
|
722 |
+
batch_size = 1
|
723 |
+
elif prompt is not None and isinstance(prompt, list):
|
724 |
+
batch_size = len(prompt)
|
725 |
+
else:
|
726 |
+
batch_size = prompt_embeds.shape[0]
|
727 |
+
|
728 |
+
device = self._execution_device
|
729 |
+
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
730 |
+
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
731 |
+
# corresponds to doing no classifier free guidance.
|
732 |
+
do_classifier_free_guidance = guidance_scale > 1.0
|
733 |
+
|
734 |
+
# 3. Encode input prompt
|
735 |
+
text_encoder_lora_scale = (
|
736 |
+
cross_attention_kwargs.get('scale', None)
|
737 |
+
if cross_attention_kwargs is not None else None)
|
738 |
+
prompt_embeds = self._encode_prompt(
|
739 |
+
prompt,
|
740 |
+
device,
|
741 |
+
num_images_per_prompt,
|
742 |
+
do_classifier_free_guidance,
|
743 |
+
negative_prompt,
|
744 |
+
prompt_embeds=prompt_embeds,
|
745 |
+
negative_prompt_embeds=negative_prompt_embeds,
|
746 |
+
lora_scale=text_encoder_lora_scale,
|
747 |
+
)
|
748 |
+
|
749 |
+
# 4. Prepare timesteps
|
750 |
+
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
751 |
+
timesteps = self.scheduler.timesteps
|
752 |
+
|
753 |
+
# 5. Prepare latent variables
|
754 |
+
num_channels_latents = self.unet.config.in_channels
|
755 |
+
latents = self.prepare_latents(
|
756 |
+
batch_size * num_images_per_prompt,
|
757 |
+
num_channels_latents,
|
758 |
+
height,
|
759 |
+
width,
|
760 |
+
prompt_embeds.dtype,
|
761 |
+
device,
|
762 |
+
generator,
|
763 |
+
latents,
|
764 |
+
)
|
765 |
+
|
766 |
+
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
767 |
+
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
768 |
+
|
769 |
+
# 7. Denoising loop
|
770 |
+
num_warmup_steps = len(
|
771 |
+
timesteps) - num_inference_steps * self.scheduler.order
|
772 |
+
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
773 |
+
for i, t in enumerate(timesteps):
|
774 |
+
# expand the latents if we are doing classifier free guidance
|
775 |
+
latent_model_input = torch.cat(
|
776 |
+
[latents] * 2) if do_classifier_free_guidance else latents
|
777 |
+
latent_model_input = self.scheduler.scale_model_input(
|
778 |
+
latent_model_input, t)
|
779 |
+
|
780 |
+
# predict the noise residual
|
781 |
+
noise_pred = self.unet(
|
782 |
+
latent_model_input,
|
783 |
+
t,
|
784 |
+
encoder_hidden_states=prompt_embeds,
|
785 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
786 |
+
return_dict=False,
|
787 |
+
)[0]
|
788 |
+
|
789 |
+
# perform guidance
|
790 |
+
if do_classifier_free_guidance:
|
791 |
+
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
792 |
+
noise_pred = noise_pred_uncond + guidance_scale * (
|
793 |
+
noise_pred_text - noise_pred_uncond)
|
794 |
+
|
795 |
+
if do_classifier_free_guidance and guidance_rescale > 0.0:
|
796 |
+
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
|
797 |
+
noise_pred = rescale_noise_cfg(
|
798 |
+
noise_pred,
|
799 |
+
noise_pred_text,
|
800 |
+
guidance_rescale=guidance_rescale)
|
801 |
+
|
802 |
+
# compute the previous noisy sample x_t -> x_t-1
|
803 |
+
latents = self.scheduler.step(
|
804 |
+
noise_pred,
|
805 |
+
t,
|
806 |
+
latents,
|
807 |
+
**extra_step_kwargs,
|
808 |
+
return_dict=False)[0]
|
809 |
+
|
810 |
+
# call the callback, if provided
|
811 |
+
condition_i = i == len(timesteps) - 1
|
812 |
+
condition_warm = (i + 1) > num_warmup_steps and (
|
813 |
+
i + 1) % self.scheduler.order == 0
|
814 |
+
if condition_i or condition_warm:
|
815 |
+
progress_bar.update()
|
816 |
+
if callback is not None and i % callback_steps == 0:
|
817 |
+
callback(i, t, latents)
|
818 |
+
latents = self.blend_h(latents, latents, self.blend_extend)
|
819 |
+
latents = self.blend_h(latents, latents, self.blend_extend)
|
820 |
+
latents = latents[:, :, :, :width // self.vae_scale_factor]
|
821 |
+
|
822 |
+
if not output_type == 'latent':
|
823 |
+
image = self.vae.decode(
|
824 |
+
latents / self.vae.config.scaling_factor, return_dict=False)[0]
|
825 |
+
image, has_nsfw_concept = self.run_safety_checker(
|
826 |
+
image, device, prompt_embeds.dtype)
|
827 |
+
else:
|
828 |
+
image = latents
|
829 |
+
has_nsfw_concept = None
|
830 |
+
|
831 |
+
if has_nsfw_concept is None:
|
832 |
+
do_denormalize = [True] * image.shape[0]
|
833 |
+
else:
|
834 |
+
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
|
835 |
+
|
836 |
+
image = self.image_processor.postprocess(
|
837 |
+
image, output_type=output_type, do_denormalize=do_denormalize)
|
838 |
+
|
839 |
+
# Offload last model to CPU
|
840 |
+
if hasattr(
|
841 |
+
self,
|
842 |
+
'final_offload_hook') and self.final_offload_hook is not None:
|
843 |
+
self.final_offload_hook.offload()
|
844 |
+
|
845 |
+
if not return_dict:
|
846 |
+
return (image, has_nsfw_concept)
|
847 |
+
|
848 |
+
return StableDiffusionPipelineOutput(
|
849 |
+
images=image, nsfw_content_detected=has_nsfw_concept)
|
txt2panoimg/pipeline_sr.py
ADDED
@@ -0,0 +1,1202 @@
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
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|
|
|
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|
1 |
+
# Copyright © Alibaba, Inc. and its affiliates.
|
2 |
+
# The implementation here is modifed based on diffusers.StableDiffusionControlNetImg2ImgPipeline,
|
3 |
+
# originally Apache 2.0 License and public available at
|
4 |
+
# https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py
|
5 |
+
|
6 |
+
import copy
|
7 |
+
import re
|
8 |
+
from typing import Any, Callable, Dict, List, Optional, Union
|
9 |
+
|
10 |
+
import numpy as np
|
11 |
+
import PIL.Image
|
12 |
+
import torch
|
13 |
+
import torch.nn.functional as F
|
14 |
+
from diffusers import (AutoencoderKL, DiffusionPipeline,
|
15 |
+
StableDiffusionControlNetImg2ImgPipeline)
|
16 |
+
from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
|
17 |
+
from diffusers.models import ControlNetModel
|
18 |
+
try:
|
19 |
+
from diffusers.models.autoencoders.vae import DecoderOutput
|
20 |
+
except:
|
21 |
+
from diffusers.models.vae import DecoderOutput
|
22 |
+
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
|
23 |
+
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
|
24 |
+
from diffusers.utils import logging, replace_example_docstring
|
25 |
+
from diffusers.utils.torch_utils import is_compiled_module
|
26 |
+
|
27 |
+
from transformers import CLIPTokenizer
|
28 |
+
|
29 |
+
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
30 |
+
|
31 |
+
EXAMPLE_DOC_STRING = """
|
32 |
+
Examples:
|
33 |
+
```py
|
34 |
+
>>> import torch
|
35 |
+
>>> from PIL import Image
|
36 |
+
>>> from txt2panoimage.pipeline_sr import StableDiffusionControlNetImg2ImgPanoPipeline
|
37 |
+
>>> base_model_path = "models/sr-base"
|
38 |
+
>>> controlnet_path = "models/sr-control"
|
39 |
+
>>> controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
|
40 |
+
>>> pipe = StableDiffusionControlNetImg2ImgPanoPipeline.from_pretrained(base_model_path, controlnet=controlnet,
|
41 |
+
... torch_dtype=torch.float16)
|
42 |
+
>>> pipe.vae.enable_tiling()
|
43 |
+
>>> # remove following line if xformers is not installed
|
44 |
+
>>> pipe.enable_xformers_memory_efficient_attention()
|
45 |
+
>>> pipe.enable_model_cpu_offload()
|
46 |
+
>>> input_image_path = 'data/test.png'
|
47 |
+
>>> image = Image.open(input_image_path)
|
48 |
+
>>> image = pipe(
|
49 |
+
... "futuristic-looking woman",
|
50 |
+
... num_inference_steps=20,
|
51 |
+
... image=image,
|
52 |
+
... height=768,
|
53 |
+
... width=1536,
|
54 |
+
... control_image=image,
|
55 |
+
... ).images[0]
|
56 |
+
|
57 |
+
```
|
58 |
+
"""
|
59 |
+
|
60 |
+
re_attention = re.compile(
|
61 |
+
r"""
|
62 |
+
\\\(|
|
63 |
+
\\\)|
|
64 |
+
\\\[|
|
65 |
+
\\]|
|
66 |
+
\\\\|
|
67 |
+
\\|
|
68 |
+
\(|
|
69 |
+
\[|
|
70 |
+
:([+-]?[.\d]+)\)|
|
71 |
+
\)|
|
72 |
+
]|
|
73 |
+
[^\\()\[\]:]+|
|
74 |
+
:
|
75 |
+
""",
|
76 |
+
re.X,
|
77 |
+
)
|
78 |
+
|
79 |
+
|
80 |
+
def parse_prompt_attention(text):
|
81 |
+
"""
|
82 |
+
Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
|
83 |
+
Accepted tokens are:
|
84 |
+
(abc) - increases attention to abc by a multiplier of 1.1
|
85 |
+
(abc:3.12) - increases attention to abc by a multiplier of 3.12
|
86 |
+
[abc] - decreases attention to abc by a multiplier of 1.1
|
87 |
+
"""
|
88 |
+
|
89 |
+
res = []
|
90 |
+
round_brackets = []
|
91 |
+
square_brackets = []
|
92 |
+
|
93 |
+
round_bracket_multiplier = 1.1
|
94 |
+
square_bracket_multiplier = 1 / 1.1
|
95 |
+
|
96 |
+
def multiply_range(start_position, multiplier):
|
97 |
+
for p in range(start_position, len(res)):
|
98 |
+
res[p][1] *= multiplier
|
99 |
+
|
100 |
+
for m in re_attention.finditer(text):
|
101 |
+
text = m.group(0)
|
102 |
+
weight = m.group(1)
|
103 |
+
|
104 |
+
if text.startswith('\\'):
|
105 |
+
res.append([text[1:], 1.0])
|
106 |
+
elif text == '(':
|
107 |
+
round_brackets.append(len(res))
|
108 |
+
elif text == '[':
|
109 |
+
square_brackets.append(len(res))
|
110 |
+
elif weight is not None and len(round_brackets) > 0:
|
111 |
+
multiply_range(round_brackets.pop(), float(weight))
|
112 |
+
elif text == ')' and len(round_brackets) > 0:
|
113 |
+
multiply_range(round_brackets.pop(), round_bracket_multiplier)
|
114 |
+
elif text == ']' and len(square_brackets) > 0:
|
115 |
+
multiply_range(square_brackets.pop(), square_bracket_multiplier)
|
116 |
+
else:
|
117 |
+
res.append([text, 1.0])
|
118 |
+
|
119 |
+
for pos in round_brackets:
|
120 |
+
multiply_range(pos, round_bracket_multiplier)
|
121 |
+
|
122 |
+
for pos in square_brackets:
|
123 |
+
multiply_range(pos, square_bracket_multiplier)
|
124 |
+
|
125 |
+
if len(res) == 0:
|
126 |
+
res = [['', 1.0]]
|
127 |
+
|
128 |
+
# merge runs of identical weights
|
129 |
+
i = 0
|
130 |
+
while i + 1 < len(res):
|
131 |
+
if res[i][1] == res[i + 1][1]:
|
132 |
+
res[i][0] += res[i + 1][0]
|
133 |
+
res.pop(i + 1)
|
134 |
+
else:
|
135 |
+
i += 1
|
136 |
+
|
137 |
+
return res
|
138 |
+
|
139 |
+
|
140 |
+
def get_prompts_with_weights(pipe: DiffusionPipeline, prompt: List[str],
|
141 |
+
max_length: int):
|
142 |
+
r"""
|
143 |
+
Tokenize a list of prompts and return its tokens with weights of each token.
|
144 |
+
|
145 |
+
No padding, starting or ending token is included.
|
146 |
+
"""
|
147 |
+
tokens = []
|
148 |
+
weights = []
|
149 |
+
truncated = False
|
150 |
+
for text in prompt:
|
151 |
+
texts_and_weights = parse_prompt_attention(text)
|
152 |
+
text_token = []
|
153 |
+
text_weight = []
|
154 |
+
for word, weight in texts_and_weights:
|
155 |
+
# tokenize and discard the starting and the ending token
|
156 |
+
token = pipe.tokenizer(word).input_ids[1:-1]
|
157 |
+
text_token += token
|
158 |
+
# copy the weight by length of token
|
159 |
+
text_weight += [weight] * len(token)
|
160 |
+
# stop if the text is too long (longer than truncation limit)
|
161 |
+
if len(text_token) > max_length:
|
162 |
+
truncated = True
|
163 |
+
break
|
164 |
+
# truncate
|
165 |
+
if len(text_token) > max_length:
|
166 |
+
truncated = True
|
167 |
+
text_token = text_token[:max_length]
|
168 |
+
text_weight = text_weight[:max_length]
|
169 |
+
tokens.append(text_token)
|
170 |
+
weights.append(text_weight)
|
171 |
+
if truncated:
|
172 |
+
logger.warning(
|
173 |
+
'Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples'
|
174 |
+
)
|
175 |
+
return tokens, weights
|
176 |
+
|
177 |
+
|
178 |
+
def pad_tokens_and_weights(tokens,
|
179 |
+
weights,
|
180 |
+
max_length,
|
181 |
+
bos,
|
182 |
+
eos,
|
183 |
+
pad,
|
184 |
+
no_boseos_middle=True,
|
185 |
+
chunk_length=77):
|
186 |
+
r"""
|
187 |
+
Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
|
188 |
+
"""
|
189 |
+
max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
|
190 |
+
weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
|
191 |
+
for i in range(len(tokens)):
|
192 |
+
tokens[i] = [
|
193 |
+
bos
|
194 |
+
] + tokens[i] + [pad] * (max_length - 1 - len(tokens[i]) - 1) + [eos]
|
195 |
+
if no_boseos_middle:
|
196 |
+
weights[i] = [1.0] + weights[i] + [1.0] * (
|
197 |
+
max_length - 1 - len(weights[i]))
|
198 |
+
else:
|
199 |
+
w = []
|
200 |
+
if len(weights[i]) == 0:
|
201 |
+
w = [1.0] * weights_length
|
202 |
+
else:
|
203 |
+
for j in range(max_embeddings_multiples):
|
204 |
+
w.append(1.0) # weight for starting token in this chunk
|
205 |
+
w += weights[i][j * (chunk_length - 2):min(
|
206 |
+
len(weights[i]), (j + 1) * (chunk_length - 2))]
|
207 |
+
w.append(1.0) # weight for ending token in this chunk
|
208 |
+
w += [1.0] * (weights_length - len(w))
|
209 |
+
weights[i] = w[:]
|
210 |
+
|
211 |
+
return tokens, weights
|
212 |
+
|
213 |
+
|
214 |
+
def get_unweighted_text_embeddings(
|
215 |
+
pipe: DiffusionPipeline,
|
216 |
+
text_input: torch.Tensor,
|
217 |
+
chunk_length: int,
|
218 |
+
no_boseos_middle: Optional[bool] = True,
|
219 |
+
):
|
220 |
+
"""
|
221 |
+
When the length of tokens is a multiple of the capacity of the text encoder,
|
222 |
+
it should be split into chunks and sent to the text encoder individually.
|
223 |
+
"""
|
224 |
+
max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)
|
225 |
+
if max_embeddings_multiples > 1:
|
226 |
+
text_embeddings = []
|
227 |
+
for i in range(max_embeddings_multiples):
|
228 |
+
# extract the i-th chunk
|
229 |
+
text_input_chunk = text_input[:, i * (chunk_length - 2):(i + 1)
|
230 |
+
* (chunk_length - 2) + 2].clone()
|
231 |
+
|
232 |
+
# cover the head and the tail by the starting and the ending tokens
|
233 |
+
text_input_chunk[:, 0] = text_input[0, 0]
|
234 |
+
text_input_chunk[:, -1] = text_input[0, -1]
|
235 |
+
text_embedding = pipe.text_encoder(text_input_chunk)[0]
|
236 |
+
|
237 |
+
if no_boseos_middle:
|
238 |
+
if i == 0:
|
239 |
+
# discard the ending token
|
240 |
+
text_embedding = text_embedding[:, :-1]
|
241 |
+
elif i == max_embeddings_multiples - 1:
|
242 |
+
# discard the starting token
|
243 |
+
text_embedding = text_embedding[:, 1:]
|
244 |
+
else:
|
245 |
+
# discard both starting and ending tokens
|
246 |
+
text_embedding = text_embedding[:, 1:-1]
|
247 |
+
|
248 |
+
text_embeddings.append(text_embedding)
|
249 |
+
text_embeddings = torch.concat(text_embeddings, axis=1)
|
250 |
+
else:
|
251 |
+
text_embeddings = pipe.text_encoder(text_input)[0]
|
252 |
+
return text_embeddings
|
253 |
+
|
254 |
+
|
255 |
+
def get_weighted_text_embeddings(
|
256 |
+
pipe: DiffusionPipeline,
|
257 |
+
prompt: Union[str, List[str]],
|
258 |
+
uncond_prompt: Optional[Union[str, List[str]]] = None,
|
259 |
+
max_embeddings_multiples: Optional[int] = 3,
|
260 |
+
no_boseos_middle: Optional[bool] = False,
|
261 |
+
skip_parsing: Optional[bool] = False,
|
262 |
+
skip_weighting: Optional[bool] = False,
|
263 |
+
):
|
264 |
+
r"""
|
265 |
+
Prompts can be assigned with local weights using brackets. For example,
|
266 |
+
prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
|
267 |
+
and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.
|
268 |
+
|
269 |
+
Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.
|
270 |
+
|
271 |
+
Args:
|
272 |
+
pipe (`DiffusionPipeline`):
|
273 |
+
Pipe to provide access to the tokenizer and the text encoder.
|
274 |
+
prompt (`str` or `List[str]`):
|
275 |
+
The prompt or prompts to guide the image generation.
|
276 |
+
uncond_prompt (`str` or `List[str]`):
|
277 |
+
The unconditional prompt or prompts for guide the image generation. If unconditional prompt
|
278 |
+
is provided, the embeddings of prompt and uncond_prompt are concatenated.
|
279 |
+
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
|
280 |
+
The max multiple length of prompt embeddings compared to the max output length of text encoder.
|
281 |
+
no_boseos_middle (`bool`, *optional*, defaults to `False`):
|
282 |
+
If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and
|
283 |
+
ending token in each of the chunk in the middle.
|
284 |
+
skip_parsing (`bool`, *optional*, defaults to `False`):
|
285 |
+
Skip the parsing of brackets.
|
286 |
+
skip_weighting (`bool`, *optional*, defaults to `False`):
|
287 |
+
Skip the weighting. When the parsing is skipped, it is forced True.
|
288 |
+
"""
|
289 |
+
max_length = (pipe.tokenizer.model_max_length
|
290 |
+
- 2) * max_embeddings_multiples + 2
|
291 |
+
if isinstance(prompt, str):
|
292 |
+
prompt = [prompt]
|
293 |
+
|
294 |
+
if not skip_parsing:
|
295 |
+
prompt_tokens, prompt_weights = get_prompts_with_weights(
|
296 |
+
pipe, prompt, max_length - 2)
|
297 |
+
if uncond_prompt is not None:
|
298 |
+
if isinstance(uncond_prompt, str):
|
299 |
+
uncond_prompt = [uncond_prompt]
|
300 |
+
uncond_tokens, uncond_weights = get_prompts_with_weights(
|
301 |
+
pipe, uncond_prompt, max_length - 2)
|
302 |
+
else:
|
303 |
+
prompt_tokens = [
|
304 |
+
token[1:-1] for token in pipe.tokenizer(
|
305 |
+
prompt, max_length=max_length, truncation=True).input_ids
|
306 |
+
]
|
307 |
+
prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
|
308 |
+
if uncond_prompt is not None:
|
309 |
+
if isinstance(uncond_prompt, str):
|
310 |
+
uncond_prompt = [uncond_prompt]
|
311 |
+
uncond_tokens = [
|
312 |
+
token[1:-1] for token in pipe.tokenizer(
|
313 |
+
uncond_prompt, max_length=max_length,
|
314 |
+
truncation=True).input_ids
|
315 |
+
]
|
316 |
+
uncond_weights = [[1.0] * len(token) for token in uncond_tokens]
|
317 |
+
|
318 |
+
# round up the longest length of tokens to a multiple of (model_max_length - 2)
|
319 |
+
max_length = max([len(token) for token in prompt_tokens])
|
320 |
+
if uncond_prompt is not None:
|
321 |
+
max_length = max(max_length,
|
322 |
+
max([len(token) for token in uncond_tokens]))
|
323 |
+
|
324 |
+
max_embeddings_multiples = min(
|
325 |
+
max_embeddings_multiples,
|
326 |
+
(max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1,
|
327 |
+
)
|
328 |
+
max_embeddings_multiples = max(1, max_embeddings_multiples)
|
329 |
+
max_length = (pipe.tokenizer.model_max_length
|
330 |
+
- 2) * max_embeddings_multiples + 2
|
331 |
+
|
332 |
+
# pad the length of tokens and weights
|
333 |
+
bos = pipe.tokenizer.bos_token_id
|
334 |
+
eos = pipe.tokenizer.eos_token_id
|
335 |
+
pad = getattr(pipe.tokenizer, 'pad_token_id', eos)
|
336 |
+
prompt_tokens, prompt_weights = pad_tokens_and_weights(
|
337 |
+
prompt_tokens,
|
338 |
+
prompt_weights,
|
339 |
+
max_length,
|
340 |
+
bos,
|
341 |
+
eos,
|
342 |
+
pad,
|
343 |
+
no_boseos_middle=no_boseos_middle,
|
344 |
+
chunk_length=pipe.tokenizer.model_max_length,
|
345 |
+
)
|
346 |
+
prompt_tokens = torch.tensor(
|
347 |
+
prompt_tokens, dtype=torch.long, device=pipe.device)
|
348 |
+
if uncond_prompt is not None:
|
349 |
+
uncond_tokens, uncond_weights = pad_tokens_and_weights(
|
350 |
+
uncond_tokens,
|
351 |
+
uncond_weights,
|
352 |
+
max_length,
|
353 |
+
bos,
|
354 |
+
eos,
|
355 |
+
pad,
|
356 |
+
no_boseos_middle=no_boseos_middle,
|
357 |
+
chunk_length=pipe.tokenizer.model_max_length,
|
358 |
+
)
|
359 |
+
uncond_tokens = torch.tensor(
|
360 |
+
uncond_tokens, dtype=torch.long, device=pipe.device)
|
361 |
+
|
362 |
+
# get the embeddings
|
363 |
+
text_embeddings = get_unweighted_text_embeddings(
|
364 |
+
pipe,
|
365 |
+
prompt_tokens,
|
366 |
+
pipe.tokenizer.model_max_length,
|
367 |
+
no_boseos_middle=no_boseos_middle,
|
368 |
+
)
|
369 |
+
prompt_weights = torch.tensor(
|
370 |
+
prompt_weights,
|
371 |
+
dtype=text_embeddings.dtype,
|
372 |
+
device=text_embeddings.device)
|
373 |
+
if uncond_prompt is not None:
|
374 |
+
uncond_embeddings = get_unweighted_text_embeddings(
|
375 |
+
pipe,
|
376 |
+
uncond_tokens,
|
377 |
+
pipe.tokenizer.model_max_length,
|
378 |
+
no_boseos_middle=no_boseos_middle,
|
379 |
+
)
|
380 |
+
uncond_weights = torch.tensor(
|
381 |
+
uncond_weights,
|
382 |
+
dtype=uncond_embeddings.dtype,
|
383 |
+
device=uncond_embeddings.device)
|
384 |
+
|
385 |
+
# assign weights to the prompts and normalize in the sense of mean
|
386 |
+
# TODO: should we normalize by chunk or in a whole (current implementation)?
|
387 |
+
if (not skip_parsing) and (not skip_weighting):
|
388 |
+
previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(
|
389 |
+
text_embeddings.dtype)
|
390 |
+
text_embeddings *= prompt_weights.unsqueeze(-1)
|
391 |
+
current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(
|
392 |
+
text_embeddings.dtype)
|
393 |
+
text_embeddings *= (previous_mean
|
394 |
+
/ current_mean).unsqueeze(-1).unsqueeze(-1)
|
395 |
+
if uncond_prompt is not None:
|
396 |
+
previous_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(
|
397 |
+
uncond_embeddings.dtype)
|
398 |
+
uncond_embeddings *= uncond_weights.unsqueeze(-1)
|
399 |
+
current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(
|
400 |
+
uncond_embeddings.dtype)
|
401 |
+
uncond_embeddings *= (previous_mean
|
402 |
+
/ current_mean).unsqueeze(-1).unsqueeze(-1)
|
403 |
+
|
404 |
+
if uncond_prompt is not None:
|
405 |
+
return text_embeddings, uncond_embeddings
|
406 |
+
return text_embeddings, None
|
407 |
+
|
408 |
+
|
409 |
+
def prepare_image(image):
|
410 |
+
if isinstance(image, torch.Tensor):
|
411 |
+
# Batch single image
|
412 |
+
if image.ndim == 3:
|
413 |
+
image = image.unsqueeze(0)
|
414 |
+
|
415 |
+
image = image.to(dtype=torch.float32)
|
416 |
+
else:
|
417 |
+
# preprocess image
|
418 |
+
if isinstance(image, (PIL.Image.Image, np.ndarray)):
|
419 |
+
image = [image]
|
420 |
+
|
421 |
+
if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
|
422 |
+
image = [np.array(i.convert('RGB'))[None, :] for i in image]
|
423 |
+
image = np.concatenate(image, axis=0)
|
424 |
+
elif isinstance(image, list) and isinstance(image[0], np.ndarray):
|
425 |
+
image = np.concatenate([i[None, :] for i in image], axis=0)
|
426 |
+
|
427 |
+
image = image.transpose(0, 3, 1, 2)
|
428 |
+
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
|
429 |
+
|
430 |
+
return image
|
431 |
+
|
432 |
+
|
433 |
+
class StableDiffusionControlNetImg2ImgPanoPipeline(
|
434 |
+
StableDiffusionControlNetImg2ImgPipeline):
|
435 |
+
r"""
|
436 |
+
Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.
|
437 |
+
|
438 |
+
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
|
439 |
+
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
|
440 |
+
|
441 |
+
In addition the pipeline inherits the following loading methods:
|
442 |
+
- *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
|
443 |
+
|
444 |
+
Args:
|
445 |
+
vae ([`AutoencoderKL`]):
|
446 |
+
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
|
447 |
+
text_encoder ([`CLIPTextModel`]):
|
448 |
+
Frozen text-encoder. Stable Diffusion uses the text portion of
|
449 |
+
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
|
450 |
+
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
|
451 |
+
tokenizer (`CLIPTokenizer`):
|
452 |
+
Tokenizer of class
|
453 |
+
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/
|
454 |
+
model_doc/clip#transformers.CLIPTokenizer).
|
455 |
+
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
|
456 |
+
controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
|
457 |
+
Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets
|
458 |
+
as a list, the outputs from each ControlNet are added together to create one combined additional
|
459 |
+
conditioning.
|
460 |
+
scheduler ([`SchedulerMixin`]):
|
461 |
+
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
|
462 |
+
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
|
463 |
+
safety_checker ([`StableDiffusionSafetyChecker`]):
|
464 |
+
Classification module that estimates whether generated images could be considered offensive or harmful.
|
465 |
+
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
|
466 |
+
feature_extractor ([`CLIPImageProcessor`]):
|
467 |
+
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
|
468 |
+
"""
|
469 |
+
_optional_components = ['safety_checker', 'feature_extractor']
|
470 |
+
|
471 |
+
def check_inputs(
|
472 |
+
self,
|
473 |
+
prompt,
|
474 |
+
image,
|
475 |
+
height,
|
476 |
+
width,
|
477 |
+
callback_steps,
|
478 |
+
negative_prompt=None,
|
479 |
+
prompt_embeds=None,
|
480 |
+
negative_prompt_embeds=None,
|
481 |
+
controlnet_conditioning_scale=1.0,
|
482 |
+
):
|
483 |
+
if height % 8 != 0 or width % 8 != 0:
|
484 |
+
raise ValueError(
|
485 |
+
f'`height` and `width` have to be divisible by 8 but are {height} and {width}.'
|
486 |
+
)
|
487 |
+
condition_1 = callback_steps is not None
|
488 |
+
condition_2 = not isinstance(callback_steps,
|
489 |
+
int) or callback_steps <= 0
|
490 |
+
if (callback_steps is None) or (condition_1 and condition_2):
|
491 |
+
raise ValueError(
|
492 |
+
f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
|
493 |
+
f' {type(callback_steps)}.')
|
494 |
+
if prompt is not None and prompt_embeds is not None:
|
495 |
+
raise ValueError(
|
496 |
+
f'Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to'
|
497 |
+
' only forward one of the two.')
|
498 |
+
elif prompt is None and prompt_embeds is None:
|
499 |
+
raise ValueError(
|
500 |
+
'Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.'
|
501 |
+
)
|
502 |
+
elif prompt is not None and (not isinstance(prompt, str)
|
503 |
+
and not isinstance(prompt, list)):
|
504 |
+
raise ValueError(
|
505 |
+
f'`prompt` has to be of type `str` or `list` but is {type(prompt)}'
|
506 |
+
)
|
507 |
+
if negative_prompt is not None and negative_prompt_embeds is not None:
|
508 |
+
raise ValueError(
|
509 |
+
f'Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:'
|
510 |
+
f' {negative_prompt_embeds}. Please make sure to only forward one of the two.'
|
511 |
+
)
|
512 |
+
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
513 |
+
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
514 |
+
raise ValueError(
|
515 |
+
'`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but'
|
516 |
+
f' got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`'
|
517 |
+
f' {negative_prompt_embeds.shape}.')
|
518 |
+
# `prompt` needs more sophisticated handling when there are multiple
|
519 |
+
# conditionings.
|
520 |
+
if isinstance(self.controlnet, MultiControlNetModel):
|
521 |
+
if isinstance(prompt, list):
|
522 |
+
logger.warning(
|
523 |
+
f'You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}'
|
524 |
+
' prompts. The conditionings will be fixed across the prompts.'
|
525 |
+
)
|
526 |
+
# Check `image`
|
527 |
+
is_compiled = hasattr(
|
528 |
+
F, 'scaled_dot_product_attention') and isinstance(
|
529 |
+
self.controlnet, torch._dynamo.eval_frame.OptimizedModule)
|
530 |
+
if (isinstance(self.controlnet, ControlNetModel) or is_compiled
|
531 |
+
and isinstance(self.controlnet._orig_mod, ControlNetModel)):
|
532 |
+
self.check_image(image, prompt, prompt_embeds)
|
533 |
+
elif (isinstance(self.controlnet, MultiControlNetModel) or is_compiled
|
534 |
+
and isinstance(self.controlnet._orig_mod, MultiControlNetModel)):
|
535 |
+
if not isinstance(image, list):
|
536 |
+
raise TypeError(
|
537 |
+
'For multiple controlnets: `image` must be type `list`')
|
538 |
+
# When `image` is a nested list:
|
539 |
+
# (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])
|
540 |
+
elif any(isinstance(i, list) for i in image):
|
541 |
+
raise ValueError(
|
542 |
+
'A single batch of multiple conditionings are supported at the moment.'
|
543 |
+
)
|
544 |
+
elif len(image) != len(self.controlnet.nets):
|
545 |
+
raise ValueError(
|
546 |
+
'For multiple controlnets: `image` must have the same length as the number of controlnets.'
|
547 |
+
)
|
548 |
+
for image_ in image:
|
549 |
+
self.check_image(image_, prompt, prompt_embeds)
|
550 |
+
else:
|
551 |
+
assert False
|
552 |
+
# Check `controlnet_conditioning_scale`
|
553 |
+
if (isinstance(self.controlnet, ControlNetModel) or is_compiled
|
554 |
+
and isinstance(self.controlnet._orig_mod, ControlNetModel)):
|
555 |
+
if not isinstance(controlnet_conditioning_scale, float):
|
556 |
+
raise TypeError(
|
557 |
+
'For single controlnet: `controlnet_conditioning_scale` must be type `float`.'
|
558 |
+
)
|
559 |
+
elif (isinstance(self.controlnet, MultiControlNetModel) or is_compiled
|
560 |
+
and isinstance(self.controlnet._orig_mod, MultiControlNetModel)):
|
561 |
+
if isinstance(controlnet_conditioning_scale, list):
|
562 |
+
if any(
|
563 |
+
isinstance(i, list)
|
564 |
+
for i in controlnet_conditioning_scale):
|
565 |
+
raise ValueError(
|
566 |
+
'A single batch of multiple conditionings are supported at the moment.'
|
567 |
+
)
|
568 |
+
elif isinstance(
|
569 |
+
controlnet_conditioning_scale,
|
570 |
+
list) and len(controlnet_conditioning_scale) != len(
|
571 |
+
self.controlnet.nets):
|
572 |
+
raise ValueError(
|
573 |
+
'For multiple controlnets: When `controlnet_conditioning_scale` '
|
574 |
+
'is specified as `list`, it must have'
|
575 |
+
' the same length as the number of controlnets')
|
576 |
+
else:
|
577 |
+
assert False
|
578 |
+
|
579 |
+
def _default_height_width(self, height, width, image):
|
580 |
+
# NOTE: It is possible that a list of images have different
|
581 |
+
# dimensions for each image, so just checking the first image
|
582 |
+
# is not _exactly_ correct, but it is simple.
|
583 |
+
while isinstance(image, list):
|
584 |
+
image = image[0]
|
585 |
+
if height is None:
|
586 |
+
if isinstance(image, PIL.Image.Image):
|
587 |
+
height = image.height
|
588 |
+
elif isinstance(image, torch.Tensor):
|
589 |
+
height = image.shape[2]
|
590 |
+
height = (height // 8) * 8 # round down to nearest multiple of 8
|
591 |
+
if width is None:
|
592 |
+
if isinstance(image, PIL.Image.Image):
|
593 |
+
width = image.width
|
594 |
+
elif isinstance(image, torch.Tensor):
|
595 |
+
width = image.shape[3]
|
596 |
+
width = (width // 8) * 8 # round down to nearest multiple of 8
|
597 |
+
return height, width
|
598 |
+
|
599 |
+
def _encode_prompt(
|
600 |
+
self,
|
601 |
+
prompt,
|
602 |
+
device,
|
603 |
+
num_images_per_prompt,
|
604 |
+
do_classifier_free_guidance,
|
605 |
+
negative_prompt=None,
|
606 |
+
max_embeddings_multiples=3,
|
607 |
+
prompt_embeds: Optional[torch.FloatTensor] = None,
|
608 |
+
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
609 |
+
lora_scale: Optional[float] = None,
|
610 |
+
):
|
611 |
+
r"""
|
612 |
+
Encodes the prompt into text encoder hidden states.
|
613 |
+
|
614 |
+
Args:
|
615 |
+
prompt (`str` or `list(int)`):
|
616 |
+
prompt to be encoded
|
617 |
+
device: (`torch.device`):
|
618 |
+
torch device
|
619 |
+
num_images_per_prompt (`int`):
|
620 |
+
number of images that should be generated per prompt
|
621 |
+
do_classifier_free_guidance (`bool`):
|
622 |
+
whether to use classifier free guidance or not
|
623 |
+
negative_prompt (`str` or `List[str]`):
|
624 |
+
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
|
625 |
+
if `guidance_scale` is less than `1`).
|
626 |
+
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
|
627 |
+
The max multiple length of prompt embeddings compared to the max output length of text encoder.
|
628 |
+
"""
|
629 |
+
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
|
630 |
+
self._lora_scale = lora_scale
|
631 |
+
|
632 |
+
if prompt is not None and isinstance(prompt, str):
|
633 |
+
batch_size = 1
|
634 |
+
elif prompt is not None and isinstance(prompt, list):
|
635 |
+
batch_size = len(prompt)
|
636 |
+
else:
|
637 |
+
batch_size = prompt_embeds.shape[0]
|
638 |
+
|
639 |
+
if negative_prompt_embeds is None:
|
640 |
+
if negative_prompt is None:
|
641 |
+
negative_prompt = [''] * batch_size
|
642 |
+
elif isinstance(negative_prompt, str):
|
643 |
+
negative_prompt = [negative_prompt] * batch_size
|
644 |
+
if batch_size != len(negative_prompt):
|
645 |
+
raise ValueError(
|
646 |
+
f'`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:'
|
647 |
+
f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
|
648 |
+
' the batch size of `prompt`.')
|
649 |
+
if prompt_embeds is None or negative_prompt_embeds is None:
|
650 |
+
if isinstance(self, TextualInversionLoaderMixin):
|
651 |
+
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
|
652 |
+
if do_classifier_free_guidance and negative_prompt_embeds is None:
|
653 |
+
negative_prompt = self.maybe_convert_prompt(
|
654 |
+
negative_prompt, self.tokenizer)
|
655 |
+
|
656 |
+
prompt_embeds1, negative_prompt_embeds1 = get_weighted_text_embeddings(
|
657 |
+
pipe=self,
|
658 |
+
prompt=prompt,
|
659 |
+
uncond_prompt=negative_prompt
|
660 |
+
if do_classifier_free_guidance else None,
|
661 |
+
max_embeddings_multiples=max_embeddings_multiples,
|
662 |
+
)
|
663 |
+
if prompt_embeds is None:
|
664 |
+
prompt_embeds = prompt_embeds1
|
665 |
+
if negative_prompt_embeds is None:
|
666 |
+
negative_prompt_embeds = negative_prompt_embeds1
|
667 |
+
|
668 |
+
bs_embed, seq_len, _ = prompt_embeds.shape
|
669 |
+
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
670 |
+
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
671 |
+
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt,
|
672 |
+
seq_len, -1)
|
673 |
+
|
674 |
+
if do_classifier_free_guidance:
|
675 |
+
bs_embed, seq_len, _ = negative_prompt_embeds.shape
|
676 |
+
negative_prompt_embeds = negative_prompt_embeds.repeat(
|
677 |
+
1, num_images_per_prompt, 1)
|
678 |
+
negative_prompt_embeds = negative_prompt_embeds.view(
|
679 |
+
bs_embed * num_images_per_prompt, seq_len, -1)
|
680 |
+
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
681 |
+
|
682 |
+
return prompt_embeds
|
683 |
+
|
684 |
+
def denoise_latents(self, latents, t, prompt_embeds, control_image,
|
685 |
+
controlnet_conditioning_scale, guess_mode,
|
686 |
+
cross_attention_kwargs, do_classifier_free_guidance,
|
687 |
+
guidance_scale, extra_step_kwargs,
|
688 |
+
views_scheduler_status):
|
689 |
+
# expand the latents if we are doing classifier free guidance
|
690 |
+
latent_model_input = torch.cat(
|
691 |
+
[latents] * 2) if do_classifier_free_guidance else latents
|
692 |
+
self.scheduler.__dict__.update(views_scheduler_status[0])
|
693 |
+
latent_model_input = self.scheduler.scale_model_input(
|
694 |
+
latent_model_input, t)
|
695 |
+
# controlnet(s) inference
|
696 |
+
if guess_mode and do_classifier_free_guidance:
|
697 |
+
# Infer ControlNet only for the conditional batch.
|
698 |
+
controlnet_latent_model_input = latents
|
699 |
+
controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
|
700 |
+
else:
|
701 |
+
controlnet_latent_model_input = latent_model_input
|
702 |
+
controlnet_prompt_embeds = prompt_embeds
|
703 |
+
down_block_res_samples, mid_block_res_sample = self.controlnet(
|
704 |
+
controlnet_latent_model_input,
|
705 |
+
t,
|
706 |
+
encoder_hidden_states=controlnet_prompt_embeds,
|
707 |
+
controlnet_cond=control_image,
|
708 |
+
conditioning_scale=controlnet_conditioning_scale,
|
709 |
+
guess_mode=guess_mode,
|
710 |
+
return_dict=False,
|
711 |
+
)
|
712 |
+
if guess_mode and do_classifier_free_guidance:
|
713 |
+
# Infered ControlNet only for the conditional batch.
|
714 |
+
# To apply the output of ControlNet to both the unconditional and conditional batches,
|
715 |
+
# add 0 to the unconditional batch to keep it unchanged.
|
716 |
+
down_block_res_samples = [
|
717 |
+
torch.cat([torch.zeros_like(d), d])
|
718 |
+
for d in down_block_res_samples
|
719 |
+
]
|
720 |
+
mid_block_res_sample = torch.cat(
|
721 |
+
[torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
|
722 |
+
# predict the noise residual
|
723 |
+
noise_pred = self.unet(
|
724 |
+
latent_model_input,
|
725 |
+
t,
|
726 |
+
encoder_hidden_states=prompt_embeds,
|
727 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
728 |
+
down_block_additional_residuals=down_block_res_samples,
|
729 |
+
mid_block_additional_residual=mid_block_res_sample,
|
730 |
+
return_dict=False,
|
731 |
+
)[0]
|
732 |
+
# perform guidance
|
733 |
+
if do_classifier_free_guidance:
|
734 |
+
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
735 |
+
noise_pred = noise_pred_uncond + guidance_scale * (
|
736 |
+
noise_pred_text - noise_pred_uncond)
|
737 |
+
# compute the previous noisy sample x_t -> x_t-1
|
738 |
+
latents = self.scheduler.step(
|
739 |
+
noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
740 |
+
return latents
|
741 |
+
|
742 |
+
def blend_v(self, a, b, blend_extent):
|
743 |
+
blend_extent = min(a.shape[2], b.shape[2], blend_extent)
|
744 |
+
for y in range(blend_extent):
|
745 |
+
b[:, :,
|
746 |
+
y, :] = a[:, :, -blend_extent
|
747 |
+
+ y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (
|
748 |
+
y / blend_extent)
|
749 |
+
return b
|
750 |
+
|
751 |
+
def blend_h(self, a, b, blend_extent):
|
752 |
+
blend_extent = min(a.shape[3], b.shape[3], blend_extent)
|
753 |
+
for x in range(blend_extent):
|
754 |
+
b[:, :, :, x] = a[:, :, :, -blend_extent
|
755 |
+
+ x] * (1 - x / blend_extent) + b[:, :, :, x] * (
|
756 |
+
x / blend_extent)
|
757 |
+
return b
|
758 |
+
|
759 |
+
def get_blocks(self, latents, control_image, tile_latent_min_size,
|
760 |
+
overlap_size):
|
761 |
+
rows_latents = []
|
762 |
+
rows_control_images = []
|
763 |
+
for i in range(0, latents.shape[2] - overlap_size, overlap_size):
|
764 |
+
row_latents = []
|
765 |
+
row_control_images = []
|
766 |
+
for j in range(0, latents.shape[3] - overlap_size, overlap_size):
|
767 |
+
latents_input = latents[:, :, i:i + tile_latent_min_size,
|
768 |
+
j:j + tile_latent_min_size]
|
769 |
+
c_start_i = self.vae_scale_factor * i
|
770 |
+
c_end_i = self.vae_scale_factor * (i + tile_latent_min_size)
|
771 |
+
c_start_j = self.vae_scale_factor * j
|
772 |
+
c_end_j = self.vae_scale_factor * (j + tile_latent_min_size)
|
773 |
+
control_image_input = control_image[:, :, c_start_i:c_end_i,
|
774 |
+
c_start_j:c_end_j]
|
775 |
+
row_latents.append(latents_input)
|
776 |
+
row_control_images.append(control_image_input)
|
777 |
+
rows_latents.append(row_latents)
|
778 |
+
rows_control_images.append(row_control_images)
|
779 |
+
return rows_latents, rows_control_images
|
780 |
+
|
781 |
+
@torch.no_grad()
|
782 |
+
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
783 |
+
def __call__(
|
784 |
+
self,
|
785 |
+
prompt: Union[str, List[str]] = None,
|
786 |
+
image: Union[torch.FloatTensor, PIL.Image.Image,
|
787 |
+
List[torch.FloatTensor], List[PIL.Image.Image]] = None,
|
788 |
+
control_image: Union[torch.FloatTensor, PIL.Image.Image,
|
789 |
+
List[torch.FloatTensor],
|
790 |
+
List[PIL.Image.Image]] = None,
|
791 |
+
height: Optional[int] = None,
|
792 |
+
width: Optional[int] = None,
|
793 |
+
strength: float = 0.8,
|
794 |
+
num_inference_steps: int = 50,
|
795 |
+
guidance_scale: float = 7.5,
|
796 |
+
negative_prompt: Optional[Union[str, List[str]]] = None,
|
797 |
+
num_images_per_prompt: Optional[int] = 1,
|
798 |
+
eta: float = 0.0,
|
799 |
+
generator: Optional[Union[torch.Generator,
|
800 |
+
List[torch.Generator]]] = None,
|
801 |
+
latents: Optional[torch.FloatTensor] = None,
|
802 |
+
prompt_embeds: Optional[torch.FloatTensor] = None,
|
803 |
+
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
804 |
+
output_type: Optional[str] = 'pil',
|
805 |
+
return_dict: bool = True,
|
806 |
+
callback: Optional[Callable[[int, int, torch.FloatTensor],
|
807 |
+
None]] = None,
|
808 |
+
callback_steps: int = 1,
|
809 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
810 |
+
controlnet_conditioning_scale: Union[float, List[float]] = 0.8,
|
811 |
+
guess_mode: bool = False,
|
812 |
+
context_size: int = 768,
|
813 |
+
):
|
814 |
+
r"""
|
815 |
+
Function invoked when calling the pipeline for generation.
|
816 |
+
|
817 |
+
Args:
|
818 |
+
prompt (`str` or `List[str]`, *optional*):
|
819 |
+
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
|
820 |
+
instead.
|
821 |
+
image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`,
|
822 |
+
`List[List[torch.FloatTensor]]`, or `List[List[PIL.Image.Image]]`):
|
823 |
+
The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
|
824 |
+
the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can
|
825 |
+
also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If
|
826 |
+
height and/or width are passed, `image` is resized according to them. If multiple ControlNets are
|
827 |
+
specified in init, images must be passed as a list such that each element of the list can be correctly
|
828 |
+
batched for input to a single controlnet.
|
829 |
+
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
830 |
+
The height in pixels of the generated image.
|
831 |
+
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
832 |
+
The width in pixels of the generated image.
|
833 |
+
num_inference_steps (`int`, *optional*, defaults to 50):
|
834 |
+
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
835 |
+
expense of slower inference.
|
836 |
+
guidance_scale (`float`, *optional*, defaults to 7.5):
|
837 |
+
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
838 |
+
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
839 |
+
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
840 |
+
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
841 |
+
usually at the expense of lower image quality.
|
842 |
+
negative_prompt (`str` or `List[str]`, *optional*):
|
843 |
+
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
844 |
+
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
845 |
+
less than `1`).
|
846 |
+
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
847 |
+
The number of images to generate per prompt.
|
848 |
+
eta (`float`, *optional*, defaults to 0.0):
|
849 |
+
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
850 |
+
[`schedulers.DDIMScheduler`], will be ignored for others.
|
851 |
+
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
852 |
+
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
|
853 |
+
to make generation deterministic.
|
854 |
+
latents (`torch.FloatTensor`, *optional*):
|
855 |
+
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
|
856 |
+
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
857 |
+
tensor will ge generated by sampling using the supplied random `generator`.
|
858 |
+
prompt_embeds (`torch.FloatTensor`, *optional*):
|
859 |
+
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
860 |
+
provided, text embeddings will be generated from `prompt` input argument.
|
861 |
+
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
862 |
+
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
863 |
+
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
864 |
+
argument.
|
865 |
+
output_type (`str`, *optional*, defaults to `"pil"`):
|
866 |
+
The output format of the generate image. Choose between
|
867 |
+
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
868 |
+
return_dict (`bool`, *optional*, defaults to `True`):
|
869 |
+
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
870 |
+
plain tuple.
|
871 |
+
callback (`Callable`, *optional*):
|
872 |
+
A function that will be called every `callback_steps` steps during inference. The function will be
|
873 |
+
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
874 |
+
callback_steps (`int`, *optional*, defaults to 1):
|
875 |
+
The frequency at which the `callback` function will be called. If not specified, the callback will be
|
876 |
+
called at every step.
|
877 |
+
cross_attention_kwargs (`dict`, *optional*):
|
878 |
+
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
879 |
+
`self.processor` in
|
880 |
+
[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/
|
881 |
+
src/diffusers/models/cross_attention.py).
|
882 |
+
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
|
883 |
+
The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
|
884 |
+
to the residual in the original unet. If multiple ControlNets are specified in init, you can set the
|
885 |
+
corresponding scale as a list. Note that by default, we use a smaller conditioning scale for inpainting
|
886 |
+
than for [`~StableDiffusionControlNetPipeline.__call__`].
|
887 |
+
guess_mode (`bool`, *optional*, defaults to `False`):
|
888 |
+
In this mode, the ControlNet encoder will try best to recognize the content of the input image even if
|
889 |
+
you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.
|
890 |
+
context_size ('int', *optional*, defaults to '768'):
|
891 |
+
tiled size when denoise the latents.
|
892 |
+
|
893 |
+
Examples:
|
894 |
+
|
895 |
+
Returns:
|
896 |
+
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
897 |
+
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
|
898 |
+
When returning a tuple, the first element is a list with the generated images, and the second element is a
|
899 |
+
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
|
900 |
+
(nsfw) content, according to the `safety_checker`.
|
901 |
+
"""
|
902 |
+
|
903 |
+
def tiled_decode(
|
904 |
+
self,
|
905 |
+
z: torch.FloatTensor,
|
906 |
+
return_dict: bool = True
|
907 |
+
) -> Union[DecoderOutput, torch.FloatTensor]:
|
908 |
+
r"""Decode a batch of images using a tiled decoder.
|
909 |
+
|
910 |
+
Args:
|
911 |
+
When this option is enabled, the VAE will split the input tensor into tiles to compute decoding in several
|
912 |
+
steps. This is useful to keep memory use constant regardless of image size. The end result of tiled
|
913 |
+
decoding is: different from non-tiled decoding due to each tile using a different decoder.
|
914 |
+
To avoid tiling artifacts, the tiles overlap and are blended together to form a smooth output.
|
915 |
+
You may still see tile-sized changes in the look of the output, but they should be much less noticeable.
|
916 |
+
z (`torch.FloatTensor`): Input batch of latent vectors. return_dict (`bool`, *optional*, defaults to
|
917 |
+
`True`):
|
918 |
+
Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
|
919 |
+
"""
|
920 |
+
_tile_overlap_factor = 1 - self.tile_overlap_factor
|
921 |
+
overlap_size = int(self.tile_latent_min_size
|
922 |
+
* _tile_overlap_factor)
|
923 |
+
blend_extent = int(self.tile_sample_min_size
|
924 |
+
* self.tile_overlap_factor)
|
925 |
+
row_limit = self.tile_sample_min_size - blend_extent
|
926 |
+
w = z.shape[3]
|
927 |
+
z = torch.cat([z, z[:, :, :, :w // 4]], dim=-1)
|
928 |
+
# Split z into overlapping 64x64 tiles and decode them separately.
|
929 |
+
# The tiles have an overlap to avoid seams between tiles.
|
930 |
+
|
931 |
+
rows = []
|
932 |
+
for i in range(0, z.shape[2], overlap_size):
|
933 |
+
row = []
|
934 |
+
tile = z[:, :, i:i + self.tile_latent_min_size, :]
|
935 |
+
tile = self.post_quant_conv(tile)
|
936 |
+
decoded = self.decoder(tile)
|
937 |
+
vae_scale_factor = decoded.shape[-1] // tile.shape[-1]
|
938 |
+
row.append(decoded)
|
939 |
+
rows.append(row)
|
940 |
+
result_rows = []
|
941 |
+
for i, row in enumerate(rows):
|
942 |
+
result_row = []
|
943 |
+
for j, tile in enumerate(row):
|
944 |
+
# blend the above tile and the left tile
|
945 |
+
# to the current tile and add the current tile to the result row
|
946 |
+
if i > 0:
|
947 |
+
tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
|
948 |
+
if j > 0:
|
949 |
+
tile = self.blend_h(row[j - 1], tile, blend_extent)
|
950 |
+
result_row.append(
|
951 |
+
self.blend_h(
|
952 |
+
tile[:, :, :row_limit, w * vae_scale_factor:],
|
953 |
+
tile[:, :, :row_limit, :w * vae_scale_factor],
|
954 |
+
tile.shape[-1] - w * vae_scale_factor))
|
955 |
+
result_rows.append(torch.cat(result_row, dim=3))
|
956 |
+
|
957 |
+
dec = torch.cat(result_rows, dim=2)
|
958 |
+
if not return_dict:
|
959 |
+
return (dec, )
|
960 |
+
|
961 |
+
return DecoderOutput(sample=dec)
|
962 |
+
|
963 |
+
self.vae.tiled_decode = tiled_decode.__get__(self.vae, AutoencoderKL)
|
964 |
+
|
965 |
+
# 0. Default height and width to unet
|
966 |
+
height, width = self._default_height_width(height, width, image)
|
967 |
+
|
968 |
+
# 1. Check inputs. Raise error if not correct
|
969 |
+
self.check_inputs(
|
970 |
+
prompt,
|
971 |
+
control_image,
|
972 |
+
height,
|
973 |
+
width,
|
974 |
+
callback_steps,
|
975 |
+
negative_prompt,
|
976 |
+
prompt_embeds,
|
977 |
+
negative_prompt_embeds,
|
978 |
+
controlnet_conditioning_scale,
|
979 |
+
)
|
980 |
+
|
981 |
+
# 2. Define call parameters
|
982 |
+
if prompt is not None and isinstance(prompt, str):
|
983 |
+
batch_size = 1
|
984 |
+
elif prompt is not None and isinstance(prompt, list):
|
985 |
+
batch_size = len(prompt)
|
986 |
+
else:
|
987 |
+
batch_size = prompt_embeds.shape[0]
|
988 |
+
|
989 |
+
device = self._execution_device
|
990 |
+
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
991 |
+
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
992 |
+
# corresponds to doing no classifier free guidance.
|
993 |
+
do_classifier_free_guidance = guidance_scale > 1.0
|
994 |
+
|
995 |
+
controlnet = self.controlnet._orig_mod if is_compiled_module(
|
996 |
+
self.controlnet) else self.controlnet
|
997 |
+
|
998 |
+
if isinstance(controlnet, MultiControlNetModel) and isinstance(
|
999 |
+
controlnet_conditioning_scale, float):
|
1000 |
+
controlnet_conditioning_scale = [controlnet_conditioning_scale
|
1001 |
+
] * len(controlnet.nets)
|
1002 |
+
|
1003 |
+
global_pool_conditions = (
|
1004 |
+
controlnet.config.global_pool_conditions if isinstance(
|
1005 |
+
controlnet, ControlNetModel) else
|
1006 |
+
controlnet.nets[0].config.global_pool_conditions)
|
1007 |
+
guess_mode = guess_mode or global_pool_conditions
|
1008 |
+
|
1009 |
+
# 3. Encode input prompt
|
1010 |
+
prompt_embeds = self._encode_prompt(
|
1011 |
+
prompt,
|
1012 |
+
device,
|
1013 |
+
num_images_per_prompt,
|
1014 |
+
do_classifier_free_guidance,
|
1015 |
+
negative_prompt,
|
1016 |
+
prompt_embeds=prompt_embeds,
|
1017 |
+
negative_prompt_embeds=negative_prompt_embeds,
|
1018 |
+
)
|
1019 |
+
# 4. Prepare image, and controlnet_conditioning_image
|
1020 |
+
image = prepare_image(image)
|
1021 |
+
|
1022 |
+
# 5. Prepare image
|
1023 |
+
if isinstance(controlnet, ControlNetModel):
|
1024 |
+
control_image = self.prepare_control_image(
|
1025 |
+
image=control_image,
|
1026 |
+
width=width,
|
1027 |
+
height=height,
|
1028 |
+
batch_size=batch_size * num_images_per_prompt,
|
1029 |
+
num_images_per_prompt=num_images_per_prompt,
|
1030 |
+
device=device,
|
1031 |
+
dtype=controlnet.dtype,
|
1032 |
+
do_classifier_free_guidance=do_classifier_free_guidance,
|
1033 |
+
guess_mode=guess_mode,
|
1034 |
+
)
|
1035 |
+
elif isinstance(controlnet, MultiControlNetModel):
|
1036 |
+
control_images = []
|
1037 |
+
|
1038 |
+
for control_image_ in control_image:
|
1039 |
+
control_image_ = self.prepare_control_image(
|
1040 |
+
image=control_image_,
|
1041 |
+
width=width,
|
1042 |
+
height=height,
|
1043 |
+
batch_size=batch_size * num_images_per_prompt,
|
1044 |
+
num_images_per_prompt=num_images_per_prompt,
|
1045 |
+
device=device,
|
1046 |
+
dtype=controlnet.dtype,
|
1047 |
+
do_classifier_free_guidance=do_classifier_free_guidance,
|
1048 |
+
guess_mode=guess_mode,
|
1049 |
+
)
|
1050 |
+
|
1051 |
+
control_images.append(control_image_)
|
1052 |
+
|
1053 |
+
control_image = control_images
|
1054 |
+
else:
|
1055 |
+
assert False
|
1056 |
+
|
1057 |
+
# 5. Prepare timesteps
|
1058 |
+
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
1059 |
+
timesteps, num_inference_steps = self.get_timesteps(
|
1060 |
+
num_inference_steps, strength, device)
|
1061 |
+
latent_timestep = timesteps[:1].repeat(batch_size
|
1062 |
+
* num_images_per_prompt)
|
1063 |
+
|
1064 |
+
# 6. Prepare latent variables
|
1065 |
+
latents = self.prepare_latents(
|
1066 |
+
image,
|
1067 |
+
latent_timestep,
|
1068 |
+
batch_size,
|
1069 |
+
num_images_per_prompt,
|
1070 |
+
prompt_embeds.dtype,
|
1071 |
+
device,
|
1072 |
+
generator,
|
1073 |
+
)
|
1074 |
+
|
1075 |
+
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
1076 |
+
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
1077 |
+
|
1078 |
+
views_scheduler_status = [copy.deepcopy(self.scheduler.__dict__)]
|
1079 |
+
# value = torch.zeros_like(latents)
|
1080 |
+
_, _, height, width = control_image.size()
|
1081 |
+
tile_latent_min_size = context_size // self.vae_scale_factor
|
1082 |
+
tile_overlap_factor = 0.5
|
1083 |
+
overlap_size = int(tile_latent_min_size * (1 - tile_overlap_factor))
|
1084 |
+
blend_extent = int(tile_latent_min_size * tile_overlap_factor)
|
1085 |
+
row_limit = tile_latent_min_size - blend_extent
|
1086 |
+
w = latents.shape[3]
|
1087 |
+
latents = torch.cat([latents, latents[:, :, :, :overlap_size]], dim=-1)
|
1088 |
+
control_image_extend = control_image[:, :, :, :overlap_size
|
1089 |
+
* self.vae_scale_factor]
|
1090 |
+
control_image = torch.cat([control_image, control_image_extend],
|
1091 |
+
dim=-1)
|
1092 |
+
|
1093 |
+
# 8. Denoising loop
|
1094 |
+
num_warmup_steps = len(
|
1095 |
+
timesteps) - num_inference_steps * self.scheduler.order
|
1096 |
+
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
1097 |
+
for i, t in enumerate(timesteps):
|
1098 |
+
latents_input, control_image_input = self.get_blocks(
|
1099 |
+
latents, control_image, tile_latent_min_size, overlap_size)
|
1100 |
+
rows = []
|
1101 |
+
for latents_input_, control_image_input_ in zip(
|
1102 |
+
latents_input, control_image_input):
|
1103 |
+
num_block = len(latents_input_)
|
1104 |
+
# get batched latents_input
|
1105 |
+
latents_input_ = torch.cat(
|
1106 |
+
latents_input_[:num_block], dim=0)
|
1107 |
+
# get batched prompt_embeds
|
1108 |
+
prompt_embeds_ = torch.cat(
|
1109 |
+
[prompt_embeds.chunk(2)[0]] * num_block
|
1110 |
+
+ [prompt_embeds.chunk(2)[1]] * num_block,
|
1111 |
+
dim=0)
|
1112 |
+
# get batched control_image_input
|
1113 |
+
control_image_input_ = torch.cat(
|
1114 |
+
[
|
1115 |
+
x[0, :, :, ][None, :, :, :]
|
1116 |
+
for x in control_image_input_[:num_block]
|
1117 |
+
] + [
|
1118 |
+
x[1, :, :, ][None, :, :, :]
|
1119 |
+
for x in control_image_input_[:num_block]
|
1120 |
+
],
|
1121 |
+
dim=0)
|
1122 |
+
latents_output = self.denoise_latents(
|
1123 |
+
latents_input_, t, prompt_embeds_,
|
1124 |
+
control_image_input_, controlnet_conditioning_scale,
|
1125 |
+
guess_mode, cross_attention_kwargs,
|
1126 |
+
do_classifier_free_guidance, guidance_scale,
|
1127 |
+
extra_step_kwargs, views_scheduler_status)
|
1128 |
+
rows.append(list(latents_output.chunk(num_block)))
|
1129 |
+
result_rows = []
|
1130 |
+
for i, row in enumerate(rows):
|
1131 |
+
result_row = []
|
1132 |
+
for j, tile in enumerate(row):
|
1133 |
+
# blend the above tile and the left tile
|
1134 |
+
# to the current tile and add the current tile to the result row
|
1135 |
+
if i > 0:
|
1136 |
+
tile = self.blend_v(rows[i - 1][j], tile,
|
1137 |
+
blend_extent)
|
1138 |
+
if j > 0:
|
1139 |
+
tile = self.blend_h(row[j - 1], tile, blend_extent)
|
1140 |
+
if j == 0:
|
1141 |
+
tile = self.blend_h(row[-1], tile, blend_extent)
|
1142 |
+
if i != len(rows) - 1:
|
1143 |
+
if j == len(row) - 1:
|
1144 |
+
result_row.append(tile[:, :, :row_limit, :])
|
1145 |
+
else:
|
1146 |
+
result_row.append(
|
1147 |
+
tile[:, :, :row_limit, :row_limit])
|
1148 |
+
else:
|
1149 |
+
if j == len(row) - 1:
|
1150 |
+
result_row.append(tile[:, :, :, :])
|
1151 |
+
else:
|
1152 |
+
result_row.append(tile[:, :, :, :row_limit])
|
1153 |
+
result_rows.append(torch.cat(result_row, dim=3))
|
1154 |
+
latents = torch.cat(result_rows, dim=2)
|
1155 |
+
|
1156 |
+
# call the callback, if provided
|
1157 |
+
condition_i = i == len(timesteps) - 1
|
1158 |
+
condition_warm = (i + 1) > num_warmup_steps and (
|
1159 |
+
i + 1) % self.scheduler.order == 0
|
1160 |
+
if condition_i or condition_warm:
|
1161 |
+
progress_bar.update()
|
1162 |
+
if callback is not None and i % callback_steps == 0:
|
1163 |
+
callback(i, t, latents)
|
1164 |
+
latents = latents[:, :, :, :w]
|
1165 |
+
|
1166 |
+
# If we do sequential model offloading, let's offload unet and controlnet
|
1167 |
+
# manually for max memory savings
|
1168 |
+
if hasattr(
|
1169 |
+
self,
|
1170 |
+
'final_offload_hook') and self.final_offload_hook is not None:
|
1171 |
+
self.unet.to('cpu')
|
1172 |
+
self.controlnet.to('cpu')
|
1173 |
+
torch.cuda.empty_cache()
|
1174 |
+
|
1175 |
+
if not output_type == 'latent':
|
1176 |
+
image = self.vae.decode(
|
1177 |
+
latents / self.vae.config.scaling_factor, return_dict=False)[0]
|
1178 |
+
image, has_nsfw_concept = self.run_safety_checker(
|
1179 |
+
image, device, prompt_embeds.dtype)
|
1180 |
+
else:
|
1181 |
+
image = latents
|
1182 |
+
has_nsfw_concept = None
|
1183 |
+
|
1184 |
+
if has_nsfw_concept is None:
|
1185 |
+
do_denormalize = [True] * image.shape[0]
|
1186 |
+
else:
|
1187 |
+
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
|
1188 |
+
|
1189 |
+
image = self.image_processor.postprocess(
|
1190 |
+
image, output_type=output_type, do_denormalize=do_denormalize)
|
1191 |
+
|
1192 |
+
# Offload last model to CPU
|
1193 |
+
if hasattr(
|
1194 |
+
self,
|
1195 |
+
'final_offload_hook') and self.final_offload_hook is not None:
|
1196 |
+
self.final_offload_hook.offload()
|
1197 |
+
|
1198 |
+
if not return_dict:
|
1199 |
+
return (image, has_nsfw_concept)
|
1200 |
+
|
1201 |
+
return StableDiffusionPipelineOutput(
|
1202 |
+
images=image, nsfw_content_detected=has_nsfw_concept)
|
txt2panoimg/text_to_360panorama_image_pipeline.py
ADDED
@@ -0,0 +1,212 @@
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright © Alibaba, Inc. and its affiliates.
|
2 |
+
import random
|
3 |
+
from typing import Any, Dict
|
4 |
+
|
5 |
+
import numpy as np
|
6 |
+
import torch
|
7 |
+
from diffusers import (ControlNetModel, DiffusionPipeline,
|
8 |
+
EulerAncestralDiscreteScheduler,
|
9 |
+
UniPCMultistepScheduler)
|
10 |
+
from PIL import Image
|
11 |
+
from RealESRGAN import RealESRGAN
|
12 |
+
|
13 |
+
from .pipeline_base import StableDiffusionBlendExtendPipeline
|
14 |
+
from .pipeline_sr import StableDiffusionControlNetImg2ImgPanoPipeline
|
15 |
+
|
16 |
+
class LazyRealESRGAN:
|
17 |
+
def __init__(self, device, scale):
|
18 |
+
self.device = device
|
19 |
+
self.scale = scale
|
20 |
+
self.model = None
|
21 |
+
self.model_path = None
|
22 |
+
|
23 |
+
def load_model(self):
|
24 |
+
if self.model is None:
|
25 |
+
self.model = RealESRGAN(self.device, scale=self.scale)
|
26 |
+
self.model.load_weights(self.model_path, download=False)
|
27 |
+
|
28 |
+
def predict(self, img):
|
29 |
+
self.load_model()
|
30 |
+
return self.model.predict(img)
|
31 |
+
|
32 |
+
class Text2360PanoramaImagePipeline(DiffusionPipeline):
|
33 |
+
""" Stable Diffusion for 360 Panorama Image Generation Pipeline.
|
34 |
+
Example:
|
35 |
+
>>> import torch
|
36 |
+
>>> from txt2panoimg import Text2360PanoramaImagePipeline
|
37 |
+
>>> prompt = 'The mountains'
|
38 |
+
>>> input = {'prompt': prompt, 'upscale': True}
|
39 |
+
>>> model_id = 'models/'
|
40 |
+
>>> txt2panoimg = Text2360PanoramaImagePipeline(model_id, torch_dtype=torch.float16)
|
41 |
+
>>> output = txt2panoimg(input)
|
42 |
+
>>> output.save('result.png')
|
43 |
+
"""
|
44 |
+
|
45 |
+
def __init__(self, model: str, device: str = 'cuda', **kwargs):
|
46 |
+
"""
|
47 |
+
Use `model` to create a stable diffusion pipeline for 360 panorama image generation.
|
48 |
+
Args:
|
49 |
+
model: model id on modelscope hub.
|
50 |
+
device: str = 'cuda'
|
51 |
+
"""
|
52 |
+
super().__init__()
|
53 |
+
|
54 |
+
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu'
|
55 |
+
) if device is None else device
|
56 |
+
if device == 'gpu':
|
57 |
+
device = torch.device('cuda')
|
58 |
+
|
59 |
+
torch_dtype = kwargs.get('torch_dtype', torch.float16)
|
60 |
+
enable_xformers_memory_efficient_attention = kwargs.get(
|
61 |
+
'enable_xformers_memory_efficient_attention', True)
|
62 |
+
|
63 |
+
model_id = model + '/sd-base/'
|
64 |
+
|
65 |
+
# init base model
|
66 |
+
self.pipe = StableDiffusionBlendExtendPipeline.from_pretrained(
|
67 |
+
model_id, torch_dtype=torch_dtype).to(device)
|
68 |
+
self.pipe.vae.enable_tiling()
|
69 |
+
self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(
|
70 |
+
self.pipe.scheduler.config)
|
71 |
+
# remove following line if xformers is not installed
|
72 |
+
try:
|
73 |
+
if enable_xformers_memory_efficient_attention:
|
74 |
+
self.pipe.enable_xformers_memory_efficient_attention()
|
75 |
+
except Exception as e:
|
76 |
+
print(e)
|
77 |
+
self.pipe.enable_model_cpu_offload()
|
78 |
+
|
79 |
+
# init controlnet-sr model
|
80 |
+
base_model_path = model + '/sr-base'
|
81 |
+
controlnet_path = model + '/sr-control'
|
82 |
+
controlnet = ControlNetModel.from_pretrained(
|
83 |
+
controlnet_path, torch_dtype=torch_dtype)
|
84 |
+
self.pipe_sr = StableDiffusionControlNetImg2ImgPanoPipeline.from_pretrained(
|
85 |
+
base_model_path, controlnet=controlnet,
|
86 |
+
torch_dtype=torch_dtype).to(device)
|
87 |
+
self.pipe_sr.scheduler = UniPCMultistepScheduler.from_config(
|
88 |
+
self.pipe.scheduler.config)
|
89 |
+
self.pipe_sr.vae.enable_tiling()
|
90 |
+
# remove following line if xformers is not installed
|
91 |
+
try:
|
92 |
+
if enable_xformers_memory_efficient_attention:
|
93 |
+
self.pipe_sr.enable_xformers_memory_efficient_attention()
|
94 |
+
except Exception as e:
|
95 |
+
print(e)
|
96 |
+
self.pipe_sr.enable_model_cpu_offload()
|
97 |
+
device = torch.device("cuda")
|
98 |
+
model_path = model + '/RealESRGAN_x2plus.pth'
|
99 |
+
self.upsampler = LazyRealESRGAN(device=device, scale=2)
|
100 |
+
self.upsampler.model_path = model_path
|
101 |
+
|
102 |
+
@staticmethod
|
103 |
+
def blend_h(a, b, blend_extent):
|
104 |
+
a = np.array(a)
|
105 |
+
b = np.array(b)
|
106 |
+
blend_extent = min(a.shape[1], b.shape[1], blend_extent)
|
107 |
+
for x in range(blend_extent):
|
108 |
+
b[:, x, :] = a[:, -blend_extent
|
109 |
+
+ x, :] * (1 - x / blend_extent) + b[:, x, :] * (
|
110 |
+
x / blend_extent)
|
111 |
+
return b
|
112 |
+
|
113 |
+
def __call__(self, inputs: Dict[str, Any],
|
114 |
+
**forward_params) -> Dict[str, Any]:
|
115 |
+
if not isinstance(inputs, dict):
|
116 |
+
raise ValueError(
|
117 |
+
f'Expected the input to be a dictionary, but got {type(input)}'
|
118 |
+
)
|
119 |
+
num_inference_steps = inputs.get('num_inference_steps', 20)
|
120 |
+
guidance_scale = inputs.get('guidance_scale', 7.5)
|
121 |
+
preset_a_prompt = 'photorealistic, trend on artstation, ((best quality)), ((ultra high res))'
|
122 |
+
add_prompt = inputs.get('add_prompt', preset_a_prompt)
|
123 |
+
preset_n_prompt = 'persons, complex texture, small objects, sheltered, blur, worst quality, '\
|
124 |
+
'low quality, zombie, logo, text, watermark, username, monochrome, '\
|
125 |
+
'complex lighting'
|
126 |
+
negative_prompt = inputs.get('negative_prompt', preset_n_prompt)
|
127 |
+
seed = inputs.get('seed', -1)
|
128 |
+
upscale = inputs.get('upscale', True)
|
129 |
+
refinement = inputs.get('refinement', True)
|
130 |
+
|
131 |
+
guidance_scale_sr_step1 = inputs.get('guidance_scale_sr_step1', 15)
|
132 |
+
guidance_scale_sr_step2 = inputs.get('guidance_scale_sr_step1', 17)
|
133 |
+
|
134 |
+
if 'prompt' in inputs.keys():
|
135 |
+
prompt = inputs['prompt']
|
136 |
+
else:
|
137 |
+
# for demo_service
|
138 |
+
prompt = forward_params.get('prompt', 'the living room')
|
139 |
+
|
140 |
+
print(f'Test with prompt: {prompt}')
|
141 |
+
|
142 |
+
if seed == -1:
|
143 |
+
seed = random.randint(0, 65535)
|
144 |
+
print(f'global seed: {seed}')
|
145 |
+
|
146 |
+
generator = torch.manual_seed(seed)
|
147 |
+
|
148 |
+
prompt = '<360panorama>, ' + prompt + ', ' + add_prompt
|
149 |
+
output_img = self.pipe(
|
150 |
+
prompt,
|
151 |
+
negative_prompt=negative_prompt,
|
152 |
+
num_inference_steps=num_inference_steps,
|
153 |
+
height=512,
|
154 |
+
width=1024,
|
155 |
+
guidance_scale=guidance_scale,
|
156 |
+
generator=generator).images[0]
|
157 |
+
|
158 |
+
if not upscale:
|
159 |
+
print('finished')
|
160 |
+
else:
|
161 |
+
print('inputs: upscale=True, running upscaler.')
|
162 |
+
print('running upscaler step1. Initial super-resolution')
|
163 |
+
sr_scale = 2.0
|
164 |
+
output_img = self.pipe_sr(
|
165 |
+
prompt.replace('<360panorama>, ', ''),
|
166 |
+
negative_prompt=negative_prompt,
|
167 |
+
image=output_img.resize(
|
168 |
+
(int(1536 * sr_scale), int(768 * sr_scale))),
|
169 |
+
num_inference_steps=7,
|
170 |
+
generator=generator,
|
171 |
+
control_image=output_img.resize(
|
172 |
+
(int(1536 * sr_scale), int(768 * sr_scale))),
|
173 |
+
strength=0.8,
|
174 |
+
controlnet_conditioning_scale=1.0,
|
175 |
+
guidance_scale=guidance_scale_sr_step1,
|
176 |
+
).images[0]
|
177 |
+
|
178 |
+
print('running upscaler step2. Super-resolution with Real-ESRGAN')
|
179 |
+
output_img = output_img.resize((1536 * 2, 768 * 2))
|
180 |
+
w = output_img.size[0]
|
181 |
+
blend_extend = 10
|
182 |
+
outscale = 2
|
183 |
+
output_img = np.array(output_img)
|
184 |
+
output_img = np.concatenate(
|
185 |
+
[output_img, output_img[:, :blend_extend, :]], axis=1)
|
186 |
+
output_img = self.upsampler.predict(
|
187 |
+
output_img)
|
188 |
+
output_img = self.blend_h(output_img, output_img,
|
189 |
+
blend_extend * outscale)
|
190 |
+
output_img = Image.fromarray(output_img[:, :w * outscale, :])
|
191 |
+
|
192 |
+
if refinement:
|
193 |
+
print(
|
194 |
+
'inputs: refinement=True, running refinement. This is a bit time-consuming.'
|
195 |
+
)
|
196 |
+
sr_scale = 4
|
197 |
+
output_img = self.pipe_sr(
|
198 |
+
prompt.replace('<360panorama>, ', ''),
|
199 |
+
negative_prompt=negative_prompt,
|
200 |
+
image=output_img.resize(
|
201 |
+
(int(1536 * sr_scale), int(768 * sr_scale))),
|
202 |
+
num_inference_steps=7,
|
203 |
+
generator=generator,
|
204 |
+
control_image=output_img.resize(
|
205 |
+
(int(1536 * sr_scale), int(768 * sr_scale))),
|
206 |
+
strength=0.8,
|
207 |
+
controlnet_conditioning_scale=1.0,
|
208 |
+
guidance_scale=guidance_scale_sr_step2,
|
209 |
+
).images[0]
|
210 |
+
print('finished')
|
211 |
+
|
212 |
+
return output_img
|