import contextlib import gc import json import logging import math import os import random import shutil import sys import time import itertools from pathlib import Path import cv2 import numpy as np from PIL import Image, ImageDraw import torch import torch.nn.functional as F import torch.utils.checkpoint from torch.utils.data import Dataset from torchvision import transforms from tqdm.auto import tqdm import accelerate from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import ProjectConfiguration, set_seed from datasets import load_dataset from huggingface_hub import create_repo, upload_folder from packaging import version from safetensors.torch import load_model from peft import LoraConfig import gradio as gr import pandas as pd import transformers from transformers import ( AutoTokenizer, PretrainedConfig, CLIPVisionModelWithProjection, CLIPImageProcessor, CLIPProcessor, ) import diffusers from diffusers import ( AutoencoderKL, DDPMScheduler, ColorGuiderPixArtModel, ColorGuiderSDModel, UNet2DConditionModel, PixArtTransformer2DModel, ColorFlowPixArtAlphaPipeline, ColorFlowSDPipeline, UniPCMultistepScheduler, ) from colorflow_utils.utils import * sys.path.append('./BidirectionalTranslation') from options.test_options import TestOptions from models import create_model from util import util from huggingface_hub import snapshot_download model_global_path = snapshot_download(repo_id="TencentARC/ColorFlow", cache_dir='./colorflow/', repo_type="model") print(model_global_path) transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) ]) weight_dtype = torch.float16 # line model line_model_path = model_global_path + '/LE/erika.pth' line_model = res_skip() line_model.load_state_dict(torch.load(line_model_path)) line_model.eval() line_model.cuda() # screen model global opt opt = TestOptions().parse(model_global_path) ScreenModel = create_model(opt, model_global_path) ScreenModel.setup(opt) ScreenModel.eval() image_processor = CLIPImageProcessor() image_encoder = CLIPVisionModelWithProjection.from_pretrained(model_global_path + '/image_encoder/').to('cuda') examples = [ [ "./assets/example_5/input.png", ["./assets/example_5/ref1.png", "./assets/example_5/ref2.png", "./assets/example_5/ref3.png"], "GrayImage(ScreenStyle)", "800x512", 0, 10 ], [ "./assets/example_4/input.jpg", ["./assets/example_4/ref1.jpg", "./assets/example_4/ref2.jpg", "./assets/example_4/ref3.jpg"], "GrayImage(ScreenStyle)", "640x640", 0, 10 ], [ "./assets/example_3/input.png", ["./assets/example_3/ref1.png", "./assets/example_3/ref2.png", "./assets/example_3/ref3.png"], "GrayImage(ScreenStyle)", "800x512", 0, 10 ], [ "./assets/example_2/input.png", ["./assets/example_2/ref1.png", "./assets/example_2/ref2.png", "./assets/example_2/ref3.png"], "GrayImage(ScreenStyle)", "800x512", 0, 10 ], [ "./assets/example_1/input.jpg", ["./assets/example_1/ref1.jpg", "./assets/example_1/ref2.jpg", "./assets/example_1/ref3.jpg"], "Sketch", "640x640", 0, 10 ], [ "./assets/example_0/input.jpg", ["./assets/example_0/ref1.jpg"], "Sketch", "640x640", 0, 10 ], ] global pipeline global MultiResNetModel def load_ckpt(input_style): global pipeline global MultiResNetModel if input_style == "Sketch": ckpt_path = model_global_path + '/sketch/' rank = 128 pretrained_model_name_or_path = 'PixArt-alpha/PixArt-XL-2-1024-MS' transformer = PixArtTransformer2DModel.from_pretrained( pretrained_model_name_or_path, subfolder="transformer", revision=None, variant=None ) pixart_config = get_pixart_config() ColorGuider = ColorGuiderPixArtModel.from_pretrained(ckpt_path) transformer_lora_config = LoraConfig( r=rank, lora_alpha=rank, init_lora_weights="gaussian", target_modules=["to_k", "to_q", "to_v", "to_out.0", "proj_in", "proj_out", "ff.net.0.proj", "ff.net.2", "proj", "linear", "linear_1", "linear_2"] ) transformer.add_adapter(transformer_lora_config) ckpt_key_t = torch.load(ckpt_path + 'transformer_lora.bin', map_location='cpu') transformer.load_state_dict(ckpt_key_t, strict=False) transformer.to('cuda', dtype=weight_dtype) ColorGuider.to('cuda', dtype=weight_dtype) pipeline = ColorFlowPixArtAlphaPipeline.from_pretrained( pretrained_model_name_or_path, transformer=transformer, colorguider=ColorGuider, safety_checker=None, revision=None, variant=None, torch_dtype=weight_dtype, ) pipeline = pipeline.to("cuda") block_out_channels = [128, 128, 256, 512, 512] MultiResNetModel = MultiHiddenResNetModel(block_out_channels, len(block_out_channels)) MultiResNetModel.load_state_dict(torch.load(ckpt_path + 'MultiResNetModel.bin', map_location='cpu'), strict=False) MultiResNetModel.to('cuda', dtype=weight_dtype) elif input_style == "GrayImage(ScreenStyle)": ckpt_path = model_global_path + '/GraySD/' rank = 64 pretrained_model_name_or_path = 'stable-diffusion-v1-5/stable-diffusion-v1-5' unet = UNet2DConditionModel.from_pretrained( pretrained_model_name_or_path, subfolder="unet", revision=None, variant=None ) ColorGuider = ColorGuiderSDModel.from_pretrained(ckpt_path) ColorGuider.to('cuda', dtype=weight_dtype) unet.to('cuda', dtype=weight_dtype) pipeline = ColorFlowSDPipeline.from_pretrained( pretrained_model_name_or_path, unet=unet, colorguider=ColorGuider, safety_checker=None, revision=None, variant=None, torch_dtype=weight_dtype, ) pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config) unet_lora_config = LoraConfig( r=rank, lora_alpha=rank, init_lora_weights="gaussian", target_modules=["to_k", "to_q", "to_v", "to_out.0", "ff.net.0.proj", "ff.net.2"],#ff.net.0.proj ff.net.2 ) pipeline.unet.add_adapter(unet_lora_config) pipeline.unet.load_state_dict(torch.load(ckpt_path + 'unet_lora.bin', map_location='cpu'), strict=False) pipeline = pipeline.to("cuda") block_out_channels = [128, 128, 256, 512, 512] MultiResNetModel = MultiHiddenResNetModel(block_out_channels, len(block_out_channels)) MultiResNetModel.load_state_dict(torch.load(ckpt_path + 'MultiResNetModel.bin', map_location='cpu'), strict=False) MultiResNetModel.to('cuda', dtype=weight_dtype) global cur_input_style cur_input_style = "Sketch" load_ckpt(cur_input_style) cur_input_style = "GrayImage(ScreenStyle)" load_ckpt(cur_input_style) def fix_random_seeds(seed): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) if torch.cuda.is_available(): torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) def process_multi_images(files): images = [Image.open(file.name) for file in files] imgs = [] for i, img in enumerate(images): imgs.append(img) return imgs def extract_lines(image): src = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY) rows = int(np.ceil(src.shape[0] / 16)) * 16 cols = int(np.ceil(src.shape[1] / 16)) * 16 patch = np.ones((1, 1, rows, cols), dtype="float32") patch[0, 0, 0:src.shape[0], 0:src.shape[1]] = src tensor = torch.from_numpy(patch).cuda() with torch.no_grad(): y = line_model(tensor) yc = y.cpu().numpy()[0, 0, :, :] yc[yc > 255] = 255 yc[yc < 0] = 0 outimg = yc[0:src.shape[0], 0:src.shape[1]] outimg = outimg.astype(np.uint8) outimg = Image.fromarray(outimg) torch.cuda.empty_cache() return outimg def to_screen_image(input_image): global opt global ScreenModel input_image = input_image.convert('RGB') input_image = get_ScreenVAE_input(input_image, opt) h = input_image['h'] w = input_image['w'] ScreenModel.set_input(input_image) fake_B, fake_B2, SCR = ScreenModel.forward(AtoB=True) images=fake_B2[:,:,:h,:w] im = util.tensor2im(images) image_pil = Image.fromarray(im) torch.cuda.empty_cache() return image_pil def extract_line_image(query_image_, input_style, resolution): if resolution == "640x640": tar_width = 640 tar_height = 640 elif resolution == "512x800": tar_width = 512 tar_height = 800 elif resolution == "800x512": tar_width = 800 tar_height = 512 else: gr.Info("Unsupported resolution") query_image = process_image(query_image_, int(tar_width*1.5), int(tar_height*1.5)) if input_style == "GrayImage(ScreenStyle)": extracted_line = to_screen_image(query_image) extracted_line = Image.blend(extracted_line.convert('L').convert('RGB'), query_image.convert('L').convert('RGB'), 0.5) input_context = extracted_line elif input_style == "Sketch": query_image = query_image.convert('L').convert('RGB') extracted_line = extract_lines(query_image) extracted_line = extracted_line.convert('L').convert('RGB') input_context = extracted_line torch.cuda.empty_cache() return input_context, extracted_line, input_context def colorize_image(VAE_input, input_context, reference_images, resolution, seed, input_style, num_inference_steps): if VAE_input is None or input_context is None: gr.Info("Please preprocess the image first") raise ValueError("Please preprocess the image first") global cur_input_style global pipeline global MultiResNetModel if input_style != cur_input_style: gr.Info(f"Loading {input_style} model...") load_ckpt(input_style) cur_input_style = input_style gr.Info(f"{input_style} model loaded") reference_images = process_multi_images(reference_images) fix_random_seeds(seed) if resolution == "640x640": tar_width = 640 tar_height = 640 elif resolution == "512x800": tar_width = 512 tar_height = 800 elif resolution == "800x512": tar_width = 800 tar_height = 512 else: gr.Info("Unsupported resolution") validation_mask = Image.open('./assets/mask.png').convert('RGB').resize((tar_width*2, tar_height*2)) gr.Info("Image retrieval in progress...") query_image_bw = process_image(input_context, int(tar_width), int(tar_height)) query_image = query_image_bw.convert('RGB') query_image_vae = process_image(VAE_input, int(tar_width*1.5), int(tar_height*1.5)) reference_images = [process_image(ref_image, tar_width, tar_height) for ref_image in reference_images] query_patches_pil = process_image_Q_varres(query_image, tar_width, tar_height) reference_patches_pil = [] for reference_image in reference_images: reference_patches_pil += process_image_ref_varres(reference_image, tar_width, tar_height) combined_image = None with torch.no_grad(): clip_img = image_processor(images=query_patches_pil, return_tensors="pt").pixel_values.to(image_encoder.device, dtype=image_encoder.dtype) query_embeddings = image_encoder(clip_img).image_embeds reference_patches_pil_gray = [rimg.convert('RGB').convert('RGB') for rimg in reference_patches_pil] clip_img = image_processor(images=reference_patches_pil_gray, return_tensors="pt").pixel_values.to(image_encoder.device, dtype=image_encoder.dtype) reference_embeddings = image_encoder(clip_img).image_embeds cosine_similarities = F.cosine_similarity(query_embeddings.unsqueeze(1), reference_embeddings.unsqueeze(0), dim=-1) sorted_indices = torch.argsort(cosine_similarities, descending=True, dim=1).tolist() top_k = 3 top_k_indices = [cur_sortlist[:top_k] for cur_sortlist in sorted_indices] combined_image = Image.new('RGB', (tar_width * 2, tar_height * 2), 'white') combined_image.paste(query_image_bw.resize((tar_width, tar_height)), (tar_width//2, tar_height//2)) idx_table = {0:[(1,0), (0,1), (0,0)], 1:[(1,3), (0,2),(0,3)], 2:[(2,0),(3,1), (3,0)], 3:[(2,3), (3,2),(3,3)]} for i in range(2): for j in range(2): idx_list = idx_table[i * 2 + j] for k in range(top_k): ref_index = top_k_indices[i * 2 + j][k] idx_y = idx_list[k][0] idx_x = idx_list[k][1] combined_image.paste(reference_patches_pil[ref_index].resize((tar_width//2-2, tar_height//2-2)), (tar_width//2 * idx_x + 1, tar_height//2 * idx_y + 1)) gr.Info("Model inference in progress...") generator = torch.Generator(device='cuda').manual_seed(seed) image = pipeline( "manga", cond_image=combined_image, cond_mask=validation_mask, num_inference_steps=num_inference_steps, generator=generator ).images[0] gr.Info("Post-processing image...") with torch.no_grad(): width, height = image.size new_width = width // 2 new_height = height // 2 left = (width - new_width) // 2 top = (height - new_height) // 2 right = left + new_width bottom = top + new_height center_crop = image.crop((left, top, right, bottom)) up_img = center_crop.resize(query_image_vae.size) test_low_color = transform(up_img).unsqueeze(0).to('cuda', dtype=weight_dtype) query_image_vae = transform(query_image_vae).unsqueeze(0).to('cuda', dtype=weight_dtype) h_color, hidden_list_color = pipeline.vae._encode(test_low_color,return_dict = False, hidden_flag = True) h_bw, hidden_list_bw = pipeline.vae._encode(query_image_vae, return_dict = False, hidden_flag = True) hidden_list_double = [torch.cat((hidden_list_color[hidden_idx], hidden_list_bw[hidden_idx]), dim = 1) for hidden_idx in range(len(hidden_list_color))] hidden_list = MultiResNetModel(hidden_list_double) output = pipeline.vae._decode(h_color.sample(),return_dict = False, hidden_list = hidden_list)[0] output[output > 1] = 1 output[output < -1] = -1 high_res_image = Image.fromarray(((output[0] * 0.5 + 0.5).permute(1, 2, 0).detach().cpu().numpy() * 255).astype(np.uint8)).convert("RGB") gr.Info("Colorization complete!") torch.cuda.empty_cache() return high_res_image, up_img, image, query_image_bw with gr.Blocks() as demo: gr.HTML( """

🎨 ColorFlow:

Retrieval-Augmented Image Sequence Colorization

Project Page | ArXiv Preprint | GitHub Repository

NOTE: Each time you switch the input style, the corresponding model will be reloaded, which may take some time. Please be patient.

Welcome to the demo of ColorFlow. Follow the steps below to explore the capabilities of our model:

  1. Choose input style: GrayImage(ScreenStyle) or Sketch.
  2. Upload your image: Use the 'Upload' button to select the image you want to colorize.
  3. Preprocess the image: Click the 'Preprocess' button to decolorize the image.
  4. Upload reference images: Upload multiple reference images to guide the colorization.
  5. Set sampling parameters (optional): Adjust the settings and click the Colorize button.

⏱️ ZeroGPU Time Limit: Hugging Face ZeroGPU has an inference time limit of 180 seconds. You may need to log in with a free account to use this demo. Large sampling steps might lead to timeout (GPU Abort). In that case, please consider logging in with a Pro account or running it on your local machine.

注意：每次切换输入样式时，相应的模型将被重新加载，可能需要一些时间。请耐心等待。

欢迎使用 ColorFlow 演示。请按照以下步骤探索我们模型的能力：

  1. 选择输入样式：灰度图(ScreenStyle)、线稿。
  2. 上传您的图像：使用“上传”按钮选择要上色的图像。
  3. 预处理图像：点击“预处理”按钮以去色图像。
  4. 上传参考图像：上传多张参考图像以指导上色。
  5. 设置采样参数（可选）：调整设置并点击 上色 按钮。

⏱️ ZeroGPU时间限制：Hugging Face ZeroGPU 的推理时间限制为 180 秒。您可能需要使用免费帐户登录以使用此演示。大采样步骤可能会导致超时（GPU 中止）。在这种情况下，请考虑使用专业帐户登录或在本地计算机上运行。

""" ) VAE_input = gr.State() input_context = gr.State() # example_loading = gr.State(value=None) with gr.Column(): with gr.Row(): input_style = gr.Radio(["GrayImage(ScreenStyle)", "Sketch"], label="Input Style", value="GrayImage(ScreenStyle)") with gr.Row(): with gr.Column(): input_image = gr.Image(type="pil", label="Image to Colorize") resolution = gr.Radio(["640x640", "512x800", "800x512"], label="Select Resolution(Width*Height)", value="640x640") extract_button = gr.Button("Preprocess (Decolorize)") extracted_image = gr.Image(type="pil", label="Decolorized Result") with gr.Row(): reference_images = gr.Files(label="Reference Images (Upload multiple)", file_count="multiple") with gr.Column(): output_gallery = gr.Gallery(label="Colorization Results", type="pil") seed = gr.Slider(label="Random Seed", minimum=0, maximum=100000, value=0, step=1) num_inference_steps = gr.Slider(label="Inference Steps", minimum=4, maximum=100, value=10, step=1) colorize_button = gr.Button("Colorize") # progress_text = gr.Textbox(label="Progress", interactive=False) extract_button.click( extract_line_image, inputs=[input_image, input_style, resolution], outputs=[extracted_image, VAE_input, input_context] ) colorize_button.click( colorize_image, inputs=[VAE_input, input_context, reference_images, resolution, seed, input_style, num_inference_steps], outputs=output_gallery ) with gr.Column(): gr.Markdown("### Quick Examples") gr.Examples( examples=examples, inputs=[input_image, reference_images, input_style, resolution, seed, num_inference_steps], label="Examples", examples_per_page=6, ) demo.launch()