import spaces import gradio as gr from PIL import Image from src.tryon_pipeline import StableDiffusionXLInpaintPipeline as TryonPipeline from src.unet_hacked_garmnet import UNet2DConditionModel as UNet2DConditionModel_ref from src.unet_hacked_tryon import UNet2DConditionModel from transformers import ( CLIPImageProcessor, CLIPVisionModelWithProjection, CLIPTextModel, CLIPTextModelWithProjection, ) from diffusers import DDPMScheduler, AutoencoderKL from typing import List import torch import os from transformers import AutoTokenizer import numpy as np from utils_mask import get_mask_location from torchvision import transforms import apply_net from preprocess.humanparsing.run_parsing import Parsing from preprocess.openpose.run_openpose import OpenPose from detectron2.data.detection_utils import convert_PIL_to_numpy, _apply_exif_orientation from torchvision.transforms.functional import to_pil_image def pil_to_binary_mask(pil_image, threshold=0): np_image = np.array(pil_image) grayscale_image = Image.fromarray(np_image).convert("L") binary_mask = np.array(grayscale_image) > threshold mask = np.zeros(binary_mask.shape, dtype=np.uint8) for i in range(binary_mask.shape[0]): for j in range(binary_mask.shape[1]): if binary_mask[i, j]: mask[i, j] = 1 mask = (mask * 255).astype(np.uint8) output_mask = Image.fromarray(mask) return output_mask base_path = 'yisol/IDM-VTON' example_path = os.path.join(os.path.dirname(__file__), 'example') unet = UNet2DConditionModel.from_pretrained( base_path, subfolder="unet", torch_dtype=torch.float16, ) unet.requires_grad_(False) tokenizer_one = AutoTokenizer.from_pretrained( base_path, subfolder="tokenizer", revision=None, use_fast=False, ) tokenizer_two = AutoTokenizer.from_pretrained( base_path, subfolder="tokenizer_2", revision=None, use_fast=False, ) noise_scheduler = DDPMScheduler.from_pretrained(base_path, subfolder="scheduler") text_encoder_one = CLIPTextModel.from_pretrained( base_path, subfolder="text_encoder", torch_dtype=torch.float16, ) text_encoder_two = CLIPTextModelWithProjection.from_pretrained( base_path, subfolder="text_encoder_2", torch_dtype=torch.float16, ) image_encoder = CLIPVisionModelWithProjection.from_pretrained( base_path, subfolder="image_encoder", torch_dtype=torch.float16, ) vae = AutoencoderKL.from_pretrained( base_path, subfolder="vae", torch_dtype=torch.float16, ) UNet_Encoder = UNet2DConditionModel_ref.from_pretrained( base_path, subfolder="unet_encoder", torch_dtype=torch.float16, ) parsing_model = Parsing(0) openpose_model = OpenPose(0) UNet_Encoder.requires_grad_(False) image_encoder.requires_grad_(False) vae.requires_grad_(False) unet.requires_grad_(False) text_encoder_one.requires_grad_(False) text_encoder_two.requires_grad_(False) tensor_transfrom = transforms.Compose( [ transforms.ToTensor(), transforms.Normalize([0.5], [0.5]), ] ) pipe = TryonPipeline.from_pretrained( base_path, unet=unet, vae=vae, feature_extractor=CLIPImageProcessor(), text_encoder=text_encoder_one, text_encoder_2=text_encoder_two, tokenizer=tokenizer_one, tokenizer_2=tokenizer_two, scheduler=noise_scheduler, image_encoder=image_encoder, torch_dtype=torch.float16, ) pipe.unet_encoder = UNet_Encoder @spaces.GPU(duration=100) # 実行時間を100秒に設定 def start_tryon( dict, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed, num_images ): device = "cuda" openpose_model.preprocessor.body_estimation.model.to(device) pipe.to(device) pipe.unet_encoder.to(device) garm_img = garm_img.convert("RGB").resize((768, 1024)) human_img_orig = dict["background"].convert("RGB") if is_checked_crop: width, height = human_img_orig.size target_width = int(min(width, height * (3 / 4))) target_height = int(min(height, width * (4 / 3))) left = (width - target_width) / 2 top = (height - target_height) / 2 right = (width + target_width) / 2 bottom = (height + target_height) / 2 cropped_img = human_img_orig.crop((left, top, right, bottom)) crop_size = cropped_img.size human_img = cropped_img.resize((768, 1024)) else: human_img = human_img_orig.resize((768, 1024)) if is_checked: keypoints = openpose_model(human_img.resize((384, 512))) model_parse, _ = parsing_model(human_img.resize((384, 512))) mask, mask_gray = get_mask_location('hd', "upper_body", model_parse, keypoints) mask = mask.resize((768, 1024)) else: mask = pil_to_binary_mask(dict['layers'][0].convert("RGB").resize((768, 1024))) mask_gray = (1 - transforms.ToTensor()(mask)) * tensor_transfrom(human_img) mask_gray = to_pil_image((mask_gray + 1.0) / 2.0) human_img_arg = _apply_exif_orientation(human_img.resize((384, 512))) human_img_arg = convert_PIL_to_numpy(human_img_arg, format="BGR") args = apply_net.create_argument_parser().parse_args( ( 'show', './configs/densepose_rcnn_R_50_FPN_s1x.yaml', './ckpt/densepose/model_final_162be9.pkl', 'dp_segm', '-v', '--opts', 'MODEL.DEVICE', 'cuda', ) ) pose_img = args.func(args, human_img_arg) pose_img = pose_img[:, :, ::-1] pose_img = Image.fromarray(pose_img).resize((768, 1024)) # テキストエンコーディングは一度だけ行う with torch.no_grad(): with torch.cuda.amp.autocast(): prompt = "model is wearing " + garment_des negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality" ( prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, ) = pipe.encode_prompt( prompt, num_images_per_prompt=1, do_classifier_free_guidance=True, negative_prompt=negative_prompt, ) prompt_c = "a photo of " + garment_des negative_prompt_c = "monochrome, lowres, bad anatomy, worst quality, low quality" ( prompt_embeds_c, _, _, _, ) = pipe.encode_prompt( prompt_c, num_images_per_prompt=1, do_classifier_free_guidance=False, negative_prompt=negative_prompt_c, ) pose_img_tensor = tensor_transfrom(pose_img).unsqueeze(0).to(device, torch.float16) garm_tensor = tensor_transfrom(garm_img).unsqueeze(0).to(device, torch.float16) # 初期化 output_images = gr.State([]) # 最初にマスク画像を一度だけ出力 yield output_images.value, mask_gray for i in range(int(num_images)): current_seed = seed + i if seed is not None and seed != -1 else None generator = ( torch.Generator(device).manual_seed(int(current_seed)) if current_seed is not None else None ) with torch.no_grad(): with torch.cuda.amp.autocast(): images = pipe( prompt_embeds=prompt_embeds.to(device, torch.float16), negative_prompt_embeds=negative_prompt_embeds.to(device, torch.float16), pooled_prompt_embeds=pooled_prompt_embeds.to(device, torch.float16), negative_pooled_prompt_embeds=negative_pooled_prompt_embeds.to( device, torch.float16 ), num_inference_steps=denoise_steps, generator=generator, strength=1.0, pose_img=pose_img_tensor.to(device, torch.float16), text_embeds_cloth=prompt_embeds_c.to(device, torch.float16), cloth=garm_tensor.to(device, torch.float16), mask_image=mask, image=human_img, height=1024, width=768, ip_adapter_image=garm_img.resize((768, 1024)), guidance_scale=2.0, )[0] if is_checked_crop: out_img = images[0].resize(crop_size) human_img_copy = human_img_orig.copy() human_img_copy.paste(out_img, (int(left), int(top))) new_image = human_img_copy else: new_image = images[0] # 画像を追加 output_images.value.append(new_image) # ギャラリーを更新しつつmasked_imgを保持 yield output_images.value, mask_gray # mask_grayを再度渡す # 最終的な結果を返す return output_images.value, mask_gray garm_list = os.listdir(os.path.join(example_path, "cloth")) garm_list_path = [os.path.join(example_path, "cloth", garm) for garm in garm_list] human_list = os.listdir(os.path.join(example_path, "human")) human_list_path = [os.path.join(example_path, "human", human) for human in human_list] human_ex_list = [] for ex_human in human_list_path: ex_dict = {} ex_dict['background'] = ex_human ex_dict['layers'] = None ex_dict['composite'] = None human_ex_list.append(ex_dict) image_blocks = gr.Blocks().queue() with image_blocks as demo: gr.Markdown("## IDM-VTON 👕👔👚") gr.Markdown( "Virtual Try-on with your image and garment image. Check out the [source codes](https://github.com/yisol/IDM-VTON) and the [model](https://huggingface.co/yisol/IDM-VTON)" ) with gr.Row(): with gr.Column(): imgs = gr.ImageEditor( sources='upload', type="pil", label='Human. Mask with pen or use auto-masking', interactive=True, ) with gr.Row(): is_checked = gr.Checkbox( label="Yes", info="Use auto-generated mask (Takes 5 seconds)", value=True ) with gr.Row(): is_checked_crop = gr.Checkbox(label="Yes", info="Use auto-crop & resizing", value=False) example = gr.Examples(inputs=imgs, examples_per_page=10, examples=human_ex_list) with gr.Column(): garm_img = gr.Image(label="Garment", sources='upload', type="pil") with gr.Row(elem_id="prompt-container"): with gr.Row(): prompt = gr.Textbox( placeholder="Description of garment ex) Short Sleeve Round Neck T-shirts", show_label=False, elem_id="prompt", ) example = gr.Examples(inputs=garm_img, examples_per_page=8, examples=garm_list_path) with gr.Column(): masked_img = gr.Image( label="Masked image output", elem_id="masked-img", show_share_button=False ) with gr.Column(): image_gallery = gr.Gallery( label="Generated Images", elem_id="output-gallery", show_label=True ) with gr.Column(): try_button = gr.Button(value="Try-on") with gr.Accordion(label="Advanced Settings", open=False): with gr.Row(): denoise_steps = gr.Number( label="Denoising Steps", minimum=20, maximum=40, value=30, step=1 ) seed = gr.Number(label="Seed", minimum=-1, maximum=2147483647, step=1, value=42) num_images = gr.Slider( label="Number of Images", minimum=1, maximum=10, step=1, value=1 # 最大値を10に変更 ) try_button.click( fn=start_tryon, inputs=[ imgs, garm_img, prompt, is_checked, is_checked_crop, denoise_steps, seed, num_images, ], outputs=[image_gallery, masked_img], api_name='tryon', ) image_blocks.launch(show_error=True)