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 spaces 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] == True : 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, ) # "stabilityai/stable-diffusion-xl-base-1.0", 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 def start_tryon(dict,garm_img,garment_des,is_checked,is_checked_crop,denoise_steps,seed, category): device = "cuda" category = int(category) if category==0: category='upper_body' elif category==1: category='lower_body' else: category='dresses' 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 aspect_ratio = width / height if not (0.45 < aspect_ratio < 0.46): 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)) 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', category, model_parse, keypoints) mask = mask.resize((768,1024)) else: mask = pil_to_binary_mask(dict['layers'][0].convert("RGB").resize((768, 1024))) # mask = transforms.ToTensor()(mask) # mask = mask.unsqueeze(0) 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')) # verbosity = getattr(args, "verbosity", None) pose_img = args.func(args,human_img_arg) pose_img = pose_img[:,:,::-1] pose_img = Image.fromarray(pose_img).resize((768,1024)) with torch.no_grad(): # Extract the images with torch.cuda.amp.autocast(): with torch.no_grad(): prompt = "model is wearing " + garment_des negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality, contortionist, amputee, polydactyly, deformed, distorted, misshapen, malformed, abnormal, mutant, defaced, shapeless, unreal, missing arms, three hands, bad face, extra fingers, cartoon, fused face, cg, ugly fingers, three legs, bad hands, fused feet, worst face, extra eyes, long fingers, three feet, missing legs, cloned face, worst feet, extra crus, huge eyes, fused crus, three thigh, bad anatomy, disconnected limbs, animate, 3d, worst thigh, extra thigh, fused thigh, missing fingers, amputation, poorly drawn face, three crus, horn, 2girl, bad arms" with torch.inference_mode(): ( 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 = "a photo of " + garment_des negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality, contortionist, amputee, polydactyly, deformed, distorted, misshapen, malformed, abnormal, mutant, defaced, shapeless, unreal, missing arms, three hands, bad face, extra fingers, cartoon, fused face, cg, ugly fingers, three legs, bad hands, fused feet, worst face, extra eyes, long fingers, three feet, missing legs, cloned face, worst feet, extra crus, huge eyes, fused crus, three thigh, bad anatomy, disconnected limbs, animate, 3d, worst thigh, extra thigh, fused thigh, missing fingers, amputation, poorly drawn face, three crus, horn, 2girl, bad arms" if not isinstance(prompt, List): prompt = [prompt] * 1 if not isinstance(negative_prompt, List): negative_prompt = [negative_prompt] * 1 with torch.inference_mode(): ( prompt_embeds_c, _, _, _, ) = pipe.encode_prompt( prompt, num_images_per_prompt=1, do_classifier_free_guidance=False, negative_prompt=negative_prompt, ) pose_img = tensor_transfrom(pose_img).unsqueeze(0).to(device,torch.float16) garm_tensor = tensor_transfrom(garm_img).unsqueeze(0).to(device,torch.float16) generator = torch.Generator(device).manual_seed(seed) if seed is not None else None 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.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: if not (0.45 < aspect_ratio < 0.46): out_img =images[0].resize(crop_size) human_img_orig.paste(out_img, (int(left), int(top))) else: return images[0], mask_gray return human_img_orig, mask_gray else: return images[0], mask_gray # return images[0], 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) ##default human image_blocks = gr.Blocks().queue() with image_blocks as demo: gr.Markdown("## VirtualFit") gr.Markdown("VirtualFIT Demo") 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) with gr.Row(): category = gr.Textbox(placeholder="0 = upper body, 1 = lower body, 2 = full body", show_label=False, elem_id="prompt") 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(): # image_out = gr.Image(label="Output", elem_id="output-img", height=400) masked_img = gr.Image(label="Masked image output", elem_id="masked-img",show_share_button=False) with gr.Column(): # image_out = gr.Image(label="Output", elem_id="output-img", height=400) image_out = gr.Image(label="Output", elem_id="output-img",show_share_button=False) 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) try_button.click(fn=start_tryon, inputs=[imgs, garm_img, prompt, is_checked,is_checked_crop, denoise_steps, seed, category], outputs=[image_out,masked_img], api_name='tryon') image_blocks.launch()