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 from PIL import Image, ImageDraw, ImageFont 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 progress=gr.Progress() @spaces.GPU def infer(person,garment,denoise_steps,seed): progress(0,desc="Starting") device = "cuda" openpose_model.preprocessor.body_estimation.model.to(device) pipe.to(device) pipe.unet_encoder.to(device) personRGB = person.convert("RGB") crop_size = personRGB.size human_img = personRGB.resize((768,1024)) garm_img= garment.convert("RGB").resize((768,1024)) progress(0.1,desc="Mask generating") 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)) mask_gray = (1-transforms.ToTensor()(mask)) * tensor_transfrom(human_img) mask_gray = to_pil_image((mask_gray+1.0)/2.0) progress(0.3,desc="DensePose processing") 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)) progress(0.5,desc="Image generating") def callback(pipe, step, timestep, callback_kwargs): progress_value = 0.5 + ((step+1.0)/denoise_steps)*(0.5/1.0) progress(progress_value, desc=f"Image generating, {step + 1}/{denoise_steps} steps") return callback_kwargs with torch.no_grad(): # Extract the images with torch.cuda.amp.autocast(): with torch.no_grad(): prompt = "model is wearing clothing" negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality" 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 clothing" negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality" 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, callback_on_step_end=callback )[0] out_img = images[0].resize(crop_size) progress(1,desc="Complete") return out_img title = "## IDM-VTON" description = "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)" example_path = os.path.join(os.path.dirname(__file__), 'example') person_list = os.listdir(os.path.join(example_path,"human")) person_images = [os.path.join(example_path,"human",person) for person in person_list] garment_list = os.listdir(os.path.join(example_path,"cloth")) garment_images = [os.path.join(example_path,"cloth",garment) for garment in garment_list] with gr.Blocks().queue() as demo: gr.Markdown(title) gr.Markdown(description) with gr.Row(): with gr.Column(): gr.Markdown("#### Person Image") person_image = gr.Image( sources=["upload"], type="pil", label="Person Image", width=512, height=512, ) gr.Examples( inputs=person_image, examples_per_page=20, examples=person_images, ) with gr.Column(): gr.Markdown("#### Garment Image") garment_image = gr.Image( sources=["upload"], type="pil", label="Garment Image", width=512, height=512, ) gr.Examples( inputs=garment_image, examples_per_page=20, examples=garment_images, ) with gr.Column(): gr.Markdown("#### Generated Image") gen_image = gr.Image( label="Generated Image", width=512, height=512, ) with gr.Row(): gen_button = gr.Button("Generate") with gr.Accordion("Advanced Options", open=False): 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) gen_button.click( fn=infer, inputs=[person_image, garment_image, denoise_steps, seed], outputs=[gen_image] ) demo.launch()