import gradio as gr import argparse, torch, os 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, ) from diffusers import AutoencoderKL from typing import List from util.common import open_folder from util.image import pil_to_binary_mask, save_output_image 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 util.pipeline import quantize_4bit, restart_cpu_offload, torch_gc parser = argparse.ArgumentParser() parser.add_argument("--share", type=str, default=False, help="Set to True to share the app publicly.") parser.add_argument("--lowvram", action="store_true", help="Enable CPU offload for model operations.") parser.add_argument("--load_mode", default=None, type=str, choices=["4bit", "8bit"], help="Quantization mode for optimization memory consumption") parser.add_argument("--fixed_vae", action="store_true", default=True, help="Use fixed vae for FP16.") args = parser.parse_args() load_mode = args.load_mode fixed_vae = args.fixed_vae dtype = torch.float16 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model_id = 'yisol/IDM-VTON' vae_model_id = 'madebyollin/sdxl-vae-fp16-fix' dtypeQuantize = dtype if(load_mode in ('4bit','8bit')): dtypeQuantize = torch.float8_e4m3fn ENABLE_CPU_OFFLOAD = args.lowvram torch.backends.cudnn.allow_tf32 = False torch.backends.cuda.allow_tf32 = False need_restart_cpu_offloading = False unet = None pipe = None UNet_Encoder = None example_path = os.path.join(os.path.dirname(__file__), 'example') def start_tryon(dict, garm_img, garment_des, category, is_checked, is_checked_crop, denoise_steps, is_randomize_seed, seed, number_of_images): global pipe, unet, UNet_Encoder, need_restart_cpu_offloading if pipe == None: unet = UNet2DConditionModel.from_pretrained( model_id, subfolder="unet", torch_dtype=dtypeQuantize, ) if load_mode == '4bit': quantize_4bit(unet) unet.requires_grad_(False) image_encoder = CLIPVisionModelWithProjection.from_pretrained( model_id, subfolder="image_encoder", torch_dtype=torch.float16, ) if load_mode == '4bit': quantize_4bit(image_encoder) if fixed_vae: vae = AutoencoderKL.from_pretrained(vae_model_id, torch_dtype=dtype) else: vae = AutoencoderKL.from_pretrained(model_id, subfolder="vae", torch_dtype=dtype, ) # "stabilityai/stable-diffusion-xl-base-1.0", UNet_Encoder = UNet2DConditionModel_ref.from_pretrained( model_id, subfolder="unet_encoder", torch_dtype=dtypeQuantize, ) if load_mode == '4bit': quantize_4bit(UNet_Encoder) UNet_Encoder.requires_grad_(False) image_encoder.requires_grad_(False) vae.requires_grad_(False) unet.requires_grad_(False) pipe_param = { 'pretrained_model_name_or_path': model_id, 'unet': unet, 'torch_dtype': dtype, 'vae': vae, 'image_encoder': image_encoder, 'feature_extractor': CLIPImageProcessor(), } pipe = TryonPipeline.from_pretrained(**pipe_param).to(device) pipe.unet_encoder = UNet_Encoder pipe.unet_encoder.to(pipe.unet.device) if load_mode == '4bit': if pipe.text_encoder is not None: quantize_4bit(pipe.text_encoder) if pipe.text_encoder_2 is not None: quantize_4bit(pipe.text_encoder_2) else: if ENABLE_CPU_OFFLOAD: need_restart_cpu_offloading =True torch_gc() parsing_model = Parsing(0) openpose_model = OpenPose(0) openpose_model.preprocessor.body_estimation.model.to(device) tensor_transfrom = transforms.Compose( [ transforms.ToTensor(), transforms.Normalize([0.5], [0.5]), ] ) if need_restart_cpu_offloading: restart_cpu_offload(pipe, load_mode) elif ENABLE_CPU_OFFLOAD: pipe.enable_model_cpu_offload() #if load_mode != '4bit' : # pipe.enable_xformers_memory_efficient_attention() 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', 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)) if pipe.text_encoder is not None: pipe.text_encoder.to(device) if pipe.text_encoder_2 is not None: pipe.text_encoder_2.to(device) with torch.no_grad(): # Extract the images with torch.cuda.amp.autocast(dtype=dtype): with torch.no_grad(): prompt = "model is wearing " + garment_des 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 " + garment_des 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,dtype) garm_tensor = tensor_transfrom(garm_img).unsqueeze(0).to(device,dtype) results = [] current_seed = seed for i in range(number_of_images): if is_randomize_seed: current_seed = torch.randint(0, 2**32, size=(1,)).item() generator = torch.Generator(device).manual_seed(current_seed) if seed != -1 else None current_seed = current_seed + i images = pipe( prompt_embeds=prompt_embeds.to(device,dtype), negative_prompt_embeds=negative_prompt_embeds.to(device,dtype), pooled_prompt_embeds=pooled_prompt_embeds.to(device,dtype), negative_pooled_prompt_embeds=negative_pooled_prompt_embeds.to(device,dtype), num_inference_steps=denoise_steps, generator=generator, strength = 1.0, pose_img = pose_img.to(device,dtype), text_embeds_cloth=prompt_embeds_c.to(device,dtype), cloth = garm_tensor.to(device,dtype), mask_image=mask, image=human_img, height=1024, width=768, ip_adapter_image = garm_img.resize((768,1024)), guidance_scale=2.0, dtype=dtype, device=device, )[0] if is_checked_crop: out_img = images[0].resize(crop_size) human_img_orig.paste(out_img, (int(left), int(top))) img_path = save_output_image(human_img_orig, base_path="outputs", base_filename='img', seed=current_seed) results.append(img_path) else: img_path = save_output_image(images[0], base_path="outputs", base_filename='img') results.append(img_path) return results, 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: if "Jensen" in ex_human or "sam1 (1)" in ex_human: 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("## V7 - IDM-VTON 👕👔👚 improved by SECourses and DEVAIEXP: 1-Click Installers Latest Version On : https://www.patreon.com/posts/103022942") 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(): category = gr.Radio(choices=["upper_body", "lower_body", "dresses"], label="Select Garment Category", value="upper_body") 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=True) example = gr.Examples( inputs=imgs, examples_per_page=2, 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(): with gr.Row(): # 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.Row(): btn_open_outputs = gr.Button("Open Outputs Folder") btn_open_outputs.click(fn=open_folder) with gr.Column(): with gr.Row(): # image_out = gr.Image(label="Output", elem_id="output-img", height=400) image_gallery = gr.Gallery(label="Generated Images", show_label=True) with gr.Row(): try_button = gr.Button(value="Try-on") denoise_steps = gr.Number(label="Denoising Steps", minimum=20, maximum=120, value=30, step=1) seed = gr.Number(label="Seed", minimum=-1, maximum=2147483647, step=1, value=1) is_randomize_seed = gr.Checkbox(label="Randomize seed for each generated image", value=True) number_of_images = gr.Number(label="Number Of Images To Generate (it will start from your input seed and increment by 1)", minimum=1, maximum=9999, value=1, step=1) try_button.click(fn=start_tryon, inputs=[imgs, garm_img, prompt, category, is_checked, is_checked_crop, denoise_steps, is_randomize_seed, seed, number_of_images], outputs=[image_gallery, masked_img],api_name='tryon') image_blocks.launch(inbrowser=True,share=args.share)