import sys sys.path.append('./') from PIL import Image try: import cv2 print("OpenCV is installed correctly.") except ImportError: print("OpenCV is not installed.") import gradio as gr 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 device = 'cuda:0' if torch.cuda.is_available() else 'cpu' 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 # Function to visualize parsing def visualize_parsing(image, mask): """ Visualize the parsing by applying a color map to the segmentation mask. """ # Ensure image is in RGB format and convert to numpy array image_array = np.array(image.convert('RGB'), dtype=np.uint8) # Create a color map num_classes = np.max(mask) + 1 colors = np.random.randint(0, 255, size=(num_classes, 3), dtype=np.uint8) # Apply color map to the mask color_mask = colors[mask.astype(int)] # Ensure color_mask is correctly shaped and typed color_mask = np.array(color_mask, dtype=np.uint8) # Combine the original image and the color mask combined_image = cv2.addWeighted(image_array, 0.5, color_mask, 0.5, 0) return Image.fromarray(combined_image) def process_densepose(human_img): """ Processes the human image using DensePose and returns the DensePose image. Assumes human_img is a dictionary with a 'background' key pointing to the image path. """ # Load image from path image_path = human_img['background'] # Assuming 'background' is the correct key if isinstance(image_path, Image.Image): image = image_path else: image = Image.open(image_path) # Only call Image.open if it's not already an Image object # Apply EXIF orientation and resize human_img_arg = _apply_exif_orientation(image.resize((384, 512))) human_img_arg = convert_PIL_to_numpy(human_img_arg, format="BGR") # Setup DensePose arguments 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] # Convert from BGR to RGB pose_img = Image.fromarray(pose_img).resize((768, 1024)) return pose_img, pose_img def process_human_parsing(human_img): """ Processes the human image to perform segmentation using a human parsing model. """ image_path = human_img['background'] # Assuming 'background' is the correct key if isinstance(image_path, Image.Image): image = image_path else: image = Image.open(image_path) # Only call Image.open if it's not already an Image object image = image.resize((384, 512)) model_parse, _ = parsing_model(image) # parsing_image = visualize_parsing(human_img, model_parse) # Visualization function needed # vis_image = visualize_parsing(image, model_parse) # state_message = "Human parsing processing completed" return model_parse def start_tryon(dict, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed): """ Preprocesses images and generates outputs using various models. Parameters: - human_img: PIL image of the human. - garm_img: PIL image of the garment. - garment_des: Description of the garment. - is_checked: Boolean flag indicating whether to use auto-generated mask. - is_checked_crop: Boolean flag indicating whether to use auto-crop & resizing. - denoise_steps: Number of denoising steps. - seed: Seed for random generator. - pose_img: DensePose image generated in the previous step. Returns: - Processed images: Depending on the conditions, it returns human_img_orig, mask_gray, and final output images. """ 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))) print(keypoints) model_parse, _ = parsing_model(human_img.resize((384,512))) print(model_parse) 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 = 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" 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,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: out_img = images[0].resize(crop_size) human_img_orig.paste(out_img, (int(left), int(top))) return human_img_orig, mask_gray else: # out_img = images[0].resize(crop_size) 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: 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(): # 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(): densepose_img_out = gr.Image(label="Output", elem_id="densepose-img",show_share_button=False) # densepose_img = gr.Gallery(label="All images", show_label=False, elem_id="all-images", columns=[3], rows=[1], object_fit="contain", height="auto") 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) densepose_state = gr.State(None) # Define the steps in sequence image_blocks = gr.Blocks().queue() with image_blocks as 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) 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_out = gr.Image(label="Output", elem_id="output-img", show_share_button=False) with gr.Column(): densepose_img_out = gr.Image(label="Dense-pose", elem_id="densepose-img", show_share_button=False) # densepose_img = gr.Gallery(label="All images", show_label=False, elem_id="all-images", columns=[3], rows=[1], object_fit="contain", height="auto") with gr.Column(): human_parse_img_out = gr.Image(label="Human-Parse", elem_id="humanparse-img", show_share_button=False) # densepose_img = gr.Gallery(label="All images", show_label=False, elem_id="all-images", columns=[3], rows=[1], object_fit="contain", height="auto") with gr.Column(): try_button = gr.Button(value="Try-on") get_denspose =gr.Button(value="Get-DensePose") get_humanparse =gr.Button(value="Get-HumanParse") 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) densepose_state = gr.State(None) # Define the steps in sequence get_denspose.click( fn=process_densepose, inputs=[imgs], outputs=[densepose_img_out, densepose_state], api_name='process_densepose' ) get_humanparse.click( fn=process_human_parsing, inputs=[imgs], outputs=[human_parse_img_out], api_name='process_humanparse' ) try_button.click( fn=start_tryon, inputs=[imgs, garm_img, prompt, is_checked, is_checked_crop, denoise_steps, seed], outputs=[image_out, masked_img], api_name='start_tryon' ) image_blocks.launch(server_name="0.0.0.0", server_port=3000)