import spaces import logging import math 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 src.enhanced_garment_net import EnhancedGarmentNetWithTimestep 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 src.background_processor import BackgroundProcessor 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) # This is suggestion from Claude for enhanced garment net #enhancedGarmentNet = EnhancedGarmentNetWithTimestep() #enhancedGarmentNet.to(dtype=torch.float16) 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 # pipe.garment_net = enhancedGarmentNet # Standard size of shein images #WIDTH = int(4160/5) #HEIGHT = int(6240/5) # Standard size on which model is trained WIDTH = int(768) HEIGHT = int(1024) POSE_WIDTH = int(WIDTH/2) # int(WIDTH/2) POSE_HEIGHT = int(HEIGHT/2) #int(HEIGHT/2) ARM_WIDTH = "dc" # "hd" # hd -> full sleeve, dc for half sleeve CATEGORY = "upper_body" # "lower_body" def is_cropping_required(width, height): # If aspect ratio is 1.33, which is same as standard 3x4 ( 768x1024 ), then no need to crop, else crop aspect_ratio = round(height/width, 2) if aspect_ratio == 1.33: return False return True @spaces.GPU def start_tryon(human_img_dict,garm_img,garment_des, background_img, is_checked,is_checked_crop,denoise_steps,seed): logging.info("Starting try on") print(f"Input: {human_img_dict}") #device = "cuda" device = 'cuda:0' if torch.cuda.is_available() else 'cpu' openpose_model.preprocessor.body_estimation.model.to(device) pipe.to(device) pipe.unet_encoder.to(device) # pipe.garment_net.to(device) if isinstance(human_img_dict, dict): human_img_orig = human_img_dict["background"].convert("RGB") # ImageEditor else: human_img_orig = human_img_dict.convert("RGB") # Image """ # Derive HEIGHT & WIDTH such that width is not more than 1000. This will cater to both Shein images (4160x6240) of 2:3 AR and model standard images ( 768x1024 ) of 3:4 AR WIDTH, HEIGHT = human_img_orig.size division_factor = math.ceil(WIDTH/1000) WIDTH = int(WIDTH/division_factor) HEIGHT = int(HEIGHT/division_factor) POSE_WIDTH = int(WIDTH/2) POSE_HEIGHT = int(HEIGHT/2) """ # is_checked_crop as True if original AR is not same as 2x3 as expected by model w, h = human_img_orig.size is_checked_crop = is_cropping_required(w, h) garm_img= garm_img.convert("RGB").resize((WIDTH,HEIGHT)) if is_checked_crop: # This will crop the image to make it Aspect Ratio of 3 x 4. And then at the end revert it back to original dimentions 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 right = (width + target_width) / 2 # for Landmark, model sizes are 594x879, so we need to reduce the height. In some case the garment on the model is # also getting removed when reducing size from bottom. So we will only reduce height from top for now top = (height - target_height) #top = (height - target_height) / 2 bottom = height #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((WIDTH, HEIGHT)) else: human_img = human_img_orig.resize((WIDTH, HEIGHT)) # Commenting out naize harmonization for now. We will have to integrate with Deep Learning based Harmonization methods # Do color transfer from background image for better image harmonization #if background_img: # human_img = BackgroundProcessor.intensity_transfer(human_img, background_img) if is_checked: # internally openpose_model is resizing human_img to resolution 384 if not passed as input keypoints = openpose_model(human_img.resize((POSE_WIDTH, POSE_HEIGHT))) model_parse, _ = parsing_model(human_img.resize((POSE_WIDTH, POSE_HEIGHT))) # internally get mask location function is resizing model_parse to 384x512 if width & height not passed mask, mask_gray = get_mask_location(ARM_WIDTH, CATEGORY, model_parse, keypoints) mask = mask.resize((WIDTH, HEIGHT)) logging.info("Mask location on model identified") else: mask = pil_to_binary_mask(human_img_dict['layers'][0].convert("RGB").resize((WIDTH, HEIGHT))) # 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((POSE_WIDTH,POSE_HEIGHT))) 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', device)) # 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((WIDTH,HEIGHT)) 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=HEIGHT, width=WIDTH, ip_adapter_image = garm_img.resize((WIDTH,HEIGHT)), 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))) final_image = human_img_orig # return human_img_orig, mask_gray else: final_image = images[0] # return images[0], mask_gray # apply background to final image if background_img: logging.info("Adding background") final_image = BackgroundProcessor.replace_background_with_removebg(final_image, background_img) return final_image, 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 = [] #human_ex_list = human_list_path # Image #""" if using ImageEditor instead of Image while taking input, use this - ImageEditor 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 # api_open=True will allow this API to be hit using curl image_blocks = gr.Blocks().queue(api_open=True) with image_blocks as demo: gr.Markdown("## Virtual Try-On 👕👔👚") gr.Markdown("Upload an image of a person and an image of a garment ✨.") with gr.Row(): with gr.Column(): # changing from ImageEditor to Image to allow easy passing of data through API # instead of passing {"dictionary": <>} ( which is failing ), we can directly pass the image imgs = gr.ImageEditor(sources='upload', type="pil", label='Human. Mask with pen or use auto-masking', interactive=True) #imgs = gr.Image(sources='upload', type='pil',label='Human. Mask with pen or use auto-masking') 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(): background_img = gr.Image(label="Background", sources='upload', type="pil") with gr.Column(): with gr.Row(): image_out = gr.Image(label="Output", elem_id="output-img", show_share_button=False) with gr.Row(): 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) 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, background_img, is_checked,is_checked_crop, denoise_steps, seed], outputs=[image_out,masked_img], api_name='tryon') image_blocks.launch()