import sys import os sys.path.append('./') os.system("pip install gradio accelerate==0.25.0 torchmetrics==1.2.1 tqdm==4.66.1 fastapi==0.111.0 transformers==4.36.2 diffusers==0.25 einops==0.7.0 bitsandbytes scipy==1.11.1 opencv-python gradio==4.24.0 fvcore cloudpickle omegaconf pycocotools basicsr av onnxruntime==1.16.2 peft==0.11.1 huggingface_hub==0.24.7 --no-deps") import spaces from fastapi import FastAPI app = FastAPI() from PIL import Image 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 import torch from typing import List from transformers import AutoTokenizer import numpy as np from utils_mask import get_mask_location from torchvision import transforms from preprocess.humanparsing.run_parsing import Parsing from preprocess.openpose.run_openpose import OpenPose from torchvision.transforms.functional import to_pil_image import apply_net from detectron2.data.detection_utils import convert_PIL_to_numpy,_apply_exif_orientation 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 = 'Keshabwi66/SmartLugaModel' 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(boy,girl,person_img,cloth_img, garment_des, denoise_steps=10, seed=42): # Assuming device is set up (e.g., "cuda" or "cpu") device="cuda" openpose_model.preprocessor.body_estimation.model.to(device) pipe.to(device) pipe.unet_encoder.to(device) # Resize and prepare images garm_img = cloth_img.convert("RGB").resize((768, 1024)) human_img = person_img.convert("RGB").resize((768,1024)) is_checked=True; 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', "upper_body", model_parse, keypoints) mask = mask.resize((768,1024)) 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 boy: prompt = "A boy is wearing"+garment_des if girl: prompt= "A girl is wearing"+garment_des # Embedding generation for prompts with torch.no_grad(): with torch.cuda.amp.autocast(): # Generate text embeddings for garment description prompt = prompt 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_cloth, _, _, _, )= pipe.encode_prompt( prompt, num_images_per_prompt=1, do_classifier_free_guidance=False, negative_prompt=negative_prompt, ) # Convert images to tensors for processing pose_img_tensor = tensor_transfrom(pose_img).unsqueeze(0).to(device, torch.float16) garm_tensor = tensor_transfrom(garm_img).unsqueeze(0).to(device, torch.float16) # Prepare the generator with optional seed generator = torch.Generator(device).manual_seed(seed) if seed is not None else None # Generate the virtual try-on output image 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_tensor.to(device, torch.float16), text_embeds_cloth=prompt_embeds_cloth.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] return images[0].resize(person_img.size) # Gradio interface for the virtual try-on model image_blocks = gr.Blocks().queue() with image_blocks as demo: gr.Markdown("## SmartLuga") with gr.Row(): with gr.Column(): person_img = gr.Image(label='Person Image', sources='upload', type="pil") boy = gr.Checkbox(label="Yes", info="Boy",value=True) girl = gr.Checkbox(label="Yes", info="Girl",value=False) with gr.Column(): cloth_img = gr.Image(label='Garment Image', sources='upload', type="pil") garment_des = gr.Textbox(placeholder="Description of garment ex) Short Sleeve Round Neck T-shirts", label="Garment Description") with gr.Column(): image_out = gr.Image(label="Output Image", elem_id="output-img", show_share_button=False) try_button = gr.Button(value="Try-on") try_button.click(fn=start_tryon, inputs=[boy,girl,person_img, cloth_img, garment_des], outputs=[image_out], api_name='tryon') image_blocks.launch()