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 from typing import List import torch import os from transformers import AutoTokenizer import numpy as np from torchvision import transforms 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 = '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, ) 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(person_img, mask_img, cloth_img, garment_des, denoise_steps=10, seed=42): # Assuming device is set up (e.g., "cuda" or "cpu") 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)) mask = pil_to_binary_mask(mask_img.convert("RGB").resize((768, 1024))) pose_img=Image.open("00006_00.jpg") # Prepare pose image (already uploaded) pose_img = pose_img.resize((768, 1024)) # Embedding generation for prompts with torch.no_grad(): with torch.cuda.amp.autocast(): # Generate text embeddings for garment description prompt = f"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_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] # 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") mask_img = gr.Image(label='Mask Image', sources='upload', type="pil") 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=[person_img, mask_img, cloth_img, garment_des], outputs=[image_out], api_name='tryon') image_blocks.launch()