import os import gradio as gr import spaces import torch import torch.nn as nn from diffusers import EulerDiscreteScheduler, AutoencoderKL, UNet2DConditionModel from huggingface_hub import hf_hub_download from transformers import SiglipImageProcessor, SiglipVisionModel from torchvision.io import read_image import torchvision.transforms.v2 as transforms from torchvision.utils import make_grid class TryOffDiff(nn.Module): def __init__(self): super().__init__() self.unet = UNet2DConditionModel.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="unet") self.transformer = torch.nn.TransformerEncoderLayer(d_model=768, nhead=8, batch_first=True) self.proj = nn.Linear(1024, 77) self.norm = nn.LayerNorm(768) def adapt_embeddings(self, x): x = self.transformer(x) x = self.proj(x.permute(0, 2, 1)).permute(0, 2, 1) return self.norm(x) def forward(self, noisy_latents, t, cond_emb): cond_emb = self.adapt_embeddings(cond_emb) return self.unet(noisy_latents, t, encoder_hidden_states=cond_emb).sample class PadToSquare: def __call__(self, img): _, h, w = img.shape # Get the original dimensions max_side = max(h, w) pad_h = (max_side - h) // 2 pad_w = (max_side - w) // 2 padding = (pad_w, pad_h, max_side - w - pad_w, max_side - h - pad_h) return transforms.functional.pad(img, padding, padding_mode="edge") # Set device device = "cuda" if torch.cuda.is_available() else "cpu" # Initialize Image Encoder img_processor = SiglipImageProcessor.from_pretrained( "google/siglip-base-patch16-512", do_resize=False, do_rescale=False, do_normalize=False ) img_enc = SiglipVisionModel.from_pretrained("google/siglip-base-patch16-512").eval().to(device) img_enc_transform = transforms.Compose([ PadToSquare(), # Custom transform to pad the image to a square transforms.Resize((512, 512)), transforms.ToDtype(torch.float32, scale=True), transforms.Normalize(mean=[0.5], std=[0.5]), ]) # Load TryOffDiff Model path_model = hf_hub_download( repo_id="rizavelioglu/tryoffdiff", filename="tryoffdiff.pth", # or one of ["ldm-1", "ldm-2", "ldm-3", ...], force_download=False ) path_scheduler = hf_hub_download( repo_id="rizavelioglu/tryoffdiff", filename="scheduler/scheduler_config.json", force_download=False ) net = TryOffDiff() net.load_state_dict(torch.load(path_model, weights_only=False)) net.eval().to(device) # Initialize VAE (only Decoder will be used) vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="vae").eval().to(device) torch.cuda.empty_cache() # Define image generation function @spaces.GPU(duration=10) @torch.no_grad() def generate_image(input_image, seed=42, guidance_scale=2.0, num_inference_steps=50, is_upscale=False): # Configure scheduler scheduler = EulerDiscreteScheduler.from_pretrained(path_scheduler) scheduler.is_scale_input_called = True # suppress warning scheduler.set_timesteps(num_inference_steps) # Set random seed generator = torch.Generator(device=device).manual_seed(seed) x = torch.randn(1, 4, 64, 64, generator=generator, device=device) # Process input image cond_image = img_enc_transform(read_image(input_image)) inputs = {k: v.to(img_enc.device) for k, v in img_processor(images=cond_image, return_tensors="pt").items()} cond_emb = img_enc(**inputs).last_hidden_state.to(device) # Prepare unconditioned embeddings uncond_emb = torch.zeros_like(cond_emb) if guidance_scale > 1 else None # Denoising loop with mixed precision with torch.autocast(device): for t in scheduler.timesteps: if guidance_scale > 1: noise_pred = net( torch.cat([x] * 2), t, torch.cat([uncond_emb, cond_emb]) ).chunk(2) noise_pred = noise_pred[0] + guidance_scale * (noise_pred[1] - noise_pred[0]) else: noise_pred = net(x, t, cond_emb) scheduler_output = scheduler.step(noise_pred, t, x) x = scheduler_output.prev_sample # Decode preds decoded = vae.decode(1 / 0.18215 * scheduler_output.pred_original_sample).sample images = (decoded / 2 + 0.5).cpu() # Create grid grid = make_grid(images, nrow=len(images), normalize=True, scale_each=True) if is_upscale: pass else: return transforms.ToPILImage()(grid) title = "Virtual Try-Off Generator" description = r""" This is the demo of the paper TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models.
Upload an image of a clothed individual to generate a standardized garment image using TryOffDiff.
Check out the project page for more information. """ article = r""" Example images are sampled from the `VITON-HD-test` set, which the models did not see during training.
**Citation**
If you find our work useful in your research, please consider giving a star ⭐ and a citation: ``` @article{velioglu2024tryoffdiff, title = {TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models}, author = {Velioglu, Riza and Bevandic, Petra and Chan, Robin and Hammer, Barbara}, journal = {arXiv}, year = {2024}, note = {\url{https://doi.org/nt3n}} } ``` """ examples = [[f"examples/{img_filename}", 42, 2.0, 20, False] for img_filename in sorted(os.listdir("examples/"))] # Create Gradio App demo = gr.Interface( fn=generate_image, inputs=[ gr.Image(type="filepath", label="Reference Image"), gr.Slider(value=42, minimum=0, maximum=1e6, step=1, label="Seed"), gr.Slider(value=2.0, minimum=1, maximum=5, step=0.5, label="Guidance Scale(s)", info="No guidance applied at s=1, hence faster inference."), gr.Slider(value=20, minimum=0, maximum=1000, step=10, label="# of Inference Steps"), gr.Checkbox(value=False, label="Upscale Output") ], outputs=gr.Image(type="pil", label="Generated Garment", height=512, width=512), title=title, description=description, article=article, examples=examples, examples_per_page=4, ) demo.launch()