init

.gitattributes

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+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

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+.gradio
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
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+
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+
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+
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+
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+
+# pyenv
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+#   intended to run in multiple environments; otherwise, check them in:
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+
+# pipenv
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+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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+
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+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

.python-version

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+3.11

README.md

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+---
+title: TryOffAnyone
+emoji: 👕
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 5.9.1
+app_file: src/app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

pyproject.toml

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+[project]
+dependencies = [
+    "accelerate>=1.2.1",
+    "diffusers>=0.32.1",
+    "gradio>=5.9.1",
+    "pillow-heif>=0.21.0",
+    "scikit-image>=0.25.0",
+    "spaces>=0.31.1",
+    "torchvision>=0.20.1",
+    "transformers>=4.47.1",
+]
+description = "TryOffAnyone: Tiled Cloth Generation from a Dressed Person "
+name = "try-off-anyone"
+readme = "README.md"
+requires-python = ">=3.11"
+version = "0.1.0"
+
+[tool.ruff]
+line-length = 120

src/app.py

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+from typing import TypedDict
+
+import diffusers.image_processor
+import gradio as gr
+import pillow_heif  # pyright: ignore[reportMissingTypeStubs]
+import spaces  # pyright: ignore[reportMissingTypeStubs]
+import torch
+from PIL import Image
+
+from pipeline import TryOffAnyone
+
+pillow_heif.register_heif_opener()  # pyright: ignore[reportUnknownMemberType]
+pillow_heif.register_avif_opener()  # pyright: ignore[reportUnknownMemberType]
+
+torch.set_float32_matmul_precision("high")
+torch.backends.cuda.matmul.allow_tf32 = True
+
+TITLE = """
+# Try Off Anyone
+
+## ⚠️ Important
+
+1. Choose an example image or upload your own
+2. Use the Pen tool to draw a mask over the clothing area you want to extract
+
+[![arxiv badge](https://img.shields.io/badge/arXiv-Paper-b31b1b.svg?style=for-the-badge)](https://arxiv.org/abs/2412.08573)
+"""
+
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+DTYPE = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
+
+pipeline_tryoff = TryOffAnyone(
+    device=DEVICE,
+    dtype=DTYPE,
+)
+mask_processor = diffusers.image_processor.VaeImageProcessor(
+    vae_scale_factor=8,
+    do_normalize=False,
+    do_binarize=True,
+    do_convert_grayscale=True,
+)
+vae_processor = diffusers.image_processor.VaeImageProcessor(
+    vae_scale_factor=8,
+)
+
+
+class ImageData(TypedDict):
+    background: Image.Image
+    composite: Image.Image
+    layers: list[Image.Image]
+
+
+@spaces.GPU
+def process(
+    image_data: ImageData,
+    image_width: int,
+    image_height: int,
+    num_inference_steps: int,
+    condition_scale: float,
+    seed: int,
+) -> Image.Image:
+    assert image_width > 0
+    assert image_height > 0
+    assert num_inference_steps > 0
+    assert condition_scale > 0
+    assert seed >= 0
+
+    # extract image and mask from image_data
+    image = image_data["background"]
+    mask = image_data["layers"][0]
+
+    # preprocess image
+    image = image.convert("RGB").resize((image_width, image_height))
+    image_preprocessed = vae_processor.preprocess(  # pyright: ignore[reportUnknownMemberType,reportAssignmentType]
+        image=image,
+        width=image_width,
+        height=image_height,
+    )[0]
+
+    # preprocess mask
+    mask = mask.getchannel("A").resize((image_width, image_height))
+    mask_preprocessed = mask_processor.preprocess(  # pyright: ignore[reportUnknownMemberType]
+        image=mask,
+        width=image_width,
+        height=image_height,
+    )[0]
+
+    # generate the TryOff image
+    gen = torch.Generator(device=DEVICE).manual_seed(seed)
+    tryoff_image = pipeline_tryoff(
+        image_preprocessed,
+        mask_preprocessed,
+        inference_steps=num_inference_steps,
+        scale=condition_scale,
+        generator=gen,
+    )[0]
+
+    return tryoff_image
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(TITLE)
+
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.ImageMask(
+                label="Input Image",
+                height=1024,  # https://github.com/gradio-app/gradio/issues/10236
+                type="pil",
+                interactive=True,
+            )
+            run_button = gr.Button(
+                value="Extract Clothing",
+            )
+            gr.Examples(
+                examples=[
+                    ["examples/model_1.jpg"],
+                    ["examples/model_2.jpg"],
+                    ["examples/model_3.jpg"],
+                    ["examples/model_4.jpg"],
+                    ["examples/model_5.jpg"],
+                    ["examples/model_6.jpg"],
+                    ["examples/model_7.jpg"],
+                    ["examples/model_8.jpg"],
+                    ["examples/model_9.jpg"],
+                ],
+                inputs=[input_image],
+            )
+        with gr.Column():
+            output_image = gr.Image(
+                label="TryOff result",
+                height=1024,
+                image_mode="RGB",
+                type="pil",
+            )
+
+    with gr.Accordion("Advanced Settings", open=True):
+        seed = gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=100_000,
+            value=69_420,
+            step=1,
+        )
+        scale = gr.Slider(
+            label="Scale",
+            minimum=0.5,
+            maximum=5,
+            value=2.5,
+            step=0.05,
+        )
+        num_inference_steps = gr.Slider(
+            label="Number of inference steps",
+            minimum=1,
+            maximum=50,
+            value=25,
+            step=1,
+        )
+        with gr.Row():
+            image_width = gr.Slider(
+                label="Image Width",
+                minimum=64,
+                maximum=1024,
+                value=384,
+                step=8,
+            )
+            image_height = gr.Slider(
+                label="Image Height",
+                minimum=64,
+                maximum=1024,
+                value=512,
+                step=8,
+            )
+
+    run_button.click(
+        fn=process,
+        inputs=[
+            input_image,
+            image_width,
+            image_height,
+            num_inference_steps,
+            scale,
+            seed,
+        ],
+        outputs=output_image,
+    )
+
+demo.launch()

src/pipeline.py

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+# type: ignore
+# Inspired from https://github.com/ixarchakos/try-off-anyone/blob/aa3045453013065573a647e4536922bac696b968/src/model/pipeline.py
+# Inspired from https://github.com/ixarchakos/try-off-anyone/blob/aa3045453013065573a647e4536922bac696b968/src/model/attention.py
+
+import torch
+from accelerate import load_checkpoint_in_model
+from diffusers import AutoencoderKL, DDIMScheduler, UNet2DConditionModel
+from diffusers.models.attention_processor import AttnProcessor
+from diffusers.utils.torch_utils import randn_tensor
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+
+class Skip(torch.nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def __call__(
+        self,
+        attn: torch.Tensor,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor = None,
+        attention_mask: torch.Tensor = None,
+        temb: torch.Tensor = None,
+    ) -> torch.Tensor:
+        return hidden_states
+
+
+def fine_tuned_modules(unet: UNet2DConditionModel) -> torch.nn.ModuleList:
+    trainable_modules = torch.nn.ModuleList()
+
+    for blocks in [unet.down_blocks, unet.mid_block, unet.up_blocks]:
+        if hasattr(blocks, "attentions"):
+            trainable_modules.append(blocks.attentions)
+        else:
+            for block in blocks:
+                if hasattr(block, "attentions"):
+                    trainable_modules.append(block.attentions)
+
+    return trainable_modules
+
+
+def skip_cross_attentions(unet: UNet2DConditionModel) -> dict[str, AttnProcessor | Skip]:
+    attn_processors = {
+        name: unet.attn_processors[name] if name.endswith("attn1.processor") else Skip()
+        for name in unet.attn_processors.keys()
+    }
+    return attn_processors
+
+
+def encode(image: torch.Tensor, vae: AutoencoderKL) -> torch.Tensor:
+    image = image.to(memory_format=torch.contiguous_format).float().to(vae.device, dtype=vae.dtype)
+    with torch.no_grad():
+        return vae.encode(image).latent_dist.sample() * vae.config.scaling_factor
+
+
+class TryOffAnyone:
+    def __init__(
+        self,
+        device: torch.device,
+        dtype: torch.dtype,
+        concat_dim: int = -2,
+    ) -> None:
+        self.concat_dim = concat_dim
+        self.device = device
+        self.dtype = dtype
+
+        self.noise_scheduler = DDIMScheduler.from_pretrained(
+            pretrained_model_name_or_path="stable-diffusion-v1-5/stable-diffusion-inpainting",
+            subfolder="scheduler",
+        )
+        self.vae = AutoencoderKL.from_pretrained(
+            pretrained_model_name_or_path="stabilityai/sd-vae-ft-mse",
+        ).to(device, dtype=dtype)
+        self.unet = UNet2DConditionModel.from_pretrained(
+            pretrained_model_name_or_path="stable-diffusion-v1-5/stable-diffusion-inpainting",
+            subfolder="unet",
+            variant="fp16",
+        ).to(device, dtype=dtype)
+
+        self.unet.set_attn_processor(skip_cross_attentions(self.unet))
+        load_checkpoint_in_model(
+            model=fine_tuned_modules(unet=self.unet),
+            checkpoint=hf_hub_download(
+                repo_id="ixarchakos/tryOffAnyone",
+                filename="model.safetensors",
+            ),
+        )
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        image: torch.Tensor,
+        mask: torch.Tensor,
+        inference_steps: int,
+        scale: float,
+        generator: torch.Generator,
+    ) -> list[Image.Image]:
+        image = image.unsqueeze(0).to(self.device, dtype=self.dtype)
+        mask = (mask.unsqueeze(0) > 0.5).to(self.device, dtype=self.dtype)
+        masked_image = image * (mask < 0.5)
+
+        masked_latent = encode(masked_image, self.vae)
+        image_latent = encode(image, self.vae)
+        mask = torch.nn.functional.interpolate(mask, size=masked_latent.shape[-2:], mode="nearest")
+
+        masked_latent_concat = torch.cat([masked_latent, image_latent], dim=self.concat_dim)
+        mask_concat = torch.cat([mask, torch.zeros_like(mask)], dim=self.concat_dim)
+
+        latents = randn_tensor(
+            shape=masked_latent_concat.shape,
+            generator=generator,
+            device=self.device,
+            dtype=self.dtype,
+        )
+
+        self.noise_scheduler.set_timesteps(inference_steps, device=self.device)
+        timesteps = self.noise_scheduler.timesteps
+
+        if do_classifier_free_guidance := (scale > 1.0):
+            masked_latent_concat = torch.cat(
+                [
+                    torch.cat([masked_latent, torch.zeros_like(image_latent)], dim=self.concat_dim),
+                    masked_latent_concat,
+                ]
+            )
+
+            mask_concat = torch.cat([mask_concat] * 2)
+
+        extra_step = {"generator": generator, "eta": 1.0}
+        for t in timesteps:
+            input_latents = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            input_latents = self.noise_scheduler.scale_model_input(input_latents, t)
+
+            input_latents = torch.cat([input_latents, mask_concat, masked_latent_concat], dim=1)
+
+            noise_pred = self.unet(
+                input_latents,
+                t.to(self.device),
+                encoder_hidden_states=None,
+                return_dict=False,
+            )[0]
+
+            if do_classifier_free_guidance:
+                noise_pred_unc, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_unc + scale * (noise_pred_text - noise_pred_unc)
+
+            latents = self.noise_scheduler.step(noise_pred, t, latents, **extra_step).prev_sample
+
+        latents = latents.split(latents.shape[self.concat_dim] // 2, dim=self.concat_dim)[0]
+        latents = 1 / self.vae.config.scaling_factor * latents
+        image = self.vae.decode(latents.to(self.device, dtype=self.dtype)).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        image = (image * 255).round().astype("uint8")
+        image = [Image.fromarray(im) for im in image]
+
+        return image