Upload app.py

---
sdk: gradio
sdk_version: 4.16.0
app_file: OOTDiffusion-main/app.py
---
```
4. Commit — HF will now look inside the subfolder

---

### ⚠️ Bigger Issue I Also Notice

Your whole OOTDiffusion repo is **inside a subfolder** (`OOTDiffusion-main/`). This means the `app.py` path imports like `from ootd.inference_ootd_hd import ...` may also break.

**Best fix long-term:** Move all contents of `OOTDiffusion-main/` up to the root so your Space looks like:
```
OOODdiffusion/
├── app.py ← root level
├── ootd/
├── run/
├── preprocess/
├── checkpoints/
├── requirements.txt
└── README.md

app.py

@@ -0,0 +1,238 @@
+"""
+app.py — OOTDiffusion Hugging Face Space
+Place this file in the ROOT of your Space repo alongside the
+OOTDiffusion source folders: ootd/, run/, preprocess/, checkpoints/
+
+README.md front-matter required:
+---
+title: OOTDiffusion Virtual Try-On
+emoji: 👗
+colorFrom: purple
+colorTo: pink
+sdk: gradio
+sdk_version: 4.16.0
+app_file: app.py
+pinned: false
+license: cc-by-nc-sa-4.0
+---
+"""
+
+import sys
+import os
+
+# ── Path setup ────────────────────────────────────────────────────────────────
+ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
+RUN_DIR  = os.path.join(ROOT_DIR, "run")
+sys.path.insert(0, ROOT_DIR)
+sys.path.insert(0, RUN_DIR)
+
+import torch
+import numpy as np
+import gradio as gr
+from PIL import Image
+
+# ── Device ────────────────────────────────────────────────────────────────────
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"[OOTDiffusion] Device: {DEVICE}")
+
+# ── Lazy-load models (loaded once on first request) ───────────────────────────
+_pipe_hd = None   # VITON-HD  — half-body
+_pipe_dc = None   # Dress Code — full-body
+
+
+def load_pipeline(model_type: str):
+    """Import and cache the requested OOTDiffusion pipeline."""
+    global _pipe_hd, _pipe_dc
+
+    if model_type == "hd":
+        if _pipe_hd is None:
+            from ootd.inference_ootd_hd import OOTDiffusionHD
+            print("[OOTDiffusion] Loading HD pipeline …")
+            _pipe_hd = OOTDiffusionHD(ROOT_DIR)
+        return _pipe_hd
+
+    else:  # dc
+        if _pipe_dc is None:
+            from ootd.inference_ootd_dc import OOTDiffusionDC
+            print("[OOTDiffusion] Loading DC pipeline …")
+            _pipe_dc = OOTDiffusionDC(ROOT_DIR)
+        return _pipe_dc
+
+
+# ── Category mapping ──────────────────────────────────────────────────────────
+CATEGORY_MAP = {
+    "Upper-body":  0,
+    "Lower-body":  1,
+    "Dress":       2,
+}
+
+
+# ── Main inference function ───────────────────────────────────────────────────
+def run_tryon(
+    model_image,
+    cloth_image,
+    model_type,
+    category_label,
+    n_samples,
+    n_steps,
+    guidance_scale,
+    seed,
+):
+    if model_image is None:
+        raise gr.Error("Please upload a model (person) image.")
+    if cloth_image is None:
+        raise gr.Error("Please upload a garment image.")
+
+    # Convert to PIL just in case Gradio passes numpy arrays
+    if isinstance(model_image, np.ndarray):
+        model_image = Image.fromarray(model_image)
+    if isinstance(cloth_image, np.ndarray):
+        cloth_image = Image.fromarray(cloth_image)
+
+    model_image = model_image.convert("RGB")
+    cloth_image = cloth_image.convert("RGB")
+
+    category_idx = CATEGORY_MAP[category_label]
+
+    try:
+        pipe = load_pipeline(model_type)
+    except Exception as e:
+        raise gr.Error(
+            f"Failed to load model: {e}\n"
+            "Make sure checkpoints/ and ootd/ folders are present."
+        )
+
+    try:
+        if model_type == "hd":
+            result = pipe(
+                model_type="hd",
+                category=category_idx,
+                image_garm=cloth_image,
+                image_vton=model_image,
+                mask=None,
+                image_ori=model_image,
+                num_samples=int(n_samples),
+                num_steps=int(n_steps),
+                guidance_scale=guidance_scale,
+                seed=int(seed),
+            )
+        else:
+            result = pipe(
+                model_type="dc",
+                category=category_idx,
+                image_garm=cloth_image,
+                image_vton=model_image,
+                mask=None,
+                image_ori=model_image,
+                num_samples=int(n_samples),
+                num_steps=int(n_steps),
+                guidance_scale=guidance_scale,
+                seed=int(seed),
+            )
+    except Exception as e:
+        raise gr.Error(f"Inference failed: {e}")
+
+    # result is expected to be a list of PIL Images
+    if isinstance(result, (list, tuple)):
+        return result
+    return [result]
+
+
+# ── Gradio UI ─────────────────────────────────────────────────────────────────
+with gr.Blocks(title="OOTDiffusion Virtual Try-On", theme=gr.themes.Soft()) as demo:
+
+    gr.Markdown(
+        """
+        # 👗 OOTDiffusion — Virtual Try-On
+        **[AAAI 2025]** Upload a *model photo* and a *garment image*, choose settings, and click **Run Try-On**.
+        > ⚠️ Non-commercial use only (CC-BY-NC-SA-4.0)
+        """
+    )
+
+    with gr.Row():
+        # ── Left column: inputs ───────────────────────────────────────────────
+        with gr.Column(scale=1):
+            model_img = gr.Image(
+                label="Model Image (person)",
+                type="pil",
+                height=400,
+            )
+            cloth_img = gr.Image(
+                label="Garment Image (clothing)",
+                type="pil",
+                height=400,
+            )
+
+        # ── Middle column: settings ───────────────────────────────────────────
+        with gr.Column(scale=1):
+            model_type = gr.Radio(
+                choices=["hd", "dc"],
+                value="hd",
+                label="Model Type",
+                info="hd = half-body (VITON-HD) | dc = full-body (Dress Code)",
+            )
+            category = gr.Dropdown(
+                choices=list(CATEGORY_MAP.keys()),
+                value="Upper-body",
+                label="Garment Category",
+                info="Only used when Model Type is 'dc'",
+            )
+            n_samples = gr.Slider(
+                minimum=1, maximum=4, step=1, value=1,
+                label="Number of Samples",
+            )
+            n_steps = gr.Slider(
+                minimum=10, maximum=40, step=5, value=20,
+                label="Denoising Steps",
+                info="More steps = better quality but slower",
+            )
+            guidance_scale = gr.Slider(
+                minimum=1.0, maximum=5.0, step=0.5, value=2.0,
+                label="Guidance Scale",
+            )
+            seed = gr.Number(
+                value=42,
+                label="Seed (-1 = random)",
+                precision=0,
+            )
+
+            run_btn = gr.Button("🚀 Run Try-On", variant="primary")
+
+        # ── Right column: outputs ─────────────────────────────────────────────
+        with gr.Column(scale=1):
+            output_gallery = gr.Gallery(
+                label="Try-On Results",
+                columns=2,
+                height=500,
+                object_fit="contain",
+            )
+
+    gr.Markdown(
+        """
+        ### Tips
+        - **HD model**: best for upper-body garments on half-body photos
+        - **DC model**: supports upper-body / lower-body / dress on full-body photos
+        - Increasing **steps** to 30–40 noticeably improves quality
+        - Set **seed = -1** for random results each run
+        """
+    )
+
+    # ── Wire up the button ────────────────────────────────────────────────────
+    run_btn.click(
+        fn=run_tryon,
+        inputs=[
+            model_img,
+            cloth_img,
+            model_type,
+            category,
+            n_samples,
+            n_steps,
+            guidance_scale,
+            seed,
+        ],
+        outputs=output_gallery,
+    )
+
+# ── Launch ────────────────────────────────────────────────────────────────────
+if __name__ == "__main__":
+    demo.launch()