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README.md

@@ -1,13 +1,18 @@
 ---
-title: Testroom
-emoji: 🏃
-colorFrom: indigo
-colorTo: gray
+title: DARKROOM
+emoji: 🎨
+colorFrom: gray
+colorTo: yellow
 sdk: gradio
-sdk_version: 6.15.2
-python_version: '3.12'
+sdk_version: 5.49.1
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# DARKROOM — AI art page restorer + HandRefiner (ZeroGPU)
+
+Serves the DARKROOM web tool and runs HandRefiner (MeshGraphormer depth +
+ControlNet inpainting) on a free, on-demand ZeroGPU. Open the Space and use it.
+
+**Setup:** New Space → SDK **Gradio** → Hardware **ZeroGPU** → upload `app.py`,
+`requirements.txt`, `README.md`.

app.py

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+"""
+DARKROOM HandRefiner — Hugging Face ZeroGPU Space
+=================================================
+Standard Gradio Interface (the pattern ZeroGPU actually supports): upload an
+image, optionally paint a mask, get the hands structurally fixed on a free
+on-demand GPU. This is the reliable shape — the previous "custom FastAPI route"
+build failed with "No @spaces.GPU function detected" because ZeroGPU only
+detects GPU functions wired into a normal Gradio app.
+
+PIPELINE: MeshGraphormer hand-mesh -> depth map -> depth ControlNet ->
+Stable Diffusion inpainting (HandRefiner). Fixes only the hand region.
+
+--------------------------------------------------------------------------
+DEPLOY  (needs a HF PRO account to CREATE a ZeroGPU Space — $9/mo)
+--------------------------------------------------------------------------
+1. huggingface.co -> New Space -> SDK: Gradio -> Hardware: ZeroGPU
+2. Upload: app.py, requirements.txt, README.md
+3. Wait for build, then use the Space UI (or call it from the DARKROOM tool
+   via the gradio_client endpoint shown on the Space's "View API" page).
+
+HONEST LIMITS:
+* Creating a ZeroGPU Space requires PRO. Using one is free within a daily quota
+  (resets 24h after first use); each fix is a few GPU-seconds.
+* GPU duration is capped (~120s max). We request 90s.
+* Stock depth ControlNet is okay-not-perfect; swap CONTROLNET_ID to
+  hr16/ControlNet-HandRefiner-pruned for finetuned quality.
+* MeshGraphormer can't fix unreadable hands or crossed fingers.
+"""
+
+import spaces                       # must precede torch for ZeroGPU
+import torch
+from PIL import Image, ImageFilter
+import gradio as gr
+
+SD_INPAINT_ID = "runwayml/stable-diffusion-inpainting"
+CONTROLNET_ID = "lllyasviel/control_v11f1p_sd15_depth"   # -> hr16/ControlNet-HandRefiner-pruned for best
+MESHGRAPHORMER_ID = "hr16/ControlNet-HandRefiner-pruned"
+MAX_SIDE = 768
+DEFAULT_PROMPT = "a detailed, anatomically correct hand with five fingers, natural proportions, same art style and lighting"
+NEG = "extra fingers, fused fingers, missing fingers, deformed, mutated, blurry, low quality"
+
+_PIPE = None
+_MESH = None
+
+def _load():
+    global _PIPE, _MESH
+    if _PIPE is not None:
+        return
+    from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, UniPCMultistepScheduler
+    from controlnet_aux import MeshGraphormerDetector
+    _MESH = MeshGraphormerDetector.from_pretrained(MESHGRAPHORMER_ID).to("cuda")
+    cn = ControlNetModel.from_pretrained(CONTROLNET_ID, torch_dtype=torch.float16)
+    pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
+        SD_INPAINT_ID, controlnet=cn, torch_dtype=torch.float16, safety_checker=None
+    )
+    pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+    _PIPE = pipe.to("cuda")
+
+def _fit(img):
+    w, h = img.size
+    s = min(1.0, MAX_SIDE / max(w, h))
+    return img.resize((max(8, int(round(w*s/8))*8), max(8, int(round(h*s/8))*8)), Image.LANCZOS), (w, h)
+
+@spaces.GPU(duration=90)
+def fix_hands(image, mask_layers, prompt, strength):
+    """ZeroGPU-allocated worker, wired directly into the Gradio Interface below."""
+    if image is None:
+        raise gr.Error("Upload an image first.")
+    _load()
+    init, (ow, oh) = _fit(image.convert("RGB"))
+    W, H = init.size
+
+    # optional hand-drawn mask from the ImageMask/Sketchpad component
+    sent_mask = None
+    if isinstance(mask_layers, dict):
+        layers = mask_layers.get("layers") or []
+        if layers:
+            m = layers[0].convert("L").resize((W, H), Image.LANCZOS)
+            if m.getbbox() is not None:
+                sent_mask = m
+
+    mg = _MESH(init)
+    depth_img, auto_mask = (mg[0], (mg[1] if len(mg) > 1 else None)) if isinstance(mg, tuple) else (mg, None)
+    depth_img = depth_img.convert("RGB").resize((W, H), Image.LANCZOS)
+    mask_img = sent_mask or (auto_mask.convert("L").resize((W, H), Image.LANCZOS) if auto_mask else None)
+    if mask_img is None:
+        raise gr.Error("No hands detected. Paint a mask over the hand and try again.")
+
+    mask_img = mask_img.filter(ImageFilter.GaussianBlur(2))
+    out = _PIPE(
+        prompt=prompt or DEFAULT_PROMPT, negative_prompt=NEG, image=init, mask_image=mask_img,
+        control_image=depth_img, num_inference_steps=30, strength=float(strength),
+        guidance_scale=7.5, controlnet_conditioning_scale=0.7,
+    ).images[0]
+    return out.resize((ow, oh), Image.LANCZOS)
+
+with gr.Blocks(title="DARKROOM HandRefiner", theme=gr.themes.Base()) as demo:
+    gr.Markdown("## 🖐️ DARKROOM HandRefiner\nUpload AI art with bad hands. It auto-detects hands "
+                "(MeshGraphormer) and regenerates them with correct geometry. Optionally paint a mask "
+                "to target a specific hand. Free GPU runs a few seconds per fix.")
+    with gr.Row():
+        with gr.Column():
+            inp = gr.ImageMask(type="pil", label="Image (optionally paint over the bad hand)")
+            prompt = gr.Textbox(value=DEFAULT_PROMPT, label="Prompt", lines=2)
+            strength = gr.Slider(0.3, 1.0, value=0.75, step=0.05, label="Fix strength (denoise)")
+            btn = gr.Button("Fix hands", variant="primary")
+        with gr.Column():
+            out = gr.Image(type="pil", label="Result")
+    btn.click(fix_hands, inputs=[inp, inp, prompt, strength], outputs=out, api_name="fix_hands")
+
+if __name__ == "__main__":
+    demo.queue().launch()

requirements-2.txt

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+spaces
+gradio==5.49.1
+torch
+diffusers
+transformers
+accelerate
+controlnet_aux
+pillow
+numpy
+scipy
+python-multipart