app.py

app.py

@@ -0,0 +1,116 @@
+"""
+Waifu-Inpaint-XL Gradio App
+----------------------------
+Free-GPU-friendly inpainting UI for ShinoharaHare/Waifu-Inpaint-XL.
+Works as-is on: HF Spaces (ZeroGPU), Kaggle Notebooks, Google Colab.
+
+Setup:
+    pip install -r requirements.txt
+    huggingface-cli login   # needed once, model is gated
+
+Run:
+    python app.py
+"""
+
+import os
+import torch
+import gradio as gr
+from diffusers import StableDiffusionXLInpaintPipeline
+from PIL import Image
+
+MODEL_ID = "ShinoharaHare/Waifu-Inpaint-XL"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+DTYPE = torch.float16 if DEVICE == "cuda" else torch.float32
+
+pipe = None  # lazy-loaded so the UI opens instantly, model loads on first click
+
+
+def load_pipeline():
+    global pipe
+    if pipe is None:
+        pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
+            MODEL_ID,
+            torch_dtype=DTYPE,
+            use_safetensors=True,
+        )
+        pipe.to(DEVICE)
+        if DEVICE == "cuda":
+            pipe.enable_vae_slicing()
+            pipe.enable_attention_slicing()
+    return pipe
+
+
+def run_inpaint(
+    editor_value,          # gr.ImageEditor output: {"background":..., "layers":[...], "composite":...}
+    prompt,
+    negative_prompt,
+    steps,
+    guidance,
+    num_variations,
+    seed,
+):
+    if editor_value is None or editor_value.get("background") is None:
+        raise gr.Error("Upload an image first.")
+
+    base_image = editor_value["background"].convert("RGB")
+
+    # Build mask from the drawn layer (painted area = white = inpaint region)
+    if not editor_value.get("layers"):
+        raise gr.Error("Paint over the area you want to inpaint (use the brush tool).")
+    mask_layer = editor_value["layers"][0]
+    mask = mask_layer.split()[-1].convert("L")  # alpha channel -> grayscale mask
+
+    p = load_pipeline()
+
+    results = []
+    base_seed = int(seed) if seed >= 0 else torch.seed()
+    for i in range(int(num_variations)):
+        gen = torch.Generator(device=DEVICE).manual_seed(base_seed + i)
+        out = p(
+            prompt=prompt,
+            negative_prompt=negative_prompt or None,
+            image=base_image,
+            mask_image=mask,
+            num_inference_steps=int(steps),
+            guidance_scale=float(guidance),
+            height=base_image.height,
+            width=base_image.width,
+            generator=gen,
+        ).images[0]
+        results.append(out)
+
+    return results
+
+
+with gr.Blocks(title="Waifu-Inpaint-XL") as demo:
+    gr.Markdown("## Waifu-Inpaint-XL — paint a mask, describe the change, generate")
+
+    with gr.Row():
+        with gr.Column():
+            editor = gr.ImageEditor(
+                label="Upload image, then paint the mask (brush tool)",
+                type="pil",
+                brush=gr.Brush(colors=["#ffffff"], default_size=25),
+            )
+            prompt = gr.Textbox(label="Prompt", placeholder="orange striped sweater, red sparkle eyes")
+            negative_prompt = gr.Textbox(label="Negative prompt (optional)", value="blurry, low quality, extra limbs")
+            with gr.Row():
+                steps = gr.Slider(10, 50, value=28, step=1, label="Steps")
+                guidance = gr.Slider(1, 12, value=5.0, step=0.5, label="Guidance scale")
+            with gr.Row():
+                num_variations = gr.Slider(1, 6, value=1, step=1, label="Variations to generate")
+                seed = gr.Number(value=-1, label="Seed (-1 = random)")
+            run_btn = gr.Button("Generate", variant="primary")
+
+        with gr.Column():
+            gallery = gr.Gallery(label="Results", columns=3, height=500)
+
+    run_btn.click(
+        fn=run_inpaint,
+        inputs=[editor, prompt, negative_prompt, steps, guidance, num_variations, seed],
+        outputs=gallery,
+    )
+
+if __name__ == "__main__":
+    # share=True gives you a public URL for free when running on Colab/Kaggle
+    demo.launch(share=os.environ.get("GRADIO_SHARE", "true").lower() == "true")