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README.md

@@ -1,13 +1,12 @@
 ---
-title: Image Outpaint
-emoji: 💻
-colorFrom: indigo
-colorTo: purple
+title: YDWD Image Outpaint
+emoji: 🖼️
+colorFrom: yellow
+colorTo: green
 sdk: gradio
-sdk_version: 6.24.0
-python_version: '3.13'
 app_file: app.py
 pinned: false
+license: openrail
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Dedicated masked outpainting backend for AI-IMAGE-YDWD.

app.py

@@ -0,0 +1,111 @@
+import random
+
+import gradio as gr
+import numpy as np
+import spaces
+import torch
+from diffusers import AutoPipelineForInpainting
+from PIL import Image, ImageFilter
+
+
+MODEL_ID = "diffusers/stable-diffusion-xl-1.0-inpainting-0.1"
+MAX_PIXELS = 1024 * 1024
+
+pipe = AutoPipelineForInpainting.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.float16,
+    variant="fp16",
+)
+pipe.enable_vae_tiling()
+pipe.enable_vae_slicing()
+pipe.to("cuda")
+
+
+def _fit_size(width: int, height: int) -> tuple[int, int]:
+    scale = min(1.0, (MAX_PIXELS / max(1, width * height)) ** 0.5)
+    fitted_w = max(64, int(round(width * scale / 8)) * 8)
+    fitted_h = max(64, int(round(height * scale / 8)) * 8)
+    return fitted_w, fitted_h
+
+
+@spaces.GPU(duration=120)
+def outpaint(
+    image: Image.Image,
+    mask: Image.Image,
+    prompt: str,
+    negative_prompt: str,
+    seed: int,
+    steps: int,
+    guidance_scale: float,
+) -> Image.Image:
+    if image is None or mask is None:
+        raise gr.Error("需要原图和外扩蒙版")
+
+    image = image.convert("RGB")
+    mask = mask.convert("L")
+    original_size = image.size
+    run_size = _fit_size(*original_size)
+    run_image = image.resize(run_size, Image.Resampling.LANCZOS)
+
+    # Keep the center protected and soften only the generated-side boundary.
+    run_mask = mask.resize(run_size, Image.Resampling.NEAREST)
+    run_mask = run_mask.point(lambda value: 255 if value >= 128 else 0)
+    run_mask = run_mask.filter(ImageFilter.GaussianBlur(radius=2.0))
+
+    seed = int(seed) if int(seed) > 0 else random.randint(0, 2**31 - 1)
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+    result = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        image=run_image,
+        mask_image=run_mask,
+        width=run_size[0],
+        height=run_size[1],
+        strength=1.0,
+        guidance_scale=float(guidance_scale),
+        num_inference_steps=int(steps),
+        generator=generator,
+    ).images[0]
+
+    if result.size != original_size:
+        result = result.resize(original_size, Image.Resampling.LANCZOS)
+    return result
+
+
+with gr.Blocks(title="YDWD Outpaint") as demo:
+    gr.Markdown("# YDWD Outpaint Backend")
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="Image")
+        mask_input = gr.Image(type="pil", image_mode="L", label="Mask")
+        output = gr.Image(type="pil", label="Result")
+    prompt_input = gr.Textbox(
+        value="Seamlessly extend the existing photograph into the masked outer area. Continue the nearby background, lighting, perspective, textures and environment naturally. Keep the original composition and person unchanged.",
+        label="Prompt",
+    )
+    negative_input = gr.Textbox(
+        value="duplicate person, repeated person, extra person, duplicated body, repeated image, collage, split image, seam, border, text, watermark, deformed anatomy",
+        label="Negative prompt",
+    )
+    seed_input = gr.Number(value=0, precision=0, label="Seed")
+    steps_input = gr.Slider(15, 35, value=24, step=1, label="Steps")
+    guidance_input = gr.Slider(3.0, 10.0, value=7.0, step=0.5, label="Guidance")
+    run_button = gr.Button("Outpaint", variant="primary")
+    run_button.click(
+        outpaint,
+        inputs=[
+            image_input,
+            mask_input,
+            prompt_input,
+            negative_input,
+            seed_input,
+            steps_input,
+            guidance_input,
+        ],
+        outputs=output,
+        api_name="outpaint",
+        concurrency_limit=1,
+    )
+
+
+if __name__ == "__main__":
+    demo.launch(show_error=True)

requirements.txt

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+diffusers>=0.36.0
+transformers>=4.51.0
+accelerate>=1.2.0
+safetensors>=0.4.5
+spaces>=0.40.0
+gradio>=5.0,<7.0