Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+import torch
+from diffusers import DiffusionPipeline
+from utils import generate_canny
+
+# 加载模型
+pipe = DiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-3.5-large-controlnet-canny",
+    torch_dtype=torch.float16,
+    variant="fp16"
+).to("cuda")
+
+# 推理函数
+def infer(image: Image.Image, prompt: str, neg_prompt: str = "bad face, blurry, text, watermark") -> Image.Image:
+    control = generate_canny(image)
+    result = pipe(
+        prompt=prompt,
+        negative_prompt=neg_prompt,
+        controlnet_conditioning_image=control,
+        num_inference_steps=30,
+        guidance_scale=9.0,
+    ).images[0]
+    return result
+
+# Gradio 界面
+demo = gr.Interface(
+    fn=infer,
+    inputs=[
+        gr.Image(type="pil", label="上传原图（自动转边缘图）"),
+        gr.Textbox(lines=2, label="Prompt（描述服装/风格）", value="a woman wearing a spring coat, photorealistic, soft lighting, 8k"),
+        gr.Textbox(lines=1, label="Negative Prompt", value="bad face, cropped, lowres, extra limbs")
+    ],
+    outputs=gr.Image(type="pil", label="生成结果"),
+    title="👗 ControlNet-Canny 高质量穿搭图生成器（SD3.5）",
+    description="上传图像 + 输入Prompt，即可生成结构一致的高清穿搭图（Stable Diffusion 3.5 + ControlNet）",
+    examples=[["example.jpg", "a fashion photo of a woman wearing a trench coat, full body, 8k"]],
+)
+
+demo.launch()