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.dockerignore

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+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+env
+.venv
+.env
+.git
+.gitignore
+.cache
+.coverage
+htmlcov/
+.pytest_cache/

Dockerfile

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+# 使用带有CUDA的基础镜像
+FROM nvcr.io/nvidia/pytorch:23.10-py3
+
+# 设置工作目录
+WORKDIR /app
+
+# 安装系统依赖
+RUN apt-get update && apt-get install -y \
+    git \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+# 复制项目文件
+COPY requirements.txt .
+COPY app.py .
+
+# 安装Python依赖
+RUN pip install --no-cache-dir -U pip && \
+    pip install --no-cache-dir -r requirements.txt
+
+# 设置环境变量
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONPATH=/app \
+    HUGGINGFACE_HUB_CACHE=/app/cache \
+    HF_HOME=/app/cache \
+    HF_HUB_ENABLE_HF_TRANSFER=1 \
+    PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512
+
+# 创建缓存目录
+RUN mkdir -p /app/cache
+
+# 暴露端口
+EXPOSE 7860
+
+# 启动命令
+CMD ["python", "app.py"]

app.py

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+import os
+import gradio as gr
+from diffusers import DiffusionPipeline
+import torch
+from huggingface_hub import login
+
+# 初始化 Hugging Face 登录
+if os.getenv('HF_TOKEN'):
+    login(token=os.getenv('HF_TOKEN'))
+
+# 模型加载函数
+def load_model():
+    model_id = "IndexTeam/Index-anisora"
+    print("正在加载模型...")
+    
+    pipe = DiffusionPipeline.from_pretrained(
+        model_id,
+        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+        variant="fp16" if torch.cuda.is_available() else None,
+        use_safetensors=True
+    )
+    
+    if torch.cuda.is_available():
+        print("使用CUDA加速")
+        pipe = pipe.to("cuda")
+        pipe.enable_attention_slicing()
+        if hasattr(pipe, 'enable_xformers_memory_efficient_attention'):
+            pipe.enable_xformers_memory_efficient_attention()
+    else:
+        print("警告：未检测到CUDA，使用CPU模式运行")
+    
+    return pipe
+
+# 加载模型
+pipe = load_model()
+
+# 生成图像函数
+def generate_image(prompt, negative_prompt, steps=30, guidance_scale=7.5, seed=None):
+    if seed is not None and seed >= 0:
+        generator = torch.Generator("cuda" if torch.cuda.is_available() else "cpu").manual_seed(int(seed))
+    else:
+        generator = None
+    
+    try:
+        with torch.inference_mode():
+            print(f"生成图像: {prompt[:50]}...")
+            image = pipe(
+                prompt=prompt,
+                negative_prompt=negative_prompt,
+                num_inference_steps=steps,
+                guidance_scale=guidance_scale,
+                generator=generator
+            ).images[0]
+        return image
+    except Exception as e:
+        print(f"生成失败: {str(e)}")
+        raise gr.Error(f"生成图像时出错: {str(e)}")
+
+# 创建Gradio界面
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # Index-anisora 演示
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(
+                label="提示词",
+                value="1girl, beautiful anime style, detailed background",
+                placeholder="描述你想要生成的图像..."
+            )
+            negative_prompt = gr.Textbox(
+                label="负面提示词",
+                value="low quality, blurry, bad anatomy",
+                placeholder="描述你不希望在图像中出现的内容..."
+            )
+            
+            with gr.Row():
+                steps = gr.Slider(
+                    label="推理步数", 
+                    minimum=10, 
+                    maximum=50, 
+                    value=30, 
+                    step=1
+                )
+                guidance_scale = gr.Slider(
+                    label="引导强度", 
+                    minimum=1.0, 
+                    maximum=20.0, 
+                    value=7.5, 
+                    step=0.5
+                )
+                seed = gr.Number(
+                    label="随机种子", 
+                    value=-1,
+                    precision=0
+                )
+            
+            submit_btn = gr.Button("生成", variant="primary")
+        
+        with gr.Column():
+            output_image = gr.Image(label="生成结果")
+    
+    # 提交按钮事件
+    submit_btn.click(
+        fn=generate_image,
+        inputs=[prompt, negative_prompt, steps, guidance_scale, seed],
+        outputs=output_image
+    )
+
+# 启动应用
+if __name__ == "__main__":
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False
+    )

requirements.txt

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+torch>=2.0.0
+torchvision
+diffusers>=0.20.0
+transformers
+accelerate
+xformers
+gradio>=3.0.0
+huggingface-hub