app.py

import gradio as gr
import numpy as np
import random
import logging
import sys

# 设置日志记录
logging.basicConfig(level=logging.INFO, 
                    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
                    stream=sys.stdout)
logger = logging.getLogger(__name__)

# 修复 Gradio JSON Schema 错误
try:
    import gradio_client.utils
    # 添加对布尔值的检查
    original_get_type = gradio_client.utils.get_type
    def patched_get_type(schema):
        if isinstance(schema, bool):
            return "bool"
        if not isinstance(schema, dict):
            return "any"
        return original_get_type(schema)
    gradio_client.utils.get_type = patched_get_type
    logger.info("Successfully patched Gradio JSON schema processing")
except Exception as e:
    logger.error(f"Failed to patch Gradio: {str(e)}")

# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch

# 使用 try/except 避免在导入模块时出错
try:
    device = "cuda" if torch.cuda.is_available() else "cpu"
    model_repo_id = "stabilityai/sdxl-turbo"
    
    logger.info(f"Using device: {device}")
    logger.info(f"Loading model: {model_repo_id}")
    
    if torch.cuda.is_available():
        torch_dtype = torch.float16
    else:
        torch_dtype = torch.float32
    
    pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
    pipe = pipe.to(device)
    logger.info("Model loaded successfully")
    
    MAX_SEED = np.iinfo(np.int32).max
    MAX_IMAGE_SIZE = 1024
except Exception as e:
    logger.error(f"Error during setup: {str(e)}")
    # 不立即抛出异常，让 Gradio 界面可以加载

# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    try:
        logger.info(f"Processing prompt: {prompt}")
        
        if randomize_seed:
            seed = random.randint(0, MAX_SEED)
            
        logger.info(f"Using seed: {seed}, width: {width}, height: {height}")
        
        generator = torch.Generator().manual_seed(seed)

        image = pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            width=width,
            height=height,
            generator=generator,
        ).images[0]

        logger.info("Image generation successful")
        return image, seed
    except Exception as e:
        logger.error(f"Error in inference: {str(e)}")
        return None, seed  # 返回 None 而不是抛出异常

# 定义示例
examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
    "A delicious ceviche cheesecake slice",
]

# 简化 CSS
css = "#col-container { margin: 0 auto; max-width: 640px; }"

# 创建简化版的 Gradio 界面
with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("# Text-to-Image Generator")
        
        # 主输入区域
        prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt")
        run_button = gr.Button("Generate Image")
        
        # 结果显示
        result = gr.Image(label="Generated Image")
        seed_text = gr.Number(label="Seed Used")
        
        # 高级设置（折叠）
        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What to exclude from the image")
            
            # 种子设置
            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
            randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
            
            # 尺寸设置
            with gr.Row():
                width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
                height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
            
            # 生成参数
            with gr.Row():
                guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=10.0, step=0.1, value=0.0)
                num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=2)
        
        # 示例
        gr.Examples(examples, inputs=prompt)
    
    # 绑定事件处理
    run_button.click(
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed_text],
    )

# 启动应用
if __name__ == "__main__":
    try:
        logger.info("Starting Gradio app")
        demo.launch()
    except Exception as e:
        logger.error(f"Error launching app: {str(e)}")