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import gradio as gr |
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from model_pipelines import load_pipelines, generate_all |
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from grace_eval import compute_sample_scores, plot_radar |
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import torch |
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import time |
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from functools import partial |
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torch.set_grad_enabled(False) |
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torch.backends.cuda.is_available = lambda: False |
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class ModelLoader: |
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_instance = None |
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def __new__(cls): |
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if cls._instance is None: |
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cls._instance = super().__new__(cls) |
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cls._instance.models = None |
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return cls._instance |
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def load(self): |
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if self.models is None: |
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print("🔄 Initializing models...") |
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start = time.time() |
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self.models = load_pipelines() |
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print(f"✅ Models loaded in {time.time()-start:.1f}s") |
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return self.models |
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def create_interface(): |
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with gr.Blocks(title="🖼️ AI Image Generator Comparison", theme=gr.themes.Soft()) as demo: |
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gr.Markdown("## 🏆 图像生成模型对比实验 (CPU模式)") |
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with gr.Tab("🆚 Arena"): |
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with gr.Row(): |
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prompt = gr.Textbox(label="✨ 输入提示词", placeholder="描述您想生成的图像...") |
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with gr.Row(): |
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generate_btn = gr.Button("🚀 生成图像", variant="primary") |
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with gr.Row(): |
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outputs = [ |
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gr.Image(label="Stable Diffusion v1.5", type="pil"), |
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gr.Image(label="Openjourney v4", type="pil"), |
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gr.Image(label="LDM 256", type="pil") |
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] |
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generate_btn.click( |
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partial(generate_all, ModelLoader().load()), |
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inputs=prompt, |
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outputs=outputs |
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) |
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with gr.Tab("📊 Leaderboard"): |
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with gr.Column(): |
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eval_prompt = gr.Textbox(label="评估用提示词") |
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eval_btn = gr.Button("生成雷达图") |
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radar_img = gr.Image(label="GRACE评估结果") |
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eval_btn.click( |
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lambda p: (plot_radar(compute_sample_scores(None, p)) or "radar.png"), |
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inputs=eval_prompt, |
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outputs=radar_img |
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) |
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with gr.Tab("📝 Report"): |
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try: |
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with open("report.md", "r", encoding="utf-8") as f: |
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gr.Markdown(f.read()) |
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except: |
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gr.Markdown("## 实验报告\n报告加载失败") |
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return demo |
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if __name__ == "__main__": |
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create_interface().launch( |
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server_name="0.0.0.0", |
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server_port=7860, |
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show_error=True, |
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enable_queue=True |
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) |
