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app.py

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+import gradio as gr
+import torch
+import json
+import os
+from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler
+from diffusers.utils import export_to_gif
+from huggingface_hub import InferenceClient, hf_hub_download
+from safetensors.torch import load_file
+import spaces
+
+# 1. 初始化 LLM 客户端 (使用 Hugging Face 免费的 Serverless API)
+client = InferenceClient("meta-llama/Llama-3.3-70B-Instruct")
+
+# 2. 初始化视频生成 Pipeline (AnimateDiff-Lightning)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+step = 4  # 4-step inference, fast and good quality
+repo = "ByteDance/AnimateDiff-Lightning"
+ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
+base = "emilianJR/epiCRealism"
+
+adapter = MotionAdapter().to(device, dtype)
+adapter.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device))
+pipe = AnimateDiffPipeline.from_pretrained(base, motion_adapter=adapter, torch_dtype=dtype).to(device)
+pipe.scheduler = EulerDiscreteScheduler.from_config(
+    pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear"
+)
+
+def generate_llm_content(query):
+    system_prompt = "You are a talented video creator. Generate a response in JSON format with 'title', 'cover_prompt', and 'video_prompt' (3s)."
+    user_prompt = f"User Query: {query}\n\nRequirements:\n- title: MAX 50 chars\n- cover_prompt: image description\n- video_prompt: 3s motion description\n\nReturn JSON ONLY."
+    
+    response = client.chat_completion(
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt}
+        ],
+        max_tokens=500,
+        response_format={"type": "json_object"}
+    )
+    return json.loads(response.choices[0].message.content)
+
+@spaces.GPU(duration=60) # 申请 ZeroGPU A100 资源
+def create_popcorn(query):
+    # Step 1: LLM 生成内容
+    content = generate_llm_content(query)
+    title = content.get("title", "Untitled Video")
+    video_prompt = content.get("video_prompt", query)
+    
+    # Step 2: 生成视频
+    print(f"Generating video for: {video_prompt}")
+    output = pipe(prompt=video_prompt, guidance_scale=1.0, num_inference_steps=4, num_frames=16)
+    
+    # 保存为 GIF (Gradio 支持展示)
+    video_path = "output_video.gif"
+    export_to_gif(output.frames[0], video_path)
+    
+    return title, content.get("cover_prompt"), video_prompt, video_path
+
+# 3. 构建 Gradio UI
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🍿 LLMPopcorn Demo")
+    gr.Markdown("Input a topic, and LLMPopcorn will generate the **Title**, **Prompts**, and a **3-second AI Video**.")
+    
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(label="Enter your video idea", placeholder="e.g., A futuristic city with flying cars")
+            btn = gr.Button("Generate Popcorn!", variant="primary")
+        
+        with gr.Column():
+            output_title = gr.Textbox(label="Generated Title")
+            output_video = gr.Image(label="Generated 3s Video (GIF)")
+            
+    with gr.Accordion("Prompt Details", open=False):
+        output_cover_prompt = gr.Textbox(label="Cover Prompt")
+        output_video_prompt = gr.Textbox(label="Video Prompt")
+
+    btn.click(
+        fn=create_popcorn,
+        inputs=[input_text],
+        outputs=[output_title, output_cover_prompt, output_video_prompt, output_video]
+    )
+
+if __name__ == "__main__":
+    demo.launch()