Create app.py

app.py

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+import gradio as gr
+import torch
+from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler
+from diffusers.utils import export_to_video
+import spaces
+import os
+
+# 1. Load the Model Components
+print("Loading AnimateDiff-Lightning... this will be fast.")
+
+# Load the motion adapter (the "video" part of the brain)
+adapter = MotionAdapter.from_pretrained(
+    "ByteDance/AnimateDiff-Lightning-4step-T2V",
+    torch_dtype=torch.float16
+)
+
+# Load the base model (the "image" part of the brain)
+# We use epiCRealism for high-quality realistic style
+pipe = AnimateDiffPipeline.from_pretrained(
+    "emilianJR/epiCRealism",
+    motion_adapter=adapter,
+    torch_dtype=torch.float16
+)
+
+# Set up the scheduler specifically for Lightning (4-step generation)
+pipe.scheduler = EulerDiscreteScheduler.from_config(
+    pipe.scheduler.config, 
+    timestep_spacing="trailing", 
+    beta_schedule="linear"
+)
+
+# Move to GPU immediately to speed up loading (ZeroGPU handles the swap)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe.to(device)
+
+# 2. Define the Generation Function
+# @spaces.GPU ensures you get a powerful GPU for this function
+@spaces.GPU(duration=60)
+def generate_video(prompt, negative_prompt):
+    print(f"Generating video for: {prompt}")
+    
+    # Generate the video frames
+    output = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=4,  # Lightning needs only 4 steps!
+        guidance_scale=1.5,     # Keep guidance low for Lightning
+        num_frames=16,          # Standard length for AnimateDiff
+    )
+    
+    frames = output.frames[0]
+    
+    # Save to MP4
+    output_path = "output.mp4"
+    export_to_video(frames, output_path)
+    
+    return output_path
+
+# 3. Build the User Interface
+with gr.Blocks(theme="soft") as demo:
+    gr.Markdown("# ⚡ AnimateDiff Lightning (Free & Fast)")
+    gr.Markdown("A truly free, open-source video generator using ByteDance's Lightning technology. fast generation.")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt_input = gr.Textbox(
+                label="Prompt", 
+                placeholder="Close up portrait of a cyberpunk woman, neon city background, rainfall, 8k, realistic",
+                lines=3
+            )
+            neg_prompt_input = gr.Textbox(
+                label="Negative Prompt", 
+                value="bad quality, worst quality, deformed, distorted, watermark",
+                lines=2
+            )
+            generate_btn = gr.Button("⚡ Generate Video", variant="primary")
+        
+        with gr.Column():
+            video_output = gr.Video(label="Generated Result")
+
+    generate_btn.click(
+        fn=generate_video,
+        inputs=[prompt_input, neg_prompt_input],
+        outputs=video_output
+    )
+
+# Launch
+demo.launch()
+
+