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
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+import spaces
+import os
+
+# 1. Hardware Detection (The Fix)
+# This checks if you have a GPU. If not, it switches to CPU mode (float32).
+if torch.cuda.is_available():
+    device = "cuda"
+    dtype = torch.float16
+    print("✅ GPU detected: Running in fast mode (float16)")
+else:
+    device = "cpu"
+    dtype = torch.float32
+    print("⚠️ No GPU detected: Running in slow mode (float32)")
+
+# 2. Load the Model Components
+print("Loading AnimateDiff-Lightning...")
+
+# STEP A: Load the standard adapter
+adapter = MotionAdapter.from_pretrained(
+    "guoyww/animatediff-motion-adapter-v1-5-2",
+    torch_dtype=dtype  # Use the detected smart type
+)
+
+# STEP B: Download the Lightning weights
+print("Downloading Lightning weights...")
+file_path = hf_hub_download(
+    repo_id="ByteDance/AnimateDiff-Lightning",
+    filename="animatediff_lightning_4step_diffusers.safetensors"
+)
+
+# STEP C: Apply the Lightning update
+adapter.load_state_dict(
+    load_file(file_path)
+)
+
+# STEP D: Load the base model
+pipe = AnimateDiffPipeline.from_pretrained(
+    "emilianJR/epiCRealism",
+    motion_adapter=adapter,
+    torch_dtype=dtype  # Use the detected smart type
+)
+
+# Set up the scheduler
+pipe.scheduler = EulerDiscreteScheduler.from_config(
+    pipe.scheduler.config, 
+    timestep_spacing="trailing", 
+    beta_schedule="linear"
+)
+
+# Move to the detected device
+pipe.to(device)
+
+# 3. Define the Generation Function
+# We wrap this in a try-except block to handle the @spaces decorator gracefully
+try:
+    @spaces.GPU(duration=60)
+    def generate_video(prompt, negative_prompt):
+        return run_inference(prompt, negative_prompt)
+except Exception:
+    # If @spaces fails (because we are on CPU), just run the function normally
+    def generate_video(prompt, negative_prompt):
+        return run_inference(prompt, negative_prompt)
+
+def run_inference(prompt, negative_prompt):
+    print(f"Generating video for: {prompt}")
+    
+    output = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=4, 
+        guidance_scale=1.5,
+        num_frames=16,
+    )
+    
+    frames = output.frames[0]
+    output_path = "output.mp4"
+    export_to_video(frames, output_path)
+    return output_path
+
+# 4. Build the UI (Removed 'theme' to fix your second error)
+with gr.Blocks() as demo:
+    gr.Markdown("# ⚡ AnimateDiff Lightning")
+    gr.Markdown("If this is running on CPU, it will take about 3-5 minutes per video.")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt_input = gr.Textbox(label="Prompt", lines=3)
+            neg_prompt_input = gr.Textbox(label="Negative Prompt", value="bad quality, deformed", lines=2)
+            generate_btn = gr.Button("Generate Video")
+        
+        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
+    )
+
+demo.launch()
+
+