Create app.py

app.py

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+import gradio as gr
+import torch
+from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+from diffusers.utils import export_to_video
+
+# Initialize the diffusion pipeline
+pipe = DiffusionPipeline.from_pretrained(
+    "heboya8/text2video-test",
+    torch_dtype=torch.float16,
+    variant="fp16"
+)
+
+# Optimize for GPU memory
+pipe.enable_model_cpu_offload()
+pipe.enable_vae_slicing()
+
+def generate_video(prompt):
+    try:
+        # Generate video frames
+        video_frames = pipe(
+            prompt,
+            num_inference_steps=50,
+            num_frames=200
+        ).frames
+        
+        # Export frames to video file
+        video_path = export_to_video(video_frames, output_video_path="output_video.mp4")
+        return video_path
+    except Exception as e:
+        return f"Error generating video: {str(e)}"
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=generate_video,
+    inputs=gr.Textbox(
+        label="Enter your prompt",
+        placeholder="e.g., a flower in a garden"
+    ),
+    outputs=gr.Video(label="Generated Video"),
+    title="Text-to-Video Generator",
+    description="Enter a text prompt to generate a video using the diffusion model."
+)
+
+# Launch the app
+interface.launch()