app.py

import torch
import gradio as gr
from diffusers import StableDiffusionPipeline

# Load model manually from Hugging Face model hub or your uploaded files
model_path = "sarthak247/Wan2.1-T2V-1.3B-nf4"  # Replace with your model path
pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
pipe.to("cuda")  # If running on GPU

def generate_video(prompt):
    """
    Generates a video from the provided prompt using the pre-loaded model.
    """
    try:
        # Generate video using the model pipeline
        video = pipe(prompt).videos[0]  # Assuming output is a video tensor
        
        # Return the generated video
        return video

    except Exception as e:
        print(f"Error during video generation: {e}")
        return "Error generating video"

# Gradio UI for video generation
iface = gr.Interface(
    fn=generate_video,
    inputs=gr.Textbox(label="Enter Text Prompt"),
    outputs=gr.Video(label="Generated Video"),
    title="Text-to-Video Generation with Wan2.1-T2V",
    description="This app generates a video based on the text prompt using the Wan2.1-T2V model."
)

# Launch the Gradio app
iface.launch()