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import gradio as gr
import torch
from diffusers import AutoencoderKLWan, WanPipeline
from diffusers.utils import export_to_video
import spaces # ZeroGPU integration
@spaces.GPU # This decorator will request a GPU during initialization
def load_pipeline():
model_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
print("Loading model. This may take several minutes...")
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
pipe.to("cuda")
print("Model loaded successfully.")
return pipe
# Preload the model during startup
PIPELINE = load_pipeline()
def generate_video(prompt, negative_prompt=""):
# Use the globally preloaded PIPELINE
output = PIPELINE(
prompt=prompt,
negative_prompt=negative_prompt,
height=480, # 480p height
width=832, # Suitable width for 480p videos
num_frames=81, # Adjust number of frames for desired video length
guidance_scale=5.0 # Recommended guidance scale for the 1.3B model
).frames[0]
video_path = "output.mp4"
export_to_video(output, video_path, fps=15)
return video_path
# Create the Gradio interface
iface = gr.Interface(
fn=generate_video,
inputs=[
gr.Textbox(label="Prompt", placeholder="Enter your video prompt here"),
gr.Textbox(label="Negative Prompt", placeholder="Optional negative prompt", value="")
],
outputs=gr.Video(label="Generated Video"),
title="Wan2.1-T2V-1.3B Video Generator",
description="Generate 480p videos using the Wan2.1-T2V-1.3B diffusers pipeline with ZeroGPU support."
)
if __name__ == "__main__":
iface.launch()
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