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import gradio as gr
import spaces
import torch
import os
from glob import glob
from diffusers import StableVideoDiffusionPipeline
from diffusers.utils import export_to_video
from PIL import Image
output_folder = "outputs"
pipe = StableVideoDiffusionPipeline.from_pretrained(
"stabilityai/stable-video-diffusion-img2vid-xt", variant="fp16"
).to("cuda")
@spaces.GPU(duration=480)
def sample(
image: Image,
width: int = 1024,
height: int = 576,
motion_bucket_id: int = 127,
fps_id: int = 30,
):
width = int(width)
height = int(height)
img = image.resize((width, height))
os.makedirs(output_folder, exist_ok=True)
base_count = len(glob(os.path.join(output_folder, "*.mp4")))
video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
frames = pipe(
img,
decode_chunk_size=3,
generator=None,
motion_bucket_id=motion_bucket_id,
noise_aug_strength=0.1,
num_frames=25,
).frames[0]
export_to_video(frames, video_path, fps=fps_id)
return video_path
with gr.Blocks() as demo:
with gr.Row():
image = gr.Image(label="Upload your image", type="pil")
video = gr.Video()
with gr.Column():
generate_btn = gr.Button("Generate")
with gr.Accordion("Advanced options", open=False):
width = gr.Number(label="Width", value=1024, minimum=1)
height = gr.Number(label="Height", value=576, minimum=1)
motion_bucket_id = gr.Slider(
label="Motion bucket id",
info="Controls how much motion to add/remove from the image",
value=60,
minimum=1,
maximum=255,
)
fps_id = gr.Slider(
label="Frames per second",
info="Video length will be 25 frames.",
value=30,
minimum=5,
step=2,
maximum=60,
)
image.upload(fn=lambda img: img, inputs=image, outputs=image, queue=False)
generate_btn.click(
fn=sample,
inputs=[image, width, height, motion_bucket_id, fps_id],
outputs=[video],
api_name="video",
)
if __name__ == "__main__":
demo.queue(max_size=20, api_open=False)
demo.launch(show_api=False) |