app.py

import gradio as gr
import torch
import os
from glob import glob
from pathlib import Path
from typing import Optional
from diffusers import StableVideoDiffusionPipeline
from diffusers.utils import load_image, export_to_video
from PIL import Image
import uuid
import random
from huggingface_hub import hf_hub_download
import spaces


MAX_64_BIT_INT = 2**63 - 1
DEFAULT_SEED = 42
DEFAULT_OUTPUT_FOLDER = "outputs"


pipe = StableVideoDiffusionPipeline.from_pretrained("vdo/stable-video-diffusion-img2vid-xt-1-1", torch_dtype=torch.float16, variant="fp16")
pipe.to("cpu")


def resize_image(image, output_size=(1024, 576)):
    target_aspect = output_size[0] / output_size[1]
    image_aspect = image.width / image.height

    if image_aspect > target_aspect:
        new_height = output_size[1]
        new_width = int(new_height * image_aspect)
        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
        left = (new_width - output_size[0]) / 2
        top = 0
        right = (new_width + output_size[0]) / 2
        bottom = output_size[1]
    else:
        new_width = output_size[0]
        new_height = int(new_width / image_aspect)
        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
        left = 0
        top = (new_height - output_size[1]) / 2
        right = output_size[0]
        bottom = (new_height + output_size[1]) / 2

    cropped_image = resized_image.crop((left, top, right, bottom))
    return cropped_image


def generate_video(image, seed, motion_bucket_id, fps_id):
    if image.mode == "RGBA":
        image = image.convert("RGB")

    generator = torch.manual_seed(seed)
    frames = pipe(image, decode_chunk_size=3, generator=generator, motion_bucket_id=motion_bucket_id, noise_aug_strength=0.1, num_frames=25).frames[0]
    return frames


def export_video(frames, video_path, fps_id):
    export_to_video(frames, video_path, fps=fps_id)


@spaces.GPU(duration=120)
def sample(
    image,
    seed=DEFAULT_SEED,
    randomize_seed=True,
    motion_bucket_id=127,
    fps_id=6,
    version="svd_xt",
    cond_aug=0.02,
    decoding_t=3,
    device="cuda",
    output_folder=DEFAULT_OUTPUT_FOLDER,
):
    if randomize_seed:
        seed = random.randint(0, MAX_64_BIT_INT)
    generator = torch.manual_seed(seed)

    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 = generate_video(image, seed, motion_bucket_id, fps_id)
    export_video(frames, video_path, fps_id)
    torch.manual_seed(seed)

    return video_path, frames, seed


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            image = gr.Image(label="Upload your image", type="pil")
            with gr.Accordion("Advanced options", open=False):
                seed = gr.Slider(label="Seed", value=DEFAULT_SEED, randomize=True, minimum=0, maximum=MAX_64_BIT_INT, step=1)
                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
                motion_bucket_id = gr.Slider(label="Motion bucket id", info="Controls how much motion to add/remove from the image", value=127, minimum=1, maximum=255)
                fps_id = gr.Slider(label="Frames per second", info="The length of your video in seconds will be 25/fps", value=6, minimum=5, maximum=30)
            generate_btn = gr.Button(value="Animate", variant="primary")
        with gr.Column():
            video = gr.Video(label="Generated video")
            gallery = gr.Gallery(label="Generated frames")

    image.upload(fn=resize_image, inputs=image, outputs=image, queue=False)
    generate_btn.click(fn=sample, inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id], outputs=[video, gallery, seed], api_name="video")


if __name__ == "__main__":
    demo.launch(share=True, show_api=False)