app.py

import gradio as gr
import torch
import os
import uuid
import random
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
from huggingface_hub import hf_hub_download


pipe = StableVideoDiffusionPipeline.from_pretrained(
    "stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16"
)
pipe.to("cuda")
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
max_64_bit_int = 2**63 - 1

# Function to sample video from the input image
def sample(
    image: Image,
    seed: Optional[int] = 42,
    randomize_seed: bool = True,
    motion_bucket_id: int = 127,
    fps_id: int = 6,
    version: str = "svd_xt",
    cond_aug: float = 0.02,
    decoding_t: int = 3,  # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
    device: str = "cuda",
    output_folder: str = "outputs",
):
    if image.mode == "RGBA":
        image = image.convert("RGB")
    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 = pipe(image, decode_chunk_size=decoding_t, generator=generator, 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)
    torch.manual_seed(seed)
    return video_path, seed

# Function to resize the uploaded image
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

# Dynamically load image files from the 'images' directory
def get_example_images():
    image_dir = "images/"
    image_files = glob(os.path.join(image_dir, "*.png")) + glob(os.path.join(image_dir, "*.jpg"))
    return image_files

# Gradio interface setup
with gr.Blocks() as demo:
    gr.Markdown('''# Stable Video Diffusion using Image 2 Video XT 
        #### Research release: generate `4s` vid from a single image at (`25 frames` at `6 fps`).''')

    with gr.Row():
        with gr.Column():
            image = gr.Image(label="Upload your image", type="pil")
            generate_btn = gr.Button("Generate")
        video = gr.Video()

    with gr.Accordion("Advanced options", open=False):
        seed = gr.Slider(label="Seed", value=42, 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", value=127, minimum=1, maximum=255)
        fps_id = gr.Slider(label="Frames per second", value=6, minimum=5, maximum=30)

    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, seed], api_name="video")

    # Dynamically load examples from the filesystem
    example_images = get_example_images()
    gr.Examples(
        examples=example_images,
        inputs=image,
        outputs=[video, seed],
        fn=sample,
        cache_examples=True,
    )

if __name__ == "__main__":
    demo.queue(max_size=20)
    demo.launch(share=True)