app.py

import gradio as gr
import torch
import numpy as np
import modin.pandas as pd
from PIL import Image
from diffusers import DiffusionPipeline
from huggingface_hub import login
import os
from glob import glob
from pathlib import Path
from typing import Optional
import uuid
import random

token = os.environ['HF_TOKEN']
login(token=token)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
torch.cuda.max_memory_allocated(device=device)
torch.cuda.empty_cache()
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt-1-1")
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)

#pipe.enable_xformers_memory_efficient_attention()
#pipe = pipe.to(device)
torch.cuda.empty_cache()

max_64_bit_int = 2**63 - 1

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_1-1",
    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)
    torch.cuda.empty_cache()
    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)
    torch.cuda.empty_cache()
    return video_path, seed

def resize_image(image, output_size=(768, 512)):
    # Calculate aspect ratios
    target_aspect = output_size[0] / output_size[1]  # Aspect ratio of the desired size
    image_aspect = image.width / image.height  # Aspect ratio of the original image

    # Resize then crop if the original image is larger
    if image_aspect > target_aspect:
        # Resize the image to match the target height, maintaining aspect ratio
        new_height = output_size[1]
        new_width = int(new_height * image_aspect)
        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
        # Calculate coordinates for cropping
        left = (new_width - output_size[0]) / 2
        top = 0
        right = (new_width + output_size[0]) / 2
        bottom = output_size[1]
    else:
        # Resize the image to match the target width, maintaining aspect ratio
        new_width = output_size[0]
        new_height = int(new_width / image_aspect)
        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
        # Calculate coordinates for cropping
        left = 0
        top = (new_height - output_size[1]) / 2
        right = output_size[0]
        bottom = (new_height + output_size[1]) / 2

    # Crop the image
    cropped_image = resized_image.crop((left, top, right, bottom))
    torch.cuda.empty_cache()
    return cropped_image

with gr.Blocks() as demo:
  #gr.Markdown('''# Community demo for Stable Video Diffusion - Img2Vid - XT ([model](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt), [paper](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets), [stability's ui waitlist](https://stability.ai/contact))
#### Research release ([_non-commercial_](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/blob/main/LICENSE)): generate `4s` vid from a single image at (`25 frames` at `6 fps`). this demo uses [🧨 diffusers for low VRAM and fast generation](https://huggingface.co/docs/diffusers/main/en/using-diffusers/svd).
  #''')
  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", 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)
      
  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",)# inputs=image, outputs=[video, seed], fn=sample, cache_examples=True,)

if __name__ == "__main__":
    demo.queue(max_size=20, api_open=False)
    demo.launch(show_api=False)