app.py

import gradio as gr
import spaces
#import gradio.helpers
import torch
import os
from glob import glob
from pathlib import Path
from typing import Optional

# from diffusers import StableVideoDiffusionPipeline
from kandinsky import get_T2V_pipeline
from diffusers.utils import load_image, export_to_video
from PIL import Image

import uuid
import random
from huggingface_hub import hf_hub_download

#gradio.helpers.CACHED_FOLDER = '/data/cache'

# pipe = StableVideoDiffusionPipeline.from_pretrained(
#     "multimodalart/stable-video-diffusion", torch_dtype=torch.float16, variant="fp16"
# )
# pipe.to("cuda")
#pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
#pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True)

device_map = {
    "dit": torch.device('cuda'), 
    "vae": torch.device('cuda'), 
    "text_embedder": torch.device('cuda')
}
pipe = get_T2V_pipeline(device_map)

max_64_bit_int = 2**63 - 1

@spaces.GPU(duration=120)
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",
    progress=gr.Progress(track_tqdm=True)
):
    # 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]
    frames = pipe(
        seed=seed,
        time_length=12,
        width = 672,
        height = 384,
        save_path=video_path,
        text=prompt,
    )
    export_to_video(frames, video_path, fps=8)
    torch.manual_seed(seed)

    return video_path, seed

def resize_image(image, output_size=(1024, 576)):
    # 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))
    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")
  gr.Examples(
    examples=[
        "images/blink_meme.png",
        "images/confused2_meme.png",
        "images/disaster_meme.png",
        "images/distracted_meme.png",
        "images/hide_meme.png",
        "images/nazare_meme.png",
        "images/success_meme.png",
        "images/willy_meme.png",
        "images/wink_meme.png"
    ],
    inputs=image,
    outputs=[video, seed],
    fn=sample,
    cache_examples="lazy",
  )

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