app.py

import subprocess
subprocess.run(
    'pip install numpy==1.26.4',
    shell=True
)

import os
import gradio as gr
import torch
import spaces
import random
from PIL import Image
import numpy as np

from glob import glob
from pathlib import Path
from typing import Optional

from diffsynth import save_video, ModelManager, SVDVideoPipeline, HunyuanDiTImagePipeline

import uuid

HF_TOKEN = os.environ.get("HF_TOKEN", None)
# Constants
MAX_SEED = np.iinfo(np.int32).max
CSS = """
footer {
    visibility: hidden;
}
"""

JS = """function () {
  gradioURL = window.location.href
  if (!gradioURL.endsWith('?__theme=dark')) {
    window.location.replace(gradioURL + '?__theme=dark');
  }
}"""


# Ensure model and scheduler are initialized in GPU-enabled function
if torch.cuda.is_available():
    model_manager = ModelManager(
        torch_dtype=torch.float16, 
        device="cuda", 
        model_id_list=["stable-video-diffusion-img2vid-xt", "ExVideo-SVD-128f-v1"],
        downloading_priority=["HuggingFace"])
    pipe = SVDVideoPipeline.from_model_manager(model_manager)


@spaces.GPU(duration=120)
def generate(
    image,
    seed: Optional[int] = -1,
    motion_bucket_id: int = 127,
    fps_id: int = 25,
    num_inference_steps: int = 10,
    num_frames: int = 50,
    output_folder: str = "outputs",
    progress=gr.Progress(track_tqdm=True)):
    
    if seed == -1:
        seed = random.randint(0, MAX_SEED)
        
    image = Image.open(image)

    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")

    video = pipe(
        input_image=image.resize((512, 512)), 
        num_frames=num_frames, 
        fps=fps_id, 
        height=512, 
        width=512,
        motion_bucket_id=motion_bucket_id,
        num_inference_steps=num_inference_steps,
        min_cfg_scale=2, 
        max_cfg_scale=2, 
        contrast_enhance_scale=1.2
    )
    model_manager.to("cpu")
    
    save_video(video, video_path, fps=fps_id)
    
    return video_path, seed


examples = [
        "./train.jpg",
        "./girl.webp",
        "./robo.jpg",
    ]



# Gradio Interface

with gr.Blocks(css=CSS, js=JS, theme="soft") as demo:
    gr.HTML("<h1><center>Exvideo📽️</center></h1>")
    gr.HTML("<p><center><a href='https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1'>ExVideo</a> image-to-video generation<br><b>Update</b>: first version</center></p>")
    with gr.Row():
        image = gr.Image(label='Upload Image', height=600, scale=2, image_mode="RGB", type="filepath")
        video = gr.Video(label="Generated Video", height=600, scale=2)
        with gr.Accordion("Advanced Options", open=True):
            with gr.Column(scale=1):
                seed = gr.Slider(
                    label="Seed (-1 Random)",
                    minimum=-1,
                    maximum=MAX_SEED,
                    step=1,
                    value=-1,
                )
                motion_bucket_id = gr.Slider(
                    label="Motion bucket id", 
                    info="Controls how much motion to add/remove from the image", 
                    value=127, 
                    step=1,
                    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,
                    step=1,
                    minimum=5, 
                    maximum=30
                )
                num_inference_steps = gr.Slider(
                    label="Inference steps", 
                    info="Inference steps",
                    step=1,
                    value=10, 
                    minimum=1, 
                    maximum=50
                )
                num_frames = gr.Slider(
                    label="Frames num", 
                    info="Frames num",
                    step=1,
                    value=50, 
                    minimum=1, 
                    maximum=128
                )               
    with gr.Row():
        submit_btn = gr.Button(value="Generate")
        #stop_btn = gr.Button(value="Stop", variant="stop")
        clear_btn = gr.ClearButton([image, seed, video])
    gr.Examples(
        examples=examples,
        inputs=image,
        outputs=[video, seed],
        fn=generate,
        cache_examples="lazy",
        examples_per_page=4,
    )

    submit_event = submit_btn.click(fn=generate, inputs=[image, seed, motion_bucket_id, fps_id,num_inference_steps, num_frames], outputs=[video, seed], api_name="video")
    #stop_btn.click(fn=None, inputs=None, outputs=None, cancels=[submit_event])
    
demo.queue().launch()