app.py

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 ModelManager, SVDVideoPipeline, HunyuanDiTImagePipeline
from diffsynth import ModelManager
from diffusers.utils import load_image, export_to_video

import uuid

HF_TOKEN = os.environ.get("HF_TOKEN", None)
# Constants
#MAX_SEED = np.iinfo(np.int32).max
MAX_SEED= 2147483647
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")

    frames = 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
    ).frames[0]
    
    export_to_video(frames, 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()