app.py

import gradio as gr
import os
import subprocess
import cv2
from moviepy.editor import VideoFileClip, concatenate_videoclips
import math

from huggingface_hub import snapshot_download

model_ids = [
    'runwayml/stable-diffusion-v1-5',
    'lllyasviel/sd-controlnet-depth', 
    'lllyasviel/sd-controlnet-canny', 
    'lllyasviel/sd-controlnet-openpose',
]
for model_id in model_ids:
    model_name = model_id.split('/')[-1]
    snapshot_download(model_id, local_dir=f'checkpoints/{model_name}')



def get_frame_count_in_duration(filepath):
    video = cv2.VideoCapture(filepath)
    fps = video.get(cv2.CAP_PROP_FPS)
    frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
    duration = frame_count / fps
    width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
    video.release()
    return gr.update(maximum=frame_count)


def cut_mp4_into_chunks(input_file, chunk_size):
    video = VideoFileClip(input_file)
    frame_count = int(video.fps * video.duration)
    num_chunks = (frame_count + chunk_size - 1) // chunk_size  # Ceiling division

    chunks = []

    for i in range(num_chunks):
        start_frame = i * chunk_size
        end_frame = min((i + 1) * chunk_size, frame_count)
        chunk = video.subclip(start_frame / video.fps, end_frame / video.fps)
        chunk_frame_count = end_frame - start_frame
        chunks.append((chunk, chunk_frame_count))

    return chunks

def run_inference(prompt, video_path, condition, video_length):
    chunk_size = 12
    chunks = cut_mp4_into_chunks(video_path, chunk_size)

    output_path = 'output/'
    os.makedirs(output_path, exist_ok=True)

    # Accessing chunks and frame counts by index
    for i, (chunk, frame_count) in enumerate(chunks):
        chunk.write_videofile(f'chunk_{i}.mp4')  # Saving the chunk to a file
        chunk_path = f'chunk_{i}.mp4'
        print(f"Chunk {i}: Frame Count = {frame_count}")

        command = f"python inference.py --prompt '{prompt}' --condition '{condition}' --video_path '{chunk_path}' --output_path '{output_path}' --video_length {frame_count}"
        subprocess.run(command, shell=True)

def working_run_inference(prompt, video_path, condition, video_length):

    
    output_path = 'output/'
    os.makedirs(output_path, exist_ok=True)

    # Construct the final video path
    video_path_output = os.path.join(output_path, f"{prompt}.mp4")

    # Check if the file already exists
    if os.path.exists(video_path_output):
        # Delete the existing file
        os.remove(video_path_output)

    if video_length > 12:
        command = f"python inference.py --prompt '{prompt}' --condition '{condition}' --video_path '{video_path}' --output_path '{output_path}' --video_length {video_length} --is_long_video"
    else:
        command = f"python inference.py --prompt '{prompt}' --condition '{condition}' --video_path '{video_path}' --output_path '{output_path}' --video_length {video_length}"
    subprocess.run(command, shell=True)

    # Construct the video path
    video_path_output = os.path.join(output_path, f"{prompt}.mp4")

        

    return "done", video_path_output 


with gr.Blocks() as demo:
    with gr.Column():
        prompt = gr.Textbox(label="prompt")
        video_path = gr.Video(source="upload", type="filepath")
        condition = gr.Textbox(label="Condition", value="depth")
        video_length = gr.Slider(label="video length", minimum=1, maximum=15, step=1, value=2)
        #seed = gr.Number(label="seed", value=42)
        submit_btn = gr.Button("Submit")
        video_res = gr.Video(label="result")
        status = gr.Textbox(label="result")
    video_path.change(fn=get_frame_count_in_duration,
                      inputs=[video_path],
                      outputs=[video_length]
                     )
    submit_btn.click(fn=run_inference, 
                     inputs=[prompt,
                             video_path,
                             condition,
                             video_length
                            ],
                    outputs=[status, video_res])

demo.queue(max_size=12).launch()