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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 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}"
else:
command = f"python inference.py --prompt '{prompt}' --condition '{condition}' --video_path '{video_path}' --output_path '{output_path}' --video_length {video_length} --is_long_video"
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() |