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import gradio as gr
import os
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}')
import subprocess
from moviepy.editor import VideoFileClip
def get_video_properties(filepath):
video = VideoFileClip(filepath)
duration = video.duration
fps = video.fps
video.close()
return duration, fps
def run_inference(prompt, video_path, condition, video_length):
# Call the function to get the video properties
duration, fps = get_video_properties(video_path)
video_length = int(video_length * fps)
output_path = 'output/'
os.makedirs(output_path, exist_ok=True)
command = f"python inference.py --prompt '{prompt}' --condition '{condition}' --video_path '{video_path}' --output_path '{output_path}' --video_length {video_length} --smoother_steps 19 20"
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
#return f"{output_path}/{prompt}.mp4"
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")
submit_btn.click(fn=run_inference,
inputs=[prompt,
video_path,
condition,
video_length
],
outputs=[status, video_res])
demo.queue(max_size=12).launch() |