| from __future__ import annotations |
| import gradio as gr |
| import os |
| import cv2 |
| import numpy as np |
| from PIL import Image |
| from moviepy.editor import * |
| from share_btn import community_icon_html, loading_icon_html, share_js |
|
|
| import pathlib |
| import shlex |
| import subprocess |
|
|
| if os.getenv('SYSTEM') == 'spaces': |
| with open('patch') as f: |
| subprocess.run(shlex.split('patch -p1'), stdin=f, cwd='ControlNet') |
|
|
| base_url = 'https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/' |
|
|
| names = [ |
| 'body_pose_model.pth', |
| 'dpt_hybrid-midas-501f0c75.pt', |
| 'hand_pose_model.pth', |
| 'mlsd_large_512_fp32.pth', |
| 'mlsd_tiny_512_fp32.pth', |
| 'network-bsds500.pth', |
| 'upernet_global_small.pth', |
| ] |
|
|
| for name in names: |
| command = f'wget https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/{name} -O {name}' |
| out_path = pathlib.Path(f'ControlNet/annotator/ckpts/{name}') |
| if out_path.exists(): |
| continue |
| subprocess.run(shlex.split(command), cwd='ControlNet/annotator/ckpts/') |
|
|
| from model import Model |
| model = Model() |
|
|
|
|
| def controlnet(i, prompt, control_task, seed_in, ddim_steps, scale): |
| img= Image.open(i) |
| np_img = np.array(img) |
| |
| a_prompt = "best quality, extremely detailed" |
| n_prompt = "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality" |
| num_samples = 1 |
| image_resolution = 512 |
| detect_resolution = 512 |
| eta = 0.0 |
| low_threshold = 100 |
| high_threshold = 200 |
| |
| if control_task == 'Canny': |
| result = model.process_canny(np_img, prompt, a_prompt, n_prompt, num_samples, |
| image_resolution, detect_resolution, ddim_steps, scale, seed_in, eta, low_threshold, high_threshold) |
| elif control_task == 'Depth': |
| result = model.process_depth(np_img, prompt, a_prompt, n_prompt, num_samples, |
| image_resolution, detect_resolution, ddim_steps, scale, seed_in, eta) |
| elif control_task == 'Pose': |
| result = model.process_pose(np_img, prompt, a_prompt, n_prompt, num_samples, |
| image_resolution, detect_resolution, ddim_steps, scale, seed_in, eta) |
| |
| |
| im = Image.fromarray(result[1]) |
| im.save("your_file" + str(i) + ".jpeg") |
| return "your_file" + str(i) + ".jpeg" |
|
|
|
|
| def get_frames(video_in): |
| frames = [] |
| |
| clip = VideoFileClip(video_in) |
| |
| |
| if clip.fps > 30: |
| print("vide rate is over 30, resetting to 30") |
| clip_resized = clip.resize(height=512) |
| clip_resized.write_videofile("video_resized.mp4", fps=30) |
| else: |
| print("video rate is OK") |
| clip_resized = clip.resize(height=512) |
| clip_resized.write_videofile("video_resized.mp4", fps=clip.fps) |
| |
| print("video resized to 512 height") |
| |
| |
| cap= cv2.VideoCapture("video_resized.mp4") |
| |
| fps = cap.get(cv2.CAP_PROP_FPS) |
| print("video fps: " + str(fps)) |
| i=0 |
| while(cap.isOpened()): |
| ret, frame = cap.read() |
| if ret == False: |
| break |
| cv2.imwrite('kang'+str(i)+'.jpg',frame) |
| frames.append('kang'+str(i)+'.jpg') |
| i+=1 |
| |
| cap.release() |
| cv2.destroyAllWindows() |
| print("broke the video into frames") |
| |
| return frames, fps |
|
|
|
|
| def create_video(frames, fps): |
| print("building video result") |
| clip = ImageSequenceClip(frames, fps=fps) |
| clip.write_videofile("movie.mp4", fps=fps) |
| |
| return 'movie.mp4' |
|
|
|
|
| def infer(prompt,video_in, control_task, seed_in, trim_value, ddim_steps, scale): |
| print(f""" |
| ——————————————— |
| {prompt} |
| ———————————————""") |
| |
| |
| break_vid = get_frames(video_in) |
| frames_list= break_vid[0] |
| fps = break_vid[1] |
| n_frame = int(trim_value*fps) |
| |
| if n_frame >= len(frames_list): |
| print("video is shorter than the cut value") |
| n_frame = len(frames_list) |
| |
| |
| result_frames = [] |
| print("set stop frames to: " + str(n_frame)) |
| |
| for i in frames_list[0:int(n_frame)]: |
| controlnet_img = controlnet(i, prompt,control_task, seed_in, ddim_steps, scale) |
| |
| |
| |
| |
| |
| result_frames.append(controlnet_img) |
| print("frame " + i + "/" + str(n_frame) + ": done;") |
|
|
| final_vid = create_video(result_frames, fps) |
| print("finished !") |
| |
| return final_vid, gr.Group.update(visible=True) |
| |
|
|
| title = """ |
| <div style="text-align: center; max-width: 700px; margin: 0 auto;"> |
| <div |
| style=" |
| display: inline-flex; |
| align-items: center; |
| gap: 0.8rem; |
| font-size: 1.75rem; |
| " |
| > |
| <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;"> |
| ControlNet Video |
| </h1> |
| </div> |
| <p style="margin-bottom: 10px; font-size: 94%"> |
| Apply ControlNet to a video |
| </p> |
| </div> |
| """ |
|
|
| article = """ |
| |
| <div class="footer"> |
| <p> |
| Follow <a href="https://twitter.com/fffiloni" target="_blank">Sylvain Filoni</a> for future updates 🤗 |
| </p> |
| </div> |
| <div id="may-like-container" style="display: flex;justify-content: center;flex-direction: column;align-items: center;margin-bottom: 30px;"> |
| <p>You may also like: </p> |
| <div id="may-like-content" style="display:flex;flex-wrap: wrap;align-items:center;height:20px;"> |
| |
| <svg height="20" width="148" style="margin-left:4px;margin-bottom: 6px;"> |
| <a href="https://huggingface.co/spaces/fffiloni/Pix2Pix-Video" target="_blank"> |
| <image href="https://img.shields.io/badge/🤗 Spaces-Pix2Pix_Video-blue" src="https://img.shields.io/badge/🤗 Spaces-Pix2Pix_Video-blue.png" height="20"/> |
| </a> |
| </svg> |
| |
| </div> |
| |
| </div> |
| |
| """ |
|
|
| with gr.Blocks(css='style.css') as demo: |
| with gr.Column(elem_id="col-container"): |
| gr.HTML(title) |
| with gr.Row(): |
| with gr.Column(): |
| video_inp = gr.Video(label="Video source", source="upload", type="filepath", elem_id="input-vid") |
| video_out = gr.Video(label="ControlNet video result", elem_id="video-output") |
| with gr.Group(elem_id="share-btn-container", visible=False) as share_group: |
| community_icon = gr.HTML(community_icon_html) |
| loading_icon = gr.HTML(loading_icon_html) |
| share_button = gr.Button("Share to community", elem_id="share-btn") |
| with gr.Column(): |
| |
| |
| prompt = gr.Textbox(label="Prompt", placeholder="enter prompt", show_label=True, elem_id="prompt-in") |
| control_task = gr.Dropdown(label="Control Task", choices=["Canny", "Depth", "Pose"], value="Pose", multiselect=False) |
| with gr.Row(): |
| seed_inp = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, value=123456) |
| trim_in = gr.Slider(label="Cut video at (s)", minimun=1, maximum=5, step=1, value=1) |
| ddim_steps = gr.Slider(label='Steps', |
| minimum=1, |
| maximum=100, |
| value=20, |
| step=1) |
| scale = gr.Slider(label='Guidance Scale', |
| minimum=0.1, |
| maximum=30.0, |
| value=9.0, |
| step=0.1) |
| |
| submit_btn = gr.Button("Generate Pix2Pix video") |
| |
| gr.HTML(""" |
| <a style="display:inline-block" href="https://huggingface.co/spaces/fffiloni/Pix2Pix-Video?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a> |
| work with longer videos / skip the queue: |
| """, elem_id="duplicate-container") |
| |
| inputs = [prompt,video_inp,control_task, seed_inp, trim_in, ddim_steps, scale] |
| outputs = [video_out, share_group] |
| |
| |
| |
| gr.HTML(article) |
| |
| submit_btn.click(infer, inputs, outputs) |
| share_button.click(None, [], [], _js=share_js) |
|
|
| |
| |
| demo.launch().queue(max_size=12) |
