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| 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 (DEFAULT_BASE_MODEL_FILENAME, DEFAULT_BASE_MODEL_REPO, | |
| DEFAULT_BASE_MODEL_URL, Model) | |
| model = Model() | |
| def controlnet(i, prompt, control_task, seed_in, ddim_steps, scale, low_threshold, high_threshold, value_threshold, distance_threshold, bg_threshold): | |
| 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 | |
| #value_threshold = 0.1 | |
| #distance_threshold = 0.1 | |
| #bg_threshold = 0.4 | |
| if control_task == 'Canny': | |
| result = model.process_canny(np_img, prompt, a_prompt, n_prompt, num_samples, | |
| image_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 == 'Hed': | |
| result = model.process_hed(np_img, prompt, a_prompt, n_prompt, num_samples, | |
| image_resolution, detect_resolution, ddim_steps, scale, seed_in, eta) | |
| elif control_task == 'Hough': | |
| result = model.process_hough(np_img, prompt, a_prompt, n_prompt, num_samples, | |
| image_resolution, detect_resolution, ddim_steps, scale, seed_in, eta, value_threshold, | |
| distance_threshold) | |
| elif control_task == 'Normal': | |
| result = model.process_normal(np_img, prompt, a_prompt, n_prompt, num_samples, | |
| image_resolution, detect_resolution, ddim_steps, scale, seed_in, eta, bg_threshold) | |
| 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) | |
| elif control_task == 'Scribble': | |
| result = model.process_scribble(np_img, prompt, a_prompt, n_prompt, num_samples, | |
| image_resolution, ddim_steps, scale, seed_in, eta) | |
| elif control_task == 'Seg': | |
| result = model.process_seg(np_img, prompt, a_prompt, n_prompt, num_samples, | |
| image_resolution, detect_resolution, ddim_steps, scale, seed_in, eta) | |
| #print(result[0]) | |
| processor_im = Image.fromarray(result[0]) | |
| processor_im.save("process_" + control_task + "_" + str(i) + ".jpeg") | |
| im = Image.fromarray(result[1]) | |
| im.save("your_file" + str(i) + ".jpeg") | |
| return "your_file" + str(i) + ".jpeg", "process_" + control_task + "_" + str(i) + ".jpeg" | |
| def change_task_options(task): | |
| if task == "Canny" : | |
| return canny_opt.update(visible=True), hough_opt.update(visible=False), normal_opt.update(visible=False) | |
| elif task == "Hough" : | |
| return canny_opt.update(visible=False),hough_opt.update(visible=True), normal_opt.update(visible=False) | |
| elif task == "Normal" : | |
| return canny_opt.update(visible=False),hough_opt.update(visible=False), normal_opt.update(visible=True) | |
| else : | |
| return canny_opt.update(visible=False),hough_opt.update(visible=False), normal_opt.update(visible=False) | |
| def get_frames(video_in): | |
| frames = [] | |
| #resize the video | |
| clip = VideoFileClip(video_in) | |
| #check fps | |
| 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") | |
| # Opens the Video file with CV2 | |
| 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 convert(gif): | |
| if gif != None: | |
| clip = VideoFileClip(gif.name) | |
| clip.write_videofile("my_gif_video.mp4") | |
| return "my_gif_video.mp4" | |
| else: | |
| pass | |
| def create_video(frames, fps, type): | |
| print("building video result") | |
| clip = ImageSequenceClip(frames, fps=fps) | |
| clip.write_videofile(type + "_result.mp4", fps=fps) | |
| return type + "_result.mp4" | |
| def infer(prompt,video_in, control_task, seed_in, trim_value, ddim_steps, scale, low_threshold, high_threshold, value_threshold, distance_threshold, bg_threshold, gif_import): | |
| print(f""" | |
| ——————————————— | |
| {prompt} | |
| ———————————————""") | |
| # 1. break video into frames and get FPS | |
| 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) | |
| # 2. prepare frames result arrays | |
| processor_result_frames = [] | |
| 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, low_threshold, high_threshold, value_threshold, distance_threshold, bg_threshold) | |
| #images = controlnet_img[0] | |
| #rgb_im = images[0].convert("RGB") | |
| # exporting the image | |
| #rgb_im.save(f"result_img-{i}.jpg") | |
| processor_result_frames.append(controlnet_img[1]) | |
| result_frames.append(controlnet_img[0]) | |
| print("frame " + i + "/" + str(n_frame) + ": done;") | |
| processor_vid = create_video(processor_result_frames, fps, "processor") | |
| final_vid = create_video(result_frames, fps, "final") | |
| files = [processor_vid, final_vid] | |
| if gif_import != None: | |
| final_gif = VideoFileClip(final_vid) | |
| final_gif.write_gif("final_result.gif") | |
| final_gif = "final_result.gif" | |
| files.append(final_gif) | |
| print("finished !") | |
| return final_vid, gr.Accordion.update(visible=True), gr.Video.update(value=processor_vid, visible=True), gr.File.update(value=files, visible=True), gr.Group.update(visible=True) | |
| def clean(): | |
| return gr.Accordion.update(visible=False),gr.Video.update(value=None, visible=False), gr.Video.update(value=None), gr.File.update(value=None, visible=False), gr.Group.update(visible=False) | |
| 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) | |
| gr.HTML(""" | |
| <a style="display:inline-block" href="https://huggingface.co/spaces/fffiloni/ControlNet-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> | |
| """, elem_id="duplicate-container") | |
| 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.Accordion("Detailed results", visible=False) as detailed_result: | |
| prep_video_out = gr.Video(label="Preprocessor video result", visible=False, elem_id="prep-video-output") | |
| files = gr.File(label="Files can be downloaded ;)", visible=False) | |
| with gr.Column(): | |
| #status = gr.Textbox() | |
| prompt = gr.Textbox(label="Prompt", placeholder="enter prompt", show_label=True, elem_id="prompt-in") | |
| with gr.Row(): | |
| control_task = gr.Dropdown(label="Control Task", choices=["Canny", "Depth", "Hed", "Hough", "Normal", "Pose", "Scribble", "Seg"], value="Pose", multiselect=False, elem_id="controltask-in") | |
| seed_inp = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, value=123456, elem_id="seed-in") | |
| with gr.Row(): | |
| trim_in = gr.Slider(label="Cut video at (s)", minimun=1, maximum=5, step=1, value=1) | |
| with gr.Accordion("Advanced Options", open=False): | |
| with gr.Tab("Diffusion Settings"): | |
| with gr.Row(visible=False) as canny_opt: | |
| low_threshold = gr.Slider(label='Canny low threshold', minimum=1, maximum=255, value=100, step=1) | |
| high_threshold = gr.Slider(label='Canny high threshold', minimum=1, maximum=255, value=200, step=1) | |
| with gr.Row(visible=False) as hough_opt: | |
| value_threshold = gr.Slider(label='Hough value threshold (MLSD)', minimum=0.01, maximum=2.0, value=0.1, step=0.01) | |
| distance_threshold = gr.Slider(label='Hough distance threshold (MLSD)', minimum=0.01, maximum=20.0, value=0.1, step=0.01) | |
| with gr.Row(visible=False) as normal_opt: | |
| bg_threshold = gr.Slider(label='Normal background threshold', minimum=0.0, maximum=1.0, value=0.4, step=0.01) | |
| 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) | |
| with gr.Tab("GIF import"): | |
| gif_import = gr.File(label="import a GIF instead", file_types=['.gif']) | |
| gif_import.change(convert, gif_import, video_inp, queue=False) | |
| with gr.Tab("Custom Model"): | |
| current_base_model = gr.Text(label='Current base model', | |
| value=DEFAULT_BASE_MODEL_URL) | |
| with gr.Row(): | |
| with gr.Column(): | |
| base_model_repo = gr.Text(label='Base model repo', | |
| max_lines=1, | |
| placeholder=DEFAULT_BASE_MODEL_REPO, | |
| interactive=True) | |
| base_model_filename = gr.Text( | |
| label='Base model file', | |
| max_lines=1, | |
| placeholder=DEFAULT_BASE_MODEL_FILENAME, | |
| interactive=True) | |
| change_base_model_button = gr.Button('Change base model') | |
| gr.HTML( | |
| '''<p>You can use other base models by specifying the repository name and filename.<br /> | |
| The base model must be compatible with Stable Diffusion v1.5.</p>''') | |
| change_base_model_button.click(fn=model.set_base_model, | |
| inputs=[ | |
| base_model_repo, | |
| base_model_filename, | |
| ], | |
| outputs=current_base_model, queue=False) | |
| submit_btn = gr.Button("Generate ControlNet video") | |
| inputs = [prompt,video_inp,control_task, seed_inp, trim_in, ddim_steps, scale, low_threshold, high_threshold, value_threshold, distance_threshold, bg_threshold, gif_import] | |
| outputs = [video_out, detailed_result, prep_video_out, files, share_group] | |
| #outputs = [status] | |
| gr.HTML(article) | |
| control_task.change(change_task_options, inputs=[control_task], outputs=[canny_opt, hough_opt, normal_opt], queue=False) | |
| submit_btn.click(clean, inputs=[], outputs=[detailed_result, prep_video_out, video_out, files, share_group], queue=False) | |
| submit_btn.click(infer, inputs, outputs) | |
| share_button.click(None, [], [], _js=share_js) | |
| demo.queue(max_size=12).launch() |