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from __future__ import annotations |
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import gradio as gr |
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import os |
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import cv2 |
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import numpy as np |
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from PIL import Image |
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from moviepy.editor import * |
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from share_btn import community_icon_html, loading_icon_html, share_js |
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import pathlib |
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import shlex |
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import subprocess |
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if os.getenv('SYSTEM') == 'spaces': |
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with open('patch') as f: |
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subprocess.run(shlex.split('patch -p1'), stdin=f, cwd='ControlNet') |
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base_url = 'https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/' |
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names = [ |
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'body_pose_model.pth', |
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'dpt_hybrid-midas-501f0c75.pt', |
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'hand_pose_model.pth', |
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'mlsd_large_512_fp32.pth', |
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'mlsd_tiny_512_fp32.pth', |
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'network-bsds500.pth', |
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'upernet_global_small.pth', |
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] |
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for name in names: |
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command = f'wget https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/{name} -O {name}' |
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out_path = pathlib.Path(f'ControlNet/annotator/ckpts/{name}') |
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if out_path.exists(): |
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continue |
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subprocess.run(shlex.split(command), cwd='ControlNet/annotator/ckpts/') |
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from model import (DEFAULT_BASE_MODEL_FILENAME, DEFAULT_BASE_MODEL_REPO, |
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DEFAULT_BASE_MODEL_URL, Model) |
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model = Model() |
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def controlnet(i, prompt, control_task, seed_in, ddim_steps, scale, low_threshold, high_threshold, value_threshold, distance_threshold, bg_threshold): |
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img= Image.open(i) |
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np_img = np.array(img) |
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a_prompt = "best quality, extremely detailed" |
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n_prompt = "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality" |
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num_samples = 1 |
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image_resolution = 512 |
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detect_resolution = 512 |
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eta = 0.0 |
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if control_task == 'Canny': |
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result = model.process_canny(np_img, prompt, a_prompt, n_prompt, num_samples, |
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image_resolution, ddim_steps, scale, seed_in, eta, low_threshold, high_threshold) |
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elif control_task == 'Depth': |
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result = model.process_depth(np_img, prompt, a_prompt, n_prompt, num_samples, |
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image_resolution, detect_resolution, ddim_steps, scale, seed_in, eta) |
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elif control_task == 'Hed': |
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result = model.process_hed(np_img, prompt, a_prompt, n_prompt, num_samples, |
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image_resolution, detect_resolution, ddim_steps, scale, seed_in, eta) |
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elif control_task == 'Hough': |
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result = model.process_hough(np_img, prompt, a_prompt, n_prompt, num_samples, |
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image_resolution, detect_resolution, ddim_steps, scale, seed_in, eta, value_threshold, |
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distance_threshold) |
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elif control_task == 'Normal': |
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result = model.process_normal(np_img, prompt, a_prompt, n_prompt, num_samples, |
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image_resolution, detect_resolution, ddim_steps, scale, seed_in, eta, bg_threshold) |
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elif control_task == 'Pose': |
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result = model.process_pose(np_img, prompt, a_prompt, n_prompt, num_samples, |
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image_resolution, detect_resolution, ddim_steps, scale, seed_in, eta) |
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elif control_task == 'Scribble': |
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result = model.process_scribble(np_img, prompt, a_prompt, n_prompt, num_samples, |
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image_resolution, ddim_steps, scale, seed_in, eta) |
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elif control_task == 'Seg': |
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result = model.process_seg(np_img, prompt, a_prompt, n_prompt, num_samples, |
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image_resolution, detect_resolution, ddim_steps, scale, seed_in, eta) |
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processor_im = Image.fromarray(result[0]) |
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processor_im.save("process_" + control_task + "_" + str(i) + ".jpeg") |
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im = Image.fromarray(result[1]) |
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im.save("your_file" + str(i) + ".jpeg") |
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return "your_file" + str(i) + ".jpeg", "process_" + control_task + "_" + str(i) + ".jpeg" |
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def change_task_options(task): |
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if task == "Canny" : |
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return canny_opt.update(visible=True), hough_opt.update(visible=False), normal_opt.update(visible=False) |
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elif task == "Hough" : |
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return canny_opt.update(visible=False),hough_opt.update(visible=True), normal_opt.update(visible=False) |
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elif task == "Normal" : |
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return canny_opt.update(visible=False),hough_opt.update(visible=False), normal_opt.update(visible=True) |
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else : |
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return canny_opt.update(visible=False),hough_opt.update(visible=False), normal_opt.update(visible=False) |
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def get_frames(video_in): |
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frames = [] |
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clip = VideoFileClip(video_in) |
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if clip.fps > 30: |
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print("vide rate is over 30, resetting to 30") |
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clip_resized = clip.resize(height=512) |
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clip_resized.write_videofile("video_resized.mp4", fps=30) |
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else: |
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print("video rate is OK") |
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clip_resized = clip.resize(height=512) |
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clip_resized.write_videofile("video_resized.mp4", fps=clip.fps) |
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print("video resized to 512 height") |
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cap= cv2.VideoCapture("video_resized.mp4") |
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fps = cap.get(cv2.CAP_PROP_FPS) |
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print("video fps: " + str(fps)) |
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i=0 |
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while(cap.isOpened()): |
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ret, frame = cap.read() |
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if ret == False: |
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break |
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cv2.imwrite('kang'+str(i)+'.jpg',frame) |
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frames.append('kang'+str(i)+'.jpg') |
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i+=1 |
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cap.release() |
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cv2.destroyAllWindows() |
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print("broke the video into frames") |
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return frames, fps |
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def convert(gif): |
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if gif != None: |
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clip = VideoFileClip(gif.name) |
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clip.write_videofile("my_gif_video.mp4") |
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return "my_gif_video.mp4" |
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else: |
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pass |
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def create_video(frames, fps, type): |
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print("building video result") |
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clip = ImageSequenceClip(frames, fps=fps) |
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clip.write_videofile(type + "_result.mp4", fps=fps) |
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return type + "_result.mp4" |
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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): |
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print(f""" |
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——————————————— |
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{prompt} |
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———————————————""") |
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break_vid = get_frames(video_in) |
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frames_list= break_vid[0] |
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fps = break_vid[1] |
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n_frame = int(trim_value*fps) |
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if n_frame >= len(frames_list): |
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print("video is shorter than the cut value") |
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n_frame = len(frames_list) |
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processor_result_frames = [] |
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result_frames = [] |
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print("set stop frames to: " + str(n_frame)) |
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for i in frames_list[0:int(n_frame)]: |
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controlnet_img = controlnet(i, prompt,control_task, seed_in, ddim_steps, scale, low_threshold, high_threshold, value_threshold, distance_threshold, bg_threshold) |
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processor_result_frames.append(controlnet_img[1]) |
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result_frames.append(controlnet_img[0]) |
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print("frame " + i + "/" + str(n_frame) + ": done;") |
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processor_vid = create_video(processor_result_frames, fps, "processor") |
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final_vid = create_video(result_frames, fps, "final") |
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files = [processor_vid, final_vid] |
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if gif_import != None: |
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final_gif = VideoFileClip(final_vid) |
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final_gif.write_gif("final_result.gif") |
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final_gif = "final_result.gif" |
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files.append(final_gif) |
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print("finished !") |
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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) |
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def clean(): |
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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) |
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title = """ |
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<div style="text-align: center; max-width: 700px; margin: 0 auto;"> |
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<div |
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style=" |
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display: inline-flex; |
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align-items: center; |
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gap: 0.8rem; |
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font-size: 1.75rem; |
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" |
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> |
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<h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;"> |
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ControlNet Video |
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</h1> |
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</div> |
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<p style="margin-bottom: 10px; font-size: 94%"> |
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Apply ControlNet to a video |
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</p> |
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</div> |
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""" |
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article = """ |
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<div class="footer"> |
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<p> |
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Follow <a href="https://twitter.com/fffiloni" target="_blank">Sylvain Filoni</a> for future updates 🤗 |
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</p> |
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</div> |
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<div id="may-like-container" style="display: flex;justify-content: center;flex-direction: column;align-items: center;margin-bottom: 30px;"> |
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<p>You may also like: </p> |
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<div id="may-like-content" style="display:flex;flex-wrap: wrap;align-items:center;height:20px;"> |
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<svg height="20" width="148" style="margin-left:4px;margin-bottom: 6px;"> |
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<a href="https://huggingface.co/spaces/fffiloni/Pix2Pix-Video" target="_blank"> |
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<image href="https://img.shields.io/badge/🤗 Spaces-Pix2Pix_Video-blue" src="https://img.shields.io/badge/🤗 Spaces-Pix2Pix_Video-blue.png" height="20"/> |
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</a> |
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</svg> |
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</div> |
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</div> |
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""" |
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with gr.Blocks(css='style.css') as demo: |
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with gr.Column(elem_id="col-container"): |
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gr.HTML(title) |
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gr.HTML(""" |
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<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> |
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""", elem_id="duplicate-container") |
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with gr.Row(): |
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with gr.Column(): |
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video_inp = gr.Video(label="Video source", source="upload", type="filepath", elem_id="input-vid") |
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video_out = gr.Video(label="ControlNet video result", elem_id="video-output") |
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with gr.Group(elem_id="share-btn-container", visible=False) as share_group: |
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community_icon = gr.HTML(community_icon_html) |
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loading_icon = gr.HTML(loading_icon_html) |
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share_button = gr.Button("Share to community", elem_id="share-btn") |
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with gr.Accordion("Detailed results", visible=False) as detailed_result: |
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prep_video_out = gr.Video(label="Preprocessor video result", visible=False, elem_id="prep-video-output") |
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files = gr.File(label="Files can be downloaded ;)", visible=False) |
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with gr.Column(): |
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prompt = gr.Textbox(label="Prompt", placeholder="enter prompt", show_label=True, elem_id="prompt-in") |
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with gr.Row(): |
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control_task = gr.Dropdown(label="Control Task", choices=["Canny", "Depth", "Hed", "Hough", "Normal", "Pose", "Scribble", "Seg"], value="Pose", multiselect=False, elem_id="controltask-in") |
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seed_inp = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, value=123456, elem_id="seed-in") |
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with gr.Row(): |
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trim_in = gr.Slider(label="Cut video at (s)", minimun=1, maximum=5, step=1, value=1) |
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with gr.Accordion("Advanced Options", open=False): |
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with gr.Tab("Diffusion Settings"): |
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with gr.Row(visible=False) as canny_opt: |
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low_threshold = gr.Slider(label='Canny low threshold', minimum=1, maximum=255, value=100, step=1) |
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high_threshold = gr.Slider(label='Canny high threshold', minimum=1, maximum=255, value=200, step=1) |
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with gr.Row(visible=False) as hough_opt: |
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value_threshold = gr.Slider(label='Hough value threshold (MLSD)', minimum=0.01, maximum=2.0, value=0.1, step=0.01) |
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distance_threshold = gr.Slider(label='Hough distance threshold (MLSD)', minimum=0.01, maximum=20.0, value=0.1, step=0.01) |
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with gr.Row(visible=False) as normal_opt: |
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bg_threshold = gr.Slider(label='Normal background threshold', minimum=0.0, maximum=1.0, value=0.4, step=0.01) |
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ddim_steps = gr.Slider(label='Steps', minimum=1, maximum=100, value=20, step=1) |
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scale = gr.Slider(label='Guidance Scale', minimum=0.1, maximum=30.0, value=9.0, step=0.1) |
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with gr.Tab("GIF import"): |
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gif_import = gr.File(label="import a GIF instead", file_types=['.gif']) |
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gif_import.change(convert, gif_import, video_inp, queue=False) |
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with gr.Tab("Custom Model"): |
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current_base_model = gr.Text(label='Current base model', |
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value=DEFAULT_BASE_MODEL_URL) |
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with gr.Row(): |
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with gr.Column(): |
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base_model_repo = gr.Text(label='Base model repo', |
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max_lines=1, |
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placeholder=DEFAULT_BASE_MODEL_REPO, |
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interactive=True) |
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base_model_filename = gr.Text( |
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label='Base model file', |
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max_lines=1, |
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placeholder=DEFAULT_BASE_MODEL_FILENAME, |
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interactive=True) |
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change_base_model_button = gr.Button('Change base model') |
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gr.HTML( |
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'''<p>You can use other base models by specifying the repository name and filename.<br /> |
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The base model must be compatible with Stable Diffusion v1.5.</p>''') |
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change_base_model_button.click(fn=model.set_base_model, |
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inputs=[ |
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base_model_repo, |
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base_model_filename, |
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], |
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outputs=current_base_model, queue=False) |
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submit_btn = gr.Button("Generate ControlNet video") |
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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] |
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outputs = [video_out, detailed_result, prep_video_out, files, share_group] |
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gr.HTML(article) |
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control_task.change(change_task_options, inputs=[control_task], outputs=[canny_opt, hough_opt, normal_opt], queue=False) |
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submit_btn.click(clean, inputs=[], outputs=[detailed_result, prep_video_out, video_out, files, share_group], queue=False) |
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submit_btn.click(infer, inputs, outputs) |
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share_button.click(None, [], [], _js=share_js) |
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demo.queue(max_size=12).launch() |
