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 = """

ControlNet Video

Apply ControlNet to a video

""" article = """

Follow Sylvain Filoni for future updates 🤗

You may also like:

""" with gr.Blocks(css='style.css') as demo: with gr.Column(elem_id="col-container"): gr.HTML(title) gr.HTML(""" Duplicate Space """, 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( '''

You can use other base models by specifying the repository name and filename.
The base model must be compatible with Stable Diffusion v1.5.

''') 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()