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, 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) #print(result[0]) im = Image.fromarray(result[1]) im.save("your_file" + str(i) + ".jpeg") return "your_file" + str(i) + ".jpeg" 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 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} ———————————————""") # 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 array 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) #images = controlnet_img[0] #rgb_im = images[0].convert("RGB") # exporting the image #rgb_im.save(f"result_img-{i}.jpg") 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) #return controlnet_img title = """

ControlNet Video

Apply ControlNet to a video

""" article = """

Follow Sylvain Filoni for future updates 🤗

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""" 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(): #status = gr.Textbox() 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 ControlNet video") gr.HTML(""" Duplicate Space 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] #outputs = [status] gr.HTML(article) submit_btn.click(infer, inputs, outputs) share_button.click(None, [], [], _js=share_js) demo.queue(max_size=12).launch()