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Added custom models option
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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=10, 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()