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Running
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A10G
import gradio as gr | |
import os | |
import cv2 | |
import numpy as np | |
from moviepy.editor import * | |
from share_btn import community_icon_html, loading_icon_html, share_js | |
from diffusers import StableDiffusionInstructPix2PixPipeline | |
import torch | |
from PIL import Image, ImageOps | |
import time | |
import psutil | |
import math | |
import random | |
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained("timbrooks/instruct-pix2pix", torch_dtype=torch.float16, safety_checker=None) | |
device = "GPU π₯" if torch.cuda.is_available() else "CPU π₯Ά" | |
if torch.cuda.is_available(): | |
pipe = pipe.to("cuda") | |
def pix2pix( | |
input_image: Image.Image, | |
instruction: str, | |
steps: int, | |
seed: int, | |
text_cfg_scale: float, | |
image_cfg_scale: float, | |
): | |
width, height = input_image.size | |
factor = 512 / max(width, height) | |
factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height) | |
width = int((width * factor) // 64) * 64 | |
height = int((height * factor) // 64) * 64 | |
input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS) | |
if instruction == "": | |
return [input_image, seed] | |
generator = torch.manual_seed(seed) | |
edited_image = pipe( | |
instruction, image=input_image, | |
guidance_scale=text_cfg_scale, image_guidance_scale=image_cfg_scale, | |
num_inference_steps=steps, generator=generator, | |
).images[0] | |
print(f"EDITED: {edited_image}") | |
return edited_image | |
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, seed_in, trim_value): | |
print(prompt) | |
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) | |
result_frames = [] | |
print("set stop frames to: " + str(n_frame)) | |
for i in frames_list[0:int(n_frame)]: | |
pil_i = Image.open(i).convert("RGB") | |
pix2pix_img = pix2pix(pil_i, prompt, 50, seed_in, 7.5, 1.5) | |
print(pix2pix_img) | |
image = Image.open(pix2pix_img) | |
rgb_im = image.convert("RGB") | |
# exporting the image | |
rgb_im.save(f"result_img-{i}.jpg") | |
result_frames.append(f"result_img-{i}.jpg") | |
print("frame " + i + "/" + str(n_frame) + ": done;") | |
final_vid = create_video(result_frames, fps) | |
print("finished !") | |
return final_vid, gr.Group.update(visible=True) | |
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;"> | |
Pix2Pix Video | |
</h1> | |
</div> | |
<p style="margin-bottom: 10px; font-size: 94%"> | |
Apply Instruct Pix2Pix Diffusion to a video | |
</p> | |
</div> | |
""" | |
article = """ | |
<div class="footer"> | |
<p> | |
Examples by <a href="https://twitter.com/CitizenPlain" target="_blank">Nathan Shipley</a> β’ | |
Follow <a href="https://twitter.com/fffiloni" target="_blank">Sylvain Filoni</a> for future updates π€ | |
</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="162" style="margin-left:4px;margin-bottom: 6px;"> | |
<a href="https://huggingface.co/spaces/timbrooks/instruct-pix2pix" target="_blank"> | |
<image href="https://img.shields.io/badge/π€ Spaces-Instruct_Pix2Pix-blue" src="https://img.shields.io/badge/π€ Spaces-Instruct_Pix2Pix-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) | |
with gr.Row(): | |
with gr.Column(): | |
video_inp = gr.Video(label="Video source", source="upload", type="filepath", elem_id="input-vid") | |
prompt = gr.Textbox(label="Prompt", placeholder="enter prompt", show_label=False, elem_id="prompt-in") | |
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) | |
with gr.Column(): | |
video_out = gr.Video(label="Pix2pix video result", elem_id="video-output") | |
gr.HTML(""" | |
<a style="display:inline-block" href="https://huggingface.co/spaces/fffiloni/Pix2Pix-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> | |
work with longer videos / skip the queue: | |
""", elem_id="duplicate-container") | |
submit_btn = gr.Button("Generate Pix2Pix video") | |
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") | |
inputs = [prompt,video_inp,seed_inp, trim_in] | |
outputs = [video_out, share_group] | |
#ex = gr.Examples( | |
# [ | |
# ["Make it a marble sculpture", "./examples/pexels-jill-burrow-7665249_512x512.mp4", 422112651, 4], | |
# ["Make it molten lava", "./examples/Ocean_Pexels_ 8953474_512x512.mp4", 43571876, 4] | |
# ], | |
# inputs=inputs, | |
# outputs=outputs, | |
# fn=infer, | |
# cache_examples=True, | |
#) | |
gr.HTML(article) | |
submit_btn.click(infer, inputs, outputs) | |
share_button.click(None, [], [], _js=share_js) | |
demo.queue(max_size=12).launch() | |