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| import gradio as gr | |
| import os | |
| import cv2 | |
| import numpy as np | |
| from moviepy.editor import * | |
| 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 🔥" if torch.cuda.is_available() else "CPU 🥶" | |
| 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 | |
| pix2pix_img.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 | |
| 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> • | |
| 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="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/🤗 Spaces-Instruct_Pix2Pix-blue" src="https://img.shields.io/badge/🤗 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", sources=["upload"], 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)", minimum=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") | |
| inputs = [prompt,video_inp,seed_inp, trim_in] | |
| outputs = [video_out] | |
| 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, show_api=False) | |
| demo.queue(max_size=12).launch(show_api=False) | |