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 DiffusionPipeline, EulerAncestralDiscreteScheduler import torch from PIL import Image import time import psutil import random #token = os.environ.get('HF_TOKEN') #pix2pix = gr.Blocks.load(name="spaces/fffiloni/instruct-pix2pix-clone", api_key=token) pipe = DiffusionPipeline.from_pretrained("timbrooks/instruct-pix2pix", torch_dtype=torch.float16, safety_checker=None) pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) pipe.enable_xformers_memory_efficient_attention() pipe.unet.to(memory_format=torch.channels_last) device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶" if torch.cuda.is_available(): pipe = pipe.to("cuda") def pix2pix( prompt, text_guidance_scale, image_guidance_scale, image, steps, neg_prompt="", width=512, height=512, seed=0, ): print(psutil.virtual_memory()) # print memory usage if seed == 0: seed = random.randint(0, 2147483647) generator = torch.Generator("cuda").manual_seed(seed) try: image = Image.open(image) ratio = min(height / image.height, width / image.width) image = image.resize((int(image.width * ratio), int(image.height * ratio)), Image.LANCZOS) result = pipe( prompt, negative_prompt=neg_prompt, image=image, num_inference_steps=int(steps), image_guidance_scale=image_guidance_scale, guidance_scale=text_guidance_scale, generator=generator, ) # return replace_nsfw_images(result) return result.images, result.nsfw_content_detected, seed except Exception as e: return None, None, error_str(e) def error_str(error, title="Error"): return ( f"""#### {title} {error}""" if error else "" ) 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)]: pix2pix_img = pix2pix(prompt,5.5,1.5,i,15,"",512,512,seed_in) images = pix2pix_img[0] rgb_im = images[0].convert("RGB") # exporting the image rgb_im.save(f"result_img-{i}.jpg") result_frames.append(f"result_img-{i}.jpg") print("frame " + i + ": done;") final_vid = create_video(result_frames, fps) print("finished !") return final_vid, gr.Group.update(visible=True) title = """

Pix2Pix Video

Apply Instruct Pix2Pix Diffusion 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(): prompt = gr.Textbox(label="Prompt", placeholder="enter prompt", show_label=False, elem_id="prompt-in") video_inp = gr.Video(label="Video source", source="upload", type="filepath", include_audio=False, elem_id="input-vid") 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=3, step=1, value=1) with gr.Column(): gr.HTML(""" Duplicate Space work with longer videos / skip the queue: """, elem_id="duplicate-container") video_out = gr.Video(label="Pix2pix video result", elem_id="video-output") 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") gr.HTML(article) inputs = [prompt,video_inp,seed_inp, trim_in] outputs = [video_out, share_group] submit_btn.click(infer, inputs, outputs) share_button.click(None, [], [], _js=share_js) demo.launch().queue(max_size=12)