from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler import gradio as gr from PIL import Image import cv2 import os, random, gc import numpy as np from transformers import pipeline import PIL.Image from diffusers.utils import load_image, export_to_video from accelerate import Accelerator from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, UniPCMultistepScheduler import torch from moviepy.video.fx.all import crop from diffusers.utils import export_to_gif import mediapy from image_tools.sizes import resize_and_crop from moviepy.editor import * from pathlib import Path from typing import Optional, List from tqdm import tqdm import supervision as sv accelerator = Accelerator(cpu=True) models =[ "runwayml/stable-diffusion-v1-5", "prompthero/openjourney-v4", "CompVis/stable-diffusion-v1-4", "stabilityai/stable-diffusion-2-1", "stablediffusionapi/edge-of-realism", "sd-dreambooth-library/fashion", "DucHaiten/DucHaitenDreamWorld", "kandinsky-community/kandinsky-2-1", "plasmo/woolitize-768sd1-5", "wavymulder/modelshoot", "Fictiverse/Stable_Diffusion_VoxelArt_Model", "darkstorm2150/Protogen_v2.2_Official_Release", "hassanblend/HassanBlend1.5.1.2", "hassanblend/hassanblend1.4", "nitrosocke/redshift-diffusion", "prompthero/openjourney-v2", "Lykon/DreamShaper", "nitrosocke/mo-di-diffusion", "dreamlike-art/dreamlike-diffusion-1.0", "dreamlike-art/dreamlike-photoreal-2.0", "digiplay/RealismEngine_v1", "digiplay/AIGEN_v1.4_diffusers", "stablediffusionapi/dreamshaper-v6", "TheLastBen/froggy-style-v21-768", "digiplay/PotoPhotoRealism_v1", ] controlnet = accelerator.prepare(ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float32)) def plex(fpath, text, neg_prompt, modil, one, two, three, four, five): gc.collect() modal=""+modil+"" pipe = accelerator.prepare(StableDiffusionControlNetImg2ImgPipeline.from_pretrained(modal, controlnet=controlnet, torch_dtype=torch.float32, use_safetensors=False, safety_checker=None)) pipe.unet.to(memory_format=torch.channels_last) pipe.scheduler = accelerator.prepare(DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)) pipe = pipe.to("cpu") prompt = text video = './video.mp4' orvid = './orvid.mp4' canvid = './canvid.mp4' frames = [] canframes = [] orframes = [] fin_frames = [] max_frames=0 cap = cv2.VideoCapture(fpath) clip = VideoFileClip(fpath) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) fps = cap.get(cv2.CAP_PROP_FPS) aspect = width / height if aspect == 1 and height >= 512: nwidth = 512 nheight = 512 prep = clip.resize(height=nheight) left = 0 top = 0 right = 512 bottom = 512 if aspect > 1 and height >= 512: nheight = 512 nwidth = int(nheight * aspect) prep = clip.resize(height=nheight) left = (nwidth - width) / 2 top = 0 right = (nwidth + width) / 2 bottom = nheight if aspect < 1 and width >= 512: nwidth = 512 nheight = int(nwidth / aspect) prep = clip.resize(height=nheight) left = 0 top = (height - nheight) / 2 right = nwidth bottom = (height + nheight) / 2 if aspect < 1 and width < 512: return None if aspect > 1 and height < 512: return None closer = crop(clip, x1=left, y1=top, x2=right, y2=bottom) if fps > 10: closer.write_videofile('./video.mp4', fps=10) fps = 10 else: closer.write_videofile('./video.mp4', fps=fps) fps = fps max_frames = int(fps * 2) for frame in tqdm(sv.get_video_frames_generator(source_path=video,)): frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) cap.release() cv2.destroyAllWindows() ncap = cv2.VideoCapture(video) total_frames = int(ncap.get(cv2.CAP_PROP_FRAME_COUNT)) if total_frames <= 0: return None b = 0 if total_frames > max_frames: max_frames = int(max_frames) if total_frames < max_frames: max_frames = int(total_frames) for b in range(int(max_frames)): frame = frames[b] original = load_image(Image.fromarray(frame)) original.save('./image.png', 'PNG') original = original.resize((512, 512)) original = original.convert("RGB") original.save('./image.png', 'PNG') orframes.append(original) cannyimage = np.array(original) cannyimage = cv2.Canny(cannyimage, 100, 200) cannyimage = cannyimage[:, :, None] cannyimage = np.concatenate([cannyimage, cannyimage, cannyimage], axis=2) cannyimage = Image.fromarray(cannyimage) canframes.append(cannyimage) generator = torch.Generator(device="cpu").manual_seed(five) imoge = pipe(prompt=prompt,image=[original],control_image=[cannyimage],guidance_scale=four,num_inference_steps=one,generator=generator,strength=two,negative_prompt=neg_prompt,controlnet_conditioning_scale=three,width=512,height=512) fin_frames.append(imoge.images[0]) b += 1 ncap.release() cv2.destroyAllWindows() export_to_video(fin_frames, video, fps=fps) export_to_video(orframes, orvid, fps=fps) export_to_video(canframes, canvid, fps=fps) return video, canvid, orvid iface = gr.Interface(fn=plex, inputs=[gr.File(label="Your video",interactive=True, file_types=['.mp4',]),gr.Textbox(label="prompt"),gr.Textbox(label="neg prompt"),gr.Dropdown(choices=models, label="Models", value=models[0], type="value"), gr.Slider(label="num inference steps", minimum=1, step=1, maximum=10, value=4), gr.Slider(label="Strength", minimum=0.01, step=0.01, maximum=20.00, value=5.00), gr.Slider(label="controlnet scale", minimum=0.01, step=0.01, maximum=0.99, value=0.80), gr.Slider(label="Guidance scale", minimum=0.01, step=0.01, maximum=10.00, value=2.00), gr.Slider(label="Manual seed", minimum=0, step=32, maximum=4836928, value=0)], outputs=[gr.Video(label="final"), gr.Video(label="canny vid"), gr.Video(label="orig")],description="Running on cpu, very slow! by JoPmt.") iface.queue(max_size=1,api_open=False) iface.launch(max_threads=1)