Spaces:
Runtime error
Runtime error
File size: 5,389 Bytes
76ce7ec e2153ed 76ce7ec d78a4ca 76ce7ec 1afcad4 76ce7ec 736c07e 76ce7ec 736c07e 76ce7ec d497512 76ce7ec |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 |
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 StableDiffusionXLControlNetImg2ImgPipeline, ControlNetModel, EulerDiscreteScheduler
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)
controlnet = accelerator.prepare(ControlNetModel.from_pretrained("diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.bfloat16, use_safetensors=True, variant="fp16", safety_checker=None))
pipe = accelerator.prepare(StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained("stabilityai/sdxl-turbo", controlnet=controlnet, torch_dtype=torch.bfloat16, use_safetensors=True, variant="fp16", safety_checker=None))
pipe.unet.to(memory_format=torch.channels_last)
pipe.scheduler = accelerator.prepare(EulerDiscreteScheduler.from_config(pipe.scheduler.config))
pipe = accelerator.prepare(pipe.to("cpu"))
def plex(fpath, text, neg_prompt, one, two, three, four, five):
gc.collect()
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),gr.Textbox(label="prompt"),gr.Textbox(label="neg prompt"), gr.Slider(label="num inference steps", minimum=2, step=1, maximum=5, value=2), gr.Slider(label="prompt strength", minimum=0.5, step=0.5, maximum=1.0, value=0.5), gr.Slider(label="controlnet scale", minimum=0.01, step=0.01, maximum=0.99, value=0.80), gr.Slider(label="Guidance scale", minimum=0.0, step=0.1, maximum=9.9, value=0.0), 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) |