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| import argparse | |
| import cv2 | |
| import time | |
| import os | |
| import shutil | |
| from pathlib import Path | |
| import gradio as gr | |
| from PIL import Image | |
| import numpy as np | |
| from io import BytesIO | |
| import os | |
| source_img = gr.Image(source="upload", type="filepath", label="init_img | 512*512 px") | |
| gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto") | |
| doc_path = os.path.expanduser('~\Documents') | |
| visions_path = os.path.expanduser('~\Documents\\visions of chaos') | |
| import subprocess | |
| import random | |
| parser = argparse.ArgumentParser() | |
| #inpaint | |
| parser.add_argument("--mask", type=str, help="thickness of the mask for seamless inpainting",choices=["thinnest", "thin", "medium", "thick", "thickest"],default="medium") | |
| parser.add_argument("--input",type=str,nargs="?",default="source_img",help="input image",) | |
| parser.add_argument("--indir2",type=str,nargs="?",default="tmp360/tiled_image/",help="dir containing image-mask pairs (`example.png` and `example_mask.png`)",) | |
| parser.add_argument("--outdir2",type=str,nargs="?",default="tmp360/original_image2/",help="temp dir to write results to",) | |
| parser.add_argument("--steps2",type=int,default=50,help="number of ddim sampling steps",) | |
| parser.add_argument("--indir3",type=str,nargs="?",default="tmp360/tiled2_image2/",help="dir containing image-mask pairs (`example.png` and `example_mask.png`)",) | |
| parser.add_argument("--outdir3",type=str,nargs="?",default="outputs/txt2seamlessimg-samples/",help="dir to write results to",) | |
| parser.add_argument("--steps3",type=int,default=50,help="number of ddim sampling steps",) | |
| ##first pass of inpainting | |
| import argparse, os, sys, glob | |
| from omegaconf import OmegaConf | |
| from PIL import Image | |
| from tqdm import tqdm | |
| import numpy as np | |
| import torch | |
| from main import instantiate_from_config | |
| from ldm.models.diffusion.ddim import DDIMSampler | |
| def make_batch(image, mask, device): | |
| image = np.array(Image.open(image).convert("RGB")) | |
| image = image.astype(np.float32)/255.0 | |
| image = image[None].transpose(0,3,1,2) | |
| image = torch.from_numpy(image) | |
| mask = np.array(Image.open(mask).convert("L")) | |
| mask = mask.astype(np.float32)/255.0 | |
| mask = mask[None,None] | |
| mask[mask < 0.5] = 0 | |
| mask[mask >= 0.5] = 1 | |
| mask = torch.from_numpy(mask) | |
| masked_image = (1-mask)*image | |
| batch = {"image": image, "mask": mask, "masked_image": masked_image} | |
| for k in batch: | |
| batch[k] = batch[k].to(device=device) | |
| batch[k] = batch[k]*2.0-1.0 | |
| return batch | |
| if __name__ == "__main__": | |
| opt = parser.parse_args() | |
| inputimg = opt.input | |
| destination = 'tmp360/original_image/example1.png' | |
| #shutil.copy(inputimg, destination) | |
| from PIL import Image | |
| import PIL | |
| img = Image.open(inputimg) # image extension *.png,*.jpg | |
| new_width = 512 | |
| new_height = 512 | |
| img = img.resize((new_width, new_height), PIL.Image.LANCZOS) | |
| img.save('tmp360/original_image/example.png') | |
| '''p = subprocess.Popen(['mkdir', 'tmp360']) | |
| p = subprocess.Popen(['mkdir', 'tmp360/original_image']) | |
| p = subprocess.Popen(['mkdir', 'tmp360/original_image2']) | |
| p = subprocess.Popen(['mkdir', 'tmp360/tiled_image']) | |
| p = subprocess.Popen(['mkdir', 'tmp360/tiled2_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image2'])''' | |
| # masks = opt.mask | |
| # thinnest = r'seamless/thinnest/1st_mask.png' | |
| # thin = r'seamless/thin/1st_mask.png' | |
| # medium = r'seamless/medium/1st_mask.png' | |
| # thick = r'seamless/thick/1st_mask.png' | |
| # thickest = r'seamless/thickest/1st_mask.png' | |
| # | |
| # if masks == thinnest: | |
| # '''p = subprocess.Popen(['mkdir', 'tmp360']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/original_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/original_image2']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled2_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image2'])''' | |
| # print('temporary directories made') | |
| # print('copying',opt.mask ,'mask to dir') | |
| # shutil.copy('C:/deepdream-test/stable/stable-diffusion-2/seamless/medium/1st_mask.png', 'C:/deepdream-test/stable/stable-diffusion-2/tmp360/tiled_image/example_mask.png') | |
| # print('thinnest mask copied') | |
| # elif masks == thin: | |
| # p = subprocess.Popen(['mkdir', 'tmp360']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/original_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/original_image2']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled2_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image2']) | |
| # print('temporary directories made') | |
| # print('copying',opt.mask ,'mask to dir') | |
| # shutil.copy('C:/deepdream-test/stable/stable-diffusion-2/seamless/thin/1st_mask.png', 'C:/deepdream-test/stable/stable-diffusion-2/tmp360/tiled_image/example_mask.png') | |
| # print(opt.mask, 'mask copied') | |
| # elif masks == medium: | |
| # p = subprocess.Popen(['mkdir', 'tmp360']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/original_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/original_image2']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled2_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image2']) | |
| # print('temporary directories made') | |
| # print('copying',opt.mask ,'mask to dir') | |
| # shutil.copy('C:/deepdream-test/stable/stable-diffusion-2/seamless/medium/1st_mask.png', 'C:/deepdream-test/stable/stable-diffusion-2/tmp360/tiled_image/example_mask.png') | |
| # elif masks == thick: | |
| # p = subprocess.Popen(['mkdir', 'tmp360']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/original_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/original_image2']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled2_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image2']) | |
| # print('temporary directories made') | |
| # print('copying',opt.mask ,'mask to dir') | |
| # shutil.copy('C:/deepdream-test/stable/stable-diffusion-2/seamless/thick/1st_mask.png', 'C:/deepdream-test/stable/stable-diffusion-2/tmp360/tiled_image/example_mask.png') | |
| # elif masks == thickest: | |
| # p = subprocess.Popen(['mkdir', 'tmp360']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/original_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/original_image2']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled2_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image2']) | |
| # print('temporary directories made') | |
| # print('copying',opt.mask ,'mask to dir') | |
| # shutil.copy('C:/deepdream-test/stable/stable-diffusion-2/seamless/thickest/1st_mask.png', 'C:/deepdream-test/stable/stable-diffusion-2/tmp360/tiled_image/example_mask.png') | |
| # | |
| # # outpath = opt.outdir | |
| # # sample_path = os.path.join(outpath, "samples") | |
| # output555= "outputs/txt2img-samples/samples/example.png" | |
| """##move opt.output to temp directory### | |
| source = output555 | |
| destination = 'tmp360/original_image/example.png' | |
| shutil.move(source, destination)""" | |
| ##tile the image | |
| #p = subprocess.Popen(['mogrify', 'convert', '-virtual-pixel', 'tile', '-filter', 'point', '-set', 'option:distort:viewport', '1024x1024', '-distort', 'SRT', '0', '-path', r'tmp360/tiled2_image', r'tmp360/original_image/example.png']) | |
| #print('image tiled') | |
| #from PIL import Image # import pillow library (can install with "pip install pillow") | |
| #im = Image.open('tmp360/tiled2_image/example.png') | |
| #im = im.crop( (1, 0, 512, 512) ) # previously, image was 826 pixels wide, cropping to 825 pixels wide | |
| #im.save('tmp360/tiled2_image/example.png') # saves the image | |
| # im.show() # opens the image | |
| subprocess.call([r'crop.bat']) | |
| print('image center cropped') | |
| masks1 = sorted(glob.glob(os.path.join(opt.indir2, "*_mask.png"))) | |
| images1 = [x.replace("_mask.png", ".png") for x in masks1] | |
| print(f"Found {len(masks1)} inputs.") | |
| config = OmegaConf.load("models/ldm/inpainting_big/config.yaml") | |
| model = instantiate_from_config(config.model) | |
| model.load_state_dict(torch.load("C:\deepdream-test\stable\stable-diffusion-2\models\ldm\inpainting_big\last.ckpt")["state_dict"], | |
| strict=False) | |
| device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") | |
| model = model.to(device) | |
| sampler = DDIMSampler(model) | |
| os.makedirs(opt.outdir2, exist_ok=True) | |
| with torch.no_grad(): | |
| with model.ema_scope(): | |
| for image, mask in tqdm(zip(images1, masks1)): | |
| outpath3 = os.path.join(opt.outdir2, os.path.split(image)[1]) | |
| batch = make_batch(image, mask, device=device) | |
| # encode masked image and concat downsampled mask | |
| c = model.cond_stage_model.encode(batch["masked_image"]) | |
| cc = torch.nn.functional.interpolate(batch["mask"], | |
| size=c.shape[-2:]) | |
| c = torch.cat((c, cc), dim=1) | |
| shape = (c.shape[1]-1,)+c.shape[2:] | |
| samples_ddim, _ = sampler.sample(S=opt.steps2, | |
| conditioning=c, | |
| batch_size=c.shape[0], | |
| shape=shape, | |
| verbose=False) | |
| x_samples_ddim = model.decode_first_stage(samples_ddim) | |
| image = torch.clamp((batch["image"]+1.0)/2.0, | |
| min=0.0, max=1.0) | |
| mask = torch.clamp((batch["mask"]+1.0)/2.0, | |
| min=0.0, max=1.0) | |
| predicted_image = torch.clamp((x_samples_ddim+1.0)/2.0, | |
| min=0.0, max=1.0) | |
| inpainted = (1-mask)*image+mask*predicted_image | |
| inpainted = inpainted.cpu().numpy().transpose(0,2,3,1)[0]*255 | |
| Image.fromarray(inpainted.astype(np.uint8)).save(outpath3) | |
| if __name__ == "__main__": | |
| #opt = parser.parse_args() | |
| #inputimg = outpath3 | |
| #destination = 'tmp360/original_image2/example.png' | |
| #shutil.copy(inputimg, destination) | |
| '''p = subprocess.Popen(['mkdir', 'tmp360']) | |
| p = subprocess.Popen(['mkdir', 'tmp360/original_image']) | |
| p = subprocess.Popen(['mkdir', 'tmp360/original_image2']) | |
| p = subprocess.Popen(['mkdir', 'tmp360/tiled_image']) | |
| p = subprocess.Popen(['mkdir', 'tmp360/tiled2_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image2'])''' | |
| # masks = opt.mask | |
| # thinnest = r'seamless/thinnest/1st_mask.png' | |
| # thin = r'seamless/thin/1st_mask.png' | |
| # medium = r'seamless/medium/1st_mask.png' | |
| # thick = r'seamless/thick/1st_mask.png' | |
| # thickest = r'seamless/thickest/1st_mask.png' | |
| # | |
| # if masks == thinnest: | |
| # '''p = subprocess.Popen(['mkdir', 'tmp360']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/original_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/original_image2']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled2_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image2'])''' | |
| # print('temporary directories made') | |
| # print('copying',opt.mask ,'mask to dir') | |
| # shutil.copy('C:/deepdream-test/stable/stable-diffusion-2/seamless/example_mask.png', 'C:/deepdream-test/stable/stable-diffusion-2/tmp360/tiled_image/example_mask.png') | |
| # print('thinnest mask copied') | |
| # elif masks == thin: | |
| # p = subprocess.Popen(['mkdir', 'tmp360']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/original_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/original_image2']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled2_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image2']) | |
| # print('temporary directories made') | |
| # print('copying',opt.mask ,'mask to dir') | |
| # shutil.copy('C:/deepdream-test/stable/stable-diffusion-2/seamless/thin/1st_mask.png', 'C:/deepdream-test/stable/stable-diffusion-2/tmp360/tiled_image/example_mask.png') | |
| # print(opt.mask, 'mask copied') | |
| # elif masks == medium: | |
| # p = subprocess.Popen(['mkdir', 'tmp360']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/original_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/original_image2']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled2_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image2']) | |
| # print('temporary directories made') | |
| # print('copying',opt.mask ,'mask to dir') | |
| # shutil.copy('C:/deepdream-test/stable/stable-diffusion-2/seamless/medium/1st_mask.png', 'C:/deepdream-test/stable/stable-diffusion-2/tmp360/tiled_image/example_mask.png') | |
| # elif masks == thick: | |
| # p = subprocess.Popen(['mkdir', 'tmp360']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/original_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/original_image2']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled2_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image2']) | |
| # print('temporary directories made') | |
| # print('copying',opt.mask ,'mask to dir') | |
| # shutil.copy('C:/deepdream-test/stable/stable-diffusion-2/seamless/thick/1st_mask.png', 'C:/deepdream-test/stable/stable-diffusion-2/tmp360/tiled_image/example_mask.png') | |
| # elif masks == thickest: | |
| # p = subprocess.Popen(['mkdir', 'tmp360']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/original_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/original_image2']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled2_image']) | |
| # p = subprocess.Popen(['mkdir', 'tmp360/tiled_image2']) | |
| # print('temporary directories made') | |
| # print('copying',opt.mask ,'mask to dir') | |
| # shutil.copy('C:/deepdream-test/stable/stable-diffusion-2/seamless/thickest/1st_mask.png', 'C:/deepdream-test/stable/stable-diffusion-2/tmp360/tiled_image/example_mask.png') | |
| # outpath = opt.outdir | |
| # sample_path = os.path.join(outpath, "samples") | |
| #output555= "outputs/txt2img-samples/samples/example.png" | |
| """##move opt.output to temp directory### | |
| source = output555 | |
| destination = 'tmp360/original_image/example.png' | |
| shutil.move(source, destination)""" | |
| ##tile the image | |
| #p = subprocess.Popen(['mogrify', 'convert', '-virtual-pixel', 'tile', '-filter', 'point', '-set', 'option:distort:viewport', '1024x1024', '-distort', 'SRT', '0', '-path', r'tmp360/tiled2_image', r'tmp360/original_image/example.png']) | |
| #print('image tiled') | |
| #from PIL import Image # import pillow library (can install with "pip install pillow") | |
| #im = Image.open('tmp360/tiled2_image/example.png') | |
| #im = im.crop( (1, 0, 512, 512) ) # previously, image was 826 pixels wide, cropping to 825 pixels wide | |
| #im.save('tmp360/tiled2_image/example.png') # saves the image | |
| # im.show() # opens the image | |
| subprocess.call([r'2ndpass.bat']) | |
| print('image center cropped') | |
| masks = sorted(glob.glob(os.path.join(opt.indir3, "*_mask.png"))) | |
| images = [x.replace("_mask.png", ".png") for x in masks] | |
| print(f"Found {len(masks)} inputs.") | |
| config = OmegaConf.load("models/ldm/inpainting_big/config.yaml") | |
| model = instantiate_from_config(config.model) | |
| model.load_state_dict(torch.load("C:\deepdream-test\stable\stable-diffusion-2\models\ldm\inpainting_big\last.ckpt")["state_dict"], | |
| strict=False) | |
| device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") | |
| model = model.to(device) | |
| sampler = DDIMSampler(model) | |
| outpath4 = opt.outdir3 | |
| base_count = len(os.listdir(outpath4)) | |
| os.makedirs(opt.outdir3, exist_ok=True) | |
| with torch.no_grad(): | |
| with model.ema_scope(): | |
| for image, mask in tqdm(zip(images, masks)): | |
| outpath4 = os.path.join(opt.outdir3, os.path.split(opt.outdir3)[1]) | |
| batch = make_batch(image, mask, device=device) | |
| # encode masked image and concat downsampled mask | |
| c = model.cond_stage_model.encode(batch["masked_image"]) | |
| cc = torch.nn.functional.interpolate(batch["mask"], | |
| size=c.shape[-2:]) | |
| c = torch.cat((c, cc), dim=1) | |
| shape = (c.shape[1]-1,)+c.shape[2:] | |
| samples_ddim, _ = sampler.sample(S=opt.steps2, | |
| conditioning=c, | |
| batch_size=c.shape[0], | |
| shape=shape, | |
| verbose=False) | |
| x_samples_ddim = model.decode_first_stage(samples_ddim) | |
| image = torch.clamp((batch["image"]+1.0)/2.0, | |
| min=0.0, max=1.0) | |
| mask = torch.clamp((batch["mask"]+1.0)/2.0, | |
| min=0.0, max=1.0) | |
| predicted_image = torch.clamp((x_samples_ddim+1.0)/2.0, | |
| min=0.0, max=1.0) | |
| inpainted = (1-mask)*image+mask*predicted_image | |
| inpainted = inpainted.cpu().numpy().transpose(0,2,3,1)[0]*255 | |
| #Image.fromarray(inpainted.astype(np.uint8)).save(outpath4) | |
| Image.fromarray(inpainted.astype(np.uint8)).save(os.path.join(outpath4, f"{base_count:05}.png")) | |
| base_count += 1 | |
| title="make seamless latent diffusion from Stable Diffusion repo" | |
| description="make seamless Stable Diffusion example" | |
| gr.Interface(fn=infer, inputs=[source_img], outputs=gallery,title=title,description=description).launch(enable_queue=True) |