Create app.py

app.py

@@ -1,347 +1,7 @@
-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
 
+def greet(name):
+    return "Hello " + name + "!!"
 
-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)
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()