Update app.py

app.py

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