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	# Originally made by Katherine Crowson (https://github.com/crowsonkb, https://twitter.com/RiversHaveWings)
	# The original BigGAN+CLIP method was by https://twitter.com/advadnoun

	import argparse
	import math
	import random
	# from email.policy import default
	from urllib.request import urlopen
	from tqdm import tqdm
	import sys
	import os

	# pip install taming-transformers doesn't work with Gumbel, but does not yet work with coco etc
	# appending the path does work with Gumbel, but gives ModuleNotFoundError: No module named 'transformers' for coco etc
	sys.path.append('taming-transformers')

	from omegaconf import OmegaConf
	from taming.models import cond_transformer, vqgan
	#import taming.modules

	import torch
	from torch import nn, optim
	from torch.nn import functional as F
	from torchvision import transforms
	from torchvision.transforms import functional as TF
	from torch.cuda import get_device_properties
	torch.backends.cudnn.benchmark = False # NR: True is a bit faster, but can lead to OOM. False is more deterministic.
	#torch.use_deterministic_algorithms(True) # NR: grid_sampler_2d_backward_cuda does not have a deterministic implementation

	from torch_optimizer import DiffGrad, AdamP

	from CLIP import clip
	import kornia.augmentation as K
	import numpy as np
	import imageio

	from PIL import ImageFile, Image, PngImagePlugin, ImageChops
	ImageFile.LOAD_TRUNCATED_IMAGES = True

	from subprocess import Popen, PIPE
	import re

	# Supress warnings
	import warnings
	warnings.filterwarnings('ignore')


	# Check for GPU and reduce the default image size if low VRAM
	default_image_size = 512 # >8GB VRAM
	if not torch.cuda.is_available():
	default_image_size = 256 # no GPU found
	elif get_device_properties(0).total_memory <= 2 33: # 2 33 = 8,589,934,592 bytes = 8 GB
	default_image_size = 304 # <8GB VRAM

	# Create the parser
	vq_parser = argparse.ArgumentParser(description='Image generation using VQGAN+CLIP')

	# Add the arguments
	vq_parser.add_argument("-p", "--prompts", type=str, help="Text prompts", default=None, dest='prompts')
	vq_parser.add_argument("-ip", "--image_prompts", type=str, help="Image prompts / target image", default=[], dest='image_prompts')
	vq_parser.add_argument("-i", "--iterations", type=int, help="Number of iterations", default=500, dest='max_iterations')
	vq_parser.add_argument("-se", "--save_every", type=int, help="Save image iterations", default=50, dest='display_freq')
	vq_parser.add_argument("-s", "--size", nargs=2, type=int, help="Image size (width height) (default: %(default)s)", default=[default_image_size,default_image_size], dest='size')
	vq_parser.add_argument("-ii", "--init_image", type=str, help="Initial image", default=None, dest='init_image')
	vq_parser.add_argument("-in", "--init_noise", type=str, help="Initial noise image (pixels or gradient)", default=None, dest='init_noise')
	vq_parser.add_argument("-iw", "--init_weight", type=float, help="Initial weight", default=0., dest='init_weight')
	vq_parser.add_argument("-m", "--clip_model", type=str, help="CLIP model (e.g. ViT-B/32, ViT-B/16)", default='ViT-B/32', dest='clip_model')
	vq_parser.add_argument("-conf", "--vqgan_config", type=str, help="VQGAN config", default=f'checkpoints/vqgan_imagenet_f16_16384.yaml', dest='vqgan_config')
	vq_parser.add_argument("-ckpt", "--vqgan_checkpoint", type=str, help="VQGAN checkpoint", default=f'checkpoints/vqgan_imagenet_f16_16384.ckpt', dest='vqgan_checkpoint')
	vq_parser.add_argument("-nps", "--noise_prompt_seeds", nargs="*", type=int, help="Noise prompt seeds", default=[], dest='noise_prompt_seeds')
	vq_parser.add_argument("-npw", "--noise_prompt_weights", nargs="*", type=float, help="Noise prompt weights", default=[], dest='noise_prompt_weights')
	vq_parser.add_argument("-lr", "--learning_rate", type=float, help="Learning rate", default=0.1, dest='step_size')
	vq_parser.add_argument("-cutm", "--cut_method", type=str, help="Cut method", choices=['original','updated','nrupdated','updatedpooling','latest'], default='latest', dest='cut_method')
	vq_parser.add_argument("-cuts", "--num_cuts", type=int, help="Number of cuts", default=32, dest='cutn')
	vq_parser.add_argument("-cutp", "--cut_power", type=float, help="Cut power", default=1., dest='cut_pow')
	vq_parser.add_argument("-sd", "--seed", type=int, help="Seed", default=None, dest='seed')
	vq_parser.add_argument("-opt", "--optimiser", type=str, help="Optimiser", choices=['Adam','AdamW','Adagrad','Adamax','DiffGrad','AdamP','RAdam','RMSprop'], default='Adam', dest='optimiser')
	vq_parser.add_argument("-o", "--output", type=str, help="Output image filename", default="output.png", dest='output')
	vq_parser.add_argument("-vid", "--video", action='store_true', help="Create video frames?", dest='make_video')
	vq_parser.add_argument("-zvid", "--zoom_video", action='store_true', help="Create zoom video?", dest='make_zoom_video')
	vq_parser.add_argument("-zs", "--zoom_start", type=int, help="Zoom start iteration", default=0, dest='zoom_start')
	vq_parser.add_argument("-zse", "--zoom_save_every", type=int, help="Save zoom image iterations", default=10, dest='zoom_frequency')
	vq_parser.add_argument("-zsc", "--zoom_scale", type=float, help="Zoom scale %%", default=0.99, dest='zoom_scale')
	vq_parser.add_argument("-zsx", "--zoom_shift_x", type=int, help="Zoom shift x (left/right) amount in pixels", default=0, dest='zoom_shift_x')
	vq_parser.add_argument("-zsy", "--zoom_shift_y", type=int, help="Zoom shift y (up/down) amount in pixels", default=0, dest='zoom_shift_y')
	vq_parser.add_argument("-cpe", "--change_prompt_every", type=int, help="Prompt change frequency", default=0, dest='prompt_frequency')
	vq_parser.add_argument("-vl", "--video_length", type=float, help="Video length in seconds (not interpolated)", default=10, dest='video_length')
	vq_parser.add_argument("-ofps", "--output_video_fps", type=float, help="Create an interpolated video (Nvidia GPU only) with this fps (min 10. best set to 30 or 60)", default=0, dest='output_video_fps')
	vq_parser.add_argument("-ifps", "--input_video_fps", type=float, help="When creating an interpolated video, use this as the input fps to interpolate from (>0 & <ofps)", default=15, dest='input_video_fps')
	vq_parser.add_argument("-d", "--deterministic", action='store_true', help="Enable cudnn.deterministic?", dest='cudnn_determinism')
	vq_parser.add_argument("-aug", "--augments", nargs='+', action='append', type=str, choices=['Ji','Sh','Gn','Pe','Ro','Af','Et','Ts','Cr','Er','Re'], help="Enabled augments (latest vut method only)", default=[], dest='augments')
	vq_parser.add_argument("-vsd", "--video_style_dir", type=str, help="Directory with video frames to style", default=None, dest='video_style_dir')
	vq_parser.add_argument("-cd", "--cuda_device", type=str, help="Cuda device to use", default="cuda:0", dest='cuda_device')


	# Execute the parse_args() method
	args = vq_parser.parse_args()

	if not args.prompts and not args.image_prompts:
	args.prompts = "A cute, smiling, Nerdy Rodent"

	if args.cudnn_determinism:
	torch.backends.cudnn.deterministic = True

	if not args.augments:
	args.augments = [['Af', 'Pe', 'Ji', 'Er']]

	# Split text prompts using the pipe character (weights are split later)
	if args.prompts:
	# For stories, there will be many phrases
	story_phrases = [phrase.strip() for phrase in args.prompts.split("^")]

	# Make a list of all phrases
	all_phrases = []
	for phrase in story_phrases:
	all_phrases.append(phrase.split("\|"))

	# First phrase
	args.prompts = all_phrases[0]

	# Split target images using the pipe character (weights are split later)
	if args.image_prompts:
	args.image_prompts = args.image_prompts.split("\|")
	args.image_prompts = [image.strip() for image in args.image_prompts]

	if args.make_video and args.make_zoom_video:
	print("Warning: Make video and make zoom video are mutually exclusive.")
	args.make_video = False

	# Make video steps directory
	if args.make_video or args.make_zoom_video:
	if not os.path.exists('steps'):
	os.mkdir('steps')

	# Fallback to CPU if CUDA is not found and make sure GPU video rendering is also disabled
	# NB. May not work for AMD cards?
	if not args.cuda_device == 'cpu' and not torch.cuda.is_available():
	args.cuda_device = 'cpu'
	args.video_fps = 0
	print("Warning: No GPU found! Using the CPU instead. The iterations will be slow.")
	print("Perhaps CUDA/ROCm or the right pytorch version is not properly installed?")

	# If a video_style_dir has been, then create a list of all the images
	if args.video_style_dir:
	print("Locating video frames...")
	video_frame_list = []
	for entry in os.scandir(args.video_style_dir):
	if (entry.path.endswith(".jpg")
	or entry.path.endswith(".png")) and entry.is_file():
	video_frame_list.append(entry.path)

	# Reset a few options - same filename, different directory
	if not os.path.exists('steps'):
	os.mkdir('steps')

	args.init_image = video_frame_list[0]
	filename = os.path.basename(args.init_image)
	cwd = os.getcwd()
	args.output = os.path.join(cwd, "steps", filename)
	num_video_frames = len(video_frame_list) # for video styling


	# Various functions and classes
	def sinc(x):
	return torch.where(x != 0, torch.sin(math.pi * x) / (math.pi * x), x.new_ones([]))


	def lanczos(x, a):
	cond = torch.logical_and(-a < x, x < a)
	out = torch.where(cond, sinc(x) * sinc(x/a), x.new_zeros([]))
	return out / out.sum()


	def ramp(ratio, width):
	n = math.ceil(width / ratio + 1)
	out = torch.empty([n])
	cur = 0
	for i in range(out.shape[0]):
	out[i] = cur
	cur += ratio
	return torch.cat([-out[1:].flip([0]), out])[1:-1]


	# For zoom video
	def zoom_at(img, x, y, zoom):
	w, h = img.size
	zoom2 = zoom * 2
	img = img.crop((x - w / zoom2, y - h / zoom2,
	x + w / zoom2, y + h / zoom2))
	return img.resize((w, h), Image.LANCZOS)


	# NR: Testing with different intital images
	def random_noise_image(w,h):
	random_image = Image.fromarray(np.random.randint(0,255,(w,h,3),dtype=np.dtype('uint8')))
	return random_image


	# create initial gradient image
	def gradient_2d(start, stop, width, height, is_horizontal):
	if is_horizontal:
	return np.tile(np.linspace(start, stop, width), (height, 1))
	else:
	return np.tile(np.linspace(start, stop, height), (width, 1)).T


	def gradient_3d(width, height, start_list, stop_list, is_horizontal_list):
	result = np.zeros((height, width, len(start_list)), dtype=float)

	for i, (start, stop, is_horizontal) in enumerate(zip(start_list, stop_list, is_horizontal_list)):
	result[:, :, i] = gradient_2d(start, stop, width, height, is_horizontal)

	return result


	def random_gradient_image(w,h):
	array = gradient_3d(w, h, (0, 0, np.random.randint(0,255)), (np.random.randint(1,255), np.random.randint(2,255), np.random.randint(3,128)), (True, False, False))
	random_image = Image.fromarray(np.uint8(array))
	return random_image


	# Used in older MakeCutouts
	def resample(input, size, align_corners=True):
	n, c, h, w = input.shape
	dh, dw = size

	input = input.view([n * c, 1, h, w])

	if dh < h:
	kernel_h = lanczos(ramp(dh / h, 2), 2).to(input.device, input.dtype)
	pad_h = (kernel_h.shape[0] - 1) // 2
	input = F.pad(input, (0, 0, pad_h, pad_h), 'reflect')
	input = F.conv2d(input, kernel_h[None, None, :, None])

	if dw < w:
	kernel_w = lanczos(ramp(dw / w, 2), 2).to(input.device, input.dtype)
	pad_w = (kernel_w.shape[0] - 1) // 2
	input = F.pad(input, (pad_w, pad_w, 0, 0), 'reflect')
	input = F.conv2d(input, kernel_w[None, None, None, :])

	input = input.view([n, c, h, w])
	return F.interpolate(input, size, mode='bicubic', align_corners=align_corners)


	class ReplaceGrad(torch.autograd.Function):
	@staticmethod
	def forward(ctx, x_forward, x_backward):
	ctx.shape = x_backward.shape
	return x_forward

	@staticmethod
	def backward(ctx, grad_in):
	return None, grad_in.sum_to_size(ctx.shape)

	replace_grad = ReplaceGrad.apply


	class ClampWithGrad(torch.autograd.Function):
	@staticmethod
	def forward(ctx, input, min, max):
	ctx.min = min
	ctx.max = max
	ctx.save_for_backward(input)
	return input.clamp(min, max)

	@staticmethod
	def backward(ctx, grad_in):
	input, = ctx.saved_tensors
	return grad_in * (grad_in * (input - input.clamp(ctx.min, ctx.max)) >= 0), None, None

	clamp_with_grad = ClampWithGrad.apply


	def vector_quantize(x, codebook):
	d = x.pow(2).sum(dim=-1, keepdim=True) + codebook.pow(2).sum(dim=1) - 2 * x @ codebook.T
	indices = d.argmin(-1)
	x_q = F.one_hot(indices, codebook.shape[0]).to(d.dtype) @ codebook
	return replace_grad(x_q, x)


	class Prompt(nn.Module):
	def __init__(self, embed, weight=1., stop=float('-inf')):
	super().__init__()
	self.register_buffer('embed', embed)
	self.register_buffer('weight', torch.as_tensor(weight))
	self.register_buffer('stop', torch.as_tensor(stop))

	def forward(self, input):
	input_normed = F.normalize(input.unsqueeze(1), dim=2)
	embed_normed = F.normalize(self.embed.unsqueeze(0), dim=2)
	dists = input_normed.sub(embed_normed).norm(dim=2).div(2).arcsin().pow(2).mul(2)
	dists = dists * self.weight.sign()
	return self.weight.abs() * replace_grad(dists, torch.maximum(dists, self.stop)).mean()


	#NR: Split prompts and weights
	def split_prompt(prompt):
	vals = prompt.rsplit(':', 2)
	vals = vals + ['', '1', '-inf'][len(vals):]
	return vals[0], float(vals[1]), float(vals[2])


	class MakeCutouts(nn.Module):
	def __init__(self, cut_size, cutn, cut_pow=1.):
	super().__init__()
	self.cut_size = cut_size
	self.cutn = cutn
	self.cut_pow = cut_pow # not used with pooling

	# Pick your own augments & their order
	augment_list = []
	for item in args.augments[0]:
	if item == 'Ji':
	augment_list.append(K.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1, p=0.7))
	elif item == 'Sh':
	augment_list.append(K.RandomSharpness(sharpness=0.3, p=0.5))
	elif item == 'Gn':
	augment_list.append(K.RandomGaussianNoise(mean=0.0, std=1., p=0.5))
	elif item == 'Pe':
	augment_list.append(K.RandomPerspective(distortion_scale=0.7, p=0.7))
	elif item == 'Ro':
	augment_list.append(K.RandomRotation(degrees=15, p=0.7))
	elif item == 'Af':
	augment_list.append(K.RandomAffine(degrees=15, translate=0.1, shear=5, p=0.7, padding_mode='zeros', keepdim=True)) # border, reflection, zeros
	elif item == 'Et':
	augment_list.append(K.RandomElasticTransform(p=0.7))
	elif item == 'Ts':
	augment_list.append(K.RandomThinPlateSpline(scale=0.8, same_on_batch=True, p=0.7))
	elif item == 'Cr':
	augment_list.append(K.RandomCrop(size=(self.cut_size,self.cut_size), pad_if_needed=True, padding_mode='reflect', p=0.5))
	elif item == 'Er':
	augment_list.append(K.RandomErasing(scale=(.1, .4), ratio=(.3, 1/.3), same_on_batch=True, p=0.7))
	elif item == 'Re':
	augment_list.append(K.RandomResizedCrop(size=(self.cut_size,self.cut_size), scale=(0.1,1), ratio=(0.75,1.333), cropping_mode='resample', p=0.5))

	self.augs = nn.Sequential(*augment_list)
	self.noise_fac = 0.1
	# self.noise_fac = False

	# Uncomment if you like seeing the list ;)
	# print(augment_list)

	# Pooling
	self.av_pool = nn.AdaptiveAvgPool2d((self.cut_size, self.cut_size))
	self.max_pool = nn.AdaptiveMaxPool2d((self.cut_size, self.cut_size))

	def forward(self, input):
	cutouts = []

	for _ in range(self.cutn):
	# Use Pooling
	cutout = (self.av_pool(input) + self.max_pool(input))/2
	cutouts.append(cutout)

	batch = self.augs(torch.cat(cutouts, dim=0))

	if self.noise_fac:
	facs = batch.new_empty([self.cutn, 1, 1, 1]).uniform_(0, self.noise_fac)
	batch = batch + facs * torch.randn_like(batch)
	return batch


	# An updated version with Kornia augments and pooling (where my version started):
	class MakeCutoutsPoolingUpdate(nn.Module):
	def __init__(self, cut_size, cutn, cut_pow=1.):
	super().__init__()
	self.cut_size = cut_size
	self.cutn = cutn
	self.cut_pow = cut_pow # Not used with pooling

	self.augs = nn.Sequential(
	K.RandomAffine(degrees=15, translate=0.1, p=0.7, padding_mode='border'),
	K.RandomPerspective(0.7,p=0.7),
	K.ColorJitter(hue=0.1, saturation=0.1, p=0.7),
	K.RandomErasing((.1, .4), (.3, 1/.3), same_on_batch=True, p=0.7),
	)

	self.noise_fac = 0.1
	self.av_pool = nn.AdaptiveAvgPool2d((self.cut_size, self.cut_size))
	self.max_pool = nn.AdaptiveMaxPool2d((self.cut_size, self.cut_size))

	def forward(self, input):
	sideY, sideX = input.shape[2:4]
	max_size = min(sideX, sideY)
	min_size = min(sideX, sideY, self.cut_size)
	cutouts = []

	for _ in range(self.cutn):
	cutout = (self.av_pool(input) + self.max_pool(input))/2
	cutouts.append(cutout)

	batch = self.augs(torch.cat(cutouts, dim=0))

	if self.noise_fac:
	facs = batch.new_empty([self.cutn, 1, 1, 1]).uniform_(0, self.noise_fac)
	batch = batch + facs * torch.randn_like(batch)
	return batch


	# An Nerdy updated version with selectable Kornia augments, but no pooling:
	class MakeCutoutsNRUpdate(nn.Module):
	def __init__(self, cut_size, cutn, cut_pow=1.):
	super().__init__()
	self.cut_size = cut_size
	self.cutn = cutn
	self.cut_pow = cut_pow
	self.noise_fac = 0.1

	# Pick your own augments & their order
	augment_list = []
	for item in args.augments[0]:
	if item == 'Ji':
	augment_list.append(K.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1, p=0.7))
	elif item == 'Sh':
	augment_list.append(K.RandomSharpness(sharpness=0.3, p=0.5))
	elif item == 'Gn':
	augment_list.append(K.RandomGaussianNoise(mean=0.0, std=1., p=0.5))
	elif item == 'Pe':
	augment_list.append(K.RandomPerspective(distortion_scale=0.5, p=0.7))
	elif item == 'Ro':
	augment_list.append(K.RandomRotation(degrees=15, p=0.7))
	elif item == 'Af':
	augment_list.append(K.RandomAffine(degrees=30, translate=0.1, shear=5, p=0.7, padding_mode='zeros', keepdim=True)) # border, reflection, zeros
	elif item == 'Et':
	augment_list.append(K.RandomElasticTransform(p=0.7))
	elif item == 'Ts':
	augment_list.append(K.RandomThinPlateSpline(scale=0.8, same_on_batch=True, p=0.7))
	elif item == 'Cr':
	augment_list.append(K.RandomCrop(size=(self.cut_size,self.cut_size), pad_if_needed=True, padding_mode='reflect', p=0.5))
	elif item == 'Er':
	augment_list.append(K.RandomErasing(scale=(.1, .4), ratio=(.3, 1/.3), same_on_batch=True, p=0.7))
	elif item == 'Re':
	augment_list.append(K.RandomResizedCrop(size=(self.cut_size,self.cut_size), scale=(0.1,1), ratio=(0.75,1.333), cropping_mode='resample', p=0.5))

	self.augs = nn.Sequential(*augment_list)


	def forward(self, input):
	sideY, sideX = input.shape[2:4]
	max_size = min(sideX, sideY)
	min_size = min(sideX, sideY, self.cut_size)
	cutouts = []
	for _ in range(self.cutn):
	size = int(torch.rand([])*self.cut_pow (max_size - min_size) + min_size)
	offsetx = torch.randint(0, sideX - size + 1, ())
	offsety = torch.randint(0, sideY - size + 1, ())
	cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]
	cutouts.append(resample(cutout, (self.cut_size, self.cut_size)))
	batch = self.augs(torch.cat(cutouts, dim=0))
	if self.noise_fac:
	facs = batch.new_empty([self.cutn, 1, 1, 1]).uniform_(0, self.noise_fac)
	batch = batch + facs * torch.randn_like(batch)
	return batch


	# An updated version with Kornia augments, but no pooling:
	class MakeCutoutsUpdate(nn.Module):
	def __init__(self, cut_size, cutn, cut_pow=1.):
	super().__init__()
	self.cut_size = cut_size
	self.cutn = cutn
	self.cut_pow = cut_pow
	self.augs = nn.Sequential(
	K.RandomHorizontalFlip(p=0.5),
	K.ColorJitter(hue=0.01, saturation=0.01, p=0.7),
	# K.RandomSolarize(0.01, 0.01, p=0.7),
	K.RandomSharpness(0.3,p=0.4),
	K.RandomAffine(degrees=30, translate=0.1, p=0.8, padding_mode='border'),
	K.RandomPerspective(0.2,p=0.4),)
	self.noise_fac = 0.1


	def forward(self, input):
	sideY, sideX = input.shape[2:4]
	max_size = min(sideX, sideY)
	min_size = min(sideX, sideY, self.cut_size)
	cutouts = []
	for _ in range(self.cutn):
	size = int(torch.rand([])*self.cut_pow (max_size - min_size) + min_size)
	offsetx = torch.randint(0, sideX - size + 1, ())
	offsety = torch.randint(0, sideY - size + 1, ())
	cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]
	cutouts.append(resample(cutout, (self.cut_size, self.cut_size)))
	batch = self.augs(torch.cat(cutouts, dim=0))
	if self.noise_fac:
	facs = batch.new_empty([self.cutn, 1, 1, 1]).uniform_(0, self.noise_fac)
	batch = batch + facs * torch.randn_like(batch)
	return batch


	# This is the original version (No pooling)
	class MakeCutoutsOrig(nn.Module):
	def __init__(self, cut_size, cutn, cut_pow=1.):
	super().__init__()
	self.cut_size = cut_size
	self.cutn = cutn
	self.cut_pow = cut_pow

	def forward(self, input):
	sideY, sideX = input.shape[2:4]
	max_size = min(sideX, sideY)
	min_size = min(sideX, sideY, self.cut_size)
	cutouts = []
	for _ in range(self.cutn):
	size = int(torch.rand([])*self.cut_pow (max_size - min_size) + min_size)
	offsetx = torch.randint(0, sideX - size + 1, ())
	offsety = torch.randint(0, sideY - size + 1, ())
	cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]
	cutouts.append(resample(cutout, (self.cut_size, self.cut_size)))
	return clamp_with_grad(torch.cat(cutouts, dim=0), 0, 1)


	def load_vqgan_model(config_path, checkpoint_path):
	global gumbel
	gumbel = False
	config = OmegaConf.load(config_path)
	if config.model.target == 'taming.models.vqgan.VQModel':
	model = vqgan.VQModel(**config.model.params)
	model.eval().requires_grad_(False)
	model.init_from_ckpt(checkpoint_path)
	elif config.model.target == 'taming.models.vqgan.GumbelVQ':
	model = vqgan.GumbelVQ(**config.model.params)
	model.eval().requires_grad_(False)
	model.init_from_ckpt(checkpoint_path)
	gumbel = True
	elif config.model.target == 'taming.models.cond_transformer.Net2NetTransformer':
	parent_model = cond_transformer.Net2NetTransformer(**config.model.params)
	parent_model.eval().requires_grad_(False)
	parent_model.init_from_ckpt(checkpoint_path)
	model = parent_model.first_stage_model
	else:
	raise ValueError(f'unknown model type: {config.model.target}')
	del model.loss
	return model


	def resize_image(image, out_size):
	ratio = image.size[0] / image.size[1]
	area = min(image.size[0] * image.size[1], out_size[0] * out_size[1])
	size = round((area * ratio)0.5), round((area / ratio)0.5)
	return image.resize(size, Image.LANCZOS)


	# Do it
	device = torch.device(args.cuda_device)
	model = load_vqgan_model(args.vqgan_config, args.vqgan_checkpoint).to(device)
	jit = True if "1.7.1" in torch.__version__ else False
	perceptor = clip.load(args.clip_model, jit=jit)[0].eval().requires_grad_(False).to(device)

	# clock=deepcopy(perceptor.visual.positional_embedding.data)
	# perceptor.visual.positional_embedding.data = clock/clock.max()
	# perceptor.visual.positional_embedding.data=clamp_with_grad(clock,0,1)

	cut_size = perceptor.visual.input_resolution
	f = 2**(model.decoder.num_resolutions - 1)

	# Cutout class options:
	# 'latest','original','updated' or 'updatedpooling'
	if args.cut_method == 'latest':
	make_cutouts = MakeCutouts(cut_size, args.cutn, cut_pow=args.cut_pow)
	elif args.cut_method == 'original':
	make_cutouts = MakeCutoutsOrig(cut_size, args.cutn, cut_pow=args.cut_pow)
	elif args.cut_method == 'updated':
	make_cutouts = MakeCutoutsUpdate(cut_size, args.cutn, cut_pow=args.cut_pow)
	elif args.cut_method == 'nrupdated':
	make_cutouts = MakeCutoutsNRUpdate(cut_size, args.cutn, cut_pow=args.cut_pow)
	else:
	make_cutouts = MakeCutoutsPoolingUpdate(cut_size, args.cutn, cut_pow=args.cut_pow)

	toksX, toksY = args.size[0] // f, args.size[1] // f
	sideX, sideY = toksX * f, toksY * f

	# Gumbel or not?
	if gumbel:
	e_dim = 256
	n_toks = model.quantize.n_embed
	z_min = model.quantize.embed.weight.min(dim=0).values[None, :, None, None]
	z_max = model.quantize.embed.weight.max(dim=0).values[None, :, None, None]
	else:
	e_dim = model.quantize.e_dim
	n_toks = model.quantize.n_e
	z_min = model.quantize.embedding.weight.min(dim=0).values[None, :, None, None]
	z_max = model.quantize.embedding.weight.max(dim=0).values[None, :, None, None]


	if args.init_image:
	if 'http' in args.init_image:
	img = Image.open(urlopen(args.init_image))
	else:
	img = Image.open(args.init_image)
	pil_image = img.convert('RGB')
	pil_image = pil_image.resize((sideX, sideY), Image.LANCZOS)
	pil_tensor = TF.to_tensor(pil_image)
	z, _ = model.encode(pil_tensor.to(device).unsqueeze(0) 2 - 1)
	elif args.init_noise == 'pixels':
	img = random_noise_image(args.size[0], args.size[1])
	pil_image = img.convert('RGB')
	pil_image = pil_image.resize((sideX, sideY), Image.LANCZOS)
	pil_tensor = TF.to_tensor(pil_image)
	z, _ = model.encode(pil_tensor.to(device).unsqueeze(0) 2 - 1)
	elif args.init_noise == 'gradient':
	img = random_gradient_image(args.size[0], args.size[1])
	pil_image = img.convert('RGB')
	pil_image = pil_image.resize((sideX, sideY), Image.LANCZOS)
	pil_tensor = TF.to_tensor(pil_image)
	z, _ = model.encode(pil_tensor.to(device).unsqueeze(0) 2 - 1)
	else:
	one_hot = F.one_hot(torch.randint(n_toks, [toksY * toksX], device=device), n_toks).float()
	# z = one_hot @ model.quantize.embedding.weight
	if gumbel:
	z = one_hot @ model.quantize.embed.weight
	else:
	z = one_hot @ model.quantize.embedding.weight

	z = z.view([-1, toksY, toksX, e_dim]).permute(0, 3, 1, 2)
	#z = torch.rand_like(z)*2 # NR: check

	z_orig = z.clone()
	z.requires_grad_(True)

	pMs = []
	normalize = transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
	std=[0.26862954, 0.26130258, 0.27577711])

	# From imagenet - Which is better?
	#normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
	# std=[0.229, 0.224, 0.225])

	# CLIP tokenize/encode
	if args.prompts:
	for prompt in args.prompts:
	txt, weight, stop = split_prompt(prompt)
	embed = perceptor.encode_text(clip.tokenize(txt).to(device)).float()
	pMs.append(Prompt(embed, weight, stop).to(device))

	for prompt in args.image_prompts:
	path, weight, stop = split_prompt(prompt)
	img = Image.open(path)
	pil_image = img.convert('RGB')
	img = resize_image(pil_image, (sideX, sideY))
	batch = make_cutouts(TF.to_tensor(img).unsqueeze(0).to(device))
	embed = perceptor.encode_image(normalize(batch)).float()
	pMs.append(Prompt(embed, weight, stop).to(device))

	for seed, weight in zip(args.noise_prompt_seeds, args.noise_prompt_weights):
	gen = torch.Generator().manual_seed(seed)
	embed = torch.empty([1, perceptor.visual.output_dim]).normal_(generator=gen)
	pMs.append(Prompt(embed, weight).to(device))


	# Set the optimiser
	def get_opt(opt_name, opt_lr):
	if opt_name == "Adam":
	opt = optim.Adam([z], lr=opt_lr) # LR=0.1 (Default)
	elif opt_name == "AdamW":
	opt = optim.AdamW([z], lr=opt_lr)
	elif opt_name == "Adagrad":
	opt = optim.Adagrad([z], lr=opt_lr)
	elif opt_name == "Adamax":
	opt = optim.Adamax([z], lr=opt_lr)
	elif opt_name == "DiffGrad":
	opt = DiffGrad([z], lr=opt_lr, eps=1e-9, weight_decay=1e-9) # NR: Playing for reasons
	elif opt_name == "AdamP":
	opt = AdamP([z], lr=opt_lr)
	elif opt_name == "RAdam":
	opt = optim.RAdam([z], lr=opt_lr)
	elif opt_name == "RMSprop":
	opt = optim.RMSprop([z], lr=opt_lr)
	else:
	print("Unknown optimiser. Are choices broken?")
	opt = optim.Adam([z], lr=opt_lr)
	return opt

	opt = get_opt(args.optimiser, args.step_size)


	# Output for the user
	print('Using device:', device)
	print('Optimising using:', args.optimiser)

	if args.prompts:
	print('Using text prompts:', args.prompts)
	if args.image_prompts:
	print('Using image prompts:', args.image_prompts)
	if args.init_image:
	print('Using initial image:', args.init_image)
	if args.noise_prompt_weights:
	print('Noise prompt weights:', args.noise_prompt_weights)


	if args.seed is None:
	seed = torch.seed()
	else:
	seed = args.seed
	torch.manual_seed(seed)
	print('Using seed:', seed)


	# Vector quantize
	def synth(z):
	if gumbel:
	z_q = vector_quantize(z.movedim(1, 3), model.quantize.embed.weight).movedim(3, 1)
	else:
	z_q = vector_quantize(z.movedim(1, 3), model.quantize.embedding.weight).movedim(3, 1)
	return clamp_with_grad(model.decode(z_q).add(1).div(2), 0, 1)


	#@torch.no_grad()
	@torch.inference_mode()
	def checkin(i, losses):
	losses_str = ', '.join(f'{loss.item():g}' for loss in losses)
	tqdm.write(f'i: {i}, loss: {sum(losses).item():g}, losses: {losses_str}')
	out = synth(z)
	info = PngImagePlugin.PngInfo()
	info.add_text('comment', f'{args.prompts}')
	TF.to_pil_image(out[0].cpu()).save(args.output, pnginfo=info)


	def ascend_txt():
	global i
	out = synth(z)
	iii = perceptor.encode_image(normalize(make_cutouts(out))).float()

	result = []

	if args.init_weight:
	# result.append(F.mse_loss(z, z_orig) * args.init_weight / 2)
	result.append(F.mse_loss(z, torch.zeros_like(z_orig)) * ((1/torch.tensor(i2 + 1))args.init_weight) / 2)

	for prompt in pMs:
	result.append(prompt(iii))

	if args.make_video:
	img = np.array(out.mul(255).clamp(0, 255)[0].cpu().detach().numpy().astype(np.uint8))[:,:,:]
	img = np.transpose(img, (1, 2, 0))
	imageio.imwrite('./steps/' + str(i) + '.png', np.array(img))

	return result # return loss


	def train(i):
	opt.zero_grad(set_to_none=True)
	lossAll = ascend_txt()

	if i % args.display_freq == 0:
	checkin(i, lossAll)

	loss = sum(lossAll)
	loss.backward()
	opt.step()

	#with torch.no_grad():
	with torch.inference_mode():
	z.copy_(z.maximum(z_min).minimum(z_max))



	i = 0 # Iteration counter
	j = 0 # Zoom video frame counter
	p = 1 # Phrase counter
	smoother = 0 # Smoother counter
	this_video_frame = 0 # for video styling

	# Messing with learning rate / optimisers
	#variable_lr = args.step_size
	#optimiser_list = [['Adam',0.075],['AdamW',0.125],['Adagrad',0.2],['Adamax',0.125],['DiffGrad',0.075],['RAdam',0.125],['RMSprop',0.02]]

	# Do it
	try:
	with tqdm() as pbar:
	while True:
	# Change generated image
	if args.make_zoom_video:
	if i % args.zoom_frequency == 0:
	out = synth(z)

	# Save image
	img = np.array(out.mul(255).clamp(0, 255)[0].cpu().detach().numpy().astype(np.uint8))[:,:,:]
	img = np.transpose(img, (1, 2, 0))
	imageio.imwrite('./steps/' + str(j) + '.png', np.array(img))

	# Time to start zooming?
	if args.zoom_start <= i:
	# Convert z back into a Pil image
	#pil_image = TF.to_pil_image(out[0].cpu())

	# Convert NP to Pil image
	pil_image = Image.fromarray(np.array(img).astype('uint8'), 'RGB')

	# Zoom
	if args.zoom_scale != 1:
	pil_image_zoom = zoom_at(pil_image, sideX/2, sideY/2, args.zoom_scale)
	else:
	pil_image_zoom = pil_image

	# Shift - https://pillow.readthedocs.io/en/latest/reference/ImageChops.html
	if args.zoom_shift_x or args.zoom_shift_y:
	# This one wraps the image
	pil_image_zoom = ImageChops.offset(pil_image_zoom, args.zoom_shift_x, args.zoom_shift_y)

	# Convert image back to a tensor again
	pil_tensor = TF.to_tensor(pil_image_zoom)

	# Re-encode
	z, _ = model.encode(pil_tensor.to(device).unsqueeze(0) 2 - 1)
	z_orig = z.clone()
	z.requires_grad_(True)

	# Re-create optimiser
	opt = get_opt(args.optimiser, args.step_size)

	# Next
	j += 1

	# Change text prompt
	if args.prompt_frequency > 0:
	if i % args.prompt_frequency == 0 and i > 0:
	# In case there aren't enough phrases, just loop
	if p >= len(all_phrases):
	p = 0

	pMs = []
	args.prompts = all_phrases[p]

	# Show user we're changing prompt
	print(args.prompts)

	for prompt in args.prompts:
	txt, weight, stop = split_prompt(prompt)
	embed = perceptor.encode_text(clip.tokenize(txt).to(device)).float()
	pMs.append(Prompt(embed, weight, stop).to(device))

	'''
	# Smooth test
	smoother = args.zoom_frequency * 15 # smoothing over x frames
	variable_lr = args.step_size * 0.25
	opt = get_opt(args.optimiser, variable_lr)
	'''

	p += 1

	'''
	if smoother > 0:
	if smoother == 1:
	opt = get_opt(args.optimiser, args.step_size)
	smoother -= 1
	'''

	'''
	# Messing with learning rate / optimisers
	if i % 225 == 0 and i > 0:
	variable_optimiser_item = random.choice(optimiser_list)
	variable_optimiser = variable_optimiser_item[0]
	variable_lr = variable_optimiser_item[1]

	opt = get_opt(variable_optimiser, variable_lr)
	print("New opt: %s, lr= %f" %(variable_optimiser,variable_lr))
	'''


	# Training time
	train(i)

	# Ready to stop yet?
	if i == args.max_iterations:
	if not args.video_style_dir:
	# we're done
	break
	else:
	if this_video_frame == (num_video_frames - 1):
	# we're done
	make_styled_video = True
	break
	else:
	# Next video frame
	this_video_frame += 1

	# Reset the iteration count
	i = -1
	pbar.reset()

	# Load the next frame, reset a few options - same filename, different directory
	args.init_image = video_frame_list[this_video_frame]
	print("Next frame: ", args.init_image)

	if args.seed is None:
	seed = torch.seed()
	else:
	seed = args.seed
	torch.manual_seed(seed)
	print("Seed: ", seed)

	filename = os.path.basename(args.init_image)
	args.output = os.path.join(cwd, "steps", filename)

	# Load and resize image
	img = Image.open(args.init_image)
	pil_image = img.convert('RGB')
	pil_image = pil_image.resize((sideX, sideY), Image.LANCZOS)
	pil_tensor = TF.to_tensor(pil_image)

	# Re-encode
	z, _ = model.encode(pil_tensor.to(device).unsqueeze(0) 2 - 1)
	z_orig = z.clone()
	z.requires_grad_(True)

	# Re-create optimiser
	opt = get_opt(args.optimiser, args.step_size)

	i += 1
	pbar.update()
	except KeyboardInterrupt:
	pass

	# All done :)

	# Video generation
	if args.make_video or args.make_zoom_video:
	init_frame = 1 # Initial video frame
	if args.make_zoom_video:
	last_frame = j
	else:
	last_frame = i # This will raise an error if that number of frames does not exist.

	length = args.video_length # Desired time of the video in seconds

	min_fps = 10
	max_fps = 60

	total_frames = last_frame-init_frame

	frames = []
	tqdm.write('Generating video...')
	for i in range(init_frame,last_frame):
	temp = Image.open("./steps/"+ str(i) +'.png')
	keep = temp.copy()
	frames.append(keep)
	temp.close()

	if args.output_video_fps > 9:
	# Hardware encoding and video frame interpolation
	print("Creating interpolated frames...")
	ffmpeg_filter = f"minterpolate='mi_mode=mci:me=hexbs:me_mode=bidir:mc_mode=aobmc:vsbmc=1:mb_size=8:search_param=32:fps={args.output_video_fps}'"
	output_file = re.compile('\.png$').sub('.mp4', args.output)
	try:
	p = Popen(['ffmpeg',
	'-y',
	'-f', 'image2pipe',
	'-vcodec', 'png',
	'-r', str(args.input_video_fps),
	'-i',
	'-',
	'-b:v', '10M',
	'-vcodec', 'h264_nvenc',
	'-pix_fmt', 'yuv420p',
	'-strict', '-2',
	'-filter:v', f'{ffmpeg_filter}',
	'-metadata', f'comment={args.prompts}',
	output_file], stdin=PIPE)
	except FileNotFoundError:
	print("ffmpeg command failed - check your installation")
	for im in tqdm(frames):
	im.save(p.stdin, 'PNG')
	p.stdin.close()
	p.wait()
	else:
	# CPU
	fps = np.clip(total_frames/length,min_fps,max_fps)
	output_file = re.compile('\.png$').sub('.mp4', args.output)
	try:
	p = Popen(['ffmpeg',
	'-y',
	'-f', 'image2pipe',
	'-vcodec', 'png',
	'-r', str(fps),
	'-i',
	'-',
	'-vcodec', 'libx264',
	'-r', str(fps),
	'-pix_fmt', 'yuv420p',
	'-crf', '17',
	'-preset', 'veryslow',
	'-metadata', f'comment={args.prompts}',
	output_file], stdin=PIPE)
	except FileNotFoundError:
	print("ffmpeg command failed - check your installation")
	for im in tqdm(frames):
	im.save(p.stdin, 'PNG')
	p.stdin.close()
	p.wait()