generate.py

# Originally made by Katherine Crowson (https://github.com/crowsonkb, https://twitter.com/RiversHaveWings)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 functools

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

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

from subprocess import Popen, PIPE
import re

# Supress warnings
import warnings

def force_async(fn):
    '''
    turns a sync function to async function using threads
    '''
    from concurrent.futures import ThreadPoolExecutor
    import asyncio
    pool = ThreadPoolExecutor()

    @functools.wraps(fn)
    def wrapper(*args, **kwargs):
        future = pool.submit(fn, *args, **kwargs)
        return asyncio.wrap_future(future)  # make it awaitable

    return wrapper

@force_async
def generate_image(args):
    # 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')

    ImageFile.LOAD_TRUNCATED_IMAGES = True

    warnings.filterwarnings('ignore')


    # Check for GPU and reduce the default image size if low VRAM
    default_image_size = 256  # >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

    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:
            hdr = { 'User-Agent' : 'Mozilla/5.0 (Windows NT 6.1; Win64; x64)' }
            req = urllib.request.Request(args.init_image, headers=hdr)
            img = Image.open(urllib.request.urlopen(req))
        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)
        if 'http' in path:
            hdr = { 'User-Agent' : 'Mozilla/5.0 (Windows NT 6.1; Win64; x64)' }
            req = urllib.request.Request(args.init_image, headers=hdr)
            img = Image.open(urllib.request.urlopen(req))
        else:
            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(i*2 + 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()     
    return args.output

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

    default_image_size = 256
    # 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()
    generate_image(args)