second_attempt.py

# Hypertron v2
# Original file is located at https://colab.research.google.com/drive/1N4UNSbtNMd31N_gAT9rAm8ZzPh62Y5ud
import sys

sys.stdout.write("Imports ...\n")
sys.stdout.flush()

sys.path.append('./CLIP')
sys.path.append('./taming-transformers')

import os
os.environ["XDG_CACHE_HOME"] = "../../.cache"
from huggingface_hub import hf_hub_download
import gradio as gr
from CLIP import clip
from omegaconf import OmegaConf
from taming.models import cond_transformer, vqgan
import torch
vqgan_model = hf_hub_download(repo_id="boris/vqgan_f16_16384", filename="model.ckpt")
vqgan_config = hf_hub_download(repo_id="boris/vqgan_f16_16384", filename="config.yaml")


device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("Using device:", device)


perceptor = (
    clip.load("ViT-B/32", jit=False)[0]
    .eval()
    .requires_grad_(False)
    .to(device)
)
def run_all(user_input,num_steps, template, width,height):
    global model
    global perceptor
    import argparse
    import math
    from pathlib import Path
    import sys
    import pandas as pd
    from IPython import display
    from base64 import b64encode
    
    from PIL import Image
    
    import torch
    from torch import nn
    import torch.optim as optim
    from torch import optim
    from torch.nn import functional as F
    from torchvision import transforms
    from torchvision.transforms import functional as TF
    import torchvision.transforms as T
    from tqdm.notebook import tqdm
    
    import kornia.augmentation as K
    import numpy as np
    import subprocess
    import imageio
    from PIL import ImageFile, Image
    #ImageFile.LOAD_TRUNCATED_IMAGES = True
    import hashlib
    from PIL.PngImagePlugin import PngImageFile, PngInfo
    import json
    import IPython
    from IPython.display import Markdown, display, Image, clear_output
    import urllib.request
    import random

    sys.stdout.write("Parsing arguments ...\n")
    sys.stdout.flush()

    def parse_args():
      desc = "Blah"
      parser = argparse.ArgumentParser(description=desc)
      parser.add_argument('--prompt', type=str, help='Text to generate image from.')
      parser.add_argument('--seed', type=int, help='Random seed.')
      parser.add_argument('--sizex', type=int, help='Image width.')
      parser.add_argument('--sizey', type=int, help='Image height.')
      parser.add_argument('--flavor', type=str, help='Flavor.')
      parser.add_argument('--template', type=str, help='Template.')
      parser.add_argument('--iterations', type=int, help='Iterations')
      parser.add_argument('--mse', type=int, help='Use MSE')
      parser.add_argument('--update', type=int, help='Update every n iterations.')
      parser.add_argument('--clip_model_1', type=str, help='CLIP model 1 to load.')
      parser.add_argument('--clip_model_2', type=str, help='CLIP model 2 to load.')
      parser.add_argument('--clip_model_3', type=str, help='CLIP model 3 to load.')
      parser.add_argument('--clip_model_4', type=str, help='CLIP model 4 to load.')
      parser.add_argument('--clip_model_5', type=str, help='CLIP model 5 to load.')
      parser.add_argument('--clip_model_6', type=str, help='CLIP model 6 to load.')
      parser.add_argument('--vqgan_model', type=str, help='VQGAN model to load.')
      parser.add_argument('--seed_image', type=str, help='Initial seed image.', default=None)
      parser.add_argument('--image_file', type=str, help='Output image name.')
      parser.add_argument('--frame_dir', type=str, help='Save frame file directory.')
      args = parser.parse_args()
      return args

    image_path = None
    flavor = 'cumin'
    #args2=parse_args();
    args2 = argparse.Namespace(
            prompt=user_input,
            seed=int(random.randint(0, 2147483647)),
            sizex=width,
            sizey=height,
            flavor=flavor,
            iterations=num_steps,
            mse=True,
            update=100,
            template=template,
            vqgan_model='ImageNet 16384',
            seed_image=image_path,
            image_file="progress.png",
            #frame_dir=intermediary_folder,
    )
    if args2.seed is not None:
        sys.stdout.write(f'Setting seed to {args2.seed} ...\n')
        sys.stdout.flush()
        import numpy as np
        np.random.seed(args2.seed)
        import random
        random.seed(args2.seed)
        #next line forces deterministic random values, but causes other issues with resampling (uncomment to see)
        #torch.use_deterministic_algorithms(True)
        torch.manual_seed(args2.seed)
        torch.cuda.manual_seed(args2.seed)
        torch.cuda.manual_seed_all(args2.seed)
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = False 





    """
    from imgtag import ImgTag    # metadata 
    from libxmp import *         # metadata
    import libxmp                # metadata
    from stegano import lsb
    import gc
    import GPUtil as GPU
    """

    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
    print('Using device:', device)


    def noise_gen(shape, octaves=5):
        n, c, h, w = shape
        noise = torch.zeros([n, c, 1, 1])
        max_octaves = min(octaves, math.log(h)/math.log(2), math.log(w)/math.log(2))
        for i in reversed(range(max_octaves)):
            h_cur, w_cur = h // 2**i, w // 2**i
            noise = F.interpolate(noise, (h_cur, w_cur), mode='bicubic', align_corners=False)
            noise += torch.randn([n, c, h_cur, w_cur]) / 5
        return noise

    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]


    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)

    def lerp(a, b, f):
        return (a * (1.0 - f)) + (b * f);

    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()


    #def parse_prompt(prompt):
    #    vals = prompt.rsplit(':', 2)
    #    vals = vals + ['', '1', '-inf'][len(vals):]
    #    return vals[0], float(vals[1]), float(vals[2])

    def parse_prompt(prompt):
        if prompt.startswith('http://') or prompt.startswith('https://'):
            vals = prompt.rsplit(':', 1)
            vals = [vals[0] + ':' + vals[1], *vals[2:]]
        else:
            vals = prompt.rsplit(':', 1)
        vals = vals + ['', '1', '-inf'][len(vals):]
        return vals[0], float(vals[1]), float(vals[2])

    def one_sided_clip_loss(input, target, labels=None, logit_scale=100):
        input_normed = F.normalize(input, dim=-1)
        target_normed = F.normalize(target, dim=-1)
        logits = input_normed @ target_normed.T * logit_scale
        if labels is None:
            labels = torch.arange(len(input), device=logits.device)
        return F.cross_entropy(logits, labels)

    class EMATensor(nn.Module):
        """implmeneted by Katherine Crowson"""
        def __init__(self, tensor, decay):
            super().__init__()
            self.tensor = nn.Parameter(tensor)
            self.register_buffer('biased', torch.zeros_like(tensor))
            self.register_buffer('average', torch.zeros_like(tensor))
            self.decay = decay
            self.register_buffer('accum', torch.tensor(1.))
            self.update()
        
        @torch.no_grad()
        def update(self):
            if not self.training:
                raise RuntimeError('update() should only be called during training')

            self.accum *= self.decay
            self.biased.mul_(self.decay)
            self.biased.add_((1 - self.decay) * self.tensor)
            self.average.copy_(self.biased)
            self.average.div_(1 - self.accum)

        def forward(self):
            if self.training:
                return self.tensor
            return self.average


    ############################################################################################
    ############################################################################################


    class MakeCutoutsJuu(nn.Module):
        def __init__(self, cut_size, cutn, cut_pow, augs):
            super().__init__()
            self.cut_size = cut_size
            self.cutn = cutn
            self.cut_pow = cut_pow
            self.augs = nn.Sequential(
                #K.RandomGaussianNoise(mean=0.0, std=0.5, p=0.1),
                K.RandomHorizontalFlip(p=0.5),
                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),
                K.ColorJitter(hue=0.01, saturation=0.01, p=0.7),
                K.RandomGrayscale(p=0.1),
            )
            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

    class MakeCutoutsMoth(nn.Module):
        def __init__(self, cut_size, cutn, cut_pow, augs, skip_augs=False):
            super().__init__()
            self.cut_size = cut_size
            self.cutn = cutn
            self.cut_pow = cut_pow
            self.skip_augs = skip_augs
            self.augs = T.Compose([
                T.RandomHorizontalFlip(p=0.5),
                T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
                T.RandomAffine(degrees=15, translate=(0.1, 0.1)),
                T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
                T.RandomPerspective(distortion_scale=0.4, p=0.7),
                T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
                T.RandomGrayscale(p=0.15),
                T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
                # T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
            ])

        def forward(self, input):
            input = T.Pad(input.shape[2]//4, fill=0)(input)
            sideY, sideX = input.shape[2:4]
            max_size = min(sideX, sideY)

            cutouts = []
            for ch in range(cutn):
                if ch > cutn - cutn//4:
                    cutout = input.clone()
                else:
                    size = int(max_size * torch.zeros(1,).normal_(mean=.8, std=.3).clip(float(self.cut_size/max_size), 1.))
                    offsetx = torch.randint(0, abs(sideX - size + 1), ())
                    offsety = torch.randint(0, abs(sideY - size + 1), ())
                    cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]

                if not self.skip_augs:
                    cutout = self.augs(cutout)
                cutouts.append(resample(cutout, (self.cut_size, self.cut_size)))
                del cutout

            cutouts = torch.cat(cutouts, dim=0)
            return cutouts

    class MakeCutoutsAaron(nn.Module):
        def __init__(self, cut_size, cutn, cut_pow, augs):
            super().__init__()
            self.cut_size = cut_size
            self.cutn = cutn
            self.cut_pow = cut_pow
            self.augs = augs
            self.av_pool = nn.AdaptiveAvgPool2d((self.cut_size, self.cut_size))
            self.max_pool = nn.AdaptiveMaxPool2d((self.cut_size, self.cut_size))

        def set_cut_pow(self, cut_pow):
            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 = []
            cutouts_full = []
            
            min_size_width = min(sideX, sideY)
            lower_bound = float(self.cut_size/min_size_width)
            
            for ii in range(self.cutn):
                size = int(min_size_width*torch.zeros(1,).normal_(mean=.8, std=.3).clip(lower_bound, 1.)) # replace .5 with a result for 224 the default large size is .95
              
                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)))

            cutouts = torch.cat(cutouts, dim=0)

            return clamp_with_grad(cutouts, 0, 1)

    class MakeCutoutsCumin(nn.Module):
        #from https://colab.research.google.com/drive/1ZAus_gn2RhTZWzOWUpPERNC0Q8OhZRTZ
        def __init__(self, cut_size, cutn, cut_pow, augs):
            super().__init__()
            self.cut_size = cut_size
            tqdm.write(f'cut size: {self.cut_size}')
            self.cutn = cutn
            self.cut_pow = cut_pow
            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))
            self.augs = nn.Sequential(
              #K.RandomHorizontalFlip(p=0.5),
              #K.RandomSharpness(0.3,p=0.4),
              #K.RandomGaussianBlur((3,3),(10.5,10.5),p=0.2),
              #K.RandomGaussianNoise(p=0.5),
              #K.RandomElasticTransform(kernel_size=(33, 33), sigma=(7,7), p=0.2),
              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),)
                
        def set_cut_pow(self, cut_pow):
          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 = []
            cutouts_full = []
            noise_fac = 0.1
            
            
            min_size_width = min(sideX, sideY)
            lower_bound = float(self.cut_size/min_size_width)
            
            for ii in range(self.cutn):
                
                
              # size = int(torch.rand([])**self.cut_pow * (max_size - min_size) + min_size)
              randsize = torch.zeros(1,).normal_(mean=.8, std=.3).clip(lower_bound,1.)
              size_mult = randsize ** self.cut_pow
              size = int(min_size_width * (size_mult.clip(lower_bound, 1.))) # replace .5 with a result for 224 the default large size is .95
              # size = int(min_size_width*torch.zeros(1,).normal_(mean=.9, std=.3).clip(lower_bound, .95)) # replace .5 with a result for 224 the default large size is .95

              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)))
            
            
            cutouts = torch.cat(cutouts, dim=0)
            cutouts = clamp_with_grad(cutouts, 0, 1)

            #if args.use_augs:
            cutouts = self.augs(cutouts)
            if self.noise_fac:
              facs = cutouts.new_empty([cutouts.shape[0], 1, 1, 1]).uniform_(0, self.noise_fac)
              cutouts = cutouts + facs * torch.randn_like(cutouts)
            return cutouts


    class MakeCutoutsHolywater(nn.Module):
      def __init__(self, cut_size, cutn, cut_pow, augs):
        super().__init__()
        self.cut_size = cut_size
        tqdm.write(f'cut size: {self.cut_size}')
        self.cutn = cutn
        self.cut_pow = cut_pow
        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))
        self.augs = nn.Sequential(
                #K.RandomGaussianNoise(mean=0.0, std=0.5, p=0.1),
                K.RandomHorizontalFlip(p=0.5),
                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),
                K.ColorJitter(hue=0.01, saturation=0.01, p=0.7),
                K.RandomGrayscale(p=0.1),
            )

      def set_cut_pow(self, cut_pow):
        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 = []
          cutouts_full = []
          noise_fac = 0.1
          min_size_width = min(sideX, sideY)
          lower_bound = float(self.cut_size/min_size_width)
          
          for ii in range(self.cutn):
            size = int(torch.rand([])**self.cut_pow * (max_size - min_size) + min_size)
            randsize = torch.zeros(1,).normal_(mean=.8, std=.3).clip(lower_bound,1.)
            size_mult = randsize ** self.cut_pow * ii + size
            size1 = int((min_size_width) * (size_mult.clip(lower_bound, 1.))) # replace .5 with a result for 224 the default large size is .95
            size2 = int((min_size_width) * torch.zeros(1,).normal_(mean=.9, std=.3).clip(lower_bound, .95)) # replace .5 with a result for 224 the default large size is .95
            offsetx = torch.randint(0, sideX - size1 + 1, ())
            offsety = torch.randint(0, sideY - size2 + 1, ())
            cutout = input[:, :, offsety:offsety + size2 + ii, offsetx:offsetx + size1 + ii]
            cutouts.append(resample(cutout, (self.cut_size, self.cut_size)))
          
          cutouts = torch.cat(cutouts, dim=0)
          cutouts = clamp_with_grad(cutouts, 0, 1)
          cutouts = self.augs(cutouts)
          facs = cutouts.new_empty([cutouts.shape[0], 1, 1, 1]).uniform_(0, self.noise_fac)
          cutouts = cutouts + facs * torch.randn_like(cutouts)
          return cutouts

    class MakeCutoutsOldHolywater(nn.Module):
        def __init__(self, cut_size, cutn, cut_pow, augs):
            super().__init__()
            self.cut_size = cut_size
            tqdm.write(f'cut size: {self.cut_size}')
            self.cutn = cutn
            self.cut_pow = cut_pow
            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))
            self.augs = nn.Sequential(
              #K.RandomHorizontalFlip(p=0.5),
              #K.RandomSharpness(0.3,p=0.4),
              #K.RandomGaussianBlur((3,3),(10.5,10.5),p=0.2),
              #K.RandomGaussianNoise(p=0.5),
              #K.RandomElasticTransform(kernel_size=(33, 33), sigma=(7,7), p=0.2),
              K.RandomAffine(degrees=180, translate=0.5, p=0.2, padding_mode='border'),
              K.RandomPerspective(0.6,p=0.9),
              K.ColorJitter(hue=0.03, saturation=0.01, p=0.1),
              K.RandomErasing((.1, .7), (.3, 1/.4), same_on_batch=True, p=0.2),)

        def set_cut_pow(self, cut_pow):
          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 = []
            cutouts_full = []
            noise_fac = 0.1
            
            
            min_size_width = min(sideX, sideY)
            lower_bound = float(self.cut_size/min_size_width)
            
            for ii in range(self.cutn):
                
                
              # size = int(torch.rand([])**self.cut_pow * (max_size - min_size) + min_size)
              randsize = torch.zeros(1,).normal_(mean=.8, std=.3).clip(lower_bound,1.)
              size_mult = randsize ** self.cut_pow
              size = int(min_size_width * (size_mult.clip(lower_bound, 1.))) # replace .5 with a result for 224 the default large size is .95
              # size = int(min_size_width*torch.zeros(1,).normal_(mean=.9, std=.3).clip(lower_bound, .95)) # replace .5 with a result for 224 the default large size is .95

              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)))
            
            
            cutouts = torch.cat(cutouts, dim=0)
            cutouts = clamp_with_grad(cutouts, 0, 1)

            #if args.use_augs:
            cutouts = self.augs(cutouts)
            if self.noise_fac:
              facs = cutouts.new_empty([cutouts.shape[0], 1, 1, 1]).uniform_(0, self.noise_fac)
              cutouts = cutouts + facs * torch.randn_like(cutouts)
            return cutouts


    class MakeCutoutsGinger(nn.Module):
        def __init__(self, cut_size, cutn, cut_pow, augs):
            super().__init__()
            self.cut_size = cut_size
            tqdm.write(f'cut size: {self.cut_size}')
            self.cutn = cutn
            self.cut_pow = cut_pow
            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))
            self.augs = augs
            '''
            nn.Sequential(
              K.RandomHorizontalFlip(p=0.5),
              K.RandomSharpness(0.3,p=0.4),
              K.RandomGaussianBlur((3,3),(10.5,10.5),p=0.2),
              K.RandomGaussianNoise(p=0.5),
              K.RandomElasticTransform(kernel_size=(33, 33), sigma=(7,7), p=0.2),
              K.RandomAffine(degrees=30, translate=0.1, p=0.8, padding_mode='border'), # padding_mode=2
              K.RandomPerspective(0.2,p=0.4, ),
              K.ColorJitter(hue=0.01, saturation=0.01, p=0.7),)
    '''

        def set_cut_pow(self, cut_pow):
          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 = []
            cutouts_full = []
            noise_fac = 0.1
            
            
            min_size_width = min(sideX, sideY)
            lower_bound = float(self.cut_size/min_size_width)
            
            for ii in range(self.cutn):
                
                
              # size = int(torch.rand([])**self.cut_pow * (max_size - min_size) + min_size)
              randsize = torch.zeros(1,).normal_(mean=.8, std=.3).clip(lower_bound,1.)
              size_mult = randsize ** self.cut_pow
              size = int(min_size_width * (size_mult.clip(lower_bound, 1.))) # replace .5 with a result for 224 the default large size is .95
              # size = int(min_size_width*torch.zeros(1,).normal_(mean=.9, std=.3).clip(lower_bound, .95)) # replace .5 with a result for 224 the default large size is .95

              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)))
            
            
            cutouts = torch.cat(cutouts, dim=0)
            cutouts = clamp_with_grad(cutouts, 0, 1)

            #if args.use_augs:
            cutouts = self.augs(cutouts)
            if self.noise_fac:
              facs = cutouts.new_empty([cutouts.shape[0], 1, 1, 1]).uniform_(0, self.noise_fac)
              cutouts = cutouts + facs * torch.randn_like(cutouts)
            return cutouts

    class MakeCutoutsZynth(nn.Module):
        def __init__(self, cut_size, cutn, cut_pow, augs):
            super().__init__()
            self.cut_size = cut_size
            tqdm.write(f'cut size: {self.cut_size}')
            self.cutn = cutn
            self.cut_pow = cut_pow
            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))
            self.augs = nn.Sequential(
            K.RandomHorizontalFlip(p=0.5),
            # 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),
            K.ColorJitter(hue=0.01, saturation=0.01, p=0.7))


        def set_cut_pow(self, cut_pow):
          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 = []
            cutouts_full = []
            noise_fac = 0.1
            
            
            min_size_width = min(sideX, sideY)
            lower_bound = float(self.cut_size/min_size_width)
            
            for ii in range(self.cutn):
                
                
              # size = int(torch.rand([])**self.cut_pow * (max_size - min_size) + min_size)
              randsize = torch.zeros(1,).normal_(mean=.8, std=.3).clip(lower_bound,1.)
              size_mult = randsize ** self.cut_pow
              size = int(min_size_width * (size_mult.clip(lower_bound, 1.))) # replace .5 with a result for 224 the default large size is .95
              # size = int(min_size_width*torch.zeros(1,).normal_(mean=.9, std=.3).clip(lower_bound, .95)) # replace .5 with a result for 224 the default large size is .95

              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)))
            
            
            cutouts = torch.cat(cutouts, dim=0)
            cutouts = clamp_with_grad(cutouts, 0, 1)

            #if args.use_augs:
            cutouts = self.augs(cutouts)
            if self.noise_fac:
              facs = cutouts.new_empty([cutouts.shape[0], 1, 1, 1]).uniform_(0, self.noise_fac)
              cutouts = cutouts + facs * torch.randn_like(cutouts)
            return cutouts

    class MakeCutoutsWyvern(nn.Module):
        def __init__(self, cut_size, cutn, cut_pow, augs):
            super().__init__()
            self.cut_size = cut_size
            tqdm.write(f'cut size: {self.cut_size}')
            self.cutn = cutn
            self.cut_pow = cut_pow
            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))
            self.augs = augs

        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):
        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.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
        elif config.model.target == 'taming.models.vqgan.GumbelVQ':
            model = vqgan.GumbelVQ(**config.model.params)
            #print(config.model.params)
            model.eval().requires_grad_(False)
            model.init_from_ckpt(checkpoint_path)
        else:
            raise ValueError(f'unknown model type: {config.model.target}')
        del model.loss
        return model

    import PIL

    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, PIL.Image.LANCZOS)

    class GaussianBlur2d(nn.Module):
        def __init__(self, sigma, window=0, mode='reflect', value=0):
            super().__init__()
            self.mode = mode
            self.value = value
            if not window:
                window = max(math.ceil((sigma * 6 + 1) / 2) * 2 - 1, 3)
            if sigma:
                kernel = torch.exp(-(torch.arange(window) - window // 2)**2 / 2 / sigma**2)
                kernel /= kernel.sum()
            else:
                kernel = torch.ones([1])
            self.register_buffer('kernel', kernel)

        def forward(self, input):
            n, c, h, w = input.shape
            input = input.view([n * c, 1, h, w])
            start_pad = (self.kernel.shape[0] - 1) // 2
            end_pad = self.kernel.shape[0] // 2
            input = F.pad(input, (start_pad, end_pad, start_pad, end_pad), self.mode, self.value)
            input = F.conv2d(input, self.kernel[None, None, None, :])
            input = F.conv2d(input, self.kernel[None, None, :, None])
            return input.view([n, c, h, w])

    BUF_SIZE = 65536
    def get_digest(path, alg=hashlib.sha256):
      hash = alg()
      #print(path)
      with open(path, 'rb') as fp:
        while True:
          data = fp.read(BUF_SIZE)
          if not data: break
          hash.update(data)
      return b64encode(hash.digest()).decode('utf-8')

    flavordict = {
        "cumin": MakeCutoutsCumin,
        "holywater": MakeCutoutsHolywater,
        "old_holywater": MakeCutoutsOldHolywater,
        "ginger": MakeCutoutsGinger,
        "zynth": MakeCutoutsZynth,
        "wyvern": MakeCutoutsWyvern,
        "aaron": MakeCutoutsAaron,
        "moth": MakeCutoutsMoth,
        "juu": MakeCutoutsJuu,
    }

    @torch.jit.script
    def gelu_impl(x):
        """OpenAI's gelu implementation."""
        return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x * (1.0 + 0.044715 * x * x)))


    def gelu(x):
        return gelu_impl(x)


    class MSEDecayLoss(nn.Module):
        def __init__(self, init_weight, mse_decay_rate, mse_epoches, mse_quantize ):
            super().__init__()
          
            self.init_weight = init_weight
            self.has_init_image = False
            self.mse_decay = init_weight / mse_epoches if init_weight else 0 
            self.mse_decay_rate = mse_decay_rate
            self.mse_weight = init_weight
            self.mse_epoches = mse_epoches
            self.mse_quantize = mse_quantize

        @torch.no_grad()
        def set_target( self, z_tensor, model ):
            z_tensor = z_tensor.detach().clone()
            if self.mse_quantize:
                z_tensor = vector_quantize(z_tensor.movedim(1, 3), model.quantize.embedding.weight).movedim(3, 1)#z.average
            self.z_orig = z_tensor
              
        def forward( self, i, z ):
            if self.is_active(i):
                return F.mse_loss(z, self.z_orig) * self.mse_weight / 2
            return 0
            
        def is_active(self, i):
            if not self.init_weight:
              return False
            if i <= self.mse_decay_rate and not self.has_init_image:
              return False
            return True

        @torch.no_grad()
        def step( self, i ):

            if i % self.mse_decay_rate == 0 and i != 0 and i < self.mse_decay_rate * self.mse_epoches:
                
                if self.mse_weight - self.mse_decay > 0 and self.mse_weight - self.mse_decay >= self.mse_decay:
                  self.mse_weight -= self.mse_decay
                else:
                  self.mse_weight = 0
                #print(f"updated mse weight: {self.mse_weight}")

                return True

            return False
      
    class TVLoss(nn.Module):
        def forward(self, input):
            input = F.pad(input, (0, 1, 0, 1), 'replicate')
            x_diff = input[..., :-1, 1:] - input[..., :-1, :-1]
            y_diff = input[..., 1:, :-1] - input[..., :-1, :-1]
            diff = x_diff**2 + y_diff**2 + 1e-8
            return diff.mean(dim=1).sqrt().mean()

    class MultiClipLoss(nn.Module):
        def __init__(self, clip_models, text_prompt, cutn, cut_pow=1., clip_weight=1. ):
            super().__init__()

            # Load Clip
            self.perceptors = []
            for cm in clip_models:
              sys.stdout.write(f"Loading {cm[0]} ...\n")
              sys.stdout.flush()
              c = clip.load(cm[0], jit=False)[0].eval().requires_grad_(False).to(device)
              self.perceptors.append( { 'res': c.visual.input_resolution, 'perceptor': c, 'weight': cm[1], 'prompts':[] } )        
            self.perceptors.sort(key=lambda e: e['res'], reverse=True)
            
            # Make Cutouts
            self.max_cut_size = self.perceptors[0]['res']
            #self.make_cuts = flavordict[flavor](self.max_cut_size, cutn, cut_pow)
            #cutouts = flavordict[flavor](self.max_cut_size, cutn, cut_pow=cut_pow, augs=args.augs)

            # Get Prompt Embedings
            #texts = [phrase.strip() for phrase in text_prompt.split("|")]
            #if text_prompt == ['']:
            #  texts = []
            texts = text_prompt
            self.pMs = []
            for prompt in texts:
              txt, weight, stop = parse_prompt(prompt)
              clip_token = clip.tokenize(txt).to(device)
              for p in self.perceptors:
                embed = p['perceptor'].encode_text(clip_token).float()
                embed_normed = F.normalize(embed.unsqueeze(0), dim=2)
                p['prompts'].append({'embed_normed':embed_normed,'weight':torch.as_tensor(weight, device=device),'stop':torch.as_tensor(stop, device=device)})
        
            # Prep Augments 
            self.normalize = transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
                                        std=[0.26862954, 0.26130258, 0.27577711])

            self.augs = nn.Sequential(
               K.RandomHorizontalFlip(p=0.5),
               K.RandomSharpness(0.3,p=0.1),
               K.RandomAffine(degrees=30, translate=0.1, p=0.8, padding_mode='border'), # padding_mode=2
               K.RandomPerspective(0.2,p=0.4, ),
               K.ColorJitter(hue=0.01, saturation=0.01, p=0.7),
               K.RandomGrayscale(p=0.15)          
           )
            self.noise_fac = 0.1

            self.clip_weight = clip_weight

        def prepare_cuts(self,img):
          cutouts = self.make_cuts(img)
          cutouts = self.augs(cutouts)
          if self.noise_fac:
              facs = cutouts.new_empty([cutouts.shape[0], 1, 1, 1]).uniform_(0, self.noise_fac)
              cutouts = cutouts + facs * torch.randn_like(cutouts)
          cutouts = self.normalize(cutouts)
          return cutouts

        def forward(self, i, img):
          cutouts = checkpoint(self.prepare_cuts, img)
          loss = []
          
          current_cuts = cutouts
          currentres = self.max_cut_size
          for p in self.perceptors:
              if currentres != p['res']:
                  current_cuts = resample(cutouts,(p['res'],p['res']))
                  currentres = p['res']

              iii = p['perceptor'].encode_image(current_cuts).float()
              input_normed = F.normalize(iii.unsqueeze(1), dim=2)
              for prompt in p['prompts']:
                dists = input_normed.sub(prompt['embed_normed']).norm(dim=2).div(2).arcsin().pow(2).mul(2)
                dists = dists * prompt['weight'].sign()
                l = prompt['weight'].abs() * replace_grad(dists, torch.maximum(dists, prompt['stop'])).mean()
                loss.append(l * p['weight'])

          return loss

    class ModelHost:
      def __init__(self, args):
        self.args = args
        self.model, self.perceptor = None, None
        self.make_cutouts = None
        self.alt_make_cutouts = None
        self.imageSize = None
        self.prompts = None
        self.opt = None
        self.normalize = None
        self.z, self.z_orig, self.z_min, self.z_max = None, None, None, None
        self.metadata = None
        self.mse_weight = 0
        self.normal_flip_optim = None
        self.usealtprompts = False

      def setup_metadata(self, seed):
        metadata = {k:v for k,v in vars(self.args).items()}
        del metadata['max_iterations']
        del metadata['display_freq']
        metadata['seed'] = seed
        if (metadata['init_image']):
          path = metadata['init_image']
          digest = get_digest(path)
          metadata['init_image'] = (path, digest)
        if (metadata['image_prompts']):
          prompts = []
          for prompt in metadata['image_prompts']:
            path = prompt
            digest = get_digest(path)
            prompts.append((path,digest))
          metadata['image_prompts'] = prompts
        self.metadata = metadata

      def setup_model(self, x):
        i = x
        device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
        """
        print('Using device:', device)
        if self.args.prompts:
            print('Using prompts:', self.args.prompts)
        if self.args.altprompts:
            print('Using alternate augment set prompts:', self.args.altprompts)
        if self.args.image_prompts:
            print('Using image prompts:', self.args.image_prompts)
        if args.seed is None:
            seed = torch.seed()
        else:
            seed = args.seed
        torch.manual_seed(seed)
        print('Using seed:', seed)
        """
        model = load_vqgan_model(vqgan_config, vqgan_model).to(device)

        active_clips = bool(self.args.clip_model2) + bool(self.args.clip_model3) + bool(self.args.clip_model4) + bool(self.args.clip_model5) + bool(self.args.clip_model6)
        if active_clips != 0: clip_weight = round(1/(active_clips+1), 2)
        clip_models = [[clip_model, 1.0]]
        if self.args.clip_model2:
          clip_models = [[self.args.clip_model, clip_weight], [self.args.clip_model2, clip_weight]]
        if self.args.clip_model3:
          clip_models = [[self.args.clip_model, clip_weight], [self.args.clip_model2, clip_weight], [self.args.clip_model3, clip_weight]]
        if self.args.clip_model4:
          clip_models = [[self.args.clip_model, clip_weight], [self.args.clip_model2, clip_weight], [self.args.clip_model3, clip_weight], [self.args.clip_model4, clip_weight]]
        if self.args.clip_model5:
          clip_models = [[self.args.clip_model, clip_weight], [self.args.clip_model2, clip_weight], [self.args.clip_model3, clip_weight], [self.args.clip_model4, clip_weight], [self.args.clip_model5, clip_weight]]
        if self.args.clip_model6:
          clip_models = [[self.args.clip_model, clip_weight], [self.args.clip_model2, clip_weight], [self.args.clip_model3, clip_weight], [self.args.clip_model4, clip_weight], [self.args.clip_model5, clip_weight], [self.args.clip_model6, clip_weight]]
        #print(clip_models)

        clip_loss = MultiClipLoss(clip_models, self.args.prompts, cutn=self.args.cutn)

        #update_random(self.args.gen_seed, 'image generation')
        
        #[0].eval().requires_grad_(False)
        #perceptor = clip.load(args.clip_model, jit=False)[0].eval().requires_grad_(False).to(device)
        #[0].eval().requires_grad_(True)

        cut_size = perceptor.visual.input_resolution
        
        if self.args.is_gumbel:
          e_dim = model.quantize.embedding_dim
        else:
          e_dim = model.quantize.e_dim
            
        f = 2**(model.decoder.num_resolutions - 1)
       
        make_cutouts = flavordict[flavor](cut_size, args.mse_cutn, cut_pow=args.mse_cut_pow,augs=args.augs)

        #make_cutouts = MakeCutouts(cut_size, args.mse_cutn, cut_pow=args.mse_cut_pow,augs=args.augs)
        if args.altprompts:
            self.usealtprompts = True
            self.alt_make_cutouts = flavordict[flavor](cut_size, args.mse_cutn, cut_pow=args.alt_mse_cut_pow,augs=args.altaugs)
            #self.alt_make_cutouts = MakeCutouts(cut_size, args.mse_cutn, cut_pow=args.alt_mse_cut_pow,augs=args.altaugs)
        
        if self.args.is_gumbel:
          n_toks = model.quantize.n_embed
        else:
          n_toks = model.quantize.n_e

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

        if self.args.is_gumbel:
            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:
            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]
        
        from PIL import Image
        import cv2
    #-------
        working_dir = self.args.folder_name
        
        if self.args.init_image != "":
            img_0 = cv2.imread(init_image)
            z, *_ = model.encode(TF.to_tensor(img_0).to(device).unsqueeze(0) * 2 - 1)
        elif not os.path.isfile(f'{working_dir}/steps/{i:04d}.png'):
            one_hot = F.one_hot(torch.randint(n_toks, [toksY * toksX], device=device), n_toks).float()
            if self.args.is_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)
        else:
            if save_all_iterations:
                img_0 = cv2.imread(
                    f'{working_dir}/steps/{i:04d}_{iterations_per_frame}.png')
            else:
                # Hack to prevent colour inversion on every frame
                img_temp = cv2.imread(f'{working_dir}/steps/{i}.png')
                imageio.imwrite('inverted_temp.png', img_temp)
                img_0 = cv2.imread('inverted_temp.png')
            center = (1*img_0.shape[1]//2, 1*img_0.shape[0]//2)
            trans_mat = np.float32(
                [[1, 0, 10],
                [0, 1, 10]]
            )
            rot_mat = cv2.getRotationMatrix2D( center, 10, 20 )

            trans_mat = np.vstack([trans_mat, [0,0,1]])
            rot_mat = np.vstack([rot_mat, [0,0,1]])
            transformation_matrix = np.matmul(rot_mat, trans_mat)

            img_0 = cv2.warpPerspective(
                img_0,
                transformation_matrix,
                (img_0.shape[1], img_0.shape[0]),
                borderMode=cv2.BORDER_WRAP
            )
            z, *_ = model.encode(TF.to_tensor(img_0).to(device).unsqueeze(0) * 2 - 1)
            
            def save_output(i, img, suffix='zoomed'):
              filename = \
                  f"{working_dir}/steps/{i:04}{'_' + suffix if suffix else ''}.png"
              imageio.imwrite(filename, np.array(img))

            save_output(i, img_0)
    #-------
        if args.init_image:
            pil_image = Image.open(args.init_image).convert('RGB')
            pil_image = pil_image.resize((sideX, sideY), Image.LANCZOS)
            z, *_ = model.encode(TF.to_tensor(pil_image).to(device).unsqueeze(0) * 2 - 1)
        else:
            one_hot = F.one_hot(torch.randint(n_toks, [toksY * toksX], device=device), n_toks).float()
            if self.args.is_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 = EMATensor(z, args.ema_val)
        
        if args.mse_with_zeros and not args.init_image:
            z_orig = torch.zeros_like(z.tensor)
        else:
            z_orig = z.tensor.clone()
        z.requires_grad_(True)
        #opt = optim.AdamW(z.parameters(), lr=args.mse_step_size, weight_decay=0.00000000)
        if self.normal_flip_optim == True:
          if randint(1,2) == 1:
            opt = torch.optim.AdamW(z.parameters(), lr=args.step_size, weight_decay=0.00000000)
            #opt = Ranger21(z.parameters(), lr=args.step_size, weight_decay=0.00000000)
          else:
            opt = optim.DiffGrad(z.parameters(), lr=args.step_size, weight_decay=0.00000000)
        else:
          opt = torch.optim.AdamW(z.parameters(), lr=args.step_size, weight_decay=0.00000000)

        self.cur_step_size =args.mse_step_size

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

        pMs = []
        altpMs = []

        for prompt in args.prompts:
            txt, weight, stop = parse_prompt(prompt)
            embed = perceptor.encode_text(clip.tokenize(txt).to(device)).float()
            pMs.append(Prompt(embed, weight, stop).to(device))
        
        for prompt in args.altprompts:
            txt, weight, stop = parse_prompt(prompt)
            embed = perceptor.encode_text(clip.tokenize(txt).to(device)).float()
            altpMs.append(Prompt(embed, weight, stop).to(device))
        
        from PIL import Image
        for prompt in args.image_prompts:
            path, weight, stop = parse_prompt(prompt)
            img = resize_image(Image.open(path).convert('RGB'), (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))
            if(self.usealtprompts):
              altpMs.append(Prompt(embed, weight).to(device))

        self.model, self.perceptor = model, perceptor
        self.make_cutouts = make_cutouts
        self.imageSize = (sideX, sideY)
        self.prompts = pMs
        self.altprompts = altpMs
        self.opt = opt
        self.normalize = normalize
        self.z, self.z_orig, self.z_min, self.z_max = z, z_orig, z_min, z_max
        self.setup_metadata(args2.seed)
        self.mse_weight = self.args.init_weight

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

      def add_metadata(self, path, i):
        imfile = PngImageFile(path)
        meta = PngInfo()
        step_meta = {'iterations':i}
        step_meta.update(self.metadata)
        #meta.add_itxt('vqgan-params', json.dumps(step_meta), zip=True)
        imfile.save(path, pnginfo=meta)
        #Hey you. This one's for Glooperpogger#7353 on Discord (Gloop has a gun), they are a nice snek

      @torch.no_grad()
      def checkin(self, i, losses, x):
          out = self.synth(self.z.average)
          TF.to_pil_image(out[0].cpu()).save(args2.image_file)

      def unique_index(self, batchpath):
          i = 0
          while i < 10000:
              if os.path.isfile(batchpath+"/"+str(i)+".png"):
                  i = i+1
              else:
                  return batchpath+"/"+str(i)+".png"

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

          result = []
          if self.args.init_weight and self.mse_weight > 0:
              result.append(F.mse_loss(self.z.tensor, self.z_orig) * self.mse_weight / 2)

          for prompt in self.prompts:
              result.append(prompt(iii))
              
          if self.usealtprompts:
            iii = self.perceptor.encode_image(self.normalize(self.alt_make_cutouts(out))).float()
            for prompt in self.altprompts:
              result.append(prompt(iii))

          """      
          img = np.array(out.mul(255).clamp(0, 255)[0].cpu().detach().numpy().astype(np.uint8))[:,:,:]
          img = np.transpose(img, (1, 2, 0))
          im_path = 'progress.png'
          imageio.imwrite(im_path, np.array(img))
          self.add_metadata(im_path, i)
          """
          return result

      def train(self, i,x):
          self.opt.zero_grad()
          mse_decay = self.args.mse_decay
          mse_decay_rate = self.args.mse_decay_rate
          lossAll = self.ascend_txt(i)

          sys.stdout.write("Iteration {}".format(i)+"\n")
          sys.stdout.flush()
        
          """
          if i < args.mse_end and i % args.mse_display_freq == 0:
            self.checkin(i, lossAll, x)
          if i == args.mse_end:
            self.checkin(i,lossAll,x)
          if i > args.mse_end and (i-args.mse_end) % args.display_freq == 0:
            self.checkin(i, lossAll, x)
          """
          if i % args2.iterations == 0:
            self.checkin(i, lossAll, x)


          
          loss = sum(lossAll)
          loss.backward()
          self.opt.step()
          with torch.no_grad():
              if self.mse_weight > 0 and self.args.init_weight and i > 0 and i%mse_decay_rate == 0:
                  if self.args.is_gumbel:
                    self.z_orig = vector_quantize(self.z.average.movedim(1, 3), self.model.quantize.embed.weight).movedim(3, 1)
                  else:
                    self.z_orig = vector_quantize(self.z.average.movedim(1, 3), self.model.quantize.embedding.weight).movedim(3, 1)
                  if self.mse_weight - mse_decay > 0:
                      self.mse_weight = self.mse_weight - mse_decay
                      #print(f"updated mse weight: {self.mse_weight}")
                  else:
                      self.mse_weight = 0
                      self.make_cutouts = flavordict[flavor](self.perceptor.visual.input_resolution, args.cutn, cut_pow=args.cut_pow, augs = args.augs)
                      if self.usealtprompts:
                          self.alt_make_cutouts = flavordict[flavor](self.perceptor.visual.input_resolution, args.cutn, cut_pow=args.alt_cut_pow, augs = args.altaugs)
                      self.z = EMATensor(self.z.average, args.ema_val)
                      self.new_step_size =args.step_size
                      self.opt = torch.optim.AdamW(self.z.parameters(), lr=args.step_size, weight_decay=0.00000000)
                      #print(f"updated mse weight: {self.mse_weight}")
              if i > args.mse_end:
                  if args.step_size != args.final_step_size and args.max_iterations > 0:
                    progress = (i-args.mse_end)/(args.max_iterations)
                    self.cur_step_size = lerp(step_size, final_step_size,progress)
                    for g in self.opt.param_groups:
                      g['lr'] = self.cur_step_size
              #self.z.copy_(self.z.maximum(self.z_min).minimum(self.z_max))

      def run(self,x):
        i = 0
        try:
            pbar = tqdm(range(int(args.max_iterations + args.mse_end)))
            while True:
              self.train(i,x)
              if i > 0 and i%args.mse_decay_rate==0 and self.mse_weight > 0:
                self.z = EMATensor(self.z.average, args.ema_val)
                self.opt = torch.optim.AdamW(self.z.parameters(), lr=args.mse_step_size, weight_decay=0.00000000)
                #self.opt = optim.Adgarad(self.z.parameters(), lr=args.mse_step_size, weight_decay=0.00000000)
              if i >= args.max_iterations + args.mse_end:
                pbar.close()
                break
              self.z.update()
              i += 1
              pbar.update()
        except KeyboardInterrupt:
            pass
        return i

    def add_noise(img):

    	# Getting the dimensions of the image
    	row , col = img.shape
    	
    	# Randomly pick some pixels in the
    	# image for coloring them white
    	# Pick a random number between 300 and 10000
    	number_of_pixels = random.randint(300, 10000)
    	for i in range(number_of_pixels):
    		
    		# Pick a random y coordinate
    		y_coord=random.randint(0, row - 1)
    		
    		# Pick a random x coordinate
    		x_coord=random.randint(0, col - 1)
    		
    		# Color that pixel to white
    		img[y_coord][x_coord] = 255
    		
    	# Randomly pick some pixels in
    	# the image for coloring them black
    	# Pick a random number between 300 and 10000
    	number_of_pixels = random.randint(300 , 10000)
    	for i in range(number_of_pixels):
    		
    		# Pick a random y coordinate
    		y_coord=random.randint(0, row - 1)
    		
    		# Pick a random x coordinate
    		x_coord=random.randint(0, col - 1)
    		
    		# Color that pixel to black
    		img[y_coord][x_coord] = 0
    		
    	return img

    import io
    import base64
    def image_to_data_url(img, ext):  
        img_byte_arr = io.BytesIO()
        img.save(img_byte_arr, format=ext)
        img_byte_arr = img_byte_arr.getvalue()
        # ext = filename.split('.')[-1]
        prefix = f'data:image/{ext};base64,'
        return prefix + base64.b64encode(img_byte_arr).decode('utf-8')
     
    import torch
    import math
    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

    def rand_perlin_2d(shape, res, fade = lambda t: 6*t**5 - 15*t**4 + 10*t**3):
        delta = (res[0] / shape[0], res[1] / shape[1])
        d = (shape[0] // res[0], shape[1] // res[1])
        
        grid = torch.stack(torch.meshgrid(torch.arange(0, res[0], delta[0]), torch.arange(0, res[1], delta[1])), dim = -1) % 1
        angles = 2*math.pi*torch.rand(res[0]+1, res[1]+1)
        gradients = torch.stack((torch.cos(angles), torch.sin(angles)), dim = -1)
        
        tile_grads = lambda slice1, slice2: gradients[slice1[0]:slice1[1], slice2[0]:slice2[1]].repeat_interleave(d[0], 0).repeat_interleave(d[1], 1)
        dot = lambda grad, shift: (torch.stack((grid[:shape[0],:shape[1],0] + shift[0], grid[:shape[0],:shape[1], 1] + shift[1]  ), dim = -1) * grad[:shape[0], :shape[1]]).sum(dim = -1)
        
        n00 = dot(tile_grads([0, -1], [0, -1]), [0,  0])
        n10 = dot(tile_grads([1, None], [0, -1]), [-1, 0])
        n01 = dot(tile_grads([0, -1],[1, None]), [0, -1])
        n11 = dot(tile_grads([1, None], [1, None]), [-1,-1])
        t = fade(grid[:shape[0], :shape[1]])
        return math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1])

    def rand_perlin_2d_octaves( desired_shape, octaves=1, persistence=0.5):
        shape = torch.tensor(desired_shape)
        shape = 2 ** torch.ceil( torch.log2( shape ) )
        shape = shape.type(torch.int)

        max_octaves = int(min(octaves,math.log(shape[0])/math.log(2), math.log(shape[1])/math.log(2)))
        res = torch.floor( shape / 2 ** max_octaves).type(torch.int)

        noise = torch.zeros(list(shape))
        frequency = 1
        amplitude = 1
        for _ in range(max_octaves):
            noise += amplitude * rand_perlin_2d(shape, (frequency*res[0], frequency*res[1]))
            frequency *= 2
            amplitude *= persistence
        
        return noise[:desired_shape[0],:desired_shape[1]]

    def rand_perlin_rgb( desired_shape, amp=0.1, octaves=6 ):
      r = rand_perlin_2d_octaves( desired_shape, octaves )
      g = rand_perlin_2d_octaves( desired_shape, octaves )
      b = rand_perlin_2d_octaves( desired_shape, octaves )
      rgb = ( torch.stack((r,g,b)) * amp + 1 ) * 0.5
      return rgb.unsqueeze(0).clip(0,1).to(device)


    def pyramid_noise_gen(shape, octaves=5, decay=1.):
        n, c, h, w = shape
        noise = torch.zeros([n, c, 1, 1])
        max_octaves = int(min(math.log(h)/math.log(2), math.log(w)/math.log(2)))
        if octaves is not None and 0 < octaves:
          max_octaves = min(octaves,max_octaves)
        for i in reversed(range(max_octaves)):
            h_cur, w_cur = h // 2**i, w // 2**i
            noise = F.interpolate(noise, (h_cur, w_cur), mode='bicubic', align_corners=False)
            noise += ( torch.randn([n, c, h_cur, w_cur]) / max_octaves ) * decay**( max_octaves - (i+1) )
        return noise

    def rand_z(model, toksX, toksY):
        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]

        one_hot = F.one_hot(torch.randint(n_toks, [toksY * toksX], device=device), n_toks).float()
        z = one_hot @ model.quantize.embedding.weight
        z = z.view([-1, toksY, toksX, e_dim]).permute(0, 3, 1, 2)

        return z


    def make_rand_init( mode, model, perlin_octaves, perlin_weight, pyramid_octaves, pyramid_decay, toksX, toksY, f ):

      if mode == 'VQGAN ZRand':
        return rand_z(model, toksX, toksY)
      elif mode == 'Perlin Noise':
        rand_init = rand_perlin_rgb((toksY * f, toksX * f), perlin_weight, perlin_octaves )
        z, *_ = model.encode(rand_init * 2 - 1)
        return z
      elif mode == 'Pyramid Noise':
        rand_init = pyramid_noise_gen( (1,3,toksY * f, toksX * f), pyramid_octaves, pyramid_decay).to(device)
        rand_init = ( rand_init * 0.5 + 0.5 ).clip(0,1)
        z, *_ = model.encode(rand_init * 2 - 1)
        return z

    # Commented out IPython magic to ensure Python compatibility.
    #@title <font color="lightgreen" size="+3">←</font> <font size="+2">💠</font> Selection of models to download <font size="+2">💠</font>
    #@markdown By default, the notebook downloads the 16384 model from ImageNet. There are others like COCO, WikiArt 1024, WikiArt 16384, FacesHQ or S-FLCKR, which are heavy, and if you are not going to use them it would be pointless to download them, so if you want to use them, simply select the models to download. (by the way, COCO 1 Stage is a lighter COCO model. WikiArt 7 Mil is a lighter (and worst) WikiArt model.)
    # %cd /content/

    #import gdown
    import os

    imagenet_1024 = False #@param {type:"boolean"}
    imagenet_16384 = True #@param {type:"boolean"}
    gumbel_8192 = False #@param {type:"boolean"}
    sber_gumbel = False #@param {type:"boolean"}
    #imagenet_cin = False #@param {type:"boolean"}
    coco = False #@param {type:"boolean"}
    coco_1stage = False #@param {type:"boolean"}
    faceshq = False #@param {type:"boolean"}
    wikiart_1024 = False #@param {type:"boolean"}
    wikiart_16384 = False #@param {type:"boolean"}
    wikiart_7mil = False #@param {type:"boolean"}
    sflckr = False #@param {type:"boolean"}

    ##@markdown Experimental models (won't probably work, if you know how to make them work, go ahead :D):
    #celebahq = False #@param {type:"boolean"}
    #ade20k = False #@param {type:"boolean"}
    #drin = False #@param {type:"boolean"}
    #gumbel = False #@param {type:"boolean"}
    #gumbel_8192 = False #@param {type:"boolean"}

    # Configure and run the model"""

    # Commented out IPython magic to ensure Python compatibility.
    #@title <font color="lightgreen" size="+3">←</font> <font size="+2">🏃‍♂️</font> **Configure & Run** <font size="+2">🏃‍♂️</font>

    import os
    import random
    import cv2
    #from google.colab import drive
    from PIL import Image
    from importlib import reload
    reload(PIL.TiffTags)
    # %cd /content/
    #@markdown >`prompts` is the list of prompts to give to the AI, separated by `|`. With more than one, it will attempt to mix them together. You can add weights to different parts of the prompt by adding a `p:x` at the end of a prompt (before a `|`) where `p` is the prompt and `x` is the weight.


    #prompts = "A fantasy landscape, by Greg Rutkowski. A lush mountain.:1 | Trending on ArtStation, unreal engine. 4K HD, realism.:0.63" #@param {type:"string"}

    prompts = args2.prompt

    width =  args2.sizex#@param {type:"number"}
    height = args2.sizey #@param {type:"number"}

    sys.stdout.write(f"Loading {args2.vqgan_model} ...\n")
    sys.stdout.flush()

    #model = "ImageNet 16384" #@param ['ImageNet 16384', 'ImageNet 1024', "Gumbel 8192", "Sber Gumbel", 'WikiArt 1024', 'WikiArt 16384', 'WikiArt 7mil', 'COCO-Stuff', 'COCO 1 Stage', 'FacesHQ', 'S-FLCKR']
    model = args2.vqgan_model

    if model == "Gumbel 8192" or model == "Sber Gumbel":
      is_gumbel = True
    else:
      is_gumbel = False

    ##@markdown The flavor effects the output greatly. Each has it's own characteristics and depending on what you choose, you'll get a widely different result with the same prompt and seed. Ginger is the default, nothing special. Cumin results more of a painting, while Holywater makes everythng super funky and/or colorful. Custom is a custom flavor, use the utilities above.
    #   Type "old_holywater" to use the old holywater flavor from Hypertron V1
    flavor = args2.flavor #'ginger' #@param ["ginger", "cumin", "holywater", "zynth", "wyvern", "aaron", "moth", "juu", "custom"]
    template = args2.template #'Balanced' #@param ["none", "----------Parameter Tweaking----------", "Balanced", "Detailed", "Consistent Creativity", "Realistic", "Smooth", "Subtle MSE", "Hyper Fast Results", "----------Complete Overhaul----------", "flag", "planet", "creature", "human", "----------Sizes----------", "Size: Square", "Size: Landscape", "Size: Poster", "----------Prompt Modifiers----------", "Better - Fast", "Better - Slow", "Movie Poster", "Negative Prompt", "Better Quality"]
    ##@markdown To use initial or target images, upload it on the left in the file browser. You can also use previous outputs by putting its path below, e.g. `batch_01/0.png`. If your previous output is saved to drive, you can use the checkbox so you don't have to type the whole path.
    init = 'default noise' #@param ["default noise", "image", "random image", "salt and pepper noise", "salt and pepper noise on init image"]

    if args2.seed_image is None:
        init_image = "" #args2.seed_image #""#@param {type:"string"}
    else:
        init_image = args2.seed_image #""#@param {type:"string"}

    if init == "random image":
      url = "https://picsum.photos/" + str(width) + "/" + str(height) + "?blur=" + str(random.randrange(5, 10))
      urllib.request.urlretrieve(url, "Init_Img/Image.png")
      init_image = "Init_Img/Image.png"
    elif init == "random image clear":
      url = "https://source.unsplash.com/random/" + str(width) + "x" + str(height)
      urllib.request.urlretrieve(url, "Init_Img/Image.png")
      init_image = "Init_Img/Image.png"
    elif init == "random image clear 2":
      url = "https://loremflickr.com/" + str(width) + "/" + str(height)
      urllib.request.urlretrieve(url, "Init_Img/Image.png")
      init_image = "Init_Img/Image.png"
    elif init == "salt and pepper noise":
      urllib.request.urlretrieve("https://i.stack.imgur.com/olrL8.png", "Init_Img/Image.png")
      import cv2
      img = cv2.imread('Init_Img/Image.png', 0)
      cv2.imwrite('Init_Img/Image.png', add_noise(img))
      init_image = "Init_Img/Image.png"
    elif init == "salt and pepper noise on init image":
      img = cv2.imread(init_image, 0)
      cv2.imwrite('Init_Img/Image.png', add_noise(img))
      init_image = "Init_Img/Image.png"
    elif init == "perlin noise":
      #For some reason Colab started crashing from this
      import noise
      import numpy as np
      from PIL import Image
      shape = (width, height)
      scale = 100
      octaves = 6
      persistence = 0.5
      lacunarity = 2.0
      seed = np.random.randint(0,100000)
      world = np.zeros(shape)
      for i in range(shape[0]):
          for j in range(shape[1]):
              world[i][j] = noise.pnoise2(i/scale, j/scale, octaves=octaves, persistence=persistence, lacunarity=lacunarity, repeatx=1024, repeaty=1024, base=seed)
      Image.fromarray(prep_world(world)).convert("L").save("Init_Img/Image.png")
      init_image = "Init_Img/Image.png"
    elif init == "black and white":
      url = "https://www.random.org/bitmaps/?format=png&width=300&height=300&zoom=1"
      urllib.request.urlretrieve(url, "Init_Img/Image.png")
      init_image = "Init_Img/Image.png"



    seed = args2.seed#@param {type:"number"}
    #@markdown >iterations excludes iterations spent during the mse phase, if it is being used. The total iterations will be more if `mse_decay_rate` is more than 0.
    iterations = args2.iterations#@param {type:"number"}
    transparent_png = False #@param {type:"boolean"}

    #@markdown <font size="+3">⚠</font> **ADVANCED SETTINGS** <font size="+3">⚠</font>
    #@markdown ---
    #@markdown ---

    #@markdown >If you want to make multiple images with different prompts, use this. Seperate different prompts for different images with a `~` (example: `prompt1~prompt1~prompt3`). Iter is the iterations you want each image to run for. If you use MSE, I'd type a pretty low number (about 10).
    multiple_prompt_batches = False #@param {type:"boolean"}
    multiple_prompt_batches_iter =  300#@param {type:"number"}

    #@markdown >`folder_name` is the name of the folder you want to output your result(s) to. Previous outputs will NOT be overwritten. By default, it will be saved to the colab's root folder, but the `save_to_drive` checkbox will save it to `MyDrive\VQGAN_Output` instead.
    folder_name = ""#@param {type:"string"}
    save_to_drive = False #@param {type:"boolean"}
    prompt_experiment = "None" #@param ['None', 'Fever Dream', 'Philipuss’s Basement', 'Vivid Turmoil', 'Mad Dad', 'Platinum', 'Negative Energy']
    if prompt_experiment == "Fever Dream":
      prompts = "<|startoftext|>" + prompts + "<|endoftext|>"
    elif prompt_experiment == "Vivid Turmoil":
      prompts = prompts.replace(" ", "¡")
      prompts = "¬" + prompts + "®"
    elif prompt_experiment == "Mad Dad":
      prompts = prompts.replace(" ", '\\s+')
    elif prompt_experiment == "Platinum":
      prompts = "~!" + prompts + "!~"
      prompts = prompts.replace(" ", '</w>')
    elif prompt_experiment == "Philipuss’s Basement":
      prompts = "<|startoftext|>" + prompts
      prompts = prompts.replace(" ", "<|endoftext|><|startoftext|>")
    elif prompt_experiment == "Lowercase":
      prompts = prompts.lower()
      
    clip_model =  "ViT-B/32" #"ViT-B/32" #@param ["ViT-L/14", "ViT-B/32", "ViT-B/16", "RN50x64", "RN50x16", "RN50x4", "RN101", "RN50"]
    clip_model2 = "None" #args2.clip_model_2 #'None' #@param ["None", "ViT-L/14", "ViT-B/32", "ViT-B/16", "RN50x64", "RN50x16", "RN50x4", "RN101", "RN50"]
    clip_model3 = "None" #args2.clip_model_3 #'None' #@param ["None", "ViT-L/14", "ViT-B/32", "ViT-B/16", "RN50x64", "RN50x16", "RN50x4", "RN101", "RN50"]
    clip_model4 = "None" #args2.clip_model_4 #'None' #@param ["None", "ViT-L/14", "ViT-B/32", "ViT-B/16", "RN50x64", "RN50x16", "RN50x4", "RN101", "RN50"]
    clip_model5 = "None" #args2.clip_model_5 #'None' #@param ["None", "ViT-L/14", "ViT-B/32", "ViT-B/16", "RN50x64", "RN50x16", "RN50x4", "RN101", "RN50"]
    clip_model6 = "None" #args2.clip_model_6 #'None' #@param ["None", "ViT-L/14", "ViT-B/32", "ViT-B/16", "RN50x64", "RN50x16", "RN50x4", "RN101", "RN50"]

    if clip_model2 == "None": clip_model2 = None
    if clip_model3 == "None": clip_model3 = None
    if clip_model4 == "None": clip_model4 = None
    if clip_model5 == "None": clip_model5 = None
    if clip_model6 == "None": clip_model6 = None

    #@markdown >Target images work like prompts, write the name of the image. You can add multiple target images by seperating them with a `|`.
    target_images = ""#@param {type:"string"}

    #@markdown ><font size="+2">☢</font> Advanced values. Values of cut_pow below 1 prioritize structure over detail, and vice versa for above 1. Step_size affects how wild the change between iterations is, and if final_step_size is not 0, step_size will interpolate towards it over time.
    #@markdown >Cutn affects on 'Creativity': less cutout will lead to more random/creative results, sometimes barely readable, while higher values (90+) lead to very stable, photo-like outputs
    cutn =  130#@param {type:"number"}
    cut_pow = 1#@param {type:"number"}
    #@markdown >Step_size is like weirdness. Lower: more accurate/realistic, slower; Higher: less accurate/more funky, faster.
    step_size = 0.1#@param {type:"number"}
    #@markdown >Start_step_size is a temporary step_size that will be active only in the first 10 iterations. It (sometimes) helps with speed. If it's set to 0, it won't be used.
    start_step_size = 0 #@param {type:"number"}
    #@markdown >Final_step_size is a goal step_size which the AI will try and reach. If set to 0, it won't be used.
    final_step_size = 0#@param {type:"number"}
    if start_step_size <= 0: start_step_size = step_size
    if final_step_size <= 0: final_step_size = step_size

    #@markdown ---

    #@markdown >EMA maintains a moving average of trained parameters. The number below is the rate of decay (higher means slower).
    ema_val =  0.98#@param {type:"number"}

    #@markdown >If you want to keep starting from the same point, set `gen_seed` to a positive number. `-1` will make it random every time. 
    gen_seed = -1#@param {type:'number'}


    init_image_in_drive = False #@param {type:"boolean"}
    if init_image_in_drive and init_image:
        init_image = '/content/drive/MyDrive/VQGAN_Output/' + init_image

    images_interval =  args2.update#@param {type:"number"}

    #I think you should give "Free Thoughts on the Proceedings of the Continental Congress" a read, really funny and actually well-written, Hamilton presented it in a bad light IMO.

    batch_size =  1#@param {type:"number"}

    #@markdown ---

    #@markdown <font size="+1">🔮</font> **MSE Regulization** <font size="+1">🔮</font>
    #Based off of this notebook: https://colab.research.google.com/drive/1gFn9u3oPOgsNzJWEFmdK-N9h_y65b8fj?usp=sharing - already in credits
    use_mse = args2.mse #@param {type:"boolean"}
    mse_images_interval = images_interval
    mse_init_weight =  0.2#@param {type:"number"}
    mse_decay_rate =  160#@param {type:"number"}
    mse_epoches = 10#@param {type:"number"}
    ##@param {type:"number"}

    #@markdown >Overwrites the usual values during the mse phase if included. If any value is 0, its normal counterpart is used instead.
    mse_with_zeros = True #@param {type:"boolean"}
    mse_step_size = 0.87 #@param {type:"number"}
    mse_cutn =  42#@param {type:"number"}
    mse_cut_pow = 0.75 #@param {type:"number"}

    #@markdown >normal_flip_optim flips between two optimizers during the normal (not MSE) phase. It can improve quality, but it's kind of experimental, use at your own risk.
    normal_flip_optim = True #@param {type:"boolean"}
    ##@markdown >Adding some TV may make the image blurrier but also helps to get rid of noise. A good value to try might be 0.1.
    #tv_weight = 0.1 #@param {type:'number'}
    #@markdown ---

    #@markdown >`altprompts` is a set of prompts that take in a different augmentation pipeline, and can have their own cut_pow. At the moment, the default "alt augment" settings flip the picture cutouts upside down before evaluating. This can be good for optical illusion images. If either cut_pow value is 0, it will use the same value as the normal prompts.
    altprompts = "" #@param {type:"string"}
    altprompt_mode = "flipped" 
    ##@param ["normal" , "flipped", "sideways"]
    alt_cut_pow = 0 #@param {type:"number"}
    alt_mse_cut_pow = 0 #@param {type:"number"}
    #altprompt_type = "upside-down" #@param ['upside-down', 'as']

    ##@markdown ---
    ##@markdown <font size="+1">💫</font> **Zooming and Moving** <font size="+1">💫</font>
    zoom = False
    ##@param {type:"boolean"}
    zoom_speed = 100 
    ##@param {type:"number"}
    zoom_frequency = 20 
    ##@param {type:"number"}

    #@markdown ---
    #@markdown On an unrelated note, if you get any errors while running this, restart the runtime and run the first cell again. If that doesn't work either, message me on Discord (Philipuss#4066).

    model_names={'ImageNet 16384': 'vqgan_imagenet_f16_16384', 'ImageNet 1024': 'vqgan_imagenet_f16_1024', "Gumbel 8192": "gumbel_8192", "Sber Gumbel": "sber_gumbel", 'imagenet_cin': 'imagenet_cin', 'WikiArt 1024': 'wikiart_1024', 'WikiArt 16384': 'wikiart_16384', 'COCO-Stuff': 'coco', 'FacesHQ': 'faceshq', 'S-FLCKR': 'sflckr', 'WikiArt 7mil': 'wikiart_7mil', 'COCO 1 Stage': 'coco_1stage'}

    if template == "Better - Fast":
      prompts = prompts + ". Detailed artwork. ArtStationHQ. unreal engine. 4K HD."
    elif template == "Better - Slow":
      prompts = prompts + ". Detailed artwork. Trending on ArtStation. unreal engine. | Rendered in Maya. " + prompts + ". 4K HD."
    elif template == "Movie Poster":
      prompts = prompts + ". Movie poster. Rendered in unreal engine. ArtStationHQ."
      width = 400
      height = 592
    elif template == 'flag':
      prompts = "A photo of a flag of the country " + prompts + " | Flag of " + prompts + ". White background."
      #import cv2
      #img = cv2.imread('templates/flag.png', 0)
      #cv2.imwrite('templates/final_flag.png', add_noise(img))
      init_image = "flag.png"
      transparent_png = True
    elif template == 'planet':
      import cv2
      img = cv2.imread('planet.png', 0)
      cv2.imwrite('final_planet.png', add_noise(img))
      prompts = "A photo of the planet " + prompts + ". Planet in the middle with black background. | The planet of " + prompts + ". Photo of a planet. Black background. Trending on ArtStation. | Colorful."
      init_image = "final_planet.png"
    elif template == 'creature':
      #import cv2
      #img = cv2.imread('templates/planet.png', 0)
      #cv2.imwrite('templates/final_planet.png', add_noise(img))
      prompts = "A photo of a creature with " + prompts + ". Animal in the middle with white background. | The creature has " + prompts + ". Photo of a creature/animal. White background. Detailed image of a creature. | White background."
      init_image = "creature.png"
      #transparent_png = True
    elif template == 'Detailed':
      prompts = prompts + ", by Puer Udger. Detailed artwork, trending on artstation. 4K HD, realism."
      flavor = "cumin"
    elif template == "human":
      init_image = "human.png"
    elif template == "Realistic":
      cutn = 200
      step_size = 0.03
      cut_pow = 0.2
      flavor = "holywater"
    elif template == "Consistent Creativity":
      flavor = "cumin"
      cut_pow = 0.01
      cutn = 136
      step_size = 0.08
      mse_step_size = 0.41
      mse_cut_pow = 0.3
      ema_val = 0.99
      normal_flip_optim = False
    elif template == "Smooth":
      flavor = "wyvern"
      step_size = 0.10
      cutn = 120
      normal_flip_optim = False
      tv_weight = 10
    elif template == "Subtle MSE":
      mse_init_weight = 0.07
      mse_decay_rate = 130
      mse_step_size = 0.2
      mse_cutn = 100
      mse_cut_pow = 0.6
    elif template == "Balanced":
      cutn = 130
      cut_pow = 1
      step_size = 0.16
      final_step_size = 0
      ema_val = 0.98
      mse_init_weight = 0.2
      mse_decay_rate = 130
      mse_with_zeros = True
      mse_step_size = 0.9
      mse_cutn = 50
      mse_cut_pow = 0.8
      normal_flip_optim = True
    elif template == "Size: Square":
      width = 450
      height = 450
    elif template == "Size: Landscape":
      width = 480
      height = 336
    elif template == "Size: Poster":
      width = 336
      height = 480
    elif template == "Negative Prompt":
      prompts = prompts.replace(":", ":-")
      prompts = prompts.replace(":--", ":")
    elif template == "Hyper Fast Results":
      step_size = 1
      ema_val = 0.3
      cutn = 30
    elif template == "Better Quality":
      prompts = prompts + ":1 | Watermark, blurry, cropped, confusing, cut, incoherent:-1"

    mse_decay = 0

    if use_mse == False:
        mse_init_weight = 0.
    else:
        mse_decay = mse_init_weight / mse_epoches
      
    if os.path.isdir('/content/drive') == False:
        if save_to_drive == True or init_image_in_drive == True:
            drive.mount('/content/drive')

    if seed == -1:
        seed = None
    if init_image == "None":
        init_image = None
    if target_images == "None" or not target_images:
        target_images = []
    else:
        target_images = target_images.split("|")
        target_images = [image.strip() for image in target_images]

    prompts = [phrase.strip() for phrase in prompts.split("|")]
    if prompts == ['']:
        prompts = []

    altprompts = [phrase.strip() for phrase in altprompts.split("|")]
    if altprompts == ['']:
        altprompts = []

    if mse_images_interval == 0: mse_images_interval = images_interval
    if mse_step_size == 0: mse_step_size = step_size
    if mse_cutn == 0: mse_cutn = cutn
    if mse_cut_pow == 0: mse_cut_pow = cut_pow
    if alt_cut_pow == 0: alt_cut_pow = cut_pow
    if alt_mse_cut_pow == 0: alt_mse_cut_pow = mse_cut_pow

    augs = nn.Sequential(
              K.RandomHorizontalFlip(p=0.5),
              K.RandomSharpness(0.3,p=0.4),
              K.RandomGaussianBlur((3,3),(4.5,4.5),p=0.3),
              #K.RandomGaussianNoise(p=0.5),
              #K.RandomElasticTransform(kernel_size=(33, 33), sigma=(7,7), p=0.2),
              K.RandomAffine(degrees=30, translate=0.1, p=0.8, padding_mode='border'), # padding_mode=2
              K.RandomPerspective(0.2,p=0.4, ),
              K.ColorJitter(hue=0.01, saturation=0.01, p=0.7),
              K.RandomGrayscale(p=0.1),
              )


    if altprompt_mode == "normal":
      altaugs = nn.Sequential(
                K.RandomRotation(degrees=90.0, return_transform=True),
                K.RandomHorizontalFlip(p=0.5),
                K.RandomSharpness(0.3,p=0.4),
                K.RandomGaussianBlur((3,3),(4.5,4.5),p=0.3),
                #K.RandomGaussianNoise(p=0.5),
                #K.RandomElasticTransform(kernel_size=(33, 33), sigma=(7,7), p=0.2),
                K.RandomAffine(degrees=30, translate=0.1, p=0.8, padding_mode='border'), # padding_mode=2
                K.RandomPerspective(0.2,p=0.4, ),
                K.ColorJitter(hue=0.01, saturation=0.01, p=0.7),
                K.RandomGrayscale(p=0.1),)
    elif altprompt_mode == "flipped":
      altaugs = nn.Sequential(
                K.RandomHorizontalFlip(p=0.5),
                #K.RandomRotation(degrees=90.0),
                K.RandomVerticalFlip(p=1),
                K.RandomSharpness(0.3,p=0.4),
                K.RandomGaussianBlur((3,3),(4.5,4.5),p=0.3),
                #K.RandomGaussianNoise(p=0.5),
                #K.RandomElasticTransform(kernel_size=(33, 33), sigma=(7,7), p=0.2),
                K.RandomAffine(degrees=30, translate=0.1, p=0.8, padding_mode='border'), # padding_mode=2
                K.RandomPerspective(0.2,p=0.4, ),
                K.ColorJitter(hue=0.01, saturation=0.01, p=0.7),
                K.RandomGrayscale(p=0.1),)
    elif altprompt_mode == "sideways":
      altaugs = nn.Sequential(
                K.RandomHorizontalFlip(p=0.5),
                #K.RandomRotation(degrees=90.0),
                K.RandomVerticalFlip(p=1),
                K.RandomSharpness(0.3,p=0.4),
                K.RandomGaussianBlur((3,3),(4.5,4.5),p=0.3),
                #K.RandomGaussianNoise(p=0.5),
                #K.RandomElasticTransform(kernel_size=(33, 33), sigma=(7,7), p=0.2),
                K.RandomAffine(degrees=30, translate=0.1, p=0.8, padding_mode='border'), # padding_mode=2
                K.RandomPerspective(0.2,p=0.4, ),
                K.ColorJitter(hue=0.01, saturation=0.01, p=0.7),
                K.RandomGrayscale(p=0.1),)




    if multiple_prompt_batches:
      prompts_all = str(prompts).split("~")
    else:
      prompts_all = prompts
      multiple_prompt_batches_iter = iterations

    if multiple_prompt_batches:
      mtpl_prmpts_btchs = len(prompts_all)
    else:
      mtpl_prmpts_btchs = 1

    #print(mtpl_prmpts_btchs)

    steps_path = './'
    zoom_path = './'

    path = './'

    iterations = multiple_prompt_batches_iter

    for pr in range(0, mtpl_prmpts_btchs):
      #print(prompts_all[pr].replace('[\'', '').replace('\']', ''))
      if multiple_prompt_batches:
        prompts = prompts_all[pr].replace('[\'', '').replace('\']', '')

      if zoom:
        mdf_iter = round(iterations/zoom_frequency)
      else:
        mdf_iter = 2
        zoom_frequency = iterations

      for iter in range(1, mdf_iter):
        if zoom:
          if iter != 0:
            image = Image.open('progress.png')
            area = (0, 0, width-zoom_speed, height-zoom_speed)
            cropped_img = image.crop(area)
            cropped_img.show()

            new_image = cropped_img.resize((width, height))
            new_image.save('zoom.png')
            init_image = 'zoom.png'

        args = argparse.Namespace(
            prompts=prompts,
            altprompts=altprompts,
            image_prompts=target_images,
            noise_prompt_seeds=[],
            noise_prompt_weights=[],
            size=[width, height],
            init_image=init_image,
            png=transparent_png,
            init_weight= mse_init_weight,
            vqgan_model=model_names[model],
            step_size=step_size,
            start_step_size = start_step_size,
            final_step_size = final_step_size,
            cutn=cutn,
            cut_pow=cut_pow,
            mse_cutn = mse_cutn,
            mse_cut_pow = mse_cut_pow,
            mse_step_size = mse_step_size,
            display_freq=images_interval,
            mse_display_freq = mse_images_interval,
            max_iterations=zoom_frequency,
            mse_end = 0,
            seed=seed,
            folder_name=folder_name,
            save_to_drive=save_to_drive,
            mse_decay_rate = mse_decay_rate,
            mse_decay = mse_decay,
            mse_with_zeros = mse_with_zeros,
            normal_flip_optim = normal_flip_optim,
            ema_val = ema_val,
            augs = augs,
            altaugs = altaugs,
            alt_cut_pow = alt_cut_pow,
            alt_mse_cut_pow = alt_mse_cut_pow,
            is_gumbel = is_gumbel,
            clip_model = clip_model,
            clip_model2 = clip_model2,
            clip_model3 = clip_model3,
            clip_model4 = clip_model4,
            clip_model5 = clip_model5,
            clip_model6 = clip_model6,
            gen_seed = gen_seed)

        mh = ModelHost(args)
        x = 0

        for x in range(batch_size):
            mh.setup_model(x)
            last_iter = mh.run(x)
            #print(last_iter)
            image_data = Image.open(args2.image_file)
            return(image_data)

        if batch_size != 1:
          #clear_output()
          #print("===============================================================================")
          q = 0
          while q < batch_size:
            display(Image('/content/' + folder_name + "/" + str(q) + '.png'))
            #print("Image" + str(q) + '.png')
            q += 1

      if zoom:
        files = os.listdir(steps_path)
        for index, file in enumerate(files):
              os.rename(os.path.join(steps_path, file),os.path.join(steps_path,''.join([str(index + 1 + zoom_frequency * iter),'.png'])))
              index = index+1

        from pathlib import Path
        import shutil

        src_path = steps_path
        trg_path = zoom_path

        for src_file in range(1, mdf_iter):
            shutil.move(os.path.join(src_path,src_file),trg_path)
    
##################### START GRADIO HERE ############################
image = gr.outputs.Image(type="pil", label="Your result")
iface = gr.Interface(
    fn=run_all, 
    inputs=[
    gr.inputs.Textbox(label="Prompt - try adding increments to your prompt such as 'oil on canvas', 'a painting', 'a book cover'",default="chalk pastel drawing of a dog wearing a funny hat"),
    gr.inputs.Slider(label="Steps - more steps can increase quality but will take longer to generate",default=300,maximum=300,minimum=1,step=1),
    gr.inputs.Dropdown(label="Style",choices=["none","Balanced","Detailed","Consistent Creativity","Realistic","Smooth","Subtle MSE","Hyper Fast Results"]),
    gr.inputs.Radio(label="Width", choices=[32,64,128,256,512],default=256),
    gr.inputs.Radio(label="Height", choices=[32,64,128,256,512],default=256),
    ], 
    outputs=image,
    title="Generate images from text with VQGAN+CLIP",
    #description="<div>By typing a prompt and pressing submit you can generate images based on this prompt. <a href='https://github.com/CompVis/latent-diffusion' target='_blank'>Latent Diffusion</a> is a text-to-image model created by <a href='https://github.com/CompVis' target='_blank'>CompVis</a>, trained on the <a href='https://laion.ai/laion-400-open-dataset/'>LAION-400M dataset.</a><br>This UI to the model was assembled by <a style='color: rgb(245, 158, 11);font-weight:bold' href='https://twitter.com/multimodalart' target='_blank'>@multimodalart</a></div>",
    #article="<h4 style='font-size: 110%;margin-top:.5em'>Biases acknowledgment</h4><div>Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exarcbates societal biases. According to the <a href='https://arxiv.org/abs/2112.10752' target='_blank'>Latent Diffusion paper</a>:<i> \"Deep learning modules tend to reproduce or exacerbate biases that are already present in the data\"</i>. The model was trained on an unfiltered version the LAION-400M dataset, which scrapped non-curated image-text-pairs from the internet (the exception being the the removal of illegal content) and is meant to be used for research purposes, such as this one. <a href='https://laion.ai/laion-400-open-dataset/' target='_blank'>You can read more on LAION's website</a></div><h4 style='font-size: 110%;margin-top:1em'>Who owns the images produced by this demo?</h4><div>Definetly not me! Probably you do. I say probably because the Copyright discussion about AI generated art is ongoing. So <a href='https://www.theverge.com/2022/2/21/22944335/us-copyright-office-reject-ai-generated-art-recent-entrance-to-paradise' target='_blank'>it may be the case that everything produced here falls automatically into the public domain</a>. But in any case it is either yours or is in the public domain.</div>"
    )
iface.launch(enable_queue=True)