sample-python/testgan.py at main · web3pm/sample-python

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# -*- coding: utf-8 -*-
"""Copy of Dan's version of VQGAN+CLIP (with pooling).ipynb"
Automatically generated by Colaboratory.
Original file is located at
    https://colab.research.google.com/drive/1FZYlb-tde1Zqidygrc3Ty-ru6hTphCfm
# Generate images from text prompts with VQGAN and CLIP (z+quantize method).
Originally made by Katherine Crowson (https://github.com/crowsonkb, https://twitter.com/RiversHaveWings). The original BigGAN+CLIP method was by https://twitter.com/advadnoun.
 Added some explanations and modifications by Eleiber#8347, pooling trick by Crimeacs#8222 (https://twitter.com/EarthML1) and the GUI was made with the help of Abulafia#3734.
Other resources:
https://www.reddit.com/r/bigsleep/comments/oohmes/tips_and_tricks_for_prompts_and_parameters/
Prompts: description:weight|description2:weight
Weights can be negative
4k / chromatic aberration effect / cinematic effect / diorama / dof / depth of field / field of view / fisheye lens effect / photorealistic / hyperrealistic / raytracing / shaders / stop motion / tilt-shift photography / ultrarealistic / vignetting. cell shading / flat colors / full of color / electric colors. anime / comic / graphic novel / visual novel. 
**Materials**: acrylic painting style / clay / coffee paint / collage / embroidery / glitch / graphite drawing / gouache / illuminated manuscript / ink drawing / medieval parchment / detailed oil painting / tempera / watercolor. isometric / lineart / lofi / lowpoly / photoshop / pixel art / vector art / voxel art. 
**Historical periods**: baroque / German Romanticism / impressionism / Luminism / pointillism / post-impressionism / Vienna Secession. 
You can also use type of painting by region: chinese painting / indian art / tibetan paintings / nordic mythology style / soviet art style, etc. According to movies or video games in Ghost in the Shell style / in Star Wars style / in Ghibli style / in Metropolis film style / Death Stranding style… (the more the entry text is related to the movie or video game, the more it will resemble.) See The 1000 Best Movies in History (2020). According to rendering program Imitate the result, it's not really using those graphics engines[r 5]):
rendered in Unreal Engine[r 6][r 7], the most used. rendered in vray. rendered in povray. zbrush/cycles. rendered in rtx. rendered in octane. rendered in cinema4d. rendered in autodesk 3ds max. rendered in houdini. etc. They can be combined. Pending: Check: https://www.g2.com/products/octanerender/competitors/alternatives Specific Effects Add brdf / caustics / global-illumination / non-photorealistic / path tracing physically based rendering / raytracing / etc. Effects / lights diamond / fire / fog / glow / incandescent / iridescent / lava / red-hot / shining / etc. Other Trending on modifiers (website): for example "trending on artstation"[r 8] black hole / dismal / grim / liminal / minimalistic / surprising / vibrant
**Other instructions**:
https://tuscriaturas.miraheze.org/wiki/Ayuda:Generar_im%C3%A1genes_con_VQGAN%2BCLIP
# @title Licensed under the MIT License
# Copyright (c) 2021 Katherine Crowson
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
!nvidia-smi
!git clone https://github.com/openai/CLIP
# !pip install taming-transformers
!git clone https://github.com/CompVis/taming-transformers.git
!pip install ftfy regex tqdm omegaconf pytorch-lightning
!pip install kornia
!pip install imageio-ffmpeg   
!pip install einops          
!mkdir steps
"""By default, the notebook downloads the 1024 and 16384 models from ImageNet. There are others like COCO-Stuff, WikiArt or S-FLCKR, which are heavy, and if you are not going to use them it would be useless to download them, so if you want to use them, simply remove the numerals at the beginning of the lines depending on the model you want (the model name is at the end of the lines)."""
#@title Selection of models to download
#@markdown By default, the notebook downloads the 1024 and 16384 models from ImageNet. There are others like COCO-Stuff, 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.
imagenet_1024 = True #@param {type:"boolean"}
imagenet_16384 = True #@param {type:"boolean"}
coco = False #@param {type:"boolean"}
faceshq = False #@param {type:"boolean"}
wikiart_1024 = False #@param {type:"boolean"}
wikiart_16384 = True #@param {type:"boolean"}
sflckr = False #@param {type:"boolean"}
openimages_8192 = False #@param {type:"boolean"}
if imagenet_1024:
  !curl -L -o vqgan_imagenet_f16_1024.yaml -C - 'http://mirror.io.community/blob/vqgan/vqgan_imagenet_f16_1024.yaml' #ImageNet 1024
  !curl -L -o vqgan_imagenet_f16_1024.ckpt -C - 'http://mirror.io.community/blob/vqgan/vqgan_imagenet_f16_1024.ckpt'  #ImageNet 1024
if imagenet_16384:
  !curl -L -o vqgan_imagenet_f16_16384.yaml -C - 'https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887/files/?p=%2Fconfigs%2Fmodel.yaml&dl=1' #ImageNet 16384
  !curl -L -o vqgan_imagenet_f16_16384.ckpt -C - 'https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887/files/?p=%2Fckpts%2Flast.ckpt&dl=1' #ImageNet 16384
if openimages_8192:
  !curl -L -o vqgan_openimages_f16_8192.yaml -C - 'https://heibox.uni-heidelberg.de/d/2e5662443a6b4307b470/files/?p=%2Fconfigs%2Fmodel.yaml&dl=1' #ImageNet 16384
  !curl -L -o vqgan_openimages_f16_8192.ckpt -C - 'https://heibox.uni-heidelberg.de/d/2e5662443a6b4307b470/files/?p=%2Fckpts%2Flast.ckpt&dl=1' #ImageNet 16384
  !curl -L -o coco.yaml -C - 'https://dl.nmkd.de/ai/clip/coco/coco.yaml' #COCO
  !curl -L -o coco.ckpt -C - 'https://dl.nmkd.de/ai/clip/coco/coco.ckpt' #COCO
if faceshq:
  !curl -L -o faceshq.yaml -C - 'https://drive.google.com/uc?export=download&id=1fHwGx_hnBtC8nsq7hesJvs-Klv-P0gzT' #FacesHQ
  !curl -L -o faceshq.ckpt -C - 'https://app.koofr.net/content/links/a04deec9-0c59-4673-8b37-3d696fe63a5d/files/get/last.ckpt?path=%2F2020-11-13T21-41-45_faceshq_transformer%2Fcheckpoints%2Flast.ckpt' #FacesHQ
if wikiart_1024: 
  !curl -L -o wikiart_1024.yaml -C - 'http://mirror.io.community/blob/vqgan/wikiart.yaml' #WikiArt 1024
  !curl -L -o wikiart_1024.ckpt -C - 'http://mirror.io.community/blob/vqgan/wikiart.ckpt' #WikiArt 1024
if wikiart_16384: 
  !curl -L -o wikiart_16384.yaml -C - 'http://eaidata.bmk.sh/data/Wikiart_16384/wikiart_f16_16384_8145600.yaml' #WikiArt 16384
  !curl -L -o wikiart_16384.ckpt -C - 'http://eaidata.bmk.sh/data/Wikiart_16384/wikiart_f16_16384_8145600.ckpt' #WikiArt 16384
  !curl -L -o sflckr.yaml -C - 'https://heibox.uni-heidelberg.de/d/73487ab6e5314cb5adba/files/?p=%2Fconfigs%2F2020-11-09T13-31-51-project.yaml&dl=1' #S-FLCKR
  !curl -L -o sflckr.ckpt -C - 'https://heibox.uni-heidelberg.de/d/73487ab6e5314cb5adba/files/?p=%2Fcheckpoints%2Flast.ckpt&dl=1' #S-FLCKR
# @title Load libraries and variables
import argparse
import math
from pathlib import Path
sys.path.insert(1, '/content/taming-transformers')
from IPython import display
from base64 import b64encode
from omegaconf import OmegaConf
from PIL import Image
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 tqdm.notebook import tqdm
from CLIP import clip
import kornia.augmentation as K
import numpy as np
import imageio
from PIL import ImageFile, Image
ImageFile.LOAD_TRUNCATED_IMAGES = True
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)
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])
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
        self.augs = nn.Sequential(
            # K.RandomHorizontalFlip(p=0.5),
            # K.RandomVerticalFlip(p=0.5),
            # K.RandomSolarize(0.01, 0.01, p=0.7),
            # K.RandomSharpness(0.3,p=0.4),
            # K.RandomResizedCrop(size=(self.cut_size,self.cut_size), scale=(0.1,1),  ratio=(0.75,1.333), cropping_mode='resample', p=0.5),
            # K.RandomCrop(size=(self.cut_size,self.cut_size), p=0.5),
            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):
            # 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)))
            # cutout = transforms.Resize(size=(self.cut_size, self.cut_size))(input)
            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
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.vqgan.GumbelVQ':
        model = vqgan.GumbelVQ(**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
        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)
"""## Settings for this run:
Mainly what you will have to modify will be `texts:`, there you can place the text or texts you want to generate (separated with `|`). It is a list because you can put more than one text, and so the AI ​​tries to 'mix' the images, giving the same priority to both texts.
To use an initial image to the model, you just have to upload a file to the Colab environment (in the section on the left), and then modify `init_image:` putting the exact name of the file. Example: `sample.png`
You can also modify the model by changing the lines that say `model:`. Currently ImageNet 1024, ImageNet 16384, WikiArt 1024, WikiArt 16384, S-FLCKR and COCO-Stuff are available. To activate them you have to have downloaded them first, and then you can simply select it.
You can also use `target_images`, which is basically putting one or more images on it that the AI ​​will take as a "target", fulfilling the same function as putting text on it. To put more than one you have to use `|` as a separator.
#@title Parameters
texts = "moon god trending on artstation" #@param {type:"string"}
width =  400#@param {type:"number"}
height = 400#@param {type:"number"}
model = "vqgan_imagenet_f16_16384" #@param ["vqgan_imagenet_f16_16384", "vqgan_imagenet_f16_1024", "vqgan_openimages_f16_8192", "wikiart_1024", "wikiart_16384", "coco", "faceshq", "sflckr"]
images_interval =  25#@param {type:"number"}
init_image = ""#@param {type:"string"}
target_images = ""#@param {type:"string"}
seed = -1#@param {type:"number"}
max_iterations = 600#@param {type:"number"}
model_names={"vqgan_imagenet_f16_16384": 'ImageNet 16384',"vqgan_imagenet_f16_1024":"ImageNet 1024", 'vqgan_openimages_f16_8192':'OpenImages 8912',
                 "wikiart_1024":"WikiArt 1024", "wikiart_16384":"WikiArt 16384", "coco":"COCO-Stuff", "faceshq":"FacesHQ", "sflckr":"S-FLCKR"}
name_model = model_names[model]     
if seed == -1:
    seed = None
if init_image == "None":
    init_image = None
if target_images == "None" or not target_images:
    target_images = []
    target_images = target_images.split("|")
    target_images = [image.strip() for image in target_images]
texts = [phrase.strip() for phrase in texts.split("|")]
if texts == ['']:
    texts = []
args = argparse.Namespace(
    prompts=texts,
    image_prompts=target_images,
    noise_prompt_seeds=[],
    noise_prompt_weights=[],
    size=[width, height],
    init_image=init_image,
    init_weight=0.,
    clip_model='ViT-B/32',
    vqgan_config=f'{model}.yaml',
    vqgan_checkpoint=f'{model}.ckpt',
    step_size=0.1,
    cutn=32,
    cut_pow=1.,
    display_freq=images_interval,
    seed=seed,
#@title Actually do the run...
from urllib.request import urlopen
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print('Using device:', device)
    print('Using texts:', texts)
if target_images:
    print('Using image prompts:', target_images)
if args.seed is None:
    seed = torch.seed()
    seed = args.seed
torch.manual_seed(seed)
print('Using seed:', seed)
model = load_vqgan_model(args.vqgan_config, args.vqgan_checkpoint).to(device)
perceptor = clip.load(args.clip_model, jit=False)[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)
make_cutouts = MakeCutouts(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
if args.vqgan_checkpoint == 'vqgan_openimages_f16_8192.ckpt':
    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]
    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]
# 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]
# normalize_imagenet = transforms.Normalize(mean=[0.485, 0.456, 0.406],
if args.init_image:
    if 'http' in args.init_image:
      img = Image.open(urlopen(args.init_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)
    z, *_ = model.encode(pil_tensor.to(device).unsqueeze(0) * 2 - 1)
    one_hot = F.one_hot(torch.randint(n_toks, [toksY * toksX], device=device), n_toks).float()
    # z = one_hot @ model.quantize.embedding.weight
    if args.vqgan_checkpoint == 'vqgan_openimages_f16_8192.ckpt':
        z = one_hot @ model.quantize.embed.weight
        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
z_orig = z.clone()
z.requires_grad_(True)
opt = optim.Adam([z], lr=args.step_size)
normalize = transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
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.image_prompts:
    path, weight, stop = parse_prompt(prompt)
    img = Image.open(path)
    pil_image = img.convert('RGB')
    img = resize_image(pil_image, (sideX, sideY))
    batch = make_cutouts(TF.to_tensor(img).unsqueeze(0).to(device))
    embed = perceptor.encode_image(normalize(batch)).float()
    pMs.append(Prompt(embed, weight, stop).to(device))
for seed, weight in zip(args.noise_prompt_seeds, args.noise_prompt_weights):
    gen = torch.Generator().manual_seed(seed)
    embed = torch.empty([1, perceptor.visual.output_dim]).normal_(generator=gen)
    pMs.append(Prompt(embed, weight).to(device))
def synth(z):
    if args.vqgan_checkpoint == 'vqgan_openimages_f16_8192.ckpt':
        z_q = vector_quantize(z.movedim(1, 3), model.quantize.embed.weight).movedim(3, 1)
        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()
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)
    TF.to_pil_image(out[0].cpu()).save('progress.png')
    display.display(display.Image('progress.png'))
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))
    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
def train(i):
    opt.zero_grad()
    lossAll = ascend_txt()
    if i % args.display_freq == 0:
        checkin(i, lossAll)
    loss = sum(lossAll)
    loss.backward()
    opt.step()
    with torch.no_grad():
        z.copy_(z.maximum(z_min).minimum(z_max))
    with tqdm() as pbar:
        while True:
            train(i)
            if i == max_iterations:
                break
            pbar.update()
except KeyboardInterrupt:
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