main.py

import argparse
import math
import random

from vqgan_clip.grad import *
from vqgan_clip.helpers import *
from vqgan_clip.inits import *
from vqgan_clip.masking import *
from vqgan_clip.optimizers import *

from urllib.request import urlopen
from tqdm import tqdm
import sys
import os

from omegaconf import OmegaConf

from taming.models import cond_transformer, vqgan

import torch
from torch import nn, optim
from torch.nn import functional as F
from torchvision import transforms
from torchvision.transforms import functional as TF
from torch.cuda import get_device_properties
torch.backends.cudnn.benchmark = False

from torch_optimizer import DiffGrad, AdamP, RAdam

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

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

from subprocess import Popen, PIPE
import re
from packaging import version

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

# Check for GPU and reduce the default image size if low VRAM
default_image_size = 512  # >8GB VRAM
if not torch.cuda.is_available():
    default_image_size = 256  # no GPU found
elif get_device_properties(0).total_memory <= 2 ** 33:  # 2 ** 33 = 8,589,934,592 bytes = 8 GB
    default_image_size = 318  # <8GB VRAM

def parse():

    vq_parser = argparse.ArgumentParser(description='Image generation using VQGAN+CLIP')

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

    args = vq_parser.parse_args()

    if not args.prompts and not args.image_prompts:
        raise Exception("You must supply a text or image prompt")

    torch.backends.cudnn.deterministic = args.cudnn_determinism

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

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

        # First phrase
        args.prompts = all_phrases[0]

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

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

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

    return args

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

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


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


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


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


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


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

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

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

    return result # return loss


def train(i):
    opt.zero_grad(set_to_none=True)
    lossAll = ascend_txt()
    
    if i % args.display_freq == 0:
        checkin(i, lossAll)
       
    loss = sum(lossAll)
    loss.backward()
    opt.step()
    
    #with torch.no_grad():
    with torch.inference_mode():
        z.copy_(z.maximum(z_min).minimum(z_max))


if __name__ == '__main__':

    args = parse()

    # Do it
    device = torch.device(args.cuda_device)
    model = load_vqgan_model(args.vqgan_config, args.vqgan_checkpoint).to(device)
    jit = True if version.parse(torch.__version__) < version.parse('1.8.0') else False
    perceptor = clip.load(args.clip_model, jit=jit)[0].eval().requires_grad_(False).to(device)
    

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

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

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

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


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

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

    z_orig = z.clone()
    z.requires_grad_(True)    
    pMs = []
    normalize = transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
                                      std=[0.26862954, 0.26130258, 0.27577711])


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


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

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


    # Set the optimiser
    opt, z = get_opt(args.optimiser, z, args.step_size)


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

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


    if args.seed is None:
        seed = torch.seed()
    else:
        seed = args.seed  
    torch.manual_seed(seed)
    print('Using seed:', seed)
    
    
    i = 0 # Iteration counter
    j = 0 # Zoom video frame counter    
    p = 1 # Phrase counter
    smoother = 0 # Smoother counter
    this_video_frame = 0 # for video styling

    with tqdm() as pbar:
        while i < args.max_iterations:
            # Change text prompt
            if args.prompt_frequency > 0:
                if i % args.prompt_frequency == 0 and i > 0:
                    # In case there aren't enough phrases, just loop
                    if p >= len(all_phrases):
                        p = 0
                    
                    pMs = []
                    args.prompts = all_phrases[p]

                    # Show user we're changing prompt                                
                    print(args.prompts)
                    
                    for prompt in args.prompts:
                        txt, weight, stop = split_prompt(prompt)
                        embed = perceptor.encode_text(clip.tokenize(txt).to(device)).float()
                        pMs.append(Prompt(embed, weight, stop).to(device))                    
                    p += 1
            train(i)
            i += 1
            pbar.update()
    
    print("done")