app.py

import torch
torch.hub.download_url_to_file('http://mirror.io.community/blob/vqgan/vqgan_imagenet_f16_16384.yaml', 'vqgan_imagenet_f16_16384.yaml')
torch.hub.download_url_to_file('http://mirror.io.community/blob/vqgan/vqgan_imagenet_f16_16384.ckpt', 'vqgan_imagenet_f16_16384.ckpt')
import argparse
import math
from pathlib import Path
import sys

sys.path.insert(1, './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 
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
import gradio as gr

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
    else:
        raise ValueError(f'unknown model type: {config.model.target}')
    del model.loss
    return model


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


def inference(text):
    texts = text
    width =  300
    height = 300
    model = "vqgan_imagenet_f16_16384" 
    images_interval =  50
    init_image = ""
    target_images = ""
    seed = 42
    max_iterations = 300

    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 = []
    else:
        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,
    )
    from urllib.request import urlopen

    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
    print('Using device:', device)
    if texts:
        print('Using texts:', texts)
    if target_images:
        print('Using image prompts:', target_images)
    if args.seed is None:
        seed = torch.seed()
    else:
        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]
    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]
    # 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],
    #                                            std=[0.229, 0.224, 0.225])

    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)
    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 args.vqgan_checkpoint == 'vqgan_openimages_f16_8192.ckpt':
            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
    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],
                                    std=[0.26862954, 0.26130258, 0.27577711])



    pMs = []

    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)
        else:
            z_q = vector_quantize(z.movedim(1, 3), model.quantize.embedding.weight).movedim(3, 1)
        return clamp_with_grad(model.decode(z_q).add(1).div(2), 0, 1)

    @torch.no_grad()
    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))

    i = 0
    try:
        with tqdm() as pbar:
            while True:
                train(i)
                if i == max_iterations:
                    break
                i += 1
                pbar.update()
    except KeyboardInterrupt:
        pass
    return "./steps/300.png"

title = "VQGAN + CLIP"
description = "Gradio demo for VQGAN + CLIP. To use it, simply add your text, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'>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. | <a href='https://colab.research.google.com/drive/1ZAus_gn2RhTZWzOWUpPERNC0Q8OhZRTZ'>Colab</a> | <a href='https://github.com/CompVis/taming-transformers'>Taming Transformers Github Repo</a> | <a href='https://github.com/openai/CLIP'>CLIP Github Repo</a></p>"

gr.Interface(
    inference, 
    gr.inputs.Textbox(label="Input"), 
    gr.outputs.Image(type="file", label="Output"),
    title=title,
    description=description,
    article=article,
    examples=[
              ['a garden by james gurney'],
              ['coral reef city artstationHQ'],
              ['a cabin in the mountains unreal engine']
    ]
    ).launch(debug=True)