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import math
from sys import path
path.append("./taming-transformers")
from taming.models import cond_transformer, vqgan
import torch
from torch import nn
from torch.nn import functional
from omegaconf import OmegaConf
import kornia.augmentation as K
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 = functional.pad(input, (0, 0, pad_h, pad_h), "reflect")
input = functional.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 = functional.pad(input, (pad_w, pad_w, 0, 0), "reflect")
input = functional.conv2d(input, kernel_w[None, None, None, :])
input = input.view([n, c, h, w])
return functional.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 = functional.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=1.0, 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 = functional.normalize(input.unsqueeze(1), dim=2)
embed_normed = functional.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=1.0):
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.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),
)
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
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
else:
raise ValueError(f"unknown model type: {config.model.target}")
del model.loss
return model
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