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247 lines
8.9 KiB
Python
247 lines
8.9 KiB
Python
import math
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from einops import rearrange, repeat
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import torch
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from torch import nn
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from torch.nn import functional as F
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from . import utils
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# Karras et al. preconditioned denoiser
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class Denoiser(nn.Module):
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"""A Karras et al. preconditioner for denoising diffusion models."""
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def __init__(self, inner_model, sigma_data=1.):
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super().__init__()
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self.inner_model = inner_model
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self.sigma_data = sigma_data
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def get_scalings(self, sigma):
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c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2)
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c_out = sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
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c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
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return c_skip, c_out, c_in
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def loss(self, input, noise, sigma, **kwargs):
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c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
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noised_input = input + noise * utils.append_dims(sigma, input.ndim)
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model_output = self.inner_model(noised_input * c_in, sigma, **kwargs)
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target = (input - c_skip * noised_input) / c_out
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return (model_output - target).pow(2).flatten(1).mean(1)
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def forward(self, input, sigma, **kwargs):
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c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
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return self.inner_model(input * c_in, sigma, **kwargs) * c_out + input * c_skip
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class DenoiserWithVariance(Denoiser):
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def loss(self, input, noise, sigma, **kwargs):
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c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
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noised_input = input + noise * utils.append_dims(sigma, input.ndim)
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model_output, logvar = self.inner_model(noised_input * c_in, sigma, return_variance=True, **kwargs)
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logvar = utils.append_dims(logvar, model_output.ndim)
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target = (input - c_skip * noised_input) / c_out
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losses = ((model_output - target) ** 2 / logvar.exp() + logvar) / 2
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return losses.flatten(1).mean(1)
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# Residual blocks
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class ResidualBlock(nn.Module):
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def __init__(self, *main, skip=None):
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super().__init__()
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self.main = nn.Sequential(*main)
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self.skip = skip if skip else nn.Identity()
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def forward(self, input):
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return self.main(input) + self.skip(input)
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# Noise level (and other) conditioning
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class ConditionedModule(nn.Module):
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pass
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class UnconditionedModule(ConditionedModule):
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def __init__(self, module):
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super().__init__()
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self.module = module
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def forward(self, input, cond=None):
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return self.module(input)
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class ConditionedSequential(nn.Sequential, ConditionedModule):
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def forward(self, input, cond):
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for module in self:
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if isinstance(module, ConditionedModule):
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input = module(input, cond)
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else:
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input = module(input)
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return input
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class ConditionedResidualBlock(ConditionedModule):
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def __init__(self, *main, skip=None):
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super().__init__()
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self.main = ConditionedSequential(*main)
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self.skip = skip if skip else nn.Identity()
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def forward(self, input, cond):
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skip = self.skip(input, cond) if isinstance(self.skip, ConditionedModule) else self.skip(input)
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return self.main(input, cond) + skip
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class AdaGN(ConditionedModule):
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def __init__(self, feats_in, c_out, num_groups, eps=1e-5, cond_key='cond'):
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super().__init__()
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self.num_groups = num_groups
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self.eps = eps
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self.cond_key = cond_key
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self.mapper = nn.Linear(feats_in, c_out * 2)
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def forward(self, input, cond):
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weight, bias = self.mapper(cond[self.cond_key]).chunk(2, dim=-1)
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input = F.group_norm(input, self.num_groups, eps=self.eps)
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return torch.addcmul(utils.append_dims(bias, input.ndim), input, utils.append_dims(weight, input.ndim) + 1)
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# Attention
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class SelfAttention2d(ConditionedModule):
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def __init__(self, c_in, n_head, norm, dropout_rate=0.):
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super().__init__()
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assert c_in % n_head == 0
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self.norm_in = norm(c_in)
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self.n_head = n_head
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self.qkv_proj = nn.Conv2d(c_in, c_in * 3, 1)
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self.out_proj = nn.Conv2d(c_in, c_in, 1)
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self.dropout = nn.Dropout(dropout_rate)
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def forward(self, input, cond):
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n, c, h, w = input.shape
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qkv = self.qkv_proj(self.norm_in(input, cond))
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qkv = qkv.view([n, self.n_head * 3, c // self.n_head, h * w]).transpose(2, 3)
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q, k, v = qkv.chunk(3, dim=1)
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scale = k.shape[3] ** -0.25
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att = ((q * scale) @ (k.transpose(2, 3) * scale)).softmax(3)
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att = self.dropout(att)
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y = (att @ v).transpose(2, 3).contiguous().view([n, c, h, w])
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return input + self.out_proj(y)
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class CrossAttention2d(ConditionedModule):
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def __init__(self, c_dec, c_enc, n_head, norm_dec, dropout_rate=0.,
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cond_key='cross', cond_key_padding='cross_padding'):
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super().__init__()
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assert c_dec % n_head == 0
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self.cond_key = cond_key
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self.cond_key_padding = cond_key_padding
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self.norm_enc = nn.LayerNorm(c_enc)
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self.norm_dec = norm_dec(c_dec)
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self.n_head = n_head
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self.q_proj = nn.Conv2d(c_dec, c_dec, 1)
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self.kv_proj = nn.Linear(c_enc, c_dec * 2)
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self.out_proj = nn.Conv2d(c_dec, c_dec, 1)
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self.dropout = nn.Dropout(dropout_rate)
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def forward(self, input, cond):
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n, c, h, w = input.shape
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q = self.q_proj(self.norm_dec(input, cond))
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q = q.view([n, self.n_head, c // self.n_head, h * w]).transpose(2, 3)
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kv = self.kv_proj(self.norm_enc(cond[self.cond_key]))
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kv = kv.view([n, -1, self.n_head * 2, c // self.n_head]).transpose(1, 2)
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k, v = kv.chunk(2, dim=1)
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scale = k.shape[3] ** -0.25
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att = ((q * scale) @ (k.transpose(2, 3) * scale))
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att = att - (cond[self.cond_key_padding][:, None, None, :]) * 10000
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att = att.softmax(3)
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att = self.dropout(att)
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y = (att @ v).transpose(2, 3)
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y = y.contiguous().view([n, c, h, w])
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return input + self.out_proj(y)
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# Downsampling/upsampling
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_kernels = {
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'linear':
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[1 / 8, 3 / 8, 3 / 8, 1 / 8],
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'cubic':
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[-0.01171875, -0.03515625, 0.11328125, 0.43359375,
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0.43359375, 0.11328125, -0.03515625, -0.01171875],
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'lanczos3':
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[0.003689131001010537, 0.015056144446134567, -0.03399861603975296,
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-0.066637322306633, 0.13550527393817902, 0.44638532400131226,
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0.44638532400131226, 0.13550527393817902, -0.066637322306633,
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-0.03399861603975296, 0.015056144446134567, 0.003689131001010537]
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}
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_kernels['bilinear'] = _kernels['linear']
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_kernels['bicubic'] = _kernels['cubic']
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class Downsample2d(nn.Module):
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def __init__(self, kernel='linear', pad_mode='reflect'):
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super().__init__()
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self.pad_mode = pad_mode
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kernel_1d = torch.tensor([_kernels[kernel]])
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self.pad = kernel_1d.shape[1] // 2 - 1
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self.register_buffer('kernel', kernel_1d.T @ kernel_1d)
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def forward(self, x):
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x = F.pad(x, (self.pad,) * 4, self.pad_mode)
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weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
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indices = torch.arange(x.shape[1], device=x.device)
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weight[indices, indices] = self.kernel.to(weight)
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return F.conv2d(x, weight, stride=2)
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class Upsample2d(nn.Module):
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def __init__(self, kernel='linear', pad_mode='reflect'):
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super().__init__()
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self.pad_mode = pad_mode
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kernel_1d = torch.tensor([_kernels[kernel]]) * 2
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self.pad = kernel_1d.shape[1] // 2 - 1
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self.register_buffer('kernel', kernel_1d.T @ kernel_1d)
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def forward(self, x):
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x = F.pad(x, ((self.pad + 1) // 2,) * 4, self.pad_mode)
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weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
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indices = torch.arange(x.shape[1], device=x.device)
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weight[indices, indices] = self.kernel.to(weight)
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return F.conv_transpose2d(x, weight, stride=2, padding=self.pad * 2 + 1)
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# Embeddings
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class FourierFeatures(nn.Module):
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def __init__(self, in_features, out_features, std=1.):
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super().__init__()
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assert out_features % 2 == 0
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self.register_buffer('weight', torch.randn([out_features // 2, in_features]) * std)
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def forward(self, input):
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f = 2 * math.pi * input @ self.weight.T
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return torch.cat([f.cos(), f.sin()], dim=-1)
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# U-Nets
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class UNet(ConditionedModule):
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def __init__(self, d_blocks, u_blocks, skip_stages=0):
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super().__init__()
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self.d_blocks = nn.ModuleList(d_blocks)
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self.u_blocks = nn.ModuleList(u_blocks)
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self.skip_stages = skip_stages
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def forward(self, input, cond):
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skips = []
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for block in self.d_blocks[self.skip_stages:]:
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input = block(input, cond)
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skips.append(input)
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for i, (block, skip) in enumerate(zip(self.u_blocks, reversed(skips))):
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input = block(input, cond, skip if i > 0 else None)
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return input
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