760 lines
23 KiB
Python
760 lines
23 KiB
Python
"""Modified from https://github.com/wzhouxiff/RestoreFormer"""
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class VectorQuantizer(nn.Module):
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"""
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see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py
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____________________________________________
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Discretization bottleneck part of the VQ-VAE.
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Inputs:
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- n_e : number of embeddings
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- e_dim : dimension of embedding
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- beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
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_____________________________________________
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"""
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def __init__(self, n_e, e_dim, beta):
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super(VectorQuantizer, self).__init__()
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self.n_e = n_e
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self.e_dim = e_dim
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self.beta = beta
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self.embedding = nn.Embedding(self.n_e, self.e_dim)
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self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
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def forward(self, z):
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"""
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Inputs the output of the encoder network z and maps it to a discrete
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one-hot vector that is the index of the closest embedding vector e_j
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z (continuous) -> z_q (discrete)
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z.shape = (batch, channel, height, width)
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quantization pipeline:
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1. get encoder input (B,C,H,W)
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2. flatten input to (B*H*W,C)
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"""
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# reshape z -> (batch, height, width, channel) and flatten
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z = z.permute(0, 2, 3, 1).contiguous()
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z_flattened = z.view(-1, self.e_dim)
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# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
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d = (
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torch.sum(z_flattened**2, dim=1, keepdim=True)
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+ torch.sum(self.embedding.weight**2, dim=1)
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- 2 * torch.matmul(z_flattened, self.embedding.weight.t())
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)
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# could possible replace this here
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# #\start...
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# find closest encodings
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min_value, min_encoding_indices = torch.min(d, dim=1)
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min_encoding_indices = min_encoding_indices.unsqueeze(1)
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min_encodings = torch.zeros(min_encoding_indices.shape[0], self.n_e).to(z)
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min_encodings.scatter_(1, min_encoding_indices, 1)
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# dtype min encodings: torch.float32
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# min_encodings shape: torch.Size([2048, 512])
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# min_encoding_indices.shape: torch.Size([2048, 1])
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# get quantized latent vectors
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z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
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# .........\end
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# with:
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# .........\start
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# min_encoding_indices = torch.argmin(d, dim=1)
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# z_q = self.embedding(min_encoding_indices)
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# ......\end......... (TODO)
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# compute loss for embedding
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loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean(
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(z_q - z.detach()) ** 2
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)
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# preserve gradients
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z_q = z + (z_q - z).detach()
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# perplexity
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e_mean = torch.mean(min_encodings, dim=0)
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perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
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# reshape back to match original input shape
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z_q = z_q.permute(0, 3, 1, 2).contiguous()
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return z_q, loss, (perplexity, min_encodings, min_encoding_indices, d)
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def get_codebook_entry(self, indices, shape):
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# shape specifying (batch, height, width, channel)
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# TODO: check for more easy handling with nn.Embedding
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min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices)
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min_encodings.scatter_(1, indices[:, None], 1)
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# get quantized latent vectors
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z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
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if shape is not None:
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z_q = z_q.view(shape)
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# reshape back to match original input shape
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z_q = z_q.permute(0, 3, 1, 2).contiguous()
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return z_q
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# pytorch_diffusion + derived encoder decoder
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def nonlinearity(x):
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# swish
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return x * torch.sigmoid(x)
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def Normalize(in_channels):
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return torch.nn.GroupNorm(
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num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
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)
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class Upsample(nn.Module):
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def __init__(self, in_channels, with_conv):
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super().__init__()
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self.with_conv = with_conv
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if self.with_conv:
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self.conv = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=3, stride=1, padding=1
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)
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def forward(self, x):
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x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
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if self.with_conv:
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x = self.conv(x)
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return x
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class Downsample(nn.Module):
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def __init__(self, in_channels, with_conv):
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super().__init__()
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self.with_conv = with_conv
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if self.with_conv:
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# no asymmetric padding in torch conv, must do it ourselves
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self.conv = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=3, stride=2, padding=0
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)
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def forward(self, x):
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if self.with_conv:
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pad = (0, 1, 0, 1)
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x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
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x = self.conv(x)
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else:
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x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
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return x
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class ResnetBlock(nn.Module):
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def __init__(
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self,
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*,
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in_channels,
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out_channels=None,
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conv_shortcut=False,
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dropout,
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temb_channels=512,
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):
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super().__init__()
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self.in_channels = in_channels
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out_channels = in_channels if out_channels is None else out_channels
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self.out_channels = out_channels
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self.use_conv_shortcut = conv_shortcut
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self.norm1 = Normalize(in_channels)
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self.conv1 = torch.nn.Conv2d(
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in_channels, out_channels, kernel_size=3, stride=1, padding=1
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)
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if temb_channels > 0:
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self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
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self.norm2 = Normalize(out_channels)
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self.dropout = torch.nn.Dropout(dropout)
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self.conv2 = torch.nn.Conv2d(
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out_channels, out_channels, kernel_size=3, stride=1, padding=1
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)
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if self.in_channels != self.out_channels:
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if self.use_conv_shortcut:
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self.conv_shortcut = torch.nn.Conv2d(
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in_channels, out_channels, kernel_size=3, stride=1, padding=1
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)
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else:
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self.nin_shortcut = torch.nn.Conv2d(
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in_channels, out_channels, kernel_size=1, stride=1, padding=0
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)
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def forward(self, x, temb):
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h = x
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h = self.norm1(h)
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h = nonlinearity(h)
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h = self.conv1(h)
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if temb is not None:
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h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None]
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h = self.norm2(h)
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h = nonlinearity(h)
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h = self.dropout(h)
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h = self.conv2(h)
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if self.in_channels != self.out_channels:
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if self.use_conv_shortcut:
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x = self.conv_shortcut(x)
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else:
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x = self.nin_shortcut(x)
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return x + h
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class MultiHeadAttnBlock(nn.Module):
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def __init__(self, in_channels, head_size=1):
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super().__init__()
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self.in_channels = in_channels
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self.head_size = head_size
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self.att_size = in_channels // head_size
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assert (
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in_channels % head_size == 0
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), "The size of head should be divided by the number of channels."
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self.norm1 = Normalize(in_channels)
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self.norm2 = Normalize(in_channels)
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self.q = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.k = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.v = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.proj_out = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.num = 0
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def forward(self, x, y=None):
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h_ = x
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h_ = self.norm1(h_)
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if y is None:
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y = h_
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else:
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y = self.norm2(y)
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q = self.q(y)
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k = self.k(h_)
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v = self.v(h_)
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# compute attention
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b, c, h, w = q.shape
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q = q.reshape(b, self.head_size, self.att_size, h * w)
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q = q.permute(0, 3, 1, 2) # b, hw, head, att
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k = k.reshape(b, self.head_size, self.att_size, h * w)
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k = k.permute(0, 3, 1, 2)
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v = v.reshape(b, self.head_size, self.att_size, h * w)
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v = v.permute(0, 3, 1, 2)
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q = q.transpose(1, 2)
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v = v.transpose(1, 2)
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k = k.transpose(1, 2).transpose(2, 3)
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scale = int(self.att_size) ** (-0.5)
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q.mul_(scale)
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w_ = torch.matmul(q, k)
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w_ = F.softmax(w_, dim=3)
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w_ = w_.matmul(v)
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w_ = w_.transpose(1, 2).contiguous() # [b, h*w, head, att]
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w_ = w_.view(b, h, w, -1)
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w_ = w_.permute(0, 3, 1, 2)
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w_ = self.proj_out(w_)
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return x + w_
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class MultiHeadEncoder(nn.Module):
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def __init__(
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self,
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ch,
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out_ch,
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ch_mult=(1, 2, 4, 8),
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num_res_blocks=2,
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attn_resolutions=(16,),
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dropout=0.0,
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resamp_with_conv=True,
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in_channels=3,
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resolution=512,
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z_channels=256,
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double_z=True,
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enable_mid=True,
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head_size=1,
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**ignore_kwargs,
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):
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super().__init__()
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self.ch = ch
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self.temb_ch = 0
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self.num_resolutions = len(ch_mult)
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self.num_res_blocks = num_res_blocks
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self.resolution = resolution
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self.in_channels = in_channels
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self.enable_mid = enable_mid
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# downsampling
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self.conv_in = torch.nn.Conv2d(
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in_channels, self.ch, kernel_size=3, stride=1, padding=1
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)
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curr_res = resolution
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in_ch_mult = (1,) + tuple(ch_mult)
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self.down = nn.ModuleList()
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for i_level in range(self.num_resolutions):
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block = nn.ModuleList()
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attn = nn.ModuleList()
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block_in = ch * in_ch_mult[i_level]
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block_out = ch * ch_mult[i_level]
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for i_block in range(self.num_res_blocks):
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block.append(
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ResnetBlock(
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in_channels=block_in,
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out_channels=block_out,
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temb_channels=self.temb_ch,
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dropout=dropout,
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)
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)
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block_in = block_out
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if curr_res in attn_resolutions:
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attn.append(MultiHeadAttnBlock(block_in, head_size))
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down = nn.Module()
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down.block = block
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down.attn = attn
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if i_level != self.num_resolutions - 1:
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down.downsample = Downsample(block_in, resamp_with_conv)
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curr_res = curr_res // 2
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self.down.append(down)
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# middle
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if self.enable_mid:
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self.mid = nn.Module()
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self.mid.block_1 = ResnetBlock(
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in_channels=block_in,
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out_channels=block_in,
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temb_channels=self.temb_ch,
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dropout=dropout,
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)
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self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size)
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self.mid.block_2 = ResnetBlock(
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in_channels=block_in,
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out_channels=block_in,
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temb_channels=self.temb_ch,
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dropout=dropout,
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)
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# end
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self.norm_out = Normalize(block_in)
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self.conv_out = torch.nn.Conv2d(
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block_in,
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2 * z_channels if double_z else z_channels,
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kernel_size=3,
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stride=1,
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padding=1,
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)
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def forward(self, x):
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hs = {}
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# timestep embedding
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temb = None
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# downsampling
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h = self.conv_in(x)
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hs["in"] = h
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for i_level in range(self.num_resolutions):
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for i_block in range(self.num_res_blocks):
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h = self.down[i_level].block[i_block](h, temb)
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if len(self.down[i_level].attn) > 0:
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h = self.down[i_level].attn[i_block](h)
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if i_level != self.num_resolutions - 1:
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# hs.append(h)
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hs["block_" + str(i_level)] = h
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h = self.down[i_level].downsample(h)
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# middle
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# h = hs[-1]
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if self.enable_mid:
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h = self.mid.block_1(h, temb)
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hs["block_" + str(i_level) + "_atten"] = h
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h = self.mid.attn_1(h)
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h = self.mid.block_2(h, temb)
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hs["mid_atten"] = h
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# end
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h = self.norm_out(h)
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h = nonlinearity(h)
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h = self.conv_out(h)
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# hs.append(h)
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hs["out"] = h
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return hs
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class MultiHeadDecoder(nn.Module):
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def __init__(
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self,
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ch,
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out_ch,
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ch_mult=(1, 2, 4, 8),
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num_res_blocks=2,
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attn_resolutions=(16,),
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dropout=0.0,
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resamp_with_conv=True,
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in_channels=3,
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resolution=512,
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z_channels=256,
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give_pre_end=False,
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enable_mid=True,
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head_size=1,
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**ignorekwargs,
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):
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super().__init__()
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self.ch = ch
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self.temb_ch = 0
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self.num_resolutions = len(ch_mult)
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self.num_res_blocks = num_res_blocks
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self.resolution = resolution
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self.in_channels = in_channels
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self.give_pre_end = give_pre_end
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self.enable_mid = enable_mid
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# compute in_ch_mult, block_in and curr_res at lowest res
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block_in = ch * ch_mult[self.num_resolutions - 1]
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curr_res = resolution // 2 ** (self.num_resolutions - 1)
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self.z_shape = (1, z_channels, curr_res, curr_res)
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print(
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"Working with z of shape {} = {} dimensions.".format(
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self.z_shape, np.prod(self.z_shape)
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)
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)
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# z to block_in
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self.conv_in = torch.nn.Conv2d(
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z_channels, block_in, kernel_size=3, stride=1, padding=1
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)
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# middle
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if self.enable_mid:
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self.mid = nn.Module()
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self.mid.block_1 = ResnetBlock(
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in_channels=block_in,
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out_channels=block_in,
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temb_channels=self.temb_ch,
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dropout=dropout,
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)
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self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size)
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self.mid.block_2 = ResnetBlock(
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in_channels=block_in,
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out_channels=block_in,
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temb_channels=self.temb_ch,
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dropout=dropout,
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)
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# upsampling
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self.up = nn.ModuleList()
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for i_level in reversed(range(self.num_resolutions)):
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block = nn.ModuleList()
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attn = nn.ModuleList()
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block_out = ch * ch_mult[i_level]
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for i_block in range(self.num_res_blocks + 1):
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block.append(
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ResnetBlock(
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in_channels=block_in,
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out_channels=block_out,
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temb_channels=self.temb_ch,
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dropout=dropout,
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)
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)
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block_in = block_out
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if curr_res in attn_resolutions:
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attn.append(MultiHeadAttnBlock(block_in, head_size))
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up = nn.Module()
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up.block = block
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up.attn = attn
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if i_level != 0:
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up.upsample = Upsample(block_in, resamp_with_conv)
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curr_res = curr_res * 2
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self.up.insert(0, up) # prepend to get consistent order
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# end
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self.norm_out = Normalize(block_in)
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self.conv_out = torch.nn.Conv2d(
|
|
block_in, out_ch, kernel_size=3, stride=1, padding=1
|
|
)
|
|
|
|
def forward(self, z):
|
|
# assert z.shape[1:] == self.z_shape[1:]
|
|
self.last_z_shape = z.shape
|
|
|
|
# timestep embedding
|
|
temb = None
|
|
|
|
# z to block_in
|
|
h = self.conv_in(z)
|
|
|
|
# middle
|
|
if self.enable_mid:
|
|
h = self.mid.block_1(h, temb)
|
|
h = self.mid.attn_1(h)
|
|
h = self.mid.block_2(h, temb)
|
|
|
|
# upsampling
|
|
for i_level in reversed(range(self.num_resolutions)):
|
|
for i_block in range(self.num_res_blocks + 1):
|
|
h = self.up[i_level].block[i_block](h, temb)
|
|
if len(self.up[i_level].attn) > 0:
|
|
h = self.up[i_level].attn[i_block](h)
|
|
if i_level != 0:
|
|
h = self.up[i_level].upsample(h)
|
|
|
|
# end
|
|
if self.give_pre_end:
|
|
return h
|
|
|
|
h = self.norm_out(h)
|
|
h = nonlinearity(h)
|
|
h = self.conv_out(h)
|
|
return h
|
|
|
|
|
|
class MultiHeadDecoderTransformer(nn.Module):
|
|
def __init__(
|
|
self,
|
|
ch,
|
|
out_ch,
|
|
ch_mult=(1, 2, 4, 8),
|
|
num_res_blocks=2,
|
|
attn_resolutions=(16,),
|
|
dropout=0.0,
|
|
resamp_with_conv=True,
|
|
in_channels=3,
|
|
resolution=512,
|
|
z_channels=256,
|
|
give_pre_end=False,
|
|
enable_mid=True,
|
|
head_size=1,
|
|
**ignorekwargs,
|
|
):
|
|
super().__init__()
|
|
self.ch = ch
|
|
self.temb_ch = 0
|
|
self.num_resolutions = len(ch_mult)
|
|
self.num_res_blocks = num_res_blocks
|
|
self.resolution = resolution
|
|
self.in_channels = in_channels
|
|
self.give_pre_end = give_pre_end
|
|
self.enable_mid = enable_mid
|
|
|
|
# compute in_ch_mult, block_in and curr_res at lowest res
|
|
block_in = ch * ch_mult[self.num_resolutions - 1]
|
|
curr_res = resolution // 2 ** (self.num_resolutions - 1)
|
|
self.z_shape = (1, z_channels, curr_res, curr_res)
|
|
print(
|
|
"Working with z of shape {} = {} dimensions.".format(
|
|
self.z_shape, np.prod(self.z_shape)
|
|
)
|
|
)
|
|
|
|
# z to block_in
|
|
self.conv_in = torch.nn.Conv2d(
|
|
z_channels, block_in, kernel_size=3, stride=1, padding=1
|
|
)
|
|
|
|
# middle
|
|
if self.enable_mid:
|
|
self.mid = nn.Module()
|
|
self.mid.block_1 = ResnetBlock(
|
|
in_channels=block_in,
|
|
out_channels=block_in,
|
|
temb_channels=self.temb_ch,
|
|
dropout=dropout,
|
|
)
|
|
self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size)
|
|
self.mid.block_2 = ResnetBlock(
|
|
in_channels=block_in,
|
|
out_channels=block_in,
|
|
temb_channels=self.temb_ch,
|
|
dropout=dropout,
|
|
)
|
|
|
|
# upsampling
|
|
self.up = nn.ModuleList()
|
|
for i_level in reversed(range(self.num_resolutions)):
|
|
block = nn.ModuleList()
|
|
attn = nn.ModuleList()
|
|
block_out = ch * ch_mult[i_level]
|
|
for i_block in range(self.num_res_blocks + 1):
|
|
block.append(
|
|
ResnetBlock(
|
|
in_channels=block_in,
|
|
out_channels=block_out,
|
|
temb_channels=self.temb_ch,
|
|
dropout=dropout,
|
|
)
|
|
)
|
|
block_in = block_out
|
|
if curr_res in attn_resolutions:
|
|
attn.append(MultiHeadAttnBlock(block_in, head_size))
|
|
up = nn.Module()
|
|
up.block = block
|
|
up.attn = attn
|
|
if i_level != 0:
|
|
up.upsample = Upsample(block_in, resamp_with_conv)
|
|
curr_res = curr_res * 2
|
|
self.up.insert(0, up) # prepend to get consistent order
|
|
|
|
# end
|
|
self.norm_out = Normalize(block_in)
|
|
self.conv_out = torch.nn.Conv2d(
|
|
block_in, out_ch, kernel_size=3, stride=1, padding=1
|
|
)
|
|
|
|
def forward(self, z, hs):
|
|
# assert z.shape[1:] == self.z_shape[1:]
|
|
# self.last_z_shape = z.shape
|
|
|
|
# timestep embedding
|
|
temb = None
|
|
|
|
# z to block_in
|
|
h = self.conv_in(z)
|
|
|
|
# middle
|
|
if self.enable_mid:
|
|
h = self.mid.block_1(h, temb)
|
|
h = self.mid.attn_1(h, hs["mid_atten"])
|
|
h = self.mid.block_2(h, temb)
|
|
|
|
# upsampling
|
|
for i_level in reversed(range(self.num_resolutions)):
|
|
for i_block in range(self.num_res_blocks + 1):
|
|
h = self.up[i_level].block[i_block](h, temb)
|
|
if len(self.up[i_level].attn) > 0:
|
|
h = self.up[i_level].attn[i_block](
|
|
h, hs["block_" + str(i_level) + "_atten"]
|
|
)
|
|
# hfeature = h.clone()
|
|
if i_level != 0:
|
|
h = self.up[i_level].upsample(h)
|
|
|
|
# end
|
|
if self.give_pre_end:
|
|
return h
|
|
|
|
h = self.norm_out(h)
|
|
h = nonlinearity(h)
|
|
h = self.conv_out(h)
|
|
return h
|
|
|
|
|
|
class RestoreFormer(nn.Module):
|
|
def __init__(
|
|
self,
|
|
n_embed=1024,
|
|
embed_dim=256,
|
|
ch=64,
|
|
out_ch=3,
|
|
ch_mult=(1, 2, 2, 4, 4, 8),
|
|
num_res_blocks=2,
|
|
attn_resolutions=(16,),
|
|
dropout=0.0,
|
|
in_channels=3,
|
|
resolution=512,
|
|
z_channels=256,
|
|
double_z=False,
|
|
enable_mid=True,
|
|
fix_decoder=False,
|
|
fix_codebook=True,
|
|
fix_encoder=False,
|
|
head_size=8,
|
|
):
|
|
super(RestoreFormer, self).__init__()
|
|
|
|
self.encoder = MultiHeadEncoder(
|
|
ch=ch,
|
|
out_ch=out_ch,
|
|
ch_mult=ch_mult,
|
|
num_res_blocks=num_res_blocks,
|
|
attn_resolutions=attn_resolutions,
|
|
dropout=dropout,
|
|
in_channels=in_channels,
|
|
resolution=resolution,
|
|
z_channels=z_channels,
|
|
double_z=double_z,
|
|
enable_mid=enable_mid,
|
|
head_size=head_size,
|
|
)
|
|
self.decoder = MultiHeadDecoderTransformer(
|
|
ch=ch,
|
|
out_ch=out_ch,
|
|
ch_mult=ch_mult,
|
|
num_res_blocks=num_res_blocks,
|
|
attn_resolutions=attn_resolutions,
|
|
dropout=dropout,
|
|
in_channels=in_channels,
|
|
resolution=resolution,
|
|
z_channels=z_channels,
|
|
enable_mid=enable_mid,
|
|
head_size=head_size,
|
|
)
|
|
|
|
self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25)
|
|
|
|
self.quant_conv = torch.nn.Conv2d(z_channels, embed_dim, 1)
|
|
self.post_quant_conv = torch.nn.Conv2d(embed_dim, z_channels, 1)
|
|
|
|
if fix_decoder:
|
|
for _, param in self.decoder.named_parameters():
|
|
param.requires_grad = False
|
|
for _, param in self.post_quant_conv.named_parameters():
|
|
param.requires_grad = False
|
|
for _, param in self.quantize.named_parameters():
|
|
param.requires_grad = False
|
|
elif fix_codebook:
|
|
for _, param in self.quantize.named_parameters():
|
|
param.requires_grad = False
|
|
|
|
if fix_encoder:
|
|
for _, param in self.encoder.named_parameters():
|
|
param.requires_grad = False
|
|
|
|
def encode(self, x):
|
|
hs = self.encoder(x)
|
|
h = self.quant_conv(hs["out"])
|
|
quant, emb_loss, info = self.quantize(h)
|
|
return quant, emb_loss, info, hs
|
|
|
|
def decode(self, quant, hs):
|
|
quant = self.post_quant_conv(quant)
|
|
dec = self.decoder(quant, hs)
|
|
|
|
return dec
|
|
|
|
def forward(self, input, **kwargs):
|
|
quant, diff, info, hs = self.encode(input)
|
|
dec = self.decode(quant, hs)
|
|
|
|
return dec, None
|