mirror of
https://github.com/brycedrennan/imaginAIry
synced 2024-11-05 12:00:15 +00:00
755 lines
22 KiB
Python
755 lines
22 KiB
Python
# pylama:ignore=W0613,W0612
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# pytorch_diffusion + derived encoder decoder
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import gc
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import logging
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import math
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import numpy as np
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import torch
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from einops import rearrange
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from torch import nn
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from imaginairy.modules.attention import LinearAttention
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from imaginairy.modules.distributions import DiagonalGaussianDistribution
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from imaginairy.utils import get_device, instantiate_from_config
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logger = logging.getLogger(__name__)
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def get_timestep_embedding(timesteps, embedding_dim):
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"""
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Matches the implementation in Denoising Diffusion Probabilistic Models:
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From Fairseq.
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Build sinusoidal embeddings.
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This matches the implementation in tensor2tensor, but differs slightly
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from the description in Section 3.5 of "Attention Is All You Need".
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"""
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assert len(timesteps.shape) == 1
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half_dim = embedding_dim // 2
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emb = math.log(10000) / (half_dim - 1)
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emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
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emb = emb.to(device=timesteps.device)
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emb = timesteps.float()[:, None] * emb[None, :]
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emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
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if embedding_dim % 2 == 1: # zero pad
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emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
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return emb
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def nonlinearity(x):
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# swish
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t = torch.sigmoid(x)
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x *= t
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del t
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return x
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def Normalize(in_channels, num_groups=32):
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return torch.nn.GroupNorm(
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num_groups=num_groups, 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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h1 = x
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h2 = self.norm1(h1)
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del h1
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h3 = nonlinearity(h2)
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del h2
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h4 = self.conv1(h3)
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del h3
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if temb is not None:
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h4 = h4 + self.temb_proj(nonlinearity(temb))[:, :, None, None]
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h5 = self.norm2(h4)
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del h4
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h6 = nonlinearity(h5)
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del h5
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h7 = self.dropout(h6)
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del h6
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h8 = self.conv2(h7)
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del h7
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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 + h8
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class LinAttnBlock(LinearAttention):
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"""to match AttnBlock usage"""
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def __init__(self, in_channels):
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super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
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class AttnBlock(nn.Module):
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def __init__(self, in_channels):
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super().__init__()
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self.in_channels = in_channels
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self.norm = 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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def forward(self, x):
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if get_device() == "cuda":
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return self.forward_cuda(x)
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h_ = x
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h_ = self.norm(h_)
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q = self.q(h_)
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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, c, h * w)
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q = q.permute(0, 2, 1) # b,hw,c
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k = k.reshape(b, c, h * w) # b,c,hw
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w_ = torch.bmm(q, k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
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w_ = w_ * (int(c) ** (-0.5))
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w_ = torch.nn.functional.softmax(w_, dim=2)
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# attend to values
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v = v.reshape(b, c, h * w)
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w_ = w_.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
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h_ = torch.bmm(v, w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
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h_ = h_.reshape(b, c, h, w)
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h_ = self.proj_out(h_)
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return x + h_
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def forward_cuda(self, x):
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h_ = x
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h_ = self.norm(h_)
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q1 = self.q(h_)
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k1 = 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 = q1.shape
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q2 = q1.reshape(b, c, h * w)
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del q1
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q = q2.permute(0, 2, 1) # b,hw,c
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del q2
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k = k1.reshape(b, c, h * w) # b,c,hw
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del k1
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h_ = torch.zeros_like(k, device=q.device)
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stats = torch.cuda.memory_stats(q.device)
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mem_active = stats["active_bytes.all.current"]
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mem_reserved = stats["reserved_bytes.all.current"]
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mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
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mem_free_torch = mem_reserved - mem_active
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mem_free_total = mem_free_cuda + mem_free_torch
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tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
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mem_required = tensor_size * 2.5
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steps = 1
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if mem_required > mem_free_total:
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steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
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slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
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for i in range(0, q.shape[1], slice_size):
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end = i + slice_size
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w1 = torch.bmm(q[:, i:end], k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
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w2 = w1 * (int(c) ** (-0.5))
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del w1
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w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
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del w2
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# attend to values
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v1 = v.reshape(b, c, h * w)
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w4 = w3.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
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del w3
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h_[:, :, i:end] = torch.bmm(
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v1, w4
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) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
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del v1, w4
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h2 = h_.reshape(b, c, h, w)
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del h_
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h3 = self.proj_out(h2)
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del h2
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h3 += x
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return h3
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def make_attn(in_channels, attn_type="vanilla"):
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assert attn_type in ["vanilla", "linear", "none"], f"attn_type {attn_type} unknown"
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logger.debug(
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f"making attention of type '{attn_type}' with {in_channels} in_channels"
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)
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if attn_type == "vanilla":
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return AttnBlock(in_channels)
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if attn_type == "none":
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return nn.Identity(in_channels)
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return LinAttnBlock(in_channels)
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class Encoder(nn.Module):
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def __init__(
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self,
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*,
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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,
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attn_resolutions,
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dropout=0.0,
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resamp_with_conv=True,
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in_channels,
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resolution,
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z_channels,
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double_z=True,
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use_linear_attn=False,
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attn_type="vanilla",
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**ignore_kwargs,
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):
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super().__init__()
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if use_linear_attn:
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attn_type = "linear"
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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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# 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.in_ch_mult = in_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(make_attn(block_in, attn_type=attn_type))
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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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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 = make_attn(block_in, attn_type=attn_type)
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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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# timestep embedding
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temb = None
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# downsampling
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hs = [self.conv_in(x)]
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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](hs[-1], 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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hs.append(h)
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if i_level != self.num_resolutions - 1:
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hs.append(self.down[i_level].downsample(hs[-1]))
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# middle
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h = hs[-1]
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h = self.mid.block_1(h, temb)
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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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# 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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return h
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class Decoder(nn.Module):
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def __init__(
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self,
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*,
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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,
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attn_resolutions,
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dropout=0.0,
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resamp_with_conv=True,
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in_channels,
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resolution,
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z_channels,
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give_pre_end=False,
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tanh_out=False,
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use_linear_attn=False,
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attn_type="vanilla",
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**ignorekwargs,
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):
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super().__init__()
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if use_linear_attn:
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attn_type = "linear"
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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.tanh_out = tanh_out
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# compute in_ch_mult, block_in and curr_res at lowest res
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in_ch_mult = (1,) + tuple(ch_mult)
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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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logger.debug(
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f"Working with z of shape {self.z_shape} = {np.prod(self.z_shape)} dimensions."
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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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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 = make_attn(block_in, attn_type=attn_type)
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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(make_attn(block_in, attn_type=attn_type))
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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)
|
|
self.conv_out = torch.nn.Conv2d(
|
|
block_in, out_ch, kernel_size=3, stride=1, padding=1
|
|
)
|
|
self.last_z_shape = None
|
|
|
|
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
|
|
h1 = self.conv_in(z)
|
|
|
|
# middle
|
|
h2 = self.mid.block_1(h1, temb)
|
|
del h1
|
|
|
|
h3 = self.mid.attn_1(h2)
|
|
del h2
|
|
|
|
h = self.mid.block_2(h3, temb)
|
|
del h3
|
|
|
|
# prepare for up sampling
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
# 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:
|
|
t = h
|
|
h = self.up[i_level].attn[i_block](t)
|
|
del t
|
|
|
|
if i_level != 0:
|
|
t = h
|
|
h = self.up[i_level].upsample(t)
|
|
del t
|
|
|
|
# end
|
|
if self.give_pre_end:
|
|
return h
|
|
|
|
h1 = self.norm_out(h)
|
|
del h
|
|
|
|
h2 = nonlinearity(h1)
|
|
del h1
|
|
|
|
h = self.conv_out(h2)
|
|
del h2
|
|
|
|
if self.tanh_out:
|
|
t = h
|
|
h = torch.tanh(t)
|
|
del t
|
|
return h
|
|
|
|
|
|
class LatentRescaler(nn.Module):
|
|
def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
|
|
super().__init__()
|
|
# residual block, interpolate, residual block
|
|
self.factor = factor
|
|
self.conv_in = nn.Conv2d(
|
|
in_channels, mid_channels, kernel_size=3, stride=1, padding=1
|
|
)
|
|
self.res_block1 = nn.ModuleList(
|
|
[
|
|
ResnetBlock(
|
|
in_channels=mid_channels,
|
|
out_channels=mid_channels,
|
|
temb_channels=0,
|
|
dropout=0.0,
|
|
)
|
|
for _ in range(depth)
|
|
]
|
|
)
|
|
self.attn = AttnBlock(mid_channels)
|
|
self.res_block2 = nn.ModuleList(
|
|
[
|
|
ResnetBlock(
|
|
in_channels=mid_channels,
|
|
out_channels=mid_channels,
|
|
temb_channels=0,
|
|
dropout=0.0,
|
|
)
|
|
for _ in range(depth)
|
|
]
|
|
)
|
|
|
|
self.conv_out = nn.Conv2d(
|
|
mid_channels,
|
|
out_channels,
|
|
kernel_size=1,
|
|
)
|
|
|
|
def forward(self, x):
|
|
x = self.conv_in(x)
|
|
for block in self.res_block1:
|
|
x = block(x, None)
|
|
x = torch.nn.functional.interpolate(
|
|
x,
|
|
size=(
|
|
int(round(x.shape[2] * self.factor)),
|
|
int(round(x.shape[3] * self.factor)),
|
|
),
|
|
)
|
|
x = self.attn(x)
|
|
for block in self.res_block2:
|
|
x = block(x, None)
|
|
x = self.conv_out(x)
|
|
return x
|
|
|
|
|
|
class Upsampler(nn.Module):
|
|
def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
|
|
super().__init__()
|
|
assert out_size >= in_size
|
|
num_blocks = int(np.log2(out_size // in_size)) + 1
|
|
factor_up = 1.0 + (out_size % in_size)
|
|
logger.debug(
|
|
f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}"
|
|
)
|
|
self.rescaler = LatentRescaler(
|
|
factor=factor_up,
|
|
in_channels=in_channels,
|
|
mid_channels=2 * in_channels,
|
|
out_channels=in_channels,
|
|
)
|
|
self.decoder = Decoder(
|
|
out_ch=out_channels,
|
|
resolution=out_size,
|
|
z_channels=in_channels,
|
|
num_res_blocks=2,
|
|
attn_resolutions=[],
|
|
in_channels=None,
|
|
ch=in_channels,
|
|
ch_mult=[ch_mult for _ in range(num_blocks)],
|
|
)
|
|
|
|
def forward(self, x):
|
|
x = self.rescaler(x)
|
|
x = self.decoder(x)
|
|
return x
|
|
|
|
|
|
class Resize(nn.Module):
|
|
def __init__(self, in_channels=None, learned=False, mode="bilinear"):
|
|
super().__init__()
|
|
self.with_conv = learned
|
|
self.mode = mode
|
|
if self.with_conv:
|
|
logger.info(
|
|
f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode" # noqa
|
|
)
|
|
raise NotImplementedError()
|
|
# assert in_channels is not None
|
|
# # no asymmetric padding in torch conv, must do it ourselves
|
|
# self.conv = torch.nn.Conv2d(
|
|
# in_channels, in_channels, kernel_size=4, stride=2, padding=1
|
|
# )
|
|
|
|
def forward(self, x, scale_factor=1.0):
|
|
if scale_factor == 1.0:
|
|
return x
|
|
|
|
x = torch.nn.functional.interpolate(
|
|
x, mode=self.mode, align_corners=False, scale_factor=scale_factor
|
|
)
|
|
return x
|
|
|
|
|
|
class FirstStagePostProcessor(nn.Module):
|
|
def __init__(
|
|
self,
|
|
ch_mult: list,
|
|
in_channels,
|
|
pretrained_model: nn.Module = None,
|
|
reshape=False,
|
|
n_channels=None,
|
|
dropout=0.0,
|
|
pretrained_config=None,
|
|
):
|
|
super().__init__()
|
|
if pretrained_config is None:
|
|
assert (
|
|
pretrained_model is not None
|
|
), 'Either "pretrained_model" or "pretrained_config" must not be None'
|
|
self.pretrained_model = pretrained_model
|
|
else:
|
|
assert (
|
|
pretrained_config is not None
|
|
), 'Either "pretrained_model" or "pretrained_config" must not be None'
|
|
self.instantiate_pretrained(pretrained_config)
|
|
|
|
self.do_reshape = reshape
|
|
|
|
if n_channels is None:
|
|
n_channels = self.pretrained_model.encoder.ch
|
|
|
|
self.proj_norm = Normalize(in_channels, num_groups=in_channels // 2)
|
|
self.proj = nn.Conv2d(
|
|
in_channels, n_channels, kernel_size=3, stride=1, padding=1
|
|
)
|
|
|
|
blocks = []
|
|
downs = []
|
|
ch_in = n_channels
|
|
for m in ch_mult:
|
|
blocks.append(
|
|
ResnetBlock(
|
|
in_channels=ch_in, out_channels=m * n_channels, dropout=dropout
|
|
)
|
|
)
|
|
ch_in = m * n_channels
|
|
downs.append(Downsample(ch_in, with_conv=False))
|
|
|
|
self.model = nn.ModuleList(blocks)
|
|
self.downsampler = nn.ModuleList(downs)
|
|
|
|
def instantiate_pretrained(self, config):
|
|
model = instantiate_from_config(config)
|
|
self.pretrained_model = model.eval()
|
|
# self.pretrained_model.train = False
|
|
for param in self.pretrained_model.parameters():
|
|
param.requires_grad = False
|
|
|
|
@torch.no_grad()
|
|
def encode_with_pretrained(self, x):
|
|
c = self.pretrained_model.encode(x)
|
|
if isinstance(c, DiagonalGaussianDistribution):
|
|
c = c.mode()
|
|
return c
|
|
|
|
def forward(self, x):
|
|
z_fs = self.encode_with_pretrained(x)
|
|
z = self.proj_norm(z_fs)
|
|
z = self.proj(z)
|
|
z = nonlinearity(z)
|
|
|
|
for submodel, downmodel in zip(self.model, self.downsampler):
|
|
z = submodel(z, temb=None)
|
|
z = downmodel(z)
|
|
|
|
if self.do_reshape:
|
|
z = rearrange(z, "b c h w -> b (h w) c")
|
|
return z
|