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-rw-r--r--ldm/modules/attention.py261
-rw-r--r--ldm/modules/diffusionmodules/__init__.py0
-rw-r--r--ldm/modules/diffusionmodules/model.py835
-rw-r--r--ldm/modules/diffusionmodules/openaimodel.py961
-rw-r--r--ldm/modules/diffusionmodules/util.py267
-rw-r--r--ldm/modules/distributions/__init__.py0
-rw-r--r--ldm/modules/distributions/distributions.py92
-rw-r--r--ldm/modules/ema.py76
-rw-r--r--ldm/modules/encoders/__init__.py0
-rw-r--r--ldm/modules/encoders/modules.py234
-rw-r--r--ldm/modules/encoders/xlmr.py137
-rw-r--r--ldm/modules/image_degradation/__init__.py2
-rw-r--r--ldm/modules/image_degradation/bsrgan.py730
-rw-r--r--ldm/modules/image_degradation/bsrgan_light.py650
-rw-r--r--ldm/modules/image_degradation/utils/test.pngbin441072 -> 0 bytes
-rw-r--r--ldm/modules/image_degradation/utils_image.py916
-rw-r--r--ldm/modules/losses/__init__.py1
-rw-r--r--ldm/modules/losses/contperceptual.py111
-rw-r--r--ldm/modules/losses/vqperceptual.py167
-rw-r--r--ldm/modules/x_transformer.py641
20 files changed, 0 insertions, 6081 deletions
diff --git a/ldm/modules/attention.py b/ldm/modules/attention.py
deleted file mode 100644
index f4eff39c..00000000
--- a/ldm/modules/attention.py
+++ /dev/null
@@ -1,261 +0,0 @@
-from inspect import isfunction
-import math
-import torch
-import torch.nn.functional as F
-from torch import nn, einsum
-from einops import rearrange, repeat
-
-from ldm.modules.diffusionmodules.util import checkpoint
-
-
-def exists(val):
- return val is not None
-
-
-def uniq(arr):
- return{el: True for el in arr}.keys()
-
-
-def default(val, d):
- if exists(val):
- return val
- return d() if isfunction(d) else d
-
-
-def max_neg_value(t):
- return -torch.finfo(t.dtype).max
-
-
-def init_(tensor):
- dim = tensor.shape[-1]
- std = 1 / math.sqrt(dim)
- tensor.uniform_(-std, std)
- return tensor
-
-
-# feedforward
-class GEGLU(nn.Module):
- def __init__(self, dim_in, dim_out):
- super().__init__()
- self.proj = nn.Linear(dim_in, dim_out * 2)
-
- def forward(self, x):
- x, gate = self.proj(x).chunk(2, dim=-1)
- return x * F.gelu(gate)
-
-
-class FeedForward(nn.Module):
- def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
- super().__init__()
- inner_dim = int(dim * mult)
- dim_out = default(dim_out, dim)
- project_in = nn.Sequential(
- nn.Linear(dim, inner_dim),
- nn.GELU()
- ) if not glu else GEGLU(dim, inner_dim)
-
- self.net = nn.Sequential(
- project_in,
- nn.Dropout(dropout),
- nn.Linear(inner_dim, dim_out)
- )
-
- def forward(self, x):
- return self.net(x)
-
-
-def zero_module(module):
- """
- Zero out the parameters of a module and return it.
- """
- for p in module.parameters():
- p.detach().zero_()
- return module
-
-
-def Normalize(in_channels):
- return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class LinearAttention(nn.Module):
- def __init__(self, dim, heads=4, dim_head=32):
- super().__init__()
- self.heads = heads
- hidden_dim = dim_head * heads
- self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
- self.to_out = nn.Conv2d(hidden_dim, dim, 1)
-
- def forward(self, x):
- b, c, h, w = x.shape
- qkv = self.to_qkv(x)
- q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
- k = k.softmax(dim=-1)
- context = torch.einsum('bhdn,bhen->bhde', k, v)
- out = torch.einsum('bhde,bhdn->bhen', context, q)
- out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
- return self.to_out(out)
-
-
-class SpatialSelfAttention(nn.Module):
- def __init__(self, in_channels):
- super().__init__()
- self.in_channels = in_channels
-
- self.norm = Normalize(in_channels)
- self.q = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.k = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.v = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.proj_out = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
-
- def forward(self, x):
- h_ = x
- h_ = self.norm(h_)
- q = self.q(h_)
- k = self.k(h_)
- v = self.v(h_)
-
- # compute attention
- b,c,h,w = q.shape
- q = rearrange(q, 'b c h w -> b (h w) c')
- k = rearrange(k, 'b c h w -> b c (h w)')
- w_ = torch.einsum('bij,bjk->bik', q, k)
-
- w_ = w_ * (int(c)**(-0.5))
- w_ = torch.nn.functional.softmax(w_, dim=2)
-
- # attend to values
- v = rearrange(v, 'b c h w -> b c (h w)')
- w_ = rearrange(w_, 'b i j -> b j i')
- h_ = torch.einsum('bij,bjk->bik', v, w_)
- h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
- h_ = self.proj_out(h_)
-
- return x+h_
-
-
-class CrossAttention(nn.Module):
- def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
- super().__init__()
- inner_dim = dim_head * heads
- context_dim = default(context_dim, query_dim)
-
- self.scale = dim_head ** -0.5
- self.heads = heads
-
- self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
- self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
- self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
-
- self.to_out = nn.Sequential(
- nn.Linear(inner_dim, query_dim),
- nn.Dropout(dropout)
- )
-
- def forward(self, x, context=None, mask=None):
- h = self.heads
-
- q = self.to_q(x)
- context = default(context, x)
- k = self.to_k(context)
- v = self.to_v(context)
-
- q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-
- sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-
- if exists(mask):
- mask = rearrange(mask, 'b ... -> b (...)')
- max_neg_value = -torch.finfo(sim.dtype).max
- mask = repeat(mask, 'b j -> (b h) () j', h=h)
- sim.masked_fill_(~mask, max_neg_value)
-
- # attention, what we cannot get enough of
- attn = sim.softmax(dim=-1)
-
- out = einsum('b i j, b j d -> b i d', attn, v)
- out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
- return self.to_out(out)
-
-
-class BasicTransformerBlock(nn.Module):
- def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):
- super().__init__()
- self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout) # is a self-attention
- self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
- self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
- heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none
- self.norm1 = nn.LayerNorm(dim)
- self.norm2 = nn.LayerNorm(dim)
- self.norm3 = nn.LayerNorm(dim)
- self.checkpoint = checkpoint
-
- def forward(self, x, context=None):
- return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
-
- def _forward(self, x, context=None):
- x = self.attn1(self.norm1(x)) + x
- x = self.attn2(self.norm2(x), context=context) + x
- x = self.ff(self.norm3(x)) + x
- return x
-
-
-class SpatialTransformer(nn.Module):
- """
- Transformer block for image-like data.
- First, project the input (aka embedding)
- and reshape to b, t, d.
- Then apply standard transformer action.
- Finally, reshape to image
- """
- def __init__(self, in_channels, n_heads, d_head,
- depth=1, dropout=0., context_dim=None):
- super().__init__()
- self.in_channels = in_channels
- inner_dim = n_heads * d_head
- self.norm = Normalize(in_channels)
-
- self.proj_in = nn.Conv2d(in_channels,
- inner_dim,
- kernel_size=1,
- stride=1,
- padding=0)
-
- self.transformer_blocks = nn.ModuleList(
- [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
- for d in range(depth)]
- )
-
- self.proj_out = zero_module(nn.Conv2d(inner_dim,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0))
-
- def forward(self, x, context=None):
- # note: if no context is given, cross-attention defaults to self-attention
- b, c, h, w = x.shape
- x_in = x
- x = self.norm(x)
- x = self.proj_in(x)
- x = rearrange(x, 'b c h w -> b (h w) c')
- for block in self.transformer_blocks:
- x = block(x, context=context)
- x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
- x = self.proj_out(x)
- return x + x_in \ No newline at end of file
diff --git a/ldm/modules/diffusionmodules/__init__.py b/ldm/modules/diffusionmodules/__init__.py
deleted file mode 100644
index e69de29b..00000000
--- a/ldm/modules/diffusionmodules/__init__.py
+++ /dev/null
diff --git a/ldm/modules/diffusionmodules/model.py b/ldm/modules/diffusionmodules/model.py
deleted file mode 100644
index 533e589a..00000000
--- a/ldm/modules/diffusionmodules/model.py
+++ /dev/null
@@ -1,835 +0,0 @@
-# pytorch_diffusion + derived encoder decoder
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import rearrange
-
-from ldm.util import instantiate_from_config
-from ldm.modules.attention import LinearAttention
-
-
-def get_timestep_embedding(timesteps, embedding_dim):
- """
- This matches the implementation in Denoising Diffusion Probabilistic Models:
- From Fairseq.
- Build sinusoidal embeddings.
- This matches the implementation in tensor2tensor, but differs slightly
- from the description in Section 3.5 of "Attention Is All You Need".
- """
- assert len(timesteps.shape) == 1
-
- half_dim = embedding_dim // 2
- emb = math.log(10000) / (half_dim - 1)
- emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
- emb = emb.to(device=timesteps.device)
- emb = timesteps.float()[:, None] * emb[None, :]
- emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
- if embedding_dim % 2 == 1: # zero pad
- emb = torch.nn.functional.pad(emb, (0,1,0,0))
- return emb
-
-
-def nonlinearity(x):
- # swish
- return x*torch.sigmoid(x)
-
-
-def Normalize(in_channels, num_groups=32):
- return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class Upsample(nn.Module):
- def __init__(self, in_channels, with_conv):
- super().__init__()
- self.with_conv = with_conv
- if self.with_conv:
- self.conv = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x):
- x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
- if self.with_conv:
- x = self.conv(x)
- return x
-
-
-class Downsample(nn.Module):
- def __init__(self, in_channels, with_conv):
- super().__init__()
- self.with_conv = with_conv
- if self.with_conv:
- # no asymmetric padding in torch conv, must do it ourselves
- self.conv = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=3,
- stride=2,
- padding=0)
-
- def forward(self, x):
- if self.with_conv:
- pad = (0,1,0,1)
- x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
- x = self.conv(x)
- else:
- x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
- return x
-
-
-class ResnetBlock(nn.Module):
- def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
- dropout, temb_channels=512):
- super().__init__()
- self.in_channels = in_channels
- out_channels = in_channels if out_channels is None else out_channels
- self.out_channels = out_channels
- self.use_conv_shortcut = conv_shortcut
-
- self.norm1 = Normalize(in_channels)
- self.conv1 = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- if temb_channels > 0:
- self.temb_proj = torch.nn.Linear(temb_channels,
- out_channels)
- self.norm2 = Normalize(out_channels)
- self.dropout = torch.nn.Dropout(dropout)
- self.conv2 = torch.nn.Conv2d(out_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- if self.in_channels != self.out_channels:
- if self.use_conv_shortcut:
- self.conv_shortcut = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- else:
- self.nin_shortcut = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=1,
- stride=1,
- padding=0)
-
- def forward(self, x, temb):
- h = x
- h = self.norm1(h)
- h = nonlinearity(h)
- h = self.conv1(h)
-
- if temb is not None:
- h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
-
- h = self.norm2(h)
- h = nonlinearity(h)
- h = self.dropout(h)
- h = self.conv2(h)
-
- if self.in_channels != self.out_channels:
- if self.use_conv_shortcut:
- x = self.conv_shortcut(x)
- else:
- x = self.nin_shortcut(x)
-
- return x+h
-
-
-class LinAttnBlock(LinearAttention):
- """to match AttnBlock usage"""
- def __init__(self, in_channels):
- super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
-
-
-class AttnBlock(nn.Module):
- def __init__(self, in_channels):
- super().__init__()
- self.in_channels = in_channels
-
- self.norm = Normalize(in_channels)
- self.q = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.k = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.v = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.proj_out = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
-
-
- def forward(self, x):
- h_ = x
- h_ = self.norm(h_)
- q = self.q(h_)
- k = self.k(h_)
- v = self.v(h_)
-
- # compute attention
- b,c,h,w = q.shape
- q = q.reshape(b,c,h*w)
- q = q.permute(0,2,1) # b,hw,c
- k = k.reshape(b,c,h*w) # b,c,hw
- w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
- w_ = w_ * (int(c)**(-0.5))
- w_ = torch.nn.functional.softmax(w_, dim=2)
-
- # attend to values
- v = v.reshape(b,c,h*w)
- w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
- 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]
- h_ = h_.reshape(b,c,h,w)
-
- h_ = self.proj_out(h_)
-
- return x+h_
-
-
-def make_attn(in_channels, attn_type="vanilla"):
- assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
- print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
- if attn_type == "vanilla":
- return AttnBlock(in_channels)
- elif attn_type == "none":
- return nn.Identity(in_channels)
- else:
- return LinAttnBlock(in_channels)
-
-
-class Model(nn.Module):
- def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
- resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
- super().__init__()
- if use_linear_attn: attn_type = "linear"
- self.ch = ch
- self.temb_ch = self.ch*4
- self.num_resolutions = len(ch_mult)
- self.num_res_blocks = num_res_blocks
- self.resolution = resolution
- self.in_channels = in_channels
-
- self.use_timestep = use_timestep
- if self.use_timestep:
- # timestep embedding
- self.temb = nn.Module()
- self.temb.dense = nn.ModuleList([
- torch.nn.Linear(self.ch,
- self.temb_ch),
- torch.nn.Linear(self.temb_ch,
- self.temb_ch),
- ])
-
- # downsampling
- self.conv_in = torch.nn.Conv2d(in_channels,
- self.ch,
- kernel_size=3,
- stride=1,
- padding=1)
-
- curr_res = resolution
- in_ch_mult = (1,)+tuple(ch_mult)
- self.down = nn.ModuleList()
- for i_level in range(self.num_resolutions):
- block = nn.ModuleList()
- attn = nn.ModuleList()
- block_in = ch*in_ch_mult[i_level]
- block_out = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks):
- 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(make_attn(block_in, attn_type=attn_type))
- down = nn.Module()
- down.block = block
- down.attn = attn
- if i_level != self.num_resolutions-1:
- down.downsample = Downsample(block_in, resamp_with_conv)
- curr_res = curr_res // 2
- self.down.append(down)
-
- # middle
- 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 = make_attn(block_in, attn_type=attn_type)
- 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]
- skip_in = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks+1):
- if i_block == self.num_res_blocks:
- skip_in = ch*in_ch_mult[i_level]
- block.append(ResnetBlock(in_channels=block_in+skip_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- if curr_res in attn_resolutions:
- attn.append(make_attn(block_in, attn_type=attn_type))
- 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, x, t=None, context=None):
- #assert x.shape[2] == x.shape[3] == self.resolution
- if context is not None:
- # assume aligned context, cat along channel axis
- x = torch.cat((x, context), dim=1)
- if self.use_timestep:
- # timestep embedding
- assert t is not None
- temb = get_timestep_embedding(t, self.ch)
- temb = self.temb.dense[0](temb)
- temb = nonlinearity(temb)
- temb = self.temb.dense[1](temb)
- else:
- temb = None
-
- # downsampling
- hs = [self.conv_in(x)]
- for i_level in range(self.num_resolutions):
- for i_block in range(self.num_res_blocks):
- h = self.down[i_level].block[i_block](hs[-1], temb)
- if len(self.down[i_level].attn) > 0:
- h = self.down[i_level].attn[i_block](h)
- hs.append(h)
- if i_level != self.num_resolutions-1:
- hs.append(self.down[i_level].downsample(hs[-1]))
-
- # middle
- h = hs[-1]
- 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](
- torch.cat([h, hs.pop()], dim=1), 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
- h = self.norm_out(h)
- h = nonlinearity(h)
- h = self.conv_out(h)
- return h
-
- def get_last_layer(self):
- return self.conv_out.weight
-
-
-class Encoder(nn.Module):
- def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
- resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
- **ignore_kwargs):
- super().__init__()
- if use_linear_attn: attn_type = "linear"
- 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
-
- # downsampling
- self.conv_in = torch.nn.Conv2d(in_channels,
- self.ch,
- kernel_size=3,
- stride=1,
- padding=1)
-
- curr_res = resolution
- in_ch_mult = (1,)+tuple(ch_mult)
- self.in_ch_mult = in_ch_mult
- self.down = nn.ModuleList()
- for i_level in range(self.num_resolutions):
- block = nn.ModuleList()
- attn = nn.ModuleList()
- block_in = ch*in_ch_mult[i_level]
- block_out = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks):
- 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(make_attn(block_in, attn_type=attn_type))
- down = nn.Module()
- down.block = block
- down.attn = attn
- if i_level != self.num_resolutions-1:
- down.downsample = Downsample(block_in, resamp_with_conv)
- curr_res = curr_res // 2
- self.down.append(down)
-
- # middle
- 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 = make_attn(block_in, attn_type=attn_type)
- self.mid.block_2 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
-
- # end
- self.norm_out = Normalize(block_in)
- self.conv_out = torch.nn.Conv2d(block_in,
- 2*z_channels if double_z else z_channels,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x):
- # timestep embedding
- temb = None
-
- # downsampling
- hs = [self.conv_in(x)]
- for i_level in range(self.num_resolutions):
- for i_block in range(self.num_res_blocks):
- h = self.down[i_level].block[i_block](hs[-1], temb)
- if len(self.down[i_level].attn) > 0:
- h = self.down[i_level].attn[i_block](h)
- hs.append(h)
- if i_level != self.num_resolutions-1:
- hs.append(self.down[i_level].downsample(hs[-1]))
-
- # middle
- h = hs[-1]
- h = self.mid.block_1(h, temb)
- h = self.mid.attn_1(h)
- h = self.mid.block_2(h, temb)
-
- # end
- h = self.norm_out(h)
- h = nonlinearity(h)
- h = self.conv_out(h)
- return h
-
-
-class Decoder(nn.Module):
- def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
- resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
- attn_type="vanilla", **ignorekwargs):
- super().__init__()
- if use_linear_attn: attn_type = "linear"
- 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.tanh_out = tanh_out
-
- # compute in_ch_mult, block_in and curr_res at lowest res
- in_ch_mult = (1,)+tuple(ch_mult)
- 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
- 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 = make_attn(block_in, attn_type=attn_type)
- 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(make_attn(block_in, attn_type=attn_type))
- 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):
- #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
- 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