/
githubmirror
/
stablediffusion
Обзор
Документация
Войти
/
githubmirror
/
stablediffusion
Код
Запросы
0
Пакеты
0
Релизы
0
Аналитика
Безопасность
main
ldm/modules/diffusionmodules/model.py
852 строки
34 KB
Robin Rombach
release more models
24 ноя 2022, 03:22
24 ноя 2022, 03:22
ca86da3
Код
Авторство
О чём код?
# pytorch_diffusion + derived encoder decoder import math import torch import torch.nn as nn import numpy as np from einops import rearrange from typing import Optional, Any from ldm.modules.attention import MemoryEfficientCrossAttention try: import xformers import xformers.ops XFORMERS_IS_AVAILBLE = True except: XFORMERS_IS_AVAILBLE = False print("No module 'xformers'. Proceeding without it.") 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 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_ class MemoryEfficientAttnBlock(nn.Module): """ Uses xformers efficient implementation, see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 Note: this is a single-head self-attention operation """ # 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) self.attention_op: Optional[Any] = None 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, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v)) q, k, v = map( lambda t: t.unsqueeze(3) .reshape(B, t.shape[1], 1, C) .permute(0, 2, 1, 3) .reshape(B * 1, t.shape[1], C) .contiguous(), (q, k, v), ) out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op) out = ( out.unsqueeze(0) .reshape(B, 1, out.shape[1], C) .permute(0, 2, 1, 3) .reshape(B, out.shape[1], C) ) out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C) out = self.proj_out(out) return x+out class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention): def forward(self, x, context=None, mask=None): b, c, h, w = x.shape x = rearrange(x, 'b c h w -> b (h w) c') out = super().forward(x, context=context, mask=mask) out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c) return x + out def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None): assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown' if XFORMERS_IS_AVAILBLE and attn_type == "vanilla": attn_type = "vanilla-xformers" print(f"making attention of type '{attn_type}' with {in_channels} in_channels") if attn_type == "vanilla": assert attn_kwargs is None return AttnBlock(in_channels) elif attn_type == "vanilla-xformers": print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...") return MemoryEfficientAttnBlock(in_channels) elif type == "memory-efficient-cross-attn": attn_kwargs["query_dim"] = in_channels return MemoryEfficientCrossAttentionWrapper(**attn_kwargs) elif attn_type == "none": return nn.Identity(in_channels) else: raise NotImplementedError() 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 if self.give_pre_end: return h h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) if self.tanh_out: h = torch.tanh(h) return h class SimpleDecoder(nn.Module): def __init__(self, in_channels, out_channels, *args, **kwargs): super().__init__() self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1), ResnetBlock(in_channels=in_channels, out_channels=2 * in_channels, temb_channels=0, dropout=0.0), ResnetBlock(in_channels=2 * in_channels, out_channels=4 * in_channels, temb_channels=0, dropout=0.0), ResnetBlock(in_channels=4 * in_channels, out_channels=2 * in_channels, temb_channels=0, dropout=0.0), nn.Conv2d(2*in_channels, in_channels, 1), Upsample(in_channels, with_conv=True)]) # end self.norm_out = Normalize(in_channels) self.conv_out = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): for i, layer in enumerate(self.model): if i in [1,2,3]: x = layer(x, None) else: x = layer(x) h = self.norm_out(x) h = nonlinearity(h) x = self.conv_out(h) return x class UpsampleDecoder(nn.Module): def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution, ch_mult=(2,2), dropout=0.0): super().__init__() # upsampling self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks block_in = in_channels curr_res = resolution // 2 ** (self.num_resolutions - 1) self.res_blocks = nn.ModuleList() self.upsample_blocks = nn.ModuleList() for i_level in range(self.num_resolutions): res_block = [] block_out = ch * ch_mult[i_level] for i_block in range(self.num_res_blocks + 1): res_block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out self.res_blocks.append(nn.ModuleList(res_block)) if i_level != self.num_resolutions - 1: self.upsample_blocks.append(Upsample(block_in, True)) curr_res = curr_res * 2 # end self.norm_out = Normalize(block_in) self.conv_out = torch.nn.Conv2d(block_in, out_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): # upsampling h = x for k, i_level in enumerate(range(self.num_resolutions)): for i_block in range(self.num_res_blocks + 1): h = self.res_blocks[i_level][i_block](h, None) if i_level != self.num_resolutions - 1: h = self.upsample_blocks[k](h) h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) 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 MergedRescaleEncoder(nn.Module): def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1): super().__init__() intermediate_chn = ch * ch_mult[-1] self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult, z_channels=intermediate_chn, double_z=False, resolution=resolution, attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, out_ch=None) self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn, mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth) def forward(self, x): x = self.encoder(x) x = self.rescaler(x) return x class MergedRescaleDecoder(nn.Module): def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8), dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1): super().__init__() tmp_chn = z_channels*ch_mult[-1] self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks, ch_mult=ch_mult, resolution=resolution, ch=ch) self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn, out_channels=tmp_chn, depth=rescale_module_depth) def forward(self, x): x = self.rescaler(x) x = self.decoder(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.+ (out_size % in_size) print(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: print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode") 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 else: x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor) return x