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import numpy as np |
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import torch |
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from diffusers.configuration_utils import ConfigMixin, register_to_config |
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from torch import nn |
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from backend.attention import attention_function_single_head_spatial |
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def nonlinearity(x): |
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return x * torch.sigmoid(x) |
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def Normalize(in_channels, num_groups=32): |
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return nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) |
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class DiagonalGaussianDistribution: |
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def __init__(self, parameters, deterministic=False): |
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self.parameters = parameters |
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self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) |
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self.logvar = torch.clamp(self.logvar, -30.0, 20.0) |
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self.deterministic = deterministic |
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self.std = torch.exp(0.5 * self.logvar) |
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self.var = torch.exp(self.logvar) |
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if self.deterministic: |
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self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) |
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def sample(self): |
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x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) |
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return x |
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def mode(self): |
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return self.mean |
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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 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) |
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@torch.inference_mode() |
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def forward(self, x): |
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try: |
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x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") |
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except Exception as e: |
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b, c, h, w = x.shape |
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out = torch.empty((b, c, h * 2, w * 2), dtype=x.dtype, layout=x.layout, device=x.device) |
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split = 8 |
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l = out.shape[1] // split |
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for i in range(0, out.shape[1], l): |
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out[:, i : i + l] = torch.nn.functional.interpolate(x[:, i : i + l].to(torch.float32), scale_factor=2.0, mode="nearest").to(x.dtype) |
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del x |
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x = out |
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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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self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) |
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@torch.inference_mode() |
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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__(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, temb_channels=512): |
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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.swish = torch.nn.SiLU(inplace=True) |
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self.norm1 = Normalize(in_channels) |
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self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) |
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if temb_channels > 0: |
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self.temb_proj = 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, inplace=True) |
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self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) |
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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 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) |
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else: |
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self.nin_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) |
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@torch.inference_mode() |
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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 = self.swish(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(self.swish(temb))[:, :, None, None] |
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h = self.norm2(h) |
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h = self.swish(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 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 = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) |
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self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) |
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self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) |
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self.proj_out = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) |
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@torch.inference_mode() |
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def forward(self, 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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h_ = attention_function_single_head_spatial(q, k, v) |
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h_ = self.proj_out(h_) |
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return x + h_ |
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class Encoder(nn.Module): |
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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", **kwargs): |
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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.conv_in = nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) |
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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(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) |
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block_in = block_out |
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if curr_res in attn_resolutions: |
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attn.append(AttnBlock(block_in)) |
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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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self.mid = nn.Module() |
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self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) |
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self.mid.attn_1 = AttnBlock(block_in) |
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self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) |
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self.norm_out = Normalize(block_in) |
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self.conv_out = nn.Conv2d(block_in, 2 * z_channels if double_z else z_channels, kernel_size=3, stride=1, padding=1) |
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@torch.inference_mode() |
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def forward(self, x): |
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temb = None |
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h = 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](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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h = self.down[i_level].downsample(h) |
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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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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__(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, **kwargs): |
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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.tanh_out = tanh_out |
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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("Working with z of shape {} = {} dimensions.".format(self.z_shape, np.prod(self.z_shape))) |
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self.conv_in = nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1) |
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self.mid = nn.Module() |
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self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) |
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self.mid.attn_1 = AttnBlock(block_in) |
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self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) |
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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(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) |
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block_in = block_out |
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if curr_res in attn_resolutions: |
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attn.append(AttnBlock(block_in)) |
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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) |
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self.norm_out = Normalize(block_in) |
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self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) |
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@torch.inference_mode() |
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def forward(self, z, **kwargs): |
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temb = None |
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h = self.conv_in(z) |
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h = self.mid.block_1(h, temb, **kwargs) |
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h = self.mid.attn_1(h, **kwargs) |
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h = self.mid.block_2(h, temb, **kwargs) |
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for i_level in reversed(range(self.num_resolutions)): |
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for i_block in range(self.num_res_blocks + 1): |
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_h = self.up[i_level].block[i_block](h, temb, **kwargs) |
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del h |
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h = self.up[i_level].attn[i_block](_h, **kwargs) if len(self.up[i_level].attn) > 0 else _h |
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if i_level != 0: |
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_h = self.up[i_level].upsample(h) |
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del h |
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h = _h |
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if self.give_pre_end: |
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return h |
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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, **kwargs) |
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if self.tanh_out: |
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h = torch.tanh(h) |
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return h |
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class IntegratedAutoencoderKL(nn.Module, ConfigMixin): |
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config_name = "config.json" |
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@register_to_config |
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def __init__(self, in_channels=3, out_channels=3, down_block_types=("DownEncoderBlock2D",), up_block_types=("UpDecoderBlock2D",), block_out_channels=(64,), layers_per_block=1, act_fn="silu", latent_channels=4, norm_num_groups=32, sample_size=32, scaling_factor=0.18215, shift_factor=0.0, latents_mean=None, latents_std=None, force_upcast=True, use_quant_conv=True, use_post_quant_conv=True): |
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super().__init__() |
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ch = block_out_channels[0] |
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ch_mult = [x // ch for x in block_out_channels] |
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self.encoder = Encoder(double_z=True, z_channels=latent_channels, resolution=256, in_channels=in_channels, out_ch=out_channels, ch=ch, ch_mult=ch_mult, num_res_blocks=layers_per_block, attn_resolutions=[], dropout=0.0) |
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self.decoder = Decoder(double_z=True, z_channels=latent_channels, resolution=256, in_channels=in_channels, out_ch=out_channels, ch=ch, ch_mult=ch_mult, num_res_blocks=layers_per_block, attn_resolutions=[], dropout=0.0) |
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self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1) if use_quant_conv else None |
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self.post_quant_conv = nn.Conv2d(latent_channels, latent_channels, 1) if use_post_quant_conv else None |
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self.embed_dim = latent_channels |
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self.scaling_factor = scaling_factor |
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self.shift_factor = shift_factor |
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if not isinstance(self.shift_factor, float): |
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self.shift_factor = 0.0 |
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def encode(self, x): |
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z = self.encoder(x) |
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if self.quant_conv is not None: |
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z = self.quant_conv(z) |
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posterior = DiagonalGaussianDistribution(z) |
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return posterior.sample() |
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def decode(self, z): |
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if self.post_quant_conv is not None: |
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z = self.post_quant_conv(z) |
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x = self.decoder(z) |
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return x |
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def process_in(self, latent): |
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return (latent - self.shift_factor) * self.scaling_factor |
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def process_out(self, latent): |
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return (latent / self.scaling_factor) + self.shift_factor |
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