Update all files for BitDance-Tokenizer-diffusers

bitdance_diffusers/modeling_autoencoder.py

@@ -0,0 +1,322 @@
+from __future__ import annotations
+
+from typing import Any, Dict, Optional, Sequence
+
+import torch
+import torch.nn.functional as F
+from einops import rearrange
+from torch import nn
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.modeling_utils import ModelMixin
+
+
+def swish(x: torch.Tensor) -> torch.Tensor:
+    return x * torch.sigmoid(x)
+
+
+class ResBlock(nn.Module):
+    def __init__(
+        self,
+        in_filters: int,
+        out_filters: int,
+        use_conv_shortcut: bool = False,
+        use_agn: bool = False,
+    ) -> None:
+        super().__init__()
+        self.in_filters = in_filters
+        self.out_filters = out_filters
+        self.use_conv_shortcut = use_conv_shortcut
+        self.use_agn = use_agn
+
+        if not use_agn:
+            self.norm1 = nn.GroupNorm(32, in_filters, eps=1e-6)
+        self.norm2 = nn.GroupNorm(32, out_filters, eps=1e-6)
+
+        self.conv1 = nn.Conv2d(in_filters, out_filters, kernel_size=3, padding=1, bias=False)
+        self.conv2 = nn.Conv2d(out_filters, out_filters, kernel_size=3, padding=1, bias=False)
+
+        if in_filters != out_filters:
+            if use_conv_shortcut:
+                self.conv_shortcut = nn.Conv2d(in_filters, out_filters, kernel_size=3, padding=1, bias=False)
+            else:
+                self.nin_shortcut = nn.Conv2d(in_filters, out_filters, kernel_size=1, padding=0, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        residual = x
+        if not self.use_agn:
+            x = self.norm1(x)
+        x = swish(x)
+        x = self.conv1(x)
+        x = self.norm2(x)
+        x = swish(x)
+        x = self.conv2(x)
+
+        if self.in_filters != self.out_filters:
+            if self.use_conv_shortcut:
+                residual = self.conv_shortcut(residual)
+            else:
+                residual = self.nin_shortcut(residual)
+
+        return x + residual
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        *,
+        ch: int,
+        out_ch: int,
+        in_channels: int,
+        num_res_blocks: int,
+        z_channels: int,
+        ch_mult: Sequence[int] = (1, 2, 2, 4),
+        resolution: Optional[int] = None,
+        double_z: bool = False,
+    ) -> None:
+        super().__init__()
+        del out_ch, double_z
+        self.in_channels = in_channels
+        self.z_channels = z_channels
+        self.resolution = resolution
+        self.num_res_blocks = num_res_blocks
+        self.num_blocks = len(ch_mult)
+
+        self.conv_in = nn.Conv2d(in_channels, ch, kernel_size=3, padding=1, bias=False)
+        self.down = nn.ModuleList()
+
+        in_ch_mult = (1,) + tuple(ch_mult)
+        block_out = ch * ch_mult[0]
+        for i_level in range(self.num_blocks):
+            block = nn.ModuleList()
+            block_in = ch * in_ch_mult[i_level]
+            block_out = ch * ch_mult[i_level]
+            for _ in range(self.num_res_blocks):
+                block.append(ResBlock(block_in, block_out))
+                block_in = block_out
+
+            down = nn.Module()
+            down.block = block
+            if i_level < self.num_blocks - 1:
+                down.downsample = nn.Conv2d(block_out, block_out, kernel_size=3, stride=2, padding=1)
+            self.down.append(down)
+
+        self.mid_block = nn.ModuleList([ResBlock(block_out, block_out) for _ in range(self.num_res_blocks)])
+        self.norm_out = nn.GroupNorm(32, block_out, eps=1e-6)
+        self.conv_out = nn.Conv2d(block_out, z_channels, kernel_size=1)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.conv_in(x)
+        for i_level in range(self.num_blocks):
+            for i_block in range(self.num_res_blocks):
+                x = self.down[i_level].block[i_block](x)
+            if i_level < self.num_blocks - 1:
+                x = self.down[i_level].downsample(x)
+
+        for block in self.mid_block:
+            x = block(x)
+
+        x = self.norm_out(x)
+        x = swish(x)
+        x = self.conv_out(x)
+        return x
+
+
+def depth_to_space(x: torch.Tensor, block_size: int) -> torch.Tensor:
+    if x.dim() < 3:
+        raise ValueError("Expected a channels-first (*CHW) tensor of at least 3 dims.")
+    c, h, w = x.shape[-3:]
+    s = block_size**2
+    if c % s != 0:
+        raise ValueError(f"Expected C divisible by {s}, but got C={c}.")
+
+    outer_dims = x.shape[:-3]
+    x = x.view(-1, block_size, block_size, c // s, h, w)
+    x = x.permute(0, 3, 4, 1, 5, 2)
+    x = x.contiguous().view(*outer_dims, c // s, h * block_size, w * block_size)
+    return x
+
+
+class Upsampler(nn.Module):
+    def __init__(self, dim: int) -> None:
+        super().__init__()
+        self.conv1 = nn.Conv2d(dim, dim * 4, kernel_size=3, padding=1)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return depth_to_space(self.conv1(x), block_size=2)
+
+
+class AdaptiveGroupNorm(nn.Module):
+    def __init__(self, z_channel: int, in_filters: int, num_groups: int = 32, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.gn = nn.GroupNorm(num_groups=num_groups, num_channels=in_filters, eps=eps, affine=False)
+        self.gamma = nn.Linear(z_channel, in_filters)
+        self.beta = nn.Linear(z_channel, in_filters)
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor, quantizer: torch.Tensor) -> torch.Tensor:
+        bsz, channels, _, _ = x.shape
+
+        scale = rearrange(quantizer, "b c h w -> b c (h w)")
+        scale = scale.var(dim=-1) + self.eps
+        scale = scale.sqrt()
+        scale = self.gamma(scale).view(bsz, channels, 1, 1)
+
+        bias = rearrange(quantizer, "b c h w -> b c (h w)")
+        bias = bias.mean(dim=-1)
+        bias = self.beta(bias).view(bsz, channels, 1, 1)
+
+        x = self.gn(x)
+        return scale * x + bias
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        *,
+        ch: int,
+        out_ch: int,
+        in_channels: int,
+        num_res_blocks: int,
+        z_channels: int,
+        ch_mult: Sequence[int] = (1, 2, 2, 4),
+        resolution: Optional[int] = None,
+        double_z: bool = False,
+    ) -> None:
+        super().__init__()
+        del in_channels, resolution, double_z
+        self.num_blocks = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+
+        block_in = ch * ch_mult[self.num_blocks - 1]
+        self.conv_in = nn.Conv2d(z_channels, block_in, kernel_size=3, padding=1, bias=True)
+        self.mid_block = nn.ModuleList([ResBlock(block_in, block_in) for _ in range(self.num_res_blocks)])
+
+        self.up = nn.ModuleList()
+        self.adaptive = nn.ModuleList()
+        for i_level in reversed(range(self.num_blocks)):
+            block = nn.ModuleList()
+            block_out = ch * ch_mult[i_level]
+            self.adaptive.insert(0, AdaptiveGroupNorm(z_channels, block_in))
+            for _ in range(self.num_res_blocks):
+                block.append(ResBlock(block_in, block_out))
+                block_in = block_out
+            up = nn.Module()
+            up.block = block
+            if i_level > 0:
+                up.upsample = Upsampler(block_in)
+            self.up.insert(0, up)
+
+        self.norm_out = nn.GroupNorm(32, block_in, eps=1e-6)
+        self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, padding=1)
+
+    def forward(self, z: torch.Tensor) -> torch.Tensor:
+        style = z.clone()
+        z = self.conv_in(z)
+
+        for block in self.mid_block:
+            z = block(z)
+
+        for i_level in reversed(range(self.num_blocks)):
+            z = self.adaptive[i_level](z, style)
+            for i_block in range(self.num_res_blocks):
+                z = self.up[i_level].block[i_block](z)
+            if i_level > 0:
+                z = self.up[i_level].upsample(z)
+
+        z = self.norm_out(z)
+        z = swish(z)
+        z = self.conv_out(z)
+        return z
+
+
+class GANDecoder(nn.Module):
+    def __init__(
+        self,
+        *,
+        ch: int,
+        out_ch: int,
+        in_channels: int,
+        num_res_blocks: int,
+        z_channels: int,
+        ch_mult: Sequence[int] = (1, 2, 2, 4),
+        resolution: Optional[int] = None,
+        double_z: bool = False,
+    ) -> None:
+        super().__init__()
+        del in_channels, resolution, double_z
+        self.num_blocks = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+
+        block_in = ch * ch_mult[self.num_blocks - 1]
+        self.conv_in = nn.Conv2d(z_channels * 2, block_in, kernel_size=3, padding=1, bias=True)
+        self.mid_block = nn.ModuleList([ResBlock(block_in, block_in) for _ in range(self.num_res_blocks)])
+
+        self.up = nn.ModuleList()
+        self.adaptive = nn.ModuleList()
+        for i_level in reversed(range(self.num_blocks)):
+            block = nn.ModuleList()
+            block_out = ch * ch_mult[i_level]
+            self.adaptive.insert(0, AdaptiveGroupNorm(z_channels, block_in))
+            for _ in range(self.num_res_blocks):
+                block.append(ResBlock(block_in, block_out))
+                block_in = block_out
+            up = nn.Module()
+            up.block = block
+            if i_level > 0:
+                up.upsample = Upsampler(block_in)
+            self.up.insert(0, up)
+
+        self.norm_out = nn.GroupNorm(32, block_in, eps=1e-6)
+        self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, padding=1)
+
+    def forward(self, z: torch.Tensor) -> torch.Tensor:
+        style = z.clone()
+        noise = torch.randn_like(z, device=z.device)
+        z = torch.cat([z, noise], dim=1)
+        z = self.conv_in(z)
+
+        for block in self.mid_block:
+            z = block(z)
+
+        for i_level in reversed(range(self.num_blocks)):
+            z = self.adaptive[i_level](z, style)
+            for i_block in range(self.num_res_blocks):
+                z = self.up[i_level].block[i_block](z)
+            if i_level > 0:
+                z = self.up[i_level].upsample(z)
+
+        z = self.norm_out(z)
+        z = swish(z)
+        z = self.conv_out(z)
+        return z
+
+
+class BitDanceAutoencoder(ModelMixin, ConfigMixin):
+    @register_to_config
+    def __init__(self, ddconfig: Dict[str, Any], gan_decoder: bool = False) -> None:
+        super().__init__()
+        self.encoder = Encoder(**ddconfig)
+        self.decoder = GANDecoder(**ddconfig) if gan_decoder else Decoder(**ddconfig)
+
+    @property
+    def z_channels(self) -> int:
+        return int(self.config.ddconfig["z_channels"])
+
+    @property
+    def patch_size(self) -> int:
+        ch_mult = self.config.ddconfig["ch_mult"]
+        return 2 ** (len(ch_mult) - 1)
+
+    def encode(self, x: torch.Tensor) -> torch.Tensor:
+        h = self.encoder(x)
+        codebook_value = torch.tensor([1.0], device=h.device, dtype=h.dtype)
+        quant_h = torch.where(h > 0, codebook_value, -codebook_value)
+        return quant_h
+
+    def decode(self, quant: torch.Tensor) -> torch.Tensor:
+        return self.decoder(quant)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        quant = self.encode(x)
+        return self.decode(quant)