134 lines
5.3 KiB
Python
134 lines
5.3 KiB
Python
#!/usr/bin/env python3
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"""
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Tiny AutoEncoder for Stable Diffusion
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(DNN for encoding / decoding SD's latent space)
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"""
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import torch
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import torch.nn as nn
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import comfy.utils
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import comfy.ops
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def conv(n_in, n_out, **kwargs):
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return comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
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class Clamp(nn.Module):
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def forward(self, x):
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return torch.tanh(x / 3) * 3
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class Block(nn.Module):
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def __init__(self, n_in: int, n_out: int, use_midblock_gn: bool = False):
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super().__init__()
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self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out))
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self.skip = comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
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self.fuse = nn.ReLU()
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if not use_midblock_gn:
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self.pool = None
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return
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n_gn = n_in * 4
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self.pool = nn.Sequential(
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comfy.ops.disable_weight_init.Conv2d(n_in, n_gn, 1, bias=False),
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comfy.ops.disable_weight_init.GroupNorm(4, n_gn),
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nn.ReLU(inplace=True),
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comfy.ops.disable_weight_init.Conv2d(n_gn, n_in, 1, bias=False),
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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if self.pool is not None:
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x = x + self.pool(x)
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return self.fuse(self.conv(x) + self.skip(x))
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class Encoder(nn.Sequential):
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def __init__(self, latent_channels: int = 4, use_gn: bool = False):
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super().__init__(
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conv(3, 64), Block(64, 64),
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conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
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conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
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conv(64, 64, stride=2, bias=False), Block(64, 64, use_gn), Block(64, 64, use_gn), Block(64, 64, use_gn),
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conv(64, latent_channels),
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)
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class Decoder(nn.Sequential):
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def __init__(self, latent_channels: int = 4, use_gn: bool = False):
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super().__init__(
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Clamp(), conv(latent_channels, 64), nn.ReLU(),
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Block(64, 64, use_gn), Block(64, 64, use_gn), Block(64, 64, use_gn), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
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Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
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Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
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Block(64, 64), conv(64, 3),
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)
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class DecoderFlux2(Decoder):
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def __init__(self, latent_channels: int = 128, use_gn: bool = True):
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if latent_channels != 128 or not use_gn:
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raise ValueError("Unexpected parameters for Flux2 TAE module")
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super().__init__(latent_channels=32, use_gn=True)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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B, C, H, W = x.shape
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x = (
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x
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.reshape(B, 32, 2, 2, H, W)
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.permute(0, 1, 4, 2, 5, 3)
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.reshape(B, 32, H * 2, W * 2)
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)
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return super().forward(x)
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class EncoderFlux2(Encoder):
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def __init__(self, latent_channels: int = 128, use_gn: bool = True):
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if latent_channels != 128 or not use_gn:
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raise ValueError("Unexpected parameters for Flux2 TAE module")
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super().__init__(latent_channels=32, use_gn=True)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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result = super().forward(x)
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B, C, H, W = result.shape
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return (
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result
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.reshape(B, C, H // 2, 2, W // 2, 2)
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.permute(0, 1, 3, 5, 2, 4)
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.reshape(B, 128, H // 2, W // 2)
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)
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class TAESD(nn.Module):
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latent_magnitude = 3
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latent_shift = 0.5
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def __init__(self, encoder_path=None, decoder_path=None, latent_channels=4):
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"""Initialize pretrained TAESD on the given device from the given checkpoints."""
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super().__init__()
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if latent_channels == 128:
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encoder_class = EncoderFlux2
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decoder_class = DecoderFlux2
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else:
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encoder_class = Encoder
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decoder_class = Decoder
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self.taesd_encoder = encoder_class(latent_channels=latent_channels)
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self.taesd_decoder = decoder_class(latent_channels=latent_channels)
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self.vae_scale = torch.nn.Parameter(torch.tensor(1.0))
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self.vae_shift = torch.nn.Parameter(torch.tensor(0.0))
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if encoder_path is not None:
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self.taesd_encoder.load_state_dict(comfy.utils.load_torch_file(encoder_path, safe_load=True))
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if decoder_path is not None:
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self.taesd_decoder.load_state_dict(comfy.utils.load_torch_file(decoder_path, safe_load=True))
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@staticmethod
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def scale_latents(x: torch.Tensor) -> torch.Tensor:
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"""raw latents -> [0, 1]"""
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return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1)
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@staticmethod
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def unscale_latents(x: torch.Tensor) -> torch.Tensor:
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"""[0, 1] -> raw latents"""
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return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude)
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def decode(self, x: torch.Tensor) -> torch.Tensor:
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x_sample = self.taesd_decoder((x - self.vae_shift) * self.vae_scale)
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x_sample = x_sample.sub(0.5).mul(2)
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return x_sample
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def encode(self, x: torch.Tensor) -> torch.Tensor:
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return (self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale) + self.vae_shift
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