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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, n_out):
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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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def forward(self, x):
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return self.fuse(self.conv(x) + self.skip(x))
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def Encoder(latent_channels=4):
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return nn.Sequential(
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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), Block(64, 64), Block(64, 64),
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conv(64, latent_channels),
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)
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def Decoder(latent_channels=4):
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return nn.Sequential(
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Clamp(), conv(latent_channels, 64), nn.ReLU(),
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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), 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 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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self.taesd_encoder = Encoder(latent_channels=latent_channels)
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self.taesd_decoder = Decoder(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):
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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):
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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):
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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):
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return (self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale) + self.vae_shift
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