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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 ldm_patched.modules.utils |
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def conv(n_in, n_out, **kwargs): |
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return nn.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 = nn.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(): |
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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, 4), |
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) |
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def Decoder(): |
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return nn.Sequential( |
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Clamp(), conv(4, 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): |
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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() |
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self.taesd_decoder = Decoder() |
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self.vae_scale = torch.nn.Parameter(torch.tensor(1.0)) |
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if encoder_path is not None: |
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self.taesd_encoder.load_state_dict(ldm_patched.modules.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(ldm_patched.modules.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_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 |
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