| |
| """ |
| Tiny AutoEncoder for Stable Diffusion |
| (DNN for encoding / decoding SD's latent space) |
| """ |
| import torch |
| import torch.nn as nn |
|
|
| import comfy.utils |
| import comfy.ops |
|
|
| def conv(n_in, n_out, **kwargs): |
| return comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs) |
|
|
| class Clamp(nn.Module): |
| def forward(self, x): |
| return torch.tanh(x / 3) * 3 |
|
|
| class Block(nn.Module): |
| def __init__(self, n_in, n_out): |
| super().__init__() |
| self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out)) |
| self.skip = comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity() |
| self.fuse = nn.ReLU() |
| def forward(self, x): |
| return self.fuse(self.conv(x) + self.skip(x)) |
|
|
| def Encoder(latent_channels=4): |
| return nn.Sequential( |
| conv(3, 64), Block(64, 64), |
| conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), |
| conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), |
| conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), |
| conv(64, latent_channels), |
| ) |
|
|
|
|
| def Decoder(latent_channels=4): |
| return nn.Sequential( |
| Clamp(), conv(latent_channels, 64), nn.ReLU(), |
| Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), |
| Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), |
| Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), |
| Block(64, 64), conv(64, 3), |
| ) |
|
|
| class TAESD(nn.Module): |
| latent_magnitude = 3 |
| latent_shift = 0.5 |
|
|
| def __init__(self, encoder_path=None, decoder_path=None, latent_channels=4): |
| """Initialize pretrained TAESD on the given device from the given checkpoints.""" |
| super().__init__() |
| self.taesd_encoder = Encoder(latent_channels=latent_channels) |
| self.taesd_decoder = Decoder(latent_channels=latent_channels) |
| self.vae_scale = torch.nn.Parameter(torch.tensor(1.0)) |
| self.vae_shift = torch.nn.Parameter(torch.tensor(0.0)) |
| if encoder_path is not None: |
| self.taesd_encoder.load_state_dict(comfy.utils.load_torch_file(encoder_path, safe_load=True)) |
| if decoder_path is not None: |
| self.taesd_decoder.load_state_dict(comfy.utils.load_torch_file(decoder_path, safe_load=True)) |
|
|
| @staticmethod |
| def scale_latents(x): |
| """raw latents -> [0, 1]""" |
| return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1) |
|
|
| @staticmethod |
| def unscale_latents(x): |
| """[0, 1] -> raw latents""" |
| return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude) |
|
|
| def decode(self, x): |
| x_sample = self.taesd_decoder((x - self.vae_shift) * self.vae_scale) |
| x_sample = x_sample.sub(0.5).mul(2) |
| return x_sample |
|
|
| def encode(self, x): |
| return (self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale) + self.vae_shift |
|
|
