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| #!/usr/bin/env python3 | |
| """ | |
| 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)) | |
| def scale_latents(x): | |
| """raw latents -> [0, 1]""" | |
| return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1) | |
| 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 | |