| |
| """ |
| Tiny AutoEncoder for Stable Diffusion |
| (DNN for encoding / decoding SD's latent space) |
| """ |
| import torch |
| import torch.nn as nn |
|
|
| def conv(n_in, n_out, **kwargs): |
| return nn.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, use_midblock_gn=False): |
| 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 = nn.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity() |
| self.fuse = nn.ReLU() |
| self.pool = None |
| if use_midblock_gn: |
| conv1x1, n_gn = lambda n_in, n_out: nn.Conv2d(n_in, n_out, 1, bias=False), n_in*4 |
| self.pool = nn.Sequential(conv1x1(n_in, n_gn), nn.GroupNorm(4, n_gn), nn.ReLU(inplace=True), conv1x1(n_gn, n_in)) |
| def forward(self, x): |
| if self.pool is not None: |
| x = x + self.pool(x) |
| return self.fuse(self.conv(x) + self.skip(x)) |
|
|
| def Encoder(latent_channels=4, use_midblock_gn=False, image_channels=3): |
| mb_kw = dict(use_midblock_gn=use_midblock_gn) |
| return nn.Sequential( |
| conv(image_channels, 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, **mb_kw), Block(64, 64, **mb_kw), Block(64, 64, **mb_kw), |
| conv(64, latent_channels), |
| ) |
|
|
| def Decoder(latent_channels=4, use_midblock_gn=False, image_channels=3): |
| mb_kw = dict(use_midblock_gn=use_midblock_gn) |
| return nn.Sequential( |
| Clamp(), conv(latent_channels, 64), nn.ReLU(), |
| Block(64, 64, **mb_kw), Block(64, 64, **mb_kw), Block(64, 64, **mb_kw), 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, image_channels), |
| ) |
|
|
| def F32Encoder(latent_channels=32, image_channels=3): |
| """Encoder variant with 32x spatial downscaling instead of 8x.""" |
| return nn.Sequential( |
| conv(image_channels, 32, stride=2), nn.ReLU(inplace=True), conv(32, 64, stride=2), nn.ReLU(inplace=True), 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 F32Decoder(latent_channels=32, image_channels=3): |
| """Decoder variant with 32x spatial upscaling instead of 8x.""" |
| return nn.Sequential( |
| Clamp(), conv(latent_channels, 256), nn.ReLU(), |
| Block(256, 256), Block(256, 256), Block(256, 256), nn.Upsample(scale_factor=2), conv(256, 128, bias=False), |
| Block(128, 128), Block(128, 128), Block(128, 128), nn.Upsample(scale_factor=2), conv(128, 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), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), |
| Block(64, 64), conv(64, image_channels), |
| ) |
|
|
| def F16Encoder(latent_channels=64, image_channels=3): |
| """Encoder variant with 16x spatial downscaling, 2x2 input patchify, and wider low-res stages.""" |
| return nn.Sequential( |
| nn.PixelUnshuffle(2), conv(image_channels * 4, 64), nn.ReLU(inplace=True), Block(64, 64), |
| conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), |
| conv(64, 128, stride=2, bias=False), Block(128, 128), Block(128, 128), Block(128, 128), |
| conv(128, 256, stride=2, bias=False), Block(256, 256), Block(256, 256), Block(256, 256), |
| conv(256, latent_channels), |
| ) |
|
|
| def F16Decoder(latent_channels=64, image_channels=3): |
| """Decoder variant with 16x spatial upscaling, wider low-res stages, and 2x2 output patchify.""" |
| return nn.Sequential( |
| Clamp(), conv(latent_channels, 256), nn.ReLU(), |
| Block(256, 256), Block(256, 256), Block(256, 256), nn.Upsample(scale_factor=2), conv(256, 128, bias=False), |
| Block(128, 128), Block(128, 128), Block(128, 128), nn.Upsample(scale_factor=2), conv(128, 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, image_channels * 4), nn.PixelShuffle(2), |
| ) |
|
|
| class TAESD(nn.Module): |
| latent_magnitude = 3 |
| latent_shift = 0.5 |
|
|
| def __init__(self, encoder_path="taesd_encoder.pth", decoder_path="taesd_decoder.pth", latent_channels=None, arch_variant=None, image_channels=None): |
| """Initialize pretrained TAESD on the given device from the given checkpoints.""" |
| super().__init__() |
| if latent_channels is None: |
| latent_channels, arch_variant = self.guess_latent_channels_and_arch(str(encoder_path)) |
| if image_channels is None: |
| image_channels = self.guess_image_channels(str(encoder_path or decoder_path)) |
| |
| self.image_channels = image_channels |
| self.latent_channels = latent_channels |
| |
| self.encoder = Encoder(latent_channels, use_midblock_gn=(arch_variant in ["flux_2"]), image_channels=image_channels) |
| self.decoder = Decoder(latent_channels, use_midblock_gn=(arch_variant in ["flux_2"]), image_channels=image_channels) |
| |
| if arch_variant == "f32": |
| self.encoder, self.decoder = F32Encoder(latent_channels, image_channels), F32Decoder(latent_channels, image_channels) |
| |
| if arch_variant == "f16": |
| self.encoder, self.decoder = F16Encoder(latent_channels, image_channels), F16Decoder(latent_channels, image_channels) |
| if encoder_path is not None: |
| self.encoder.load_state_dict(torch.load(encoder_path, map_location="cpu", weights_only=True)) |
| if decoder_path is not None: |
| self.decoder.load_state_dict(torch.load(decoder_path, map_location="cpu", weights_only=True)) |
|
|
| def guess_latent_channels(self, encoder_path): |
| """Guess latent channel count based on encoder filename""" |
| return self.guess_latent_channels_and_arch(encoder_path)[0] |
|
|
| def guess_latent_channels_and_arch(self, encoder_path): |
| """Guess latent channel count and architecture variant based on encoder filename""" |
| if "taef1" in encoder_path: |
| return 16, None |
| if "taef2" in encoder_path: |
| return 32, "flux_2" |
| if "taesd3" in encoder_path: |
| return 16, None |
| if "taesana" in encoder_path: |
| return 32, "f32" |
| if "taeqi2_1" in encoder_path: |
| return 64, "f16" |
| return 4, None |
|
|
| def guess_image_channels(self, encoder_path): |
| """Guess image channel count (3 for RGB, 4 for RGBA) based on encoder filename""" |
| if "taeqi2_1" in encoder_path: |
| return 4 |
| return 3 |
|
|
| @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) |
|
|
|
|
| @torch.no_grad() |
| def main(): |
| from PIL import Image |
| import sys |
| import torchvision.transforms.functional as TF |
| dev = torch.device("cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu") |
| print("Using device", dev) |
| taesd = TAESD().to(dev) |
| |
| im_mode = "RGBA" if taesd.image_channels == 4 else "RGB" |
| for im_path in sys.argv[1:]: |
| im = TF.to_tensor(Image.open(im_path).convert(im_mode)).unsqueeze(0).to(dev) |
|
|
| |
| im_enc = taesd.scale_latents(taesd.encoder(im)).mul_(255).round_().byte() |
| |
| im_enc = im_enc[0].unflatten(0, (-1, 4)).transpose(0, 1).flatten(1, 2) |
| enc_path = im_path + ".encoded.png" |
| TF.to_pil_image(im_enc).save(enc_path) |
| print(f"Encoded {im_path} to {enc_path}") |
|
|
| |
| im_enc = TF.to_tensor(Image.open(enc_path)) |
| im_enc = im_enc.unflatten(1, (taesd.latent_channels // 4, -1)).transpose(0, 1).flatten(0, 1) |
| im_enc = taesd.unscale_latents(im_enc.unsqueeze(0).to(dev)) |
| im_dec = taesd.decoder(im_enc).clamp(0, 1) |
| dec_path = im_path + ".decoded.png" |
| print(f"Decoded {enc_path} to {dec_path}") |
| TF.to_pil_image(im_dec[0]).save(dec_path) |
|
|
| if __name__ == "__main__": |
| main() |
|
|
