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"""
Tiny AutoEncoder for Stable Diffusion
(DNN for encoding / decoding SD's latent space)
https://github.com/madebyollin/taesd
"""
import os
import torch
import torch.nn as nn
from modules import devices, paths_internal, shared
sd_vae_taesd_models = {}
def conv(n_in, n_out, **kwargs):
return nn.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
class Clamp(nn.Module):
@staticmethod
def forward(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 = nn.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 decoder():
return nn.Sequential(
Clamp(), conv(4, 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),
)
def encoder():
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, 4),
)
class TAESDDecoder(nn.Module):
latent_magnitude = 3
latent_shift = 0.5
def __init__(self, decoder_path="taesd_decoder.pth"):
"""Initialize pretrained TAESD on the given device from the given checkpoints."""
super().__init__()
self.decoder = decoder()
self.decoder.load_state_dict(
torch.load(decoder_path, map_location='cpu' if devices.device.type != 'cuda' else None))
class TAESDEncoder(nn.Module):
latent_magnitude = 3
latent_shift = 0.5
def __init__(self, encoder_path="taesd_encoder.pth"):
"""Initialize pretrained TAESD on the given device from the given checkpoints."""
super().__init__()
self.encoder = encoder()
self.encoder.load_state_dict(
torch.load(encoder_path, map_location='cpu' if devices.device.type != 'cuda' else None))
def download_model(model_path, model_url):
if not os.path.exists(model_path):
os.makedirs(os.path.dirname(model_path), exist_ok=True)
print(f'Downloading TAESD model to: {model_path}')
torch.hub.download_url_to_file(model_url, model_path)
def decoder_model():
model_name = "taesdxl_decoder.pth" if getattr(shared.sd_model, 'is_sdxl', False) else "taesd_decoder.pth"
loaded_model = sd_vae_taesd_models.get(model_name)
if loaded_model is None:
model_path = os.path.join(paths_internal.models_path, "VAE-taesd", model_name)
download_model(model_path, 'https://github.com/madebyollin/taesd/raw/main/' + model_name)
if os.path.exists(model_path):
loaded_model = TAESDDecoder(model_path)
loaded_model.eval()
loaded_model.to(devices.device, devices.dtype)
sd_vae_taesd_models[model_name] = loaded_model
else:
raise FileNotFoundError('TAESD model not found')
return loaded_model.decoder
def encoder_model():
model_name = "taesdxl_encoder.pth" if getattr(shared.sd_model, 'is_sdxl', False) else "taesd_encoder.pth"
loaded_model = sd_vae_taesd_models.get(model_name)
if loaded_model is None:
model_path = os.path.join(paths_internal.models_path, "VAE-taesd", model_name)
download_model(model_path, 'https://github.com/madebyollin/taesd/raw/main/' + model_name)
if os.path.exists(model_path):
loaded_model = TAESDEncoder(model_path)
loaded_model.eval()
loaded_model.to(devices.device, devices.dtype)
sd_vae_taesd_models[model_name] = loaded_model
else:
raise FileNotFoundError('TAESD model not found')
return loaded_model.encoder
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