stable-diffusion-webui/modules/sd_vae_taesd.py

89 lines
2.6 KiB
Python

"""
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
sd_vae_taesd = None
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),
)
class TAESD(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))
@staticmethod
def unscale_latents(x):
"""[0, 1] -> raw latents"""
return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude)
def download_model(model_path):
model_url = 'https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth'
if not os.path.exists(model_path):
os.makedirs(os.path.dirname(model_path), exist_ok=True)
print(f'Downloading TAESD decoder to: {model_path}')
torch.hub.download_url_to_file(model_url, model_path)
def model():
global sd_vae_taesd
if sd_vae_taesd is None:
model_path = os.path.join(paths_internal.models_path, "VAE-taesd", "taesd_decoder.pth")
download_model(model_path)
if os.path.exists(model_path):
sd_vae_taesd = TAESD(model_path)
sd_vae_taesd.eval()
sd_vae_taesd.to(devices.device, devices.dtype)
else:
raise FileNotFoundError('TAESD model not found')
return sd_vae_taesd.decoder