mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-10-31 10:03:40 +00:00
89 lines
2.6 KiB
Python
89 lines
2.6 KiB
Python
"""
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Tiny AutoEncoder for Stable Diffusion
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(DNN for encoding / decoding SD's latent space)
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https://github.com/madebyollin/taesd
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"""
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import os
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import torch
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import torch.nn as nn
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from modules import devices, paths_internal
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sd_vae_taesd = None
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def conv(n_in, n_out, **kwargs):
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return nn.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
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class Clamp(nn.Module):
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@staticmethod
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def forward(x):
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return torch.tanh(x / 3) * 3
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class Block(nn.Module):
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def __init__(self, n_in, n_out):
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super().__init__()
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self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out))
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self.skip = nn.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
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self.fuse = nn.ReLU()
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def forward(self, x):
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return self.fuse(self.conv(x) + self.skip(x))
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def decoder():
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return nn.Sequential(
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Clamp(), conv(4, 64), nn.ReLU(),
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Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
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Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
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Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
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Block(64, 64), conv(64, 3),
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)
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class TAESD(nn.Module):
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latent_magnitude = 3
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latent_shift = 0.5
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def __init__(self, decoder_path="taesd_decoder.pth"):
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"""Initialize pretrained TAESD on the given device from the given checkpoints."""
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super().__init__()
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self.decoder = decoder()
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self.decoder.load_state_dict(
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torch.load(decoder_path, map_location='cpu' if devices.device.type != 'cuda' else None))
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@staticmethod
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def unscale_latents(x):
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"""[0, 1] -> raw latents"""
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return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude)
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def download_model(model_path):
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model_url = 'https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth'
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if not os.path.exists(model_path):
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os.makedirs(os.path.dirname(model_path), exist_ok=True)
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print(f'Downloading TAESD decoder to: {model_path}')
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torch.hub.download_url_to_file(model_url, model_path)
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def model():
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global sd_vae_taesd
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if sd_vae_taesd is None:
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model_path = os.path.join(paths_internal.models_path, "VAE-taesd", "taesd_decoder.pth")
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download_model(model_path)
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if os.path.exists(model_path):
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sd_vae_taesd = TAESD(model_path)
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sd_vae_taesd.eval()
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sd_vae_taesd.to(devices.device, devices.dtype)
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else:
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raise FileNotFoundError('TAESD model not found')
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return sd_vae_taesd.decoder
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