mirror of https://github.com/vladmandic/automatic
89 lines
4.1 KiB
Python
89 lines
4.1 KiB
Python
import torch
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import torch.nn as nn
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from modules import devices
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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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def forward(self, 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 Encoder(latent_channels=4):
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return nn.Sequential(
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conv(3, 64), Block(64, 64),
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conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
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conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
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conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
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conv(64, latent_channels),
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)
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def Decoder(latent_channels=4):
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from modules import shared
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if shared.opts.taesd_layers == 1:
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return nn.Sequential(
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Clamp(), conv(latent_channels, 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.Identity(), conv(64, 64, bias=False),
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Block(64, 64), Block(64, 64), Block(64, 64), nn.Identity(), conv(64, 64, bias=False),
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Block(64, 64), conv(64, 3),
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)
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elif shared.opts.taesd_layers == 2:
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return nn.Sequential(
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Clamp(), conv(latent_channels, 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.Identity(), conv(64, 64, bias=False),
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Block(64, 64), conv(64, 3),
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)
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else:
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return nn.Sequential(
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Clamp(), conv(latent_channels, 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): # pylint: disable=abstract-method
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latent_magnitude = 3
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latent_shift = 0.5
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def __init__(self, encoder_path=None, decoder_path=None, latent_channels=None):
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super().__init__()
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self.dtype = devices.dtype_vae if devices.dtype_vae != torch.bfloat16 else torch.float16 # taesd does not support bf16
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if latent_channels is None:
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latent_channels = self.guess_latent_channels(str(decoder_path), str(encoder_path))
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self.encoder = Encoder(latent_channels)
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self.decoder = Decoder(latent_channels)
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if encoder_path is not None:
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self.encoder.load_state_dict(torch.load(encoder_path, map_location="cpu"), strict=False)
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self.encoder.eval()
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self.encoder = self.encoder.to(devices.device, dtype=self.dtype)
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if decoder_path is not None:
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self.decoder.load_state_dict(torch.load(decoder_path, map_location="cpu"), strict=False)
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self.decoder.eval()
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self.decoder = self.decoder.to(devices.device, dtype=self.dtype)
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def guess_latent_channels(self, decoder_path, encoder_path):
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return 16 if ("f1" in encoder_path or "f1" in decoder_path) or ("sd3" in encoder_path or "sd3" in decoder_path) else 4
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@staticmethod
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def scale_latents(x):
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return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1) # raw latents -> [0, 1]
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@staticmethod
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def unscale_latents(x):
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return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude) # [0, 1] -> raw latents
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