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modules/sd_vae_taesd.py

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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
+
+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 = 2
+    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 decode():
+    global sd_vae_taesd
+
+    if sd_vae_taesd is None:
+        model_path = os.path.join(paths_internal.models_path, "VAE-approx", "taesd_decoder.pth")
+        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('Tiny AE mdoel not found')
+
+    return sd_vae_taesd.decoder