ldm_patched

ldm_patched/taesd/taesd.py

@@ -0,0 +1,77 @@
+#!/usr/bin/env python3
+"""
+Tiny AutoEncoder for Stable Diffusion
+(DNN for encoding / decoding SD's latent space)
+"""
+import torch
+import torch.nn as nn
+
+import ldm_patched.modules.utils
+import ldm_patched.modules.ops
+
+def conv(n_in, n_out, **kwargs):
+    return ldm_patched.modules.ops.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
+
+class Clamp(nn.Module):
+    def forward(self, 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 = ldm_patched.modules.ops.disable_weight_init.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 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),
+    )
+
+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, encoder_path=None, decoder_path=None):
+        """Initialize pretrained TAESD on the given device from the given checkpoints."""
+        super().__init__()
+        self.taesd_encoder = Encoder()
+        self.taesd_decoder = Decoder()
+        self.vae_scale = torch.nn.Parameter(torch.tensor(1.0))
+        if encoder_path is not None:
+            self.taesd_encoder.load_state_dict(ldm_patched.modules.utils.load_torch_file(encoder_path, safe_load=True))
+        if decoder_path is not None:
+            self.taesd_decoder.load_state_dict(ldm_patched.modules.utils.load_torch_file(decoder_path, safe_load=True))
+
+    @staticmethod
+    def scale_latents(x):
+        """raw latents -> [0, 1]"""
+        return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1)
+
+    @staticmethod
+    def unscale_latents(x):
+        """[0, 1] -> raw latents"""
+        return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude)
+
+    def decode(self, x):
+        x_sample = self.taesd_decoder(x * self.vae_scale)
+        x_sample = x_sample.sub(0.5).mul(2)
+        return x_sample
+
+    def encode(self, x):
+        return self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale