Add RealPLKSR LayerNorm

libs/spandrel/spandrel/architectures/PLKSR/__arch/RealPLKSR.py

@@ -8,6 +8,20 @@
 from spandrel.util import store_hyperparameters
 
 
+class LayerNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(dim))
+        self.bias = nn.Parameter(torch.zeros(dim))
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        u = x.mean(1, keepdim=True)
+        s = (x - u).pow(2).mean(1, keepdim=True)
+        x = (x - u) / torch.sqrt(s + self.eps)
+        return self.weight[:, None, None] * x + self.bias[:, None, None]
+
+
 class DCCM(nn.Sequential):
     "Doubled Convolutional Channel Mixer"
 
@@ -56,11 +70,15 @@ def __init__(
         dim: int,
         kernel_size: int,
         split_ratio: float,
-        norm_groups: int,
         use_ea: bool = True,
+        norm_groups: int = 4,
+        use_layer_norm: bool = False,
     ):
         super().__init__()
 
+        # Layer Norm
+        self.layer_norm = LayerNorm(dim) if use_layer_norm else nn.Identity()
+
         # Local Texture
         self.channel_mixer = DCCM(dim)
 
@@ -80,11 +98,16 @@ def __init__(
         self.refine = nn.Conv2d(dim, dim, 1, 1, 0)
         trunc_normal_(self.refine.weight, std=0.02)
 
-        # Group Normalization
-        self.norm = nn.GroupNorm(norm_groups, dim)
+        if not use_layer_norm:
+            self.norm = nn.GroupNorm(norm_groups, dim)
+            nn.init.constant_(self.norm.bias, 0)
+            nn.init.constant_(self.norm.weight, 1.0)
+        else:
+            self.norm = nn.Identity()
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         x_skip = x
+        x = self.layer_norm(x)
         x = self.channel_mixer(x)
         x = self.lk(x)
         x = self.attn(x)
@@ -114,6 +137,7 @@ def __init__(
         norm_groups: int = 4,
         dropout: float = 0,
         dysample: bool = False,
+        layer_norm: bool = False,
     ):
         super().__init__()
 
@@ -128,11 +152,11 @@ def __init__(
         self.feats = nn.Sequential(
             *[nn.Conv2d(in_ch, dim, 3, 1, 1)]
             + [
-                PLKBlock(dim, kernel_size, split_ratio, norm_groups, use_ea)
+                PLKBlock(dim, kernel_size, split_ratio, use_ea, norm_groups, layer_norm)
                 for _ in range(n_blocks)
             ]
             + [nn.Dropout2d(dropout)]
-            + [nn.Conv2d(dim, 3 * upscaling_factor**2, 3, 1, 1)]
+            + [nn.Conv2d(dim, out_ch * upscaling_factor**2, 3, 1, 1)]
         )
         trunc_normal_(self.feats[0].weight, std=0.02)
         trunc_normal_(self.feats[-1].weight, std=0.02)

libs/spandrel/spandrel/architectures/PLKSR/__init__.py

@@ -1,7 +1,8 @@
 from __future__ import annotations
 
 import math
-from typing import Literal, Sequence, Union
+from collections.abc import Sequence
+from typing import Literal, Union
 
 from typing_extensions import override
 
@@ -41,6 +42,7 @@ def load(self, state_dict: StateDict) -> ImageModelDescriptor[_PLKSR]:
         kernel_size = 17
         split_ratio = 0.25
         use_ea = True
+        supports_half = True
 
         dim = state_dict["feats.0.weight"].shape[0]
 
@@ -118,10 +120,14 @@ def load(self, state_dict: StateDict) -> ImageModelDescriptor[_PLKSR]:
             n_blocks = total_feat_layers - 3
             kernel_size = state_dict["feats.1.lk.conv.weight"].shape[2]
             split_ratio = state_dict["feats.1.lk.conv.weight"].shape[0] / dim
-
+            use_layer_norm = "feats.1.layer_norm.bias" in state_dict
             use_dysample = "to_img.init_pos" in state_dict
             if use_dysample:
                 more_tags.append("DySample")
+            if use_layer_norm:
+                more_tags.append("LayerNorm")
+            else:
+                supports_half = False
 
             model = RealPLKSR(
                 dim=dim,
@@ -132,6 +138,7 @@ def load(self, state_dict: StateDict) -> ImageModelDescriptor[_PLKSR]:
                 use_ea=use_ea,
                 norm_groups=4,  # un-detectable
                 dysample=use_dysample,
+                layer_norm=use_layer_norm,
             )
         else:
             raise ValueError("Unknown model type")
@@ -142,7 +149,7 @@ def load(self, state_dict: StateDict) -> ImageModelDescriptor[_PLKSR]:
             architecture=self,
             purpose="Restoration" if scale == 1 else "SR",
             tags=[f"{dim}dim", f"{n_blocks}nb", f"{kernel_size}ks", *more_tags],
-            supports_half=False,
+            supports_half=supports_half,
             supports_bfloat16=True,
             scale=scale,
             input_channels=3,

tests/__snapshots__/test_PLKSR.ambr

@@ -11,7 +11,7 @@
     scale=4,
     size_requirements=SizeRequirements(minimum=0, multiple_of=1, square=False),
     supports_bfloat16=True,
-    supports_half=False,
+    supports_half=True,
     tags=list([
       '64dim',
       '12nb',
@@ -33,7 +33,7 @@
     scale=2,
     size_requirements=SizeRequirements(minimum=0, multiple_of=1, square=False),
     supports_bfloat16=True,
-    supports_half=False,
+    supports_half=True,
     tags=list([
       '64dim',
       '28nb',
@@ -55,7 +55,7 @@
     scale=3,
     size_requirements=SizeRequirements(minimum=0, multiple_of=1, square=False),
     supports_bfloat16=True,
-    supports_half=False,
+    supports_half=True,
     tags=list([
       '64dim',
       '28nb',
@@ -77,7 +77,7 @@
     scale=4,
     size_requirements=SizeRequirements(minimum=0, multiple_of=1, square=False),
     supports_bfloat16=True,
-    supports_half=False,
+    supports_half=True,
     tags=list([
       '64dim',
       '28nb',
@@ -154,3 +154,49 @@
     tiling=<ModelTiling.SUPPORTED: 1>,
   )
 # ---
+# name: test_RealPLKSR_LayerNorm_2x
+  ImageModelDescriptor(
+    architecture=PLKSRArch(
+      id='PLKSR',
+      name='PLKSR',
+    ),
+    input_channels=3,
+    output_channels=3,
+    purpose='SR',
+    scale=2,
+    size_requirements=SizeRequirements(minimum=0, multiple_of=1, square=False),
+    supports_bfloat16=True,
+    supports_half=True,
+    tags=list([
+      '64dim',
+      '28nb',
+      '17ks',
+      'Real',
+      'LayerNorm',
+    ]),
+    tiling=<ModelTiling.SUPPORTED: 1>,
+  )
+# ---
+# name: test_RealPLKSR_LayerNorm_4x
+  ImageModelDescriptor(
+    architecture=PLKSRArch(
+      id='PLKSR',
+      name='PLKSR',
+    ),
+    input_channels=3,
+    output_channels=3,
+    purpose='SR',
+    scale=4,
+    size_requirements=SizeRequirements(minimum=0, multiple_of=1, square=False),
+    supports_bfloat16=True,
+    supports_half=True,
+    tags=list([
+      '64dim',
+      '28nb',
+      '17ks',
+      'Real',
+      'LayerNorm',
+    ]),
+    tiling=<ModelTiling.SUPPORTED: 1>,
+  )
+# ---

tests/test_PLKSR.py

@@ -52,6 +52,7 @@ def test_load():
         lambda: RealPLKSR(split_ratio=0.75),
         lambda: RealPLKSR(use_ea=False),
         lambda: RealPLKSR(dysample=True),
+        lambda: RealPLKSR(layer_norm=True),
     )
 
 
@@ -68,6 +69,12 @@ def test_size_requirements():
     )
     assert_size_requirements(file.load_model())
 
+    file = ModelFile.from_url(
+        "/~https://github.com/the-database/traiNNer-redux/releases/download/pretrained-models/4x_DF2K_Redux_RealPLKSRLayerNorm_50k.safetensors",
+        name="4x_DF2K_Redux_RealPLKSRLayerNorm_50k.safetensors",
+    )
+    assert_size_requirements(file.load_model())
+
 
 def test_PLKSR_official_x4(snapshot):
     file = ModelFile.from_url(
@@ -172,3 +179,29 @@ def test_RealPLKSR_DySample(snapshot):
         model,
         [TestImage.SR_16, TestImage.SR_32, TestImage.SR_64],
     )
+
+
+def test_RealPLKSR_LayerNorm_4x(snapshot):
+    file = ModelFile.from_url(
+        "/~https://github.com/the-database/traiNNer-redux/releases/download/pretrained-models/4x_DF2K_Redux_RealPLKSRLayerNorm_50k.safetensors",
+        name="4x_DF2K_Redux_RealPLKSRLayerNorm_50k.safetensors",
+    )
+    model = file.load_model()
+    assert model == snapshot(exclude=disallowed_props)
+    assert isinstance(model.model, RealPLKSR)
+    assert_image_inference(
+        file, model, [TestImage.SR_16, TestImage.SR_32, TestImage.SR_64]
+    )
+
+
+def test_RealPLKSR_LayerNorm_2x(snapshot):
+    file = ModelFile.from_url(
+        "/~https://github.com/the-database/traiNNer-redux/releases/download/pretrained-models/2x_DF2K_Redux_RealPLKSRLayerNorm_450k.safetensors",
+        name="2x_DF2K_Redux_RealPLKSRLayerNorm_450k.safetensors",
+    )
+    model = file.load_model()
+    assert model == snapshot(exclude=disallowed_props)
+    assert isinstance(model.model, RealPLKSR)
+    assert_image_inference(
+        file, model, [TestImage.SR_16, TestImage.SR_32, TestImage.SR_64]
+    )