Add support for DRCT

README.md

@@ -104,6 +104,7 @@ Spandrel currently supports a limited amount of network architectures. If the ar
 - [DCTLSA](https://github.com/zengkun301/DCTLSA) | [Models](https://github.com/zengkun301/DCTLSA/tree/main/pretrained)
 - [ATD](https://github.com/LabShuHangGU/Adaptive-Token-Dictionary) | [Models](https://drive.google.com/drive/folders/1D3BvTS1xBcaU1mp50k3pBzUWb7qjRvmB?usp=sharing)
 - [AdaCode](https://github.com/kechunl/AdaCode) | [Models](https://github.com/kechunl/AdaCode/releases/tag/v0-pretrain_models)
+- [DRCT](https://github.com/ming053l/DRCT)
 
 #### Face Restoration
 

libs/spandrel/spandrel/__helpers/main_registry.py

@@ -6,6 +6,7 @@
     DAT,
     DCTLSA,
     DITN,
+    DRCT,
     ESRGAN,
     FBCNN,
     GFPGAN,
@@ -75,5 +76,6 @@
     ArchSupport.from_architecture(DRUNet.DRUNetArch()),
     ArchSupport.from_architecture(DnCNN.DnCNNArch()),
     ArchSupport.from_architecture(IPT.IPTArch()),
+    ArchSupport.from_architecture(DRCT.DRCTArch()),
     ArchSupport.from_architecture(ESRGAN.ESRGANArch()),
 )

libs/spandrel/spandrel/architectures/DRCT/__init__.py

@@ -0,0 +1,174 @@
+import math
+
+from typing_extensions import override
+
+from spandrel.util import KeyCondition, get_seq_len
+
+from ...__helpers.model_descriptor import (
+    Architecture,
+    ImageModelDescriptor,
+    SizeRequirements,
+    StateDict,
+)
+from .arch.drct_arch import DRCT
+
+
+def _get_upscale_pixelshuffle(
+    state_dict: StateDict, key_prefix: str = "upsample"
+) -> int:
+    upscale = 1
+
+    for i in range(0, 10, 2):
+        key = f"{key_prefix}.{i}.weight"
+        if key not in state_dict:
+            break
+
+        shape = state_dict[key].shape
+        num_feat = shape[1]
+        upscale *= math.isqrt(shape[0] // num_feat)
+
+    return upscale
+
+
+class DRCTArch(Architecture[DRCT]):
+    def __init__(self) -> None:
+        super().__init__(
+            id="DRCT",
+            detect=KeyCondition.has_all(
+                "conv_first.weight",
+                "conv_first.bias",
+                "layers.0.swin1.norm1.weight",
+                "layers.0.swin1.norm1.bias",
+                "layers.0.swin1.attn.relative_position_bias_table",
+                "layers.0.swin1.attn.relative_position_index",
+                "layers.0.swin1.attn.qkv.weight",
+                "layers.0.swin1.attn.proj.weight",
+                "layers.0.swin1.attn.proj.bias",
+                "layers.0.swin1.norm2.weight",
+                "layers.0.swin1.mlp.fc1.weight",
+                "layers.0.swin1.mlp.fc1.bias",
+                "layers.0.swin1.mlp.fc2.weight",
+                "layers.0.adjust1.weight",
+                "layers.0.swin2.norm1.weight",
+                "layers.0.adjust2.weight",
+                "layers.0.swin3.norm1.weight",
+                "layers.0.adjust3.weight",
+                "layers.0.swin4.norm1.weight",
+                "layers.0.adjust4.weight",
+                "layers.0.swin5.norm1.weight",
+                "layers.0.adjust5.weight",
+                "norm.weight",
+                "norm.bias",
+            ),
+        )
+
+    @override
+    def load(self, state_dict: StateDict) -> ImageModelDescriptor[DRCT]:
+        # Defaults
+        img_size = 64
+        patch_size = 1  # cannot be detected
+        in_chans = 3
+        embed_dim = 180
+        depths = (6, 6, 6, 6, 6, 6)
+        num_heads = (6, 6, 6, 6, 6, 6)
+        window_size = 16
+        mlp_ratio = 2.0
+        qkv_bias = True
+        ape = False
+        patch_norm = True
+        upscale = 2
+        img_range = 1.0  # cannot be deduced from state_dict
+        upsampler = ""
+        resi_connection = "1conv"
+        gc = 32
+
+        # detect
+        in_chans = state_dict["conv_first.weight"].shape[1]
+        embed_dim = state_dict["conv_first.weight"].shape[0]
+
+        num_layers = get_seq_len(state_dict, "layers")
+        depths = (6,) * num_layers
+        num_heads = []
+        for i in range(num_layers):
+            num_heads.append(
+                state_dict[f"layers.{i}.swin1.attn.relative_position_bias_table"].shape[
+                    1
+                ]
+            )
+
+        mlp_ratio = state_dict["layers.0.swin1.mlp.fc1.weight"].shape[0] / embed_dim
+
+        window_square = state_dict[
+            "layers.0.swin1.attn.relative_position_bias_table"
+        ].shape[0]
+        window_size = (math.isqrt(window_square) + 1) // 2
+
+        if "conv_last.weight" in state_dict:
+            upsampler = "pixelshuffle"
+            upscale = _get_upscale_pixelshuffle(state_dict, "upsample")
+        else:
+            upsampler = ""
+            upscale = 1
+
+        if "conv_after_body.weight" in state_dict:
+            resi_connection = "1conv"
+        else:
+            resi_connection = "identity"
+
+        qkv_bias = "layers.0.swin1.attn.qkv.bias" in state_dict
+        gc = state_dict["layers.0.adjust1.weight"].shape[0]
+
+        patch_norm = "patch_embed.norm.weight" in state_dict
+        ape = "absolute_pos_embed" in state_dict
+
+        if "layers.0.swin2.attn_mask" in state_dict:
+            img_size = (
+                math.isqrt(state_dict["layers.0.swin2.attn_mask"].shape[0])
+                * window_size
+                * patch_size
+            )
+        else:
+            # we only know that the input size is <= window_size,
+            # so we just assume that the input size is window_size
+            img_size = window_size * patch_size
+
+        model = DRCT(
+            img_size=img_size,
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            depths=depths,
+            num_heads=num_heads,
+            window_size=window_size,
+            mlp_ratio=mlp_ratio,
+            qkv_bias=qkv_bias,
+            ape=ape,
+            patch_norm=patch_norm,
+            upscale=upscale,
+            img_range=img_range,
+            upsampler=upsampler,
+            resi_connection=resi_connection,
+            gc=gc,
+        )
+
+        size_tag = ["large"] if len(depths) >= 10 else []
+        tags = [
+            *size_tag,
+            f"s{img_size}w{window_size}",
+            f"{embed_dim}dim",
+            f"{resi_connection}",
+        ]
+
+        return ImageModelDescriptor(
+            model,
+            state_dict,
+            architecture=self,
+            purpose="Restoration" if upscale == 1 else "SR",
+            tags=tags,
+            supports_half=False,  # Too much weirdness to support this at the moment
+            supports_bfloat16=True,
+            scale=upscale,
+            input_channels=in_chans,
+            output_channels=in_chans,
+            size_requirements=SizeRequirements(),
+        )

libs/spandrel/spandrel/architectures/DRCT/arch/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Chia-Ming Lee
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.