make __getattr__ on Module return object, not Any

PyTorch chose to make it Any because they expect its users' code
to be "highly dynamic": https://github.com/pytorch/pytorch/pull/104321

It is not the case for us, in Refiners having untyped code
goes contrary to one of our core principles.

Note that there is currently an open PR in PyTorch to
return `Module | Tensor`, but in practice this is not always
correct either: https://github.com/pytorch/pytorch/pull/115074

I also moved Residuals-related code from SD1 to latent_diffusion
because SDXL should not depend on SD1.

scripts/conversion/convert_diffusers_controlnet.py

@@ -29,7 +29,7 @@ def convert(args: Args) -> dict[str, torch.Tensor]:
     )
     unet = SD1UNet(in_channels=4)
     adapter = SD1ControlnetAdapter(unet, name="mycn").inject()
-    controlnet = unet.Controlnet
+    controlnet = adapter.controlnet
 
     condition = torch.randn(1, 3, 512, 512)
     adapter.set_controlnet_condition(condition=condition)

scripts/conversion/convert_diffusers_unet.py

@@ -39,7 +39,7 @@ def setup_converter(args: Args) -> ModelConverter:
     target.set_timestep(timestep=timestep)
     target.set_clip_text_embedding(clip_text_embedding=clip_text_embeddings)
     added_cond_kwargs = {}
-    if source_has_time_ids:
+    if isinstance(target, SDXLUNet):
         added_cond_kwargs = {"text_embeds": torch.randn(1, 1280), "time_ids": torch.randn(1, 6)}
         target.set_time_ids(time_ids=added_cond_kwargs["time_ids"])
         target.set_pooled_text_embedding(pooled_text_embedding=added_cond_kwargs["text_embeds"])

scripts/conversion/convert_segment_anything.py

@@ -11,7 +11,7 @@ from torch import Tensor
 import refiners.fluxion.layers as fl
 from refiners.fluxion.model_converter import ModelConverter
 from refiners.fluxion.utils import load_tensors, manual_seed, save_to_safetensors
-from refiners.foundationals.segment_anything.image_encoder import SAMViTH
+from refiners.foundationals.segment_anything.image_encoder import PositionalEncoder, SAMViTH
 from refiners.foundationals.segment_anything.mask_decoder import MaskDecoder
 from refiners.foundationals.segment_anything.prompt_encoder import MaskEncoder, PointEncoder
 
@@ -136,7 +136,8 @@ def convert_vit(vit: nn.Module) -> dict[str, Tensor]:
         source_state_dict=source_state_dict, target_state_dict=target_state_dict, state_dict_mapping=mapping
     )
 
-    embed = pos_embed.reshape_as(refiners_sam_vit_h.PositionalEncoder.Parameter.weight)
+    positional_encoder = refiners_sam_vit_h.layer("PositionalEncoder", PositionalEncoder)
+    embed = pos_embed.reshape_as(positional_encoder.layer("Parameter", fl.Parameter).weight)
     converted_source["PositionalEncoder.Parameter.weight"] = embed  # type: ignore
     converted_source.update(rel_items)
 

src/refiners/fluxion/adapters/adapter.py

@@ -43,14 +43,12 @@ class Adapter(Generic[T]):
         ), "Call the Chain constructor in the setup_adapter context."
         self._target = [target]
 
-        if not isinstance(self.target, fl.ContextModule):
+        if isinstance(target, fl.ContextModule):
+            assert isinstance(target, fl.ContextModule)
+            with target.no_parent_refresh():
                 yield
-            return
-
-        _old_can_refresh_parent = target._can_refresh_parent
-        target._can_refresh_parent = False
+        else:
             yield
-        target._can_refresh_parent = _old_can_refresh_parent
 
     def inject(self: TAdapter, parent: fl.Chain | None = None) -> TAdapter:
         """Inject the adapter.

src/refiners/fluxion/layers/chain.py

@@ -3,7 +3,7 @@ import re
 import sys
 import traceback
 from collections import defaultdict
-from typing import Any, Callable, Iterable, Iterator, TypeVar, cast, overload
+from typing import Any, Callable, Iterable, Iterator, Sequence, TypeVar, cast, overload
 
 import torch
 from torch import Tensor, cat, device as Device, dtype as DType
@@ -362,6 +362,48 @@ class Chain(ContextModule):
                 recurse=recurse,
             )
 
+    def layer(self, key: str | int | Sequence[str | int], layer_type: type[T] = Module) -> T:
+        """Access a layer of the Chain given its type.
+
+        Example:
+            ```py
+            # same as my_chain["Linear_2"], asserts it is a Linear
+            my_chain.layer("Linear_2", fl.Linear)
+
+
+            # same as my_chain[3], asserts it is a Linear
+            my_chain.layer(3, fl.Linear)
+
+            # probably won't work
+            my_chain.layer("Conv2d", fl.Linear)
+
+
+            # same as my_chain["foo"][42]["bar"],
+            # assuming bar is a MyType and all parents are Chains
+            my_chain.layer(("foo", 42, "bar"), fl.MyType)
+            ```
+
+        Args:
+            key: The key or path of the layer.
+            layer_type: The type of the layer.
+
+        Yields:
+            The layer.
+
+        Raises:
+            AssertionError: If the layer doesn't exist or the type is invalid.
+        """
+        if isinstance(key, (str, int)):
+            r = self[key]
+            assert isinstance(r, layer_type), f"layer {key} is {type(r)}, not {layer_type}"
+            return r
+        if len(key) == 0:
+            assert isinstance(self, layer_type), f"layer is {type(self)}, not {layer_type}"
+            return self
+        if len(key) == 1:
+            return self.layer(key[0], layer_type)
+        return self.layer(key[0], Chain).layer(key[1:], layer_type)
+
     def layers(
         self,
         layer_type: type[T],
@@ -576,6 +618,7 @@ class Chain(ContextModule):
         require extra GPU memory since the weights are in the leaves and hence not copied.
         """
         if hasattr(self, "_pre_structural_copy"):
+            assert callable(self._pre_structural_copy)
             self._pre_structural_copy()
 
         modules = [structural_copy(m) for m in self]
@@ -586,6 +629,7 @@ class Chain(ContextModule):
             clone.append(module=module)
 
         if hasattr(clone, "_post_structural_copy"):
+            assert callable(clone._post_structural_copy)
             clone._post_structural_copy(self)
 
         return clone

src/refiners/fluxion/layers/module.py

@@ -1,11 +1,12 @@
+import contextlib
 import sys
 from collections import defaultdict
 from inspect import Parameter, signature
 from pathlib import Path
 from types import ModuleType
-from typing import TYPE_CHECKING, Any, DefaultDict, Generator, Sequence, TypedDict, TypeVar, cast
+from typing import TYPE_CHECKING, Any, DefaultDict, Generator, Iterator, Sequence, TypedDict, TypeVar, cast
 
-from torch import device as Device, dtype as DType
+from torch import Tensor, device as Device, dtype as DType
 from torch.nn.modules.module import Module as TorchModule
 
 from refiners.fluxion.context import Context, ContextProvider
@@ -29,7 +30,13 @@ class Module(TorchModule):
     def __init__(self, *args: Any, **kwargs: Any) -> None:
         super().__init__(*args, *kwargs)  # type: ignore[reportUnknownMemberType]
 
-    def __getattr__(self, name: str) -> Any:
+    def __getattr__(self, name: str) -> object:
+        # Note: PyTorch returns `Any` as of 2.2 and is considering
+        # going back to `Tensor | Module`, but the truth is it is
+        # impossible to type `__getattr__` correctly.
+        # Because PyTorch assumes its users write highly dynamic code,
+        # it returns Python's top type `Any`. In Refiners, static type
+        # checking is a core design value, hence we return `object` instead.
         return super().__getattr__(name=name)
 
     def __setattr__(self, name: str, value: Any) -> None:
@@ -220,10 +227,19 @@ class ContextModule(Module):
 
         return super().get_path(parent=parent or self.parent, top=top)
 
+    @contextlib.contextmanager
+    def no_parent_refresh(self) -> Iterator[None]:
+        _old_can_refresh_parent = self._can_refresh_parent
+        self._can_refresh_parent = False
+        yield
+        self._can_refresh_parent = _old_can_refresh_parent
+
 
 class WeightedModule(Module):
     """A module with a weight (Tensor) attribute."""
 
+    weight: Tensor
+
     @property
     def device(self) -> Device:
         """Return the device of the module's weight."""

src/refiners/foundationals/latent_diffusion/auto_encoder.py

@@ -157,7 +157,7 @@ class Decoder(Chain):
         )
         resnet_layers[0].insert(1, attention_layer)
         for _, layer in zip(range(3), resnet_layers[1:]):
-            channels: int = layer[-1].out_channels
+            channels: int = layer.layer(-1, Resnet).out_channels
             layer.insert(-1, Upsample(channels=channels, upsample_factor=2, device=device, dtype=dtype))
         super().__init__(
             Conv2d(

src/refiners/foundationals/latent_diffusion/freeu.py

@@ -73,7 +73,7 @@ class FreeUResidualConcatenator(fl.Concatenate):
 class SDFreeUAdapter(Generic[T], fl.Chain, Adapter[T]):
     def __init__(self, target: T, backbone_scales: list[float], skip_scales: list[float]) -> None:
         assert len(backbone_scales) == len(skip_scales)
-        assert len(backbone_scales) <= len(target.UpBlocks)
+        assert len(backbone_scales) <= len(target.layer("UpBlocks", fl.Chain))
         self.backbone_scales = backbone_scales
         self.skip_scales = skip_scales
         with self.setup_adapter(target):
@@ -88,7 +88,7 @@ class SDFreeUAdapter(Generic[T], fl.Chain, Adapter[T]):
 
     def eject(self) -> None:
         for n in range(len(self.backbone_scales)):
-            block = self.target.UpBlocks[n]
+            block = self.target.layer(("UpBlocks", n), fl.Chain)
             concat = block.ensure_find(FreeUResidualConcatenator)
             block.replace(concat, ResidualConcatenator(-n - 2))
         super().eject()

src/refiners/foundationals/latent_diffusion/image_prompt.py

@@ -307,11 +307,11 @@ class CrossAttentionAdapter(fl.Chain, Adapter[fl.Attention]):
 
     @property
     def image_key_projection(self) -> fl.Linear:
-        return self.image_cross_attention.Distribute[1].Linear
+        return self.image_cross_attention.layer(("Distribute", 1, "Linear"), fl.Linear)
 
     @property
     def image_value_projection(self) -> fl.Linear:
-        return self.image_cross_attention.Distribute[2].Linear
+        return self.image_cross_attention.layer(("Distribute", 2, "Linear"), fl.Linear)
 
     @property
     def scale(self) -> float:

src/refiners/foundationals/latent_diffusion/model.py

@@ -9,17 +9,15 @@ import refiners.fluxion.layers as fl
 from refiners.foundationals.latent_diffusion.auto_encoder import LatentDiffusionAutoencoder
 from refiners.foundationals.latent_diffusion.solvers import Solver
 
-T = TypeVar("T", bound="fl.Module")
-
 TLatentDiffusionModel = TypeVar("TLatentDiffusionModel", bound="LatentDiffusionModel")
 
 
 class LatentDiffusionModel(fl.Module, ABC):
     def __init__(
         self,
-        unet: fl.Module,
+        unet: fl.Chain,
         lda: LatentDiffusionAutoencoder,
-        clip_text_encoder: fl.Module,
+        clip_text_encoder: fl.Chain,
         solver: Solver,
         device: Device | str = "cpu",
         dtype: DType = torch.float32,

src/refiners/foundationals/latent_diffusion/stable_diffusion_1/controlnet.py

@@ -1,5 +1,3 @@
-from typing import Iterable, cast
-
 from torch import Tensor, device as Device, dtype as DType
 
 from refiners.fluxion.adapters.adapter import Adapter
@@ -93,28 +91,31 @@ class Controlnet(Passthrough):
 
         # We run the condition encoder at each step. Caching the result
         # is not worth it as subsequent runs take virtually no time (FG-374).
-        self.DownBlocks[0].append(
+
+        self.layer(("DownBlocks", 0), Chain).append(
             Residual(
                 UseContext("controlnet", f"condition_{name}"),
                 ConditionEncoder(device=device, dtype=dtype),
             ),
         )
         for residual_block in self.layers(ResidualBlock):
-            chain = residual_block.Chain
+            chain = residual_block.layer("Chain", Chain)
             RangeAdapter2d(
-                target=chain.Conv2d_1,
+                target=chain.layer("Conv2d_1", Conv2d),
                 channels=residual_block.out_channels,
                 embedding_dim=1280,
                 context_key=f"timestep_embedding_{name}",
                 device=device,
                 dtype=dtype,
             ).inject(chain)
-        for n, block in enumerate(cast(Iterable[Chain], self.DownBlocks)):
-            assert hasattr(block[0], "out_channels"), (
+        for n, block in enumerate(self.layer("DownBlocks", DownBlocks)):
+            assert isinstance(block, Chain)
+            b0 = block[0]
+            assert hasattr(b0, "out_channels"), (
                 "The first block of every subchain in DownBlocks is expected to respond to `out_channels`,"
-                f" {block[0]} does not."
+                f" {b0} does not."
             )
-            out_channels: int = block[0].out_channels
+            assert isinstance(out_channels := b0.out_channels, int)
             block.append(
                 Passthrough(
                     Conv2d(
@@ -123,7 +124,7 @@ class Controlnet(Passthrough):
                     Lambda(self._store_nth_residual(n)),
                 )
             )
-        self.MiddleBlock.append(
+        self.layer("MiddleBlock", MiddleBlock).append(
             Passthrough(
                 Conv2d(in_channels=1280, out_channels=1280, kernel_size=1, device=device, dtype=dtype),
                 Lambda(self._store_nth_residual(12)),
@@ -166,6 +167,10 @@ class SD1ControlnetAdapter(Chain, Adapter[SD1UNet]):
         self.target.remove(self._controlnet[0])
         super().eject()
 
+    @property
+    def controlnet(self) -> Controlnet:
+        return self._controlnet[0]
+
     def init_context(self) -> Contexts:
         return {"controlnet": {f"condition_{self.name}": None}}
 

src/refiners/foundationals/latent_diffusion/stable_diffusion_1/t2i_adapter.py

@@ -25,7 +25,7 @@ class SD1T2IAdapter(T2IAdapter[SD1UNet]):
 
     def inject(self: "SD1T2IAdapter", parent: fl.Chain | None = None) -> "SD1T2IAdapter":
         for n, feat in zip(self.residual_indices, self._features, strict=True):
-            block = self.target.DownBlocks[n]
+            block = self.target.layer(("DownBlocks", n), fl.Chain)
             for t2i_layer in block.layers(layer_type=T2IFeatures):
                 assert t2i_layer.name != self.name, f"T2I-Adapter named {self.name} is already injected"
             block.insert_before_type(ResidualAccumulator, feat)
@@ -33,5 +33,5 @@ class SD1T2IAdapter(T2IAdapter[SD1UNet]):
 
     def eject(self: "SD1T2IAdapter") -> None:
         for n, feat in zip(self.residual_indices, self._features, strict=True):
-            self.target.DownBlocks[n].remove(feat)
+            self.target.layer(("DownBlocks", n), fl.Chain).remove(feat)
         super().eject()

src/refiners/foundationals/latent_diffusion/stable_diffusion_1/unet.py

@@ -6,6 +6,11 @@ import refiners.fluxion.layers as fl
 from refiners.fluxion.context import Contexts
 from refiners.foundationals.latent_diffusion.cross_attention import CrossAttentionBlock2d
 from refiners.foundationals.latent_diffusion.range_adapter import RangeAdapter2d, RangeEncoder
+from refiners.foundationals.latent_diffusion.unet import (
+    ResidualAccumulator,
+    ResidualBlock,
+    ResidualConcatenator,
+)
 
 
 class TimestepEncoder(fl.Passthrough):
@@ -22,54 +27,6 @@ class TimestepEncoder(fl.Passthrough):
         )
 
 
-class ResidualBlock(fl.Sum):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        num_groups: int = 32,
-        eps: float = 1e-5,
-        device: Device | str | None = None,
-        dtype: DType | None = None,
-    ) -> None:
-        if in_channels % num_groups != 0 or out_channels % num_groups != 0:
-            raise ValueError("Number of input and output channels must be divisible by num_groups.")
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.num_groups = num_groups
-        self.eps = eps
-        shortcut = (
-            fl.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, device=device, dtype=dtype)
-            if in_channels != out_channels
-            else fl.Identity()
-        )
-        super().__init__(
-            fl.Chain(
-                fl.GroupNorm(channels=in_channels, num_groups=num_groups, eps=eps, device=device, dtype=dtype),
-                fl.SiLU(),
-                fl.Conv2d(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    kernel_size=3,
-                    padding=1,
-                    device=device,
-                    dtype=dtype,
-                ),
-                fl.GroupNorm(channels=out_channels, num_groups=num_groups, eps=eps, device=device, dtype=dtype),
-                fl.SiLU(),
-                fl.Conv2d(
-                    in_channels=out_channels,
-                    out_channels=out_channels,
-                    kernel_size=3,
-                    padding=1,
-                    device=device,
-                    dtype=dtype,
-                ),
-            ),
-            shortcut,
-        )
-
-
 class CLIPLCrossAttention(CrossAttentionBlock2d):
     def __init__(
         self,
@@ -205,34 +162,6 @@ class MiddleBlock(fl.Chain):
         )
 
 
-class ResidualAccumulator(fl.Passthrough):
-    def __init__(self, n: int) -> None:
-        self.n = n
-
-        super().__init__(
-            fl.Residual(
-                fl.UseContext(context="unet", key="residuals").compose(func=lambda residuals: residuals[self.n])
-            ),
-            fl.SetContext(context="unet", key="residuals", callback=self.update),
-        )
-
-    def update(self, residuals: list[Tensor | float], x: Tensor) -> None:
-        residuals[self.n] = x
-
-
-class ResidualConcatenator(fl.Chain):
-    def __init__(self, n: int) -> None:
-        self.n = n
-
-        super().__init__(
-            fl.Concatenate(
-                fl.Identity(),
-                fl.UseContext(context="unet", key="residuals").compose(lambda residuals: residuals[self.n]),
-                dim=1,
-            ),
-        )
-
-
 class SD1UNet(fl.Chain):
     """Stable Diffusion 1.5 U-Net.
 
@@ -275,9 +204,9 @@ class SD1UNet(fl.Chain):
             ),
         )
         for residual_block in self.layers(ResidualBlock):
-            chain = residual_block.Chain
+            chain = residual_block.layer("Chain", fl.Chain)
             RangeAdapter2d(
-                target=chain.Conv2d_1,
+                target=chain.layer("Conv2d_1", fl.Conv2d),
                 channels=residual_block.out_channels,
                 embedding_dim=1280,
                 context_key="timestep_embedding",

src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/t2i_adapter.py

@@ -2,7 +2,7 @@ from torch import Tensor
 
 import refiners.fluxion.layers as fl
 from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import ResidualAccumulator
-from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet
+from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import MiddleBlock, SDXLUNet
 from refiners.foundationals.latent_diffusion.t2i_adapter import ConditionEncoderXL, T2IAdapter, T2IFeatures
 
 
@@ -31,18 +31,19 @@ class SDXLT2IAdapter(T2IAdapter[SDXLUNet]):
 
         # Note: `strict=False` because `residual_indices` is shorter than `_features` due to MiddleBlock (see below)
         for n, feat in zip(self.residual_indices, self._features, strict=False):
-            block = self.target.DownBlocks[n]
+            block = self.target.layer(("DownBlocks", n), fl.Chain)
             sanity_check_t2i(block)
             block.insert_before_type(ResidualAccumulator, feat)
 
         # Special case: the MiddleBlock has no ResidualAccumulator (this is done via a subsequent layer) so just append
-        sanity_check_t2i(self.target.MiddleBlock)
-        self.target.MiddleBlock.append(self._features[-1])
+        mid_block = self.target.layer("MiddleBlock", MiddleBlock)
+        sanity_check_t2i(mid_block)
+        mid_block.append(self._features[-1])
         return super().inject(parent)
 
     def eject(self: "SDXLT2IAdapter") -> None:
         # See `inject` re: `strict=False`
         for n, feat in zip(self.residual_indices, self._features, strict=False):
-            self.target.DownBlocks[n].remove(feat)
-        self.target.MiddleBlock.remove(self._features[-1])
+            self.target.layer(("DownBlocks", n), fl.Chain).remove(feat)
+        self.target.layer("MiddleBlock", MiddleBlock).remove(self._features[-1])
         super().eject()

src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/text_encoder.py

@@ -72,7 +72,9 @@ class DoubleTextEncoder(fl.Chain):
             fl.Parallel(text_encoder_l[:-2], text_encoder_g),
             fl.Lambda(func=self.concatenate_embeddings),
         )
-        TextEncoderWithPooling(target=text_encoder_g, projection=projection).inject(parent=self.Parallel)
+        TextEncoderWithPooling(target=text_encoder_g, projection=projection).inject(
+            parent=self.layer("Parallel", fl.Parallel)
+        )
 
     def __call__(self, text: str) -> tuple[Float[Tensor, "1 77 2048"], Float[Tensor, "1 1280"]]:
         return super().__call__(text)

src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/unet.py

@@ -10,7 +10,7 @@ from refiners.foundationals.latent_diffusion.range_adapter import (
     RangeEncoder,
     compute_sinusoidal_embedding,
 )
-from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import (
+from refiners.foundationals.latent_diffusion.unet import (
     ResidualAccumulator,
     ResidualBlock,
     ResidualConcatenator,
@@ -267,9 +267,9 @@ class SDXLUNet(fl.Chain):
             OutputBlock(device=device, dtype=dtype),
         )
         for residual_block in self.layers(ResidualBlock):
-            chain = residual_block.Chain
+            chain = residual_block.layer("Chain", fl.Chain)
             RangeAdapter2d(
-                target=chain.Conv2d_1,
+                target=chain.layer("Conv2d_1", fl.Conv2d),
                 channels=residual_block.out_channels,
                 embedding_dim=1280,
                 context_key="timestep_embedding",

src/refiners/foundationals/latent_diffusion/unet.py

@@ -0,0 +1,79 @@
+from torch import Tensor, device as Device, dtype as DType
+
+import refiners.fluxion.layers as fl
+
+
+class ResidualBlock(fl.Sum):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        num_groups: int = 32,
+        eps: float = 1e-5,
+        device: Device | str | None = None,
+        dtype: DType | None = None,
+    ) -> None:
+        if in_channels % num_groups != 0 or out_channels % num_groups != 0:
+            raise ValueError("Number of input and output channels must be divisible by num_groups.")
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.num_groups = num_groups
+        self.eps = eps
+        shortcut = (
+            fl.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, device=device, dtype=dtype)
+            if in_channels != out_channels
+            else fl.Identity()
+        )
+        super().__init__(
+            fl.Chain(
+                fl.GroupNorm(channels=in_channels, num_groups=num_groups, eps=eps, device=device, dtype=dtype),
+                fl.SiLU(),
+                fl.Conv2d(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    kernel_size=3,
+                    padding=1,
+                    device=device,
+                    dtype=dtype,
+                ),
+                fl.GroupNorm(channels=out_channels, num_groups=num_groups, eps=eps, device=device, dtype=dtype),
+                fl.SiLU(),
+                fl.Conv2d(
+                    in_channels=out_channels,
+                    out_channels=out_channels,
+                    kernel_size=3,
+                    padding=1,
+                    device=device,
+                    dtype=dtype,
+                ),
+            ),
+            shortcut,
+        )
+
+
+class ResidualAccumulator(fl.Passthrough):
+    def __init__(self, n: int) -> None:
+        self.n = n
+
+        super().__init__(
+            fl.Residual(
+                fl.UseContext(context="unet", key="residuals").compose(func=lambda residuals: residuals[self.n])
+            ),
+            fl.SetContext(context="unet", key="residuals", callback=self.update),
+        )
+
+    def update(self, residuals: list[Tensor | float], x: Tensor) -> None:
+        residuals[self.n] = x
+
+
+class ResidualConcatenator(fl.Chain):
+    def __init__(self, n: int) -> None:
+        self.n = n
+
+        super().__init__(
+            fl.Concatenate(
+                fl.Identity(),
+                fl.UseContext(context="unet", key="residuals").compose(lambda residuals: residuals[self.n]),
+                dim=1,
+            ),
+        )

src/refiners/foundationals/segment_anything/prompt_encoder.py

@@ -188,6 +188,7 @@ class MaskEncoder(fl.Chain):
     def get_no_mask_dense_embedding(
         self, image_embedding_size: tuple[int, int], batch_size: int = 1
     ) -> Float[Tensor, "batch embedding_dim image_embedding_height image_embedding_width"]:
-        return self.no_mask_embedding.reshape(1, -1, 1, 1).expand(
+        no_mask_embedding = cast(Tensor, self.no_mask_embedding)
+        return no_mask_embedding.reshape(1, -1, 1, 1).expand(
             batch_size, -1, image_embedding_size[0], image_embedding_size[1]
         )

tests/adapters/test_adapter.py

@@ -22,8 +22,8 @@ def chain() -> Chain:
 
 
 def test_weighted_module_adapter_insertion(chain: Chain):
-    parent = chain.Chain
-    adaptee = parent.Linear
+    parent = chain.layer("Chain", Chain)
+    adaptee = parent.layer("Linear", Linear)
 
     adapter = DummyLinearAdapter(adaptee).inject(parent)
 
@@ -39,7 +39,7 @@ def test_weighted_module_adapter_insertion(chain: Chain):
 
 def test_chain_adapter_insertion(chain: Chain):
     parent = chain
-    adaptee = parent.Chain
+    adaptee = parent.layer("Chain", Chain)
 
     adapter = DummyChainAdapter(adaptee)
     assert adaptee.parent == parent
@@ -58,20 +58,20 @@ def test_chain_adapter_insertion(chain: Chain):
 
 
 def test_weighted_module_adapter_structural_copy(chain: Chain):
-    parent = chain.Chain
-    adaptee = parent.Linear
+    parent = chain.layer("Chain", Chain)
+    adaptee = parent.layer("Linear", Linear)
 
     DummyLinearAdapter(adaptee).inject(parent)
 
     clone = chain.structural_copy()
-    cloned_adapter = clone.Chain.DummyLinearAdapter
+    cloned_adapter = clone.layer(("Chain", "DummyLinearAdapter"), DummyLinearAdapter)
     assert cloned_adapter.parent == clone.Chain
     assert cloned_adapter.target == adaptee
 
 
 def test_chain_adapter_structural_copy(chain: Chain):
     # Chain adapters cannot be copied by default.
-    adapter = DummyChainAdapter(chain.Chain).inject()
+    adapter = DummyChainAdapter(chain.layer("Chain", Chain)).inject()
 
     with pytest.raises(RuntimeError):
         chain.structural_copy()

tests/adapters/test_lora.py

@@ -27,6 +27,7 @@ def test_properties(lora: LinearLora, conv_lora: Lora) -> None:
     assert conv_lora.scale == 1.0
     assert conv_lora.in_channels == conv_lora.down.in_channels == 16
     assert conv_lora.out_channels == conv_lora.up.out_channels == 8
+    assert isinstance(conv_lora.down, fl.Conv2d) and isinstance(conv_lora.up, fl.Conv2d)
     assert conv_lora.kernel_size == (conv_lora.down.kernel_size[0], conv_lora.up.kernel_size[0]) == (3, 1)
     # padding is set so the spatial dimensions are preserved
     assert conv_lora.padding == (conv_lora.down.padding[0], conv_lora.up.padding[0]) == (0, 1)
@@ -39,10 +40,12 @@ def test_scale_setter(lora: LinearLora) -> None:
 
 
 def test_from_weights(lora: LinearLora, conv_lora: Conv2dLora) -> None:
+    assert isinstance(lora.down, fl.Linear) and isinstance(lora.up, fl.Linear)
     new_lora = LinearLora.from_weights("test", down=lora.down.weight, up=lora.up.weight)
     x = torch.randn(1, 320)
     assert torch.allclose(lora(x), new_lora(x))
 
+    assert isinstance(conv_lora.down, fl.Conv2d) and isinstance(conv_lora.up, fl.Conv2d)
     new_conv_lora = Conv2dLora.from_weights("conv_test", down=conv_lora.down.weight, up=conv_lora.up.weight)
     x = torch.randn(1, 16, 64, 64)
     assert torch.allclose(conv_lora(x), new_conv_lora(x))

tests/adapters/test_range_adapter.py

@@ -15,8 +15,9 @@ def test_range_encoder_dtype_after_adaptation(test_device: torch.device):  # FG-
     dtype = torch.float64
     chain = Chain(RangeEncoder(320, 1280, device=test_device, dtype=dtype))
 
-    adaptee = chain.RangeEncoder.Linear_1
-    adapter = DummyLinearAdapter(adaptee).inject(chain.RangeEncoder)
+    range_encoder = chain.layer("RangeEncoder", RangeEncoder)
+    adaptee = range_encoder.layer("Linear_1", Linear)
+    adapter = DummyLinearAdapter(adaptee).inject(range_encoder)
 
     assert adapter.parent == chain.RangeEncoder
 

tests/fluxion/layers/test_chain.py

@@ -30,8 +30,9 @@ def test_chain_getitem_accessor() -> None:
 
 def test_chain_find_parent():
     chain = fl.Chain(fl.Chain(fl.Linear(1, 1)))
+    subchain = chain.layer("Chain", fl.Chain)
 
-    assert chain.find_parent(chain.Chain.Linear) == chain.Chain
+    assert chain.find_parent(subchain.layer("Linear", fl.Linear)) == subchain
     assert chain.find_parent(fl.Linear(1, 1)) is None
 
 
@@ -64,17 +65,20 @@ def test_chain_walk() -> None:
         fl.Chain(),
     )
 
-    assert list(chain.walk()) == [(chain.Sum, chain), (chain.Chain, chain)]
+    sum_ = chain.layer("Sum", fl.Sum)
+    sum_chain = sum_.layer("Chain", fl.Chain)
+
+    assert list(chain.walk()) == [(sum_, chain), (chain.Chain, chain)]
     assert list(chain.walk(fl.Linear)) == [
-        (chain.Sum.Chain.Linear, chain.Sum.Chain),
-        (chain.Sum.Linear, chain.Sum),
+        (sum_chain.Linear, sum_chain),
+        (sum_.Linear, sum_),
     ]
 
     assert list(chain.walk(recurse=True)) == [
-        (chain.Sum, chain),
-        (chain.Sum.Chain, chain.Sum),
-        (chain.Sum.Chain.Linear, chain.Sum.Chain),
-        (chain.Sum.Linear, chain.Sum),
+        (sum_, chain),
+        (sum_chain, sum_),
+        (sum_chain.Linear, sum_chain),
+        (sum_.Linear, sum_),
         (chain.Chain, chain),
     ]
 
@@ -98,6 +102,29 @@ def test_chain_walk_stop_iteration() -> None:
     assert len(list(chain.walk(predicate))) == 1
 
 
+def test_chain_layer() -> None:
+    chain = fl.Chain(
+        fl.Sum(fl.Chain(), fl.Chain()),
+    )
+
+    sum_ = chain.layer(0, fl.Sum)
+    assert chain.layer("Sum", fl.Sum) == sum_
+    assert chain.layer("Sum", fl.Chain) == sum_
+
+    chain_2 = chain.layer((0, 1), fl.Chain)
+    assert chain.layer((0, 1)) == chain_2
+    assert chain.layer((0, "Chain_2"), fl.Chain) == chain_2
+    assert chain.layer(("Sum", "Chain_2"), fl.Chain) == chain_2
+
+    assert chain.layer((), fl.Chain) == chain
+
+    with pytest.raises(AssertionError):
+        chain.layer((0, 1), fl.Sum)
+
+    with pytest.raises(AssertionError):
+        chain.layer((), fl.Sum)
+
+
 def test_chain_layers() -> None:
     chain = fl.Chain(
         fl.Chain(fl.Chain(fl.Chain())),
@@ -214,11 +241,12 @@ def test_chain_replace() -> None:
         fl.Linear(1, 1),
         fl.Chain(fl.Linear(1, 1), fl.Linear(1, 1)),
     )
+    subchain = chain.layer("Chain", fl.Chain)
 
-    assert isinstance(chain.Chain[1], fl.Linear)
-    chain.Chain.replace(chain.Chain[1], fl.Conv2d(1, 1, 1))
+    assert isinstance(subchain[1], fl.Linear)
+    subchain.replace(subchain[1], fl.Conv2d(1, 1, 1))
     assert len(chain) == 3
-    assert isinstance(chain.Chain[1], fl.Conv2d)
+    assert isinstance(subchain[1], fl.Conv2d)
 
 
 def test_chain_structural_copy() -> None:
@@ -236,15 +264,18 @@ def test_chain_structural_copy() -> None:
 
     m2 = m.structural_copy()
 
-    assert m.Linear == m2.Linear
-    assert m.Sum.Linear_1 == m2.Sum.Linear_1
-    assert m.Sum.Linear_2 == m2.Sum.Linear_2
+    m_sum = m.layer("Sum", fl.Sum)
+    m2_sum = m2.layer("Sum", fl.Sum)
 
-    assert m.Sum != m2.Sum
+    assert m.Linear == m2.Linear
+    assert m_sum.Linear_1 == m2_sum.Linear_1
+    assert m_sum.Linear_2 == m2_sum.Linear_2
+
+    assert m_sum != m2_sum
     assert m != m2
 
-    assert m.Sum.parent == m
-    assert m2.Sum.parent == m2
+    assert m_sum.parent == m
+    assert m2_sum.parent == m2
 
     y2 = m2(x)
     assert y2.shape == (7, 12)

tests/fluxion/test_module.py

@@ -10,9 +10,12 @@ def test_module_get_path() -> None:
         fl.Sum(),
     )
 
-    assert chain.Sum_1.Linear_2.get_path(parent=chain.Sum_1) == "Chain.Sum_1.Linear_2"
-    assert chain.Sum_1.Linear_2.get_path(parent=chain.Sum_1, top=chain.Sum_1) == "Sum.Linear_2"
-    assert chain.Sum_1.get_path() == "Chain.Sum_1"
+    sum_1 = chain.layer("Sum_1", fl.Sum)
+    linear_2 = sum_1.layer("Linear_2", fl.Linear)
+
+    assert linear_2.get_path(parent=sum_1) == "Chain.Sum_1.Linear_2"
+    assert linear_2.get_path(parent=sum_1, top=sum_1) == "Sum.Linear_2"
+    assert sum_1.get_path() == "Chain.Sum_1"
 
 
 def test_module_basic_attributes() -> None:

tests/foundationals/clip/test_concepts.py

@@ -85,13 +85,14 @@ def cat_embedding_textual_inversion(test_textual_inversion_path: Path) -> torch.
 
 
 def test_tokenizer_with_special_character():
-    clip_tokenizer = fl.Chain(CLIPTokenizer())
-    token_extender = TokenExtender(clip_tokenizer.CLIPTokenizer)
-    new_token_id = max(clip_tokenizer.CLIPTokenizer.token_to_id_mapping.values()) + 42
+    clip_tokenizer_chain = fl.Chain(CLIPTokenizer())
+    original_clip_tokenizer = clip_tokenizer_chain.layer("CLIPTokenizer", CLIPTokenizer)
+    token_extender = TokenExtender(original_clip_tokenizer)
+    new_token_id = max(original_clip_tokenizer.token_to_id_mapping.values()) + 42
     token_extender.add_token("*", new_token_id)
-    token_extender.inject(clip_tokenizer)
+    token_extender.inject(clip_tokenizer_chain)
 
-    adapted_clip_tokenizer = clip_tokenizer.ensure_find(CLIPTokenizer)
+    adapted_clip_tokenizer = clip_tokenizer_chain.ensure_find(CLIPTokenizer)
 
     assert torch.allclose(
         adapted_clip_tokenizer.encode("*"),

tests/foundationals/latent_diffusion/test_freeu.py

@@ -4,6 +4,7 @@ import pytest
 import torch
 
 from refiners.fluxion import manual_seed
+from refiners.fluxion.layers import Chain
 from refiners.fluxion.utils import no_grad
 from refiners.foundationals.latent_diffusion import SD1UNet, SDXLUNet
 from refiners.foundationals.latent_diffusion.freeu import FreeUResidualConcatenator, SDFreeUAdapter
@@ -33,7 +34,7 @@ def test_freeu_adapter(unet: SD1UNet | SDXLUNet) -> None:
 
 
 def test_freeu_adapter_too_many_scales(unet: SD1UNet | SDXLUNet) -> None:
-    num_blocks = len(unet.UpBlocks)
+    num_blocks = len(unet.layer("UpBlocks", Chain))
 
     with pytest.raises(AssertionError):
         SDFreeUAdapter(unet, backbone_scales=[1.2] * (num_blocks + 1), skip_scales=[0.9] * (num_blocks + 1))

tests/foundationals/segment_anything/test_sam.py

@@ -16,10 +16,11 @@ from tests.foundationals.segment_anything.utils import (
 )
 from torch import Tensor
 
+import refiners.fluxion.layers as fl
 from refiners.fluxion import manual_seed
 from refiners.fluxion.model_converter import ModelConverter
 from refiners.fluxion.utils import image_to_tensor, load_tensors, no_grad
-from refiners.foundationals.segment_anything.image_encoder import FusedSelfAttention
+from refiners.foundationals.segment_anything.image_encoder import FusedSelfAttention, RelativePositionAttention
 from refiners.foundationals.segment_anything.model import SegmentAnythingH
 from refiners.foundationals.segment_anything.transformer import TwoWayTransformerLayer
 
@@ -104,17 +105,22 @@ def test_fused_self_attention(facebook_sam_h: FacebookSAM) -> None:
     manual_seed(seed=0)
     x = torch.randn(25, 14, 14, 1280, device=facebook_sam_h.device)
 
-    attention = cast(nn.Module, facebook_sam_h.image_encoder.blocks[0].attn)  # type: ignore
+    attention = cast(nn.Module, facebook_sam_h.image_encoder.blocks[0].attn)
 
     refiners_attention = FusedSelfAttention(
         embedding_dim=1280, num_heads=16, spatial_size=(14, 14), device=facebook_sam_h.device
     )
-    refiners_attention.Linear_1.weight = attention.qkv.weight  # type: ignore
-    refiners_attention.Linear_1.bias = attention.qkv.bias  # type: ignore
-    refiners_attention.Linear_2.weight = attention.proj.weight  # type: ignore
-    refiners_attention.Linear_2.bias = attention.proj.bias  # type: ignore
-    refiners_attention.RelativePositionAttention.horizontal_embedding = attention.rel_pos_w
-    refiners_attention.RelativePositionAttention.vertical_embedding = attention.rel_pos_h
+
+    rpa = refiners_attention.layer("RelativePositionAttention", RelativePositionAttention)
+    linear_1 = refiners_attention.layer("Linear_1", fl.Linear)
+    linear_2 = refiners_attention.layer("Linear_2", fl.Linear)
+
+    linear_1.weight = attention.qkv.weight
+    linear_1.bias = attention.qkv.bias
+    linear_2.weight = attention.proj.weight
+    linear_2.bias = attention.proj.bias
+    rpa.horizontal_embedding = attention.rel_pos_w
+    rpa.vertical_embedding = attention.rel_pos_h
 
     y_1 = attention(x)
     assert y_1.shape == x.shape