make set_scale for T2I-Adapter really dynamic

Before this change, `set_scale` had only an impact on the condition
encoder. So calling `set_scale` after `set_condition_features` had no
effect at runtime.

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

@@ -1,5 +1,3 @@
-from typing import cast, Iterable
-
 from torch import Tensor
 
 from refiners.foundationals.latent_diffusion.t2i_adapter import T2IAdapter, T2IFeatures, ConditionEncoder
@@ -13,9 +11,11 @@ class SD1T2IAdapter(T2IAdapter[SD1UNet]):
         target: SD1UNet,
         name: str,
         condition_encoder: ConditionEncoder | None = None,
+        scale: float = 1.0,
         weights: dict[str, Tensor] | None = None,
     ) -> None:
         self.residual_indices = (2, 5, 8, 11)
+        self._features = [T2IFeatures(name=name, index=i, scale=scale) for i in range(4)]
         super().__init__(
             target=target,
             name=name,
@@ -24,23 +24,14 @@ class SD1T2IAdapter(T2IAdapter[SD1UNet]):
         )
 
     def inject(self: "SD1T2IAdapter", parent: fl.Chain | None = None) -> "SD1T2IAdapter":
-        for n, block in enumerate(cast(Iterable[fl.Chain], self.target.DownBlocks)):
+        for n, feat in zip(self.residual_indices, self._features, strict=True):
-            if n not in self.residual_indices:
+            block = self.target.DownBlocks[n]
-                continue
             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(
+            block.insert_before_type(ResidualAccumulator, feat)
-                ResidualAccumulator, T2IFeatures(name=self.name, index=self.residual_indices.index(n))
-            )
         return super().inject(parent)
 
     def eject(self: "SD1T2IAdapter") -> None:
-        for n, block in enumerate(cast(Iterable[fl.Chain], self.target.DownBlocks)):
+        for n, feat in zip(self.residual_indices, self._features, strict=True):
-            if n not in self.residual_indices:
+            self.target.DownBlocks[n].remove(feat)
-                continue
-            t2i_layers = [
-                t2i_layer for t2i_layer in block.layers(layer_type=T2IFeatures) if t2i_layer.name == self.name
-            ]
-            assert len(t2i_layers) == 1
-            block.remove(t2i_layers.pop())
         super().eject()

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

@@ -1,5 +1,3 @@
-from typing import cast, Iterable
-
 from torch import Tensor
 
 from refiners.foundationals.latent_diffusion.t2i_adapter import T2IAdapter, T2IFeatures, ConditionEncoderXL
@@ -14,9 +12,11 @@ class SDXLT2IAdapter(T2IAdapter[SDXLUNet]):
         target: SDXLUNet,
         name: str,
         condition_encoder: ConditionEncoderXL | None = None,
+        scale: float = 1.0,
         weights: dict[str, Tensor] | None = None,
     ) -> None:
         self.residual_indices = (3, 5, 8)  # the UNet's middle block is handled separately (see `inject` and `eject`)
+        self._features = [T2IFeatures(name=name, index=i, scale=scale) for i in range(4)]
         super().__init__(
             target=target,
             name=name,
@@ -29,29 +29,20 @@ class SDXLT2IAdapter(T2IAdapter[SDXLUNet]):
             for t2i_layer in block.layers(layer_type=T2IFeatures):
                 assert t2i_layer.name != self.name, f"T2I-Adapter named {self.name} is already injected"
 
-        for n, block in enumerate(cast(Iterable[fl.Chain], self.target.DownBlocks)):
+        # Note: `strict=False` because `residual_indices` is shorter than `_features` due to MiddleBlock (see below)
-            if n not in self.residual_indices:
+        for n, feat in zip(self.residual_indices, self._features, strict=False):
-                continue
+            block = self.target.DownBlocks[n]
             sanity_check_t2i(block)
-            block.insert_before_type(
+            block.insert_before_type(ResidualAccumulator, feat)
-                ResidualAccumulator, T2IFeatures(name=self.name, index=self.residual_indices.index(n))
+
-            )
-        sanity_check_t2i(self.target.MiddleBlock)
         # Special case: the MiddleBlock has no ResidualAccumulator (this is done via a subsequent layer) so just append
-        self.target.MiddleBlock.append(T2IFeatures(name=self.name, index=-1))
+        sanity_check_t2i(self.target.MiddleBlock)
+        self.target.MiddleBlock.append(self._features[-1])
         return super().inject(parent)
 
     def eject(self: "SDXLT2IAdapter") -> None:
-        def eject_t2i(block: fl.Module) -> None:
+        # See `inject` re: `strict=False`
-            t2i_layers = [
+        for n, feat in zip(self.residual_indices, self._features, strict=False):
-                t2i_layer for t2i_layer in block.layers(layer_type=T2IFeatures) if t2i_layer.name == self.name
+            self.target.DownBlocks[n].remove(feat)
-            ]
+        self.target.MiddleBlock.remove(self._features[-1])
-            assert len(t2i_layers) == 1
-            block.remove(t2i_layers.pop())
-
-        for n, block in enumerate(cast(Iterable[fl.Chain], self.target.DownBlocks)):
-            if n not in self.residual_indices:
-                continue
-            eject_t2i(block)
-        eject_t2i(self.target.MiddleBlock)
         super().eject()

src/refiners/foundationals/latent_diffusion/t2i_adapter.py

@@ -117,13 +117,9 @@ class ConditionEncoder(fl.Chain):
                 )
                 for i in range(1, len(channels))
             ),
-            fl.UseContext(context="t2iadapter", key="features").compose(func=self.scale_outputs),
+            fl.UseContext(context="t2iadapter", key="features"),
         )
 
-    def scale_outputs(self, features: list[Tensor]) -> tuple[Tensor, ...]:
-        assert len(features) == 4
-        return tuple([x * self.scale for x in features])
-
     def init_context(self) -> Contexts:
         return {"t2iadapter": {"features": []}}
 
@@ -157,23 +153,25 @@ class ConditionEncoderXL(ConditionEncoder, fl.Chain):
                 channels[1], channels[2], num_residual_blocks, downsample=True, device=device, dtype=dtype
             ),
             StatefulResidualBlocks(channels[2], channels[3], num_residual_blocks, device=device, dtype=dtype),
-            fl.UseContext(context="t2iadapter", key="features").compose(func=self.scale_outputs),
+            fl.UseContext(context="t2iadapter", key="features"),
         )
 
 
 class T2IFeatures(fl.Residual):
-    def __init__(self, name: str, index: int) -> None:
+    def __init__(self, name: str, index: int, scale: float = 1.0) -> None:
         self.name = name
         self.index = index
+        self.scale = scale
         super().__init__(
             fl.UseContext(context="t2iadapter", key=f"condition_features_{self.name}").compose(
-                func=lambda features: features[self.index]
+                func=lambda features: self.scale * features[self.index]
             )
         )
 
 
 class T2IAdapter(Generic[T], fl.Chain, Adapter[T]):
     _condition_encoder: list[ConditionEncoder]  # prevent PyTorch module registration
+    _features: list[T2IFeatures] = []
 
     def __init__(
         self,
@@ -207,7 +205,8 @@ class T2IAdapter(Generic[T], fl.Chain, Adapter[T]):
         self.set_context("t2iadapter", {f"condition_features_{self.name}": features})
 
     def set_scale(self, scale: float) -> None:
-        self.condition_encoder.scale = scale
+        for f in self._features:
+            f.scale = scale
 
     def init_context(self) -> Contexts:
         return {"t2iadapter": {f"condition_features_{self.name}": None}}