allow passing inclusions and exlusions to SDLoraManager

src/refiners/foundationals/latent_diffusion/lora.py

@@ -18,6 +18,10 @@ class SDLoraManager:
     def __init__(
         self,
         target: LatentDiffusionModel,
+        unet_inclusions: list[str] | None = None,
+        unet_exclusions: list[str] | None = None,
+        text_encoder_inclusions: list[str] | None = None,
+        text_encoder_exclusions: list[str] | None = None,
     ) -> None:
         """Initialize the LoRA manager.
 
@@ -25,6 +29,10 @@ class SDLoraManager:
             target: The target model to manage the LoRAs for.
         """
         self.target = target
+        self.unet_inclusions = unet_inclusions
+        self.unet_exclusions = unet_exclusions
+        self.text_encoder_inclusions = text_encoder_inclusions
+        self.text_encoder_exclusions = text_encoder_exclusions
 
     @property
     def unet(self) -> fl.Chain:
@@ -114,7 +122,12 @@ class SDLoraManager:
                 (keys are the names of the LoRAs, values are the LoRAs to add to the text encoder)
         """
         text_encoder_loras = {key: loras[key] for key in loras.keys() if "text" in key}
-        auto_attach_loras(text_encoder_loras, self.clip_text_encoder)
+        auto_attach_loras(
+            text_encoder_loras,
+            self.clip_text_encoder,
+            exclude=self.text_encoder_exclusions,
+            include=self.text_encoder_inclusions,
+        )
 
     def add_loras_to_unet(self, loras: dict[str, Lora[Any]], /) -> None:
         """Add multiple LoRAs to the U-Net.
@@ -124,10 +137,21 @@ class SDLoraManager:
                 (keys are the names of the LoRAs, values are the LoRAs to add to the U-Net)
         """
         unet_loras = {key: loras[key] for key in loras.keys() if "unet" in key}
-        exclude = [
-            block for s, block in self.unet_exclusions.items() if all([s not in key for key in unet_loras.keys()])
+
+        if self.unet_exclusions is None:
+            auto_exclusions = {
+                "time": "TimestepEncoder",
+                "res": "ResidualBlock",
+                "downsample": "Downsample",
+                "upsample": "Upsample",
+            }
+            exclusions = [
+                block for s, block in auto_exclusions.items() if all([s not in key for key in unet_loras.keys()])
             ]
-        auto_attach_loras(unet_loras, self.unet, exclude=exclude)
+        else:
+            exclusions = self.unet_exclusions
+
+        auto_attach_loras(unet_loras, self.unet, exclude=exclusions, include=self.unet_inclusions)
 
     def remove_loras(self, *names: str) -> None:
         """Remove multiple LoRAs from the target.
@@ -206,15 +230,6 @@ class SDLoraManager:
         """List of all the LoraAdapters managed by the SDLoraManager."""
         return list(self.unet.layers(LoraAdapter)) + list(self.clip_text_encoder.layers(LoraAdapter))
 
-    @property
-    def unet_exclusions(self) -> dict[str, str]:
-        return {
-            "time": "TimestepEncoder",
-            "res": "ResidualBlock",
-            "downsample": "Downsample",
-            "upsample": "Upsample",
-        }
-
     @property
     def scales(self) -> dict[str, float]:
         """The scales of all the LoRAs managed by the SDLoraManager."""