(doc/foundationals) add SDLoraManager, related docstrings

docs/reference/foundationals/latent_diffusion.md

@@ -5,3 +5,5 @@
 ::: refiners.foundationals.latent_diffusion.stable_diffusion_1
 
 ::: refiners.foundationals.latent_diffusion.solvers
+
+::: refiners.foundationals.latent_diffusion.lora

src/refiners/foundationals/latent_diffusion/lora.py

@@ -8,20 +8,34 @@ from refiners.foundationals.latent_diffusion.model import LatentDiffusionModel
 
 
 class SDLoraManager:
+    """Manage LoRAs for a Stable Diffusion model.
+
+    Note:
+        In the context of SDLoraManager, a "LoRA" is a set of ["LoRA layers"][refiners.fluxion.adapters.lora.Lora]
+        that can be attached to a target model.
+    """
+
     def __init__(
         self,
         target: LatentDiffusionModel,
     ) -> None:
+        """Initialize the LoRA manager.
+
+        Args:
+            target: The target model to manage the LoRAs for.
+        """
         self.target = target
 
     @property
     def unet(self) -> fl.Chain:
+        """The Stable Diffusion's U-Net model."""
         unet = self.target.unet
         assert isinstance(unet, fl.Chain)
         return unet
 
     @property
     def clip_text_encoder(self) -> fl.Chain:
+        """The Stable Diffusion's text encoder."""
         clip_text_encoder = self.target.clip_text_encoder
         assert isinstance(clip_text_encoder, fl.Chain)
         return clip_text_encoder
@@ -33,23 +47,44 @@ class SDLoraManager:
         tensors: dict[str, Tensor],
         scale: float = 1.0,
     ) -> None:
-        """Load the LoRA weights from a dictionary of tensors.
+        """Load a single LoRA from a `state_dict`.
 
-        Expects the keys to be in the commonly found formats on CivitAI's hub.
+        Warning:
+            This method expects the keys of the `state_dict` to be in the commonly found formats on CivitAI's hub.
+
+        Args:
+            name: The name of the LoRA.
+            tensors: The `state_dict` of the LoRA to load.
+            scale: The scale to use for the LoRA.
+
+        Raises:
+            AssertionError: If the Manager already has a LoRA with the same name.
         """
         assert name not in self.names, f"LoRA {name} already exists"
+
+        # load LoRA the state_dict
         loras = Lora.from_dict(
-            name, {key: value.to(device=self.target.device, dtype=self.target.dtype) for key, value in tensors.items()}
+            name,
+            state_dict={
+                key: value.to(
+                    device=self.target.device,
+                    dtype=self.target.dtype,
+                )
+                for key, value in tensors.items()
+            },
         )
+        # sort all the LoRA's keys using the `sort_keys` method
         loras = {key: loras[key] for key in sorted(loras.keys(), key=SDLoraManager.sort_keys)}
 
         # if no key contains "unet" or "text", assume all keys are for the unet
         if all("unet" not in key and "text" not in key for key in loras.keys()):
             loras = {f"unet_{key}": value for key, value in loras.items()}
 
+        # attach the LoRA to the target
         self.add_loras_to_unet(loras)
         self.add_loras_to_text_encoder(loras)
 
+        # set the scale of the LoRA
         self.set_scale(name, scale)
 
     def add_multiple_loras(
@@ -58,14 +93,36 @@ class SDLoraManager:
         tensors: dict[str, dict[str, Tensor]],
         scale: dict[str, float] | None = None,
     ) -> None:
+        """Load multiple LoRAs from a dictionary of `state_dict`.
+
+        Args:
+            tensors: The dictionary of `state_dict` of the LoRAs to load
+                (keys are the names of the LoRAs, values are the `state_dict` of the LoRAs).
+            scale: The scales to use for the LoRAs.
+
+        Raises:
+            AssertionError: If the manager already has a LoRA with the same name.
+        """
         for name, lora_tensors in tensors.items():
             self.add_loras(name, tensors=lora_tensors, scale=scale[name] if scale else 1.0)
 
     def add_loras_to_text_encoder(self, loras: dict[str, Lora], /) -> None:
+        """Add multiple LoRAs to the text encoder.
+
+        Args:
+            loras: The dictionary of LoRAs to add to the text encoder.
+                (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}
         SDLoraManager.auto_attach(text_encoder_loras, self.clip_text_encoder)
 
     def add_loras_to_unet(self, loras: dict[str, Lora], /) -> None:
+        """Add multiple LoRAs to the U-Net.
+
+        Args:
+            loras: The dictionary of LoRAs to add to the U-Net.
+                (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()])
@@ -73,6 +130,11 @@ class SDLoraManager:
         SDLoraManager.auto_attach(unet_loras, self.unet, exclude=exclude)
 
     def remove_loras(self, *names: str) -> None:
+        """Remove mulitple LoRAs from the target.
+
+        Args:
+            names: The names of the LoRAs to remove.
+        """
         for lora_adapter in self.lora_adapters:
             for name in names:
                 lora_adapter.remove_lora(name)
@@ -81,21 +143,47 @@ class SDLoraManager:
                 lora_adapter.eject()
 
     def remove_all(self) -> None:
+        """Remove all the LoRAs from the target."""
         for lora_adapter in self.lora_adapters:
             lora_adapter.eject()
 
     def get_loras_by_name(self, name: str, /) -> list[Lora]:
+        """Get the LoRA layers with the given name.
+
+        Args:
+            name: The name of the LoRA.
+        """
         return [lora for lora in self.loras if lora.name == name]
 
     def get_scale(self, name: str, /) -> float:
+        """Get the scale of the LoRA with the given name.
+
+        Args:
+            name: The name of the LoRA.
+
+        Returns:
+            The scale of the LoRA layers with the given name.
+        """
         loras = self.get_loras_by_name(name)
         assert all([lora.scale == loras[0].scale for lora in loras]), "lora scales are not all the same"
         return loras[0].scale
 
     def set_scale(self, name: str, scale: float, /) -> None:
+        """Set the scale of the LoRA with the given name.
+
+        Args:
+            name: The name of the LoRA.
+            scale: The new scale to set.
+        """
         self.update_scales({name: scale})
 
     def update_scales(self, scales: dict[str, float], /) -> None:
+        """Update the scales of mulitple LoRAs.
+
+        Args:
+            scales: The scales to update.
+                (keys are the names of the LoRAs, values are the new scales to set)
+        """
         assert all([name in self.names for name in scales]), f"Scales keys must be a subset of {self.names}"
         for name, scale in scales.items():
             for lora in self.get_loras_by_name(name):
@@ -103,14 +191,17 @@ class SDLoraManager:
 
     @property
     def loras(self) -> list[Lora]:
+        """List of all the LoRA layers managed by the SDLoraManager."""
         return list(self.unet.layers(Lora)) + list(self.clip_text_encoder.layers(Lora))
 
     @property
     def names(self) -> list[str]:
+        """List of all the LoRA names managed the SDLoraManager"""
         return list(set(lora.name for lora in self.loras))
 
     @property
     def lora_adapters(self) -> list[LoraAdapter]:
+        """List of all the LoraAdapters managed by the SDLoraManager."""
         return list(self.unet.layers(LoraAdapter)) + list(self.clip_text_encoder.layers(LoraAdapter))
 
     @property
@@ -124,6 +215,7 @@ class SDLoraManager:
 
     @property
     def scales(self) -> dict[str, float]:
+        """The scales of all the LoRAs managed by the SDLoraManager."""
         return {name: self.get_scale(name) for name in self.names}
 
     @staticmethod