(doc/fluxion/ld) add SDXLAutoencoder docstrings

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

@@ -10,10 +10,24 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDX
 
 
 class SDXLAutoencoder(LatentDiffusionAutoencoder):
+    """Stable Diffusion XL autoencoder model.
+
+    Attributes:
+        encoder_scale: The encoder scale to use.
+    """
+
     encoder_scale: float = 0.13025
 
 
 class StableDiffusion_XL(LatentDiffusionModel):
+    """Stable Diffusion XL model.
+
+    Attributes:
+        unet: The U-Net model.
+        clip_text_encoder: The text encoder.
+        lda (SDXLAutoencoder): The image autoencoder.
+    """
+
     unet: SDXLUNet
     clip_text_encoder: DoubleTextEncoder
     lda: SDXLAutoencoder
@@ -27,6 +41,16 @@ class StableDiffusion_XL(LatentDiffusionModel):
         device: Device | str = "cpu",
         dtype: DType = torch.float32,
     ) -> None:
+        """Initializes the model.
+
+        Args:
+            unet: The SDXLUNet U-Net model to use.
+            lda: The SDXLAutoencoder image autoencoder to use.
+            clip_text_encoder: The DoubleTextEncoder text encoder to use.
+            solver: The solver to use.
+            device: The PyTorch device to use.
+            dtype: The PyTorch data type to use.
+        """
         unet = unet or SDXLUNet(in_channels=4)
         lda = lda or SDXLAutoencoder()
         clip_text_encoder = clip_text_encoder or DoubleTextEncoder()
@@ -42,6 +66,13 @@ class StableDiffusion_XL(LatentDiffusionModel):
         )
 
     def compute_clip_text_embedding(self, text: str, negative_text: str | None = None) -> tuple[Tensor, Tensor]:
+        """Compute the CLIP text embedding associated with the given prompt and negative prompt.
+
+        Args:
+            text: The prompt to compute the CLIP text embedding of.
+            negative_text: The negative prompt to compute the CLIP text embedding of.
+                If not provided, the negative prompt is assumed to be empty (i.e., `""`).
+        """
         conditional_embedding, conditional_pooled_embedding = self.clip_text_encoder(text)
         if text == negative_text:
             return torch.cat(tensors=(conditional_embedding, conditional_embedding), dim=0), torch.cat(
@@ -57,6 +88,7 @@ class StableDiffusion_XL(LatentDiffusionModel):
 
     @property
     def default_time_ids(self) -> Tensor:
+        """The default time IDs to use."""
         # [original_height, original_width, crop_top, crop_left, target_height, target_width]
         # See https://arxiv.org/abs/2307.01952 > 2.2 Micro-Conditioning
         time_ids = torch.tensor(data=[1024, 1024, 0, 0, 1024, 1024], device=self.device)
@@ -71,6 +103,14 @@ class StableDiffusion_XL(LatentDiffusionModel):
         time_ids: Tensor,
         **_: Tensor,
     ) -> None:
+        """Sets the various context parameters required by the U-Net model.
+
+        Args:
+            timestep: The timestep to set.
+            clip_text_embedding: The CLIP text embedding to set.
+            pooled_text_embedding: The pooled CLIP text embedding to set.
+            time_ids: The time IDs to set.
+        """
         self.unet.set_timestep(timestep=timestep)
         self.unet.set_clip_text_embedding(clip_text_embedding=clip_text_embedding)
         self.unet.set_pooled_text_embedding(pooled_text_embedding=pooled_text_embedding)
@@ -98,6 +138,12 @@ class StableDiffusion_XL(LatentDiffusionModel):
         )
 
     def set_self_attention_guidance(self, enable: bool, scale: float = 1.0) -> None:
+        """Sets the self-attention guidance.
+
+        Args:
+            enable: Whether to enable self-attention guidance or not.
+            scale: The scale to use.
+        """
         if enable:
             if sag := self._find_sag_adapter():
                 sag.scale = scale
@@ -108,9 +154,11 @@ class StableDiffusion_XL(LatentDiffusionModel):
                 sag.eject()
 
     def has_self_attention_guidance(self) -> bool:
+        """Whether the model has self-attention guidance or not."""
         return self._find_sag_adapter() is not None
 
     def _find_sag_adapter(self) -> SDXLSAGAdapter | None:
+        """Finds the self-attention guidance adapter."""
         for p in self.unet.get_parents():
             if isinstance(p, SDXLSAGAdapter):
                 return p
@@ -127,6 +175,19 @@ class StableDiffusion_XL(LatentDiffusionModel):
         time_ids: Tensor,
         **kwargs: Tensor,
     ) -> Tensor:
+        """Compute the self-attention guidance.
+
+        Args:
+            x: The input tensor.
+            noise: The noise tensor.
+            step: The step to compute the self-attention guidance at.
+            clip_text_embedding: The CLIP text embedding to compute the self-attention guidance with.
+            pooled_text_embedding: The pooled CLIP text embedding to compute the self-attention guidance with.
+            time_ids: The time IDs to compute the self-attention guidance with.
+
+        Returns:
+            The computed self-attention guidance.
+        """
         sag = self._find_sag_adapter()
         assert sag is not None