fix miscellaneous typos

docs/guides/adapting_sdxl/index.md

@@ -364,7 +364,7 @@ python scripts/conversion/convert_diffusers_ip_adapter.py --from ip-adapter-plus
 
 This will download and convert both IP-Adapter and CLIP Image Encoder pretrained weights.
 
-Then, in your Python code, simply instantiate a [`SDXLIPAdapter`][refiners.foundationals.latent_diffusion.stable_diffusion_xl.image_prompt.SDXLIPAdapter] targetting our `sdxl.unet`, and inject it using a simple `.inject()` call:
+Then, in your Python code, simply instantiate a [`SDXLIPAdapter`][refiners.foundationals.latent_diffusion.stable_diffusion_xl.image_prompt.SDXLIPAdapter] targeting our `sdxl.unet`, and inject it using a simple `.inject()` call:
 
 ```py
 # IP-Adapter

src/refiners/fluxion/layers/chain.py

@@ -55,7 +55,7 @@ class Chain(ContextModule):
 
     This layer is the main building block of Fluxion.
     It is used to compose other layers in a sequential manner.
-    Similary to [`torch.nn.Sequential`][torch.nn.Sequential],
+    Similarly to [`torch.nn.Sequential`][torch.nn.Sequential],
     it calls each of its sub-layers in order, chaining their outputs as inputs to the next sublayer.
     However, it also provides additional methods to manipulate its sub-layers and their context.
 

src/refiners/foundationals/latent_diffusion/lora.py

@@ -131,7 +131,7 @@ class SDLoraManager:
         SDLoraManager.auto_attach(unet_loras, self.unet, exclude=exclude)
 
     def remove_loras(self, *names: str) -> None:
-        """Remove mulitple LoRAs from the target.
+        """Remove multiple LoRAs from the target.
 
         Args:
             names: The names of the LoRAs to remove.
@@ -179,7 +179,7 @@ class SDLoraManager:
         self.update_scales({name: scale})
 
     def update_scales(self, scales: dict[str, float], /) -> None:
-        """Update the scales of mulitple LoRAs.
+        """Update the scales of multiple LoRAs.
 
         Args:
             scales: The scales to update.