From 9066dc18392b6c1b048f08831fa7423ac2fac767 Mon Sep 17 00:00:00 2001
From: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date: Fri, 27 Jun 2025 17:41:27 +1000
Subject: [PATCH] tidy(nodes): remove extraneous comments & add useful ones

---
 invokeai/app/invocations/flux_denoise.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/invokeai/app/invocations/flux_denoise.py b/invokeai/app/invocations/flux_denoise.py
index f601e0c2fe..2ef40c8d94 100644
--- a/invokeai/app/invocations/flux_denoise.py
+++ b/invokeai/app/invocations/flux_denoise.py
@@ -147,6 +147,9 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         description=FieldDescriptions.vae,
         input=Input.Connection,
     )
+    # This node accepts a images for features like FLUX Fill, ControlNet, and Kontext, but needs to operate on them in
+    # latent space. We'll run the VAE to encode them in this node instead of requiring the user to run the VAE in
+    # upstream nodes.
 
     ip_adapter: IPAdapterField | list[IPAdapterField] | None = InputField(
         description=FieldDescriptions.ip_adapter, title="IP-Adapter", default=None, input=Input.Connection
@@ -386,29 +389,26 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
 
             kontext_extension = None
             if self.kontext_conditioning is not None:
-                # We need a VAE to encode the reference image. We can reuse the
-                # controlnet_vae field as it serves a similar purpose (image to latents).
                 if not self.controlnet_vae:
                     raise ValueError("A VAE (e.g., controlnet_vae) must be provided to use Kontext conditioning.")
 
                 kontext_extension = KontextExtension(
                     kontext_field=self.kontext_conditioning,
                     context=context,
-                    vae_field=self.controlnet_vae,  # Pass the VAE field
+                    vae_field=self.controlnet_vae,
                     device=TorchDevice.choose_torch_device(),
                     dtype=inference_dtype,
                 )
 
             final_img, final_img_ids = x, img_ids
-            original_seq_len = x.shape[1]  # Store the original sequence length
+            original_seq_len = x.shape[1]
             if kontext_extension is not None:
                 final_img, final_img_ids = kontext_extension.apply(final_img, final_img_ids)
 
-            # The denoise function will now use the combined tensors
             x = denoise(
                 model=transformer,
-                img=final_img,  # Pass the combined image tokens
-                img_ids=final_img_ids,  # Pass the combined image IDs
+                img=final_img,
+                img_ids=final_img_ids,
                 pos_regional_prompting_extension=pos_regional_prompting_extension,
                 neg_regional_prompting_extension=neg_regional_prompting_extension,
                 timesteps=timesteps,