diff --git a/invokeai/backend/stable_diffusion/denoise_context.py b/invokeai/backend/stable_diffusion/denoise_context.py
index 26c3b02c3b..bcebb2945e 100644
--- a/invokeai/backend/stable_diffusion/denoise_context.py
+++ b/invokeai/backend/stable_diffusion/denoise_context.py
@@ -31,6 +31,30 @@ class UNetKwargs:
 
 @dataclass
 class DenoiseInputs:
+    """Initial variables passed to denoise. Supposed to be unchanged.
+
+    Variables:
+        orig_latents: The latent-space image to denoise.
+            Shape: [batch, channels, latent_height, latent_width]
+            - If we are inpainting, this is the initial latent image before noise has been added.
+            - If we are generating a new image, this should be initialized to zeros.
+            - In some cases, this may be a partially-noised latent image (e.g. when running the SDXL refiner).
+        scheduler_step_kwargs: kwargs forwarded to the scheduler.step() method.
+        conditioning_data: Text conditionging data.
+        noise: Noise used for two purposes:
+            Shape: [1 or batch, channels, latent_height, latent_width]
+            1. Used by the scheduler to noise the initial `latents` before denoising.
+            2. Used to noise the `masked_latents` when inpainting.
+            `noise` should be None if the `latents` tensor has already been noised.
+        seed: The seed used to generate the noise for the denoising process.
+            HACK(ryand): seed is only used in a particular case when `noise` is None, but we need to re-generate the
+            same noise used earlier in the pipeline. This should really be handled in a clearer way.
+        timesteps: The timestep schedule for the denoising process.
+        init_timestep: The first timestep in the schedule. This is used to determine the initial noise level, so
+            should be populated if you want noise applied *even* if timesteps is empty.
+        attention_processor_cls: Class of attention processor that is used.
+    """
+
     orig_latents: torch.Tensor
     scheduler_step_kwargs: dict[str, Any]
     conditioning_data: TextConditioningData
@@ -43,6 +67,41 @@ class DenoiseInputs:
 
 @dataclass
 class DenoiseContext:
+    """Context with all variables in denoise
+
+    Variables:
+        inputs: Initial variables passed to denoise. Supposed to be unchanged.
+        scheduler: Scheduler which used to apply noise predictions.
+        unet: UNet model.
+        latents: Current state of latent-space image in denoising process.
+            None until `pre_denoise_loop` callback.
+            Shape: [batch, channels, latent_height, latent_width]
+        step_index: Current denoising step index.
+            None until `pre_step` callback.
+        timestep: Current denoising step timestep.
+            None until `pre_step` callback.
+        unet_kwargs: Arguments which will be passed to U Net model.
+            Available in `pre_unet`/`post_unet` callbacks, otherwice will be None.
+        step_output: SchedulerOutput class returned from step function(normally, generated by scheduler).
+            Supposed to be used only in `post_step` callback, otherwice can be None.
+        latent_model_input: Scaled version of `latents`, which will be passed to unet_kwargs initialization.
+            Available in events inside step(between `pre_step` and `post_stop`).
+            Shape: [batch, channels, latent_height, latent_width]
+        conditioning_mode: [TMP] Defines on which conditionings current unet call will be runned.
+            Available in `pre_unet`/`post_unet` callbacks, otherwice will be None.
+            Can be "negative", "positive" or "both"
+        negative_noise_pred: [TMP] Noise predictions from negative conditioning.
+            Available in `apply_cfg` and `post_apply_cfg` callbacks, otherwice will be None.
+            Shape: [batch, channels, latent_height, latent_width]
+        positive_noise_pred: [TMP] Noise predictions from positive conditioning.
+            Available in `apply_cfg` and `post_apply_cfg` callbacks, otherwice will be None.
+            Shape: [batch, channels, latent_height, latent_width]
+        noise_pred: Combined noise prediction from passed conditionings.
+            Available in `apply_cfg` and `post_apply_cfg` callbacks, otherwice will be None.
+            Shape: [batch, channels, latent_height, latent_width]
+        extra: Dictionary for extensions to pass extra info about denoise process to other extensions.
+    """
+
     inputs: DenoiseInputs
 
     scheduler: SchedulerMixin