diff --git a/diffusion/models/models.py b/diffusion/models/models.py
index 5ab83398..e2eed1a8 100644
--- a/diffusion/models/models.py
+++ b/diffusion/models/models.py
@@ -44,8 +44,8 @@ def stable_diffusion_2(
     prompts.
 
     Args:
-        model_name (str, optional): Name of the model to load. Defaults to 'stabilityai/stable-diffusion-2-base'.
-        pretrained (bool, optional): Whether to load pretrained weights. Defaults to True.
+        model_name (str): Name of the model to load. Defaults to 'stabilityai/stable-diffusion-2-base'.
+        pretrained (bool): Whether to load pretrained weights. Defaults to True.
         prediction_type (str): The type of prediction to use. Must be one of 'sample',
             'epsilon', or 'v_prediction'. Default: `epsilon`.
         train_metrics (list, optional): List of metrics to compute during training. If None, defaults to
@@ -54,12 +54,12 @@ def stable_diffusion_2(
             [MeanSquaredError(), FrechetInceptionDistance(normalize=True)].
         val_guidance_scales (list, optional): List of scales to use for validation guidance. If None, defaults to
             [1.0, 3.0, 7.0].
-        val_seed (int, optional): Seed to use for generating evaluation images. Defaults to 1138.
+        val_seed (int): Seed to use for generating evaluation images. Defaults to 1138.
         loss_bins (list, optional): List of tuples of (min, max) values to use for loss binning. If None, defaults to
             [(0, 1)].
-        precomputed_latents (bool, optional): Whether to use precomputed latents. Defaults to False.
-        encode_latents_in_fp16 (bool, optional): Whether to encode latents in fp16. Defaults to True.
-        fsdp (bool, optional): Whether to use FSDP. Defaults to True.
+        precomputed_latents (bool): Whether to use precomputed latents. Defaults to False.
+        encode_latents_in_fp16 (bool): Whether to encode latents in fp16. Defaults to True.
+        fsdp (bool): Whether to use FSDP. Defaults to True.
     """
     if train_metrics is None:
         train_metrics = [MeanSquaredError()]
@@ -144,13 +144,14 @@ def stable_diffusion_xl(
     prompts. Currently uses UNet and VAE config from SDXL, but text encoder/tokenizer from SD2.
 
     Args:
-        model_name (str, optional): Name of the model to load. Determines the text encoder, tokenizer,
+        model_name (str): Name of the model to load. Determines the text encoder, tokenizer,
             and noise scheduler. Defaults to 'stabilityai/stable-diffusion-2-base'.
-        unet_model_name (str, optional): Name of the UNet model to load. Defaults to
+        unet_model_name (str): Name of the UNet model to load. Defaults to
             'stabilityai/stable-diffusion-xl-base-1.0'.
-        vae_model_name (str, optional): Name of the VAE model to load. Defaults to
-            'madebyollin/sdxl-vae-fp16-fix'.
-        pretrained (bool, optional): Whether to load pretrained weights. Defaults to True.
+        vae_model_name (str): Name of the VAE model to load. Defaults to
+            'madebyollin/sdxl-vae-fp16-fix' as the official VAE checkpoint (from
+            'stabilityai/stable-diffusion-xl-base-1.0') is not compatible with fp16.
+        pretrained (bool): Whether to load pretrained weights. Defaults to True.
         prediction_type (str): The type of prediction to use. Must be one of 'sample',
             'epsilon', or 'v_prediction'. Default: `epsilon`.
         train_metrics (list, optional): List of metrics to compute during training. If None, defaults to
@@ -159,12 +160,12 @@ def stable_diffusion_xl(
             [MeanSquaredError(), FrechetInceptionDistance(normalize=True)].
         val_guidance_scales (list, optional): List of scales to use for validation guidance. If None, defaults to
             [1.0, 3.0, 7.0].
-        val_seed (int, optional): Seed to use for generating evaluation images. Defaults to 1138.
+        val_seed (int): Seed to use for generating evaluation images. Defaults to 1138.
         loss_bins (list, optional): List of tuples of (min, max) values to use for loss binning. If None, defaults to
             [(0, 1)].
-        precomputed_latents (bool, optional): Whether to use precomputed latents. Defaults to False.
-        encode_latents_in_fp16 (bool, optional): Whether to encode latents in fp16. Defaults to True.
-        fsdp (bool, optional): Whether to use FSDP. Defaults to True.
+        precomputed_latents (bool): Whether to use precomputed latents. Defaults to False.
+        encode_latents_in_fp16 (bool): Whether to encode latents in fp16. Defaults to True.
+        fsdp (bool): Whether to use FSDP. Defaults to True.
     """
     if train_metrics is None:
         train_metrics = [MeanSquaredError()]
@@ -180,7 +181,7 @@ def stable_diffusion_xl(
             metric.requires_grad_(False)
 
     if pretrained:
-        unet = UNet2DConditionModel.from_pretrained(unet_model_name, subfolder='unet')
+        raise NotImplementedError('Full SDXL pipeline not implemented yet.')
     else:
         config = PretrainedConfig.get_config_dict(unet_model_name, subfolder='unet')
         # Currently not doing micro-conditioning, so set config appropriately