[From Single File] Allow vae to be loaded

src/diffusers/loaders.py

@@ -1387,6 +1387,9 @@ def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
                 An instance of `CLIPTextModel` to use, specifically the
                 [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. If this
                 parameter is `None`, the function loads a new instance of `CLIPTextModel` by itself if needed.
+            vae (`AutoencoderKL`, *optional*, defaults to `None`):
+                Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. If
+                this parameter is `None`, the function will load a new instance of [CLIP] by itself, if needed.
             tokenizer ([`~transformers.CLIPTokenizer`], *optional*, defaults to `None`):
                 An instance of `CLIPTokenizer` to use. If this parameter is `None`, the function loads a new instance
                 of `CLIPTokenizer` by itself if needed.
@@ -1435,6 +1438,7 @@ def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
         load_safety_checker = kwargs.pop("load_safety_checker", True)
         prediction_type = kwargs.pop("prediction_type", None)
         text_encoder = kwargs.pop("text_encoder", None)
+        vae = kwargs.pop("vae", None)
         controlnet = kwargs.pop("controlnet", None)
         tokenizer = kwargs.pop("tokenizer", None)
 
@@ -1525,6 +1529,7 @@ def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
             load_safety_checker=load_safety_checker,
             prediction_type=prediction_type,
             text_encoder=text_encoder,
+            vae=vae,
             tokenizer=tokenizer,
         )
 

src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

@@ -1107,6 +1107,7 @@ def download_from_original_stable_diffusion_ckpt(
     pipeline_class: DiffusionPipeline = None,
     local_files_only=False,
     vae_path=None,
+    vae=None,
     text_encoder=None,
     tokenizer=None,
 ) -> DiffusionPipeline:
@@ -1156,6 +1157,9 @@ def download_from_original_stable_diffusion_ckpt(
             The pipeline class to use. Pass `None` to determine automatically.
         local_files_only (`bool`, *optional*, defaults to `False`):
             Whether or not to only look at local files (i.e., do not try to download the model).
+        vae (`AutoencoderKL`, *optional*, defaults to `None`):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. If
+            this parameter is `None`, the function will load a new instance of [CLIP] by itself, if needed.
         text_encoder (`CLIPTextModel`, *optional*, defaults to `None`):
             An instance of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel)
             to use, specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)
@@ -1361,7 +1365,7 @@ def download_from_original_stable_diffusion_ckpt(
         unet.load_state_dict(converted_unet_checkpoint)
 
     # Convert the VAE model.
-    if vae_path is None:
+    if vae_path is None and vae is None:
         vae_config = create_vae_diffusers_config(original_config, image_size=image_size)
         converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
 
@@ -1385,7 +1389,7 @@ def download_from_original_stable_diffusion_ckpt(
                 set_module_tensor_to_device(vae, param_name, "cpu", value=param)
         else:
             vae.load_state_dict(converted_vae_checkpoint)
-    else:
+    elif vae is None:
         vae = AutoencoderKL.from_pretrained(vae_path)
 
     if model_type == "FrozenOpenCLIPEmbedder":