update

src/diffusers/loaders/single_file.py

@@ -55,31 +55,6 @@ REFINER_PIPELINES = [
     "StableDiffusionXLControlNetImg2ImgPipeline",
 ]
 
-LOADABLE_CLASSES = {
-    "diffusers": {
-        "ControlNetModel": "create_controlnet_model",
-        "AutoencoderKL": "create_vae_model",
-        "UNet2DConditionModel": "create_unet_model",
-    }
-}
-
-
-def extract_pipeline_component_names(pipeline_class):
-    components = inspect.signature(pipeline_class).parameters.keys()
-    return components
-
-
-def check_valid_url(pretrained_model_link_or_path):
-    # check if url prefix is valid
-    # remove huggingface url prefix from model path
-    has_valid_url_prefix = False
-    for prefix in VALID_URL_PREFIXES:
-        if pretrained_model_link_or_path.startswith(prefix):
-            pretrained_model_link_or_path = pretrained_model_link_or_path[len(prefix) :]
-            has_valid_url_prefix = True
-
-    return has_valid_url_prefix, pretrained_model_link_or_path
-
 
 def _extract_repo_id_and_weights_name(pretrained_model_name_or_path):
     pattern = r"([^/]+)/([^/]+)/(?:blob/main/)?(.+)"
@@ -174,8 +149,9 @@ def set_additional_components(
     **kwargs,
 ):
     components = {}
+    model_type = kwargs.get("model_type", None)
     if pipeline_class_name in REFINER_PIPELINES:
-        model_type = infer_model_type(pipeline_class_name, original_config)
+        model_type = infer_model_type(original_config, model_type=model_type)
         is_refiner = model_type == "SDXL-Refiner"
         components.update(
             {
@@ -206,8 +182,7 @@ class FromSingleFileMixin:
                       `"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.ckpt"`) on the Hub.
                     - A path to a *file* containing all pipeline weights.
             torch_dtype (`str` or `torch.dtype`, *optional*):
-                Override the default `torch.dtype` and load the model with another dtype. If `"auto"` is passed, the
-                dtype is automatically derived from the model's weights.
+                Override the default `torch.dtype` and load the model with another dtype.
             force_download (`bool`, *optional*, defaults to `False`):
                 Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                 cached versions if they exist.
@@ -291,6 +266,7 @@ class FromSingleFileMixin:
             )
             checkpoint = load_state_dict(checkpoint_path)
 
+        # some checkpoints contain the model state dict under a "state_dict" key
         while "state_dict" in checkpoint:
             checkpoint = checkpoint["state_dict"]
 
@@ -340,11 +316,9 @@ class FromSingleFileMixin:
                     continue
                 init_kwargs.update(components)
 
-        additional_components = set(optional_kwargs - init_kwargs.keys())
+        additional_components = set_additional_components(class_name, original_config, **kwargs)
         if additional_components:
-            components = set_additional_components(class_name, original_config, **kwargs)
-            if components:
-                init_kwargs.update(components)
+            init_kwargs.update(additional_components)
 
         init_kwargs.update(passed_pipe_kwargs)
         pipe = pipeline_class(**init_kwargs)

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -792,7 +792,6 @@ class StableDiffusionXLImg2ImgPipeline(
             self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
         )
         expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
-
         if (
             expected_add_embed_dim > passed_add_embed_dim
             and (expected_add_embed_dim - passed_add_embed_dim) == self.unet.config.addition_time_embed_dim