umm I think we should update the code itself so that component_name and cache_dir can be None - they are only needed for custom code, which we don't support yet with AutoModel

I just tried setting component_name and cache_dir to None values and ran the tests from #11401 (comment) and they ran fine for me.

what do you mean? the first line would already throw an error, no?
os.path.join(None,None)

I meant when I changed to the original code:

It didn't error out for me 👀

Oh, they should run fine even without it, because of this line

well, it is wrapped inside a try/exception.....
but what I meant we should update this function so that componenet_name and cache_dir aree optional argument (it is meant to be, they aree only needed for custom code)

We should catch the specific Exception here instead of making it generic. This will help eliminate other side-effects.

I think if we move this import inside the try block, we should be able to get rid of the circular import problem: https://github.com/huggingface/diffusers/actions/runs/14787632114/job/41518909088?pr=11401#step:15:68

let's jjust pass None here since we ddon't actually have a cached_dir so not very meaningful
and update the other function so it works with cache_dir=None and component_name=NOne

move this section under a if subfolder is not None:

if subfolder is not None:
     try: 
          ...

I think we are not supporting local path for pretrained_model_or_path here, are we? it would be a bit more complex if we do

What would be the complexity related to supporting local path here ?

Shouldn't we also be able to load models from structure where there is no folder heirarchy using this code, Following is a working example

try:
    control_net = AutoModel.from_pretrained(
        "ishan24/Sana_600M_1024px_ControlNet_diffusers",
        torch_dtype=torch.float16
    )
    print(f"test passed!")
except Exception as e:
    print(f"test failed: {e}")

I think for local path, you can append subfolder to the path as well ( we don't have to consider that for now)

like unet = AutoModel.from_pretrained("ishan24/SDXL_diffusers/unet")

ohk, so we dont want to support flat repo's like this to load models

Because doing the following would mean that we dont support above case

if subfolder is not None:
     try: 
          ...

ohh no we need to support flat repos, we need to all repos (we don't need to support some edge cases with local path when useer include subfolder directly in the file path, not as subfolder argument)

so basically here
if subfolder is not None -> we try model_index.json approach first, if that fails, we try the config.json approach
if subfolder is None -> we try the config.json approach directly(I think it is not meaningful to use model_index.json when subfolder is None because you need use subfolder as name to locate the info there, no?)

got it, made the change

well, it is wrapped inside a try/exception.....
but what I meant we should update this function so that componenet_name and cache_dir aree optional argument (it is meant to be, they aree only needed for custom code)

Would avoid using exceptions for control flow and simplify this a bit

        load_config_kwargs = {
            "cache_dir": cache_dir,
            "force_download": force_download,
            "proxies": proxies,
            "token": token,
            "local_files_only": local_files_only,
            "revision": revision,
        }

        library = None
        orig_class_name = None
        from diffusers import pipelines

        # Always attempt to fetch model_index.json first
        try:
            cls.config_name = "model_index.json"
            config = cls.load_config(pretrained_model_or_path, **load_config_kwargs)

            if subfolder is not None and subfolder in config:
                library, orig_class_name = config[subfolder]

        except EntryNotFoundError as e:
            logger.debug(e)

        # Unable to load from model_index.json so fallback to loading from config
        if library is None and orig_class_name is None:
            cls.config_name = "config.json"
            load_config_kwargs.update({"subfolder": subfolder})

            config = cls.load_config(pretrained_model_or_path, **load_config_kwargs)
            orig_class_name = config["_class_name"]
            library = "diffusers"

        model_cls, _ = get_class_obj_and_candidates(
            library_name=library,
            class_name=orig_class_name,
            importable_classes=ALL_IMPORTABLE_CLASSES,
            pipelines=pipelines,
            is_pipeline_module=hasattr(pipelines, library), 
        )