Original issue posted here.
Essentially I’m loading & saving the stable cascade pipeline from HF using the following code snippet:
model = StableCascadeCombinedPipeline.from_pretrained(HF_MODEL_ID,
variant="bf16",
torch_dtype=torch.float16,
cache_dir=CACHE_DIR)
model.save_pretrained(save_directory="model/", from_pt=True)
This works great until I try to load that model from my file system:
model = StableCascadeCombinedPipeline.from_pretrained("model/",
variant="bf16",
torch_dtype=torch.float16,
local_files_only=True)
Loading pipeline components...: 100%
9/9 [00:05<00:00, 2.32it/s]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[12], line 1
----> 1 model = StableCascadeCombinedPipeline.from_pretrained("model/",
2 variant="bf16",
3 torch_dtype=torch.float16,
4 local_files_only=True)
File /opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:119, in validate_hf_hub_args.<locals>._inner_fn(*args, **kwargs)
116 if check_use_auth_token:
117 kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs)
--> 119 return fn(*args, **kwargs)
File /opt/conda/lib/python3.10/site-packages/diffusers/pipelines/pipeline_utils.py:910, in DiffusionPipeline.from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
905 raise ValueError(
906 f"Pipeline {pipeline_class} expected {expected_modules}, but only {passed_modules} were passed."
907 )
909 # 8. Instantiate the pipeline
--> 910 model = pipeline_class(**init_kwargs)
912 # 9. Save where the model was instantiated from
913 model.register_to_config(_name_or_path=pretrained_model_name_or_path)
File /opt/conda/lib/python3.10/site-packages/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py:88, in StableCascadeCombinedPipeline.__init__(self, tokenizer, text_encoder, decoder, scheduler, vqgan, prior_prior, prior_text_encoder, prior_tokenizer, prior_scheduler, prior_feature_extractor, prior_image_encoder)
72 def __init__(
73 self,
74 tokenizer: CLIPTokenizer,
(...)
84 prior_image_encoder: Optional[CLIPVisionModelWithProjection] = None,
85 ):
86 super().__init__()
---> 88 self.register_modules(
89 text_encoder=text_encoder,
90 tokenizer=tokenizer,
91 decoder=decoder,
92 scheduler=scheduler,
93 vqgan=vqgan,
94 prior_text_encoder=prior_text_encoder,
95 prior_tokenizer=prior_tokenizer,
96 prior_prior=prior_prior,
97 prior_scheduler=prior_scheduler,
98 prior_feature_extractor=prior_feature_extractor,
99 prior_image_encoder=prior_image_encoder,
100 )
101 self.prior_pipe = StableCascadePriorPipeline(
102 prior=prior_prior,
103 text_encoder=prior_text_encoder,
(...)
107 feature_extractor=prior_feature_extractor,
108 )
109 self.decoder_pipe = StableCascadeDecoderPipeline(
110 text_encoder=text_encoder,
111 tokenizer=tokenizer,
(...)
114 vqgan=vqgan,
115 )
File /opt/conda/lib/python3.10/site-packages/diffusers/pipelines/pipeline_utils.py:154, in DiffusionPipeline.register_modules(self, **kwargs)
152 register_dict = {name: (None, None)}
153 else:
--> 154 library, class_name = _fetch_class_library_tuple(module)
155 register_dict = {name: (library, class_name)}
157 # save model index config
File /opt/conda/lib/python3.10/site-packages/diffusers/pipelines/pipeline_loading_utils.py:488, in _fetch_class_library_tuple(module)
486 # register the config from the original module, not the dynamo compiled one
487 not_compiled_module = _unwrap_model(module)
--> 488 library = not_compiled_module.__module__.split(".")[0]
490 # check if the module is a pipeline module
491 module_path_items = not_compiled_module.__module__.split(".")
AttributeError: 'list' object has no attribute '__module__'
I’m not sure where I’m going wrong here, some suggested workarounds were to modify src/diffusers/pipelines/pipeline_loading_utils.py but I’d rather do this by the book.