ValueError: Trying to set a tensor of shape torch.Size([77, 1280]) in "weight" (which has shape torch.Size([77, 512])), this look incorrect.

[ScottishFold007]

I have try "pip install git+https://github.com/kashif/diffusers.git@wuerstchen-v3",but still occur the things:

A matching Triton is not available, some optimizations will not be enabled. Error caught was: No module named 'triton' WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.autotrackable has been moved to tensorflow.python.trackable.autotrackable. The old module will be deleted in version 2.11. WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11. WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base_delegate has been moved to tensorflow.python.trackable.base_delegate. The old module will be deleted in version 2.11. WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.data_structures has been moved to tensorflow.python.trackable.data_structures. The old module will be deleted in version 2.11. WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.graph_view has been moved to tensorflow.python.checkpoint.graph_view. The old module will be deleted in version 2.11. WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.util has been moved to tensorflow.python.checkpoint.checkpoint. The old module will be deleted in version 2.11. WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.checkpoint_management has been moved to tensorflow.python.checkpoint.checkpoint_management. The old module will be deleted in version 2.9. WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.resource has been moved to tensorflow.python.trackable.resource. The old module will be deleted in version 2.11. WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.asset has been moved to tensorflow.python.trackable.asset. The old module will be deleted in version 2.11. WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.python_state has been moved to tensorflow.python.trackable.python_state. The old module will be deleted in version 2.11. WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.saving.checkpoint_options has been moved to tensorflow.python.checkpoint.checkpoint_options. The old module will be deleted in version 2.11. Loading pipeline components...: 0% 0/3 [00:00<?, ?it/s]

ValueError Traceback (most recent call last) ~\AppData\Local\Temp\ipykernel_4928\1705133580.py in 5 num_images_per_prompt = 2 6 model_path= r"D:\all_models_archives\stable-cascade" ----> 7 prior = StableCascadePriorPipeline.from_pretrained(model_path, torch_dtype=torch.bfloat16, use_safetensors =True).to(device) 8 decoder = StableCascadeDecoderPipeline.from_pretrained(model_path, torch_dtype=torch.float16, use_safetensors =True).to(device) 9

~\anaconda3\lib\site-packages\huggingface_hub\utils_validators.py in _inner_fn(*args, *kwargs) 116 kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.name, has_token=has_token, kwargs=kwargs) 117 --> 118 return fn(args, **kwargs) 119 120 return _inner_fn # type: ignore

~\anaconda3\lib\site-packages\diffusers\pipelines\pipeline_utils.py in from_pretrained(cls, pretrained_model_name_or_path, **kwargs) 1261 else: 1262 # load sub model -> 1263 loaded_sub_model = load_sub_model( 1264 library_name=library_name, 1265 class_name=class_name,

~\anaconda3\lib\site-packages\diffusers\pipelines\pipeline_utils.py in load_sub_model(library_name, class_name, importable_classes, pipelines, is_pipeline_module, pipeline_class, torch_dtype, provider, sess_options, device_map, max_memory, offload_folder, offload_state_dict, model_variants, name, from_flax, variant, low_cpu_mem_usage, cached_folder, revision) 529 # check if the module is in a subdirectory 530 if os.path.isdir(os.path.join(cached_folder, name)): --> 531 loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs) 532 else: 533 # else load from the root directory

~\AppData\Roaming\Python\Python39\site-packages\transformers\modeling_utils.py in from_pretrained(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs) 3478 offload_index, 3479 error_msgs, -> 3480 ) = cls._load_pretrained_model( 3481 model, 3482 state_dict,

~\AppData\Roaming\Python\Python39\site-packages\transformers\modeling_utils.py in _load_pretrained_model(cls, model, state_dict, loaded_keys, resolved_archive_file, pretrained_model_name_or_path, ignore_mismatched_sizes, sharded_metadata, _fast_init, low_cpu_mem_usage, device_map, offload_folder, offload_state_dict, dtype, is_quantized, keep_in_fp32_modules) 3868 if low_cpu_mem_usage: 3869 if not is_fsdp_enabled() or is_fsdp_enabled_and_dist_rank_0(): -> 3870 new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model( 3871 model_to_load, 3872 state_dict,

~\AppData\Roaming\Python\Python39\site-packages\transformers\modeling_utils.py in _load_state_dict_into_meta_model(model, state_dict, loaded_state_dict_keys, start_prefix, expected_keys, device_map, offload_folder, offload_index, state_dict_folder, state_dict_index, dtype, is_quantized, is_safetensors, keep_in_fp32_modules) 741 elif not is_quantized: 742 # For backward compatibility with older versions of accelerate --> 743 set_module_tensor_to_device(model, param_name, param_device, **set_module_kwargs) 744 else: 745 if param.dtype == torch.int8 and param_name.replace("weight", "SCB") in state_dict.keys():

~\anaconda3\lib\site-packages\accelerate\utils\modeling.py in set_module_tensor_to_device(module, tensor_name, device, value, dtype, fp16_statistics, tied_params_map) 343 if value is not None: 344 if old_value.shape != value.shape: --> 345 raise ValueError( 346 f'Trying to set a tensor of shape {value.shape} in "{tensor_name}" (which has shape {old_value.shape}), this look incorrect.' 347 )

ValueError: Trying to set a tensor of shape torch.Size([77, 1280]) in "weight" (which has shape torch.Size([77, 512])), this look incorrect.

[davejhahn]

I have the same problem, I'm running on Ubuntu though. In the error it says:

If you want to instead overwrite randomly initialized weights, please make sure to pass both `low_cpu_mem_usage=False` and `ignore_mismatched_sizes=True`. For more information, see also: https://github.com/huggingface/diffusers/issues/1619#issuecomment-1345604389 as an example.

However, looking at the issues associated with the link it seems like this is something that needs to be updated in the app itself.

[KylanDixonMF]

I believe you are correct, I am also running in Ubuntu and get the size mismatch.

embedding.1.weight expected shape tensor(..., device='meta', size=(320, 64, 1, 1)), but got torch.Size([320, 16, 1, 1]). If you want to instead overwrite randomly initialized weights, please make sure to pass bothlow_cpu_mem_usage=Falseandignore_mismatched_sizes=True. For more information, see also: https://github.com/huggingface/diffusers/issues/1619#issuecomment-1345604389 as an example.

prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=torch.bfloat16).to(device) decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", torch_dtype=torch.float16, ignore_mismatched_sizes=True).to(device)

I have made these adjustments and to no avail.

[jcplus]

I'm running this on M2 and getting this error:

WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:
    PyTorch 2.1.0a0+git4add06e with CUDA None (you have 2.1.1)
    Python  3.11.5 (you have 3.11.5)
  Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)
  Memory-efficient attention, SwiGLU, sparse and more won't be available.
  Set XFORMERS_MORE_DETAILS=1 for more details
RUNNING ON: mps
Loading pipeline components...:  67%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                         | 4/6 [00:01<00:00,  3.07it/s]The config attributes {'c_in': 16} were passed to StableCascadeUnet, but are not expected and will be ignored. Please verify your config.json configuration file.
/opt/homebrew/lib/python3.11/site-packages/diffusers/models/lora.py:384: FutureWarning: `LoRACompatibleLinear` is deprecated and will be removed in version 1.0.0. Use of `LoRACompatibleLinear` is deprecated. Please switch to PEFT backend by installing PEFT: `pip install peft`.
  deprecate("LoRACompatibleLinear", "1.0.0", deprecation_message)
Loading pipeline components...: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:40<00:00,  6.82s/it]
Fetching 13 files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:00<00:00, 20.46it/s]
Loading pipeline components...:  40%|█████████████████████████████████████████████████████████████████████▏                                                                                                       | 2/5 [00:00<00:00, 16.00it/s]The config attributes {'c_in': 4} were passed to StableCascadeUnet, but are not expected and will be ignored. Please verify your config.json configuration file.
Loading pipeline components...:  40%|█████████████████████████████████████████████████████████████████████▏                                                                                                       | 2/5 [00:05<00:08,  2.72s/it]
Traceback (most recent call last):
  File "/Users/it6/cascade/gradio_app/app.py", line 14, in <module>
    decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", torch_dtype=torch.half)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/homebrew/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/opt/homebrew/lib/python3.11/site-packages/diffusers/pipelines/pipeline_utils.py", line 1263, in from_pretrained
    loaded_sub_model = load_sub_model(
                       ^^^^^^^^^^^^^^^
  File "/opt/homebrew/lib/python3.11/site-packages/diffusers/pipelines/pipeline_utils.py", line 531, in load_sub_model
    loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/homebrew/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/opt/homebrew/lib/python3.11/site-packages/diffusers/models/modeling_utils.py", line 669, in from_pretrained
    unexpected_keys = load_model_dict_into_meta(
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/homebrew/lib/python3.11/site-packages/diffusers/models/modeling_utils.py", line 154, in load_model_dict_into_meta
    raise ValueError(
ValueError: Cannot load /Users/it6/.cache/huggingface/hub/models--stabilityai--stable-cascade/snapshots/e3aee2fd11a00865f5c085d3e741f2e51aef12d3/decoder because embedding.1.weight expected shape tensor(..., device='meta', size=(320, 64, 1, 1)), but got torch.Size([320, 16, 1, 1]). If you want to instead overwrite randomly initialized weights, please make sure to pass both `low_cpu_mem_usage=False` and `ignore_mismatched_sizes=True`. For more information, see also: https://github.com/huggingface/diffusers/issues/1619#issuecomment-1345604389 as an example.

[KylanDixonMF]

It appears that no matter how you run it, there is an issue with the mismatched types that setting: ignore_mismatched_sizes=True doesn't have any effect at this point. At least from what I have seen so far.