TypeError: function takes exactly 5 arguments (1 given)

[Fqlox]

Hi,

I tried to launch the project on windows 10 with a 3090, added import os os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
and got the followings errors :

(anytext) E:\repo\AnyText>python demo.py
2024-01-08 11:50:28,862 - modelscope - INFO - PyTorch version 2.0.1+cu118 Found.
2024-01-08 11:50:28,866 - modelscope - INFO - TensorFlow version 2.13.0 Found.
2024-01-08 11:50:28,866 - modelscope - INFO - Loading ast index from C:\Users\USER\.cache\modelscope\ast_indexer
2024-01-08 11:50:28,963 - modelscope - INFO - Loading done! Current index file version is 1.10.0, with md5 407792a6ca3bfb6c73e1d4358a891444 and a total number of 946 components indexed
2024-01-08 11:50:34,252 - modelscope - INFO - Use user-specified model revision: v1.1.1
2024-01-08 11:50:38,802 - modelscope - WARNING - ('PIPELINES', 'my-anytext-task', 'anytext-pipeline') not found in ast index file
A matching Triton is not available, some optimizations will not be enabled.
Error caught was: No module named 'triton'
ControlLDM: Running in eps-prediction mode
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 768 and using 8 heads.
DiffusionWrapper has 859.52 M params.
making attention of type 'vanilla-xformers' with 512 in_channels
building MemoryEfficientAttnBlock with 512 in_channels...
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
making attention of type 'vanilla-xformers' with 512 in_channels
building MemoryEfficientAttnBlock with 512 in_channels...
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 768 and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 8 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 768 and using 8 heads.
Loaded model config from [models_yaml/anytext_sd15.yaml]
Loaded state_dict from [C:\Users\USER\.cache\modelscope\hub\damo\cv_anytext_text_generation_editing\anytext_v1.1.ckpt]
2024-01-08 11:50:58,008 - modelscope - INFO - initiate model from C:\Users\USER\.cache\modelscope\hub\damo\cv_anytext_text_generation_editing\nlp_csanmt_translation_zh2en
2024-01-08 11:50:58,008 - modelscope - INFO - initiate model from location C:\Users\USER\.cache\modelscope\hub\damo\cv_anytext_text_generation_editing\nlp_csanmt_translation_zh2en.
2024-01-08 11:50:58,014 - modelscope - INFO - initialize model from C:\Users\USER\.cache\modelscope\hub\damo\cv_anytext_text_generation_editing\nlp_csanmt_translation_zh2en
{'hidden_size': 1024, 'filter_size': 4096, 'num_heads': 16, 'num_encoder_layers': 24, 'num_decoder_layers': 6, 'attention_dropout': 0.0, 'residual_dropout': 0.0, 'relu_dropout': 0.0, 'layer_preproc': 'layer_norm', 'layer_postproc': 'none', 'shared_embedding_and_softmax_weights': True, 'shared_source_target_embedding': True, 'initializer_scale': 0.1, 'position_info_type': 'absolute', 'max_relative_dis': 16, 'num_semantic_encoder_layers': 4, 'src_vocab_size': 50000, 'trg_vocab_size': 50000, 'seed': 1234, 'beam_size': 4, 'lp_rate': 0.6, 'max_decoded_trg_len': 100, 'device_map': None, 'device': 'cuda'}
2024-01-08 11:50:58,026 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.
2024-01-08 11:50:58,027 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'src_lang': 'zh', 'tgt_lang': 'en', 'src_bpe': {'file': 'bpe.zh'}, 'model_dir': 'C:\\Users\\USER\\.cache\\modelscope\\hub\\damo\\cv_anytext_text_generation_editing\\nlp_csanmt_translation_zh2en'}. trying to build by task and model information.
2024-01-08 11:50:58,027 - modelscope - WARNING - No preprocessor key ('csanmt-translation', 'translation') found in PREPROCESSOR_MAP, skip building preprocessor.
Traceback (most recent call last):
  File "E:\conda\envs\anytext\lib\site-packages\modelscope\utils\registry.py", line 212, in build_from_cfg
    return obj_cls(**args)
  File "E:\conda\envs\anytext\lib\site-packages\modelscope\pipelines\nlp\translation_pipeline.py", line 54, in __init__
    self._src_vocab = dict([
  File "E:\conda\envs\anytext\lib\site-packages\modelscope\pipelines\nlp\translation_pipeline.py", line 54, in <listcomp>
    self._src_vocab = dict([
  File "E:\conda\envs\anytext\lib\encodings\cp1252.py", line 23, in decode
    return codecs.charmap_decode(input,self.errors,decoding_table)[0]
UnicodeDecodeError: 'charmap' codec can't decode byte 0x81 in position 29: character maps to <undefined>

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "E:\conda\envs\anytext\lib\site-packages\modelscope\utils\registry.py", line 212, in build_from_cfg
    return obj_cls(**args)
  File "C:\Users\USER\.cache\modelscope\modelscope_modules\cv_anytext_text_generation_editing\ms_wrapper.py", line 336, in __init__
    pipe_model = AnyTextModel(model_dir=model, **kwargs)
  File "C:\Users\USER\.cache\modelscope\modelscope_modules\cv_anytext_text_generation_editing\ms_wrapper.py", line 46, in __init__
    self.init_model(**kwargs)
  File "C:\Users\USER\.cache\modelscope\modelscope_modules\cv_anytext_text_generation_editing\ms_wrapper.py", line 240, in init_model
    self.trans_pipe = pipeline(task=Tasks.translation, model=os.path.join(self.model_dir, 'nlp_csanmt_translation_zh2en'))
  File "E:\conda\envs\anytext\lib\site-packages\modelscope\pipelines\builder.py", line 170, in pipeline
    return build_pipeline(cfg, task_name=task)
  File "E:\conda\envs\anytext\lib\site-packages\modelscope\pipelines\builder.py", line 65, in build_pipeline
    return build_from_cfg(
  File "E:\conda\envs\anytext\lib\site-packages\modelscope\utils\registry.py", line 215, in build_from_cfg
    raise type(e)(f'{obj_cls.__name__}: {e}')
TypeError: function takes exactly 5 arguments (1 given)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "E:\repo\AnyText\demo.py", line 53, in <module>
    inference = pipeline('my-anytext-task', model='damo/cv_anytext_text_generation_editing', model_revision='v1.1.1', use_fp16=not args.use_fp32, use_translator=not args.no_translator, font_path=args.font_path)
  File "E:\conda\envs\anytext\lib\site-packages\modelscope\pipelines\builder.py", line 170, in pipeline
    return build_pipeline(cfg, task_name=task)
  File "E:\conda\envs\anytext\lib\site-packages\modelscope\pipelines\builder.py", line 65, in build_pipeline
    return build_from_cfg(
  File "E:\conda\envs\anytext\lib\site-packages\modelscope\utils\registry.py", line 215, in build_from_cfg
    raise type(e)(f'{obj_cls.__name__}: {e}')
TypeError: AnyTextPipeline: function takes exactly 5 arguments (1 given)

(anytext) E:\repo\AnyText>

[QueenPuxxi]

I had the same problem. How should I solve it?

[dvdcjw]

Same here