TypeError: AnyTextPipeline: function takes exactly 5 arguments (1 given)

本地操作系统：Windows11

我的CUDA版本：11.8

我做了什么： 1.启动Anaconda虚拟环境anytext 2.在项目文件夹目录下输入python inference.py

终端命令行日志： (anytext) PS D:\GitHubProject\AnyText> python .\inference.py 2024-01-11 17:27:52,120 - modelscope - INFO - PyTorch version 2.0.1+cu118 Found. 2024-01-11 17:27:52,123 - modelscope - INFO - TensorFlow version 2.13.0 Found. 2024-01-11 17:27:52,124 - modelscope - INFO - Loading ast index from C:\Users\92923.cache\modelscope\ast_indexer 2024-01-11 17:27:52,753 - modelscope - INFO - Loading done! Current index file version is 1.10.0, with md5 dece09f2ebbe99e0a53c20441372b40d and a total number of 946 components indexed 2024-01-11 17:27:57,202 - modelscope - INFO - Use user-specified model revision: v1.1.1 2024-01-11 17:28:12,416 - 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\92923.cache\modelscope\hub\damo\cv_anytext_text_generation_editing\anytext_v1.1.ckpt] 2024-01-11 17:28:32,619 - modelscope - INFO - initiate model from C:\Users\92923.cache\modelscope\hub\damo\cv_anytext_text_generation_editing\nlp_csanmt_translation_zh2en 2024-01-11 17:28:32,619 - modelscope - INFO - initiate model from location C:\Users\92923.cache\modelscope\hub\damo\cv_anytext_text_generation_editing\nlp_csanmt_translation_zh2en. 2024-01-11 17:28:32,622 - modelscope - INFO - initialize model from C:\Users\92923.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'} 2024-01-11 17:28:32,656 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file. 2024-01-11 17:28:32,657 - 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\92923\.cache\modelscope\hub\damo\cv_anytext_text_generation_editing\nlp_csanmt_translation_zh2en'}. trying to build by task and model information. 2024-01-11 17:28:32,657 - modelscope - WARNING - No preprocessor key ('csanmt-translation', 'translation') found in PREPROCESSOR_MAP, skip building preprocessor. Traceback (most recent call last): File "D:\Anaconda3\envs\anytext\lib\site-packages\modelscope\utils\registry.py", line 212, in build_from_cfg return obj_cls(**args) File "D:\Anaconda3\envs\anytext\lib\site-packages\modelscope\pipelines\nlp\translation_pipeline.py", line 54, in init self._src_vocab = dict([ File "D:\Anaconda3\envs\anytext\lib\site-packages\modelscope\pipelines\nlp\translation_pipeline.py", line 54, in self._src_vocab = dict([ UnicodeDecodeError: 'gbk' codec can't decode byte 0x84 in position 7: illegal multibyte sequence

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "D:\Anaconda3\envs\anytext\lib\site-packages\modelscope\utils\registry.py", line 212, in build_from_cfg return obj_cls(args) File "C:\Users\92923.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\92923.cache\modelscope\modelscope_modules\cv_anytext_text_generation_editing\ms_wrapper.py", line 46, in init self.init_model(**kwargs) File "C:\Users\92923.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 "D:\Anaconda3\envs\anytext\lib\site-packages\modelscope\pipelines\builder.py", line 170, in pipeline return build_pipeline(cfg, task_name=task) File "D:\Anaconda3\envs\anytext\lib\site-packages\modelscope\pipelines\builder.py", line 65, in build_pipeline return build_from_cfg( File "D:\Anaconda3\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 "D:\GitHubProject\AnyText\inference.py", line 3, in pipe = pipeline('my-anytext-task', model='damo/cv_anytext_text_generation_editing', model_revision='v1.1.1') File "D:\Anaconda3\envs\anytext\lib\site-packages\modelscope\pipelines\builder.py", line 170, in pipeline return build_pipeline(cfg, task_name=task) File "D:\Anaconda3\envs\anytext\lib\site-packages\modelscope\pipelines\builder.py", line 65, in build_pipeline return build_from_cfg( File "D:\Anaconda3\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)

tyxsspa / AnyText

TypeError: AnyTextPipeline: function takes exactly 5 arguments (1 given) #51