mirror of
https://github.com/OpenVGLab/OmniLottie.git
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55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
"""
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OmniLottie Decoder Configuration
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"""
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from transformers import PretrainedConfig
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from typing import Optional
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class LottieDecoderConfig(PretrainedConfig):
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"""
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Configuration class for LottieDecoder model, inheriting from PretrainedConfig
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Stores configuration parameters for the LottieDecoder model,
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supporting Hugging Face's standard save and load mechanisms.
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Args:
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pix_len (int): Maximum length for image/video tokens, default 4560
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text_len (int): Maximum length for text tokens, default 1500
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base_model_path (str): Path or name of base Qwen2.5-VL model
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vocab_size (int): Vocabulary size, extended to 192400 to support Lottie tokens
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bos_token_id (int): Beginning-of-sequence token ID for Lottie
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eos_token_id (int): End-of-sequence token ID for Lottie
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pad_token_id (int): Padding token ID
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torch_dtype (str): Model weight data type, default "bfloat16"
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attn_implementation (str): Attention implementation method, default "eager"
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"""
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model_type = "lottie_decoder"
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def __init__(
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self,
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pix_len: int = 4560,
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text_len: int = 1500,
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base_model_path: str = "Qwen/Qwen2.5-VL-3B-Instruct",
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vocab_size: int = 192400,
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bos_token_id: int = 192398,
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eos_token_id: int = 192399,
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pad_token_id: int = 151643,
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torch_dtype: str = "bfloat16",
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attn_implementation: str = "eager",
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**kwargs
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):
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super().__init__(
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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pad_token_id=pad_token_id,
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**kwargs
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)
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self.pix_len = pix_len
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self.text_len = text_len
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self.base_model_path = base_model_path
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self.vocab_size = vocab_size
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self.torch_dtype = torch_dtype
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self.attn_implementation = attn_implementation
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