upload LFQ implementation

configuration_lfq_tokenizer.py

@@ -0,0 +1,43 @@
+"""
+Hugging Face compatible implementation of Open-MAGVIt2
+Code reference: https://github.com/TencentARC/Open-MAGVIT2
+"""
+
+
+from transformers import PretrainedConfig
+
+
+class EncoderDecoderConfig(PretrainedConfig):
+    model_type = "resnet_encoder_decoder"
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.ch = kwargs.get("ch", 128)
+        self.in_channels = kwargs.get("in_channels", 3)
+        self.out_ch = kwargs.get("out_ch", 3)
+        self.z_channels = kwargs.get("z_channels", 18)
+        self.num_res_blocks = kwargs.get("num_res_blocks", 2)
+        self.ch_mult = kwargs.get("ch_mult", [1, 1, 2, 2, 4])
+
+
+class QuantizerConfig(PretrainedConfig):
+    model_type = "lfq_quantizer"
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.dim = kwargs.get("dim", 18)
+        self.codebook_size = kwargs.get("codebook_size", 262144)
+        self.batch_maximization_weight = kwargs.get("batch_maximization_weight", 1.0)
+        self.sample_minimization_weight = kwargs.get("sample_minimization_weight", 1.0)
+
+
+class LFQTokenizerConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a :class:`~transform
+    """
+    model_type = "lfq_tokenizer"
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.encoder_decoder_config = kwargs.get("encoder_decoder_config", EncoderDecoderConfig())
+        self.quantizer_config = kwargs.get("quantizer_config", QuantizerConfig())