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| import json |
| import copy |
| from typing import Optional, Union, List |
| from transformers.configuration_utils import PretrainedConfig |
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|
| class InternVLUGenerationDecoderConfig(PretrainedConfig): |
| model_type = "internvlu_generation_decoder" |
| is_composition = False |
|
|
| def __init__( |
| self, |
| decoder_projector_type: str = "mlp2x_gelu", |
| vae_projector_type: str = "mlp2x_gelu", |
| visual_input_hidden_size: int = 4096, |
| input_hidden_size: int = 1536, |
| vae_input_hidden_size: int = 64, |
| output_hidden_size: int = 2304, |
| decoder_config: Optional[Union[str, dict]] = None, |
| padding_encoder_hidden_states=True, |
| pad_to_fix_length: bool = True, |
| sigmas_as_weight: bool = False, |
| discrete_timestep=False, |
| weighting_scheme: Optional[str] = ( |
| "logit_normal" |
| ), |
| logit_mean: float = 0.0, |
| logit_std: float = 1.0, |
| mode_scale: float = 1.29, |
| all_cfg_scale: float = 0.0, |
| part_cfg_scale: float = 0.0, |
| mask_weight_type: Optional[str] = None, |
| max_sequence_length: int = 512, |
| gen_image_height: int = 512, |
| gen_image_width: int = 512, |
| video_position_scale_factor: float = 1.0, |
| vlm_cond_position_scale_factor: float = 1.0, |
| vae_downsample_factor: int = 32, |
| region_weighting: bool = False, |
| flow_shift: float = 3.0, |
| vlm_select_layer: Optional[List[int]] = [-1], |
| qk_norm: str = None, |
| **kwargs, |
| ): |
| super().__init__(**kwargs) |
|
|
| self.decoder_projector_type = decoder_projector_type |
| self.vae_projector_type = vae_projector_type |
|
|
| self.visual_input_hidden_size = visual_input_hidden_size |
| self.input_hidden_size = input_hidden_size |
| self.vae_input_hidden_size = vae_input_hidden_size |
| self.output_hidden_size = output_hidden_size |
| self.decoder_config = decoder_config |
|
|
| self.padding_encoder_hidden_states = padding_encoder_hidden_states |
| self.pad_to_fix_length = pad_to_fix_length |
|
|
| self.sigmas_as_weight = sigmas_as_weight |
| self.discrete_timestep = discrete_timestep |
| self.weighting_scheme = weighting_scheme |
| self.logit_mean = logit_mean |
| self.logit_std = logit_std |
| self.mode_scale = mode_scale |
|
|
| self.all_cfg_scale = all_cfg_scale |
| self.part_cfg_scale = part_cfg_scale |
|
|
| self.mask_weight_type = mask_weight_type |
| self.gen_image_height = gen_image_height |
| self.gen_image_width = gen_image_width |
| self.video_position_scale_factor = video_position_scale_factor |
| self.vlm_cond_position_scale_factor = vlm_cond_position_scale_factor |
|
|
| self.vae_downsample_factor = vae_downsample_factor |
| self.region_weighting = region_weighting |
|
|
| self.vlm_select_layer = vlm_select_layer |
|
|
| self.max_sequence_length = max_sequence_length |
|
|
| self.qk_norm = qk_norm |
|
|
| self.flow_shift = flow_shift |
|
|
| if isinstance(decoder_config, str): |
| with open(decoder_config, "r") as f: |
| self.decoder_config = json.load(f) |
| else: |
| self.decoder_config = decoder_config |
|
|
| def to_dict(self): |
| """ |
| Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. |
| |
| Returns: |
| `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, |
| """ |
|
|
| output = copy.deepcopy(self.__dict__) |
| output["model_type"] = self.__class__.model_type |
| output["is_composition"] = self.__class__.is_composition |
|
|
| return output |
|
|
