SigLIP vision encoder configuration for FastVideo.
Classes¶
dataclass
¶
SiglipVisionArchConfig(stacked_params_mapping: list = (lambda: [('qkv_proj', 'q_proj', 'q'), ('qkv_proj', 'k_proj', 'k'), ('qkv_proj', 'v_proj', 'v')])(), architectures: list[str] = (lambda: ['SiglipVisionModel'])(), _supported_attention_backends: tuple[AttentionBackendEnum, ...] = (FLASH_ATTN, TORCH_SDPA), output_hidden_states: bool = False, use_return_dict: bool = True, attention_dropout: float = 0.0, dtype: str | None = None, hidden_act: str = 'gelu_pytorch_tanh', hidden_size: int = 1152, image_size: int = 384, intermediate_size: int = 4304, layer_norm_eps: float = 1e-06, model_type: str = 'siglip_vision_model', num_attention_heads: int = 16, num_channels: int = 3, num_hidden_layers: int = 27, patch_size: int = 14)
Bases: ImageEncoderArchConfig
Architecture configuration for SigLIP vision encoder.
Fields match the config.json from HuggingFace SigLIP checkpoints.
dataclass
¶
SiglipVisionConfig(arch_config: ImageEncoderArchConfig = SiglipVisionArchConfig(), prefix: str = 'siglip', quant_config: QuantizationConfig | None = None, lora_config: Any | None = None, num_hidden_layers_override: int | None = None, require_post_norm: bool | None = None, enable_scale: bool = True, is_causal: bool = False)
Bases: ImageEncoderConfig
Configuration for SigLIP vision encoder.