# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/janus/modular_janus.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_janus.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # Copyright 2025 Deepseek AI and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import List from ...configuration_utils import PretrainedConfig from ...utils import logging from ..auto import CONFIG_MAPPING, AutoConfig logger = logging.get_logger(__name__) class JanusVisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`JanusVisionModel`]. It is used to instantiate a `JanusVisionModel` according to the specified arguments, defining the model architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 1024): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. num_channels (`int`, *optional*, defaults to 3): The number of input channels. patch_size (`int`, *optional*, defaults to 16): The size (resolution) of each patch. image_size (`int`, *optional*, defaults to 384): The size (resolution) of each image. attention_dropout (`float`, *optional*, defaults to 0.0): Dropout probability for attention weights. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the layer normalization layers. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"`, and `"gelu_new"` are supported. mlp_ratio (`float`, *optional*, defaults to 4.0): Ratio of MLP hidden dimensionality to embedding dimensionality. attention_bias (`bool`, *optional*, defaults to `True`): Whether to add a bias to the queries, keys, and values in the attention layers. hidden_dropout_rate (`float`, *optional*, defaults to 0.0): The dropout probability for fully connected layers in the encoder. projection_dim (`int`, *optional*, defaults to 2048): Dimensionality of the MLP projection head. projection_dropout (`float`, *optional*, defaults to 0.0): Dropout probability for the projection layer. use_qk_norm (`bool`, *optional*, defaults to `False`): Whether to normalize the query and key matrices. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated normal initializer for initializing all weight matrices. depth (`int`, *optional*, defaults to 2): Number of hidden layers in the aligner module. num_image_tokens (`int`, *optional*, defaults to 576): Number of image tokens. """ model_type = "janus_vision_model" base_config_key = "vision_config" def __init__( self, hidden_size=1024, num_hidden_layers=24, num_attention_heads=16, num_channels=3, patch_size=16, image_size=384, attention_dropout=0.0, layer_norm_eps=1e-6, hidden_act="gelu", mlp_ratio=4.0, attention_bias=True, hidden_dropout_rate=0.0, projection_dim=2048, projection_dropout=0.0, use_qk_norm=False, initializer_range=0.02, depth=2, num_image_tokens=576, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.num_channels = num_channels self.patch_size = patch_size self.image_size = image_size self.attention_dropout = attention_dropout self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.mlp_ratio = mlp_ratio self.attention_bias = attention_bias self.hidden_dropout_rate = hidden_dropout_rate self.projection_dim = projection_dim self.projection_dropout = projection_dropout self.use_qk_norm = use_qk_norm self.initializer_range = initializer_range self.depth = depth self.num_image_tokens = num_image_tokens class JanusVQVAEConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`JanusVQVAEModel`]. It is used to instantiate a `JanusVQVAEModel` according to the specified arguments, defining the model architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Instantiating a configuration with the defaults will yield a similar configuration to the VQModel of the [deepseek-community/Janus-Pro-1B](https://huggingface.co/deepseek-community/Janus-Pro-1B). Args: embed_dim (`int`, *optional*, defaults to 8): Dimensionality of each embedding vector. num_embeddings (`int`, *optional*, defaults to 16384): Number of codebook embeddings. double_latent (`bool`, *optional*, defaults to `False`): Whether to use double z channels. latent_channels (`int`, *optional*, defaults to 256): Number of channels for the latent space. num_patches (`int`, *optional*, defaults to 32): Num of patches the input images can be divided into. in_channels (`int`, *optional*, defaults to 3): Number of input channels. out_channels (`int`, *optional*, defaults to 3): Number of out channels. base_channels (`int`, *optional*, defaults to 128): Base channel count. channel_multiplier (`List[int]`, *optional*, defaults to `[1, 1, 2, 2, 4]`): Channel multipliers for each resolution. num_res_blocks (`int`, *optional*, defaults to 2): Number of residual blocks. dropout (`float`, *optional*, defaults to 0.0): Dropout rate. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. projection_dim (`int`, *optional*, defaults to 2048): Dimensionality of the MLP projection head. num_hidden_layers (`int`, *optional*, defaults to 2): Number of hidden layers in VAVAE MLP Connecter module. hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. image_token_embed_dim (`int`, *optional*, defaults to 2048): Dimension of image embeddings. It should be same as the dimensionality of text embeddings. """ model_type = "janus_vqgan" base_config_key = "vq_config" def __init__( self, embed_dim: int = 8, num_embeddings: int = 16384, double_latent: bool = False, latent_channels: int = 256, num_patches: int = 32, in_channels: int = 3, out_channels: int = 3, base_channels: int = 128, channel_multiplier: List[int] = [1, 1, 2, 2, 4], num_res_blocks: int = 2, dropout: float = 0.0, initializer_range=0.02, projection_dim=2048, num_hidden_layers=2, hidden_act="gelu", image_token_embed_dim=2048, **kwargs, ): super().__init__(**kwargs) self.embed_dim = embed_dim self.num_embeddings = num_embeddings self.double_latent = double_latent self.latent_channels = latent_channels self.in_channels = in_channels self.base_channels = base_channels self.channel_multiplier = channel_multiplier self.num_res_blocks = num_res_blocks self.dropout = dropout self.initializer_range = initializer_range self.num_patches = num_patches self.out_channels = out_channels self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.hidden_act = hidden_act self.image_token_embed_dim = image_token_embed_dim class JanusConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`JanusModel`]. It is used to instantiate an Janus model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Janus-1B or Janus-7B models. e.g. [deepseek-community/Janus-Pro-1B](https://huggingface.co/deepseek-community/Janus-Pro-1B) or [deepseek-community/Janus-Pro-7B](https://huggingface.co/deepseek-community/Janus-Pro-7B) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`): The config object or dictionary of the text backbone. vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `JanusVisionConfig`): The config object or dictionary of the vision backbone. vq_config (`Union[AutoConfig, dict]`, *optional*, defaults to `JanusVQVAEConfig`): The config object or dictionary of the VQVAE backbone. image_token_id (`int`, *optional*, defaults to 100581): Token index of a placeholder image token. Example: ```python >>> from transformers import JanusForConditionalGeneration, JanusConfig, JanusVisionConfig, JanusVQVAEConfig, LlamaConfig >>> # Initializing a Janus vision config >>> vision_config = JanusVisionConfig() >>> # Initializing a Llama config >>> text_config = LlamaConfig() >>> # Initializing a VQ config >>> vq_config = JanusVQVAEConfig() >>> # Initializing a Janus Pro 1B style configuration >>> configuration = JanusConfig(vision_config=vision_config, text_config=text_config, vq_config=vq_config) >>> # Initializing a model from the Janus Pro 1B style configuration >>> model = JanusForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "janus" sub_configs = { "text_config": AutoConfig, "vision_config": JanusVisionConfig, "vq_config": JanusVQVAEConfig, } def __init__( self, text_config=None, vision_config=None, vq_config=None, image_token_id=100581, **kwargs, ): if isinstance(text_config, dict): text_config["model_type"] = text_config.get("model_type", "llama") self.text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config) elif text_config is None: logger.info("`text_config` is None. Initializing with default values") self.text_config = CONFIG_MAPPING["llama"]() elif isinstance(text_config, PretrainedConfig): self.text_config = text_config else: raise ValueError( f"Invalid type for `text_config`. Must be either `dict` or `LlamaConfig`." f" Type found: {type(text_config)}" ) if vision_config is None: logger.info("`vision_config` is None. Initializing with default JanusVisionConfig values") self.vision_config = JanusVisionConfig() elif isinstance(vision_config, dict): self.vision_config = JanusVisionConfig(**vision_config) elif isinstance(vision_config, JanusVisionConfig): self.vision_config = vision_config else: raise ValueError( f"Invalid type for `vision_config`. Must be either `dict` or `JanusVisionConfig`." f" Type found: {type(vision_config)}" ) if vq_config is None: logger.info("`vq_config` is None. Initializing with default JanusVQVAEConfig values") self.vq_config = JanusVQVAEConfig() elif isinstance(vq_config, dict): self.vq_config = JanusVQVAEConfig(**vq_config) elif isinstance(vq_config, JanusVQVAEConfig): self.vq_config = vq_config else: raise ValueError( f"Invalid type for `vq_config`. Must be either `dict` or `JanusVQVAEConfig`." f" Type found: {type(vq_config)}" ) # This dimension is required when decoding discrete image tokens to continuous input. self.vq_config.num_patches = self.vision_config.image_size // self.vision_config.patch_size # The default is only the index for the 1B model, 7B uses a different one self.image_token_id = image_token_id super().__init__(**kwargs) __all__ = ["JanusVQVAEConfig", "JanusVisionConfig", "JanusConfig"]