# coding=utf-8 # Copyright 2025 The HustVL Team and The HuggingFace Inc. team. All rights reserved. # # This code is based on Qwen2.5-VL, which is derived from EleutherAI's GPT-NeoX library # and the GPT-NeoX and OPT implementations. It has been modified to create DiffusionVL. # # 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. """DiffusionVL (Qwen2.5-VL based) model configuration.""" from typing import List, Optional, Union from transformers.configuration_utils import PretrainedConfig class DiffusionVL_Qwen2_5_VL_VisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`DiffusionVL_Qwen2_5_VL_VisionModel`]. It is used to instantiate the vision encoder according to the specified arguments. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: depth (`int`, *optional*, defaults to 32): Number of vision transformer layers. hidden_size (`int`, *optional*, defaults to 1280): Dimensionality of the encoder layers and the pooler layer. hidden_act (`str`, *optional*, defaults to `"silu"`): The non-linear activation function in the encoder. intermediate_size (`int`, *optional*, defaults to 3420): Dimensionality of the "intermediate" (i.e., feed-forward) layer. num_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer. in_channels (`int`, *optional*, defaults to 3): Number of input channels. patch_size (`int`, *optional*, defaults to 14): The size of each image patch. spatial_merge_size (`int`, *optional*, defaults to 2): The spatial merge size for patch merging. temporal_patch_size (`int`, *optional*, defaults to 2): The temporal patch size for video processing. tokens_per_second (`int`, *optional*, defaults to 4): Number of tokens per second for video processing. window_size (`int`, *optional*, defaults to 112): Window size for windowed attention. out_hidden_size (`int`, *optional*, defaults to 3584): Output hidden size after the vision encoder. fullatt_block_indexes (`List[int]`, *optional*): Indices of blocks that use full attention instead of windowed attention. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing weight matrices. Example: ```python >>> from configuration_diffusionvl_qwen2_5_vl import DiffusionVL_Qwen2_5_VL_VisionConfig >>> # Initializing a DiffusionVL vision configuration >>> configuration = DiffusionVL_Qwen2_5_VL_VisionConfig() ``` """ model_type = "diffusionvl_qwen2_5_vl_vision" base_config_key = "vision_config" def __init__( self, depth: int = 32, hidden_size: int = 1280, hidden_act: str = "silu", intermediate_size: int = 3420, num_heads: int = 16, in_channels: int = 3, patch_size: int = 14, spatial_merge_size: int = 2, temporal_patch_size: int = 2, tokens_per_second: int = 4, window_size: int = 112, out_hidden_size: int = 3584, fullatt_block_indexes: Optional[List[int]] = None, initializer_range: float = 0.02, **kwargs, ): super().__init__(**kwargs) self.depth = depth self.hidden_size = hidden_size self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.num_heads = num_heads self.in_channels = in_channels self.patch_size = patch_size self.spatial_merge_size = spatial_merge_size self.temporal_patch_size = temporal_patch_size self.tokens_per_second = tokens_per_second self.window_size = window_size self.out_hidden_size = out_hidden_size self.fullatt_block_indexes = fullatt_block_indexes or [7, 15, 23, 31] self.initializer_range = initializer_range class DiffusionVL_Qwen2_5_VL_Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`DiffusionVL_Qwen2_5_VL_ForConditionalGeneration`]. It is used to instantiate a DiffusionVL model according to the specified arguments. DiffusionVL extends Qwen2.5-VL architecture with BD3LM (Block Diffusion Language Model) for diffusion-based text generation instead of autoregressive decoding. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 152064): Vocabulary size of the DiffusionVL model. hidden_size (`int`, *optional*, defaults to 3584): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 18944): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 28): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 28): Number of attention heads for each attention layer. num_key_value_heads (`int`, *optional*, defaults to 4): Number of key-value heads for Grouped Query Attention (GQA). hidden_act (`str`, *optional*, defaults to `"silu"`): The non-linear activation function in the decoder. max_position_embeddings (`int`, *optional*, defaults to 128000): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-6): The epsilon used by the RMS normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether to use the past key/values attentions. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. vision_config (`DiffusionVL_Qwen2_5_VL_VisionConfig`, *optional*): The configuration for the vision encoder. image_token_id (`int`, *optional*, defaults to 151655): The token index for image placeholder. video_token_id (`int`, *optional*, defaults to 151656): The token index for video placeholder. vision_start_token_id (`int`, *optional*, defaults to 151652): The token index denoting start of vision input. vision_end_token_id (`int`, *optional*, defaults to 151653): The token index denoting end of vision input. enable_bd3lm (`bool`, *optional*, defaults to `True`): Whether to enable BD3LM diffusion-based generation. bd3lm_block_size (`int`, *optional*, defaults to 8): Block size for BD3LM generation. bd3lm_cross_attn (`bool`, *optional*, defaults to `True`): Whether to use cross-attention in BD3LM. mask_token_id (`int`, *optional*, defaults to 151671): The token index for mask token used in diffusion. rope_theta (`float`, *optional*, defaults to 1000000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for RoPE embeddings. Example: ```python >>> from transformers import AutoModelForCausalLM >>> from configuration_diffusionvl_qwen2_5_vl import DiffusionVL_Qwen2_5_VL_Config >>> # Initializing a DiffusionVL configuration >>> configuration = DiffusionVL_Qwen2_5_VL_Config() >>> # Initializing a model from the configuration >>> model = AutoModelForCausalLM.from_pretrained( ... "path/to/model", config=configuration, trust_remote_code=True ... ) >>> # Accessing the model configuration >>> configuration = model.config ``` """ model_type = "diffusionvl_qwenvl" sub_configs = {"vision_config": DiffusionVL_Qwen2_5_VL_VisionConfig} keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size: int = 152064, hidden_size: int = 3584, intermediate_size: int = 18944, num_hidden_layers: int = 28, num_attention_heads: int = 28, num_key_value_heads: int = 4, hidden_act: str = "silu", max_position_embeddings: int = 128000, initializer_range: float = 0.02, rms_norm_eps: float = 1e-6, use_cache: bool = True, tie_word_embeddings: bool = False, attention_dropout: float = 0.0, # Vision configuration vision_config: Optional[Union[DiffusionVL_Qwen2_5_VL_VisionConfig, dict]] = None, # Multimodal token IDs image_token_id: int = 151655, video_token_id: int = 151656, vision_start_token_id: int = 151652, vision_end_token_id: int = 151653, # BD3LM diffusion parameters enable_bd3lm: bool = True, bd3lm_block_size: int = 8, bd3lm_cross_attn: bool = True, bd3lm_antithetic_sampling: bool = True, bd3lm_sampling_eps_min: float = 1e-3, bd3lm_sampling_eps_max: float = 1.0, mask_token_id: int = 151671, # RoPE parameters rope_theta: float = 1000000.0, rope_scaling: Optional[dict] = None, **kwargs, ): # Remove text_config from kwargs to avoid GenerationConfig issues # (text_config is only needed for train code, HF config uses flattened params) kwargs.pop("text_config", None) # Text model configuration self.vocab_size = vocab_size self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.attention_dropout = attention_dropout self.rope_theta = rope_theta self.rope_scaling = rope_scaling or { "mrope_section": [16, 24, 24], "rope_type": "default", "type": "default", } # Vision configuration if vision_config is None: self.vision_config = DiffusionVL_Qwen2_5_VL_VisionConfig() elif isinstance(vision_config, dict): self.vision_config = DiffusionVL_Qwen2_5_VL_VisionConfig(**vision_config) elif isinstance(vision_config, DiffusionVL_Qwen2_5_VL_VisionConfig): self.vision_config = vision_config else: self.vision_config = DiffusionVL_Qwen2_5_VL_VisionConfig() # Multimodal token IDs self.image_token_id = image_token_id self.video_token_id = video_token_id self.vision_start_token_id = vision_start_token_id self.vision_end_token_id = vision_end_token_id # BD3LM diffusion configuration self.enable_bd3lm = enable_bd3lm self.bd3lm_block_size = bd3lm_block_size self.bd3lm_cross_attn = bd3lm_cross_attn self.bd3lm_antithetic_sampling = bd3lm_antithetic_sampling self.bd3lm_sampling_eps_min = bd3lm_sampling_eps_min self.bd3lm_sampling_eps_max = bd3lm_sampling_eps_max self.mask_token_id = mask_token_id super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) __all__ = ["DiffusionVL_Qwen2_5_VL_Config", "DiffusionVL_Qwen2_5_VL_VisionConfig"]