# coding=utf-8 # Copyright 2025 The HustVL Team and The HuggingFace Inc. team. All rights reserved. # # This code is based on Qwen2.5 and SigLIP. 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 (SigLIP + Qwen2.5) model configuration.""" from typing import List, Optional, Union from transformers.configuration_utils import PretrainedConfig class DiffusionVL_Qwen2_5_VisionConfig(PretrainedConfig): """ Configuration for SigLIP vision encoder used in DiffusionVL-Qwen2.5. Args: hidden_size: Dimension of the encoder layers (1152 for SigLIP-SO400M). intermediate_size: Dimension of the MLP layers. num_hidden_layers: Number of transformer layers. num_attention_heads: Number of attention heads. num_channels: Number of input channels. image_size: Input image resolution. patch_size: Patch size for patch embedding. hidden_act: Activation function. layer_norm_eps: Layer normalization epsilon. attention_dropout: Attention dropout probability. """ model_type = "diffusionvl_qwen2_5_vision" base_config_key = "vision_config" def __init__( self, hidden_size: int = 1152, intermediate_size: int = 4304, num_hidden_layers: int = 26, # SigLIP uses 27 layers, but last one is removed num_attention_heads: int = 16, num_channels: int = 3, image_size: int = 384, patch_size: int = 14, hidden_act: str = "gelu_pytorch_tanh", layer_norm_eps: float = 1e-6, attention_dropout: float = 0.0, **kwargs, ): super().__init__(**kwargs) 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_channels = num_channels self.image_size = image_size self.patch_size = patch_size self.hidden_act = hidden_act self.layer_norm_eps = layer_norm_eps self.attention_dropout = attention_dropout class DiffusionVL_Qwen2_5_Config(PretrainedConfig): """ Configuration for DiffusionVL-Qwen2.5 model. This model uses: - SigLIP as the vision encoder (external ViT) - PoolerProjector as the MM projector (Conv2d + MLP) - Qwen2.5 as the LLM backbone (standard RoPE, not M-RoPE) - BD3LM for diffusion-based generation Args: vocab_size: Vocabulary size. hidden_size: Dimension of the hidden representations. intermediate_size: Dimension of the MLP representations. num_hidden_layers: Number of hidden layers. num_attention_heads: Number of attention heads. num_key_value_heads: Number of key-value heads for GQA. hidden_act: Activation function. max_position_embeddings: Maximum sequence length. initializer_range: Standard deviation for weight initialization. rms_norm_eps: Epsilon for RMS normalization. use_cache: Whether to use KV cache. tie_word_embeddings: Whether to tie input and output embeddings. attention_dropout: Attention dropout probability. vision_config: Vision encoder configuration. mm_hidden_size: Vision encoder hidden size for projector. enable_bd3lm: Whether to enable BD3LM. bd3lm_block_size: Block size for BD3LM. mask_token_id: Token ID for mask token. rope_theta: RoPE base period. sliding_window: Sliding window size for attention. """ model_type = "diffusionvl_qwen" sub_configs = {"vision_config": DiffusionVL_Qwen2_5_VisionConfig} keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, # Text model parameters (Qwen2.5 compatible) 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 = 32768, 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_VisionConfig, dict]] = None, # MM projector mm_hidden_size: int = 1152, # SigLIP hidden size # BD3LM diffusion parameters enable_bd3lm: bool = True, bd3lm_block_size: int = 8, 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 (standard RoPE, not M-RoPE) rope_theta: float = 1000000.0, rope_scaling: Optional[dict] = None, # Sliding window attention sliding_window: int = 32768, max_window_layers: int = 28, use_sliding_window: bool = False, **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 self.sliding_window = sliding_window self.max_window_layers = max_window_layers self.use_sliding_window = use_sliding_window # Vision configuration if vision_config is None: self.vision_config = DiffusionVL_Qwen2_5_VisionConfig() elif isinstance(vision_config, dict): self.vision_config = DiffusionVL_Qwen2_5_VisionConfig(**vision_config) elif isinstance(vision_config, DiffusionVL_Qwen2_5_VisionConfig): self.vision_config = vision_config else: self.vision_config = DiffusionVL_Qwen2_5_VisionConfig() # MM projector self.mm_hidden_size = mm_hidden_size # BD3LM diffusion configuration self.enable_bd3lm = enable_bd3lm self.bd3lm_block_size = bd3lm_block_size 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_Config", "DiffusionVL_Qwen2_5_VisionConfig"]