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qwen-image
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Artiprocher
255
diffsynth/models/qwen_image_text_encoder.py
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255
diffsynth/models/qwen_image_text_encoder.py
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from transformers import Qwen2_5_VLModel
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import torch
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from typing import Optional, Union
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class QwenImageTextEncoder(torch.nn.Module):
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def __init__(self):
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super().__init__()
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from transformers import Qwen2_5_VLConfig
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config = Qwen2_5_VLConfig(**{
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"architectures": [
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"Qwen2_5_VLForConditionalGeneration"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 3584,
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"image_token_id": 151655,
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"initializer_range": 0.02,
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"intermediate_size": 18944,
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"max_position_embeddings": 128000,
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"max_window_layers": 28,
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"model_type": "qwen2_5_vl",
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"num_attention_heads": 28,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
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"rms_norm_eps": 1e-06,
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"rope_scaling": {
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"mrope_section": [
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16,
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24,
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24
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],
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"rope_type": "default",
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"type": "default"
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},
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"rope_theta": 1000000.0,
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"sliding_window": 32768,
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"text_config": {
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"architectures": [
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"Qwen2_5_VLForConditionalGeneration"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 3584,
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"image_token_id": None,
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"initializer_range": 0.02,
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"intermediate_size": 18944,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 128000,
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"max_window_layers": 28,
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"model_type": "qwen2_5_vl_text",
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"num_attention_heads": 28,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
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"rms_norm_eps": 1e-06,
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"rope_scaling": {
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"mrope_section": [
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16,
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24,
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24
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],
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"rope_type": "default",
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"type": "default"
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},
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"rope_theta": 1000000.0,
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"sliding_window": None,
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"torch_dtype": "float32",
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"use_cache": True,
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"use_sliding_window": False,
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"video_token_id": None,
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"vision_end_token_id": 151653,
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"vision_start_token_id": 151652,
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"vision_token_id": 151654,
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"vocab_size": 152064
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},
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"tie_word_embeddings": False,
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"torch_dtype": "float32",
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"transformers_version": "4.54.0",
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"use_cache": True,
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"use_sliding_window": False,
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"video_token_id": 151656,
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"vision_config": {
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"depth": 32,
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"fullatt_block_indexes": [
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7,
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15,
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23,
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31
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],
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"hidden_act": "silu",
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"hidden_size": 1280,
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"in_channels": 3,
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"in_chans": 3,
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"initializer_range": 0.02,
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"intermediate_size": 3420,
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"model_type": "qwen2_5_vl",
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"num_heads": 16,
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"out_hidden_size": 3584,
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"patch_size": 14,
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"spatial_merge_size": 2,
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"spatial_patch_size": 14,
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"temporal_patch_size": 2,
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"tokens_per_second": 2,
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"torch_dtype": "float32",
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"window_size": 112
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},
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"vision_end_token_id": 151653,
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"vision_start_token_id": 151652,
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"vision_token_id": 151654,
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"vocab_size": 152064
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})
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self.model = Qwen2_5_VLModel(config)
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self.lm_head = torch.nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
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def forward(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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labels: Optional[torch.LongTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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pixel_values: Optional[torch.Tensor] = None,
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pixel_values_videos: Optional[torch.FloatTensor] = None,
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image_grid_thw: Optional[torch.LongTensor] = None,
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video_grid_thw: Optional[torch.LongTensor] = None,
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rope_deltas: Optional[torch.LongTensor] = None,
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cache_position: Optional[torch.LongTensor] = None,
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second_per_grid_ts: Optional[torch.Tensor] = None,
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logits_to_keep: Union[int, torch.Tensor] = 0,
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**kwargs,
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):
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r"""
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
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(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
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image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
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The temporal, height and width of feature shape of each image in LLM.
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video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
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The temporal, height and width of feature shape of each video in LLM.
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rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
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The rope index difference between sequence length and multimodal rope.
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second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*):
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The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs.
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Example:
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```python
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>>> from PIL import Image
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>>> import requests
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>>> from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
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>>> model = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
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>>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
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>>> messages = [
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{
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"role": "user",
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"content": [
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{"type": "image"},
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{"type": "text", "text": "What is shown in this image?"},
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],
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},
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]
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>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
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>>> image = Image.open(requests.get(url, stream=True).raw)
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>>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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>>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos])
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>>> # Generate
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>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
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>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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"The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..."
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```"""
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output_attentions = False
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output_hidden_states = True
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outputs = self.model(
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input_ids=input_ids,
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pixel_values=pixel_values,
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pixel_values_videos=pixel_values_videos,
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image_grid_thw=image_grid_thw,
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video_grid_thw=video_grid_thw,
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second_per_grid_ts=second_per_grid_ts,
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position_ids=position_ids,
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attention_mask=attention_mask,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=True,
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cache_position=cache_position,
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**kwargs,
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)
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return outputs.hidden_states
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@staticmethod
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def state_dict_converter():
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return QwenImageTextEncoderStateDictConverter()
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class QwenImageTextEncoderStateDictConverter():
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def __init__(self):
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pass
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def from_diffusers(self, state_dict):
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state_dict_ = {}
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for k, v in state_dict.items():
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if k.startswith("visual."):
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k = "model." + k
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elif k.startswith("model."):
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k = k.replace("model.", "model.language_model.")
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state_dict_[k] = v
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return state_dict_
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