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# 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 Processor - Combines image processor and tokenizer.
"""
import re
from typing import List, Optional, Union
import torch
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
from transformers.video_utils import VideoInput
IMAGE_TOKEN_INDEX = -200
DEFAULT_IMAGE_TOKEN = "<image>"
class DiffusionVL_Qwen2_5_VL_ProcessorKwargs(ProcessingKwargs, total=False):
"""Keyword arguments for DiffusionVL_Qwen2_5_VL_Processor."""
_defaults = {
"text_kwargs": {
"padding": False,
},
}
def tokenizer_image_token(
prompt: str,
tokenizer,
image_token_index: int = IMAGE_TOKEN_INDEX,
return_tensors: Optional[str] = None,
) -> Union[List[int], torch.Tensor]:
"""
Tokenize text with image placeholders, replacing <image> with IMAGE_TOKEN_INDEX.
Args:
prompt: Input text containing <image> placeholders.
tokenizer: The tokenizer to use for encoding text.
image_token_index: The token index to use for image placeholders.
return_tensors: If "pt", return a PyTorch tensor.
Returns:
List of token IDs or a PyTorch tensor.
"""
prompt_chunks = prompt.split(DEFAULT_IMAGE_TOKEN)
input_ids = []
offset = 0
if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0:
# First chunk has text
input_ids = tokenizer(prompt_chunks[0], add_special_tokens=False).input_ids
offset = 1
for chunk_idx in range(offset, len(prompt_chunks)):
chunk = prompt_chunks[chunk_idx]
# Add image token
input_ids.append(image_token_index)
# Add text after image
if len(chunk) > 0:
input_ids.extend(tokenizer(chunk, add_special_tokens=False).input_ids)
if return_tensors == "pt":
return torch.tensor(input_ids, dtype=torch.long)
return input_ids
class DiffusionVL_Qwen2_5_VL_Processor(ProcessorMixin):
r"""
Constructs a DiffusionVL processor which wraps an image processor and a tokenizer into a single processor.
[`DiffusionVL_Qwen2_5_VL_Processor`] offers all the functionalities of [`Qwen2VLImageProcessor`] and [`Qwen2TokenizerFast`].
See the [`~DiffusionVL_Qwen2_5_VL_Processor.__call__`] and [`~DiffusionVL_Qwen2_5_VL_Processor.decode`] for more information.
This processor uses LLaVA-style image token handling:
- `<image>` in text is replaced with `IMAGE_TOKEN_INDEX` (-200) in input_ids
- The model's `prepare_inputs_labels_for_multimodal` replaces -200 with actual image features
Args:
image_processor ([`Qwen2VLImageProcessor`], *optional*):
The image processor is a required input.
tokenizer ([`Qwen2TokenizerFast`], *optional*):
The tokenizer is a required input.
chat_template (`str`, *optional*):
A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string.
Example:
```python
>>> from transformers import AutoProcessor
>>> from PIL import Image
>>> processor = AutoProcessor.from_pretrained("path/to/model", trust_remote_code=True)
>>> # Prepare text with image placeholder
>>> messages = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "Describe this image."}]}]
>>> text = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
>>> # Process image and text
>>> image = Image.open("image.jpg")
>>> inputs = processor(text=[text], images=[image], return_tensors="pt")
```
"""
attributes = ["image_processor", "tokenizer"]
image_processor_class = "Qwen2VLImageProcessor"
tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
def __init__(
self,
image_processor=None,
tokenizer=None,
chat_template: Optional[str] = None,
**kwargs,
):
self.image_token = DEFAULT_IMAGE_TOKEN
self.image_token_index = IMAGE_TOKEN_INDEX
super().__init__(image_processor, tokenizer, chat_template=chat_template)
def __call__(
self,
images: Optional[ImageInput] = None,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
videos: Optional[VideoInput] = None,
**kwargs: Unpack[DiffusionVL_Qwen2_5_VL_ProcessorKwargs],
) -> BatchFeature:
"""
Main method to prepare for the model one or several sequences and image(s).
This method forwards the `text` and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`]
if `text` is not `None` to encode the text. To prepare the vision inputs, this method forwards the `images`
and `kwargs` arguments to Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__call__`] if `images` is not `None`.
The text should contain `<image>` placeholders where images should be inserted.
These will be replaced with `IMAGE_TOKEN_INDEX` (-200) in the output input_ids.
Args:
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, *optional*):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array, or PyTorch
tensor. Both channels-first and channels-last formats are supported.
text (`str`, `List[str]`, *optional*):
The sequence or batch of sequences to be encoded. Each sequence should be a string containing
`<image>` placeholders where images will be inserted.
videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, *optional*):
The video or batch of videos to be prepared. Currently not fully supported.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors of a particular framework. Acceptable values are:
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model.
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
- **image_grid_thw** -- List of image 3D grid dimensions. Returned when `images` is not `None`.
"""
output_kwargs = self._merge_kwargs(
DiffusionVL_Qwen2_5_VL_ProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
# Process images
image_inputs = {}
if images is not None:
image_inputs = self.image_processor(
images=images, **output_kwargs.get("images_kwargs", {})
)
# Handle text input
if text is None:
return BatchFeature(data=image_inputs)
if not isinstance(text, list):
text = [text]
# Tokenize with LLaVA-style image token handling
return_tensors = output_kwargs.get("text_kwargs", {}).pop("return_tensors", None)
all_input_ids = []
for t in text:
input_ids = tokenizer_image_token(
t, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors=None
)
all_input_ids.append(input_ids)
# Pad sequences
max_len = max(len(ids) for ids in all_input_ids)
padded_input_ids = []
attention_masks = []
pad_token_id = (
self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else 0
)
for ids in all_input_ids:
padding_length = max_len - len(ids)
padded_ids = ids + [pad_token_id] * padding_length
mask = [1] * len(ids) + [0] * padding_length
padded_input_ids.append(padded_ids)
attention_masks.append(mask)
text_inputs = {
"input_ids": padded_input_ids,
"attention_mask": attention_masks,
}
return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
def build_conversation_input_ids(
self,
messages: List[dict],
images: Optional[List] = None,
add_generation_prompt: bool = True,
) -> dict:
"""
Build input_ids from conversation messages in LLaVA format.
This method converts a list of messages into a prompt string with `<image>` placeholders.
Uses LLaVA-style chat template format (trained format).
Args:
messages: List of message dicts with 'role' and 'content' keys.
Content can be a string or a list of dicts with 'type' key ('text' or 'image').
images: Optional list of images (used for validation).
add_generation_prompt: Whether to add generation prompt at the end.
Returns:
dict with 'text' key containing the prompt string with `<image>` placeholders.
"""
# Build LLaVA-style prompt directly
# Format: <|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<image>\nPrompt<|im_end|>\n<|im_start|>assistant\n
text_parts = []
for message in messages:
role = message.get("role", "user")
content = message.get("content", "")
text_parts.append(f"<|im_start|>{role}\n")
# Handle content - can be string or list of content items
if isinstance(content, str):
text_parts.append(content)
elif isinstance(content, list):
for item in content:
if isinstance(item, dict):
if item.get("type") == "image":
text_parts.append(DEFAULT_IMAGE_TOKEN)
elif item.get("type") == "text":
text_parts.append(item.get("text", ""))
else:
text_parts.append(str(item))
text_parts.append("<|im_end|>\n")
if add_generation_prompt:
text_parts.append("<|im_start|>assistant\n")
text = "".join(text_parts)
return {"text": text}
def batch_decode(self, *args, **kwargs):
"""
Decode a batch of token IDs to text.
This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`].
Please refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
Decode token IDs to text.
This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`].
Please refer to the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
def model_input_names(self) -> List[str]:
"""Return the list of model input names."""
tokenizer_names = self.tokenizer.model_input_names
image_processor_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_names + image_processor_names))
__all__ = ["DiffusionVL_Qwen2_5_VL_Processor", "tokenizer_image_token"]
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