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[Model][HunyuanVL] Use native transformers processor and adapt to transformers 5.13 (#47872)

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Co-authored-by: manayang <manayang@tencent.com>
This commit is contained in:
manayang
2026-07-08 07:58:23 +00:00
committed by GitHub
co-authored by manayang
parent 7cc2e8e74f
commit 51e5372f3d
4 changed files with 21 additions and 712 deletions
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tests/models/registry.py
+1 -1
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@@ -936,7 +936,7 @@ _MULTIMODAL_EXAMPLE_MODELS = {
"HunYuanVLForConditionalGeneration": _HfExamplesInfo(
"tencent/HunyuanOCR",
hf_overrides={"num_experts": 0},
is_available_online=False,
min_transformers_version="5.13",
),
"Idefics3ForConditionalGeneration": _HfExamplesInfo(
"HuggingFaceM4/Idefics3-8B-Llama3",
vllm/model_executor/models/hunyuan_vision.py
+20 -8
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@@ -31,7 +31,11 @@ from typing import Annotated, Any, Literal, TypeAlias
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import BatchFeature
from transformers import BatchFeature, HunYuanVLProcessor
from transformers.models.hunyuan_vl.image_processing_hunyuan_vl import (
HunYuanVLImageProcessor,
smart_resize,
)
from vllm.config import VllmConfig
from vllm.config.multimodal import BaseDummyOptions
@@ -75,11 +79,6 @@ from vllm.transformers_utils.configs.hunyuan_vl import (
HunYuanVLConfig,
HunYuanVLVisionConfig,
)
from vllm.transformers_utils.processors.hunyuan_vl import HunYuanVLProcessor
from vllm.transformers_utils.processors.hunyuan_vl_image import (
HunYuanVLImageProcessor,
smart_resize,
)
from vllm.utils.tensor_schema import TensorSchema, TensorShape
from .interfaces import (
@@ -574,9 +573,12 @@ class HunYuanVLProcessingInfo(BaseProcessingInfo):
self,
**kwargs: object,
) -> HunYuanVLProcessor:
# transformers>=5.13 replaced `use_fast` with `backend`; pin the
# PIL backend to match the released HunyuanOCR checkpoint packing.
kwargs.pop("use_fast", None)
kwargs.setdefault("backend", "pil")
return self.ctx.get_hf_processor(
HunYuanVLProcessor,
use_fast=kwargs.pop("use_fast", True),
**kwargs,
)
@@ -724,8 +726,18 @@ class HunYuanVLMultiModalProcessor(BaseMultiModalProcessor[HunYuanVLProcessingIn
mm_kwargs: Mapping[str, object],
tok_kwargs: Mapping[str, object],
) -> BatchFeature:
hf_processor = self.info.get_hf_processor(**mm_kwargs)
# HunYuanVLProcessor requires image placeholders wrapped with start/end tokens.
if mm_data.get("images") is not None and prompt:
img_tok = hf_processor.image_token
wrapped = (
f"{hf_processor.image_start_token}{img_tok}"
f"{hf_processor.image_end_token}"
)
if img_tok in prompt and wrapped not in prompt:
prompt = prompt.replace(img_tok, wrapped)
return self.info.ctx.call_hf_processor(
self.info.get_hf_processor(**mm_kwargs),
hf_processor,
dict(text=prompt, **mm_data),
dict(**mm_kwargs, **tok_kwargs),
)
vllm/transformers_utils/processors/hunyuan_vl.py
-226
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@@ -1,226 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# adapted from https://github.com/ManaEstras/transformers/blob/v4.57.1.hyvl/src/transformers/models/hunyuan_vl/processing_hunyuan_vl.py
import numpy as np
import torch
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.processing_utils import ProcessorMixin
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
from transformers.video_utils import VideoInput
class HunYuanVLProcessor(ProcessorMixin):
attributes = ["image_processor", "tokenizer"]
valid_kwargs = ["chat_template"]
image_processor_class = "AutoImageProcessor"
tokenizer_class = "AutoTokenizer" # ("AutoTokenizer", None)
def __init__(
self,
image_processor=None,
tokenizer=None,
chat_template=None,
**kwargs,
):
# TODO Fix the init
self.tokenizer = tokenizer
self.image_token_id = 120120 # self.tokenizer.image_token_id
self.image_token = self.tokenizer.convert_ids_to_tokens(self.image_token_id)
self.im_start_token_id = 120118 # self.tokenizer.im_start_id
self.im_start_token = self.tokenizer.convert_ids_to_tokens(
self.im_start_token_id
)
self.im_end_token_id = 120119 # self.tokenizer.im_end_id
self.im_end_token = self.tokenizer.convert_ids_to_tokens(self.im_end_token_id)
self.placeholder_token = self.tokenizer.convert_ids_to_tokens(
self.tokenizer.vocab_size - 1
)
self.pad_id = 120002 # self.tokenizer.pad_token_id
super().__init__(image_processor, tokenizer, chat_template=chat_template)
def __call__(
self,
images: ImageInput = None,
text: TextInput
| PreTokenizedInput
| list[TextInput]
| list[PreTokenizedInput] = None,
videos: VideoInput = None,
**kwargs,
) -> BatchFeature:
image_inputs = {}
if images is not None:
image_inputs = self.image_processor(images=images)
image_grid_thw = image_inputs["image_grid_thw"]
if not isinstance(text, list):
text = [text]
text = text.copy() # below lines change text in-place
image_tokens_cumsum = [0]
if images is not None:
index = 0
for i in range(len(text)):
while self.image_token in text[i]:
grid_h, grid_w = image_grid_thw[index][-2:]
patch_h = grid_h // self.image_processor.merge_size
patch_w = grid_w // self.image_processor.merge_size
num_image_tokens = patch_h * (patch_w + 1) + 2
image_tokens_cumsum.append(
image_tokens_cumsum[-1] + num_image_tokens
)
# text[i] = text[i].replace(self.image_token, self.im_start_token + self.placeholder_token * num_image_tokens + self.im_end_token, 1) # noqa: E501
text[i] = text[i].replace(
self.image_token, self.placeholder_token * num_image_tokens, 1
)
index += 1
text[i] = text[i].replace(self.placeholder_token, self.image_token)
# text[i] = self.tokenizer.bos_token + text[i]
text_inputs = self.tokenizer(text, add_special_tokens=False, **kwargs)
self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
input_ids = text_inputs["input_ids"]
position_ids = torch.arange(len(input_ids[0]))
position_ids_w = torch.arange(len(input_ids[0]))
position_ids_h = torch.arange(len(input_ids[0]))
position_ids_t = torch.arange(len(input_ids[0]))
if images is not None:
image_token_pos_indices = torch.where(input_ids[0] == self.image_token_id)[
0
]
for i in range(len(image_grid_thw)):
grid_h, grid_w = image_grid_thw[i][-2:]
patch_h = grid_h // self.image_processor.merge_size
patch_w = grid_w // self.image_processor.merge_size
start_pos = image_token_pos_indices[image_tokens_cumsum[i]].item() + 1
replace_num = (patch_w + 1) * patch_h
position_ids_w[start_pos : start_pos + replace_num] = torch.tensor(
list(range(patch_w + 1)) * patch_h, dtype=torch.int64
)
patch_h_list = []
for h in range(patch_h):
patch_h_list += [h] * (patch_w + 1)
position_ids_h[start_pos : start_pos + replace_num] = torch.tensor(
patch_h_list, dtype=torch.int64
)
position_ids_t[start_pos : start_pos + replace_num] = 0
position_ids = torch.stack(
[position_ids, position_ids_w, position_ids_h, position_ids_t]
).unsqueeze(0)
text_inputs["position_ids"] = position_ids
attention_mask = input_ids.ne(self.pad_id)
text_inputs["attention_mask"] = attention_mask
text_inputs["imgs_pos"] = [self.get_imgs_pos(e) for e in input_ids]
# image_inputs["imgs"] = [[image_inputs["pixel_values"]]]
return_tensors = kwargs.pop("return_tensors", None)
return BatchFeature(
data={**text_inputs, **image_inputs},
tensor_type=return_tensors,
)
def batch_decode(self, *args, **kwargs):
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
return self.tokenizer.decode(*args, **kwargs)
def post_process_image_text_to_text(
self,
generated_outputs,
skip_special_tokens=True,
clean_up_tokenization_spaces=False,
**kwargs,
):
assert 0
def apply_chat_template(self, *args, **kwargs):
kwargs["return_dict"] = False
return self.tokenizer.apply_chat_template(*args, **kwargs)
def get_imgs_pos(self, doc_ids):
doc_ids = np.array(doc_ids, dtype=np.int64)
img_begin_index = np.where(doc_ids == self.im_start_token_id)[0]
img_end_index = np.where(doc_ids == self.im_end_token_id)[0]
imgs_pos = np.concatenate(
(
np.reshape(img_begin_index + 1, (-1, 1)),
np.reshape(img_end_index, (-1, 1)),
),
axis=-1,
).tolist()
return imgs_pos
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
def split_image_into_patch_blocks(
pixel_values: torch.Tensor, # shape: [batch_size, 3, H, W]
patch_size: int = 16, # e.g. 16
adaptor_patch_div: int = 4, # e.g. 4 --> each patch_size is cut into 4x4 small regions, i.e. patch_size // 4 # noqa: E501
) -> torch.Tensor:
"""
Split the input image tensor (supporting batch) into large patches of size `patch_size`,
and then further divide each large patch into smaller regions of size
(patch_size // adaptor_patch_div) x (patch_size // adaptor_patch_div).
Each small region is extracted as a tensor of shape [3, patch_size, patch_size].
The final output contains all such small region tensors.
Args:
pixel_values: Input image tensor of shape [batch_size, 3, H, W].
patch_size: Size of the large patch, e.g., 16.
adaptor_patch_div: Each large patch is divided into
(patch_size // adaptor_patch_div) x (patch_size // adaptor_patch_div)
smaller regions.
Returns:
patches: A tensor of shape [N, 3, patch_size, patch_size],
where N = batch_size * (H // patch_size) * (W // patch_size) * (patch_size // adaptor_patch_div)^2.
Each element in the batch corresponds to one small image region.
""" # noqa: E501
batch_size, channels, height, width = pixel_values.shape
assert channels == 3, "Pixel values must have 3 channels in dim=1"
assert height % patch_size == 0 and width % patch_size == 0, (
"H and W must be divisible by patch_size"
)
patch_height_num = height // patch_size
patch_width_num = width // patch_size
# Reshape to [B, 3, ph, ps, pw, ps]
img = pixel_values.reshape(
batch_size, 3, patch_height_num, patch_size, patch_width_num, patch_size
)
# Further split each psxps patch into (ps//aps)x(ps//aps) small regions
img = img.reshape(
batch_size,
3,
patch_height_num,
patch_size // adaptor_patch_div, # ps // aps
adaptor_patch_div,
patch_width_num,
patch_size // adaptor_patch_div, # ps // aps
adaptor_patch_div,
)
# Permute to group the small regions: [B, ph, pw, ps//aps, ps//aps, 3, aps, aps]
img = img.permute(0, 2, 5, 3, 6, 1, 4, 7)
# Reshape into [B * ph * pw * (ps//aps)^2, 3, patch_size, patch_size]
patches = img.reshape(-1, 3, patch_size, patch_size)
return patches
vllm/transformers_utils/processors/hunyuan_vl_image.py
-477
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@@ -1,477 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# adapted from https://github.com/ManaEstras/transformers/blob/v4.57.1.hyvl/src/transformers/models/hunyuan_vl/image_processing_hunyuan_vl.py
"""Image processor class for HunYuanVL."""
# isort conflicts with ruff for transformers imports
# isort: skip_file
import math
import numpy as np
import torchvision.transforms as transforms
from transformers import AutoImageProcessor
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
from transformers.image_transforms import (
convert_to_rgb,
)
from transformers.image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_flat_list_of_images,
make_list_of_images,
valid_images,
validate_preprocess_arguments,
)
from transformers.utils import TensorType, logging
from transformers.video_utils import VideoInput, make_batched_videos
logger = logging.get_logger(__name__)
def smart_resize(
height: int,
width: int,
factor: int = 16,
min_pixels: int = 512 * 512,
max_pixels: int = 2048 * 2048,
):
"""Rescales the image so that the following conditions are met:
1. Both dimensions (height and width) are divisible by 'factor'.
2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].
3. The aspect ratio of the image is maintained as closely as possible.
"""
if max(height, width) / min(height, width) > 200:
raise ValueError(
"absolute aspect ratio must be smaller than 200, got "
f"{max(height, width) / min(height, width)}"
)
h_bar = round(height / factor) * factor
w_bar = round(width / factor) * factor
if h_bar * w_bar > max_pixels:
beta = math.sqrt((height * width) / max_pixels)
h_bar = max(factor, math.floor(height / beta / factor) * factor)
w_bar = max(factor, math.floor(width / beta / factor) * factor)
elif h_bar * w_bar < min_pixels:
beta = math.sqrt(min_pixels / (height * width))
h_bar = math.ceil(height * beta / factor) * factor
w_bar = math.ceil(width * beta / factor) * factor
return h_bar, w_bar
class HunYuanVLImageProcessor(BaseImageProcessor):
model_input_names = [
"pixel_values",
"image_grid_thw",
"pixel_values_videos",
"video_grid_thw",
]
def __init__(
self,
do_resize: bool = True,
size: dict[str, int] | None = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_rescale: bool = True,
rescale_factor: int | float = 1 / 255,
do_normalize: bool = True,
image_mean: float | list[float] | None = None,
image_std: float | list[float] | None = None,
do_convert_rgb: bool = True,
min_pixels: int | None = None,
max_pixels: int | None = None,
patch_size: int = 16,
temporal_patch_size: int = 2,
merge_size: int = 2,
**kwargs,
) -> None:
super().__init__(**kwargs)
if size is not None and (
"shortest_edge" not in size or "longest_edge" not in size
):
raise ValueError(
"size must contain 'shortest_edge' and 'longest_edge' keys."
)
else:
size = {"shortest_edge": 512 * 512, "longest_edge": 2048 * 2048}
# backward compatibility: override size with min_pixels and max_pixels
# if they are provided.
if min_pixels is not None:
size["shortest_edge"] = min_pixels
if max_pixels is not None:
size["longest_edge"] = max_pixels
self.min_pixels = size["shortest_edge"]
self.max_pixels = size["longest_edge"]
self.size = size
self.do_resize = do_resize
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
self.patch_size = patch_size
self.temporal_patch_size = temporal_patch_size
self.merge_size = merge_size
self.do_convert_rgb = do_convert_rgb
# hard-code
def _preprocess(
self,
images: ImageInput | VideoInput,
do_resize: bool | None = None,
size: dict[str, int] | None = None,
resample: PILImageResampling = None,
do_rescale: bool | None = None,
rescale_factor: float | None = None,
do_normalize: bool | None = None,
image_mean: float | list[float] | None = None,
image_std: float | list[float] | None = None,
patch_size: int = 16,
temporal_patch_size: int = 2,
merge_size: int = 2,
do_convert_rgb: bool | None = None,
data_format: ChannelDimension | None = ChannelDimension.FIRST,
input_data_format: str | ChannelDimension | None = None,
):
"""
Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`.
Args:
images (`ImageInput`):
Image or batch of images to preprocess. Expects pixel values ranging from 0 to 255. If pixel values range from 0 to 1, set `do_rescale=False`.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
size (`dict[str, int]`, *optional*, defaults to `self.size`):
Size of the image after resizing. `shortest_edge` and `longest_edge` keys must be present.
resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
Resampling filter to use if resizing the image. This can be one of the `PILImageResampling` enums.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image.
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
Scale factor to use if rescaling the image.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the image.
image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
Mean to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
Standard deviation to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
patch_size (`int`, *optional*, defaults to `self.patch_size`):
The spatial patch size of the vision encoder.
temporal_patch_size (`int`, *optional*, defaults to `self.temporal_patch_size`):
The temporal patch size of the vision encoder.
merge_size (`int`, *optional*, defaults to `self.merge_size`):
The merge size of the vision encoder to llm encoder.
do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
Whether to convert the image to RGB.
data_format (`ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
""" # noqa: E501
images = make_list_of_images(images)
if do_convert_rgb:
images = [convert_to_rgb(image) for image in images]
width, height = images[0].width, images[0].height
resized_width, resized_height = width, height
processed_images = []
for image in images:
if do_resize:
resized_height, resized_width = smart_resize(
height=height,
width=width,
factor=patch_size * merge_size,
min_pixels=self.min_pixels,
max_pixels=self.max_pixels,
)
image = image.resize((resized_width, resized_height))
if do_normalize:
image = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize(self.image_mean, self.image_std),
]
)(image)
processed_images.append(image)
patches = np.array(processed_images)
channel = patches.shape[1]
grid_t = patches.shape[0] // temporal_patch_size
grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
patches = patches.reshape(
1,
channel,
grid_h // merge_size,
merge_size,
patch_size,
grid_w // merge_size,
merge_size,
patch_size,
)
patches = patches.transpose(0, 2, 3, 5, 6, 1, 4, 7)
flatten_patches = patches.reshape(
1 * grid_h * grid_w, channel * patch_size * patch_size
)
return flatten_patches, (grid_t, grid_h, grid_w)
def preprocess(
self,
images: ImageInput,
videos: VideoInput = None,
do_resize: bool | None = None,
size: dict[str, int] | None = None,
min_pixels: int | None = None,
max_pixels: int | None = None,
resample: PILImageResampling = None,
do_rescale: bool | None = None,
rescale_factor: float | None = None,
do_normalize: bool | None = None,
image_mean: float | list[float] | None = None,
image_std: float | list[float] | None = None,
patch_size: int | None = None,
temporal_patch_size: int | None = None,
merge_size: int | None = None,
do_convert_rgb: bool | None = None,
return_tensors: str | TensorType | None = None,
data_format: ChannelDimension | None = ChannelDimension.FIRST,
input_data_format: str | ChannelDimension | None = None,
):
"""
Args:
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
videos (`VideoInput`):
Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If
passing in videos with pixel values between 0 and 1, set `do_rescale=False`.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
size (`dict[str, int]`, *optional*, defaults to `self.size`):
Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
the longest edge resized to keep the input aspect ratio.
resample (`int`, *optional*, defaults to `self.resample`):
Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
has an effect if `do_resize` is set to `True`.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image.
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the image.
image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
`True`.
min_pixels (`int`, *optional*, defaults to `self.min_pixels`):
The min pixels of the image to resize the image.
max_pixels (`int`, *optional*, defaults to `self.max_pixels`):
The max pixels of the image to resize the image.
patch_size (`int`, *optional*, defaults to `self.patch_size`):
The spatial patch size of the vision encoder.
temporal_patch_size (`int`, *optional*, defaults to `self.temporal_patch_size`):
The temporal patch size of the vision encoder.
merge_size (`int`, *optional*, defaults to `self.merge_size`):
The merge size of the vision encoder to llm encoder.
do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
Whether to convert the image to RGB.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
""" # noqa: E501
min_pixels = min_pixels if min_pixels is not None else self.min_pixels
max_pixels = max_pixels if max_pixels is not None else self.max_pixels
if size is not None:
if "shortest_edge" not in size or "longest_edge" not in size:
raise ValueError(
"size must contain 'shortest_edge' and 'longest_edge' keys."
)
min_pixels = size["shortest_edge"]
elif min_pixels is not None and max_pixels is not None:
# backward compatibility: override size with min_pixels and max_pixels
# if they are provided.
size = {"shortest_edge": min_pixels, "longest_edge": max_pixels}
else:
size = {**self.size}
do_resize = do_resize if do_resize is not None else self.do_resize
resample = resample if resample is not None else self.resample
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
rescale_factor = (
rescale_factor if rescale_factor is not None else self.rescale_factor
)
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
image_mean = image_mean if image_mean is not None else self.image_mean
image_std = image_std if image_std is not None else self.image_std
patch_size = patch_size if patch_size is not None else self.patch_size
temporal_patch_size = (
temporal_patch_size
if temporal_patch_size is not None
else self.temporal_patch_size
)
merge_size = merge_size if merge_size is not None else self.merge_size
do_convert_rgb = (
do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
)
if images is not None:
images = make_flat_list_of_images(images)
if images is not None and not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
validate_preprocess_arguments(
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
do_resize=do_resize,
size=size,
resample=resample,
)
data = {}
if images is not None:
pixel_values, vision_grid_thws = [], []
for image in images:
patches, image_grid_thw = self._preprocess(
image,
do_resize=do_resize,
size=size,
resample=resample,
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
patch_size=patch_size,
temporal_patch_size=temporal_patch_size,
merge_size=merge_size,
data_format=data_format,
do_convert_rgb=do_convert_rgb,
input_data_format=input_data_format,
)
pixel_values.extend(patches)
vision_grid_thws.append(image_grid_thw)
pixel_values = np.array(pixel_values)
vision_grid_thws = np.array(vision_grid_thws)
data.update(
{"pixel_values": pixel_values, "image_grid_thw": vision_grid_thws}
)
# kept for BC only and should be removed after v5.0
if videos is not None:
logger.warning(
"`HunYuanVLV1ImageProcessor` works only with image inputs "
"and doesn't process videos anymore. "
"This is a deprecated behavior and will be removed in v5.0. "
"Your videos should be forwarded to `HunYuanVLV1VideoProcessor`. "
)
videos = make_batched_videos(videos)
pixel_values_videos, vision_grid_thws_videos = [], []
for images in videos:
patches, video_grid_thw = self._preprocess(
images,
do_resize=do_resize,
size=size,
resample=resample,
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
patch_size=patch_size,
temporal_patch_size=temporal_patch_size,
merge_size=merge_size,
data_format=data_format,
do_convert_rgb=do_convert_rgb,
input_data_format=input_data_format,
)
pixel_values_videos.extend(patches)
vision_grid_thws_videos.append(video_grid_thw)
data.update(
{
"pixel_values_videos": np.array(pixel_values_videos),
"video_grid_thw": np.array(vision_grid_thws_videos),
}
)
return BatchFeature(data=data, tensor_type=return_tensors)
def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None):
"""
A utility that returns number of image patches for a given image size.
Args:
height (`int`):
Height of the input image.
width (`int`):
Width of the input image.
images_kwargs (`dict`, *optional*):
Any kwargs to override defaults of the image processor.
Returns:
`int`: Number of image patches per image.
"""
min_pixels = (
images_kwargs["min_pixels"]
if "min_pixels" in images_kwargs
else self.size["shortest_edge"]
)
max_pixels = (
images_kwargs["max_pixels"]
if "max_pixels" in images_kwargs
else self.size["longest_edge"]
)
patch_size = images_kwargs.get("patch_size", self.patch_size)
merge_size = images_kwargs.get("merge_size", self.merge_size)
factor = patch_size * merge_size
resized_height, resized_width = smart_resize(
height, width, factor, min_pixels=min_pixels, max_pixels=max_pixels
)
grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
return grid_h * (grid_w + 1) + 2
AutoImageProcessor.register("HunYuanVLImageProcessor", HunYuanVLImageProcessor)