# 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)