# GlmImage ## Overview GLM-Image is an image generation model adopts a hybrid autoregressive + diffusion decoder architecture, effectively pushing the upper bound of visual fidelity and fine-grained details. In general image generation quality, it aligns with industry-standard LDM-based approaches, while demonstrating significant advantages in knowledge-intensive image generation scenarios. Model architecture: a hybrid autoregressive + diffusion decoder design、 + Autoregressive generator: a 9B-parameter model initialized from [GLM-4-9B-0414](https://huggingface.co/zai-org/GLM-4-9B-0414), with an expanded vocabulary to incorporate visual tokens. The model first generates a compact encoding of approximately 256 tokens, then expands to 1K–4K tokens, corresponding to 1K–2K high-resolution image outputs. + Diffusion Decoder: a 7B-parameter decoder based on a single-stream DiT architecture for latent-space image decoding. It is equipped with a Glyph Encoder text module, significantly improving accurate text rendering within images. Post-training with decoupled reinforcement learning: the model introduces a fine-grained, modular feedback strategy using the GRPO algorithm, substantially enhancing both semantic understanding and visual detail quality. + Autoregressive module: provides low-frequency feedback signals focused on aesthetics and semantic alignment, improving instruction following and artistic expressiveness. + Decoder module: delivers high-frequency feedback targeting detail fidelity and text accuracy, resulting in highly realistic textures, lighting, and color reproduction, as well as more precise text rendering. GLM-Image supports both text-to-image and image-to-image generation within a single model + Text-to-image: generates high-detail images from textual descriptions, with particularly strong performance in information-dense scenarios. + Image-to-image: supports a wide range of tasks, including image editing, style transfer, multi-subject consistency, and identity-preserving generation for people and objects. + `GlmImageForConditionalGeneration` is the AR part of GLM-Image model, and for full image generation pipeline, please refer to [here](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/glm_image). This model was contributed by [Raushan Turganbay](https://huggingface.co/RaushanTurganbay) and [Yuxuan Zhang](https://huggingface.co/ZHANGYUXUAN-zR). ## Usage examples Using GLM-Image with image input to generate vision token for DIT using. ```python from transformers import GlmImageForConditionalGeneration, AutoProcessor import torch model = GlmImageForConditionalGeneration.from_pretrained( pretrained_model_name_or_path="zai-org/GLM-Image/vision_language_encoder", dtype=torch.bfloat16, device_map="cuda:0" ) processor = AutoProcessor.from_pretrained( pretrained_model_name_or_path="zai-org/GLM-Image/processor", use_fast=True ) # Case1 T2I prompt = "现代美食杂志风格的甜点制作教程图，主题为覆盆子慕斯蛋糕。整体布局干净明亮，分为四个主要区域：顶部左侧是黑色粗体标题“覆盆子慕斯蛋糕制作指南”，右侧搭配光线柔和的成品蛋糕特写照片，蛋糕呈淡粉色，表面点缀新鲜覆盆子与薄荷叶；左下方为配料清单区域，标题“配料”使用简洁字体，下方列有“面粉 150g”“鸡蛋 3个”“细砂糖 120g”“覆盆子果泥 200g”“明胶片 10g”“淡奶油 300ml”“新鲜覆盆子”等配料，每种配料旁配有简约线图标（如面粉袋、鸡蛋、糖罐等）；右下方是四个等大的步骤方框，每个方框内含高清微距实拍图及对应操作说明，从上到下依次为：步骤1展示打蛋器打发白色泡沫（对应说明“打发蛋白至干性发泡”），步骤2展示红白相间的混合物被刮刀翻拌（对应说明“轻柔翻拌果泥与面糊”），步骤3展示粉色液体被倒入圆形模具（对应说明“倒入模具并冷藏4小时”），步骤4展示成品蛋糕表面装饰覆盆子与薄荷叶（对应说明“用覆盆子和薄荷装饰”）；底部边缘设浅棕色信息条，左侧图标分别代表“准备时间：30分钟”“烹饪时间：20分钟”“份量：8人份”。整体色调以奶油白、淡粉色为主，背景带轻微纸质纹理，图文排版紧凑有序，信息层级分明。" target_h, target_w = 1152, 768 use_reference_images = False reference_image_paths = None # ## Case2 # prompt = "Replace the background of the snow forest with an underground station featuring an automatic escalator." # cond_0 = "cond.jpg" # target_h, target_w = 1152, 768 # use_reference_images = True # reference_image_paths = [cond_0] ## Case3 # prompt = "Make the man in the first figure and the child from the second image bow at the same time in a respectful KTV." # cond_0 = "cond_0.jpg" # cond_1 = "cond_1.jpg" # target_h, target_w = 1152, 768 # use_reference_images = True # reference_image_paths = [cond_0, cond_1] def build_messages(prompt, use_reference_images, reference_image_paths): content = [] if use_reference_images: for img_path in reference_image_paths: content.append({"type": "image", "url": img_path}) content.append({"type": "text", "text": prompt}) return [{"role": "user", "content": content}] def compute_generation_params(image_grid_thw, use_reference_images): grid_sizes = [] for i in range(image_grid_thw.shape[0]): t, h, w = image_grid_thw[i].tolist() grid_sizes.append(int(h * w)) target_output_length = grid_sizes[0] if use_reference_images: max_new_tokens = grid_sizes[-1] + 1 output_start_offset = 0 output_length = grid_sizes[-1] else: total_tokens = sum(grid_sizes) max_new_tokens = total_tokens + 1 output_start_offset = sum(grid_sizes[1:]) output_length = target_output_length return max_new_tokens, output_start_offset, output_length messages = build_messages(prompt, use_reference_images, reference_image_paths if use_reference_images else None) inputs = processor.apply_chat_template( messages, target_h=target_h, target_w=target_w, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) image_grid_thw = inputs.get('image_grid_thw') print(f"image_grid_thw: {image_grid_thw}") max_new_tokens, output_start_offset, output_length = compute_generation_params( image_grid_thw, use_reference_images ) print(f"use_reference_images: {use_reference_images}") print(f"max_new_tokens: {max_new_tokens}") print(f"output_start_offset: {output_start_offset}") print(f"output_length: {output_length}") outputs = model.generate( **inputs, max_new_tokens=max_new_tokens, do_sample=True ) input_length = inputs["input_ids"].shape[-1] output_tokens = outputs[0][input_length:][output_start_offset:output_start_offset + output_length] print(f"Input length: {input_length}") print(f"Total generated tokens: {outputs[0].shape[-1] - input_length}") print(f"Extracted output tokens shape: {output_tokens.shape}") print(f"Output tokens: {output_tokens}") ``` ## GlmImageConfig[[transformers.GlmImageConfig]] #### transformers.GlmImageConfig[[transformers.GlmImageConfig]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/glm_image/configuration_glm_image.py#L275) This is the configuration class to store the configuration of a [GlmImageModel](/docs/transformers/v5.3.0/en/model_doc/glm_image#transformers.GlmImageModel). It is used to instantiate a GLM-Image model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of GLM-Image [zai-org/GLM-Image](https://huggingface.co/zai-org/GLM-Image) architecture. Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.3.0/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/v5.3.0/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. ```python >>> from transformers import Glm4vForConditionalGeneration, Glm4vConfig >>> # Initializing a GLM-Image style configuration >>> configuration = Glm4vConfig() >>> # Initializing a model from the GLM-Image style configuration >>> model = Glm4vForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` **Parameters:** text_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `GlmImageTextConfig`) : The config object or dictionary of the text backbone. vision_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `GlmImageVisionConfig`) : The config object or dictionary of the vision backbone. vq_config (`Union[Dict, GlmImageVQVAEConfig]`, *optional*) : GlmImageVQVAEConfig instance containing the configuration for the VQ-VAE model. image_token_id (`int`, *optional*, defaults to 167855) : The image token index to encode the image prompt. image_start_token_id (`int`, *optional*, defaults to 16384) : The image start token index to encode the start of image. image_end_token_id (`int`, *optional*, defaults to 16385) : The image end token index to encode the end of image. tie_word_embeddings (`bool`, *optional*, defaults to `False`) : Whether the model's input and output word embeddings should be tied. ## GlmImageVisionConfig[[transformers.GlmImageVisionConfig]] #### transformers.GlmImageVisionConfig[[transformers.GlmImageVisionConfig]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/glm_image/configuration_glm_image.py#L67) This is the configuration class to store the configuration of a [GlmImageVisionModel](/docs/transformers/v5.3.0/en/model_doc/glm_image#transformers.GlmImageVisionModel). It is used to instantiate an GlmImageVisionModel model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of GLM-Image [zai-org/GLM-Image](https://huggingface.co/zai-org/GLM-Image). **Parameters:** depth (`int`, *optional*, defaults to 40) : Number of layers (depth) in the model. hidden_size (`int`, *optional*, defaults to 1536) : Dimensionality of the encoder layers and the pooler layer. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`) : The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. attention_bias (`bool`, *optional*, defaults to `True`) : Whether to add a bias to the queries, keys and values. attention_dropout (`float`, *optional*, defaults to 0.0) : Dropout probability for attention weights. num_heads (`int`, *optional*, defaults to 16) : Number of attention heads for each attention layer in the Transformer architecture. in_channels (`int`, *optional*, defaults to 3) : Number of input channels. image_size (`int` or `list[int]`, *optional*, defaults to 2048) : The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 16) : The size (resolution) of each patch. layer_norm_eps (`float`, *optional*, defaults to 1e-06) : The epsilon used by the layer normalization layers. spatial_merge_size (`int`, *optional*, defaults to 1) : The size used for merging spatial dimensions. intermediate_size (`int`, *optional*, defaults to 6144) : Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. initializer_range (`float`, *optional*, defaults to 0.02) : The standard deviation of the truncated_normal_initializer for initializing all weight matrices. ## GlmImageTextConfig[[transformers.GlmImageTextConfig]] #### transformers.GlmImageTextConfig[[transformers.GlmImageTextConfig]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/glm_image/configuration_glm_image.py#L141) This is the configuration class to store the configuration of a [GlmImageTextModel](/docs/transformers/v5.3.0/en/model_doc/glm_image#transformers.GlmImageTextModel). It is used to instantiate a GLM-Image model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of GLM-Image [zai-org/GLM-Image](https://huggingface.co/zai-org/GLM-Image). Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.3.0/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/v5.3.0/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. ```python >>> from transformers import GlmImageTextModel, GlmImageConfig >>> # Initializing a GlmImageConfig style configuration >>> configuration = GlmImageConfig() >>> # Initializing a model from the GlmImageConfig style configuration >>> model = GlmImageTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` **Parameters:** vocab_size (`int`, *optional*, defaults to 168064) : Vocabulary size of the GlmImage model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [GlmImageModel](/docs/transformers/v5.3.0/en/model_doc/glm_image#transformers.GlmImageModel) hidden_size (`int`, *optional*, defaults to 4096) : Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 13696) : Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 40) : Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 32) : Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 2) : This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`) : The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 131072) : The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02) : The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-05) : The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`) : Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. attention_dropout (`float`, *optional*, defaults to 0.0) : The dropout ratio for the attention probabilities. rope_parameters (`RopeParameters`, *optional*) : Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. pad_token_id (`int`, *optional*, defaults to 167841) : The id of the padding token. vision_vocab_size (`int`, *optional*, defaults to 16512) : Vision vocabulary size of the GlmImage model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [GlmImageVisionModel](/docs/transformers/v5.3.0/en/model_doc/glm_image#transformers.GlmImageVisionModel) attention_bias (`bool`, *optional*, defaults to `True`) : Whether to add a bias to the queries, keys and values. eos_token_id (`int`, *optional*, defaults to 16385) : The id of the end of sequence token. ## GlmImageVQVAEConfig[[transformers.GlmImageVQVAEConfig]] #### transformers.GlmImageVQVAEConfig[[transformers.GlmImageVQVAEConfig]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/glm_image/configuration_glm_image.py#L25) This is the configuration class to store the configuration of a `GlmImageVQModel`. It is used to instantiate a `GlmImageVQModel` according to the specified arguments, defining the model architecture. Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.3.0/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/v5.3.0/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. Instantiating a configuration with the defaults will yield a similar configuration to the VQModel of the [zai-org/GLM-Image](https://huggingface.co/zai-org/GLM-Image) architecture. **Parameters:** embed_dim (`int`, *optional*, defaults to 2048) : Dimensionality of each embedding vector. num_embeddings (`int`, *optional*, defaults to 16384) : Number of codebook embeddings. latent_channels (`int`, *optional*, defaults to 1536) : Number of channels for the latent space. in_channels (`int`, *optional*, defaults to 3) : Number of input channels. initializer_range (`float`, *optional*, defaults to 0.02) : The standard deviation of the truncated_normal_initializer for initializing all weight matrices. ## GlmImageImageProcessor[[transformers.GlmImageImageProcessor]] #### transformers.GlmImageImageProcessor[[transformers.GlmImageImageProcessor]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/glm_image/image_processing_glm_image.py#L112) Constructs a Qwen2-VL image processor that dynamically resizes images based on the original images. preprocesstransformers.GlmImageImageProcessor.preprocesshttps://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/glm_image/image_processing_glm_image.py#L330[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "do_resize", "val": ": bool | None = None"}, {"name": "size", "val": ": dict[str, int] | None = None"}, {"name": "min_pixels", "val": ": int | None = None"}, {"name": "max_pixels", "val": ": int | None = None"}, {"name": "resample", "val": ": PIL.Image.Resampling | None = None"}, {"name": "do_rescale", "val": ": bool | None = None"}, {"name": "rescale_factor", "val": ": float | None = None"}, {"name": "do_normalize", "val": ": bool | None = None"}, {"name": "image_mean", "val": ": float | list[float] | None = None"}, {"name": "image_std", "val": ": float | list[float] | None = None"}, {"name": "patch_size", "val": ": int | None = None"}, {"name": "temporal_patch_size", "val": ": int | None = None"}, {"name": "merge_size", "val": ": int | None = None"}, {"name": "do_convert_rgb", "val": ": bool | None = None"}, {"name": "return_tensors", "val": ": str | transformers.utils.generic.TensorType | None = None"}, {"name": "data_format", "val": ": transformers.image_utils.ChannelDimension | None = "}, {"name": "input_data_format", "val": ": str | transformers.image_utils.ChannelDimension | None = None"}]- **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`. - **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.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.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.0 **Parameters:** do_resize (`bool`, *optional*, defaults to `True`) : Whether to resize the image's (height, width) dimensions. size (`dict[str, int]`, *optional*, defaults to `{"shortest_edge" : 56 * 56, "longest_edge": 28 * 28 * 1280}`): Size of the image after resizing. `shortest_edge` and `longest_edge` keys must be present. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`) : Resampling filter to use when resizing the image. do_rescale (`bool`, *optional*, defaults to `True`) : Whether to rescale the image by the specified scale `rescale_factor`. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`) : Scale factor to use if rescaling the image. do_normalize (`bool`, *optional*, defaults to `True`) : Whether to normalize the image. image_mean (`float` or `list[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`) : Mean to use if normalizing the image. This is a float or list of floats for each channel in the image. image_std (`float` or `list[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`) : Standard deviation to use if normalizing the image. This is a float or list of floats for each channel in the image. do_convert_rgb (`bool`, *optional*, defaults to `True`) : Whether to convert the image to RGB. min_pixels (`int`, *optional*, defaults to `56 * 56`) : The min pixels of the image to resize the image. max_pixels (`int`, *optional*, defaults to `28 * 28 * 1280`) : The max pixels of the image to resize the image. patch_size (`int`, *optional*, defaults to 14) : The spatial patch size of the vision encoder. temporal_patch_size (`int`, *optional*, defaults to 2) : The temporal patch size of the vision encoder. merge_size (`int`, *optional*, defaults to 2) : The merge size of the vision encoder to llm encoder. ## GlmImageImageProcessorFast[[transformers.GlmImageImageProcessorFast]] #### transformers.GlmImageImageProcessorFast[[transformers.GlmImageImageProcessorFast]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/glm_image/image_processing_glm_image_fast.py#L87) Constructs a GlmImageImageProcessorFast image processor. preprocesstransformers.GlmImageImageProcessorFast.preprocesshttps://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/glm_image/image_processing_glm_image_fast.py#L142[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.glm_image.image_processing_glm_image.GlmImageImageProcessorKwargs]"}]- **images** (`Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]`) -- 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`. - **min_pixels** (`int`, *kwargs*, *optional*, defaults to `56 * 56`) -- The min pixels of the image to resize the image. - **max_pixels** (`int`, *kwargs*, *optional*, defaults to `28 * 28 * 1280`) -- The max pixels of the image to resize the image. - **patch_size** (`int`, *kwargs*, *optional*, defaults to 14) -- The spatial patch size of the vision encoder. - **temporal_patch_size** (`int`, *kwargs*, *optional*, defaults to 2) -- The temporal patch size of the vision encoder. - **merge_size** (`int`, *kwargs*, *optional*, defaults to 2) -- The merge size of the vision encoder to llm encoder. - **return_tensors** (`str` or [TensorType](/docs/transformers/v5.3.0/en/internal/file_utils#transformers.TensorType), *optional*) -- Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors. - ****kwargs** ([ImagesKwargs](/docs/transformers/v5.3.0/en/main_classes/processors#transformers.ImagesKwargs), *optional*) -- Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.0`~feature_extraction_utils.BatchFeature`- **data** (`dict`, *optional*) -- Dictionary of lists/arrays/tensors returned by the __call__/pad methods ('input_values', 'attention_mask', etc.). - **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization. - **skip_tensor_conversion** (`list[str]` or `set[str]`, *optional*) -- List or set of keys that should NOT be converted to tensors, even when `tensor_type` is specified. **Parameters:** min_pixels (`int`, *kwargs*, *optional*, defaults to `56 * 56`) : The min pixels of the image to resize the image. max_pixels (`int`, *kwargs*, *optional*, defaults to `28 * 28 * 1280`) : The max pixels of the image to resize the image. patch_size (`int`, *kwargs*, *optional*, defaults to 14) : The spatial patch size of the vision encoder. temporal_patch_size (`int`, *kwargs*, *optional*, defaults to 2) : The temporal patch size of the vision encoder. merge_size (`int`, *kwargs*, *optional*, defaults to 2) : The merge size of the vision encoder to llm encoder. - ****kwargs** ([ImagesKwargs](/docs/transformers/v5.3.0/en/main_classes/processors#transformers.ImagesKwargs), *optional*) : Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments. **Returns:** ``~feature_extraction_utils.BatchFeature`` - **data** (`dict`, *optional*) -- Dictionary of lists/arrays/tensors returned by the __call__/pad methods ('input_values', 'attention_mask', etc.). - **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization. - **skip_tensor_conversion** (`list[str]` or `set[str]`, *optional*) -- List or set of keys that should NOT be converted to tensors, even when `tensor_type` is specified. ## GlmImageProcessor[[transformers.GlmImageProcessor]] #### transformers.GlmImageProcessor[[transformers.GlmImageProcessor]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/glm_image/processing_glm_image.py#L62) Constructs a GLM-Image processor which wraps a GLM-Image image processor and a GLM-Image tokenizer into a single processor. `__call__()` and [decode()](/docs/transformers/v5.3.0/en/main_classes/processors#transformers.ProcessorMixin.decode) for more information. **Parameters:** image_processor ([GlmImageProcessor](/docs/transformers/v5.3.0/en/model_doc/glm_image#transformers.GlmImageProcessor), *optional*) : The image processor is a required input. tokenizer ([PreTrainedTokenizerFast](/docs/transformers/v5.3.0/en/main_classes/tokenizer#transformers.TokenizersBackend), *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. ## GlmImageVisionModel[[transformers.GlmImageVisionModel]] #### transformers.GlmImageVisionModel[[transformers.GlmImageVisionModel]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/glm_image/modeling_glm_image.py#L668) forwardtransformers.GlmImageVisionModel.forwardhttps://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/glm_image/modeling_glm_image.py#L720[{"name": "pixel_values", "val": ": Tensor"}, {"name": "grid_thw", "val": ": Tensor"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **pixel_values** (`torch.Tensor` of shape `(total_patches, num_channels * patch_size * patch_size)`) -- Packed pixel values. - **grid_thw** (`torch.Tensor` of shape `(num_images, 3)`) -- The temporal, height and width of feature shape of each image.0`torch.Tensor` of shape `(total_patches, hidden_size)`Hidden states. The [GlmImageVisionModel](/docs/transformers/v5.3.0/en/model_doc/glm_image#transformers.GlmImageVisionModel) forward method, overrides the `__call__` special method. Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them. **Parameters:** pixel_values (`torch.Tensor` of shape `(total_patches, num_channels * patch_size * patch_size)`) : Packed pixel values. grid_thw (`torch.Tensor` of shape `(num_images, 3)`) : The temporal, height and width of feature shape of each image. **Returns:** ``torch.Tensor` of shape `(total_patches, hidden_size)`` Hidden states. ## GlmImageTextModel[[transformers.GlmImageTextModel]] #### transformers.GlmImageTextModel[[transformers.GlmImageTextModel]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/glm_image/modeling_glm_image.py#L841) The bare Glm Image Text Model outputting raw hidden-states without any specific head on to. This model inherits from [PreTrainedModel](/docs/transformers/v5.3.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. forwardtransformers.GlmImageTextModel.forwardhttps://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/glm_image/modeling_glm_image.py#L861[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "use_cache", "val": ": bool | None = None"}, {"name": "cache_position", "val": ": torch.LongTensor | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.modeling_flash_attention_utils.FlashAttentionKwargs]"}]- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of input sequence tokens in the vocabulary. Padding will be ignored by default. Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.3.0/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.3.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.3.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#input-ids) - **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) -- Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) - **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) - **past_key_values** (`~cache_utils.Cache`, *optional*) -- Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Only [Cache](/docs/transformers/v5.3.0/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). If no `past_key_values` are passed, [DynamicCache](/docs/transformers/v5.3.0/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default. The model will output the same cache format that is fed as input. If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. - **inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. - **use_cache** (`bool`, *optional*) -- If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). - **cache_position** (`torch.LongTensor` of shape `(sequence_length)`, *optional*) -- Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.0[BaseModelOutputWithPast](/docs/transformers/v5.3.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPast](/docs/transformers/v5.3.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (`None`) and inputs. The [GlmImageTextModel](/docs/transformers/v5.3.0/en/model_doc/glm_image#transformers.GlmImageTextModel) forward method, overrides the `__call__` special method. Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them. - **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. - **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/v5.3.0/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. - **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. - **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. **Parameters:** config ([GlmImageTextConfig](/docs/transformers/v5.3.0/en/model_doc/glm_image#transformers.GlmImageTextConfig)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.3.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. **Returns:** `[BaseModelOutputWithPast](/docs/transformers/v5.3.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or `tuple(torch.FloatTensor)`` A [BaseModelOutputWithPast](/docs/transformers/v5.3.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (`None`) and inputs. ## GlmImageVQVAE[[transformers.GlmImageVQVAE]] #### transformers.GlmImageVQVAE[[transformers.GlmImageVQVAE]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/glm_image/modeling_glm_image.py#L640) The VQ-VAE model used in GlmImage for encoding/decoding images into discrete tokens. This model follows the "Make-a-scene: Scene-based text-to-image generation with human priors" paper from [ Oran Gafni, Adam Polyak, Oron Ashual, Shelly Sheynin, Devi Parikh, and Yaniv Taigman](https://huggingface.co/papers/2203.13131). This model inherits from [PreTrainedModel](/docs/transformers/v5.3.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. _forward_unimplementedtransformers.GlmImageVQVAE.forwardhttps://github.com/huggingface/transformers/blob/v5.3.0/src/torch/nn/modules/module.py#L391[{"name": "*input", "val": ": typing.Any"}] Define the computation performed at every call. Should be overridden by all subclasses. Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them. **Parameters:** config ([GlmImageVQVAEConfig](/docs/transformers/v5.3.0/en/model_doc/glm_image#transformers.GlmImageVQVAEConfig)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.3.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. ## GlmImageModel[[transformers.GlmImageModel]] #### transformers.GlmImageModel[[transformers.GlmImageModel]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/glm_image/modeling_glm_image.py#L949) The bare Glm Image Model outputting raw hidden-states without any specific head on top. This model inherits from [PreTrainedModel](/docs/transformers/v5.3.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. forwardtransformers.GlmImageModel.forwardhttps://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/glm_image/modeling_glm_image.py#L1289[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "pixel_values", "val": ": torch.Tensor | None = None"}, {"name": "image_grid_thw", "val": ": torch.LongTensor | None = None"}, {"name": "images_per_sample", "val": ": torch.LongTensor | None = None"}, {"name": "rope_deltas", "val": ": torch.LongTensor | None = None"}, {"name": "cache_position", "val": ": torch.LongTensor | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of input sequence tokens in the vocabulary. Padding will be ignored by default. Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.3.0/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.3.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.3.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#input-ids) - **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) -- Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) - **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) - **past_key_values** (`~cache_utils.Cache`, *optional*) -- Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Only [Cache](/docs/transformers/v5.3.0/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). If no `past_key_values` are passed, [DynamicCache](/docs/transformers/v5.3.0/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default. The model will output the same cache format that is fed as input. If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. - **inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. - **pixel_values** (`torch.Tensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) -- The tensors corresponding to the input images. Pixel values can be obtained using `image_processor_class`. See `image_processor_class.__call__` for details (`processor_class` uses `image_processor_class` for processing images). - **image_grid_thw** (`torch.LongTensor` of shape `(total_images_in_batch, 3)`, *optional*) -- The temporal, height and width of feature shape of each image in LLM. Images are packed across all samples in the batch. - **images_per_sample** (`torch.LongTensor` of shape `(batch_size,)`, *optional*) -- Number of images (including target grids) for each sample in the batch. - **rope_deltas** (`torch.LongTensor` of shape `(batch_size, )`, *optional*) -- The rope index difference between sequence length and multimodal rope. - **cache_position** (`torch.LongTensor` of shape `(sequence_length)`, *optional*) -- Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.0`GlmImageModelOutputWithPast` or `tuple(torch.FloatTensor)`A `GlmImageModelOutputWithPast` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (`None`) and inputs. The [GlmImageModel](/docs/transformers/v5.3.0/en/model_doc/glm_image#transformers.GlmImageModel) forward method, overrides the `__call__` special method. Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them. - **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*, defaults to `None`) -- Sequence of hidden-states at the output of the last layer of the model. - **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/v5.3.0/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. - **hidden_states** (`tuple[torch.FloatTensor]`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. - **attentions** (`tuple[torch.FloatTensor]`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. - **rope_deltas** (`torch.LongTensor` of shape `(batch_size, )`, *optional*) -- The rope index difference between sequence length and multimodal rope. **Parameters:** config ([GlmImageModel](/docs/transformers/v5.3.0/en/model_doc/glm_image#transformers.GlmImageModel)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.3.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. **Returns:** ``GlmImageModelOutputWithPast` or `tuple(torch.FloatTensor)`` A `GlmImageModelOutputWithPast` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (`None`) and inputs. #### get_image_features[[transformers.GlmImageModel.get_image_features]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/glm_image/modeling_glm_image.py#L1202) - **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model. - **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. - **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. - **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. **Parameters:** pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) : The tensors corresponding to the input images. image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*) : The temporal, height and width of feature shape of each image in LLM. **Returns:** `[BaseModelOutputWithPooling](/docs/transformers/v5.3.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`` A [BaseModelOutputWithPooling](/docs/transformers/v5.3.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([GlmImageConfig](/docs/transformers/v5.3.0/en/model_doc/glm_image#transformers.GlmImageConfig)) and inputs. ## GlmImageForConditionalGeneration[[transformers.GlmImageForConditionalGeneration]] #### transformers.GlmImageForConditionalGeneration[[transformers.GlmImageForConditionalGeneration]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/glm_image/modeling_glm_image.py#L1455) forwardtransformers.GlmImageForConditionalGeneration.forwardhttps://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/glm_image/modeling_glm_image.py#L1489[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "pixel_values", "val": ": torch.Tensor | None = None"}, {"name": "image_grid_thw", "val": ": torch.LongTensor | None = None"}, {"name": "images_per_sample", "val": ": torch.LongTensor | None = None"}, {"name": "cache_position", "val": ": torch.LongTensor | None = None"}, {"name": "logits_to_keep", "val": ": int | torch.Tensor = 0"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}] labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. image_grid_thw (`torch.LongTensor` of shape `(total_images_in_batch, 3)`, *optional*): The temporal, height and width of feature shape of each image in LLM. Images are packed across all samples in the batch. images_per_sample (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Number of images (including target grids) for each sample in the batch. Example: ```python >>> from PIL import Image >>> import httpx >>> from io import BytesIO >>> from transformers import AutoProcessor, GlmImageForConditionalGeneration >>> model = GlmImageForConditionalGeneration.from_pretrained("zai-org/GLM-Image") >>> processor = AutoProcessor.from_pretrained("zai-org/GLM-Image") >>> messages = [ { "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": "Add a truck of this photo.28 40"}, ], }, ] >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg" >>> with httpx.stream("GET", url) as response: ... image = Image.open(BytesIO(response.read())) >>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) >>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos]) >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "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 ..." ``` #### get_image_features[[transformers.GlmImageForConditionalGeneration.get_image_features]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/glm_image/modeling_glm_image.py#L1471) - **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model. - **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. - **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. - **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. **Parameters:** pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) : The tensors corresponding to the input images. image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*) : The temporal, height and width of feature shape of each image in LLM. **Returns:** `[BaseModelOutputWithPooling](/docs/transformers/v5.3.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`` A [BaseModelOutputWithPooling](/docs/transformers/v5.3.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([GlmImageConfig](/docs/transformers/v5.3.0/en/model_doc/glm_image#transformers.GlmImageConfig)) and inputs.