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README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

chat_template.jinja

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+{{bos_token}}{% for message in messages %}{{'<|im_start|>' + message['role'] + '
+'}}{% if message['content'] is string %}{{ message['content'] }}{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' %}{{ '<image>' }}{% elif content['type'] == 'text' %}{{ content['text'] }}{% endif %}{% endfor %}{% endif %}{{'<|im_end|>
+'}}{% endfor %}{% if add_generation_prompt %}{{'<|im_start|>assistant
+' }}{% endif %}

config.json

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+{
+  "architectures": [
+    "Lfm2VlForConditionalGeneration"
+  ],
+  "auto_map": {
+    "AutoConfig": "modeling_lfm2_vl.Lfm2VlConfig",
+    "AutoModelForImageTextToText": "modeling_lfm2_vl.Lfm2VlForConditionalGeneration"
+  },
+  "do_image_splitting": true,
+  "downsample_factor": 2,
+  "encoder_patch_size": 16,
+  "image_token_index": 396,
+  "max_image_tokens": 256,
+  "max_num_patches": 1024,
+  "max_pixels_tolerance": 2.0,
+  "max_tiles": 10,
+  "min_image_tokens": 64,
+  "min_tiles": 2,
+  "model_type": "lfm2-vl",
+  "projector_bias": true,
+  "projector_hidden_act": "gelu",
+  "projector_hidden_size": 2560,
+  "text_config": {
+    "_name_or_path": "LiquidAI/LFM2-350M",
+    "architectures": [
+      "Lfm2ForCausalLM"
+    ],
+    "block_auto_adjust_ff_dim": true,
+    "block_dim": 1024,
+    "block_ff_dim": 6656,
+    "block_ffn_dim_multiplier": 1.0,
+    "block_mlp_init_scale": 1.0,
+    "block_multiple_of": 256,
+    "block_norm_eps": 1e-05,
+    "block_out_init_scale": 1.0,
+    "block_use_swiglu": true,
+    "block_use_xavier_init": true,
+    "conv_L_cache": 3,
+    "conv_bias": false,
+    "conv_dim": 1024,
+    "conv_dim_out": 1024,
+    "conv_use_xavier_init": true,
+    "eos_token_id": 7,
+    "hidden_size": 1024,
+    "initializer_range": 0.02,
+    "intermediate_size": 6656,
+    "layer_types": [
+      "conv",
+      "conv",
+      "full_attention",
+      "conv",
+      "conv",
+      "full_attention",
+      "conv",
+      "conv",
+      "full_attention",
+      "conv",
+      "full_attention",
+      "conv",
+      "full_attention",
+      "conv",
+      "full_attention",
+      "conv"
+    ],
+    "max_position_embeddings": 128000,
+    "model_type": "lfm2",
+    "norm_eps": 1e-05,
+    "num_attention_heads": 16,
+    "num_heads": 16,
+    "num_hidden_layers": 16,
+    "num_key_value_heads": 8,
+    "rope_theta": 1000000.0,
+    "torch_dtype": "bfloat16",
+    "use_cache": true,
+    "use_pos_enc": true,
+    "vocab_size": 65536
+  },
+  "tile_size": 512,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.0",
+  "use_image_special_tokens": true,
+  "use_thumbnail": false,
+  "vision_config": {
+    "attention_dropout": 0.0,
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 768,
+    "intermediate_size": 3072,
+    "layer_norm_eps": 1e-06,
+    "model_type": "siglip2_vision_model",
+    "num_attention_heads": 12,
+    "num_channels": 3,
+    "num_hidden_layers": 12,
+    "num_patches": 256,
+    "patch_size": 16,
+    "torch_dtype": "bfloat16",
+    "vision_use_head": false
+  },
+  "vision_feature_layer": -1
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 7,
+  "pad_token_id": 0,
+  "transformers_version": "4.55.0"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:163cab2c6c394a8532c405d63603e7b25f9220a344df950288411d1fba0b0567
+size 901692416

modeling_lfm2_vl.py

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+"""PyTorch LFM2-VL model."""
+
+from dataclasses import dataclass
+
+import torch
+from torch import nn
+from transformers import AutoConfig, AutoModel
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache
+from transformers.configuration_utils import PretrainedConfig
+from transformers.generation import GenerationMixin
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from transformers.modeling_outputs import BaseModelOutputWithPast, ModelOutput
+from transformers.modeling_utils import PreTrainedModel
+from transformers.models.lfm2.configuration_lfm2 import Lfm2Config
+from transformers.models.siglip2.configuration_siglip2 import Siglip2VisionConfig
+from transformers.models.siglip2.modeling_siglip2 import Siglip2VisionModel
+from transformers.processing_utils import Unpack
+from transformers.utils import can_return_tuple, logging
+
+logger = logging.get_logger(__name__)
+
+
+class Lfm2VlConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Lfm2VlForConditionalGeneration`]. It is used to instantiate an
+    Lfm2Vl model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the Lfm2-VL-1.6B.
+
+    e.g. [LiquidAI/LFM2-VL-1.6B](https://huggingface.co/LiquidAI/LFM2-VL-1.6B)
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vision_config (`AutoConfig | dict`,  *optional*, defaults to `Siglip2ImageConfig`):
+            The config object or dictionary of the vision backbone.
+        text_config (`AutoConfig | dict`, *optional*, defaults to `Lfm2Config`):
+            The config object or dictionary of the text backbone.
+        image_token_id (`int`, *optional*, defaults to 396):
+            The image token index to encode the image prompt.
+        projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
+            The activation function used by the multimodal projector.
+        projector_hidden_size (`int`, *optional*, defaults to 2056):
+            The hidden size of the multimodal projector.
+        projector_bias (`bool`, *optional*, defaults to `True`):
+            Whether to use bias in the multimodal projector.
+        downsample_factor (`int`, *optional*, defaults to 2):
+            The downsample_factor factor of the vision backbone.
+        vision_feature_layer (`int`, *optional*, defaults to -1):
+            The layer of the vision tower to use as features.
+        min_image_tokens (`int`, *optional*, defaults to 64):
+            The minimum number of image tokens for smart resize.
+        max_image_tokens (`int`, *optional*, defaults to 256):
+            The maximum number of image tokens for smart resize.
+        encoder_patch_size (`int`, *optional*, defaults to 16):
+            The patch size of the encoder.
+        max_num_patches (`int`, *optional*, defaults to 1024):
+            The maximum number of image tokens passed to the encoder per image or tile.
+        use_image_special_tokens (`bool`, *optional*, defaults to `True`):
+            Whether to use image special tokens.
+        do_image_splitting (`bool`, *optional*, defaults to `True`):
+            Whether to split large images into tiles.
+        min_tiles (`int`, *optional*, defaults to 2):
+            The minimum number of tiles to split the image into.
+        max_tiles (`int`, *optional*, defaults to 10):
+            The maximum number of tiles to split the image into.
+        tile_size (`int`, *optional*, defaults to 512):
+            The size of the tile to split the image into.
+        max_pixels_tolerance (`float`, *optional*, defaults to 2.0):
+            The maximum tolerance for the number of pixels in the image before splitting.
+        use_thumbnail (`bool`, *optional*, defaults to `True`):
+            Whether to append the thumbnail of the image when splitting.
+    """
+
+    model_type = "lfm2-vl"
+    attribute_map = {
+        "image_token_id": "image_token_index",
+    }
+    sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}
+
+    def __init__(
+        self,
+        vision_config=None,
+        text_config=None,
+        image_token_index=396,
+        projector_hidden_act="gelu",
+        projector_hidden_size=2560,
+        projector_bias=True,
+        downsample_factor=2,
+        vision_feature_layer=-1,
+        min_image_tokens=64,
+        max_image_tokens=256,
+        encoder_patch_size=16,
+        max_num_patches=1024,
+        use_image_special_tokens=True,
+        do_image_splitting=True,
+        min_tiles=2,
+        max_tiles=10,
+        tile_size=512,
+        max_pixels_tolerance=2.0,
+        use_thumbnail=True,
+        torch_dtype=torch.bfloat16,
+        **kwargs,
+    ):
+        self.vision_config = vision_config
+        self.text_config = text_config
+        self.image_token_index = image_token_index
+        self.projector_hidden_act = projector_hidden_act
+        self.projector_hidden_size = projector_hidden_size
+        self.projector_bias = projector_bias
+        self.downsample_factor = downsample_factor
+        self.vision_feature_layer = vision_feature_layer
+        self.min_image_tokens = min_image_tokens
+        self.max_image_tokens = max_image_tokens
+        self.encoder_patch_size = encoder_patch_size
+        self.max_num_patches = max_num_patches
+        self.use_image_special_tokens = use_image_special_tokens
+        self.do_image_splitting = do_image_splitting
+        self.min_tiles = min_tiles
+        self.max_tiles = max_tiles
+        self.tile_size = tile_size
+        self.max_pixels_tolerance = max_pixels_tolerance
+        self.use_thumbnail = use_thumbnail
+        self.torch_dtype = torch_dtype
+        
+        if isinstance(vision_config, dict):
+            vision_config = Siglip2VisionConfig(**vision_config)
+        elif vision_config is None:
+            vision_config = Siglip2VisionConfig()
+        self.vision_config = vision_config
+
+        self.vision_config = vision_config
+
+        if isinstance(text_config, dict):
+            text_config = Lfm2Config(**text_config)
+        elif text_config is None:
+            text_config = Lfm2Config()
+
+        self.text_config = text_config
+
+        super().__init__(**kwargs)
+
+
+@dataclass
+class Lfm2VlModelOutputWithPast(BaseModelOutputWithPast):
+    r"""
+    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+        Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+        `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+        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.
+    image_hidden_states (`torch.FloatTensor`, *optional*):
+        A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
+        image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+    """
+
+    image_hidden_states: torch.FloatTensor | None = None
+
+
+@dataclass
+class Lfm2VlCausalLMOutputWithPast(ModelOutput):
+    r"""
+    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+        Language modeling loss (for next-token prediction).
+    logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+        Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+        Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+        `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+        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.
+    image_hidden_states (`torch.FloatTensor`, *optional*):
+        A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
+        image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+    """
+
+    loss: torch.FloatTensor | None = None
+    logits: torch.FloatTensor | None = None
+    past_key_values: list[torch.FloatTensor] | None = None
+    hidden_states: tuple[torch.FloatTensor] | None = None
+    attentions: tuple[torch.FloatTensor] | None = None
+    image_hidden_states: torch.FloatTensor | None = None
+
+
+class Lfm2VlMultiModalProjector(nn.Module):
+    def __init__(self, config: Lfm2VlConfig):
+        super().__init__()
+        in_channels = config.vision_config.hidden_size * (config.downsample_factor**2)
+        self.layer_norm = nn.LayerNorm(in_channels)
+        self.linear_1 = nn.Linear(
+            in_channels,
+            config.projector_hidden_size,
+            bias=config.projector_bias,
+        )
+        self.act = ACT2FN[config.projector_hidden_act]
+        self.linear_2 = nn.Linear(
+            config.projector_hidden_size,
+            config.text_config.hidden_size,
+            bias=config.projector_bias,
+        )
+
+    def forward(self, image_features):
+        image_features = self.layer_norm(image_features)
+        hidden_states = self.linear_1(image_features)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        return hidden_states
+
+
+class PixelUnshuffleBlock(nn.Module):
+    def __init__(self, factor: int):
+        super().__init__()
+        self.factor = factor
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        n, w, h, c = x.size()
+        if w % self.factor != 0:
+            x = torch.concat(
+                [
+                    x,
+                    torch.zeros(
+                        (n, self.factor - (w % self.factor), h, c), dtype=x.dtype
+                    ).to(x.device),
+                ],
+                dim=1,
+            ).contiguous()
+            n, w, h, c = x.size()
+        x = x.contiguous()
+        if h % self.factor != 0:
+            x = torch.concat(
+                [
+                    x,
+                    torch.zeros(
+                        (n, w, self.factor - (h % self.factor), c), dtype=x.dtype
+                    ).to(x.device),
+                ],
+                dim=2,
+            ).contiguous()
+            n, w, h, c = x.size()
+        x = x.view(n, w, int(h / self.factor), int(c * self.factor))
+        x = x.permute(0, 2, 1, 3).contiguous()
+        x = x.view(
+            n, int(h / self.factor), int(w / self.factor), int(c * self.factor**2)
+        )
+        x = x.permute(0, 2, 1, 3).contiguous()
+        return x
+
+
+class Lfm2VlPreTrainedModel(PreTrainedModel):
+    config: Lfm2VlConfig
+    base_model_prefix = ""
+    supports_gradient_checkpointing = True
+    _skip_keys_device_placement = ["past_key_values"]
+
+    _supports_flash_attn = True
+    _supports_sdpa = True
+
+    _can_compile_fullgraph = False
+    _supports_flex_attn = True
+    _supports_attention_backend = True
+
+
+class Lfm2VlModel(Lfm2VlPreTrainedModel):
+    _checkpoint_conversion_mapping = {"language_model.model": "language_model"}
+
+    def __init__(self, config: Lfm2VlConfig):
+        super().__init__(config)
+        self.vision_tower = Siglip2VisionModel(config.vision_config)
+
+        if config.vision_feature_layer != -1:
+            self.vision_tower.vision_model.encoder.layers = (
+                self.vision_tower.vision_model.encoder.layers[
+                    : config.vision_feature_layer + 1
+                ]
+            )
+        if config.downsample_factor > 1:
+            self.pixel_unshuffle = PixelUnshuffleBlock(config.downsample_factor)
+        else:
+            self.pixel_unshuffle = nn.Identity()
+
+        self.multi_modal_projector = Lfm2VlMultiModalProjector(config)
+        self.language_model = AutoModel.from_config(config.text_config)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.language_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.language_model.set_input_embeddings(value)
+
+    def set_decoder(self, decoder):
+        self.language_model = decoder
+
+    def get_decoder(self):
+        return self.language_model
+
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        spatial_shapes: torch.Tensor,
+        pixel_attention_mask: torch.Tensor,
+        **kwargs,
+    ) -> list[torch.Tensor]:
+        """
+        Obtains image last hidden states from the vision tower and apply multimodal projection.
+
+        Args:
+            pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`):
+               The tensors corresponding to the input images.
+            spatial_shapes (`torch.Tensor` of shape `(batch_size, 2)`):
+                The spatial shapes of the input images.
+            pixel_attention_mask (`torch.Tensor` of shape `(batch_size, height, width)`):
+                The pixel attention mask of the input images.
+        Returns:
+            image_features (`list[torch.Tensor]`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
+        """
+        image_outputs = self.vision_tower(
+            pixel_values=pixel_values,
+            spatial_shapes=spatial_shapes,
+            pixel_attention_mask=pixel_attention_mask,
+        ).last_hidden_state
+
+        img_feature_lengths = pixel_attention_mask.sum(dim=1)
+        image_features = []
+
+        for img_idx in range(image_outputs.size(0)):
+            feature = image_outputs[img_idx]
+            # unpad the image representation
+            feature = feature[: img_feature_lengths[img_idx], :].unsqueeze(0)
+
+            feature_org_h, feature_org_w = spatial_shapes[img_idx]
+            feature = feature.reshape(1, feature_org_h, feature_org_w, -1)
+            feature = self.pixel_unshuffle(feature)
+
+            # project the image representation
+            img_embedding = self.multi_modal_projector(feature)
+
+            # flatten here to handle variable length in naflex
+            img_embedding = img_embedding.reshape(-1, img_embedding.size(-1))
+            image_features.append(img_embedding)
+
+        return image_features
+
+    def get_placeholder_mask(
+        self,
+        input_ids: torch.LongTensor | None,
+        inputs_embeds: torch.FloatTensor,
+        image_features: torch.FloatTensor,
+    ):
+        """
+        Obtains multimodal placeholdr mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+        equal to the length of multimodal features. If the lengths are different, an error is raised.
+        """
+        if input_ids is None:
+            special_image_mask = inputs_embeds == self.get_input_embeddings()(
+                torch.tensor(
+                    self.config.image_token_id,
+                    dtype=torch.long,
+                    device=inputs_embeds.device,
+                )
+            )
+            special_image_mask = special_image_mask.all(-1)
+        else:
+            special_image_mask = input_ids == self.config.image_token_id
+        n_image_tokens = special_image_mask.sum()
+        special_image_mask = (
+            special_image_mask.unsqueeze(-1)
+            .expand_as(inputs_embeds)
+            .to(inputs_embeds.device)
+        )
+        n_image_features = image_features.shape[0]
+        if inputs_embeds[special_image_mask].numel() != image_features.numel():
+            raise ValueError(
+                f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
+            )
+        return special_image_mask
+
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        pixel_values: torch.FloatTensor = None,
+        spatial_shapes: torch.Tensor = None,
+        pixel_attention_mask: torch.Tensor = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+        image_sizes: torch.Tensor = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple | Lfm2VlModelOutputWithPast:
+        """
+        spatial_shapes (`torch.Tensor` of shape `(batch_size, 2)`, *optional*):
+            The spatial shapes of the input images.
+        pixel_attention_mask (`torch.Tensor` of shape `(batch_size, height, width)`, *optional*):
+            The pixel attention mask of the input images.
+        """
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You must specify exactly one of input_ids or inputs_embeds"
+            )
+
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+        if pixel_values is not None:
+            image_features = self.get_image_features(
+                pixel_values=pixel_values,
+                spatial_shapes=spatial_shapes,
+                pixel_attention_mask=pixel_attention_mask,
+            )
+            image_features = torch.cat(image_features, dim=0).to(
+                inputs_embeds.device, inputs_embeds.dtype
+            )
+            special_image_mask = self.get_placeholder_mask(
+                input_ids=input_ids,
+                inputs_embeds=inputs_embeds,
+                image_features=image_features,
+            )
+            inputs_embeds = inputs_embeds.masked_scatter(
+                special_image_mask, image_features
+            )
+
+        outputs = self.language_model(
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=True,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        return Lfm2VlModelOutputWithPast(
+            last_hidden_state=outputs.last_hidden_state,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=image_features if pixel_values is not None else None,
+        )
+
+
+class Lfm2VlForConditionalGeneration(Lfm2VlPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config: Lfm2VlConfig):
+        super().__init__(config)
+        self.model = Lfm2VlModel(config)
+        self.lm_head = nn.Linear(
+            config.text_config.hidden_size, config.text_config.vocab_size, bias=False
+        )
+        self.post_init()
+
+    def _supports_default_dynamic_cache(self):
+        return False
+
+    def get_input_embeddings(self):
+        return self.model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.model.set_input_embeddings(value)
+
+    def get_output_embeddings(self) -> nn.Module:
+        return self.lm_head
+
+    def set_decoder(self, decoder):
+        self.model.set_decoder(decoder)
+
+    def get_decoder(self):
+        return self.model.get_decoder()
+
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        spatial_shapes: torch.Tensor,
+        pixel_attention_mask: torch.Tensor,
+        **kwargs,
+    ):
+        return self.model.get_image_features(
+            pixel_values=pixel_values,
+            spatial_shapes=spatial_shapes,
+            pixel_attention_mask=pixel_attention_mask,
+            **kwargs,
+        )
+
+    @property
+    def language_model(self):
+        return self.model.language_model
+
+    @property
+    def vision_tower(self):
+        return self.model.vision_tower
+
+    @property
+    def multi_modal_projector(self):
+        return self.model.multi_modal_projector
+
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        pixel_values: torch.FloatTensor = None,
+        spatial_shapes: torch.Tensor = None,
+        pixel_attention_mask: torch.Tensor = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        image_sizes: torch.Tensor | None = None,
+        **kwargs,
+    ) -> tuple | Lfm2VlCausalLMOutputWithPast:
+        r"""
+        pixel_values (`torch.FloatTensor` of shape `(batch_size, channels, height, width)`, *optional*):
+            The input image tensors.
+        spatial_shapes (`torch.Tensor` of shape `(batch_size, 2)`, *optional*):
+            The spatial shapes of the input images.
+        pixel_attention_mask (`torch.Tensor` of shape `(batch_size, height, width)`, *optional*):
+            The pixel attention mask of the input images.
+        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]`.
+
+        Example:
+
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import AutoProcessor, AutoModelForImageTextToText
+        >>> from transformers.image_utils import load_image
+
+        >>> model = AutoModelForImageTextToText.from_pretrained(
+        ...     "LiquidAI/LFM2-VL-1.6B",
+        ...     trust_remote_code=True
+        ... )
+        >>> processor = AutoProcessor.from_pretrained(
+        ...     "LiquidAI/LFM2-VL-1.6B",
+        ...     trust_remote_code=True
+        ... )
+
+        >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+        >>> image = load_image(url)
+
+        >>> conversation = [
+        ...     {
+        ...         "role": "user",
+        ...         "content": [
+        ...             {"type": "image", "image": image},
+        ...             {"type": "text", "text": "What is in this image?"},
+        ...         ],
+        ...     },
+        ... ]
+
+        >>> inputs = processor.apply_chat_template(
+        ...     conversation,
+        ...     add_generation_prompt=True,
+        ...     tokenize=True,
+        ...     return_dict=True,
+        ...     return_tensors="pt"
+        ... )
+
+        >>> # Generate
+        >>> outputs = model.generate(**inputs, max_new_tokens=45)
+        >>> processor.batch_decode(outputs, skip_special_tokens=True)[0]
+        'This image depicts a vibrant street scene in what appears to be a Chinatown or similar cultural area. The focal point is a large red stop sign with white lettering, mounted on a pole.'
+        ```"""
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        outputs = self.model(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            spatial_shapes=spatial_shapes,
+            pixel_attention_mask=pixel_attention_mask,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=True,
+            cache_position=cache_position,
+            image_sizes=image_sizes,
+            **kwargs,
+        )
+
+        hidden_states = outputs[0]
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = (
+            slice(-logits_to_keep, None)
+            if isinstance(logits_to_keep, int)
+            else logits_to_keep
+        )
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                vocab_size=self.config.text_config.vocab_size,
+                **kwargs,
+            )
+
+        return Lfm2VlCausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=outputs.image_hidden_states,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        inputs_embeds=None,
+        pixel_values=None,
+        attention_mask=None,
+        cache_position=None,
+        logits_to_keep=None,
+        **kwargs,
+    ):
+        # Overwritten -- in specific circumstances we don't want to forward image inputs to the model
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            cache_position=cache_position,
+            logits_to_keep=logits_to_keep,
+            **kwargs,
+        )
+
+        if cache_position[0] == 0:
+            # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
+            # Otherwise we need pixel values to be passed to model
+            model_inputs["pixel_values"] = pixel_values
+
+        return model_inputs
+
+
+__all__ = ["Lfm2VlForConditionalGeneration", "Lfm2VlModel", "Lfm2VlPreTrainedModel"]

preprocessor_config.json

@@ -0,0 +1,27 @@
+{
+  "auto_map": {
+    "AutoProcessor": "processing_lfm2_vl.Lfm2VlProcessor"
+  },
+  "do_convert_rgb": null,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": false,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "Siglip2ImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "input_data_format": "channels_last",
+  "max_num_patches": 1024,
+  "patch_size": 16,
+  "processor_class": "Lfm2VlProcessor",
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "return_tensors": "pt"
+}

processing_lfm2_vl.py

@@ -0,0 +1,645 @@
+import math
+from typing import Union
+
+from PIL import Image
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.image_utils import ImageInput, make_nested_list_of_images
+from transformers.image_transforms import to_pil_image
+from transformers.processing_utils import (
+    ImagesKwargs,
+    ProcessingKwargs,
+    ProcessorMixin,
+    Unpack,
+)
+from transformers.tokenization_utils_base import BatchEncoding, TextInput
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+# resize adapted from qwen2.5
+# https://github.com/QwenLM/Qwen2.5-VL/blob/main/qwen-vl-utils/src/qwen_vl_utils/vision_process.py
+def round_by_factor(number: float, factor: int) -> int:
+    """Returns the closest integer to 'number' that is divisible by 'factor'."""
+    return round(number / factor) * factor
+
+
+def ceil_by_factor(number: float, factor: int) -> int:
+    """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
+    return math.ceil(number / factor) * factor
+
+
+def floor_by_factor(number: float, factor: int) -> int:
+    """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
+    return math.floor(number / factor) * factor
+
+
+def find_closest_aspect_ratio(
+    aspect_ratio: float,
+    target_ratios: list[tuple[int, int]],
+    width: int,
+    height: int,
+    image_size: int,
+) -> tuple[int, int]:
+    """Find the closest aspect ratio from target_ratios to match the input aspect ratio.
+
+    Args:
+        aspect_ratio: The aspect ratio to match (width/height).
+        target_ratios: List of possible aspect ratios as tuples of (width, height) integers.
+        width: Original image width in pixels.
+        height: Original image height in pixels.
+        image_size: Base size for calculating target area.
+
+    Returns:
+        tuple[int, int]: The best matching ratio as (width, height) integers.
+    """
+    best_ratio_diff = float("inf")
+    best_ratio = (1, 1)
+    area = width * height
+
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+
+        # update best ratio if we found a closer match
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        # if equally close, prefer the ratio that better matches the original image area
+        elif ratio_diff == best_ratio_diff:
+            target_area = image_size * image_size * ratio[0] * ratio[1]
+            if area > 0.5 * target_area:
+                best_ratio = ratio
+
+    return best_ratio
+
+
+class Lfm2VlImagesKwargs(ImagesKwargs, total=False):
+    return_row_col_info: bool | None
+    max_image_size: dict[str, int] | None
+
+
+class Lfm2VlProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Lfm2VlImagesKwargs
+
+    _defaults = {
+        "text_kwargs": {
+            "add_special_tokens": False,
+            "padding": False,
+            "is_split_into_words": False,
+        },
+        "images_kwargs": {
+            "do_resize": False,
+        },
+    }
+
+
+class Lfm2VlProcessor(ProcessorMixin):
+    r"""
+    Constructs a Lfm2Vl processor which wraps a Lfm2Tokenizer tokenizer and Lfm2Vl image processor into a single processor.
+
+    [`Lfm2VlProcessor`] offers all the functionalities of [`Siglip2ImageProcessor`] and [`Lfm2Tokenizer`].
+
+    Args:
+        image_processor (`Siglip2ImageProcessor`):
+            An instance of [`Siglip2ImageProcessor`]. The image processor is a required input.
+        tokenizer (`PreTrainedTokenizerBase`):
+            An instance of [`PreTrainedTokenizerBase`]. This should correspond with the model's text model. The tokenizer is a required input.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    image_processor_class = "Siglip2ImageProcessor"
+    tokenizer_class = "AutoTokenizer"
+
+    def __init__(
+        self,
+        image_processor,
+        tokenizer,
+        chat_template: str,
+        use_image_special_tokens: bool,
+        downsample_factor: int,
+        do_image_splitting: bool,
+        min_tiles: int,
+        max_tiles: int,
+        use_thumbnail: bool,
+        min_image_tokens: int,
+        max_image_tokens: int,
+        encoder_patch_size: int,
+        tile_size: int,
+        max_pixels_tolerance: float,
+        max_num_patches: int,
+        auto_map: dict[str, str] = None,
+        **kwargs,
+    ):
+        self.image_token = getattr(tokenizer, "image_token", "<image>")
+        self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
+        self.use_image_special_tokens = use_image_special_tokens
+        self.image_start_token = getattr(
+            tokenizer, "image_start_token", "<|image_start|>"
+        )
+        self.image_end_token = getattr(tokenizer, "image_end_token", "<|image_end|>")
+        self.image_thumbnail_token = getattr(
+            tokenizer, "image_thumbnail", "<|img_thumbnail|>"
+        )
+        self.downsample_factor = downsample_factor
+        self.do_image_splitting = do_image_splitting
+        self.min_tiles = min_tiles
+        self.max_tiles = max_tiles
+        self.use_thumbnail = use_thumbnail
+        self.min_image_tokens = min_image_tokens
+        self.max_image_tokens = max_image_tokens
+        self.encoder_patch_size = encoder_patch_size
+        self.tile_size = tile_size
+        self.max_pixels_tolerance = max_pixels_tolerance
+        self.chat_template = chat_template
+        self.auto_map = auto_map
+        super().__init__(
+            image_processor, tokenizer, chat_template=chat_template, **kwargs
+        )
+        self.max_num_patches = max_num_patches
+        self.image_processor.max_num_patches = max_num_patches
+
+    def _high_res_preprocessor(
+        self,
+        image: Image.Image,
+        min_tiles,
+        max_tiles,
+        tile_size,
+    ) -> tuple[list[Image.Image], int, int, int]:
+        """Process a high resolution image into patches.
+        This method splits a high resolution image into a grid of smaller patches while trying to maintain
+        the original aspect ratio. It finds the optimal grid configuration within the specified tile constraints.
+        """
+        orig_width, orig_height = image.size
+        aspect_ratio = orig_width / orig_height
+
+        # generate valid patch grid configurations (width, height)
+        target_ratios = [
+            (w, h)
+            for n in range(min_tiles, max_tiles + 1)
+            for w in range(1, n + 1)
+            for h in range(1, n + 1)
+            if min_tiles <= w * h <= max_tiles
+        ]
+        target_ratios = sorted(set(target_ratios), key=lambda x: x[0] * x[1])
+
+        # default to 1x1 if no valid configurations found
+        if not target_ratios:
+            return [], 0, 0
+
+        # find best matching grid configuration
+        grid_width, grid_height = find_closest_aspect_ratio(
+            aspect_ratio, target_ratios, orig_width, orig_height, tile_size
+        )
+
+        target_width = tile_size * grid_width
+        target_height = tile_size * grid_height
+        total_patches = grid_width * grid_height
+
+        # resize and split image into patches
+        resized_img = image.resize((target_width, target_height))
+        patches = []
+
+        for i in range(total_patches):
+            # calculate patch coordinates
+            col = i % grid_width
+            row = i // grid_width
+            box = (
+                col * tile_size,
+                row * tile_size,
+                (col + 1) * tile_size,
+                (row + 1) * tile_size,
+            )
+            patch = resized_img.crop(box)
+            patches.append(patch)
+
+        num_rows = grid_height
+        num_columns = grid_width
+
+        return patches, num_rows, num_columns
+
+    def _smart_resize(
+        self,
+        image: Image.Image,
+        downsample_factor: int,
+        min_image_tokens: int,
+        max_image_tokens: int,
+        encoder_patch_size: int,
+    ) -> Image.Image:
+        """
+        Rescales the image so that the following conditions are met:
+        1. Both dimensions (height and width) are divisible by 'encoder_patch_size' * 'downsample_factor'.
+           This ensures no padding is needed in the downsampling step.
+        2. The total number of pixels is within the range ['smart_resize_min_pixels', 'smart_resize_max_pixels'].
+        3. The aspect ratio of the image is maintained as closely as possible.
+        """
+        width, height = image.size
+
+        total_factor = encoder_patch_size * downsample_factor
+        smart_resize_min_pixels = (
+            min_image_tokens
+            * encoder_patch_size ** 2
+            * downsample_factor ** 2
+        )
+        smart_resize_max_pixels = (
+            max_image_tokens
+            * encoder_patch_size ** 2
+            * downsample_factor ** 2
+        )
+
+        h_bar = max(total_factor, round_by_factor(height, total_factor))
+        w_bar = max(total_factor, round_by_factor(width, total_factor))
+
+        if h_bar * w_bar > smart_resize_max_pixels:
+            beta = math.sqrt((height * width) / smart_resize_max_pixels)
+            h_bar = max(total_factor, floor_by_factor(height / beta, total_factor))
+            w_bar = max(total_factor, floor_by_factor(width / beta, total_factor))
+        elif h_bar * w_bar < smart_resize_min_pixels:
+            beta = math.sqrt(smart_resize_min_pixels / (height * width))
+            h_bar = ceil_by_factor(height * beta, total_factor)
+            w_bar = ceil_by_factor(width * beta, total_factor)
+
+        resized_img = image.resize((w_bar, h_bar))
+        return resized_img
+
+    def _get_tokens_num(self, image_height: int, image_width: int) -> int:
+        num_patches_height = image_height // self.encoder_patch_size
+        num_patches_width = image_width // self.encoder_patch_size
+
+        dwn_num_patches_height = math.ceil(num_patches_height / self.downsample_factor)
+        dwn_num_patches_width = math.ceil(num_patches_width / self.downsample_factor)
+
+        return dwn_num_patches_height * dwn_num_patches_width
+
+    def _is_img_too_large(
+        self,
+        image: Image.Image,
+        max_image_tokens: int,
+        encoder_patch_size: int,
+        max_pixels_tolerance: float,
+    ) -> bool:
+        """Check if the image is too large to be processed as one tile."""
+        width, height = image.size
+
+        h_bar = max(encoder_patch_size, round_by_factor(height, encoder_patch_size))
+        w_bar = max(encoder_patch_size, round_by_factor(width, encoder_patch_size))
+        return (
+            h_bar * w_bar
+            > max_image_tokens
+            * encoder_patch_size ** 2
+            * self.downsample_factor ** 2
+            * max_pixels_tolerance
+        )
+
+    def _resize_and_maybe_split(
+        self,
+        image: ImageInput,
+        downsample_factor: int,
+        min_tiles: int,
+        max_tiles: int,
+        use_thumbnail: bool,
+        min_image_tokens: int,
+        max_image_tokens: int,
+        encoder_patch_size: int,
+        tile_size: int,
+        max_pixels_tolerance: float,
+    ) -> tuple[list[Image.Image], int, int, int, int]:
+        """Apply smart resize and maybe split the image into tiles if image too large.
+        Return:
+            image_tiles: ImageInput
+            num_tokens_per_tile: int
+            num_rows: int
+            num_cols: int
+            num_thumbnail_tokens: int
+        """
+        image = to_pil_image(image)
+        do_image_splitting = not min_tiles == max_tiles == 1
+        if (
+            self._is_img_too_large(
+                image,
+                max_image_tokens,
+                encoder_patch_size,
+                max_pixels_tolerance,
+            )
+            and do_image_splitting
+        ):
+            image_tiles, num_rows, num_cols = self._high_res_preprocessor(
+                image, min_tiles, max_tiles, tile_size
+            )
+            if len(image_tiles) > 1:
+                num_thumbnail_tokens = 0
+                if use_thumbnail:
+                    thumbnail_image = self._smart_resize(
+                        image,
+                        downsample_factor,
+                        min_image_tokens,
+                        max_image_tokens,
+                        encoder_patch_size,
+                    )
+                    num_thumbnail_tokens = self._get_tokens_num(
+                        thumbnail_image.height, thumbnail_image.width
+                    )
+                    image_tiles.append(thumbnail_image)
+
+                return (
+                    image_tiles,
+                    self._get_tokens_num(tile_size, tile_size),
+                    num_rows,
+                    num_cols,
+                    num_thumbnail_tokens,
+                )
+        else:
+            image = self._smart_resize(
+                image,
+                downsample_factor,
+                min_image_tokens,
+                max_image_tokens,
+                encoder_patch_size,
+            )
+            return [image], self._get_tokens_num(image.height, image.width), 1, 1, 0
+
+    def process_vision(
+        self,
+        text: list[str],
+        images: list[list[ImageInput]],
+        use_image_special_tokens: bool,
+        downsample_factor: int,
+        min_tiles: int,
+        max_tiles: int,
+        use_thumbnail: bool,
+        min_image_tokens: int,
+        max_image_tokens: int,
+        encoder_patch_size: int,
+        tile_size: int,
+        max_pixels_tolerance: float,
+        output_kwargs: dict,
+    ):
+        if text is not None:
+            n_images_in_text = [sample.count(self.image_token) for sample in text]
+
+        n_images_in_images = [len(sublist) for sublist in images]
+
+        if n_images_in_images != n_images_in_text:
+            raise ValueError(
+                f"The number of images in the text {n_images_in_text} and images {n_images_in_images} should be the same."
+            )
+
+        prompt_strings = []
+        image_inputs = []
+
+        for sample_text, sample_images in zip(text, images, strict=False):
+            split_sample = sample_text.split(self.image_token)
+            sample_tiles = []
+            sample_text_with_image_tokens = ""
+            for i, image in enumerate(sample_images):
+                sample_text_with_image_tokens += split_sample[i]
+                if use_image_special_tokens:
+                    sample_text_with_image_tokens += self.image_start_token
+                (
+                    image_tiles,
+                    num_tokens_per_tile,
+                    num_rows,
+                    num_cols,
+                    num_thumbnail_tokens,
+                ) = self._resize_and_maybe_split(
+                    image,
+                    downsample_factor,
+                    min_tiles,
+                    max_tiles,
+                    use_thumbnail,
+                    min_image_tokens,
+                    max_image_tokens,
+                    encoder_patch_size,
+                    tile_size,
+                    max_pixels_tolerance,
+                )
+
+                if len(image_tiles) > 1:
+                    for row in range(num_rows):
+                        for col in range(num_cols):
+                            if use_image_special_tokens:
+                                sample_text_with_image_tokens += (
+                                    f"<|img_row_{row + 1}_col_{col + 1}|>"
+                                )
+                            sample_text_with_image_tokens += (
+                                self.image_token * num_tokens_per_tile
+                            )
+
+                    if num_thumbnail_tokens > 0:
+                        if use_image_special_tokens:
+                            sample_text_with_image_tokens += self.image_thumbnail_token
+                        sample_text_with_image_tokens += (
+                            self.image_token * num_thumbnail_tokens
+                        )
+                else:
+                    sample_text_with_image_tokens += (
+                        self.image_token * num_tokens_per_tile
+                    )
+
+                if use_image_special_tokens:
+                    sample_text_with_image_tokens += self.image_end_token
+
+                sample_text_with_image_tokens += split_sample[i + 1]
+                sample_tiles.extend(image_tiles)
+
+            prompt_strings.append(sample_text_with_image_tokens)
+            image_inputs.append(sample_tiles)
+
+        image_inputs = self.image_processor(
+            image_inputs, **output_kwargs["images_kwargs"]
+        )
+
+        if text is None:
+            return None, image_inputs
+
+        return prompt_strings, image_inputs
+
+    def __call__(
+        self,
+        images: ImageInput | list[ImageInput] | list[list[ImageInput]] = None,
+        text: Union[TextInput, "PreTokenizedInput", list[TextInput], list["PreTokenizedInput"]] = None,
+        use_image_special_tokens: bool | None = None,
+        downsample_factor: int | None = None,
+        min_image_tokens: int | None = None,
+        max_image_tokens: int | None = None,
+        do_image_splitting: bool | None = None,
+        min_tiles: int | None = None,
+        max_tiles: int | None = None,
+        use_thumbnail: bool | None = None,
+        encoder_patch_size: int | None = None,
+        tile_size: int | None = None,
+        max_pixels_tolerance: float | None = None,
+        **kwargs: Unpack[Lfm2VlProcessorKwargs],
+    ) -> BatchEncoding:
+        """
+        Processes the input prompts and returns a BatchFeature.
+
+        Example:
+
+        ```python
+        >>> import requests
+        >>> from transformers import AutoProcessor
+        >>> from transformers.image_utils import load_image
+        >>> processor = AutoProcessor.from_pretrained("LiquidAI/LFM2-VL-1.6B", trust_remote_code=True)
+
+        >>> url1 = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
+        >>> url2 = "https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg"
+
+        >>> image1, image2 = load_image(url1), load_image(url2)
+        >>> images = [image1, image2]
+
+        >>> conversation = [
+        ...     {
+        ...         "role": "user",
+        ...         "content": [
+        ...             {"type": "image", "url": image1},
+        ...             {"type": "image", "url": image2},
+        ...             {"type": "text", "text": "Compare the two images."},
+        ...         ],
+        ...     },
+        ... ]
+        >>> chat_inputs = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
+        >>> outputs = processor(images=images, text=chat_inputs, return_tensors="pt")
+        >>> input_ids = outputs.input_ids
+        >>> input_tokens = processor.tokenizer.batch_decode(input_ids)
+        >>> print(input_tokens)
+        '['user\nCompare the two images.\nassistant\n']'
+        ```
+
+        Args:
+            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`, *optional*):
+                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                tensor. If is of type `list[ImageInput]`, it's assumed that this is for a single prompt i.e. of batch size 1.
+            text (`TextInput`, *optional*):
+                The sequence or batch of sequences to be encoded.
+                Wherever an image token, `<image>` is encountered it is expanded to a proper sequence of image tokens.
+            return_tensors (`str | TensorType`, *optional*):
+                If set, will return tensors of a particular framework. See [`PreTrainedTokenizerFast.__call__`] for more
+                information.
+        """
+        use_image_special_tokens = (
+            use_image_special_tokens
+            if use_image_special_tokens is not None
+            else self.use_image_special_tokens
+        )
+        downsample_factor = (
+            downsample_factor
+            if downsample_factor is not None
+            else self.downsample_factor
+        )
+        do_image_splitting = (
+            do_image_splitting
+            if do_image_splitting is not None
+            else self.do_image_splitting
+        )
+
+        min_tiles = min_tiles if min_tiles is not None else self.min_tiles
+        max_tiles = max_tiles if max_tiles is not None else self.max_tiles
+
+        if not do_image_splitting:
+            min_tiles = 1
+            max_tiles = 1
+            logger.debug(
+                "Image splitting is disabled, setting min_tiles and max_tiles to 1. Set do_image_splitting=True to enable splitting."
+            )
+
+        if do_image_splitting and min_tiles > max_tiles:
+            raise ValueError("min_tiles must be less than or equal to max_tiles")
+
+        use_thumbnail = (
+            use_thumbnail if use_thumbnail is not None else self.use_thumbnail
+        )
+        min_image_tokens = (
+            min_image_tokens if min_image_tokens is not None else self.min_image_tokens
+        )
+        max_image_tokens = (
+            max_image_tokens if max_image_tokens is not None else self.max_image_tokens
+        )
+        encoder_patch_size = (
+            encoder_patch_size
+            if encoder_patch_size is not None
+            else self.encoder_patch_size
+        )
+        tile_size = tile_size if tile_size is not None else self.tile_size
+        max_pixels_tolerance = (
+            max_pixels_tolerance
+            if max_pixels_tolerance is not None
+            else self.max_pixels_tolerance
+        )
+
+        if text is None and images is None:
+            raise ValueError("You must provide one of `text` or `images`.")
+
+        output_kwargs = self._merge_kwargs(
+            Lfm2VlProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        if text is not None:
+            if isinstance(text, str):
+                text = [text]
+            elif not isinstance(text, list) and not isinstance(text[0], str):
+                raise ValueError(
+                    "Invalid input text. Please provide a string, or a list of strings"
+                )
+            n_images_in_text = sum([sample.count(self.image_token) for sample in text])
+            if n_images_in_text > 0 and (images is None):
+                raise ValueError(
+                    f"We detected {n_images_in_text} tokens in the text but no images were passed"
+                )
+
+        inputs = {}
+
+        if images is not None:
+            images = make_nested_list_of_images(images)
+            text, vision_inputs = self.process_vision(
+                text,
+                images,
+                use_image_special_tokens,
+                downsample_factor,
+                min_tiles,
+                max_tiles,
+                use_thumbnail,
+                min_image_tokens,
+                max_image_tokens,
+                encoder_patch_size,
+                tile_size,
+                max_pixels_tolerance,
+                output_kwargs,
+            )
+            inputs.update(vision_inputs)
+
+        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+
+        if text is not None:
+            text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+            self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
+            inputs.update(text_inputs)
+
+        return BatchFeature(inputs, tensor_type=return_tensors)
+
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to LFM2Tokeniser's [`~PreTrainedTokenizer.batch_decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        batched_decode_output = self.tokenizer.batch_decode(*args, **kwargs)
+        return batched_decode_output
+
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to LFM2Tokeniser's [`~PreTrainedTokenizer.decode`]. Please refer to
+        the docstring of this method for more information.
+        """
+        decode_output = self.tokenizer.decode(*args, **kwargs)
+        return decode_output
+
+    @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(image_processor_input_names + tokenizer_input_names))
+
+
+__all__ = ["Lfm2VlProcessor"]

processor_config.json

@@ -0,0 +1,18 @@
+{
+  "auto_map": {
+    "AutoProcessor": "processing_lfm2_vl.Lfm2VlProcessor"
+  },
+  "do_image_splitting": true,
+  "downsample_factor": 2,
+  "encoder_patch_size": 16,
+  "max_image_tokens": 256,
+  "max_num_patches": 1024,
+  "max_pixels_tolerance": 2.0,
+  "max_tiles": 10,
+  "min_image_tokens": 64,
+  "min_tiles": 2,
+  "processor_class": "Lfm2VlProcessor",
+  "tile_size": 512,
+  "use_image_special_tokens": true,
+  "use_thumbnail": false
+}

special_tokens_map.json

@@ -0,0 +1,27 @@
+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "image_end_token": "<|image_end|>",
+  "image_start_token": "<|image_start|>",
+  "image_thumbnail": "<|img_thumbnail|>",
+  "image_token": "<image>",
+  "pad_token": {
+    "content": "<|pad|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,4088 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<|pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<|startoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<|fim_pre|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<|fim_mid|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "5": {
+      "content": "<|fim_suf|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "6": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "7": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "8": {
+      "content": "<|tool_list_start|>",
+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
+    },
+    "9": {
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+}