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mlx-community
/
LFM2-VL-1.6B-8bit

Image-Text-to-Text
Transformers
Safetensors
MLX
English
lfm2-vl
liquid
lfm2
edge
conversational
custom_code
8-bit precision
Model card Files
xet
Community
prince-canuma commited on
Commit
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Files changed (13) hide show
  1. README.md +28 -0
  2. chat_template.jinja +4 -0
  3. config.json +278 -0
  4. generation_config.json +7 -0
  5. model.safetensors +3 -0
  6. model.safetensors.index.json +772 -0
  7. modeling_lfm2_vl.py +688 -0
  8. preprocessor_config.json +27 -0
  9. processing_lfm2_vl.py +645 -0
  10. processor_config.json +18 -0
  11. special_tokens_map.json +27 -0
  12. tokenizer.json +0 -0
  13. tokenizer_config.json +4088 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: other
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+ license_name: lfm1.0
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+ license_link: LICENSE
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+ language:
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+ - en
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+ pipeline_tag: image-text-to-text
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+ tags:
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+ - liquid
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+ - lfm2
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+ - lfm2-vl
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+ - edge
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+ - mlx
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+ ---
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+
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+ # mlx-community/LFM2-VL-1.6B-8bit
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+ This model was converted to MLX format from [`LiquidAI/LFM2-VL-1.6B`]() using mlx-vlm version **0.3.2**.
19
+ Refer to the [original model card](https://huggingface.co/LiquidAI/LFM2-VL-1.6B) for more details on the model.
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+ ## Use with mlx
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+
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+ ```bash
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+ pip install -U mlx-vlm
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+ ```
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+
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+ ```bash
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+ python -m mlx_vlm.generate --model mlx-community/LFM2-VL-1.6B-8bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
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+ ```
chat_template.jinja ADDED
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+ {{bos_token}}{% for message in messages %}{{'<|im_start|>' + message['role'] + '
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+ '}}{% 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|>
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+ '}}{% endfor %}{% if add_generation_prompt %}{{'<|im_start|>assistant
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+ ' }}{% endif %}
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+ {
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+ "architectures": [
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+ "Lfm2VlForConditionalGeneration"
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+ "auto_map": {
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+ "AutoModelForImageTextToText": "modeling_lfm2_vl.Lfm2VlForConditionalGeneration"
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+ "vision_tower.encoder.layers.9.mlp.fc2.weight": "model.safetensors",
761
+ "vision_tower.encoder.layers.9.self_attn.k_proj.bias": "model.safetensors",
762
+ "vision_tower.encoder.layers.9.self_attn.k_proj.weight": "model.safetensors",
763
+ "vision_tower.encoder.layers.9.self_attn.out_proj.bias": "model.safetensors",
764
+ "vision_tower.encoder.layers.9.self_attn.out_proj.weight": "model.safetensors",
765
+ "vision_tower.encoder.layers.9.self_attn.q_proj.bias": "model.safetensors",
766
+ "vision_tower.encoder.layers.9.self_attn.q_proj.weight": "model.safetensors",
767
+ "vision_tower.encoder.layers.9.self_attn.v_proj.bias": "model.safetensors",
768
+ "vision_tower.encoder.layers.9.self_attn.v_proj.weight": "model.safetensors",
769
+ "vision_tower.post_layernorm.bias": "model.safetensors",
770
+ "vision_tower.post_layernorm.weight": "model.safetensors"
771
+ }
772
+ }
modeling_lfm2_vl.py ADDED
@@ -0,0 +1,688 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """PyTorch LFM2-VL model."""
2
+
3
+ from dataclasses import dataclass
4
+
5
+ import torch
6
+ from torch import nn
7
+ from transformers import AutoConfig, AutoModel
8
+ from transformers.activations import ACT2FN
9
+ from transformers.cache_utils import Cache
10
+ from transformers.configuration_utils import PretrainedConfig
11
+ from transformers.generation import GenerationMixin
12
+ from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
13
+ from transformers.modeling_outputs import BaseModelOutputWithPast, ModelOutput
14
+ from transformers.modeling_utils import PreTrainedModel
15
+ from transformers.models.lfm2.configuration_lfm2 import Lfm2Config
16
+ from transformers.models.siglip2.configuration_siglip2 import Siglip2VisionConfig
17
+ from transformers.models.siglip2.modeling_siglip2 import Siglip2VisionModel
18
+ from transformers.processing_utils import Unpack
19
+ from transformers.utils import can_return_tuple, logging
20
+
21
+ logger = logging.get_logger(__name__)
22
+
23
+
24
+ class Lfm2VlConfig(PretrainedConfig):
25
+ r"""
26
+ This is the configuration class to store the configuration of a [`Lfm2VlForConditionalGeneration`]. It is used to instantiate an
27
+ Lfm2Vl model according to the specified arguments, defining the model architecture. Instantiating a configuration
28
+ with the defaults will yield a similar configuration to that of the Lfm2-VL-1.6B.
29
+
30
+ e.g. [LiquidAI/LFM2-VL-1.6B](https://huggingface.co/LiquidAI/LFM2-VL-1.6B)
31
+
32
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
33
+ documentation from [`PretrainedConfig`] for more information.
34
+
35
+ Args:
36
+ vision_config (`AutoConfig | dict`, *optional*, defaults to `Siglip2ImageConfig`):
37
+ The config object or dictionary of the vision backbone.
38
+ text_config (`AutoConfig | dict`, *optional*, defaults to `Lfm2Config`):
39
+ The config object or dictionary of the text backbone.
40
+ image_token_id (`int`, *optional*, defaults to 396):
41
+ The image token index to encode the image prompt.
42
+ projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
43
+ The activation function used by the multimodal projector.
44
+ projector_hidden_size (`int`, *optional*, defaults to 2056):
45
+ The hidden size of the multimodal projector.
46
+ projector_bias (`bool`, *optional*, defaults to `True`):
47
+ Whether to use bias in the multimodal projector.
48
+ downsample_factor (`int`, *optional*, defaults to 2):
49
+ The downsample_factor factor of the vision backbone.
50
+ vision_feature_layer (`int`, *optional*, defaults to -1):
51
+ The layer of the vision tower to use as features.
52
+ min_image_tokens (`int`, *optional*, defaults to 64):
53
+ The minimum number of image tokens for smart resize.
54
+ max_image_tokens (`int`, *optional*, defaults to 256):
55
+ The maximum number of image tokens for smart resize.
56
+ encoder_patch_size (`int`, *optional*, defaults to 16):
57
+ The patch size of the encoder.
58
+ max_num_patches (`int`, *optional*, defaults to 1024):
59
+ The maximum number of image tokens passed to the encoder per image or tile.
60
+ use_image_special_tokens (`bool`, *optional*, defaults to `True`):
61
+ Whether to use image special tokens.
62
+ do_image_splitting (`bool`, *optional*, defaults to `True`):
63
+ Whether to split large images into tiles.
64
+ min_tiles (`int`, *optional*, defaults to 2):
65
+ The minimum number of tiles to split the image into.
66
+ max_tiles (`int`, *optional*, defaults to 10):
67
+ The maximum number of tiles to split the image into.
68
+ tile_size (`int`, *optional*, defaults to 512):
69
+ The size of the tile to split the image into.
70
+ max_pixels_tolerance (`float`, *optional*, defaults to 2.0):
71
+ The maximum tolerance for the number of pixels in the image before splitting.
72
+ use_thumbnail (`bool`, *optional*, defaults to `True`):
73
+ Whether to append the thumbnail of the image when splitting.
74
+ """
75
+
76
+ model_type = "lfm2-vl"
77
+ attribute_map = {
78
+ "image_token_id": "image_token_index",
79
+ }
80
+ sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}
81
+
82
+ def __init__(
83
+ self,
84
+ vision_config=None,
85
+ text_config=None,
86
+ image_token_index=396,
87
+ projector_hidden_act="gelu",
88
+ projector_hidden_size=2560,
89
+ projector_bias=True,
90
+ downsample_factor=2,
91
+ vision_feature_layer=-1,
92
+ min_image_tokens=64,
93
+ max_image_tokens=256,
94
+ encoder_patch_size=16,
95
+ max_num_patches=1024,
96
+ use_image_special_tokens=True,
97
+ do_image_splitting=True,
98
+ min_tiles=2,
99
+ max_tiles=10,
100
+ tile_size=512,
101
+ max_pixels_tolerance=2.0,
102
+ use_thumbnail=True,
103
+ torch_dtype=torch.bfloat16,
104
+ **kwargs,
105
+ ):
106
+ self.vision_config = vision_config
107
+ self.text_config = text_config
108
+ self.image_token_index = image_token_index
109
+ self.projector_hidden_act = projector_hidden_act
110
+ self.projector_hidden_size = projector_hidden_size
111
+ self.projector_bias = projector_bias
112
+ self.downsample_factor = downsample_factor
113
+ self.vision_feature_layer = vision_feature_layer
114
+ self.min_image_tokens = min_image_tokens
115
+ self.max_image_tokens = max_image_tokens
116
+ self.encoder_patch_size = encoder_patch_size
117
+ self.max_num_patches = max_num_patches
118
+ self.use_image_special_tokens = use_image_special_tokens
119
+ self.do_image_splitting = do_image_splitting
120
+ self.min_tiles = min_tiles
121
+ self.max_tiles = max_tiles
122
+ self.tile_size = tile_size
123
+ self.max_pixels_tolerance = max_pixels_tolerance
124
+ self.use_thumbnail = use_thumbnail
125
+ self.torch_dtype = torch_dtype
126
+
127
+ if isinstance(vision_config, dict):
128
+ vision_config = Siglip2VisionConfig(**vision_config)
129
+ elif vision_config is None:
130
+ vision_config = Siglip2VisionConfig()
131
+ self.vision_config = vision_config
132
+
133
+ self.vision_config = vision_config
134
+
135
+ if isinstance(text_config, dict):
136
+ text_config = Lfm2Config(**text_config)
137
+ elif text_config is None:
138
+ text_config = Lfm2Config()
139
+
140
+ self.text_config = text_config
141
+
142
+ super().__init__(**kwargs)
143
+
144
+
145
+ @dataclass
146
+ class Lfm2VlModelOutputWithPast(BaseModelOutputWithPast):
147
+ r"""
148
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
149
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
150
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
151
+
152
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
153
+ `past_key_values` input) to speed up sequential decoding.
154
+ image_hidden_states (`torch.FloatTensor`, *optional*):
155
+ A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
156
+ image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
157
+ """
158
+
159
+ image_hidden_states: torch.FloatTensor | None = None
160
+
161
+
162
+ @dataclass
163
+ class Lfm2VlCausalLMOutputWithPast(ModelOutput):
164
+ r"""
165
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
166
+ Language modeling loss (for next-token prediction).
167
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
168
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
169
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
170
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
171
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
172
+
173
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
174
+ `past_key_values` input) to speed up sequential decoding.
175
+ image_hidden_states (`torch.FloatTensor`, *optional*):
176
+ A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
177
+ image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
178
+ """
179
+
180
+ loss: torch.FloatTensor | None = None
181
+ logits: torch.FloatTensor | None = None
182
+ past_key_values: list[torch.FloatTensor] | None = None
183
+ hidden_states: tuple[torch.FloatTensor] | None = None
184
+ attentions: tuple[torch.FloatTensor] | None = None
185
+ image_hidden_states: torch.FloatTensor | None = None
186
+
187
+
188
+ class Lfm2VlMultiModalProjector(nn.Module):
189
+ def __init__(self, config: Lfm2VlConfig):
190
+ super().__init__()
191
+ in_channels = config.vision_config.hidden_size * (config.downsample_factor**2)
192
+ self.layer_norm = nn.LayerNorm(in_channels)
193
+ self.linear_1 = nn.Linear(
194
+ in_channels,
195
+ config.projector_hidden_size,
196
+ bias=config.projector_bias,
197
+ )
198
+ self.act = ACT2FN[config.projector_hidden_act]
199
+ self.linear_2 = nn.Linear(
200
+ config.projector_hidden_size,
201
+ config.text_config.hidden_size,
202
+ bias=config.projector_bias,
203
+ )
204
+
205
+ def forward(self, image_features):
206
+ image_features = self.layer_norm(image_features)
207
+ hidden_states = self.linear_1(image_features)
208
+ hidden_states = self.act(hidden_states)
209
+ hidden_states = self.linear_2(hidden_states)
210
+ return hidden_states
211
+
212
+
213
+ class PixelUnshuffleBlock(nn.Module):
214
+ def __init__(self, factor: int):
215
+ super().__init__()
216
+ self.factor = factor
217
+
218
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
219
+ n, w, h, c = x.size()
220
+ if w % self.factor != 0:
221
+ x = torch.concat(
222
+ [
223
+ x,
224
+ torch.zeros(
225
+ (n, self.factor - (w % self.factor), h, c), dtype=x.dtype
226
+ ).to(x.device),
227
+ ],
228
+ dim=1,
229
+ ).contiguous()
230
+ n, w, h, c = x.size()
231
+ x = x.contiguous()
232
+ if h % self.factor != 0:
233
+ x = torch.concat(
234
+ [
235
+ x,
236
+ torch.zeros(
237
+ (n, w, self.factor - (h % self.factor), c), dtype=x.dtype
238
+ ).to(x.device),
239
+ ],
240
+ dim=2,
241
+ ).contiguous()
242
+ n, w, h, c = x.size()
243
+ x = x.view(n, w, int(h / self.factor), int(c * self.factor))
244
+ x = x.permute(0, 2, 1, 3).contiguous()
245
+ x = x.view(
246
+ n, int(h / self.factor), int(w / self.factor), int(c * self.factor**2)
247
+ )
248
+ x = x.permute(0, 2, 1, 3).contiguous()
249
+ return x
250
+
251
+
252
+ class Lfm2VlPreTrainedModel(PreTrainedModel):
253
+ config: Lfm2VlConfig
254
+ base_model_prefix = ""
255
+ supports_gradient_checkpointing = True
256
+ _skip_keys_device_placement = ["past_key_values"]
257
+
258
+ _supports_flash_attn = True
259
+ _supports_sdpa = True
260
+
261
+ _can_compile_fullgraph = False
262
+ _supports_flex_attn = True
263
+ _supports_attention_backend = True
264
+
265
+
266
+ class Lfm2VlModel(Lfm2VlPreTrainedModel):
267
+ _checkpoint_conversion_mapping = {"language_model.model": "language_model"}
268
+
269
+ def __init__(self, config: Lfm2VlConfig):
270
+ super().__init__(config)
271
+ self.vision_tower = Siglip2VisionModel(config.vision_config)
272
+
273
+ if config.vision_feature_layer != -1:
274
+ self.vision_tower.vision_model.encoder.layers = (
275
+ self.vision_tower.vision_model.encoder.layers[
276
+ : config.vision_feature_layer + 1
277
+ ]
278
+ )
279
+ if config.downsample_factor > 1:
280
+ self.pixel_unshuffle = PixelUnshuffleBlock(config.downsample_factor)
281
+ else:
282
+ self.pixel_unshuffle = nn.Identity()
283
+
284
+ self.multi_modal_projector = Lfm2VlMultiModalProjector(config)
285
+ self.language_model = AutoModel.from_config(config.text_config)
286
+ self.post_init()
287
+
288
+ def get_input_embeddings(self):
289
+ return self.language_model.get_input_embeddings()
290
+
291
+ def set_input_embeddings(self, value):
292
+ self.language_model.set_input_embeddings(value)
293
+
294
+ def set_decoder(self, decoder):
295
+ self.language_model = decoder
296
+
297
+ def get_decoder(self):
298
+ return self.language_model
299
+
300
+ def get_image_features(
301
+ self,
302
+ pixel_values: torch.FloatTensor,
303
+ spatial_shapes: torch.Tensor,
304
+ pixel_attention_mask: torch.Tensor,
305
+ **kwargs,
306
+ ) -> list[torch.Tensor]:
307
+ """
308
+ Obtains image last hidden states from the vision tower and apply multimodal projection.
309
+
310
+ Args:
311
+ pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`):
312
+ The tensors corresponding to the input images.
313
+ spatial_shapes (`torch.Tensor` of shape `(batch_size, 2)`):
314
+ The spatial shapes of the input images.
315
+ pixel_attention_mask (`torch.Tensor` of shape `(batch_size, height, width)`):
316
+ The pixel attention mask of the input images.
317
+ Returns:
318
+ image_features (`list[torch.Tensor]`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
319
+ """
320
+ image_outputs = self.vision_tower(
321
+ pixel_values=pixel_values,
322
+ spatial_shapes=spatial_shapes,
323
+ pixel_attention_mask=pixel_attention_mask,
324
+ ).last_hidden_state
325
+
326
+ img_feature_lengths = pixel_attention_mask.sum(dim=1)
327
+ image_features = []
328
+
329
+ for img_idx in range(image_outputs.size(0)):
330
+ feature = image_outputs[img_idx]
331
+ # unpad the image representation
332
+ feature = feature[: img_feature_lengths[img_idx], :].unsqueeze(0)
333
+
334
+ feature_org_h, feature_org_w = spatial_shapes[img_idx]
335
+ feature = feature.reshape(1, feature_org_h, feature_org_w, -1)
336
+ feature = self.pixel_unshuffle(feature)
337
+
338
+ # project the image representation
339
+ img_embedding = self.multi_modal_projector(feature)
340
+
341
+ # flatten here to handle variable length in naflex
342
+ img_embedding = img_embedding.reshape(-1, img_embedding.size(-1))
343
+ image_features.append(img_embedding)
344
+
345
+ return image_features
346
+
347
+ def get_placeholder_mask(
348
+ self,
349
+ input_ids: torch.LongTensor | None,
350
+ inputs_embeds: torch.FloatTensor,
351
+ image_features: torch.FloatTensor,
352
+ ):
353
+ """
354
+ Obtains multimodal placeholdr mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
355
+ equal to the length of multimodal features. If the lengths are different, an error is raised.
356
+ """
357
+ if input_ids is None:
358
+ special_image_mask = inputs_embeds == self.get_input_embeddings()(
359
+ torch.tensor(
360
+ self.config.image_token_id,
361
+ dtype=torch.long,
362
+ device=inputs_embeds.device,
363
+ )
364
+ )
365
+ special_image_mask = special_image_mask.all(-1)
366
+ else:
367
+ special_image_mask = input_ids == self.config.image_token_id
368
+ n_image_tokens = special_image_mask.sum()
369
+ special_image_mask = (
370
+ special_image_mask.unsqueeze(-1)
371
+ .expand_as(inputs_embeds)
372
+ .to(inputs_embeds.device)
373
+ )
374
+ n_image_features = image_features.shape[0]
375
+ if inputs_embeds[special_image_mask].numel() != image_features.numel():
376
+ raise ValueError(
377
+ f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
378
+ )
379
+ return special_image_mask
380
+
381
+ @can_return_tuple
382
+ def forward(
383
+ self,
384
+ input_ids: torch.LongTensor = None,
385
+ attention_mask: torch.Tensor | None = None,
386
+ position_ids: torch.LongTensor | None = None,
387
+ pixel_values: torch.FloatTensor = None,
388
+ spatial_shapes: torch.Tensor = None,
389
+ pixel_attention_mask: torch.Tensor = None,
390
+ past_key_values: Cache | None = None,
391
+ inputs_embeds: torch.FloatTensor | None = None,
392
+ use_cache: bool | None = None,
393
+ output_attentions: bool | None = None,
394
+ output_hidden_states: bool | None = None,
395
+ return_dict: bool | None = None,
396
+ cache_position: torch.LongTensor | None = None,
397
+ image_sizes: torch.Tensor = None,
398
+ **kwargs: Unpack[FlashAttentionKwargs],
399
+ ) -> tuple | Lfm2VlModelOutputWithPast:
400
+ """
401
+ spatial_shapes (`torch.Tensor` of shape `(batch_size, 2)`, *optional*):
402
+ The spatial shapes of the input images.
403
+ pixel_attention_mask (`torch.Tensor` of shape `(batch_size, height, width)`, *optional*):
404
+ The pixel attention mask of the input images.
405
+ """
406
+ output_attentions = (
407
+ output_attentions
408
+ if output_attentions is not None
409
+ else self.config.output_attentions
410
+ )
411
+ output_hidden_states = (
412
+ output_hidden_states
413
+ if output_hidden_states is not None
414
+ else self.config.output_hidden_states
415
+ )
416
+ return_dict = (
417
+ return_dict if return_dict is not None else self.config.use_return_dict
418
+ )
419
+
420
+ if (input_ids is None) ^ (inputs_embeds is not None):
421
+ raise ValueError(
422
+ "You must specify exactly one of input_ids or inputs_embeds"
423
+ )
424
+
425
+ if inputs_embeds is None:
426
+ inputs_embeds = self.get_input_embeddings()(input_ids)
427
+
428
+ if pixel_values is not None:
429
+ image_features = self.get_image_features(
430
+ pixel_values=pixel_values,
431
+ spatial_shapes=spatial_shapes,
432
+ pixel_attention_mask=pixel_attention_mask,
433
+ )
434
+ image_features = torch.cat(image_features, dim=0).to(
435
+ inputs_embeds.device, inputs_embeds.dtype
436
+ )
437
+ special_image_mask = self.get_placeholder_mask(
438
+ input_ids=input_ids,
439
+ inputs_embeds=inputs_embeds,
440
+ image_features=image_features,
441
+ )
442
+ inputs_embeds = inputs_embeds.masked_scatter(
443
+ special_image_mask, image_features
444
+ )
445
+
446
+ outputs = self.language_model(
447
+ attention_mask=attention_mask,
448
+ position_ids=position_ids,
449
+ past_key_values=past_key_values,
450
+ inputs_embeds=inputs_embeds,
451
+ use_cache=use_cache,
452
+ output_attentions=output_attentions,
453
+ output_hidden_states=output_hidden_states,
454
+ return_dict=True,
455
+ cache_position=cache_position,
456
+ **kwargs,
457
+ )
458
+
459
+ return Lfm2VlModelOutputWithPast(
460
+ last_hidden_state=outputs.last_hidden_state,
461
+ past_key_values=outputs.past_key_values,
462
+ hidden_states=outputs.hidden_states,
463
+ attentions=outputs.attentions,
464
+ image_hidden_states=image_features if pixel_values is not None else None,
465
+ )
466
+
467
+
468
+ class Lfm2VlForConditionalGeneration(Lfm2VlPreTrainedModel, GenerationMixin):
469
+ _tied_weights_keys = ["lm_head.weight"]
470
+
471
+ def __init__(self, config: Lfm2VlConfig):
472
+ super().__init__(config)
473
+ self.model = Lfm2VlModel(config)
474
+ self.lm_head = nn.Linear(
475
+ config.text_config.hidden_size, config.text_config.vocab_size, bias=False
476
+ )
477
+ self.post_init()
478
+
479
+ def _supports_default_dynamic_cache(self):
480
+ return False
481
+
482
+ def get_input_embeddings(self):
483
+ return self.model.get_input_embeddings()
484
+
485
+ def set_input_embeddings(self, value):
486
+ self.model.set_input_embeddings(value)
487
+
488
+ def get_output_embeddings(self) -> nn.Module:
489
+ return self.lm_head
490
+
491
+ def set_decoder(self, decoder):
492
+ self.model.set_decoder(decoder)
493
+
494
+ def get_decoder(self):
495
+ return self.model.get_decoder()
496
+
497
+ def get_image_features(
498
+ self,
499
+ pixel_values: torch.FloatTensor,
500
+ spatial_shapes: torch.Tensor,
501
+ pixel_attention_mask: torch.Tensor,
502
+ **kwargs,
503
+ ):
504
+ return self.model.get_image_features(
505
+ pixel_values=pixel_values,
506
+ spatial_shapes=spatial_shapes,
507
+ pixel_attention_mask=pixel_attention_mask,
508
+ **kwargs,
509
+ )
510
+
511
+ @property
512
+ def language_model(self):
513
+ return self.model.language_model
514
+
515
+ @property
516
+ def vision_tower(self):
517
+ return self.model.vision_tower
518
+
519
+ @property
520
+ def multi_modal_projector(self):
521
+ return self.model.multi_modal_projector
522
+
523
+ @can_return_tuple
524
+ def forward(
525
+ self,
526
+ input_ids: torch.LongTensor = None,
527
+ pixel_values: torch.FloatTensor = None,
528
+ spatial_shapes: torch.Tensor = None,
529
+ pixel_attention_mask: torch.Tensor = None,
530
+ attention_mask: torch.Tensor | None = None,
531
+ position_ids: torch.LongTensor | None = None,
532
+ past_key_values: Cache | None = None,
533
+ inputs_embeds: torch.FloatTensor | None = None,
534
+ labels: torch.LongTensor | None = None,
535
+ use_cache: bool | None = None,
536
+ output_attentions: bool | None = None,
537
+ output_hidden_states: bool | None = None,
538
+ return_dict: bool | None = None,
539
+ cache_position: torch.LongTensor | None = None,
540
+ logits_to_keep: int | torch.Tensor = 0,
541
+ image_sizes: torch.Tensor | None = None,
542
+ **kwargs,
543
+ ) -> tuple | Lfm2VlCausalLMOutputWithPast:
544
+ r"""
545
+ pixel_values (`torch.FloatTensor` of shape `(batch_size, channels, height, width)`, *optional*):
546
+ The input image tensors.
547
+ spatial_shapes (`torch.Tensor` of shape `(batch_size, 2)`, *optional*):
548
+ The spatial shapes of the input images.
549
+ pixel_attention_mask (`torch.Tensor` of shape `(batch_size, height, width)`, *optional*):
550
+ The pixel attention mask of the input images.
551
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
552
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
553
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
554
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
555
+
556
+ Example:
557
+
558
+ ```python
559
+ >>> from PIL import Image
560
+ >>> import requests
561
+ >>> from transformers import AutoProcessor, AutoModelForImageTextToText
562
+ >>> from transformers.image_utils import load_image
563
+
564
+ >>> model = AutoModelForImageTextToText.from_pretrained(
565
+ ... "LiquidAI/LFM2-VL-1.6B",
566
+ ... trust_remote_code=True
567
+ ... )
568
+ >>> processor = AutoProcessor.from_pretrained(
569
+ ... "LiquidAI/LFM2-VL-1.6B",
570
+ ... trust_remote_code=True
571
+ ... )
572
+
573
+ >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
574
+ >>> image = load_image(url)
575
+
576
+ >>> conversation = [
577
+ ... {
578
+ ... "role": "user",
579
+ ... "content": [
580
+ ... {"type": "image", "image": image},
581
+ ... {"type": "text", "text": "What is in this image?"},
582
+ ... ],
583
+ ... },
584
+ ... ]
585
+
586
+ >>> inputs = processor.apply_chat_template(
587
+ ... conversation,
588
+ ... add_generation_prompt=True,
589
+ ... tokenize=True,
590
+ ... return_dict=True,
591
+ ... return_tensors="pt"
592
+ ... )
593
+
594
+ >>> # Generate
595
+ >>> outputs = model.generate(**inputs, max_new_tokens=45)
596
+ >>> processor.batch_decode(outputs, skip_special_tokens=True)[0]
597
+ '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.'
598
+ ```"""
599
+ output_attentions = (
600
+ output_attentions
601
+ if output_attentions is not None
602
+ else self.config.output_attentions
603
+ )
604
+ output_hidden_states = (
605
+ output_hidden_states
606
+ if output_hidden_states is not None
607
+ else self.config.output_hidden_states
608
+ )
609
+ return_dict = (
610
+ return_dict if return_dict is not None else self.config.use_return_dict
611
+ )
612
+
613
+ outputs = self.model(
614
+ input_ids=input_ids,
615
+ pixel_values=pixel_values,
616
+ spatial_shapes=spatial_shapes,
617
+ pixel_attention_mask=pixel_attention_mask,
618
+ attention_mask=attention_mask,
619
+ position_ids=position_ids,
620
+ past_key_values=past_key_values,
621
+ inputs_embeds=inputs_embeds,
622
+ use_cache=use_cache,
623
+ output_attentions=output_attentions,
624
+ output_hidden_states=output_hidden_states,
625
+ return_dict=True,
626
+ cache_position=cache_position,
627
+ image_sizes=image_sizes,
628
+ **kwargs,
629
+ )
630
+
631
+ hidden_states = outputs[0]
632
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
633
+ slice_indices = (
634
+ slice(-logits_to_keep, None)
635
+ if isinstance(logits_to_keep, int)
636
+ else logits_to_keep
637
+ )
638
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
639
+
640
+ loss = None
641
+ if labels is not None:
642
+ loss = self.loss_function(
643
+ logits=logits,
644
+ labels=labels,
645
+ vocab_size=self.config.text_config.vocab_size,
646
+ **kwargs,
647
+ )
648
+
649
+ return Lfm2VlCausalLMOutputWithPast(
650
+ loss=loss,
651
+ logits=logits,
652
+ past_key_values=outputs.past_key_values,
653
+ hidden_states=outputs.hidden_states,
654
+ attentions=outputs.attentions,
655
+ image_hidden_states=outputs.image_hidden_states,
656
+ )
657
+
658
+ def prepare_inputs_for_generation(
659
+ self,
660
+ input_ids,
661
+ past_key_values=None,
662
+ inputs_embeds=None,
663
+ pixel_values=None,
664
+ attention_mask=None,
665
+ cache_position=None,
666
+ logits_to_keep=None,
667
+ **kwargs,
668
+ ):
669
+ # Overwritten -- in specific circumstances we don't want to forward image inputs to the model
670
+ model_inputs = super().prepare_inputs_for_generation(
671
+ input_ids,
672
+ past_key_values=past_key_values,
673
+ inputs_embeds=inputs_embeds,
674
+ attention_mask=attention_mask,
675
+ cache_position=cache_position,
676
+ logits_to_keep=logits_to_keep,
677
+ **kwargs,
678
+ )
679
+
680
+ if cache_position[0] == 0:
681
+ # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
682
+ # Otherwise we need pixel values to be passed to model
683
+ model_inputs["pixel_values"] = pixel_values
684
+
685
+ return model_inputs
686
+
687
+
688
+ __all__ = ["Lfm2VlForConditionalGeneration", "Lfm2VlModel", "Lfm2VlPreTrainedModel"]
preprocessor_config.json ADDED
@@ -0,0 +1,27 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "auto_map": {
3
+ "AutoProcessor": "processing_lfm2_vl.Lfm2VlProcessor"
4
+ },
5
+ "do_convert_rgb": null,
6
+ "do_normalize": true,
7
+ "do_rescale": true,
8
+ "do_resize": false,
9
+ "image_mean": [
10
+ 0.5,
11
+ 0.5,
12
+ 0.5
13
+ ],
14
+ "image_processor_type": "Siglip2ImageProcessor",
15
+ "image_std": [
16
+ 0.5,
17
+ 0.5,
18
+ 0.5
19
+ ],
20
+ "input_data_format": "channels_last",
21
+ "max_num_patches": 1024,
22
+ "patch_size": 16,
23
+ "processor_class": "Lfm2VlProcessor",
24
+ "resample": 2,
25
+ "rescale_factor": 0.00392156862745098,
26
+ "return_tensors": "pt"
27
+ }
processing_lfm2_vl.py ADDED
@@ -0,0 +1,645 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import math
2
+ from typing import Union
3
+
4
+ from PIL import Image
5
+ from transformers.feature_extraction_utils import BatchFeature
6
+ from transformers.image_utils import ImageInput, make_nested_list_of_images
7
+ from transformers.image_transforms import to_pil_image
8
+ from transformers.processing_utils import (
9
+ ImagesKwargs,
10
+ ProcessingKwargs,
11
+ ProcessorMixin,
12
+ Unpack,
13
+ )
14
+ from transformers.tokenization_utils_base import BatchEncoding, TextInput
15
+ from transformers.utils import logging
16
+
17
+ logger = logging.get_logger(__name__)
18
+
19
+
20
+ # resize adapted from qwen2.5
21
+ # https://github.com/QwenLM/Qwen2.5-VL/blob/main/qwen-vl-utils/src/qwen_vl_utils/vision_process.py
22
+ def round_by_factor(number: float, factor: int) -> int:
23
+ """Returns the closest integer to 'number' that is divisible by 'factor'."""
24
+ return round(number / factor) * factor
25
+
26
+
27
+ def ceil_by_factor(number: float, factor: int) -> int:
28
+ """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
29
+ return math.ceil(number / factor) * factor
30
+
31
+
32
+ def floor_by_factor(number: float, factor: int) -> int:
33
+ """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
34
+ return math.floor(number / factor) * factor
35
+
36
+
37
+ def find_closest_aspect_ratio(
38
+ aspect_ratio: float,
39
+ target_ratios: list[tuple[int, int]],
40
+ width: int,
41
+ height: int,
42
+ image_size: int,
43
+ ) -> tuple[int, int]:
44
+ """Find the closest aspect ratio from target_ratios to match the input aspect ratio.
45
+
46
+ Args:
47
+ aspect_ratio: The aspect ratio to match (width/height).
48
+ target_ratios: List of possible aspect ratios as tuples of (width, height) integers.
49
+ width: Original image width in pixels.
50
+ height: Original image height in pixels.
51
+ image_size: Base size for calculating target area.
52
+
53
+ Returns:
54
+ tuple[int, int]: The best matching ratio as (width, height) integers.
55
+ """
56
+ best_ratio_diff = float("inf")
57
+ best_ratio = (1, 1)
58
+ area = width * height
59
+
60
+ for ratio in target_ratios:
61
+ target_aspect_ratio = ratio[0] / ratio[1]
62
+ ratio_diff = abs(aspect_ratio - target_aspect_ratio)
63
+
64
+ # update best ratio if we found a closer match
65
+ if ratio_diff < best_ratio_diff:
66
+ best_ratio_diff = ratio_diff
67
+ best_ratio = ratio
68
+ # if equally close, prefer the ratio that better matches the original image area
69
+ elif ratio_diff == best_ratio_diff:
70
+ target_area = image_size * image_size * ratio[0] * ratio[1]
71
+ if area > 0.5 * target_area:
72
+ best_ratio = ratio
73
+
74
+ return best_ratio
75
+
76
+
77
+ class Lfm2VlImagesKwargs(ImagesKwargs, total=False):
78
+ return_row_col_info: bool | None
79
+ max_image_size: dict[str, int] | None
80
+
81
+
82
+ class Lfm2VlProcessorKwargs(ProcessingKwargs, total=False):
83
+ images_kwargs: Lfm2VlImagesKwargs
84
+
85
+ _defaults = {
86
+ "text_kwargs": {
87
+ "add_special_tokens": False,
88
+ "padding": False,
89
+ "is_split_into_words": False,
90
+ },
91
+ "images_kwargs": {
92
+ "do_resize": False,
93
+ },
94
+ }
95
+
96
+
97
+ class Lfm2VlProcessor(ProcessorMixin):
98
+ r"""
99
+ Constructs a Lfm2Vl processor which wraps a Lfm2Tokenizer tokenizer and Lfm2Vl image processor into a single processor.
100
+
101
+ [`Lfm2VlProcessor`] offers all the functionalities of [`Siglip2ImageProcessor`] and [`Lfm2Tokenizer`].
102
+
103
+ Args:
104
+ image_processor (`Siglip2ImageProcessor`):
105
+ An instance of [`Siglip2ImageProcessor`]. The image processor is a required input.
106
+ tokenizer (`PreTrainedTokenizerBase`):
107
+ An instance of [`PreTrainedTokenizerBase`]. This should correspond with the model's text model. The tokenizer is a required input.
108
+ """
109
+
110
+ attributes = ["image_processor", "tokenizer"]
111
+ image_processor_class = "Siglip2ImageProcessor"
112
+ tokenizer_class = "AutoTokenizer"
113
+
114
+ def __init__(
115
+ self,
116
+ image_processor,
117
+ tokenizer,
118
+ chat_template: str,
119
+ use_image_special_tokens: bool,
120
+ downsample_factor: int,
121
+ do_image_splitting: bool,
122
+ min_tiles: int,
123
+ max_tiles: int,
124
+ use_thumbnail: bool,
125
+ min_image_tokens: int,
126
+ max_image_tokens: int,
127
+ encoder_patch_size: int,
128
+ tile_size: int,
129
+ max_pixels_tolerance: float,
130
+ max_num_patches: int,
131
+ auto_map: dict[str, str] = None,
132
+ **kwargs,
133
+ ):
134
+ self.image_token = getattr(tokenizer, "image_token", "<image>")
135
+ self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
136
+ self.use_image_special_tokens = use_image_special_tokens
137
+ self.image_start_token = getattr(
138
+ tokenizer, "image_start_token", "<|image_start|>"
139
+ )
140
+ self.image_end_token = getattr(tokenizer, "image_end_token", "<|image_end|>")
141
+ self.image_thumbnail_token = getattr(
142
+ tokenizer, "image_thumbnail", "<|img_thumbnail|>"
143
+ )
144
+ self.downsample_factor = downsample_factor
145
+ self.do_image_splitting = do_image_splitting
146
+ self.min_tiles = min_tiles
147
+ self.max_tiles = max_tiles
148
+ self.use_thumbnail = use_thumbnail
149
+ self.min_image_tokens = min_image_tokens
150
+ self.max_image_tokens = max_image_tokens
151
+ self.encoder_patch_size = encoder_patch_size
152
+ self.tile_size = tile_size
153
+ self.max_pixels_tolerance = max_pixels_tolerance
154
+ self.chat_template = chat_template
155
+ self.auto_map = auto_map
156
+ super().__init__(
157
+ image_processor, tokenizer, chat_template=chat_template, **kwargs
158
+ )
159
+ self.max_num_patches = max_num_patches
160
+ self.image_processor.max_num_patches = max_num_patches
161
+
162
+ def _high_res_preprocessor(
163
+ self,
164
+ image: Image.Image,
165
+ min_tiles,
166
+ max_tiles,
167
+ tile_size,
168
+ ) -> tuple[list[Image.Image], int, int, int]:
169
+ """Process a high resolution image into patches.
170
+ This method splits a high resolution image into a grid of smaller patches while trying to maintain
171
+ the original aspect ratio. It finds the optimal grid configuration within the specified tile constraints.
172
+ """
173
+ orig_width, orig_height = image.size
174
+ aspect_ratio = orig_width / orig_height
175
+
176
+ # generate valid patch grid configurations (width, height)
177
+ target_ratios = [
178
+ (w, h)
179
+ for n in range(min_tiles, max_tiles + 1)
180
+ for w in range(1, n + 1)
181
+ for h in range(1, n + 1)
182
+ if min_tiles <= w * h <= max_tiles
183
+ ]
184
+ target_ratios = sorted(set(target_ratios), key=lambda x: x[0] * x[1])
185
+
186
+ # default to 1x1 if no valid configurations found
187
+ if not target_ratios:
188
+ return [], 0, 0
189
+
190
+ # find best matching grid configuration
191
+ grid_width, grid_height = find_closest_aspect_ratio(
192
+ aspect_ratio, target_ratios, orig_width, orig_height, tile_size
193
+ )
194
+
195
+ target_width = tile_size * grid_width
196
+ target_height = tile_size * grid_height
197
+ total_patches = grid_width * grid_height
198
+
199
+ # resize and split image into patches
200
+ resized_img = image.resize((target_width, target_height))
201
+ patches = []
202
+
203
+ for i in range(total_patches):
204
+ # calculate patch coordinates
205
+ col = i % grid_width
206
+ row = i // grid_width
207
+ box = (
208
+ col * tile_size,
209
+ row * tile_size,
210
+ (col + 1) * tile_size,
211
+ (row + 1) * tile_size,
212
+ )
213
+ patch = resized_img.crop(box)
214
+ patches.append(patch)
215
+
216
+ num_rows = grid_height
217
+ num_columns = grid_width
218
+
219
+ return patches, num_rows, num_columns
220
+
221
+ def _smart_resize(
222
+ self,
223
+ image: Image.Image,
224
+ downsample_factor: int,
225
+ min_image_tokens: int,
226
+ max_image_tokens: int,
227
+ encoder_patch_size: int,
228
+ ) -> Image.Image:
229
+ """
230
+ Rescales the image so that the following conditions are met:
231
+ 1. Both dimensions (height and width) are divisible by 'encoder_patch_size' * 'downsample_factor'.
232
+ This ensures no padding is needed in the downsampling step.
233
+ 2. The total number of pixels is within the range ['smart_resize_min_pixels', 'smart_resize_max_pixels'].
234
+ 3. The aspect ratio of the image is maintained as closely as possible.
235
+ """
236
+ width, height = image.size
237
+
238
+ total_factor = encoder_patch_size * downsample_factor
239
+ smart_resize_min_pixels = (
240
+ min_image_tokens
241
+ * encoder_patch_size ** 2
242
+ * downsample_factor ** 2
243
+ )
244
+ smart_resize_max_pixels = (
245
+ max_image_tokens
246
+ * encoder_patch_size ** 2
247
+ * downsample_factor ** 2
248
+ )
249
+
250
+ h_bar = max(total_factor, round_by_factor(height, total_factor))
251
+ w_bar = max(total_factor, round_by_factor(width, total_factor))
252
+
253
+ if h_bar * w_bar > smart_resize_max_pixels:
254
+ beta = math.sqrt((height * width) / smart_resize_max_pixels)
255
+ h_bar = max(total_factor, floor_by_factor(height / beta, total_factor))
256
+ w_bar = max(total_factor, floor_by_factor(width / beta, total_factor))
257
+ elif h_bar * w_bar < smart_resize_min_pixels:
258
+ beta = math.sqrt(smart_resize_min_pixels / (height * width))
259
+ h_bar = ceil_by_factor(height * beta, total_factor)
260
+ w_bar = ceil_by_factor(width * beta, total_factor)
261
+
262
+ resized_img = image.resize((w_bar, h_bar))
263
+ return resized_img
264
+
265
+ def _get_tokens_num(self, image_height: int, image_width: int) -> int:
266
+ num_patches_height = image_height // self.encoder_patch_size
267
+ num_patches_width = image_width // self.encoder_patch_size
268
+
269
+ dwn_num_patches_height = math.ceil(num_patches_height / self.downsample_factor)
270
+ dwn_num_patches_width = math.ceil(num_patches_width / self.downsample_factor)
271
+
272
+ return dwn_num_patches_height * dwn_num_patches_width
273
+
274
+ def _is_img_too_large(
275
+ self,
276
+ image: Image.Image,
277
+ max_image_tokens: int,
278
+ encoder_patch_size: int,
279
+ max_pixels_tolerance: float,
280
+ ) -> bool:
281
+ """Check if the image is too large to be processed as one tile."""
282
+ width, height = image.size
283
+
284
+ h_bar = max(encoder_patch_size, round_by_factor(height, encoder_patch_size))
285
+ w_bar = max(encoder_patch_size, round_by_factor(width, encoder_patch_size))
286
+ return (
287
+ h_bar * w_bar
288
+ > max_image_tokens
289
+ * encoder_patch_size ** 2
290
+ * self.downsample_factor ** 2
291
+ * max_pixels_tolerance
292
+ )
293
+
294
+ def _resize_and_maybe_split(
295
+ self,
296
+ image: ImageInput,
297
+ downsample_factor: int,
298
+ min_tiles: int,
299
+ max_tiles: int,
300
+ use_thumbnail: bool,
301
+ min_image_tokens: int,
302
+ max_image_tokens: int,
303
+ encoder_patch_size: int,
304
+ tile_size: int,
305
+ max_pixels_tolerance: float,
306
+ ) -> tuple[list[Image.Image], int, int, int, int]:
307
+ """Apply smart resize and maybe split the image into tiles if image too large.
308
+ Return:
309
+ image_tiles: ImageInput
310
+ num_tokens_per_tile: int
311
+ num_rows: int
312
+ num_cols: int
313
+ num_thumbnail_tokens: int
314
+ """
315
+ image = to_pil_image(image)
316
+ do_image_splitting = not min_tiles == max_tiles == 1
317
+ if (
318
+ self._is_img_too_large(
319
+ image,
320
+ max_image_tokens,
321
+ encoder_patch_size,
322
+ max_pixels_tolerance,
323
+ )
324
+ and do_image_splitting
325
+ ):
326
+ image_tiles, num_rows, num_cols = self._high_res_preprocessor(
327
+ image, min_tiles, max_tiles, tile_size
328
+ )
329
+ if len(image_tiles) > 1:
330
+ num_thumbnail_tokens = 0
331
+ if use_thumbnail:
332
+ thumbnail_image = self._smart_resize(
333
+ image,
334
+ downsample_factor,
335
+ min_image_tokens,
336
+ max_image_tokens,
337
+ encoder_patch_size,
338
+ )
339
+ num_thumbnail_tokens = self._get_tokens_num(
340
+ thumbnail_image.height, thumbnail_image.width
341
+ )
342
+ image_tiles.append(thumbnail_image)
343
+
344
+ return (
345
+ image_tiles,
346
+ self._get_tokens_num(tile_size, tile_size),
347
+ num_rows,
348
+ num_cols,
349
+ num_thumbnail_tokens,
350
+ )
351
+ else:
352
+ image = self._smart_resize(
353
+ image,
354
+ downsample_factor,
355
+ min_image_tokens,
356
+ max_image_tokens,
357
+ encoder_patch_size,
358
+ )
359
+ return [image], self._get_tokens_num(image.height, image.width), 1, 1, 0
360
+
361
+ def process_vision(
362
+ self,
363
+ text: list[str],
364
+ images: list[list[ImageInput]],
365
+ use_image_special_tokens: bool,
366
+ downsample_factor: int,
367
+ min_tiles: int,
368
+ max_tiles: int,
369
+ use_thumbnail: bool,
370
+ min_image_tokens: int,
371
+ max_image_tokens: int,
372
+ encoder_patch_size: int,
373
+ tile_size: int,
374
+ max_pixels_tolerance: float,
375
+ output_kwargs: dict,
376
+ ):
377
+ if text is not None:
378
+ n_images_in_text = [sample.count(self.image_token) for sample in text]
379
+
380
+ n_images_in_images = [len(sublist) for sublist in images]
381
+
382
+ if n_images_in_images != n_images_in_text:
383
+ raise ValueError(
384
+ f"The number of images in the text {n_images_in_text} and images {n_images_in_images} should be the same."
385
+ )
386
+
387
+ prompt_strings = []
388
+ image_inputs = []
389
+
390
+ for sample_text, sample_images in zip(text, images, strict=False):
391
+ split_sample = sample_text.split(self.image_token)
392
+ sample_tiles = []
393
+ sample_text_with_image_tokens = ""
394
+ for i, image in enumerate(sample_images):
395
+ sample_text_with_image_tokens += split_sample[i]
396
+ if use_image_special_tokens:
397
+ sample_text_with_image_tokens += self.image_start_token
398
+ (
399
+ image_tiles,
400
+ num_tokens_per_tile,
401
+ num_rows,
402
+ num_cols,
403
+ num_thumbnail_tokens,
404
+ ) = self._resize_and_maybe_split(
405
+ image,
406
+ downsample_factor,
407
+ min_tiles,
408
+ max_tiles,
409
+ use_thumbnail,
410
+ min_image_tokens,
411
+ max_image_tokens,
412
+ encoder_patch_size,
413
+ tile_size,
414
+ max_pixels_tolerance,
415
+ )
416
+
417
+ if len(image_tiles) > 1:
418
+ for row in range(num_rows):
419
+ for col in range(num_cols):
420
+ if use_image_special_tokens:
421
+ sample_text_with_image_tokens += (
422
+ f"<|img_row_{row + 1}_col_{col + 1}|>"
423
+ )
424
+ sample_text_with_image_tokens += (
425
+ self.image_token * num_tokens_per_tile
426
+ )
427
+
428
+ if num_thumbnail_tokens > 0:
429
+ if use_image_special_tokens:
430
+ sample_text_with_image_tokens += self.image_thumbnail_token
431
+ sample_text_with_image_tokens += (
432
+ self.image_token * num_thumbnail_tokens
433
+ )
434
+ else:
435
+ sample_text_with_image_tokens += (
436
+ self.image_token * num_tokens_per_tile
437
+ )
438
+
439
+ if use_image_special_tokens:
440
+ sample_text_with_image_tokens += self.image_end_token
441
+
442
+ sample_text_with_image_tokens += split_sample[i + 1]
443
+ sample_tiles.extend(image_tiles)
444
+
445
+ prompt_strings.append(sample_text_with_image_tokens)
446
+ image_inputs.append(sample_tiles)
447
+
448
+ image_inputs = self.image_processor(
449
+ image_inputs, **output_kwargs["images_kwargs"]
450
+ )
451
+
452
+ if text is None:
453
+ return None, image_inputs
454
+
455
+ return prompt_strings, image_inputs
456
+
457
+ def __call__(
458
+ self,
459
+ images: ImageInput | list[ImageInput] | list[list[ImageInput]] = None,
460
+ text: Union[TextInput, "PreTokenizedInput", list[TextInput], list["PreTokenizedInput"]] = None,
461
+ use_image_special_tokens: bool | None = None,
462
+ downsample_factor: int | None = None,
463
+ min_image_tokens: int | None = None,
464
+ max_image_tokens: int | None = None,
465
+ do_image_splitting: bool | None = None,
466
+ min_tiles: int | None = None,
467
+ max_tiles: int | None = None,
468
+ use_thumbnail: bool | None = None,
469
+ encoder_patch_size: int | None = None,
470
+ tile_size: int | None = None,
471
+ max_pixels_tolerance: float | None = None,
472
+ **kwargs: Unpack[Lfm2VlProcessorKwargs],
473
+ ) -> BatchEncoding:
474
+ """
475
+ Processes the input prompts and returns a BatchFeature.
476
+
477
+ Example:
478
+
479
+ ```python
480
+ >>> import requests
481
+ >>> from transformers import AutoProcessor
482
+ >>> from transformers.image_utils import load_image
483
+ >>> processor = AutoProcessor.from_pretrained("LiquidAI/LFM2-VL-1.6B", trust_remote_code=True)
484
+
485
+ >>> url1 = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
486
+ >>> url2 = "https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg"
487
+
488
+ >>> image1, image2 = load_image(url1), load_image(url2)
489
+ >>> images = [image1, image2]
490
+
491
+ >>> conversation = [
492
+ ... {
493
+ ... "role": "user",
494
+ ... "content": [
495
+ ... {"type": "image", "url": image1},
496
+ ... {"type": "image", "url": image2},
497
+ ... {"type": "text", "text": "Compare the two images."},
498
+ ... ],
499
+ ... },
500
+ ... ]
501
+ >>> chat_inputs = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
502
+ >>> outputs = processor(images=images, text=chat_inputs, return_tensors="pt")
503
+ >>> input_ids = outputs.input_ids
504
+ >>> input_tokens = processor.tokenizer.batch_decode(input_ids)
505
+ >>> print(input_tokens)
506
+ '['user\nCompare the two images.\nassistant\n']'
507
+ ```
508
+
509
+ Args:
510
+ images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`, *optional*):
511
+ The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
512
+ tensor. If is of type `list[ImageInput]`, it's assumed that this is for a single prompt i.e. of batch size 1.
513
+ text (`TextInput`, *optional*):
514
+ The sequence or batch of sequences to be encoded.
515
+ Wherever an image token, `<image>` is encountered it is expanded to a proper sequence of image tokens.
516
+ return_tensors (`str | TensorType`, *optional*):
517
+ If set, will return tensors of a particular framework. See [`PreTrainedTokenizerFast.__call__`] for more
518
+ information.
519
+ """
520
+ use_image_special_tokens = (
521
+ use_image_special_tokens
522
+ if use_image_special_tokens is not None
523
+ else self.use_image_special_tokens
524
+ )
525
+ downsample_factor = (
526
+ downsample_factor
527
+ if downsample_factor is not None
528
+ else self.downsample_factor
529
+ )
530
+ do_image_splitting = (
531
+ do_image_splitting
532
+ if do_image_splitting is not None
533
+ else self.do_image_splitting
534
+ )
535
+
536
+ min_tiles = min_tiles if min_tiles is not None else self.min_tiles
537
+ max_tiles = max_tiles if max_tiles is not None else self.max_tiles
538
+
539
+ if not do_image_splitting:
540
+ min_tiles = 1
541
+ max_tiles = 1
542
+ logger.debug(
543
+ "Image splitting is disabled, setting min_tiles and max_tiles to 1. Set do_image_splitting=True to enable splitting."
544
+ )
545
+
546
+ if do_image_splitting and min_tiles > max_tiles:
547
+ raise ValueError("min_tiles must be less than or equal to max_tiles")
548
+
549
+ use_thumbnail = (
550
+ use_thumbnail if use_thumbnail is not None else self.use_thumbnail
551
+ )
552
+ min_image_tokens = (
553
+ min_image_tokens if min_image_tokens is not None else self.min_image_tokens
554
+ )
555
+ max_image_tokens = (
556
+ max_image_tokens if max_image_tokens is not None else self.max_image_tokens
557
+ )
558
+ encoder_patch_size = (
559
+ encoder_patch_size
560
+ if encoder_patch_size is not None
561
+ else self.encoder_patch_size
562
+ )
563
+ tile_size = tile_size if tile_size is not None else self.tile_size
564
+ max_pixels_tolerance = (
565
+ max_pixels_tolerance
566
+ if max_pixels_tolerance is not None
567
+ else self.max_pixels_tolerance
568
+ )
569
+
570
+ if text is None and images is None:
571
+ raise ValueError("You must provide one of `text` or `images`.")
572
+
573
+ output_kwargs = self._merge_kwargs(
574
+ Lfm2VlProcessorKwargs,
575
+ tokenizer_init_kwargs=self.tokenizer.init_kwargs,
576
+ **kwargs,
577
+ )
578
+
579
+ if text is not None:
580
+ if isinstance(text, str):
581
+ text = [text]
582
+ elif not isinstance(text, list) and not isinstance(text[0], str):
583
+ raise ValueError(
584
+ "Invalid input text. Please provide a string, or a list of strings"
585
+ )
586
+ n_images_in_text = sum([sample.count(self.image_token) for sample in text])
587
+ if n_images_in_text > 0 and (images is None):
588
+ raise ValueError(
589
+ f"We detected {n_images_in_text} tokens in the text but no images were passed"
590
+ )
591
+
592
+ inputs = {}
593
+
594
+ if images is not None:
595
+ images = make_nested_list_of_images(images)
596
+ text, vision_inputs = self.process_vision(
597
+ text,
598
+ images,
599
+ use_image_special_tokens,
600
+ downsample_factor,
601
+ min_tiles,
602
+ max_tiles,
603
+ use_thumbnail,
604
+ min_image_tokens,
605
+ max_image_tokens,
606
+ encoder_patch_size,
607
+ tile_size,
608
+ max_pixels_tolerance,
609
+ output_kwargs,
610
+ )
611
+ inputs.update(vision_inputs)
612
+
613
+ return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
614
+
615
+ if text is not None:
616
+ text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
617
+ self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
618
+ inputs.update(text_inputs)
619
+
620
+ return BatchFeature(inputs, tensor_type=return_tensors)
621
+
622
+ def batch_decode(self, *args, **kwargs):
623
+ """
624
+ This method forwards all its arguments to LFM2Tokeniser's [`~PreTrainedTokenizer.batch_decode`]. Please
625
+ refer to the docstring of this method for more information.
626
+ """
627
+ batched_decode_output = self.tokenizer.batch_decode(*args, **kwargs)
628
+ return batched_decode_output
629
+
630
+ def decode(self, *args, **kwargs):
631
+ """
632
+ This method forwards all its arguments to LFM2Tokeniser's [`~PreTrainedTokenizer.decode`]. Please refer to
633
+ the docstring of this method for more information.
634
+ """
635
+ decode_output = self.tokenizer.decode(*args, **kwargs)
636
+ return decode_output
637
+
638
+ @property
639
+ def model_input_names(self):
640
+ tokenizer_input_names = self.tokenizer.model_input_names
641
+ image_processor_input_names = self.image_processor.model_input_names
642
+ return list(dict.fromkeys(image_processor_input_names + tokenizer_input_names))
643
+
644
+
645
+ __all__ = ["Lfm2VlProcessor"]
processor_config.json ADDED
@@ -0,0 +1,18 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "auto_map": {
3
+ "AutoProcessor": "processing_lfm2_vl.Lfm2VlProcessor"
4
+ },
5
+ "do_image_splitting": true,
6
+ "downsample_factor": 2,
7
+ "encoder_patch_size": 16,
8
+ "max_image_tokens": 256,
9
+ "max_num_patches": 1024,
10
+ "max_pixels_tolerance": 1.5,
11
+ "max_tiles": 10,
12
+ "min_image_tokens": 64,
13
+ "min_tiles": 2,
14
+ "processor_class": "Lfm2VlProcessor",
15
+ "tile_size": 512,
16
+ "use_image_special_tokens": true,
17
+ "use_thumbnail": true
18
+ }
special_tokens_map.json ADDED
@@ -0,0 +1,27 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<|startoftext|>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "<|im_end|>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "image_end_token": "<|image_end|>",
17
+ "image_start_token": "<|image_start|>",
18
+ "image_thumbnail": "<|img_thumbnail|>",
19
+ "image_token": "<image>",
20
+ "pad_token": {
21
+ "content": "<|pad|>",
22
+ "lstrip": false,
23
+ "normalized": false,
24
+ "rstrip": false,
25
+ "single_word": false
26
+ }
27
+ }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
@@ -0,0 +1,4088 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