Refactoring of ImageProcessorFast

docs/source/en/model_doc/blip.md

@@ -61,6 +61,11 @@ The original code can be found [here](https://github.com/salesforce/BLIP).
 [[autodoc]] BlipImageProcessor
     - preprocess
 
+## BlipImageProcessorFast
+
+[[autodoc]] BlipImageProcessorFast
+    - preprocess
+
 <frameworkcontent>
 <pt>
 

docs/source/en/model_doc/clip.md

@@ -251,6 +251,11 @@ The resource should ideally demonstrate something new instead of duplicating an
 [[autodoc]] CLIPImageProcessor
     - preprocess
 
+## CLIPImageProcessorFast
+
+[[autodoc]] CLIPImageProcessorFast
+    - preprocess
+
 ## CLIPFeatureExtractor
 
 [[autodoc]] CLIPFeatureExtractor

docs/source/en/model_doc/convnext.md

@@ -64,6 +64,11 @@ If you're interested in submitting a resource to be included here, please feel f
 [[autodoc]] ConvNextImageProcessor
     - preprocess
 
+## ConvNextImageProcessorFast
+
+[[autodoc]] ConvNextImageProcessorFast
+    - preprocess
+
 <frameworkcontent>
 <pt>
 

docs/source/en/model_doc/deit.md

@@ -125,6 +125,11 @@ If you're interested in submitting a resource to be included here, please feel f
 [[autodoc]] DeiTImageProcessor
     - preprocess
 
+## DeiTImageProcessorFast
+
+[[autodoc]] DeiTImageProcessorFast
+    - preprocess
+
 <frameworkcontent>
 <pt>
 

docs/source/en/model_doc/llava.md

@@ -195,6 +195,11 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 [[autodoc]] LlavaImageProcessor
     - preprocess
 
+## LlavaImageProcessorFast
+
+[[autodoc]] LlavaImageProcessorFast
+    - preprocess
+
 ## LlavaProcessor
 
 [[autodoc]] LlavaProcessor

docs/source/en/model_doc/llava_next.md

@@ -288,6 +288,11 @@ model = AutoModelForImageTextToText.from_pretrained(
 [[autodoc]] LlavaNextImageProcessor
     - preprocess
 
+## LlavaNextImageProcessorFast
+
+[[autodoc]] LlavaNextImageProcessorFast
+    - preprocess
+
 ## LlavaNextProcessor
 
 [[autodoc]] LlavaNextProcessor

docs/source/en/model_doc/llava_onevision.md

@@ -100,8 +100,8 @@ import torch
 from PIL import Image
 import requests
 
-processor = AutoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf") 
-model = LlavaOnevisionForConditionalGeneration.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf", torch_dtype=torch.float16, low_cpu_mem_usage=True) 
+processor = AutoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf")
+model = LlavaOnevisionForConditionalGeneration.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf", torch_dtype=torch.float16, low_cpu_mem_usage=True)
 model.to("cuda:0")
 
 # prepare image and text prompt, using the appropriate prompt template
@@ -298,8 +298,8 @@ First make sure to install flash-attn. Refer to the [original repository of Flas
 from transformers import LlavaOnevisionForConditionalGeneration
 
 model = LlavaOnevisionForConditionalGeneration.from_pretrained(
-    model_id, 
-    torch_dtype=torch.float16, 
+    model_id,
+    torch_dtype=torch.float16,
     low_cpu_mem_usage=True,
     use_flash_attention_2=True
 ).to(0)
@@ -318,6 +318,11 @@ model = LlavaOnevisionForConditionalGeneration.from_pretrained(
 
 [[autodoc]] LlavaOnevisionImageProcessor
 
+## LlavaOnevisionImageProcessorFast
+
+[[autodoc]] LlavaOnevisionImageProcessorFast
+    - preprocess
+
 ## LlavaOnevisionVideoProcessor
 
 [[autodoc]] LlavaOnevisionVideoProcessor

docs/source/en/model_doc/siglip.md

@@ -214,6 +214,11 @@ Below is an expected speedup diagram that compares inference time between the na
 [[autodoc]] SiglipImageProcessor
     - preprocess
 
+## SiglipImageProcessorFast
+
+[[autodoc]] SiglipImageProcessorFast
+    - preprocess
+
 ## SiglipProcessor
 
 [[autodoc]] SiglipProcessor

docs/source/ja/model_doc/blip.md

@@ -61,6 +61,11 @@ BLIP は、次のようなさまざまなマルチモーダル タスクを実
 [[autodoc]] BlipImageProcessor
     - preprocess
 
+## BlipImageProcessorFast
+
+[[autodoc]] BlipImageProcessorFast
+    - preprocess
+
 <frameworkcontent>
 <pt>
 

docs/source/ja/model_doc/clip.md

@@ -133,6 +133,11 @@ CLIP を使い始めるのに役立つ公式 Hugging Face およびコミュニ
 [[autodoc]] CLIPImageProcessor
     - preprocess
 
+## CLIPImageProcessorFast
+
+[[autodoc]] CLIPImageProcessorFast
+    - preprocess
+
 ## CLIPFeatureExtractor
 
 [[autodoc]] CLIPFeatureExtractor

docs/source/ja/model_doc/convnext.md

@@ -64,6 +64,11 @@ ConvNeXT の使用を開始するのに役立つ公式 Hugging Face およびコ
 [[autodoc]] ConvNextImageProcessor
     - preprocess
 
+## ConvNextImageProcessorFast
+
+[[autodoc]] ConvNextImageProcessorFast
+    - preprocess
+
 <frameworkcontent>
 <pt>
 

docs/source/ja/model_doc/deit.md

@@ -98,6 +98,11 @@ DeiT を始めるのに役立つ公式 Hugging Face およびコミュニティ
 [[autodoc]] DeiTImageProcessor
     - preprocess
 
+## DeiTImageProcessorFast
+
+[[autodoc]] DeiTImageProcessorFast
+    - preprocess
+
 <frameworkcontent>
 <pt>
 

examples/modular-transformers/modeling_new_task_model.py

@@ -452,10 +452,7 @@ def prepare_inputs_for_generation(
         return model_inputs
 
     def resize_token_embeddings(
-        self,
-        new_num_tokens: Optional[int] = None,
-        pad_to_multiple_of=None,
-        mean_resizing=True
+        self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None, mean_resizing=True
     ) -> nn.Embedding:
         model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
 

examples/modular-transformers/modular_new_task_model.py

@@ -70,10 +70,7 @@ def forward(
         return (embeddings,) + vlm_outputs
 
     def resize_token_embeddings(
-        self,
-        new_num_tokens: Optional[int] = None,
-        pad_to_multiple_of=None,
-        mean_resizing=True
+        self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None, mean_resizing=True
     ) -> nn.Embedding:
         model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
 

src/transformers/__init__.py

@@ -1308,11 +1308,19 @@
     ]
 else:
     _import_structure["image_processing_utils_fast"] = ["BaseImageProcessorFast"]
+    _import_structure["models.blip"].append("BlipImageProcessorFast")
+    _import_structure["models.clip"].append("CLIPImageProcessorFast")
+    _import_structure["models.convnext"].append("ConvNextImageProcessorFast")
     _import_structure["models.deformable_detr"].append("DeformableDetrImageProcessorFast")
+    _import_structure["models.deit"].append("DeiTImageProcessorFast")
     _import_structure["models.detr"].append("DetrImageProcessorFast")
+    _import_structure["models.llava"].append("LlavaImageProcessorFast")
+    _import_structure["models.llava_next"].append("LlavaNextImageProcessorFast")
+    _import_structure["models.llava_onevision"].append("LlavaOnevisionImageProcessorFast")
     _import_structure["models.pixtral"].append("PixtralImageProcessorFast")
     _import_structure["models.qwen2_vl"].append("Qwen2VLImageProcessorFast")
     _import_structure["models.rt_detr"].append("RTDetrImageProcessorFast")
+    _import_structure["models.siglip"].append("SiglipImageProcessorFast")
     _import_structure["models.vit"].append("ViTImageProcessorFast")
 
 try:
@@ -6442,11 +6450,19 @@
         from .utils.dummy_torchvision_objects import *
     else:
         from .image_processing_utils_fast import BaseImageProcessorFast
+        from .models.blip import BlipImageProcessorFast
+        from .models.clip import CLIPImageProcessorFast
+        from .models.convnext import ConvNextImageProcessorFast
         from .models.deformable_detr import DeformableDetrImageProcessorFast
+        from .models.deit import DeiTImageProcessorFast
         from .models.detr import DetrImageProcessorFast
+        from .models.llava import LlavaImageProcessorFast
+        from .models.llava_next import LlavaNextImageProcessorFast
+        from .models.llava_onevision import LlavaOnevisionImageProcessorFast
         from .models.pixtral import PixtralImageProcessorFast
         from .models.qwen2_vl import Qwen2VLImageProcessorFast
         from .models.rt_detr import RTDetrImageProcessorFast
+        from .models.siglip import SiglipImageProcessorFast
         from .models.vit import ViTImageProcessorFast
 
     try: