Add Idefics2/3 and SmolVLM Fast image processors + improvements for fast image processors

docs/source/en/model_doc/idefics2.md

@@ -162,7 +162,7 @@ To load and run a model using Flash Attention-2, simply change the code snippet
 ```diff
 model = Idefics2ForConditionalGeneration.from_pretrained(
     "HuggingFaceM4/idefics2-8b",
-+    torch_dtype=torch.float16,    
++    torch_dtype=torch.float16,
 +    attn_implementation="flash_attention_2",
 ).to(device)
 ```
@@ -184,7 +184,7 @@ Quantizing a model is as simple as passing a `quantization_config` to the model.
 + )
 model = Idefics2ForConditionalGeneration.from_pretrained(
     "HuggingFaceM4/idefics2-8b",
-+    torch_dtype=torch.float16,    
++    torch_dtype=torch.float16,
 +    quantization_config=quantization_config,
 ).to(device)
 ```
@@ -218,7 +218,10 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 [[autodoc]] Idefics2ImageProcessor
     - preprocess
 
+## Idefics2ImageProcessorFast
+[[autodoc]] Idefics2ImageProcessorFast
+    - preprocess
 
 ## Idefics2Processor
 [[autodoc]] Idefics2Processor
-    - __call__
+    - __call__

docs/source/en/model_doc/idefics3.md

@@ -80,6 +80,9 @@ This model was contributed by [amyeroberts](https://huggingface.co/amyeroberts)
 [[autodoc]] Idefics3ImageProcessor
     - preprocess
 
+## Idefics3ImageProcessorFast
+[[autodoc]] Idefics3ImageProcessorFast
+    - preprocess
 
 ## Idefics3Processor
 [[autodoc]] Idefics3Processor

docs/source/en/model_doc/smolvlm.md

@@ -32,7 +32,7 @@ SmolVLM2 is an adaptation of the Idefics3 model with two main differences:
 
 Input images are processed either by upsampling (if resizing is enabled) or at their original resolution. The resizing behavior depends on two parameters: do_resize and size.
 
-Videos should not be upsampled. 
+Videos should not be upsampled.
 
 If `do_resize` is set to `True`, the model resizes images so that the longest edge is 4*512 pixels by default.
 The default resizing behavior can be customized by passing a dictionary to the `size` parameter. For example, `{"longest_edge": 4 * 512}` is the default, but you can change it to a different value if needed.
@@ -192,11 +192,14 @@ print(generated_texts[0])
 [[autodoc]] SmolVLMForConditionalGeneration
     - forward
 
-
 ## SmolVLMImageProcessor
 [[autodoc]] SmolVLMImageProcessor
     - preprocess
 
+## SmolVLMImageProcessorFast
+[[autodoc]] SmolVLMImageProcessorFast
+    - preprocess
+
 ## SmolVLMVideoProcessor
 [[autodoc]] SmolVLMVideoProcessor
     - preprocess

src/transformers/commands/add_fast_image_processor.py

@@ -396,7 +396,7 @@ def add_fast_image_processor_file(
 
     content_header = get_fast_image_processing_content_header(content_base_file)
     content_base_file = (
-        f"@auto_docstring(\n"
+        f"@auto_docstring\n"
         f"class {fast_image_processor_name}(BaseImageProcessorFast):\n"
         "    # This generated class can be used as a starting point for the fast image processor.\n"
         "    # if the image processor is only used for simple augmentations, such as resizing, center cropping, rescaling, or normalizing,\n"

src/transformers/image_processing_utils_fast.py

@@ -18,11 +18,7 @@
 
 import numpy as np
 
-from .image_processing_utils import (
-    BaseImageProcessor,
-    BatchFeature,
-    get_size_dict,
-)
+from .image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
 from .image_transforms import (
     convert_to_rgb,
     get_resize_output_image_size,
@@ -188,6 +184,7 @@ class DefaultFastImageProcessorKwargs(TypedDict, total=False):
     data_format: Optional[ChannelDimension]
     input_data_format: Optional[Union[str, ChannelDimension]]
     device: Optional["torch.device"]
+    disable_grouping: Optional[bool]
 
 
 @auto_docstring
@@ -481,18 +478,35 @@ def _prepare_input_images(
     ) -> list["torch.Tensor"]:
         """
         Prepare the input images for processing.
+
+        Args:
+            images (`ImageInput`):
+                The input images to process.
+            do_convert_rgb (`bool`, *optional*):
+                Whether to convert the images to RGB.
+            input_data_format (`str` or `ChannelDimension`, *optional*):
+                The input data format of the images.
+            device (`torch.device`, *optional*):
+                The device to put the processed images on.
+
+        Returns:
+            List[`torch.Tensor`]: The processed images.
         """
+
+        # Get structured images (potentially nested)
         images = self._prepare_images_structure(images)
-        process_image_fn = partial(
-            self._process_image,
-            do_convert_rgb=do_convert_rgb,
-            input_data_format=input_data_format,
-            device=device,
+
+        process_image_partial = partial(
+            self._process_image, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device
         )
-        # todo: yoni - check if we can parallelize this efficiently
-        processed_images = []
-        for image in images:
-            processed_images.append(process_image_fn(image))
+
+        # Check if we have nested structure, assuming the nesting is consistent
+        has_nested_structure = len(images) > 0 and isinstance(images[0], (list, tuple))
+
+        if has_nested_structure:
+            processed_images = [[process_image_partial(img) for img in nested_list] for nested_list in images]
+        else:
+            processed_images = [process_image_partial(img) for img in images]
 
         return processed_images
 
@@ -618,11 +632,12 @@ def _preprocess(
         do_normalize: bool,
         image_mean: Optional[Union[float, list[float]]],
         image_std: Optional[Union[float, list[float]]],
+        disable_grouping: Optional[bool],
         return_tensors: Optional[Union[str, TensorType]],
         **kwargs,
     ) -> BatchFeature:
         # Group images by size for batched resizing
-        grouped_images, grouped_images_index = group_images_by_shape(images)
+        grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
         resized_images_grouped = {}
         for shape, stacked_images in grouped_images.items():
             if do_resize:
@@ -632,7 +647,7 @@ def _preprocess(
 
         # Group images by size for further processing
         # Needed in case do_resize is False, or resize returns images with different sizes
-        grouped_images, grouped_images_index = group_images_by_shape(resized_images)
+        grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping)
         processed_images_grouped = {}
         for shape, stacked_images in grouped_images.items():
             if do_center_crop:
@@ -652,47 +667,3 @@ def to_dict(self):
         encoder_dict = super().to_dict()
         encoder_dict.pop("_valid_processor_keys", None)
         return encoder_dict
-
-
-class SemanticSegmentationMixin:
-    def post_process_semantic_segmentation(self, outputs, target_sizes: Optional[list[tuple]] = None):
-        """
-        Converts the output of [`MobileNetV2ForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch.
-
-        Args:
-            outputs ([`MobileNetV2ForSemanticSegmentation`]):
-                Raw outputs of the model.
-            target_sizes (`List[Tuple]` of length `batch_size`, *optional*):
-                List of tuples corresponding to the requested final size (height, width) of each prediction. If unset,
-                predictions will not be resized.
-
-        Returns:
-            semantic_segmentation: `List[torch.Tensor]` of length `batch_size`, where each item is a semantic
-            segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is
-            specified). Each entry of each `torch.Tensor` correspond to a semantic class id.
-        """
-        logits = outputs.logits
-
-        # Resize logits and compute semantic segmentation maps
-        if target_sizes is not None:
-            if len(logits) != len(target_sizes):
-                raise ValueError(
-                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"
-                )
-
-            # if is_torch_tensor(target_sizes):
-            #     target_sizes = target_sizes.numpy()
-
-            semantic_segmentation = []
-
-            for idx in range(len(logits)):
-                resized_logits = torch.nn.functional.interpolate(
-                    logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
-                )
-                semantic_map = resized_logits[0].argmax(dim=0)
-                semantic_segmentation.append(semantic_map)
-        else:
-            semantic_segmentation = logits.argmax(dim=1)
-            semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
-
-        return semantic_segmentation

src/transformers/image_transforms.py

@@ -841,37 +841,134 @@ def _cast_tensor_to_float(x):
     return x.float()
 
 
+def _flatten_nested_images(nested_images):
+    """Helper function to flatten a single level of nested image structures and group by shape."""
+    grouped_images = {}
+    grouped_images_index = {}
+
+    for i, sublist in enumerate(nested_images):
+        for j, image in enumerate(sublist):
+            shape = image.shape[1:]
+            if shape not in grouped_images:
+                grouped_images[shape] = []
+            grouped_images[shape].append(image)
+            grouped_images_index[(i, j)] = (shape, len(grouped_images[shape]) - 1)
+
+    return grouped_images, grouped_images_index
+
+
+def _reconstruct_nested_structure(indices, processed_images):
+    """Helper function to reconstruct a single level nested structure."""
+    # Find the maximum outer index
+    max_outer_idx = max(idx[0] for idx in indices.keys())
+
+    # Create the outer list
+    result = [None] * (max_outer_idx + 1)
+
+    # Group indices by outer index
+    nested_indices = {}
+    for i, j in indices.keys():
+        if i not in nested_indices:
+            nested_indices[i] = []
+        nested_indices[i].append(j)
+
+    for i in range(max_outer_idx + 1):
+        if i in nested_indices:
+            inner_max_idx = max(nested_indices[i])
+            inner_list = [None] * (inner_max_idx + 1)
+            for j in range(inner_max_idx + 1):
+                if (i, j) in indices:
+                    shape, idx = indices[(i, j)]
+                    inner_list[j] = processed_images[shape][idx]
+            result[i] = inner_list
+
+    return result
+
+
 def group_images_by_shape(
-    images: list["torch.Tensor"],
-) -> tuple[dict[tuple[int, int], list["torch.Tensor"]], dict[int, tuple[tuple[int, int], int]]]:
+    images: Union[list["torch.Tensor"], "torch.Tensor"],
+    is_nested: bool = False,
+    disable_grouping: Optional[bool] = None,
+) -> tuple[
+    dict[tuple[int, int], list["torch.Tensor"]], dict[Union[int, tuple[int, int]], tuple[tuple[int, int], int]]
+]:
     """
     Groups images by shape.
     Returns a dictionary with the shape as key and a list of images with that shape as value,
     and a dictionary with the index of the image in the original list as key and the shape and index in the grouped list as value.
+
+    The function supports both flat lists of tensors and nested structures.
+    The input must be either all flat or all nested, not a mix of both.
+
+    Args:
+        images (Union[list["torch.Tensor"], "torch.Tensor"]):
+            A list of images or a single tensor
+
+    Returns:
+        tuple[dict[tuple[int, int], list["torch.Tensor"]], dict[Union[int, tuple[int, int]], tuple[tuple[int, int], int]]]:
+            - A dictionary with shape as key and list of images with that shape as value
+            - A dictionary mapping original indices to (shape, index) tuples
     """
-    grouped_images = {}
-    grouped_images_index = {}
-    for i, image in enumerate(images):
-        shape = image.shape[1:]
-        if shape not in grouped_images:
-            grouped_images[shape] = []
-        grouped_images[shape].append(image)
-        grouped_images_index[i] = (shape, len(grouped_images[shape]) - 1)
-    # stack images with the same shape
-    grouped_images = {shape: torch.stack(images, dim=0) for shape, images in grouped_images.items()}
+    if not is_nested:
+        disable_grouping = images[0].device == "cpu" if disable_grouping is None else disable_grouping
+        if disable_grouping:
+            return {i: images[i].unsqueeze(0) for i in range(len(images))}, {i: (i, 0) for i in range(len(images))}
+
+        grouped_images = {}
+        grouped_images_index = {}
+        for i, image in enumerate(images):
+            shape = image.shape[1:]
+            if shape not in grouped_images:
+                grouped_images[shape] = []
+            grouped_images[shape].append(image)
+            grouped_images_index[i] = (shape, len(grouped_images[shape]) - 1)
+
+        # Stack images with the same shape
+        grouped_images = {shape: torch.stack(imgs, dim=0) for shape, imgs in grouped_images.items()}
+        return grouped_images, grouped_images_index
+
+    disable_grouping = images[0][0].device == "cpu" if disable_grouping is None else disable_grouping
+
+    if disable_grouping:
+        return {(i, j): images[i][j].unsqueeze(0) for i in range(len(images)) for j in range(len(images[i]))}, {
+            (i, j): ((i, j), 0) for i in range(len(images)) for j in range(len(images[i]))
+        }
+
+    # Handle single level nested structure
+    grouped_images, grouped_images_index = _flatten_nested_images(images)
+
+    # Stack images with the same shape
+    grouped_images = {shape: torch.stack(imgs, dim=0) for shape, imgs in grouped_images.items()}
+
     return grouped_images, grouped_images_index
 
 
 def reorder_images(
-    processed_images: dict[tuple[int, int], "torch.Tensor"], grouped_images_index: dict[int, tuple[int, int]]
-) -> list["torch.Tensor"]:
+    processed_images: dict[tuple[int, int], "torch.Tensor"],
+    grouped_images_index: dict[Union[int, tuple[int, int]], tuple[tuple[int, int], int]],
+    is_nested: bool = False,
+) -> Union[list["torch.Tensor"], "torch.Tensor"]:
     """
-    Reconstructs a list of images in the original order.
+    Reconstructs images in the original order, preserving the original structure (nested or not).
+    The input structure is either all flat or all nested.
+
+    Args:
+        processed_images (dict[tuple[int, int], "torch.Tensor"]):
+            Dictionary mapping shapes to batched processed images.
+        grouped_images_index (dict[Union[int, tuple[int, int]], tuple[tuple[int, int], int]]):
+            Dictionary mapping original indices to (shape, index) tuples.
+
+    Returns:
+        Union[list["torch.Tensor"], "torch.Tensor"]:
+            Images in the original structure.
     """
-    return [
-        processed_images[grouped_images_index[i][0]][grouped_images_index[i][1]]
-        for i in range(len(grouped_images_index))
-    ]
+    if not is_nested:
+        return [
+            processed_images[grouped_images_index[i][0]][grouped_images_index[i][1]]
+            for i in range(len(grouped_images_index))
+        ]
+
+    return _reconstruct_nested_structure(grouped_images_index, processed_images)
 
 
 class NumpyToTensor:

src/transformers/models/auto/image_processing_auto.py

@@ -95,8 +95,8 @@
             ("groupvit", ("CLIPImageProcessor", "CLIPImageProcessorFast")),
             ("hiera", ("BitImageProcessor", "BitImageProcessorFast")),
             ("idefics", ("IdeficsImageProcessor",)),
-            ("idefics2", ("Idefics2ImageProcessor",)),
-            ("idefics3", ("Idefics3ImageProcessor",)),
+            ("idefics2", ("Idefics2ImageProcessor", "Idefics2ImageProcessorFast")),
+            ("idefics3", ("Idefics3ImageProcessor", "Idefics3ImageProcessorFast")),
             ("ijepa", ("ViTImageProcessor", "ViTImageProcessorFast")),
             ("imagegpt", ("ImageGPTImageProcessor",)),
             ("instructblip", ("BlipImageProcessor", "BlipImageProcessorFast")),
@@ -147,6 +147,7 @@
             ("shieldgemma2", ("Gemma3ImageProcessor", "Gemma3ImageProcessorFast")),
             ("siglip", ("SiglipImageProcessor", "SiglipImageProcessorFast")),
             ("siglip2", ("Siglip2ImageProcessor", "Siglip2ImageProcessorFast")),
+            ("smolvlm", ("SmolVLMImageProcessor", "SmolVLMImageProcessorFast")),
             ("superglue", ("SuperGlueImageProcessor",)),
             ("swiftformer", ("ViTImageProcessor", "ViTImageProcessorFast")),
             ("swin", ("ViTImageProcessor", "ViTImageProcessorFast")),