Add image processors refactor to v5 migration guide#45556
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yonigozlan wants to merge 1 commit intohuggingface:mainfrom
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Add image processors refactor to v5 migration guide#45556yonigozlan wants to merge 1 commit intohuggingface:mainfrom
yonigozlan wants to merge 1 commit intohuggingface:mainfrom
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
stevhliu
approved these changes
Apr 21, 2026
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| ### Image Processors | ||
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| The old slow/fast dual-file design — a PIL-based `image_processing_<model>.py` paired with a torchvision-based `image_processing_<model>_fast.py` — has been replaced with a named-backend architecture: |
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| The old slow/fast dual-file design — a PIL-based `image_processing_<model>.py` paired with a torchvision-based `image_processing_<model>_fast.py` — has been replaced with a named-backend architecture: | |
| The old slow/fast dual-file design has been replaced with a named-backend architecture. Each model previously had a PIL-based `image_processing_<model>.py` and a torchvision-based `image_processing_<model>_fast.py`. The new layout is: |
| - `image_processing_<model>.py` → **torchvision** backend (default; was previously `FooImageProcessorFast`) | ||
| - `image_processing_pil_<model>.py` → **PIL** backend (was previously `FooImageProcessor`) | ||
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| Processor classes now inherit from `TorchvisionBackend` or `PilBackend` (defined in `image_processing_backends.py`), which provide ready-made implementations of all standard operations (`resize`, `rescale`, `normalize`, `center_crop`, `pad`) and a default `_preprocess` pipeline. `BaseImageProcessor` (in `image_processing_utils`) handles the shared preprocessing boilerplate — kwargs validation, default-filling from class attributes, and input preparation — so model-specific processors contain only what is genuinely unique to the model. Most processors now simply inherit from a backend and declare class-attribute defaults; only processors with custom logic (e.g. patch tiling) need to override `_preprocess`. |
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| Processor classes now inherit from `TorchvisionBackend` or `PilBackend` (defined in `image_processing_backends.py`), which provide ready-made implementations of all standard operations (`resize`, `rescale`, `normalize`, `center_crop`, `pad`) and a default `_preprocess` pipeline. `BaseImageProcessor` (in `image_processing_utils`) handles the shared preprocessing boilerplate — kwargs validation, default-filling from class attributes, and input preparation — so model-specific processors contain only what is genuinely unique to the model. Most processors now simply inherit from a backend and declare class-attribute defaults; only processors with custom logic (e.g. patch tiling) need to override `_preprocess`. | |
| Processor classes now inherit from `TorchvisionBackend` or `PilBackend` (defined in `image_processing_backends.py`), which provide ready-made implementations of all standard operations (`resize`, `rescale`, `normalize`, `center_crop`, `pad`) and a default `_preprocess` pipeline. `BaseImageProcessor` (in `image_processing_utils`) handles shared preprocessing boilerplate: kwargs validation, default-filling from class attributes, and input preparation. Model-specific processors contain only what is unique to the model. Most processors inherit from a backend and declare class-attribute defaults. Only those with custom logic (e.g. patch tiling) need to override `_preprocess`. |
| - Minor change: `XXXFastImageProcessorKwargs` is removed in favor of `XXXImageProcessorKwargs` which will be shared between fast and slow processors (https://github.com/huggingface/transformers/pull/40931) | ||
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| ### Image Processors |
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| ### Image Processors | |
| ### Image processors |
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What does this PR do?
As discussed internally @vasqu
Cc @stevhliu