Fix issue with from pretrained and kwargs in image processors #41997
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I have a quick question about the: image_processor_dict.update(kwargs)
image_processor = cls(**image_processor_dict)Fix. Because you are adding user given I think the only way to check wether the kwargs are or are not in the init function is by inspecting its signature, but I don't think it's clean and it doesn't solve the underlying core pattern. Because processors can do whatever they want in their init, even use env vars for all they care while |
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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. |
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No problem feel free to ask questions! The idea is that all the init logic/setting of attributes and checking of kwargs should be in the class init, to avoid having different results when loading models in different ways (from_pretrained, from_dict, direct instantiation etc.). |
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I agree, if you can enforce some kind of contract where the And thank you for your openness! |
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Added logic to more clearly use cls.valid_kwargs to update the image processor dict ;).
In that case, if the kwarg is in the valid_kwargs attribute of the processor class, it will still be set as an instance attribute (see the logic here in the base fast image processor class) |
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Oh ok perfect thank you @yonigozlan didn't know about |
molbap
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Nov 3, 2025
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Looks fine to me, good use of valid_kwargs but imo we should also get rid of the mutated kwargs!
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[For maintainers] Suggested jobs to run (before merge) run-slow: pix2struct |
zucchini-nlp
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Nov 5, 2025
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| image_seq_length (`int`, *optional*): | ||
| The number of image tokens to be used for each image in the input. | ||
| Added for backward compatibility but this should be set as a processor attribute in future models. |
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sorry for nitty-picking after PR is merged, For my own understanding, do we need it in here, because not all image processors are VLM-specific and image_seq_length isn't always needed for them
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No worries, I agree with you we shouldn't really have this here, as this should be a processor attribute, but a lot of models on the hub have it as an image processor attribute, so I put it here for BC...
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…gface#41997) * accept kwargs in image proc from_pretrained * only use kwargs that are in cls.valid_kwargs * remove specific logic for _from_auto * add image_seq_length to Images_kwargs for backward compatibility * fix missing image kwargs in pix2struct
Abdennacer-Badaoui
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Nov 10, 2025
…gface#41997) * accept kwargs in image proc from_pretrained * only use kwargs that are in cls.valid_kwargs * remove specific logic for _from_auto * add image_seq_length to Images_kwargs for backward compatibility * fix missing image kwargs in pix2struct
yonigozlan
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* remove attributes and add all missing sub processors to their auto classes * remove all mentions of .attributes * cleanup * fix processor tests * fix modular * remove last attributes * fixup * fixes after merge * fix wrong tokenizer in auto florence2 * fix missing audio_processor + nits * Override __init__ in NewProcessor and change hf-internal-testing-repo (temporarily) * fix auto tokenizer test * add init to markup_lm * update CustomProcessor in custom_processing * remove print * nit * fix test modeling owlv2 * fix test_processing_layoutxlm * Fix owlv2, wav2vec2, markuplm, voxtral issues * add support for loading and saving multiple tokenizer natively * remove exclude_attributes from save_pretrained * Run slow v2 (#41914) * Super * Super * Super * Super --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com> * Fix `detectron2` installation in docker files (#41975) * detectron2 - part 1 * detectron2 - part 2 --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com> * Fix `autoawq[kernels]` installation in quantization docker file (#41978) fix autoawq[kernels] Co-authored-by: ydshieh <ydshieh@users.noreply.github.com> * add support for saving encoder only so any parakeet model can be loaded for inference (#41969) * add support for saving encoder only so any decoder model can be loaded Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * use convolution_bias * convert modular * convolution_bias in convertion script --------- Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> Co-authored-by: Eustache Le Bihan <eulebihan@gmail.com> Co-authored-by: eustlb <94853470+eustlb@users.noreply.github.com> * Use indices as position_ids in modernebert (#41789) * Use indices as position_ids in modernebert * Move position_ids init to the branch * test tensor parallel: make tests for dense model more robust (#41968) * make test forward and backward more robust * refactor compile part of test tensor parallel * linting * pass rank around instead of calling it over and over * Run slow v2 (#41914) * Super * Super * Super * Super --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com> * Fix `detectron2` installation in docker files (#41975) * detectron2 - part 1 * detectron2 - part 2 --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com> * Fix `autoawq[kernels]` installation in quantization docker file (#41978) fix autoawq[kernels] Co-authored-by: ydshieh <ydshieh@users.noreply.github.com> * add support for saving encoder only so any parakeet model can be loaded for inference (#41969) * add support for saving encoder only so any decoder model can be loaded Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> * use convolution_bias * convert modular * convolution_bias in convertion script --------- Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> Co-authored-by: Eustache Le Bihan <eulebihan@gmail.com> Co-authored-by: eustlb <94853470+eustlb@users.noreply.github.com> --------- Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com> Co-authored-by: ydshieh <ydshieh@users.noreply.github.com> Co-authored-by: Nithin Rao <nithinrao.koluguri@gmail.com> Co-authored-by: Eustache Le Bihan <eulebihan@gmail.com> Co-authored-by: eustlb <94853470+eustlb@users.noreply.github.com> * fix: dict[RopeParameters] to dict[str, RopeParameters] (#41963) * docs: add continuous batching page (#41847) * docs: add continuous batching page * docs(cb): add `generate_batch` example * docs(cb): add `opentelemtry` and `serving` section * feat: add `TODO` note about opentelemetry dependency * docs(cb): add supported features * docs(cb): add unsupported features * docs(cb): add `ContinuousBatchingManager` example * docs(cb): x reference CB in optimizing inference * Fix `torchcodec` version in quantization docker file (#41988) check Co-authored-by: ydshieh <ydshieh@users.noreply.github.com> * [kernels] Add Tests & CI for kernels (#41765) * first commit * add tests * add kernel config * add more tests * add ci * small fix * change branch name * update tests * nit * change test name * revert jobs * addressing review * reenable all jobs * address second review * Move the Mi355 to regular docker (#41989) * Move the Mi355 to regular docker * Disable gfx950 compilation for FA on AMD * More data in benchmarking (#41848) * Reduce scope of cross-generate * Rm generate_sall configs * Workflow benchmarks more * Prevent crash when FA is not installed * fix (CI): Refactor SSH runners (#41991) * Change ssh runner type * Add wait step to SSH runner workflow * Rename wait step to wait2 in ssh-runner.yml * Remove wait step from ssh-runner.yml Removed the wait step from the SSH runner workflow. * Update runner type for single GPU A10 instance * Update SSH runner version to 1.90.3 * Add sha256sum to ssh-runner workflow * Update runner type and remove unused steps * fix 3 failed test cases for video_llama_3 model on Intel XPU (#41931) * fix 3 failed test cases for video_llama_3 model on Intel XPU Signed-off-by: Liu, Kaixuan <kaixuan.liu@intel.com> * update Signed-off-by: Liu, Kaixuan <kaixuan.liu@intel.com> * adjust format Signed-off-by: Liu, Kaixuan <kaixuan.liu@intel.com> * update code Signed-off-by: Liu, Kaixuan <kaixuan.liu@intel.com> --------- Signed-off-by: Liu, Kaixuan <kaixuan.liu@intel.com> * Integrate colqwen2.5 using colqwen2 modelling code (#40600) * adding option for 2.5 * minor - arg in conversion script * getting started on modelling.py * minor - shouldve been using modular * adressing comments + fixing datatype/device _get method * minor * commiting suggestion Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> * docs + first test * ruff fix * minor fix * ruff fix * model fix * model fix * fine-grained check, with a hardcoded score from the original Hf implementation. * minor ruff * update tests values with CI hardware * adding 2.5 to conversion script * Apply style fixes --------- Co-authored-by: Sahil Kabir <sahilkabir@Sahils-MacBook-Pro.local> Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> Co-authored-by: yonigozlan <yoni.gozlan@huggingface.co> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * Fixed wrong padding value in OWLv2 (#41938) * Update image_processing_owlv2_fast.py fixed padding value * fixed padding value * Change padding constant value from 0.5 to 0.0 * Fixed missed padding value in modular_owlv2.py --------- Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> * Fix `run slow v2`: empty report when there is only one model (#42002) fix Co-authored-by: ydshieh <ydshieh@users.noreply.github.com> * [kernels] change import time in KernelConfig (#42004) * change import time * style * DOC Fix typo in argument name: pseudoquant (#41994) The correct argument name is pseudoquantization. Since there is no error on passing wrong arguments name (which is arguably an anti-pattern), this is difficult for users to debug. * Fix `torch+deepspeed` docker file (#41985) * fix * delete --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com> * Correct syntax error in trainer.md (#42001) A comma is missing between two parameters in the signature of compute_loss function. * Reduce the number of benchmark in the CI (#42008) Changed how benchmark cfgs are chosen * Fix continuous batching tests (#42012) * Fix continuous batching tests * make fixup * add back `logging_dir` (#42013) * add back * Apply style fixes --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * Fix issue with from pretrained and kwargs in image processors (#41997) * accept kwargs in image proc from_pretrained * only use kwargs that are in cls.valid_kwargs * remove specific logic for _from_auto * add image_seq_length to Images_kwargs for backward compatibility * fix missing image kwargs in pix2struct * Fix default image_rows and image_cols initialization in Idefics3 and SmolVLM processors (#41871) * Fix default image_rows and image_cols initialization in Idefics3 and SmolVLM processors * Fix default initialization of image_rows and image_cols in Idefics3 and SmolVLM processors * Add GLPNImageProcessorFast (#41725) * Add GLPNImageProcessorFast for torch backend * Address review feedback - Simplified to_dict() method - Keep tensors as torch instead of converting to numpy for heterogeneous shapes - Removed unnecessary shape guards in post_process_depth_estimation - Improved variable names (tgt -> target_size, d -> resized) - Removed unnecessary GLPNImageProcessorKwargs class * Address review feedback - Simplified to_dict() method - Keep tensors as torch instead of converting to numpy for heterogeneous shapes - Removed unnecessary shape guards in post_process_depth_estimation - Improved variable names (tgt -> target_size, d -> resized) - Removed unnecessary GLPNImageProcessorKwargs class * commits after 2nd review * Address all review feedback and add explicit batched test - Simplified to_dict() with descriptive variable names (d->output_dict) - Fixed resize operation: changed from crop to proper resize with interpolation - Added padding for heterogeneous batch shapes in both slow and fast processors - Fused rescale and normalize operations for efficiency - Improved all variable names (tgt->target_size, d->depth_4d->resized) - Added GLPNImageProcessorKwargs class in slow processor and imported in fast - Renamed test_equivalence_slow_fast to test_slow_fast_equivalence - Added explicit test_slow_fast_equivalence_batched test - All 20 tests passing * using padding from utils * simplify glpn image processor fast * fix docstring --------- Co-authored-by: yonigozlan <yoni.gozlan@huggingface.co> Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> * add fuyu fast image processors (#41817) * added fast processor for fuyu (#36978) * updated docs for fuyu model (#36978) * updated test_image_processing and image_processing_fuyu_fast * updated fuyu.md and image_processing_fuyu_fast (#36978) * updated test_image_processing_fuyu (#36978) * formatted image_processing_fuyu_fast and test_image_processing_fuyu (#36978) * updated tests and fuyu fast image processing (#36978) * Merge branch 'fuyu-fast-image-processors' of https://github.com/DeXtAr47-oss/transformers into fuyu-fast-image-processors * fixed format (#36978) * formatted files (#36978) * formatted files * revert unnecessary changes * clean up and process by group --------- Co-authored-by: yonigozlan <yoni.gozlan@huggingface.co> * [kernels] Fix XPU layernorm kernel (#41583) * fix * add comment * better fix * style * Update src/transformers/modeling_utils.py Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> --------- Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> * [v5] Deprecate Text2Text and related pipelines (#41996) * Deprecate Text2Text and related pipelines * Try a restructure * make fixup * logging -> logger * [FPQuant] MXFP8 and MXFP4 backwards support (#41897) * FP-Quant backwards * fp-quant v0.3.0 docker * availability version bump * fp_quant==0.3.1 * fp_quant v0.3.2 * add working auto_docstring for processors * add auto_docstring to processors first part * add auto_docstring to processors part 2 * modifs after review * fully working auto_docstring and check_docstring with placeholder docstrings * Working check_docstrings for Typed dicts * Add recurring processor args to auto_docstring and add support for removing redundant docstring and placeholders * replace placeholders with real docstrings * fix copies * fixup * remove unwanted changes * fix unprotected imports * Fix unprotected imports * fix unprotected imports * Add __call__ to all docs of processors * nits docs --------- Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> Signed-off-by: Liu, Kaixuan <kaixuan.liu@intel.com> Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com> Co-authored-by: ydshieh <ydshieh@users.noreply.github.com> Co-authored-by: Nithin Rao <nithinrao.koluguri@gmail.com> Co-authored-by: Eustache Le Bihan <eulebihan@gmail.com> Co-authored-by: eustlb <94853470+eustlb@users.noreply.github.com> Co-authored-by: Rémi Ouazan <83456801+remi-or@users.noreply.github.com> Co-authored-by: Ferdinand Mom <47445085+3outeille@users.noreply.github.com> Co-authored-by: Ryan Mullins <ryanmullins@google.com> Co-authored-by: Luc Georges <McPatate@users.noreply.github.com> Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com> Co-authored-by: Guillaume LEGENDRE <glegendre01@gmail.com> Co-authored-by: kaixuanliu <kaixuan.liu@intel.com> Co-authored-by: Sahil Kabir <66221472+sahil-kabir@users.noreply.github.com> Co-authored-by: Sahil Kabir <sahilkabir@Sahils-MacBook-Pro.local> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: James <67161633+gjamesgoenawan@users.noreply.github.com> Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com> Co-authored-by: Yacklin Wong <139425274+Yacklin@users.noreply.github.com> Co-authored-by: Matt <Rocketknight1@users.noreply.github.com> Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> Co-authored-by: MilkClouds <claude@maum.ai> Co-authored-by: ARAVINDHAN T <arvindhant01@gmail.com> Co-authored-by: Pritam Das <79273068+DeXtAr47-oss@users.noreply.github.com> Co-authored-by: Andrei Panferov <andrei@panferov.org>
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What does this PR do?
Fixes #41955.
Fixes an issue raised in #41954. Instead of setting attributes from kwargs after instantiating the image processor in
from_pretrained, we update the image processor dict with the kwargs before instantiating the object. This allows custom logic in the init to take into account the custom kwargs passed tofrom_pretrained.In the PR linked, the issue was that
max_pixelsis supposed to overwritesize["longest_edge"]when passed to the init, but infrom_pretrained,max_pixelswas never passed to the init and only set as an attribute after instantiating the image processor.