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The Wire · Showcase

PYTORCH-IMAGE-MODELS FIXES POSITIONAL EMBEDDING RESAMPLING BUG

By RepoJournal · Filed · About Hugging Face

A shortcut in positional embedding resampling was silently skipping necessary transformations when token counts matched but grid shapes differed.

The `resample_abs_pos_embed` function in pytorch-image-models had a critical logic flaw [1]. When source and target grids held the same number of patches, the function returned early without checking if the grid dimensions themselves had changed. That means a rectangular source grid like (2, 8) being resampled to a square (4, 4) target would skip the resampling entirely, breaking model behavior on architectures that use explicit grid specifications [2]. The fix adds proper dimension checking and includes a regression test to catch this case in the future. This matters because vision transformers increasingly use non-square patch grids for efficiency, and silent failures on grid reshaping can corrupt embeddings silently. Ship this before you use pytorch-image-models with any rectangular source grids.

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Action items

References

  1. [1] Fix positional embedding resampling for equal-token grid changes ↗ huggingface/pytorch-image-models
  2. [2] Fix equal-token positional embedding resampling huggingface/pytorch-image-models

Quick answers

What shipped in Hugging Face on August 10, 2026?
A shortcut in positional embedding resampling was silently skipping necessary transformations when token counts matched but grid shapes differed. In total, 1 commits and 1 pull requests landed.
Who contributed to Hugging Face on August 10, 2026?
1 developer shipped this update, including aswanth-07.
What were the notable Hugging Face updates?
Fix positional embedding resampling for equal-token grid changes and Fix equal-token positional embedding resampling.

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