Multi-framework support

Removed: this page used to document kornia.to_tensorflow(), kornia.to_jax() and kornia.to_numpy() — functions that lazily transpiled the whole library to another framework, powered by the third-party Ivy project. In September 2026 we tested that claim end to end, found it did not hold up, and removed the functions along with the advertising in the README and on the landing page. This page now records what was found, for anyone who lands here from an old link.

What we found

Testing used kornia 0.9.0rc1, ivy 1.0.0.5 (its latest release, from June 2025), torch 2.14.0, tensorflow 2.21.0, and jax 0.10.2 / jaxlib 0.10.2 — following the exact examples this page used to show.

• kornia.to_tensorflow(), to_jax() and to_numpy() crashed immediately with TypeError: unhashable type when kornia was checked out into a directory whose path contains the substring kornia — the default name git clone gives the repository. Ivy’s transpiler decides whether a module belongs to kornia with a plain substring check against each module’s file path, so it also recursed into unrelated parts of PyTorch’s dependency tree (ctypes, dill, unittest.mock, …) and hit an object it could not hash.

• Even from a differently named checkout, to_tensorflow() segfaulted during transpilation whenever transformers was installed in the same environment — which it was by default for kornia contributors, since both packages shipped in the same dev extra (no longer the case: transformers moved to the optional sd extra). The crash happened inside Ivy’s Hugging Face integration lookup, which imports triton as a side effect.

• With transformers absent and the environment’s ruff executable on PATH (Ivy shells out to it to format generated code), to_tensorflow() did transpile and run rgb_to_grayscale correctly.

• to_numpy() transpiled but failed at call time: the generated code checked np.bfloat16, an attribute NumPy does not have.

• to_jax() failed outright: Ivy requires flax>=0.8.0, which this page never mentioned; after installing it, the call failed instead with module 'jaxlib' has no attribute 'xla_extension', an incompatibility between Ivy and current jaxlib releases.

None of these were bugs in kornia’s own code — all five are in Ivy’s transpiler or in its compatibility with current TensorFlow/JAX/NumPy releases.

Is Ivy still maintained?

As of September 2026, ivy-llc/ivy (which now redirects to unifyai/ivy) had a single commit in the previous twelve weeks — a rebrand, not a fix — and its last PyPI release, 1.0.0.5, shipped in June 2025. It carries close to a thousand open issues, including unmerged fixes for exactly the kind of NumPy/JAX version drift this page ran into. The unifyai organization itself is active, but its recent development effort is going into an unrelated product line, not Ivy.

If Ivy becomes reliable again, multi-framework support is worth revisiting — but as a fresh integration, not a restoration of kornia.to_tensorflow()/to_jax()/to_numpy() from this page. Their implementation is gone; kornia.to_tensorflow(), kornia.to_jax(), kornia.to_numpy() and their kornia.transpiler counterparts now only raise a clear error pointing here if something still calls them.