We’ve encountered what appears to be a CoreML regression between macOS 26.0.1 and macOS 26.1 Beta.
In macOS 26.0.1, CoreML models run and produce correct results. However, in macOS 26.1 Beta, the same models produce scrambled or corrupted outputs, suggesting that tensor memory is being read or written incorrectly. The behavior is consistent with a low-level stride or pointer arithmetic issue — for example, using 16-bit strides on 32-bit data or other mismatches in tensor layout handling.
Reproduction
Install ON1 Photo RAW 2026 or ON1 Resize 2026 on macOS 26.0.1.
Use the newest Highest Quality resize model, which is Stable Diffusion–based and runs through CoreML.
Observe correct, high-quality results.
Upgrade to macOS 26.1 Beta and run the same operation again.
The output becomes visually scrambled or corrupted.
We are also seeing similar issues with another Stable Diffusion UNet model that previously worked correctly on macOS 26.0.1. This suggests the regression may affect multiple diffusion-style architectures, likely due to a change in CoreML’s tensor stride, layout computation, or memory alignment between these versions.
Notes
The affected models are exported using standard CoreML conversion pipelines.
No custom operators or third-party CoreML runtime layers are used.
The issue reproduces consistently across multiple machines.
It would be helpful to know if there were changes to CoreML’s tensor layout, precision handling, or MLCompute backend between macOS 26.0.1 and 26.1 Beta, or if this is a known regression in the current beta.
