Tokenizer-Generator Coupling in Medical Image Generation
Latent medical image generators usually treat the tokenizer as fixed preprocessing. We test whether this separation holds in a controlled ChestMNIST study at $64\times64$ that crosses discrete tokenizers, generator families, and sampler settings under a shared latent grid, with continuous-latent reference cells. The rankings depend jointly on the tokenizer, generator, and sampler. The best quantizer changes with the generator, and validation-based sampler selection changes the apparent generator ranking. We interpret this through a rate-distortion-modelability framing in which modelability is conditional on the generator, sampler, and inference budget. The interaction persists when the vo...
| Authors | Liam Chalcroft |
|---|---|
| Primary category | cs.CV |
| Published | 7 August 2026 |
| Last updated at source | 27 September 2026 |
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