docs/inrflow_integration.md

INRFlow integration

INRFlow is implemented as a self-contained built-in ADAM model plugin. It uses the existing manifest-driven Training and Generations interfaces, job queue, checkpoint catalog, progress reporting, pause/cancel behavior, live previews, and asset registry. No separate workspace or UI redesign is required.

What is implemented

• RGB coordinate-value fields in ambient image space; there is no VAE or other modality-specific compressor.
• A patch-local cross-attention encoder that turns coordinate/RGB pairs into spatial context latents.
• A time-conditioned transformer trunk for global context.
• A query decoder that predicts one RGB velocity per requested coordinate-value pair.
• Conditional independent continuous flow matching from Gaussian noise at t=0 to image data at t=1, with uniform or logit-normal time sampling.
• Random point-query subsampling during training.
• EMA model weights, periodic and final checkpoints, optimizer-state resume, deterministic preview seeds, and training metadata.
• Euler and Heun generation, live ODE previews, query chunking, Smart Generation, and square resolution-flexible coordinate queries.

Comparison with regular Flow Matching

For a useful architecture comparison, train both backends from the same dataset at the same native resolution, use the same number of optimizer updates, and generate with the same seed and Euler/Heun step count. Keep INRFlow's output at Native for the primary comparison. Its alternative output resolutions test the continuous coordinate representation, but they are not an apples-to-apples quality comparison because they extrapolate away from the training grid.

ADAM's default INRFlow is deliberately much smaller than the reference research models so it can be explored on a consumer GPU. It preserves the defining model structure and loss, but it should not be treated as a reproduction of published quality or scaling results.

Boundaries

MaskGIT remains out of scope. PixelRow is a separate autoregressive-row plugin, and Glow is not implemented. None of those architectures share INRFlow's checkpoint format.

Method reference: INRFlow: Flow Matching for INRs in Ambient Space. Reference implementation: apple/ml-inrflow.