title: Tiled Inference: Model-Bound High-Resolution Contract description: "Tiled inference is a model-bound high-resolution strategy; partitioning, overlap, blending, global context, coordinate mapping, and output review must be evaluated together on the pinned pipeline, while detection tiles and generative or retouch tiles remain separate contracts." category: techniques tags: [tiled-inference, high-resolution, seam-review, object-detection, image-editing, evaluation] aliases: ["High-Resolution Tiling", "Tile-Based Inference"]

Tiled Inference: Model-Bound High-Resolution Contract¶

Tiling is a way to present part of a large image to a constrained model. It is not a universal high-resolution recipe: a detector, a VAE decoder, and an image-editing model have different input, state, merge, and failure contracts. Treat the tiled workflow as a versioned pipeline, then test the assembled output rather than assuming that overlap removes every artifact.

Separate the task families¶

SAHI's slicing guide describes a detection workflow: split a large image, run a detector on each slice, then merge predictions back into source coordinates. It can preserve small-object detail for a detector, but it does not define a generative, retouching, or segmentation stitcher.

Diffusers' memory guide documents VAE tiling for supported pipelines. That operation splits encoding or decoding into overlapping tiles to reduce peak memory; the guide also warns that separately decoded tiles can vary in tone. It is not a guarantee that every model has a compatible spatial or latent tiling interface.

Keep these contracts distinct:

Do not apply a detector's merge rule to pixels or latents, and do not call a generated seam repair a recovered source fact.

Declare the tile plan¶

Record the exact source and execution contract:

• source digest, dimensions, orientation, crop policy, and input color interpretation;
• model/checkpoint, encoder or decoder, runtime, backend, and preprocessing versions;
• tile geometry, overlap, padding, traversal/order, and boundary ownership;
• full-image or low-resolution guidance, if the named model supports it;
• coordinate transform from every tile result to the original image; and
• merge, blend, mask, and failure behavior.

Choose geometry only after measuring the target model on the target device. Nominal VRAM, a neighboring model, or an example tile size is not evidence that a configuration fits or preserves detail.

Preserve the intended image¶

For pixel-producing work, compare a tiled result with a supported full-frame or canonical non-tiled baseline when one exists. Inspect seams, tone, gradients, texture, text, product geometry, faces, and protected regions at the original delivery resolution. A visual check must include overlap boundaries, not only a reduced preview.

For detection or segmentation, retain each tile's candidate coordinates, merge rule, confidence/uncertainty, and the source coordinate system used for review. Duplicate suppression, thresholding, and edge clipping are configuration choices that must be measured on source-disjoint holdouts.

Global context, cross-tile state, latent blending, and seam refinement are model-specific features. Use them only when documented for the pinned pipeline, and bind any state reset or fallback behavior to the job receipt.

Acceptance and failure handling¶

Measure peak memory, cold and warm latency, throughput, and output quality on the actual device. A release candidate passes only if the recorded tile plan stays within its measured resource envelope and preserves the task's acceptance criteria on held-out full-resolution images.

If a tile cannot be processed or an assembly check fails, return a visible failure or a reviewable partial state. Do not silently change the model, resize away source detail, omit edge tiles, or substitute a lower-quality output.

Related pages¶

• low vram inference strategies
• temporal tiling
• defect detection small objects
• upscaler evaluation