README.md

metadata

license: bsd-3-clause
tags:
  - coreai
  - neural-engine
  - super-resolution
  - image-to-image
  - apple-silicon
  - real-esrgan
library_name: coreai

Real-ESRGAN-CoreAI

Real-ESRGAN (SRVGGNetCompact) 4× super-resolution as CoreAI .aimodel assets for the Apple Neural Engine — fp16, static 128×128 input, exported from the original xinntao/Real-ESRGAN checkpoints (BSD-3-Clause).

To our knowledge the first super-resolution model in coreai-community.

asset	source checkpoint	num_conv	size
realesr_general_x4v3_float16_static128.aimodel	realesr-general-x4v3	32	4.6 MB
realesr_general_wdn_x4v3_float16_static128.aimodel	realesr-general-wdn-x4v3 (denoising)	32	4.6 MB
realesr_animevideov3_float16_static128.aimodel	realesr-animevideov3	16	2.3 MB

Numbers (measured, M5 Max, macOS 27)

Parity — fp16 on ANE vs fp32 PyTorch, 7 real 128² tiles per variant: general min 68.56 / mean 69.36 dB · general-wdn min 58.15 / mean 65.09 dB · anime min 64.11 / mean 69.43 dB. Full tiled pipeline vs an independent fp32 oracle: 66.85 dB, max |Δ| = 1 LSB.

Why the ANE: wall-clock ties a well-tuned GPU path at this tile size while drawing ≈4.5–4.9× less energy per frame (~17 W vs ~83 W over idle), with no thermal throttling.

Usage

Static shape: input x = [1, 3, 128, 128] fp16 NCHW in [0,1]; output [1, 3, 512, 512]. Tile larger images (overlap 8 recommended, feathered blend). A ready-made Swift package that does exactly this — tiling, compositing, variant selection, tests — is xocialize/coreai-realesrgan-swift.

import CoreAI
let model = try await AIModel(contentsOf: url,
    options: SpecializationOptions(preferredComputeUnitKind: .neuralEngine))
let fn = try model.loadFunction(named: "main")!
let out = try await fn.run(inputs: ["x": input])  // first load pays ~8 s E5RT specialization, OS-cached

Reproducibility

srvgg_export.py (in this repo) re-creates every asset from the original .pth checkpoints: PyTorch → torch.export → coreai-torch TorchConverter → .aimodel. No opaque binaries.

Provenance & license

Architecture and weights: xinntao/Real-ESRGAN, BSD-3-Clause (code and released checkpoints). This repo redistributes the same weights in a converted container under the same license, with the conversion script included.