mlboydaisuke / AdcSR-CoreAI

AdcSR ×4 Super-Resolution — Core AI

On-device ×4 super-resolution with AdcSR ( Adversarial Diffusion Compression , CVPR 2025) converted for Apple's Core AI stack. AdcSR compresses the one-step diffusion model OSEDiff into a small diffusion-GAN : a pruned Stable Diffusion 2.1 UNet + a half-size VAE decoder, run in one forward pass — no iterative denoising, no prompt, no noise — so it is fast and small enough to run fully on-device, including iPhone.

What it is

• fp32, ~1.7 GB. Output matches the torch reference (cosine 1.000012). fp32 because the pruned SD-2.1 UNet's attention/group-norm overflow in fp16 (NaN on smooth tiles).
• Image → image, one step. Input a low-resolution tile, get a 4× tile back. No text, no noise.
• 456 M parameters (pruned SD-2.1 UNet + half VAE decoder).
• The graph outputs the raw SR; AdcSR's per-image color-match is applied host-side by SuperResolver after tiling (baking it per-tile blows up uniform tiles).

I/O contract (per tile)

• input: lr [1,3,128,128] in [-1,1] (a low-resolution tile).
• output: sr [1,3,512,512] in [-1,1] (×4), with the reference's per-image color-match baked in.

Usage (CoreAIKit)

import CoreAIKitVision

let sr = try await SuperResolver(model: .adcsrX4)   // downloads this repo on first use
let big = try await sr.upscale(cgImage)             // ×4; tiles any-size input + feather-blends

SuperResolver splits any-size input into overlapping 128-px LR windows, runs each, and blends (and caps very large inputs so the result stays a reasonable size).

License & attribution

• AdcSR (method + the pruning/training code): Apache-2.0 — Bingchen Li et al., Adversarial Diffusion Compression for Real-World Image Super-Resolution , CVPR 2025.
• Weights are derived from Stable Diffusion 2.1 (via OSEDiff) and therefore carry the CreativeML Open RAIL++-M license — commercial use is permitted under its use-based restrictions, the same license under which Apple distributes Stable Diffusion for Core ML.

This Core AI conversion inherits both. See LICENSE (Apache-2.0, AdcSR) and the SD-2.1 OpenRAIL++-M terms.