license: openrail
library_name: coreml
pipeline_tag: text-to-image
base_model: ByteDance/Hyper-SD
base_model_relation: quantized
tags:
- coreml
- core-ml
- ios
- macos
- apple
- on-device
- text-to-image
- stable-diffusion
- one-step
- lora
- arxiv:2404.13686
Hyper-SD (1-Step) — Core ML
ByteDance, 2024
Single-step text-to-image from SD1.5 via TCD distillation. 512×512. Chunked UNet (6-bit).
Core ML conversion of ByteDance/Hyper-SD for on-device inference on iPhone, iPad and Mac. Converted with coremltools; the packages are stateless, so all sequencing and buffering lives in your Swift code.
| Task | text to image |
| Upstream | ByteDance/Hyper-SD |
| Packages | 4 |
| Download size | 905 MB |
| Minimum iOS | 17.0 |
| Peak RAM | ~1000 MB |
Files
| File | Size | Compute units | SHA-256 |
|---|---|---|---|
HyperSDTextEncoder.mlpackage.zip |
216 MB | cpuAndNeuralEngine |
201b0fcc3573811a… |
HyperSDUnetChunk1.mlpackage.zip |
310 MB | cpuAndNeuralEngine |
279da11b8231aeeb… |
HyperSDUnetChunk2.mlpackage.zip |
290 MB | cpuAndNeuralEngine |
0a700d11a105da58… |
HyperSDVAEDecoder.mlpackage.zip |
87 MB | cpuAndGPU |
1260371542d845a2… |
vocab.json |
1 MB | - |
e089ad92ba36837a… |
merges.txt |
512 KB | - |
9fd691f7c8039210… |
| Total | 905 MB |
compute_units is not a suggestion -- it is the configuration the conversion was verified against. Moving a package to a different compute unit can silently change the numerics (FP16 attention overflow) or crash on the GPU.
Download
hf download mlboydaisuke/coreml-zoo --include "hypersd/*" --local-dir ./hypersd
unzip './hypersd/hypersd/*.zip' -d ./hypersd
Use in Swift
import CoreML
let config = MLModelConfiguration()
config.computeUnits = .cpuAndNeuralEngine // as converted — see the table above
// Unzip the .mlpackage, drop it into your Xcode target and Xcode compiles it
// at build time:
let model = try HyperSDTextEncoder(configuration: config)
// ...or compile a downloaded .mlpackage at runtime:
let compiled = try await MLModel.compileModel(at: mlpackageURL)
let model = try MLModel(contentsOf: compiled, configuration: config)
This model is split into 4 Core ML packages that are driven in sequence from Swift. Load them one at a time, copy the outputs out of the
MLMultiArraybuffers and release each model before loading the next — two large Core ML models resident at once will OOM on an iPhone.
Demo
- Sample app —
sample_apps/HyperSDDemo, a standalone SwiftUI project. - Models Zoo — this model is downloadable and runnable inside the Models Zoo app on the App Store, no build required.
Conversion
- Script:
convert_hypersd.py - Pitfalls hit during conversion (FP16 overflow, ANE buffer limits, stride handling):
docs/coreml_conversion_notes.md - Model index: CoreML-Models
License
The conversion inherits the upstream license: OpenRAIL-M.
Credits
- Upstream authors: ByteDance/Hyper-SD, 2024
- Core ML conversion: john-rocky (Daisuke Majima)