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mlboydaisuke/Hyper-SD-1step-CoreML

sourceHugging Faceopenrailupdated 1mo agoView on Hugging Face
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Model Card

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).

<p><img src="https://huggingface.co/mlboydaisuke/Hyper-SD-1step-CoreML/resolve/main/media/1c6281beb3.png" alt="Hyper-SD (1-Step) demo" width="49%"> <img src="https://huggingface.co/mlboydaisuke/Hyper-SD-1step-CoreML/resolve/main/media/008e4341e0.png" alt="Hyper-SD (1-Step) demo" width="49%"></p>

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.

Tasktext to image
UpstreamByteDance/Hyper-SD
Packages4
Download size905 MB
Minimum iOS17.0
Peak RAM~1000 MB

Files

FileSizeCompute unitsSHA-256
HyperSDTextEncoder.mlpackage.zip216 MBcpuAndNeuralEngine201b0fcc3573811a…
HyperSDUnetChunk1.mlpackage.zip310 MBcpuAndNeuralEngine279da11b8231aeeb…
HyperSDUnetChunk2.mlpackage.zip290 MBcpuAndNeuralEngine0a700d11a105da58…
HyperSDVAEDecoder.mlpackage.zip87 MBcpuAndGPU1260371542d845a2…
vocab.json1 MB-e089ad92ba36837a…
merges.txt512 KB-9fd691f7c8039210…
Total905 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

bash
hf download mlboydaisuke/coreml-zoo --include "hypersd/*" --local-dir ./hypersd
unzip './hypersd/hypersd/*.zip' -d ./hypersd

Use in Swift

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 MLMultiArray buffers and release each model before loading the next — two large Core ML models resident at once will OOM on an iPhone.

Demo

Conversion

License

The conversion inherits the upstream license: OpenRAIL-M.

Credits

  • Upstream authors: ByteDance/Hyper-SD, 2024
  • Core ML conversion: john-rocky (Daisuke Majima)

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More models in this format: Core ML Model Zoo — 46 models, each with the recipe that produced it.

Want a different model on-device? Open a request — free, open weights only; the export and its measured numbers get published publicly.

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