Add TokForge app links

03c659e verified 3 months ago

4 kB

license: creativeml-openrail-m
tags:
  - text-to-image
  - stable-diffusion
  - dreamshaper
  - coreml
  - apple-neural-engine
  - palettized
  - tokforge
base_model:
  - Lykon/dreamshaper-8
pipeline_tag: text-to-image
library_name: ml-stable-diffusion

TokForge

Website: https://tokforge.ai
Discord: https://discord.gg/Acv3CBtfVm
Google Play: https://play.google.com/store/apps/details?id=dev.tokforge
iOS TestFlight: https://testflight.apple.com/join/jnufjzRr

Runs on-device in the TokForge app.

TokForge — DreamShaper 8 · CoreML 6-bit (Apple Neural Engine)

A 6-bit palettized Apple CoreML conversion of DreamShaper 8 (Lykon, SD-1.5 realistic finetune), built for on-device image generation in the TokForge iOS app. Converted with Apple ml-stable-diffusion (torch2coreml) using SPLIT_EINSUM_V2 attention and --quantize-nbits 6 (6-bit palettized weights), so it compiles fast on the Apple Neural Engine — the ANE-fast beautiful default replacing the FP16 finetune that hit a >11-minute ANE graph compile.

Part of the TokForge iOS · CoreML Image Models collection.

Files

File	Size	Contents
`DreamShaper-8_palettized_split_einsum_v2_compiled.zip`	~874 MB	The compiled Swift-CLI resource bundle (a single ZIP of `Resources/`)
`Resources/`	~913 MB	The unzipped tree: `TextEncoder.mlmodelc` / `Unet.mlmodelc` / `VAEDecoder.mlmodelc` / `VAEEncoder.mlmodelc` + `vocab.json` + `merges.txt`

The ZIP expands to one wrapper folder (Resources/) holding the compiled .mlmodelc models plus the CLIP vocab.json + merges.txt — the exact layout Apples StableDiffusionPipeline (and the TokForge installer) loads.

Recommended render settings (standard SD-1.5)

attention:    split_einsum_v2 (Apple Neural Engine)
compute:      .cpuAndNeuralEngine  (palettized -> fast ANE compile)
steps:        20   (8 = fast floor, 40 = extra refinement)
cfg-scale:    7.5
resolution:   512x512  (SD-1.5 native; baked into the compiled model)

How this was built

Loaded Lykon/dreamshaper-8 (SD-1.5 diffusers format).
Converted UNet + text encoder + VAE decoder + VAE encoder to CoreML with Apple ml-stable-diffusion python_coreml_stable_diffusion.torch2coreml, --attention-implementation SPLIT_EINSUM_V2 (ANE-shaped attention).
Applied 6-bit palettization (--quantize-nbits 6) — the iOS-17 ANE runtime feature that makes the graph compile fast on the Neural Engine.
Bundled the compiled resources for the Swift CLI (--bundle-resources-for-swift-cli).

Conversion peaked at ~9.96 GB RAM (no --chunk-unet needed); runs on Apple silicon, iOS 17+ (6-bit palettized weights require the iOS-17 runtime).

License & attribution

License: CreativeML OpenRAIL-M (inherited from DreamShaper 8 / Stable Diffusion 1.5). Use is subject to the OpenRAIL-M use restrictions.
Base model: DreamShaper 8 by Lykon — https://huggingface.co/Lykon/dreamshaper-8 (a Stable Diffusion 1.5 finetune). All credit for the model weights is Lykons.
Conversion tooling: Apple ml-stable-diffusion — https://github.com/apple/ml-stable-diffusion (6-bit palettization, SPLIT_EINSUM_V2 attention).
Built on top of Stable Diffusion 1.5 (Runway/CompVis/Stability).

This repository is a redistribution for on-device use — a format conversion (PyTorch -> CoreML) and 6-bit palettization of Lykons DreamShaper 8. No weights were retrained. The original OpenRAIL-M terms and attribution requirements propagate to this conversion and any images generated with it. No additional restrictions are imposed by this repackaging.