license: creativeml-openrail-m
base_model: RunDiffusion/Juggernaut-X-Hyper
pipeline_tag: text-to-image
tags:
- core-ml
- coreml
- stable-diffusion
- apple-silicon
- ios
- quantized
Juggernaut X Hyper — Core ML (8-bit)
Generated on-device from this exact Core ML build (6 steps, guidance 2.0, trailing timestep spacing, 1024x1024, seed 42 — 12 s on an M3 Ultra).
Photorealism + speed — Hyper-SD low-step variant of the Juggernaut X line.
Core ML conversion for Apple silicon (iOS / iPadOS / macOS, Neural Engine), built with Apple's ml-stable-diffusion for mindfire-image.
Original model
Converted from RunDiffusion/Juggernaut-X-Hyper — go there for the original weights, full model card and licence.
Demo prompt
The prompt and settings used for this model's demo image (also the reference example shipped in mindfire-image):
Prompt
Cinematic mid shot photo of an astronaut walking through a neon-lit Tokyo alley at night, hyperdetailed photography, skin details, shallow depth of field
| Setting | Value |
|---|---|
| Steps | 6 |
| Guidance (CFG) | 2.0 |
| Size | 1024x1024 |
Hyper-SD: few steps at low guidance. The Unet is chunked (UnetChunk1/2.mlmodelc) to fit Neural Engine per-model limits.
Modifications from the base model
Converted from PyTorch/diffusers to Core ML (.mlmodelc) and quantized to 8-bit palettized
weights. No fine-tuning — behaviour tracks the base model, though quantization can shift outputs
slightly.
Licence
Inherited from the base model: creativeml-openrail-m. This carries the OpenRAIL use-based restrictions
(Attachment A), which bind you as a downstream user of this conversion exactly as they do for the
base model. Read the base model's licence before use or redistribution.