coreml-juggernaut-x-hyper-8bit

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

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.