---
license: creativeml-openrail-m
base_model: RunDiffusion/Juggernaut-X-Hyper
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- diffusers
library_name: diffusers
pipeline_tag: text-to-image
---

# Juggernaut X-Hyper — fp16 diffusers tree

A **format conversion** of [RunDiffusion/Juggernaut-X-Hyper](https://huggingface.co/RunDiffusion/Juggernaut-X-Hyper).
The weights are RunDiffusion's; nothing has been retrained, merged, or fine-tuned.

## Why this repo exists

The upstream repo cannot be loaded by `diffusers`/MLX as published:

- its diffusers tree stores weights as **PyTorch pickle** (`.bin`), not safetensors;
- its tokenizers ship without the legacy CLIP pair (`vocab.json` / `merges.txt`)
  that SDXL text-encoding pipelines expect.

The only directly usable weights upstream are in the root single-file
checkpoint, `JuggernautXRundiffusion_Hyper.safetensors`.

## Changes made

1. Loaded the upstream single-file checkpoint with
   `StableDiffusionXLPipeline.from_single_file(...)` and re-saved it as a
   standard fp16 diffusers tree (`safe_serialization=True`, `variant="fp16"`).
2. Restored `vocab.json` and `merges.txt` for `tokenizer/` and `tokenizer_2/`
   from [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).
   SDXL fine-tunes do not retrain the CLIP tokenizers, so these files are
   byte-identical across SDXL models (`vocab.json` 1,059,962 bytes,
   `merges.txt` 524,619 bytes).

No weight values were altered. The conversion script is
[`sdxl_single_file_to_diffusers.py`](https://github.com/Gatcha-man/mindfire-image/blob/main/scripts/sdxl_single_file_to_diffusers.py).

## Recommended settings

Per the upstream model card:

| Setting | Value |
|---|---|
| Steps | 4–8 (start at 6) |
| CFG scale | 1.0–2.0 |
| Sampler | DPM++ SDE or TCD |
| Resolution | 1024×1024 (SDXL native) |

Verified on an M-series Mac Studio via MLX: 6 steps, CFG 2.0, 1024×1024,
~7.5s per image.

## License

**CreativeML Open RAIL-M**, inherited unchanged from the upstream model —
see the [original model card](https://huggingface.co/RunDiffusion/Juggernaut-X-Hyper)
and the [license text](https://huggingface.co/spaces/CompVis/stable-diffusion-license).

Upstream states: *"This model may not be deployed behind paid API services
without explicit licensing."* Commercial licensing:
[juggernaut@rundiffusion.com](mailto:juggernaut@rundiffusion.com).

The RAIL-M use-based restrictions apply to this copy exactly as they apply to
the original.

## Attribution

Juggernaut X-Hyper by **RunDiffusion / KandooAI**.
Original: <https://huggingface.co/RunDiffusion/Juggernaut-X-Hyper>