Mouserat/Illustrious-XL-v2.0-diffusers-mnn

Illustrious-XL-v2.0 (MNN)

MNN format conversion of OnomaAIResearch/Illustrious-XL-v2.0, for on-device image generation in nezumi-ai — a private, fully offline AI chat app for Android. The MNN image-generation engine in this model is one of several on-device inference backends used by the app.

A Windows CLI (nezumi-ai-sd-cli) is also provided for testing/debugging on desktop, but the primary target platform is Android.

Platform note: nezumi-ai-sd-cli currently builds for Windows only (.exe). A Linux build is planned. The Android app is the main way to use this model.

Requirements note: unlike the app's general 6GB RAM minimum, SDXL / Illustrious models require 8GB RAM minimum on Android (verified by testing). This is higher than the LLM-only requirement due to SDXL's larger UNet and dual text encoders. 8GB+ recommended for comfortable use.

Variants

File	UNet quantization	Size	Notes
Illustrious-XL-v2.0-diffusers-mnn-int4-block32.zip	4-bit, block size 32	3.01 GB	Smaller / faster, more quality loss — recommended for 8GB devices
Illustrious-XL-v2.0-diffusers-mnn-int8-block128.zip	8-bit, block size 128	4.08 GB	Larger, closer to original quality — for 12GB+ devices

Both share the same CLIP/VAE settings (see below).

Model Provenance

Field	Value
Base model	OnomaAIResearch/Illustrious-XL-v2.0
Format	MNN (clip1.mnn, clip2.mnn, unet.mnn, vae_decoder_fp16.mnn, tokenizers)
Conversion tool	convert_hf_to_mnn_sdxl.py (nezumi-ai)

Conversion settings

# int4-block32
python convert_hf_to_mnn_sdxl.py --model OnomaAIResearch/Illustrious-XL-v2.0 \
    --out ./out/int4-block32 --size 1024 --clip-skip 2 \
    --unet-bits 4 --unet-block 32 --clip-bits 8 --vae-bits 8

# int8-block128
python convert_hf_to_mnn_sdxl.py --model OnomaAIResearch/Illustrious-XL-v2.0 \
    --out ./out/int8-block128 --size 1024 --clip-skip 2 \
    --unet-bits 8 --unet-block 128 --clip-bits 8 --vae-bits 8

No fine-tuning or retraining was performed — weights are unchanged from the original checkpoint aside from format conversion and the quantization above.

License

• Original model license: CreativeML Open RAIL-M — all credit for the weights and training goes to OnomaAI Research.
• Redistribution: 可（元モデルのライセンス上、量子化・派生物の再配布は許可されています）
• Commercial use: RAIL-Mの条件内で可
• Attribution: 要（本README内に明記）

This checkpoint inherits the original model's use-based restrictions in full (see Attachment A of the full license text), including prohibitions on use for exploiting minors, generating disinformation, harassment, discrimination, unauthorized medical advice, and law-enforcement/immigration profiling. On Android, note that nezumi-ai includes an ImageSafetyChecker (NSFW detection with auto-block/blur), which complements — but does not replace — compliance with these restrictions.

Note: the conversion script itself is part of the nezumi-ai project and licensed separately under LGPL v3 / a commercial license (see LICENSE.md). That license applies to the code, not to this model checkpoint.

Requirements (Android)

Item	Minimum	Recommended
Android Version	12 (API 30)	14+ (API 34+)
RAM (SDXL / Illustrious)	8GB	12GB+
Storage	4GB (int4) / 5GB (int8), plus space for other models	8GB+
GPU/NPU	Optional	Snapdragon / Mali / Adreno (OpenCL)

Usage

Android (primary)

Used automatically by the nezumi-ai app's image-generation feature (MNN backend, GPU/OpenCL → CPU fallback). Download/select this model from within the app; manual extraction is not required on Android.

Windows CLI (testing/debugging)

Distributed as zip archives — extract before use:

unzip Illustrious-XL-v2.0-diffusers-mnn-int8-block128.zip -d C:\sdxl-model

nezumi-ai-sd-cli "C:\sdxl-model" "1girl, cute, cat ears" --steps 20 --width 1024 --height 1024 --backend cpu --out out.png

Options

Option	Description	Default
<model_path>	Path to the extracted MNN model folder	—
<prompt>	Text prompt	—
--negative <text>	Negative prompt	empty
--width <n> / --height <n>	Image size	512 / 512
--steps <n>	Sampling steps	20
--cfg <f>	CFG scale	7.0
--seed <n>	Seed (negative = random)	-1
--scheduler <name>	euler|ddim|dpm|dpm++2m|dpm++2m-karras|lcm|eulera|unipc	dpm++2m
--backend <name>	cpu|opencl	cpu
--out <path>	.ppm always works; .png needs stb_image_write.h	—

SDXL/Illustrious系では--width 1024 --height 1024を推奨します（デフォルトの512は非対応解像度のため画質が崩れます）。

Roadmap

• Linux build of nezumi-ai-sd-cli

Disclaimer

This is an unofficial, community conversion and is not affiliated with or endorsed by OnomaAI Research.