Mouserat/CyberRealistic-mnn

CyberRealistic (MNN)

This repository provides an MNN-format conversion of cyberdelia/CyberRealistic (CyberRealistic_FINAL_FP16.safetensors) for use with the nezumi-ai image generation engine — a fully offline AI chat app for Android supporting on-device image generation.

A Windows CLI (nezumi-ai-sd-cli) is also provided for testing/debugging on desktop, but the primary target platform is Android. This model is not bundled with the app — users download it separately and individually agree to its license terms.

Platform note: nezumi-ai-sd-cli currently builds for Windows only (.exe). A Linux build is planned.

Variants

SD1.5 models are distributed in a single quantization variant (unlike the SDXL/Illustrious line, which offers int4/int8 variants):

File	UNet quantization	Size
CyberRealistic-mnn-int8-block32.zip	8-bit, block size 32	~1.23 GB

Model Provenance

Field	Value
Base model	cyberdelia/CyberRealistic, CyberRealistic_FINAL_FP16.safetensors
Original source	Photorealistic SD1.5 checkpoint by Cyberdelia, optimized for portraits and lifelike scenes with minimal prompt engineering
Format	MNN (clip_v2.mnn + .weight, unet.mnn + .weight, vae_decoder_fp16.mnn + .weight, token_emb.bin, pos_emb.bin, tokenizer.json)
Conversion tool	convert_hf_to_mnn_sd.py (nezumi-ai)

Conversion steps

The upstream model is distributed as single .safetensors checkpoints rather than a diffusers-format repository, so an extra pre-conversion step is required.

# 1. Download the checkpoint
curl -L -o CyberRealistic_FINAL_FP16.safetensors \
  "https://huggingface.co/cyberdelia/CyberRealistic/resolve/main/CyberRealistic_FINAL_FP16.safetensors"

# 2. Convert single-file safetensors -> diffusers format
python -c "
from diffusers import StableDiffusionPipeline
import torch

pipe = StableDiffusionPipeline.from_single_file(
    './CyberRealistic_FINAL_FP16.safetensors',
    torch_dtype=torch.float16,
    safety_checker=None,
)
pipe.save_pretrained('./CyberRealistic_diffusers')
"

# 3. Convert diffusers format -> MNN
python convert_hf_to_mnn_sd.py \
    --model ./CyberRealistic_diffusers \
    --out ./out/CyberRealistic \
    --size 512 \
    --unet-bits 8 \
    --unet-block 32 \
    --clip-bits 8 \
    --vae-bits 8

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

Output files

clip_v2.mnn                0.13 MB
clip_v2.mnn.weight       148.92 MB
model.json                 0.00 MB
pos_emb.bin                 0.23 MB
token_emb.bin              72.38 MB
tokenizer.json               2.12 MB
unet.mnn                    1.13 MB
unet.mnn.weight          911.38 MB
vae_decoder_fp16.mnn        0.22 MB
vae_decoder_fp16.mnn.weight 94.38 MB
TOTAL                    1230.89 MB

License

The upstream model's license labeling is inconsistent across sources, so we report both here rather than assuming one is correct:

• Hugging Face repository tag: creativeml-openrail-m (as declared in cyberdelia/CyberRealistic's repo metadata)
• Model card body text: refers to the "CreativeML Open RAIL++-M License" (a distinct, related license more commonly used for SDXL-era models)

RAIL-M and RAIL++-M share the same core structure and use-based restrictions (Attachment A), differing mainly in scope of applicability (RAIL++-M extends coverage to text encoders in addition to the diffusion model itself). We are not aware of this discrepancy having been resolved by the upstream author as of this writing. Given the ambiguity, this repository:

• Uses creativeml-openrail-m as the declared HF license tag (matching the upstream repo's own tag)

• Documents the RAIL++-M wording found in the upstream model card here, for transparency

• Applies the more inclusive set of use-based restrictions from both variants, to be safe

• Commercial use: The upstream model card states commercial and non-commercial use are both permitted, with proper credit and no malicious use. This is consistent with both RAIL-M and RAIL++-M.

• Redistribution: Permitted under either license variant.

• Attribution: Required — credit to Cyberdelia (see above).

This checkpoint inherits the original model's use-based restrictions in full (see Attachment A of the RAIL-M full text), including prohibitions on use for exploiting minors, generating disinformation, harassment, discrimination, unauthorized medical advice, and law-enforcement/immigration profiling.

Please review the upstream model card and the RAIL-M full text yourself before relying on this for commercial use — this is a summary, not legal advice.

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 31)	14+ (API 34+)
RAM	4GB	6GB+
Storage	3GB free	5GB+
GPU	Optional — OpenCL-capable GPU (Adreno, Mali, PowerVR)	Recommended

SD1.5 requirements are lower than SDXL/Illustrious (8GB RAM minimum) — see that model's README for comparison.

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 a zip archive. Extract it before use — in PowerShell:

Expand-Archive CyberRealistic-mnn-int8-block32.zip C:\sd-model

Then run:

nezumi-ai-sd-cli "C:\sd-model" "RAW photo, realistic photo of a 22-year-old woman, natural lighting, depth of field, candid moment, color graded" --steps 25 --width 512 --height 512 --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>	Sampling scheduler: euler|ddim|dpm|dpm++2m|dpm++2m-karras|lcm|eulera|unipc	dpm++2m
--backend <name>	cpu|opencl	cpu
--out <path>	Output path. .ppm always works; .png requires stb_image_write.h	—

The upstream author recommends 25–30 steps, dpm++2m-karras or eulera, and CFG 7.0–8.0 at 512×512 (native), with hires-fix for higher resolutions. VAE is already baked in — no separate VAE file needed.

Roadmap

• Linux build of nezumi-ai-sd-cli
• macOS build
• SDXL support
• Additional quantization variants

Disclaimer

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