You can deploy this model and start earning money today!

Mouserat/
dreamshaper-8-mnn

Name: dreamshaper-8-mnn
Availability: InStock

creativeml-openrail-m

Views

No views yet

DreamShaper 8 (MNN)

This repository provides an MNN-format conversion of Lykon/dreamshaper-8 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
`DreamShaper_8-mnn-int8-block32.zip`	8-bit, block size 32	~1.23 GB

Model Provenance

Field	Value
Base model	Lykon/dreamshaper-8
Original source	General-purpose SD1.5 checkpoint by Lykon, fine-tuned from runwayml/stable-diffusion-v1-5; aims to handle photorealism, art, and anime in one model
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 settings

bash

python convert_hf_to_mnn_sd.py \
    --model Lykon/dreamshaper-8 \
    --out ./out/DreamShaper_8 \
    --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 format conversion and the 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

Original model license: CreativeML Open RAIL-M (full text) — all credit for the weights and training goes to Lykon.
Redistribution: Permitted under the original model's license.
Commercial use: Permitted within the terms of the RAIL-M license.
Attribution: Required (see above).

Please read and comply with the original license before using this model.

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.

Note on provenance: the primary Civitai-linked repository (Lykon/DreamShaper) is tagged license: other and points to the Civitai model page rather than specifying terms directly. This conversion instead uses Lykon/dreamshaper-8, the diffusers-format release of the same v8 weights, which is explicitly tagged creativeml-openrail-m.

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 DreamShaper_8-mnn-int8-block32.zip C:\sd-model

Then run:

nezumi-ai-sd-cli "C:\sd-model" "portrait of a fantasy warrior, detailed armor, dramatic lighting" --steps 20 --width 512 --height 640 --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`	—

DreamShaper works well across styles (photoreal, art, anime). The author suggests CLIP skip 2 for some outputs; dpm++2m-karras or eulera at CFG 6–8 with 20–25 steps is a reasonable starting point.

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 Lykon.

Mouserat/dreamshaper-8-mnn