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LuminaRS
asdf98/LuminaRS
LuminaRS is a machine learning model from asdf98. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
A novel ~90M parameter image generation model for art/illustration that runs on mobile devices (2-4GB VRAM).
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Updated Apr 27, 2026
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At a glance
- Access
- Public
- Created
- Apr 27, 2026
- Updated
- Apr 27, 2026
- SHA
- fa58f41a
Model card
From the Hugging Face model README
LuminaRS — Lightweight Recursive Art Image Generator
A novel ~90M parameter image generation model for art/illustration that runs on mobile devices (2-4GB VRAM).
Why LuminaRS?
| Problem | Current Solutions | LuminaRS |
|---|---|---|
| Heavy models (6-12GB) | SDXL, Flux | ~90M params, <500MB |
| Can't run mobile | Quantized SD (quality loss) | Designed small from scratch |
| Poor prompt adherence | SD 1.5 | TRM-style recursive reasoning |
| No art specialization | General photo models | Art-focused training stages |
| Unstable training | Diffusion (score matching) | Flow matching (stable ODE) |
Architecture (Novel Contributions)
1. Recursive Shared-Weight Refinement (from TRM)
Inspired by Tiny Recursive Models — beat 200x larger LLMs with 7M params.
for _ in range(T): z = z + unet(z, text, t) # shared-weight refinement
Effective depth = T x L without Tx parameters.
2. Flow Matching (instead of Diffusion)
- v(x_t, t) = x_clean - x_noise (straight-line velocity)
- 10-12 inference steps vs 50+ for diffusion
- No score matching instability
3. ConvNeXt + MQA Cross-Attention
Depthwise 7x7 conv, Adaptive LayerNorm (time), MQA cross-attn (text), GELU MLP
4. Staged Freeze/Thaw Training
| Stage | What's Trained | LR |
|---|---|---|
| 1 | All denoiser params | 1e-4 |
| 2 | Cross-attention only | 1e-5 |
| 3 | All params, joint | 1e-6 |
VAE and CLIP always frozen.
Parameter Budget
| Component | Params |
|---|---|
| Encoder | ~35M |
| Bottleneck | ~15M |
| Decoder | ~35M |
| Embeds | ~5M |
| Total trainable | ~90M |
| VAE (frozen) | ~83M |
| CLIP (frozen) | ~303M |
| Inference VRAM (b=1) | ~1.5-2GB |
Quick Start
from luminars.model import LuminaRS
from luminars.config import LuminaRSConfig
from luminars.sampler import sample_flow
cfg = LuminaRSConfig()
model = LuminaRS(cfg)
latents = sample_flow(model, text_emb, (1,16,32,32), 12)
Files
- luminars/ -- model, config, loss, sampler, train helpers
- train.py -- main training script
- LuminaRS_Colab.ipynb -- Colab notebook
Research Foundations
- TRM (Jolicoeur-Martineau 2025): Recursive reasoning
- SnapGen (2024): Mobile UNet design
- ZigMa (2024): Mamba diffusion
- Flow Matching (Lipman 2023): Stable ODE generation
- MQA (Shazeer 2019): Multi-query attention
- ConvNeXt (Liu 2022): Modernized CNN
MIT License
- Created
- Apr 27, 2026
- Updated
- Apr 27, 2026