File size: 2,473 Bytes
dc40562 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | # 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](https://arxiv.org/abs/2510.04871) — beat 200x larger LLMs with 7M params.
```python
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
```python
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
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