| # LuminaRS — Lightweight Recursive Art Image Generator |
|
|
| A novel ~90M parameter image generation model for art/illustration that runs on mobile devices (2-4GB VRAM). |
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|
| ## 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) |
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|
| ### 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 |
| |
