IRIS: Iterative Recurrent Image Synthesis

A novel architecture for mobile-first, high-quality text-to-image generation under 3-4GB RAM

🚀 Train It Now!

← Download IRIS_Training_Notebook.ipynb from this repo and upload to Colab!

Quick start: Download IRIS_Training_Notebook.ipynb, open it in Colab (or Kaggle), enable GPU, and run all cells. Trains end-to-end in ~2-3 hours on a free T4.

The notebook includes:

📦 Auto-downloads architecture code from this repo
🎨 Trains on Pokémon BLIP Captions dataset (833 image-caption pairs)
🔬 Stage 1: Wavelet VAE training with frequency-aware loss
⚡ Stage 2: Rectified Flow generator training with CLIP conditioning
📊 Visualizations: reconstructions, generated samples, loss curves, GRFM internals
💾 Checkpoint saving for continued training

🎯 Why IRIS?

Current image generation models face critical limitations:

Problem	Current State	IRIS Solution
Too heavy for mobile	SD3: 2B params, FLUX: 12B params	48-136M params, <600MB inference
Quadratic attention	O(N²) self-attention	O(N log N) Fourier + O(N) recurrence
Too many inference steps	20-50 NFE typical	1-4 steps with consistency distillation
Old models look bad	SD 1.5 era quality insufficient	Modern rectified flow + frequency-aware latent
Quantization degrades quality	INT4/INT8 drops aesthetics	Architecture-level efficiency, no quantization needed
No editing support	Separate heavy editing models	Iterative core naturally extends to editing

🏗️ Architecture Overview

IRIS introduces a Prelude-Core-Coda architecture with shared-weight iterative refinement:

Text ──→ CLIP-L/14 ──→ text_tokens [77×768]
                                                    
Image ──→ HaarDWT ──→ WaveletVAE ──→ z₀ [C×H/16×W/16]
                                      │
                                      ▼ (+ noise via Rectified Flow)
                              ┌─────────────┐
                              │   PRELUDE    │ ← 2 conv blocks (unique weights)
                              └──────┬──────┘
                                     │
                              ┌──────▼──────┐
                              │    CORE     │ ← GRFM + CrossAttn + FFN
                              │  (shared    │   Iterated 4-16× (same weights!)
                              │   weights)  │   Iteration-aware via adaLN
                              └──────┬──────┘
                                     │
                              ┌──────▼──────┐
                              │    CODA     │ ← 2 local-attention blocks
                              └──────┬──────┘
                                     │
                              ▼ predicted velocity
                              └──→ WaveletVAE Decode ──→ HaarIDWT ──→ Image

🔬 Key Innovations

1. GRFM (Gated Recurrent Fourier Mixer) — Novel Token Mixing

Three complementary pathways fused via learned adaptive gating:

Fourier Global Pathway (O(N log N)): RFFT2 → Block-diagonal MLP → SoftShrink → IRFFT2
Gated Linear Recurrence (O(N)): Bidirectional RG-LRU scan with variance-preserving updates
Manhattan Spatial Gate: Per-head learnable spatial decay D_{nm} = γ^Manhattan(n,m)

output = gate × x_fourier + (1 - gate) × x_recurrent + α × x_spatial

2. Recurrent Depth Core (Huginn paradigm, novel for images)

Shared-weight core block iterated 4-16× (same model, adaptive quality!)
4-layer block × 8 iterations = 32 effective layers from just 4 layers of params
48M unique params → 270-524M effective capacity

3. Wavelet-Frequency Latent Space

Haar DWT preprocessing preserves frequency structure in latent space
16× total spatial compression (lossless wavelet + learned VAE)

4. Dual-Axis Recurrence (Novel)

Recurrence over noise schedule (diffusion) AND computational depth (core iterations)

📊 Model Variants

Variant	Generator Params	Total Memory (fp16)	Mobile Fit
IRIS-Tiny	19M	545 MB	✅ Ultra-mobile
IRIS-Small	47M	597 MB	✅ Mobile
IRIS-Base	135M	760 MB	✅ Consumer GPU

🔧 Quick Start

from iris_model import create_iris_small
import torch

model = create_iris_small()
text_tokens = torch.randn(1, 77, 768)  # Replace with CLIP-L/14 embeddings

# Fast mobile inference (4 iterations, 4 steps)
images = model.generate(text_tokens, num_steps=4, num_iterations=4)

# Quality inference (8 iterations, 4 steps)
images = model.generate(text_tokens, num_steps=4, num_iterations=8)

📐 Mathematical Foundations

Rectified Flow Training

z_t = (1-t)·z₀ + t·ε,  v_target = ε - z₀
L = w(t) · ||v_θ(z_t, t, c) - v_target||²,  w(t) = t/(1-t)
t ~ Logit-Normal(0, 1)

GRFM Pathways

Fourier:    RFFT2 → BlockDiagMLP → SoftShrink(λ) → IRFFT2       [O(N log N)]
Recurrence: h_t = a_t⊙h_{t-1} + √(1-a_t²)⊙(i_t⊙x_t)           [O(N)]
Spatial:    D_{nm} = γ^(|row_n-row_m| + |col_n-col_m|)           [O(N×window)]

🏋️ Training Recipe

Stage	Data	Est. Cost
1. VAE	ImageNet + CC3M	20 GPU-hrs
2. Class-Cond	ImageNet 256px	100 GPU-hrs
3. Text-Image	CC3M/CC12M	200 GPU-hrs
4. Aesthetic	JourneyDB	50 GPU-hrs
5. Distill	Self-distill	30 GPU-hrs

Total: ~~400 A100 GPU-hours (~~$1,600) | Stages 1-2 run on free Colab T4

📚 Research Foundations

Concept	Source	How Used
Recurrent Depth	Huginn (2502.05171)	Prelude-Core-Coda
Fourier Mixing	AFNO (2111.13587)	GRFM pathway
Gated Recurrence	Griffin RG-LRU (2402.19427)	GRFM pathway
Manhattan Decay	RMT (2309.11523)	GRFM pathway
Wavelet Diffusion	WaveDiff (2211.16152)	Latent space
Rectified Flow	RF (2209.03003), SD3	Training objective
Consistency Models	CM (2303.01469)	Distillation
adaLN-Zero	DiT (2212.09748)	Conditioning
Efficient Training	PixArt-α (2310.00426)	Training recipe
Mobile Design	SnapGen (2412.09619)	DWSConv, tiny VAE

📄 Files

File	Description
`IRIS_Training_Notebook.ipynb`	🔥 Complete Colab/Kaggle training notebook
`iris_model.py`	Architecture implementation (~1200 lines)
`train_iris.py`	CLI training pipeline (all 5 stages)
`test_iris.py`	Validation test suite (9 tests, all passing)
`ARCHITECTURE.md`	Detailed math specification

✅ Verified Properties

✅ Haar DWT/IDWT roundtrip lossless (error < 1e-5)
✅ WaveletVAE: 256×256→16×16 latent (48× compression)
✅ GRFM forward/backward correct, all gradients flow
✅ Variable iteration counts work (adaptive compute)
✅ Full training step with rectified flow loss
✅ End-to-end generation pipeline
✅ IRIS-Tiny: 545 MB total inference (< 3GB ✅)
✅ IRIS-Small: 597 MB total inference (< 3GB ✅)
✅ 16× iteration gives 10.9× effective capacity

📜 License

Apache 2.0

@misc{iris2026,
  title={IRIS: Iterative Recurrent Image Synthesis for Mobile-First Image Generation},
  year={2026},
  note={Novel architecture: GRFM + Recurrent Depth + Wavelet Latent Space}
}

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