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3ce19a2 | 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 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 | # One Pass Is Not Enough: Recursive Latent Refinement for Generative Models
[**Project page**](https://mehdie79.github.io/Portfolio_Mehdi_Esmaeilzadeh/project/rtm-imle/) |
[**Paper**](https://arxiv.org/abs/2605.15309)
Official implementation of the **Recursive Token Mapper (RTM)**, which
replaces the single-pass MLP mapper of style-based generators with a
small recursive block that refines the latent across H weight-shared
cycles.
*Left: baseline single-pass mapper. Right: RTM (ours). Recursive
refinement produces more diverse coat patterns and better sample quality
(FID 4.79 vs 4.99, Recall 0.565 vs 0.507 on AFHQ-v1 512x512).*
## Abstract
Despite remarkable progress, image generation is far from solved. The
dominant metric, FID, conflates sample fidelity with mode coverage and is
close to being saturated. Yet a model can still exhibit mode collapse
while achieving a low FID, since a handful of sharp, near-duplicate
images can outscore a model that faithfully covers the full data
distribution. We argue that precision and recall are essential
complements to FID, and that because FID is already saturated, the more
meaningful goal is to improve diversity and coverage. Achieving high
recall requires a model that explicitly prioritizes mode coverage,
unlike most generative models, which optimize sample fidelity. We
introduce **RTM**, which replaces the single-pass latent mapping in
style-based generators with an iterative refinement process, and show
that this consistently improves both quality and diversity. Integrated
with Implicit Maximum Likelihood Estimation (IMLE), which optimizes mode
coverage by design, RTM achieves the highest precision and recall among
current state-of-the-art approaches while maintaining competitive FID,
with improvements across CIFAR-10, CelebA-HQ at 256x256, and nine
few-shot benchmarks. RTM also improves StyleGAN2 and StyleGAN2-ADA on
CIFAR-10 and AFHQ-v1 at 512x512, demonstrating that the benefit is not
specific to IMLE. Unlike flow-matching baselines that achieve
competitive FID at the expense of coverage, recursive refinement
improves both quality and diversity simultaneously.
## Architecture
The mapper M projects noise z into a small set of latent tokens that are
refined through H weight-shared cycles of token-mixing and
channel-mixing MLPs, then projected back into the style vector w.
## Repository layout
```
.
├── assets/ # Hero image and architecture diagram
├── rtm_core.py # RTM building blocks
├── models.py hps.py sampler.py train.py evaluate.py
├── prepare_cifar10.py prepare_celeba_hq.py
├── helpers/ visual/ lpips/
├── scripts/ # CIFAR-10 + CelebA-HQ-256 train/eval
├── fewshot/ # Few-shot pipeline (Obama, AnimalFace-cat, ...)
│ ├── train.py evaluate.py models.py hps.py sampler.py
│ ├── README.md
│ └── scripts/
└── studiogan/ # StyleGAN2 / StyleGAN2-ADA + RTM (AFHQ, CIFAR-10)
├── src/ # Vendored PyTorch-StudioGAN with RTM patched in
├── scripts/ # AFHQ-v1 + CIFAR-10 train/eval
└── README.md
```
## Setup
```bash
virtualenv -p python venv && source venv/bin/activate
pip install -r requirements.txt
pip install -i https://test.pypi.org/simple/ dciknn-cuda==0.1.15
```
The StudioGAN pipeline shares the same environment, plus a few extra
dependencies; see `studiogan/README.md`.
## Datasets
```bash
python prepare_cifar10.py --out_dir ./datasets/cifar10
python prepare_celeba_hq.py --src /path/to/celeba-hq --out_dir ./datasets/celeba-hq-256
```
For the few-shot benchmarks drop
each image folder under `./datasets/<name>/` and point `--data_root` at
it.
For AFHQ-v1 (StudioGAN pipeline) follow the standard StudioGAN data
layout: `<AFHQ>/{train,valid}/{cat,dog,wild}/*.png`.
## Training
CIFAR-10 (RS-IMLE + RTM):
```bash
bash scripts/train_cifar10.sh
```
CelebA-HQ-256 (RS-IMLE + RTM):
```bash
bash scripts/train_celebahq256.sh
```
Few-shot (RS-IMLE + RTM):
```bash
cd fewshot && bash scripts/train.sh /path/to/dataset
```
StyleGAN2-ADA + RTM:
```bash
DATA_DIR=/path/to/AFHQ NUM_GPUS=4 bash studiogan/scripts/train_afhq.sh
DATA_DIR=/path/to/cifar-10 bash studiogan/scripts/train_cifar10.sh
```
## Pretrained checkpoints
Checkpoints will be released soon!
## Evaluation
RS-IMLE on CIFAR-10 (FID with 50,000 samples, Precision/Recall with
50,000):
```bash
bash scripts/eval_cifar10.sh /path/to/model.th
```
RS-IMLE on CelebA-HQ-256 (FID with 30,000 samples, Precision/Recall with
30,000):
```bash
bash scripts/eval_celebahq256.sh /path/to/model.th
```
Few-shot (FID with 5000 samples, Precision/Recall with 1000):
```bash
cd fewshot && bash scripts/evaluate.sh /path/to/model.th /path/to/dataset
```
StyleGAN2-ADA + RTM on AFHQ-v1 / CIFAR-10 (FID and improved
Precision/Recall via StudioGAN's PRDC):
```bash
CKPT_DIR=./runs/afhq_rtm/checkpoints/<exp> DATA_DIR=/path/to/AFHQ bash studiogan/scripts/eval_afhq.sh
CKPT_DIR=./runs/cifar10_rtm/checkpoints/<exp> DATA_DIR=/path/to/cifar-10 bash studiogan/scripts/eval_cifar10.sh
```
All evaluation scripts use the same configuration that produced the
headline numbers in the paper.
## Citation
If you find this work useful, please cite:
```bibtex
@misc{esmaeilzadeh2026onepass,
title = {One Pass Is Not Enough: Recursive Latent Refinement for Generative Models},
author = {Mehdi Esmaeilzadeh and Alexia Jolicoeur-Martineau and Chirag Vashist and Ke Li},
year = {2026},
eprint = {2605.15309},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2605.15309},
doi = {10.48550/arXiv.2605.15309}
}
```
## Acknowledgments
This work has been built on top of these codebases:
- [RS-IMLE (Vashist et al., ECCV 2024)](https://github.com/serchirag/rs-imle)
- [Tiny Recursive Model (Jolicoeur-Martineau, 2025)](https://github.com/SamsungSAILMontreal/TinyRecursiveModels)
- [PyTorch-StudioGAN (POSTECH-CVLab)](https://github.com/POSTECH-CVLab/PyTorch-StudioGAN) — see `studiogan/LICENSE-StudioGAN` and `studiogan/LICENSE-NVIDIA` for upstream licenses.
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