MIRO — ablations and single-reward specialists

This repository hosts the 15 ablation / baseline checkpoints that accompany the main MIRO release at nicolas-dufour/miro.

Dufour, Degeorge, Ghosh, Kalogeiton, Picard. MIRO: MultI-Reward cOnditioned pretraining improves T2I quality and efficiency. ICML 2026.

📄 Paper · 🌐 Project page · 💻 Code · 🐍 pip install miro-t2i

Layout

Every variant lives in its own subfolder and is loaded via the variant= argument:

from miro import MiroPipeline
import torch

pipe = MiroPipeline.from_pretrained(
    "nicolas-dufour/miro-ablations",
    variant="miro-no-clip",            # ← the subfolder name
).to("cuda", torch.float16)

Each MiroPipeline instance exposes pipe.coherence_keys, which lists the reward axes the loaded checkpoint was trained on. reward_targets={...} will raise ValueError if you pass a key that's not in this list.

Variants

Reward ablations (8) — full MIRO recipe minus one signal

Same architecture and training data as the main MIRO, with one reward signal turned off so you can isolate its contribution.

Single-reward specialists (7) — paper baselines

Each is trained on only one reward signal — the controls the paper compares MIRO against. pipe.coherence_keys is a 1-tuple for these.

What's in each subfolder

miro-<variant>/
├── model.safetensors      # fp32 EMA weights (~1.4 GB) — ready for finetuning
├── config.json            # network kwargs + sampler defaults
├── uncond_embedding.npy   # precomputed FLAN-T5-XL unconditional embedding
├── teaser.jpg             # shared masonry gallery
└── README.md              # per-variant model card

Citation

@inproceedings{dufour2026miro,
  title     = {{MIRO}: {M}ult{I}-{R}eward c{O}nditioned pretraining improves {T2I} quality and efficiency},
  author    = {Dufour, Nicolas and Degeorge, Lucas and Ghosh, Arijit and Kalogeiton, Vicky and Picard, David},
  booktitle = {International Conference on Machine Learning (ICML)},
  year      = {2026}
}

License

MIT — see LICENSE.