# SigMa reproduction bundle

Reproduction of ICML 2026 paper **σ: Sigmoid Modulation for Ultra High Resolution Diffusion**
(OpenReview [47JZSOkw5C](https://openreview.net/forum?id=47JZSOkw5C); code [github.com/bxuanz/SigMa](https://github.com/bxuanz/SigMa)).

This bundle verifies SigMa's **training-free RoPE modulation mechanism** — the algorithmic core both
paper claims rest on — by running the paper's *own released* `FluxPosEmbed` positional-encoding module
at the exact FLUX latent grids up to 16 megapixels. No model weights are needed: SigMa lives entirely
in a weight-free RoPE layer.

## Contents
- `scripts/sigma_core.py` — SigMa RoPE math **copied verbatim** from `SigMa/flux/transformer_flux.py`
  (only lifted out of the diffusers-heavy module to dodge an unrelated `huggingface-hub` version clash;
  no logic changed — line provenance is in the file header).
- `scripts/repro_sigma_mechanism.py` — the reproduction: drives the real `FluxPosEmbed` across
  resolutions 1024²→4096² and the 28-step flow schedule; writes `outputs/*.csv`, `arrays.npz`, `summary.json`.
- `scripts/make_figures.py` — renders the figures (`outputs/fig_*.png`, `outputs/sigma_figure.html`).
- `outputs/` — all metrics (CSV/JSON), arrays, and figures.
- `poster/` — the Chenruishuo/posterly poster (`poster.html`, self-contained `poster_embed.html`,
  `poster_preview.png/pdf`, `GATE_REPORT.json`).
- `scripts/scaffold_icml_logbook.py`, `scripts/validate_icml_logbook.py` — challenge tooling.

## Rerun
```bash
pip install torch numpy matplotlib plotly
git clone https://github.com/bxuanz/SigMa            # source of the verbatim RoPE math
cp scripts/sigma_core.py scripts/repro_sigma_mechanism.py scripts/make_figures.py .
python repro_sigma_mechanism.py    # ~12 s on CPU -> outputs/
python make_figures.py             # -> outputs/fig_*.png, sigma_figure.html
```

## What is NOT in scope
End-to-end 16-MP FLUX image generation and the SOTA FID/PickScore benchmarks are **not** regenerated:
`black-forest-labs/FLUX.1-Krea-dev` (12B) is gated and Hugging Face Jobs returned 402 (no credits).
The mechanism enabling those results is what this bundle verifies.