---
title: Follow the Mean (FLUX.2)
emoji: 🪷
colorFrom: pink
colorTo: indigo
sdk: gradio
sdk_version: 6.14.0
app_file: app.py
pinned: false
license: apache-2.0
short_description: Training-free reference-guided generation with FLUX.2-klein
models:
  - black-forest-labs/FLUX.2-klein-4B
tags:
  - flux
  - flux.2
  - flow-matching
  - reference-guided
  - rmg
  - training-free
---

# Follow the Mean — FLUX.2 reference-guided generation

Training-free **Reference-Mean Guidance (RMG)** applied to a frozen
[`FLUX.2-klein-4B`](https://huggingface.co/black-forest-labs/FLUX.2-klein-4B).

The prompt says *what* the image is; the reference set biases *which way* the
flow goes. Change the reference set, and the flow changes.

* Paper: [Follow the Mean: Reference-Guided Flow Matching](https://arxiv.org/abs/2605.10302)
* Code:  [github.com/pedrocurvo/follow-the-mean](https://github.com/pedrocurvo/follow-the-mean)

## How it works

For deterministic flow-matching interpolants, the velocity field points toward
an endpoint mean. RMG approximates a *target* endpoint mean from a small
reference set and adds the residual correction during sampling:

```
u_guided(x_t, t) = u_theta(x_t, t)
    + beta_t * (mu_ref(x_t, t) - mu_theta(x_t, t)) / (1 - t)
```

No FLUX.2 weights are updated; the control signal lives entirely in the
reference set. This Space implements that callback on top of the
`Flux2KleinPipeline` from `diffusers`.