---
language: en
license: apache-2.0
base_model: black-forest-labs/FLUX.2-klein-base-4B
library_name: diffusers
tags:
  - audio-synthesis
  - room-impulse-response
  - acoustics
  - lora
  - flux2
  - arxiv:2604.20329
pipeline_tag: image-to-image
---

# echo-plantain

A LoRA adapter on FLUX.2 Klein (4B) that predicts the magnitude spectrogram of a room impulse response from a top-down schematic of the room. Reframes acoustic modeling as image-to-image generation: the source image is a schematic showing room geometry plus source and listener positions, the target is the RIR spectrogram in RGB, and an inverse bijection recovers a mono RIR suitable for audio convolution.

This adapter tests whether the recipe from *Image Generators are Generalist Vision Learners* (Gabeur et al., 2026; [arXiv:2604.20329](https://arxiv.org/abs/2604.20329)) extends to physics-grounded prediction tasks where the input is a 2D image and the output is a signal that captures the response of a physical system.

## Method

1. **Reframe room acoustics as image-to-image.** Source: a 768 × 768 top-down schematic of a rectangular room with the audio source rendered as a red ⊕ glyph, the listener as a blue ⊙ glyph, and floor brightness encoding surface absorption (lighter = more reflective). Target: the room impulse response, computed via the image-source method, encoded as an RGB spectrogram.
2. **Bijective magnitude↔RGB encoding.** Linear-amplitude STFT magnitude → dB clipped to [−100, 0] → curve `u ∈ [0, 1]` → 7-segment Hamiltonian path through the corners of the RGB cube (black → blue → cyan → green → yellow → red → magenta → white). The wider dB range relative to speech encodings captures the full RIR dynamic range from direct-arrival peak to late-reverberation tail.
3. **Audio params.** 16 kHz, n_fft = 1024, hop = 256, 1-second clips. STFT (513 frequency bins × 63 time frames) placed top-left in a 768 × 768 canvas with silence padding.

Training data: 10,000 randomly-generated rectangular rooms via [pyroomacoustics](https://github.com/LCAV/pyroomacoustics). Dimensions uniform on 3–12 m × 3–12 m with 2.4–4.0 m ceiling; surface absorption uniform on [0.05, 0.50]; source and listener positions uniform inside the room with minimum 0.5 m separation. RIRs computed by image-source method up to reflection order 6.

## Status

Training in progress. Weights will be added when complete.

## Training

| | |
|---|---|
| Base | `black-forest-labs/FLUX.2-klein-base-4B` |
| Adapter | LoRA, rank 256 on transformer attention + rank 32 on text encoder |
| Resolution | 768 × 768 |
| Batch size | 4 |
| Optimizer | AdamW, lr 1e-4, cosine schedule, 300-step warmup |
| Max steps | 15 000 |
| Mixed precision | bf16 |
| Training data | 10 000 synthetic rooms (pyroomacoustics, image-source method, max order 6) |
| Audio params | 16 kHz, n_fft 1024, hop 256, 1-second RIR clips |
| Spectrogram encoding | Linear magnitude → dB clipped [−100, 0] → Hilbert RGB-cube path |

## Usage

```python
import torch
from PIL import Image
from diffusers import Flux2KleinPipeline

pipe = Flux2KleinPipeline.from_pretrained(
    "black-forest-labs/FLUX.2-klein-base-4B", torch_dtype=torch.bfloat16,
).to("cuda")
pipe.load_lora_weights("phanerozoic/echo-plantain")

# A top-down schematic of the target room (see `render_schematic.py` for the
# renderer convention: walls as outline, source as red ⊕, listener as blue ⊙,
# floor brightness encoding absorption).
schematic = Image.open("room_schematic.png").convert("RGB").resize((768, 768))

prompt = (
    "Generate a room impulse response spectrogram for the depicted space. "
    "Time on horizontal axis (early reflections at left, late reverb tail extending right), "
    "frequency on vertical axis. Energy encoded in RGB along a Hilbert path through "
    "the color cube: black is below noise floor, blue/cyan is faint reflections, "
    "green/yellow is strong reflections, red/magenta is direct-arrival energy."
)
img = pipe(
    image=schematic, prompt=prompt, height=768, width=768,
    guidance_scale=4.0, num_inference_steps=20,
).images[0]
```

The decoder (RGB → magnitude → mono RIR) is in `decode_rir.py`. The recovered RIR can be convolved with any dry signal to apply the predicted room reverb.

## License

The LoRA adapter weights in this repository are released under the Apache License 2.0, matching the license of the base model FLUX.2 Klein 4B.

### Training data attribution

The training data is fully synthetic, generated at preparation time from random rectangular room geometries via the [pyroomacoustics](https://github.com/LCAV/pyroomacoustics) Python library (Scheibler, Bezzam, Dokmanić, 2018). Pyroomacoustics is distributed under the MIT License. No external dataset is required to reproduce the training corpus; the dataset-generation script is included in this repository.

### Base model

Base model FLUX.2 Klein 4B is distributed by Black Forest Labs under the Apache License 2.0. See https://huggingface.co/black-forest-labs/FLUX.2-klein-base-4B for the original model card.

## References

- Gabeur, Long, Peng, et al. *Image Generators are Generalist Vision Learners.* [arXiv:2604.20329](https://arxiv.org/abs/2604.20329) (2026).
- Scheibler, Bezzam, Dokmanić. *Pyroomacoustics: A Python package for audio room simulation and array processing algorithms.* ICASSP 2018.
- Allen, Berkley. *Image method for efficiently simulating small-room acoustics.* JASA 1979.