README.md

---
license: apache-2.0
language:
- en
pipeline_tag: image-to-image
tags:
- text-to-image
- image-editing
- flux
- diffusion-single-file
---

This is a BF16 conversion of [`black-forest-labs/FLUX.2-small-decoder`](https://huggingface.co/black-forest-labs/FLUX.2-small-decoder), converted using `diffusers` and `safetensors`. Weights cast from FP32 to BF16; `bn` statistics preserved as-is.

`FLUX.2 Small Decoder` is a distilled VAE decoder that serves as a **drop-in replacement** for the standard FLUX.2 decoder. It delivers faster decoding and lower VRAM usage with minimal to zero quality loss. The encoder remains unchanged.

# **Key Features**

  1. **~1.4x faster decoding** compared to the full decoder.
  2. **~1.4x less VRAM** at decode time, enabling higher resolutions without running out of memory.
  3. **~28M decoder parameters** (vs ~50M in the full decoder) thanks to narrower channel widths (`[96, 192, 384, 384]` vs `[128, 256, 512, 512]`).
  4. **Minimal quality loss** — images are almost identical.
  5. Available under the Apache 2.0 license.

Compatible with all open FLUX.2 models:
- [FLUX.2-klein-4B](https://huggingface.co/black-forest-labs/FLUX.2-klein-4B)
- [FLUX.2-klein-9B](https://huggingface.co/black-forest-labs/FLUX.2-klein-9B)
- [FLUX.2-klein-9b-kv](https://huggingface.co/black-forest-labs/FLUX.2-klein-9b-kv)
- [FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev)

# **Usage**

```shell
pip install git+https://github.com/huggingface/diffusers.git
```

```python
import torch
from diffusers import Flux2KleinPipeline, AutoencoderKLFlux2

device = "cuda"
dtype = torch.bfloat16

vae = AutoencoderKLFlux2.from_pretrained("InsecureErasure/FLUX.2-small-decoder-BF16", torch_dtype=dtype)
pipe = Flux2KleinPipeline.from_pretrained("black-forest-labs/FLUX.2-klein-4B", vae=vae, torch_dtype=dtype)
pipe.enable_model_cpu_offload()

prompt = "A black cat holding a sign that says 'hello world' in typewriter font"
image = pipe(
    prompt=prompt,
    height=1024,
    width=1024,
    guidance_scale=1.0,
    num_inference_steps=4,
    generator=torch.Generator(device=device).manual_seed(0)
).images[0]
image.save("flux-klein-small-decoder.png")
```

---
# License

This model is licensed under the https://www.apache.org/licenses/LICENSE-2.0.