flux-3-image-klein

Ungated mirror of black-forest-labs/flux-3-image-klein. All credit to Black Forest Labs. This is not an official Black Forest Labs release. The model is licensed under the FLUX Kommunity License (non-commercial), included here as LICENSE.md with NOTICE.md; any rights to use it are granted directly by Black Forest Labs under that license.

FLUX 3 Image Klein

FLUX 3 Image Klein is a 5.6B open weights model for text-to-image generation and image editing. It takes a prompt and up to 10 reference images and returns one image. Text-to-image, single-reference edits and multi-reference compositions run through the same model, with Qwen3-VL-4B-Instruct as the text encoder.

The 5.6B parameter label refers to the image transformer; the text encoder is separate.

This repository holds the distilled checkpoint for generation and image editing. For fine-tuning, LoRA training and research, use FLUX 3 Image Klein Base.

Inference code, prompt upsampling and the bounding box format: black-forest-labs/flux-3-image-klein on GitHub.

This repository includes the BF16 transformer and FP8 and NVFP4 variants, together with the Qwen3-VL-4B-Instruct text encoder and image autoencoder.

Key Features

1. Text-to-image, single-reference editing and multi-reference generation in one model.
2. Bounding boxes in the prompt. Place elements with boxes on a 0 to 1000 grid, then recolor, move, replace or remove them through the prompt. Boxes guide placement and scale; they do not constrain pixels to a hard mask.
3. Text rendering within generated images.
4. Released under the FLUX Kommunity License. The license permits non-commercial use and provides a commercial-output exception for Qualifying Users, subject to its terms. See License below.

Download

Download the root BF16 distilled package, including its text encoder and image autoencoder, without downloading the quantized variants:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="black-forest-labs/flux-3-image-klein",
    local_dir="weights/flux-3-image-klein",
    allow_patterns=[
        "model.safetensors", "config.json", "manifest.json",
        "ae.safetensors", "text_encoder/*",
        "README.md", "LICENSE.md", "NOTICE.md",
    ],
)

To download a quantized distilled transformer instead, replace the three root transformer patterns (model.safetensors, config.json, and manifest.json) with variants/fp8r/* or variants/nvfp4/*. Keep ae.safetensors and text_encoder/* for the other components.

These files contain the model package, not a complete inference application. Use an inference implementation that supports flow-map sampling and this checkpoint format. Quantized packages need a loader that supports their format; their file size is not a measure of total GPU memory use.

Using with reference code

The distilled checkpoint is served on the BFL API as FLUX 3 Image Klein. The reference inference code is black-forest-labs/flux-3-image-klein.

From the reference implementation checkout, install the package and download the two components the CLI loads besides this checkpoint. The CLI reads its weights from weights/ (set WEIGHTS_DIR for another root), where the download above puts this repository.

pip install -e .

# safety classifier, always loaded
hf download Falconsai/nsfw_image_detection --local-dir weights/nsfw-classifier
# prompt upsampler (18.8 GB), rewrites the prompt first; skip it and add --no-pup to send prompts as typed
hf download black-forest-labs/flux-3-image-klein-pup --local-dir weights/pup

# text-to-image
python scripts/cli.py "A wide-angle photograph of a lone American bison in a misty geothermal landscape, half hidden by white steam, a slate-blue stream and evergreens fading into the mist behind. Muted, cool palette, soft overcast light." \
  --model distilled --out bison.png

# image editing; repeat --image for multiple references
python scripts/cli.py "The same lone American bison in the same misty geothermal landscape, now at night under a full moon: the steam lit from behind, cool blue light, deep shadows, stars above the evergreens." \
  --model distilled --image bison.png --out bison_night.png

Using with Diffusers

FLUX 3 Image Klein uses a modular pipeline. Pass prompt for text-to-image generation, or pass image with a prompt for editing. image can be one image or a list of reference images.

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

import torch
from diffusers import ModularPipeline

pipe = ModularPipeline.from_pretrained("black-forest-labs/flux-3-image-klein")
pipe.load_components(dtype=torch.bfloat16)
pipe.to("cuda")

image = pipe(
    prompt="A wide-angle photograph of a lone American bison in a misty geothermal landscape, half hidden by white steam, a slate-blue stream and evergreens fading into the mist behind. Muted, cool palette, soft overcast light.",
    generator=torch.Generator().manual_seed(0),
    output="images",
)[0]
image.save("bison.png")

night = pipe(
    prompt="The same lone American bison in th