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@ -14,60 +14,10 @@ tags:
![Teaser](./editing.jpg)
![Teaser](./others.jpg)
The FLUX.2 [klein] model family are our fastest image models to date. FLUX.2 [klein] unifies generation and editing in a single compact architecture, **delivering state-of-the-art quality with end-to-end inference in as low as under a second**. Built for applications that require real-time image generation without sacrificing quality, and runs on consumer hardware, with as little as 13GB VRAM.
FLUX.2 [klein] 4B Base is a 4 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities.
It's a full-capacity foundation model. Undistilled, preserving complete training signal for maximum flexibility. Ideal for fine-tuning, LoRA training, research, and custom pipelines where control matters more than speed. Higher output diversity than the distilled models.
This repository holds an FP8 version of FLUX.2 [klein] Base 4B. The main repository of this model (full BF16 weights) can be found [here](https://huggingface.co/black-forest-labs/FLUX.2-klein-base-4B).
`FLUX.2 [klein] 4B Base` is a 4 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities.
For more information, please read our [blog post](https://bfl.ai/blog/flux2-klein-towards-interactive-visual-intelligence).
# **Key Features**
1. Exceptional speed and quality-to-size ratio.
2. Ideal for local deployment and fine-tuning on limited hardware.
3. Trained without step or guidance distillation, making FLUX.2 [klein] 4B Base more efficient and flexible.
4. Open weights for customization and fine-tuning to drive science, research, and empower artists to iterate with speed.
5. Outputs can be used for commercial purposes, as described in the [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).
# **Usage**
We provide a reference implementation of FLUX.2 [klein] 4B Base, as well as sampling code, in a dedicated [GitHub repository](https://github.com/black-forest-labs/flux2). Developers and creatives looking to build on top of FLUX.2 [klein] 4B Base are encouraged to use this as a starting point.
FLUX.2 [klein] 4B Base is also available in both [ComfyUI](https://github.com/comfyanonymous/ComfyUI) and [Diffusers](https://github.com/huggingface/diffusers).
## **Using with Diffusers 🧨**
To use FLUX.2 [klein] 4B Base with the 🧨 Diffusers python library, first install or upgrade diffusers:
```python
pip install -U diffusers
Then you can use Flux2KleinPipeline to run the model:
import torch
from diffusers import Flux2KleinPipeline
device = "cuda"
dtype = torch.bfloat16
pipe = Flux2KleinPipeline.from_pretrained("black-forest-labs/FLUX.2-klein-base-4B", torch_dtype=dtype)
pipe.enable_model_cpu_offload() # save some VRAM by offloading the model to CPU
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=4.0,
num_inference_steps=50,
generator=torch.Generator(device=device).manual_seed(0)
).images[0]
image.save("flux-klein.png")
```
This repository holds an [FP8 version](https://huggingface.co/black-forest-labs/FLUX.2-klein-base-4b-fp8/blob/main/flux-2-klein-base-4b-fp8.safetensors) of FLUX.2 [klein] 4B Base. The main repository of this model (full BF16 weights) can be found [here](https://huggingface.co/black-forest-labs/FLUX.2-klein-base-4B).
---
Limitations
@ -117,4 +67,4 @@ This model is licensed under the https://www.apache.org/licenses/LICENSE-2.0.
Trademarks & IP
This project may contain trademarks or logos for projects, products, or services. Use of Black Forest Labs and FLUX trademarks or logos in modified versions of this project must not cause confusion or imply sponsorship or endorsement. Any use of third-party trademarks, intellectual property or logos are subject to those third-party's policies.
This project may contain trademarks or logos for projects, products, or services. Use of Black Forest Labs and FLUX trademarks or logos in modified versions of this project must not cause confusion or imply sponsorship or endorsement. Any use of third-party trademarks, intellectual property or logos are subject to those third-party's policies.