FLUX.2 klein base 4B
FLUX.2 klein base 4B is an Apache-2.0 licensed image-to-image model from black-forest-labs. It is a 4 billion parameter rectified flow transformer that unifies image generation and editing in a single compact architecture and supports multi-reference editing. It is an undistilled, full-capacity foundation model intended for flexibility, fine-tuning, LoRA training, research, and custom pipelines. The model card states it can run on consumer hardware with as little as 13GB VRAM and is available via ComfyUI and Diffusers.
What do you want to do?
Choose a task. Get the software, files, settings, and steps to run it.
Create images locally with FLUX.2 Klein
Generate images from text prompts in ComfyUI with the fast distilled or more adjustable base 4B model. Save your results and reuse the visual workflow.
Edit images locally with FLUX.2 Klein
Upload an image, describe the changes you want and save the edited result in ComfyUI. This reference-guided workflow supports a range of visual edits; it does not use an inpainting mask.
Open a guide for its software, hardware requirements, and setup steps.
At a glance
- Parameters
- 3.88B
- Architecture
- unknown
- License
- apache-2.0
- Software
- Diffusers
Will it run on your machine?
Save your machine to see a personalized rating and its reasoning.
Add my hardwareBest for
- Text-to-image generation
- Image editing
- Multi-reference editing
- Fine-tuning and LoRA training
- Research and custom pipelines
Tradeoffs
- Not intended or able to provide factual information.
- Rendered text may be inaccurate or distorted.
- May represent or amplify biases from training data.
- May fail to generate output matching prompts.
- Prompt following is heavily influenced by prompting style.
Ways to run it
Diffusers · See official guide
Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.
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=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")Explore its uses
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