FLUX.3 Klein: Black Forest Labs releases 5.5B image model on Hugging Face
Black Forest Labs has released the model flux-3-image-klein on Hugging Face. It has 5.5 billion parameters and is meant for image generation and image editing. The Model Card is still empty, so details are unclear.
What this is about
Black Forest Labs, the company behind the FLUX image models, released the model flux-3-image-klein on Hugging Face on October 13, 2026. According to the metadata, it has 5.5 billion parameters (parameters are, roughly speaking, the dials a model tunes during training) and works with the diffusers library, a widely used Python toolkit for image models. The task is listed as image-to-image, and the tags mention image generation, image editing and FLUX.3.
The Model Card is empty in the source we have. Anything beyond the metadata is therefore unconfirmed.
What the source tells us
- Name and size: "Klein" (German for "small") hints at a smaller variant of the FLUX.3 family. That's a guess from the name, and the source doesn't confirm it. At 5.5 billion parameters, the model is fairly compact for an image model.
- Task: The image-to-image pipeline and the image-editing tag suggest the model changes existing images based on an instruction. Whether it also generates images from text alone isn't clear from the source.
- Language: English is listed as the language, so prompts should probably be written in English.
- License: Hugging Face shows "other". The terms, for example for commercial use, aren't in the data. Earlier FLUX releases came with different licenses. Check the license file in the repository.
- Interest: Over 102,000 downloads and 633 likes show strong community interest.
- Formats: The weights are available as safetensors. No GGUF version is listed.
What's missing
There are no benchmarks, no details on training data, resolution, speed, memory requirements or architecture, and no sample images. It's also unclear whether the model is gated, meaning you have to accept terms before downloading.
What you need to try it
There are no official hardware specs. As a rough estimate, 5.5 billion parameters at 4-bit quantization (numbers are stored with fewer bits, which saves memory) need about 3 to 5 GB of VRAM (video memory). At full 16-bit precision, the model alone is around 11 GB, and the text encoder and image decoder come on top. The source doesn't describe those. A graphics card with 12 to 16 GB of VRAM is probably realistic. That's an estimate, not a maker's figure. Whether it runs on a Mac, CPU or Google Colab isn't confirmed, though diffusers generally supports several platforms.
Our take
For local users, a 5.5-billion-parameter model from a well-known vendor is interesting because it could run on consumer hardware. Without a Model Card, examples and benchmarks, there's no way to judge how good it is or how it stacks up against other image models. The license also needs clarifying before you use it commercially. Your best bet is to wait for an updated Model Card or an announcement from the maker.
Sources
- Hugging Face Trending huggingface.co
- Hugging Face Trending huggingface.co
- Google News (GB) news.google.com
- Google News (ES) news.google.com
- Google News (IT) news.google.com
- Google News (TW) news.google.com
- r/StableDiffusion x.com
Try it yourself: FLUX.3 Image Klein
Explained step by step, no prior knowledge needed – on your own computer or free in your browser.
What can I do with it?
According to the metadata, the model is for image generation and image editing. You could load an image and have it changed with an English instruction. Exact capabilities aren't confirmed, since there's no Model Card.
Who is it worth it for?
Interesting for tinkerers with a graphics card who want to test new image models. If you need reliable quality figures or clear license terms, it's better to wait for the Model Card.
What your computer needs
Only one variant, with weights in safetensors format, is known. The source doesn't mention quantized versions. As a rule of thumb, roughly 12 to 16 GB of VRAM should be enough for 16-bit, and less with quantization.
- Maker specs are missing, so these values are estimates.
- 5.5B parameters come to about 11 GB at 16-bit for the weights alone.
- With 4-bit quantization, about 3 to 5 GB of VRAM is conceivable, if such versions appear.
- According to the source, no GGUF version is available.
Gated access: You need a free Hugging Face account. Log in on the model page and accept the license. Then create an access token (type Read) at huggingface.co/settings/tokens and run hf auth login once before you start.
How do I find out what my computer has?
- Windows: Press Ctrl + Shift + Esc → Task Manager → Performance tab → click GPU on the left. At the bottom you'll see Dedicated GPU memory (that's your VRAM). You'll find system memory under Memory.
- Mac: Apple menu at the top left → About This Mac. You'll see your chip (e.g. M2) and memory (e.g. 16 GB). On Apple chips, graphics and processor share this memory.
- Linux: Type
nvidia-smiin the terminal (shows your graphics card and its memory) andfree -hfor system memory.
Guide
Google Colab (in your browser, no installation)
Google Colab is a free service from Google. It gives you a computer with a graphics card in your browser for a few hours. All you need is a Google account. For this model: Colab Pro only – Free Colab has only 12.7 GB of RAM – not enough for this model.
- Download the notebook. Click the button and save the file.
- Open Colab. Go to colab.research.google.com and sign in with your Google account.
- Upload the notebook. In the window that appears, click Upload on the left (or choose File → Upload notebook at the top) and select the file
colab.ipynb. - Turn on the GPU. In the top menu, go to Runtime → Change runtime type → under Hardware accelerator, select T4 GPU (with Colab Pro: L4 or A100) → Save.
- Start everything. Runtime → Run all (keyboard shortcut Ctrl + F9). If you see the warning “This notebook was not authored by Google,” click Run anyway.
- Wait. The cells run one after another. A green checkmark appears to the left of each cell when it's done. Installation and download take about 15 minutes here.
- Try it out. The result appears below the last cell. To test something else, change the text in the cell and run it again with Shift + Enter.
- Clean up. When you're done: Runtime → Disconnect and delete runtime. This saves your free quota.
Good to know: The free GPU is limited (usually a few hours per day). After about 90 minutes of inactivity, Colab disconnects, and you'll have to start everything over.
Windows 10 / 11
This model runs on Python. Our script handles the setup automatically: it creates an isolated Python environment, installs the required packages (PyTorch, approx. 3 GB) and downloads the model (32 GB (about 43 min at 100 Mbit/s)).
- Update your graphics driver (NVIDIA): install the latest driver. Without an NVIDIA card, it runs on the CPU – much more slowly.
- Install Python. Download Python 3.12 from python.org and run the installer. Important: At the bottom of the first window, tick Add python.exe to PATH, then click Install Now.
- Create a folder and download the files. Create a folder, e.g.
Documents\AI-Test, and save both files into it: - Open a terminal in the folder. Open the folder in File Explorer, right-click an empty spot → Open in Terminal.
- Run it. Paste this command and press Enter:
Installation and download are running now. The first time, this can take 10–30 minutes.powershell -ExecutionPolicy Bypass -File try_windows.ps1 - Result. The folder now contains the generated image
output.png. - Try your own input: Open
demo.pyin an editor, change the sample text, and save. Then run this in the terminal:.\.venv-flux-3-image-klein\Scripts\Activate.ps1 python demo.py
macOS
Macs with an Apple chip (M1 to M5) are ideal. Older Intel Macs are very slow.
- Open Terminal. Press Cmd + Space, type Terminal, and press Enter.
- Install the developer tools (first time only; includes Python):
Click Install in the window. If you see "already installed," everything is already in place.xcode-select --install - Download the files – both end up in your Downloads folder:
- Run it:
The first time, installation and download take 10–30 minutes.cd ~/Downloads bash try_mac.sh - Result. The folder now contains the generated image
output.png.
Linux
- Check your GPU.
nvidia-smishould show your card. If it doesn't, install the driver on Ubuntu and restart:sudo ubuntu-drivers install - Install the Python basics (Ubuntu/Debian):
sudo apt update && sudo apt install -y python3-venv python3-pip git - Put the files in a folder and open a terminal there:
- Run it:
bash try_linux.sh - Result. The folder now contains the generated image
output.png.
Try it
Make the sky a warm sunset with orange and pink clouds.Replace the background with a snowy mountain landscape.Turn this photo into a watercolor painting.If something goes wrong
The Model Card is empty and there are no usage examples. Look through the repository files.
The license is "other". Read the license file before using the model commercially.
You may have to accept terms and log in on Hugging Face to download the weights.
You need a recent version of diffusers, possibly straight from GitHub.
“out of memory,” a crash, or your computer freezes
The model is too big for your VRAM (video memory). Pick a smaller variant from the table above and close other programs, especially games and browsers with lots of tabs.
It runs, but extremely slowly
Your CPU is probably doing the work instead of your graphics card. In Ollama, the PROCESSOR column of ollama ps shows how much runs on the GPU. If it says "100% CPU", update your graphics driver or pick a smaller variant that fits entirely into VRAM (video memory).
“ollama” or “python” is not found
Close the terminal window and open it again so it recognizes the newly installed program. For Python on Windows: reinstall it and check Add python.exe to PATH.
Windows: "Running scripts is disabled on this system"
Run the script with exactly the command above: powershell -ExecutionPolicy Bypass -File try_windows.ps1. This allows execution for this one time only.
Error message when loading the model (version, "trust_remote_code", unknown architecture)
Brand-new models often need the latest software version. With Ollama: update Ollama. With Python: pip install -U transformers in the activated environment. The Model Card usually lists the exact version.
Tokenstrom generates the scripts and notebook automatically. Please take a quick look before running them – they're plain, readable text. Mind the model's license: custom license (see Model Card).
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