161 lines
7.8 KiB
Markdown
Executable File
161 lines
7.8 KiB
Markdown
Executable File
# FLUX.2
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**Frontier Visual Intelligence** — State-of-the-art image generation and editing from [Black Forest Labs](https://bfl.ai).
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---
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<p align="center">
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<a href="https://docs.bfl.ai">API Docs</a> •
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<a href="https://huggingface.co/black-forest-labs">Hugging Face</a> •
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<a href="https://bfl.ai/blog">Blog</a>
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</p>
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This repo contains minimal inference code to run image generation & editing with our FLUX.2 open-weight models.
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## News
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- **[15.01.2026]** Today, we release the FLUX.2 [klein] family of models, our fastest models yet. Sub-second generation on consumer GPUs. Read more about it in our [blog post](https://bfl.ai/blog/flux2-klein-towards-interactive-visual-intelligence).
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- **[25.11.2025]** We are releasing FLUX.2 [dev], a 32B parameter model for text-to-image generation, and image editing (single reference image and multiple reference images).
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## Model Overview
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| Name | Step-distilled | Guidance-distilled | Text-to-Image | Image Editing (Single reference) | Image Editing (Multi-reference) | License |
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| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
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| [FLUX.2 [klein] 4B](https://huggingface.co/black-forest-labs/FLUX.2-klein-4B) | ✅ | ✅ | ✅ | ✅ | ✅ | [apache-2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) |
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| [FLUX.2 [klein] 9B](https://huggingface.co/black-forest-labs/FLUX.2-klein-9B) | ✅ | ✅ | ✅ | ✅ | ✅ | [FLUX Non-Commercial License](model_licenses/LICENSE-FLUX-NON-COMMERICAL) |
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| [FLUX.2 [klein] 4B Base](https://huggingface.co/black-forest-labs/FLUX.2-klein-base-4B) | ❌ | ❌ | ✅ | ✅ | ✅ | [apache-2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) |
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| [FLUX.2 [klein] 9B Base](https://huggingface.co/black-forest-labs/FLUX.2-klein-base-9B) | ❌ | ❌ | ✅ | ✅ | ✅ | [FLUX Non-Commercial License](model_licenses/LICENSE-FLUX-NON-COMMERICAL) |
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| [FLUX.2 [dev]](https://huggingface.co/black-forest-labs/FLUX.2-dev) | ❌ | ✅ | ✅ | ✅ | ✅ | [FLUX.2-dev Non-Commercial License](model_licenses/LICENSE-FLUX-DEV) |
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**All models support**: Text-to-Image ✅ | Single-ref Editing ✅ | Multi-ref Editing ✅
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## Which Model Should I Use?
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| Need | Recommended |
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|------|-------------|
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| Real-time apps, interactive workflows | [klein] 4B or 9B (distilled) |
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| Consumer GPU (e.g. RTX 3090/4070) | [klein] 4B |
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| Fine-tuning, LoRA training | [klein] Base or FLUX.2 [dev] |
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| Maximum quality, no latency constraints | FLUX.2 [dev] |
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## `FLUX.2 [klein]`
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FLUX.2 [klein] is our fastest model family — generating and editing (multiple) images in under a second without sacrificing quality. Built for real-time applications, creative iteration, and deployment on consumer hardware.
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### Key Capabilities
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- **Sub-second inference** — Generate or edit images under a second on modern hardware
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- **Unified generation & editing** — Text-to-image, image editing, and multi-reference in one model
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- **Runs on consumer GPUs** — Klein 4B fits in ~8GB VRAM (RTX 3090/4070 and up)
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- **Apache 2.0 on 4B** — Open-source, fine-tuning, and customization
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### Performance
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Klein models define the Pareto frontier for quality vs. latency and VRAM across text-to-image, single-reference editing, and multi-reference generation:
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<p align="center">
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<img src="assets/klein_benchmark.jpg" alt="FLUX.2 [klein] vs Baselines — Elo vs Latency and VRAM" width="800"/>
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</p>
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<sub>Higher Elo + Lower Latency/VRAM = Better.</sub>
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### The Klein Family
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| Model | Best For |
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|:---|:---|
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| **[klein] 4B** | Maximum speed, consumer hardware, edge deployment |
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| **[klein] 9B** | Best quality-to-latency ratio, production apps |
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| **[klein] 4B Base** | Fine-tuning on limited hardware, full customization |
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| **[klein] 9B Base** | Research, LoRA training, maximum output diversity |
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**Distilled vs Base:**
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- Use **Distilled** (4-step) for production apps and real-time generation
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- Use **Base** (50-step) for fine-tuning, LoRA training, and maximum flexibility
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**Licensing:** 4B models are [Apache 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md). 9B models use the [FLUX.2-dev Non-Commercial License](model_licenses/LICENSE-FLUX-DEV).
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### Text-to-image examples
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Example focused on realism
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Example focused on output diversity
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### Editing examples
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## `FLUX.2 [dev]`
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`FLUX.2 [dev]` is a 32B parameter flow matching transformer model capable of generating and editing (multiple) images. The model is released under the [FLUX.2-dev Non-Commercial License](model_licenses/LICENSE-FLUX-DEV) and can be found [here](https://huggingface.co/black-forest-labs/FLUX.2-dev).
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Note that the below script for `FLUX.2 [dev]` needs considerable amount of VRAM (H100-equivalent GPU). We partnered with Hugging Face to make quantized versions that run on consumer hardware; below you can find instructions on how to run it on a RTX 4090 with a remote text encoder, for other quantization sizes and combinations, check the [diffusers quantization guide here](docs/flux2_dev_hf.md).
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### Text-to-image examples
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### Editing examples
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### Prompt upsampling
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`FLUX.2 [dev]` benefits significantly from prompt upsampling. The inference script below offers the option to use both local prompt upsampling with the same model we use for text encoding ([`Mistral-Small-3.2-24B-Instruct-2506`](https://huggingface.co/mistralai/Mistral-Small-3.2-24B-Instruct-2506)), or alternatively, use any model on [OpenRouter](https://openrouter.ai/) via an API call.
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See the [upsampling guide](docs/flux2_with_prompt_upsampling.md) for additional details and guidance on when to use upsampling.
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## `FLUX.2` autoencoder
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The FLUX.2 autoencoder has considerably improved over the [FLUX.1 autoencoder](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/ae.safetensors). The autoencoder is released under [Apache 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) and can be found [here](https://huggingface.co/black-forest-labs/FLUX.2-dev/blob/main/ae.safetensors). For more information, see our [technical blogpost](https://bfl.ai/research/representation-comparison).
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## Local installation
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The inference code was tested on GB200 using CUDA 12.9 and Python 3.12.
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```bash
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python3.12 -m venv .venv
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source .venv/bin/activate
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pip install -e . --extra-index-url https://download.pytorch.org/whl/cu129 --no-cache-dir
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```
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## Run the CLI
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Before running the CLI, you may download the weights from [here](https://huggingface.co/black-forest-labs/FLUX.2-dev) and set the following environment variables.
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```bash
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export FLUX2_MODEL_PATH="<flux2_path>"
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export AE_MODEL_PATH="<ae_path>"
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export KLEIN_4B_MODEL_PATH="<klein_4b_path>"
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export KLEIN_4B_BASE_MODEL_PATH="<klein_4b_base_path>"
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export KLEIN_9B_MODEL_PATH="<klein_9b_path>"
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export KLEIN_9B_BASE_MODEL_PATH="<klein_9b_base_path>"
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```
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If you don't set the environment variables, the weights will be downloaded automatically.
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You can start an interactive session to do both text to image generation as well as editing (one or multiple) images with the following command:
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```bash
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PYTHONPATH=src python scripts/cli.py
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```
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## Watermarking
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We've added an option to embed invisible watermarks directly into the generated images
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via the [invisible watermark library](https://github.com/ShieldMnt/invisible-watermark).
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Additionally, we are recommending implementing a solution to mark the metadata of your outputs, such as [C2PA](https://c2pa.org/)
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## Citation
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If you find the provided code or models useful for your research, consider citing them as:
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```bib
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@misc{flux-2-2025,
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author={Black Forest Labs},
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title={{FLUX.2: Frontier Visual Intelligence}},
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year={2025},
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howpublished={\url{https://bfl.ai/blog/flux-2}},
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}
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```
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