FLUX.2

FLUX.2 is Black Forest Labs’ second-generation rectified-flow transformer family. It uses a 32-channel VAE and a large language model as its text encoder. InvokeAI supports these variants and detects them on install:

Variant	Text encoder	Notes
Klein 4B	Qwen3 4B	Distilled for 4 steps
Klein 9B	Qwen3 8B	Distilled for 4 steps
Klein 4B Base / Klein 9B Base	Qwen3 4B / Qwen3 8B	Undistilled foundation models; 28 steps
FLUX.2 [dev]	Mistral Small 3.1	32B guidance-distilled transformer; 28 steps; non-commercial license

Every variant can take reference images for image editing (see Reference images).

Klein vs. Klein Base

A Klein model and its Base model have identical architectures, so InvokeAI tells them apart by the file or repository name: a name containing “base” (and not “distilled”) is identified as Base. If a model was identified wrongly, change its Variant in the model’s edit form in the Model Manager. It matters: Base models need 28 steps, distilled ones 4.

License

Model	License	Commercial use	Gated
FLUX.2 Klein 4B	Apache 2.0	Allowed	No
FLUX.2 Klein 9B	FLUX Non-Commercial License	The weights may not be used commercially; generated images may, except to train a competing model	Yes, on Black Forest Labs’ repositories
FLUX.2 [dev]	FLUX [dev] Non-Commercial License	As for Klein 9B	Yes, on Black Forest Labs’ repositories

The Qwen3 and Mistral text encoders are Apache 2.0. Some repackaged copies show a different license in their metadata; the original model’s license is the one that applies.

This is a summary, not legal advice. Read the full license on the model’s HuggingFace page before using a model commercially.

Hardware

See the System Requirements table:

• Klein 4B — 12 GB VRAM and 16 GB RAM at 1024×1024; the FP8 build works with 8 GB+. The GGUF Q4 build runs on 6–8 GB.
• Klein 9B — 12 GB VRAM and 32 GB RAM. The int8 build is 9.5 GB on any supported GPU. The FP8 build stays that small only on an RTX 40-series (or newer) card with fp8_compute enabled; otherwise it expands to about 18 GB. The Qwen3 8B encoder is about 15 GB and streams on a 12 GB card.
• FLUX.2 [dev] — the full Diffusers pipeline is about 80 GB. The NF4 Diffusers build runs on about 18 GB of VRAM with offloading, and the GGUF Q3_K_M transformer fits about 12 GB with offloading.

See also FP8 Storage and SDNQ quantization.

Installing

The FLUX.2 Klein bundle in the Model Manager is the quickest start. It installs FLUX.2 Klein 4B (GGUF Q4) together with the FLUX.2 VAE and the Qwen3 4B encoder.

A FLUX.2 model needs three components:

Component	Diffusers install	Single-file / GGUF / FP8 / int8 install
Transformer	bundled	the checkpoint itself
VAE (FLUX.2)	bundled	installed separately
Text encoder	bundled	Klein 4B: Qwen3 4B · Klein 9B: Qwen3 8B · [dev]: Mistral

Starter models that are not Diffusers pipelines install the VAE and the matching encoder as dependencies. Pick them in the Components section next to the model. If you have a FLUX.2 Diffusers model of the matching variant installed, InvokeAI uses its encoder and VAE automatically, so you don’t need standalone ones.

Klein starter models

Starter model	Format	Size
FLUX.2 Klein 4B (Diffusers) / 9B (Diffusers)	Diffusers pipeline	~16 GB / ~35 GB
FLUX.2 Klein 4B	bf16 single file	~8 GB
FLUX.2 Klein 4B (FP8) / 9B (FP8)	FP8 single file	~4 GB / ~9.5 GB
FLUX.2 Klein 9B (int8)	int8 single file (community repack)	~9.5 GB, same size in memory on every supported GPU
FLUX.2 Klein 4B (GGUF Q4 / Q8)	GGUF	~2.6 GB / ~4.3 GB
FLUX.2 Klein 9B (GGUF Q4 / Q8)	GGUF	~5.8 GB / ~10 GB
FLUX.2 Klein 4B (SDNQ dynamic 4-bit) / 9B (SDNQ dynamic 4-bit + SVD)	SDNQ Diffusers pipeline	~5 GB / ~13 GB, self-contained

Standalone Qwen3 encoders are available as starter models too: FLUX.2 Klein Qwen3 4B Encoder (~8 GB) and its FP4 mixed build (~3.6 GB download), and FLUX.2 Klein Qwen3 8B Encoder (~16 GB) with FP4 mixed (~6.3 GB download) and int8 (~9.4 GB) builds. A 4B encoder only works with Klein 4B models, an 8B encoder only with Klein 9B.

When you install an FP8 checkpoint, FP8 Storage is switched on for it automatically, which keeps the file’s fp8 weights instead of expanding them.

FLUX.2 [dev] starter models

Starter model	Format	Size
FLUX.2 [dev] (Diffusers)	Diffusers pipeline	~80 GB
FLUX.2 [dev] (Diffusers, NF4)	NF4-quantized transformer and text encoder	~18 GB VRAM with offload
FLUX.2 [dev] Transformer (GGUF Q3_K_M / Q4_K_M / Q5_K_M / Q6_K / Q8_0)	GGUF	~15.9 / 20 / 24 / 27.9 / 35.5 GB

The GGUF transformers need a separate Mistral encoder. Q3_K_M and Q4_K_M install the GGUF Q4 encoder, the larger quants the GGUF Q8 encoder. Other encoder starters are the Comfy-Org builds — FP8 (~18 GB, best quality for its size), BF16 (~35.6 GB) and FP4 mixed (~12.3 GB download) — and a GGUF IQ4_XS build (~11.1 GB).

License and access

FLUX.2 [dev] is released under a non-commercial license. Some Black Forest Labs repositories are gated on HuggingFace: if a download asks for an access token, accept the license on the model’s HuggingFace page and add your HuggingFace token under API Keys in the Model Manager.

Generation settings

Selecting a FLUX.2 model applies these defaults:

Variant	Steps	Guidance	Scheduler	Size
Klein 4B / 9B	4	—	Euler	1024×1024
Klein 4B Base / 9B Base	28	—	Euler	1024×1024
[dev]	28	3.5	Euler	1024×1024

• Guidance — the slider next to Steps sets FLUX.2 [dev]‘s distilled guidance (0–20). It has no effect on Klein models.
• Scheduler — Euler, Heun (2nd order; better quality at about twice the time per step) or LCM (for few steps).
• Size — width and height must be multiples of 16.
• Negative prompt — not available in the Generate tab or on the Canvas.

Text-to-image, image-to-image, inpainting and outpainting all work on the Canvas. FLUX.2 has no ControlNet or IP-Adapter support.

Reference images

Every FLUX.2 model can edit or combine images from up to five reference images. Add them on the Generate tab or the Canvas and describe the result you want in the prompt. No extra model is needed.

In the workflow editor, wrap each image in a Kontext Conditioning - FLUX node and connect it (or a collection of them) to the Reference Images input of FLUX2 Denoise.

Regional guidance

Regional guidance layers with a prompt are supported on the Canvas. Regional reference images are not. Regional prompts are ignored when reference images are attached; InvokeAI logs a warning when that happens.

LoRA

FLUX.2 LoRAs are labelled with the variant they were trained for, and the Generate tab only offers LoRAs that match the selected model’s variant. LoRAs patch the transformer and, where they include text-encoder layers, the Qwen3 or Mistral encoder. In workflows use Apply LoRA - Flux2 Klein or Apply LoRA - FLUX.2 [dev] (and their Collection versions).

PiD super-resolution decode

FLUX.2 Klein latents can be decoded with a PiD decoder for a 4× super-resolved image. Install PiD Decoder FLUX.2 (2K) or PiD Decoder FLUX.2 (2K to 4K); see PiD Super-Resolution Decode.

Workflow nodes

Node	Purpose
Main Model - Flux2 Klein / Main Model - FLUX.2 [dev]	Loads the transformer, encoder and VAE
Prompt - Flux2 Klein / Prompt - FLUX.2 [dev]	Encodes the prompt
FLUX2 Denoise	Runs the diffusion; takes the VAE and optional reference images
Latents to Image - FLUX2 / Image to Latents - FLUX2	VAE decode / encode

FLUX2 Denoise also has a CFG Scale and a negative conditioning input, which the Generate tab leaves off (CFG 1.0).