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Vladimir Mandic edited this page Oct 29, 2024 · 33 revisions

Black Forest Labs FLUX.1

FLUX.1 family consists of 3 variations:

  • Pro
    Model weights are NOT released, model is available only via Black Forest Labs
  • Dev
    Open-weight, guidance-distilled from Pro variation, available for non-commercial applications
  • Schnell
    Open-weight, timestep-distilled from Dev variation, available under Apache2.0 license

Additionally SD.Next includes pre-quantized variations of FLUX.1 Dev variation: qint8, qint4 and nf4
Pick variant that uses less memory as model in original form has very high requirements

[!IMPORTANT] Allow gated access This is a gated model, you need to accept the terms and conditions to use it
For more information see Gated Access Wiki

[!IMPORTANT] Set offloading Set appropriate offloading setting before loading the model to avoid out-of-memory errors
For more information see Offloading Wiki

[!IMPORTANT] Choose quantization Check compatibility of different quantizations with your platform and GPU!
For more information see Quantization Wiki

[!TIP] Use reference models Use of reference models is recommended over manually downloaded models!
Simply select it from Networks -> Models -> Reference
and model will be auto-downloaded on first use

Components

FLUX.1 model consists of:

When using reference models, all components will be loaded as needed.
If using manually downloaded model, you need to ensure that all components are correctly configured and available.
Note that majority of available downloads are not actually all-in-one models and are instead just a part of the full model with individual components.

Tip

For convience, you can add setting that allow quick replacements of model components
to your quicksettings by adding
Settings -> User Interface -> Quicksettings list -> sd_model_checkpoint, sd_unet, sd_vae, sd_text_encoder

image

Fine-tunes

Diffusers

There are already many FLUX.1 unofficial variations available
Any Diffuser-based variation can be downloaded and loaded into SD.Next using Models -> Huggingface -> Download
For example, interesting variation is a merge of Dev and Schnell variations by sayakpaul: sayakpaul/FLUX.1-merged

LoRAs

SD.Next includes support for FLUX.1 LoRAs

Since LoRA keys vary significantly between tools used to train LoRA as well as LoRA types,
support for additional LoRAs will be added as needed - please report any non-functional LoRAs!

Also note that compatibility of LoRA depends on the quantization type! If you have issues loading LoRA, try switching your FLUX.1 base model to different quantization type

All-in-one

Typical all-in-one safetensors file is over 20GB in size and contains full model with transformer, both text-encoders and VAE
Since text encoders and VAE are same between all FLUX.1 models, using all-in-one safetensors is not recommended due to large duplication of data

Unet/Transformer

Unet/Transformer component of FLUX.1 is a typical model fine-tune and is around 11GB in size

To load a Unet/Transformer safetensors file:

  1. Download safetensors or gguf file from desired source and place it in models/UNET folder
    example: FastFlux Unchained
  2. Load FLUX.1 model as usual and then
  3. Replace transformer with one in desired safetensors file using:
    Settings -> Execution & Models -> UNet

Text Encoder

SD.Next allows changing optional text encoder on-the-fly

Go to Settings -> Models -> Text encoder and select the desired text encoder
T5 enhances text rendering and some details, but its otherwise very lightly used and optional
Loading lighter T5 will greatly decrease model resource usage, but may not be compatible with all offloading modes

Tip

To use prompt attention syntax with FLUX.1, set
Settings -> Execution -> Prompt attention to xhinker

Example image with different encoder quantization options
flux-encoder

VAE

SD.Next allows changing VAE model used by FLUX.1 on-the-fly
There are no alternative VAE models released, so this setting is mostly for future use

Tip

To enable image previews during generate, set Settings -> Live Preview -> Method to TAESD

To further speed up generation, you can disable "full quality" which triggers use of TAESD instead of full VAE to decode final image

Scheduler

FLUX.1 at the moment supports only Euler FlowMatch scheduler, additional schedulers will be added in the future
Due to specifics of flow-matching methods, number of steps also has strong influence on the image composition, not just on the way how its resolved

Example image at different steps
flux-steps

Additionally, sampler can be tuned with shift parameter which roughly modifies how long does model spend on composition vs actual diffusion

Example image with different sampler shift values flux-shift

ControlNet

Support for all InstantX/Shakker-Labs models including Union-Pro

FLUX.1 ControlNets are large at over 6GB on top of already very large FLUX.1 model
as such, you may need to use offloading:sequential which is not as fast, but uses far less memory

When using union model, you must also select control mode in the control unit

Notes

Performance

Performance and memory usage of different FLUX.1 variations:

dtype time (sec) performance memory offload note
bf16 >32 GB none *1
bf16 50.47 0.40 it/s balanced *2
bf16 94.28 0.21 it/s 1.89 GB sequential
nf4 14.69 1.36 it/s 17.92 GB none
nf4 21.02 0.95 it/s balanced *2
nf4 sequential err
qint8 15.42 1.30 it/s 18.85 GB none
qint8 balanced err
qint8 sequential err
qint4 18.37 1.09 it/s 11.38 GB none
qint4 balanced err
qint4 sequential err

Notes:

  • *1: Memory usage exceeeds 32GB and is not recommended
  • *2: Balanced offload VRAM usage is not included since it depends on desired threshold

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