Skip to content

FLUX

Jump to bottom
Vladimir Mandic edited this page Aug 31, 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

To use either any variations or quantizations, simply select it from Networks -> Reference
and model will be auto-downloaded on first use

note: Do not download base model manually!

Tip

Set appropriate offloading setting before loading the model to avoid out-of-memory errors

Notes

  • FLUX.1 is based on Flow-matching scheduling, only supported sampler is Euler Flow Match (Default)
    Setting any other sampler will be ignored
  • Use of FLUX.1 LoRAs is included with limited support - not all LoRAs are supported with more variations coming soon
  • FLUX.1 VAE does not support FP16, it is recommended to use BF16 if you have a compatible GPU
    Otherwise, VAE will be upcast to FP32 which takes more memory and time
  • 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

Offloading

FLUX.1 is a massive model at ~32GB and as such it is recommended to use offloading
To set offloading, see Settings -> Diffusers -> Model offload mode:

  • Recommended for high VRAM GPUs: Balanced
    Faster but requires compatible GPU and sufficient VRAM
  • Recommended for low VRAM GPUs: Sequential
    Much slower but allows FLUX.1 to run on GPUs with 6GB VRAM

Quantization

Quantization can significantly reduce memory requirements, but it can also slightly reduce quality of outputs
Also, different quantization options are very platform and GPU dependent and are not supported on all platforms

  • qint8 and qint8 quantization require optimum-quanto which will be auto-installed on first use
    note: qint quantization requires torch==2.4.0
    note: is not compatible with balanced offload
  • nf4 quantization requires bitsandbytes which will be auto-installed on first use
    note: bitsandbytes package is not compatible with all platforms and gpus

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 *3
qint8 15.42 1.30 it/s 18.85 GB none
qint8 balanced *4
qint8 sequential *5
qint4 18.37 1.09 it/s 11.38 GB none
qint4 balanced *4
qint4 sequential *5

Notes:

  • *1: memory usage exceeeds 32GB and is not recommended
  • *2: balanced offload VRAM usage is not included since it depends on desired threshold
  • *3: nf4 quantization is not compatible with sequential offload

    Blockwise quantization only supports 16/32-bit floats, but got torch.uint8

  • *4: qint quantization is not compatible with balanced offload

    TypeError: QBytesTensor.new() missing 5 required positional arguments

  • *5: qint quantization is not compatible with sequential offload

    RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!

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 singnificantly 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!

Single-file Safetensors

Loading of single-file safetensors is experimental:

  • Supported for transformer (otherwise known as UNet) part of the FLUX.1 model only!
  • Safetensors that contain full model with VAE and text-encoder are not supported at the moment and will be added in the future
  • Safetensors in pre-quantized format are not supported at the moment and will be added in the future

To load a Unet safetensors file:

  1. Download safetensors 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

Tip

For convience, you can add that setting to your quicksettings by adding Settings -> User Interface -> Quicksettings list -> sd_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

If you want to frequently switch between text encoders, you can add that setting to quicksettings by adding Settings -> User Interface -> Quicksettings list -> sd_text_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

If you want to frequently switch between text encoders, you can add that setting to quicksettings by adding Settings -> User Interface -> Quicksettings list -> sd_vae

Example quicksettings

image

ToDo / Future

Additional core support will be added in diffusers==0.31 and subsequently included in SD.Next:

© SD.Next

Clone this wiki locally