Text To Image

Open-weight models

We currently offer two open-weight text-to-image models.

Open-weights usage

The weights will be downloaded automatically to checkpoints/ from HuggingFace once you start one of the demos. Alternatively, you may download the weights manually and put them in checkpoints/, or you can also manually link them with the following environment variables:

export FLUX_MODEL=<your model path here>
export FLUX_AE=<your autoencoder path here>

For interactive sampling run

python -m flux t2i --name <name> --loop

where name is one of flux-dev or flux-schnell. Or to generate a single sample run

python -m flux t2i --name <name> \
  --height <height> --width <width> \
  --prompt "<prompt>"

TRT engine infernece

We provide exports in BF16, FP8, and FP4 precision. Note that you need to install the repository with TensorRT support as outlined here.

python -m flux t2i --name=<name> --loop --trt --trt_transformer_precision <precision>

where <trt_transformer_precision> is either bf16, fp8, or fp4. For ONNX exports, height and width have to be within 768 and 1344.

Streamlit and Gradio

We also provide a streamlit demo that does both text-to-image and image-to-image. The demo can be run via

streamlit run demo_st.py

We also offer a Gradio-based demo for an interactive experience. To run the Gradio demo:

python demo_gr.py --name flux-schnell --device cuda

Options:

• --name: Choose the model to use (options: "flux-schnell", "flux-dev")
• --device: Specify the device to use (default: "cuda" if available, otherwise "cpu")
• --offload: Offload model to CPU when not in use
• --share: Create a public link to your demo

To run the demo with the dev model and create a public link:

python demo_gr.py --name flux-dev --share

Diffusers integration

FLUX.1 [schnell] and FLUX.1 [dev] are integrated with the 🧨 diffusers library. To use it with diffusers, install it:

pip install git+https://github.com/huggingface/diffusers.git

Then you can use FluxPipeline to run the model

import torch
from diffusers import FluxPipeline

model_id = "black-forest-labs/FLUX.1-schnell" #you can also use `black-forest-labs/FLUX.1-dev`

pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power

prompt = "A cat holding a sign that says hello world"
seed = 42
image = pipe(
    prompt,
    output_type="pil",
    num_inference_steps=4, #use a larger number if you are using [dev]
    generator=torch.Generator("cpu").manual_seed(seed)
).images[0]
image.save("flux-schnell.png")

To learn more check out the diffusers documentation