FLUX.1-dev | NVIDIA NeMo AutoModel

FLUX.1-dev is a 12B parameter text-to-image diffusion transformer from Black Forest Labs, trained with flow matching. It produces high-fidelity images and is designed for non-commercial research and development use.


Task	Text-to-Image
Architecture	DiT (Flow Matching)
Parameters	12B
HF Org	black-forest-labs

Available Models

FLUX.1-dev: 12B parameters

Task

Text-to-Image (T2I)

Example HF Models

Model	HF ID
FLUX.1-dev	`black-forest-labs/FLUX.1-dev`

Example Recipes

Recipe	Description
flux_t2i_flow.yaml	Fine-tune — FLUX.1-dev with flow matching
flux_t2i_flow.yaml	Pretrain — FLUX.1-dev with flow matching

Try with NeMo AutoModel

1. Install (full instructions):

pip install "nemo-automodel[diffusion,diffusion-media]"

2. Clone the repo to get the example recipes:

git clone https://github.com/NVIDIA-NeMo/Automodel.git
cd Automodel

3. Run the recipe from inside the repo:

torchrun --nproc-per-node=8 \
  examples/diffusion/finetune/finetune.py \
  -c examples/diffusion/finetune/flux_t2i_flow.yaml

Run with Docker

1. Pull the container and mount a checkpoint directory:

docker run --gpus all -it --rm \
  --shm-size=8g \
  -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
  nvcr.io/nvidia/nemo-automodel:26.06.00

2. Navigate to the AutoModel directory (where the recipes are):

cd /opt/Automodel

3. Install media dependencies (not installed in the container by default):

uv pip install ".[diffusion-media]"

4. Run the recipe:

torchrun --nproc-per-node=8 \
  examples/diffusion/finetune/finetune.py \
  -c examples/diffusion/finetune/flux_t2i_flow.yaml

See the Installation Guide and Diffusion Fine-Tuning Guide.

Training

See the Diffusion Training and Fine-Tuning Guide and Dataset Preparation.

Hugging Face Model Cards

black-forest-labs/FLUX.1-dev