Qwen-Image#

Qwen-Image is Alibaba Cloud’s text-to-image diffusion transformer. NeMo AutoModel supports Qwen-Image training via its flow-matching pipeline with a dedicated qwen_image adapter, enabling FSDP2 parallelization, multiresolution bucketed dataloading and LoRA-style fine-tuning.

Task

Text-to-Image

Architecture

DiT (Flow Matching)

HF Org

Qwen

Available Models#

  • Qwen-Image

Task#

  • Text-to-Image (T2I)

Example HF Models#

Model

HF ID

Qwen-Image

Qwen/Qwen-Image

Example Recipes#

Recipe

Description

qwen_image_t2i_flow.yaml

Fine-tune — Qwen-Image with flow matching

qwen_image_t2i_flow.yaml

Pretrain — Qwen-Image with flow matching

Try with NeMo AutoModel#

1. Install (full instructions):

pip install nemo-automodel

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/qwen_image_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:26.02.00

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

cd /opt/Automodel

3. Run the recipe:

torchrun --nproc-per-node=8 \
  examples/diffusion/finetune/finetune.py \
  -c examples/diffusion/finetune/qwen_image_t2i_flow.yaml

See the Installation Guide and Diffusion Training and Fine-Tuning Guide.

Fine-Tuning#

See the Diffusion Training and Fine-Tuning Guide.

Hugging Face Model Cards#