> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/automodel/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/automodel/_mcp/server.

# Qwen-Image

[Qwen-Image](https://huggingface.co/Qwen/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](https://huggingface.co/Qwen) |

## Available Models

* **Qwen-Image**

## Task

* Text-to-Image (T2I)

## Example HF Models

| Model      | HF ID                                                       |
| ---------- | ----------------------------------------------------------- |
| Qwen-Image | [`Qwen/Qwen-Image`](https://huggingface.co/Qwen/Qwen-Image) |

## Example Recipes

| Recipe                                                                                                                                 | Description                               |
| -------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------- |
| [qwen\_image\_t2i\_flow.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/diffusion/finetune/qwen_image_t2i_flow.yaml) | Fine-tune — Qwen-Image with flow matching |
| [qwen\_image\_t2i\_flow.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/diffusion/pretrain/qwen_image_t2i_flow.yaml) | Pretrain — Qwen-Image with flow matching  |

## Try with NeMo AutoModel

**1. Install** ([full instructions](/get-started/installation)):

```bash
pip install nemo-automodel
```

**2. Clone the repo** to get the example recipes:

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

**3. Run the recipe** from inside the repo:

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

**1. Pull the container** and mount a checkpoint directory:

```bash
docker run --gpus all -it --rm \
  --shm-size=8g \
  -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
  nvcr.io/nvidia/nemo-automodel:26.04.00
```

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

```bash
cd /opt/Automodel
```

**3. Run the recipe**:

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

See the [Installation Guide](/get-started/installation) and [Diffusion Training and Fine-Tuning Guide](/recipes-e2e-examples/diffusion-fine-tuning).

## Fine-Tuning

See the [Diffusion Training and Fine-Tuning Guide](/recipes-e2e-examples/diffusion-fine-tuning).

## Hugging Face Model Cards

* [Qwen/Qwen-Image](https://huggingface.co/Qwen/Qwen-Image)