---
license: other
license_name: qwen-research
license_link: LICENSE
pipeline_tag: text-to-image
tags:
- diffusers
- qwen
- image-generation
- image-editing
- rgba
---
🤖 ModelScope |
🤗 HuggingFace |
đź“‘ Blog |
🖥️ Demo |
🫨 Discord |
đź’¬ WeChat
## Introduction
We are excited to open-source **Qwen-Image-2.1**, a unified text-to-image generation and image editing model in the Qwen family. With just **7B parameters in its visual generation component** (32 Single-Stream DiT layers), Qwen-Image-2.1 balances generation quality, inference efficiency, and versatility.
Four key improvements define this release:
- **Compact and Efficient** — A lightweight architecture with mixed-granularity attention and prefix KV cache reuse delivers strong image quality at low computational cost.
- **Native Transparency, Unified Creation and Editing** — Generate regular or transparent (RGBA) images from text, edit transparent layers, and extract subjects from photographs—all in one model.
- **Versatile Editing** — Support up to **10 reference images**, specify local edits via circles, painted annotations, or separate masks, and preserve identity for people and products.
- **Realistic Textures and Refined Aesthetics** — Improved typography, portrait lighting, and fine details for more visually compelling results.
For more details, see the [GitHub repo](https://github.com/QwenLM/Qwen-Image-2.1) and [Blog](https://qwen.ai/blog?id=qwen-image-2.1).
## Quick Start
### Installation
```bash
pip install torch>=2.4.0
pip install transformers>=5.17
pip install git+https://github.com/huggingface/diffusers
pip install accelerate pillow
```
### Text-to-Image
```python
import torch
from diffusers import QwenImage21Pipeline
pipe = QwenImage21Pipeline.from_pretrained(
"Qwen/Qwen-Image-2.1", torch_dtype=torch.bfloat16
).to("cuda")
image = pipe(
prompt="A neon shop sign that reads \"QWEN IMAGE 2.1\", rainy night, reflections on wet pavement",
width=2048, height=2048,
num_inference_steps=40,
generator=torch.Generator("cuda").manual_seed(42),
).images[0]
image.save("t2i_example.png")
```
### Image Editing
```python
import torch
from PIL import Image
from diffusers import QwenImage21Pipeline
pipe = QwenImage21Pipeline.from_pretrained(
"Qwen/Qwen-Image-2.1", torch_dtype=torch.bfloat16
).to("cuda")
input_image = Image.open("input.png")
image = pipe(
prompt="Change the background to a sunset beach",
image=input_image,
num_inference_steps=40,
generator=torch.Generator("cuda").manual_seed(42),
).images[0]
image.save("edit_example.png")
```
### Transparent Image Generation (RGBA)
Use the recommended prompt format for transparent images:
```python
image = pipe(
prompt="This is an RGBA image with transparency. A cute cartoon dragon sticker. The image has alpha channel and the background is transparent.",
width=2048, height=2048,
num_inference_steps=40,
generator=torch.Generator("cuda").manual_seed(42),
).images[0]
image.save("transparent_example.png")
```
### Supported Aspect Ratios
```python
aspect_ratios = {
"1:1": (2048, 2048),
"4:3": (2400, 1792),
"3:4": (1792, 2400),
"3:2": (2528, 1696),
"2:3": (1696, 2528),
"16:9": (2752, 1536),
"9:16": (1536, 2752),
}
```
### Memory Optimization
```python
pipe = QwenImage21Pipeline.from_pretrained(
"Qwen/Qwen-Image-2.1", torch_dtype=torch.bfloat16
)
pipe.enable_model_cpu_offload()
```
## Showcase
Native transparent image generation
Group photograph generated from six portrait references
Text rendering
## License
This model is licensed under the [Qwen Research License Agreement](./LICENSE).
