Qwen-Image-2.1

🤖 <a href="https://modelscope.cn/models/Qwen/Qwen-Image-2.1">ModelScope</a>&nbsp;&nbsp;| &nbsp;&nbsp;🤗 <a href="https://huggingface.co/Qwen/Qwen-Image-2.1">HuggingFace</a>&nbsp;&nbsp;| &nbsp;&nbsp;📑 <a href="https://qwen.ai/blog?id=qwen-image-2.1">Blog</a>&nbsp;&nbsp;| &nbsp;&nbsp;🖥️ <a href="https://huggingface.co/spaces/Qwen/Qwen-Image-2.1">Demo</a>&nbsp;&nbsp;| &nbsp;&nbsp;🫨 <a href="https://discord.gg/BEYSk3pkSu">Discord</a>&nbsp;&nbsp;| &nbsp;&nbsp;💬 <a href="https://huggingface.co/Qwen/Qwen-Image-2.1/blob/main/assets/qr.png">WeChat</a> 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). pip install torch>=2.4.0 pip install transformers>=5.17 pip install git+https://github.com/huggingface/diffusers pip install accelerate pillow 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") 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") Use the recommended prompt format for transparent images: 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") 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), } pipe = QwenImage21Pipeline.from_pretrained( "Qwen/Qwen-Image-2.1", torch_dtype=torch.bfloat16 ) pipe.enable_model_cpu_offload() This model is licensed under the [Qwen Research License Agreement](./LICENSE).