theally
about 2 months ago
This model was originally uploaded to HuggingFace / Civitai. If it's yours, please contact us so we can transfer it to your account!
We are thrilled to release Qwen-Image, an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing. Experiments show strong general capabilities in both image generation and editing, with exceptional performance in text rendering, especially for Chinese.
One of its standout capabilities is high-fidelity text rendering across diverse images. Whether it’s alphabetic languages like English or logographic scripts like Chinese, Qwen-Image preserves typographic details, layout coherence, and contextual harmony with stunning accuracy. Text isn’t just overlaid—it’s seamlessly integrated into the visual fabric.
Beyond text, Qwen-Image excels at general image generation with support for a wide range of artistic styles. From photorealistic scenes to impressionist paintings, from anime aesthetics to minimalist design, the model adapts fluidly to creative prompts, making it a versatile tool for artists, designers, and storytellers.
When it comes to image editing, Qwen-Image goes far beyond simple adjustments. It enables advanced operations such as style transfer, object insertion or removal, detail enhancement, text editing within images, and even human pose manipulation—all with intuitive input and coherent output. This level of control brings professional-grade editing within reach of everyday users.
But Qwen-Image doesn’t just create or edit—it understands. It supports a suite of image understanding tasks, including object detection, semantic segmentation, depth and edge (Canny) estimation, novel view synthesis, and super-resolution. These capabilities, while technically distinct, can all be seen as specialized forms of intelligent image editing, powered by deep visual comprehension.
Together, these features make Qwen-Image not just a tool for generating pretty pictures, but a comprehensive foundation model for intelligent visual creation and manipulation—where language, layout, and imagery converge.
License Agreement
Qwen-Image is licensed under Apache 2.0.
Original Text and Models: https://huggingface.co/Qwen/Qwen-Image
What is Qwen-Image?
Qwen-Image is a highly specialized Image generation AI Model of type Safetensors / Checkpoint AI Model created by AI community user theally. Derived from the powerful Stable Diffusion (Qwen) model, Qwen-Image has undergone an extensive fine-tuning process, leveraging the power of a dataset consisting of images generated by other AI models or user-contributed data. This fine-tuning process ensures that Qwen-Image is capable of generating images that are highly relevant to the specific use-cases it was designed for, such as base model, qwen, qwen-image.
With a rating of 0 and over 0 ratings, Qwen-Image is a popular choice among users for generating high-quality images from text prompts.
Can I download Qwen-Image?
Yes! You can download the latest version of Qwen-Image from here.
How to use Qwen-Image?
To use Qwen-Image, download the model checkpoint file and set up an UI for running Stable Diffusion models (for example, AUTOMATIC1111). Then, provide the model with a detailed text prompt to generate an image. Experiment with different prompts and settings to achieve the desired results. If this sounds a bit complicated, check out our initial guide to Stable Diffusion – it might be of help. And if you really want to dive deep into AI image generation and understand how set up AUTOMATIC1111 to use Safetensors / Checkpoint AI Models like Qwen-Image, check out our crash course in AI image generation.
Download (37.2 GB)
Download available on desktop only
You'll need to use a program like A1111 to run this – learn how in our crash course
Popularity
170
~10
Info
Base model: Qwen
Version qwen_2.5_vl_7b_fp8_scaled: 1 File
To download these files, please visit this page from a desktop computer.
About this version: qwen_2.5_vl_7b_fp8_scaled
qwen_2.5_vl_7b_fp8_scaled text encoder
