Qwen-Image Technical Report

• URL: http://arxiv.org/abs/2508.02324v1
• Date: Mon, 04 Aug 2025 11:49:20 GMT
• Title: Qwen-Image Technical Report
• Authors: Chenfei Wu, Jiahao Li, Jingren Zhou, Junyang Lin, Kaiyuan Gao, Kun Yan, Sheng-ming Yin, Shuai Bai, Xiao Xu, Yilei Chen, Yuxiang Chen, Zecheng Tang, Zekai Zhang, Zhengyi Wang, An Yang, Bowen Yu, Chen Cheng, Dayiheng Liu, Deqing Li, Hang Zhang, Hao Meng, Hu Wei, Jingyuan Ni, Kai Chen, Kuan Cao, Liang Peng, Lin Qu, Minggang Wu, Peng Wang, Shuting Yu, Tingkun Wen, Wensen Feng, Xiaoxiao Xu, Yi Wang, Yichang Zhang, Yongqiang Zhu, Yujia Wu, Yuxuan Cai, Zenan Liu,
• Abstract summary: We present Qwen-Image, an image generation foundation model that achieves significant advances in complex text rendering and precise image editing.<n>We design a comprehensive data pipeline that includes large-scale data collection, filtering, annotation, synthesis, and balancing.<n>Qwen-Image performs exceptionally well in alphabetic languages such as English, and also achieves remarkable progress on more challenging logographic languages like Chinese.
• Score: 86.46471547116158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Qwen-Image, an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing. To address the challenges of complex text rendering, we design a comprehensive data pipeline that includes large-scale data collection, filtering, annotation, synthesis, and balancing. Moreover, we adopt a progressive training strategy that starts with non-text-to-text rendering, evolves from simple to complex textual inputs, and gradually scales up to paragraph-level descriptions. This curriculum learning approach substantially enhances the model's native text rendering capabilities. As a result, Qwen-Image not only performs exceptionally well in alphabetic languages such as English, but also achieves remarkable progress on more challenging logographic languages like Chinese. To enhance image editing consistency, we introduce an improved multi-task training paradigm that incorporates not only traditional text-to-image (T2I) and text-image-to-image (TI2I) tasks but also image-to-image (I2I) reconstruction, effectively aligning the latent representations between Qwen2.5-VL and MMDiT. Furthermore, we separately feed the original image into Qwen2.5-VL and the VAE encoder to obtain semantic and reconstructive representations, respectively. This dual-encoding mechanism enables the editing module to strike a balance between preserving semantic consistency and maintaining visual fidelity. Qwen-Image achieves state-of-the-art performance, demonstrating its strong capabilities in both image generation and editing across multiple benchmarks.

Related papers

• UniGlyph: Unified Segmentation-Conditioned Diffusion for Precise Visual Text Synthesis [38.658170067715965]
  We propose a segmentation-guided framework that uses pixel-level visual text masks as unified conditional inputs.<n>Our approach achieves state-of-the-art performance on the AnyText benchmark.<n>We also introduce two new benchmarks: GlyphMM-benchmark for testing layout and glyph consistency in complex, and MiniText-benchmark for assessing generation quality in small-scale text regions.
  arXiv  Detail & Related papers  (2025-07-01T17:42:19Z)
• Unified Autoregressive Visual Generation and Understanding with Continuous Tokens [52.21981295470491]
  We present UniFluid, a unified autoregressive framework for joint visual generation and understanding.<n>Our unified autoregressive architecture processes multimodal image and text inputs, generating discrete tokens for text and continuous tokens for image.<n>We find though there is an inherent trade-off between the image generation and understanding task, a carefully tuned training recipe enables them to improve each other.
  arXiv  Detail & Related papers  (2025-03-17T17:58:30Z)
• Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation Model [69.09404597939744]
  Seedream 2.0 is a native Chinese-English bilingual image generation foundation model.<n>It adeptly manages text prompt in both Chinese and English, supporting bilingual image generation and text rendering.<n>It is integrated with a self-developed bilingual large language model as a text encoder, allowing it to learn native knowledge directly from massive data.
  arXiv  Detail & Related papers  (2025-03-10T17:58:33Z)
• Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation [81.45400849638347]
  In-image machine translation (IIMT) aims to translate an image containing texts in source language into an image containing translations in target language. In this paper, we propose an end-to-end IIMT model consisting of four modules. Our model achieves competitive performance compared to cascaded models with only 70.9% of parameters, and significantly outperforms the pixel-level end-to-end IIMT model.
  arXiv  Detail & Related papers  (2024-07-03T08:15:39Z)
• Prompt-Consistency Image Generation (PCIG): A Unified Framework Integrating LLMs, Knowledge Graphs, and Controllable Diffusion Models [20.19571676239579]
  We introduce a novel diffusion-based framework to enhance the alignment of generated images with their corresponding descriptions. Our framework is built upon a comprehensive analysis of inconsistency phenomena, categorizing them based on their manifestation in the image. We then integrate a state-of-the-art controllable image generation model with a visual text generation module to generate an image that is consistent with the original prompt.
  arXiv  Detail & Related papers  (2024-06-24T06:12:16Z)
• SPIRE: Semantic Prompt-Driven Image Restoration [66.26165625929747]
  We develop SPIRE, a Semantic and restoration Prompt-driven Image Restoration framework. Our approach is the first framework that supports fine-level instruction through language-based quantitative specification of the restoration strength. Our experiments demonstrate the superior restoration performance of SPIRE compared to the state of the arts.
  arXiv  Detail & Related papers  (2023-12-18T17:02:30Z)
• Story Visualization by Online Text Augmentation with Context Memory [64.86944645907771]
  We propose a novel memory architecture for the Bi-directional Transformer framework with an online text augmentation. The proposed method significantly outperforms the state of the arts in various metrics including FID, character F1, frame accuracy, BLEU-2/3, and R-precision.
  arXiv  Detail & Related papers  (2023-08-15T05:08:12Z)
• Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation [10.39028769374367]
  We present a new framework that takes text-to-image synthesis to the realm of image-to-image translation. Our method harnesses the power of a pre-trained text-to-image diffusion model to generate a new image that complies with the target text.
  arXiv  Detail & Related papers  (2022-11-22T20:39:18Z)
• Scaling Autoregressive Models for Content-Rich Text-to-Image Generation [95.02406834386814]
  Parti treats text-to-image generation as a sequence-to-sequence modeling problem. Parti uses a Transformer-based image tokenizer, ViT-VQGAN, to encode images as sequences of discrete tokens. PartiPrompts (P2) is a new holistic benchmark of over 1600 English prompts.
  arXiv  Detail & Related papers  (2022-06-22T01:11:29Z)
• ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation [22.47279425592133]
  We propose ERNIE-ViLG, a unified generative pre-training framework for bidirectional image-text generation. For the text-to-image generation process, we propose an end-to-end training method to jointly learn the visual sequence generator and the image reconstructor. We train a 10-billion parameter ERNIE-ViLG model on a large-scale dataset of 145 million (Chinese) image-text pairs.
  arXiv  Detail & Related papers  (2021-12-31T03:53:33Z)
• Text to Image Generation with Semantic-Spatial Aware GAN [41.73685713621705]
  A text to image generation (T2I) model aims to generate photo-realistic images which are semantically consistent with the text descriptions. We propose a novel framework Semantic-Spatial Aware GAN, which is trained in an end-to-end fashion so that the text encoder can exploit better text information.
  arXiv  Detail & Related papers  (2021-04-01T15:48:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.