Built upon Ovis-U1, Ovis-Image is a 7B text-to-image model specifically optimized for high-quality text rendering, designed to operate efficiently under stringent computational
constraints.
The overall architecture of Ovis-Image.
🏆 Highlights
Strong text rendering at a compact 7B scale: Ovis-Image is a 7B text-to-image model that delivers text rendering quality comparable to much larger 20B-class systems such as Qwen-Image and competitive with leading closed-source models like GPT4o in text-centric scenarios, while remaining small enough to run on widely accessible hardware.
High fidelity on text-heavy, layout-sensitive prompts: The model excels on prompts that demand tight alignment between linguistic content and rendered typography (e.g., posters, banners, logos, UI mockups, infographics), producing legible, correctly spelled, and semantically consistent text across diverse fonts, sizes, and aspect ratios without compromising overall visual quality.
Efficiency and deployability: With its 7B parameter budget and streamlined architecture, Ovis-Image fits on a single high-end GPU with moderate memory, supports low-latency interactive use, and scales to batch production serving, bringing near–frontier text rendering to applications where tens-of-billions–parameter models are impractical.
✨ Showcase
Here are some examples demonstrating the capabilities of Ovis-Image.
Ovis-Image examples
🛠️ Inference
Inference with Diffusers
First, install the diffusers library with support for Ovis-Image.
Next, use the OvisImagePipeline to generate the image.
python
1import torch
2from diffusers import OvisImagePipeline
34pipe = OvisImagePipeline.from_pretrained("AIDC-AI/Ovis-Image-7B", torch_dtype=torch.bfloat16)5pipe.to("cuda")6prompt ="A creative 3D artistic render where the text \"OVIS-IMAGE\" is written in a bold, expressive handwritten brush style using thick, wet oil paint. The paint is a mix of vibrant rainbow colors (red, blue, yellow) swirling together like toothpaste or impasto art. You can see the ridges of the brush bristles and the glossy, wet texture of the paint. The background is a clean artist's canvas. Dynamic lighting creates soft shadows behind the floating paint strokes. Colorful, expressive, tactile texture, 4k detail."7image = pipe(prompt, negative_prompt="", num_inference_steps=50, guidance_scale=5.0).images[0]8image.save("ovis_image.png")
Inference with Pytorch
Ovis-Image has been tested with Python 3.10, Torch 2.6.0, and Transformers 4.57.1. For a full list of package dependencies, please see requirements.txt.
1python ovis_image/test.py \2 --model_path AIDC-AI/Ovis-Image-7B/ovis_image.safetensors \3 --vae_path AIDC-AI/Ovis-Image-7B/ae.safetensors \4 --ovis_path AIDC-AI/Ovis-Image-7B/Ovis2.5-2B \5 --image_size 1024\6 --denoising_steps 50\7 --cfg_scale 5.0\8 --prompt "A creative 3D artistic render where the text \"OVIS-IMAGE\" is written in a bold, expressive handwritten brush style using thick, wet oil paint. The paint is a mix of vibrant rainbow colors (red, blue, yellow) swirling together like toothpaste or impasto art. You can see the ridges of the brush bristles and the glossy, wet texture of the paint. The background is a clean artist's canvas. Dynamic lighting creates soft shadows behind the floating paint strokes. Colorful, expressive, tactile texture, 4k detail."\
Alternatively, you can try Ovis-Image directly in your browser on
Evaluation of text rendering ability on LongText-Bench.
Model
#Params.
LongText-Bench-EN
LongText-Bench-ZN
Kolors 2.0
-
0.258
0.329
GPT4o
-
0.956
0.619
Seedream 3.0
-
0.896
0.878
OmniGen2
3B+4B
0.561
0.059
Janus-Pro
7B
0.019
0.006
BLIP3-o
7B+1B
0.021
0.018
FLUX.1-dev
11B+12B
0.607
0.005
BAGEL
7B+7B
0.373
0.310
HiDream-I1-Full
11B+17B
0.543
0.024
Qwen-Image
7B+20B
0.943
0.946
Ovis-Image
2B+7B
0.922
0.964
Evaluation of text-to-image generation ability on DPG-Bench.
Model
#Params.
Global
Entity
Attribute
Relation
Other
Overall
Seedream 3.0
-
94.31
92.65
91.36
92.78
88.24
88.27
GPT4o
-
88.89
88.94
89.84
92.63
90.96
85.15
Ovis-U1
2B+1B
82.37
90.08
88.68
93.35
85.20
83.72
OmniGen2
3B+4B
88.81
88.83
90.18
89.37
90.27
83.57
Janus-Pro
7B
86.90
88.90
89.40
89.32
89.48
84.19
BAGEL
7B+7B
88.94
90.37
91.29
90.82
88.67
85.07
HiDream-I1-Full
11B+17B
76.44
90.22
89.48
93.74
91.83
85.89
UniWorld-V1
7B+12B
83.64
88.39
88.44
89.27
87.22
81.38
Qwen-Image
7B+20B
91.32
91.56
92.02
94.31
92.73
88.32
Ovis-Image
2B+7B
82.37
92.38
90.42
93.98
91.20
86.59
Evaluation of text-to-image generation ability on GenEval.
Model
#Params.
Single object
Two object
Counting
Colors
Position
Attribute binding
Overall
Seedream 3.0
-
0.99
0.96
0.91
0.93
0.47
0.80
0.84
GPT4o
-
0.99
0.92
0.85
0.92
0.75
0.61
0.84
Ovis-U1
2B+1B
0.98
0.98
0.90
0.92
0.79
0.75
0.89
OmniGen2
3B+4B
1.00
0.95
0.64
0.88
0.55
0.76
0.80
Janus-Pro
7B
0.99
0.89
0.59
0.90
0.79
0.66
0.80
BAGEL
7B+7B
0.99
0.94
0.81
0.88
0.64
0.63
0.82
HiDream-I1-Full
11B+17B
1.00
0.98
0.79
0.91
0.60
0.72
0.83
UniWorld-V1
7B+12B
0.99
0.93
0.79
0.89
0.49
0.70
0.80
Qwen-Image
7B+20B
0.99
0.92
0.89
0.88
0.76
0.77
0.87
Ovis-Image
2B+7B
1.00
0.97
0.76
0.86
0.67
0.80
0.84
Evaluation of text-to-image generation ability on OneIG-EN.
Model
#Params.
Alignment
Text
Reasoning
Style
Diversity
Overall
Kolors 2.0
-
0.820
0.427
0.262
0.360
0.300
0.434
Imagen4
-
0.857
0.805
0.338
0.377
0.199
0.515
Seedream 3.0
-
0.818
0.865
0.275
0.413
0.277
0.530
GPT4o
-
0.851
0.857
0.345
0.462
0.151
0.533
Ovis-U1
2B+1B
0.816
0.034
0.226
0.443
0.191
0.342
CogView4
6B
0.786
0.641
0.246
0.353
0.205
0.446
Janus-Pro
7B
0.553
0.001
0.139
0.276
0.365
0.267
OmniGen2
3B+4B
0.804
0.680
0.271
0.377
0.242
0.475
BLIP3-o
7B+1B
0.711
0.013
0.223
0.361
0.229
0.307
FLUX.1-dev
11B+12B
0.786
0.523
0.253
0.368
0.238
0.434
BAGEL
7B+7B
0.769
0.244
0.173
0.367
0.251
0.361
BAGEL+CoT
7B+7B
0.793
0.020
0.206
0.390
0.209
0.324
HiDream-I1-Full
11B+17B
0.829
0.707
0.317
0.347
0.186
0.477
HunyuanImage-2.1
7B+17B
0.835
0.816
0.299
0.355
0.127
0.486
Qwen-Image
7B+20B
0.882
0.891
0.306
0.418
0.197
0.539
Ovis-Image
2B+7B
0.858
0.914
0.308
0.386
0.186
0.530
Evaluation of text-to-image generation ability on OneIG-ZN.
Model
#Params.
Alignment
Text
Reasoning
Style
Diversity
Overall
Kolors 2.0
-
0.738
0.502
0.226
0.331
0.333
0.426
Seedream 3.0
-
0.793
0.928
0.281
0.397
0.243
0.528
GPT4o
-
0.812
0.650
0.300
0.449
0.159
0.474
CogView4
6B
0.700
0.193
0.236
0.348
0.214
0.338
Janus-Pro
7B
0.324
0.148
0.104
0.264
0.358
0.240
BLIP3-o
7B+1B
0.608
0.092
0.213
0.369
0.233
0.303
BAGEL
7B+7B
0.672
0.365
0.186
0.357
0.268
0.370
BAGEL+CoT
7B+7B
0.719
0.127
0.219
0.385
0.197
0.329
HiDream-I1-Full
11B+17B
0.620
0.205
0.256
0.304
0.300
0.337
HunyuanImage-2.1
7B+17B
0.775
0.896
0.271
0.348
0.114
0.481
Qwen-Image
7B+20B
0.825
0.963
0.267
0.405
0.279
0.548
Ovis-Image
2B+7B
0.805
0.961
0.273
0.368
0.198
0.521
📚 Citation
If you find Ovis-Image useful for your research or applications, please cite our technical report:
bibtex
1@article{wang2025ovis_image,
2 title={Ovis-Image Technical Report},
3 author={Wang, Guo-Hua and Cao, Liangfu and Cui, Tianyu and Fu, Minghao and Chen, Xiaohao and Zhan, Pengxin and Zhao, Jianshan and Li, Lan and Fu, Bowen and Liu, Jiaqi and Chen, Qing-Guo},
4 journal={arXiv preprint arXiv:2511.22982},
5 year={2025}
6}
🙏 Acknowledgments
The code is built upon Ovis and FLUX. We thank their authors for open-sourcing their great work.
📄 License
This project is licensed under the Apache License, Version 2.0 (SPDX-License-Identifier: Apache-2.0).
🚨 Disclaimer
We used compliance checking algorithms during the training process, to ensure the compliance of the trained model(s) to the best of our ability. Due to complex data and the diversity of language model usage scenarios, we cannot guarantee that the model is completely free of copyright issues or improper content. If you believe anything infringes on your rights or generates improper content, please contact us, and we will promptly address the matter.
🔥 We are hiring!
We are looking for both interns and full-time researchers to join our team, focusing on multimodal understanding, generation, reasoning, AI agents, and unified multimodal models. If you are interested in exploring these exciting areas, please reach out to us at qingguo.cqg@alibaba-inc.com.
