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Show-o2 1.5B-HQ
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Show-o2 1.5B-HQ is an open-weights image model from Show Lab, NUS, released June 2025. Its published size is 1.5B parameters. Licence: apache-2.0.
Model information sources
Release date ↗ · Weights licence ↗ · Parameter count ↗
1.5B = the language-model backbone (Qwen2.5-1.5B-Instruct) of the small Show-o2 variant, 'approximately 1.5B LLM parameters' per the makers; the HQ checkpoint is that model further trained at 512/1024 resolution. Flow head, fusion/projector layers and the external Wan2.1 3D causal VAE are not included; no total is published.
Score within the LMArena text-to-image comparison set only.
$ how it compares
The image models ranked either side of Show-o2 1.5B-HQ, out of 197 with a score, then more from Show Lab, NUS.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 144 |  UniWorld-V1 | 40±2 | comparison only | ||
| 145 |  UniPic2-Metaquery-9B | 40±6 | comparison only | ||
| 146 | Show-o2 1.5B-HQ | 40±3 | local | ||
| 147 | Skywork UniPic 1.5B | 40±6 | comparison only | ||
| 148 |  Stable Diffusion XL 0.9 (base) | 40±6 | local | ||
| more from Show Lab, NUS | |||||
| 123 | Show-o2 1.5B | 45±5 | comparison only | ||
| 138 | Show-o2 7B | 42±3 | local | ||
| 159 | Show-o (show-o-demo-512) | 36±3 | local | ||
$ what it's good at
text to image▂▅▅▃▄▄█▃▁▂▁▁ #90 of 126 * · 96% of its score · 1 test
Beats Latent Diffusion text2img-large (LDM-KL-8-G, LAION-400M), VQ-Diffusion (ITHQ), OmniGen2 here, though they rank higher overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| GenExam (relaxed overall, GPT-5 judge) | 10.1% ✎ | ▄█▂▂▂▂▆▄·▂·▂ beats 2/16 | 96% |
$ where to run it
Other ways to run it
- Run it as published · needs ~7.9 GB, NVIDIA only · from github.com/showlab (official)
git clone https://github.com/showlab/Show-o && cd Show-o/show-o2 && bash build_env.sh && wandb login && wget https://huggingface.co/Wan-AI/Wan2.1-T2V-14B/resolve/main/Wan2.1_VAE.pth && python3 inference_t2i.py config=configs/showo2_1.5b_demo_1024x1024.yaml batch_size=4 guidance_scale=7.5 num_inference_steps=50
Official weights and documentation: huggingface.co/showlab ↗
$ how the score was worked out · GenExam: A Multidisciplinary… 96%
Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works
| source | results | share of the score |
|---|---|---|
 GenExam: A Multidisciplinary Text-to-Image Exam (arXiv v5) | 1 | 96% |
| prior · pull toward the middle | 4% | |
| weighted average | = 40.0 |
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data updated · sources and licences · json