README.md

metadata

license: apache-2.0
language:
  - en
  - zh
pipeline_tag: image-to-image
library_name: transformers

MingTok: A Unified Tokenizer for Visual Understanding and Generation without Vector Quantization

📑 Technical Report ｜ 📖 Project Page ｜ 🤗 Hugging Face ｜ 🤖 ModelScope ｜ 💾 GitHub

Key Features

• 🖼️ First Continuous Unified Vision Tokenizer: MingTok enables unified vision understanding and generation via a continuous latent space, eliminating quantization while preserving semantic and perceptual fidelity.
• 🎯 High-Fidelity Image Reconstruction: A three-stage architecture (encoding, expansion, reconstruction) captures fine details and global structure for accurate, high-quality image recovery.
• ⚡ Accelerated Autoregressive Convergence: Masked modeling with multi-level supervision shapes a compact, semantically rich latent space, enabling faster and more stable autoregressive training.

Figure 1: Conceptual comparison and qualitative examples of MingTok.

Usage

# build MingTok

from mingtok.modeling_mingtok import MingTok

mingtok_model = MingTok.from_pretrained("inclusionAI/MingTok-Vision")
mingtok_model = mingtok_model.cuda()

img_path = "mingtok/asset/mingtok.png"
save_path = "mingtok/asset/mingtok_recon.png"

# loading original image
image = Image.open(img_path).convert("RGB")
processor = CenterCropProcessor(image_size=512, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
image = processor(image).cuda().unsqueeze(0)

# performing reconstruction
with torch.no_grad():
  image_recon = mingtok_model.forward_enc_dec(image)
  # latent = mingtok_model.low_level_encoder(image)
  # semantic_feat = mingtok_model.semantic_decoder(latent)['x_norm_patchtokens']
  # image_recon = mingtok_model.forward_pixel_decoder(semantic_feat)


output_mean = torch.Tensor([0.5,0.5,0.5]).view(1,-1,1,1).cuda()
output_std = torch.Tensor([0.5,0.5,0.5]).view(1,-1,1,1).cuda()
output_image = (image_recon*output_std + output_mean)[0]
output_image = T.ToPILImage()(output_image)
output_image.save(save_path)

Performance

Image Reconstruction

Tokenizer	Res.	# Tokens	rFID ↓	PSNR ↑	SSIM ↑	LPIPS ↓
Specialized tokenizers
SD-VAE	256	1024	1.06	28.62	0.86	-
GigaTok	256	256	0.51	21.32	0.69	0.21
VA-VAE	256	256	0.26	28.59	0.80	0.09
HieraTok	256	256	1.04	23.90	0.72	0.09
DC-AE	512	64	0.22	26.15	0.71	0.08
MAE-Tok	512	128	0.62	-	-	-
TexTok	512	256	0.73	24.45	0.66	0.19
Unified tokenizers
UniTok	256	256	0.38	-	-	-
TokenFlow	384	729	0.63	22.77	0.73	-
MingTok-Vision	512	256	0.54	30.77	0.62	0.14
MingTok-Vision †	512	256	0.38	31.09	0.64	0.12

† denotes using semantic decoder after joint pre-training.

Reference

@article{huang2025mingunivision,
  title={Ming-UniVision: Joint Image Understanding and Generation with a Unified Continuous Tokenizer},
  author={Huang, Ziyuan and Zheng, DanDan and Zou, Cheng and Liu, Rui and Wang, Xiaolong and Ji, Kaixiang and Chai, Weilong and Sun, Jianxin and Wang, Libin and Lv, Yongjie and Huang, Taozhi and Liu, Jiajia and Guo, Qingpei and Yang, Ming and Chen, Jingdong and Zhou, Jun},
  journal={arXiv preprint arXiv:2510.06590},
  year={2025}
}