Q-DiT4SR: Exploration of Detail-Preserving Diffusion Transformer Quantization for Real-World Image Super-Resolution
Fuente:
arXiv
Guardado en:
| Autores principales: | Zhang, Xun, Yang, Kaicheng, Lu, Hongliang, Qin, Haotong, Guo, Yong, Zhang, Yulun |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolution
por: Duan, Zheng-Peng, et al.
Publicado: (2025)
por: Duan, Zheng-Peng, et al.
Publicado: (2025)
RobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization
por: Yang, Kaicheng, et al.
Publicado: (2025)
por: Yang, Kaicheng, et al.
Publicado: (2025)
QArtSR: Quantization via Reverse-Module and Timestep-Retraining in One-Step Diffusion based Image Super-Resolution
por: Zhu, Libo, et al.
Publicado: (2025)
por: Zhu, Libo, et al.
Publicado: (2025)
PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution
por: Zhu, Libo, et al.
Publicado: (2024)
por: Zhu, Libo, et al.
Publicado: (2024)
QuantSR+: Pushing the Limit of Quantized Image Super-Resolution Networks
por: Qin, Haotong, et al.
Publicado: (2026)
por: Qin, Haotong, et al.
Publicado: (2026)
TreeQ: Pushing the Quantization Boundary of Diffusion Transformer via Tree-Structured Mixed-Precision Search
por: Yang, Kaicheng, et al.
Publicado: (2025)
por: Yang, Kaicheng, et al.
Publicado: (2025)
Binarized Diffusion Model for Image Super-Resolution
por: Chen, Zheng, et al.
Publicado: (2024)
por: Chen, Zheng, et al.
Publicado: (2024)
2DQuant: Low-bit Post-Training Quantization for Image Super-Resolution
por: Liu, Kai, et al.
Publicado: (2024)
por: Liu, Kai, et al.
Publicado: (2024)
HiT-SR: Hierarchical Transformer for Efficient Image Super-Resolution
por: Zhang, Xiang, et al.
Publicado: (2024)
por: Zhang, Xiang, et al.
Publicado: (2024)
BiMaCoSR: Binary One-Step Diffusion Model Leveraging Flexible Matrix Compression for Real Super-Resolution
por: Liu, Kai, et al.
Publicado: (2025)
por: Liu, Kai, et al.
Publicado: (2025)
TaQ-DiT: Time-aware Quantization for Diffusion Transformers
por: Liu, Xinyan, et al.
Publicado: (2024)
por: Liu, Xinyan, et al.
Publicado: (2024)
Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers
por: Chen, Lei, et al.
Publicado: (2024)
por: Chen, Lei, et al.
Publicado: (2024)
StructSR: Refuse Spurious Details in Real-World Image Super-Resolution
por: Li, Yachao, et al.
Publicado: (2025)
por: Li, Yachao, et al.
Publicado: (2025)
LSGQuant: Layer-Sensitivity Guided Quantization for One-Step Diffusion Real-World Video Super-Resolution
por: Wu, Tianxing, et al.
Publicado: (2026)
por: Wu, Tianxing, et al.
Publicado: (2026)
TinySR: Pruning Diffusion for Real-World Image Super-Resolution
por: Dong, Linwei, et al.
Publicado: (2025)
por: Dong, Linwei, et al.
Publicado: (2025)
QuantVSR: Low-Bit Post-Training Quantization for Real-World Video Super-Resolution
por: Chai, Bowen, et al.
Publicado: (2025)
por: Chai, Bowen, et al.
Publicado: (2025)
One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation
por: Li, Jianze, et al.
Publicado: (2025)
por: Li, Jianze, et al.
Publicado: (2025)
HQ-DiT: Efficient Diffusion Transformer with FP4 Hybrid Quantization
por: Liu, Wenxuan, et al.
Publicado: (2024)
por: Liu, Wenxuan, et al.
Publicado: (2024)
DiT-IC: Aligned Diffusion Transformer for Efficient Image Compression
por: Shi, Junqi, et al.
Publicado: (2026)
por: Shi, Junqi, et al.
Publicado: (2026)
DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-Resolution
por: Chen, Zheng, et al.
Publicado: (2025)
por: Chen, Zheng, et al.
Publicado: (2025)
TQ-DiT: Efficient Time-Aware Quantization for Diffusion Transformers
por: Hwang, Younghye, et al.
Publicado: (2025)
por: Hwang, Younghye, et al.
Publicado: (2025)
PTQ4DiT: Post-training Quantization for Diffusion Transformers
por: Wu, Junyi, et al.
Publicado: (2024)
por: Wu, Junyi, et al.
Publicado: (2024)
Quant-dLLM: Post-Training Extreme Low-Bit Quantization for Diffusion Large Language Models
por: Zhang, Tianao, et al.
Publicado: (2025)
por: Zhang, Tianao, et al.
Publicado: (2025)
Bird-SR: Bidirectional Reward-Guided Diffusion for Real-World Image Super-Resolution
por: Fan, Zihao, et al.
Publicado: (2026)
por: Fan, Zihao, et al.
Publicado: (2026)
ControlSR: Taming Diffusion Models for Consistent Real-World Image Super Resolution
por: Wan, Yuhao, et al.
Publicado: (2024)
por: Wan, Yuhao, et al.
Publicado: (2024)
Inf-DiT: Upsampling Any-Resolution Image with Memory-Efficient Diffusion Transformer
por: Yang, Zhuoyi, et al.
Publicado: (2024)
por: Yang, Zhuoyi, et al.
Publicado: (2024)
TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution
por: Dong, Linwei, et al.
Publicado: (2024)
por: Dong, Linwei, et al.
Publicado: (2024)
FiDeSR: High-Fidelity and Detail-Preserving One-Step Diffusion Super-Resolution
por: Kim, Aro, et al.
Publicado: (2026)
por: Kim, Aro, et al.
Publicado: (2026)
DiT-JSCC: Rethinking Deep JSCC with Diffusion Transformers and Semantic Representations
por: Tan, Kailin, et al.
Publicado: (2026)
por: Tan, Kailin, et al.
Publicado: (2026)
SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution
por: Wu, Rongyuan, et al.
Publicado: (2023)
por: Wu, Rongyuan, et al.
Publicado: (2023)
Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding
por: Li, Zhimin, et al.
Publicado: (2024)
por: Li, Zhimin, et al.
Publicado: (2024)
DiT4Edit: Diffusion Transformer for Image Editing
por: Feng, Kunyu, et al.
Publicado: (2024)
por: Feng, Kunyu, et al.
Publicado: (2024)
Preserving Full Degradation Details for Blind Image Super-Resolution
por: Liu, Hongda, et al.
Publicado: (2024)
por: Liu, Hongda, et al.
Publicado: (2024)
DiffST: Spatiotemporal-Aware Diffusion for Real-World Space-Time Video Super-Resolution
por: Chen, Zheng, et al.
Publicado: (2026)
por: Chen, Zheng, et al.
Publicado: (2026)
LRQ-DiT: Log-Rotation Post-Training Quantization of Diffusion Transformers for Image and Video Generation
por: Yang, Lianwei, et al.
Publicado: (2025)
por: Yang, Lianwei, et al.
Publicado: (2025)
See More Details: Efficient Image Super-Resolution by Experts Mining
por: Zamfir, Eduard, et al.
Publicado: (2024)
por: Zamfir, Eduard, et al.
Publicado: (2024)
FP4DiT: Towards Effective Floating Point Quantization for Diffusion Transformers
por: Chen, Ruichen, et al.
Publicado: (2025)
por: Chen, Ruichen, et al.
Publicado: (2025)
VQ4DiT: Efficient Post-Training Vector Quantization for Diffusion Transformers
por: Deng, Juncan, et al.
Publicado: (2024)
por: Deng, Juncan, et al.
Publicado: (2024)
DiT as Real-Time Rerenderer: Streaming Video Stylization with Autoregressive Diffusion Transformer
por: Lyu, Hengye, et al.
Publicado: (2026)
por: Lyu, Hengye, et al.
Publicado: (2026)
CasSR: Activating Image Power for Real-World Image Super-Resolution
por: Chen, Haolan, et al.
Publicado: (2024)
por: Chen, Haolan, et al.
Publicado: (2024)
Ejemplares similares
-
DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolution
por: Duan, Zheng-Peng, et al.
Publicado: (2025) -
RobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization
por: Yang, Kaicheng, et al.
Publicado: (2025) -
QArtSR: Quantization via Reverse-Module and Timestep-Retraining in One-Step Diffusion based Image Super-Resolution
por: Zhu, Libo, et al.
Publicado: (2025) -
PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution
por: Zhu, Libo, et al.
Publicado: (2024) -
QuantSR+: Pushing the Limit of Quantized Image Super-Resolution Networks
por: Qin, Haotong, et al.
Publicado: (2026)