Shiva-DiT: Residual-Based Differentiable Top- k Selection for Efficient Diffusion Transformers

arXiv:2602.05605 · cs.LG, cs.AI, cs.CV · Submitted 2026-02-05 · Read on arXiv

cs.LG, cs.AI, cs.CV

Submitted: 2026-02-05

Updated: 2026-09-02

Code: https://github.com/huggingface/peft

Terminology

• Transformer — 47× in this paper · explained in 2 episodes
• Pruning — 39× in this paper · explained in 4 episodes
• Sparsity — 13× in this paper · explained in 4 episodes
• Consistency — 11× in this paper · explained in 3 episodes
• Distillation — 11× in this paper · explained in 2 episodes
• Fidelity — 11× in this paper · explained in 2 episodes
• Diffusion Model — 10× in this paper · explained in 8 episodes
• Diffusion Models — 10× in this paper · explained in 6 episodes
• Alignment — 9× in this paper · explained in 1 episode
• Adaptation — 8× in this paper · explained in 1 episode
• Redundancy — 6× in this paper · explained in 2 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Cosine Similarity — 4× in this paper · explained in 3 episodes
• Gradient Flow — 4× in this paper · explained in 2 episodes
• Classifier-Free Guidance — 3× in this paper
• Computational overhead — 3× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Flow Matching — 3× in this paper · explained in 8 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Diffusion Process — 2× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Allocator — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Exponential Moving Average — 1× in this paper
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Residual-Based Straight-Through Estimator — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Structural Integrity — 1× in this paper · explained in 3 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation
• Token Pruning for Caching Better: 9 Times Acceleration on Stable Diffusion for Free

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks