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FORA: Fast-Forward Caching in Diffusion Transformer Acceleration
Pratheba Selvaraju, Tianyu Ding, Tianyi Chen, Ilya Zharkov, Luming Liang
TL;DR
Larger diffusion transformers raise inference costs that make real-time applications less practical. FORA caches and reuses attention and MLP outputs across denoising steps; experiments report improved inference speed, with a cache interval of 7 offering a balance before FID degrades at larger intervals.
Problem
The larger size of diffusion transformers increases inference costs, making them less suitable for real-time applications.
Source: §1 · p. 1, §2.1 · p. 3Method
FORA uses static caching to recompute and store features at intervals, then reuse them for subsequent denoising steps without retraining.
Source: §3 · p. 4, §3.2 · p. 5Results
FORA shows notable inference-speed and computational-efficiency improvements, and a cache interval of 7 provides a balance before larger intervals significantly degrade FID.
Source: §1 · p. 3, §3.3 · p. 6Takeaways & Limitations
FORA is described as a training-free, plug-and-play approach that can integrate with fast samplers and reduce inference costs while preserving image quality.
Source: §4.2 · p. 7, §5 · p. 9Static caching applies uniformly across denoising steps and may not capture dynamic, context-dependent feature similarities.
Source: §3.3 · p. 6, §5 · p. 9
Abstract
from arXiv · showhide
Diffusion transformers (DiT) have become the de facto choice for generating high-quality images and videos, largely due to their scalability, which enables the construction of larger models for enhanced performance. However, the increased size of these models leads to higher inference costs, making them less attractive for real-time applications. We present Fast-FORward CAching (FORA), a simple yet effective approach designed to accelerate DiT by exploiting the repetitive nature of the diffusion process. FORA implements a caching mechanism that stores and reuses intermediate outputs from the attention and MLP layers across denoising steps, thereby reducing computational overhead. This approach does not require model retraining and seamlessly integrates with existing transformer-based diffusion models. Experiments show that FORA can speed up diffusion transformers several times over while only minimally affecting performance metrics such as the IS Score and FID. By enabling faster processing with minimal trade-offs in quality, FORA represents a significant advancement in deploying diffusion transformers for real-time applications. Code will be made publicly available at: https://github.com/prathebaselva/FORA.
1 Introduction
Diffusion transformers scale to high-quality image and video generation, but their larger size raises inference costs that hinder real-time use. FORA addresses the transformer caching gap by reusing intermediate layer outputs during denoising, without retraining.
1 Introduction
- Larger diffusion transformers improve image and video generation performance but increase inference costs, limiting real-time applications.Source: §1 · p. 1
- Existing caching-based efficiency methods primarily target U-Net diffusion models, leaving transformer-based models unaddressed.Source: §1 · p. 1
- FORA reuses attention and MLP outputs across denoising steps to reduce computational overhead in transformer-based diffusion models.The caching approach is described as training-free and compatible with existing transformer-based models.Source: §1 · pp. 2–3
- Experiments report notable improvements in inference speed and computational efficiency, supporting FORA's potential for real-time use.Source: §1 · p. 3
2 Related work
Diffusion transformers offer a scalable alternative to U-Net models, while their computational demands remain a deployment challenge. Prior efficiency work spans training and inference, but caching methods largely target U-Net diffusion rather than transformers.
2.1 Diffusion model
- Diffusion transformers leverage transformer scalability to increase model capacity, while large-scale inference speed and resource needs bottleneck real-time deployment.Source: §2.1 · p. 3
2.2 Efficiency enhancements of diffusion model
- Efficiency research addresses both training and inference costs, including training strategies that reduce costs and emissions while maintaining competitive image quality.PIXART is cited for training-cost and CO2-emission reductions; DREAM is described as achieving several-times-faster training convergence.Source: §2.2 · p. 3
- Inference-efficiency approaches include reducing sampling steps, model compression, and caching that exploits repeated computation across denoising.Source: §2.2 · p. 3
- A 250-step DDIM heatmap shows high consecutive-step feature similarity, especially late in diffusion, motivating caching to reduce computation.Source: Figure 2 · p. 4
- Prior caching methods target U-Net diffusion, whereas this work addresses transformer-based diffusion with training-free, plug-and-play caching.Source: §2.2 · p. 4
3 Fast-Forward Caching for Accelerated Sampling
FORA accelerates DiT sampling by caching attention and MLP features across denoising steps. Its static cache interval trades recomputation for reuse, with quality and efficiency depending on the interval and caching applied uniformly across steps.
3.1 Motivations
- Consecutive denoising outputs and layer features are similar, attention and MLP concentrate computational overhead, and the sampling objective remains consistent across steps.Source: §3.1 · pp. 4–5
3.2 FORA
- FORA caches attention and MLP features when t mod N = 0, then reuses them for the next N−1 steps instead of recomputing them.The paper describes the strategy as applicable to similar attention or high-compute layers in transformer backbones.Source: §3 · p. 4, §3.2 · pp. 5–6
- For ImageNet class-conditional generation, a static cache threshold of 3 provides the reported FID and computational speed-up balance for DiT-XL/2-G(cfg-1.50)@.Both the baseline and FORA use 250 DDIM sampling steps.Source: Table 1 · p. 6
3.3 Discussion
- N max=7 provides a reported balance, while larger cache intervals significantly degrade the Fréchet Inception Distance (FID) score and image quality.The optimal cache interval depends on task requirements and the desired speed-quality trade-off.Source: §3.3 · p. 6
- Static caching applies reuse uniformly across denoising steps, so it may miss differences in feature similarity between stages; dynamic strategies are left for future work.Source: §3.3 · p. 6
4 Experiments
The experiments evaluate FORA on DiT for ImageNet class-conditional generation and PIXART-α for text-conditional generation. They report faster sampling with quality trade-offs examined through cache-interval and guidance-strength ablations.
4.4 Results
- For text-conditional generation, FORA with IDDPM is described as preserving image quality, while Table 2 reports accelerated sampling with comparable COCO FID-30K scores.The table compares static caching (N = 2) with baseline PIXART-α using Inception V3 and CLIP-ViT-B-32 features.Source: Table 2 · p. 7, §4.4 · p. 8
4.1 Datasets and encoders
- ImageNet supports the class-conditional evaluation, while MSCOCO supports text-conditional evaluation using T5 large as the text encoder.The selected evaluation models are DiT and PIXART-α, both with Transformer backbones; FORA is evaluated with them and integrated with fast samplers.Source: §4.1 · p. 7, §4.2 · p. 7
4.3 Experimental setup
- The evaluation uses DiT with DDIM at guidance strength 1.5 and PIXART-α with IDDPM or DPM-Solver at guidance strength 4.5.DiT evaluation samples 50,000 ImageNet images; PIXART-α uses qualitative sample texts and quantitative MS-COCO-30K evaluation, with experiments on a single A100 GPU.Source: §4.2 · p. 7, §4.3 · pp. 7–8
4.5 Ablation studies
- As sampling steps increase, proportionally adjusting the caching interval yields comparable results while reducing attention and MLP recomputations.The interval ablation reports no negative impact on FID or IS as intervals increase with sampling steps.Source: Table 3 · p. 8, §4.5 · p. 8
4.4 Results
- 3 to 4× speed-up is reported for class-conditional generation, with a cache interval of 3 balancing FID and speed-up.Increasing the interval also makes background elements gain importance relative to the class subject.Source: §4.4 · p. 8
4.5 Ablation studies
- Guidance strength and cache interval both affect composition; guidance strength 1.5 often produces clutter, while higher strengths improve clarity and composition.The ImageNet cfg-scale ablation reports a positive correlation between higher guidance scale and higher IS.Source: Figure 5 · p. 9, §4.5 · p. 9, Table 4 · p. 9
5 Conclusion
The conclusion presents FORA as a training-free, plug-and-play caching mechanism for transformer-based diffusion models that reduces computational overhead. It also identifies static caching’s inability to fully capture complex similarity patterns as a limitation.
5 Conclusion
- FORA is presented as a caching mechanism applicable to transformer-based diffusion models that reduces computational overhead.The conclusion describes it as training-free, plug-and-play, and compatible with fast samplers.Source: §5 · p. 9
- Experiments and ablation studies are reported to demonstrate the method’s efficiency and versatility.Source: §5 · p. 9
- Static caching cannot fully use the complex similarity patterns in transformer-based diffusion models.The conclusion describes those similarities as dynamic and context-dependent across diffusion stages.Source: §5 · p. 9