Sayak Paul Brings KV Caching to Flow-Based Image Diffusion Models

Sayak Paul published a long-form post, "KV Caching in Flow Models," systematically transferring KV caching—previously used mainly in autoregressive language models—to flow-based image generation models built on the MMDiT architecture, complete with intuition, pseudocode, implementation, and benchmarks.Confirmed

• The post was motivated by QwenImage 2.1's KV caching mechanism, and the author notes that Flux.2-Klein-KV may have been the earliest to apply this approach to reference image tokens
• Benchmarks focus on real-world scenarios, zeroing in on the speed-vs-memory trade-off the author cares most about; he concedes that a more complete comparison should also include quantitative reporting on output quality
• The writing style follows Andrej Karpathy's pedagogy: build intuition first, then present pseudocode and implementation. The author says it worked well and discloses AI-assisted writing in the follow-up
• The author found that combining traditional diffusion caching techniques (e.g., TaylorSeer) with KV caching causes a perceptible drop in image quality, and ran controlled experiments to back this up—providing empirical evidence for choosing text-to-image inference acceleration schemes
• The author found that in Flux.2-Klein-KV, besides reference image tokens, text projections can also be KV-cached with no visible generation failures, and has flagged this to @bflaiNot yet confirmed
• The viability of KV-caching text projections still needs more rigorous validation; if it holds, computation could be further compressed, but currently there is no failure visible from eyeballing the outputsWhy it matters
• This is a systematic attempt to migrate KV caching from language models to image flow models, offering new tools and empirical data for accelerating text-to-image inference
• The experiments show that not all acceleration techniques stack cleanly—KV caching combined with TaylorSeer actually degrades quality, directly informing engineering decisions

2026-10-07 ~ 2026-10-07 · 7 related posts

• [source] KV Caching Comes to Flow Models: Implementation, Benchmarks and Quirky Experiments — RisingSayak · 2026-10-07
• [source] Deep dive: KV caching for flow-based image generation, with intuition, pseudocode and benchmarks — RisingSayak · 2026-10-07
• Writing technical deep-dives Karpathy-style: intuition first, then pseudocode — RisingSayak · 2026-10-07
• KV-caching benchmarks in the piece focus on speed-memory trade-offs, quality metrics still lacking — RisingSayak · 2026-10-07
• Text projections in Flux.2-Klein-KV appear KV-cacheable, outputs show no failures yet — RisingSayak · 2026-10-07
• [source] Combining TaylorSeer-style diffusion caching with KV caching degrades image quality noticeably — RisingSayak · 2026-10-07
• Follow-up: Combining TaylorSeer with KV Caching Degrades Quality in Flow Models — RisingSayak · 2026-10-07