ai-toolkit fork speeds up Stable Diffusion training and rendering

By PulseAugur Editorial · [1 sources] · 2026-06-26 19:37

An optimized fork of the ai-toolkit has been released, introducing caching for transformer quants to reduce model training startup times. The update also enables the use of a local ComfyUI server for faster and more controlled rendering of sample images. These optimizations, particularly the caching and local server integration, can significantly decrease rendering times, with one user reporting a reduction from two minutes to under a minute on a 4090 GPU. AI

IMPACT This update improves the efficiency of local AI model training and rendering workflows for users of Stable Diffusion and related tools.

RANK_REASON This is a software update for a specific tool, not a frontier release, significant industry move, or academic research.

Read on r/StableDiffusion →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ai-toolkit fork speeds up Stable Diffusion training and rendering

COVERAGE [1]

r/StableDiffusion TIER_2 English(EN) · /u/Incognit0ErgoSum · 2026-06-26 19:37

My optimized fork of ai-toolkit now supports caching of transformer quants (to speed up startup time when training the same model) and using a local ComfyUI server (on the same machine) to render sample images, which is way faster and has more control. Krea 2 training is vram optimized.

<div class="md">Check out the latest commit here: <a href="https://github.com/envy-ai/ai-toolkit-envy-optimized">https://github.com/envy-ai/ai-toolkit-envy-optimized</a> YMMV, but on my 4090, rendering of 2 krea sample images went from 2 minutes to…

COVERAGE [1]

My optimized fork of ai-toolkit now supports caching of transformer quants (to speed up startup time when training the same model) and using a local ComfyUI server (on the same machine) to render sample images, which is way faster and has more control. Krea 2 training is vram optimized.

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