Research & Insights

Technical deep-dives into deterministic AI

Exploring the technical challenges of reproducible AI generation, pipeline integration, and production workflows.

Much of this research builds on foundational work by Thinking Machines Lab on defeating nondeterminism in neural network inference (He, 2025).

Featured

technicalJan 15, 2025 · 8 min read

Why Temperature=0 Isn't Enough for Deterministic AI

Batch processing introduces hidden variance even with temperature=0 and fixed seeds. Here's the technical breakdown of where non-determinism actually comes from.

All Articles

technicalJan 10, 2025

Complete RNG State Management in PyTorch

PyTorch, NumPy, and Python each maintain separate RNG states. Capturing and restoring all three is essential for true reproducibility.

12 min read

tutorialJan 5, 2025

Building Deterministic Workflows in ComfyUI

Step-by-step guide to creating reproducible image generation pipelines using deterministic nodes and proper state management.

15 min read

case studyDec 20, 2024

Integrating AI Generation into VFX Pipelines

How to connect AI image generation with USD, Houdini TOPs, and Nuke for production-ready workflows with version control.

10 min read

technicalDec 15, 2024

The Batch Variance Problem: A Deep Dive

Why batch_size > 1 breaks reproducibility in diffusion models, and the mathematical explanation behind it.

14 min read

announcementDec 1, 2024

Deterministic Nodes v2.0: What's New

Major release with improved RNG locking, better ComfyUI integration, and new pipeline export features.

5 min read