From ddd159ef23b2f1dccbbdb380113c45754cb41ac2 Mon Sep 17 00:00:00 2001 From: John Pollock Date: Fri, 15 Aug 2025 06:13:34 -0500 Subject: [PATCH] docs: Clarify GGUF performance gain comparison in README Update the README to specify that the "up to 10% faster GGUF inference" claim for DisTorch2 is a direct comparison against the previous DisTorch V1 implementation. This clarification helps manage user expectations and provides a more accurate performance context. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 0c00aa0..a5fe0b9 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ [^1]: This **enhances memory management,** not parallel processing. Workflow steps still execute sequentially, but with components (in full or in part) loaded across your specified devices. *Performance gains* come from avoiding repeated model loading/unloading when VRAM is constrained. *Capability gains* come from offloading as much of the model (VAE/CLIP/UNet) off of your main **compute** device as possible—allowing you to maximize latent space for actual computation. 1. **Universal .safetensors Support**: Native DisTorch2 distribution for all `.safetensors` models. -2. **Up to 10% Faster GGUF Inference**: The new DisTorch2 logic provides potential speedups for GGUF models. +2. **Up to 10% Faster GGUF Inference versus DisTorch1**: The new DisTorch2 logic provides potential speedups for GGUF models versus the DisTorch V1 method. 3. **Bespoke WanVideoWrapper Integration**: Tightly integrated, stable support for WanVideoWrapper with eight bespoke MultiGPU nodes.

DisTorch: How It Works