From c3a5850e06991051593472a8b3167d7210317796 Mon Sep 17 00:00:00 2001 From: Extraltodeus Date: Tue, 14 May 2024 20:34:58 +0200 Subject: [PATCH] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 773c188..b39edfc 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,8 @@ My own version "from scratch" of a self-rescaling CFG / anti-burn. It ain't much but it's honest work. ## Last update (14.05.24): -- added node: preset loader. Can do what the other can and MUCH MORE! (note: mostly tested with SDXL) -- added node: "Excellent attention" developped by myself and based on this [astonishingly easy to understand research paper](https://github.com/Extraltodeus/temp/blob/main/ihave.jpg)! But in short This node allows to disable the input layer 8 on self and cross attention. But also to apply a custom modification on cross attention middle layer 0. I have only tested with SDXL. Also for some reason the Juggernaut model does not play too well with it. You can find a grid example of this node's settings in the "grids_example" folder. +- added node: **preset loader**. Can do what the other can and MUCH MORE! (note: mostly tested with SDXL) +- added node: "**Excellent attention**" developped by myself and based on this [astonishingly easy to understand research paper](https://github.com/Extraltodeus/temp/blob/main/ihave.jpg)! But in short This node allows to disable the input layer 8 on self and cross attention. But also to apply a custom modification on cross attention middle layer 0. I have only tested with SDXL. Also for some reason the Juggernaut model does not play too well with it. You can find a grid example of this node's settings in the "grids_example" folder. - Attention modifier presets! Try them with the preset loader! - I discovered that disabling the input layer 8 on the cross or self attention tends to make more random images. This layer seems to be the main responsible for the overall composition while not being alone at the task. Disabling it for the self attention for generating the positive denoised is risky regarding the image coherence but tends to create more interesting images more driven by your prompt. For the cross attention it creates more coherent images with a bit less prompt following. The results are most interesting. Doing it in the negative seems to be an improvement. You will find the possibility to do experiment about that through the presets loader and the "excellent attention" node.