From 9d6618e8f65fb43d8e2daa0b3cc3c0f611c26946 Mon Sep 17 00:00:00 2001 From: TilmannR Date: Tue, 20 Feb 2024 22:00:31 +0100 Subject: [PATCH] Update readme.md Fixed typos "straighforward" and "sugested". --- gdf/readme.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/gdf/readme.md b/gdf/readme.md index 9a63691..753dff6 100644 --- a/gdf/readme.md +++ b/gdf/readme.md @@ -5,7 +5,7 @@ GDF is a simple framework for working with diffusion models. It implements most , EDM, Rectified Flows, etc.) and makes it very easy to switch between them or combine different parts of different frameworks -Using GDF is very straighforward, first of all just define an instance of the GDF class: +Using GDF is very straightforward, first of all just define an instance of the GDF class: ```python from gdf import GDF @@ -45,7 +45,7 @@ So it's very easy to extend this framework with custom schedulers, scalers, targ When you define your training loop you can get all you need by just doing: ```python -shift, loss_shift = 1, 1 # this can be set to higher values as per what the Simple Diffusion paper sugested for high resolution +shift, loss_shift = 1, 1 # this can be set to higher values as per what the Simple Diffusion paper suggested for high resolution for inputs, extra_conditions in dataloader_iterator: noised, noise, target, logSNR, noise_cond, loss_weight = gdf.diffuse(inputs, shift=shift, loss_shift=loss_shift) pred = diffusion_model(noised, noise_cond, extra_conditions)