Merge branch 'sd3' of https://github.com/kohya-ss/sd-scripts into sd3

README.md

@@ -19,7 +19,9 @@ Nov 7, 2024:
 - The distribution of timesteps during SD3/3.5 training has been adjusted. This applies to both fine-tuning and LoRA training. PR [#1768](https://github.com/kohya-ss/sd-scripts/pull/1768) Thanks to Dango233!
   - Previously, the side closer to noise was more sampled, but now it is uniform by default. This may improve the problem of difficulty in learning details.
   - Specifically, the problem of double shifting has been fixed. The default for `--weighting_scheme` has been changed to `uniform` (the previous default was `logit_normal`).
-  - A new option `--training_shift` has been added. The default is 1.0, and all timesteps are sampled uniformly. If less than 1.0, the side closer to the image is more sampled, and if more than 1.0, the side closer to noise is more sampled.
+  - A new option `--training_shift` has been added. The default is 1.0, and all timesteps are sampled uniformly. If less than 1.0, the side closer to the image is more sampled (training more on image details), and if more than 1.0, the side closer to noise is more sampled (training more on overall structure).
+  - The effect of a shift in uniform distribution is shown in the figure below.
+  - ![Figure_1](https://github.com/user-attachments/assets/99a72c67-adfb-4440-81d4-a718985ff350)
 
 Oct 31, 2024:
 
@@ -693,7 +695,8 @@ Other options are described below.
    - The weighting scheme is described in the section 3.1 of the [SD3 paper](https://arxiv.org/abs/2403.03206v1). 
    - The uniform distribution is the default. If you want to change the distribution, see `--help` for options. 
    - `--training_shift` is the shift value for the training distribution of timesteps.
-
+   - The effect of a shift in uniform distribution is shown in the figure below.
+   - ![Figure_1](https://github.com/user-attachments/assets/99a72c67-adfb-4440-81d4-a718985ff350)
 
 Technical details of multi-resolution training for SD3.5M: