diff --git a/README.md b/README.md
index 51e4635b..165eed34 100644
--- a/README.md
+++ b/README.md
@@ -9,6 +9,13 @@ __Please update PyTorch to 2.4.0. We have tested with `torch==2.4.0` and `torchv
 The command to install PyTorch is as follows:
 `pip3 install torch==2.4.0 torchvision==0.19.0 --index-url https://download.pytorch.org/whl/cu124`
 
+Aug 20, 2024:
+FLUX.1 supports multi-resolution inference, so training at multiple resolutions may be possible and the results may be improved (like 1024x1024, 768x768 and 512x512 ... you can use any resolution).
+
+The script seems to support multi-resolution even in the current version, __if `--cache_latents_to_disk` is not specified__. Please try if you are interested. See [FLUX.1 Multi-resolution training](#flux1-multi-resolution-training) for details.
+
+We will support multi-resolution caching to disk in the near future.
+
 Aug 19, 2024:
 In `flux_train.py`, the memory consumption during model saving is reduced when `--save_precision` is set to the same value as `--mixed_precision` (about 22GB). Please set the same value unless there is a reason.
 
@@ -159,6 +166,51 @@ In the case of LoRA models are trained with `bf16`, we are not sure which is bet
 
 The script can merge multiple LoRA models. If you want to merge multiple LoRA models, specify `--concat` option to work the merged LoRA model properly.
 
+### FLUX.1 Multi-resolution training
+
+You can define multiple resolutions in the dataset configuration file. __Caching latents to disk is not supported yet.__
+
+The dataset configuration file is like below. You can define multiple resolutions with different batch sizes. The resolutions are defined in the `[[datasets]]` section. The `[[datasets.subsets]]` section is for the dataset directory. Please specify the same directory for each resolution.
+
+```
+[general]
+# define common settings here
+flip_aug = true
+color_aug = false
+keep_tokens_separator= "|||"
+shuffle_caption = false
+caption_tag_dropout_rate = 0
+caption_extension = ".txt"
+
+[[datasets]]
+# define the first resolution here
+batch_size = 2
+enable_bucket = true
+resolution = [1024, 1024]
+
+  [[datasets.subsets]]
+  image_dir = "path/to/image/dir"
+  num_repeats = 1
+
+[[datasets]]
+# define the second resolution here
+batch_size = 3
+enable_bucket = true
+resolution = [768, 768]
+
+  [[datasets.subsets]]
+  image_dir = "path/to/image/dir"
+  num_repeats = 1
+
+[[datasets]]
+# define the third resolution here
+batch_size = 4
+enable_bucket = true
+resolution = [512, 512]
+
+  [[datasets.subsets]]
+  image_dir = "path/to/image/dir"
+  num_repeats = 1
 ```
 
 ## SD3 training