update README, remove unnecessary autocast

README.md

@@ -13,13 +13,13 @@ The command to install PyTorch is as follows:
 
 Oct 19, 2024:
 
-- Added an implementation of Differential Output Preservation (temporary name) for SDXL/FLUX.1 LoRA training.
+- Added an implementation of Differential Output Preservation (temporary name) for SDXL/FLUX.1 LoRA training. SD1/2 is not tested yet. This is an experimental feature. 
   - A method to make the output of LoRA closer to the output when LoRA is not applied, with captions that do not contain trigger words.
   - Define a Dataset subset for the regularization image (`is_reg = true`) with `.toml`. Add `custom_attributes.diff_output_preservation = true`.
     - See [dataset configuration](docs/config_README-en.md) for the regularization dataset.
-  - Specify "number of training images x number of epochs >= number of regularization images x number of epochs".
-  - Specify a large value for `--prior_loss_weight` option (not dataset config). We recommend 10-1000.
-  - Set the loss in the training without using the regularization image to be close to the loss in the training using DOP.
+  - Specify "number of training images x number of repeats >= number of regularization images x number of repeats".
+  - Specify a large value for `--prior_loss_weight` option (not dataset config). The appropriate value is unknown, but try around 10-100. Note that the default is 1.0.
+  - You may want to start with 2/3 to 3/4 of the loss value when DOP is not applied. If it is 1/2, DOP may not be working.
 ```
 [[datasets.subsets]]
 image_dir = "path/to/image/dir"
@@ -28,8 +28,6 @@ is_reg = true
 custom_attributes.diff_output_preservation = true # Add this
 ```
 
-
-
 Oct 13, 2024:
 
 - Fixed an issue where it took a long time to load the image size when initializing the dataset, especially when the number of images in the dataset was large.

flux_train_network.py

@@ -453,7 +453,7 @@ def call_dit(img, img_ids, t5_out, txt_ids, l_pooled, timesteps, guidance_vec, t
 
             if len(diff_output_pr_indices) > 0:
                 network.set_multiplier(0.0)
-                with torch.no_grad(), accelerator.autocast():
+                with torch.no_grad():
                     model_pred_prior = call_dit(
                         img=packed_noisy_model_input[diff_output_pr_indices],
                         img_ids=img_ids[diff_output_pr_indices],