Change the default `weighting_scheme` in the SD3 scripts (#8639)

* change to logit_normal as the weighting scheme

* sensible default mote

examples/dreambooth/README_sd3.md

@@ -11,6 +11,8 @@ The `train_dreambooth_sd3.py` script shows how to implement the training procedu
 huggingface-cli login
 ```
 
+This will also allow us to push the trained model parameters to the Hugging Face Hub platform. 
+
 ## Running locally with PyTorch
 
 ### Installing the dependencies
@@ -52,8 +54,6 @@ write_basic_config()
 ```
 
 When running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups. 
-Note also that we use PEFT library as backend for LoRA training, make sure to have `peft>=0.6.0` installed in your environment.
-
 
 ### Dog toy example
 
@@ -72,8 +72,6 @@ snapshot_download(
 )
 ```
 
-This will also allow us to push the trained LoRA parameters to the Hugging Face Hub platform. 
-
 Now, we can launch training using:
 
 ```bash
@@ -116,6 +114,8 @@ To better track our training experiments, we're using the following flags in the
 
 [LoRA](https://huggingface.co/docs/peft/conceptual_guides/adapter#low-rank-adaptation-lora) is a popular parameter-efficient fine-tuning technique that allows you to achieve full-finetuning like performance but with a fraction of learnable parameters.
 
+Note also that we use PEFT library as backend for LoRA training, make sure to have `peft>=0.6.0` installed in your environment.
+
 To perform DreamBooth with LoRA, run:
 
 ```bash
@@ -142,3 +142,7 @@ accelerate launch train_dreambooth_lora_sd3.py \
   --seed="0" \
   --push_to_hub
 ```
+
+## Other notes
+
+We default to the "logit_normal" weighting scheme for the loss following the SD3 paper. Thanks to @bghira for helping us discover that for other weighting schemes supported from the training script, training may incur numerical instabilities. 
\ No newline at end of file

examples/dreambooth/train_dreambooth_lora_sd3.py

@@ -477,7 +477,10 @@ def parse_args(input_args=None):
         ),
     )
     parser.add_argument(
-        "--weighting_scheme", type=str, default="sigma_sqrt", choices=["sigma_sqrt", "logit_normal", "mode", "cosmap"]
+        "--weighting_scheme",
+        type=str,
+        default="logit_normal",
+        choices=["sigma_sqrt", "logit_normal", "mode", "cosmap"],
     )
     parser.add_argument(
         "--logit_mean", type=float, default=0.0, help="mean to use when using the `'logit_normal'` weighting scheme."

examples/dreambooth/train_dreambooth_sd3.py

@@ -472,7 +472,10 @@ def parse_args(input_args=None):
         ),
     )
     parser.add_argument(
-        "--weighting_scheme", type=str, default="sigma_sqrt", choices=["sigma_sqrt", "logit_normal", "mode", "cosmap"]
+        "--weighting_scheme",
+        type=str,
+        default="logit_normal",
+        choices=["sigma_sqrt", "logit_normal", "mode", "cosmap"],
     )
     parser.add_argument(
         "--logit_mean", type=float, default=0.0, help="mean to use when using the `'logit_normal'` weighting scheme."