add DoRA training feature to sdxl dreambooth lora script (#7235)

* dora in canonical script

* add mention of DoRA to readme

examples/dreambooth/README_sdxl.md

@@ -243,3 +243,29 @@ accelerate launch train_dreambooth_lora_sdxl.py \
 
 > [!CAUTION]
 > Min-SNR gamma is not supported with the EDM-style training yet. When training with the PlaygroundAI model, it's recommended to not pass any "variant".
+
+### DoRA training 
+The script now supports DoRA training too!
+> Proposed in [DoRA: Weight-Decomposed Low-Rank Adaptation](https://arxiv.org/abs/2402.09353), 
+**DoRA** is very similar to LoRA, except it decomposes the pre-trained weight into two components, **magnitude** and **direction** and employs LoRA for _directional_ updates to efficiently minimize the number of trainable parameters. 
+The authors found that by using DoRA, both the learning capacity and training stability of LoRA are enhanced without any additional overhead during inference. 
+
+> [!NOTE]
+> 💡DoRA training is still _experimental_  
+> and is likely to require different hyperparameter values to perform best compared to a LoRA.
+> Specifically, we've noticed 2 differences to take into account your training: 
+> 1. **LoRA seem to converge faster than DoRA** (so a set of parameters that may lead to overfitting when training a LoRA may be working well for a DoRA)
+> 2. **DoRA quality superior to LoRA especially in lower ranks** the difference in quality of DoRA of rank 8 and LoRA of rank 8 appears to be more significant than when training ranks of 32 or 64 for example.  
+> This is also aligned with some of the quantitative analysis shown in the paper. 
+
+**Usage**
+1. To use DoRA you need to install `peft` from main: 
+```bash
+pip install git+https://github.com/huggingface/peft.git
+```
+2. Enable DoRA training by adding this flag
+```bash
+--use_dora
+```
+**Inference** 
+The inference is the same as if you train a regular LoRA 🤗
\ No newline at end of file

examples/dreambooth/train_dreambooth_lora_sdxl.py

@@ -647,6 +647,15 @@ def parse_args(input_args=None):
         default=4,
         help=("The dimension of the LoRA update matrices."),
     )
+    parser.add_argument(
+        "--use_dora",
+        action="store_true",
+        default=False,
+        help=(
+            "Wether to train a DoRA as proposed in- DoRA: Weight-Decomposed Low-Rank Adaptation https://arxiv.org/abs/2402.09353. "
+            "Note: to use DoRA you need to install peft from main, `pip install git+https://github.com/huggingface/peft.git`"
+        ),
+    )
 
     if input_args is not None:
         args = parser.parse_args(input_args)
@@ -1147,6 +1156,7 @@ def main(args):
     # now we will add new LoRA weights to the attention layers
     unet_lora_config = LoraConfig(
         r=args.rank,
+        use_dora=args.use_dora,
         lora_alpha=args.rank,
         init_lora_weights="gaussian",
         target_modules=["to_k", "to_q", "to_v", "to_out.0"],
@@ -1158,6 +1168,7 @@ def main(args):
     if args.train_text_encoder:
         text_lora_config = LoraConfig(
             r=args.rank,
+            use_dora=args.use_dora,
             lora_alpha=args.rank,
             init_lora_weights="gaussian",
             target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],