[research_projects] Update README.md to include a note about NF5 T5-xxl (#9775)

Update README.md

examples/research_projects/flux_lora_quantization/README.md

@@ -5,7 +5,8 @@
 
 This example shows how to fine-tune [Flux.1 Dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) with LoRA and quantization. We show this by using the [`Norod78/Yarn-art-style`](https://huggingface.co/datasets/Norod78/Yarn-art-style) dataset. Steps below summarize the workflow:
 
-* We precompute the text embeddings in `compute_embeddings.py` and serialize them into a parquet file. 
+* We precompute the text embeddings in `compute_embeddings.py` and serialize them into a parquet file.
+  * Even though optional, we load the T5-xxl in NF4 to further reduce the memory foot-print. 
 * `train_dreambooth_lora_flux_miniature.py` takes care of training:
   * Since we already precomputed the text embeddings, we don't load the text encoders.
   * We load the VAE and use it to precompute the image latents and we then delete it. 
@@ -163,4 +164,4 @@ image.save("yarn_merged.png")
 |-------|-------|
 | ![Image A](https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/quantized_flux_training/merged.png) | ![Image B](https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/quantized_flux_training/unmerged.png) |
 
-As we can notice the first column result follows the style more closely.
\ No newline at end of file
+As we can notice the first column result follows the style more closely.