update readme

README.md

@@ -9,10 +9,8 @@ __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 18, 2024:
-Memory-efficient training based on 2kpr's implementation is implemented in `flux_train.py`. Thanks to 2kpr. See [FLUX.1 fine-tuning](#flux1-fine-tuning) for details.
-
+Memory-efficient training based on 2kpr's implementation is implemented in `flux_train.py`. Thanks to 2kpr! See [FLUX.1 fine-tuning](#flux1-fine-tuning) for details.
 
 Aug 17, 2024:
 Added a script `flux_train.py` to train FLUX.1. The script is experimental and not an optimized version. It needs >28GB VRAM for training. 
@@ -118,6 +116,8 @@ accelerate launch  --mixed_precision bf16 --num_cpu_threads_per_process 1 flux_t
 
 (Combine the command into one line.)
 
+Sample image generation during training is not tested yet.
+
 Options are almost the same as LoRA training. The difference is `--blockwise_fused_optimizer`, `--double_blocks_to_swap` and `--cpu_offload_checkpointing`.  `--single_blocks_to_swap` is also available.
 
 `--blockwise_fused_optimizer` enables the fusing of the optimizer for each block. This is similar to `--fused_backward_pass`. Any optimizer can be used, but Adafactor is recommended for memory efficiency. `--fused_optimizer_groups` is deprecated due to the addition of this option for FLUX.1 training.