Update README.md

README.md

@@ -26,8 +26,9 @@ TangoFlux consists of FluxTransformer blocks, which are Diffusion Transformers (
 
 ## Installation
 
+git clone 代码文件
 ```bash
-pip install git+https://github.com/declare-lab/TangoFlux
+pip install -e .
 ```
 
 ## Inference
@@ -77,13 +78,13 @@ We use the `accelerate` package from Hugging Face for multi-GPU training. Run `a
 `tangoflux_config.yaml` defines the training file paths and model hyperparameters:
 
 ```bash
-CUDA_VISIBLE_DEVICES=0,1 accelerate launch --config_file='configs/accelerator_config.yaml' tangoflux/train.py   --checkpointing_steps="best" --save_every=5 --config='configs/tangoflux_config.yaml'
+HIP_VISIBLE_DEVICES=0,1 accelerate launch --config_file='configs/accelerator_config.yaml' tangoflux/train.py   --checkpointing_steps="best" --save_every=5 --config='configs/tangoflux_config.yaml'
 ```
 
 To perform DPO training, modify the training files such that each data point contains "chosen", "reject", "caption" and "duration" fields. Please specify the path to your training files in `configs/tangoflux_config.yaml`. An example has been provided in `train_dpo.json`. Replace it with your own audio.
 
 ```bash
-CUDA_VISIBLE_DEVICES=0,1 accelerate launch --config_file='configs/accelerator_config.yaml' tangoflux/train_dpo.py   --checkpointing_steps="best" --save_every=5 --config='configs/tangoflux_config.yaml'
+HIP_VISIBLE_DEVICES=0,1 accelerate launch --config_file='configs/accelerator_config.yaml' tangoflux/train_dpo.py   --checkpointing_steps="best" --save_every=5 --config='configs/tangoflux_config.yaml'
 ```
 
 ## Evaluation Scripts