update readme to match configs

README.md

@@ -21,13 +21,13 @@ HiRO (High Resolution Output) is a diffusion model which generates downscaled fi
 
 1. Download this repository. Optionally, you can just download a subset of the `forcing_data` and `initial_conditions` for the period you are interested in.
 
-2. Update paths in the `inference_config.yaml`. Specifically, update `experiment_dir`, `checkpoint_path`, `initial_condition.path` and `forcing_loader.dataset.path`.
+2. Install code dependencies with `pip install fme`.
 
-3. Install code dependencies with `pip install fme`.
+3. Update paths in the ACE inference config file `ace2s_inference_config_global.yaml`. Specifically, update `experiment_dir`, `checkpoint_path`, `initial_condition.path` and `forcing_loader.dataset.path`. 
 
-4. Run ACE inference with `python -m fme.ace.inference inference_config.yaml`.
+4. Update paths in the HiRO downscaling inference config file `hiro_downscaling_ace2s_pnw_output.yaml`. Specifically, update `experiment_dir`, `model.checkpoint_path`, and `data.coarse`. The directory path in `data.coarse` should point to the same ACE inference `experiment_dir` from step 3. Optionally, if you wish to change the region and/or time selection of the area(s) to downscale you may edit those in the downscaling config (see [downscalimg inference docs](https://ai2-climate-emulator.readthedocs.io/en/latest/downscaling_inference.html) for more details). An example of global downscaling is also provided in `hiro_downscaling_ace2s_global_output.yaml`.
 
-5. Update paths in the `downscaling_config.yaml`. Specifically, update `experiment_dir`, `model.checkpoint_path`, and `data.coarse`. `data.coarse` data path(s) should point to the saved ACE inference output from step 4. 
+4. Run the script `run-hiro-ace.sh`.
 
 
 ### Strengths and weaknesses