update

Author: Luciennnnnnn

examples/OmniGen2-RL/README.md

@@ -26,7 +26,7 @@ We provide ready-to-use scripts to perform a full Best-of-N workflow on the GEdi
 
 **1.Generate Candidates**
 ```bash
-bash evaluation/GEdit-Bench/omnigen2_16samples.sh
+bash evaluation/GEdit-Bench/omnigen2_16samples.sh # default using 8 GPUs
 ```
 
 > **⚠️ Important Note on Resource Usage**
@@ -47,7 +47,6 @@ bash evaluation/GEdit-Bench/omnigen2_16samples.sh --world_size 4 --rank 1
 # ...and so on for ranks 2 and 3.
 ```
 
-- **GPU Configuration**: You can adjust the number of GPUs used per machine by modifying the num_gpus_per_machine variable inside the script.
 - **Monitoring Progress**: The scripts utilize nohup for background execution. We recommend monitoring the file (specified in the script file) to track the status and progress of the generation process.
 
 **2.Score and Select**

examples/OmniGen2-RL/evaluation/GEdit-Bench/flux_kontext_dev_16samples.sh

@@ -43,7 +43,7 @@ echo "WORLD_SIZE: $WORLD_SIZE"
 global_shift_index=0
 total_num_images=606
 
-num_gpus_per_machine=8
+num_gpus_per_machine=$(python -c "import torch; print(torch.cuda.device_count())")
 # Calculate images per machine, rounding up to ensure all data is covered
 num_images_per_machine=$(( (total_num_images + WORLD_SIZE - 1) / WORLD_SIZE ))
 shift_index=$((RANK * num_images_per_machine))

examples/OmniGen2-RL/evaluation/GEdit-Bench/flux_kontext_dev_16samples_select_best_editscore_pass1.sh

@@ -43,7 +43,7 @@ echo "WORLD_SIZE: $WORLD_SIZE"
 global_shift_index=0
 total_num_images=606
 
-num_gpus_per_machine=8
+num_gpus_per_machine=$(python -c "import torch; print(torch.cuda.device_count())")
 # Calculate images per machine, rounding up to ensure all data is covered
 num_images_per_machine=$(( (total_num_images + WORLD_SIZE - 1) / WORLD_SIZE ))
 shift_index=$((RANK * num_images_per_machine))

examples/OmniGen2-RL/evaluation/GEdit-Bench/flux_kontext_dev_16samples_select_best_editscore_pass4.sh

@@ -43,7 +43,7 @@ echo "WORLD_SIZE: $WORLD_SIZE"
 global_shift_index=0
 total_num_images=606
 
-num_gpus_per_machine=8
+num_gpus_per_machine=$(python -c "import torch; print(torch.cuda.device_count())")
 # Calculate images per machine, rounding up to ensure all data is covered
 num_images_per_machine=$(( (total_num_images + WORLD_SIZE - 1) / WORLD_SIZE ))
 shift_index=$((RANK * num_images_per_machine))

examples/OmniGen2-RL/evaluation/GEdit-Bench/omnigen2_16samples.sh

@@ -46,7 +46,7 @@ echo "WORLD_SIZE: $WORLD_SIZE"
 global_shift_index=0
 total_num_images=606
 
-num_gpus_per_machine=8
+num_gpus_per_machine=$(python -c "import torch; print(torch.cuda.device_count())")
 # Calculate images per machine, rounding up to ensure all data is covered
 num_images_per_machine=$(( (total_num_images + WORLD_SIZE - 1) / WORLD_SIZE ))
 shift_index=$((RANK * num_images_per_machine))

examples/OmniGen2-RL/evaluation/GEdit-Bench/omnigen2_16samples_select_best_editscore_pass1.sh

@@ -43,7 +43,7 @@ echo "WORLD_SIZE: $WORLD_SIZE"
 global_shift_index=0
 total_num_images=606
 
-num_gpus_per_machine=8
+num_gpus_per_machine=$(python -c "import torch; print(torch.cuda.device_count())")
 # Calculate images per machine, rounding up to ensure all data is covered
 num_images_per_machine=$(( (total_num_images + WORLD_SIZE - 1) / WORLD_SIZE ))
 shift_index=$((RANK * num_images_per_machine))

examples/OmniGen2-RL/evaluation/GEdit-Bench/omnigen2_16samples_select_best_editscore_pass4.sh

@@ -43,7 +43,7 @@ echo "WORLD_SIZE: $WORLD_SIZE"
 global_shift_index=0
 total_num_images=606
 
-num_gpus_per_machine=8
+num_gpus_per_machine=$(python -c "import torch; print(torch.cuda.device_count())")
 # Calculate images per machine, rounding up to ensure all data is covered
 num_images_per_machine=$(( (total_num_images + WORLD_SIZE - 1) / WORLD_SIZE ))
 shift_index=$((RANK * num_images_per_machine))

examples/OmniGen2-RL/evaluation/GEdit-Bench/qwen_image_16samples.sh

@@ -43,7 +43,7 @@ echo "WORLD_SIZE: $WORLD_SIZE"
 global_shift_index=0
 total_num_images=606
 
-num_gpus_per_machine=8
+num_gpus_per_machine=$(python -c "import torch; print(torch.cuda.device_count())")
 # Calculate images per machine, rounding up to ensure all data is covered
 num_images_per_machine=$(( (total_num_images + WORLD_SIZE - 1) / WORLD_SIZE ))
 shift_index=$((RANK * num_images_per_machine))

examples/OmniGen2-RL/evaluation/GEdit-Bench/qwen_image_16samples_select_best_editscore_pass1.sh

@@ -43,7 +43,7 @@ echo "WORLD_SIZE: $WORLD_SIZE"
 global_shift_index=0
 total_num_images=606
 
-num_gpus_per_machine=8
+num_gpus_per_machine=$(python -c "import torch; print(torch.cuda.device_count())")
 # Calculate images per machine, rounding up to ensure all data is covered
 num_images_per_machine=$(( (total_num_images + WORLD_SIZE - 1) / WORLD_SIZE ))
 shift_index=$((RANK * num_images_per_machine))

examples/OmniGen2-RL/evaluation/GEdit-Bench/qwen_image_16samples_select_best_editscore_pass4.sh

@@ -43,7 +43,7 @@ echo "WORLD_SIZE: $WORLD_SIZE"
 global_shift_index=0
 total_num_images=606
 
-num_gpus_per_machine=8
+num_gpus_per_machine=$(python -c "import torch; print(torch.cuda.device_count())")
 # Calculate images per machine, rounding up to ensure all data is covered
 num_images_per_machine=$(( (total_num_images + WORLD_SIZE - 1) / WORLD_SIZE ))
 shift_index=$((RANK * num_images_per_machine))