parser.add_argument("--force-cpu-for-conditioning-latents",default=default_arguments['force-cpu-for-conditioning-latents'],action='store_true',help="Forces computing conditional latents to be done on the CPU (if you constantyl OOM on low chunk counts)")
parser.add_argument("--force-cpu-for-conditioning-latents",default=default_arguments['force-cpu-for-conditioning-latents'],action='store_true',help="Forces computing conditional latents to be done on the CPU (if you constantyl OOM on low chunk counts)")
parser.add_argument("--prune-nonfinal-outputs",default=default_arguments['prune-nonfinal-outputs'],action='store_true',help="Deletes non-final output files on completing a generation")
parser.add_argument("--prune-nonfinal-outputs",default=default_arguments['prune-nonfinal-outputs'],action='store_true',help="Deletes non-final output files on completing a generation")
parser.add_argument("--use-bigvgan-vocoder",default=default_arguments['use-bigvgan-vocoder'],action='store_true',help="Uses BigVGAN in place of the default vocoder")
parser.add_argument("--device-override",default=default_arguments['device-override'],help="A device string to override pass through Torch")
parser.add_argument("--device-override",default=default_arguments['device-override'],help="A device string to override pass through Torch")
parser.add_argument("--sample-batch-size",default=default_arguments['sample-batch-size'],type=int,help="Sets how many batches to use during the autoregressive samples pass")
parser.add_argument("--sample-batch-size",default=default_arguments['sample-batch-size'],type=int,help="Sets how many batches to use during the autoregressive samples pass")
parser.add_argument("--concurrency-count",type=int,default=default_arguments['concurrency-count'],help="How many Gradio events to process at once")
parser.add_argument("--concurrency-count",type=int,default=default_arguments['concurrency-count'],help="How many Gradio events to process at once")