Merge pull request #48 from damian0815/feat_cli_args_override_json_file
CLI args override values in JSON file
This commit is contained in:
Victor Hall
GitHub
commit
7f6145098a
126
train.py
126
train.py
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@ -965,94 +965,68 @@ def main(args):
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logging.info(f"{Fore.LIGHTWHITE_EX} **** Finished training ****{Style.RESET_ALL}")
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logging.info(f"{Fore.LIGHTWHITE_EX} ***************************{Style.RESET_ALL}")
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def update_old_args(t_args):
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"""
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Update old args to new args to deal with json config loading and missing args for compatibility
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"""
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if not hasattr(t_args, "shuffle_tags"):
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print(f" Config json is missing 'shuffle_tags' flag")
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t_args.__dict__["shuffle_tags"] = False
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if not hasattr(t_args, "save_full_precision"):
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print(f" Config json is missing 'save_full_precision' flag")
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t_args.__dict__["save_full_precision"] = False
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if not hasattr(t_args, "notebook"):
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print(f" Config json is missing 'notebook' flag")
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t_args.__dict__["notebook"] = False
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if not hasattr(t_args, "disable_unet_training"):
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print(f" Config json is missing 'disable_unet_training' flag")
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t_args.__dict__["disable_unet_training"] = False
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if not hasattr(t_args, "rated_dataset"):
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print(f" Config json is missing 'rated_dataset' flag")
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t_args.__dict__["rated_dataset"] = False
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if not hasattr(t_args, "rated_dataset_target_dropout_percent"):
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print(f" Config json is missing 'rated_dataset_target_dropout_percent' flag")
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t_args.__dict__["rated_dataset_target_dropout_percent"] = 50
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if not hasattr(t_args, "validation_config"):
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print(f" Config json is missing 'validation_config'")
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t_args.__dict__["validation_config"] = None
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if __name__ == "__main__":
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supported_resolutions = [256, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024, 1088, 1152]
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supported_precisions = ['fp16', 'fp32']
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argparser = argparse.ArgumentParser(description="EveryDream2 Training options")
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argparser.add_argument("--config", type=str, required=False, default=None, help="JSON config file to load options from")
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args, _ = argparser.parse_known_args()
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args, argv = argparser.parse_known_args()
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if args.config is not None:
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print(f"Loading training config from {args.config}, all other command options will be ignored!")
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print(f"Loading training config from {args.config}.")
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with open(args.config, 'rt') as f:
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t_args = argparse.Namespace()
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t_args.__dict__.update(json.load(f))
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update_old_args(t_args) # update args to support older configs
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args = argparser.parse_args(namespace=t_args)
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args.__dict__.update(json.load(f))
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if len(argv) > 0:
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print(f"Config .json loaded but there are additional CLI arguments -- these will override values in {args.config}.")
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else:
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print("No config file specified, using command line args")
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argparser = argparse.ArgumentParser(description="EveryDream2 Training options")
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argparser.add_argument("--amp", action="store_true", default=False, help="Enables automatic mixed precision compute, recommended on")
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argparser.add_argument("--batch_size", type=int, default=2, help="Batch size (def: 2)")
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argparser.add_argument("--ckpt_every_n_minutes", type=int, default=None, help="Save checkpoint every n minutes, def: 20")
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argparser.add_argument("--clip_grad_norm", type=float, default=None, help="Clip gradient norm (def: disabled) (ex: 1.5), useful if loss=nan?")
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argparser.add_argument("--clip_skip", type=int, default=0, help="Train using penultimate layer (def: 0) (2 is 'penultimate')", choices=[0, 1, 2, 3, 4])
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argparser.add_argument("--cond_dropout", type=float, default=0.04, help="Conditional drop out as decimal 0.0-1.0, see docs for more info (def: 0.04)")
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argparser.add_argument("--data_root", type=str, default="input", help="folder where your training images are")
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argparser.add_argument("--disable_textenc_training", action="store_true", default=False, help="disables training of text encoder (def: False)")
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argparser.add_argument("--disable_unet_training", action="store_true", default=False, help="disables training of unet (def: False) NOT RECOMMENDED")
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argparser.add_argument("--disable_xformers", action="store_true", default=False, help="disable xformers, may reduce performance (def: False)")
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argparser.add_argument("--flip_p", type=float, default=0.0, help="probability of flipping image horizontally (def: 0.0) use 0.0 to 1.0, ex 0.5, not good for specific faces!")
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argparser.add_argument("--gpuid", type=int, default=0, help="id of gpu to use for training, (def: 0) (ex: 1 to use GPU_ID 1)")
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argparser.add_argument("--gradient_checkpointing", action="store_true", default=False, help="enable gradient checkpointing to reduce VRAM use, may reduce performance (def: False)")
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argparser.add_argument("--grad_accum", type=int, default=1, help="Gradient accumulation factor (def: 1), (ex, 2)")
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argparser.add_argument("--hf_repo_subfolder", type=str, default=None, help="Subfolder inside the huggingface repo to download, if the model is not in the root of the repo.")
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argparser.add_argument("--logdir", type=str, default="logs", help="folder to save logs to (def: logs)")
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argparser.add_argument("--log_step", type=int, default=25, help="How often to log training stats, def: 25, recommend default!")
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argparser.add_argument("--lowvram", action="store_true", default=False, help="automatically overrides various args to support 12GB gpu")
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argparser.add_argument("--lr", type=float, default=None, help="Learning rate, if using scheduler is maximum LR at top of curve")
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argparser.add_argument("--lr_decay_steps", type=int, default=0, help="Steps to reach minimum LR, default: automatically set")
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argparser.add_argument("--lr_scheduler", type=str, default="constant", help="LR scheduler, (default: constant)", choices=["constant", "linear", "cosine", "polynomial"])
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argparser.add_argument("--lr_warmup_steps", type=int, default=None, help="Steps to reach max LR during warmup (def: 0.02 of lr_decay_steps), non-functional for constant")
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argparser.add_argument("--max_epochs", type=int, default=300, help="Maximum number of epochs to train for")
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argparser.add_argument("--notebook", action="store_true", default=False, help="disable keypresses and uses tqdm.notebook for jupyter notebook (def: False)")
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argparser.add_argument("--project_name", type=str, default="myproj", help="Project name for logs and checkpoints, ex. 'tedbennett', 'superduperV1'")
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argparser.add_argument("--resolution", type=int, default=512, help="resolution to train", choices=supported_resolutions)
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argparser.add_argument("--resume_ckpt", type=str, required=True, default="sd_v1-5_vae.ckpt", help="The checkpoint to resume from, either a local .ckpt file, a converted Diffusers format folder, or a Huggingface.co repo id such as stabilityai/stable-diffusion-2-1 ")
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argparser.add_argument("--sample_prompts", type=str, default="sample_prompts.txt", help="File with prompts to generate test samples from (def: sample_prompts.txt)")
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argparser.add_argument("--sample_steps", type=int, default=250, help="Number of steps between samples (def: 250)")
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argparser.add_argument("--save_ckpt_dir", type=str, default=None, help="folder to save checkpoints to (def: root training folder)")
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argparser.add_argument("--save_every_n_epochs", type=int, default=None, help="Save checkpoint every n epochs, def: 0 (disabled)")
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argparser.add_argument("--save_full_precision", action="store_true", default=False, help="save ckpts at full FP32")
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argparser.add_argument("--save_optimizer", action="store_true", default=False, help="saves optimizer state with ckpt, useful for resuming training later")
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argparser.add_argument("--scale_lr", action="store_true", default=False, help="automatically scale up learning rate based on batch size and grad accumulation (def: False)")
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argparser.add_argument("--seed", type=int, default=555, help="seed used for samples and shuffling, use -1 for random")
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argparser.add_argument("--shuffle_tags", action="store_true", default=False, help="randomly shuffles CSV tags in captions, for booru datasets")
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argparser.add_argument("--useadam8bit", action="store_true", default=False, help="Use AdamW 8-Bit optimizer, recommended!")
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argparser.add_argument("--validation_config", type=str, default="validation_default.json", help="validation config file (def: validation_config.json)")
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argparser.add_argument("--wandb", action="store_true", default=False, help="enable wandb logging instead of tensorboard, requires env var WANDB_API_KEY")
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argparser.add_argument("--write_schedule", action="store_true", default=False, help="write schedule of images and their batches to file (def: False)")
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argparser.add_argument("--rated_dataset", action="store_true", default=False, help="enable rated image set training, to less often train on lower rated images through the epochs")
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argparser.add_argument("--rated_dataset_target_dropout_percent", type=int, default=50, help="how many images (in percent) should be included in the last epoch (Default 50)")
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args, _ = argparser.parse_known_args()
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argparser = argparse.ArgumentParser(description="EveryDream2 Training options")
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argparser.add_argument("--amp", action="store_true", default=False, help="Enables automatic mixed precision compute, recommended on")
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argparser.add_argument("--batch_size", type=int, default=2, help="Batch size (def: 2)")
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argparser.add_argument("--ckpt_every_n_minutes", type=int, default=None, help="Save checkpoint every n minutes, def: 20")
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argparser.add_argument("--clip_grad_norm", type=float, default=None, help="Clip gradient norm (def: disabled) (ex: 1.5), useful if loss=nan?")
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argparser.add_argument("--clip_skip", type=int, default=0, help="Train using penultimate layer (def: 0) (2 is 'penultimate')", choices=[0, 1, 2, 3, 4])
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argparser.add_argument("--cond_dropout", type=float, default=0.04, help="Conditional drop out as decimal 0.0-1.0, see docs for more info (def: 0.04)")
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argparser.add_argument("--data_root", type=str, default="input", help="folder where your training images are")
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argparser.add_argument("--disable_textenc_training", action="store_true", default=False, help="disables training of text encoder (def: False)")
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argparser.add_argument("--disable_unet_training", action="store_true", default=False, help="disables training of unet (def: False) NOT RECOMMENDED")
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argparser.add_argument("--disable_xformers", action="store_true", default=False, help="disable xformers, may reduce performance (def: False)")
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argparser.add_argument("--flip_p", type=float, default=0.0, help="probability of flipping image horizontally (def: 0.0) use 0.0 to 1.0, ex 0.5, not good for specific faces!")
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argparser.add_argument("--gpuid", type=int, default=0, help="id of gpu to use for training, (def: 0) (ex: 1 to use GPU_ID 1)")
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argparser.add_argument("--gradient_checkpointing", action="store_true", default=False, help="enable gradient checkpointing to reduce VRAM use, may reduce performance (def: False)")
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argparser.add_argument("--grad_accum", type=int, default=1, help="Gradient accumulation factor (def: 1), (ex, 2)")
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argparser.add_argument("--hf_repo_subfolder", type=str, default=None, help="Subfolder inside the huggingface repo to download, if the model is not in the root of the repo.")
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argparser.add_argument("--logdir", type=str, default="logs", help="folder to save logs to (def: logs)")
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argparser.add_argument("--log_step", type=int, default=25, help="How often to log training stats, def: 25, recommend default!")
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argparser.add_argument("--lowvram", action="store_true", default=False, help="automatically overrides various args to support 12GB gpu")
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argparser.add_argument("--lr", type=float, default=None, help="Learning rate, if using scheduler is maximum LR at top of curve")
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argparser.add_argument("--lr_decay_steps", type=int, default=0, help="Steps to reach minimum LR, default: automatically set")
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argparser.add_argument("--lr_scheduler", type=str, default="constant", help="LR scheduler, (default: constant)", choices=["constant", "linear", "cosine", "polynomial"])
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argparser.add_argument("--lr_warmup_steps", type=int, default=None, help="Steps to reach max LR during warmup (def: 0.02 of lr_decay_steps), non-functional for constant")
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argparser.add_argument("--max_epochs", type=int, default=300, help="Maximum number of epochs to train for")
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argparser.add_argument("--notebook", action="store_true", default=False, help="disable keypresses and uses tqdm.notebook for jupyter notebook (def: False)")
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argparser.add_argument("--project_name", type=str, default="myproj", help="Project name for logs and checkpoints, ex. 'tedbennett', 'superduperV1'")
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argparser.add_argument("--resolution", type=int, default=512, help="resolution to train", choices=supported_resolutions)
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argparser.add_argument("--resume_ckpt", type=str, required=not ('resume_ckpt' in args), default="sd_v1-5_vae.ckpt", help="The checkpoint to resume from, either a local .ckpt file, a converted Diffusers format folder, or a Huggingface.co repo id such as stabilityai/stable-diffusion-2-1 ")
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argparser.add_argument("--sample_prompts", type=str, default="sample_prompts.txt", help="File with prompts to generate test samples from (def: sample_prompts.txt)")
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argparser.add_argument("--sample_steps", type=int, default=250, help="Number of steps between samples (def: 250)")
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argparser.add_argument("--save_ckpt_dir", type=str, default=None, help="folder to save checkpoints to (def: root training folder)")
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argparser.add_argument("--save_every_n_epochs", type=int, default=None, help="Save checkpoint every n epochs, def: 0 (disabled)")
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argparser.add_argument("--save_full_precision", action="store_true", default=False, help="save ckpts at full FP32")
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argparser.add_argument("--save_optimizer", action="store_true", default=False, help="saves optimizer state with ckpt, useful for resuming training later")
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argparser.add_argument("--scale_lr", action="store_true", default=False, help="automatically scale up learning rate based on batch size and grad accumulation (def: False)")
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argparser.add_argument("--seed", type=int, default=555, help="seed used for samples and shuffling, use -1 for random")
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argparser.add_argument("--shuffle_tags", action="store_true", default=False, help="randomly shuffles CSV tags in captions, for booru datasets")
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argparser.add_argument("--useadam8bit", action="store_true", default=False, help="Use AdamW 8-Bit optimizer, recommended!")
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argparser.add_argument("--wandb", action="store_true", default=False, help="enable wandb logging instead of tensorboard, requires env var WANDB_API_KEY")
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argparser.add_argument("--write_schedule", action="store_true", default=False, help="write schedule of images and their batches to file (def: False)")
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argparser.add_argument("--rated_dataset", action="store_true", default=False, help="enable rated image set training, to less often train on lower rated images through the epochs")
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argparser.add_argument("--rated_dataset_target_dropout_percent", type=int, default=50, help="how many images (in percent) should be included in the last epoch (Default 50)")
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# load CLI args to overwrite existing config args
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args = argparser.parse_args(args=argv, namespace=args)
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print(f" Args:")
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pprint.pprint(args.__dict__)
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pprint.pprint(vars(args))
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main(args)
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