Create parser.py

utils/parser.py

@@ -0,0 +1,452 @@
+import argparse
+import os
+
+def parse_args(input_args=None):
+    parser = argparse.ArgumentParser(description="Train Consistency Encoder.")
+    parser.add_argument(
+        "--pretrained_model_name_or_path",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to pretrained model or model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--pretrained_vae_model_name_or_path",
+        type=str,
+        default=None,
+        help="Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.",
+    )
+    parser.add_argument(
+        "--revision",
+        type=str,
+        default=None,
+        required=False,
+        help="Revision of pretrained model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--variant",
+        type=str,
+        default=None,
+        help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
+    )
+    
+    # parser.add_argument(
+    #     "--instance_data_dir",
+    #     type=str,
+    #     required=True,
+    #     help=("A folder containing the training data. "),
+    # )
+
+    parser.add_argument(
+        "--data_config_path",
+        type=str,
+        required=True,
+        help=("A folder containing the training data. "),
+    )
+
+    parser.add_argument(
+        "--cache_dir",
+        type=str,
+        default=None,
+        help="The directory where the downloaded models and datasets will be stored.",
+    )
+
+    parser.add_argument(
+        "--image_column",
+        type=str,
+        default="image",
+        help="The column of the dataset containing the target image. By "
+        "default, the standard Image Dataset maps out 'file_name' "
+        "to 'image'.",
+    )
+    parser.add_argument(
+        "--caption_column",
+        type=str,
+        default=None,
+        help="The column of the dataset containing the instance prompt for each image",
+    )
+
+    parser.add_argument("--repeats", type=int, default=1, help="How many times to repeat the training data.")
+
+    parser.add_argument(
+        "--instance_prompt",
+        type=str,
+        default=None,
+        required=True,
+        help="The prompt with identifier specifying the instance, e.g. 'photo of a TOK dog', 'in the style of TOK'",
+    )
+
+    parser.add_argument(
+        "--validation_prompt",
+        type=str,
+        default=None,
+        help="A prompt that is used during validation to verify that the model is learning.",
+    )
+    parser.add_argument(
+        "--num_train_vis_images",
+        type=int,
+        default=2,
+        help="Number of images that should be generated during validation with `validation_prompt`.",
+    )
+    parser.add_argument(
+        "--num_validation_images",
+        type=int,
+        default=2,
+        help="Number of images that should be generated during validation with `validation_prompt`.",
+    )
+
+    parser.add_argument(
+        "--validation_vis_steps",
+        type=int,
+        default=500,
+        help=(
+            "Run dreambooth validation every X steps. Dreambooth validation consists of running the prompt"
+            " `args.validation_prompt` multiple times: `args.num_validation_images`."
+        ),
+    )
+
+    parser.add_argument(
+        "--train_vis_steps",
+        type=int,
+        default=500,
+        help=(
+            "Run dreambooth validation every X steps. Dreambooth validation consists of running the prompt"
+            " `args.validation_prompt` multiple times: `args.num_validation_images`."
+        ),
+    )
+
+    parser.add_argument(
+        "--vis_lcm",
+        type=bool,
+        default=True,
+        help=(
+            "Also log results of LCM inference",
+        ),
+    )
+
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="lora-dreambooth-model",
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+
+    parser.add_argument("--save_only_encoder", action="store_true", help="Only save the encoder and not the full accelerator state")
+
+    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
+
+    parser.add_argument("--freeze_encoder_unet", action="store_true", help="Don't train encoder unet")
+    parser.add_argument("--predict_word_embedding", action="store_true", help="Predict word embeddings in addition to KV features")
+    parser.add_argument("--ip_adapter_feature_extractor_path", type=str, help="Path to pre-trained feature extractor for IP-adapter")
+    parser.add_argument("--ip_adapter_model_path", type=str, help="Path to pre-trained IP-adapter.")
+    parser.add_argument("--ip_adapter_tokens", type=int, default=16, help="Number of tokens to use in IP-adapter cross attention mechanism")
+    parser.add_argument("--optimize_adapter", action="store_true", help="Optimize IP-adapter parameters (projector + cross-attention layers)")
+    parser.add_argument("--adapter_attention_scale", type=float, default=1.0, help="Relative strength of the adapter cross attention layers")
+    parser.add_argument("--adapter_lr", type=float, help="Learning rate for the adapter parameters. Defaults to the global LR if not provided")
+
+    parser.add_argument("--noisy_encoder_input", action="store_true", help="Noise the encoder input to the same step as the decoder?")
+
+    # related to CFG:
+    parser.add_argument("--adapter_drop_chance", type=float, default=0.0, help="Chance to drop adapter condition input during training")
+    parser.add_argument("--text_drop_chance", type=float, default=0.0, help="Chance to drop text condition during training")
+    parser.add_argument("--kv_drop_chance", type=float, default=0.0, help="Chance to drop KV condition during training")
+
+    
+    
+    parser.add_argument(
+        "--resolution",
+        type=int,
+        default=1024,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+
+    parser.add_argument(
+        "--crops_coords_top_left_h",
+        type=int,
+        default=0,
+        help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."),
+    )
+
+    parser.add_argument(
+        "--crops_coords_top_left_w",
+        type=int,
+        default=0,
+        help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."),
+    )
+
+    parser.add_argument(
+        "--center_crop",
+        default=False,
+        action="store_true",
+        help=(
+            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
+            " cropped. The images will be resized to the resolution first before cropping."
+        ),
+    )
+
+    parser.add_argument(
+        "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
+    )
+
+    parser.add_argument("--num_train_epochs", type=int, default=1)
+
+    parser.add_argument(
+        "--max_train_steps",
+        type=int,
+        default=None,
+        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
+    )
+
+    parser.add_argument(
+        "--checkpointing_steps",
+        type=int,
+        default=500,
+        help=(
+            "Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
+            " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
+            " training using `--resume_from_checkpoint`."
+        ),
+    )
+
+    parser.add_argument(
+        "--checkpoints_total_limit",
+        type=int,
+        default=5,
+        help=("Max number of checkpoints to store."),
+    )
+
+    parser.add_argument(
+        "--resume_from_checkpoint",
+        type=str,
+        default=None,
+        help=(
+            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
+            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
+        ),
+    )
+
+    parser.add_argument("--max_timesteps_for_x0_loss", type=int, default=1001)
+
+    parser.add_argument(
+        "--gradient_accumulation_steps",
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.",
+    )
+
+    parser.add_argument(
+        "--gradient_checkpointing",
+        action="store_true",
+        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
+    )
+
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=1e-4,
+        help="Initial learning rate (after the potential warmup period) to use.",
+    )
+
+    parser.add_argument(
+        "--scale_lr",
+        action="store_true",
+        default=False,
+        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
+    )
+
+    parser.add_argument(
+        "--lr_scheduler",
+        type=str,
+        default="constant",
+        help=(
+            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
+            ' "constant", "constant_with_warmup"]'
+        ),
+    )
+
+    parser.add_argument(
+        "--snr_gamma",
+        type=float,
+        default=None,
+        help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
+        "More details here: https://arxiv.org/abs/2303.09556.",
+    )
+
+    parser.add_argument(
+        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
+    )
+
+    parser.add_argument(
+        "--lr_num_cycles",
+        type=int,
+        default=1,
+        help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
+    )
+
+    parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
+
+    parser.add_argument(
+        "--dataloader_num_workers",
+        type=int,
+        default=0,
+        help=(
+            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
+        ),
+    )
+
+    parser.add_argument("--adam_weight_decay", type=float, default=1e-04, help="Weight decay to use for unet params")
+
+    parser.add_argument(
+        "--adam_epsilon",
+        type=float,
+        default=1e-08,
+        help="Epsilon value for the Adam optimizer and Prodigy optimizers.",
+    )
+
+    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
+    
+    parser.add_argument(
+        "--logging_dir",
+        type=str,
+        default="logs",
+        help=(
+            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
+        ),
+    )
+    parser.add_argument(
+        "--allow_tf32",
+        action="store_true",
+        help=(
+            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
+            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+        ),
+    )
+
+    parser.add_argument(
+        "--report_to",
+        type=str,
+        default="wandb",
+        help=(
+            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
+            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
+        ),
+    )
+
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default=None,
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
+            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+        ),
+    )
+
+    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
+
+    parser.add_argument(
+        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
+    )
+
+    parser.add_argument(
+        "--rank",
+        type=int,
+        default=4,
+        help=("The dimension of the LoRA update matrices."),
+    )
+
+    parser.add_argument(
+        "--pretrained_lcm_lora_path",
+        type=str,
+        default="latent-consistency/lcm-lora-sdxl",
+        help=("Path for lcm lora pretrained"),
+        )
+    
+    parser.add_argument(
+        "--losses_config_path",
+        type=str,
+        required=True,
+        help=("A yaml file containing losses to use and their weights."),
+    )
+    
+    parser.add_argument(
+        "--lcm_every_k_steps",
+        type=int,
+        default=-1,
+        help="How often to run lcm. If -1, lcm is not run."
+    )
+
+    parser.add_argument(
+        "--lcm_batch_size",
+        type=int,
+        default=1,
+        help="Batch size for lcm."
+    )
+    parser.add_argument(
+        "--lcm_max_timestep",
+        type=int,
+        default=1000,
+        help="Max timestep to use with LCM."
+    )
+
+    parser.add_argument(
+        "--lcm_sample_scale_every_k_steps",
+        type=int,
+        default=-1,
+        help="How often to change lcm scale. If -1, scale is fixed at 1."
+    )
+
+    parser.add_argument(
+        "--lcm_min_scale",
+        type=float,
+        default=0.1,
+        help="When sampling lcm scale, the minimum scale to use."
+    )
+
+    parser.add_argument(
+        "--scale_lcm_by_max_step",
+        action="store_true",
+        help="scale LCM lora alpha linearly by the maximal timestep sampled that iteration"
+    )
+
+    parser.add_argument(
+        "--lcm_sample_full_lcm_prob",
+        type=float,
+        default=0.2,
+        help="When sampling lcm scale, the probability of using full lcm (scale of 1)."
+    )
+
+    parser.add_argument(
+        "--run_on_cpu",
+        action="store_true",
+        help="whether to run on cpu or not"
+    )
+
+    parser.add_argument(
+        "--experiment_name",
+        type=str,
+        help=("A short description of the experiment to add to the wand run log. "),
+    )
+    parser.add_argument("--encoder_lora_rank", type=int, default=0, help="Rank of Lora in unet encoder. 0 means no lora")
+
+    parser.add_argument("--kvcopy_lora_rank", type=int, default=0, help="Rank of lora in the kvcopy modules. 0 means no lora")
+
+
+    if input_args is not None:
+        args = parser.parse_args(input_args)
+    else:
+        args = parser.parse_args()
+
+    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    if env_local_rank != -1 and env_local_rank != args.local_rank:
+        args.local_rank = env_local_rank
+
+    args.optimizer = "AdamW"
+
+    return args