203 lines
13 KiB
Python
203 lines
13 KiB
Python
import argparse
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import json
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import random
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import time
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import typing as t
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from pathlib import Path
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from mflux.community.in_context_lora.in_context_loras import LORA_NAME_MAP, LORA_REPO_ID
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from mflux.ui import defaults as ui_defaults
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class ModelSpecAction(argparse.Action):
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def __call__(self, parser, namespace, values, option_string=None):
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if values in ["dev", "schnell"]:
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setattr(namespace, self.dest, values)
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return
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if values.count("/") != 1:
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raise argparse.ArgumentError(
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self, 'Value must be either "dev", "schnell", or "' f'in format "org/model". Got: {values}'
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)
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# If we got here, values contains exactly one slash
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setattr(namespace, self.dest, values)
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# fmt: off
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class CommandLineParser(argparse.ArgumentParser):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.supports_metadata_config = False
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self.supports_image_generation = False
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self.supports_controlnet = False
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self.supports_image_to_image = False
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self.supports_lora = False
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def add_general_arguments(self) -> None:
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self.add_argument("--low-ram", action="store_true", help="Enable low-RAM mode to reduce memory usage (may impact performance).")
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def add_model_arguments(self, path_type: t.Literal["load", "save"] = "load", require_model_arg: bool = True) -> None:
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self.add_argument("--model", "-m", type=str, required=require_model_arg, action=ModelSpecAction, help=f"The model to use ({' or '.join(ui_defaults.MODEL_CHOICES)} or a compatible huggingface repo_id org/model).")
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if path_type == "load":
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self.add_argument("--path", type=str, default=None, help="Local path for loading a model from disk")
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else:
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self.add_argument("--path", type=str, required=True, help="Local path for saving a model to disk.")
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self.add_argument("--base-model", type=str, required=False, choices=ui_defaults.MODEL_CHOICES, help="When using a third-party huggingface model, explicitly specify whether the base model is dev or schnell")
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self.add_argument("--quantize", "-q", type=int, choices=ui_defaults.QUANTIZE_CHOICES, default=None, help=f"Quantize the model ({' or '.join(map(str, ui_defaults.QUANTIZE_CHOICES))}, Default is None)")
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def add_lora_arguments(self) -> None: # fmt: off
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self.supports_lora = True
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lora_group = self.add_argument_group("LoRA configuration")
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lora_group.add_argument("--lora-style", type=str, choices=sorted(LORA_NAME_MAP.keys()), help="Style of the LoRA to use (e.g., 'storyboard' for film storyboard style)")
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self.add_argument("--lora-paths", type=str, nargs="*", default=None, help="Local safetensors for applying LORA from disk")
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self.add_argument("--lora-scales", type=float, nargs="*", default=None, help="Scaling factor to adjust the impact of LoRA weights on the model. A value of 1.0 applies the LoRA weights as they are.")
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lora_group.add_argument("--lora-name", type=str, help="Name of the LoRA to download from Hugging Face")
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lora_group.add_argument("--lora-repo-id", type=str, default=LORA_REPO_ID, help=f"Hugging Face repository ID for LoRAs (default: {LORA_REPO_ID})")
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# fmt: on
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def _add_image_generator_common_arguments(self) -> None:
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self.supports_image_generation = True
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self.add_argument("--height", type=int, default=ui_defaults.HEIGHT, help=f"Image height (Default is {ui_defaults.HEIGHT})")
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self.add_argument("--width", type=int, default=ui_defaults.WIDTH, help=f"Image width (Default is {ui_defaults.HEIGHT})")
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self.add_argument("--steps", type=int, default=None, help="Inference Steps")
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self.add_argument("--guidance", type=float, default=ui_defaults.GUIDANCE_SCALE, help=f"Guidance Scale (Default is {ui_defaults.GUIDANCE_SCALE})")
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def add_image_generator_arguments(self, supports_metadata_config=False) -> None:
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self.add_argument("--prompt", type=str, required=(not supports_metadata_config), default=None, help="The textual description of the image to generate.")
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self.add_argument("--seed", type=int, default=None, nargs='+', help="Specify 1+ Entropy Seeds (Default is 1 time-based random-seed)")
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self.add_argument("--auto-seeds", type=int, default=-1, help="Auto generate N Entropy Seeds (random ints between 0 and 1 billion")
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self._add_image_generator_common_arguments()
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if supports_metadata_config:
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self.add_metadata_config()
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def add_image_to_image_arguments(self, required=False) -> None:
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self.supports_image_to_image = True
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self.add_argument("--image-path", type=Path, required=required, default=None, help="Local path to init image")
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self.add_argument("--image-strength", type=float, required=False, default=ui_defaults.IMAGE_STRENGTH, help=f"Controls how strongly the init image influences the output image. A value of 0.0 means no influence. (Default is {ui_defaults.IMAGE_STRENGTH})")
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def add_batch_image_generator_arguments(self) -> None:
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self.add_argument("--prompts-file", type=Path, required=True, default=argparse.SUPPRESS, help="Local path for a file that holds a batch of prompts.")
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self.add_argument("--global-seed", type=int, default=argparse.SUPPRESS, help="Entropy Seed (used for all prompts in the batch)")
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self._add_image_generator_common_arguments()
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def add_output_arguments(self) -> None:
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self.add_argument("--metadata", action="store_true", help="Export image metadata as a JSON file.")
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self.add_argument("--output", type=str, default="image.png", help="The filename for the output image. Default is \"image.png\".")
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self.add_argument('--stepwise-image-output-dir', type=str, default=None, help='[EXPERIMENTAL] Output dir to write step-wise images and their final composite image to. This feature may change in future versions.')
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def add_controlnet_arguments(self) -> None:
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self.supports_controlnet = True
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self.add_argument("--controlnet-image-path", type=str, required=False, help="Local path of the image to use as input for controlnet.")
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self.add_argument("--controlnet-strength", type=float, default=ui_defaults.CONTROLNET_STRENGTH, help=f"Controls how strongly the control image influences the output image. A value of 0.0 means no influence. (Default is {ui_defaults.CONTROLNET_STRENGTH})")
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self.add_argument("--controlnet-save-canny", action="store_true", help="If set, save the Canny edge detection reference input image.")
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def add_metadata_config(self) -> None:
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self.supports_metadata_config = True
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self.add_argument("--config-from-metadata", "-C", type=Path, required=False, default=argparse.SUPPRESS, help="Re-use the parameters from prior metadata. Params from metadata are secondary to other args you provide.")
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def add_training_arguments(self) -> None:
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self.add_argument("--train-config", type=str, required=False, help="Local path of the training configuration file")
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self.add_argument("--train-checkpoint", type=str, required=False, help="Local path of the checkpoint file which specifies how to continue the training process")
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def parse_args(self, **kwargs) -> argparse.Namespace:
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namespace = super().parse_args()
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# Check if either training arguments are provided
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has_training_args = (hasattr(namespace, "train_config") and namespace.train_config is not None) or \
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(hasattr(namespace, "train_checkpoint") and namespace.train_checkpoint is not None)
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# Only enforce model requirement for path if we're not in training mode
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if hasattr(namespace, "path") and namespace.path is not None and namespace.model is None and not has_training_args:
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self.error("--model must be specified when using --path")
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if getattr(namespace, "config_from_metadata", False):
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prior_gen_metadata = json.load(namespace.config_from_metadata.open("rt"))
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if namespace.model is None:
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# when not provided by CLI flag, find it in the config file
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namespace.model = prior_gen_metadata.get("model", None)
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if namespace.base_model is None:
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namespace.base_model = prior_gen_metadata.get("base_model", None)
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if namespace.prompt is None:
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namespace.prompt = prior_gen_metadata.get("prompt", None)
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# all configs from the metadata config defers to any explicitly defined args
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guidance_default = self.get_default("guidance")
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guidance_from_metadata = prior_gen_metadata.get("guidance")
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if namespace.guidance == guidance_default and guidance_from_metadata:
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namespace.guidance = guidance_from_metadata
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if namespace.quantize is None:
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namespace.quantize = prior_gen_metadata.get("quantize", None)
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seed_from_metadata = prior_gen_metadata.get("seed", None)
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if namespace.seed is None and seed_from_metadata is not None:
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namespace.seed = [seed_from_metadata]
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if namespace.seed is None:
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# not passed by user, not populated by metadata
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namespace.seed = [int(time.time())]
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if namespace.steps is None:
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namespace.steps = prior_gen_metadata.get("steps", None)
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if self.supports_lora:
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if namespace.lora_paths is None:
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namespace.lora_paths = prior_gen_metadata.get("lora_paths", None)
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elif namespace.lora_paths:
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# merge the loras from cli and config file
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namespace.lora_paths = prior_gen_metadata.get("lora_paths", []) + namespace.lora_paths
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if namespace.lora_scales is None:
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namespace.lora_scales = prior_gen_metadata.get("lora_scales", None)
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elif namespace.lora_scales:
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# merge the loras from cli and config file
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namespace.lora_scales = prior_gen_metadata.get("lora_scales", []) + namespace.lora_scales
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if self.supports_image_to_image:
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if namespace.image_path is None:
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namespace.image_path = prior_gen_metadata.get("image_path", None)
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if namespace.image_strength == self.get_default("image_strength") and (img_strength_from_metadata := prior_gen_metadata.get("image_strength", None)):
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namespace.image_strength = img_strength_from_metadata
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if self.supports_controlnet:
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if namespace.controlnet_image_path is None:
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namespace.controlnet_image_path = prior_gen_metadata.get("controlnet_image_path", None)
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if namespace.controlnet_strength == self.get_default("controlnet_strength") and (cnet_strength_from_metadata := prior_gen_metadata.get("controlnet_strength", None)):
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namespace.controlnet_strength = cnet_strength_from_metadata
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if namespace.controlnet_save_canny == self.get_default("controlnet_save_canny") and (cnet_canny_from_metadata := prior_gen_metadata.get("controlnet_save_canny", None)):
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namespace.controlnet_save_canny = cnet_canny_from_metadata
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# Only require model if we're not in training mode
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if namespace.model is None and not has_training_args:
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self.error("--model / -m must be provided, or 'model' must be specified in the config file.")
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if self.supports_image_generation and namespace.seed is None and namespace.auto_seeds > 0:
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# choose N int seeds in the range of 0 < value < 1 billion
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namespace.seed = [random.randint(0, int(1e7)) for _ in range(namespace.auto_seeds)]
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if self.supports_image_generation and namespace.seed is None:
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# final default: did not obtain seed from metadata, --seed, or --auto-seeds
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namespace.seed = [int(time.time())]
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if self.supports_image_generation and len(namespace.seed) > 1:
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# auto append seed-$value to output names for multi image generations
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# e.g. output.png -> output_seed_101.png output_seed_102.png, etc
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output_path = Path(namespace.output)
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namespace.output = str(output_path.with_stem(output_path.stem + "_seed_{seed}"))
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if self.supports_image_generation and namespace.prompt is None:
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# not supplied by CLI and not supplied by metadata config file
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self.error("--prompt argument required or 'prompt' required in metadata config file")
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if self.supports_image_generation and namespace.steps is None:
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namespace.steps = ui_defaults.MODEL_INFERENCE_STEPS.get(namespace.model, None)
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if namespace.low_ram and len(namespace.seed) > 1:
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self.error("--low-ram cannot be used with multiple seeds")
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return namespace
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