import os import time from typing import List, Optional, Literal, Union, TYPE_CHECKING, Dict import random import torch from toolkit.prompt_utils import PromptEmbeds ImgExt = Literal['jpg', 'png', 'webp'] SaveFormat = Literal['safetensors', 'diffusers'] if TYPE_CHECKING: from toolkit.guidance import GuidanceType class SaveConfig: def __init__(self, **kwargs): self.save_every: int = kwargs.get('save_every', 1000) self.dtype: str = kwargs.get('dtype', 'float16') self.max_step_saves_to_keep: int = kwargs.get('max_step_saves_to_keep', 5) self.save_format: SaveFormat = kwargs.get('save_format', 'safetensors') if self.save_format not in ['safetensors', 'diffusers']: raise ValueError(f"save_format must be safetensors or diffusers, got {self.save_format}") self.push_to_hub: bool = kwargs.get("push_to_hub", False) self.hf_repo_id: Optional[str] = kwargs.get("hf_repo_id", None) self.hf_private: Optional[str] = kwargs.get("hf_private", False) class LogingConfig: def __init__(self, **kwargs): self.log_every: int = kwargs.get('log_every', 100) self.verbose: bool = kwargs.get('verbose', False) self.use_wandb: bool = kwargs.get('use_wandb', False) class SampleConfig: def __init__(self, **kwargs): self.sampler: str = kwargs.get('sampler', 'ddpm') self.sample_every: int = kwargs.get('sample_every', 100) self.width: int = kwargs.get('width', 512) self.height: int = kwargs.get('height', 512) self.prompts: list[str] = kwargs.get('prompts', []) self.neg = kwargs.get('neg', False) self.seed = kwargs.get('seed', 0) self.walk_seed = kwargs.get('walk_seed', False) self.guidance_scale = kwargs.get('guidance_scale', 7) self.sample_steps = kwargs.get('sample_steps', 20) self.network_multiplier = kwargs.get('network_multiplier', 1) self.guidance_rescale = kwargs.get('guidance_rescale', 0.0) self.ext: ImgExt = kwargs.get('format', 'jpg') self.adapter_conditioning_scale = kwargs.get('adapter_conditioning_scale', 1.0) self.refiner_start_at = kwargs.get('refiner_start_at', 0.5) # step to start using refiner on sample if it exists self.extra_values = kwargs.get('extra_values', []) class LormModuleSettingsConfig: def __init__(self, **kwargs): self.contains: str = kwargs.get('contains', '4nt$3') self.extract_mode: str = kwargs.get('extract_mode', 'ratio') # min num parameters to attach to self.parameter_threshold: int = kwargs.get('parameter_threshold', 0) self.extract_mode_param: dict = kwargs.get('extract_mode_param', 0.25) class LoRMConfig: def __init__(self, **kwargs): self.extract_mode: str = kwargs.get('extract_mode', 'ratio') self.do_conv: bool = kwargs.get('do_conv', False) self.extract_mode_param: dict = kwargs.get('extract_mode_param', 0.25) self.parameter_threshold: int = kwargs.get('parameter_threshold', 0) module_settings = kwargs.get('module_settings', []) default_module_settings = { 'extract_mode': self.extract_mode, 'extract_mode_param': self.extract_mode_param, 'parameter_threshold': self.parameter_threshold, } module_settings = [{**default_module_settings, **module_setting, } for module_setting in module_settings] self.module_settings: List[LormModuleSettingsConfig] = [LormModuleSettingsConfig(**module_setting) for module_setting in module_settings] def get_config_for_module(self, block_name): for setting in self.module_settings: contain_pieces = setting.contains.split('|') if all(contain_piece in block_name for contain_piece in contain_pieces): return setting # try replacing the . with _ contain_pieces = setting.contains.replace('.', '_').split('|') if all(contain_piece in block_name for contain_piece in contain_pieces): return setting # do default return LormModuleSettingsConfig(**{ 'extract_mode': self.extract_mode, 'extract_mode_param': self.extract_mode_param, 'parameter_threshold': self.parameter_threshold, }) NetworkType = Literal['lora', 'locon', 'lorm'] class NetworkConfig: def __init__(self, **kwargs): self.type: NetworkType = kwargs.get('type', 'lora') rank = kwargs.get('rank', None) linear = kwargs.get('linear', None) if rank is not None: self.rank: int = rank # rank for backward compatibility self.linear: int = rank elif linear is not None: self.rank: int = linear self.linear: int = linear self.conv: int = kwargs.get('conv', None) self.alpha: float = kwargs.get('alpha', 1.0) self.linear_alpha: float = kwargs.get('linear_alpha', self.alpha) self.conv_alpha: float = kwargs.get('conv_alpha', self.conv) self.dropout: Union[float, None] = kwargs.get('dropout', None) self.network_kwargs: dict = kwargs.get('network_kwargs', {}) self.lorm_config: Union[LoRMConfig, None] = None lorm = kwargs.get('lorm', None) if lorm is not None: self.lorm_config: LoRMConfig = LoRMConfig(**lorm) if self.type == 'lorm': # set linear to arbitrary values so it makes them self.linear = 4 self.rank = 4 if self.lorm_config.do_conv: self.conv = 4 self.transformer_only = kwargs.get('transformer_only', True) AdapterTypes = Literal['t2i', 'ip', 'ip+', 'clip', 'ilora', 'photo_maker', 'control_net'] CLIPLayer = Literal['penultimate_hidden_states', 'image_embeds', 'last_hidden_state'] class AdapterConfig: def __init__(self, **kwargs): self.type: AdapterTypes = kwargs.get('type', 't2i') # t2i, ip, clip, control_net self.in_channels: int = kwargs.get('in_channels', 3) self.channels: List[int] = kwargs.get('channels', [320, 640, 1280, 1280]) self.num_res_blocks: int = kwargs.get('num_res_blocks', 2) self.downscale_factor: int = kwargs.get('downscale_factor', 8) self.adapter_type: str = kwargs.get('adapter_type', 'full_adapter') self.image_dir: str = kwargs.get('image_dir', None) self.test_img_path: str = kwargs.get('test_img_path', None) self.train: str = kwargs.get('train', False) self.image_encoder_path: str = kwargs.get('image_encoder_path', None) self.name_or_path = kwargs.get('name_or_path', None) num_tokens = kwargs.get('num_tokens', None) if num_tokens is None and self.type.startswith('ip'): if self.type == 'ip+': num_tokens = 16 num_tokens = 16 elif self.type == 'ip': num_tokens = 4 self.num_tokens: int = num_tokens self.train_image_encoder: bool = kwargs.get('train_image_encoder', False) self.train_only_image_encoder: bool = kwargs.get('train_only_image_encoder', False) if self.train_only_image_encoder: self.train_image_encoder = True self.train_only_image_encoder_positional_embedding: bool = kwargs.get( 'train_only_image_encoder_positional_embedding', False) self.image_encoder_arch: str = kwargs.get('image_encoder_arch', 'clip') # clip vit vit_hybrid, safe self.safe_reducer_channels: int = kwargs.get('safe_reducer_channels', 512) self.safe_channels: int = kwargs.get('safe_channels', 2048) self.safe_tokens: int = kwargs.get('safe_tokens', 8) self.quad_image: bool = kwargs.get('quad_image', False) # clip vision self.trigger = kwargs.get('trigger', 'tri993r') self.trigger_class_name = kwargs.get('trigger_class_name', None) self.class_names = kwargs.get('class_names', []) self.clip_layer: CLIPLayer = kwargs.get('clip_layer', None) if self.clip_layer is None: if self.type.startswith('ip+'): self.clip_layer = 'penultimate_hidden_states' else: self.clip_layer = 'last_hidden_state' # text encoder self.text_encoder_path: str = kwargs.get('text_encoder_path', None) self.text_encoder_arch: str = kwargs.get('text_encoder_arch', 'clip') # clip t5 self.train_scaler: bool = kwargs.get('train_scaler', False) self.scaler_lr: Optional[float] = kwargs.get('scaler_lr', None) # trains with a scaler to easy channel bias but merges it in on save self.merge_scaler: bool = kwargs.get('merge_scaler', False) # for ilora self.head_dim: int = kwargs.get('head_dim', 1024) self.num_heads: int = kwargs.get('num_heads', 1) self.ilora_down: bool = kwargs.get('ilora_down', True) self.ilora_mid: bool = kwargs.get('ilora_mid', True) self.ilora_up: bool = kwargs.get('ilora_up', True) self.flux_only_double: bool = kwargs.get('flux_only_double', False) class EmbeddingConfig: def __init__(self, **kwargs): self.trigger = kwargs.get('trigger', 'custom_embedding') self.tokens = kwargs.get('tokens', 4) self.init_words = kwargs.get('init_words', '*') self.save_format = kwargs.get('save_format', 'safetensors') self.trigger_class_name = kwargs.get('trigger_class_name', None) # used for inverted masked prior ContentOrStyleType = Literal['balanced', 'style', 'content'] LossTarget = Literal['noise', 'source', 'unaugmented', 'differential_noise'] class TrainConfig: def __init__(self, **kwargs): self.noise_scheduler = kwargs.get('noise_scheduler', 'ddpm') self.content_or_style: ContentOrStyleType = kwargs.get('content_or_style', 'balanced') self.content_or_style_reg: ContentOrStyleType = kwargs.get('content_or_style', 'balanced') self.steps: int = kwargs.get('steps', 1000) self.lr = kwargs.get('lr', 1e-6) self.unet_lr = kwargs.get('unet_lr', self.lr) self.text_encoder_lr = kwargs.get('text_encoder_lr', self.lr) self.refiner_lr = kwargs.get('refiner_lr', self.lr) self.embedding_lr = kwargs.get('embedding_lr', self.lr) self.adapter_lr = kwargs.get('adapter_lr', self.lr) self.optimizer = kwargs.get('optimizer', 'adamw') self.optimizer_params = kwargs.get('optimizer_params', {}) self.lr_scheduler = kwargs.get('lr_scheduler', 'constant') self.lr_scheduler_params = kwargs.get('lr_scheduler_params', {}) self.min_denoising_steps: int = kwargs.get('min_denoising_steps', 0) self.max_denoising_steps: int = kwargs.get('max_denoising_steps', 1000) self.batch_size: int = kwargs.get('batch_size', 1) self.orig_batch_size: int = self.batch_size self.dtype: str = kwargs.get('dtype', 'fp32') self.xformers = kwargs.get('xformers', False) self.sdp = kwargs.get('sdp', False) self.train_unet = kwargs.get('train_unet', True) self.train_text_encoder = kwargs.get('train_text_encoder', False) self.train_refiner = kwargs.get('train_refiner', True) self.train_turbo = kwargs.get('train_turbo', False) self.show_turbo_outputs = kwargs.get('show_turbo_outputs', False) self.min_snr_gamma = kwargs.get('min_snr_gamma', None) self.snr_gamma = kwargs.get('snr_gamma', None) # trains a gamma, offset, and scale to adjust loss to adapt to timestep differentials # this should balance the learning rate across all timesteps over time self.learnable_snr_gos = kwargs.get('learnable_snr_gos', False) self.noise_offset = kwargs.get('noise_offset', 0.0) self.skip_first_sample = kwargs.get('skip_first_sample', False) self.force_first_sample = kwargs.get('force_first_sample', False) self.gradient_checkpointing = kwargs.get('gradient_checkpointing', True) self.weight_jitter = kwargs.get('weight_jitter', 0.0) self.merge_network_on_save = kwargs.get('merge_network_on_save', False) self.max_grad_norm = kwargs.get('max_grad_norm', 1.0) self.start_step = kwargs.get('start_step', None) self.free_u = kwargs.get('free_u', False) self.adapter_assist_name_or_path: Optional[str] = kwargs.get('adapter_assist_name_or_path', None) self.adapter_assist_type: Optional[str] = kwargs.get('adapter_assist_type', 't2i') # t2i, control_net self.noise_multiplier = kwargs.get('noise_multiplier', 1.0) self.target_noise_multiplier = kwargs.get('target_noise_multiplier', 1.0) self.img_multiplier = kwargs.get('img_multiplier', 1.0) self.noisy_latent_multiplier = kwargs.get('noisy_latent_multiplier', 1.0) self.latent_multiplier = kwargs.get('latent_multiplier', 1.0) self.negative_prompt = kwargs.get('negative_prompt', None) self.max_negative_prompts = kwargs.get('max_negative_prompts', 1) # multiplier applied to loos on regularization images self.reg_weight = kwargs.get('reg_weight', 1.0) self.num_train_timesteps = kwargs.get('num_train_timesteps', 1000) self.random_noise_shift = kwargs.get('random_noise_shift', 0.0) # automatically adapte the vae scaling based on the image norm self.adaptive_scaling_factor = kwargs.get('adaptive_scaling_factor', False) # dropout that happens before encoding. It functions independently per text encoder self.prompt_dropout_prob = kwargs.get('prompt_dropout_prob', 0.0) # match the norm of the noise before computing loss. This will help the model maintain its # current understandin of the brightness of images. self.match_noise_norm = kwargs.get('match_noise_norm', False) # set to -1 to accumulate gradients for entire epoch # warning, only do this with a small dataset or you will run out of memory # This is legacy but left in for backwards compatibility self.gradient_accumulation_steps = kwargs.get('gradient_accumulation_steps', 1) # this will do proper gradient accumulation where you will not see a step until the end of the accumulation # the method above will show a step every accumulation self.gradient_accumulation = kwargs.get('gradient_accumulation', 1) if self.gradient_accumulation > 1: if self.gradient_accumulation_steps != 1: raise ValueError("gradient_accumulation and gradient_accumulation_steps are mutually exclusive") # short long captions will double your batch size. This only works when a dataset is # prepared with a json caption file that has both short and long captions in it. It will # Double up every image and run it through with both short and long captions. The idea # is that the network will learn how to generate good images with both short and long captions self.short_and_long_captions = kwargs.get('short_and_long_captions', False) # if above is NOT true, this will make it so the long caption foes to te2 and the short caption goes to te1 for sdxl only self.short_and_long_captions_encoder_split = kwargs.get('short_and_long_captions_encoder_split', False) # basically gradient accumulation but we run just 1 item through the network # and accumulate gradients. This can be used as basic gradient accumulation but is very helpful # for training tricks that increase batch size but need a single gradient step self.single_item_batching = kwargs.get('single_item_batching', False) match_adapter_assist = kwargs.get('match_adapter_assist', False) self.match_adapter_chance = kwargs.get('match_adapter_chance', 0.0) self.loss_target: LossTarget = kwargs.get('loss_target', 'noise') # noise, source, unaugmented, differential_noise # When a mask is passed in a dataset, and this is true, # we will predict noise without a the LoRa network and use the prediction as a target for # unmasked reign. It is unmasked regularization basically self.inverted_mask_prior = kwargs.get('inverted_mask_prior', False) self.inverted_mask_prior_multiplier = kwargs.get('inverted_mask_prior_multiplier', 0.5) # legacy if match_adapter_assist and self.match_adapter_chance == 0.0: self.match_adapter_chance = 1.0 # standardize inputs to the meand std of the model knowledge self.standardize_images = kwargs.get('standardize_images', False) self.standardize_latents = kwargs.get('standardize_latents', False) if self.train_turbo and not self.noise_scheduler.startswith("euler"): raise ValueError(f"train_turbo is only supported with euler and wuler_a noise schedulers") self.dynamic_noise_offset = kwargs.get('dynamic_noise_offset', False) self.do_cfg = kwargs.get('do_cfg', False) self.do_random_cfg = kwargs.get('do_random_cfg', False) self.cfg_scale = kwargs.get('cfg_scale', 1.0) self.max_cfg_scale = kwargs.get('max_cfg_scale', self.cfg_scale) self.cfg_rescale = kwargs.get('cfg_rescale', None) if self.cfg_rescale is None: self.cfg_rescale = self.cfg_scale # applies the inverse of the prediction mean and std to the target to correct # for norm drift self.correct_pred_norm = kwargs.get('correct_pred_norm', False) self.correct_pred_norm_multiplier = kwargs.get('correct_pred_norm_multiplier', 1.0) self.loss_type = kwargs.get('loss_type', 'mse') # scale the prediction by this. Increase for more detail, decrease for less self.pred_scaler = kwargs.get('pred_scaler', 1.0) # repeats the prompt a few times to saturate the encoder self.prompt_saturation_chance = kwargs.get('prompt_saturation_chance', 0.0) # applies negative loss on the prior to encourage network to diverge from it self.do_prior_divergence = kwargs.get('do_prior_divergence', False) ema_config: Union[Dict, None] = kwargs.get('ema_config', None) if ema_config is not None: ema_config['use_ema'] = True print(f"Using EMA") else: ema_config = {'use_ema': False} self.ema_config: EMAConfig = EMAConfig(**ema_config) # adds an additional loss to the network to encourage it output a normalized standard deviation self.target_norm_std = kwargs.get('target_norm_std', None) self.target_norm_std_value = kwargs.get('target_norm_std_value', 1.0) self.linear_timesteps = kwargs.get('linear_timesteps', False) self.linear_timesteps2 = kwargs.get('linear_timesteps2', False) self.disable_sampling = kwargs.get('disable_sampling', False) class ModelConfig: def __init__(self, **kwargs): self.name_or_path: str = kwargs.get('name_or_path', None) self.is_v2: bool = kwargs.get('is_v2', False) self.is_xl: bool = kwargs.get('is_xl', False) self.is_pixart: bool = kwargs.get('is_pixart', False) self.is_pixart_sigma: bool = kwargs.get('is_pixart_sigma', False) self.is_auraflow: bool = kwargs.get('is_auraflow', False) self.is_v3: bool = kwargs.get('is_v3', False) self.is_flux: bool = kwargs.get('is_flux', False) if self.is_pixart_sigma: self.is_pixart = True self.use_flux_cfg = kwargs.get('use_flux_cfg', False) self.is_ssd: bool = kwargs.get('is_ssd', False) self.is_vega: bool = kwargs.get('is_vega', False) self.is_v_pred: bool = kwargs.get('is_v_pred', False) self.dtype: str = kwargs.get('dtype', 'float16') self.vae_path = kwargs.get('vae_path', None) self.refiner_name_or_path = kwargs.get('refiner_name_or_path', None) self._original_refiner_name_or_path = self.refiner_name_or_path self.refiner_start_at = kwargs.get('refiner_start_at', 0.5) self.lora_path = kwargs.get('lora_path', None) # mainly for decompression loras for distilled models self.assistant_lora_path = kwargs.get('assistant_lora_path', None) self.latent_space_version = kwargs.get('latent_space_version', None) # only for SDXL models for now self.use_text_encoder_1: bool = kwargs.get('use_text_encoder_1', True) self.use_text_encoder_2: bool = kwargs.get('use_text_encoder_2', True) self.experimental_xl: bool = kwargs.get('experimental_xl', False) if self.name_or_path is None: raise ValueError('name_or_path must be specified') if self.is_ssd: # sed sdxl as true since it is mostly the same architecture self.is_xl = True if self.is_vega: self.is_xl = True # for text encoder quant. Only works with pixart currently self.text_encoder_bits = kwargs.get('text_encoder_bits', 16) # 16, 8, 4 self.unet_path = kwargs.get("unet_path", None) self.unet_sample_size = kwargs.get("unet_sample_size", None) self.vae_device = kwargs.get("vae_device", None) self.vae_dtype = kwargs.get("vae_dtype", self.dtype) self.te_device = kwargs.get("te_device", None) self.te_dtype = kwargs.get("te_dtype", self.dtype) # only for flux for now self.quantize = kwargs.get("quantize", False) self.low_vram = kwargs.get("low_vram", False) self.attn_masking = kwargs.get("attn_masking", False) if self.attn_masking and not self.is_flux: raise ValueError("attn_masking is only supported with flux models currently") pass class EMAConfig: def __init__(self, **kwargs): self.use_ema: bool = kwargs.get('use_ema', False) self.ema_decay: float = kwargs.get('ema_decay', 0.999) # feeds back the decay difference into the parameter self.use_feedback: bool = kwargs.get('use_feedback', False) class ReferenceDatasetConfig: def __init__(self, **kwargs): # can pass with a side by side pait or a folder with pos and neg folder self.pair_folder: str = kwargs.get('pair_folder', None) self.pos_folder: str = kwargs.get('pos_folder', None) self.neg_folder: str = kwargs.get('neg_folder', None) self.network_weight: float = float(kwargs.get('network_weight', 1.0)) self.pos_weight: float = float(kwargs.get('pos_weight', self.network_weight)) self.neg_weight: float = float(kwargs.get('neg_weight', self.network_weight)) # make sure they are all absolute values no negatives self.pos_weight = abs(self.pos_weight) self.neg_weight = abs(self.neg_weight) self.target_class: str = kwargs.get('target_class', '') self.size: int = kwargs.get('size', 512) class SliderTargetConfig: def __init__(self, **kwargs): self.target_class: str = kwargs.get('target_class', '') self.positive: str = kwargs.get('positive', '') self.negative: str = kwargs.get('negative', '') self.multiplier: float = kwargs.get('multiplier', 1.0) self.weight: float = kwargs.get('weight', 1.0) self.shuffle: bool = kwargs.get('shuffle', False) class GuidanceConfig: def __init__(self, **kwargs): self.target_class: str = kwargs.get('target_class', '') self.guidance_scale: float = kwargs.get('guidance_scale', 1.0) self.positive_prompt: str = kwargs.get('positive_prompt', '') self.negative_prompt: str = kwargs.get('negative_prompt', '') class SliderConfigAnchors: def __init__(self, **kwargs): self.prompt = kwargs.get('prompt', '') self.neg_prompt = kwargs.get('neg_prompt', '') self.multiplier = kwargs.get('multiplier', 1.0) class SliderConfig: def __init__(self, **kwargs): targets = kwargs.get('targets', []) anchors = kwargs.get('anchors', []) anchors = [SliderConfigAnchors(**anchor) for anchor in anchors] self.anchors: List[SliderConfigAnchors] = anchors self.resolutions: List[List[int]] = kwargs.get('resolutions', [[512, 512]]) self.prompt_file: str = kwargs.get('prompt_file', None) self.prompt_tensors: str = kwargs.get('prompt_tensors', None) self.batch_full_slide: bool = kwargs.get('batch_full_slide', True) self.use_adapter: bool = kwargs.get('use_adapter', None) # depth self.adapter_img_dir = kwargs.get('adapter_img_dir', None) self.low_ram = kwargs.get('low_ram', False) # expand targets if shuffling from toolkit.prompt_utils import get_slider_target_permutations self.targets: List[SliderTargetConfig] = [] targets = [SliderTargetConfig(**target) for target in targets] # do permutations if shuffle is true print(f"Building slider targets") for target in targets: if target.shuffle: target_permutations = get_slider_target_permutations(target, max_permutations=8) self.targets = self.targets + target_permutations else: self.targets.append(target) print(f"Built {len(self.targets)} slider targets (with permutations)") class DatasetConfig: """ Dataset config for sd-datasets """ def __init__(self, **kwargs): self.type = kwargs.get('type', 'image') # sd, slider, reference # will be legacy self.folder_path: str = kwargs.get('folder_path', None) # can be json or folder path self.dataset_path: str = kwargs.get('dataset_path', None) self.default_caption: str = kwargs.get('default_caption', None) random_triggers = kwargs.get('random_triggers', []) # if they are a string, load them from a file if isinstance(random_triggers, str) and os.path.exists(random_triggers): with open(random_triggers, 'r') as f: random_triggers = f.read().splitlines() # remove empty lines random_triggers = [line for line in random_triggers if line.strip() != ''] self.random_triggers: List[str] = random_triggers self.random_triggers_max: int = kwargs.get('random_triggers_max', 1) self.caption_ext: str = kwargs.get('caption_ext', None) self.random_scale: bool = kwargs.get('random_scale', False) self.random_crop: bool = kwargs.get('random_crop', False) self.resolution: int = kwargs.get('resolution', 512) self.scale: float = kwargs.get('scale', 1.0) self.buckets: bool = kwargs.get('buckets', True) self.bucket_tolerance: int = kwargs.get('bucket_tolerance', 64) self.is_reg: bool = kwargs.get('is_reg', False) self.network_weight: float = float(kwargs.get('network_weight', 1.0)) self.token_dropout_rate: float = float(kwargs.get('token_dropout_rate', 0.0)) self.shuffle_tokens: bool = kwargs.get('shuffle_tokens', False) self.caption_dropout_rate: float = float(kwargs.get('caption_dropout_rate', 0.0)) self.keep_tokens: int = kwargs.get('keep_tokens', 0) # #of first tokens to always keep unless caption dropped self.flip_x: bool = kwargs.get('flip_x', False) self.flip_y: bool = kwargs.get('flip_y', False) self.augments: List[str] = kwargs.get('augments', []) self.control_path: str = kwargs.get('control_path', None) # depth maps, etc # instead of cropping ot match image, it will serve the full size control image (clip images ie for ip adapters) self.full_size_control_images: bool = kwargs.get('full_size_control_images', False) self.alpha_mask: bool = kwargs.get('alpha_mask', False) # if true, will use alpha channel as mask self.mask_path: str = kwargs.get('mask_path', None) # focus mask (black and white. White has higher loss than black) self.unconditional_path: str = kwargs.get('unconditional_path', None) # path where matching unconditional images are located self.invert_mask: bool = kwargs.get('invert_mask', False) # invert mask self.mask_min_value: float = kwargs.get('mask_min_value', 0.0) # min value for . 0 - 1 self.poi: Union[str, None] = kwargs.get('poi', None) # if one is set and in json data, will be used as auto crop scale point of interes self.num_repeats: int = kwargs.get('num_repeats', 1) # number of times to repeat dataset # cache latents will store them in memory self.cache_latents: bool = kwargs.get('cache_latents', False) # cache latents to disk will store them on disk. If both are true, it will save to disk, but keep in memory self.cache_latents_to_disk: bool = kwargs.get('cache_latents_to_disk', False) self.cache_clip_vision_to_disk: bool = kwargs.get('cache_clip_vision_to_disk', False) self.standardize_images: bool = kwargs.get('standardize_images', False) # https://albumentations.ai/docs/api_reference/augmentations/transforms # augmentations are returned as a separate image and cannot currently be cached self.augmentations: List[dict] = kwargs.get('augmentations', None) self.shuffle_augmentations: bool = kwargs.get('shuffle_augmentations', False) has_augmentations = self.augmentations is not None and len(self.augmentations) > 0 if (len(self.augments) > 0 or has_augmentations) and (self.cache_latents or self.cache_latents_to_disk): print(f"WARNING: Augments are not supported with caching latents. Setting cache_latents to False") self.cache_latents = False self.cache_latents_to_disk = False # legacy compatability legacy_caption_type = kwargs.get('caption_type', None) if legacy_caption_type: self.caption_ext = legacy_caption_type self.caption_type = self.caption_ext self.guidance_type: GuidanceType = kwargs.get('guidance_type', 'targeted') # ip adapter / reference dataset self.clip_image_path: str = kwargs.get('clip_image_path', None) # depth maps, etc self.clip_image_augmentations: List[dict] = kwargs.get('clip_image_augmentations', None) self.clip_image_shuffle_augmentations: bool = kwargs.get('clip_image_shuffle_augmentations', False) self.replacements: List[str] = kwargs.get('replacements', []) self.loss_multiplier: float = kwargs.get('loss_multiplier', 1.0) self.num_workers: int = kwargs.get('num_workers', 2) self.prefetch_factor: int = kwargs.get('prefetch_factor', 2) self.extra_values: List[float] = kwargs.get('extra_values', []) self.square_crop: bool = kwargs.get('square_crop', False) # apply same augmentations to control images. Usually want this true unless special case self.replay_transforms: bool = kwargs.get('replay_transforms', True) def preprocess_dataset_raw_config(raw_config: List[dict]) -> List[dict]: """ This just splits up the datasets by resolutions so you dont have to do it manually :param raw_config: :return: """ # split up datasets by resolutions new_config = [] for dataset in raw_config: resolution = dataset.get('resolution', 512) if isinstance(resolution, list): resolution_list = resolution else: resolution_list = [resolution] for res in resolution_list: dataset_copy = dataset.copy() dataset_copy['resolution'] = res new_config.append(dataset_copy) return new_config class GenerateImageConfig: def __init__( self, prompt: str = '', prompt_2: Optional[str] = None, width: int = 512, height: int = 512, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: str = '', negative_prompt_2: Optional[str] = None, seed: int = -1, network_multiplier: float = 1.0, guidance_rescale: float = 0.0, # the tag [time] will be replaced with milliseconds since epoch output_path: str = None, # full image path output_folder: str = None, # folder to save image in if output_path is not specified output_ext: str = ImgExt, # extension to save image as if output_path is not specified output_tail: str = '', # tail to add to output filename add_prompt_file: bool = False, # add a prompt file with generated image adapter_image_path: str = None, # path to adapter image adapter_conditioning_scale: float = 1.0, # scale for adapter conditioning latents: Union[torch.Tensor | None] = None, # input latent to start with, extra_kwargs: dict = None, # extra data to save with prompt file refiner_start_at: float = 0.5, # start at this percentage of a step. 0.0 to 1.0 . 1.0 is the end extra_values: List[float] = None, # extra values to save with prompt file ): self.width: int = width self.height: int = height self.num_inference_steps: int = num_inference_steps self.guidance_scale: float = guidance_scale self.guidance_rescale: float = guidance_rescale self.prompt: str = prompt self.prompt_2: str = prompt_2 self.negative_prompt: str = negative_prompt self.negative_prompt_2: str = negative_prompt_2 self.latents: Union[torch.Tensor | None] = latents self.output_path: str = output_path self.seed: int = seed if self.seed == -1: # generate random one self.seed = random.randint(0, 2 ** 32 - 1) self.network_multiplier: float = network_multiplier self.output_folder: str = output_folder self.output_ext: str = output_ext self.add_prompt_file: bool = add_prompt_file self.output_tail: str = output_tail self.gen_time: int = int(time.time() * 1000) self.adapter_image_path: str = adapter_image_path self.adapter_conditioning_scale: float = adapter_conditioning_scale self.extra_kwargs = extra_kwargs if extra_kwargs is not None else {} self.refiner_start_at = refiner_start_at self.extra_values = extra_values if extra_values is not None else [] # prompt string will override any settings above self._process_prompt_string() # handle dual text encoder prompts if nothing passed if negative_prompt_2 is None: self.negative_prompt_2 = negative_prompt if prompt_2 is None: self.prompt_2 = self.prompt # parse prompt paths if self.output_path is None and self.output_folder is None: raise ValueError('output_path or output_folder must be specified') elif self.output_path is not None: self.output_folder = os.path.dirname(self.output_path) self.output_ext = os.path.splitext(self.output_path)[1][1:] self.output_filename_no_ext = os.path.splitext(os.path.basename(self.output_path))[0] else: self.output_filename_no_ext = '[time]_[count]' if len(self.output_tail) > 0: self.output_filename_no_ext += '_' + self.output_tail self.output_path = os.path.join(self.output_folder, self.output_filename_no_ext + '.' + self.output_ext) # adjust height self.height = max(64, self.height - self.height % 8) # round to divisible by 8 self.width = max(64, self.width - self.width % 8) # round to divisible by 8 def set_gen_time(self, gen_time: int = None): if gen_time is not None: self.gen_time = gen_time else: self.gen_time = int(time.time() * 1000) def _get_path_no_ext(self, count: int = 0, max_count=0): # zero pad count count_str = str(count).zfill(len(str(max_count))) # replace [time] with gen time filename = self.output_filename_no_ext.replace('[time]', str(self.gen_time)) # replace [count] with count filename = filename.replace('[count]', count_str) return filename def get_image_path(self, count: int = 0, max_count=0): filename = self._get_path_no_ext(count, max_count) ext = self.output_ext # if it does not start with a dot add one if ext[0] != '.': ext = '.' + ext filename += ext # join with folder return os.path.join(self.output_folder, filename) def get_prompt_path(self, count: int = 0, max_count=0): filename = self._get_path_no_ext(count, max_count) filename += '.txt' # join with folder return os.path.join(self.output_folder, filename) def save_image(self, image, count: int = 0, max_count=0): # make parent dirs os.makedirs(self.output_folder, exist_ok=True) self.set_gen_time() # TODO save image gen header info for A1111 and us, our seeds probably wont match image.save(self.get_image_path(count, max_count)) # do prompt file if self.add_prompt_file: self.save_prompt_file(count, max_count) def save_prompt_file(self, count: int = 0, max_count=0): # save prompt file with open(self.get_prompt_path(count, max_count), 'w') as f: prompt = self.prompt if self.prompt_2 is not None: prompt += ' --p2 ' + self.prompt_2 if self.negative_prompt is not None: prompt += ' --n ' + self.negative_prompt if self.negative_prompt_2 is not None: prompt += ' --n2 ' + self.negative_prompt_2 prompt += ' --w ' + str(self.width) prompt += ' --h ' + str(self.height) prompt += ' --seed ' + str(self.seed) prompt += ' --cfg ' + str(self.guidance_scale) prompt += ' --steps ' + str(self.num_inference_steps) prompt += ' --m ' + str(self.network_multiplier) prompt += ' --gr ' + str(self.guidance_rescale) # get gen info f.write(self.prompt) def _process_prompt_string(self): # we will try to support all sd-scripts where we can # FROM SD-SCRIPTS # --n Treat everything until the next option as a negative prompt. # --w Specify the width of the generated image. # --h Specify the height of the generated image. # --d Specify the seed for the generated image. # --l Specify the CFG scale for the generated image. # --s Specify the number of steps during generation. # OURS and some QOL additions # --m Specify the network multiplier for the generated image. # --p2 Prompt for the second text encoder (SDXL only) # --n2 Negative prompt for the second text encoder (SDXL only) # --gr Specify the guidance rescale for the generated image (SDXL only) # --seed Specify the seed for the generated image same as --d # --cfg Specify the CFG scale for the generated image same as --l # --steps Specify the number of steps during generation same as --s # --network_multiplier Specify the network multiplier for the generated image same as --m # process prompt string and update values if it has some if self.prompt is not None and len(self.prompt) > 0: # process prompt string prompt = self.prompt prompt = prompt.strip() p_split = prompt.split('--') self.prompt = p_split[0].strip() if len(p_split) > 1: for split in p_split[1:]: # allows multi char flags flag = split.split(' ')[0].strip() content = split[len(flag):].strip() if flag == 'p2': self.prompt_2 = content elif flag == 'n': self.negative_prompt = content elif flag == 'n2': self.negative_prompt_2 = content elif flag == 'w': self.width = int(content) elif flag == 'h': self.height = int(content) elif flag == 'd': self.seed = int(content) elif flag == 'seed': self.seed = int(content) elif flag == 'l': self.guidance_scale = float(content) elif flag == 'cfg': self.guidance_scale = float(content) elif flag == 's': self.num_inference_steps = int(content) elif flag == 'steps': self.num_inference_steps = int(content) elif flag == 'm': self.network_multiplier = float(content) elif flag == 'network_multiplier': self.network_multiplier = float(content) elif flag == 'gr': self.guidance_rescale = float(content) elif flag == 'a': self.adapter_conditioning_scale = float(content) elif flag == 'ref': self.refiner_start_at = float(content) elif flag == 'ev': # split by comma self.extra_values = [float(val) for val in content.split(',')] elif flag == 'extra_values': # split by comma self.extra_values = [float(val) for val in content.split(',')] def post_process_embeddings( self, conditional_prompt_embeds: PromptEmbeds, unconditional_prompt_embeds: Optional[PromptEmbeds] = None, ): # this is called after prompt embeds are encoded. We can override them in the future here pass