mirror of
https://github.com/ostris/ai-toolkit.git
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306 lines
13 KiB
Python
306 lines
13 KiB
Python
import os
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import time
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from typing import List, Optional
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import random
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class SaveConfig:
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def __init__(self, **kwargs):
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self.save_every: int = kwargs.get('save_every', 1000)
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self.dtype: str = kwargs.get('save_dtype', 'float16')
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self.max_step_saves_to_keep: int = kwargs.get('max_step_saves_to_keep', 5)
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class LogingConfig:
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def __init__(self, **kwargs):
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self.log_every: int = kwargs.get('log_every', 100)
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self.verbose: bool = kwargs.get('verbose', False)
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self.use_wandb: bool = kwargs.get('use_wandb', False)
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class SampleConfig:
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def __init__(self, **kwargs):
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self.sample_every: int = kwargs.get('sample_every', 100)
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self.width: int = kwargs.get('width', 512)
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self.height: int = kwargs.get('height', 512)
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self.prompts: list[str] = kwargs.get('prompts', [])
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self.neg = kwargs.get('neg', False)
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self.seed = kwargs.get('seed', 0)
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self.walk_seed = kwargs.get('walk_seed', False)
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self.guidance_scale = kwargs.get('guidance_scale', 7)
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self.sample_steps = kwargs.get('sample_steps', 20)
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self.network_multiplier = kwargs.get('network_multiplier', 1)
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self.guidance_rescale = kwargs.get('guidance_rescale', 0.0)
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class NetworkConfig:
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def __init__(self, **kwargs):
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self.type: str = kwargs.get('type', 'lora')
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rank = kwargs.get('rank', None)
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linear = kwargs.get('linear', None)
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if rank is not None:
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self.rank: int = rank # rank for backward compatibility
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self.linear: int = rank
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elif linear is not None:
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self.rank: int = linear
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self.linear: int = linear
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self.conv: int = kwargs.get('conv', None)
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self.alpha: float = kwargs.get('alpha', 1.0)
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self.linear_alpha: float = kwargs.get('linear_alpha', self.alpha)
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self.conv_alpha: float = kwargs.get('conv_alpha', self.conv)
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class TrainConfig:
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def __init__(self, **kwargs):
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self.noise_scheduler = kwargs.get('noise_scheduler', 'ddpm')
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self.steps: int = kwargs.get('steps', 1000)
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self.lr = kwargs.get('lr', 1e-6)
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self.optimizer = kwargs.get('optimizer', 'adamw')
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self.lr_scheduler = kwargs.get('lr_scheduler', 'constant')
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self.max_denoising_steps: int = kwargs.get('max_denoising_steps', 50)
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self.batch_size: int = kwargs.get('batch_size', 1)
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self.dtype: str = kwargs.get('dtype', 'fp32')
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self.xformers = kwargs.get('xformers', False)
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self.train_unet = kwargs.get('train_unet', True)
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self.train_text_encoder = kwargs.get('train_text_encoder', True)
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self.noise_offset = kwargs.get('noise_offset', 0.0)
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self.optimizer_params = kwargs.get('optimizer_params', {})
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self.skip_first_sample = kwargs.get('skip_first_sample', False)
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self.gradient_checkpointing = kwargs.get('gradient_checkpointing', True)
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class ModelConfig:
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def __init__(self, **kwargs):
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self.name_or_path: str = kwargs.get('name_or_path', None)
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self.is_v2: bool = kwargs.get('is_v2', False)
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self.is_xl: bool = kwargs.get('is_xl', False)
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self.is_v_pred: bool = kwargs.get('is_v_pred', False)
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self.dtype: str = kwargs.get('dtype', 'float16')
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if self.name_or_path is None:
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raise ValueError('name_or_path must be specified')
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class SliderTargetConfig:
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def __init__(self, **kwargs):
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self.target_class: str = kwargs.get('target_class', '')
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self.positive: str = kwargs.get('positive', '')
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self.negative: str = kwargs.get('negative', '')
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self.multiplier: float = kwargs.get('multiplier', 1.0)
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self.weight: float = kwargs.get('weight', 1.0)
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class SliderConfigAnchors:
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def __init__(self, **kwargs):
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self.prompt = kwargs.get('prompt', '')
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self.neg_prompt = kwargs.get('neg_prompt', '')
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self.multiplier = kwargs.get('multiplier', 1.0)
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class SliderConfig:
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def __init__(self, **kwargs):
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targets = kwargs.get('targets', [])
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targets = [SliderTargetConfig(**target) for target in targets]
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self.targets: List[SliderTargetConfig] = targets
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anchors = kwargs.get('anchors', [])
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anchors = [SliderConfigAnchors(**anchor) for anchor in anchors]
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self.anchors: List[SliderConfigAnchors] = anchors
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self.resolutions: List[List[int]] = kwargs.get('resolutions', [[512, 512]])
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self.prompt_file: str = kwargs.get('prompt_file', None)
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self.prompt_tensors: str = kwargs.get('prompt_tensors', None)
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self.batch_full_slide: bool = kwargs.get('batch_full_slide', True)
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class GenerateImageConfig:
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def __init__(
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self,
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prompt: str = '',
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prompt_2: Optional[str] = None,
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width: int = 512,
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height: int = 512,
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num_inference_steps: int = 50,
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guidance_scale: float = 7.5,
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negative_prompt: str = '',
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negative_prompt_2: Optional[str] = None,
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seed: int = -1,
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network_multiplier: float = 1.0,
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guidance_rescale: float = 0.0,
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# the tag [time] will be replaced with milliseconds since epoch
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output_path: str = None, # full image path
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output_folder: str = None, # folder to save image in if output_path is not specified
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output_ext: str = 'png', # extension to save image as if output_path is not specified
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output_tail: str = '', # tail to add to output filename
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add_prompt_file: bool = False, # add a prompt file with generated image
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):
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self.width: int = width
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self.height: int = height
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self.num_inference_steps: int = num_inference_steps
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self.guidance_scale: float = guidance_scale
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self.guidance_rescale: float = guidance_rescale
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self.prompt: str = prompt
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self.prompt_2: str = prompt_2
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self.negative_prompt: str = negative_prompt
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self.negative_prompt_2: str = negative_prompt_2
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self.output_path: str = output_path
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self.seed: int = seed
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if self.seed == -1:
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# generate random one
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self.seed = random.randint(0, 2 ** 32 - 1)
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self.network_multiplier: float = network_multiplier
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self.output_folder: str = output_folder
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self.output_ext: str = output_ext
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self.add_prompt_file: bool = add_prompt_file
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self.output_tail: str = output_tail
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self.gen_time: int = int(time.time() * 1000)
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# prompt string will override any settings above
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self._process_prompt_string()
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# handle dual text encoder prompts if nothing passed
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if negative_prompt_2 is None:
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self.negative_prompt_2 = negative_prompt
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if prompt_2 is None:
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self.prompt_2 = prompt
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# parse prompt paths
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if self.output_path is None and self.output_folder is None:
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raise ValueError('output_path or output_folder must be specified')
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elif self.output_path is not None:
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self.output_folder = os.path.dirname(self.output_path)
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self.output_ext = os.path.splitext(self.output_path)[1][1:]
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self.output_filename_no_ext = os.path.splitext(os.path.basename(self.output_path))[0]
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else:
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self.output_filename_no_ext = '[time]_[count]'
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if len(self.output_tail) > 0:
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self.output_filename_no_ext += '_' + self.output_tail
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self.output_path = os.path.join(self.output_folder, self.output_filename_no_ext + '.' + self.output_ext)
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# adjust height
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self.height = max(64, self.height - self.height % 8) # round to divisible by 8
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self.width = max(64, self.width - self.width % 8) # round to divisible by 8
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def set_gen_time(self, gen_time: int = None):
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if gen_time is not None:
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self.gen_time = gen_time
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else:
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self.gen_time = int(time.time() * 1000)
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def _get_path_no_ext(self, count: int = 0, max_count=0):
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# zero pad count
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count_str = str(count).zfill(len(str(max_count)))
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# replace [time] with gen time
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filename = self.output_filename_no_ext.replace('[time]', str(self.gen_time))
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# replace [count] with count
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filename = filename.replace('[count]', count_str)
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return filename
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def get_image_path(self, count: int = 0, max_count=0):
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filename = self._get_path_no_ext(count, max_count)
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filename += '.' + self.output_ext
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# join with folder
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return os.path.join(self.output_folder, filename)
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def get_prompt_path(self, count: int = 0, max_count=0):
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filename = self._get_path_no_ext(count, max_count)
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filename += '.txt'
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# join with folder
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return os.path.join(self.output_folder, filename)
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def save_image(self, image, count: int = 0, max_count=0):
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# make parent dirs
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os.makedirs(self.output_folder, exist_ok=True)
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self.set_gen_time()
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# TODO save image gen header info for A1111 and us, our seeds probably wont match
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image.save(self.get_image_path(count, max_count))
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# do prompt file
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if self.add_prompt_file:
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self.save_prompt_file(count, max_count)
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def save_prompt_file(self, count: int = 0, max_count=0):
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# save prompt file
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with open(self.get_prompt_path(count, max_count), 'w') as f:
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prompt = self.prompt
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if self.prompt_2 is not None:
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prompt += ' --p2 ' + self.prompt_2
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if self.negative_prompt is not None:
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prompt += ' --n ' + self.negative_prompt
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if self.negative_prompt_2 is not None:
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prompt += ' --n2 ' + self.negative_prompt_2
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prompt += ' --w ' + str(self.width)
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prompt += ' --h ' + str(self.height)
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prompt += ' --seed ' + str(self.seed)
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prompt += ' --cfg ' + str(self.guidance_scale)
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prompt += ' --steps ' + str(self.num_inference_steps)
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prompt += ' --m ' + str(self.network_multiplier)
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prompt += ' --gr ' + str(self.guidance_rescale)
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# get gen info
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f.write(self.prompt)
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def _process_prompt_string(self):
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# we will try to support all sd-scripts where we can
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# FROM SD-SCRIPTS
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# --n Treat everything until the next option as a negative prompt.
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# --w Specify the width of the generated image.
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# --h Specify the height of the generated image.
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# --d Specify the seed for the generated image.
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# --l Specify the CFG scale for the generated image.
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# --s Specify the number of steps during generation.
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# OURS and some QOL additions
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# --m Specify the network multiplier for the generated image.
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# --p2 Prompt for the second text encoder (SDXL only)
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# --n2 Negative prompt for the second text encoder (SDXL only)
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# --gr Specify the guidance rescale for the generated image (SDXL only)
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# --seed Specify the seed for the generated image same as --d
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# --cfg Specify the CFG scale for the generated image same as --l
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# --steps Specify the number of steps during generation same as --s
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# --network_multiplier Specify the network multiplier for the generated image same as --m
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# process prompt string and update values if it has some
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if self.prompt is not None and len(self.prompt) > 0:
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# process prompt string
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prompt = self.prompt
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prompt = prompt.strip()
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p_split = prompt.split('--')
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self.prompt = p_split[0].strip()
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if len(p_split) > 1:
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for split in p_split[1:]:
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# allows multi char flags
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flag = split.split(' ')[0].strip()
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content = split[len(flag):].strip()
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if flag == 'p2':
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self.prompt_2 = content
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elif flag == 'n':
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self.negative_prompt = content
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elif flag == 'n2':
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self.negative_prompt_2 = content
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elif flag == 'w':
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self.width = int(content)
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elif flag == 'h':
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self.height = int(content)
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elif flag == 'd':
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self.seed = int(content)
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elif flag == 'seed':
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self.seed = int(content)
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elif flag == 'l':
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self.guidance_scale = float(content)
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elif flag == 'cfg':
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self.guidance_scale = float(content)
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elif flag == 's':
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self.num_inference_steps = int(content)
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elif flag == 'steps':
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self.num_inference_steps = int(content)
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elif flag == 'm':
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self.network_multiplier = float(content)
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elif flag == 'network_multiplier':
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self.network_multiplier = float(content)
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elif flag == 'gr':
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self.guidance_rescale = float(content)
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