import functools import pprint import random from pathlib import Path import yaml from modules import shared from modules.loaders import loaders_samplers from modules.logging_colors import logger def default_preset(): return { 'temperature': 1, 'temperature_last': False, 'dynamic_temperature': False, 'dynatemp_low': 1, 'dynatemp_high': 1, 'dynatemp_exponent': 1, 'smoothing_factor': 0, 'smoothing_curve': 1, 'top_p': 1, 'min_p': 0, 'top_k': 0, 'repetition_penalty': 1, 'presence_penalty': 0, 'frequency_penalty': 0, 'repetition_penalty_range': 1024, 'typical_p': 1, 'tfs': 1, 'top_a': 0, 'epsilon_cutoff': 0, 'eta_cutoff': 0, 'guidance_scale': 1, 'penalty_alpha': 0, 'mirostat_mode': 0, 'mirostat_tau': 5, 'mirostat_eta': 0.1, 'do_sample': True, 'encoder_repetition_penalty': 1, 'no_repeat_ngram_size': 0, 'dry_multiplier': 0, 'dry_base': 1.75, 'dry_allowed_length': 2, 'dry_sequence_breakers': '"\\n", ":", "\\"", "*"', 'sampler_priority': 'temperature\ndynamic_temperature\nquadratic_sampling\ntop_k\ntop_p\ntypical_p\nepsilon_cutoff\neta_cutoff\ntfs\ntop_a\nmin_p\nmirostat' } def presets_params(): return [k for k in default_preset()] def load_preset(name, verbose=False): generate_params = default_preset() if name not in ['None', None, '']: path = Path(f'presets/{name}.yaml') if path.exists(): with open(path, 'r') as infile: preset = yaml.safe_load(infile) for k in preset: generate_params[k] = preset[k] else: logger.error(f"The preset \"{name}\" does not exist under \"{path}\". Using the default parameters.") if verbose: logger.info(f"\"{name}\" preset:") pprint.PrettyPrinter(indent=4, width=1, sort_dicts=False).pprint(remove_defaults(generate_params)) return generate_params @functools.cache def load_preset_memoized(name): return load_preset(name) def load_preset_for_ui(name, state): generate_params = load_preset(name, verbose=True) state.update(generate_params) return state, *[generate_params[k] for k in presets_params()] def random_preset(state): params_and_values = { 'remove_tail_tokens': { 'top_p': [0.5, 0.8, 0.9, 0.95, 0.99], 'min_p': [0.5, 0.2, 0.1, 0.05, 0.01], 'top_k': [3, 5, 10, 20, 30, 40], 'typical_p': [0.2, 0.575, 0.95], 'tfs': [0.5, 0.8, 0.9, 0.95, 0.99], 'top_a': [0.5, 0.2, 0.1, 0.05, 0.01], 'epsilon_cutoff': [1, 3, 5, 7, 9], 'eta_cutoff': [3, 6, 9, 12, 15, 18], }, 'flatten_distribution': { 'temperature': [0.1, 0.5, 0.7, 0.8, 1, 1.2, 1.5, 2.0, 5.0], 'dynamic_temperature': [ [0.1, 1], [0.1, 1.5], [0.1, 2], [0.1, 5], [0.5, 1], [0.5, 1.5], [0.5, 2], [0.5, 5], [0.8, 1], [0.8, 1.5], [0.8, 2], [0.8, 5], [1, 1.5], [1, 2], [1, 5] ], 'smoothing_factor': [0.2, 0.3, 0.6, 1.2], }, 'repetition': { 'repetition_penalty': [1, 1.05, 1.1, 1.15, 1.20, 1.25], 'presence_penalty': [0, 0.1, 0.2, 0.4, 0.6, 0.8, 1.0, 2.0], 'frequency_penalty': [0, 0.1, 0.2, 0.4, 0.6, 0.8, 1.0, 2.0], }, 'other': { 'temperature_last': [True, False], } } generate_params = default_preset() for cat in params_and_values: choices = list(params_and_values[cat].keys()) if shared.args.loader is not None: choices = [x for x in choices if loader_contains(x)] if len(choices) > 0: choice = random.choice(choices) value = random.choice(params_and_values[cat][choice]) if choice == 'dynamic_temperature': generate_params['dynamic_temperature'] = True generate_params['dynatemp_low'] = value[0] generate_params['dynatemp_high'] = value[1] else: generate_params[choice] = value state.update(generate_params) logger.info("GENERATED_PRESET=") pprint.PrettyPrinter(indent=4, width=1, sort_dicts=False).pprint(remove_defaults(state)) return state, *[generate_params[k] for k in presets_params()] def loader_contains(sampler): if sampler == 'dynamic_temperature' and 'dynatemp_low' in loaders_samplers[shared.args.loader]: return True else: return sampler in loaders_samplers[shared.args.loader] def remove_defaults(state): defaults = default_preset() data = {k: state[k] for k in presets_params()} for k in list(data.keys()): if data[k] == defaults[k]: del data[k] return data def generate_preset_yaml(state): data = remove_defaults(state) return yaml.dump(data, sort_keys=False)