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| import functools | |
| import random | |
| from pathlib import Path | |
| import yaml | |
| from modules import shared | |
| from modules.loaders import loaders_samplers | |
| def default_preset(): | |
| return { | |
| 'temperature': 1, | |
| 'temperature_last': False, | |
| 'top_p': 1, | |
| 'min_p': 0, | |
| 'top_k': 0, | |
| 'repetition_penalty': 1, | |
| 'presence_penalty': 0, | |
| 'frequency_penalty': 0, | |
| 'repetition_penalty_range': 0, | |
| '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, | |
| 'min_length': 0, | |
| 'num_beams': 1, | |
| 'length_penalty': 1, | |
| 'early_stopping': False, | |
| } | |
| def presets_params(): | |
| return [k for k in default_preset()] | |
| def load_preset(name): | |
| generate_params = default_preset() | |
| if name not in ['None', None, '']: | |
| with open(Path(f'presets/{name}.yaml'), 'r') as infile: | |
| preset = yaml.safe_load(infile) | |
| for k in preset: | |
| generate_params[k] = preset[k] | |
| generate_params['temperature'] = min(1.99, generate_params['temperature']) | |
| return generate_params | |
| def load_preset_memoized(name): | |
| return load_preset(name) | |
| def load_preset_for_ui(name, state): | |
| generate_params = load_preset(name) | |
| 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': [0.5, 0.7, 0.8, 1, 1.2, 1.5, 2.0], | |
| }, | |
| '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 x in loaders_samplers[shared.args.loader]] | |
| if len(choices) > 0: | |
| choice = random.choice(choices) | |
| generate_params[choice] = random.choice(params_and_values[cat][choice]) | |
| state.update(generate_params) | |
| return state, *[generate_params[k] for k in presets_params()] | |
| def generate_preset_yaml(state): | |
| defaults = default_preset() | |
| data = {k: state[k] for k in presets_params()} | |
| # Remove entries that are identical to the defaults | |
| for k in list(data.keys()): | |
| if data[k] == defaults[k]: | |
| del data[k] | |
| return yaml.dump(data, sort_keys=False) | |