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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, | |
'min_length': 0, | |
'num_beams': 1, | |
'length_penalty': 1, | |
'early_stopping': False, | |
'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): | |
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.") | |
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': [0.1, 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) | |