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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': 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,
}
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
@functools.cache
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)
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