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diffuser sampler settings

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Vladimir Mandic
2023-07-04 14:10:31 -04:00
parent 18ef9e6fd7
commit 191da73d48
3 changed files with 65 additions and 37 deletions
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modules/sd_samplers_diffusers.py
+33 -17
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@@ -7,7 +7,7 @@ from diffusers import (
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
KDPM2DiscreteScheduler,
# KDPM2DiscreteScheduler,
PNDMScheduler,
UniPCMultistepScheduler,
)
@@ -15,41 +15,57 @@ from modules import sd_samplers_common
config = {
'All': { 'num_train_timesteps': 1000, 'beta_start': 0.0001, 'beta_end': 0.02, 'beta_schedule': 'linear', 'prediction_type': 'epsilon' },
'UniPC': { 'solver_order': 2, 'thresholding': False, 'dynamic_thresholding_ratio': 0.995, 'sample_max_value': 1.0, 'predict_x0': 'bh2', 'lower_order_final': True },
'DDIM': { 'clip_sample': True, 'set_alpha_to_one': True, 'steps_offset': 0, 'thresholding': False, 'dynamic_thresholding_ratio': 0.995, 'clip_sample_range': 1.0, 'sample_max_value': 1.0, 'timestep_spacing': 'leading', 'rescale_betas_zero_snr': False },
'DEIS': { 'solver_order': 2, 'thresholding': False, 'dynamic_thresholding_ratio': 0.995, 'sample_max_value': 1.0, 'algorithm_type': "deis", 'solver_type': "logrho", 'lower_order_final': True },
'UniPC': { 'solver_order': 2, 'thresholding': False, 'sample_max_value': 1.0, 'predict_x0': 'bh2', 'lower_order_final': True },
'DDIM': { 'clip_sample': True, 'set_alpha_to_one': True, 'steps_offset': 0, 'thresholding': False, 'clip_sample_range': 1.0, 'sample_max_value': 1.0, 'timestep_spacing': 'leading', 'rescale_betas_zero_snr': False },
'DDPM': { 'variance_type': "fixed_small", 'clip_sample': True, 'thresholding': False, 'clip_sample_range': 1.0, 'sample_max_value': 1.0 },
'DEIS': { 'solver_order': 2, 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "deis", 'solver_type': "logrho", 'lower_order_final': True },
'Euler': { 'interpolation_type': "linear", 'use_karras_sigmas': False },
'Euler a': {},
'Heun': { 'use_karras_sigmas': False },
'PNDM': { 'skip_prk_steps': False, 'set_alpha_to_one': False, 'steps_offset': 0 },
'DPM 1S': { 'solver_order': 2, 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False },
'DPM 2M': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False },
}
samplers_data_diffusers = [
sd_samplers_common.SamplerData('UniPC', lambda model: DiffusionSampler('UniPC', UniPCMultistepScheduler, model), [], {}),
sd_samplers_common.SamplerData('DDIM', lambda model: DiffusionSampler('DDIM', DDIMScheduler, model), [], {}),
# sd_samplers_common.SamplerData('DDPM', lambda model: DiffusionSampler('DDPM', DDPMScheduler, model), [], {}),
sd_samplers_common.SamplerData('DDPM', lambda model: DiffusionSampler('DDPM', DDPMScheduler, model), [], {}),
sd_samplers_common.SamplerData('DEIS', lambda model: DiffusionSampler('DEIS', DEISMultistepScheduler, model), [], {}),
# sd_samplers_common.SamplerData('DPM++ 2M', lambda model: DiffusionSampler('DPM++ 2M', DPMSolverMultistepScheduler, model), [], {}),
# sd_samplers_common.SamplerData('DPM++ 1S', lambda model: DiffusionSampler('DPM++ 1S', DPMSolverSinglestepScheduler, model), [], {}),
# sd_samplers_common.SamplerData('DPM++ 2M SDE', lambda model: DiffusionSampler('DPM++ 2M SDE', DPMSolverMultistepScheduler, model, algorithm_type="sde-dpmsolver++"), [], {}),
# sd_samplers_common.SamplerData('DPM++ 2M Karras', lambda model: DiffusionSampler('DPM++ 2M Karras', DPMSolverMultistepScheduler, model, use_karras_sigmas=True), [], {}),
# sd_samplers_common.SamplerData('DPM++ 1S Karras', lambda model: DiffusionSampler('DPM++ 1S Karras', DPMSolverSinglestepScheduler, model, use_karras_sigmas=True), [], {}),
# sd_samplers_common.SamplerData('DPM++ 2M SDE Karras', lambda model: DiffusionSampler('DPM++ 2M SDE Karras', DPMSolverMultistepScheduler, model, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++"), [], {}),
# sd_samplers_common.SamplerData('Euler', lambda model: DiffusionSampler('Euler', EulerDiscreteScheduler, model), [], {}),
sd_samplers_common.SamplerData('DPM 1S', lambda model: DiffusionSampler('DPM++ 1S', DPMSolverSinglestepScheduler, model), [], {}),
sd_samplers_common.SamplerData('DPM 2M', lambda model: DiffusionSampler('DPM++ 2M', DPMSolverMultistepScheduler, model), [], {}),
sd_samplers_common.SamplerData('Euler', lambda model: DiffusionSampler('Euler', EulerDiscreteScheduler, model), [], {}),
sd_samplers_common.SamplerData('Euler a', lambda model: DiffusionSampler('Euler a', EulerAncestralDiscreteScheduler, model), [], {}),
# sd_samplers_common.SamplerData('Heun', lambda model: DiffusionSampler('Heun', HeunDiscreteScheduler, model), [], {}),
# sd_samplers_common.SamplerData('DPM2++ 2M', lambda model: DiffusionSampler('KDPM2', KDPM2DiscreteScheduler, model), [], {}),
# sd_samplers_common.SamplerData('PNDM', lambda model: DiffusionSampler('PNDM', PNDMScheduler, model), [], {}),
sd_samplers_common.SamplerData('Heun', lambda model: DiffusionSampler('Heun', HeunDiscreteScheduler, model), [], {}),
sd_samplers_common.SamplerData('PNDM', lambda model: DiffusionSampler('PNDM', PNDMScheduler, model), [], {}),
]
class DiffusionSampler:
def __init__(self, name, constructor, model, **kwargs):
from modules.shared import opts, log
self.config = config['All'].copy()
for key, value in config.get(name, {}).items(): # diffusers defaults
if key in self.config:
self.config[key] = value
self.config[key] = value
for key, value in model.scheduler.config.items(): # model defaults
if key in self.config:
self.config[key] = value
for key, value in kwargs.items(): # user args
if key in self.config:
self.config[key] = value
if opts.schedulers_prediction_type != 'default':
self.config['prediction_type'] = opts.schedulers_prediction_type
if opts.schedulers_beta_schedule != 'default':
self.config['beta_schedule'] = opts.schedulers_beta_schedule
if 'use_karras_sigmas' in self.config:
self.config['use_karras_sigmas'] = opts.schedulers_use_karras
if 'thresholding' in self.config:
self.config['thresholding'] = opts.schedulers_use_thresholding
if 'lower_order_final' in self.config:
self.config['lower_order_final'] = opts.schedulers_use_loworder
if 'solver_order' in self.config:
self.config['solver_order'] = opts.schedulers_solver_order
if name.startswith('DPM'):
self.config['algorithm_type'] = opts.schedulers_dpm_solver
self.sampler = constructor(**self.config)
self.sampler.name = name
log.debug(f'Diffusers sampler: {name} {self.config}')