mirror of https://github.com/vladmandic/automatic
72 lines
4.5 KiB
Python
72 lines
4.5 KiB
Python
from diffusers import (
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DDIMScheduler,
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DDPMScheduler,
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DEISMultistepScheduler,
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DPMSolverMultistepScheduler,
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DPMSolverSinglestepScheduler,
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EulerAncestralDiscreteScheduler,
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EulerDiscreteScheduler,
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HeunDiscreteScheduler,
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# KDPM2DiscreteScheduler,
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PNDMScheduler,
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UniPCMultistepScheduler,
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)
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from modules import sd_samplers_common
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config = {
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'All': { 'num_train_timesteps': 1000, 'beta_start': 0.0001, 'beta_end': 0.02, 'beta_schedule': 'linear', 'prediction_type': 'epsilon' },
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'UniPC': { 'solver_order': 2, 'thresholding': False, 'sample_max_value': 1.0, 'predict_x0': 'bh2', 'lower_order_final': True },
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'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 },
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'DDPM': { 'variance_type': "fixed_small", 'clip_sample': True, 'thresholding': False, 'clip_sample_range': 1.0, 'sample_max_value': 1.0 },
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'DEIS': { 'solver_order': 2, 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "deis", 'solver_type': "logrho", 'lower_order_final': True },
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'Euler': { 'interpolation_type': "linear", 'use_karras_sigmas': False },
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'Euler a': {},
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'Heun': { 'use_karras_sigmas': False },
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'PNDM': { 'skip_prk_steps': False, 'set_alpha_to_one': False, 'steps_offset': 0 },
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'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 },
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'DPM 2M': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False },
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}
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samplers_data_diffusers = [
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sd_samplers_common.SamplerData('UniPC', lambda model: DiffusionSampler('UniPC', UniPCMultistepScheduler, model), [], {}),
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sd_samplers_common.SamplerData('DDIM', lambda model: DiffusionSampler('DDIM', DDIMScheduler, model), [], {}),
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sd_samplers_common.SamplerData('DDPM', lambda model: DiffusionSampler('DDPM', DDPMScheduler, model), [], {}),
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sd_samplers_common.SamplerData('DEIS', lambda model: DiffusionSampler('DEIS', DEISMultistepScheduler, model), [], {}),
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sd_samplers_common.SamplerData('DPM 1S', lambda model: DiffusionSampler('DPM++ 1S', DPMSolverSinglestepScheduler, model), [], {}),
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sd_samplers_common.SamplerData('DPM 2M', lambda model: DiffusionSampler('DPM++ 2M', DPMSolverMultistepScheduler, model), [], {}),
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sd_samplers_common.SamplerData('Euler', lambda model: DiffusionSampler('Euler', EulerDiscreteScheduler, model), [], {}),
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sd_samplers_common.SamplerData('Euler a', lambda model: DiffusionSampler('Euler a', EulerAncestralDiscreteScheduler, model), [], {}),
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sd_samplers_common.SamplerData('Heun', lambda model: DiffusionSampler('Heun', HeunDiscreteScheduler, model), [], {}),
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sd_samplers_common.SamplerData('PNDM', lambda model: DiffusionSampler('PNDM', PNDMScheduler, model), [], {}),
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]
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class DiffusionSampler:
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def __init__(self, name, constructor, model, **kwargs):
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from modules.shared import opts, log
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self.config = config['All'].copy()
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for key, value in config.get(name, {}).items(): # diffusers defaults
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self.config[key] = value
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for key, value in model.scheduler.config.items(): # model defaults
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if key in self.config:
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self.config[key] = value
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for key, value in kwargs.items(): # user args
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if key in self.config:
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self.config[key] = value
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if opts.schedulers_prediction_type != 'default':
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self.config['prediction_type'] = opts.schedulers_prediction_type
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if opts.schedulers_beta_schedule != 'default':
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self.config['beta_schedule'] = opts.schedulers_beta_schedule
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if 'use_karras_sigmas' in self.config:
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self.config['use_karras_sigmas'] = opts.schedulers_use_karras
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if 'thresholding' in self.config:
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self.config['thresholding'] = opts.schedulers_use_thresholding
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if 'lower_order_final' in self.config:
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self.config['lower_order_final'] = opts.schedulers_use_loworder
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if 'solver_order' in self.config:
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self.config['solver_order'] = opts.schedulers_solver_order
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if name.startswith('DPM'):
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self.config['algorithm_type'] = opts.schedulers_dpm_solver
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self.sampler = constructor(**self.config)
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self.sampler.name = name
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log.debug(f'Diffusers sampler: {name} {self.config}')
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