mirror of
https://github.com/vladmandic/sdnext.git
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139 lines
8.8 KiB
Python
139 lines
8.8 KiB
Python
import os
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import inspect
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from modules import shared
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from modules import sd_samplers_common
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debug = shared.log.trace if os.environ.get('SD_SAMPLER_DEBUG', None) is not None else lambda *args, **kwargs: None
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debug('Trace: SAMPLER')
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try:
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from diffusers import (
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DDIMScheduler,
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DDPMScheduler,
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UniPCMultistepScheduler,
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DEISMultistepScheduler,
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DPMSolverMultistepScheduler,
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DPMSolverSinglestepScheduler,
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DPMSolverSDEScheduler,
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EulerAncestralDiscreteScheduler,
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EulerDiscreteScheduler,
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HeunDiscreteScheduler,
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KDPM2DiscreteScheduler,
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KDPM2AncestralDiscreteScheduler,
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LMSDiscreteScheduler,
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PNDMScheduler,
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LCMScheduler,
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SASolverScheduler,
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)
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except Exception as e:
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import diffusers
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shared.log.error(f'Diffusers import error: version={diffusers.__version__} error: {e}')
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config = {
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# beta_start, beta_end are typically per-scheduler, but we don't want them as they should be taken from the model itself as those are values model was trained on
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# prediction_type is ideally set in model as well, but it maybe needed that we do auto-detect of model type in the future
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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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'DDIM': { 'clip_sample': False, 'set_alpha_to_one': True, 'steps_offset': 0, 'clip_sample_range': 1.0, 'sample_max_value': 1.0, 'timestep_spacing': 'linspace', 'rescale_betas_zero_snr': False },
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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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'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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'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, 'final_sigmas_type': 'sigma_min' },
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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, 'final_sigmas_type': 'zero' },
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'DPM SDE': { 'use_karras_sigmas': False },
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'Euler a': { 'rescale_betas_zero_snr': False },
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'Euler': { 'interpolation_type': "linear", 'use_karras_sigmas': False, 'rescale_betas_zero_snr': False },
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'Heun': { 'use_karras_sigmas': False },
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'DDPM': { 'variance_type': "fixed_small", 'clip_sample': False, 'thresholding': False, 'clip_sample_range': 1.0, 'sample_max_value': 1.0, 'timestep_spacing': 'linspace', 'rescale_betas_zero_snr': False },
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'KDPM2': { 'steps_offset': 0 },
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'KDPM2 a': { 'steps_offset': 0 },
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'LMSD': { 'use_karras_sigmas': False, 'timestep_spacing': 'linspace', 'steps_offset': 0 },
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'PNDM': { 'skip_prk_steps': False, 'set_alpha_to_one': False, 'steps_offset': 0 },
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'LCM': { 'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': "scaled_linear", 'set_alpha_to_one': True, 'rescale_betas_zero_snr': False, 'thresholding': False },
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'SA Solver': {'predictor_order': 2, 'corrector_order': 2, 'thresholding': False, 'lower_order_final': True, 'use_karras_sigmas': False, 'timestep_spacing': 'linspace'},
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}
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samplers_data_diffusers = [
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sd_samplers_common.SamplerData('Default', None, [], {}),
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sd_samplers_common.SamplerData('UniPC', lambda model: DiffusionSampler('UniPC', UniPCMultistepScheduler, model), [], {}),
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sd_samplers_common.SamplerData('DEIS', lambda model: DiffusionSampler('DEIS', DEISMultistepScheduler, model), [], {}),
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sd_samplers_common.SamplerData('PNDM', lambda model: DiffusionSampler('PNDM', PNDMScheduler, model), [], {}),
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sd_samplers_common.SamplerData('DDPM', lambda model: DiffusionSampler('DDPM', DDPMScheduler, model), [], {}),
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sd_samplers_common.SamplerData('DDIM', lambda model: DiffusionSampler('DDIM', DDIMScheduler, model), [], {}),
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sd_samplers_common.SamplerData('LMSD', lambda model: DiffusionSampler('LMSD', LMSDiscreteScheduler, model), [], {}),
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sd_samplers_common.SamplerData('KDPM2', lambda model: DiffusionSampler('KDPM2', KDPM2DiscreteScheduler, model), [], {}),
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sd_samplers_common.SamplerData('KDPM2 a', lambda model: DiffusionSampler('KDPM2 a', KDPM2AncestralDiscreteScheduler, 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('DPM SDE', lambda model: DiffusionSampler('DPM SDE', DPMSolverSDEScheduler, 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('LCM', lambda model: DiffusionSampler('LCM', LCMScheduler, model), [], {}),
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sd_samplers_common.SamplerData('SA Solver', lambda model: DiffusionSampler('SA Solver', SASolverScheduler, 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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if name == 'Default':
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return
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self.name = name
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self.config = {}
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if not hasattr(model, 'scheduler'):
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return
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for key, value in config.get('All', {}).items(): # apply global defaults
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self.config[key] = value
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# shared.log.debug(f'Sampler: name={name} type=all config={self.config}')
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for key, value in config.get(name, {}).items(): # apply diffusers per-scheduler defaults
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self.config[key] = value
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# shared.log.debug(f'Sampler: name={name} type=scheduler config={self.config}')
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if hasattr(model.scheduler, 'scheduler_config'): # find model defaults
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orig_config = model.scheduler.scheduler_config
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else:
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orig_config = model.scheduler.config
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for key, value in orig_config.items(): # apply model defaults
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if key in self.config:
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self.config[key] = value
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# shared.log.debug(f'Sampler: name={name} type=model config={self.config}')
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for key, value in kwargs.items(): # apply user args, if any
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if key in self.config:
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self.config[key] = value
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# shared.log.debug(f'Sampler: name={name} type=user config={self.config}')
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# finally apply user preferences
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if shared.opts.schedulers_prediction_type != 'default':
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self.config['prediction_type'] = shared.opts.schedulers_prediction_type
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if shared.opts.schedulers_beta_schedule != 'default':
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self.config['beta_schedule'] = shared.opts.schedulers_beta_schedule
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if 'use_karras_sigmas' in self.config:
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self.config['use_karras_sigmas'] = shared.opts.schedulers_use_karras
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if 'thresholding' in self.config:
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self.config['thresholding'] = shared.opts.schedulers_use_thresholding
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if 'lower_order_final' in self.config:
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self.config['lower_order_final'] = shared.opts.schedulers_use_loworder
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if 'solver_order' in self.config:
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self.config['solver_order'] = shared.opts.schedulers_solver_order
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if 'predict_x0' in self.config:
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self.config['predict_x0'] = shared.opts.uni_pc_variant
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if 'beta_start' in self.config and shared.opts.schedulers_beta_start > 0:
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self.config['beta_start'] = shared.opts.schedulers_beta_start
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if 'beta_end' in self.config and shared.opts.schedulers_beta_end > 0:
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self.config['beta_end'] = shared.opts.schedulers_beta_end
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if 'rescale_betas_zero_snr' in self.config:
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self.config['rescale_betas_zero_snr'] = shared.opts.schedulers_rescale_betas
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if 'num_train_timesteps' in self.config:
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self.config['num_train_timesteps'] = shared.opts.schedulers_timesteps_range
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if name == 'DPM++ 2M':
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self.config['algorithm_type'] = shared.opts.schedulers_dpm_solver
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if name == 'DEIS':
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self.config['algorithm_type'] = 'deis'
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# validate all config params
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signature = inspect.signature(constructor, follow_wrapped=True)
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possible = signature.parameters.keys()
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debug(f'Sampler: sampler="{name}" config={self.config} signature={possible}')
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for key in self.config.copy().keys():
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if key not in possible:
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shared.log.warning(f'Sampler: sampler="{name}" config={self.config} invalid={key}')
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del self.config[key]
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self.sampler = constructor(**self.config)
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self.sampler.name = name
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