diff --git a/modules/errors.py b/modules/errors.py index 32bf3f9dd..5faf6f9b7 100644 --- a/modules/errors.py +++ b/modules/errors.py @@ -10,6 +10,10 @@ install_traceback() already_displayed = {} +class ValidationError(ValueError): + """Expected validation failure: display() reports the message without a traceback.""" + + def install(suppress=None): if suppress is None: suppress = [] @@ -23,6 +27,9 @@ def display(e: Exception, task: str, suppress=None): suppress = [] if isinstance(e, ErrorLimiterAbort): return + if isinstance(e, ValidationError): + log.error(f"{task or 'error'}: {e}") + return log.error(f"{task or 'error'}: {type(e).__name__}") """ trace = traceback.format_exc() diff --git a/modules/processing_helpers.py b/modules/processing_helpers.py index 1135ceccc..1d81bbd56 100644 --- a/modules/processing_helpers.py +++ b/modules/processing_helpers.py @@ -584,9 +584,9 @@ def update_sampler(p, sd_model, second_pass=False): if sampler_selection == 'None': return sampler = sd_samplers.find_sampler(sampler_selection) - if sampler is None: - log.warning(f'Sampler: "{sampler_selection}" not found') - sampler = sd_samplers.all_samplers_map.get("UniPC") + resolved = sampler is not None + if not resolved: + log.warning(f'Sampler: name="{sampler_selection}" not found') sched_override_keys = [ 'schedulers_prediction_type', 'schedulers_beta_schedule', 'schedulers_timesteps', 'schedulers_sigma', 'schedulers_use_thresholding', 'schedulers_use_loworder', @@ -596,8 +596,8 @@ def update_sampler(p, sd_model, second_pass=False): 'schedulers_timestep_spacing', 'schedulers_timesteps_range', ] scheduler_overrides = {k: getattr(p, k) for k in sched_override_keys if getattr(p, k, None) is not None} - sampler = sd_samplers.create_sampler(sampler.name, sd_model, scheduler_overrides=scheduler_overrides) - if sampler is None or sampler_selection == 'Default': + sampler = sd_samplers.create_sampler(sampler.name if resolved else sampler_selection, sd_model, scheduler_overrides=scheduler_overrides) + if sampler is None or not resolved or sampler_selection == 'Default': if second_pass: p.hr_sampler = 'Default' else: diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index d38ab0acf..9fbdf0a5a 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -1,6 +1,6 @@ import os import copy -from modules import shared +from modules import shared, errors from modules.logger import log @@ -15,7 +15,7 @@ loaded_config = None def find_sampler(name:str): if name is None or name == 'None': - return all_samplers_map.get("UniPC", None) + return all_samplers_map.get("Default", None) for sampler in all_samplers: if sampler.name.lower() == name.lower() or name in sampler.aliases: return sampler @@ -107,6 +107,8 @@ def create_sampler(name, model, scheduler_overrides=None): config = find_sampler_config(name) if config is None or config.constructor is None: + if debug or not shared.opts.schedulers_fallback: + raise errors.ValidationError(f'Sampler: name="{name}" unknown') return restore_default(model, name) from modules import sd_samplers_diffusers @@ -126,16 +128,16 @@ def create_sampler(name, model, scheduler_overrides=None): pass elif (model is not None) and (is_flow and not requires_flow): log.error(f'Sampler: "{sampler.name}" cls={sampler.sampler.__class__.__name__} pipe={model.__class__.__name__} type={pred_type} model requires sampler with discrete prediction') - if not debug: - return restore_default(model, name) + if debug or not shared.opts.schedulers_fallback: + raise errors.ValidationError(f'Sampler: name="{sampler.name}" cls={sampler.sampler.__class__.__name__} type={pred_type} model requires sampler with discrete prediction') else: - raise ValueError(f'Sampler: name="{sampler.name}" cls={sampler.sampler.__class__.__name__} type={pred_type} model requires sampler with discrete prediction') + return restore_default(model, name) elif (model is not None) and (not is_flow and requires_flow): log.error(f'Sampler: "{sampler.name}" cls={sampler.sampler.__class__.__name__} pipe={model.__class__.__name__} type={pred_type} model requires sampler with flow prediction') - if not debug: - return restore_default(model, name) + if debug or not shared.opts.schedulers_fallback: + raise errors.ValidationError(f'Sampler: name="{sampler.name}" cls={sampler.sampler.__class__.__name__} type={pred_type} model requires sampler with flow prediction') else: - raise ValueError(f'Sampler: name="{sampler.name}" cls={sampler.sampler.__class__.__name__} type={pred_type} model requires sampler with flow prediction') + return restore_default(model, name) # assign sampler if model is not None: diff --git a/modules/sd_samplers_diffusers.py b/modules/sd_samplers_diffusers.py index a6e10f63a..043eda07a 100644 --- a/modules/sd_samplers_diffusers.py +++ b/modules/sd_samplers_diffusers.py @@ -450,7 +450,7 @@ class DiffusionSampler: sigma_applied = True if not sigma_applied: if debug or not shared.opts.schedulers_fallback: - raise ValueError(f'Sampler: name="{name}" does not support sigma="{sched_sigma}"') + raise errors.ValidationError(f'Sampler: name="{name}" does not support sigma="{sched_sigma}"') else: log.warning(f'Sampler: name="{name}" does not support sigma="{sched_sigma}", using default schedule') else: @@ -519,6 +519,8 @@ class DiffusionSampler: log.error(f'Sampler: "{name}" {e}') if debug: errors.display(e, 'Samplers') + if debug or not shared.opts.schedulers_fallback: + raise errors.ValidationError(f'Sampler: name="{name}" {e}') from e self.sampler = None return @@ -527,7 +529,7 @@ class DiffusionSampler: cls_source = inspect.getsource(constructor) if '"flow_prediction"' not in cls_source and "'flow_prediction'" not in cls_source: if debug or not shared.opts.schedulers_fallback: - raise ValueError(f'Sampler: name="{name}" does not appear to support flow_prediction') + raise errors.ValidationError(f'Sampler: name="{name}" does not appear to support flow_prediction') else: log.warning(f'Sampler: name="{name}" does not support flow_prediction') self.sampler = None @@ -547,7 +549,7 @@ class DiffusionSampler: default_accept_sigmas = (model is not None) and hasattr(model.default_scheduler, 'set_timesteps') and "sigmas" in set(inspect.signature(model.default_scheduler.set_timesteps).parameters.keys()) if default_accept_sigmas and not accept_sigmas: if debug or not shared.opts.schedulers_fallback: - raise ValueError(f'Sampler: name="{name}" does not accept sigmas') + raise errors.ValidationError(f'Sampler: name="{name}" does not accept sigmas') else: log.warning(f'Sampler: name="{name}" does not accept sigmas') self.sampler = None @@ -557,7 +559,7 @@ class DiffusionSampler: if default_accept_scale_noise and not accept_scale_noise: log.warning(f'Sampler: name="{name}" does not implement scale noise') if debug or not shared.opts.schedulers_fallback: - raise ValueError(f'Sampler: name="{name}" does not implement scale noise') + raise errors.ValidationError(f'Sampler: name="{name}" does not implement scale noise') else: log.warning(f'Sampler: name="{name}" does not implement scale noise') self.sampler = None