From 4d95cf47ceb371bb5820ea002b18144fc0d75fc0 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Wed, 13 Aug 2025 14:49:11 -0400 Subject: [PATCH] restore default sampler on mismatch Signed-off-by: Vladimir Mandic --- modules/paths.py | 2 +- modules/sd_samplers.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/paths.py b/modules/paths.py index 71d2f67a0..f5d0f190c 100644 --- a/modules/paths.py +++ b/modules/paths.py @@ -130,7 +130,7 @@ def check_cache(opts): from modules.modelstats import stat if opts.hfcache_dir != prev_default: size, _mtime = stat(prev_default) - if (size//1024//1024 > 0): + if size//1024//1024 > 0: log.warning(f'Cache location changed: previous="{prev_default}" size={size//1024//1024} MB') size, _mtime = stat(opts.hfcache_dir) log.debug(f'Huggingface cache: path="{opts.hfcache_dir}" size={size//1024//1024} MB') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 0b965eaf6..644e9dd16 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -78,7 +78,7 @@ def create_sampler(name, model): if model is not None: if getattr(model, "default_scheduler", None) is None: model.default_scheduler = copy.deepcopy(model.scheduler) - requires_flow = ('FlowMatch' in model.default_scheduler.__class__.__name__) or (getattr(model.scheduler.config, 'prediction_type', None) == 'flow_prediction') + requires_flow = ('FlowMatch' in model.default_scheduler.__class__.__name__) or (getattr(model.default_scheduler.config, 'prediction_type', None) == 'flow_prediction') else: requires_flow = False @@ -98,7 +98,7 @@ def create_sampler(name, model): # validate sampler prediction type if (model is not None) and (is_flow and not requires_flow): shared.log.error(f'Sampler: "{sampler.name}" cls={sampler.sampler.__class__.__name__} pipe={model.__class__.__name__} model requires sampler with discrete prediction') - # return restore_default(model) + return restore_default(model) if (model is not None) and (not is_flow and requires_flow): shared.log.error(f'Sampler: "{sampler.name}" cls={sampler.sampler.__class__.__name__} pipe={model.__class__.__name__} model requires sampler with flow prediction') return restore_default(model)