fix(samplers): report validation failures without backtrace

Sampler capability gates raised plain ValueError when schedulers_fallback is
disabled, so the API middleware and the gradio call wrapper printed a full
backtrace for an expected outcome. Add errors.ValidationError, raise it from
the gates, and report it message-only in errors.display; the UI error box and
the API error response already carry the message.

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()

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
 
 
@@ -108,7 +108,7 @@ def create_sampler(name, model, scheduler_overrides=None):
 
     if config is None or config.constructor is None:
         if debug or not shared.opts.schedulers_fallback:
-            raise ValueError(f'Sampler: name="{name}" unknown')
+            raise errors.ValidationError(f'Sampler: name="{name}" unknown')
         return restore_default(model, name)
 
     from modules import sd_samplers_diffusers
@@ -129,13 +129,13 @@ def create_sampler(name, model, scheduler_overrides=None):
     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 debug or not shared.opts.schedulers_fallback:
-            raise ValueError(f'Sampler: name="{sampler.name}" cls={sampler.sampler.__class__.__name__} type={pred_type} model requires sampler with discrete prediction')
+            raise errors.ValidationError(f'Sampler: name="{sampler.name}" cls={sampler.sampler.__class__.__name__} type={pred_type} model requires sampler with discrete prediction')
         else:
             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 debug or not shared.opts.schedulers_fallback:
-            raise ValueError(f'Sampler: name="{sampler.name}" cls={sampler.sampler.__class__.__name__} type={pred_type} model requires sampler with flow prediction')
+            raise errors.ValidationError(f'Sampler: name="{sampler.name}" cls={sampler.sampler.__class__.__name__} type={pred_type} model requires sampler with flow prediction')
         else:
             return restore_default(model, name)
 

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:
@@ -520,7 +520,7 @@ class DiffusionSampler:
             if debug:
                 errors.display(e, 'Samplers')
             if debug or not shared.opts.schedulers_fallback:
-                raise
+                raise errors.ValidationError(f'Sampler: name="{name}" {e}') from e
             self.sampler = None
             return
 
@@ -529,7 +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
@@ -549,7 +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
@@ -559,7 +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