cleanup

modules/processing_helpers.py

@@ -351,11 +351,10 @@ def validate_sample(tensor):
         cast = sample.astype(np.uint8)
     if len(w) > 0:
         nans = np.isnan(sample).sum()
-        shared.log.error(f'Failed to validate samples: sample={sample.shape} invalid={nans}')
         cast = np.nan_to_num(sample)
         minimum, maximum, mean = np.min(cast), np.max(cast), np.mean(cast)
         cast = cast.astype(np.uint8)
-        shared.log.warning(f'Attempted to correct samples: min={minimum:.2f} max={maximum:.2f} mean={mean:.2f}')
+        shared.log.error(f'Failed to validate samples: sample={sample.shape} min={minimum:.2f} max={maximum:.2f} mean={mean:.2f} invalid={nans}')
     return cast
 
 

modules/sd_models.py

@@ -1296,8 +1296,7 @@ def switch_pipe(cls: diffusers.DiffusionPipeline, pipeline: diffusers.DiffusionP
 
 
 def clean_diffuser_pipe(pipe):
-    n = getattr(pipe.__class__, '__name__', '')
-    if 'StableDiffusionXL' in n and 'requires_aesthetics_score' in pipe.config and hasattr(pipe, '_internal_dict'):
+    if pipe is not None and shared.sd_model_type == 'sdxl' and 'requires_aesthetics_score' in pipe.config and hasattr(pipe, '_internal_dict'):
         # diffusers adds requires_aesthetics_score with img2img and complains if requires_aesthetics_score exist in txt2img
         internal_dict = dict(pipe._internal_dict) # pylint: disable=protected-access
         internal_dict.pop('requires_aesthetics_score', None)