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fix processing skip

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-07-27 10:27:58 -04:00
parent a42187fef1
commit a1ef13098b
4 changed files with 22 additions and 26 deletions
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cli/api-txt2img.py
+1 -1
View File
@@ -63,7 +63,7 @@ def generate(args): # pylint: disable=redefined-outer-name
info = data['info']
log.info(f'image received: size={image.size} time={t1-t0:.2f} info="{info}"')
if args.output:
image.save(args.output)
image.save(args.output, exif=image._getexif())
log.info(f'image saved: size={image.size} filename={args.output}')
else:
log.warning(f'no images received: {data}')
modules/processing.py
+1
View File
@@ -117,6 +117,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
timer.process.reset()
debug(f'Process images: {vars(p)}')
if not hasattr(p.sd_model, 'sd_checkpoint_info'):
shared.log.error('Processing: incomplete model')
return None
if p.scripts is not None and isinstance(p.scripts, scripts_manager.ScriptRunner):
p.scripts.before_process(p)
modules/processing_diffusers.py
+4 -5
View File
@@ -76,12 +76,11 @@ def process_pre(p: processing.StableDiffusionProcessing):
shared.log.error(f'Processing apply: {e}')
errors.display(e, 'apply')
if hasattr(shared.sd_model, 'unet'):
sd_models.move_model(shared.sd_model.unet, devices.device)
if hasattr(shared.sd_model, 'transformer'):
sd_models.move_model(shared.sd_model.transformer, devices.device)
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
sd_models.move_model(shared.sd_model, devices.device)
# if hasattr(shared.sd_model, 'unet'):
# sd_models.move_model(shared.sd_model.unet, devices.device)
# if hasattr(shared.sd_model, 'transformer'):
# sd_models.move_model(shared.sd_model.transformer, devices.device)
timer.process.record('pre')
modules/sd_models.py
+16 -20
View File
@@ -813,7 +813,7 @@ def backup_pipe_components(pipe):
'sd_checkpoint_info': getattr(pipe, "sd_checkpoint_info", None),
'sd_model_checkpoint': getattr(pipe, "sd_model_checkpoint", None),
'embedding_db': getattr(pipe, "embedding_db", None),
'loaded_loras': getattr(pipe, "loaded_loras", None),
'loaded_loras': getattr(pipe, "loaded_loras", {}),
'sd_model_hash': getattr(pipe, "sd_model_hash", None),
'has_accelerate': getattr(pipe, "has_accelerate", None),
'current_attn_name': getattr(pipe, "current_attn_name", None),
@@ -828,28 +828,24 @@ def backup_pipe_components(pipe):
def restore_pipe_components(pipe, components):
if pipe is None or components is None:
return
if hasattr(pipe, 'sd_checkpoint_info'):
pipe.sd_checkpoint_info = components['sd_checkpoint_info']
if hasattr(pipe, 'sd_model_checkpoint'):
pipe.sd_model_checkpoint = components['sd_model_checkpoint']
if hasattr(pipe, 'embedding_db'):
pipe.embedding_db = components['embedding_db']
if hasattr(pipe, 'loaded_loras'):
pipe.loaded_loras = components['loaded_loras'] if components['loaded_loras'] is not None else {}
if hasattr(pipe, 'sd_model_hash'):
pipe.sd_model_hash = components['sd_model_hash']
if hasattr(pipe, 'has_accelerate'):
pipe.has_accelerate = components['has_accelerate']
if hasattr(pipe, 'current_attn_name'):
pipe.current_attn_name = components['current_attn_name']
if hasattr(pipe, 'default_scheduler'):
pipe.default_scheduler = components['default_scheduler']
if hasattr(pipe, 'image_encoder') and components['image_encoder'] is not None:
pipe.sd_checkpoint_info = components['sd_checkpoint_info']
pipe.sd_model_checkpoint = components['sd_model_checkpoint']
pipe.embedding_db = components['embedding_db']
pipe.loaded_loras = components['loaded_loras'] if components['loaded_loras'] is not None else {}
pipe.sd_model_hash = components['sd_model_hash']
pipe.has_accelerate = components['has_accelerate']
pipe.current_attn_name = components['current_attn_name']
pipe.default_scheduler = components['default_scheduler']
if components['image_encoder'] is not None:
pipe.image_encoder = components['image_encoder']
if hasattr(pipe, 'feature_extractor') and components['feature_extractor'] is not None:
if components['feature_extractor'] is not None:
pipe.feature_extractor = components['feature_extractor']
if hasattr(pipe, 'mask_processor') and components['mask_processor'] is not None:
if components['mask_processor'] is not None:
pipe.mask_processor = components['mask_processor']
if components['restore_pipeline'] is not None:
pipe.restore_pipeline = components['restore_pipeline']
if pipe.__class__.__name__ in ['FluxPipeline', 'StableDiffusion3Pipeline']:
pipe.register_modules(image_encoder = components['image_encoder'])
pipe.register_modules(feature_extractor = components['feature_extractor'])