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- all guidance values set to -1 to enable using model defaults

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- log default values used if not overriden by user
- rename inconsistent guidance variables:

p.cfg_scale
p.image_cfg_scale -> p.cfg_image
p.diffusers_guidance_rescale -> p.cfg_rescale
p.pag_scale -> p.cfg_true
p.pag_adaptive -> p.cfg_adaptive

Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2026-05-16 11:06:23 +02:00
parent 632032308d
commit f67562b912
30 changed files with 199 additions and 167 deletions
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modules/processing_args.py
+27 -10
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@@ -179,6 +179,18 @@ def get_params(model):
return possible
def get_defaults(model, kwargs):
remove = ['return_dict', 'output_type', 'num_images_per_prompt', 'callback', 'callback_on_step_end_tensor_inputs']
try:
signature = inspect.signature(type(model).__call__, follow_wrapped=True)
defaults = {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty and v.default is not None} # get all defaults
defaults = {k: v for k, v in defaults.items() if k not in kwargs} # only log defaults that are not already set by kwargs
defaults = {k: v for k, v in defaults.items() if k not in remove} # remove common args that are not useful to log
log.debug(f'Pipeline: cls={model.__class__.__name__} defaults={defaults}')
except Exception as e:
log.error(f'Pipeline defaults: {e}')
def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:list | None=None, negative_prompts_2:list | None=None, prompt_attention:str | None=None, desc:str | None='', **kwargs):
t0 = time.time()
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
@@ -245,17 +257,20 @@ def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:l
if hasattr(model, 'scheduler') and hasattr(model.scheduler, 'noise_sampler_seed') and hasattr(model.scheduler, 'noise_sampler'):
model.scheduler.noise_sampler = None # noise needs to be reset instead of using cached values
model.scheduler.noise_sampler_seed = p.seeds # some schedulers have internal noise generator and do not use pipeline generator
if 'seed' in possible and p.seed is not None:
if ('seed' in possible) and (p.seed is not None) and (p.seed > -1):
args['seed'] = p.seed
if 'noise_sampler_seed' in possible and p.seeds is not None:
if ('noise_sampler_seed' in possible) and (p.seeds is not None):
args['noise_sampler_seed'] = p.seeds
if 'guidance_scale' in possible and p.cfg_scale is not None and p.cfg_scale > 0:
if ('guidance_scale' in possible) and (p.cfg_scale is not None) and (p.cfg_scale > -1):
args['guidance_scale'] = p.cfg_scale
if 'img_guidance_scale' in possible and hasattr(p, 'image_cfg_scale') and p.image_cfg_scale is not None and p.image_cfg_scale > 0:
args['img_guidance_scale'] = p.image_cfg_scale
if ('img_guidance_scale' in possible) and hasattr(p, 'cfg_image') and (p.cfg_image is not None) and (p.cfg_image > -1):
args['img_guidance_scale'] = p.cfg_image
if getattr(getattr(model, 'config', None), 'is_distilled', False) and args.get('guidance_scale', 0) > 1 and not getattr(p, 'distilled_warned', False):
log.warning(f'Pipeline: cls={model.__class__.__name__} distilled=True cfg_scale={args["guidance_scale"]} ignored, forced to 1')
p.distilled_warned = True
if 'generator' in possible:
generator = get_generator(p)
args['generator'] = generator
@@ -272,15 +287,14 @@ def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:l
if 'Kandinsky' in model.__class__.__name__ or 'Cosmos2' in model.__class__.__name__ or 'OmniGen2' in model.__class__.__name__:
kwargs['output_type'] = 'np' # only set latent if model has vae
if 'StableCascade' in model.__class__.__name__:
kwargs.pop("guidance_scale") # remove
kwargs.pop("num_inference_steps") # remove
if 'prior_num_inference_steps' in possible:
args["prior_num_inference_steps"] = p.steps
args["num_inference_steps"] = p.refiner_steps
if 'prior_guidance_scale' in possible:
if 'prior_guidance_scale' in possible and (p.cfg_scale is not None) and (p.cfg_scale > -1):
args["prior_guidance_scale"] = p.cfg_scale
if 'decoder_guidance_scale' in possible:
args["decoder_guidance_scale"] = p.image_cfg_scale
if 'decoder_guidance_scale' in possible and (p.cfg_image is not None) and (p.cfg_image > -1):
args["decoder_guidance_scale"] = p.cfg_image
if 'Flex2' in model.__class__.__name__:
if len(getattr(p, 'init_images', [])) > 0:
args['inpaint_image'] = p.init_images[0] if isinstance(p.init_images, list) else p.init_images
@@ -334,13 +348,14 @@ def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:l
if type(kwargs[arg]) == float or type(kwargs[arg]) == int:
if kwargs[arg] <= -1: # skip -1 as default value
continue
if kwargs[arg] is None: # skip None values
continue
args[arg] = kwargs[arg]
# optional preprocess
if hasattr(model, 'preprocess') and callable(model.preprocess):
model.preprocess(p, args)
# handle task specific args
if sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.MODULAR:
task_kwargs = task_modular_kwargs(p, model)
@@ -402,6 +417,8 @@ def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:l
sd_hijack_hypertile.hypertile_set(p, hr=len(getattr(p, 'init_images', [])) > 0)
get_defaults(model, args)
# debug info
clean = args.copy()
clean.pop('cross_attention_kwargs', None)