skip_load_model -> force_full_load (#11390)

This should be a bit more clear and less prone to potential breakage if the
logic of the load models changes a bit.

Author: comfyanonymous

comfy/sampler_helpers.py

@@ -122,21 +122,20 @@ def estimate_memory(model, noise_shape, conds):
     minimum_memory_required = model.model.memory_required([noise_shape[0]] + list(noise_shape[1:]), cond_shapes=cond_shapes_min)
     return memory_required, minimum_memory_required
 
-def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, skip_load_model=False):
+def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, force_full_load=False):
     executor = comfy.patcher_extension.WrapperExecutor.new_executor(
         _prepare_sampling,
         comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.PREPARE_SAMPLING, model_options, is_model_options=True)
     )
-    return executor.execute(model, noise_shape, conds, model_options=model_options, skip_load_model=skip_load_model)
+    return executor.execute(model, noise_shape, conds, model_options=model_options, force_full_load=force_full_load)
 
-def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, skip_load_model=False):
+def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, force_full_load=False):
     real_model: BaseModel = None
     models, inference_memory = get_additional_models(conds, model.model_dtype())
     models += get_additional_models_from_model_options(model_options)
     models += model.get_nested_additional_models()  # TODO: does this require inference_memory update?
     memory_required, minimum_memory_required = estimate_memory(model, noise_shape, conds)
-    models_list = [model] if not skip_load_model else []
-    comfy.model_management.load_models_gpu(models_list + models, memory_required=memory_required + inference_memory, minimum_memory_required=minimum_memory_required + inference_memory)
+    comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required + inference_memory, minimum_memory_required=minimum_memory_required + inference_memory, force_full_load=force_full_load)
     real_model = model.model
 
     return real_model, conds, models

comfy_extras/nodes_train.py

@@ -44,7 +44,7 @@ def outer_sample(
                 noise.shape,
                 self.conds,
                 self.model_options,
-                skip_load_model=True, # skip load model as we manage it in TrainLoraNode.execute()
+                force_full_load=True,  # mirror behavior in TrainLoraNode.execute() to keep model loaded
             )
         )
         device = self.model_patcher.load_device