Unload models and load them back in lowvram mode no free vram.

comfy/model_management.py

@@ -352,6 +352,7 @@ def unload_model_clones(model, unload_weights_only=True, force_unload=True):
 def free_memory(memory_required, device, keep_loaded=[]):
     unloaded_model = []
     can_unload = []
+    unloaded_models = []
 
     for i in range(len(current_loaded_models) -1, -1, -1):
         shift_model = current_loaded_models[i]
@@ -369,7 +370,7 @@ def free_memory(memory_required, device, keep_loaded=[]):
         unloaded_model.append(i)
 
     for i in sorted(unloaded_model, reverse=True):
-        current_loaded_models.pop(i)
+        unloaded_models.append(current_loaded_models.pop(i))
 
     if len(unloaded_model) > 0:
         soft_empty_cache()
@@ -378,6 +379,7 @@ def free_memory(memory_required, device, keep_loaded=[]):
             mem_free_total, mem_free_torch = get_free_memory(device, torch_free_too=True)
             if mem_free_torch > mem_free_total * 0.25:
                 soft_empty_cache()
+    return unloaded_models
 
 def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None):
     global vram_state
@@ -421,7 +423,13 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
         for d in devs:
             if d != torch.device("cpu"):
                 free_memory(extra_mem, d, models_already_loaded)
-        return
+                free_mem = get_free_memory(d)
+                if free_mem < minimum_memory_required:
+                    logging.info("Unloading models for lowram load.") #TODO: partial model unloading when this case happens, also handle the opposite case where models can be unlowvramed.
+                    models_to_load = free_memory(minimum_memory_required, d)
+                    logging.info("{} models unloaded.".format(len(models_to_load)))
+        if len(models_to_load) == 0:
+            return
 
     logging.info(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}")