fix model lookups

modules/devices.py

@@ -73,8 +73,7 @@ def torch_gc(force=False):
         if backend == "directml":
             practical_used = round(100 * torch.cuda.memory_allocated() / (1 << 30) / gpu.get('total', 1))
             shared.log.info(f'Practical GPU memory utilization: {practical_used}%')
-
-    if shared.opts.disable_gc and not force:
+    if not force:
         return
     collected = gc.collect()
     if cuda_ok:

modules/sd_models.py

@@ -491,7 +491,7 @@ class ModelData:
         self.lock = threading.Lock()
 
     def get_sd_model(self):
-        if self.sd_model is None:
+        if self.sd_model is None and shared.opts.sd_model_checkpoint != 'None':
             with self.lock:
                 try:
                     if shared.backend == shared.Backend.ORIGINAL:
@@ -511,7 +511,7 @@ class ModelData:
         self.sd_model = v
 
     def get_sd_refiner(self):
-        if self.sd_refiner is None:
+        if self.sd_refiner is None and shared.opts.sd_model_refiner != 'None':
             with self.lock:
                 try:
                     if shared.backend == shared.Backend.ORIGINAL:

modules/shared.py

@@ -398,7 +398,6 @@ options_templates.update(options_section(('cuda', "Compute Settings"), {
     "cuda_compile_precompile": OptionInfo(False, "Model compile precompile"),
     "cuda_compile_verbose": OptionInfo(False, "Model compile verbose mode"),
     "cuda_compile_errors": OptionInfo(True, "Model compile suppress errors"),
-    "disable_gc": OptionInfo(True, "Disable Torch memory garbage collection"),
     "ipex_optimize": OptionInfo(True if devices.backend == "ipex" else False, "Enable IPEX Optimize for Intel GPUs"),
     "directml_memory_provider": OptionInfo(default_memory_provider, '[DirectML] Memory stats provider', gr.Dropdown, lambda: {"choices": memory_providers}),
 }))