cleanup

modules/devices.py

@@ -37,21 +37,21 @@ def get_gpu_info():
         try:
             if torch.version.cuda:
                 return {
-                    'device': f'{torch.cuda.get_device_name(torch.cuda.current_device())} ({str(torch.cuda.device_count())}) ({torch.cuda.get_arch_list()[-1]}) {str(torch.cuda.get_device_capability(device))}',
+                    'device': f'{torch.cuda.get_device_name(torch.cuda.current_device())} n={torch.cuda.device_count()} arch={torch.cuda.get_arch_list()[-1]} cap={torch.cuda.get_device_capability(device)}',
                     'cuda': torch.version.cuda,
                     'cudnn': torch.backends.cudnn.version(),
                     'driver': get_driver(),
                 }
             elif torch.version.hip:
                 return {
-                    'device': f'{torch.cuda.get_device_name(torch.cuda.current_device())} ({str(torch.cuda.device_count())})',
+                    'device': f'{torch.cuda.get_device_name(torch.cuda.current_device())} n={torch.cuda.device_count()}',
                     'hip': torch.version.hip,
                 }
             else:
                 try:
                     import intel_extension_for_pytorch as ipex# pylint: disable=import-error, unused-import
                     return {
-                        'device': f'{torch.xpu.get_device_name(torch.xpu.current_device())} ({str(torch.xpu.device_count())})',
+                        'device': f'{torch.xpu.get_device_name(torch.xpu.current_device())} n={torch.xpu.device_count()}',
                         'ipex': ipex.__version__,
                     }
                 except Exception:

modules/sd_hijack_clip.py

@@ -179,7 +179,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
                     used_embeddings[embedding.name] = embedding
             z = self.process_tokens(tokens, multipliers)
             zs.append(z)
-        self.hijack.embedding_db.embeddings_used = [name for name in used_embeddings.keys()]
+        self.hijack.embedding_db.embeddings_used = list(used_embeddings)
         return torch.hstack(zs)
 
     def process_tokens(self, remade_batch_tokens, batch_multipliers):

modules/sd_models_config.py

@@ -21,12 +21,9 @@ def is_using_v_parameterization_for_sd2(state_dict):
     """
     Detects whether unet in state_dict is using v-parameterization. Returns True if it is. You're welcome.
     """
-
     import ldm.modules.diffusionmodules.openaimodel
-    from modules import devices
 
     device = devices.cpu
-
     with sd_disable_initialization.DisableInitialization():
         unet = ldm.modules.diffusionmodules.openaimodel.UNetModel(
             use_checkpoint=True,