Merge branch 'main' into mps-fp16-fixes

invokeai/backend/install/migrate_to_3.py

@@ -76,6 +76,10 @@ class MigrateTo3(object):
         Create a unique name for a model for use within models.yaml.
         '''
         done = False
+        
+        # some model names have slashes in them, which really screws things up
+        name = name.replace('/','_')
+        
         key = ModelManager.create_key(name,info.base_type,info.model_type)
         unique_name = key
         counter = 1

invokeai/backend/install/model_install_backend.py

@@ -223,7 +223,7 @@ class ModelInstall(object):
         try:
             model_result = None
             info = info or ModelProbe().heuristic_probe(path,self.prediction_helper)
-            model_name = path.stem if info.format=='checkpoint' else path.name
+            model_name = path.stem if path.is_file() else path.name
             if self.mgr.model_exists(model_name, info.base_type, info.model_type):
                 raise ValueError(f'A model named "{model_name}" is already installed.')
             attributes = self._make_attributes(path,info)

invokeai/backend/model_management/lora.py

@@ -1,18 +1,17 @@
 from __future__ import annotations
 
 import copy
-from pathlib import Path
 from contextlib import contextmanager
-from typing import Optional, Dict, Tuple, Any
+from pathlib import Path
+from typing import Any, Dict, Optional, Tuple
 
 import torch
+from compel.embeddings_provider import BaseTextualInversionManager
+from diffusers.models import UNet2DConditionModel
 from safetensors.torch import load_file
 from torch.utils.hooks import RemovableHandle
-
-from diffusers.models import UNet2DConditionModel
 from transformers import CLIPTextModel
 
-from compel.embeddings_provider import BaseTextualInversionManager
 
 class LoRALayerBase:
     #rank: Optional[int]
@@ -539,9 +538,10 @@ class ModelPatcher:
                             original_weights[module_key] = module.weight.detach().to(device="cpu", copy=True)
 
                         # enable autocast to calc fp16 loras on cpu
-                        with torch.autocast(device_type="cpu"):
-                            layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0
-                            layer_weight = layer.get_weight() * lora_weight * layer_scale
+                        #with torch.autocast(device_type="cpu"):
+                        layer.to(dtype=torch.float32)
+                        layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0
+                        layer_weight = layer.get_weight() * lora_weight * layer_scale
 
                         if module.weight.shape != layer_weight.shape:
                             # TODO: debug on lycoris

invokeai/backend/model_management/model_manager.py

@@ -731,12 +731,12 @@ class ModelManager(object):
 
                             if model_path.is_relative_to(self.app_config.root_path):
                                 model_path = model_path.relative_to(self.app_config.root_path)
-                                try:
-                                    model_config: ModelConfigBase = model_class.probe_config(str(model_path))
-                                    self.models[model_key] = model_config
-                                    new_models_found = True
-                                except NotImplementedError as e:
-                                    self.logger.warning(e)
+                            try:
+                                model_config: ModelConfigBase = model_class.probe_config(str(model_path))
+                                self.models[model_key] = model_config
+                                new_models_found = True
+                            except NotImplementedError as e:
+                                self.logger.warning(e)
 
         imported_models = self.autoimport()