Merge branch 'main' into feat/clip_skip

invokeai/backend/install/invokeai_configure.py

@@ -430,13 +430,13 @@ to allow InvokeAI to download restricted styles & subjects from the "Concept Lib
             max_height=len(PRECISION_CHOICES) + 1,
             scroll_exit=True,
         )
-        self.max_loaded_models = self.add_widget_intelligent(
+        self.max_cache_size = self.add_widget_intelligent(
             IntTitleSlider,
-            name="Number of models to cache in CPU memory (each will use 2-4 GB!)",
+            name="Size of the RAM cache used for fast model switching (GB)",
-            value=old_opts.max_loaded_models,
+            value=old_opts.max_cache_size,
-            out_of=10,
+            out_of=20,
-            lowest=1,
+            lowest=3,
-            begin_entry_at=4,
+            begin_entry_at=6,
             scroll_exit=True,
         )
         self.nextrely += 1
@@ -539,7 +539,7 @@ https://huggingface.co/spaces/CompVis/stable-diffusion-license
                 "outdir",
                 "nsfw_checker",
                 "free_gpu_mem",
-                "max_loaded_models",
+                "max_cache_size",
                 "xformers_enabled",
                 "always_use_cpu",
         ]:
@@ -555,9 +555,6 @@ https://huggingface.co/spaces/CompVis/stable-diffusion-license
         new_opts.license_acceptance = self.license_acceptance.value
         new_opts.precision = PRECISION_CHOICES[self.precision.value[0]]
         
-        # widget library workaround to make max_loaded_models an int rather than a float
-        new_opts.max_loaded_models = int(new_opts.max_loaded_models)
-
         return new_opts
 
 

invokeai/backend/install/model_install_backend.py

@@ -193,7 +193,10 @@ class ModelInstall(object):
                 models_installed.update(self._install_path(path))
 
             # folders style or similar
-            elif path.is_dir() and any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin'}]):
+            elif path.is_dir() and any([(path/x).exists() for x in \
+                                        {'config.json','model_index.json','learned_embeds.bin','pytorch_lora_weights.bin'}
+                                        ]
+                                       ):
                 models_installed.update(self._install_path(path))
 
             # recursive scan

invokeai/backend/model_management/model_cache.py

@@ -8,7 +8,7 @@ The cache returns context manager generators designed to load the
 model into the GPU within the context, and unload outside the
 context. Use like this:
 
-   cache = ModelCache(max_models_cached=6)
+   cache = ModelCache(max_cache_size=7.5)
    with cache.get_model('runwayml/stable-diffusion-1-5') as SD1,
           cache.get_model('stabilityai/stable-diffusion-2') as SD2:
        do_something_in_GPU(SD1,SD2)
@@ -91,7 +91,7 @@ class ModelCache(object):
         logger: types.ModuleType = logger
     ):
         '''
-        :param max_models: Maximum number of models to cache in CPU RAM [4]
+        :param max_cache_size: Maximum size of the RAM cache [6.0 GB]
         :param execution_device: Torch device to load active model into [torch.device('cuda')]
         :param storage_device: Torch device to save inactive model in [torch.device('cpu')]
         :param precision: Precision for loaded models [torch.float16]
@@ -126,16 +126,6 @@ class ModelCache(object):
             key += f":{submodel_type}"
         return key
 
-    #def get_model(
-    #    self,
-    #    repo_id_or_path: Union[str, Path],
-    #    model_type: ModelType = ModelType.Diffusers,
-    #    subfolder: Path = None,
-    #    submodel: ModelType = None,
-    #    revision: str = None,
-    #    attach_model_part: Tuple[ModelType, str] = (None, None),
-    #    gpu_load: bool = True,
-    #) -> ModelLocker:  # ?? what does it return
     def _get_model_info(
         self,
         model_path: str,

invokeai/backend/model_management/model_manager.py

@@ -785,7 +785,7 @@ class ModelManager(object):
                     if path in known_paths or path.parent in scanned_dirs:
                         scanned_dirs.add(path)
                         continue
-                    if any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin'}]):
+                    if any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin','pytorch_lora_weights.bin'}]):
                         new_models_found.update(installer.heuristic_import(path))
                         scanned_dirs.add(path)
 
@@ -794,7 +794,8 @@ class ModelManager(object):
                     if path in known_paths or path.parent in scanned_dirs:
                         continue
                     if path.suffix in {'.ckpt','.bin','.pth','.safetensors','.pt'}:
-                        new_models_found.update(installer.heuristic_import(path))
+                        import_result = installer.heuristic_import(path)
+                        new_models_found.update(import_result)
 
             self.logger.info(f'Scanned {items_scanned} files and directories, imported {len(new_models_found)} models')
             installed.update(new_models_found)

invokeai/backend/model_management/model_probe.py

@@ -78,7 +78,6 @@ class ModelProbe(object):
             format_type = 'diffusers' if model_path.is_dir() else 'checkpoint'
         else:
             format_type = 'diffusers' if isinstance(model,(ConfigMixin,ModelMixin)) else 'checkpoint'
-
         model_info = None
         try:
             model_type = cls.get_model_type_from_folder(model_path, model) \
@@ -105,7 +104,7 @@ class ModelProbe(object):
                                      ) else 512,
             )
         except Exception:
-            return None
+            raise
 
         return model_info
 
@@ -127,6 +126,8 @@ class ModelProbe(object):
                 return ModelType.Vae
             elif any(key.startswith(v) for v in {"lora_te_", "lora_unet_"}):
                 return ModelType.Lora
+            elif any(key.endswith(v) for v in {"to_k_lora.up.weight", "to_q_lora.down.weight"}):
+                return ModelType.Lora
             elif any(key.startswith(v) for v in {"control_model", "input_blocks"}):
                 return ModelType.ControlNet
             elif key in {"emb_params", "string_to_param"}:
@@ -137,7 +138,7 @@ class ModelProbe(object):
             if len(ckpt) < 10 and all(isinstance(v, torch.Tensor) for v in ckpt.values()):
                 return ModelType.TextualInversion
         
-        raise ValueError("Unable to determine model type")
+        raise ValueError(f"Unable to determine model type for {model_path}")
 
     @classmethod
     def get_model_type_from_folder(cls, folder_path: Path, model: ModelMixin)->ModelType:
@@ -167,7 +168,7 @@ class ModelProbe(object):
             return type
 
         # give up
-        raise ValueError("Unable to determine model type")
+        raise ValueError("Unable to determine model type for {folder_path}")
 
     @classmethod
     def _scan_and_load_checkpoint(cls,model_path: Path)->dict:

invokeai/frontend/install/model_install.py

@@ -678,9 +678,8 @@ def select_and_download_models(opt: Namespace):
 
     # this is where the TUI is called
     else:
-        # needed because the torch library is loaded, even though we don't use it
+        # needed to support the probe() method running under a subprocess
-        # currently commented out because it has started generating errors (?)
+        torch.multiprocessing.set_start_method("spawn")
-        # torch.multiprocessing.set_start_method("spawn")
 
         # the third argument is needed in the Windows 11 environment in
         # order to launch and resize a console window running this program

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketConnected.ts

@@ -1,6 +1,5 @@
 import { log } from 'app/logging/useLogger';
 import { appSocketConnected, socketConnected } from 'services/events/actions';
-import { receivedPageOfImages } from 'services/api/thunks/image';
 import { receivedOpenAPISchema } from 'services/api/thunks/schema';
 import { startAppListening } from '../..';
 
@@ -14,19 +13,10 @@ export const addSocketConnectedEventListener = () => {
 
       moduleLog.debug({ timestamp }, 'Connected');
 
-      const { nodes, config, gallery } = getState();
+      const { nodes, config } = getState();
 
       const { disabledTabs } = config;
 
-      if (!gallery.ids.length) {
-        dispatch(
-          receivedPageOfImages({
-            categories: ['general'],
-            is_intermediate: false,
-          })
-        );
-      }
-
       if (!nodes.schema && !disabledTabs.includes('nodes')) {
         dispatch(receivedOpenAPISchema());
       }

invokeai/frontend/web/src/common/hooks/useIsReadyToInvoke.ts

@@ -6,10 +6,15 @@ import { validateSeedWeights } from 'common/util/seedWeightPairs';
 import { generationSelector } from 'features/parameters/store/generationSelectors';
 import { systemSelector } from 'features/system/store/systemSelectors';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
+import {
+  modelsApi,
+  useGetMainModelsQuery,
+} from '../../services/api/endpoints/models';
 
 const readinessSelector = createSelector(
   [stateSelector, activeTabNameSelector],
-  ({ generation, system, batch }, activeTabName) => {
+  (state, activeTabName) => {
+    const { generation, system, batch } = state;
     const { shouldGenerateVariations, seedWeights, initialImage, seed } =
       generation;
 
@@ -32,6 +37,13 @@ const readinessSelector = createSelector(
       reasonsWhyNotReady.push('No initial image selected');
     }
 
+    const { isSuccess: mainModelsSuccessfullyLoaded } =
+      modelsApi.endpoints.getMainModels.select()(state);
+    if (!mainModelsSuccessfullyLoaded) {
+      isReady = false;
+      reasonsWhyNotReady.push('Models are not loaded');
+    }
+
     // TODO: job queue
     // Cannot generate if already processing an image
     if (isProcessing) {

invokeai/frontend/web/src/features/gallery/components/ImageGalleryContent.tsx

@@ -182,6 +182,15 @@ const ImageGalleryContent = () => {
     return () => osInstance()?.destroy();
   }, [scroller, initialize, osInstance]);
 
+  useEffect(() => {
+    dispatch(
+      receivedPageOfImages({
+        categories: ['general'],
+        is_intermediate: false,
+      })
+    );
+  }, [dispatch]);
+
   const handleClickImagesCategory = useCallback(() => {
     dispatch(imageCategoriesChanged(IMAGE_CATEGORIES));
     dispatch(setGalleryView('images'));