tidy(ui): organise tool module

- Canvas generation mode is replace with a boolean `sendToCanvas` flag. When off, images generated on the canvas go to the gallery. When on, they get added to the staging area.
- When an image result is received, if its destination is the canvas, staging is automatically started.
- Updated queue list to show the destination column.
- Added `IconSwitch` component to represent binary choices, used for the new `sendToCanvas` flag and image viewer toggle.
- Remove the queue actions menu in `QueueControls`. Move the queue count badge to the cancel button.
- Redo layout of `QueueControls` to prevent duplicate queue count badges.
- Fix issue where gallery and options panels could show thru transparent regions of queue tab.
- Disable panel hotkeys when on mm/queue tabs.

invokeai/app/invocations/fields.py

@@ -45,13 +45,11 @@ class UIType(str, Enum, metaclass=MetaEnum):
     SDXLRefinerModel = "SDXLRefinerModelField"
     ONNXModel = "ONNXModelField"
     VAEModel = "VAEModelField"
-    FluxVAEModel = "FluxVAEModelField"
     LoRAModel = "LoRAModelField"
     ControlNetModel = "ControlNetModelField"
     IPAdapterModel = "IPAdapterModelField"
     T2IAdapterModel = "T2IAdapterModelField"
     T5EncoderModel = "T5EncoderModelField"
-    CLIPEmbedModel = "CLIPEmbedModelField"
     SpandrelImageToImageModel = "SpandrelImageToImageModelField"
     # endregion
 
@@ -130,7 +128,6 @@ class FieldDescriptions:
     noise = "Noise tensor"
     clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
     t5_encoder = "T5 tokenizer and text encoder"
-    clip_embed_model = "CLIP Embed loader"
     unet = "UNet (scheduler, LoRAs)"
     transformer = "Transformer"
     vae = "VAE"

invokeai/app/invocations/flux_text_encoder.py

@@ -40,10 +40,7 @@ class FluxTextEncoderInvocation(BaseInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> FluxConditioningOutput:
-        # Note: The T5 and CLIP encoding are done in separate functions to ensure that all model references are locally
-        # scoped. This ensures that the T5 model can be freed and gc'd before loading the CLIP model (if necessary).
-        t5_embeddings = self._t5_encode(context)
-        clip_embeddings = self._clip_encode(context)
+        t5_embeddings, clip_embeddings = self._encode_prompt(context)
         conditioning_data = ConditioningFieldData(
             conditionings=[FLUXConditioningInfo(clip_embeds=clip_embeddings, t5_embeds=t5_embeddings)]
         )
@@ -51,7 +48,12 @@ class FluxTextEncoderInvocation(BaseInvocation):
         conditioning_name = context.conditioning.save(conditioning_data)
         return FluxConditioningOutput.build(conditioning_name)
 
-    def _t5_encode(self, context: InvocationContext) -> torch.Tensor:
+    def _encode_prompt(self, context: InvocationContext) -> tuple[torch.Tensor, torch.Tensor]:
+        # Load CLIP.
+        clip_tokenizer_info = context.models.load(self.clip.tokenizer)
+        clip_text_encoder_info = context.models.load(self.clip.text_encoder)
+
+        # Load T5.
         t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer)
         t5_text_encoder_info = context.models.load(self.t5_encoder.text_encoder)
 
@@ -68,15 +70,6 @@ class FluxTextEncoderInvocation(BaseInvocation):
 
             prompt_embeds = t5_encoder(prompt)
 
-        assert isinstance(prompt_embeds, torch.Tensor)
-        return prompt_embeds
-
-    def _clip_encode(self, context: InvocationContext) -> torch.Tensor:
-        clip_tokenizer_info = context.models.load(self.clip.tokenizer)
-        clip_text_encoder_info = context.models.load(self.clip.text_encoder)
-
-        prompt = [self.prompt]
-
         with (
             clip_text_encoder_info as clip_text_encoder,
             clip_tokenizer_info as clip_tokenizer,
@@ -88,5 +81,6 @@ class FluxTextEncoderInvocation(BaseInvocation):
 
             pooled_prompt_embeds = clip_encoder(prompt)
 
+        assert isinstance(prompt_embeds, torch.Tensor)
         assert isinstance(pooled_prompt_embeds, torch.Tensor)
-        return pooled_prompt_embeds
+        return prompt_embeds, pooled_prompt_embeds

invokeai/app/invocations/flux_text_to_image.py

@@ -58,7 +58,13 @@ class FluxTextToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        latents = self._run_diffusion(context)
+        # Load the conditioning data.
+        cond_data = context.conditioning.load(self.positive_text_conditioning.conditioning_name)
+        assert len(cond_data.conditionings) == 1
+        flux_conditioning = cond_data.conditionings[0]
+        assert isinstance(flux_conditioning, FLUXConditioningInfo)
+
+        latents = self._run_diffusion(context, flux_conditioning.clip_embeds, flux_conditioning.t5_embeds)
         image = self._run_vae_decoding(context, latents)
         image_dto = context.images.save(image=image)
         return ImageOutput.build(image_dto)
@@ -66,19 +72,11 @@ class FluxTextToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     def _run_diffusion(
         self,
         context: InvocationContext,
+        clip_embeddings: torch.Tensor,
+        t5_embeddings: torch.Tensor,
     ):
-        inference_dtype = torch.bfloat16
-
-        # Load the conditioning data.
-        cond_data = context.conditioning.load(self.positive_text_conditioning.conditioning_name)
-        assert len(cond_data.conditionings) == 1
-        flux_conditioning = cond_data.conditionings[0]
-        assert isinstance(flux_conditioning, FLUXConditioningInfo)
-        flux_conditioning = flux_conditioning.to(dtype=inference_dtype)
-        t5_embeddings = flux_conditioning.t5_embeds
-        clip_embeddings = flux_conditioning.clip_embeds
-
         transformer_info = context.models.load(self.transformer.transformer)
+        inference_dtype = torch.bfloat16
 
         # Prepare input noise.
         x = get_noise(
@@ -90,19 +88,24 @@ class FluxTextToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
             seed=self.seed,
         )
 
-        x, img_ids = prepare_latent_img_patches(x)
+        img, img_ids = prepare_latent_img_patches(x)
 
         is_schnell = "schnell" in transformer_info.config.config_path
 
         timesteps = get_schedule(
             num_steps=self.num_steps,
-            image_seq_len=x.shape[1],
+            image_seq_len=img.shape[1],
             shift=not is_schnell,
         )
 
         bs, t5_seq_len, _ = t5_embeddings.shape
         txt_ids = torch.zeros(bs, t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device())
 
+        # HACK(ryand): Manually empty the cache. Currently we don't check the size of the model before loading it from
+        # disk. Since the transformer model is large (24GB), there's a good chance that it will OOM on 32GB RAM systems
+        # if the cache is not empty.
+        context.models._services.model_manager.load.ram_cache.make_room(24 * 2**30)
+
         with transformer_info as transformer:
             assert isinstance(transformer, Flux)
 
@@ -137,7 +140,7 @@ class FluxTextToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
 
             x = denoise(
                 model=transformer,
-                img=x,
+                img=img,
                 img_ids=img_ids,
                 txt=t5_embeddings,
                 txt_ids=txt_ids,

invokeai/app/invocations/model.py

@@ -157,7 +157,7 @@ class FluxModelLoaderOutput(BaseInvocationOutput):
     title="Flux Main Model",
     tags=["model", "flux"],
     category="model",
-    version="1.0.4",
+    version="1.0.3",
     classification=Classification.Prototype,
 )
 class FluxModelLoaderInvocation(BaseInvocation):
@@ -169,35 +169,23 @@ class FluxModelLoaderInvocation(BaseInvocation):
         input=Input.Direct,
     )
 
-    t5_encoder_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.t5_encoder, ui_type=UIType.T5EncoderModel, input=Input.Direct, title="T5 Encoder"
-    )
-
-    clip_embed_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.clip_embed_model,
-        ui_type=UIType.CLIPEmbedModel,
+    t5_encoder: ModelIdentifierField = InputField(
+        description=FieldDescriptions.t5_encoder,
+        ui_type=UIType.T5EncoderModel,
         input=Input.Direct,
-        title="CLIP Embed",
-    )
-
-    vae_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.vae_model, ui_type=UIType.FluxVAEModel, title="VAE"
     )
 
     def invoke(self, context: InvocationContext) -> FluxModelLoaderOutput:
-        for key in [self.model.key, self.t5_encoder_model.key, self.clip_embed_model.key, self.vae_model.key]:
-            if not context.models.exists(key):
-                raise ValueError(f"Unknown model: {key}")
-
-        transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
-        vae = self.vae_model.model_copy(update={"submodel_type": SubModelType.VAE})
-
-        tokenizer = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
-        clip_encoder = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
-
-        tokenizer2 = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
-        t5_encoder = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
+        model_key = self.model.key
 
+        if not context.models.exists(model_key):
+            raise ValueError(f"Unknown model: {model_key}")
+        transformer = self._get_model(context, SubModelType.Transformer)
+        tokenizer = self._get_model(context, SubModelType.Tokenizer)
+        tokenizer2 = self._get_model(context, SubModelType.Tokenizer2)
+        clip_encoder = self._get_model(context, SubModelType.TextEncoder)
+        t5_encoder = self._get_model(context, SubModelType.TextEncoder2)
+        vae = self._get_model(context, SubModelType.VAE)
         transformer_config = context.models.get_config(transformer)
         assert isinstance(transformer_config, CheckpointConfigBase)
 
@@ -209,6 +197,52 @@ class FluxModelLoaderInvocation(BaseInvocation):
             max_seq_len=max_seq_lengths[transformer_config.config_path],
         )
 
+    def _get_model(self, context: InvocationContext, submodel: SubModelType) -> ModelIdentifierField:
+        match submodel:
+            case SubModelType.Transformer:
+                return self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
+            case SubModelType.VAE:
+                return self._pull_model_from_mm(
+                    context,
+                    SubModelType.VAE,
+                    "FLUX.1-schnell_ae",
+                    ModelType.VAE,
+                    BaseModelType.Flux,
+                )
+            case submodel if submodel in [SubModelType.Tokenizer, SubModelType.TextEncoder]:
+                return self._pull_model_from_mm(
+                    context,
+                    submodel,
+                    "clip-vit-large-patch14",
+                    ModelType.CLIPEmbed,
+                    BaseModelType.Any,
+                )
+            case submodel if submodel in [SubModelType.Tokenizer2, SubModelType.TextEncoder2]:
+                return self._pull_model_from_mm(
+                    context,
+                    submodel,
+                    self.t5_encoder.name,
+                    ModelType.T5Encoder,
+                    BaseModelType.Any,
+                )
+            case _:
+                raise Exception(f"{submodel.value} is not a supported submodule for a flux model")
+
+    def _pull_model_from_mm(
+        self,
+        context: InvocationContext,
+        submodel: SubModelType,
+        name: str,
+        type: ModelType,
+        base: BaseModelType,
+    ):
+        if models := context.models.search_by_attrs(name=name, base=base, type=type):
+            if len(models) != 1:
+                raise Exception(f"Multiple models detected for selected model with name {name}")
+            return ModelIdentifierField.from_config(models[0]).model_copy(update={"submodel_type": submodel})
+        else:
+            raise ValueError(f"Please install the {base}:{type} model named {name} via starter models")
+
 
 @invocation(
     "main_model_loader",

invokeai/app/services/workflow_records/default_workflows/Flux Text to Image.json

@@ -2,13 +2,13 @@
   "name": "FLUX Text to Image",
   "author": "InvokeAI",
   "description": "A simple text-to-image workflow using FLUX dev or schnell models. Prerequisite model downloads: T5 Encoder, CLIP-L Encoder, and FLUX VAE. Quantized and un-quantized versions can be found in the starter models tab within your Model Manager. We recommend 4 steps for FLUX schnell models and 30 steps for FLUX dev models.",
-  "version": "1.0.4",
+  "version": "1.0.0",
   "contact": "",
   "tags": "text2image, flux",
   "notes": "Prerequisite model downloads: T5 Encoder, CLIP-L Encoder, and FLUX VAE. Quantized and un-quantized versions can be found in the starter models tab within your Model Manager. We recommend 4 steps for FLUX schnell models and 30 steps for FLUX dev models.",
   "exposedFields": [
     {
-      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "nodeId": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
       "fieldName": "model"
     },
     {
@@ -20,8 +20,8 @@
       "fieldName": "num_steps"
     },
     {
-      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "fieldName": "t5_encoder_model"
+      "nodeId": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
+      "fieldName": "t5_encoder"
     }
   ],
   "meta": {
@@ -30,12 +30,12 @@
   },
   "nodes": [
     {
-      "id": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "id": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
       "type": "invocation",
       "data": {
-        "id": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+        "id": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
         "type": "flux_model_loader",
-        "version": "1.0.4",
+        "version": "1.0.3",
         "label": "",
         "notes": "",
         "isOpen": true,
@@ -44,25 +44,31 @@
         "inputs": {
           "model": {
             "name": "model",
-            "label": ""
+            "label": "Model (Starter Models can be found in Model Manager)",
+            "value": {
+              "key": "f04a7a2f-c74d-4538-8d5e-879a53501662",
+              "hash": "random:4875da7a9508444ffa706f61961c260d0c6729f6181a86b31fad06df1277b850",
+              "name": "FLUX Dev (Quantized)",
+              "base": "flux",
+              "type": "main"
+            }
           },
-          "t5_encoder_model": {
-            "name": "t5_encoder_model",
-            "label": ""
-          },
-          "clip_embed_model": {
-            "name": "clip_embed_model",
-            "label": ""
-          },
-          "vae_model": {
-            "name": "vae_model",
-            "label": ""
+          "t5_encoder": {
+            "name": "t5_encoder",
+            "label": "T 5 Encoder (Starter Models can be found in Model Manager)",
+            "value": {
+              "key": "20dcd9ec-5fbb-4012-8401-049e707da5e5",
+              "hash": "random:f986be43ff3502169e4adbdcee158afb0e0a65a1edc4cab16ae59963630cfd8f",
+              "name": "t5_bnb_int8_quantized_encoder",
+              "base": "any",
+              "type": "t5_encoder"
+            }
           }
         }
       },
       "position": {
-        "x": 381.1882713063478,
-        "y": -95.89663532854017
+        "x": 337.09365228062825,
+        "y": 40.63469521079861
       }
     },
     {
@@ -201,45 +207,45 @@
   ],
   "edges": [
     {
-      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90max_seq_len-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_max_seq_len",
+      "id": "reactflow__edge-4f0207c2-ff40-41fd-b047-ad33fbb1c33amax_seq_len-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_max_seq_len",
       "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "source": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
       "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
       "sourceHandle": "max_seq_len",
       "targetHandle": "t5_max_seq_len"
     },
     {
-      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90vae-159bdf1b-79e7-4174-b86e-d40e646964c8vae",
+      "id": "reactflow__edge-4f0207c2-ff40-41fd-b047-ad33fbb1c33avae-159bdf1b-79e7-4174-b86e-d40e646964c8vae",
       "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "source": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
       "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
       "sourceHandle": "vae",
       "targetHandle": "vae"
     },
     {
-      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90t5_encoder-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_encoder",
+      "id": "reactflow__edge-4f0207c2-ff40-41fd-b047-ad33fbb1c33atransformer-159bdf1b-79e7-4174-b86e-d40e646964c8transformer",
       "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "source": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
+      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "sourceHandle": "transformer",
+      "targetHandle": "transformer"
+    },
+    {
+      "id": "reactflow__edge-4f0207c2-ff40-41fd-b047-ad33fbb1c33at5_encoder-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_encoder",
+      "type": "default",
+      "source": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
       "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
       "sourceHandle": "t5_encoder",
       "targetHandle": "t5_encoder"
     },
     {
-      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90clip-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cclip",
+      "id": "reactflow__edge-4f0207c2-ff40-41fd-b047-ad33fbb1c33aclip-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cclip",
       "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "source": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
       "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
       "sourceHandle": "clip",
       "targetHandle": "clip"
     },
-    {
-      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90transformer-159bdf1b-79e7-4174-b86e-d40e646964c8transformer",
-      "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
-      "sourceHandle": "transformer",
-      "targetHandle": "transformer"
-    },
     {
       "id": "reactflow__edge-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cconditioning-159bdf1b-79e7-4174-b86e-d40e646964c8positive_text_conditioning",
       "type": "default",

invokeai/backend/flux/sampling.py

@@ -111,7 +111,16 @@ def denoise(
     step_callback: Callable[[], None],
     guidance: float = 4.0,
 ):
-    # guidance_vec is ignored for schnell.
+    dtype = model.txt_in.bias.dtype
+
+    # TODO(ryand): This shouldn't be necessary if we manage the dtypes properly in the caller.
+    img = img.to(dtype=dtype)
+    img_ids = img_ids.to(dtype=dtype)
+    txt = txt.to(dtype=dtype)
+    txt_ids = txt_ids.to(dtype=dtype)
+    vec = vec.to(dtype=dtype)
+
+    # this is ignored for schnell
     guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
     for t_curr, t_prev in tqdm(list(zip(timesteps[:-1], timesteps[1:], strict=True))):
         t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
@@ -159,9 +168,9 @@ def prepare_latent_img_patches(latent_img: torch.Tensor) -> tuple[torch.Tensor,
         img = repeat(img, "1 ... -> bs ...", bs=bs)
 
     # Generate patch position ids.
-    img_ids = torch.zeros(h // 2, w // 2, 3, device=img.device, dtype=img.dtype)
-    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=img.device, dtype=img.dtype)[:, None]
-    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=img.device, dtype=img.dtype)[None, :]
+    img_ids = torch.zeros(h // 2, w // 2, 3, device=img.device)
+    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=img.device)[:, None]
+    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=img.device)[None, :]
     img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
 
     return img, img_ids

invokeai/backend/model_manager/load/load_default.py

@@ -72,7 +72,6 @@ class ModelLoader(ModelLoaderBase):
             pass
 
         config.path = str(self._get_model_path(config))
-        self._ram_cache.make_room(self.get_size_fs(config, Path(config.path), submodel_type))
         loaded_model = self._load_model(config, submodel_type)
 
         self._ram_cache.put(

invokeai/backend/model_manager/load/model_cache/model_cache_base.py

@@ -193,6 +193,15 @@ class ModelCacheBase(ABC, Generic[T]):
         """
         pass
 
+    @abstractmethod
+    def exists(
+        self,
+        key: str,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> bool:
+        """Return true if the model identified by key and submodel_type is in the cache."""
+        pass
+
     @abstractmethod
     def cache_size(self) -> int:
         """Get the total size of the models currently cached."""

invokeai/backend/model_manager/load/model_cache/model_cache_default.py

@@ -1,6 +1,22 @@
 # Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Development team
 # TODO: Add Stalker's proper name to copyright
-""" """
+"""
+Manage a RAM cache of diffusion/transformer models for fast switching.
+They are moved between GPU VRAM and CPU RAM as necessary. If the cache
+grows larger than a preset maximum, then the least recently used
+model will be cleared and (re)loaded from disk when next needed.
+
+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_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)
+
+
+"""
 
 import gc
 import math
@@ -24,64 +40,45 @@ from invokeai.backend.model_manager.load.model_util import calc_model_size_by_da
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
-# Size of a GB in bytes.
-GB = 2**30
+# Maximum size of the cache, in gigs
+# Default is roughly enough to hold three fp16 diffusers models in RAM simultaneously
+DEFAULT_MAX_CACHE_SIZE = 6.0
+
+# amount of GPU memory to hold in reserve for use by generations (GB)
+DEFAULT_MAX_VRAM_CACHE_SIZE = 2.75
+
+# actual size of a gig
+GIG = 1073741824
 
 # Size of a MB in bytes.
 MB = 2**20
 
 
 class ModelCache(ModelCacheBase[AnyModel]):
-    """A cache for managing models in memory.
-
-    The cache is based on two levels of model storage:
-    - execution_device: The device where most models are executed (typically "cuda", "mps", or "cpu").
-    - storage_device: The device where models are offloaded when not in active use (typically "cpu").
-
-    The model cache is based on the following assumptions:
-    - storage_device_mem_size > execution_device_mem_size
-    - disk_to_storage_device_transfer_time >> storage_device_to_execution_device_transfer_time
-
-    A copy of all models in the cache is always kept on the storage_device. A subset of the models also have a copy on
-    the execution_device.
-
-    Models are moved between the storage_device and the execution_device as necessary. Cache size limits are enforced
-    on both the storage_device and the execution_device. The execution_device cache uses a smallest-first offload
-    policy. The storage_device cache uses a least-recently-used (LRU) offload policy.
-
-    Note: Neither of these offload policies has really been compared against alternatives. It's likely that different
-    policies would be better, although the optimal policies are likely heavily dependent on usage patterns and HW
-    configuration.
-
-    The cache returns context manager generators designed to load the model into the execution device (often GPU) within
-    the context, and unload outside the context.
-
-    Example usage:
-    ```
-    cache = ModelCache(max_cache_size=7.5, max_vram_cache_size=6.0)
-    with cache.get_model('runwayml/stable-diffusion-1-5') as SD1:
-        do_something_on_gpu(SD1)
-    ```
-    """
+    """Implementation of ModelCacheBase."""
 
     def __init__(
         self,
-        max_cache_size: float,
-        max_vram_cache_size: float,
+        max_cache_size: float = DEFAULT_MAX_CACHE_SIZE,
+        max_vram_cache_size: float = DEFAULT_MAX_VRAM_CACHE_SIZE,
         execution_device: torch.device = torch.device("cuda"),
         storage_device: torch.device = torch.device("cpu"),
+        precision: torch.dtype = torch.float16,
+        sequential_offload: bool = False,
         lazy_offloading: bool = True,
+        sha_chunksize: int = 16777216,
         log_memory_usage: bool = False,
         logger: Optional[Logger] = None,
     ):
         """
         Initialize the model RAM cache.
 
-        :param max_cache_size: Maximum size of the storage_device cache in GBs.
-        :param max_vram_cache_size: Maximum size of the execution_device cache in GBs.
+        :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 lazy_offloading: Keep model in VRAM until another model needs to be loaded.
+        :param precision: Precision for loaded models [torch.float16]
+        :param lazy_offloading: Keep model in VRAM until another model needs to be loaded
+        :param sequential_offload: Conserve VRAM by loading and unloading each stage of the pipeline sequentially
         :param log_memory_usage: If True, a memory snapshot will be captured before and after every model cache
             operation, and the result will be logged (at debug level). There is a time cost to capturing the memory
             snapshots, so it is recommended to disable this feature unless you are actively inspecting the model cache's
@@ -89,6 +86,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
         """
         # allow lazy offloading only when vram cache enabled
         self._lazy_offloading = lazy_offloading and max_vram_cache_size > 0
+        self._precision: torch.dtype = precision
         self._max_cache_size: float = max_cache_size
         self._max_vram_cache_size: float = max_vram_cache_size
         self._execution_device: torch.device = execution_device
@@ -147,6 +145,15 @@ class ModelCache(ModelCacheBase[AnyModel]):
             total += cache_record.size
         return total
 
+    def exists(
+        self,
+        key: str,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> bool:
+        """Return true if the model identified by key and submodel_type is in the cache."""
+        key = self._make_cache_key(key, submodel_type)
+        return key in self._cached_models
+
     def put(
         self,
         key: str,
@@ -196,7 +203,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
         # more stats
         if self.stats:
             stats_name = stats_name or key
-            self.stats.cache_size = int(self._max_cache_size * GB)
+            self.stats.cache_size = int(self._max_cache_size * GIG)
             self.stats.high_watermark = max(self.stats.high_watermark, self.cache_size())
             self.stats.in_cache = len(self._cached_models)
             self.stats.loaded_model_sizes[stats_name] = max(
@@ -224,13 +231,10 @@ class ModelCache(ModelCacheBase[AnyModel]):
             return model_key
 
     def offload_unlocked_models(self, size_required: int) -> None:
-        """Offload models from the execution_device to make room for size_required.
-
-        :param size_required: The amount of space to clear in the execution_device cache, in bytes.
-        """
-        reserved = self._max_vram_cache_size * GB
+        """Move any unused models from VRAM."""
+        reserved = self._max_vram_cache_size * GIG
         vram_in_use = torch.cuda.memory_allocated() + size_required
-        self.logger.debug(f"{(vram_in_use/GB):.2f}GB VRAM needed for models; max allowed={(reserved/GB):.2f}GB")
+        self.logger.debug(f"{(vram_in_use/GIG):.2f}GB VRAM needed for models; max allowed={(reserved/GIG):.2f}GB")
         for _, cache_entry in sorted(self._cached_models.items(), key=lambda x: x[1].size):
             if vram_in_use <= reserved:
                 break
@@ -241,7 +245,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
                 cache_entry.loaded = False
                 vram_in_use = torch.cuda.memory_allocated() + size_required
                 self.logger.debug(
-                    f"Removing {cache_entry.key} from VRAM to free {(cache_entry.size/GB):.2f}GB; vram free = {(torch.cuda.memory_allocated()/GB):.2f}GB"
+                    f"Removing {cache_entry.key} from VRAM to free {(cache_entry.size/GIG):.2f}GB; vram free = {(torch.cuda.memory_allocated()/GIG):.2f}GB"
                 )
 
         TorchDevice.empty_cache()
@@ -299,7 +303,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
         self.logger.debug(
             f"Moved model '{cache_entry.key}' from {source_device} to"
             f" {target_device} in {(end_model_to_time-start_model_to_time):.2f}s."
-            f"Estimated model size: {(cache_entry.size/GB):.3f} GB."
+            f"Estimated model size: {(cache_entry.size/GIG):.3f} GB."
             f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
         )
 
@@ -322,14 +326,14 @@ class ModelCache(ModelCacheBase[AnyModel]):
                     f"Moving model '{cache_entry.key}' from {source_device} to"
                     f" {target_device} caused an unexpected change in VRAM usage. The model's"
                     " estimated size may be incorrect. Estimated model size:"
-                    f" {(cache_entry.size/GB):.3f} GB.\n"
+                    f" {(cache_entry.size/GIG):.3f} GB.\n"
                     f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
                 )
 
     def print_cuda_stats(self) -> None:
         """Log CUDA diagnostics."""
-        vram = "%4.2fG" % (torch.cuda.memory_allocated() / GB)
-        ram = "%4.2fG" % (self.cache_size() / GB)
+        vram = "%4.2fG" % (torch.cuda.memory_allocated() / GIG)
+        ram = "%4.2fG" % (self.cache_size() / GIG)
 
         in_ram_models = 0
         in_vram_models = 0
@@ -349,20 +353,17 @@ class ModelCache(ModelCacheBase[AnyModel]):
                 )
 
     def make_room(self, size: int) -> None:
-        """Make enough room in the cache to accommodate a new model of indicated size.
-
-        Note: This function deletes all of the cache's internal references to a model in order to free it. If there are
-        external references to the model, there's nothing that the cache can do about it, and those models will not be
-        garbage-collected.
-        """
+        """Make enough room in the cache to accommodate a new model of indicated size."""
+        # calculate how much memory this model will require
+        # multiplier = 2 if self.precision==torch.float32 else 1
         bytes_needed = size
-        maximum_size = self.max_cache_size * GB  # stored in GB, convert to bytes
+        maximum_size = self.max_cache_size * GIG  # stored in GB, convert to bytes
         current_size = self.cache_size()
 
         if current_size + bytes_needed > maximum_size:
             self.logger.debug(
-                f"Max cache size exceeded: {(current_size/GB):.2f}/{self.max_cache_size:.2f} GB, need an additional"
-                f" {(bytes_needed/GB):.2f} GB"
+                f"Max cache size exceeded: {(current_size/GIG):.2f}/{self.max_cache_size:.2f} GB, need an additional"
+                f" {(bytes_needed/GIG):.2f} GB"
             )
 
         self.logger.debug(f"Before making_room: cached_models={len(self._cached_models)}")
@@ -379,7 +380,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
 
             if not cache_entry.locked:
                 self.logger.debug(
-                    f"Removing {model_key} from RAM cache to free at least {(size/GB):.2f} GB (-{(cache_entry.size/GB):.2f} GB)"
+                    f"Removing {model_key} from RAM cache to free at least {(size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)"
                 )
                 current_size -= cache_entry.size
                 models_cleared += 1

invokeai/backend/quantization/bnb_llm_int8.py

@@ -54,10 +54,8 @@ class InvokeLinear8bitLt(bnb.nn.Linear8bitLt):
 
         # See `bnb.nn.Linear8bitLt._save_to_state_dict()` for the serialization logic of SCB and weight_format.
         scb = state_dict.pop(prefix + "SCB", None)
-
-        # Currently, we only support weight_format=0.
-        weight_format = state_dict.pop(prefix + "weight_format", None)
-        assert weight_format == 0
+        # weight_format is unused, but we pop it so we can validate that there are no unexpected keys.
+        _weight_format = state_dict.pop(prefix + "weight_format", None)
 
         # TODO(ryand): Technically, we should be using `strict`, `missing_keys`, `unexpected_keys`, and `error_msgs`
         # rather than raising an exception to correctly implement this API.
@@ -91,14 +89,6 @@ class InvokeLinear8bitLt(bnb.nn.Linear8bitLt):
             )
             self.bias = bias if bias is None else torch.nn.Parameter(bias)
 
-        # Reset the state. The persisted fields are based on the initialization behaviour in
-        # `bnb.nn.Linear8bitLt.__init__()`.
-        new_state = bnb.MatmulLtState()
-        new_state.threshold = self.state.threshold
-        new_state.has_fp16_weights = False
-        new_state.use_pool = self.state.use_pool
-        self.state = new_state
-
 
 def _convert_linear_layers_to_llm_8bit(
     module: torch.nn.Module, ignore_modules: set[str], outlier_threshold: float, prefix: str = ""

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -43,11 +43,6 @@ class FLUXConditioningInfo:
     clip_embeds: torch.Tensor
     t5_embeds: torch.Tensor
 
-    def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
-        self.clip_embeds = self.clip_embeds.to(device=device, dtype=dtype)
-        self.t5_embeds = self.t5_embeds.to(device=device, dtype=dtype)
-        return self
-
 
 @dataclass
 class ConditioningFieldData:

invokeai/backend/util/__init__.py

@@ -3,9 +3,10 @@ Initialization file for invokeai.backend.util
 """
 
 from invokeai.backend.util.logging import InvokeAILogger
-from invokeai.backend.util.util import Chdir, directory_size
+from invokeai.backend.util.util import GIG, Chdir, directory_size
 
 __all__ = [
+    "GIG",
     "directory_size",
     "Chdir",
     "InvokeAILogger",

invokeai/backend/util/util.py

@@ -7,6 +7,9 @@ from pathlib import Path
 
 from PIL import Image
 
+# actual size of a gig
+GIG = 1073741824
+
 
 def slugify(value: str, allow_unicode: bool = False) -> str:
     """

invokeai/frontend/web/public/locales/en.json

@@ -706,8 +706,6 @@
         "availableModels": "Available Models",
         "baseModel": "Base Model",
         "cancel": "Cancel",
-        "clipEmbed": "CLIP Embed",
-        "clipVision": "CLIP Vision",
         "config": "Config",
         "convert": "Convert",
         "convertingModelBegin": "Converting Model. Please wait.",
@@ -795,7 +793,6 @@
         "settings": "Settings",
         "simpleModelPlaceholder": "URL or path to a local file or diffusers folder",
         "source": "Source",
-        "spandrelImageToImage": "Image to Image (Spandrel)",
         "starterModels": "Starter Models",
         "starterModelsInModelManager": "Starter Models can be found in Model Manager",
         "syncModels": "Sync Models",
@@ -804,7 +801,6 @@
         "loraTriggerPhrases": "LoRA Trigger Phrases",
         "mainModelTriggerPhrases": "Main Model Trigger Phrases",
         "typePhraseHere": "Type phrase here",
-        "t5Encoder": "T5 Encoder",
         "upcastAttention": "Upcast Attention",
         "uploadImage": "Upload Image",
         "urlOrLocalPath": "URL or Local Path",
@@ -1654,13 +1650,6 @@
         "storeNotInitialized": "Store is not initialized"
     },
     "controlLayers": {
-        "saveCanvasToGallery": "Save Canvas To Gallery",
-        "saveBboxToGallery": "Save Bbox To Gallery",
-        "savedToGalleryOk": "Saved to Gallery",
-        "savedToGalleryError": "Error saving to gallery",
-        "mergeVisible": "Merge Visible",
-        "mergeVisibleOk": "Merged visible layers",
-        "mergeVisibleError": "Error merging visible layers",
         "clearHistory": "Clear History",
         "generateMode": "Generate",
         "generateModeDesc": "Create individual images. Generated images are added directly to the gallery.",

invokeai/frontend/web/src/app/components/App.tsx

@@ -16,7 +16,6 @@ import { DynamicPromptsModal } from 'features/dynamicPrompts/components/DynamicP
 import { useStarterModelsToast } from 'features/modelManagerV2/hooks/useStarterModelsToast';
 import { ClearQueueConfirmationsAlertDialog } from 'features/queue/components/ClearQueueConfirmationAlertDialog';
 import { StylePresetModal } from 'features/stylePresets/components/StylePresetForm/StylePresetModal';
-import { activeStylePresetIdChanged } from 'features/stylePresets/store/stylePresetSlice';
 import RefreshAfterResetModal from 'features/system/components/SettingsModal/RefreshAfterResetModal';
 import SettingsModal from 'features/system/components/SettingsModal/SettingsModal';
 import { configChanged } from 'features/system/store/configSlice';
@@ -44,17 +43,10 @@ interface Props {
     action: 'sendToImg2Img' | 'sendToCanvas' | 'useAllParameters';
   };
   selectedWorkflowId?: string;
-  selectedStylePresetId?: string;
-  destination?: TabName;
+  destination?: TabName | undefined;
 }
 
-const App = ({
-  config = DEFAULT_CONFIG,
-  selectedImage,
-  selectedWorkflowId,
-  selectedStylePresetId,
-  destination,
-}: Props) => {
+const App = ({ config = DEFAULT_CONFIG, selectedImage, selectedWorkflowId, destination }: Props) => {
   const language = useAppSelector(selectLanguage);
   const logger = useLogger('system');
   const dispatch = useAppDispatch();
@@ -93,12 +85,6 @@ const App = ({
     }
   }, [selectedWorkflowId, getAndLoadWorkflow]);
 
-  useEffect(() => {
-    if (selectedStylePresetId) {
-      dispatch(activeStylePresetIdChanged(selectedStylePresetId));
-    }
-  }, [dispatch, selectedStylePresetId]);
-
   useEffect(() => {
     if (destination) {
       dispatch(setActiveTab(destination));

invokeai/frontend/web/src/app/components/InvokeAIUI.tsx

@@ -45,7 +45,6 @@ interface Props extends PropsWithChildren {
     action: 'sendToImg2Img' | 'sendToCanvas' | 'useAllParameters';
   };
   selectedWorkflowId?: string;
-  selectedStylePresetId?: string;
   destination?: TabName;
   customStarUi?: CustomStarUi;
   socketOptions?: Partial<ManagerOptions & SocketOptions>;
@@ -67,7 +66,6 @@ const InvokeAIUI = ({
   queueId,
   selectedImage,
   selectedWorkflowId,
-  selectedStylePresetId,
   destination,
   customStarUi,
   socketOptions,
@@ -229,7 +227,6 @@ const InvokeAIUI = ({
                 config={config}
                 selectedImage={selectedImage}
                 selectedWorkflowId={selectedWorkflowId}
-                selectedStylePresetId={selectedStylePresetId}
                 destination={destination}
               />
             </AppDndContext>

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

@@ -1,74 +1,52 @@
-import { useStore } from '@nanostores/react';
 import type { WritableAtom } from 'nanostores';
-import { atom } from 'nanostores';
-import { useCallback, useState } from 'react';
+import { useCallback, useMemo, useState } from 'react';
 
-type UseBoolean = {
-  isTrue: boolean;
-  setTrue: () => void;
-  setFalse: () => void;
-  set: (value: boolean) => void;
-  toggle: () => void;
-};
-
-/**
- * Creates a hook to manage a boolean state. The boolean is stored in a nanostores atom.
- * Returns a tuple containing the hook and the atom. Use this for global boolean state.
- * @param initialValue Initial value of the boolean
- */
-export const buildUseBoolean = (initialValue: boolean): [() => UseBoolean, WritableAtom<boolean>] => {
-  const $boolean = atom(initialValue);
-
-  const setTrue = () => {
-    $boolean.set(true);
-  };
-  const setFalse = () => {
-    $boolean.set(false);
-  };
-  const set = (value: boolean) => {
-    $boolean.set(value);
-  };
-  const toggle = () => {
-    $boolean.set(!$boolean.get());
-  };
-
-  const useBoolean = () => {
-    const isTrue = useStore($boolean);
-
-    return {
-      isTrue,
-      setTrue,
-      setFalse,
-      set,
-      toggle,
-    };
-  };
-
-  return [useBoolean, $boolean] as const;
-};
-
-/**
- * Hook to manage a boolean state. Use this for a local boolean state.
- * @param initialValue Initial value of the boolean
- */
 export const useBoolean = (initialValue: boolean) => {
   const [isTrue, set] = useState(initialValue);
+  const setTrue = useCallback(() => set(true), []);
+  const setFalse = useCallback(() => set(false), []);
+  const toggle = useCallback(() => set((v) => !v), []);
 
-  const setTrue = useCallback(() => {
-    set(true);
-  }, [set]);
-  const setFalse = useCallback(() => {
-    set(false);
-  }, [set]);
-  const toggle = useCallback(() => {
-    set((val) => !val);
-  }, [set]);
+  const api = useMemo(
+    () => ({
+      isTrue,
+      set,
+      setTrue,
+      setFalse,
+      toggle,
+    }),
+    [isTrue, set, setTrue, setFalse, toggle]
+  );
 
-  return {
-    isTrue,
-    setTrue,
-    setFalse,
-    set,
-    toggle,
+  return api;
+};
+
+export const buildUseBoolean = ($boolean: WritableAtom<boolean>) => {
+  return () => {
+    const setTrue = useCallback(() => {
+      $boolean.set(true);
+    }, []);
+    const setFalse = useCallback(() => {
+      $boolean.set(false);
+    }, []);
+    const set = useCallback((value: boolean) => {
+      $boolean.set(value);
+    }, []);
+    const toggle = useCallback(() => {
+      $boolean.set(!$boolean.get());
+    }, []);
+
+    const api = useMemo(
+      () => ({
+        setTrue,
+        setFalse,
+        set,
+        toggle,
+        $boolean,
+      }),
+      [set, setFalse, setTrue, toggle]
+    );
+
+    return api;
   };
 };

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/EntityListActionBarSelectedEntityOpacity.tsx

@@ -157,7 +157,7 @@ export const SelectedEntityOpacity = memo(() => {
             clampValueOnBlur={false}
             variant="outline"
           >
-            <NumberInputField paddingInlineEnd={7} _focusVisible={{ zIndex: 0 }} />
+            <NumberInputField paddingInlineEnd={7} />
             <PopoverTrigger>
               <IconButton
                 aria-label="open-slider"

invokeai/frontend/web/src/features/controlLayers/components/CanvasPanelContent.tsx

@@ -1,22 +1,37 @@
-import { Divider, Flex } from '@invoke-ai/ui-library';
+import { Box, ContextMenu, Divider, Flex, MenuList } from '@invoke-ai/ui-library';
 import { useAppSelector } from 'app/store/storeHooks';
 import { CanvasAddEntityButtons } from 'features/controlLayers/components/CanvasAddEntityButtons';
 import { CanvasEntityList } from 'features/controlLayers/components/CanvasEntityList/CanvasEntityList';
 import { EntityListActionBar } from 'features/controlLayers/components/CanvasEntityList/EntityListActionBar';
+import { CanvasEntityListMenuItems } from 'features/controlLayers/components/CanvasEntityList/EntityListActionBarAddLayerMenuItems';
 import { CanvasManagerProviderGate } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { selectHasEntities } from 'features/controlLayers/store/selectors';
-import { memo } from 'react';
+import { memo, useCallback } from 'react';
 
 export const CanvasPanelContent = memo(() => {
   const hasEntities = useAppSelector(selectHasEntities);
+  const renderMenu = useCallback(
+    () => (
+      <MenuList>
+        <CanvasEntityListMenuItems />
+      </MenuList>
+    ),
+    []
+  );
 
   return (
     <CanvasManagerProviderGate>
       <Flex flexDir="column" gap={2} w="full" h="full">
         <EntityListActionBar />
         <Divider py={0} />
-        {!hasEntities && <CanvasAddEntityButtons />}
-        {hasEntities && <CanvasEntityList />}
+        <ContextMenu<HTMLDivElement> renderMenu={renderMenu} stopImmediatePropagation stopPropagation>
+          {(ref) => (
+            <Box ref={ref} w="full" h="full">
+              {!hasEntities && <CanvasAddEntityButtons />}
+              {hasEntities && <CanvasEntityList />}
+            </Box>
+          )}
+        </ContextMenu>
       </Flex>
     </CanvasManagerProviderGate>
   );

invokeai/frontend/web/src/features/controlLayers/components/ControlLayersToolbar.tsx

@@ -2,7 +2,6 @@
 import { Flex, Spacer } from '@invoke-ai/ui-library';
 import { CanvasResetViewButton } from 'features/controlLayers/components/CanvasResetViewButton';
 import { CanvasScale } from 'features/controlLayers/components/CanvasScale';
-import { SaveToGalleryButton } from 'features/controlLayers/components/SaveToGalleryButton';
 import { CanvasSettingsPopover } from 'features/controlLayers/components/Settings/CanvasSettingsPopover';
 import { ToolChooser } from 'features/controlLayers/components/Tool/ToolChooser';
 import { ToolFillColorPicker } from 'features/controlLayers/components/Tool/ToolFillColorPicker';
@@ -28,7 +27,6 @@ export const ControlLayersToolbar = memo(() => {
         <CanvasResetViewButton />
         <Spacer />
         <ToolFillColorPicker />
-        <SaveToGalleryButton />
         <CanvasSettingsPopover />
         <ViewerToggle />
       </Flex>

invokeai/frontend/web/src/features/controlLayers/components/SaveToGalleryButton.tsx

@@ -1,53 +0,0 @@
-import { IconButton, useShiftModifier } from '@invoke-ai/ui-library';
-import { logger } from 'app/logging/logger';
-import { buildUseBoolean } from 'common/hooks/useBoolean';
-import { isOk, withResultAsync } from 'common/util/result';
-import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
-import { toast } from 'features/toast/toast';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiFloppyDiskBold } from 'react-icons/pi';
-import { serializeError } from 'serialize-error';
-
-const log = logger('canvas');
-
-const [useIsSaving] = buildUseBoolean(false);
-
-export const SaveToGalleryButton = memo(() => {
-  const { t } = useTranslation();
-  const shift = useShiftModifier();
-  const canvasManager = useCanvasManager();
-  const isSaving = useIsSaving();
-
-  const onClick = useCallback(async () => {
-    isSaving.setTrue();
-
-    const rect = shift ? canvasManager.stateApi.getBbox().rect : canvasManager.stage.getVisibleRect('raster_layer');
-
-    const result = await withResultAsync(() =>
-      canvasManager.compositor.rasterizeAndUploadCompositeRasterLayer(rect, true)
-    );
-
-    if (isOk(result)) {
-      toast({ title: t('controlLayers.savedToGalleryOk') });
-    } else {
-      log.error({ error: serializeError(result.error) }, 'Failed to save canvas to gallery');
-      toast({ title: t('controlLayers.savedToGalleryError'), status: 'error' });
-    }
-
-    isSaving.setFalse();
-  }, [canvasManager.compositor, canvasManager.stage, canvasManager.stateApi, isSaving, shift, t]);
-
-  return (
-    <IconButton
-      variant="ghost"
-      onClick={onClick}
-      icon={<PiFloppyDiskBold />}
-      isLoading={isSaving.isTrue}
-      aria-label={shift ? t('controlLayers.saveBboxToGallery') : t('controlLayers.saveCanvasToGallery')}
-      tooltip={shift ? t('controlLayers.saveBboxToGallery') : t('controlLayers.saveCanvasToGallery')}
-    />
-  );
-});
-
-SaveToGalleryButton.displayName = 'SaveToGalleryButton';

invokeai/frontend/web/src/features/controlLayers/components/common/CanvasEntityGroupList.tsx

@@ -2,12 +2,11 @@ import type { SystemStyleObject } from '@invoke-ai/ui-library';
 import { Button, Collapse, Flex, Icon, Spacer, Text } from '@invoke-ai/ui-library';
 import { useBoolean } from 'common/hooks/useBoolean';
 import { CanvasEntityAddOfTypeButton } from 'features/controlLayers/components/common/CanvasEntityAddOfTypeButton';
-import { CanvasEntityMergeVisibleButton } from 'features/controlLayers/components/common/CanvasEntityMergeVisibleButton';
 import { CanvasEntityTypeIsHiddenToggle } from 'features/controlLayers/components/common/CanvasEntityTypeIsHiddenToggle';
 import { useEntityTypeTitle } from 'features/controlLayers/hooks/useEntityTypeTitle';
 import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
 import type { PropsWithChildren } from 'react';
-import { memo, useMemo } from 'react';
+import { memo } from 'react';
 import { PiCaretDownBold } from 'react-icons/pi';
 
 type Props = PropsWithChildren<{
@@ -22,9 +21,6 @@ const _hover: SystemStyleObject = {
 export const CanvasEntityGroupList = memo(({ isSelected, type, children }: Props) => {
   const title = useEntityTypeTitle(type);
   const collapse = useBoolean(true);
-  const canMergeVisible = useMemo(() => type === 'raster_layer' || type === 'inpaint_mask', [type]);
-  const canHideAll = useMemo(() => type !== 'ip_adapter', [type]);
-
   return (
     <Flex flexDir="column" w="full">
       <Flex w="full">
@@ -58,9 +54,8 @@ export const CanvasEntityGroupList = memo(({ isSelected, type, children }: Props
           </Text>
           <Spacer />
         </Flex>
-        {canMergeVisible && <CanvasEntityMergeVisibleButton type={type} />}
         <CanvasEntityAddOfTypeButton type={type} />
-        {canHideAll && <CanvasEntityTypeIsHiddenToggle type={type} />}
+        {type !== 'ip_adapter' && <CanvasEntityTypeIsHiddenToggle type={type} />}
       </Flex>
       <Collapse in={collapse.isTrue}>
         <Flex flexDir="column" gap={2} pt={2}>

invokeai/frontend/web/src/features/controlLayers/components/common/CanvasEntityHeader.tsx

@@ -56,7 +56,7 @@ export const CanvasEntityHeader = memo(({ children, ...rest }: FlexProps) => {
   }, [entityIdentifier]);
 
   return (
-    <ContextMenu renderMenu={renderMenu}>
+    <ContextMenu renderMenu={renderMenu} stopImmediatePropagation>
       {(ref) => (
         <Flex ref={ref} gap={2} alignItems="center" p={2} {...rest}>
           {children}

invokeai/frontend/web/src/features/controlLayers/components/common/CanvasEntityMergeVisibleButton.tsx

@@ -1,88 +0,0 @@
-import { IconButton } from '@invoke-ai/ui-library';
-import { logger } from 'app/logging/logger';
-import { useAppDispatch } from 'app/store/storeHooks';
-import { isOk, withResultAsync } from 'common/util/result';
-import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
-import { inpaintMaskAdded, rasterLayerAdded } from 'features/controlLayers/store/canvasSlice';
-import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
-import { imageDTOToImageObject } from 'features/controlLayers/store/types';
-import { toast } from 'features/toast/toast';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiStackBold } from 'react-icons/pi';
-import { serializeError } from 'serialize-error';
-
-const log = logger('canvas');
-
-type Props = {
-  type: CanvasEntityIdentifier['type'];
-};
-
-export const CanvasEntityMergeVisibleButton = memo(({ type }: Props) => {
-  const { t } = useTranslation();
-  const dispatch = useAppDispatch();
-  const canvasManager = useCanvasManager();
-  const onClick = useCallback(async () => {
-    if (type === 'raster_layer') {
-      const rect = canvasManager.stage.getVisibleRect('raster_layer');
-      const result = await withResultAsync(() =>
-        canvasManager.compositor.rasterizeAndUploadCompositeRasterLayer(rect, false)
-      );
-
-      if (isOk(result)) {
-        dispatch(
-          rasterLayerAdded({
-            isSelected: true,
-            overrides: {
-              objects: [imageDTOToImageObject(result.value)],
-              position: { x: Math.floor(rect.x), y: Math.floor(rect.y) },
-            },
-            deleteOthers: true,
-          })
-        );
-        toast({ title: t('controlLayers.mergeVisibleOk') });
-      } else {
-        log.error({ error: serializeError(result.error) }, 'Failed to merge visible');
-        toast({ title: t('controlLayers.mergeVisibleError'), status: 'error' });
-      }
-    } else if (type === 'inpaint_mask') {
-      const rect = canvasManager.stage.getVisibleRect('inpaint_mask');
-      const result = await withResultAsync(() =>
-        canvasManager.compositor.rasterizeAndUploadCompositeInpaintMask(rect, false)
-      );
-
-      if (isOk(result)) {
-        dispatch(
-          inpaintMaskAdded({
-            isSelected: true,
-            overrides: {
-              objects: [imageDTOToImageObject(result.value)],
-              position: { x: Math.floor(rect.x), y: Math.floor(rect.y) },
-            },
-            deleteOthers: true,
-          })
-        );
-        toast({ title: t('controlLayers.mergeVisibleOk') });
-      } else {
-        log.error({ error: serializeError(result.error) }, 'Failed to merge visible');
-        toast({ title: t('controlLayers.mergeVisibleError'), status: 'error' });
-      }
-    } else {
-      log.error({ type }, 'Unsupported type for merge visible');
-    }
-  }, [canvasManager.compositor, canvasManager.stage, dispatch, t, type]);
-
-  return (
-    <IconButton
-      size="sm"
-      aria-label={t('controlLayers.mergeVisible')}
-      tooltip={t('controlLayers.mergeVisible')}
-      variant="link"
-      icon={<PiStackBold />}
-      onClick={onClick}
-      alignSelf="stretch"
-    />
-  );
-});
-
-CanvasEntityMergeVisibleButton.displayName = 'CanvasEntityMergeVisibleButton';

invokeai/frontend/web/src/features/controlLayers/konva/CanvasCompositorModule.ts

@@ -147,19 +147,6 @@ export class CanvasCompositorModule extends CanvasModuleABC {
     return stableHash(data);
   };
 
-  rasterizeAndUploadCompositeRasterLayer = async (rect: Rect, saveToGallery: boolean) => {
-    this.log.trace({ rect }, 'Rasterizing composite raster layer');
-
-    const canvas = this.getCompositeRasterLayerCanvas(rect);
-    const blob = await canvasToBlob(canvas);
-
-    if (this.manager._isDebugging) {
-      previewBlob(blob, 'Composite raster layer canvas');
-    }
-
-    return uploadImage(blob, 'composite-raster-layer.png', 'general', !saveToGallery);
-  };
-
   getCompositeRasterLayerImageDTO = async (rect: Rect): Promise<ImageDTO> => {
     let imageDTO: ImageDTO | null = null;
 
@@ -174,21 +161,17 @@ export class CanvasCompositorModule extends CanvasModuleABC {
       }
     }
 
-    imageDTO = await this.rasterizeAndUploadCompositeRasterLayer(rect, false);
-    this.manager.cache.imageNameCache.set(hash, imageDTO.image_name);
-    return imageDTO;
-  };
+    this.log.trace({ rect }, 'Rasterizing composite raster layer');
 
-  rasterizeAndUploadCompositeInpaintMask = async (rect: Rect, saveToGallery: boolean) => {
-    this.log.trace({ rect }, 'Rasterizing composite inpaint mask');
-
-    const canvas = this.getCompositeInpaintMaskCanvas(rect);
+    const canvas = this.getCompositeRasterLayerCanvas(rect);
     const blob = await canvasToBlob(canvas);
     if (this.manager._isDebugging) {
-      previewBlob(blob, 'Composite inpaint mask canvas');
+      previewBlob(blob, 'Composite raster layer canvas');
     }
 
-    return uploadImage(blob, 'composite-inpaint-mask.png', 'general', !saveToGallery);
+    imageDTO = await uploadImage(blob, 'composite-raster-layer.png', 'general', true);
+    this.manager.cache.imageNameCache.set(hash, imageDTO.image_name);
+    return imageDTO;
   };
 
   getCompositeInpaintMaskImageDTO = async (rect: Rect): Promise<ImageDTO> => {
@@ -205,7 +188,15 @@ export class CanvasCompositorModule extends CanvasModuleABC {
       }
     }
 
-    imageDTO = await this.rasterizeAndUploadCompositeInpaintMask(rect, false);
+    this.log.trace({ rect }, 'Rasterizing composite inpaint mask');
+
+    const canvas = this.getCompositeInpaintMaskCanvas(rect);
+    const blob = await canvasToBlob(canvas);
+    if (this.manager._isDebugging) {
+      previewBlob(blob, 'Composite inpaint mask canvas');
+    }
+
+    imageDTO = await uploadImage(blob, 'composite-inpaint-mask.png', 'general', true);
     this.manager.cache.imageNameCache.set(hash, imageDTO.image_name);
     return imageDTO;
   };

invokeai/frontend/web/src/features/controlLayers/konva/CanvasStageModule.ts

@@ -2,7 +2,7 @@ import type { CanvasManager } from 'features/controlLayers/konva/CanvasManager';
 import { CanvasModuleABC } from 'features/controlLayers/konva/CanvasModuleABC';
 import { CANVAS_SCALE_BY } from 'features/controlLayers/konva/constants';
 import { getPrefixedId, getRectUnion } from 'features/controlLayers/konva/util';
-import type { CanvasEntityIdentifier, Coordinate, Dimensions, Rect } from 'features/controlLayers/store/types';
+import type { Coordinate, Dimensions, Rect } from 'features/controlLayers/store/types';
 import type Konva from 'konva';
 import type { KonvaEventObject } from 'konva/lib/Node';
 import { clamp } from 'lodash-es';
@@ -76,36 +76,35 @@ export class CanvasStageModule extends CanvasModuleABC {
     });
   };
 
-  getVisibleRect = (type?: Exclude<CanvasEntityIdentifier['type'], 'ip_adapter'>): Rect => {
+  getVisibleRect = (): Rect => {
     const rects = [];
 
     for (const adapter of this.manager.adapters.getAll()) {
-      if (!adapter.state.isEnabled) {
-        continue;
+      if (adapter.state.isEnabled) {
+        rects.push(adapter.transformer.getRelativeRect());
       }
-      if (type && adapter.state.type !== type) {
-        continue;
-      }
-      rects.push(adapter.transformer.getRelativeRect());
     }
 
-    return getRectUnion(...rects);
+    const rectUnion = getRectUnion(...rects);
+
+    if (rectUnion.width === 0 || rectUnion.height === 0) {
+      // fall back to the bbox if there is no content
+      return this.manager.stateApi.getBbox().rect;
+    } else {
+      return rectUnion;
+    }
   };
 
   fitBboxToStage = () => {
-    const { rect } = this.manager.stateApi.getBbox();
-    this.log.trace({ rect }, 'Fitting bbox to stage');
-    this.fitRect(rect);
+    this.log.trace('Fitting bbox to stage');
+    const bbox = this.manager.stateApi.getBbox();
+    this.fitRect(bbox.rect);
   };
 
   fitLayersToStage() {
+    this.log.trace('Fitting layers to stage');
     const rect = this.getVisibleRect();
-    if (rect.width === 0 || rect.height === 0) {
-      this.fitBboxToStage();
-    } else {
-      this.log.trace({ rect }, 'Fitting layers to stage');
-      this.fitRect(rect);
-    }
+    this.fitRect(rect);
   }
 
   fitRect = (rect: Rect) => {

invokeai/frontend/web/src/features/controlLayers/konva/util.test.ts

@@ -1,47 +0,0 @@
-import { getPrefixedId, getRectUnion } from 'features/controlLayers/konva/util';
-import { describe, expect, it } from 'vitest';
-
-describe('util', () => {
-  describe('getPrefixedId', () => {
-    it('should return a prefixed id', () => {
-      expect(getPrefixedId('foo').split(':')[0]).toBe('foo');
-    });
-  });
-
-  describe('getRectUnion', () => {
-    it('should return the union of rects (2 rects)', () => {
-      const rect1 = { x: 0, y: 0, width: 10, height: 10 };
-      const rect2 = { x: 5, y: 5, width: 10, height: 10 };
-      const union = getRectUnion(rect1, rect2);
-      expect(union).toEqual({ x: 0, y: 0, width: 15, height: 15 });
-    });
-    it('should return the union of rects (3 rects)', () => {
-      const rect1 = { x: 0, y: 0, width: 10, height: 10 };
-      const rect2 = { x: 5, y: 5, width: 10, height: 10 };
-      const rect3 = { x: 10, y: 10, width: 10, height: 10 };
-      const union = getRectUnion(rect1, rect2, rect3);
-      expect(union).toEqual({ x: 0, y: 0, width: 20, height: 20 });
-    });
-    it('should return the union of rects (2 rects none from zero)', () => {
-      const rect1 = { x: 5, y: 5, width: 10, height: 10 };
-      const rect2 = { x: 10, y: 10, width: 10, height: 10 };
-      const union = getRectUnion(rect1, rect2);
-      expect(union).toEqual({ x: 5, y: 5, width: 15, height: 15 });
-    });
-    it('should return the union of rects (2 rects with negative x/y)', () => {
-      const rect1 = { x: -5, y: -5, width: 10, height: 10 };
-      const rect2 = { x: 0, y: 0, width: 10, height: 10 };
-      const union = getRectUnion(rect1, rect2);
-      expect(union).toEqual({ x: -5, y: -5, width: 15, height: 15 });
-    });
-    it('should return the union of the first rect if only one rect is provided', () => {
-      const rect = { x: 0, y: 0, width: 10, height: 10 };
-      const union = getRectUnion(rect);
-      expect(union).toEqual(rect);
-    });
-    it('should fall back on an empty rect if no rects are provided', () => {
-      const union = getRectUnion();
-      expect(union).toEqual({ x: 0, y: 0, width: 0, height: 0 });
-    });
-  });
-});

invokeai/frontend/web/src/features/controlLayers/konva/util.ts

@@ -302,13 +302,10 @@ export const konvaNodeToCanvas = (node: Konva.Node, bbox?: Rect): HTMLCanvasElem
  * @returns A Promise that resolves with Blob of the node cropped to the bounding box
  */
 export const canvasToBlob = (canvas: HTMLCanvasElement): Promise<Blob> => {
-  return new Promise((resolve, reject) => {
+  return new Promise((resolve) => {
     canvas.toBlob((blob) => {
-      if (!blob) {
-        reject('Failed to convert canvas to blob');
-      } else {
-        resolve(blob);
-      }
+      assert(blob, 'blob is null');
+      resolve(blob);
     });
   });
 };
@@ -421,25 +418,19 @@ export function snapToNearest(value: number, candidateValues: number[], threshol
 }
 
 /**
- * Gets the union of any number of rects.
- * @params rects The rects to union
+ * Gets the union of two rects
+ * @param rect1 The first rect
+ * @param rect2 The second rect
  * @returns The union of the two rects
  */
 export const getRectUnion = (...rects: Rect[]): Rect => {
-  const firstRect = rects.shift();
-
-  if (!firstRect) {
-    return getEmptyRect();
-  }
-
   const rect = rects.reduce<Rect>((acc, r) => {
     const x = Math.min(acc.x, r.x);
     const y = Math.min(acc.y, r.y);
     const width = Math.max(acc.x + acc.width, r.x + r.width) - x;
     const height = Math.max(acc.y + acc.height, r.y + r.height) - y;
     return { x, y, width, height };
-  }, firstRect);
-
+  }, getEmptyRect());
   return rect;
 };
 

invokeai/frontend/web/src/features/controlLayers/store/canvasSlice.ts

@@ -123,14 +123,9 @@ export const canvasSlice = createSlice({
     rasterLayerAdded: {
       reducer: (
         state,
-        action: PayloadAction<{
-          id: string;
-          overrides?: Partial<CanvasRasterLayerState>;
-          isSelected?: boolean;
-          deleteOthers?: boolean;
-        }>
+        action: PayloadAction<{ id: string; overrides?: Partial<CanvasRasterLayerState>; isSelected?: boolean }>
       ) => {
-        const { id, overrides, isSelected, deleteOthers } = action.payload;
+        const { id, overrides, isSelected } = action.payload;
         const entity: CanvasRasterLayerState = {
           id,
           name: null,
@@ -142,25 +137,12 @@ export const canvasSlice = createSlice({
           position: { x: 0, y: 0 },
         };
         merge(entity, overrides);
-
-        if (deleteOthers) {
-          state.rasterLayers.entities = [entity];
-        } else {
-          state.rasterLayers.entities.push(entity);
-        }
-
+        state.rasterLayers.entities.push(entity);
         if (isSelected) {
           state.selectedEntityIdentifier = getEntityIdentifier(entity);
         }
       },
-      prepare: (payload: {
-        overrides?: Partial<CanvasRasterLayerState>;
-        isSelected?: boolean;
-        /**
-         * asdf
-         */
-        deleteOthers?: boolean;
-      }) => ({
+      prepare: (payload: { overrides?: Partial<CanvasRasterLayerState>; isSelected?: boolean }) => ({
         payload: { ...payload, id: getPrefixedId('raster_layer') },
       }),
     },
@@ -621,14 +603,9 @@ export const canvasSlice = createSlice({
     inpaintMaskAdded: {
       reducer: (
         state,
-        action: PayloadAction<{
-          id: string;
-          overrides?: Partial<CanvasInpaintMaskState>;
-          isSelected?: boolean;
-          deleteOthers?: boolean;
-        }>
+        action: PayloadAction<{ id: string; overrides?: Partial<CanvasInpaintMaskState>; isSelected?: boolean }>
       ) => {
-        const { id, overrides, isSelected, deleteOthers } = action.payload;
+        const { id, overrides, isSelected } = action.payload;
         const entity: CanvasInpaintMaskState = {
           id,
           name: null,
@@ -644,22 +621,12 @@ export const canvasSlice = createSlice({
           },
         };
         merge(entity, overrides);
-
-        if (deleteOthers) {
-          state.inpaintMasks.entities = [entity];
-        } else {
-          state.inpaintMasks.entities.push(entity);
-        }
-
+        state.inpaintMasks.entities.push(entity);
         if (isSelected) {
           state.selectedEntityIdentifier = getEntityIdentifier(entity);
         }
       },
-      prepare: (payload?: {
-        overrides?: Partial<CanvasInpaintMaskState>;
-        isSelected?: boolean;
-        deleteOthers?: boolean;
-      }) => ({
+      prepare: (payload?: { overrides?: Partial<CanvasInpaintMaskState>; isSelected?: boolean }) => ({
         payload: { ...payload, id: getPrefixedId('inpaint_mask') },
       }),
     },

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelManagerPanel/ModelList.tsx

@@ -179,12 +179,12 @@ const ModelList = () => {
         {/* T5 Encoders List */}
         {isLoadingT5EncoderModels && <FetchingModelsLoader loadingMessage="Loading T5 Encoder Models..." />}
         {!isLoadingT5EncoderModels && filteredT5EncoderModels.length > 0 && (
-          <ModelListWrapper title={t('modelManager.t5Encoder')} modelList={filteredT5EncoderModels} key="t5-encoder" />
+          <ModelListWrapper title="T5 Encoder" modelList={filteredT5EncoderModels} key="t5-encoder" />
         )}
         {/* Clip Embed List */}
         {isLoadingClipEmbedModels && <FetchingModelsLoader loadingMessage="Loading Clip Embed Models..." />}
         {!isLoadingClipEmbedModels && filteredClipEmbedModels.length > 0 && (
-          <ModelListWrapper title={t('modelManager.clipEmbed')} modelList={filteredClipEmbedModels} key="clip-embed" />
+          <ModelListWrapper title="Clip Embed" modelList={filteredClipEmbedModels} key="clip-embed" />
         )}
         {/* Spandrel Image to Image List */}
         {isLoadingSpandrelImageToImageModels && (
@@ -192,7 +192,7 @@ const ModelList = () => {
         )}
         {!isLoadingSpandrelImageToImageModels && filteredSpandrelImageToImageModels.length > 0 && (
           <ModelListWrapper
-            title={t('modelManager.spandrelImageToImage')}
+            title="Image-to-Image"
             modelList={filteredSpandrelImageToImageModels}
             key="spandrel-image-to-image"
           />

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelManagerPanel/ModelTypeFilter.tsx

@@ -19,10 +19,11 @@ export const ModelTypeFilter = memo(() => {
       controlnet: 'ControlNet',
       vae: 'VAE',
       t2i_adapter: t('common.t2iAdapter'),
-      t5_encoder: t('modelManager.t5Encoder'),
-      clip_embed: t('modelManager.clipEmbed'),
+      t5_encoder: 'T5Encoder',
+      clip_embed: 'Clip Embed',
       ip_adapter: t('common.ipAdapter'),
-      spandrel_image_to_image: t('modelManager.spandrelImageToImage'),
+      clip_vision: 'Clip Vision',
+      spandrel_image_to_image: 'Image-to-Image',
     }),
     [t]
   );

invokeai/frontend/web/src/features/nodes/components/NodeEditor.tsx

@@ -4,7 +4,6 @@ import { Flex } from '@invoke-ai/ui-library';
 import { IAINoContentFallback } from 'common/components/IAIImageFallback';
 import { AddNodeCmdk } from 'features/nodes/components/flow/AddNodeCmdk/AddNodeCmdk';
 import TopPanel from 'features/nodes/components/flow/panels/TopPanel/TopPanel';
-import WorkflowEditorSettings from 'features/nodes/components/flow/panels/TopRightPanel/WorkflowEditorSettings';
 import { LoadWorkflowFromGraphModal } from 'features/workflowLibrary/components/LoadWorkflowFromGraphModal/LoadWorkflowFromGraphModal';
 import { SaveWorkflowAsDialog } from 'features/workflowLibrary/components/SaveWorkflowAsDialog/SaveWorkflowAsDialog';
 import { memo } from 'react';
@@ -40,7 +39,6 @@ const NodeEditor = () => {
           <LoadWorkflowFromGraphModal />
         </>
       )}
-      <WorkflowEditorSettings />
       {isLoading && <IAINoContentFallback label={t('nodes.loadingNodes')} icon={MdDeviceHub} />}
     </Flex>
   );

invokeai/frontend/web/src/features/nodes/components/flow/AddNodeCmdk/AddNodeCmdk.tsx

@@ -163,6 +163,7 @@ const cmdkRootSx: SystemStyleObject = {
 export const AddNodeCmdk = memo(() => {
   const { t } = useTranslation();
   const addNodeCmdk = useAddNodeCmdk();
+  const addNodeCmdkIsOpen = useStore(addNodeCmdk.$boolean);
   const inputRef = useRef<HTMLInputElement>(null);
   const [searchTerm, setSearchTerm] = useState('');
   const addNode = useAddNode();
@@ -191,7 +192,7 @@ export const AddNodeCmdk = memo(() => {
 
   return (
     <Modal
-      isOpen={addNodeCmdk.isTrue}
+      isOpen={addNodeCmdkIsOpen}
       onClose={onClose}
       useInert={false}
       initialFocusRef={inputRef}

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/InputFieldRenderer.tsx

@@ -6,8 +6,6 @@ import {
   isBoardFieldInputTemplate,
   isBooleanFieldInputInstance,
   isBooleanFieldInputTemplate,
-  isCLIPEmbedModelFieldInputInstance,
-  isCLIPEmbedModelFieldInputTemplate,
   isColorFieldInputInstance,
   isColorFieldInputTemplate,
   isControlNetModelFieldInputInstance,
@@ -18,8 +16,6 @@ import {
   isFloatFieldInputTemplate,
   isFluxMainModelFieldInputInstance,
   isFluxMainModelFieldInputTemplate,
-  isFluxVAEModelFieldInputInstance,
-  isFluxVAEModelFieldInputTemplate,
   isImageFieldInputInstance,
   isImageFieldInputTemplate,
   isIntegerFieldInputInstance,
@@ -53,12 +49,10 @@ import { memo } from 'react';
 
 import BoardFieldInputComponent from './inputs/BoardFieldInputComponent';
 import BooleanFieldInputComponent from './inputs/BooleanFieldInputComponent';
-import CLIPEmbedModelFieldInputComponent from './inputs/CLIPEmbedModelFieldInputComponent';
 import ColorFieldInputComponent from './inputs/ColorFieldInputComponent';
 import ControlNetModelFieldInputComponent from './inputs/ControlNetModelFieldInputComponent';
 import EnumFieldInputComponent from './inputs/EnumFieldInputComponent';
 import FluxMainModelFieldInputComponent from './inputs/FluxMainModelFieldInputComponent';
-import FluxVAEModelFieldInputComponent from './inputs/FluxVAEModelFieldInputComponent';
 import ImageFieldInputComponent from './inputs/ImageFieldInputComponent';
 import IPAdapterModelFieldInputComponent from './inputs/IPAdapterModelFieldInputComponent';
 import LoRAModelFieldInputComponent from './inputs/LoRAModelFieldInputComponent';
@@ -128,13 +122,6 @@ const InputFieldRenderer = ({ nodeId, fieldName }: InputFieldProps) => {
   if (isT5EncoderModelFieldInputInstance(fieldInstance) && isT5EncoderModelFieldInputTemplate(fieldTemplate)) {
     return <T5EncoderModelFieldInputComponent nodeId={nodeId} field={fieldInstance} fieldTemplate={fieldTemplate} />;
   }
-  if (isCLIPEmbedModelFieldInputInstance(fieldInstance) && isCLIPEmbedModelFieldInputTemplate(fieldTemplate)) {
-    return <CLIPEmbedModelFieldInputComponent nodeId={nodeId} field={fieldInstance} fieldTemplate={fieldTemplate} />;
-  }
-
-  if (isFluxVAEModelFieldInputInstance(fieldInstance) && isFluxVAEModelFieldInputTemplate(fieldTemplate)) {
-    return <FluxVAEModelFieldInputComponent nodeId={nodeId} field={fieldInstance} fieldTemplate={fieldTemplate} />;
-  }
 
   if (isLoRAModelFieldInputInstance(fieldInstance) && isLoRAModelFieldInputTemplate(fieldTemplate)) {
     return <LoRAModelFieldInputComponent nodeId={nodeId} field={fieldInstance} fieldTemplate={fieldTemplate} />;

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/inputs/CLIPEmbedModelFieldInputComponent.tsx

@@ -1,60 +0,0 @@
-import { Combobox, Flex, FormControl, Tooltip } from '@invoke-ai/ui-library';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { useGroupedModelCombobox } from 'common/hooks/useGroupedModelCombobox';
-import { fieldCLIPEmbedValueChanged } from 'features/nodes/store/nodesSlice';
-import type { CLIPEmbedModelFieldInputInstance, CLIPEmbedModelFieldInputTemplate } from 'features/nodes/types/field';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { useClipEmbedModels } from 'services/api/hooks/modelsByType';
-import type { ClipEmbedModelConfig } from 'services/api/types';
-
-import type { FieldComponentProps } from './types';
-
-type Props = FieldComponentProps<CLIPEmbedModelFieldInputInstance, CLIPEmbedModelFieldInputTemplate>;
-
-const CLIPEmbedModelFieldInputComponent = (props: Props) => {
-  const { nodeId, field } = props;
-  const { t } = useTranslation();
-  const disabledTabs = useAppSelector((s) => s.config.disabledTabs);
-  const dispatch = useAppDispatch();
-  const [modelConfigs, { isLoading }] = useClipEmbedModels();
-  const _onChange = useCallback(
-    (value: ClipEmbedModelConfig | null) => {
-      if (!value) {
-        return;
-      }
-      dispatch(
-        fieldCLIPEmbedValueChanged({
-          nodeId,
-          fieldName: field.name,
-          value,
-        })
-      );
-    },
-    [dispatch, field.name, nodeId]
-  );
-  const { options, value, onChange, placeholder, noOptionsMessage } = useGroupedModelCombobox({
-    modelConfigs,
-    onChange: _onChange,
-    isLoading,
-    selectedModel: field.value,
-  });
-
-  return (
-    <Flex w="full" alignItems="center" gap={2}>
-      <Tooltip label={!disabledTabs.includes('models') && t('modelManager.starterModelsInModelManager')}>
-        <FormControl className="nowheel nodrag" isDisabled={!options.length} isInvalid={!value}>
-          <Combobox
-            value={value}
-            placeholder={placeholder}
-            options={options}
-            onChange={onChange}
-            noOptionsMessage={noOptionsMessage}
-          />
-        </FormControl>
-      </Tooltip>
-    </Flex>
-  );
-};
-
-export default memo(CLIPEmbedModelFieldInputComponent);

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/inputs/FluxVAEModelFieldInputComponent.tsx

@@ -1,60 +0,0 @@
-import { Combobox, Flex, FormControl, Tooltip } from '@invoke-ai/ui-library';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { useGroupedModelCombobox } from 'common/hooks/useGroupedModelCombobox';
-import { fieldFluxVAEModelValueChanged } from 'features/nodes/store/nodesSlice';
-import type { FluxVAEModelFieldInputInstance, FluxVAEModelFieldInputTemplate } from 'features/nodes/types/field';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { useFluxVAEModels } from 'services/api/hooks/modelsByType';
-import type { VAEModelConfig } from 'services/api/types';
-
-import type { FieldComponentProps } from './types';
-
-type Props = FieldComponentProps<FluxVAEModelFieldInputInstance, FluxVAEModelFieldInputTemplate>;
-
-const FluxVAEModelFieldInputComponent = (props: Props) => {
-  const { nodeId, field } = props;
-  const { t } = useTranslation();
-  const disabledTabs = useAppSelector((s) => s.config.disabledTabs);
-  const dispatch = useAppDispatch();
-  const [modelConfigs, { isLoading }] = useFluxVAEModels();
-  const _onChange = useCallback(
-    (value: VAEModelConfig | null) => {
-      if (!value) {
-        return;
-      }
-      dispatch(
-        fieldFluxVAEModelValueChanged({
-          nodeId,
-          fieldName: field.name,
-          value,
-        })
-      );
-    },
-    [dispatch, field.name, nodeId]
-  );
-  const { options, value, onChange, placeholder, noOptionsMessage } = useGroupedModelCombobox({
-    modelConfigs,
-    onChange: _onChange,
-    isLoading,
-    selectedModel: field.value,
-  });
-
-  return (
-    <Flex w="full" alignItems="center" gap={2}>
-      <Tooltip label={!disabledTabs.includes('models') && t('modelManager.starterModelsInModelManager')}>
-        <FormControl className="nowheel nodrag" isDisabled={!options.length} isInvalid={!value}>
-          <Combobox
-            value={value}
-            placeholder={placeholder}
-            options={options}
-            onChange={onChange}
-            noOptionsMessage={noOptionsMessage}
-          />
-        </FormControl>
-      </Tooltip>
-    </Flex>
-  );
-};
-
-export default memo(FluxVAEModelFieldInputComponent);

invokeai/frontend/web/src/features/nodes/components/flow/panels/TopRightPanel/WorkflowEditorSettings.tsx

@@ -14,9 +14,9 @@ import {
   ModalHeader,
   ModalOverlay,
   Switch,
+  useDisclosure,
 } from '@invoke-ai/ui-library';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { buildUseBoolean } from 'common/hooks/useBoolean';
 import ReloadNodeTemplatesButton from 'features/nodes/components/flow/panels/TopRightPanel/ReloadSchemaButton';
 import {
   selectionModeChanged,
@@ -32,17 +32,20 @@ import {
   shouldSnapToGridChanged,
   shouldValidateGraphChanged,
 } from 'features/nodes/store/workflowSettingsSlice';
-import type { ChangeEvent } from 'react';
+import type { ChangeEvent, ReactNode } from 'react';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import { SelectionMode } from 'reactflow';
 
 const formLabelProps: FormLabelProps = { flexGrow: 1 };
-export const [useWorkflowEditorSettingsModal] = buildUseBoolean(false);
 
-const WorkflowEditorSettings = () => {
+type Props = {
+  children: (props: { onOpen: () => void }) => ReactNode;
+};
+
+const WorkflowEditorSettings = ({ children }: Props) => {
+  const { isOpen, onOpen, onClose } = useDisclosure();
   const dispatch = useAppDispatch();
-  const modal = useWorkflowEditorSettingsModal();
 
   const shouldSnapToGrid = useAppSelector(selectShouldSnapToGrid);
   const selectionMode = useAppSelector(selectSelectionMode);
@@ -96,72 +99,76 @@ const WorkflowEditorSettings = () => {
   const { t } = useTranslation();
 
   return (
-    <Modal isOpen={modal.isTrue} onClose={modal.setFalse} size="2xl" isCentered useInert={false}>
-      <ModalOverlay />
-      <ModalContent>
-        <ModalHeader>{t('nodes.workflowSettings')}</ModalHeader>
-        <ModalCloseButton />
-        <ModalBody>
-          <Flex flexDirection="column" gap={4} py={4}>
-            <Heading size="sm">{t('parameters.general')}</Heading>
-            <FormControlGroup orientation="vertical" formLabelProps={formLabelProps}>
-              <FormControl>
-                <Flex w="full">
-                  <FormLabel>{t('nodes.animatedEdges')}</FormLabel>
-                  <Switch onChange={handleChangeShouldAnimate} isChecked={shouldAnimateEdges} />
-                </Flex>
-                <FormHelperText>{t('nodes.animatedEdgesHelp')}</FormHelperText>
-              </FormControl>
-              <Divider />
-              <FormControl>
-                <Flex w="full">
-                  <FormLabel>{t('nodes.snapToGrid')}</FormLabel>
-                  <Switch isChecked={shouldSnapToGrid} onChange={handleChangeShouldSnap} />
-                </Flex>
-                <FormHelperText>{t('nodes.snapToGridHelp')}</FormHelperText>
-              </FormControl>
-              <Divider />
-              <FormControl>
-                <Flex w="full">
-                  <FormLabel>{t('nodes.colorCodeEdges')}</FormLabel>
-                  <Switch isChecked={shouldColorEdges} onChange={handleChangeShouldColor} />
-                </Flex>
-                <FormHelperText>{t('nodes.colorCodeEdgesHelp')}</FormHelperText>
-              </FormControl>
-              <Divider />
-              <FormControl>
-                <Flex w="full">
-                  <FormLabel>{t('nodes.fullyContainNodes')}</FormLabel>
-                  <Switch isChecked={selectionMode === SelectionMode.Full} onChange={handleChangeSelectionMode} />
-                </Flex>
-                <FormHelperText>{t('nodes.fullyContainNodesHelp')}</FormHelperText>
-              </FormControl>
-              <Divider />
-              <FormControl>
-                <Flex w="full">
-                  <FormLabel>{t('nodes.showEdgeLabels')}</FormLabel>
-                  <Switch isChecked={shouldShowEdgeLabels} onChange={handleChangeShouldShowEdgeLabels} />
-                </Flex>
-                <FormHelperText>{t('nodes.showEdgeLabelsHelp')}</FormHelperText>
-              </FormControl>
-              <Divider />
-              <Heading size="sm" pt={4}>
-                {t('common.advanced')}
-              </Heading>
-              <FormControl>
-                <Flex w="full">
-                  <FormLabel>{t('nodes.validateConnections')}</FormLabel>
-                  <Switch isChecked={shouldValidateGraph} onChange={handleChangeShouldValidate} />
-                </Flex>
-                <FormHelperText>{t('nodes.validateConnectionsHelp')}</FormHelperText>
-              </FormControl>
-              <Divider />
-            </FormControlGroup>
-            <ReloadNodeTemplatesButton />
-          </Flex>
-        </ModalBody>
-      </ModalContent>
-    </Modal>
+    <>
+      {children({ onOpen })}
+
+      <Modal isOpen={isOpen} onClose={onClose} size="2xl" isCentered useInert={false}>
+        <ModalOverlay />
+        <ModalContent>
+          <ModalHeader>{t('nodes.workflowSettings')}</ModalHeader>
+          <ModalCloseButton />
+          <ModalBody>
+            <Flex flexDirection="column" gap={4} py={4}>
+              <Heading size="sm">{t('parameters.general')}</Heading>
+              <FormControlGroup orientation="vertical" formLabelProps={formLabelProps}>
+                <FormControl>
+                  <Flex w="full">
+                    <FormLabel>{t('nodes.animatedEdges')}</FormLabel>
+                    <Switch onChange={handleChangeShouldAnimate} isChecked={shouldAnimateEdges} />
+                  </Flex>
+                  <FormHelperText>{t('nodes.animatedEdgesHelp')}</FormHelperText>
+                </FormControl>
+                <Divider />
+                <FormControl>
+                  <Flex w="full">
+                    <FormLabel>{t('nodes.snapToGrid')}</FormLabel>
+                    <Switch isChecked={shouldSnapToGrid} onChange={handleChangeShouldSnap} />
+                  </Flex>
+                  <FormHelperText>{t('nodes.snapToGridHelp')}</FormHelperText>
+                </FormControl>
+                <Divider />
+                <FormControl>
+                  <Flex w="full">
+                    <FormLabel>{t('nodes.colorCodeEdges')}</FormLabel>
+                    <Switch isChecked={shouldColorEdges} onChange={handleChangeShouldColor} />
+                  </Flex>
+                  <FormHelperText>{t('nodes.colorCodeEdgesHelp')}</FormHelperText>
+                </FormControl>
+                <Divider />
+                <FormControl>
+                  <Flex w="full">
+                    <FormLabel>{t('nodes.fullyContainNodes')}</FormLabel>
+                    <Switch isChecked={selectionMode === SelectionMode.Full} onChange={handleChangeSelectionMode} />
+                  </Flex>
+                  <FormHelperText>{t('nodes.fullyContainNodesHelp')}</FormHelperText>
+                </FormControl>
+                <Divider />
+                <FormControl>
+                  <Flex w="full">
+                    <FormLabel>{t('nodes.showEdgeLabels')}</FormLabel>
+                    <Switch isChecked={shouldShowEdgeLabels} onChange={handleChangeShouldShowEdgeLabels} />
+                  </Flex>
+                  <FormHelperText>{t('nodes.showEdgeLabelsHelp')}</FormHelperText>
+                </FormControl>
+                <Divider />
+                <Heading size="sm" pt={4}>
+                  {t('common.advanced')}
+                </Heading>
+                <FormControl>
+                  <Flex w="full">
+                    <FormLabel>{t('nodes.validateConnections')}</FormLabel>
+                    <Switch isChecked={shouldValidateGraph} onChange={handleChangeShouldValidate} />
+                  </Flex>
+                  <FormHelperText>{t('nodes.validateConnectionsHelp')}</FormHelperText>
+                </FormControl>
+                <Divider />
+              </FormControlGroup>
+              <ReloadNodeTemplatesButton />
+            </Flex>
+          </ModalBody>
+        </ModalContent>
+      </Modal>
+    </>
   );
 };
 

invokeai/frontend/web/src/features/nodes/hooks/useConnection.ts

@@ -2,12 +2,12 @@ import { useStore } from '@nanostores/react';
 import { useAppStore } from 'app/store/storeHooks';
 import { $mouseOverNode } from 'features/nodes/hooks/useMouseOverNode';
 import {
+  $addNodeCmdk,
   $didUpdateEdge,
   $edgePendingUpdate,
   $pendingConnection,
   $templates,
   edgesChanged,
-  useAddNodeCmdk,
 } from 'features/nodes/store/nodesSlice';
 import { selectNodes, selectNodesSlice } from 'features/nodes/store/selectors';
 import { getFirstValidConnection } from 'features/nodes/store/util/getFirstValidConnection';
@@ -21,7 +21,6 @@ export const useConnection = () => {
   const store = useAppStore();
   const templates = useStore($templates);
   const updateNodeInternals = useUpdateNodeInternals();
-  const addNodeCmdk = useAddNodeCmdk();
 
   const onConnectStart = useCallback<OnConnectStart>(
     (event, { nodeId, handleId, handleType }) => {
@@ -108,9 +107,9 @@ export const useConnection = () => {
       $pendingConnection.set(null);
     } else {
       // The mouse is not over a node - we should open the add node popover
-      addNodeCmdk.setTrue();
+      $addNodeCmdk.set(true);
     }
-  }, [addNodeCmdk, store, templates, updateNodeInternals]);
+  }, [store, templates, updateNodeInternals]);
 
   const api = useMemo(() => ({ onConnectStart, onConnect, onConnectEnd }), [onConnectStart, onConnect, onConnectEnd]);
   return api;

invokeai/frontend/web/src/features/nodes/store/nodesSlice.ts

@@ -7,13 +7,11 @@ import { SHARED_NODE_PROPERTIES } from 'features/nodes/types/constants';
 import type {
   BoardFieldValue,
   BooleanFieldValue,
-  CLIPEmbedModelFieldValue,
   ColorFieldValue,
   ControlNetModelFieldValue,
   EnumFieldValue,
   FieldValue,
   FloatFieldValue,
-  FluxVAEModelFieldValue,
   ImageFieldValue,
   IntegerFieldValue,
   IPAdapterModelFieldValue,
@@ -32,12 +30,10 @@ import type {
 import {
   zBoardFieldValue,
   zBooleanFieldValue,
-  zCLIPEmbedModelFieldValue,
   zColorFieldValue,
   zControlNetModelFieldValue,
   zEnumFieldValue,
   zFloatFieldValue,
-  zFluxVAEModelFieldValue,
   zImageFieldValue,
   zIntegerFieldValue,
   zIPAdapterModelFieldValue,
@@ -351,12 +347,6 @@ export const nodesSlice = createSlice({
     fieldT5EncoderValueChanged: (state, action: FieldValueAction<T5EncoderModelFieldValue>) => {
       fieldValueReducer(state, action, zT5EncoderModelFieldValue);
     },
-    fieldCLIPEmbedValueChanged: (state, action: FieldValueAction<CLIPEmbedModelFieldValue>) => {
-      fieldValueReducer(state, action, zCLIPEmbedModelFieldValue);
-    },
-    fieldFluxVAEModelValueChanged: (state, action: FieldValueAction<FluxVAEModelFieldValue>) => {
-      fieldValueReducer(state, action, zFluxVAEModelFieldValue);
-    },
     fieldEnumModelValueChanged: (state, action: FieldValueAction<EnumFieldValue>) => {
       fieldValueReducer(state, action, zEnumFieldValue);
     },
@@ -419,8 +409,6 @@ export const {
   fieldStringValueChanged,
   fieldVaeModelValueChanged,
   fieldT5EncoderValueChanged,
-  fieldCLIPEmbedValueChanged,
-  fieldFluxVAEModelValueChanged,
   nodeEditorReset,
   nodeIsIntermediateChanged,
   nodeIsOpenChanged,
@@ -444,7 +432,8 @@ export const $didUpdateEdge = atom(false);
 export const $lastEdgeUpdateMouseEvent = atom<MouseEvent | null>(null);
 
 export const $viewport = atom<Viewport>({ x: 0, y: 0, zoom: 1 });
-export const [useAddNodeCmdk, $addNodeCmdk] = buildUseBoolean(false);
+export const $addNodeCmdk = atom(false);
+export const useAddNodeCmdk = buildUseBoolean($addNodeCmdk);
 
 /* eslint-disable-next-line @typescript-eslint/no-explicit-any */
 const migrateNodesState = (state: any): any => {
@@ -525,8 +514,6 @@ export const isAnyNodeOrEdgeMutation = isAnyOf(
   fieldStringValueChanged,
   fieldVaeModelValueChanged,
   fieldT5EncoderValueChanged,
-  fieldCLIPEmbedValueChanged,
-  fieldFluxVAEModelValueChanged,
   nodesChanged,
   nodeIsIntermediateChanged,
   nodeIsOpenChanged,

invokeai/frontend/web/src/features/nodes/types/field.ts

@@ -151,14 +151,6 @@ const zT5EncoderModelFieldType = zFieldTypeBase.extend({
   name: z.literal('T5EncoderModelField'),
   originalType: zStatelessFieldType.optional(),
 });
-const zCLIPEmbedModelFieldType = zFieldTypeBase.extend({
-  name: z.literal('CLIPEmbedModelField'),
-  originalType: zStatelessFieldType.optional(),
-});
-const zFluxVAEModelFieldType = zFieldTypeBase.extend({
-  name: z.literal('FluxVAEModelField'),
-  originalType: zStatelessFieldType.optional(),
-});
 const zSchedulerFieldType = zFieldTypeBase.extend({
   name: z.literal('SchedulerField'),
   originalType: zStatelessFieldType.optional(),
@@ -183,8 +175,6 @@ const zStatefulFieldType = z.union([
   zT2IAdapterModelFieldType,
   zSpandrelImageToImageModelFieldType,
   zT5EncoderModelFieldType,
-  zCLIPEmbedModelFieldType,
-  zFluxVAEModelFieldType,
   zColorFieldType,
   zSchedulerFieldType,
 ]);
@@ -677,53 +667,7 @@ export const isT5EncoderModelFieldInputInstance = (val: unknown): val is T5Encod
 export const isT5EncoderModelFieldInputTemplate = (val: unknown): val is T5EncoderModelFieldInputTemplate =>
   zT5EncoderModelFieldInputTemplate.safeParse(val).success;
 
-// #endregion
-
-// #region FluxVAEModelField
-
-export const zFluxVAEModelFieldValue = zModelIdentifierField.optional();
-const zFluxVAEModelFieldInputInstance = zFieldInputInstanceBase.extend({
-  value: zFluxVAEModelFieldValue,
-});
-const zFluxVAEModelFieldInputTemplate = zFieldInputTemplateBase.extend({
-  type: zFluxVAEModelFieldType,
-  originalType: zFieldType.optional(),
-  default: zFluxVAEModelFieldValue,
-});
-
-export type FluxVAEModelFieldValue = z.infer<typeof zFluxVAEModelFieldValue>;
-
-export type FluxVAEModelFieldInputInstance = z.infer<typeof zFluxVAEModelFieldInputInstance>;
-export type FluxVAEModelFieldInputTemplate = z.infer<typeof zFluxVAEModelFieldInputTemplate>;
-export const isFluxVAEModelFieldInputInstance = (val: unknown): val is FluxVAEModelFieldInputInstance =>
-  zFluxVAEModelFieldInputInstance.safeParse(val).success;
-export const isFluxVAEModelFieldInputTemplate = (val: unknown): val is FluxVAEModelFieldInputTemplate =>
-  zFluxVAEModelFieldInputTemplate.safeParse(val).success;
-
-// #endregion
-
-// #region CLIPEmbedModelField
-
-export const zCLIPEmbedModelFieldValue = zModelIdentifierField.optional();
-const zCLIPEmbedModelFieldInputInstance = zFieldInputInstanceBase.extend({
-  value: zCLIPEmbedModelFieldValue,
-});
-const zCLIPEmbedModelFieldInputTemplate = zFieldInputTemplateBase.extend({
-  type: zCLIPEmbedModelFieldType,
-  originalType: zFieldType.optional(),
-  default: zCLIPEmbedModelFieldValue,
-});
-
-export type CLIPEmbedModelFieldValue = z.infer<typeof zCLIPEmbedModelFieldValue>;
-
-export type CLIPEmbedModelFieldInputInstance = z.infer<typeof zCLIPEmbedModelFieldInputInstance>;
-export type CLIPEmbedModelFieldInputTemplate = z.infer<typeof zCLIPEmbedModelFieldInputTemplate>;
-export const isCLIPEmbedModelFieldInputInstance = (val: unknown): val is CLIPEmbedModelFieldInputInstance =>
-  zCLIPEmbedModelFieldInputInstance.safeParse(val).success;
-export const isCLIPEmbedModelFieldInputTemplate = (val: unknown): val is CLIPEmbedModelFieldInputTemplate =>
-  zCLIPEmbedModelFieldInputTemplate.safeParse(val).success;
-
-// #endregion
+// #endregio
 
 // #region SchedulerField
 
@@ -814,8 +758,6 @@ export const zStatefulFieldValue = z.union([
   zT2IAdapterModelFieldValue,
   zSpandrelImageToImageModelFieldValue,
   zT5EncoderModelFieldValue,
-  zFluxVAEModelFieldValue,
-  zCLIPEmbedModelFieldValue,
   zColorFieldValue,
   zSchedulerFieldValue,
 ]);
@@ -846,8 +788,6 @@ const zStatefulFieldInputInstance = z.union([
   zT2IAdapterModelFieldInputInstance,
   zSpandrelImageToImageModelFieldInputInstance,
   zT5EncoderModelFieldInputInstance,
-  zFluxVAEModelFieldInputInstance,
-  zCLIPEmbedModelFieldInputInstance,
   zColorFieldInputInstance,
   zSchedulerFieldInputInstance,
 ]);
@@ -879,8 +819,6 @@ const zStatefulFieldInputTemplate = z.union([
   zT2IAdapterModelFieldInputTemplate,
   zSpandrelImageToImageModelFieldInputTemplate,
   zT5EncoderModelFieldInputTemplate,
-  zFluxVAEModelFieldInputTemplate,
-  zCLIPEmbedModelFieldInputTemplate,
   zColorFieldInputTemplate,
   zSchedulerFieldInputTemplate,
   zStatelessFieldInputTemplate,

invokeai/frontend/web/src/features/nodes/util/schema/buildFieldInputInstance.ts

@@ -23,8 +23,6 @@ const FIELD_VALUE_FALLBACK_MAP: Record<StatefulFieldType['name'], FieldValue> =
   VAEModelField: undefined,
   ControlNetModelField: undefined,
   T5EncoderModelField: undefined,
-  FluxVAEModelField: undefined,
-  CLIPEmbedModelField: undefined,
 };
 
 export const buildFieldInputInstance = (id: string, template: FieldInputTemplate): FieldInputInstance => {

invokeai/frontend/web/src/features/nodes/util/schema/buildFieldInputTemplate.ts

@@ -2,7 +2,6 @@ import { FieldParseError } from 'features/nodes/types/error';
 import type {
   BoardFieldInputTemplate,
   BooleanFieldInputTemplate,
-  CLIPEmbedModelFieldInputTemplate,
   ColorFieldInputTemplate,
   ControlNetModelFieldInputTemplate,
   EnumFieldInputTemplate,
@@ -10,7 +9,6 @@ import type {
   FieldType,
   FloatFieldInputTemplate,
   FluxMainModelFieldInputTemplate,
-  FluxVAEModelFieldInputTemplate,
   ImageFieldInputTemplate,
   IntegerFieldInputTemplate,
   IPAdapterModelFieldInputTemplate,
@@ -240,34 +238,6 @@ const buildT5EncoderModelFieldInputTemplate: FieldInputTemplateBuilder<T5Encoder
   return template;
 };
 
-const buildCLIPEmbedModelFieldInputTemplate: FieldInputTemplateBuilder<CLIPEmbedModelFieldInputTemplate> = ({
-  schemaObject,
-  baseField,
-  fieldType,
-}) => {
-  const template: CLIPEmbedModelFieldInputTemplate = {
-    ...baseField,
-    type: fieldType,
-    default: schemaObject.default ?? undefined,
-  };
-
-  return template;
-};
-
-const buildFluxVAEModelFieldInputTemplate: FieldInputTemplateBuilder<FluxVAEModelFieldInputTemplate> = ({
-  schemaObject,
-  baseField,
-  fieldType,
-}) => {
-  const template: FluxVAEModelFieldInputTemplate = {
-    ...baseField,
-    type: fieldType,
-    default: schemaObject.default ?? undefined,
-  };
-
-  return template;
-};
-
 const buildLoRAModelFieldInputTemplate: FieldInputTemplateBuilder<LoRAModelFieldInputTemplate> = ({
   schemaObject,
   baseField,
@@ -453,8 +423,6 @@ export const TEMPLATE_BUILDER_MAP: Record<StatefulFieldType['name'], FieldInputT
   SpandrelImageToImageModelField: buildSpandrelImageToImageModelFieldInputTemplate,
   VAEModelField: buildVAEModelFieldInputTemplate,
   T5EncoderModelField: buildT5EncoderModelFieldInputTemplate,
-  CLIPEmbedModelField: buildCLIPEmbedModelFieldInputTemplate,
-  FluxVAEModelField: buildFluxVAEModelFieldInputTemplate,
 } as const;
 
 export const buildFieldInputTemplate = (

invokeai/frontend/web/src/features/queue/components/ClearQueueConfirmationAlertDialog.tsx

@@ -5,16 +5,19 @@ import { useAppDispatch } from 'app/store/storeHooks';
 import { buildUseBoolean } from 'common/hooks/useBoolean';
 import { listCursorChanged, listPriorityChanged } from 'features/queue/store/queueSlice';
 import { toast } from 'features/toast/toast';
+import { atom } from 'nanostores';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { useClearQueueMutation, useGetQueueStatusQuery } from 'services/api/endpoints/queue';
 
-const [useClearQueueConfirmationAlertDialog] = buildUseBoolean(false);
+const $boolean = atom(false);
+const useClearQueueConfirmationAlertDialog = buildUseBoolean($boolean);
 
 export const useClearQueue = () => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
   const dialog = useClearQueueConfirmationAlertDialog();
+  const isOpen = useStore(dialog.$boolean);
   const { data: queueStatus } = useGetQueueStatusQuery();
   const isConnected = useStore($isConnected);
   const [trigger, { isLoading }] = useClearQueueMutation({
@@ -48,7 +51,7 @@ export const useClearQueue = () => {
 
   return {
     clearQueue,
-    isOpen: dialog.isTrue,
+    isOpen,
     openDialog: dialog.setTrue,
     closeDialog: dialog.setFalse,
     isLoading,

invokeai/frontend/web/src/features/queue/components/QueueList/useOriginText.ts

@@ -12,9 +12,5 @@ export const useOriginText = (origin: SessionQueueItemDTO['origin']) => {
     return t('queue.workflows');
   }
 
-  if (origin === 'upscaling') {
-    return t('queue.upscaling');
-  }
-
   return t('queue.other');
 };

invokeai/frontend/web/src/features/system/components/SettingsModal/RefreshAfterResetModal.tsx

@@ -8,27 +8,31 @@ import {
   ModalOverlay,
   Text,
 } from '@invoke-ai/ui-library';
+import { useStore } from '@nanostores/react';
 import { buildUseBoolean } from 'common/hooks/useBoolean';
+import { atom } from 'nanostores';
 import { memo, useEffect, useState } from 'react';
 import { useTranslation } from 'react-i18next';
 
-export const [useRefreshAfterResetModal] = buildUseBoolean(false);
+const $refreshAfterResetModalState = atom(false);
+export const useRefreshAfterResetModal = buildUseBoolean($refreshAfterResetModalState);
 
 const RefreshAfterResetModal = () => {
   const { t } = useTranslation();
   const [countdown, setCountdown] = useState(3);
 
   const refreshModal = useRefreshAfterResetModal();
+  const isOpen = useStore(refreshModal.$boolean);
 
   useEffect(() => {
-    if (!refreshModal.isTrue) {
+    if (!isOpen) {
       return;
     }
     const i = window.setInterval(() => setCountdown((prev) => prev - 1), 1000);
     return () => {
       window.clearInterval(i);
     };
-  }, [refreshModal.isTrue]);
+  }, [isOpen]);
 
   useEffect(() => {
     if (countdown <= 0) {
@@ -40,7 +44,7 @@ const RefreshAfterResetModal = () => {
     <>
       <Modal
         closeOnOverlayClick={false}
-        isOpen={refreshModal.isTrue}
+        isOpen={isOpen}
         onClose={refreshModal.setFalse}
         isCentered
         closeOnEsc={false}

invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsModal.tsx

@@ -14,6 +14,7 @@ import {
   Switch,
   Text,
 } from '@invoke-ai/ui-library';
+import { useStore } from '@nanostores/react';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
 import ScrollableContent from 'common/components/OverlayScrollbars/ScrollableContent';
@@ -41,6 +42,7 @@ import {
 } from 'features/system/store/systemSlice';
 import { selectShouldShowProgressInViewer } from 'features/ui/store/uiSelectors';
 import { setShouldShowProgressInViewer } from 'features/ui/store/uiSlice';
+import { atom } from 'nanostores';
 import type { ChangeEvent } from 'react';
 import { memo, useCallback, useEffect } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -66,7 +68,8 @@ type SettingsModalProps = {
   config?: ConfigOptions;
 };
 
-export const [useSettingsModal] = buildUseBoolean(false);
+const $settingsModal = atom(false);
+export const useSettingsModal = buildUseBoolean($settingsModal);
 
 const SettingsModal = ({ config = defaultConfig }: SettingsModalProps) => {
   const dispatch = useAppDispatch();
@@ -93,6 +96,7 @@ const SettingsModal = ({ config = defaultConfig }: SettingsModalProps) => {
     refetchIntermediatesCount,
   } = useClearIntermediates(Boolean(config?.shouldShowClearIntermediates));
   const settingsModal = useSettingsModal();
+  const settingsModalIsOpen = useStore(settingsModal.$boolean);
   const refreshModal = useRefreshAfterResetModal();
 
   const shouldUseCpuNoise = useAppSelector(selectShouldUseCPUNoise);
@@ -106,10 +110,10 @@ const SettingsModal = ({ config = defaultConfig }: SettingsModalProps) => {
   const clearStorage = useClearStorage();
 
   useEffect(() => {
-    if (settingsModal.isTrue && Boolean(config?.shouldShowClearIntermediates)) {
+    if (settingsModalIsOpen && Boolean(config?.shouldShowClearIntermediates)) {
       refetchIntermediatesCount();
     }
-  }, [config?.shouldShowClearIntermediates, refetchIntermediatesCount, settingsModal.isTrue]);
+  }, [config?.shouldShowClearIntermediates, refetchIntermediatesCount, settingsModalIsOpen]);
 
   const handleClickResetWebUI = useCallback(() => {
     clearStorage();
@@ -161,7 +165,7 @@ const SettingsModal = ({ config = defaultConfig }: SettingsModalProps) => {
   );
 
   return (
-    <Modal isOpen={settingsModal.isTrue} onClose={settingsModal.setFalse} size="2xl" isCentered useInert={false}>
+    <Modal isOpen={settingsModalIsOpen} onClose={settingsModal.setFalse} size="2xl" isCentered useInert={false}>
       <ModalOverlay />
       <ModalContent maxH="80vh" h="68rem">
         <ModalHeader bg="none">{t('common.settingsLabel')}</ModalHeader>

invokeai/frontend/web/src/features/workflowLibrary/components/WorkflowLibraryMenu/SettingsMenuItem.tsx

@@ -1,17 +1,20 @@
 import { MenuItem } from '@invoke-ai/ui-library';
-import { useWorkflowEditorSettingsModal } from 'features/nodes/components/flow/panels/TopRightPanel/WorkflowEditorSettings';
+import WorkflowEditorSettings from 'features/nodes/components/flow/panels/TopRightPanel/WorkflowEditorSettings';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { RiSettings4Line } from 'react-icons/ri';
 
 const DownloadWorkflowMenuItem = () => {
   const { t } = useTranslation();
-  const modal = useWorkflowEditorSettingsModal();
 
   return (
-    <MenuItem as="button" icon={<RiSettings4Line />} onClick={modal.setTrue}>
-      {t('nodes.workflowSettings')}
-    </MenuItem>
+    <WorkflowEditorSettings>
+      {({ onOpen }) => (
+        <MenuItem as="button" icon={<RiSettings4Line />} onClick={onOpen}>
+          {t('nodes.workflowSettings')}
+        </MenuItem>
+      )}
+    </WorkflowEditorSettings>
   );
 };
 

invokeai/frontend/web/src/services/api/hooks/modelsByType.ts

@@ -7,7 +7,6 @@ import {
   isControlNetModelConfig,
   isControlNetOrT2IAdapterModelConfig,
   isFluxMainModelModelConfig,
-  isFluxVAEModelConfig,
   isIPAdapterModelConfig,
   isLoRAModelConfig,
   isNonRefinerMainModelConfig,
@@ -53,4 +52,3 @@ export const useSpandrelImageToImageModels = buildModelsHook(isSpandrelImageToIm
 export const useIPAdapterModels = buildModelsHook(isIPAdapterModelConfig);
 export const useEmbeddingModels = buildModelsHook(isTIModelConfig);
 export const useVAEModels = buildModelsHook(isVAEModelConfig);
-export const useFluxVAEModels = buildModelsHook(isFluxVAEModelConfig);

invokeai/frontend/web/src/services/api/schema.ts

@@ -5920,22 +5920,8 @@ export type components = {
             use_cache?: boolean;
             /** @description Flux model (Transformer) to load */
             model: components["schemas"]["ModelIdentifierField"];
-            /**
-             * T5 Encoder
-             * @description T5 tokenizer and text encoder
-             */
-            t5_encoder_model: components["schemas"]["ModelIdentifierField"];
-            /**
-             * CLIP Embed
-             * @description CLIP Embed loader
-             */
-            clip_embed_model: components["schemas"]["ModelIdentifierField"];
-            /**
-             * VAE
-             * @description VAE model to load
-             * @default null
-             */
-            vae_model?: components["schemas"]["ModelIdentifierField"];
+            /** @description T5 tokenizer and text encoder */
+            t5_encoder: components["schemas"]["ModelIdentifierField"];
             /**
              * type
              * @default flux_model_loader
@@ -15341,7 +15327,7 @@ export type components = {
          *     used, and the type will be ignored. They are included here for backwards compatibility.
          * @enum {string}
          */
-        UIType: "MainModelField" | "FluxMainModelField" | "SDXLMainModelField" | "SDXLRefinerModelField" | "ONNXModelField" | "VAEModelField" | "FluxVAEModelField" | "LoRAModelField" | "ControlNetModelField" | "IPAdapterModelField" | "T2IAdapterModelField" | "T5EncoderModelField" | "CLIPEmbedModelField" | "SpandrelImageToImageModelField" | "SchedulerField" | "AnyField" | "CollectionField" | "CollectionItemField" | "DEPRECATED_Boolean" | "DEPRECATED_Color" | "DEPRECATED_Conditioning" | "DEPRECATED_Control" | "DEPRECATED_Float" | "DEPRECATED_Image" | "DEPRECATED_Integer" | "DEPRECATED_Latents" | "DEPRECATED_String" | "DEPRECATED_BooleanCollection" | "DEPRECATED_ColorCollection" | "DEPRECATED_ConditioningCollection" | "DEPRECATED_ControlCollection" | "DEPRECATED_FloatCollection" | "DEPRECATED_ImageCollection" | "DEPRECATED_IntegerCollection" | "DEPRECATED_LatentsCollection" | "DEPRECATED_StringCollection" | "DEPRECATED_BooleanPolymorphic" | "DEPRECATED_ColorPolymorphic" | "DEPRECATED_ConditioningPolymorphic" | "DEPRECATED_ControlPolymorphic" | "DEPRECATED_FloatPolymorphic" | "DEPRECATED_ImagePolymorphic" | "DEPRECATED_IntegerPolymorphic" | "DEPRECATED_LatentsPolymorphic" | "DEPRECATED_StringPolymorphic" | "DEPRECATED_UNet" | "DEPRECATED_Vae" | "DEPRECATED_CLIP" | "DEPRECATED_Collection" | "DEPRECATED_CollectionItem" | "DEPRECATED_Enum" | "DEPRECATED_WorkflowField" | "DEPRECATED_IsIntermediate" | "DEPRECATED_BoardField" | "DEPRECATED_MetadataItem" | "DEPRECATED_MetadataItemCollection" | "DEPRECATED_MetadataItemPolymorphic" | "DEPRECATED_MetadataDict";
+        UIType: "MainModelField" | "FluxMainModelField" | "SDXLMainModelField" | "SDXLRefinerModelField" | "ONNXModelField" | "VAEModelField" | "LoRAModelField" | "ControlNetModelField" | "IPAdapterModelField" | "T2IAdapterModelField" | "T5EncoderModelField" | "SpandrelImageToImageModelField" | "SchedulerField" | "AnyField" | "CollectionField" | "CollectionItemField" | "DEPRECATED_Boolean" | "DEPRECATED_Color" | "DEPRECATED_Conditioning" | "DEPRECATED_Control" | "DEPRECATED_Float" | "DEPRECATED_Image" | "DEPRECATED_Integer" | "DEPRECATED_Latents" | "DEPRECATED_String" | "DEPRECATED_BooleanCollection" | "DEPRECATED_ColorCollection" | "DEPRECATED_ConditioningCollection" | "DEPRECATED_ControlCollection" | "DEPRECATED_FloatCollection" | "DEPRECATED_ImageCollection" | "DEPRECATED_IntegerCollection" | "DEPRECATED_LatentsCollection" | "DEPRECATED_StringCollection" | "DEPRECATED_BooleanPolymorphic" | "DEPRECATED_ColorPolymorphic" | "DEPRECATED_ConditioningPolymorphic" | "DEPRECATED_ControlPolymorphic" | "DEPRECATED_FloatPolymorphic" | "DEPRECATED_ImagePolymorphic" | "DEPRECATED_IntegerPolymorphic" | "DEPRECATED_LatentsPolymorphic" | "DEPRECATED_StringPolymorphic" | "DEPRECATED_UNet" | "DEPRECATED_Vae" | "DEPRECATED_CLIP" | "DEPRECATED_Collection" | "DEPRECATED_CollectionItem" | "DEPRECATED_Enum" | "DEPRECATED_WorkflowField" | "DEPRECATED_IsIntermediate" | "DEPRECATED_BoardField" | "DEPRECATED_MetadataItem" | "DEPRECATED_MetadataItemCollection" | "DEPRECATED_MetadataItemPolymorphic" | "DEPRECATED_MetadataDict";
         /** UNetField */
         UNetField: {
             /** @description Info to load unet submodel */

invokeai/frontend/web/src/services/api/types.ts

@@ -51,7 +51,7 @@ export type VAEModelConfig = S['VAECheckpointConfig'] | S['VAEDiffusersConfig'];
 export type ControlNetModelConfig = S['ControlNetDiffusersConfig'] | S['ControlNetCheckpointConfig'];
 export type IPAdapterModelConfig = S['IPAdapterInvokeAIConfig'] | S['IPAdapterCheckpointConfig'];
 export type T2IAdapterModelConfig = S['T2IAdapterConfig'];
-export type ClipEmbedModelConfig = S['CLIPEmbedDiffusersConfig'];
+type ClipEmbedModelConfig = S['CLIPEmbedDiffusersConfig'];
 export type T5EncoderModelConfig = S['T5EncoderConfig'];
 export type T5EncoderBnbQuantizedLlmInt8bModelConfig = S['T5EncoderBnbQuantizedLlmInt8bConfig'];
 export type SpandrelImageToImageModelConfig = S['SpandrelImageToImageConfig'];
@@ -82,10 +82,6 @@ export const isVAEModelConfig = (config: AnyModelConfig): config is VAEModelConf
   return config.type === 'vae';
 };
 
-export const isFluxVAEModelConfig = (config: AnyModelConfig): config is VAEModelConfig => {
-  return config.type === 'vae' && config.base === 'flux';
-};
-
 export const isControlNetModelConfig = (config: AnyModelConfig): config is ControlNetModelConfig => {
   return config.type === 'controlnet';
 };

invokeai/version/invokeai_version.py

@@ -1 +1 @@
-__version__ = "4.2.9.dev10"
+__version__ = "4.2.9.dev8"

pyproject.toml

@@ -130,6 +130,8 @@ dependencies = [
 
 [project.scripts]
 "invokeai-web" = "invokeai.app.run_app:run_app"
+"invokeai-import-images" = "invokeai.frontend.install.import_images:main"
+"invokeai-db-maintenance" = "invokeai.backend.util.db_maintenance:main"
 
 [project.urls]
 "Homepage" = "https://invoke-ai.github.io/InvokeAI/"