Fix bug with partial offload of model buffers.

Closes #7384

SECURITY.md

@@ -0,0 +1,14 @@
+# Security Policy
+
+## Supported Versions
+
+Only the latest version of Invoke will receive security updates. 
+We do not currently maintain multiple versions of the application with updates.
+
+## Reporting a Vulnerability
+
+To report a vulnerability, contact the Invoke team directly at security@invoke.ai
+
+At this time, we do not maintain a formal bug bounty program. 
+
+You can also share identified security issues with our team on huntr.com

docs/contributing/MODEL_MANAGER.md

@@ -1364,7 +1364,6 @@ the in-memory loaded model:
 |----------------|-----------------|------------------|
 | `config`       | AnyModelConfig         | A copy of the model's configuration record for retrieving base type, etc. |
 | `model`        | AnyModel               | The instantiated model (details below) |
-| `locker`       | ModelLockerBase        | A context manager that mediates the movement of the model into VRAM |
 
 ### get_model_by_key(key, [submodel]) -> LoadedModel
 

docs/contributing/index.md

@@ -38,7 +38,7 @@ This project is a combined effort of dedicated people from across the world. [C
 
 ## Code of Conduct
 
-The InvokeAI community is a welcoming place, and we want your help in maintaining that. Please review our [Code of Conduct](https://github.com/invoke-ai/InvokeAI/blob/main/CODE_OF_CONDUCT.md) to learn more - it's essential to maintaining a respectful and inclusive environment.
+The InvokeAI community is a welcoming place, and we want your help in maintaining that. Please review our [Code of Conduct](https://github.com/invoke-ai/InvokeAI/blob/main/docs/CODE_OF_CONDUCT.md) to learn more - it's essential to maintaining a respectful and inclusive environment.
 
 By making a contribution to this project, you certify that:
 

docs/nodes/defaultNodes.md

@@ -99,7 +99,6 @@ their descriptions.
 | Scale Latents                                                 | Scales latents by a given factor.                                                                                                                    |
 | Segment Anything Processor                                    | Applies segment anything processing to image                                                                                                         |
 | Show Image                                                    | Displays a provided image, and passes it forward in the pipeline.                                                                                    |
-| Step Param Easing                                             | Experimental per-step parameter easing for denoising steps                                                                                           |
 | String Primitive Collection                                   | A collection of string primitive values                                                                                                              |
 | String Primitive                                              | A string primitive value                                                                                                                             |
 | Subtract Integers                                             | Subtracts two numbers                                                                                                                                |

invokeai/app/api/routers/model_manager.py

@@ -37,7 +37,7 @@ from invokeai.backend.model_manager.config import (
     ModelFormat,
     ModelType,
 )
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import CacheStats
+from invokeai.backend.model_manager.load.model_cache.cache_stats import CacheStats
 from invokeai.backend.model_manager.metadata.fetch.huggingface import HuggingFaceMetadataFetch
 from invokeai.backend.model_manager.metadata.metadata_base import ModelMetadataWithFiles, UnknownMetadataException
 from invokeai.backend.model_manager.search import ModelSearch

invokeai/app/api/routers/session_queue.py

@@ -110,7 +110,7 @@ async def cancel_by_batch_ids(
 @session_queue_router.put(
     "/{queue_id}/cancel_by_destination",
     operation_id="cancel_by_destination",
-    responses={200: {"model": CancelByBatchIDsResult}},
+    responses={200: {"model": CancelByDestinationResult}},
 )
 async def cancel_by_destination(
     queue_id: str = Path(description="The queue id to perform this operation on"),

invokeai/app/invocations/baseinvocation.py

@@ -63,6 +63,7 @@ class Classification(str, Enum, metaclass=MetaEnum):
     - `Prototype`: The invocation is not yet stable and may be removed from the application at any time. Workflows built around this invocation may break, and we are *not* committed to supporting this invocation.
     - `Deprecated`: The invocation is deprecated and may be removed in a future version.
     - `Internal`: The invocation is not intended for use by end-users. It may be changed or removed at any time, but is exposed for users to play with.
+    - `Special`: The invocation is a special case and does not fit into any of the other classifications.
     """
 
     Stable = "stable"
@@ -70,6 +71,7 @@ class Classification(str, Enum, metaclass=MetaEnum):
     Prototype = "prototype"
     Deprecated = "deprecated"
     Internal = "internal"
+    Special = "special"
 
 
 class UIConfigBase(BaseModel):

invokeai/app/invocations/blend_latents.py

@@ -1,98 +1,120 @@
-from typing import Any, Union
+from typing import Optional, Union
 
 import numpy as np
-import numpy.typing as npt
 import torch
+import torchvision.transforms as T
+from PIL import Image
+from torchvision.transforms.functional import resize as tv_resize
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, LatentsField
+from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, LatentsField
 from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
 from invokeai.backend.util.devices import TorchDevice
 
 
+def slerp(
+    t: Union[float, np.ndarray],
+    v0: Union[torch.Tensor, np.ndarray],
+    v1: Union[torch.Tensor, np.ndarray],
+    device: torch.device,
+    DOT_THRESHOLD: float = 0.9995,
+):
+    """
+    Spherical linear interpolation
+    Args:
+        t (float/np.ndarray): Float value between 0.0 and 1.0
+        v0 (np.ndarray): Starting vector
+        v1 (np.ndarray): Final vector
+        DOT_THRESHOLD (float): Threshold for considering the two vectors as
+                            colineal. Not recommended to alter this.
+    Returns:
+        v2 (np.ndarray): Interpolation vector between v0 and v1
+    """
+    inputs_are_torch = False
+    if not isinstance(v0, np.ndarray):
+        inputs_are_torch = True
+        v0 = v0.detach().cpu().numpy()
+    if not isinstance(v1, np.ndarray):
+        inputs_are_torch = True
+        v1 = v1.detach().cpu().numpy()
+
+    dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
+    if np.abs(dot) > DOT_THRESHOLD:
+        v2 = (1 - t) * v0 + t * v1
+    else:
+        theta_0 = np.arccos(dot)
+        sin_theta_0 = np.sin(theta_0)
+        theta_t = theta_0 * t
+        sin_theta_t = np.sin(theta_t)
+        s0 = np.sin(theta_0 - theta_t) / sin_theta_0
+        s1 = sin_theta_t / sin_theta_0
+        v2 = s0 * v0 + s1 * v1
+
+    if inputs_are_torch:
+        v2 = torch.from_numpy(v2).to(device)
+
+    return v2
+
+
 @invocation(
     "lblend",
     title="Blend Latents",
-    tags=["latents", "blend"],
+    tags=["latents", "blend", "mask"],
     category="latents",
-    version="1.0.3",
+    version="1.1.0",
 )
 class BlendLatentsInvocation(BaseInvocation):
-    """Blend two latents using a given alpha. Latents must have same size."""
+    """Blend two latents using a given alpha. If a mask is provided, the second latents will be masked before blending.
+    Latents must have same size. Masking functionality added by @dwringer."""
 
-    latents_a: LatentsField = InputField(
-        description=FieldDescriptions.latents,
-        input=Input.Connection,
-    )
-    latents_b: LatentsField = InputField(
-        description=FieldDescriptions.latents,
-        input=Input.Connection,
-    )
-    alpha: float = InputField(default=0.5, description=FieldDescriptions.blend_alpha)
+    latents_a: LatentsField = InputField(description=FieldDescriptions.latents, input=Input.Connection)
+    latents_b: LatentsField = InputField(description=FieldDescriptions.latents, input=Input.Connection)
+    mask: Optional[ImageField] = InputField(default=None, description="Mask for blending in latents B")
+    alpha: float = InputField(ge=0, default=0.5, description=FieldDescriptions.blend_alpha)
+
+    def prep_mask_tensor(self, mask_image: Image.Image) -> torch.Tensor:
+        if mask_image.mode != "L":
+            mask_image = mask_image.convert("L")
+        mask_tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
+        if mask_tensor.dim() == 3:
+            mask_tensor = mask_tensor.unsqueeze(0)
+        return mask_tensor
+
+    def replace_tensor_from_masked_tensor(
+        self, tensor: torch.Tensor, other_tensor: torch.Tensor, mask_tensor: torch.Tensor
+    ):
+        output = tensor.clone()
+        mask_tensor = mask_tensor.expand(output.shape)
+        if output.dtype != torch.float16:
+            output = torch.add(output, mask_tensor * torch.sub(other_tensor, tensor))
+        else:
+            output = torch.add(output, mask_tensor.half() * torch.sub(other_tensor, tensor))
+        return output
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents_a = context.tensors.load(self.latents_a.latents_name)
         latents_b = context.tensors.load(self.latents_b.latents_name)
+        if self.mask is None:
+            mask_tensor = torch.zeros(latents_a.shape[-2:])
+        else:
+            mask_tensor = self.prep_mask_tensor(context.images.get_pil(self.mask.image_name))
+            mask_tensor = tv_resize(mask_tensor, latents_a.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
+
+        latents_b = self.replace_tensor_from_masked_tensor(latents_b, latents_a, mask_tensor)
 
         if latents_a.shape != latents_b.shape:
-            raise Exception("Latents to blend must be the same size.")
+            raise ValueError("Latents to blend must be the same size.")
 
         device = TorchDevice.choose_torch_device()
 
-        def slerp(
-            t: Union[float, npt.NDArray[Any]],  # FIXME: maybe use np.float32 here?
-            v0: Union[torch.Tensor, npt.NDArray[Any]],
-            v1: Union[torch.Tensor, npt.NDArray[Any]],
-            DOT_THRESHOLD: float = 0.9995,
-        ) -> Union[torch.Tensor, npt.NDArray[Any]]:
-            """
-            Spherical linear interpolation
-            Args:
-                t (float/np.ndarray): Float value between 0.0 and 1.0
-                v0 (np.ndarray): Starting vector
-                v1 (np.ndarray): Final vector
-                DOT_THRESHOLD (float): Threshold for considering the two vectors as
-                                    colineal. Not recommended to alter this.
-            Returns:
-                v2 (np.ndarray): Interpolation vector between v0 and v1
-            """
-            inputs_are_torch = False
-            if not isinstance(v0, np.ndarray):
-                inputs_are_torch = True
-                v0 = v0.detach().cpu().numpy()
-            if not isinstance(v1, np.ndarray):
-                inputs_are_torch = True
-                v1 = v1.detach().cpu().numpy()
-
-            dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
-            if np.abs(dot) > DOT_THRESHOLD:
-                v2 = (1 - t) * v0 + t * v1
-            else:
-                theta_0 = np.arccos(dot)
-                sin_theta_0 = np.sin(theta_0)
-                theta_t = theta_0 * t
-                sin_theta_t = np.sin(theta_t)
-                s0 = np.sin(theta_0 - theta_t) / sin_theta_0
-                s1 = sin_theta_t / sin_theta_0
-                v2 = s0 * v0 + s1 * v1
-
-            if inputs_are_torch:
-                v2_torch: torch.Tensor = torch.from_numpy(v2).to(device)
-                return v2_torch
-            else:
-                assert isinstance(v2, np.ndarray)
-                return v2
-
         # blend
-        bl = slerp(self.alpha, latents_a, latents_b)
-        assert isinstance(bl, torch.Tensor)
-        blended_latents: torch.Tensor = bl  # for type checking convenience
+        blended_latents = slerp(self.alpha, latents_a, latents_b, device)
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         blended_latents = blended_latents.to("cpu")
-
-        TorchDevice.empty_cache()
+        torch.cuda.empty_cache()
 
         name = context.tensors.save(tensor=blended_latents)
-        return LatentsOutput.build(latents_name=name, latents=blended_latents, seed=self.latents_a.seed)
+        return LatentsOutput.build(latents_name=name, latents=blended_latents)

invokeai/app/invocations/compel.py

@@ -82,10 +82,11 @@ class CompelInvocation(BaseInvocation):
             # apply all patches while the model is on the target device
             text_encoder_info.model_on_device() as (cached_weights, text_encoder),
             tokenizer_info as tokenizer,
-            LoRAPatcher.apply_lora_patches(
+            LoRAPatcher.apply_smart_lora_patches(
                 model=text_encoder,
                 patches=_lora_loader(),
                 prefix="lora_te_",
+                dtype=TorchDevice.choose_torch_dtype(),
                 cached_weights=cached_weights,
             ),
             # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
@@ -179,10 +180,11 @@ class SDXLPromptInvocationBase:
             # apply all patches while the model is on the target device
             text_encoder_info.model_on_device() as (cached_weights, text_encoder),
             tokenizer_info as tokenizer,
-            LoRAPatcher.apply_lora_patches(
+            LoRAPatcher.apply_smart_lora_patches(
                 text_encoder,
                 patches=_lora_loader(),
                 prefix=lora_prefix,
+                dtype=TorchDevice.choose_torch_dtype(),
                 cached_weights=cached_weights,
             ),
             # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.

invokeai/app/invocations/denoise_latents.py

@@ -1003,10 +1003,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
             ModelPatcher.apply_freeu(unet, self.unet.freeu_config),
             SeamlessExt.static_patch_model(unet, self.unet.seamless_axes),  # FIXME
             # Apply the LoRA after unet has been moved to its target device for faster patching.
-            LoRAPatcher.apply_lora_patches(
+            LoRAPatcher.apply_smart_lora_patches(
                 model=unet,
                 patches=_lora_loader(),
                 prefix="lora_unet_",
+                dtype=unet.dtype,
                 cached_weights=cached_weights,
             ),
         ):

invokeai/app/invocations/fields.py

@@ -250,6 +250,11 @@ class FluxConditioningField(BaseModel):
     """A conditioning tensor primitive value"""
 
     conditioning_name: str = Field(description="The name of conditioning tensor")
+    mask: Optional[TensorField] = Field(
+        default=None,
+        description="The mask associated with this conditioning tensor. Excluded regions should be set to False, "
+        "included regions should be set to True.",
+    )
 
 
 class SD3ConditioningField(BaseModel):

invokeai/app/invocations/flux_denoise.py

@@ -30,6 +30,7 @@ from invokeai.backend.flux.controlnet.xlabs_controlnet_flux import XLabsControlN
 from invokeai.backend.flux.denoise import denoise
 from invokeai.backend.flux.extensions.inpaint_extension import InpaintExtension
 from invokeai.backend.flux.extensions.instantx_controlnet_extension import InstantXControlNetExtension
+from invokeai.backend.flux.extensions.regional_prompting_extension import RegionalPromptingExtension
 from invokeai.backend.flux.extensions.xlabs_controlnet_extension import XLabsControlNetExtension
 from invokeai.backend.flux.extensions.xlabs_ip_adapter_extension import XLabsIPAdapterExtension
 from invokeai.backend.flux.ip_adapter.xlabs_ip_adapter_flux import XlabsIpAdapterFlux
@@ -42,6 +43,7 @@ from invokeai.backend.flux.sampling_utils import (
     pack,
     unpack,
 )
+from invokeai.backend.flux.text_conditioning import FluxTextConditioning
 from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
 from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
 from invokeai.backend.lora.lora_patcher import LoRAPatcher
@@ -56,7 +58,7 @@ from invokeai.backend.util.devices import TorchDevice
     title="FLUX Denoise",
     tags=["image", "flux"],
     category="image",
-    version="3.2.1",
+    version="3.2.2",
     classification=Classification.Prototype,
 )
 class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
@@ -87,10 +89,10 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         input=Input.Connection,
         title="Transformer",
     )
-    positive_text_conditioning: FluxConditioningField = InputField(
+    positive_text_conditioning: FluxConditioningField | list[FluxConditioningField] = InputField(
         description=FieldDescriptions.positive_cond, input=Input.Connection
     )
-    negative_text_conditioning: FluxConditioningField | None = InputField(
+    negative_text_conditioning: FluxConditioningField | list[FluxConditioningField] | None = InputField(
         default=None,
         description="Negative conditioning tensor. Can be None if cfg_scale is 1.0.",
         input=Input.Connection,
@@ -139,36 +141,12 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         name = context.tensors.save(tensor=latents)
         return LatentsOutput.build(latents_name=name, latents=latents, seed=None)
 
-    def _load_text_conditioning(
-        self, context: InvocationContext, conditioning_name: str, dtype: torch.dtype
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        # Load the conditioning data.
-        cond_data = context.conditioning.load(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=dtype)
-        t5_embeddings = flux_conditioning.t5_embeds
-        clip_embeddings = flux_conditioning.clip_embeds
-        return t5_embeddings, clip_embeddings
-
     def _run_diffusion(
         self,
         context: InvocationContext,
     ):
         inference_dtype = torch.bfloat16
 
-        # Load the conditioning data.
-        pos_t5_embeddings, pos_clip_embeddings = self._load_text_conditioning(
-            context, self.positive_text_conditioning.conditioning_name, inference_dtype
-        )
-        neg_t5_embeddings: torch.Tensor | None = None
-        neg_clip_embeddings: torch.Tensor | None = None
-        if self.negative_text_conditioning is not None:
-            neg_t5_embeddings, neg_clip_embeddings = self._load_text_conditioning(
-                context, self.negative_text_conditioning.conditioning_name, inference_dtype
-            )
-
         # Load the input latents, if provided.
         init_latents = context.tensors.load(self.latents.latents_name) if self.latents else None
         if init_latents is not None:
@@ -183,15 +161,45 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
             dtype=inference_dtype,
             seed=self.seed,
         )
+        b, _c, latent_h, latent_w = noise.shape
+        packed_h = latent_h // 2
+        packed_w = latent_w // 2
+
+        # Load the conditioning data.
+        pos_text_conditionings = self._load_text_conditioning(
+            context=context,
+            cond_field=self.positive_text_conditioning,
+            packed_height=packed_h,
+            packed_width=packed_w,
+            dtype=inference_dtype,
+            device=TorchDevice.choose_torch_device(),
+        )
+        neg_text_conditionings: list[FluxTextConditioning] | None = None
+        if self.negative_text_conditioning is not None:
+            neg_text_conditionings = self._load_text_conditioning(
+                context=context,
+                cond_field=self.negative_text_conditioning,
+                packed_height=packed_h,
+                packed_width=packed_w,
+                dtype=inference_dtype,
+                device=TorchDevice.choose_torch_device(),
+            )
+        pos_regional_prompting_extension = RegionalPromptingExtension.from_text_conditioning(
+            pos_text_conditionings, img_seq_len=packed_h * packed_w
+        )
+        neg_regional_prompting_extension = (
+            RegionalPromptingExtension.from_text_conditioning(neg_text_conditionings, img_seq_len=packed_h * packed_w)
+            if neg_text_conditionings
+            else None
+        )
 
         transformer_info = context.models.load(self.transformer.transformer)
         is_schnell = "schnell" in transformer_info.config.config_path
 
         # Calculate the timestep schedule.
-        image_seq_len = noise.shape[-1] * noise.shape[-2] // 4
         timesteps = get_schedule(
             num_steps=self.num_steps,
-            image_seq_len=image_seq_len,
+            image_seq_len=packed_h * packed_w,
             shift=not is_schnell,
         )
 
@@ -228,28 +236,17 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         inpaint_mask = self._prep_inpaint_mask(context, x)
 
-        b, _c, latent_h, latent_w = x.shape
         img_ids = generate_img_ids(h=latent_h, w=latent_w, batch_size=b, device=x.device, dtype=x.dtype)
 
-        pos_bs, pos_t5_seq_len, _ = pos_t5_embeddings.shape
-        pos_txt_ids = torch.zeros(
-            pos_bs, pos_t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device()
-        )
-        neg_txt_ids: torch.Tensor | None = None
-        if neg_t5_embeddings is not None:
-            neg_bs, neg_t5_seq_len, _ = neg_t5_embeddings.shape
-            neg_txt_ids = torch.zeros(
-                neg_bs, neg_t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device()
-            )
-
         # Pack all latent tensors.
         init_latents = pack(init_latents) if init_latents is not None else None
         inpaint_mask = pack(inpaint_mask) if inpaint_mask is not None else None
         noise = pack(noise)
         x = pack(x)
 
-        # Now that we have 'packed' the latent tensors, verify that we calculated the image_seq_len correctly.
-        assert image_seq_len == x.shape[1]
+        # Now that we have 'packed' the latent tensors, verify that we calculated the image_seq_len, packed_h, and
+        # packed_w correctly.
+        assert packed_h * packed_w == x.shape[1]
 
         # Prepare inpaint extension.
         inpaint_extension: InpaintExtension | None = None
@@ -299,10 +296,11 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
             if config.format in [ModelFormat.Checkpoint]:
                 # The model is non-quantized, so we can apply the LoRA weights directly into the model.
                 exit_stack.enter_context(
-                    LoRAPatcher.apply_lora_patches(
+                    LoRAPatcher.apply_smart_lora_patches(
                         model=transformer,
                         patches=self._lora_iterator(context),
                         prefix=FLUX_LORA_TRANSFORMER_PREFIX,
+                        dtype=inference_dtype,
                         cached_weights=cached_weights,
                     )
                 )
@@ -314,7 +312,7 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 # The model is quantized, so apply the LoRA weights as sidecar layers. This results in slower inference,
                 # than directly patching the weights, but is agnostic to the quantization format.
                 exit_stack.enter_context(
-                    LoRAPatcher.apply_lora_sidecar_patches(
+                    LoRAPatcher.apply_lora_wrapper_patches(
                         model=transformer,
                         patches=self._lora_iterator(context),
                         prefix=FLUX_LORA_TRANSFORMER_PREFIX,
@@ -338,12 +336,8 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 model=transformer,
                 img=x,
                 img_ids=img_ids,
-                txt=pos_t5_embeddings,
-                txt_ids=pos_txt_ids,
-                vec=pos_clip_embeddings,
-                neg_txt=neg_t5_embeddings,
-                neg_txt_ids=neg_txt_ids,
-                neg_vec=neg_clip_embeddings,
+                pos_regional_prompting_extension=pos_regional_prompting_extension,
+                neg_regional_prompting_extension=neg_regional_prompting_extension,
                 timesteps=timesteps,
                 step_callback=self._build_step_callback(context),
                 guidance=self.guidance,
@@ -357,6 +351,43 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         x = unpack(x.float(), self.height, self.width)
         return x
 
+    def _load_text_conditioning(
+        self,
+        context: InvocationContext,
+        cond_field: FluxConditioningField | list[FluxConditioningField],
+        packed_height: int,
+        packed_width: int,
+        dtype: torch.dtype,
+        device: torch.device,
+    ) -> list[FluxTextConditioning]:
+        """Load text conditioning data from a FluxConditioningField or a list of FluxConditioningFields."""
+        # Normalize to a list of FluxConditioningFields.
+        cond_list = [cond_field] if isinstance(cond_field, FluxConditioningField) else cond_field
+
+        text_conditionings: list[FluxTextConditioning] = []
+        for cond_field in cond_list:
+            # Load the text embeddings.
+            cond_data = context.conditioning.load(cond_field.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=dtype, device=device)
+            t5_embeddings = flux_conditioning.t5_embeds
+            clip_embeddings = flux_conditioning.clip_embeds
+
+            # Load the mask, if provided.
+            mask: Optional[torch.Tensor] = None
+            if cond_field.mask is not None:
+                mask = context.tensors.load(cond_field.mask.tensor_name)
+                mask = mask.to(device=device)
+                mask = RegionalPromptingExtension.preprocess_regional_prompt_mask(
+                    mask, packed_height, packed_width, dtype, device
+                )
+
+            text_conditionings.append(FluxTextConditioning(t5_embeddings, clip_embeddings, mask))
+
+        return text_conditionings
+
     @classmethod
     def prep_cfg_scale(
         cls, cfg_scale: float | list[float], timesteps: list[float], cfg_scale_start_step: int, cfg_scale_end_step: int

invokeai/app/invocations/flux_text_encoder.py

@@ -1,11 +1,18 @@
 from contextlib import ExitStack
-from typing import Iterator, Literal, Tuple
+from typing import Iterator, Literal, Optional, Tuple
 
 import torch
 from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    FluxConditioningField,
+    Input,
+    InputField,
+    TensorField,
+    UIComponent,
+)
 from invokeai.app.invocations.model import CLIPField, T5EncoderField
 from invokeai.app.invocations.primitives import FluxConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
@@ -15,6 +22,7 @@ from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
 from invokeai.backend.lora.lora_patcher import LoRAPatcher
 from invokeai.backend.model_manager.config import ModelFormat
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData, FLUXConditioningInfo
+from invokeai.backend.util.devices import TorchDevice
 
 
 @invocation(
@@ -22,7 +30,7 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import Condit
     title="FLUX Text Encoding",
     tags=["prompt", "conditioning", "flux"],
     category="conditioning",
-    version="1.1.0",
+    version="1.1.1",
     classification=Classification.Prototype,
 )
 class FluxTextEncoderInvocation(BaseInvocation):
@@ -41,7 +49,10 @@ class FluxTextEncoderInvocation(BaseInvocation):
     t5_max_seq_len: Literal[256, 512] = InputField(
         description="Max sequence length for the T5 encoder. Expected to be 256 for FLUX schnell models and 512 for FLUX dev models."
     )
-    prompt: str = InputField(description="Text prompt to encode.")
+    prompt: str = InputField(description="Text prompt to encode.", ui_component=UIComponent.Textarea)
+    mask: Optional[TensorField] = InputField(
+        default=None, description="A mask defining the region that this conditioning prompt applies to."
+    )
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> FluxConditioningOutput:
@@ -54,7 +65,9 @@ class FluxTextEncoderInvocation(BaseInvocation):
         )
 
         conditioning_name = context.conditioning.save(conditioning_data)
-        return FluxConditioningOutput.build(conditioning_name)
+        return FluxConditioningOutput(
+            conditioning=FluxConditioningField(conditioning_name=conditioning_name, mask=self.mask)
+        )
 
     def _t5_encode(self, context: InvocationContext) -> torch.Tensor:
         t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer)
@@ -99,10 +112,11 @@ class FluxTextEncoderInvocation(BaseInvocation):
             if clip_text_encoder_config.format in [ModelFormat.Diffusers]:
                 # The model is non-quantized, so we can apply the LoRA weights directly into the model.
                 exit_stack.enter_context(
-                    LoRAPatcher.apply_lora_patches(
+                    LoRAPatcher.apply_smart_lora_patches(
                         model=clip_text_encoder,
                         patches=self._clip_lora_iterator(context),
                         prefix=FLUX_LORA_CLIP_PREFIX,
+                        dtype=TorchDevice.choose_torch_dtype(),
                         cached_weights=cached_weights,
                     )
                 )

invokeai/app/invocations/image_panels.py

@@ -0,0 +1,59 @@
+from pydantic import ValidationInfo, field_validator
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import InputField, OutputField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+
+
+@invocation_output("image_panel_coordinate_output")
+class ImagePanelCoordinateOutput(BaseInvocationOutput):
+    x_left: int = OutputField(description="The left x-coordinate of the panel.")
+    y_top: int = OutputField(description="The top y-coordinate of the panel.")
+    width: int = OutputField(description="The width of the panel.")
+    height: int = OutputField(description="The height of the panel.")
+
+
+@invocation(
+    "image_panel_layout",
+    title="Image Panel Layout",
+    tags=["image", "panel", "layout"],
+    category="image",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class ImagePanelLayoutInvocation(BaseInvocation):
+    """Get the coordinates of a single panel in a grid. (If the full image shape cannot be divided evenly into panels,
+    then the grid may not cover the entire image.)
+    """
+
+    width: int = InputField(description="The width of the entire grid.")
+    height: int = InputField(description="The height of the entire grid.")
+    num_cols: int = InputField(ge=1, default=1, description="The number of columns in the grid.")
+    num_rows: int = InputField(ge=1, default=1, description="The number of rows in the grid.")
+    panel_col_idx: int = InputField(ge=0, default=0, description="The column index of the panel to be processed.")
+    panel_row_idx: int = InputField(ge=0, default=0, description="The row index of the panel to be processed.")
+
+    @field_validator("panel_col_idx")
+    def validate_panel_col_idx(cls, v: int, info: ValidationInfo) -> int:
+        if v < 0 or v >= info.data["num_cols"]:
+            raise ValueError(f"panel_col_idx must be between 0 and {info.data['num_cols'] - 1}")
+        return v
+
+    @field_validator("panel_row_idx")
+    def validate_panel_row_idx(cls, v: int, info: ValidationInfo) -> int:
+        if v < 0 or v >= info.data["num_rows"]:
+            raise ValueError(f"panel_row_idx must be between 0 and {info.data['num_rows'] - 1}")
+        return v
+
+    def invoke(self, context: InvocationContext) -> ImagePanelCoordinateOutput:
+        x_left = self.panel_col_idx * (self.width // self.num_cols)
+        y_top = self.panel_row_idx * (self.height // self.num_rows)
+        width = self.width // self.num_cols
+        height = self.height // self.num_rows
+        return ImagePanelCoordinateOutput(x_left=x_left, y_top=y_top, width=width, height=height)

invokeai/app/invocations/param_easing.py

@@ -1,43 +1,4 @@
-import io
-from typing import Literal, Optional
-
-import matplotlib.pyplot as plt
 import numpy as np
-import PIL.Image
-from easing_functions import (
-    BackEaseIn,
-    BackEaseInOut,
-    BackEaseOut,
-    BounceEaseIn,
-    BounceEaseInOut,
-    BounceEaseOut,
-    CircularEaseIn,
-    CircularEaseInOut,
-    CircularEaseOut,
-    CubicEaseIn,
-    CubicEaseInOut,
-    CubicEaseOut,
-    ElasticEaseIn,
-    ElasticEaseInOut,
-    ElasticEaseOut,
-    ExponentialEaseIn,
-    ExponentialEaseInOut,
-    ExponentialEaseOut,
-    LinearInOut,
-    QuadEaseIn,
-    QuadEaseInOut,
-    QuadEaseOut,
-    QuarticEaseIn,
-    QuarticEaseInOut,
-    QuarticEaseOut,
-    QuinticEaseIn,
-    QuinticEaseInOut,
-    QuinticEaseOut,
-    SineEaseIn,
-    SineEaseInOut,
-    SineEaseOut,
-)
-from matplotlib.ticker import MaxNLocator
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.fields import InputField
@@ -65,191 +26,3 @@ class FloatLinearRangeInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
         param_list = list(np.linspace(self.start, self.stop, self.steps))
         return FloatCollectionOutput(collection=param_list)
-
-
-EASING_FUNCTIONS_MAP = {
-    "Linear": LinearInOut,
-    "QuadIn": QuadEaseIn,
-    "QuadOut": QuadEaseOut,
-    "QuadInOut": QuadEaseInOut,
-    "CubicIn": CubicEaseIn,
-    "CubicOut": CubicEaseOut,
-    "CubicInOut": CubicEaseInOut,
-    "QuarticIn": QuarticEaseIn,
-    "QuarticOut": QuarticEaseOut,
-    "QuarticInOut": QuarticEaseInOut,
-    "QuinticIn": QuinticEaseIn,
-    "QuinticOut": QuinticEaseOut,
-    "QuinticInOut": QuinticEaseInOut,
-    "SineIn": SineEaseIn,
-    "SineOut": SineEaseOut,
-    "SineInOut": SineEaseInOut,
-    "CircularIn": CircularEaseIn,
-    "CircularOut": CircularEaseOut,
-    "CircularInOut": CircularEaseInOut,
-    "ExponentialIn": ExponentialEaseIn,
-    "ExponentialOut": ExponentialEaseOut,
-    "ExponentialInOut": ExponentialEaseInOut,
-    "ElasticIn": ElasticEaseIn,
-    "ElasticOut": ElasticEaseOut,
-    "ElasticInOut": ElasticEaseInOut,
-    "BackIn": BackEaseIn,
-    "BackOut": BackEaseOut,
-    "BackInOut": BackEaseInOut,
-    "BounceIn": BounceEaseIn,
-    "BounceOut": BounceEaseOut,
-    "BounceInOut": BounceEaseInOut,
-}
-
-EASING_FUNCTION_KEYS = Literal[tuple(EASING_FUNCTIONS_MAP.keys())]
-
-
-# actually I think for now could just use CollectionOutput (which is list[Any]
-@invocation(
-    "step_param_easing",
-    title="Step Param Easing",
-    tags=["step", "easing"],
-    category="step",
-    version="1.0.2",
-)
-class StepParamEasingInvocation(BaseInvocation):
-    """Experimental per-step parameter easing for denoising steps"""
-
-    easing: EASING_FUNCTION_KEYS = InputField(default="Linear", description="The easing function to use")
-    num_steps: int = InputField(default=20, description="number of denoising steps")
-    start_value: float = InputField(default=0.0, description="easing starting value")
-    end_value: float = InputField(default=1.0, description="easing ending value")
-    start_step_percent: float = InputField(default=0.0, description="fraction of steps at which to start easing")
-    end_step_percent: float = InputField(default=1.0, description="fraction of steps after which to end easing")
-    # if None, then start_value is used prior to easing start
-    pre_start_value: Optional[float] = InputField(default=None, description="value before easing start")
-    # if None, then end value is used prior to easing end
-    post_end_value: Optional[float] = InputField(default=None, description="value after easing end")
-    mirror: bool = InputField(default=False, description="include mirror of easing function")
-    # FIXME: add alt_mirror option (alternative to default or mirror), or remove entirely
-    # alt_mirror: bool = InputField(default=False, description="alternative mirroring by dual easing")
-    show_easing_plot: bool = InputField(default=False, description="show easing plot")
-
-    def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
-        log_diagnostics = False
-        # convert from start_step_percent to nearest step <= (steps * start_step_percent)
-        # start_step = int(np.floor(self.num_steps * self.start_step_percent))
-        start_step = int(np.round(self.num_steps * self.start_step_percent))
-        # convert from end_step_percent to nearest step >= (steps * end_step_percent)
-        # end_step = int(np.ceil((self.num_steps - 1) * self.end_step_percent))
-        end_step = int(np.round((self.num_steps - 1) * self.end_step_percent))
-
-        # end_step = int(np.ceil(self.num_steps * self.end_step_percent))
-        num_easing_steps = end_step - start_step + 1
-
-        # num_presteps = max(start_step - 1, 0)
-        num_presteps = start_step
-        num_poststeps = self.num_steps - (num_presteps + num_easing_steps)
-        prelist = list(num_presteps * [self.pre_start_value])
-        postlist = list(num_poststeps * [self.post_end_value])
-
-        if log_diagnostics:
-            context.logger.debug("start_step: " + str(start_step))
-            context.logger.debug("end_step: " + str(end_step))
-            context.logger.debug("num_easing_steps: " + str(num_easing_steps))
-            context.logger.debug("num_presteps: " + str(num_presteps))
-            context.logger.debug("num_poststeps: " + str(num_poststeps))
-            context.logger.debug("prelist size: " + str(len(prelist)))
-            context.logger.debug("postlist size: " + str(len(postlist)))
-            context.logger.debug("prelist: " + str(prelist))
-            context.logger.debug("postlist: " + str(postlist))
-
-        easing_class = EASING_FUNCTIONS_MAP[self.easing]
-        if log_diagnostics:
-            context.logger.debug("easing class: " + str(easing_class))
-        easing_list = []
-        if self.mirror:  # "expected" mirroring
-            # if number of steps is even, squeeze duration down to (number_of_steps)/2
-            # and create reverse copy of list to append
-            # if number of steps is odd, squeeze duration down to ceil(number_of_steps/2)
-            # and create reverse copy of list[1:end-1]
-            # but if even then number_of_steps/2 === ceil(number_of_steps/2), so can just use ceil always
-
-            base_easing_duration = int(np.ceil(num_easing_steps / 2.0))
-            if log_diagnostics:
-                context.logger.debug("base easing duration: " + str(base_easing_duration))
-            even_num_steps = num_easing_steps % 2 == 0  # even number of steps
-            easing_function = easing_class(
-                start=self.start_value,
-                end=self.end_value,
-                duration=base_easing_duration - 1,
-            )
-            base_easing_vals = []
-            for step_index in range(base_easing_duration):
-                easing_val = easing_function.ease(step_index)
-                base_easing_vals.append(easing_val)
-                if log_diagnostics:
-                    context.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(easing_val))
-            if even_num_steps:
-                mirror_easing_vals = list(reversed(base_easing_vals))
-            else:
-                mirror_easing_vals = list(reversed(base_easing_vals[0:-1]))
-            if log_diagnostics:
-                context.logger.debug("base easing vals: " + str(base_easing_vals))
-                context.logger.debug("mirror easing vals: " + str(mirror_easing_vals))
-            easing_list = base_easing_vals + mirror_easing_vals
-
-        # FIXME: add alt_mirror option (alternative to default or mirror), or remove entirely
-        # elif self.alt_mirror:  # function mirroring (unintuitive behavior (at least to me))
-        #     # half_ease_duration = round(num_easing_steps - 1 / 2)
-        #     half_ease_duration = round((num_easing_steps - 1) / 2)
-        #     easing_function = easing_class(start=self.start_value,
-        #                                    end=self.end_value,
-        #                                    duration=half_ease_duration,
-        #                                    )
-        #
-        #     mirror_function = easing_class(start=self.end_value,
-        #                                    end=self.start_value,
-        #                                    duration=half_ease_duration,
-        #                                    )
-        #     for step_index in range(num_easing_steps):
-        #         if step_index <= half_ease_duration:
-        #             step_val = easing_function.ease(step_index)
-        #         else:
-        #             step_val = mirror_function.ease(step_index - half_ease_duration)
-        #         easing_list.append(step_val)
-        #         if log_diagnostics: logger.debug(step_index, step_val)
-        #
-
-        else:  # no mirroring (default)
-            easing_function = easing_class(
-                start=self.start_value,
-                end=self.end_value,
-                duration=num_easing_steps - 1,
-            )
-            for step_index in range(num_easing_steps):
-                step_val = easing_function.ease(step_index)
-                easing_list.append(step_val)
-                if log_diagnostics:
-                    context.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(step_val))
-
-        if log_diagnostics:
-            context.logger.debug("prelist size: " + str(len(prelist)))
-            context.logger.debug("easing_list size: " + str(len(easing_list)))
-            context.logger.debug("postlist size: " + str(len(postlist)))
-
-        param_list = prelist + easing_list + postlist
-
-        if self.show_easing_plot:
-            plt.figure()
-            plt.xlabel("Step")
-            plt.ylabel("Param Value")
-            plt.title("Per-Step Values Based On Easing: " + self.easing)
-            plt.bar(range(len(param_list)), param_list)
-            # plt.plot(param_list)
-            ax = plt.gca()
-            ax.xaxis.set_major_locator(MaxNLocator(integer=True))
-            buf = io.BytesIO()
-            plt.savefig(buf, format="png")
-            buf.seek(0)
-            im = PIL.Image.open(buf)
-            im.show()
-            buf.close()
-
-        # output array of size steps, each entry list[i] is param value for step i
-        return FloatCollectionOutput(collection=param_list)

invokeai/app/invocations/primitives.py

@@ -4,7 +4,13 @@ from typing import Optional
 
 import torch
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    invocation,
+    invocation_output,
+)
 from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     BoundingBoxField,
@@ -533,3 +539,23 @@ class BoundingBoxInvocation(BaseInvocation):
 
 
 # endregion
+
+
+@invocation(
+    "image_batch",
+    title="Image Batch",
+    tags=["primitives", "image", "batch", "internal"],
+    category="primitives",
+    version="1.0.0",
+    classification=Classification.Special,
+)
+class ImageBatchInvocation(BaseInvocation):
+    """Create a batched generation, where the workflow is executed once for each image in the batch."""
+
+    images: list[ImageField] = InputField(min_length=1, description="The images to batch over", input=Input.Direct)
+
+    def __init__(self):
+        raise NotImplementedError("This class should never be executed or instantiated directly.")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        raise NotImplementedError("This class should never be executed or instantiated directly.")

invokeai/app/invocations/sd3_text_encoder.py

@@ -21,6 +21,7 @@ from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
 from invokeai.backend.lora.lora_patcher import LoRAPatcher
 from invokeai.backend.model_manager.config import ModelFormat
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData, SD3ConditioningInfo
+from invokeai.backend.util.devices import TorchDevice
 
 # The SD3 T5 Max Sequence Length set based on the default in diffusers.
 SD3_T5_MAX_SEQ_LEN = 256
@@ -150,10 +151,11 @@ class Sd3TextEncoderInvocation(BaseInvocation):
             if clip_text_encoder_config.format in [ModelFormat.Diffusers]:
                 # The model is non-quantized, so we can apply the LoRA weights directly into the model.
                 exit_stack.enter_context(
-                    LoRAPatcher.apply_lora_patches(
+                    LoRAPatcher.apply_smart_lora_patches(
                         model=clip_text_encoder,
                         patches=self._clip_lora_iterator(context, clip_model),
                         prefix=FLUX_LORA_CLIP_PREFIX,
+                        dtype=TorchDevice.choose_torch_dtype(),
                         cached_weights=cached_weights,
                     )
                 )

invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py

@@ -207,7 +207,9 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
         with (
             ExitStack() as exit_stack,
             unet_info as unet,
-            LoRAPatcher.apply_lora_patches(model=unet, patches=_lora_loader(), prefix="lora_unet_"),
+            LoRAPatcher.apply_smart_lora_patches(
+                model=unet, patches=_lora_loader(), prefix="lora_unet_", dtype=unet.dtype
+            ),
         ):
             assert isinstance(unet, UNet2DConditionModel)
             latents = latents.to(device=unet.device, dtype=unet.dtype)

invokeai/app/services/invocation_stats/invocation_stats_default.py

@@ -20,7 +20,7 @@ from invokeai.app.services.invocation_stats.invocation_stats_common import (
     NodeExecutionStatsSummary,
 )
 from invokeai.app.services.invoker import Invoker
-from invokeai.backend.model_manager.load.model_cache import CacheStats
+from invokeai.backend.model_manager.load.model_cache.cache_stats import CacheStats
 
 # Size of 1GB in bytes.
 GB = 2**30

invokeai/app/services/model_load/model_load_base.py

@@ -7,7 +7,7 @@ from typing import Callable, Optional
 
 from invokeai.backend.model_manager import AnyModel, AnyModelConfig, SubModelType
 from invokeai.backend.model_manager.load import LoadedModel, LoadedModelWithoutConfig
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 
 
 class ModelLoadServiceBase(ABC):
@@ -24,7 +24,7 @@ class ModelLoadServiceBase(ABC):
 
     @property
     @abstractmethod
-    def ram_cache(self) -> ModelCacheBase[AnyModel]:
+    def ram_cache(self) -> ModelCache:
         """Return the RAM cache used by this loader."""
 
     @abstractmethod

invokeai/app/services/model_load/model_load_default.py

@@ -18,7 +18,7 @@ from invokeai.backend.model_manager.load import (
     ModelLoaderRegistry,
     ModelLoaderRegistryBase,
 )
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
@@ -30,7 +30,7 @@ class ModelLoadService(ModelLoadServiceBase):
     def __init__(
         self,
         app_config: InvokeAIAppConfig,
-        ram_cache: ModelCacheBase[AnyModel],
+        ram_cache: ModelCache,
         registry: Optional[Type[ModelLoaderRegistryBase]] = ModelLoaderRegistry,
     ):
         """Initialize the model load service."""
@@ -45,7 +45,7 @@ class ModelLoadService(ModelLoadServiceBase):
         self._invoker = invoker
 
     @property
-    def ram_cache(self) -> ModelCacheBase[AnyModel]:
+    def ram_cache(self) -> ModelCache:
         """Return the RAM cache used by this loader."""
         return self._ram_cache
 
@@ -78,15 +78,14 @@ class ModelLoadService(ModelLoadServiceBase):
         self, model_path: Path, loader: Optional[Callable[[Path], AnyModel]] = None
     ) -> LoadedModelWithoutConfig:
         cache_key = str(model_path)
-        ram_cache = self.ram_cache
         try:
-            return LoadedModelWithoutConfig(_locker=ram_cache.get(key=cache_key))
+            return LoadedModelWithoutConfig(cache_record=self._ram_cache.get(key=cache_key), cache=self._ram_cache)
         except IndexError:
             pass
 
         def torch_load_file(checkpoint: Path) -> AnyModel:
             scan_result = scan_file_path(checkpoint)
-            if scan_result.infected_files != 0:
+            if scan_result.infected_files != 0 or scan_result.scan_err:
                 raise Exception("The model at {checkpoint} is potentially infected by malware. Aborting load.")
             result = torch_load(checkpoint, map_location="cpu")
             return result
@@ -109,5 +108,5 @@ class ModelLoadService(ModelLoadServiceBase):
         )
         assert loader is not None
         raw_model = loader(model_path)
-        ram_cache.put(key=cache_key, model=raw_model)
-        return LoadedModelWithoutConfig(_locker=ram_cache.get(key=cache_key))
+        self._ram_cache.put(key=cache_key, model=raw_model)
+        return LoadedModelWithoutConfig(cache_record=self._ram_cache.get(key=cache_key), cache=self._ram_cache)

invokeai/app/services/model_manager/model_manager_default.py

@@ -16,7 +16,8 @@ from invokeai.app.services.model_load.model_load_base import ModelLoadServiceBas
 from invokeai.app.services.model_load.model_load_default import ModelLoadService
 from invokeai.app.services.model_manager.model_manager_base import ModelManagerServiceBase
 from invokeai.app.services.model_records.model_records_base import ModelRecordServiceBase
-from invokeai.backend.model_manager.load import ModelCache, ModelLoaderRegistry
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 

invokeai/app/services/session_queue/session_queue_common.py

@@ -16,6 +16,7 @@ from pydantic import (
 from pydantic_core import to_jsonable_python
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation
+from invokeai.app.invocations.fields import ImageField
 from invokeai.app.services.shared.graph import Graph, GraphExecutionState, NodeNotFoundError
 from invokeai.app.services.workflow_records.workflow_records_common import (
     WorkflowWithoutID,
@@ -51,11 +52,7 @@ class SessionQueueItemNotFoundError(ValueError):
 
 # region Batch
 
-BatchDataType = Union[
-    StrictStr,
-    float,
-    int,
-]
+BatchDataType = Union[StrictStr, float, int, ImageField]
 
 
 class NodeFieldValue(BaseModel):

invokeai/backend/flux/custom_block_processor.py

@@ -1,9 +1,10 @@
 import einops
 import torch
 
+from invokeai.backend.flux.extensions.regional_prompting_extension import RegionalPromptingExtension
 from invokeai.backend.flux.extensions.xlabs_ip_adapter_extension import XLabsIPAdapterExtension
 from invokeai.backend.flux.math import attention
-from invokeai.backend.flux.modules.layers import DoubleStreamBlock
+from invokeai.backend.flux.modules.layers import DoubleStreamBlock, SingleStreamBlock
 
 
 class CustomDoubleStreamBlockProcessor:
@@ -13,7 +14,12 @@ class CustomDoubleStreamBlockProcessor:
 
     @staticmethod
     def _double_stream_block_forward(
-        block: DoubleStreamBlock, img: torch.Tensor, txt: torch.Tensor, vec: torch.Tensor, pe: torch.Tensor
+        block: DoubleStreamBlock,
+        img: torch.Tensor,
+        txt: torch.Tensor,
+        vec: torch.Tensor,
+        pe: torch.Tensor,
+        attn_mask: torch.Tensor | None = None,
     ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """This function is a direct copy of DoubleStreamBlock.forward(), but it returns some of the intermediate
         values.
@@ -40,7 +46,7 @@ class CustomDoubleStreamBlockProcessor:
         k = torch.cat((txt_k, img_k), dim=2)
         v = torch.cat((txt_v, img_v), dim=2)
 
-        attn = attention(q, k, v, pe=pe)
+        attn = attention(q, k, v, pe=pe, attn_mask=attn_mask)
         txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
 
         # calculate the img bloks
@@ -63,11 +69,15 @@ class CustomDoubleStreamBlockProcessor:
         vec: torch.Tensor,
         pe: torch.Tensor,
         ip_adapter_extensions: list[XLabsIPAdapterExtension],
+        regional_prompting_extension: RegionalPromptingExtension,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         """A custom implementation of DoubleStreamBlock.forward() with additional features:
         - IP-Adapter support
         """
-        img, txt, img_q = CustomDoubleStreamBlockProcessor._double_stream_block_forward(block, img, txt, vec, pe)
+        attn_mask = regional_prompting_extension.get_double_stream_attn_mask(block_index)
+        img, txt, img_q = CustomDoubleStreamBlockProcessor._double_stream_block_forward(
+            block, img, txt, vec, pe, attn_mask=attn_mask
+        )
 
         # Apply IP-Adapter conditioning.
         for ip_adapter_extension in ip_adapter_extensions:
@@ -81,3 +91,48 @@ class CustomDoubleStreamBlockProcessor:
             )
 
         return img, txt
+
+
+class CustomSingleStreamBlockProcessor:
+    """A class containing a custom implementation of SingleStreamBlock.forward() with additional features (masking,
+    etc.)
+    """
+
+    @staticmethod
+    def _single_stream_block_forward(
+        block: SingleStreamBlock,
+        x: torch.Tensor,
+        vec: torch.Tensor,
+        pe: torch.Tensor,
+        attn_mask: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        """This function is a direct copy of SingleStreamBlock.forward()."""
+        mod, _ = block.modulation(vec)
+        x_mod = (1 + mod.scale) * block.pre_norm(x) + mod.shift
+        qkv, mlp = torch.split(block.linear1(x_mod), [3 * block.hidden_size, block.mlp_hidden_dim], dim=-1)
+
+        q, k, v = einops.rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=block.num_heads)
+        q, k = block.norm(q, k, v)
+
+        # compute attention
+        attn = attention(q, k, v, pe=pe, attn_mask=attn_mask)
+        # compute activation in mlp stream, cat again and run second linear layer
+        output = block.linear2(torch.cat((attn, block.mlp_act(mlp)), 2))
+        return x + mod.gate * output
+
+    @staticmethod
+    def custom_single_block_forward(
+        timestep_index: int,
+        total_num_timesteps: int,
+        block_index: int,
+        block: SingleStreamBlock,
+        img: torch.Tensor,
+        vec: torch.Tensor,
+        pe: torch.Tensor,
+        regional_prompting_extension: RegionalPromptingExtension,
+    ) -> torch.Tensor:
+        """A custom implementation of SingleStreamBlock.forward() with additional features:
+        - Masking
+        """
+        attn_mask = regional_prompting_extension.get_single_stream_attn_mask(block_index)
+        return CustomSingleStreamBlockProcessor._single_stream_block_forward(block, img, vec, pe, attn_mask=attn_mask)

invokeai/backend/flux/denoise.py

@@ -7,6 +7,7 @@ from tqdm import tqdm
 from invokeai.backend.flux.controlnet.controlnet_flux_output import ControlNetFluxOutput, sum_controlnet_flux_outputs
 from invokeai.backend.flux.extensions.inpaint_extension import InpaintExtension
 from invokeai.backend.flux.extensions.instantx_controlnet_extension import InstantXControlNetExtension
+from invokeai.backend.flux.extensions.regional_prompting_extension import RegionalPromptingExtension
 from invokeai.backend.flux.extensions.xlabs_controlnet_extension import XLabsControlNetExtension
 from invokeai.backend.flux.extensions.xlabs_ip_adapter_extension import XLabsIPAdapterExtension
 from invokeai.backend.flux.model import Flux
@@ -18,14 +19,8 @@ def denoise(
     # model input
     img: torch.Tensor,
     img_ids: torch.Tensor,
-    # positive text conditioning
-    txt: torch.Tensor,
-    txt_ids: torch.Tensor,
-    vec: torch.Tensor,
-    # negative text conditioning
-    neg_txt: torch.Tensor | None,
-    neg_txt_ids: torch.Tensor | None,
-    neg_vec: torch.Tensor | None,
+    pos_regional_prompting_extension: RegionalPromptingExtension,
+    neg_regional_prompting_extension: RegionalPromptingExtension | None,
     # sampling parameters
     timesteps: list[float],
     step_callback: Callable[[PipelineIntermediateState], None],
@@ -61,9 +56,9 @@ def denoise(
                     total_num_timesteps=total_steps,
                     img=img,
                     img_ids=img_ids,
-                    txt=txt,
-                    txt_ids=txt_ids,
-                    y=vec,
+                    txt=pos_regional_prompting_extension.regional_text_conditioning.t5_embeddings,
+                    txt_ids=pos_regional_prompting_extension.regional_text_conditioning.t5_txt_ids,
+                    y=pos_regional_prompting_extension.regional_text_conditioning.clip_embeddings,
                     timesteps=t_vec,
                     guidance=guidance_vec,
                 )
@@ -78,9 +73,9 @@ def denoise(
         pred = model(
             img=img,
             img_ids=img_ids,
-            txt=txt,
-            txt_ids=txt_ids,
-            y=vec,
+            txt=pos_regional_prompting_extension.regional_text_conditioning.t5_embeddings,
+            txt_ids=pos_regional_prompting_extension.regional_text_conditioning.t5_txt_ids,
+            y=pos_regional_prompting_extension.regional_text_conditioning.clip_embeddings,
             timesteps=t_vec,
             guidance=guidance_vec,
             timestep_index=step_index,
@@ -88,6 +83,7 @@ def denoise(
             controlnet_double_block_residuals=merged_controlnet_residuals.double_block_residuals,
             controlnet_single_block_residuals=merged_controlnet_residuals.single_block_residuals,
             ip_adapter_extensions=pos_ip_adapter_extensions,
+            regional_prompting_extension=pos_regional_prompting_extension,
         )
 
         step_cfg_scale = cfg_scale[step_index]
@@ -97,15 +93,15 @@ def denoise(
             # TODO(ryand): Add option to run positive and negative predictions in a single batch for better performance
             # on systems with sufficient VRAM.
 
-            if neg_txt is None or neg_txt_ids is None or neg_vec is None:
+            if neg_regional_prompting_extension is None:
                 raise ValueError("Negative text conditioning is required when cfg_scale is not 1.0.")
 
             neg_pred = model(
                 img=img,
                 img_ids=img_ids,
-                txt=neg_txt,
-                txt_ids=neg_txt_ids,
-                y=neg_vec,
+                txt=neg_regional_prompting_extension.regional_text_conditioning.t5_embeddings,
+                txt_ids=neg_regional_prompting_extension.regional_text_conditioning.t5_txt_ids,
+                y=neg_regional_prompting_extension.regional_text_conditioning.clip_embeddings,
                 timesteps=t_vec,
                 guidance=guidance_vec,
                 timestep_index=step_index,
@@ -113,6 +109,7 @@ def denoise(
                 controlnet_double_block_residuals=None,
                 controlnet_single_block_residuals=None,
                 ip_adapter_extensions=neg_ip_adapter_extensions,
+                regional_prompting_extension=neg_regional_prompting_extension,
             )
             pred = neg_pred + step_cfg_scale * (pred - neg_pred)
 

invokeai/backend/flux/extensions/regional_prompting_extension.py

@@ -0,0 +1,276 @@
+from typing import Optional
+
+import torch
+import torchvision
+
+from invokeai.backend.flux.text_conditioning import FluxRegionalTextConditioning, FluxTextConditioning
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import Range
+from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.util.mask import to_standard_float_mask
+
+
+class RegionalPromptingExtension:
+    """A class for managing regional prompting with FLUX.
+
+    This implementation is inspired by https://arxiv.org/pdf/2411.02395 (though there are significant differences).
+    """
+
+    def __init__(
+        self,
+        regional_text_conditioning: FluxRegionalTextConditioning,
+        restricted_attn_mask: torch.Tensor | None = None,
+    ):
+        self.regional_text_conditioning = regional_text_conditioning
+        self.restricted_attn_mask = restricted_attn_mask
+
+    def get_double_stream_attn_mask(self, block_index: int) -> torch.Tensor | None:
+        order = [self.restricted_attn_mask, None]
+        return order[block_index % len(order)]
+
+    def get_single_stream_attn_mask(self, block_index: int) -> torch.Tensor | None:
+        order = [self.restricted_attn_mask, None]
+        return order[block_index % len(order)]
+
+    @classmethod
+    def from_text_conditioning(cls, text_conditioning: list[FluxTextConditioning], img_seq_len: int):
+        """Create a RegionalPromptingExtension from a list of text conditionings.
+
+        Args:
+            text_conditioning (list[FluxTextConditioning]): The text conditionings to use for regional prompting.
+            img_seq_len (int): The image sequence length (i.e. packed_height * packed_width).
+        """
+        regional_text_conditioning = cls._concat_regional_text_conditioning(text_conditioning)
+        attn_mask_with_restricted_img_self_attn = cls._prepare_restricted_attn_mask(
+            regional_text_conditioning, img_seq_len
+        )
+        return cls(
+            regional_text_conditioning=regional_text_conditioning,
+            restricted_attn_mask=attn_mask_with_restricted_img_self_attn,
+        )
+
+    # Keeping _prepare_unrestricted_attn_mask for reference as an alternative masking strategy:
+    #
+    # @classmethod
+    # def _prepare_unrestricted_attn_mask(
+    #     cls,
+    #     regional_text_conditioning: FluxRegionalTextConditioning,
+    #     img_seq_len: int,
+    # ) -> torch.Tensor:
+    #     """Prepare an 'unrestricted' attention mask. In this context, 'unrestricted' means that:
+    #     - img self-attention is not masked.
+    #     - img regions attend to both txt within their own region and to global prompts.
+    #     """
+    #     device = TorchDevice.choose_torch_device()
+
+    #     # Infer txt_seq_len from the t5_embeddings tensor.
+    #     txt_seq_len = regional_text_conditioning.t5_embeddings.shape[1]
+
+    #     # In the attention blocks, the txt seq and img seq are concatenated and then attention is applied.
+    #     # Concatenation happens in the following order: [txt_seq, img_seq].
+    #     # There are 4 portions of the attention mask to consider as we prepare it:
+    #     # 1. txt attends to itself
+    #     # 2. txt attends to corresponding regional img
+    #     # 3. regional img attends to corresponding txt
+    #     # 4. regional img attends to itself
+
+    #     # Initialize empty attention mask.
+    #     regional_attention_mask = torch.zeros(
+    #         (txt_seq_len + img_seq_len, txt_seq_len + img_seq_len), device=device, dtype=torch.float16
+    #     )
+
+    #     for image_mask, t5_embedding_range in zip(
+    #         regional_text_conditioning.image_masks, regional_text_conditioning.t5_embedding_ranges, strict=True
+    #     ):
+    #         # 1. txt attends to itself
+    #         regional_attention_mask[
+    #             t5_embedding_range.start : t5_embedding_range.end, t5_embedding_range.start : t5_embedding_range.end
+    #         ] = 1.0
+
+    #         # 2. txt attends to corresponding regional img
+    #         # Note that we reshape to (1, img_seq_len) to ensure broadcasting works as desired.
+    #         fill_value = image_mask.view(1, img_seq_len) if image_mask is not None else 1.0
+    #         regional_attention_mask[t5_embedding_range.start : t5_embedding_range.end, txt_seq_len:] = fill_value
+
+    #         # 3. regional img attends to corresponding txt
+    #         # Note that we reshape to (img_seq_len, 1) to ensure broadcasting works as desired.
+    #         fill_value = image_mask.view(img_seq_len, 1) if image_mask is not None else 1.0
+    #         regional_attention_mask[txt_seq_len:, t5_embedding_range.start : t5_embedding_range.end] = fill_value
+
+    #     # 4. regional img attends to itself
+    #     # Allow unrestricted img self attention.
+    #     regional_attention_mask[txt_seq_len:, txt_seq_len:] = 1.0
+
+    #     # Convert attention mask to boolean.
+    #     regional_attention_mask = regional_attention_mask > 0.5
+
+    #     return regional_attention_mask
+
+    @classmethod
+    def _prepare_restricted_attn_mask(
+        cls,
+        regional_text_conditioning: FluxRegionalTextConditioning,
+        img_seq_len: int,
+    ) -> torch.Tensor | None:
+        """Prepare a 'restricted' attention mask. In this context, 'restricted' means that:
+        - img self-attention is only allowed within regions.
+        - img regions only attend to txt within their own region, not to global prompts.
+        """
+        # Identify background region. I.e. the region that is not covered by any region masks.
+        background_region_mask: None | torch.Tensor = None
+        for image_mask in regional_text_conditioning.image_masks:
+            if image_mask is not None:
+                if background_region_mask is None:
+                    background_region_mask = torch.ones_like(image_mask)
+                background_region_mask *= 1 - image_mask
+
+        if background_region_mask is None:
+            # There are no region masks, short-circuit and return None.
+            # TODO(ryand): We could restrict txt-txt attention across multiple global prompts, but this would
+            # is a rare use case and would make the logic here significantly more complicated.
+            return None
+
+        device = TorchDevice.choose_torch_device()
+
+        # Infer txt_seq_len from the t5_embeddings tensor.
+        txt_seq_len = regional_text_conditioning.t5_embeddings.shape[1]
+
+        # In the attention blocks, the txt seq and img seq are concatenated and then attention is applied.
+        # Concatenation happens in the following order: [txt_seq, img_seq].
+        # There are 4 portions of the attention mask to consider as we prepare it:
+        # 1. txt attends to itself
+        # 2. txt attends to corresponding regional img
+        # 3. regional img attends to corresponding txt
+        # 4. regional img attends to itself
+
+        # Initialize empty attention mask.
+        regional_attention_mask = torch.zeros(
+            (txt_seq_len + img_seq_len, txt_seq_len + img_seq_len), device=device, dtype=torch.float16
+        )
+
+        for image_mask, t5_embedding_range in zip(
+            regional_text_conditioning.image_masks, regional_text_conditioning.t5_embedding_ranges, strict=True
+        ):
+            # 1. txt attends to itself
+            regional_attention_mask[
+                t5_embedding_range.start : t5_embedding_range.end, t5_embedding_range.start : t5_embedding_range.end
+            ] = 1.0
+
+            if image_mask is not None:
+                # 2. txt attends to corresponding regional img
+                # Note that we reshape to (1, img_seq_len) to ensure broadcasting works as desired.
+                regional_attention_mask[t5_embedding_range.start : t5_embedding_range.end, txt_seq_len:] = (
+                    image_mask.view(1, img_seq_len)
+                )
+
+                # 3. regional img attends to corresponding txt
+                # Note that we reshape to (img_seq_len, 1) to ensure broadcasting works as desired.
+                regional_attention_mask[txt_seq_len:, t5_embedding_range.start : t5_embedding_range.end] = (
+                    image_mask.view(img_seq_len, 1)
+                )
+
+                # 4. regional img attends to itself
+                image_mask = image_mask.view(img_seq_len, 1)
+                regional_attention_mask[txt_seq_len:, txt_seq_len:] += image_mask @ image_mask.T
+            else:
+                # We don't allow attention between non-background image regions and global prompts. This helps to ensure
+                # that regions focus on their local prompts. We do, however, allow attention between background regions
+                # and global prompts. If we didn't do this, then the background regions would not attend to any txt
+                # embeddings, which we found experimentally to cause artifacts.
+
+                # 2. global txt attends to background region
+                # Note that we reshape to (1, img_seq_len) to ensure broadcasting works as desired.
+                regional_attention_mask[t5_embedding_range.start : t5_embedding_range.end, txt_seq_len:] = (
+                    background_region_mask.view(1, img_seq_len)
+                )
+
+                # 3. background region attends to global txt
+                # Note that we reshape to (img_seq_len, 1) to ensure broadcasting works as desired.
+                regional_attention_mask[txt_seq_len:, t5_embedding_range.start : t5_embedding_range.end] = (
+                    background_region_mask.view(img_seq_len, 1)
+                )
+
+        # Allow background regions to attend to themselves.
+        regional_attention_mask[txt_seq_len:, txt_seq_len:] += background_region_mask.view(img_seq_len, 1)
+        regional_attention_mask[txt_seq_len:, txt_seq_len:] += background_region_mask.view(1, img_seq_len)
+
+        # Convert attention mask to boolean.
+        regional_attention_mask = regional_attention_mask > 0.5
+
+        return regional_attention_mask
+
+    @classmethod
+    def _concat_regional_text_conditioning(
+        cls,
+        text_conditionings: list[FluxTextConditioning],
+    ) -> FluxRegionalTextConditioning:
+        """Concatenate regional text conditioning data into a single conditioning tensor (with associated masks)."""
+        concat_t5_embeddings: list[torch.Tensor] = []
+        concat_t5_embedding_ranges: list[Range] = []
+        image_masks: list[torch.Tensor | None] = []
+
+        # Choose global CLIP embedding.
+        # Use the first global prompt's CLIP embedding as the global CLIP embedding. If there is no global prompt, use
+        # the first prompt's CLIP embedding.
+        global_clip_embedding: torch.Tensor = text_conditionings[0].clip_embeddings
+        for text_conditioning in text_conditionings:
+            if text_conditioning.mask is None:
+                global_clip_embedding = text_conditioning.clip_embeddings
+                break
+
+        cur_t5_embedding_len = 0
+        for text_conditioning in text_conditionings:
+            concat_t5_embeddings.append(text_conditioning.t5_embeddings)
+
+            concat_t5_embedding_ranges.append(
+                Range(start=cur_t5_embedding_len, end=cur_t5_embedding_len + text_conditioning.t5_embeddings.shape[1])
+            )
+
+            image_masks.append(text_conditioning.mask)
+
+            cur_t5_embedding_len += text_conditioning.t5_embeddings.shape[1]
+
+        t5_embeddings = torch.cat(concat_t5_embeddings, dim=1)
+
+        # Initialize the txt_ids tensor.
+        pos_bs, pos_t5_seq_len, _ = t5_embeddings.shape
+        t5_txt_ids = torch.zeros(
+            pos_bs, pos_t5_seq_len, 3, dtype=t5_embeddings.dtype, device=TorchDevice.choose_torch_device()
+        )
+
+        return FluxRegionalTextConditioning(
+            t5_embeddings=t5_embeddings,
+            clip_embeddings=global_clip_embedding,
+            t5_txt_ids=t5_txt_ids,
+            image_masks=image_masks,
+            t5_embedding_ranges=concat_t5_embedding_ranges,
+        )
+
+    @staticmethod
+    def preprocess_regional_prompt_mask(
+        mask: Optional[torch.Tensor], packed_height: int, packed_width: int, dtype: torch.dtype, device: torch.device
+    ) -> torch.Tensor:
+        """Preprocess a regional prompt mask to match the target height and width.
+        If mask is None, returns a mask of all ones with the target height and width.
+        If mask is not None, resizes the mask to the target height and width using 'nearest' interpolation.
+
+        packed_height and packed_width are the target height and width of the mask in the 'packed' latent space.
+
+        Returns:
+            torch.Tensor: The processed mask. shape: (1, 1, packed_height * packed_width).
+        """
+
+        if mask is None:
+            return torch.ones((1, 1, packed_height * packed_width), dtype=dtype, device=device)
+
+        mask = to_standard_float_mask(mask, out_dtype=dtype)
+
+        tf = torchvision.transforms.Resize(
+            (packed_height, packed_width), interpolation=torchvision.transforms.InterpolationMode.NEAREST
+        )
+
+        # Add a batch dimension to the mask, because torchvision expects shape (batch, channels, h, w).
+        mask = mask.unsqueeze(0)  # Shape: (1, h, w) -> (1, 1, h, w)
+        resized_mask = tf(mask)
+
+        # Flatten the height and width dimensions into a single image_seq_len dimension.
+        return resized_mask.flatten(start_dim=2)

invokeai/backend/flux/ip_adapter/state_dict_utils.py

@@ -41,10 +41,12 @@ def infer_xlabs_ip_adapter_params_from_state_dict(state_dict: dict[str, torch.Te
     hidden_dim = state_dict["double_blocks.0.processor.ip_adapter_double_stream_k_proj.weight"].shape[0]
     context_dim = state_dict["double_blocks.0.processor.ip_adapter_double_stream_k_proj.weight"].shape[1]
     clip_embeddings_dim = state_dict["ip_adapter_proj_model.proj.weight"].shape[1]
+    clip_extra_context_tokens = state_dict["ip_adapter_proj_model.proj.weight"].shape[0] // context_dim
 
     return XlabsIpAdapterParams(
         num_double_blocks=num_double_blocks,
         context_dim=context_dim,
         hidden_dim=hidden_dim,
         clip_embeddings_dim=clip_embeddings_dim,
+        clip_extra_context_tokens=clip_extra_context_tokens,
     )

invokeai/backend/flux/ip_adapter/xlabs_ip_adapter_flux.py

@@ -31,13 +31,16 @@ class XlabsIpAdapterParams:
     hidden_dim: int
 
     clip_embeddings_dim: int
+    clip_extra_context_tokens: int
 
 
 class XlabsIpAdapterFlux(torch.nn.Module):
     def __init__(self, params: XlabsIpAdapterParams):
         super().__init__()
         self.image_proj = ImageProjModel(
-            cross_attention_dim=params.context_dim, clip_embeddings_dim=params.clip_embeddings_dim
+            cross_attention_dim=params.context_dim,
+            clip_embeddings_dim=params.clip_embeddings_dim,
+            clip_extra_context_tokens=params.clip_extra_context_tokens,
         )
         self.ip_adapter_double_blocks = IPAdapterDoubleBlocks(
             num_double_blocks=params.num_double_blocks, context_dim=params.context_dim, hidden_dim=params.hidden_dim

invokeai/backend/flux/math.py

@@ -5,10 +5,10 @@ from einops import rearrange
 from torch import Tensor
 
 
-def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
+def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, attn_mask: Tensor | None = None) -> Tensor:
     q, k = apply_rope(q, k, pe)
 
-    x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+    x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
     x = rearrange(x, "B H L D -> B L (H D)")
 
     return x
@@ -24,12 +24,12 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     out = torch.einsum("...n,d->...nd", pos, omega)
     out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
     out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
-    return out.float()
+    return out.to(dtype=pos.dtype, device=pos.device)
 
 
 def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
-    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
-    xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+    xq_ = xq.view(*xq.shape[:-1], -1, 1, 2)
+    xk_ = xk.view(*xk.shape[:-1], -1, 1, 2)
     xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
     xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
-    return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
+    return xq_out.view(*xq.shape), xk_out.view(*xk.shape)

invokeai/backend/flux/model.py

@@ -5,7 +5,11 @@ from dataclasses import dataclass
 import torch
 from torch import Tensor, nn
 
-from invokeai.backend.flux.custom_block_processor import CustomDoubleStreamBlockProcessor
+from invokeai.backend.flux.custom_block_processor import (
+    CustomDoubleStreamBlockProcessor,
+    CustomSingleStreamBlockProcessor,
+)
+from invokeai.backend.flux.extensions.regional_prompting_extension import RegionalPromptingExtension
 from invokeai.backend.flux.extensions.xlabs_ip_adapter_extension import XLabsIPAdapterExtension
 from invokeai.backend.flux.modules.layers import (
     DoubleStreamBlock,
@@ -95,6 +99,7 @@ class Flux(nn.Module):
         controlnet_double_block_residuals: list[Tensor] | None,
         controlnet_single_block_residuals: list[Tensor] | None,
         ip_adapter_extensions: list[XLabsIPAdapterExtension],
+        regional_prompting_extension: RegionalPromptingExtension,
     ) -> Tensor:
         if img.ndim != 3 or txt.ndim != 3:
             raise ValueError("Input img and txt tensors must have 3 dimensions.")
@@ -117,7 +122,6 @@ class Flux(nn.Module):
             assert len(controlnet_double_block_residuals) == len(self.double_blocks)
         for block_index, block in enumerate(self.double_blocks):
             assert isinstance(block, DoubleStreamBlock)
-
             img, txt = CustomDoubleStreamBlockProcessor.custom_double_block_forward(
                 timestep_index=timestep_index,
                 total_num_timesteps=total_num_timesteps,
@@ -128,6 +132,7 @@ class Flux(nn.Module):
                 vec=vec,
                 pe=pe,
                 ip_adapter_extensions=ip_adapter_extensions,
+                regional_prompting_extension=regional_prompting_extension,
             )
 
             if controlnet_double_block_residuals is not None:
@@ -140,7 +145,17 @@ class Flux(nn.Module):
             assert len(controlnet_single_block_residuals) == len(self.single_blocks)
 
         for block_index, block in enumerate(self.single_blocks):
-            img = block(img, vec=vec, pe=pe)
+            assert isinstance(block, SingleStreamBlock)
+            img = CustomSingleStreamBlockProcessor.custom_single_block_forward(
+                timestep_index=timestep_index,
+                total_num_timesteps=total_num_timesteps,
+                block_index=block_index,
+                block=block,
+                img=img,
+                vec=vec,
+                pe=pe,
+                regional_prompting_extension=regional_prompting_extension,
+            )
 
             if controlnet_single_block_residuals is not None:
                 img[:, txt.shape[1] :, ...] += controlnet_single_block_residuals[block_index]

invokeai/backend/flux/modules/layers.py

@@ -66,10 +66,7 @@ class RMSNorm(torch.nn.Module):
         self.scale = nn.Parameter(torch.ones(dim))
 
     def forward(self, x: Tensor):
-        x_dtype = x.dtype
-        x = x.float()
-        rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
-        return (x * rrms).to(dtype=x_dtype) * self.scale
+        return torch.nn.functional.rms_norm(x, self.scale.shape, self.scale, eps=1e-6)
 
 
 class QKNorm(torch.nn.Module):

invokeai/backend/flux/text_conditioning.py

@@ -0,0 +1,36 @@
+from dataclasses import dataclass
+
+import torch
+
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import Range
+
+
+@dataclass
+class FluxTextConditioning:
+    t5_embeddings: torch.Tensor
+    clip_embeddings: torch.Tensor
+    # If mask is None, the prompt is a global prompt.
+    mask: torch.Tensor | None
+
+
+@dataclass
+class FluxRegionalTextConditioning:
+    # Concatenated text embeddings.
+    # Shape: (1, concatenated_txt_seq_len, 4096)
+    t5_embeddings: torch.Tensor
+    # Shape: (1, concatenated_txt_seq_len, 3)
+    t5_txt_ids: torch.Tensor
+
+    # Global CLIP embeddings.
+    # Shape: (1, 768)
+    clip_embeddings: torch.Tensor
+
+    # A binary mask indicating the regions of the image that the prompt should be applied to. If None, the prompt is a
+    # global prompt.
+    # image_masks[i] is the mask for the ith prompt.
+    # image_masks[i] has shape (1, image_seq_len) and dtype torch.bool.
+    image_masks: list[torch.Tensor | None]
+
+    # List of ranges that represent the embedding ranges for each mask.
+    # t5_embedding_ranges[i] contains the range of the t5 embeddings that correspond to image_masks[i].
+    t5_embedding_ranges: list[Range]

invokeai/backend/lora/lora_layer_wrappers.py

@@ -0,0 +1,133 @@
+import torch
+
+from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
+from invokeai.backend.lora.layers.concatenated_lora_layer import ConcatenatedLoRALayer
+from invokeai.backend.lora.layers.lora_layer import LoRALayer
+
+
+class LoRASidecarWrapper(torch.nn.Module):
+    def __init__(self, orig_module: torch.nn.Module, lora_layers: list[AnyLoRALayer], lora_weights: list[float]):
+        super().__init__()
+        self._orig_module = orig_module
+        self._lora_layers = lora_layers
+        self._lora_weights = lora_weights
+
+    @property
+    def orig_module(self) -> torch.nn.Module:
+        return self._orig_module
+
+    def add_lora_layer(self, lora_layer: AnyLoRALayer, lora_weight: float):
+        self._lora_layers.append(lora_layer)
+        self._lora_weights.append(lora_weight)
+
+    @torch.no_grad()
+    def _get_lora_patched_parameters(
+        self, orig_params: dict[str, torch.Tensor], lora_layers: list[AnyLoRALayer], lora_weights: list[float]
+    ) -> dict[str, torch.Tensor]:
+        params: dict[str, torch.Tensor] = {}
+        for lora_layer, lora_weight in zip(lora_layers, lora_weights, strict=True):
+            layer_params = lora_layer.get_parameters(self._orig_module)
+            for param_name, param_weight in layer_params.items():
+                if orig_params[param_name].shape != param_weight.shape:
+                    param_weight = param_weight.reshape(orig_params[param_name].shape)
+
+                if param_name not in params:
+                    params[param_name] = param_weight * (lora_layer.scale() * lora_weight)
+                else:
+                    params[param_name] += param_weight * (lora_layer.scale() * lora_weight)
+
+        return params
+
+
+class LoRALinearWrapper(LoRASidecarWrapper):
+    def _lora_linear_forward(self, input: torch.Tensor, lora_layer: LoRALayer, lora_weight: float) -> torch.Tensor:
+        """An optimized implementation of the residual calculation for a Linear LoRALayer."""
+        x = torch.nn.functional.linear(input, lora_layer.down)
+        if lora_layer.mid is not None:
+            x = torch.nn.functional.linear(x, lora_layer.mid)
+        x = torch.nn.functional.linear(x, lora_layer.up, bias=lora_layer.bias)
+        x *= lora_weight * lora_layer.scale()
+        return x
+
+    def _concatenated_lora_forward(
+        self, input: torch.Tensor, concatenated_lora_layer: ConcatenatedLoRALayer, lora_weight: float
+    ) -> torch.Tensor:
+        """An optimized implementation of the residual calculation for a Linear ConcatenatedLoRALayer."""
+        x_chunks: list[torch.Tensor] = []
+        for lora_layer in concatenated_lora_layer.lora_layers:
+            x_chunk = torch.nn.functional.linear(input, lora_layer.down)
+            if lora_layer.mid is not None:
+                x_chunk = torch.nn.functional.linear(x_chunk, lora_layer.mid)
+            x_chunk = torch.nn.functional.linear(x_chunk, lora_layer.up, bias=lora_layer.bias)
+            x_chunk *= lora_weight * lora_layer.scale()
+            x_chunks.append(x_chunk)
+
+        # TODO(ryand): Generalize to support concat_axis != 0.
+        assert concatenated_lora_layer.concat_axis == 0
+        x = torch.cat(x_chunks, dim=-1)
+        return x
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        # Split the LoRA layers into those that have optimized implementations and those that don't.
+        optimized_layer_types = (LoRALayer, ConcatenatedLoRALayer)
+        optimized_layers = [
+            (layer, weight)
+            for layer, weight in zip(self._lora_layers, self._lora_weights, strict=True)
+            if isinstance(layer, optimized_layer_types)
+        ]
+        non_optimized_layers = [
+            (layer, weight)
+            for layer, weight in zip(self._lora_layers, self._lora_weights, strict=True)
+            if not isinstance(layer, optimized_layer_types)
+        ]
+
+        # First, calculate the residual for LoRA layers for which there is an optimized implementation.
+        residual = None
+        for lora_layer, lora_weight in optimized_layers:
+            if isinstance(lora_layer, LoRALayer):
+                added_residual = self._lora_linear_forward(input, lora_layer, lora_weight)
+            elif isinstance(lora_layer, ConcatenatedLoRALayer):
+                added_residual = self._concatenated_lora_forward(input, lora_layer, lora_weight)
+            else:
+                raise ValueError(f"Unsupported LoRA layer type: {type(lora_layer)}")
+
+            if residual is None:
+                residual = added_residual
+            else:
+                residual += added_residual
+
+        # Next, calculate the residuals for the LoRA layers for which there is no optimized implementation.
+        if non_optimized_layers:
+            unoptimized_layers, unoptimized_weights = zip(*non_optimized_layers, strict=True)
+            params = self._get_lora_patched_parameters(
+                orig_params={"weight": self._orig_module.weight, "bias": self._orig_module.bias},
+                lora_layers=unoptimized_layers,
+                lora_weights=unoptimized_weights,
+            )
+            added_residual = torch.nn.functional.linear(input, params["weight"], params.get("bias", None))
+            if residual is None:
+                residual = added_residual
+            else:
+                residual += added_residual
+
+        return self.orig_module(input) + residual
+
+
+class LoRAConv1dWrapper(LoRASidecarWrapper):
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        params = self._get_lora_patched_parameters(
+            orig_params={"weight": self._orig_module.weight, "bias": self._orig_module.bias},
+            lora_layers=self._lora_layers,
+            lora_weights=self._lora_weights,
+        )
+        return self.orig_module(input) + torch.nn.functional.conv1d(input, params["weight"], params.get("bias", None))
+
+
+class LoRAConv2dWrapper(LoRASidecarWrapper):
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        params = self._get_lora_patched_parameters(
+            orig_params={"weight": self._orig_module.weight, "bias": self._orig_module.bias},
+            lora_layers=self._lora_layers,
+            lora_weights=self._lora_weights,
+        )
+        return self.orig_module(input) + torch.nn.functional.conv2d(input, params["weight"], params.get("bias", None))

invokeai/backend/lora/lora_patcher.py

@@ -4,19 +4,126 @@ from typing import Dict, Iterable, Optional, Tuple
 import torch
 
 from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
-from invokeai.backend.lora.layers.concatenated_lora_layer import ConcatenatedLoRALayer
-from invokeai.backend.lora.layers.lora_layer import LoRALayer
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
-from invokeai.backend.lora.sidecar_layers.concatenated_lora.concatenated_lora_linear_sidecar_layer import (
-    ConcatenatedLoRALinearSidecarLayer,
+from invokeai.backend.lora.lora_layer_wrappers import (
+    LoRAConv1dWrapper,
+    LoRAConv2dWrapper,
+    LoRALinearWrapper,
+    LoRASidecarWrapper,
 )
-from invokeai.backend.lora.sidecar_layers.lora.lora_linear_sidecar_layer import LoRALinearSidecarLayer
-from invokeai.backend.lora.sidecar_layers.lora_sidecar_module import LoRASidecarModule
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
 
 
 class LoRAPatcher:
+    @staticmethod
+    @torch.no_grad()
+    @contextmanager
+    def apply_smart_lora_patches(
+        model: torch.nn.Module,
+        patches: Iterable[Tuple[LoRAModelRaw, float]],
+        prefix: str,
+        dtype: torch.dtype,
+        cached_weights: Optional[Dict[str, torch.Tensor]] = None,
+    ):
+        """Apply 'smart' LoRA patching that chooses whether to use direct patching or a sidecar wrapper for each module."""
+
+        # original_weights are stored for unpatching layers that are directly patched.
+        original_weights = OriginalWeightsStorage(cached_weights)
+        # original_modules are stored for unpatching layers that are wrapped in a LoRASidecarWrapper.
+        original_modules: dict[str, torch.nn.Module] = {}
+        try:
+            for patch, patch_weight in patches:
+                LoRAPatcher._apply_smart_lora_patch(
+                    model=model,
+                    prefix=prefix,
+                    patch=patch,
+                    patch_weight=patch_weight,
+                    original_weights=original_weights,
+                    original_modules=original_modules,
+                    dtype=dtype,
+                )
+
+            yield
+        finally:
+            # Restore directly patched layers.
+            for param_key, weight in original_weights.get_changed_weights():
+                model.get_parameter(param_key).copy_(weight)
+
+            # Restore LoRASidecarWrapper modules.
+            # Note: This logic assumes no nested modules in original_modules.
+            for module_key, orig_module in original_modules.items():
+                module_parent_key, module_name = LoRAPatcher._split_parent_key(module_key)
+                parent_module = model.get_submodule(module_parent_key)
+                LoRAPatcher._set_submodule(parent_module, module_name, orig_module)
+
+    @staticmethod
+    @torch.no_grad()
+    def _apply_smart_lora_patch(
+        model: torch.nn.Module,
+        prefix: str,
+        patch: LoRAModelRaw,
+        patch_weight: float,
+        original_weights: OriginalWeightsStorage,
+        original_modules: dict[str, torch.nn.Module],
+        dtype: torch.dtype,
+    ):
+        """Apply a single LoRA patch to a model using the 'smart' patching strategy that chooses whether to use direct
+        patching or a sidecar wrapper for each module.
+        """
+        if patch_weight == 0:
+            return
+
+        # If the layer keys contain a dot, then they are not flattened, and can be directly used to access model
+        # submodules. If the layer keys do not contain a dot, then they are flattened, meaning that all '.' have been
+        # replaced with '_'. Non-flattened keys are preferred, because they allow submodules to be accessed directly
+        # without searching, but some legacy code still uses flattened keys.
+        layer_keys_are_flattened = "." not in next(iter(patch.layers.keys()))
+
+        prefix_len = len(prefix)
+
+        for layer_key, layer in patch.layers.items():
+            if not layer_key.startswith(prefix):
+                continue
+
+            module_key, module = LoRAPatcher._get_submodule(
+                model, layer_key[prefix_len:], layer_key_is_flattened=layer_keys_are_flattened
+            )
+
+            # Decide whether to use direct patching or a sidecar wrapper.
+            # Direct patching is preferred, because it results in better runtime speed.
+            # Reasons to use sidecar patching:
+            # - The module is already wrapped in a LoRASidecarWrapper.
+            # - The module is quantized.
+            # - The module is on the CPU (and we don't want to store a second full copy of the original weights on the
+            #   CPU, since this would double the RAM usage)
+            # NOTE: For now, we don't check if the layer is quantized here. We assume that this is checked in the caller
+            # and that the caller will use the 'apply_lora_wrapper_patches' method if the layer is quantized.
+            # TODO(ryand): Handle the case where we are running without a GPU. Should we set a config flag that allows
+            # forcing full patching even on the CPU?
+            if isinstance(module, LoRASidecarWrapper) or LoRAPatcher._is_any_part_of_layer_on_cpu(module):
+                LoRAPatcher._apply_lora_layer_wrapper_patch(
+                    model=model,
+                    module_to_patch=module,
+                    module_to_patch_key=module_key,
+                    patch=layer,
+                    patch_weight=patch_weight,
+                    original_modules=original_modules,
+                    dtype=dtype,
+                )
+            else:
+                LoRAPatcher._apply_lora_layer_patch(
+                    module_to_patch=module,
+                    module_to_patch_key=module_key,
+                    patch=layer,
+                    patch_weight=patch_weight,
+                    original_weights=original_weights,
+                )
+
+    @staticmethod
+    def _is_any_part_of_layer_on_cpu(layer: torch.nn.Module) -> bool:
+        return any(p.device.type == "cpu" for p in layer.parameters())
+
     @staticmethod
     @torch.no_grad()
     @contextmanager
@@ -40,7 +147,7 @@ class LoRAPatcher:
         original_weights = OriginalWeightsStorage(cached_weights)
         try:
             for patch, patch_weight in patches:
-                LoRAPatcher.apply_lora_patch(
+                LoRAPatcher._apply_lora_patch(
                     model=model,
                     prefix=prefix,
                     patch=patch,
@@ -56,7 +163,7 @@ class LoRAPatcher:
 
     @staticmethod
     @torch.no_grad()
-    def apply_lora_patch(
+    def _apply_lora_patch(
         model: torch.nn.Module,
         prefix: str,
         patch: LoRAModelRaw,
@@ -91,48 +198,67 @@ class LoRAPatcher:
                 model, layer_key[prefix_len:], layer_key_is_flattened=layer_keys_are_flattened
             )
 
-            # All of the LoRA weight calculations will be done on the same device as the module weight.
-            # (Performance will be best if this is a CUDA device.)
-            device = module.weight.device
-            dtype = module.weight.dtype
+            LoRAPatcher._apply_lora_layer_patch(
+                module_to_patch=module,
+                module_to_patch_key=module_key,
+                patch=layer,
+                patch_weight=patch_weight,
+                original_weights=original_weights,
+            )
 
-            layer_scale = layer.scale()
+    @staticmethod
+    @torch.no_grad()
+    def _apply_lora_layer_patch(
+        module_to_patch: torch.nn.Module,
+        module_to_patch_key: str,
+        patch: AnyLoRALayer,
+        patch_weight: float,
+        original_weights: OriginalWeightsStorage,
+    ):
+        # All of the LoRA weight calculations will be done on the same device as the module weight.
+        # (Performance will be best if this is a CUDA device.)
+        device = module_to_patch.weight.device
+        dtype = module_to_patch.weight.dtype
 
-            # We intentionally move to the target device first, then cast. Experimentally, this was found to
-            # be significantly faster for 16-bit CPU tensors being moved to a CUDA device than doing the
-            # same thing in a single call to '.to(...)'.
-            layer.to(device=device)
-            layer.to(dtype=torch.float32)
+        layer_scale = patch.scale()
 
-            # TODO(ryand): Using torch.autocast(...) over explicit casting may offer a speed benefit on CUDA
-            # devices here. Experimentally, it was found to be very slow on CPU. More investigation needed.
-            for param_name, lora_param_weight in layer.get_parameters(module).items():
-                param_key = module_key + "." + param_name
-                module_param = module.get_parameter(param_name)
+        # We intentionally move to the target device first, then cast. Experimentally, this was found to
+        # be significantly faster for 16-bit CPU tensors being moved to a CUDA device than doing the
+        # same thing in a single call to '.to(...)'.
+        patch.to(device=device)
+        patch.to(dtype=torch.float32)
 
-                # Save original weight
-                original_weights.save(param_key, module_param)
+        # TODO(ryand): Using torch.autocast(...) over explicit casting may offer a speed benefit on CUDA
+        # devices here. Experimentally, it was found to be very slow on CPU. More investigation needed.
+        for param_name, lora_param_weight in patch.get_parameters(module_to_patch).items():
+            param_key = module_to_patch_key + "." + param_name
+            module_param = module_to_patch.get_parameter(param_name)
 
-                if module_param.shape != lora_param_weight.shape:
-                    lora_param_weight = lora_param_weight.reshape(module_param.shape)
+            # Save original weight
+            original_weights.save(param_key, module_param)
 
-                lora_param_weight *= patch_weight * layer_scale
-                module_param += lora_param_weight.to(dtype=dtype)
+            if module_param.shape != lora_param_weight.shape:
+                lora_param_weight = lora_param_weight.reshape(module_param.shape)
 
-            layer.to(device=TorchDevice.CPU_DEVICE)
+            lora_param_weight *= patch_weight * layer_scale
+            module_param += lora_param_weight.to(dtype=dtype)
+
+        patch.to(device=TorchDevice.CPU_DEVICE)
 
     @staticmethod
     @torch.no_grad()
     @contextmanager
-    def apply_lora_sidecar_patches(
+    def apply_lora_wrapper_patches(
         model: torch.nn.Module,
         patches: Iterable[Tuple[LoRAModelRaw, float]],
         prefix: str,
         dtype: torch.dtype,
     ):
-        """Apply one or more LoRA sidecar patches to a model within a context manager. Sidecar patches incur some
-        overhead compared to normal LoRA patching, but they allow for LoRA layers to applied to base layers in any
-        quantization format.
+        """Apply one or more LoRA wrapper patches to a model within a context manager. Wrapper patches incur some
+        runtime overhead compared to normal LoRA patching, but they enable:
+        - LoRA layers to be applied to quantized models
+        - LoRA layers to be applied to CPU layers without needing to store a full copy of the original weights (i.e.
+          avoid doubling the memory requirements).
 
         Args:
             model (torch.nn.Module): The model to patch.
@@ -140,14 +266,11 @@ class LoRAPatcher:
                 associated weights. An iterator is used so that the LoRA patches do not need to be loaded into memory
                 all at once.
             prefix (str): The keys in the patches will be filtered to only include weights with this prefix.
-            dtype (torch.dtype): The compute dtype of the sidecar layers. This cannot easily be inferred from the model,
-                since the sidecar layers are typically applied on top of quantized layers whose weight dtype is
-                different from their compute dtype.
         """
         original_modules: dict[str, torch.nn.Module] = {}
         try:
             for patch, patch_weight in patches:
-                LoRAPatcher._apply_lora_sidecar_patch(
+                LoRAPatcher._apply_lora_wrapper_patch(
                     model=model,
                     prefix=prefix,
                     patch=patch,
@@ -165,7 +288,7 @@ class LoRAPatcher:
                 LoRAPatcher._set_submodule(parent_module, module_name, orig_module)
 
     @staticmethod
-    def _apply_lora_sidecar_patch(
+    def _apply_lora_wrapper_patch(
         model: torch.nn.Module,
         patch: LoRAModelRaw,
         patch_weight: float,
@@ -173,7 +296,7 @@ class LoRAPatcher:
         original_modules: dict[str, torch.nn.Module],
         dtype: torch.dtype,
     ):
-        """Apply a single LoRA sidecar patch to a model."""
+        """Apply a single LoRA wrapper patch to a model."""
 
         if patch_weight == 0:
             return
@@ -194,28 +317,47 @@ class LoRAPatcher:
                 model, layer_key[prefix_len:], layer_key_is_flattened=layer_keys_are_flattened
             )
 
-            # Initialize the LoRA sidecar layer.
-            lora_sidecar_layer = LoRAPatcher._initialize_lora_sidecar_layer(module, layer, patch_weight)
+            LoRAPatcher._apply_lora_layer_wrapper_patch(
+                model=model,
+                module_to_patch=module,
+                module_to_patch_key=module_key,
+                patch=layer,
+                patch_weight=patch_weight,
+                original_modules=original_modules,
+                dtype=dtype,
+            )
 
-            # Replace the original module with a LoRASidecarModule if it has not already been done.
-            if module_key in original_modules:
-                # The module has already been patched with a LoRASidecarModule. Append to it.
-                assert isinstance(module, LoRASidecarModule)
-                lora_sidecar_module = module
-            else:
-                # The module has not yet been patched with a LoRASidecarModule. Create one.
-                lora_sidecar_module = LoRASidecarModule(module, [])
-                original_modules[module_key] = module
-                module_parent_key, module_name = LoRAPatcher._split_parent_key(module_key)
-                module_parent = model.get_submodule(module_parent_key)
-                LoRAPatcher._set_submodule(module_parent, module_name, lora_sidecar_module)
+    @staticmethod
+    @torch.no_grad()
+    def _apply_lora_layer_wrapper_patch(
+        model: torch.nn.Module,
+        module_to_patch: torch.nn.Module,
+        module_to_patch_key: str,
+        patch: AnyLoRALayer,
+        patch_weight: float,
+        original_modules: dict[str, torch.nn.Module],
+        dtype: torch.dtype,
+    ):
+        """Apply a single LoRA wrapper patch to a model."""
 
-            # Move the LoRA sidecar layer to the same device/dtype as the orig module.
-            # TODO(ryand): Experiment with moving to the device first, then casting. This could be faster.
-            lora_sidecar_layer.to(device=lora_sidecar_module.orig_module.weight.device, dtype=dtype)
+        # Replace the original module with a LoRASidecarWrapper if it has not already been done.
+        if not isinstance(module_to_patch, LoRASidecarWrapper):
+            lora_wrapper_layer = LoRAPatcher._initialize_lora_wrapper_layer(module_to_patch)
+            original_modules[module_to_patch_key] = module_to_patch
+            module_parent_key, module_name = LoRAPatcher._split_parent_key(module_to_patch_key)
+            module_parent = model.get_submodule(module_parent_key)
+            LoRAPatcher._set_submodule(module_parent, module_name, lora_wrapper_layer)
+            orig_module = module_to_patch
+        else:
+            assert module_to_patch_key in original_modules
+            lora_wrapper_layer = module_to_patch
+            orig_module = module_to_patch.orig_module
 
-            # Add the LoRA sidecar layer to the LoRASidecarModule.
-            lora_sidecar_module.add_lora_layer(lora_sidecar_layer)
+        # Move the LoRA layer to the same device/dtype as the orig module.
+        patch.to(device=orig_module.weight.device, dtype=dtype)
+
+        # Add the LoRA wrapper layer to the LoRASidecarWrapper.
+        lora_wrapper_layer.add_lora_layer(patch, patch_weight)
 
     @staticmethod
     def _split_parent_key(module_key: str) -> tuple[str, str]:
@@ -236,17 +378,13 @@ class LoRAPatcher:
             raise ValueError(f"Invalid module key: {module_key}")
 
     @staticmethod
-    def _initialize_lora_sidecar_layer(orig_layer: torch.nn.Module, lora_layer: AnyLoRALayer, patch_weight: float):
-        # TODO(ryand): Add support for more original layer types and LoRA layer types.
-        if isinstance(orig_layer, torch.nn.Linear) or (
-            isinstance(orig_layer, LoRASidecarModule) and isinstance(orig_layer.orig_module, torch.nn.Linear)
-        ):
-            if isinstance(lora_layer, LoRALayer):
-                return LoRALinearSidecarLayer(lora_layer=lora_layer, weight=patch_weight)
-            elif isinstance(lora_layer, ConcatenatedLoRALayer):
-                return ConcatenatedLoRALinearSidecarLayer(concatenated_lora_layer=lora_layer, weight=patch_weight)
-            else:
-                raise ValueError(f"Unsupported Linear LoRA layer type: {type(lora_layer)}")
+    def _initialize_lora_wrapper_layer(orig_layer: torch.nn.Module):
+        if isinstance(orig_layer, torch.nn.Linear):
+            return LoRALinearWrapper(orig_layer, [], [])
+        elif isinstance(orig_layer, torch.nn.Conv1d):
+            return LoRAConv1dWrapper(orig_layer, [], [])
+        elif isinstance(orig_layer, torch.nn.Conv2d):
+            return LoRAConv2dWrapper(orig_layer, [], [])
         else:
             raise ValueError(f"Unsupported layer type: {type(orig_layer)}")
 

invokeai/backend/lora/sidecar_layers/concatenated_lora/concatenated_lora_linear_sidecar_layer.py

@@ -1,34 +0,0 @@
-import torch
-
-from invokeai.backend.lora.layers.concatenated_lora_layer import ConcatenatedLoRALayer
-
-
-class ConcatenatedLoRALinearSidecarLayer(torch.nn.Module):
-    def __init__(
-        self,
-        concatenated_lora_layer: ConcatenatedLoRALayer,
-        weight: float,
-    ):
-        super().__init__()
-
-        self._concatenated_lora_layer = concatenated_lora_layer
-        self._weight = weight
-
-    def forward(self, input: torch.Tensor) -> torch.Tensor:
-        x_chunks: list[torch.Tensor] = []
-        for lora_layer in self._concatenated_lora_layer.lora_layers:
-            x_chunk = torch.nn.functional.linear(input, lora_layer.down)
-            if lora_layer.mid is not None:
-                x_chunk = torch.nn.functional.linear(x_chunk, lora_layer.mid)
-            x_chunk = torch.nn.functional.linear(x_chunk, lora_layer.up, bias=lora_layer.bias)
-            x_chunk *= self._weight * lora_layer.scale()
-            x_chunks.append(x_chunk)
-
-        # TODO(ryand): Generalize to support concat_axis != 0.
-        assert self._concatenated_lora_layer.concat_axis == 0
-        x = torch.cat(x_chunks, dim=-1)
-        return x
-
-    def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
-        self._concatenated_lora_layer.to(device=device, dtype=dtype)
-        return self

invokeai/backend/lora/sidecar_layers/lora/lora_linear_sidecar_layer.py

@@ -1,27 +0,0 @@
-import torch
-
-from invokeai.backend.lora.layers.lora_layer import LoRALayer
-
-
-class LoRALinearSidecarLayer(torch.nn.Module):
-    def __init__(
-        self,
-        lora_layer: LoRALayer,
-        weight: float,
-    ):
-        super().__init__()
-
-        self._lora_layer = lora_layer
-        self._weight = weight
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = torch.nn.functional.linear(x, self._lora_layer.down)
-        if self._lora_layer.mid is not None:
-            x = torch.nn.functional.linear(x, self._lora_layer.mid)
-        x = torch.nn.functional.linear(x, self._lora_layer.up, bias=self._lora_layer.bias)
-        x *= self._weight * self._lora_layer.scale()
-        return x
-
-    def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
-        self._lora_layer.to(device=device, dtype=dtype)
-        return self

invokeai/backend/lora/sidecar_layers/lora_sidecar_module.py

@@ -1,24 +0,0 @@
-import torch
-
-
-class LoRASidecarModule(torch.nn.Module):
-    """A LoRA sidecar module that wraps an original module and adds LoRA layers to it."""
-
-    def __init__(self, orig_module: torch.nn.Module, lora_layers: list[torch.nn.Module]):
-        super().__init__()
-        self.orig_module = orig_module
-        self._lora_layers = lora_layers
-
-    def add_lora_layer(self, lora_layer: torch.nn.Module):
-        self._lora_layers.append(lora_layer)
-
-    def forward(self, input: torch.Tensor) -> torch.Tensor:
-        x = self.orig_module(input)
-        for lora_layer in self._lora_layers:
-            x += lora_layer(input)
-        return x
-
-    def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
-        self._orig_module.to(device=device, dtype=dtype)
-        for lora_layer in self._lora_layers:
-            lora_layer.to(device=device, dtype=dtype)

invokeai/backend/model_manager/load/__init__.py

@@ -8,7 +8,7 @@ from pathlib import Path
 
 from invokeai.backend.model_manager.load.load_base import LoadedModel, LoadedModelWithoutConfig, ModelLoaderBase
 from invokeai.backend.model_manager.load.load_default import ModelLoader
-from invokeai.backend.model_manager.load.model_cache.model_cache_default import ModelCache
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry, ModelLoaderRegistryBase
 
 # This registers the subclasses that implement loaders of specific model types

invokeai/backend/model_manager/load/load_base.py

@@ -5,7 +5,6 @@ Base class for model loading in InvokeAI.
 
 from abc import ABC, abstractmethod
 from contextlib import contextmanager
-from dataclasses import dataclass
 from logging import Logger
 from pathlib import Path
 from typing import Any, Dict, Generator, Optional, Tuple
@@ -18,19 +17,17 @@ from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     SubModelType,
 )
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase, ModelLockerBase
+from invokeai.backend.model_manager.load.model_cache.cache_record import CacheRecord
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 
 
-@dataclass
 class LoadedModelWithoutConfig:
-    """
-    Context manager object that mediates transfer from RAM<->VRAM.
+    """Context manager object that mediates transfer from RAM<->VRAM.
 
     This is a context manager object that has two distinct APIs:
 
     1. Older API (deprecated):
-    Use the LoadedModel object directly as a context manager.
-    It will move the model into VRAM (on CUDA devices), and
+    Use the LoadedModel object directly as a context manager.  It will move the model into VRAM (on CUDA devices), and
     return the model in a form suitable for passing to torch.
     Example:
     ```
@@ -40,13 +37,9 @@ class LoadedModelWithoutConfig:
     ```
 
     2. Newer API (recommended):
-    Call the LoadedModel's `model_on_device()` method in a
-    context. It returns a tuple consisting of a copy of
-    the model's state dict in CPU RAM followed by a copy
-    of the model in VRAM. The state dict is provided to allow
-    LoRAs and other model patchers to return the model to
-    its unpatched state without expensive copy and restore
-    operations.
+    Call the LoadedModel's `model_on_device()` method in a context. It returns a tuple consisting of a copy of the
+    model's state dict in CPU RAM followed by a copy of the model in VRAM. The state dict is provided to allow LoRAs and
+    other model patchers to return the model to its unpatched state without expensive copy and restore operations.
 
     Example:
     ```
@@ -55,43 +48,42 @@ class LoadedModelWithoutConfig:
         image = vae.decode(latents)[0]
     ```
 
-    The state_dict should be treated as a read-only object and
-    never modified. Also be aware that some loadable models do
-    not have a state_dict, in which case this value will be None.
+    The state_dict should be treated as a read-only object and never modified. Also be aware that some loadable models
+    do not have a state_dict, in which case this value will be None.
     """
 
-    _locker: ModelLockerBase
+    def __init__(self, cache_record: CacheRecord, cache: ModelCache):
+        self._cache_record = cache_record
+        self._cache = cache
 
     def __enter__(self) -> AnyModel:
-        """Context entry."""
-        self._locker.lock()
+        self._cache.lock(self._cache_record.key)
         return self.model
 
     def __exit__(self, *args: Any, **kwargs: Any) -> None:
-        """Context exit."""
-        self._locker.unlock()
+        self._cache.unlock(self._cache_record.key)
 
     @contextmanager
     def model_on_device(self) -> Generator[Tuple[Optional[Dict[str, torch.Tensor]], AnyModel], None, None]:
         """Return a tuple consisting of the model's state dict (if it exists) and the locked model on execution device."""
-        locked_model = self._locker.lock()
+        self._cache.lock(self._cache_record.key)
         try:
-            state_dict = self._locker.get_state_dict()
-            yield (state_dict, locked_model)
+            yield (self._cache_record.cached_model.get_cpu_state_dict(), self._cache_record.cached_model.model)
         finally:
-            self._locker.unlock()
+            self._cache.unlock(self._cache_record.key)
 
     @property
     def model(self) -> AnyModel:
         """Return the model without locking it."""
-        return self._locker.model
+        return self._cache_record.cached_model.model
 
 
-@dataclass
 class LoadedModel(LoadedModelWithoutConfig):
     """Context manager object that mediates transfer from RAM<->VRAM."""
 
-    config: Optional[AnyModelConfig] = None
+    def __init__(self, config: Optional[AnyModelConfig], cache_record: CacheRecord, cache: ModelCache):
+        super().__init__(cache_record=cache_record, cache=cache)
+        self.config = config
 
 
 # TODO(MM2):
@@ -110,7 +102,7 @@ class ModelLoaderBase(ABC):
         self,
         app_config: InvokeAIAppConfig,
         logger: Logger,
-        ram_cache: ModelCacheBase[AnyModel],
+        ram_cache: ModelCache,
     ):
         """Initialize the loader."""
         pass
@@ -138,6 +130,6 @@ class ModelLoaderBase(ABC):
 
     @property
     @abstractmethod
-    def ram_cache(self) -> ModelCacheBase[AnyModel]:
+    def ram_cache(self) -> ModelCache:
         """Return the ram cache associated with this loader."""
         pass

invokeai/backend/model_manager/load/load_default.py

@@ -14,7 +14,8 @@ from invokeai.backend.model_manager import (
 )
 from invokeai.backend.model_manager.config import DiffusersConfigBase
 from invokeai.backend.model_manager.load.load_base import LoadedModel, ModelLoaderBase
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase, ModelLockerBase
+from invokeai.backend.model_manager.load.model_cache.cache_record import CacheRecord
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache, get_model_cache_key
 from invokeai.backend.model_manager.load.model_util import calc_model_size_by_fs
 from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
 from invokeai.backend.util.devices import TorchDevice
@@ -28,7 +29,7 @@ class ModelLoader(ModelLoaderBase):
         self,
         app_config: InvokeAIAppConfig,
         logger: Logger,
-        ram_cache: ModelCacheBase[AnyModel],
+        ram_cache: ModelCache,
     ):
         """Initialize the loader."""
         self._app_config = app_config
@@ -54,11 +55,11 @@ class ModelLoader(ModelLoaderBase):
             raise InvalidModelConfigException(f"Files for model '{model_config.name}' not found at {model_path}")
 
         with skip_torch_weight_init():
-            locker = self._load_and_cache(model_config, submodel_type)
-        return LoadedModel(config=model_config, _locker=locker)
+            cache_record = self._load_and_cache(model_config, submodel_type)
+        return LoadedModel(config=model_config, cache_record=cache_record, cache=self._ram_cache)
 
     @property
-    def ram_cache(self) -> ModelCacheBase[AnyModel]:
+    def ram_cache(self) -> ModelCache:
         """Return the ram cache associated with this loader."""
         return self._ram_cache
 
@@ -66,10 +67,10 @@ class ModelLoader(ModelLoaderBase):
         model_base = self._app_config.models_path
         return (model_base / config.path).resolve()
 
-    def _load_and_cache(self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> ModelLockerBase:
+    def _load_and_cache(self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> CacheRecord:
         stats_name = ":".join([config.base, config.type, config.name, (submodel_type or "")])
         try:
-            return self._ram_cache.get(config.key, submodel_type, stats_name=stats_name)
+            return self._ram_cache.get(key=get_model_cache_key(config.key, submodel_type), stats_name=stats_name)
         except IndexError:
             pass
 
@@ -78,16 +79,11 @@ class ModelLoader(ModelLoaderBase):
         loaded_model = self._load_model(config, submodel_type)
 
         self._ram_cache.put(
-            config.key,
-            submodel_type=submodel_type,
+            get_model_cache_key(config.key, submodel_type),
             model=loaded_model,
         )
 
-        return self._ram_cache.get(
-            key=config.key,
-            submodel_type=submodel_type,
-            stats_name=stats_name,
-        )
+        return self._ram_cache.get(key=get_model_cache_key(config.key, submodel_type), stats_name=stats_name)
 
     def get_size_fs(
         self, config: AnyModelConfig, model_path: Path, submodel_type: Optional[SubModelType] = None

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

@@ -1,6 +0,0 @@
-"""Init file for ModelCache."""
-
-from .model_cache_base import ModelCacheBase, CacheStats  # noqa F401
-from .model_cache_default import ModelCache  # noqa F401
-
-_all__ = ["ModelCacheBase", "ModelCache", "CacheStats"]

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

@@ -0,0 +1,31 @@
+from dataclasses import dataclass
+
+from invokeai.backend.model_manager.load.model_cache.cached_model.cached_model_only_full_load import (
+    CachedModelOnlyFullLoad,
+)
+from invokeai.backend.model_manager.load.model_cache.cached_model.cached_model_with_partial_load import (
+    CachedModelWithPartialLoad,
+)
+
+
+@dataclass
+class CacheRecord:
+    """A class that represents a model in the model cache."""
+
+    # Cache key.
+    key: str
+    # Model in memory.
+    cached_model: CachedModelWithPartialLoad | CachedModelOnlyFullLoad
+    # If locks > 0, the model is actively being used, so we should do our best to keep it on the compute device.
+    _locks: int = 0
+
+    def lock(self) -> None:
+        self._locks += 1
+
+    def unlock(self) -> None:
+        self._locks -= 1
+        assert self._locks >= 0
+
+    @property
+    def is_locked(self) -> bool:
+        return self._locks > 0

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

@@ -0,0 +1,15 @@
+from dataclasses import dataclass, field
+from typing import Dict
+
+
+@dataclass
+class CacheStats(object):
+    """Collect statistics on cache performance."""
+
+    hits: int = 0  # cache hits
+    misses: int = 0  # cache misses
+    high_watermark: int = 0  # amount of cache used
+    in_cache: int = 0  # number of models in cache
+    cleared: int = 0  # number of models cleared to make space
+    cache_size: int = 0  # total size of cache
+    loaded_model_sizes: Dict[str, int] = field(default_factory=dict)

invokeai/backend/model_manager/load/model_cache/cached_model/cached_model_only_full_load.py

@@ -0,0 +1,81 @@
+from typing import Any
+
+import torch
+
+
+class CachedModelOnlyFullLoad:
+    """A wrapper around a PyTorch model to handle full loads and unloads between the CPU and the compute device.
+
+    Note: "VRAM" is used throughout this class to refer to the memory on the compute device. It could be CUDA memory,
+    MPS memory, etc.
+    """
+
+    def __init__(self, model: torch.nn.Module | Any, compute_device: torch.device, total_bytes: int):
+        """Initialize a CachedModelOnlyFullLoad.
+
+        Args:
+            model (torch.nn.Module | Any): The model to wrap. Should be on the CPU.
+            compute_device (torch.device): The compute device to move the model to.
+            total_bytes (int): The total size (in bytes) of all the weights in the model.
+        """
+        # model is often a torch.nn.Module, but could be any model type. Throughout this class, we handle both cases.
+        self._model = model
+        self._compute_device = compute_device
+        self._total_bytes = total_bytes
+        self._is_in_vram = False
+
+    @property
+    def model(self) -> torch.nn.Module:
+        return self._model
+
+    def get_cpu_state_dict(self) -> dict[str, torch.Tensor] | None:
+        """Get a read-only copy of the model's state dict in RAM."""
+        # TODO(ryand): Document this better and implement it.
+        return None
+
+    def total_bytes(self) -> int:
+        """Get the total size (in bytes) of all the weights in the model."""
+        return self._total_bytes
+
+    def cur_vram_bytes(self) -> int:
+        """Get the size (in bytes) of the weights that are currently in VRAM."""
+        if self._is_in_vram:
+            return self._total_bytes
+        else:
+            return 0
+
+    def is_in_vram(self) -> bool:
+        """Return true if the model is currently in VRAM."""
+        return self._is_in_vram
+
+    def full_load_to_vram(self) -> int:
+        """Load all weights into VRAM (if supported by the model).
+
+        Returns:
+            The number of bytes loaded into VRAM.
+        """
+        if self._is_in_vram:
+            # Already in VRAM.
+            return 0
+
+        if not hasattr(self._model, "to"):
+            # Model doesn't support moving to a device.
+            return 0
+
+        self._model.to(self._compute_device)
+        self._is_in_vram = True
+        return self._total_bytes
+
+    def full_unload_from_vram(self) -> int:
+        """Unload all weights from VRAM.
+
+        Returns:
+            The number of bytes unloaded from VRAM.
+        """
+        if not self._is_in_vram:
+            # Already in RAM.
+            return 0
+
+        self._model.to("cpu")
+        self._is_in_vram = False
+        return self._total_bytes

invokeai/backend/model_manager/load/model_cache/cached_model/cached_model_with_partial_load.py

@@ -0,0 +1,150 @@
+import itertools
+
+import torch
+
+from invokeai.backend.model_manager.load.model_cache.torch_function_autocast_context import (
+    add_autocast_to_module_forward,
+)
+from invokeai.backend.util.calc_tensor_size import calc_tensor_size
+
+
+def set_nested_attr(obj: object, attr: str, value: object):
+    """A helper function that extends setattr() to support nested attributes.
+
+    Example:
+        set_nested_attr(model, "module.encoder.conv1.weight", new_conv1_weight)
+    """
+    attrs = attr.split(".")
+    for attr in attrs[:-1]:
+        obj = getattr(obj, attr)
+    setattr(obj, attrs[-1], value)
+
+
+class CachedModelWithPartialLoad:
+    """A wrapper around a PyTorch model to handle partial loads and unloads between the CPU and the compute device.
+
+    Note: "VRAM" is used throughout this class to refer to the memory on the compute device. It could be CUDA memory,
+    MPS memory, etc.
+    """
+
+    def __init__(self, model: torch.nn.Module, compute_device: torch.device):
+        self._model = model
+        self._compute_device = compute_device
+
+        # A CPU read-only copy of the model's state dict.
+        self._cpu_state_dict: dict[str, torch.Tensor] = model.state_dict()
+
+        # Monkey-patch the model to add autocasting to the model's forward method.
+        add_autocast_to_module_forward(model, compute_device)
+
+        self._total_bytes = sum(
+            calc_tensor_size(p) for p in itertools.chain(self._model.parameters(), self._model.buffers())
+        )
+        self._cur_vram_bytes: int | None = None
+
+    @property
+    def model(self) -> torch.nn.Module:
+        return self._model
+
+    def get_cpu_state_dict(self) -> dict[str, torch.Tensor] | None:
+        """Get a read-only copy of the model's state dict in RAM."""
+        # TODO(ryand): Document this better.
+        return self._cpu_state_dict
+
+    def total_bytes(self) -> int:
+        """Get the total size (in bytes) of all the weights in the model."""
+        return self._total_bytes
+
+    def cur_vram_bytes(self) -> int:
+        """Get the size (in bytes) of the weights that are currently in VRAM."""
+        if self._cur_vram_bytes is None:
+            self._cur_vram_bytes = sum(
+                calc_tensor_size(p)
+                for p in itertools.chain(self._model.parameters(), self._model.buffers())
+                if p.device.type == self._compute_device.type
+            )
+        return self._cur_vram_bytes
+
+    def full_load_to_vram(self) -> int:
+        """Load all weights into VRAM."""
+        return self.partial_load_to_vram(self.total_bytes())
+
+    def full_unload_from_vram(self) -> int:
+        """Unload all weights from VRAM."""
+        return self.partial_unload_from_vram(self.total_bytes())
+
+    @torch.no_grad()
+    def partial_load_to_vram(self, vram_bytes_to_load: int) -> int:
+        """Load more weights into VRAM without exceeding vram_bytes_to_load.
+
+        Returns:
+            The number of bytes loaded into VRAM.
+        """
+        vram_bytes_loaded = 0
+
+        for key, param in itertools.chain(self._model.named_parameters(), self._model.named_buffers()):
+            # Skip parameters that are already on the compute device.
+            if param.device.type == self._compute_device.type:
+                continue
+
+            # Check the size of the parameter.
+            param_size = calc_tensor_size(param)
+            if vram_bytes_loaded + param_size > vram_bytes_to_load:
+                # TODO(ryand): Should we just break here? If we couldn't fit this parameter into VRAM, is it really
+                # worth continuing to search for a smaller parameter that would fit?
+                continue
+
+            # Copy the parameter to the compute device.
+            # We use the 'overwrite' strategy from torch.nn.Module._apply().
+            # TODO(ryand): For some edge cases (e.g. quantized models?), we may need to support other strategies (e.g.
+            # swap).
+            if isinstance(param, torch.nn.Parameter):
+                assert param.is_leaf
+                out_param = torch.nn.Parameter(
+                    param.to(self._compute_device, copy=True), requires_grad=param.requires_grad
+                )
+                set_nested_attr(self._model, key, out_param)
+                # We did not port the param.grad handling from torch.nn.Module._apply(), because we do not expect to be
+                # handling gradients. We assert that this assumption is true.
+                assert param.grad is None
+            else:
+                # Handle buffers.
+                set_nested_attr(self._model, key, param.to(self._compute_device, copy=True))
+
+            vram_bytes_loaded += param_size
+
+        if self._cur_vram_bytes is not None:
+            self._cur_vram_bytes += vram_bytes_loaded
+
+        return vram_bytes_loaded
+
+    @torch.no_grad()
+    def partial_unload_from_vram(self, vram_bytes_to_free: int) -> int:
+        """Unload weights from VRAM until vram_bytes_to_free bytes are freed. Or the entire model is unloaded.
+
+        Returns:
+            The number of bytes unloaded from VRAM.
+        """
+        vram_bytes_freed = 0
+
+        for key, param in itertools.chain(self._model.named_parameters(), self._model.named_buffers()):
+            if vram_bytes_freed >= vram_bytes_to_free:
+                break
+
+            if param.device.type != self._compute_device.type:
+                continue
+
+            if isinstance(param, torch.nn.Parameter):
+                # Create a new parameter, but inject the existing CPU tensor into it.
+                out_param = torch.nn.Parameter(self._cpu_state_dict[key], requires_grad=param.requires_grad)
+                set_nested_attr(self._model, key, out_param)
+            else:
+                # Handle buffers.
+                set_nested_attr(self._model, key, self._cpu_state_dict[key])
+
+            vram_bytes_freed += calc_tensor_size(param)
+
+        if self._cur_vram_bytes is not None:
+            self._cur_vram_bytes -= vram_bytes_freed
+
+        return vram_bytes_freed

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

@@ -0,0 +1,538 @@
+import gc
+from logging import Logger
+from typing import Dict, List, Optional
+
+import torch
+
+from invokeai.backend.model_manager import AnyModel, SubModelType
+from invokeai.backend.model_manager.load.memory_snapshot import MemorySnapshot
+from invokeai.backend.model_manager.load.model_cache.cache_record import CacheRecord
+from invokeai.backend.model_manager.load.model_cache.cache_stats import CacheStats
+from invokeai.backend.model_manager.load.model_cache.cached_model.cached_model_only_full_load import (
+    CachedModelOnlyFullLoad,
+)
+from invokeai.backend.model_manager.load.model_cache.cached_model.cached_model_with_partial_load import (
+    CachedModelWithPartialLoad,
+)
+from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data
+from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.util.logging import InvokeAILogger
+from invokeai.backend.util.prefix_logger_adapter import PrefixedLoggerAdapter
+
+# Size of a GB in bytes.
+GB = 2**30
+
+# Size of a MB in bytes.
+MB = 2**20
+
+
+# TODO(ryand): Where should this go? The ModelCache shouldn't be concerned with submodels.
+def get_model_cache_key(model_key: str, submodel_type: Optional[SubModelType] = None) -> str:
+    """Get the cache key for a model based on the optional submodel type."""
+    if submodel_type:
+        return f"{model_key}:{submodel_type.value}"
+    else:
+        return model_key
+
+
+class ModelCache:
+    """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)
+    ```
+    """
+
+    def __init__(
+        self,
+        max_cache_size: float,
+        max_vram_cache_size: float,
+        execution_device: torch.device = torch.device("cuda"),
+        storage_device: torch.device = torch.device("cpu"),
+        lazy_offloading: bool = True,
+        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 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 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
+            behaviour.
+        :param logger: InvokeAILogger to use (otherwise creates one)
+        """
+        # allow lazy offloading only when vram cache enabled
+        # TODO(ryand): Think about what lazy_offloading should mean in the new model cache.
+        self._lazy_offloading = lazy_offloading and max_vram_cache_size > 0
+        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
+        self._storage_device: torch.device = storage_device
+        self._logger = PrefixedLoggerAdapter(
+            logger or InvokeAILogger.get_logger(self.__class__.__name__), "MODEL CACHE"
+        )
+        self._log_memory_usage = log_memory_usage
+        self._stats: Optional[CacheStats] = None
+
+        self._cached_models: Dict[str, CacheRecord] = {}
+        self._cache_stack: List[str] = []
+
+    @property
+    def max_cache_size(self) -> float:
+        """Return the cap on cache size."""
+        return self._max_cache_size
+
+    @max_cache_size.setter
+    def max_cache_size(self, value: float) -> None:
+        """Set the cap on cache size."""
+        self._max_cache_size = value
+
+    @property
+    def max_vram_cache_size(self) -> float:
+        """Return the cap on vram cache size."""
+        return self._max_vram_cache_size
+
+    @max_vram_cache_size.setter
+    def max_vram_cache_size(self, value: float) -> None:
+        """Set the cap on vram cache size."""
+        self._max_vram_cache_size = value
+
+    @property
+    def stats(self) -> Optional[CacheStats]:
+        """Return collected CacheStats object."""
+        return self._stats
+
+    @stats.setter
+    def stats(self, stats: CacheStats) -> None:
+        """Set the CacheStats object for collecting cache statistics."""
+        self._stats = stats
+
+    def put(self, key: str, model: AnyModel) -> None:
+        """Add a model to the cache."""
+        if key in self._cached_models:
+            self._logger.debug(
+                f"Attempted to add model {key} ({model.__class__.__name__}), but it already exists in the cache. No action necessary."
+            )
+            return
+
+        size = calc_model_size_by_data(self._logger, model)
+        self.make_room(size)
+
+        # Wrap model.
+        if isinstance(model, torch.nn.Module):
+            wrapped_model = CachedModelWithPartialLoad(model, self._execution_device)
+        else:
+            wrapped_model = CachedModelOnlyFullLoad(model, self._execution_device, size)
+
+        # running_on_cpu = self._execution_device == torch.device("cpu")
+        # state_dict = model.state_dict() if isinstance(model, torch.nn.Module) and not running_on_cpu else None
+        cache_record = CacheRecord(key=key, cached_model=wrapped_model)
+        self._cached_models[key] = cache_record
+        self._cache_stack.append(key)
+        self._logger.debug(
+            f"Added model {key} (Type: {model.__class__.__name__}, Wrap mode: {wrapped_model.__class__.__name__}, Model size: {size/MB:.2f}MB)"
+        )
+
+    def get(self, key: str, stats_name: Optional[str] = None) -> CacheRecord:
+        """Retrieve a model from the cache.
+
+        :param key: Model key
+        :param stats_name: A human-readable id for the model for the purposes of stats reporting.
+
+        Raises IndexError if the model is not in the cache.
+        """
+        if key in self._cached_models:
+            if self.stats:
+                self.stats.hits += 1
+        else:
+            if self.stats:
+                self.stats.misses += 1
+            self._logger.debug(f"Cache miss: {key}")
+            raise IndexError(f"The model with key {key} is not in the cache.")
+
+        cache_entry = self._cached_models[key]
+
+        # more stats
+        if self.stats:
+            stats_name = stats_name or key
+            self.stats.cache_size = int(self._max_cache_size * GB)
+            self.stats.high_watermark = max(self.stats.high_watermark, self._get_ram_in_use())
+            self.stats.in_cache = len(self._cached_models)
+            self.stats.loaded_model_sizes[stats_name] = max(
+                self.stats.loaded_model_sizes.get(stats_name, 0), cache_entry.cached_model.total_bytes()
+            )
+
+        # this moves the entry to the top (right end) of the stack
+        self._cache_stack = [k for k in self._cache_stack if k != key]
+        self._cache_stack.append(key)
+
+        self._logger.debug(f"Cache hit: {key} (Type: {cache_entry.cached_model.model.__class__.__name__})")
+
+        return cache_entry
+
+    def lock(self, key: str) -> None:
+        """Lock a model for use and move it into VRAM."""
+        cache_entry = self._cached_models[key]
+        cache_entry.lock()
+
+        self._logger.debug(f"Locking model {key} (Type: {cache_entry.cached_model.model.__class__.__name__})")
+
+        try:
+            self._load_locked_model(cache_entry)
+            self._logger.debug(
+                f"Finished locking model {key} (Type: {cache_entry.cached_model.model.__class__.__name__})"
+            )
+        except torch.cuda.OutOfMemoryError:
+            self._logger.warning("Insufficient GPU memory to load model. Aborting")
+            cache_entry.unlock()
+            raise
+        except Exception:
+            cache_entry.unlock()
+            raise
+
+        self._log_cache_state()
+
+    def unlock(self, key: str) -> None:
+        """Unlock a model."""
+        cache_entry = self._cached_models[key]
+        cache_entry.unlock()
+        self._logger.debug(f"Unlocked model {key} (Type: {cache_entry.cached_model.model.__class__.__name__})")
+
+    def _load_locked_model(self, cache_entry: CacheRecord) -> None:
+        """Helper function for self.lock(). Loads a locked model into VRAM."""
+        vram_available = self._get_vram_available()
+
+        # Calculate model_vram_needed, the amount of additional VRAM that will be used if we fully load the model into
+        # VRAM.
+        model_cur_vram_bytes = cache_entry.cached_model.cur_vram_bytes()
+        model_total_bytes = cache_entry.cached_model.total_bytes()
+        model_vram_needed = model_total_bytes - model_cur_vram_bytes
+
+        # The amount of VRAM that must be freed to make room for model_vram_needed.
+        vram_bytes_to_free = max(0, model_vram_needed - vram_available)
+
+        self._logger.debug(
+            f"Before unloading: {self._get_vram_state_str(model_cur_vram_bytes, model_total_bytes, vram_available)}"
+        )
+
+        # Make room for the model in VRAM.
+        # 1. If the model can fit entirely in VRAM, then make enough room for it to be loaded fully.
+        # 2. If the model can't fit fully into VRAM, then unload all other models and load as much of the model as
+        #    possible.
+        vram_bytes_freed = self._offload_unlocked_models(vram_bytes_to_free)
+        self._logger.debug(f"Unloaded models (if necessary): vram_bytes_freed={(vram_bytes_freed/MB):.2f}MB")
+
+        # Check the updated vram_available after offloading.
+        vram_available = self._get_vram_available()
+        self._logger.debug(
+            f"After unloading: {self._get_vram_state_str(model_cur_vram_bytes, model_total_bytes, vram_available)}"
+        )
+
+        # Move as much of the model as possible into VRAM.
+        model_bytes_loaded = 0
+        if isinstance(cache_entry.cached_model, CachedModelWithPartialLoad):
+            model_bytes_loaded = cache_entry.cached_model.partial_load_to_vram(vram_available)
+        elif isinstance(cache_entry.cached_model, CachedModelOnlyFullLoad):  # type: ignore
+            # Partial load is not supported, so we have not choice but to try and fit it all into VRAM.
+            model_bytes_loaded = cache_entry.cached_model.full_load_to_vram()
+        else:
+            raise ValueError(f"Unsupported cached model type: {type(cache_entry.cached_model)}")
+
+        model_cur_vram_bytes = cache_entry.cached_model.cur_vram_bytes()
+        vram_available = self._get_vram_available()
+        self._logger.debug(f"Loaded model onto execution device: model_bytes_loaded={(model_bytes_loaded/MB):.2f}MB, ")
+        self._logger.debug(
+            f"After loading: {self._get_vram_state_str(model_cur_vram_bytes, model_total_bytes, vram_available)}"
+        )
+
+    def _get_vram_available(self) -> int:
+        """Get the amount of VRAM available in the cache."""
+        return int(self._max_vram_cache_size * GB) - self._get_vram_in_use()
+
+    def _get_vram_in_use(self) -> int:
+        """Get the amount of VRAM currently in use."""
+        return sum(ce.cached_model.cur_vram_bytes() for ce in self._cached_models.values())
+
+    def _get_ram_available(self) -> int:
+        """Get the amount of RAM available in the cache."""
+        return int(self._max_cache_size * GB) - self._get_ram_in_use()
+
+    def _get_ram_in_use(self) -> int:
+        """Get the amount of RAM currently in use."""
+        return sum(ce.cached_model.total_bytes() for ce in self._cached_models.values())
+
+    def _capture_memory_snapshot(self) -> Optional[MemorySnapshot]:
+        if self._log_memory_usage:
+            return MemorySnapshot.capture()
+        return None
+
+    def _get_vram_state_str(self, model_cur_vram_bytes: int, model_total_bytes: int, vram_available: int) -> str:
+        """Helper function for preparing a VRAM state log string."""
+        model_cur_vram_bytes_percent = model_cur_vram_bytes / model_total_bytes if model_total_bytes > 0 else 0
+        return (
+            f"model_total={model_total_bytes/MB:.0f} MB, "
+            + f"model_vram={model_cur_vram_bytes/MB:.0f} MB ({model_cur_vram_bytes_percent:.1%} %), "
+            + f"vram_total={int(self._max_vram_cache_size * GB)/MB:.0f} MB, "
+            + f"vram_available={(vram_available/MB):.0f} MB, "
+        )
+
+    def _offload_unlocked_models(self, vram_bytes_to_free: int) -> int:
+        """Offload models from the execution_device until vram_bytes_to_free bytes are freed, or all models are
+        offloaded. Of course, locked models are not offloaded.
+
+        Returns:
+            int: The number of bytes freed.
+        """
+        self._logger.debug(f"Offloading unlocked models with goal of freeing {vram_bytes_to_free/MB:.2f}MB of VRAM.")
+        vram_bytes_freed = 0
+        # TODO(ryand): Give more thought to the offloading policy used here.
+        cache_entries_increasing_size = sorted(self._cached_models.values(), key=lambda x: x.cached_model.total_bytes())
+        for cache_entry in cache_entries_increasing_size:
+            if vram_bytes_freed >= vram_bytes_to_free:
+                break
+            if cache_entry.is_locked:
+                continue
+
+            if isinstance(cache_entry.cached_model, CachedModelWithPartialLoad):
+                cache_entry_bytes_freed = cache_entry.cached_model.partial_unload_from_vram(
+                    vram_bytes_to_free - vram_bytes_freed
+                )
+            elif isinstance(cache_entry.cached_model, CachedModelOnlyFullLoad):  # type: ignore
+                cache_entry_bytes_freed = cache_entry.cached_model.full_unload_from_vram()
+            else:
+                raise ValueError(f"Unsupported cached model type: {type(cache_entry.cached_model)}")
+            if cache_entry_bytes_freed > 0:
+                self._logger.debug(
+                    f"Unloaded {cache_entry.key} from VRAM to free {(cache_entry_bytes_freed/MB):.0f} MB."
+                )
+            vram_bytes_freed += cache_entry_bytes_freed
+
+        TorchDevice.empty_cache()
+        return vram_bytes_freed
+
+    # def _move_model_to_device(self, cache_entry: CacheRecord, target_device: torch.device) -> None:
+    #     """Move model into the indicated device.
+
+    #     :param cache_entry: The CacheRecord for the model
+    #     :param target_device: The torch.device to move the model into
+
+    #     May raise a torch.cuda.OutOfMemoryError
+    #     """
+    #     self._logger.debug(f"Called to move {cache_entry.key} to {target_device}")
+    #     source_device = cache_entry.device
+
+    #     # Note: We compare device types only so that 'cuda' == 'cuda:0'.
+    #     # This would need to be revised to support multi-GPU.
+    #     if torch.device(source_device).type == torch.device(target_device).type:
+    #         return
+
+    #     # Some models don't have a `to` method, in which case they run in RAM/CPU.
+    #     if not hasattr(cache_entry.model, "to"):
+    #         return
+
+    #     # This roundabout method for moving the model around is done to avoid
+    #     # the cost of moving the model from RAM to VRAM and then back from VRAM to RAM.
+    #     # When moving to VRAM, we copy (not move) each element of the state dict from
+    #     # RAM to a new state dict in VRAM, and then inject it into the model.
+    #     # This operation is slightly faster than running `to()` on the whole model.
+    #     #
+    #     # When the model needs to be removed from VRAM we simply delete the copy
+    #     # of the state dict in VRAM, and reinject the state dict that is cached
+    #     # in RAM into the model. So this operation is very fast.
+    #     start_model_to_time = time.time()
+    #     snapshot_before = self._capture_memory_snapshot()
+
+    #     try:
+    #         if cache_entry.state_dict is not None:
+    #             assert hasattr(cache_entry.model, "load_state_dict")
+    #             if target_device == self._storage_device:
+    #                 cache_entry.model.load_state_dict(cache_entry.state_dict, assign=True)
+    #             else:
+    #                 new_dict: Dict[str, torch.Tensor] = {}
+    #                 for k, v in cache_entry.state_dict.items():
+    #                     new_dict[k] = v.to(target_device, copy=True)
+    #                 cache_entry.model.load_state_dict(new_dict, assign=True)
+    #         cache_entry.model.to(target_device)
+    #         cache_entry.device = target_device
+    #     except Exception as e:  # blow away cache entry
+    #         self._delete_cache_entry(cache_entry)
+    #         raise e
+
+    #     snapshot_after = self._capture_memory_snapshot()
+    #     end_model_to_time = time.time()
+    #     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"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
+    #     )
+
+    #     if (
+    #         snapshot_before is not None
+    #         and snapshot_after is not None
+    #         and snapshot_before.vram is not None
+    #         and snapshot_after.vram is not None
+    #     ):
+    #         vram_change = abs(snapshot_before.vram - snapshot_after.vram)
+
+    #         # If the estimated model size does not match the change in VRAM, log a warning.
+    #         if not math.isclose(
+    #             vram_change,
+    #             cache_entry.size,
+    #             rel_tol=0.1,
+    #             abs_tol=10 * MB,
+    #         ):
+    #             self._logger.debug(
+    #                 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"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
+    #             )
+
+    def _log_cache_state(self, title: str = "Model cache state:", include_entry_details: bool = True):
+        ram_size_bytes = self._max_cache_size * GB
+        ram_in_use_bytes = self._get_ram_in_use()
+        ram_in_use_bytes_percent = ram_in_use_bytes / ram_size_bytes if ram_size_bytes > 0 else 0
+        ram_available_bytes = self._get_ram_available()
+        ram_available_bytes_percent = ram_available_bytes / ram_size_bytes if ram_size_bytes > 0 else 0
+
+        vram_size_bytes = self._max_vram_cache_size * GB
+        vram_in_use_bytes = self._get_vram_in_use()
+        vram_in_use_bytes_percent = vram_in_use_bytes / vram_size_bytes if vram_size_bytes > 0 else 0
+        vram_available_bytes = self._get_vram_available()
+        vram_available_bytes_percent = vram_available_bytes / vram_size_bytes if vram_size_bytes > 0 else 0
+
+        log = f"{title}\n"
+
+        log_format = "  {:<30} Limit: {:>7.1f} MB, Used: {:>7.1f} MB ({:>5.1%}), Available: {:>7.1f} MB ({:>5.1%})\n"
+        log += log_format.format(
+            f"Storage Device ({self._storage_device.type})",
+            ram_size_bytes / MB,
+            ram_in_use_bytes / MB,
+            ram_in_use_bytes_percent,
+            ram_available_bytes / MB,
+            ram_available_bytes_percent,
+        )
+        log += log_format.format(
+            f"Compute Device ({self._execution_device.type})",
+            vram_size_bytes / MB,
+            vram_in_use_bytes / MB,
+            vram_in_use_bytes_percent,
+            vram_available_bytes / MB,
+            vram_available_bytes_percent,
+        )
+
+        if torch.cuda.is_available():
+            log += "  {:<30} {} MB\n".format("CUDA Memory Allocated:", torch.cuda.memory_allocated() / MB)
+        log += "  {:<30} {}\n".format("Total models:", len(self._cached_models))
+
+        if include_entry_details and len(self._cached_models) > 0:
+            log += "  Models:\n"
+            log_format = (
+                "    {:<80} total={:>7.1f} MB, vram={:>7.1f} MB ({:>5.1%}), ram={:>7.1f} MB ({:>5.1%}), locked={}\n"
+            )
+            for cache_record in self._cached_models.values():
+                total_bytes = cache_record.cached_model.total_bytes()
+                cur_vram_bytes = cache_record.cached_model.cur_vram_bytes()
+                cur_vram_bytes_percent = cur_vram_bytes / total_bytes if total_bytes > 0 else 0
+                cur_ram_bytes = total_bytes - cur_vram_bytes
+                cur_ram_bytes_percent = cur_ram_bytes / total_bytes if total_bytes > 0 else 0
+
+                log += log_format.format(
+                    f"{cache_record.key} ({cache_record.cached_model.model.__class__.__name__}):",
+                    total_bytes / MB,
+                    cur_vram_bytes / MB,
+                    cur_vram_bytes_percent,
+                    cur_ram_bytes / MB,
+                    cur_ram_bytes_percent,
+                    cache_record.is_locked,
+                )
+
+        self._logger.debug(log)
+
+    def make_room(self, bytes_needed: 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.
+        """
+        self._logger.debug(f"Making room for {bytes_needed/MB:.2f}MB of RAM.")
+        self._log_cache_state(title="Before dropping models:")
+
+        ram_bytes_available = self._get_ram_available()
+        ram_bytes_to_free = max(0, bytes_needed - ram_bytes_available)
+
+        ram_bytes_freed = 0
+        pos = 0
+        models_cleared = 0
+        while ram_bytes_freed < ram_bytes_to_free and pos < len(self._cache_stack):
+            model_key = self._cache_stack[pos]
+            cache_entry = self._cached_models[model_key]
+
+            if not cache_entry.is_locked:
+                ram_bytes_freed += cache_entry.cached_model.total_bytes()
+                self._logger.debug(
+                    f"Dropping {model_key} from RAM cache to free {(cache_entry.cached_model.total_bytes()/MB):.2f}MB."
+                )
+                self._delete_cache_entry(cache_entry)
+                del cache_entry
+                models_cleared += 1
+            else:
+                pos += 1
+
+        if models_cleared > 0:
+            # There would likely be some 'garbage' to be collected regardless of whether a model was cleared or not, but
+            # there is a significant time cost to calling `gc.collect()`, so we want to use it sparingly. (The time cost
+            # is high even if no garbage gets collected.)
+            #
+            # Calling gc.collect(...) when a model is cleared seems like a good middle-ground:
+            # - If models had to be cleared, it's a signal that we are close to our memory limit.
+            # - If models were cleared, there's a good chance that there's a significant amount of garbage to be
+            #   collected.
+            #
+            # Keep in mind that gc is only responsible for handling reference cycles. Most objects should be cleaned up
+            # immediately when their reference count hits 0.
+            if self.stats:
+                self.stats.cleared = models_cleared
+            gc.collect()
+
+        TorchDevice.empty_cache()
+        self._logger.debug(f"Dropped {models_cleared} models to free {ram_bytes_freed/MB:.2f}MB of RAM.")
+        self._log_cache_state(title="After dropping models:")
+
+    def _delete_cache_entry(self, cache_entry: CacheRecord) -> None:
+        self._cache_stack.remove(cache_entry.key)
+        del self._cached_models[cache_entry.key]

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

@@ -1,221 +0,0 @@
-# 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.
-"""
-
-from abc import ABC, abstractmethod
-from dataclasses import dataclass, field
-from logging import Logger
-from typing import Dict, Generic, Optional, TypeVar
-
-import torch
-
-from invokeai.backend.model_manager.config import AnyModel, SubModelType
-
-
-class ModelLockerBase(ABC):
-    """Base class for the model locker used by the loader."""
-
-    @abstractmethod
-    def lock(self) -> AnyModel:
-        """Lock the contained model and move it into VRAM."""
-        pass
-
-    @abstractmethod
-    def unlock(self) -> None:
-        """Unlock the contained model, and remove it from VRAM."""
-        pass
-
-    @abstractmethod
-    def get_state_dict(self) -> Optional[Dict[str, torch.Tensor]]:
-        """Return the state dict (if any) for the cached model."""
-        pass
-
-    @property
-    @abstractmethod
-    def model(self) -> AnyModel:
-        """Return the model."""
-        pass
-
-
-T = TypeVar("T")
-
-
-@dataclass
-class CacheRecord(Generic[T]):
-    """
-    Elements of the cache:
-
-    key: Unique key for each model, same as used in the models database.
-    model: Model in memory.
-    state_dict: A read-only copy of the model's state dict in RAM. It will be
-                used as a template for creating a copy in the VRAM.
-    size: Size of the model
-    loaded: True if the model's state dict is currently in VRAM
-
-    Before a model is executed, the state_dict template is copied into VRAM,
-    and then injected into the model. When the model is finished, the VRAM
-    copy of the state dict is deleted, and the RAM version is reinjected
-    into the model.
-
-    The state_dict should be treated as a read-only attribute. Do not attempt
-    to patch or otherwise modify it. Instead, patch the copy of the state_dict
-    after it is loaded into the execution device (e.g. CUDA) using the `LoadedModel`
-    context manager call `model_on_device()`.
-    """
-
-    key: str
-    model: T
-    device: torch.device
-    state_dict: Optional[Dict[str, torch.Tensor]]
-    size: int
-    loaded: bool = False
-    _locks: int = 0
-
-    def lock(self) -> None:
-        """Lock this record."""
-        self._locks += 1
-
-    def unlock(self) -> None:
-        """Unlock this record."""
-        self._locks -= 1
-        assert self._locks >= 0
-
-    @property
-    def locked(self) -> bool:
-        """Return true if record is locked."""
-        return self._locks > 0
-
-
-@dataclass
-class CacheStats(object):
-    """Collect statistics on cache performance."""
-
-    hits: int = 0  # cache hits
-    misses: int = 0  # cache misses
-    high_watermark: int = 0  # amount of cache used
-    in_cache: int = 0  # number of models in cache
-    cleared: int = 0  # number of models cleared to make space
-    cache_size: int = 0  # total size of cache
-    loaded_model_sizes: Dict[str, int] = field(default_factory=dict)
-
-
-class ModelCacheBase(ABC, Generic[T]):
-    """Virtual base class for RAM model cache."""
-
-    @property
-    @abstractmethod
-    def storage_device(self) -> torch.device:
-        """Return the storage device (e.g. "CPU" for RAM)."""
-        pass
-
-    @property
-    @abstractmethod
-    def execution_device(self) -> torch.device:
-        """Return the exection device (e.g. "cuda" for VRAM)."""
-        pass
-
-    @property
-    @abstractmethod
-    def lazy_offloading(self) -> bool:
-        """Return true if the cache is configured to lazily offload models in VRAM."""
-        pass
-
-    @property
-    @abstractmethod
-    def max_cache_size(self) -> float:
-        """Return the maximum size the RAM cache can grow to."""
-        pass
-
-    @max_cache_size.setter
-    @abstractmethod
-    def max_cache_size(self, value: float) -> None:
-        """Set the cap on vram cache size."""
-
-    @property
-    @abstractmethod
-    def max_vram_cache_size(self) -> float:
-        """Return the maximum size the VRAM cache can grow to."""
-        pass
-
-    @max_vram_cache_size.setter
-    @abstractmethod
-    def max_vram_cache_size(self, value: float) -> float:
-        """Set the maximum size the VRAM cache can grow to."""
-        pass
-
-    @abstractmethod
-    def offload_unlocked_models(self, size_required: int) -> None:
-        """Offload from VRAM any models not actively in use."""
-        pass
-
-    @abstractmethod
-    def move_model_to_device(self, cache_entry: CacheRecord[AnyModel], target_device: torch.device) -> None:
-        """Move model into the indicated device."""
-        pass
-
-    @property
-    @abstractmethod
-    def stats(self) -> Optional[CacheStats]:
-        """Return collected CacheStats object."""
-        pass
-
-    @stats.setter
-    @abstractmethod
-    def stats(self, stats: CacheStats) -> None:
-        """Set the CacheStats object for collectin cache statistics."""
-        pass
-
-    @property
-    @abstractmethod
-    def logger(self) -> Logger:
-        """Return the logger used by the cache."""
-        pass
-
-    @abstractmethod
-    def make_room(self, size: int) -> None:
-        """Make enough room in the cache to accommodate a new model of indicated size."""
-        pass
-
-    @abstractmethod
-    def put(
-        self,
-        key: str,
-        model: T,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> None:
-        """Store model under key and optional submodel_type."""
-        pass
-
-    @abstractmethod
-    def get(
-        self,
-        key: str,
-        submodel_type: Optional[SubModelType] = None,
-        stats_name: Optional[str] = None,
-    ) -> ModelLockerBase:
-        """
-        Retrieve model using key and optional submodel_type.
-
-        :param key: Opaque model key
-        :param submodel_type: Type of the submodel to fetch
-        :param stats_name: A human-readable id for the model for the purposes of
-        stats reporting.
-
-        This may raise an IndexError if the model is not in the cache.
-        """
-        pass
-
-    @abstractmethod
-    def cache_size(self) -> int:
-        """Get the total size of the models currently cached."""
-        pass
-
-    @abstractmethod
-    def print_cuda_stats(self) -> None:
-        """Log debugging information on CUDA usage."""
-        pass

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

@@ -1,426 +0,0 @@
-# Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Development team
-# TODO: Add Stalker's proper name to copyright
-""" """
-
-import gc
-import math
-import time
-from contextlib import suppress
-from logging import Logger
-from typing import Dict, List, Optional
-
-import torch
-
-from invokeai.backend.model_manager import AnyModel, SubModelType
-from invokeai.backend.model_manager.load.memory_snapshot import MemorySnapshot, get_pretty_snapshot_diff
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import (
-    CacheRecord,
-    CacheStats,
-    ModelCacheBase,
-    ModelLockerBase,
-)
-from invokeai.backend.model_manager.load.model_cache.model_locker import ModelLocker
-from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data
-from invokeai.backend.util.devices import TorchDevice
-from invokeai.backend.util.logging import InvokeAILogger
-
-# Size of a GB in bytes.
-GB = 2**30
-
-# 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)
-    ```
-    """
-
-    def __init__(
-        self,
-        max_cache_size: float,
-        max_vram_cache_size: float,
-        execution_device: torch.device = torch.device("cuda"),
-        storage_device: torch.device = torch.device("cpu"),
-        precision: torch.dtype = torch.float16,
-        lazy_offloading: bool = True,
-        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 execution_device: Torch device to load active model into [torch.device('cuda')]
-        :param storage_device: Torch device to save inactive model in [torch.device('cpu')]
-        :param precision: Precision for loaded models [torch.float16]
-        :param lazy_offloading: Keep model in VRAM until another model needs to be loaded
-        :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
-            behaviour.
-        :param logger: InvokeAILogger to use (otherwise creates one)
-        """
-        # allow lazy offloading only when vram cache enabled
-        self._lazy_offloading = lazy_offloading and max_vram_cache_size > 0
-        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
-        self._storage_device: torch.device = storage_device
-        self._logger = logger or InvokeAILogger.get_logger(self.__class__.__name__)
-        self._log_memory_usage = log_memory_usage
-        self._stats: Optional[CacheStats] = None
-
-        self._cached_models: Dict[str, CacheRecord[AnyModel]] = {}
-        self._cache_stack: List[str] = []
-
-    @property
-    def logger(self) -> Logger:
-        """Return the logger used by the cache."""
-        return self._logger
-
-    @property
-    def lazy_offloading(self) -> bool:
-        """Return true if the cache is configured to lazily offload models in VRAM."""
-        return self._lazy_offloading
-
-    @property
-    def storage_device(self) -> torch.device:
-        """Return the storage device (e.g. "CPU" for RAM)."""
-        return self._storage_device
-
-    @property
-    def execution_device(self) -> torch.device:
-        """Return the exection device (e.g. "cuda" for VRAM)."""
-        return self._execution_device
-
-    @property
-    def max_cache_size(self) -> float:
-        """Return the cap on cache size."""
-        return self._max_cache_size
-
-    @max_cache_size.setter
-    def max_cache_size(self, value: float) -> None:
-        """Set the cap on cache size."""
-        self._max_cache_size = value
-
-    @property
-    def max_vram_cache_size(self) -> float:
-        """Return the cap on vram cache size."""
-        return self._max_vram_cache_size
-
-    @max_vram_cache_size.setter
-    def max_vram_cache_size(self, value: float) -> None:
-        """Set the cap on vram cache size."""
-        self._max_vram_cache_size = value
-
-    @property
-    def stats(self) -> Optional[CacheStats]:
-        """Return collected CacheStats object."""
-        return self._stats
-
-    @stats.setter
-    def stats(self, stats: CacheStats) -> None:
-        """Set the CacheStats object for collectin cache statistics."""
-        self._stats = stats
-
-    def cache_size(self) -> int:
-        """Get the total size of the models currently cached."""
-        total = 0
-        for cache_record in self._cached_models.values():
-            total += cache_record.size
-        return total
-
-    def put(
-        self,
-        key: str,
-        model: AnyModel,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> None:
-        """Store model under key and optional submodel_type."""
-        key = self._make_cache_key(key, submodel_type)
-        if key in self._cached_models:
-            return
-        size = calc_model_size_by_data(self.logger, model)
-        self.make_room(size)
-
-        running_on_cpu = self.execution_device == torch.device("cpu")
-        state_dict = model.state_dict() if isinstance(model, torch.nn.Module) and not running_on_cpu else None
-        cache_record = CacheRecord(key=key, model=model, device=self.storage_device, state_dict=state_dict, size=size)
-        self._cached_models[key] = cache_record
-        self._cache_stack.append(key)
-
-    def get(
-        self,
-        key: str,
-        submodel_type: Optional[SubModelType] = None,
-        stats_name: Optional[str] = None,
-    ) -> ModelLockerBase:
-        """
-        Retrieve model using key and optional submodel_type.
-
-        :param key: Opaque model key
-        :param submodel_type: Type of the submodel to fetch
-        :param stats_name: A human-readable id for the model for the purposes of
-        stats reporting.
-
-        This may raise an IndexError if the model is not in the cache.
-        """
-        key = self._make_cache_key(key, submodel_type)
-        if key in self._cached_models:
-            if self.stats:
-                self.stats.hits += 1
-        else:
-            if self.stats:
-                self.stats.misses += 1
-            raise IndexError(f"The model with key {key} is not in the cache.")
-
-        cache_entry = self._cached_models[key]
-
-        # more stats
-        if self.stats:
-            stats_name = stats_name or key
-            self.stats.cache_size = int(self._max_cache_size * GB)
-            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(
-                self.stats.loaded_model_sizes.get(stats_name, 0), cache_entry.size
-            )
-
-        # this moves the entry to the top (right end) of the stack
-        with suppress(Exception):
-            self._cache_stack.remove(key)
-        self._cache_stack.append(key)
-        return ModelLocker(
-            cache=self,
-            cache_entry=cache_entry,
-        )
-
-    def _capture_memory_snapshot(self) -> Optional[MemorySnapshot]:
-        if self._log_memory_usage:
-            return MemorySnapshot.capture()
-        return None
-
-    def _make_cache_key(self, model_key: str, submodel_type: Optional[SubModelType] = None) -> str:
-        if submodel_type:
-            return f"{model_key}:{submodel_type.value}"
-        else:
-            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
-        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")
-        for _, cache_entry in sorted(self._cached_models.items(), key=lambda x: x[1].size):
-            if vram_in_use <= reserved:
-                break
-            if not cache_entry.loaded:
-                continue
-            if not cache_entry.locked:
-                self.move_model_to_device(cache_entry, self.storage_device)
-                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"
-                )
-
-        TorchDevice.empty_cache()
-
-    def move_model_to_device(self, cache_entry: CacheRecord[AnyModel], target_device: torch.device) -> None:
-        """Move model into the indicated device.
-
-        :param cache_entry: The CacheRecord for the model
-        :param target_device: The torch.device to move the model into
-
-        May raise a torch.cuda.OutOfMemoryError
-        """
-        self.logger.debug(f"Called to move {cache_entry.key} to {target_device}")
-        source_device = cache_entry.device
-
-        # Note: We compare device types only so that 'cuda' == 'cuda:0'.
-        # This would need to be revised to support multi-GPU.
-        if torch.device(source_device).type == torch.device(target_device).type:
-            return
-
-        # Some models don't have a `to` method, in which case they run in RAM/CPU.
-        if not hasattr(cache_entry.model, "to"):
-            return
-
-        # This roundabout method for moving the model around is done to avoid
-        # the cost of moving the model from RAM to VRAM and then back from VRAM to RAM.
-        # When moving to VRAM, we copy (not move) each element of the state dict from
-        # RAM to a new state dict in VRAM, and then inject it into the model.
-        # This operation is slightly faster than running `to()` on the whole model.
-        #
-        # When the model needs to be removed from VRAM we simply delete the copy
-        # of the state dict in VRAM, and reinject the state dict that is cached
-        # in RAM into the model. So this operation is very fast.
-        start_model_to_time = time.time()
-        snapshot_before = self._capture_memory_snapshot()
-
-        try:
-            if cache_entry.state_dict is not None:
-                assert hasattr(cache_entry.model, "load_state_dict")
-                if target_device == self.storage_device:
-                    cache_entry.model.load_state_dict(cache_entry.state_dict, assign=True)
-                else:
-                    new_dict: Dict[str, torch.Tensor] = {}
-                    for k, v in cache_entry.state_dict.items():
-                        new_dict[k] = v.to(target_device, copy=True)
-                    cache_entry.model.load_state_dict(new_dict, assign=True)
-            cache_entry.model.to(target_device)
-            cache_entry.device = target_device
-        except Exception as e:  # blow away cache entry
-            self._delete_cache_entry(cache_entry)
-            raise e
-
-        snapshot_after = self._capture_memory_snapshot()
-        end_model_to_time = time.time()
-        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"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
-        )
-
-        if (
-            snapshot_before is not None
-            and snapshot_after is not None
-            and snapshot_before.vram is not None
-            and snapshot_after.vram is not None
-        ):
-            vram_change = abs(snapshot_before.vram - snapshot_after.vram)
-
-            # If the estimated model size does not match the change in VRAM, log a warning.
-            if not math.isclose(
-                vram_change,
-                cache_entry.size,
-                rel_tol=0.1,
-                abs_tol=10 * MB,
-            ):
-                self.logger.debug(
-                    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"{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)
-
-        in_ram_models = 0
-        in_vram_models = 0
-        locked_in_vram_models = 0
-        for cache_record in self._cached_models.values():
-            if hasattr(cache_record.model, "device"):
-                if cache_record.model.device == self.storage_device:
-                    in_ram_models += 1
-                else:
-                    in_vram_models += 1
-                if cache_record.locked:
-                    locked_in_vram_models += 1
-
-                self.logger.debug(
-                    f"Current VRAM/RAM usage: {vram}/{ram}; models_in_ram/models_in_vram(locked) ="
-                    f" {in_ram_models}/{in_vram_models}({locked_in_vram_models})"
-                )
-
-    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.
-        """
-        bytes_needed = size
-        maximum_size = self.max_cache_size * GB  # 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"
-            )
-
-        self.logger.debug(f"Before making_room: cached_models={len(self._cached_models)}")
-
-        pos = 0
-        models_cleared = 0
-        while current_size + bytes_needed > maximum_size and pos < len(self._cache_stack):
-            model_key = self._cache_stack[pos]
-            cache_entry = self._cached_models[model_key]
-            device = cache_entry.model.device if hasattr(cache_entry.model, "device") else None
-            self.logger.debug(
-                f"Model: {model_key}, locks: {cache_entry._locks}, device: {device}, loaded: {cache_entry.loaded}"
-            )
-
-            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)"
-                )
-                current_size -= cache_entry.size
-                models_cleared += 1
-                self._delete_cache_entry(cache_entry)
-                del cache_entry
-
-            else:
-                pos += 1
-
-        if models_cleared > 0:
-            # There would likely be some 'garbage' to be collected regardless of whether a model was cleared or not, but
-            # there is a significant time cost to calling `gc.collect()`, so we want to use it sparingly. (The time cost
-            # is high even if no garbage gets collected.)
-            #
-            # Calling gc.collect(...) when a model is cleared seems like a good middle-ground:
-            # - If models had to be cleared, it's a signal that we are close to our memory limit.
-            # - If models were cleared, there's a good chance that there's a significant amount of garbage to be
-            #   collected.
-            #
-            # Keep in mind that gc is only responsible for handling reference cycles. Most objects should be cleaned up
-            # immediately when their reference count hits 0.
-            if self.stats:
-                self.stats.cleared = models_cleared
-            gc.collect()
-
-        TorchDevice.empty_cache()
-        self.logger.debug(f"After making room: cached_models={len(self._cached_models)}")
-
-    def _delete_cache_entry(self, cache_entry: CacheRecord[AnyModel]) -> None:
-        self._cache_stack.remove(cache_entry.key)
-        del self._cached_models[cache_entry.key]

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

@@ -1,64 +0,0 @@
-"""
-Base class and implementation of a class that moves models in and out of VRAM.
-"""
-
-from typing import Dict, Optional
-
-import torch
-
-from invokeai.backend.model_manager import AnyModel
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import (
-    CacheRecord,
-    ModelCacheBase,
-    ModelLockerBase,
-)
-
-
-class ModelLocker(ModelLockerBase):
-    """Internal class that mediates movement in and out of GPU."""
-
-    def __init__(self, cache: ModelCacheBase[AnyModel], cache_entry: CacheRecord[AnyModel]):
-        """
-        Initialize the model locker.
-
-        :param cache: The ModelCache object
-        :param cache_entry: The entry in the model cache
-        """
-        self._cache = cache
-        self._cache_entry = cache_entry
-
-    @property
-    def model(self) -> AnyModel:
-        """Return the model without moving it around."""
-        return self._cache_entry.model
-
-    def get_state_dict(self) -> Optional[Dict[str, torch.Tensor]]:
-        """Return the state dict (if any) for the cached model."""
-        return self._cache_entry.state_dict
-
-    def lock(self) -> AnyModel:
-        """Move the model into the execution device (GPU) and lock it."""
-        self._cache_entry.lock()
-        try:
-            if self._cache.lazy_offloading:
-                self._cache.offload_unlocked_models(self._cache_entry.size)
-            self._cache.move_model_to_device(self._cache_entry, self._cache.execution_device)
-            self._cache_entry.loaded = True
-            self._cache.logger.debug(f"Locking {self._cache_entry.key} in {self._cache.execution_device}")
-            self._cache.print_cuda_stats()
-        except torch.cuda.OutOfMemoryError:
-            self._cache.logger.warning("Insufficient GPU memory to load model. Aborting")
-            self._cache_entry.unlock()
-            raise
-        except Exception:
-            self._cache_entry.unlock()
-            raise
-
-        return self.model
-
-    def unlock(self) -> None:
-        """Call upon exit from context."""
-        self._cache_entry.unlock()
-        if not self._cache.lazy_offloading:
-            self._cache.offload_unlocked_models(0)
-            self._cache.print_cuda_stats()

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

@@ -0,0 +1,33 @@
+from typing import Any, Callable
+
+import torch
+from torch.overrides import TorchFunctionMode
+
+
+def add_autocast_to_module_forward(m: torch.nn.Module, to_device: torch.device):
+    """Monkey-patch m.forward(...) with a new forward(...) method that activates device autocasting for its duration."""
+    old_forward = m.forward
+
+    def new_forward(*args: Any, **kwargs: Any):
+        with TorchFunctionAutocastDeviceContext(to_device):
+            return old_forward(*args, **kwargs)
+
+    m.forward = new_forward
+
+
+def _cast_to_device_and_run(
+    func: Callable[..., Any], args: tuple[Any, ...], kwargs: dict[str, Any], to_device: torch.device
+):
+    args_on_device = [a.to(to_device) if isinstance(a, torch.Tensor) else a for a in args]
+    kwargs_on_device = {k: v.to(to_device) if isinstance(v, torch.Tensor) else v for k, v in kwargs.items()}
+    return func(*args_on_device, **kwargs_on_device)
+
+
+class TorchFunctionAutocastDeviceContext(TorchFunctionMode):
+    def __init__(self, to_device: torch.device):
+        self._to_device = to_device
+
+    def __torch_function__(
+        self, func: Callable[..., Any], types, args: tuple[Any, ...] = (), kwargs: dict[str, Any] | None = None
+    ):
+        return _cast_to_device_and_run(func, args, kwargs or {}, self._to_device)

invokeai/backend/model_manager/load/model_loaders/lora.py

@@ -26,7 +26,7 @@ from invokeai.backend.model_manager import (
     SubModelType,
 )
 from invokeai.backend.model_manager.load.load_default import ModelLoader
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 
 
@@ -40,7 +40,7 @@ class LoRALoader(ModelLoader):
         self,
         app_config: InvokeAIAppConfig,
         logger: Logger,
-        ram_cache: ModelCacheBase[AnyModel],
+        ram_cache: ModelCache,
     ):
         """Initialize the loader."""
         super().__init__(app_config, logger, ram_cache)

invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py

@@ -25,6 +25,7 @@ from invokeai.backend.model_manager.config import (
     DiffusersConfigBase,
     MainCheckpointConfig,
 )
+from invokeai.backend.model_manager.load.model_cache.model_cache import get_model_cache_key
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 from invokeai.backend.util.silence_warnings import SilenceWarnings
@@ -132,5 +133,5 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
             if subtype == submodel_type:
                 continue
             if submodel := getattr(pipeline, subtype.value, None):
-                self._ram_cache.put(config.key, submodel_type=subtype, model=submodel)
+                self._ram_cache.put(get_model_cache_key(config.key, subtype), model=submodel)
         return getattr(pipeline, submodel_type.value)

invokeai/backend/model_manager/probe.py

@@ -469,7 +469,7 @@ class ModelProbe(object):
         """
         # scan model
         scan_result = scan_file_path(checkpoint)
-        if scan_result.infected_files != 0:
+        if scan_result.infected_files != 0 or scan_result.scan_err:
             raise Exception("The model {model_name} is potentially infected by malware. Aborting import.")
 
 
@@ -485,6 +485,7 @@ MODEL_NAME_TO_PREPROCESSOR = {
     "lineart anime": "lineart_anime_image_processor",
     "lineart_anime": "lineart_anime_image_processor",
     "lineart": "lineart_image_processor",
+    "soft": "hed_image_processor",
     "softedge": "hed_image_processor",
     "hed": "hed_image_processor",
     "shuffle": "content_shuffle_image_processor",

invokeai/backend/model_manager/starter_models.py

@@ -298,13 +298,12 @@ ip_adapter_sdxl = StarterModel(
     previous_names=["IP Adapter SDXL"],
 )
 ip_adapter_flux = StarterModel(
-    name="Standard Reference (XLabs FLUX IP-Adapter)",
+    name="Standard Reference (XLabs FLUX IP-Adapter v2)",
     base=BaseModelType.Flux,
-    source="https://huggingface.co/XLabs-AI/flux-ip-adapter/resolve/main/ip_adapter.safetensors",
+    source="https://huggingface.co/XLabs-AI/flux-ip-adapter-v2/resolve/main/ip_adapter.safetensors",
     description="References images with a more generalized/looser degree of precision.",
     type=ModelType.IPAdapter,
     dependencies=[clip_vit_l_image_encoder],
-    previous_names=["XLabs FLUX IP-Adapter"],
 )
 # endregion
 # region ControlNet

invokeai/backend/model_manager/util/model_util.py

@@ -44,7 +44,7 @@ def _fast_safetensors_reader(path: str) -> Dict[str, torch.Tensor]:
     return checkpoint
 
 
-def read_checkpoint_meta(path: Union[str, Path], scan: bool = False) -> Dict[str, torch.Tensor]:
+def read_checkpoint_meta(path: Union[str, Path], scan: bool = True) -> Dict[str, torch.Tensor]:
     if str(path).endswith(".safetensors"):
         try:
             path_str = path.as_posix() if isinstance(path, Path) else path
@@ -55,7 +55,7 @@ def read_checkpoint_meta(path: Union[str, Path], scan: bool = False) -> Dict[str
     else:
         if scan:
             scan_result = scan_file_path(path)
-            if scan_result.infected_files != 0:
+            if scan_result.infected_files != 0 or scan_result.scan_err:
                 raise Exception(f'The model file "{path}" is potentially infected by malware. Aborting import.')
         if str(path).endswith(".gguf"):
             # The GGUF reader used here uses numpy memmap, so these tensors are not loaded into memory during this function

invokeai/backend/util/prefix_logger_adapter.py

@@ -0,0 +1,12 @@
+import logging
+from typing import Any, MutableMapping
+
+
+# Issue with type hints related to LoggerAdapter: https://github.com/python/typeshed/issues/7855
+class PrefixedLoggerAdapter(logging.LoggerAdapter):  # type: ignore
+    def __init__(self, logger: logging.Logger, prefix: str):
+        super().__init__(logger, {})
+        self.prefix = prefix
+
+    def process(self, msg: str, kwargs: MutableMapping[str, Any]) -> tuple[str, MutableMapping[str, Any]]:
+        return f"[{self.prefix}] {msg}", kwargs

invokeai/frontend/web/package.json

@@ -58,7 +58,7 @@
     "@dagrejs/dagre": "^1.1.4",
     "@dagrejs/graphlib": "^2.2.4",
     "@fontsource-variable/inter": "^5.1.0",
-    "@invoke-ai/ui-library": "^0.0.43",
+    "@invoke-ai/ui-library": "^0.0.44",
     "@nanostores/react": "^0.7.3",
     "@reduxjs/toolkit": "2.2.3",
     "@roarr/browser-log-writer": "^1.3.0",

invokeai/frontend/web/pnpm-lock.yaml

@@ -24,8 +24,8 @@ dependencies:
     specifier: ^5.1.0
     version: 5.1.0
   '@invoke-ai/ui-library':
-    specifier: ^0.0.43
-    version: 0.0.43(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.1.0)(@types/react@18.3.11)(i18next@23.15.1)(react-dom@18.3.1)(react@18.3.1)
+    specifier: ^0.0.44
+    version: 0.0.44(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.1.0)(@types/react@18.3.11)(i18next@23.15.1)(react-dom@18.3.1)(react@18.3.1)
   '@nanostores/react':
     specifier: ^0.7.3
     version: 0.7.3(nanostores@0.11.3)(react@18.3.1)
@@ -515,8 +515,8 @@ packages:
     resolution: {integrity: sha512-MV6D4VLRIHr4PkW4zMyqfrNS1mPlCTiCXwvYGtDFQYr+xHFfonhAuf9WjsSc0nyp2m0OdkSLnzmVKkZFLo25Tg==}
     dev: false
 
-  /@chakra-ui/anatomy@2.3.4:
-    resolution: {integrity: sha512-fFIYN7L276gw0Q7/ikMMlZxP7mvnjRaWJ7f3Jsf9VtDOi6eAYIBRrhQe6+SZ0PGmoOkRaBc7gSE5oeIbgFFyrw==}
+  /@chakra-ui/anatomy@2.3.5:
+    resolution: {integrity: sha512-3im33cUOxCbISjaBlINE2u8BOwJSCdzpjCX0H+0JxK2xz26UaVA5xeI3NYHUoxDnr/QIrgfrllGxS0szYwOcyg==}
     dev: false
 
   /@chakra-ui/breakpoint-utils@2.0.8:
@@ -573,12 +573,12 @@ packages:
       react: 18.3.1
     dev: false
 
-  /@chakra-ui/hooks@2.4.2(react@18.3.1):
-    resolution: {integrity: sha512-LRKiVE1oA7afT5tbbSKAy7Uas2xFHE6IkrQdbhWCHmkHBUtPvjQQDgwtnd4IRZPmoEfNGwoJ/MQpwOM/NRTTwA==}
+  /@chakra-ui/hooks@2.4.3(react@18.3.1):
+    resolution: {integrity: sha512-Sr2zsoTZw3p7HbrUy4aLpTIkE2XXUelAUgg3NGwMzrmx75bE0qVyiuuTFOuyEzGxYVV2Fe8QtcKKilm6RwzTGg==}
     peerDependencies:
       react: '>=18'
     dependencies:
-      '@chakra-ui/utils': 2.2.2(react@18.3.1)
+      '@chakra-ui/utils': 2.2.3(react@18.3.1)
       '@zag-js/element-size': 0.31.1
       copy-to-clipboard: 3.3.3
       framesync: 6.1.2
@@ -596,13 +596,13 @@ packages:
       react: 18.3.1
     dev: false
 
-  /@chakra-ui/icons@2.2.4(@chakra-ui/react@2.10.2)(react@18.3.1):
+  /@chakra-ui/icons@2.2.4(@chakra-ui/react@2.10.4)(react@18.3.1):
     resolution: {integrity: sha512-l5QdBgwrAg3Sc2BRqtNkJpfuLw/pWRDwwT58J6c4PqQT6wzXxyNa8Q0PForu1ltB5qEiFb1kxr/F/HO1EwNa6g==}
     peerDependencies:
       '@chakra-ui/react': '>=2.0.0'
       react: '>=18'
     dependencies:
-      '@chakra-ui/react': 2.10.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(@types/react@18.3.11)(framer-motion@11.10.0)(react-dom@18.3.1)(react@18.3.1)
+      '@chakra-ui/react': 2.10.4(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(@types/react@18.3.11)(framer-motion@11.10.0)(react-dom@18.3.1)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -825,8 +825,8 @@ packages:
       react: 18.3.1
     dev: false
 
-  /@chakra-ui/react@2.10.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(@types/react@18.3.11)(framer-motion@11.10.0)(react-dom@18.3.1)(react@18.3.1):
-    resolution: {integrity: sha512-TfIHTqTlxTHYJZBtpiR5EZasPUrLYKJxdbHkdOJb5G1OQ+2c5kKl5XA7c2pMtsEptzb7KxAAIB62t3hxdfWp1w==}
+  /@chakra-ui/react@2.10.4(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(@types/react@18.3.11)(framer-motion@11.10.0)(react-dom@18.3.1)(react@18.3.1):
+    resolution: {integrity: sha512-XyRWnuZ1Uw7Mlj5pKUGO5/WhnIHP/EOrpy6lGZC1yWlkd0eIfIpYMZ1ALTZx4KPEdbBaes48dgiMT2ROCqLhkA==}
     peerDependencies:
       '@emotion/react': '>=11'
       '@emotion/styled': '>=11'
@@ -834,10 +834,10 @@ packages:
       react: '>=18'
       react-dom: '>=18'
     dependencies:
-      '@chakra-ui/hooks': 2.4.2(react@18.3.1)
-      '@chakra-ui/styled-system': 2.11.2(react@18.3.1)
-      '@chakra-ui/theme': 3.4.6(@chakra-ui/styled-system@2.11.2)(react@18.3.1)
-      '@chakra-ui/utils': 2.2.2(react@18.3.1)
+      '@chakra-ui/hooks': 2.4.3(react@18.3.1)
+      '@chakra-ui/styled-system': 2.12.1(react@18.3.1)
+      '@chakra-ui/theme': 3.4.7(@chakra-ui/styled-system@2.12.1)(react@18.3.1)
+      '@chakra-ui/utils': 2.2.3(react@18.3.1)
       '@emotion/react': 11.13.3(@types/react@18.3.11)(react@18.3.1)
       '@emotion/styled': 11.13.0(@emotion/react@11.13.3)(@types/react@18.3.11)(react@18.3.1)
       '@popperjs/core': 2.11.8
@@ -868,10 +868,10 @@ packages:
       react: 18.3.1
     dev: false
 
-  /@chakra-ui/styled-system@2.11.2(react@18.3.1):
-    resolution: {integrity: sha512-y++z2Uop+hjfZX9mbH88F1ikazPv32asD2er56zMJBemUAzweXnHTpiCQbluEDSUDhqmghVZAdb+5L4XLbsRxA==}
+  /@chakra-ui/styled-system@2.12.1(react@18.3.1):
+    resolution: {integrity: sha512-DQph1nDiCPtgze7nDe0a36530ByXb5VpPosKGyWMvKocVeZJcDtYG6XM0+V5a0wKuFBXsViBBRIFUTiUesJAcg==}
     dependencies:
-      '@chakra-ui/utils': 2.2.2(react@18.3.1)
+      '@chakra-ui/utils': 2.2.3(react@18.3.1)
       csstype: 3.1.3
     transitivePeerDependencies:
       - react
@@ -915,14 +915,14 @@ packages:
       color2k: 2.0.3
     dev: false
 
-  /@chakra-ui/theme-tools@2.2.6(@chakra-ui/styled-system@2.11.2)(react@18.3.1):
-    resolution: {integrity: sha512-3UhKPyzKbV3l/bg1iQN9PBvffYp+EBOoYMUaeTUdieQRPFzo2jbYR0lNCxqv8h5aGM/k54nCHU2M/GStyi9F2A==}
+  /@chakra-ui/theme-tools@2.2.7(@chakra-ui/styled-system@2.12.1)(react@18.3.1):
+    resolution: {integrity: sha512-K/VJd0QcnKik7m+qZTkggqNLep6+MPUu8IP5TUpHsnSM5R/RVjsJIR7gO8IZVAIMIGLLTIhGshHxeMekqv6LcQ==}
     peerDependencies:
       '@chakra-ui/styled-system': '>=2.0.0'
     dependencies:
-      '@chakra-ui/anatomy': 2.3.4
-      '@chakra-ui/styled-system': 2.11.2(react@18.3.1)
-      '@chakra-ui/utils': 2.2.2(react@18.3.1)
+      '@chakra-ui/anatomy': 2.3.5
+      '@chakra-ui/styled-system': 2.12.1(react@18.3.1)
+      '@chakra-ui/utils': 2.2.3(react@18.3.1)
       color2k: 2.0.3
     transitivePeerDependencies:
       - react
@@ -948,15 +948,15 @@ packages:
       '@chakra-ui/theme-tools': 2.1.2(@chakra-ui/styled-system@2.9.2)
     dev: false
 
-  /@chakra-ui/theme@3.4.6(@chakra-ui/styled-system@2.11.2)(react@18.3.1):
-    resolution: {integrity: sha512-ZwFBLfiMC3URwaO31ONXoKH9k0TX0OW3UjdPF3EQkQpYyrk/fm36GkkzajjtdpWEd7rzDLRsQjPmvwNaSoNDtg==}
+  /@chakra-ui/theme@3.4.7(@chakra-ui/styled-system@2.12.1)(react@18.3.1):
+    resolution: {integrity: sha512-pfewthgZTFNUYeUwGvhPQO/FTIyf375cFV1AT8N1y0aJiw4KDe7YTGm7p0aFy4AwAjH2ydMgeEx/lua4tx8qyQ==}
     peerDependencies:
       '@chakra-ui/styled-system': '>=2.8.0'
     dependencies:
-      '@chakra-ui/anatomy': 2.3.4
-      '@chakra-ui/styled-system': 2.11.2(react@18.3.1)
-      '@chakra-ui/theme-tools': 2.2.6(@chakra-ui/styled-system@2.11.2)(react@18.3.1)
-      '@chakra-ui/utils': 2.2.2(react@18.3.1)
+      '@chakra-ui/anatomy': 2.3.5
+      '@chakra-ui/styled-system': 2.12.1(react@18.3.1)
+      '@chakra-ui/theme-tools': 2.2.7(@chakra-ui/styled-system@2.12.1)(react@18.3.1)
+      '@chakra-ui/utils': 2.2.3(react@18.3.1)
     transitivePeerDependencies:
       - react
     dev: false
@@ -981,8 +981,8 @@ packages:
       lodash.mergewith: 4.6.2
     dev: false
 
-  /@chakra-ui/utils@2.2.2(react@18.3.1):
-    resolution: {integrity: sha512-jUPLT0JzRMWxpdzH6c+t0YMJYrvc5CLericgITV3zDSXblkfx3DsYXqU11DJTSGZI9dUKzM1Wd0Wswn4eJwvFQ==}
+  /@chakra-ui/utils@2.2.3(react@18.3.1):
+    resolution: {integrity: sha512-cldoCQuexZ6e07/9hWHKD4l1QXXlM1Nax9tuQOBvVf/EgwNZt3nZu8zZRDFlhAOKCTQDkmpLTTu+eXXjChNQOw==}
     peerDependencies:
       react: '>=16.8.0'
     dependencies:
@@ -1675,20 +1675,20 @@ packages:
       prettier: 3.3.3
     dev: true
 
-  /@invoke-ai/ui-library@0.0.43(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.1.0)(@types/react@18.3.11)(i18next@23.15.1)(react-dom@18.3.1)(react@18.3.1):
-    resolution: {integrity: sha512-t3fPYyks07ue3dEBPJuTHbeDLnDckDCOrtvc07mMDbLOnlPEZ0StaeiNGH+oO8qLzAuMAlSTdswgHfzTc2MmPw==}
+  /@invoke-ai/ui-library@0.0.44(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.1.0)(@types/react@18.3.11)(i18next@23.15.1)(react-dom@18.3.1)(react@18.3.1):
+    resolution: {integrity: sha512-PDseHmdr8oi8cmrpx3UwIYHn4NduAJX2R0pM0pyM54xrCMPMgYiCbC/eOs8Gt4fBc2ziiPZ9UGoW4evnE3YJsg==}
     peerDependencies:
       '@fontsource-variable/inter': ^5.0.16
       react: ^18.2.0
       react-dom: ^18.2.0
     dependencies:
-      '@chakra-ui/anatomy': 2.3.4
-      '@chakra-ui/icons': 2.2.4(@chakra-ui/react@2.10.2)(react@18.3.1)
+      '@chakra-ui/anatomy': 2.2.2
+      '@chakra-ui/icons': 2.2.4(@chakra-ui/react@2.10.4)(react@18.3.1)
       '@chakra-ui/layout': 2.3.1(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/portal': 2.1.0(react-dom@18.3.1)(react@18.3.1)
-      '@chakra-ui/react': 2.10.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(@types/react@18.3.11)(framer-motion@11.10.0)(react-dom@18.3.1)(react@18.3.1)
-      '@chakra-ui/styled-system': 2.11.2(react@18.3.1)
-      '@chakra-ui/theme-tools': 2.2.6(@chakra-ui/styled-system@2.11.2)(react@18.3.1)
+      '@chakra-ui/react': 2.10.4(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(@types/react@18.3.11)(framer-motion@11.10.0)(react-dom@18.3.1)(react@18.3.1)
+      '@chakra-ui/styled-system': 2.9.2
+      '@chakra-ui/theme-tools': 2.1.2(@chakra-ui/styled-system@2.9.2)
       '@emotion/react': 11.13.3(@types/react@18.3.11)(react@18.3.1)
       '@emotion/styled': 11.13.0(@emotion/react@11.13.3)(@types/react@18.3.11)(react@18.3.1)
       '@fontsource-variable/inter': 5.1.0

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

@@ -96,7 +96,9 @@
         "new": "Neu",
         "ok": "OK",
         "close": "Schließen",
-        "clipboard": "Zwischenablage"
+        "clipboard": "Zwischenablage",
+        "generating": "Generieren",
+        "loadingModel": "Lade Modell"
     },
     "gallery": {
         "galleryImageSize": "Bildgröße",
@@ -591,7 +593,15 @@
         "loraTriggerPhrases": "LoRA-Auslösephrasen",
         "installingBundle": "Bündel wird installiert",
         "triggerPhrases": "Auslösephrasen",
-        "mainModelTriggerPhrases": "Hauptmodell-Auslösephrasen"
+        "mainModelTriggerPhrases": "Hauptmodell-Auslösephrasen",
+        "noDefaultSettings": "Für dieses Modell sind keine Standardeinstellungen konfiguriert. Besuchen Sie den Modell-Manager, um Standardeinstellungen hinzuzufügen.",
+        "defaultSettingsOutOfSync": "Einige Einstellungen stimmen nicht mit den Standardeinstellungen des Modells überein:",
+        "clipLEmbed": "CLIP-L einbetten",
+        "clipGEmbed": "CLIP-G einbetten",
+        "hfTokenLabel": "HuggingFace-Token (für einige Modelle erforderlich)",
+        "hfTokenHelperText": "Für die Nutzung einiger Modelle ist ein HF-Token erforderlich. Klicken Sie hier, um Ihr Token zu erstellen oder zu erhalten.",
+        "hfForbidden": "Sie haben keinen Zugriff auf dieses HF-Modell",
+        "hfTokenInvalid": "Ungültiges oder fehlendes HF-Token"
     },
     "parameters": {
         "images": "Bilder",
@@ -768,7 +778,8 @@
         "deletedPrivateBoardsCannotbeRestored": "Gelöschte Boards können nicht wiederhergestellt werden. Wenn Sie „Nur Board löschen“ wählen, werden die Bilder in einen privaten, nicht kategorisierten Status für den Ersteller des Bildes versetzt.",
         "assetsWithCount_one": "{{count}} in der Sammlung",
         "assetsWithCount_other": "{{count}} in der Sammlung",
-        "deletedBoardsCannotbeRestored": "Gelöschte Ordner können nicht wiederhergestellt werden. Die Auswahl von \"Nur Ordner löschen\" verschiebt Bilder in einen unkategorisierten Zustand."
+        "deletedBoardsCannotbeRestored": "Gelöschte Ordner können nicht wiederhergestellt werden. Die Auswahl von \"Nur Ordner löschen\" verschiebt Bilder in einen unkategorisierten Zustand.",
+        "updateBoardError": "Fehler beim Aktualisieren des Ordners"
     },
     "queue": {
         "status": "Status",
@@ -840,7 +851,8 @@
         "upscaling": "Hochskalierung",
         "canvas": "Leinwand",
         "prompts_one": "Prompt",
-        "prompts_other": "Prompts"
+        "prompts_other": "Prompts",
+        "batchSize": "Stapelgröße"
     },
     "metadata": {
         "negativePrompt": "Negativ Beschreibung",
@@ -871,7 +883,9 @@
         "recallParameter": "{{label}} Abrufen",
         "parsingFailed": "Parsing Fehlgeschlagen",
         "canvasV2Metadata": "Leinwand",
-        "guidance": "Führung"
+        "guidance": "Führung",
+        "seamlessXAxis": "Nahtlose X Achse",
+        "seamlessYAxis": "Nahtlose Y Achse"
     },
     "popovers": {
         "noiseUseCPU": {
@@ -1078,6 +1092,21 @@
         },
         "patchmatchDownScaleSize": {
             "heading": "Herunterskalieren"
+        },
+        "paramHeight": {
+            "heading": "Höhe",
+            "paragraphs": [
+                "Höhe des generierten Bildes. Muss ein Vielfaches von 8 sein."
+            ]
+        },
+        "paramUpscaleMethod": {
+            "heading": "Vergrößerungsmethode",
+            "paragraphs": [
+                "Methode zum Hochskalieren des Bildes für High Resolution Fix."
+            ]
+        },
+        "paramHrf": {
+            "heading": "High Resolution Fix aktivieren"
         }
     },
     "invocationCache": {
@@ -1392,7 +1421,13 @@
         "pullBboxIntoLayerOk": "Bbox in die Ebene gezogen",
         "saveBboxToGallery": "Bbox in Galerie speichern",
         "tool": {
-            "bbox": "Bbox"
+            "bbox": "Bbox",
+            "brush": "Pinsel",
+            "eraser": "Radiergummi",
+            "colorPicker": "Farbwähler",
+            "view": "Ansicht",
+            "rectangle": "Rechteck",
+            "move": "Verschieben"
         },
         "transform": {
             "fitToBbox": "An Bbox anpassen",
@@ -1434,7 +1469,6 @@
         "deleteReferenceImage": "Referenzbild löschen",
         "referenceImage": "Referenzbild",
         "opacity": "Opazität",
-        "resetCanvas": "Leinwand zurücksetzen",
         "removeBookmark": "Lesezeichen entfernen",
         "rasterLayer": "Raster-Ebene",
         "rasterLayers_withCount_visible": "Raster-Ebenen ({{count}})",
@@ -1511,7 +1545,30 @@
         "layer_one": "Ebene",
         "layer_other": "Ebenen",
         "layer_withCount_one": "Ebene ({{count}})",
-        "layer_withCount_other": "Ebenen ({{count}})"
+        "layer_withCount_other": "Ebenen ({{count}})",
+        "fill": {
+            "fillStyle": "Füllstil",
+            "diagonal": "Diagonal",
+            "vertical": "Vertikal",
+            "fillColor": "Füllfarbe",
+            "grid": "Raster",
+            "solid": "Solide",
+            "crosshatch": "Kreuzschraffur",
+            "horizontal": "Horizontal"
+        },
+        "filter": {
+            "apply": "Anwenden",
+            "reset": "Zurücksetzen",
+            "cancel": "Abbrechen",
+            "spandrel_filter": {
+                "label": "Bild-zu-Bild Modell",
+                "description": "Ein Bild-zu-Bild Modell auf der ausgewählten Ebene ausführen.",
+                "model": "Modell"
+            },
+            "filters": "Filter",
+            "filterType": "Filtertyp",
+            "filter": "Filter"
+        }
     },
     "upsell": {
         "shareAccess": "Zugang teilen",

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

@@ -122,6 +122,7 @@
         "goTo": "Go to",
         "hotkeysLabel": "Hotkeys",
         "loadingImage": "Loading Image",
+        "loadingModel": "Loading Model",
         "imageFailedToLoad": "Unable to Load Image",
         "img2img": "Image To Image",
         "inpaint": "inpaint",
@@ -175,7 +176,8 @@
         "reset": "Reset",
         "none": "None",
         "new": "New",
-        "generating": "Generating"
+        "generating": "Generating",
+        "warnings": "Warnings"
     },
     "hrf": {
         "hrf": "High Resolution Fix",
@@ -262,7 +264,8 @@
         "iterations_one": "Iteration",
         "iterations_other": "Iterations",
         "generations_one": "Generation",
-        "generations_other": "Generations"
+        "generations_other": "Generations",
+        "batchSize": "Batch Size"
     },
     "invocationCache": {
         "invocationCache": "Invocation Cache",
@@ -976,6 +979,8 @@
         "zoomOutNodes": "Zoom Out",
         "betaDesc": "This invocation is in beta. Until it is stable, it may have breaking changes during app updates. We plan to support this invocation long-term.",
         "prototypeDesc": "This invocation is a prototype. It may have breaking changes during app updates and may be removed at any time.",
+        "internalDesc": "This invocation is used internally by Invoke. It may have breaking changes during app updates and may be removed at any time.",
+        "specialDesc": "This invocation some special handling in the app. For example, Batch nodes are used to queue multiple graphs from a single workflow.",
         "imageAccessError": "Unable to find image {{image_name}}, resetting to default",
         "boardAccessError": "Unable to find board {{board_id}}, resetting to default",
         "modelAccessError": "Unable to find model {{key}}, resetting to default",
@@ -1014,8 +1019,11 @@
             "addingImagesTo": "Adding images to",
             "invoke": "Invoke",
             "missingFieldTemplate": "Missing field template",
-            "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}} missing input",
+            "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}}: missing input",
             "missingNodeTemplate": "Missing node template",
+            "collectionEmpty": "{{nodeLabel}} -> {{fieldLabel}} empty collection",
+            "collectionTooFewItems": "{{nodeLabel}} -> {{fieldLabel}}: too few items, minimum {{minItems}}",
+            "collectionTooManyItems": "{{nodeLabel}} -> {{fieldLabel}}: too many items, maximum {{maxItems}}",
             "noModelSelected": "No model selected",
             "noT5EncoderModelSelected": "No T5 Encoder model selected for FLUX generation",
             "noFLUXVAEModelSelected": "No VAE model selected for FLUX generation",
@@ -1024,26 +1032,14 @@
             "fluxModelIncompatibleBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), bbox height is {{height}}",
             "fluxModelIncompatibleScaledBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), scaled bbox width is {{width}}",
             "fluxModelIncompatibleScaledBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), scaled bbox height is {{height}}",
-            "canvasIsFiltering": "Canvas is filtering",
-            "canvasIsTransforming": "Canvas is transforming",
-            "canvasIsRasterizing": "Canvas is rasterizing",
-            "canvasIsCompositing": "Canvas is compositing",
+            "canvasIsFiltering": "Canvas is busy (filtering)",
+            "canvasIsTransforming": "Canvas is busy (transforming)",
+            "canvasIsRasterizing": "Canvas is busy (rasterizing)",
+            "canvasIsCompositing": "Canvas is busy (compositing)",
+            "canvasIsSelectingObject": "Canvas is busy (selecting object)",
             "noPrompts": "No prompts generated",
             "noNodesInGraph": "No nodes in graph",
-            "systemDisconnected": "System disconnected",
-            "layer": {
-                "controlAdapterNoModelSelected": "no Control Adapter model selected",
-                "controlAdapterIncompatibleBaseModel": "incompatible Control Adapter base model",
-                "t2iAdapterIncompatibleBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, bbox width is {{width}}",
-                "t2iAdapterIncompatibleBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, bbox height is {{height}}",
-                "t2iAdapterIncompatibleScaledBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, scaled bbox width is {{width}}",
-                "t2iAdapterIncompatibleScaledBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, scaled bbox height is {{height}}",
-                "ipAdapterNoModelSelected": "no IP adapter selected",
-                "ipAdapterIncompatibleBaseModel": "incompatible IP Adapter base model",
-                "ipAdapterNoImageSelected": "no IP Adapter image selected",
-                "rgNoPromptsOrIPAdapters": "no text prompts or IP Adapters",
-                "rgNoRegion": "no region selected"
-            }
+            "systemDisconnected": "System disconnected"
         },
         "maskBlur": "Mask Blur",
         "negativePromptPlaceholder": "Negative Prompt",
@@ -1311,8 +1307,9 @@
         "controlNetBeginEnd": {
             "heading": "Begin / End Step Percentage",
             "paragraphs": [
-                "The part of the of the denoising process that will have the Control Adapter applied.",
-                "Generally, Control Adapters applied at the start of the process guide composition, and Control Adapters applied at the end guide details."
+                "This setting determines which portion of the denoising (generation) process incorporates the guidance from this layer.",
+                "• Start Step (%): Specifies when to begin applying the guidance from this layer during the generation process.",
+                "• End Step (%): Specifies when to stop applying this layer's guidance and revert general guidance from the model and other settings."
             ]
         },
         "controlNetControlMode": {
@@ -1322,7 +1319,7 @@
         "controlNetProcessor": {
             "heading": "Processor",
             "paragraphs": [
-                "Method of processing the input image to guide the generation process. Different processors will providedifferent effects or styles in your generated images."
+                "Method of processing the input image to guide the generation process. Different processors will provide different effects or styles in your generated images."
             ]
         },
         "controlNetResizeMode": {
@@ -1330,13 +1327,15 @@
             "paragraphs": ["Method to fit Control Adapter's input image size to the output generation size."]
         },
         "ipAdapterMethod": {
-            "heading": "Method",
-            "paragraphs": ["Method by which to apply the current IP Adapter."]
+            "heading": "Mode",
+            "paragraphs": ["The mode defines how the reference image will guide the generation process."]
         },
         "controlNetWeight": {
             "heading": "Weight",
             "paragraphs": [
-                "Weight of the Control Adapter. Higher weight will lead to larger impacts on the final image."
+                "Adjusts how strongly the layer influences the generation process",
+                "• Higher Weight (.75-2): Creates a more significant impact on the final result.",
+                "• Lower Weight (0-.75): Creates a smaller impact on the final result."
             ]
         },
         "dynamicPrompts": {
@@ -1658,7 +1657,6 @@
         "newControlLayerError": "Problem Creating Control Layer",
         "newRasterLayerOk": "Created Raster Layer",
         "newRasterLayerError": "Problem Creating Raster Layer",
-        "newFromImage": "New from Image",
         "pullBboxIntoLayerOk": "Bbox Pulled Into Layer",
         "pullBboxIntoLayerError": "Problem Pulling BBox Into Layer",
         "pullBboxIntoReferenceImageOk": "Bbox Pulled Into ReferenceImage",
@@ -1671,7 +1669,7 @@
         "mergingLayers": "Merging layers",
         "clearHistory": "Clear History",
         "bboxOverlay": "Show Bbox Overlay",
-        "resetCanvas": "Reset Canvas",
+        "newSession": "New Session",
         "clearCaches": "Clear Caches",
         "recalculateRects": "Recalculate Rects",
         "clipToBbox": "Clip Strokes to Bbox",
@@ -1703,8 +1701,12 @@
         "controlLayer": "Control Layer",
         "inpaintMask": "Inpaint Mask",
         "regionalGuidance": "Regional Guidance",
-        "canvasAsRasterLayer": "$t(controlLayers.canvas) as $t(controlLayers.rasterLayer)",
-        "canvasAsControlLayer": "$t(controlLayers.canvas) as $t(controlLayers.controlLayer)",
+        "referenceImageRegional": "Reference Image (Regional)",
+        "referenceImageGlobal": "Reference Image (Global)",
+        "asRasterLayer": "As $t(controlLayers.rasterLayer)",
+        "asRasterLayerResize": "As $t(controlLayers.rasterLayer) (Resize)",
+        "asControlLayer": "As $t(controlLayers.controlLayer)",
+        "asControlLayerResize": "As $t(controlLayers.controlLayer) (Resize)",
         "referenceImage": "Reference Image",
         "regionalReferenceImage": "Regional Reference Image",
         "globalReferenceImage": "Global Reference Image",
@@ -1772,6 +1774,7 @@
         "pullBboxIntoLayer": "Pull Bbox into Layer",
         "pullBboxIntoReferenceImage": "Pull Bbox into Reference Image",
         "showProgressOnCanvas": "Show Progress on Canvas",
+        "useImage": "Use Image",
         "prompt": "Prompt",
         "negativePrompt": "Negative Prompt",
         "beginEndStepPercentShort": "Begin/End %",
@@ -1780,8 +1783,26 @@
         "newGallerySessionDesc": "This will clear the canvas and all settings except for your model selection. Generations will be sent to the gallery.",
         "newCanvasSession": "New Canvas Session",
         "newCanvasSessionDesc": "This will clear the canvas and all settings except for your model selection. Generations will be staged on the canvas.",
+        "resetCanvasLayers": "Reset Canvas Layers",
+        "resetGenerationSettings": "Reset Generation Settings",
         "replaceCurrent": "Replace Current",
         "controlLayerEmptyState": "<UploadButton>Upload an image</UploadButton>, drag an image from the <GalleryButton>gallery</GalleryButton> onto this layer, or draw on the canvas to get started.",
+        "referenceImageEmptyState": "<UploadButton>Upload an image</UploadButton> or drag an image from the <GalleryButton>gallery</GalleryButton> onto this layer to get started.",
+        "warnings": {
+            "problemsFound": "Problems found",
+            "unsupportedModel": "layer not supported for selected base model",
+            "controlAdapterNoModelSelected": "no Control Layer model selected",
+            "controlAdapterIncompatibleBaseModel": "incompatible Control Layer base model",
+            "controlAdapterNoControl": "no control selected/drawn",
+            "ipAdapterNoModelSelected": "no Reference Image model selected",
+            "ipAdapterIncompatibleBaseModel": "incompatible Reference Image base model",
+            "ipAdapterNoImageSelected": "no Reference Image image selected",
+            "rgNoPromptsOrIPAdapters": "no text prompts or Reference Images",
+            "rgNegativePromptNotSupported": "Negative Prompt not supported for selected base model",
+            "rgReferenceImagesNotSupported": "regional Reference Images not supported for selected base model",
+            "rgAutoNegativeNotSupported": "Auto-Negative not supported for selected base model",
+            "rgNoRegion": "no region drawn"
+        },
         "controlMode": {
             "controlMode": "Control Mode",
             "balanced": "Balanced (recommended)",
@@ -1790,10 +1811,13 @@
             "megaControl": "Mega Control"
         },
         "ipAdapterMethod": {
-            "ipAdapterMethod": "IP Adapter Method",
+            "ipAdapterMethod": "Mode",
             "full": "Style and Composition",
+            "fullDesc": "Applies visual style (colors, textures) & composition (layout, structure).",
             "style": "Style Only",
-            "composition": "Composition Only"
+            "styleDesc": "Applies visual style (colors, textures) without considering its layout.",
+            "composition": "Composition Only",
+            "compositionDesc": "Replicates layout & structure while ignoring the reference's style."
         },
         "fill": {
             "fillColor": "Fill Color",
@@ -2109,11 +2133,73 @@
     "whatsNew": {
         "whatsNewInInvoke": "What's New in Invoke",
         "items": [
-            "<StrongComponent>SD 3.5</StrongComponent>: Support for Text-to-Image in Workflows with SD 3.5 Medium and Large.",
-            "<StrongComponent>Canvas</StrongComponent>: Streamlined Control Layer processing and improved default Control settings."
+            "<StrongComponent>Workflows</StrongComponent>: Run a workflow for a collection of images using the new <StrongComponent>Image Batch</StrongComponent> node.",
+            "<StrongComponent>FLUX</StrongComponent>: Support for XLabs IP Adapter v2."
         ],
         "readReleaseNotes": "Read Release Notes",
         "watchRecentReleaseVideos": "Watch Recent Release Videos",
         "watchUiUpdatesOverview": "Watch UI Updates Overview"
+    },
+    "supportVideos": {
+        "supportVideos": "Support Videos",
+        "gettingStarted": "Getting Started",
+        "controlCanvas": "Control Canvas",
+        "watch": "Watch",
+        "studioSessionsDesc1": "Check out the <StudioSessionsPlaylistLink /> for Invoke deep dives.",
+        "studioSessionsDesc2": "Join our <DiscordLink /> to participate in the live sessions and ask questions. Sessions are uploaded to the playlist the following week.",
+        "videos": {
+            "creatingYourFirstImage": {
+                "title": "Creating Your First Image",
+                "description": "Introduction to creating an image from scratch using Invoke's tools."
+            },
+            "usingControlLayersAndReferenceGuides": {
+                "title": "Using Control Layers and Reference Guides",
+                "description": "Learn how to guide your image creation with control layers and reference images."
+            },
+            "understandingImageToImageAndDenoising": {
+                "title": "Understanding Image-to-Image and Denoising",
+                "description": "Overview of image-to-image transformations and denoising in Invoke."
+            },
+            "exploringAIModelsAndConceptAdapters": {
+                "title": "Exploring AI Models and Concept Adapters",
+                "description": "Dive into AI models and how to use concept adapters for creative control."
+            },
+            "creatingAndComposingOnInvokesControlCanvas": {
+                "title": "Creating and Composing on Invoke's Control Canvas",
+                "description": "Learn to compose images using Invoke's control canvas."
+            },
+            "upscaling": {
+                "title": "Upscaling",
+                "description": "How to upscale images with Invoke's tools to enhance resolution."
+            },
+            "howDoIGenerateAndSaveToTheGallery": {
+                "title": "How Do I Generate and Save to the Gallery?",
+                "description": "Steps to generate and save images to the gallery."
+            },
+            "howDoIEditOnTheCanvas": {
+                "title": "How Do I Edit on the Canvas?",
+                "description": "Guide to editing images directly on the canvas."
+            },
+            "howDoIDoImageToImageTransformation": {
+                "title": "How Do I Do Image-to-Image Transformation?",
+                "description": "Tutorial on performing image-to-image transformations in Invoke."
+            },
+            "howDoIUseControlNetsAndControlLayers": {
+                "title": "How Do I Use Control Nets and Control Layers?",
+                "description": "Learn to apply control layers and controlnets to your images."
+            },
+            "howDoIUseGlobalIPAdaptersAndReferenceImages": {
+                "title": "How Do I Use Global IP Adapters and Reference Images?",
+                "description": "Introduction to adding reference images and global IP adapters."
+            },
+            "howDoIUseInpaintMasks": {
+                "title": "How Do I Use Inpaint Masks?",
+                "description": "How to apply inpaint masks for image correction and variation."
+            },
+            "howDoIOutpaint": {
+                "title": "How Do I Outpaint?",
+                "description": "Guide to outpainting beyond the original image borders."
+            }
+        }
     }
 }

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

@@ -13,7 +13,7 @@
         "discordLabel": "Discord",
         "back": "Atrás",
         "loading": "Cargando",
-        "postprocessing": "Postprocesado",
+        "postprocessing": "Postprocesamiento",
         "txt2img": "De texto a imagen",
         "accept": "Aceptar",
         "cancel": "Cancelar",
@@ -64,7 +64,7 @@
         "prevPage": "Página Anterior",
         "red": "Rojo",
         "alpha": "Transparencia",
-        "outputs": "Salidas",
+        "outputs": "Resultados",
         "learnMore": "Aprende más",
         "enabled": "Activado",
         "disabled": "Desactivado",
@@ -73,7 +73,32 @@
         "created": "Creado",
         "save": "Guardar",
         "unknownError": "Error Desconocido",
-        "blue": "Azul"
+        "blue": "Azul",
+        "clipboard": "Portapapeles",
+        "loadingImage": "Cargando la imagen",
+        "inpaint": "inpaint",
+        "ipAdapter": "Adaptador IP",
+        "t2iAdapter": "Adaptador T2I",
+        "apply": "Aplicar",
+        "openInViewer": "Abrir en el visor",
+        "off": "Apagar",
+        "generating": "Generando",
+        "ok": "De acuerdo",
+        "placeholderSelectAModel": "Seleccionar un modelo",
+        "reset": "Restablecer",
+        "none": "Ninguno",
+        "new": "Nuevo",
+        "dontShowMeThese": "No mostrar estos",
+        "loadingModel": "Cargando el modelo",
+        "view": "Ver",
+        "edit": "Editar",
+        "safetensors": "Safetensors",
+        "toResolve": "Para resolver",
+        "localSystem": "Sistema local",
+        "notInstalled": "No $t(common.installed)",
+        "outpaint": "outpaint",
+        "simple": "Sencillo",
+        "close": "Cerrar"
     },
     "gallery": {
         "galleryImageSize": "Tamaño de la imagen",
@@ -85,7 +110,63 @@
         "deleteImage_other": "Eliminar {{count}} Imágenes",
         "deleteImagePermanent": "Las imágenes eliminadas no se pueden restaurar.",
         "assets": "Activos",
-        "autoAssignBoardOnClick": "Asignación automática de tableros al hacer clic"
+        "autoAssignBoardOnClick": "Asignar automática tableros al hacer clic",
+        "gallery": "Galería",
+        "noImageSelected": "Sin imágenes seleccionadas",
+        "bulkDownloadRequestFailed": "Error al preparar la descarga",
+        "oldestFirst": "La más antigua primero",
+        "sideBySide": "conjuntamente",
+        "selectForCompare": "Seleccionar para comparar",
+        "alwaysShowImageSizeBadge": "Mostrar siempre las dimensiones de la imagen",
+        "currentlyInUse": "Esta imagen se utiliza actualmente con las siguientes funciones:",
+        "unableToLoad": "No se puede cargar la galería",
+        "selectAllOnPage": "Seleccionar todo en la página",
+        "selectAnImageToCompare": "Seleccione una imagen para comparar",
+        "bulkDownloadFailed": "Error en la descarga",
+        "compareHelp2": "Presione <Kbd> M </Kbd> para recorrer los modos de comparación.",
+        "move": "Mover",
+        "copy": "Copiar",
+        "drop": "Gota",
+        "displayBoardSearch": "Tablero de búsqueda",
+        "deleteSelection": "Borrar selección",
+        "downloadSelection": "Descargar selección",
+        "openInViewer": "Abrir en el visor",
+        "searchImages": "Búsqueda por metadatos",
+        "swapImages": "Intercambiar imágenes",
+        "sortDirection": "Orden de clasificación",
+        "showStarredImagesFirst": "Mostrar imágenes destacadas primero",
+        "go": "Ir",
+        "bulkDownloadRequested": "Preparando la descarga",
+        "image": "imagen",
+        "compareHelp4": "Presione <Kbd> Z </Kbd> o <Kbd> Esc </Kbd> para salir.",
+        "viewerImage": "Ver imagen",
+        "dropOrUpload": "$t(gallery.drop) o cargar",
+        "displaySearch": "Buscar imagen",
+        "download": "Descargar",
+        "exitBoardSearch": "Finalizar búsqueda",
+        "exitSearch": "Salir de la búsqueda de imágenes",
+        "featuresWillReset": "Si elimina esta imagen, dichas funciones se restablecerán inmediatamente.",
+        "jump": "Omitir",
+        "loading": "Cargando",
+        "newestFirst": "La más nueva primero",
+        "unstarImage": "Dejar de ser favorita",
+        "bulkDownloadRequestedDesc": "Su solicitud de descarga se está preparando. Esto puede tardar unos minutos.",
+        "hover": "Desplazar",
+        "compareHelp1": "Mantenga presionada la tecla <Kbd> Alt </Kbd> mientras hace clic en una imagen de la galería o utiliza las teclas de flecha para cambiar la imagen de comparación.",
+        "stretchToFit": "Estirar para encajar",
+        "exitCompare": "Salir de la comparación",
+        "starImage": "Imágenes favoritas",
+        "dropToUpload": "$t(gallery.drop) para cargar",
+        "slider": "Deslizador",
+        "assetsTab": "Archivos que has cargado para utilizarlos en tus proyectos.",
+        "imagesTab": "Imágenes que ha creado y guardado en Invoke.",
+        "compareImage": "Comparar imagen",
+        "boardsSettings": "Ajustes de los tableros",
+        "imagesSettings": "Configuración de imágenes de la galería",
+        "compareHelp3": "Presione <Kbd> C </Kbd> para intercambiar las imágenes comparadas.",
+        "showArchivedBoards": "Mostrar paneles archivados",
+        "closeViewer": "Cerrar visor",
+        "openViewer": "Abrir visor"
     },
     "modelManager": {
         "modelManager": "Gestor de Modelos",
@@ -131,7 +212,13 @@
         "modelDeleted": "Modelo eliminado",
         "modelDeleteFailed": "Error al borrar el modelo",
         "settings": "Ajustes",
-        "syncModels": "Sincronizar las plantillas"
+        "syncModels": "Sincronizar las plantillas",
+        "clipEmbed": "Incrustar CLIP",
+        "addModels": "Añadir modelos",
+        "advanced": "Avanzado",
+        "clipGEmbed": "Incrustar CLIP-G",
+        "cancel": "Cancelar",
+        "clipLEmbed": "Incrustar CLIP-L"
     },
     "parameters": {
         "images": "Imágenes",
@@ -158,19 +245,19 @@
         "useSeed": "Usar Semilla",
         "useAll": "Usar Todo",
         "info": "Información",
-        "showOptionsPanel": "Mostrar panel de opciones",
+        "showOptionsPanel": "Mostrar panel lateral (O o T)",
         "symmetry": "Simetría",
         "copyImage": "Copiar la imagen",
         "general": "General",
         "denoisingStrength": "Intensidad de la eliminación del ruido",
-        "seamlessXAxis": "Eje x",
-        "seamlessYAxis": "Eje y",
+        "seamlessXAxis": "Eje X sin juntas",
+        "seamlessYAxis": "Eje Y sin juntas",
         "scheduler": "Programador",
         "positivePromptPlaceholder": "Prompt Positivo",
         "negativePromptPlaceholder": "Prompt Negativo",
         "controlNetControlMode": "Modo de control",
         "clipSkip": "Omitir el CLIP",
-        "maskBlur": "Difuminar",
+        "maskBlur": "Desenfoque de máscara",
         "patchmatchDownScaleSize": "Reducir a escala",
         "coherenceMode": "Modo"
     },
@@ -202,16 +289,19 @@
         "serverError": "Error en el servidor",
         "canceled": "Procesando la cancelación",
         "connected": "Conectado al servidor",
-        "uploadFailedInvalidUploadDesc": "Debe ser una sola imagen PNG o JPEG",
-        "parameterSet": "Conjunto de parámetros",
-        "parameterNotSet": "Parámetro no configurado",
+        "uploadFailedInvalidUploadDesc": "Deben ser imágenes PNG o JPEG.",
+        "parameterSet": "Parámetro recuperado",
+        "parameterNotSet": "Parámetro no recuperado",
         "problemCopyingImage": "No se puede copiar la imagen",
         "errorCopied": "Error al copiar",
         "baseModelChanged": "Modelo base cambiado",
-        "addedToBoard": "Añadido al tablero",
+        "addedToBoard": "Se agregó a los activos del panel {{name}}",
         "baseModelChangedCleared_one": "Borrado o desactivado {{count}} submodelo incompatible",
         "baseModelChangedCleared_many": "Borrados o desactivados {{count}} submodelos incompatibles",
-        "baseModelChangedCleared_other": "Borrados o desactivados {{count}} submodelos incompatibles"
+        "baseModelChangedCleared_other": "Borrados o desactivados {{count}} submodelos incompatibles",
+        "addedToUncategorized": "Añadido a los activos del tablero $t(boards.uncategorized)",
+        "imagesWillBeAddedTo": "Las imágenes subidas se añadirán a los activos del panel {{boardName}}.",
+        "layerCopiedToClipboard": "Capa copiada en el portapapeles"
     },
     "accessibility": {
         "invokeProgressBar": "Activar la barra de progreso",
@@ -226,7 +316,8 @@
         "mode": "Modo",
         "submitSupportTicket": "Enviar Ticket de Soporte",
         "toggleRightPanel": "Activar o desactivar el panel derecho (G)",
-        "toggleLeftPanel": "Activar o desactivar el panel izquierdo (T)"
+        "toggleLeftPanel": "Activar o desactivar el panel izquierdo (T)",
+        "uploadImages": "Cargar imagen(es)"
     },
     "nodes": {
         "zoomInNodes": "Acercar",
@@ -238,7 +329,8 @@
         "showMinimapnodes": "Mostrar el minimapa",
         "reloadNodeTemplates": "Recargar las plantillas de nodos",
         "loadWorkflow": "Cargar el flujo de trabajo",
-        "downloadWorkflow": "Descargar el flujo de trabajo en un archivo JSON"
+        "downloadWorkflow": "Descargar el flujo de trabajo en un archivo JSON",
+        "boardAccessError": "No se puede encontrar el panel {{board_id}}, se está restableciendo al valor predeterminado"
     },
     "boards": {
         "autoAddBoard": "Agregar panel automáticamente",
@@ -255,7 +347,7 @@
         "bottomMessage": "Al eliminar este panel y las imágenes que contiene, se restablecerán las funciones que los estén utilizando actualmente.",
         "deleteBoardAndImages": "Borrar el panel y las imágenes",
         "loading": "Cargando...",
-        "deletedBoardsCannotbeRestored": "Los paneles eliminados no se pueden restaurar. Al Seleccionar 'Borrar Solo el Panel' transferirá las imágenes a un estado sin categorizar.",
+        "deletedBoardsCannotbeRestored": "Los paneles eliminados no se pueden restaurar. Al Seleccionar 'Borrar solo el panel' transferirá las imágenes a un estado sin categorizar.",
         "move": "Mover",
         "menuItemAutoAdd": "Agregar automáticamente a este panel",
         "searchBoard": "Buscando paneles…",
@@ -263,29 +355,33 @@
         "downloadBoard": "Descargar panel",
         "deleteBoardOnly": "Borrar solo el panel",
         "myBoard": "Mi panel",
-        "noMatching": "No hay paneles que coincidan",
+        "noMatching": "Sin paneles coincidentes",
         "imagesWithCount_one": "{{count}} imagen",
         "imagesWithCount_many": "{{count}} imágenes",
         "imagesWithCount_other": "{{count}} imágenes",
         "assetsWithCount_one": "{{count}} activo",
         "assetsWithCount_many": "{{count}} activos",
         "assetsWithCount_other": "{{count}} activos",
-        "hideBoards": "Ocultar Paneles",
-        "addPrivateBoard": "Agregar un tablero privado",
-        "addSharedBoard": "Agregar Panel Compartido",
+        "hideBoards": "Ocultar paneles",
+        "addPrivateBoard": "Agregar un panel privado",
+        "addSharedBoard": "Añadir panel compartido",
         "boards": "Paneles",
-        "archiveBoard": "Archivar Panel",
+        "archiveBoard": "Archivar panel",
         "archived": "Archivado",
         "selectedForAutoAdd": "Seleccionado para agregar automáticamente",
-        "unarchiveBoard": "Desarchivar el tablero",
-        "noBoards": "No hay tableros {{boardType}}",
-        "shared": "Carpetas compartidas",
-        "deletedPrivateBoardsCannotbeRestored": "Los tableros eliminados no se pueden restaurar. Al elegir \"Eliminar solo tablero\", las imágenes se colocan en un estado privado y sin categoría para el creador de la imagen."
+        "unarchiveBoard": "Desarchivar el panel",
+        "noBoards": "No hay paneles {{boardType}}",
+        "shared": "Paneles compartidos",
+        "deletedPrivateBoardsCannotbeRestored": "Los paneles eliminados no se pueden restaurar. Al elegir \"Eliminar solo el panel\", las imágenes se colocan en un estado privado y sin categoría para el creador de la imagen.",
+        "viewBoards": "Ver paneles",
+        "private": "Paneles privados",
+        "updateBoardError": "No se pudo actualizar el panel"
     },
     "accordions": {
         "compositing": {
             "title": "Composición",
-            "infillTab": "Relleno"
+            "infillTab": "Relleno",
+            "coherenceTab": "Parámetros de la coherencia"
         },
         "generation": {
             "title": "Generación"
@@ -309,7 +405,10 @@
             "workflows": "Flujos de trabajo",
             "models": "Modelos",
             "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
-            "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)"
+            "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)",
+            "upscaling": "Upscaling",
+            "gallery": "Galería",
+            "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)"
         }
     },
     "queue": {
@@ -317,12 +416,81 @@
         "front": "Delante",
         "batchQueuedDesc_one": "Se agregó {{count}} sesión a {{direction}} la cola",
         "batchQueuedDesc_many": "Se agregaron {{count}} sesiones a {{direction}} la cola",
-        "batchQueuedDesc_other": "Se agregaron {{count}} sesiones a {{direction}} la cola"
+        "batchQueuedDesc_other": "Se agregaron {{count}} sesiones a {{direction}} la cola",
+        "clearQueueAlertDialog": "Al vaciar la cola se cancela inmediatamente cualquier elemento de procesamiento y se vaciará la cola por completo. Los filtros pendientes se cancelarán.",
+        "time": "Tiempo",
+        "clearFailed": "Error al vaciar la cola",
+        "cancelFailed": "Error al cancelar el elemento",
+        "resumeFailed": "Error al reanudar el proceso",
+        "pause": "Pausar",
+        "pauseTooltip": "Pausar el proceso",
+        "cancelBatchSucceeded": "Lote cancelado",
+        "pruneSucceeded": "Se purgaron {{item_count}} elementos completados de la cola",
+        "pruneFailed": "Error al purgar la cola",
+        "cancelBatchFailed": "Error al cancelar los lotes",
+        "pauseFailed": "Error al pausar el proceso",
+        "status": "Estado",
+        "origin": "Origen",
+        "destination": "Destino",
+        "generations_one": "Generación",
+        "generations_many": "Generaciones",
+        "generations_other": "Generaciones",
+        "resume": "Reanudar",
+        "queueEmpty": "Cola vacía",
+        "cancelItem": "Cancelar elemento",
+        "cancelBatch": "Cancelar lote",
+        "openQueue": "Abrir la cola",
+        "completed": "Completado",
+        "enqueueing": "Añadir lotes a la cola",
+        "clear": "Limpiar",
+        "pauseSucceeded": "Proceso pausado",
+        "resumeSucceeded": "Proceso reanudado",
+        "resumeTooltip": "Reanudar proceso",
+        "cancel": "Cancelar",
+        "cancelTooltip": "Cancelar artículo actual",
+        "pruneTooltip": "Purgar {{item_count}} elementos completados",
+        "batchQueued": "Lote en cola",
+        "pending": "Pendiente",
+        "item": "Elemento",
+        "total": "Total",
+        "in_progress": "En proceso",
+        "failed": "Fallido",
+        "completedIn": "Completado en",
+        "upscaling": "Upscaling",
+        "canvas": "Lienzo",
+        "generation": "Generación",
+        "workflows": "Flujo de trabajo",
+        "other": "Otro",
+        "queueFront": "Añadir al principio de la cola",
+        "gallery": "Galería",
+        "batchFieldValues": "Valores de procesamiento por lotes",
+        "session": "Sesión",
+        "notReady": "La cola aún no está lista",
+        "graphQueued": "Gráfico en cola",
+        "clearQueueAlertDialog2": "¿Estás seguro que deseas vaciar la cola?",
+        "next": "Siguiente",
+        "iterations_one": "Interacción",
+        "iterations_many": "Interacciones",
+        "iterations_other": "Interacciones",
+        "current": "Actual",
+        "queue": "Cola",
+        "queueBack": "Añadir a la cola",
+        "cancelSucceeded": "Elemento cancelado",
+        "clearTooltip": "Cancelar y limpiar todos los elementos",
+        "clearSucceeded": "Cola vaciada",
+        "canceled": "Cancelado",
+        "batch": "Lote",
+        "graphFailedToQueue": "Error al poner el gráfico en cola",
+        "batchFailedToQueue": "Error al poner en cola el lote",
+        "prompts_one": "Prompt",
+        "prompts_many": "Prompts",
+        "prompts_other": "Prompts",
+        "prune": "Eliminar"
     },
     "upsell": {
         "inviteTeammates": "Invitar compañeros de equipo",
         "shareAccess": "Compartir acceso",
-        "professionalUpsell": "Disponible en la edición profesional de Invoke. Haz clic aquí o visita invoke.com/pricing para obtener más detalles."
+        "professionalUpsell": "Disponible en la edición profesional de Invoke. Haga clic aquí o visite invoke.com/pricing para obtener más detalles."
     },
     "controlLayers": {
         "layer_one": "Capa",
@@ -330,6 +498,415 @@
         "layer_other": "Capas",
         "layer_withCount_one": "({{count}}) capa",
         "layer_withCount_many": "({{count}}) capas",
-        "layer_withCount_other": "({{count}}) capas"
+        "layer_withCount_other": "({{count}}) capas",
+        "copyToClipboard": "Copiar al portapapeles"
+    },
+    "whatsNew": {
+        "readReleaseNotes": "Leer las notas de la versión",
+        "watchRecentReleaseVideos": "Ver videos de versiones recientes",
+        "watchUiUpdatesOverview": "Descripción general de las actualizaciones de la interfaz de usuario de Watch",
+        "whatsNewInInvoke": "Novedades en Invoke",
+        "items": [
+            "<StrongComponent>SD 3.5</StrongComponent>: compatibilidad con SD 3.5 Medium y Large.",
+            "<StrongComponent>Lienzo</StrongComponent>: Se ha simplificado el procesamiento de la capa de control y se ha mejorado la configuración predeterminada del control."
+        ]
+    },
+    "invocationCache": {
+        "enableFailed": "Error al activar la cache",
+        "cacheSize": "Tamaño de la caché",
+        "hits": "Accesos a la caché",
+        "invocationCache": "Caché",
+        "misses": "Errores de la caché",
+        "clear": "Limpiar",
+        "maxCacheSize": "Tamaño máximo de la caché",
+        "enableSucceeded": "Cache activada",
+        "clearFailed": "Error al borrar la cache",
+        "enable": "Activar",
+        "useCache": "Uso de la caché",
+        "disableSucceeded": "Caché desactivada",
+        "clearSucceeded": "Caché borrada",
+        "disable": "Desactivar",
+        "disableFailed": "Error al desactivar la caché"
+    },
+    "hrf": {
+        "hrf": "Solución de alta resolución",
+        "enableHrf": "Activar corrección de alta resolución",
+        "metadata": {
+            "enabled": "Corrección de alta resolución activada",
+            "strength": "Forzar la corrección de alta resolución",
+            "method": "Método de corrección de alta resolución"
+        },
+        "upscaleMethod": "Método de expansión"
+    },
+    "prompt": {
+        "addPromptTrigger": "Añadir activador de los avisos",
+        "compatibleEmbeddings": "Incrustaciones compatibles",
+        "noMatchingTriggers": "No hay activadores coincidentes"
+    },
+    "hotkeys": {
+        "hotkeys": "Atajo del teclado",
+        "canvas": {
+            "selectViewTool": {
+                "desc": "Selecciona la herramienta de Visualización.",
+                "title": "Visualización"
+            },
+            "cancelFilter": {
+                "title": "Cancelar el filtro",
+                "desc": "Cancelar el filtro pendiente."
+            },
+            "applyTransform": {
+                "title": "Aplicar la transformación",
+                "desc": "Aplicar la transformación pendiente a la capa seleccionada."
+            },
+            "applyFilter": {
+                "desc": "Aplicar el filtro pendiente a la capa seleccionada.",
+                "title": "Aplicar filtro"
+            },
+            "selectBrushTool": {
+                "title": "Pincel",
+                "desc": "Selecciona la herramienta pincel."
+            },
+            "selectBboxTool": {
+                "desc": "Seleccionar la herramienta de selección del marco.",
+                "title": "Selección del marco"
+            },
+            "selectMoveTool": {
+                "desc": "Selecciona la herramienta Mover.",
+                "title": "Mover"
+            },
+            "selectRectTool": {
+                "title": "Rectángulo",
+                "desc": "Selecciona la herramienta Rectángulo."
+            },
+            "decrementToolWidth": {
+                "title": "Reducir el ancho de la herramienta",
+                "desc": "Disminuye la anchura de la herramienta pincel o goma de borrar, según la que esté seleccionada."
+            },
+            "incrementToolWidth": {
+                "title": "Incrementar la anchura de la herramienta",
+                "desc": "Aumenta la anchura de la herramienta pincel o goma de borrar, según la que esté seleccionada."
+            },
+            "fitBboxToCanvas": {
+                "title": "Ajustar bordes al lienzo",
+                "desc": "Escala y posiciona la vista para ajustarla a los bodes."
+            },
+            "fitLayersToCanvas": {
+                "title": "Ajustar capas al lienzo",
+                "desc": "Escala y posiciona la vista para que se ajuste a todas las capas visibles."
+            },
+            "setFillToWhite": {
+                "title": "Establecer color en blanco",
+                "desc": "Establece el color actual de la herramienta en blanco."
+            },
+            "resetSelected": {
+                "title": "Restablecer capa",
+                "desc": "Restablecer la capa seleccionada. Solo se aplica a Máscara de retoque y Guía regional."
+            },
+            "setZoomTo400Percent": {
+                "desc": "Ajuste la aplicación del lienzo al 400%.",
+                "title": "Ampliar al 400%"
+            },
+            "transformSelected": {
+                "desc": "Transformar la capa seleccionada.",
+                "title": "Transformar"
+            },
+            "selectColorPickerTool": {
+                "title": "Selector de color",
+                "desc": "Seleccione la herramienta de selección de color."
+            },
+            "selectEraserTool": {
+                "title": "Borrador",
+                "desc": "Selecciona la herramienta Borrador."
+            },
+            "setZoomTo100Percent": {
+                "title": "Ampliar al 100%",
+                "desc": "Ajuste ampliar el lienzo al 100%."
+            },
+            "undo": {
+                "title": "Deshacer",
+                "desc": "Deshacer la última acción en el lienzo."
+            },
+            "nextEntity": {
+                "desc": "Seleccione la siguiente capa de la lista.",
+                "title": "Capa siguiente"
+            },
+            "redo": {
+                "title": "Rehacer",
+                "desc": "Rehacer la última acción en el lienzo."
+            },
+            "prevEntity": {
+                "title": "Capa anterior",
+                "desc": "Seleccione la capa anterior de la lista."
+            },
+            "title": "Lienzo",
+            "setZoomTo200Percent": {
+                "title": "Ampliar al 200%",
+                "desc": "Ajuste la ampliación del lienzo al 200%."
+            },
+            "setZoomTo800Percent": {
+                "title": "Ampliar al 800%",
+                "desc": "Ajuste la ampliación del lienzo al 800%."
+            },
+            "filterSelected": {
+                "desc": "Filtra la capa seleccionada. Solo se aplica a las capas Ráster y Control.",
+                "title": "Filtrar"
+            },
+            "cancelTransform": {
+                "title": "Cancelar transformación",
+                "desc": "Cancelar la transformación pendiente."
+            },
+            "deleteSelected": {
+                "title": "Borrar la capa",
+                "desc": "Borrar la capa seleccionada."
+            },
+            "quickSwitch": {
+                "desc": "Cambiar entre las dos últimas capas seleccionadas. Si una capa está seleccionada, cambia siempre entre ella y la última capa no seleccionada.",
+                "title": "Cambio rápido de capa"
+            }
+        },
+        "app": {
+            "selectModelsTab": {
+                "title": "Seleccione la pestaña Modelos",
+                "desc": "Selecciona la pestaña Modelos."
+            },
+            "focusPrompt": {
+                "desc": "Mueve el foco del cursor a la indicación positiva.",
+                "title": "Enfoque"
+            },
+            "toggleLeftPanel": {
+                "title": "Alternar panel izquierdo",
+                "desc": "Mostrar u ocultar el panel izquierdo."
+            },
+            "selectQueueTab": {
+                "title": "Seleccione la pestaña Cola",
+                "desc": "Seleccione la pestaña Cola."
+            },
+            "selectCanvasTab": {
+                "title": "Seleccione la pestaña Lienzo",
+                "desc": "Selecciona la pestaña Lienzo."
+            },
+            "clearQueue": {
+                "title": "Vaciar cola",
+                "desc": "Cancelar y variar todos los elementos de la cola."
+            },
+            "selectUpscalingTab": {
+                "title": "Selecciona la pestaña Ampliar",
+                "desc": "Selecciona la pestaña Aumento de escala."
+            },
+            "togglePanels": {
+                "desc": "Muestra u oculta los paneles izquierdo y derecho a la vez.",
+                "title": "Alternar paneles"
+            },
+            "toggleRightPanel": {
+                "title": "Alternar panel derecho",
+                "desc": "Mostrar u ocultar el panel derecho."
+            },
+            "invokeFront": {
+                "desc": "Pone en cola la solicitud de compilación y la agrega al principio de la cola.",
+                "title": "Invocar (frente)"
+            },
+            "cancelQueueItem": {
+                "title": "Cancelar",
+                "desc": "Cancelar el elemento de la cola que se está procesando."
+            },
+            "invoke": {
+                "desc": "Pone en cola la solicitud de compilación y la agrega al final de la cola.",
+                "title": "Invocar"
+            },
+            "title": "Aplicación",
+            "selectWorkflowsTab": {
+                "title": "Seleccione la pestaña Flujos de trabajo",
+                "desc": "Selecciona la pestaña Flujos de trabajo."
+            },
+            "resetPanelLayout": {
+                "title": "Reiniciar la posición del panel",
+                "desc": "Restablece los paneles izquierdo y derecho a su tamaño y disposición por defecto."
+            }
+        },
+        "workflows": {
+            "addNode": {
+                "title": "Añadir nodo",
+                "desc": "Abrir añadir nodo."
+            },
+            "selectAll": {
+                "title": "Seleccionar todo",
+                "desc": "Seleccione todos los nodos y enlaces."
+            },
+            "deleteSelection": {
+                "desc": "Borrar todos los nodos y enlaces seleccionados.",
+                "title": "Borrar"
+            },
+            "undo": {
+                "desc": "Deshaga la última acción.",
+                "title": "Deshacer"
+            },
+            "redo": {
+                "desc": "Rehacer la última acción.",
+                "title": "Rehacer"
+            },
+            "pasteSelection": {
+                "desc": "Pegar nodos y bordes copiados.",
+                "title": "Pegar"
+            },
+            "title": "Flujos de trabajo",
+            "copySelection": {
+                "desc": "Copiar nodos y bordes seleccionados.",
+                "title": "Copiar"
+            },
+            "pasteSelectionWithEdges": {
+                "desc": "Pega los nodos copiados, los enlaces y todos los enlaces conectados a los nodos copiados.",
+                "title": "Pegar con enlaces"
+            }
+        },
+        "viewer": {
+            "useSize": {
+                "title": "Usar dimensiones",
+                "desc": "Utiliza las dimensiones de la imagen actual como el tamaño del borde."
+            },
+            "remix": {
+                "title": "Remezcla",
+                "desc": "Recupera todos los metadatos excepto la semilla de la imagen actual."
+            },
+            "loadWorkflow": {
+                "desc": "Carga el flujo de trabajo guardado de la imagen actual (si tiene uno).",
+                "title": "Cargar flujo de trabajo"
+            },
+            "recallAll": {
+                "desc": "Recupera todos los metadatos de la imagen actual.",
+                "title": "Recuperar todos los metadatos"
+            },
+            "recallPrompts": {
+                "desc": "Recuerde las indicaciones positivas y negativas de la imagen actual.",
+                "title": "Recordatorios"
+            },
+            "recallSeed": {
+                "title": "Recuperar semilla",
+                "desc": "Recupera la semilla de la imagen actual."
+            },
+            "runPostprocessing": {
+                "title": "Ejecutar posprocesamiento",
+                "desc": "Ejecutar el posprocesamiento seleccionado en la imagen actual."
+            },
+            "toggleMetadata": {
+                "title": "Mostrar/ocultar los metadatos",
+                "desc": "Mostrar u ocultar la superposición de metadatos de la imagen actual."
+            },
+            "nextComparisonMode": {
+                "desc": "Desplácese por los modos de comparación.",
+                "title": "Siguiente comparación"
+            },
+            "title": "Visor de imágenes",
+            "toggleViewer": {
+                "title": "Mostrar/Ocultar el visor de imágenes",
+                "desc": "Mostrar u ocultar el visor de imágenes. Solo disponible en la pestaña Lienzo."
+            },
+            "swapImages": {
+                "title": "Intercambiar imágenes en la comparación",
+                "desc": "Intercambia las imágenes que se están comparando."
+            }
+        },
+        "gallery": {
+            "clearSelection": {
+                "title": "Limpiar selección",
+                "desc": "Borrar la selección actual, si hay alguna."
+            },
+            "galleryNavUp": {
+                "title": "Subir",
+                "desc": "Navega hacia arriba en la cuadrícula de la galería y selecciona esa imagen. Si estás en la parte superior de la página, ve a la página anterior."
+            },
+            "galleryNavLeft": {
+                "title": "Izquierda",
+                "desc": "Navegue hacia la izquierda en la rejilla de la galería, seleccionando esa imagen. Si está en la primera imagen de la fila, vaya a la fila anterior. Si está en la primera imagen de la página, vaya a la página anterior."
+            },
+            "galleryNavDown": {
+                "title": "Bajar",
+                "desc": "Navegue hacia abajo en la parrilla de la galería, seleccionando esa imagen. Si se encuentra al final de la página, vaya a la página siguiente."
+            },
+            "galleryNavRight": {
+                "title": "A la derecha",
+                "desc": "Navegue hacia la derecha en la rejilla de la galería, seleccionando esa imagen. Si está en la última imagen de la fila, vaya a la fila siguiente. Si está en la última imagen de la página, vaya a la página siguiente."
+            },
+            "galleryNavUpAlt": {
+                "desc": "Igual que arriba, pero selecciona la imagen de comparación, abriendo el modo de comparación si no está ya abierto.",
+                "title": "Arriba (Comparar imagen)"
+            },
+            "deleteSelection": {
+                "desc": "Borrar todas las imágenes seleccionadas. Por defecto, se le pedirá que confirme la eliminación. Si las imágenes están actualmente en uso en la aplicación, se te avisará.",
+                "title": "Borrar"
+            },
+            "title": "Galería",
+            "selectAllOnPage": {
+                "title": "Seleccionar todo en la página",
+                "desc": "Seleccionar todas las imágenes en la página actual."
+            }
+        },
+        "searchHotkeys": "Buscar teclas de acceso rápido",
+        "noHotkeysFound": "Sin teclas de acceso rápido",
+        "clearSearch": "Limpiar la búsqueda"
+    },
+    "metadata": {
+        "guidance": "Orientación",
+        "createdBy": "Creado por",
+        "noImageDetails": "Sin detalles en la imagen",
+        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)",
+        "height": "Altura",
+        "imageDimensions": "Dimensiones de la imagen",
+        "seamlessXAxis": "Eje X sin juntas",
+        "seamlessYAxis": "Eje Y sin juntas",
+        "generationMode": "Modo de generación",
+        "scheduler": "Programador",
+        "width": "Ancho",
+        "Threshold": "Umbral de ruido",
+        "canvasV2Metadata": "Lienzo",
+        "metadata": "Metadatos",
+        "model": "Modelo",
+        "allPrompts": "Todas las indicaciones",
+        "cfgScale": "Escala CFG",
+        "imageDetails": "Detalles de la imagen",
+        "negativePrompt": "Indicación negativa",
+        "noMetaData": "Sin metadatos",
+        "parameterSet": "Parámetro {{parameter}} establecido",
+        "vae": "Autocodificador",
+        "workflow": "Flujo de trabajo",
+        "seed": "Semilla",
+        "strength": "Forzar imagen a imagen",
+        "recallParameters": "Parámetros de recuperación",
+        "recallParameter": "Recuperar {{label}}",
+        "steps": "Pasos",
+        "noRecallParameters": "Sin parámetros para recuperar",
+        "parsingFailed": "Error al analizar"
+    },
+    "system": {
+        "logLevel": {
+            "debug": "Depurar",
+            "info": "Información",
+            "warn": "Advertir",
+            "fatal": "Grave",
+            "error": "Error",
+            "trace": "Rastro",
+            "logLevel": "Nivel del registro"
+        },
+        "enableLogging": "Activar registro",
+        "logNamespaces": {
+            "workflows": "Flujos de trabajo",
+            "system": "Sistema",
+            "metadata": "Metadatos",
+            "gallery": "Galería",
+            "logNamespaces": "Espacios para los nombres de registro",
+            "generation": "Generación",
+            "events": "Eventos",
+            "canvas": "Lienzo",
+            "config": "Ajustes",
+            "models": "Modelos",
+            "queue": "Cola"
+        }
+    },
+    "newUserExperience": {
+        "downloadStarterModels": "Descargar modelos de inicio",
+        "toGetStarted": "Para empezar, introduzca un mensaje en el cuadro y haga clic en <StrongComponent>Invocar</StrongComponent> para generar su primera imagen. Seleccione una plantilla para mejorar los resultados. Puede elegir guardar sus imágenes directamente en <StrongComponent>Galería</StrongComponent> o editarlas en <StrongComponent>Lienzo</StrongComponent>.",
+        "importModels": "Importar modelos",
+        "noModelsInstalled": "Parece que no tienes ningún modelo instalado",
+        "gettingStartedSeries": "¿Desea más orientación? Consulte nuestra <LinkComponent>Serie de introducción</LinkComponent> para obtener consejos sobre cómo aprovechar todo el potencial de Invoke Studio.",
+        "toGetStartedLocal": "Para empezar, asegúrate de descargar o importar los modelos necesarios para ejecutar Invoke. A continuación, introduzca un mensaje en el cuadro y haga clic en <StrongComponent>Invocar</StrongComponent> para generar su primera imagen. Seleccione una plantilla para mejorar los resultados. Puede elegir guardar sus imágenes directamente en <StrongComponent>Galería</StrongComponent> o editarlas en el <StrongComponent>Lienzo</StrongComponent>."
     }
 }

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

@@ -327,7 +327,6 @@
                 "t2iAdapterIncompatibleBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, la hauteur de la bounding box est {{height}}",
                 "t2iAdapterIncompatibleBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, la largeur de la bounding box est {{width}}",
                 "ipAdapterIncompatibleBaseModel": "modèle de base d'IP adapter incompatible",
-                "rgNoRegion": "aucune zone sélectionnée",
                 "controlAdapterNoModelSelected": "aucun modèle de Control Adapter sélectionné"
             },
             "noPrompts": "Aucun prompts généré",
@@ -1985,7 +1984,6 @@
         "inpaintMask_withCount_many": "Remplir les masques",
         "inpaintMask_withCount_other": "Remplir les masques",
         "newImg2ImgCanvasFromImage": "Nouvelle Img2Img à partir de l'image",
-        "resetCanvas": "Réinitialiser la Toile",
         "bboxOverlay": "Afficher la superposition des Bounding Box",
         "moveToFront": "Déplacer vers le permier plan",
         "moveToBack": "Déplacer vers l'arrière plan",
@@ -2034,7 +2032,6 @@
             "help2": "Commencez par un point <Bold>Inclure</Bold> au sein de l'objet cible. Ajoutez d'autres points pour affiner la sélection. Moins de points produisent généralement de meilleurs résultats.",
             "help3": "Inversez la sélection pour sélectionner tout sauf l'objet cible."
         },
-        "canvasAsControlLayer": "$t(controlLayers.canvas) en tant que $t(controlLayers.controlLayer)",
         "convertRegionalGuidanceTo": "Convertir $t(controlLayers.regionalGuidance) vers",
         "copyRasterLayerTo": "Copier $t(controlLayers.rasterLayer) vers",
         "newControlLayer": "Nouveau $t(controlLayers.controlLayer)",
@@ -2044,8 +2041,7 @@
         "convertInpaintMaskTo": "Convertir $t(controlLayers.inpaintMask) vers",
         "copyControlLayerTo": "Copier $t(controlLayers.controlLayer) vers",
         "newInpaintMask": "Nouveau $t(controlLayers.inpaintMask)",
-        "newRasterLayer": "Nouveau $t(controlLayers.rasterLayer)",
-        "canvasAsRasterLayer": "$t(controlLayers.canvas) en tant que $t(controlLayers.rasterLayer)"
+        "newRasterLayer": "Nouveau $t(controlLayers.rasterLayer)"
     },
     "upscaling": {
         "exceedsMaxSizeDetails": "La limite maximale d'agrandissement est de {{maxUpscaleDimension}}x{{maxUpscaleDimension}} pixels. Veuillez essayer une image plus petite ou réduire votre sélection d'échelle.",

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

@@ -94,7 +94,10 @@
         "view": "Vista",
         "close": "Chiudi",
         "clipboard": "Appunti",
-        "ok": "Ok"
+        "ok": "Ok",
+        "generating": "Generazione",
+        "loadingModel": "Caricamento del modello",
+        "warnings": "Avvisi"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -597,7 +600,18 @@
         "huggingFace": "HuggingFace",
         "huggingFaceRepoID": "HuggingFace Repository ID",
         "clipEmbed": "CLIP Embed",
-        "t5Encoder": "T5 Encoder"
+        "t5Encoder": "T5 Encoder",
+        "hfTokenInvalidErrorMessage": "Gettone HuggingFace non valido o mancante.",
+        "hfTokenRequired": "Stai tentando di scaricare un modello che richiede un gettone HuggingFace valido.",
+        "hfTokenUnableToVerifyErrorMessage": "Impossibile verificare il gettone HuggingFace. Ciò è probabilmente dovuto a un errore di rete. Riprova più tardi.",
+        "hfTokenHelperText": "Per utilizzare alcuni modelli è necessario un gettone HF. Fai clic qui per creare o ottenere il tuo gettone.",
+        "hfTokenInvalid": "Gettone HF non valido o mancante",
+        "hfTokenUnableToVerify": "Impossibile verificare il gettone HF",
+        "hfTokenSaved": "Gettone HF salvato",
+        "hfForbidden": "Non hai accesso a questo modello HF",
+        "hfTokenLabel": "Gettone HuggingFace (richiesto per alcuni modelli)",
+        "hfForbiddenErrorMessage": "Consigliamo di visitare la pagina del repository su HuggingFace.com. Il proprietario potrebbe richiedere l'accettazione dei termini per poter effettuare il download.",
+        "hfTokenInvalidErrorMessage2": "Aggiornalo in "
     },
     "parameters": {
         "images": "Immagini",
@@ -658,11 +672,15 @@
                 "ipAdapterIncompatibleBaseModel": "Il modello base dell'adattatore IP non è compatibile",
                 "ipAdapterNoImageSelected": "Nessuna immagine dell'adattatore IP selezionata",
                 "rgNoPromptsOrIPAdapters": "Nessun prompt o adattatore IP",
-                "rgNoRegion": "Nessuna regione selezionata",
                 "t2iAdapterIncompatibleBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, larghezza riquadro è {{width}}",
                 "t2iAdapterIncompatibleBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, altezza riquadro è {{height}}",
                 "t2iAdapterIncompatibleScaledBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, larghezza del riquadro scalato {{width}}",
-                "t2iAdapterIncompatibleScaledBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, altezza del riquadro scalato {{height}}"
+                "t2iAdapterIncompatibleScaledBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, altezza del riquadro scalato {{height}}",
+                "rgNegativePromptNotSupported": "prompt negativo non supportato per il modello base selezionato",
+                "rgAutoNegativeNotSupported": "auto-negativo non supportato per il modello base selezionato",
+                "emptyLayer": "livello vuoto",
+                "unsupportedModel": "livello non supportato per il modello base selezionato",
+                "rgReferenceImagesNotSupported": "immagini di riferimento regionali non supportate per il modello base selezionato"
             },
             "fluxModelIncompatibleBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), altezza riquadro è {{height}}",
             "fluxModelIncompatibleBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), larghezza riquadro è {{width}}",
@@ -674,7 +692,11 @@
             "canvasIsTransforming": "La tela sta trasformando",
             "canvasIsRasterizing": "La tela sta rasterizzando",
             "canvasIsCompositing": "La tela è in fase di composizione",
-            "canvasIsFiltering": "La tela sta filtrando"
+            "canvasIsFiltering": "La tela sta filtrando",
+            "collectionTooManyItems": "{{nodeLabel}} -> {{fieldLabel}}: troppi elementi, massimo {{maxItems}}",
+            "canvasIsSelectingObject": "La tela è occupata (selezione dell'oggetto)",
+            "collectionTooFewItems": "{{nodeLabel}} -> {{fieldLabel}}: troppi pochi elementi, minimo {{minItems}}",
+            "collectionEmpty": "{{nodeLabel}} -> {{fieldLabel}} raccolta vuota"
         },
         "useCpuNoise": "Usa la CPU per generare rumore",
         "iterations": "Iterazioni",
@@ -699,7 +721,9 @@
         "staged": "Maschera espansa",
         "optimizedImageToImage": "Immagine-a-immagine ottimizzata",
         "sendToCanvas": "Invia alla Tela",
-        "coherenceMinDenoise": "Riduzione minima del rumore"
+        "coherenceMinDenoise": "Min rid. rumore",
+        "recallMetadata": "Richiama i metadati",
+        "disabledNoRasterContent": "Disabilitato (nessun contenuto Raster)"
     },
     "settings": {
         "models": "Modelli",
@@ -737,7 +761,8 @@
         "confirmOnNewSession": "Conferma su nuova sessione",
         "enableModelDescriptions": "Abilita le descrizioni dei modelli nei menu a discesa",
         "modelDescriptionsDisabled": "Descrizioni dei modelli nei menu a discesa disabilitate",
-        "modelDescriptionsDisabledDesc": "Le descrizioni dei modelli nei menu a discesa sono state disabilitate. Abilitale nelle Impostazioni."
+        "modelDescriptionsDisabledDesc": "Le descrizioni dei modelli nei menu a discesa sono state disabilitate. Abilitale nelle Impostazioni.",
+        "showDetailedInvocationProgress": "Mostra dettagli avanzamento"
     },
     "toast": {
         "uploadFailed": "Caricamento fallito",
@@ -956,7 +981,9 @@
         "saveToGallery": "Salva nella Galleria",
         "noMatchingWorkflows": "Nessun flusso di lavoro corrispondente",
         "noWorkflows": "Nessun flusso di lavoro",
-        "workflowHelpText": "Hai bisogno di aiuto? Consulta la nostra guida <LinkComponent>Introduzione ai flussi di lavoro</LinkComponent>."
+        "workflowHelpText": "Hai bisogno di aiuto? Consulta la nostra guida <LinkComponent>Introduzione ai flussi di lavoro</LinkComponent>.",
+        "specialDesc": "Questa invocazione comporta una gestione speciale nell'applicazione. Ad esempio, i nodi Lotto vengono utilizzati per mettere in coda più grafici da un singolo flusso di lavoro.",
+        "internalDesc": "Questa invocazione è utilizzata internamente da Invoke. Potrebbe subire modifiche significative durante gli aggiornamenti dell'app e potrebbe essere rimossa in qualsiasi momento."
     },
     "boards": {
         "autoAddBoard": "Aggiungi automaticamente bacheca",
@@ -1077,7 +1104,8 @@
         "workflows": "Flussi di lavoro",
         "generation": "Generazione",
         "other": "Altro",
-        "gallery": "Galleria"
+        "gallery": "Galleria",
+        "batchSize": "Dimensione del lotto"
     },
     "models": {
         "noMatchingModels": "Nessun modello corrispondente",
@@ -1180,7 +1208,8 @@
             "heading": "Percentuale passi Inizio / Fine",
             "paragraphs": [
                 "La parte del processo di rimozione del rumore in cui verrà applicato l'adattatore di controllo.",
-                "In genere, gli adattatori di controllo applicati all'inizio del processo guidano la composizione, mentre quelli applicati alla fine guidano i dettagli."
+                "In genere, gli adattatori di controllo applicati all'inizio del processo guidano la composizione, mentre quelli applicati alla fine guidano i dettagli.",
+                "• Passo finale (%): specifica quando interrompere l'applicazione della guida di questo livello e ripristinare la guida generale dal modello e altre impostazioni."
             ]
         },
         "noiseUseCPU": {
@@ -1263,8 +1292,9 @@
         },
         "paramDenoisingStrength": {
             "paragraphs": [
-                "Quanto rumore viene aggiunto all'immagine in ingresso.",
-                "0 risulterà in un'immagine identica, mentre 1 risulterà in un'immagine completamente nuova."
+                "Controlla la differenza tra l'immagine generata e il/i livello/i raster.",
+                "Una forza inferiore rimane più vicina ai livelli raster visibili combinati. Una forza superiore si basa maggiormente sul prompt globale.",
+                "Se non sono presenti livelli raster con contenuto visibile, questa impostazione viene ignorata."
             ],
             "heading": "Forza di riduzione del rumore"
         },
@@ -1276,14 +1306,16 @@
         },
         "infillMethod": {
             "paragraphs": [
-                "Metodo di riempimento durante il processo di Outpainting o Inpainting."
+                "Metodo di riempimento durante il processo di Outpaint o Inpaint."
             ],
             "heading": "Metodo di riempimento"
         },
         "controlNetWeight": {
             "heading": "Peso",
             "paragraphs": [
-                "Peso dell'adattatore di controllo. Un peso maggiore porterà a impatti maggiori sull'immagine finale."
+                "Regola la forza con cui il livello influenza il processo di generazione",
+                "• Peso maggiore (0.75-2): crea un impatto più significativo sul risultato finale.",
+                "• Peso inferiore (0-0.75): crea un impatto minore sul risultato finale."
             ]
         },
         "paramCFGScale": {
@@ -1444,7 +1476,7 @@
             "heading": "Livello minimo di riduzione del rumore",
             "paragraphs": [
                 "Intensità minima di riduzione rumore per la modalità di Coerenza",
-                "L'intensità minima di riduzione del rumore per la regione di coerenza durante l'inpainting o l'outpainting"
+                "L'intensità minima di riduzione del rumore per la regione di coerenza durante l'inpaint o l'outpaint"
             ]
         },
         "compositingMaskBlur": {
@@ -1498,7 +1530,7 @@
         "optimizedDenoising": {
             "heading": "Immagine-a-immagine ottimizzata",
             "paragraphs": [
-                "Abilita 'Immagine-a-immagine ottimizzata' per una scala di riduzione del rumore più graduale per le trasformazioni da immagine a immagine e di inpainting con modelli Flux. Questa impostazione migliora la capacità di controllare la quantità di modifica applicata a un'immagine, ma può essere disattivata se preferisci usare la scala di riduzione rumore standard. Questa impostazione è ancora in fase di messa a punto ed è in stato beta."
+                "Abilita 'Immagine-a-immagine ottimizzata' per una scala di riduzione del rumore più graduale per le trasformazioni da immagine a immagine e di inpaint con modelli Flux. Questa impostazione migliora la capacità di controllare la quantità di modifica applicata a un'immagine, ma può essere disattivata se preferisci usare la scala di riduzione rumore standard. Questa impostazione è ancora in fase di messa a punto ed è in stato beta."
             ]
         },
         "paramGuidance": {
@@ -1733,8 +1765,7 @@
         "newRegionalReferenceImageError": "Problema nella creazione dell'immagine di riferimento regionale",
         "newControlLayerOk": "Livello di controllo creato",
         "bboxOverlay": "Mostra sovrapposizione riquadro",
-        "resetCanvas": "Reimposta la tela",
-        "outputOnlyMaskedRegions": "Solo regioni mascherate in uscita",
+        "outputOnlyMaskedRegions": "In uscita solo le regioni generate",
         "enableAutoNegative": "Abilita Auto Negativo",
         "disableAutoNegative": "Disabilita Auto Negativo",
         "showHUD": "Mostra HUD",
@@ -1771,7 +1802,7 @@
         "globalReferenceImage_withCount_many": "Immagini di riferimento Globali",
         "globalReferenceImage_withCount_other": "Immagini di riferimento Globali",
         "controlMode": {
-            "balanced": "Bilanciato",
+            "balanced": "Bilanciato (consigliato)",
             "controlMode": "Modalità di controllo",
             "prompt": "Prompt",
             "control": "Controllo",
@@ -1782,10 +1813,13 @@
         "beginEndStepPercentShort": "Inizio/Fine %",
         "stagingOnCanvas": "Genera immagini nella",
         "ipAdapterMethod": {
-            "full": "Completo",
+            "full": "Stile e Composizione",
             "style": "Solo Stile",
             "composition": "Solo Composizione",
-            "ipAdapterMethod": "Metodo Adattatore IP"
+            "ipAdapterMethod": "Metodo Adattatore IP",
+            "fullDesc": "Applica lo stile visivo (colori, texture) e la composizione (disposizione, struttura).",
+            "styleDesc": "Applica lo stile visivo (colori, texture) senza considerare la disposizione.",
+            "compositionDesc": "Replica disposizione e struttura ignorando lo stile di riferimento."
         },
         "showingType": "Mostra {{type}}",
         "dynamicGrid": "Griglia dinamica",
@@ -1881,7 +1915,10 @@
             "lineart_anime_edge_detection": {
                 "description": "Genera una mappa dei bordi dal livello selezionato utilizzando il modello di rilevamento dei bordi Lineart Anime.",
                 "label": "Rilevamento bordi Lineart Anime"
-            }
+            },
+            "forMoreControl": "Per un maggiore controllo, fare clic su Avanzate qui sotto.",
+            "advanced": "Avanzate",
+            "processingLayerWith": "Elaborazione del livello con il filtro {{type}}."
         },
         "controlLayers_withCount_hidden": "Livelli di controllo ({{count}} nascosti)",
         "regionalGuidance_withCount_hidden": "Guida regionale ({{count}} nascosti)",
@@ -2016,8 +2053,6 @@
         "convertControlLayerTo": "Converti $t(controlLayers.controlLayer) in",
         "newRasterLayer": "Nuovo $t(controlLayers.rasterLayer)",
         "newRegionalGuidance": "Nuova $t(controlLayers.regionalGuidance)",
-        "canvasAsRasterLayer": "$t(controlLayers.canvas) come $t(controlLayers.rasterLayer)",
-        "canvasAsControlLayer": "$t(controlLayers.canvas) come $t(controlLayers.controlLayer)",
         "convertInpaintMaskTo": "Converti $t(controlLayers.inpaintMask) in",
         "copyRegionalGuidanceTo": "Copia $t(controlLayers.regionalGuidance) in",
         "convertRasterLayerTo": "Converti $t(controlLayers.rasterLayer) in",
@@ -2025,7 +2060,18 @@
         "newControlLayer": "Nuovo $t(controlLayers.controlLayer)",
         "newInpaintMask": "Nuova $t(controlLayers.inpaintMask)",
         "replaceCurrent": "Sostituisci corrente",
-        "mergeDown": "Unire in basso"
+        "mergeDown": "Unire in basso",
+        "mergingLayers": "Unione dei livelli",
+        "controlLayerEmptyState": "<UploadButton>Carica un'immagine</UploadButton>, trascina un'immagine dalla <GalleryButton>galleria</GalleryButton> su questo livello oppure disegna sulla tela per iniziare.",
+        "useImage": "Usa immagine",
+        "resetGenerationSettings": "Ripristina impostazioni di generazione",
+        "referenceImageEmptyState": "Per iniziare, <UploadButton>carica un'immagine</UploadButton> oppure trascina un'immagine dalla <GalleryButton>galleria</GalleryButton> su questo livello.",
+        "asRasterLayer": "Come $t(controlLayers.rasterLayer)",
+        "asRasterLayerResize": "Come $t(controlLayers.rasterLayer) (Ridimensiona)",
+        "asControlLayer": "Come $t(controlLayers.controlLayer)",
+        "asControlLayerResize": "Come $t(controlLayers.controlLayer) (Ridimensiona)",
+        "newSession": "Nuova sessione",
+        "resetCanvasLayers": "Ripristina livelli Tela"
     },
     "ui": {
         "tabs": {
@@ -2057,7 +2103,9 @@
         "postProcessingMissingModelWarning": "Visita <LinkComponent>Gestione modelli</LinkComponent> per installare un modello di post-elaborazione (da immagine a immagine).",
         "exceedsMaxSize": "Le impostazioni di ampliamento superano il limite massimo delle dimensioni",
         "exceedsMaxSizeDetails": "Il limite massimo di ampliamento è {{maxUpscaleDimension}}x{{maxUpscaleDimension}} pixel. Prova un'immagine più piccola o diminuisci la scala selezionata.",
-        "upscale": "Amplia"
+        "upscale": "Amplia",
+        "incompatibleBaseModel": "Architettura del modello principale non supportata per l'ampliamento",
+        "incompatibleBaseModelDesc": "L'ampliamento è supportato solo per i modelli di architettura SD1.5 e SDXL. Cambia il modello principale per abilitare l'ampliamento."
     },
     "upsell": {
         "inviteTeammates": "Invita collaboratori",
@@ -2119,12 +2167,13 @@
     },
     "whatsNew": {
         "whatsNewInInvoke": "Novità in Invoke",
-        "line2": "Supporto Flux esteso, ora con immagini di riferimento globali",
-        "line3": "Tooltip e menu contestuali migliorati",
         "readReleaseNotes": "Leggi le note di rilascio",
         "watchRecentReleaseVideos": "Guarda i video su questa versione",
-        "line1": "Strumento <ItalicComponent>Seleziona oggetto</ItalicComponent> per la selezione e la modifica precise degli oggetti",
-        "watchUiUpdatesOverview": "Guarda le novità dell'interfaccia"
+        "watchUiUpdatesOverview": "Guarda le novità dell'interfaccia",
+        "items": [
+            "<StrongComponent>Flussi di lavoro</StrongComponent>: esegui un flusso di lavoro per una raccolta di immagini utilizzando il nuovo nodo <StrongComponent>Lotto di immagini</StrongComponent>.",
+            "<StrongComponent>Tela</StrongComponent>: elaborazione semplificata del livello di controllo e impostazioni di controllo predefinite migliorate."
+        ]
     },
     "system": {
         "logLevel": {
@@ -2150,5 +2199,67 @@
             "logNamespaces": "Elementi del registro"
         },
         "enableLogging": "Abilita la registrazione"
+    },
+    "supportVideos": {
+        "gettingStarted": "Iniziare",
+        "supportVideos": "Video di supporto",
+        "videos": {
+            "usingControlLayersAndReferenceGuides": {
+                "title": "Utilizzo di livelli di controllo e guide di riferimento",
+                "description": "Scopri come guidare la creazione delle tue immagini con livelli di controllo e immagini di riferimento."
+            },
+            "creatingYourFirstImage": {
+                "description": "Introduzione alla creazione di un'immagine da zero utilizzando gli strumenti di Invoke.",
+                "title": "Creazione della tua prima immagine"
+            },
+            "understandingImageToImageAndDenoising": {
+                "description": "Panoramica delle trasformazioni immagine-a-immagine e della riduzione del rumore in Invoke.",
+                "title": "Comprendere immagine-a-immagine e riduzione del rumore"
+            },
+            "howDoIDoImageToImageTransformation": {
+                "description": "Tutorial su come eseguire trasformazioni da immagine a immagine in Invoke.",
+                "title": "Come si esegue la trasformazione da immagine-a-immagine?"
+            },
+            "howDoIUseInpaintMasks": {
+                "title": "Come si usano le maschere Inpaint?",
+                "description": "Come applicare maschere inpaint per la correzione e la variazione delle immagini."
+            },
+            "howDoIOutpaint": {
+                "description": "Guida all'outpainting oltre i confini dell'immagine originale.",
+                "title": "Come posso eseguire l'outpainting?"
+            },
+            "exploringAIModelsAndConceptAdapters": {
+                "description": "Approfondisci i modelli di intelligenza artificiale e scopri come utilizzare gli adattatori concettuali per il controllo creativo.",
+                "title": "Esplorazione dei modelli di IA e degli adattatori concettuali"
+            },
+            "upscaling": {
+                "title": "Ampliamento",
+                "description": "Come ampliare le immagini con gli strumenti di Invoke per migliorarne la risoluzione."
+            },
+            "creatingAndComposingOnInvokesControlCanvas": {
+                "description": "Impara a comporre immagini utilizzando la tela di controllo di Invoke.",
+                "title": "Creare e comporre sulla tela di controllo di Invoke"
+            },
+            "howDoIGenerateAndSaveToTheGallery": {
+                "description": "Passaggi per generare e salvare le immagini nella galleria.",
+                "title": "Come posso generare e salvare nella Galleria?"
+            },
+            "howDoIEditOnTheCanvas": {
+                "title": "Come posso apportare modifiche sulla tela?",
+                "description": "Guida alla modifica delle immagini direttamente sulla tela."
+            },
+            "howDoIUseControlNetsAndControlLayers": {
+                "title": "Come posso utilizzare le Reti di Controllo e i Livelli di Controllo?",
+                "description": "Impara ad applicare livelli di controllo e reti di controllo alle tue immagini."
+            },
+            "howDoIUseGlobalIPAdaptersAndReferenceImages": {
+                "title": "Come si utilizzano gli adattatori IP globali e le immagini di riferimento?",
+                "description": "Introduzione all'aggiunta di immagini di riferimento e adattatori IP globali."
+            }
+        },
+        "controlCanvas": "Tela di Controllo",
+        "watch": "Guarda",
+        "studioSessionsDesc1": "Dai un'occhiata a <StudioSessionsPlaylistLink /> per approfondimenti su Invoke.",
+        "studioSessionsDesc2": "Unisciti al nostro <DiscordLink /> per partecipare alle sessioni live e fare domande. Le sessioni vengono caricate sulla playlist la settimana successiva."
     }
 }

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

@@ -16,7 +16,7 @@
         "discordLabel": "Discord",
         "nodes": "ワークフロー",
         "txt2img": "txt2img",
-        "postprocessing": "Post Processing",
+        "postprocessing": "ポストプロセス",
         "t2iAdapter": "T2I アダプター",
         "communityLabel": "コミュニティ",
         "dontAskMeAgain": "次回から確認しない",
@@ -71,8 +71,8 @@
         "orderBy": "並び順:",
         "enabled": "有効",
         "notInstalled": "未インストール",
-        "positivePrompt": "プロンプト",
-        "negativePrompt": "除外する要素",
+        "positivePrompt": "ポジティブプロンプト",
+        "negativePrompt": "ネガティブプロンプト",
         "selected": "選択済み",
         "aboutDesc": "Invokeを業務で利用する場合はマークしてください:",
         "beta": "ベータ",
@@ -80,7 +80,20 @@
         "editor": "エディタ",
         "safetensors": "Safetensors",
         "tab": "タブ",
-        "toResolve": "解決方法"
+        "toResolve": "解決方法",
+        "openInViewer": "ビューアで開く",
+        "placeholderSelectAModel": "モデルを選択",
+        "clipboard": "クリップボード",
+        "apply": "適用",
+        "loadingImage": "画像をロード中",
+        "off": "オフ",
+        "view": "ビュー",
+        "edit": "編集",
+        "ok": "OK",
+        "reset": "リセット",
+        "none": "なし",
+        "new": "新規",
+        "close": "閉じる"
     },
     "gallery": {
         "galleryImageSize": "画像のサイズ",
@@ -125,12 +138,114 @@
         "compareHelp1": "<Kbd>Alt</Kbd> キーを押しながらギャラリー画像をクリックするか、矢印キーを使用して比較画像を変更します。",
         "compareHelp3": "<Kbd>C</Kbd>を押して、比較した画像を入れ替えます。",
         "compareHelp4": "<Kbd>[Z</Kbd>]または<Kbd>[Esc</Kbd>]を押して終了します。",
-        "compareHelp2": "<Kbd>M</Kbd> キーを押して比較モードを切り替えます。"
+        "compareHelp2": "<Kbd>M</Kbd> キーを押して比較モードを切り替えます。",
+        "move": "移動",
+        "openViewer": "ビューアを開く",
+        "closeViewer": "ビューアを閉じる",
+        "exitSearch": "画像検索を終了",
+        "oldestFirst": "最古から",
+        "showStarredImagesFirst": "スター付き画像を最初に",
+        "exitBoardSearch": "ボード検索を終了",
+        "showArchivedBoards": "アーカイブされたボードを表示",
+        "searchImages": "メタデータで検索",
+        "gallery": "ギャラリー",
+        "newestFirst": "最新から",
+        "jump": "ジャンプ",
+        "go": "進む",
+        "sortDirection": "並び替え順",
+        "displayBoardSearch": "ボード検索",
+        "displaySearch": "画像を検索",
+        "boardsSettings": "ボード設定",
+        "imagesSettings": "ギャラリー画像設定"
     },
     "hotkeys": {
         "searchHotkeys": "ホットキーを検索",
         "clearSearch": "検索をクリア",
-        "noHotkeysFound": "ホットキーが見つかりません"
+        "noHotkeysFound": "ホットキーが見つかりません",
+        "viewer": {
+            "runPostprocessing": {
+                "title": "ポストプロセスを実行"
+            },
+            "useSize": {
+                "title": "サイズを使用"
+            },
+            "recallPrompts": {
+                "title": "プロンプトを再使用"
+            },
+            "recallAll": {
+                "title": "全てのメタデータを再使用"
+            },
+            "recallSeed": {
+                "title": "シード値を再使用"
+            }
+        },
+        "canvas": {
+            "redo": {
+                "title": "やり直し"
+            },
+            "transformSelected": {
+                "title": "変形"
+            },
+            "undo": {
+                "title": "取り消し"
+            },
+            "selectEraserTool": {
+                "title": "消しゴムツール"
+            },
+            "cancelTransform": {
+                "title": "変形をキャンセル"
+            },
+            "resetSelected": {
+                "title": "レイヤーをリセット"
+            },
+            "applyTransform": {
+                "title": "変形を適用"
+            },
+            "selectColorPickerTool": {
+                "title": "スポイトツール"
+            },
+            "fitBboxToCanvas": {
+                "title": "バウンディングボックスをキャンバスにフィット"
+            },
+            "selectBrushTool": {
+                "title": "ブラシツール"
+            },
+            "selectMoveTool": {
+                "title": "移動ツール"
+            },
+            "selectBboxTool": {
+                "title": "バウンディングボックスツール"
+            },
+            "title": "キャンバス",
+            "fitLayersToCanvas": {
+                "title": "レイヤーをキャンバスにフィット"
+            }
+        },
+        "workflows": {
+            "undo": {
+                "title": "取り消し"
+            },
+            "redo": {
+                "title": "やり直し"
+            }
+        },
+        "app": {
+            "toggleLeftPanel": {
+                "title": "左パネルをトグル",
+                "desc": "左パネルを表示または非表示。"
+            },
+            "title": "アプリケーション",
+            "invoke": {
+                "title": "Invoke"
+            },
+            "cancelQueueItem": {
+                "title": "キャンセル"
+            },
+            "clearQueue": {
+                "title": "キューをクリア"
+            }
+        },
+        "hotkeys": "ホットキー"
     },
     "modelManager": {
         "modelManager": "モデルマネージャ",
@@ -165,7 +280,7 @@
         "convertToDiffusers": "ディフューザーに変換",
         "alpha": "アルファ",
         "modelConverted": "モデル変換が完了しました",
-        "predictionType": "予測タイプ（安定したディフュージョン 2.x モデルおよび一部の安定したディフュージョン 1.x モデル用）",
+        "predictionType": "予測タイプ（SD 2.x モデルおよび一部のSD 1.x モデル用）",
         "selectModel": "モデルを選択",
         "advanced": "高度な設定",
         "modelDeleted": "モデルが削除されました",
@@ -178,7 +293,9 @@
         "convertToDiffusersHelpText1": "このモデルは 🧨 Diffusers フォーマットに変換されます。",
         "convertToDiffusersHelpText3": "チェックポイントファイルは、InvokeAIルートフォルダ内にある場合、ディスクから削除されます。カスタムロケーションにある場合は、削除されません。",
         "convertToDiffusersHelpText4": "これは一回限りのプロセスです。コンピュータの仕様によっては、約30秒から60秒かかる可能性があります。",
-        "cancel": "キャンセル"
+        "cancel": "キャンセル",
+        "uploadImage": "画像をアップロード",
+        "addModels": "モデルを追加"
     },
     "parameters": {
         "images": "画像",
@@ -200,7 +317,19 @@
         "info": "情報",
         "showOptionsPanel": "オプションパネルを表示",
         "iterations": "生成回数",
-        "general": "基本設定"
+        "general": "基本設定",
+        "setToOptimalSize": "サイズをモデルに最適化",
+        "invoke": {
+            "addingImagesTo": "画像の追加先"
+        },
+        "aspect": "縦横比",
+        "lockAspectRatio": "縦横比を固定",
+        "scheduler": "スケジューラー",
+        "sendToUpscale": "アップスケーラーに転送",
+        "useSize": "サイズを使用",
+        "postProcessing": "ポストプロセス (Shift + U)",
+        "denoisingStrength": "ノイズ除去強度",
+        "recallMetadata": "メタデータを再使用"
     },
     "settings": {
         "models": "モデル",
@@ -213,7 +342,11 @@
     },
     "toast": {
         "uploadFailed": "アップロード失敗",
-        "imageCopied": "画像をコピー"
+        "imageCopied": "画像をコピー",
+        "imageUploadFailed": "画像のアップロードに失敗しました",
+        "uploadFailedInvalidUploadDesc": "画像はPNGかJPGである必要があります。",
+        "sentToUpscale": "アップスケーラーに転送しました",
+        "imageUploaded": "画像をアップロードしました"
     },
     "accessibility": {
         "invokeProgressBar": "進捗バー",
@@ -226,7 +359,10 @@
         "resetUI": "$t(accessibility.reset) UI",
         "mode": "モード:",
         "about": "Invoke について",
-        "submitSupportTicket": "サポート依頼を送信する"
+        "submitSupportTicket": "サポート依頼を送信する",
+        "uploadImages": "画像をアップロード",
+        "toggleLeftPanel": "左パネルをトグル (T)",
+        "toggleRightPanel": "右パネルをトグル (G)"
     },
     "metadata": {
         "Threshold": "ノイズ閾値",
@@ -237,7 +373,8 @@
         "scheduler": "スケジューラー",
         "positivePrompt": "ポジティブプロンプト",
         "strength": "Image to Image 強度",
-        "recallParameters": "パラメータを呼び出す"
+        "recallParameters": "パラメータを再使用",
+        "recallParameter": "{{label}} を再使用"
     },
     "queue": {
         "queueEmpty": "キューが空です",
@@ -297,14 +434,22 @@
         "prune": "刈り込み",
         "prompts_other": "プロンプト",
         "iterations_other": "繰り返し",
-        "generations_other": "生成"
+        "generations_other": "生成",
+        "canvas": "キャンバス",
+        "workflows": "ワークフロー",
+        "upscaling": "アップスケール",
+        "generation": "生成",
+        "other": "その他",
+        "gallery": "ギャラリー"
     },
     "models": {
         "noMatchingModels": "一致するモデルがありません",
         "loading": "読み込み中",
         "noMatchingLoRAs": "一致するLoRAがありません",
         "noModelsAvailable": "使用可能なモデルがありません",
-        "selectModel": "モデルを選択してください"
+        "selectModel": "モデルを選択してください",
+        "concepts": "コンセプト",
+        "addLora": "LoRAを追加"
     },
     "nodes": {
         "addNode": "ノードを追加",
@@ -339,7 +484,8 @@
         "cannotConnectOutputToOutput": "出力から出力には接続できません",
         "cannotConnectToSelf": "自身のノードには接続できません",
         "colorCodeEdges": "カラー-Code Edges",
-        "loadingNodes": "ノードを読み込み中..."
+        "loadingNodes": "ノードを読み込み中...",
+        "scheduler": "スケジューラー"
     },
     "boards": {
         "autoAddBoard": "自動追加するボード",
@@ -362,7 +508,18 @@
         "deleteBoardAndImages": "ボードと画像の削除",
         "deleteBoardOnly": "ボードのみ削除",
         "deletedBoardsCannotbeRestored": "削除されたボードは復元できません",
-        "movingImagesToBoard_other": "{{count}} の画像をボードに移動:"
+        "movingImagesToBoard_other": "{{count}} の画像をボードに移動:",
+        "hideBoards": "ボードを隠す",
+        "assetsWithCount_other": "{{count}} のアセット",
+        "addPrivateBoard": "プライベートボードを追加",
+        "addSharedBoard": "共有ボードを追加",
+        "boards": "ボード",
+        "private": "プライベートボード",
+        "shared": "共有ボード",
+        "archiveBoard": "ボードをアーカイブ",
+        "archived": "アーカイブ完了",
+        "unarchiveBoard": "アーカイブされていないボード",
+        "imagesWithCount_other": "{{count}} の画像"
     },
     "invocationCache": {
         "invocationCache": "呼び出しキャッシュ",
@@ -387,6 +544,33 @@
             "paragraphs": [
                 "生成された画像の縦横比。"
             ]
+        },
+        "regionalGuidanceAndReferenceImage": {
+            "heading": "領域ガイダンスと領域参照画像"
+        },
+        "regionalReferenceImage": {
+            "heading": "領域参照画像"
+        },
+        "paramScheduler": {
+            "heading": "スケジューラー"
+        },
+        "regionalGuidance": {
+            "heading": "領域ガイダンス"
+        },
+        "rasterLayer": {
+            "heading": "ラスターレイヤー"
+        },
+        "globalReferenceImage": {
+            "heading": "全域参照画像"
+        },
+        "paramUpscaleMethod": {
+            "heading": "アップスケール手法"
+        },
+        "upscaleModel": {
+            "heading": "アップスケールモデル"
+        },
+        "paramAspect": {
+            "heading": "縦横比"
         }
     },
     "accordions": {
@@ -427,5 +611,79 @@
         "tabs": {
             "queue": "キュー"
         }
+    },
+    "controlLayers": {
+        "globalReferenceImage_withCount_other": "全域参照画像",
+        "regionalReferenceImage": "領域参照画像",
+        "saveLayerToAssets": "レイヤーをアセットに保存",
+        "global": "全域",
+        "inpaintMasks_withCount_hidden": "インペイントマスク ({{count}} hidden)",
+        "opacity": "透明度",
+        "canvasContextMenu": {
+            "newRegionalGuidance": "新規領域ガイダンス",
+            "bboxGroup": "バウンディングボックスから作成",
+            "cropCanvasToBbox": "キャンバスをバウンディングボックスでクロップ",
+            "newGlobalReferenceImage": "新規全域参照画像",
+            "newRegionalReferenceImage": "新規領域参照画像"
+        },
+        "regionalGuidance": "領域ガイダンス",
+        "globalReferenceImage": "全域参照画像",
+        "moveForward": "前面へ移動",
+        "copyInpaintMaskTo": "$t(controlLayers.inpaintMask) をコピー",
+        "transform": {
+            "fitToBbox": "バウンディングボックスにフィット",
+            "transform": "変形",
+            "apply": "適用",
+            "cancel": "キャンセル",
+            "reset": "リセット"
+        },
+        "cropLayerToBbox": "レイヤーをバウンディングボックスでクロップ",
+        "convertInpaintMaskTo": "$t(controlLayers.inpaintMask)を変換",
+        "regionalGuidance_withCount_other": "領域ガイダンス",
+        "tool": {
+            "colorPicker": "スポイト",
+            "brush": "ブラシ",
+            "rectangle": "矩形",
+            "move": "移動",
+            "eraser": "消しゴム"
+        },
+        "saveCanvasToGallery": "キャンバスをギャラリーに保存",
+        "saveBboxToGallery": "バウンディングボックスをギャラリーへ保存",
+        "moveToBack": "最背面へ移動",
+        "duplicate": "複製",
+        "addLayer": "レイヤーを追加",
+        "rasterLayer": "ラスターレイヤー",
+        "inpaintMasks_withCount_visible": "({{count}}) インペイントマスク",
+        "regional": "領域",
+        "rectangle": "矩形",
+        "moveBackward": "背面へ移動",
+        "moveToFront": "最前面へ移動",
+        "mergeDown": "レイヤーを統合",
+        "inpaintMask_withCount_other": "インペイントマスク",
+        "canvas": "キャンバス",
+        "fitBboxToLayers": "バウンディングボックスをレイヤーにフィット",
+        "removeBookmark": "ブックマークを外す",
+        "savedToGalleryOk": "ギャラリーに保存しました"
+    },
+    "stylePresets": {
+        "clearTemplateSelection": "選択したテンプレートをクリア",
+        "choosePromptTemplate": "プロンプトテンプレートを選択",
+        "myTemplates": "自分のテンプレート",
+        "flatten": "選択中のテンプレートをプロンプトに展開",
+        "uploadImage": "画像をアップロード",
+        "defaultTemplates": "デフォルトテンプレート",
+        "createPromptTemplate": "プロンプトテンプレートを作成",
+        "promptTemplateCleared": "プロンプトテンプレートをクリアしました",
+        "searchByName": "名前で検索",
+        "toggleViewMode": "表示モードを切り替え"
+    },
+    "upscaling": {
+        "upscaleModel": "アップスケールモデル",
+        "postProcessingModel": "ポストプロセスモデル",
+        "upscale": "アップスケール"
+    },
+    "sdxl": {
+        "denoisingStrength": "ノイズ除去強度",
+        "scheduler": "スケジューラー"
     }
 }

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

@@ -236,7 +236,6 @@
                 "controlAdapterIncompatibleBaseModel": "niet-compatibele basismodel voor controle-adapter",
                 "ipAdapterIncompatibleBaseModel": "niet-compatibele basismodel voor IP-adapter",
                 "ipAdapterNoImageSelected": "geen afbeelding voor IP-adapter geselecteerd",
-                "rgNoRegion": "geen gebied geselecteerd",
                 "rgNoPromptsOrIPAdapters": "geen tekstprompts of IP-adapters",
                 "ipAdapterNoModelSelected": "geen IP-adapter geselecteerd"
             }

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

@@ -10,7 +10,24 @@
         "load": "Załaduj",
         "statusDisconnected": "Odłączono od serwera",
         "githubLabel": "GitHub",
-        "discordLabel": "Discord"
+        "discordLabel": "Discord",
+        "clipboard": "Schowek",
+        "aboutDesc": "Wykorzystujesz Invoke do pracy? Sprawdź:",
+        "ai": "SI",
+        "areYouSure": "Czy jesteś pewien?",
+        "copyError": "$t(gallery.copy) Błąd",
+        "apply": "Zastosuj",
+        "copy": "Kopiuj",
+        "or": "albo",
+        "add": "Dodaj",
+        "off": "Wyłączony",
+        "accept": "Zaakceptuj",
+        "cancel": "Anuluj",
+        "advanced": "Zawansowane",
+        "back": "Do tyłu",
+        "auto": "Automatyczny",
+        "beta": "Beta",
+        "close": "Wyjdź"
     },
     "gallery": {
         "galleryImageSize": "Rozmiar obrazów",
@@ -65,6 +82,42 @@
         "uploadImage": "Wgrywanie obrazu",
         "previousImage": "Poprzedni obraz",
         "nextImage": "Następny obraz",
-        "menu": "Menu"
+        "menu": "Menu",
+        "mode": "Tryb"
+    },
+    "boards": {
+        "cancel": "Anuluj",
+        "noBoards": "Brak tablic typu {{boardType}}",
+        "imagesWithCount_one": "{{count}} zdjęcie",
+        "imagesWithCount_few": "{{count}} zdjęcia",
+        "imagesWithCount_many": "{{count}} zdjęcia",
+        "private": "Prywatne tablice",
+        "updateBoardError": "Błąd aktualizacji tablicy",
+        "uncategorized": "Nieskategoryzowane",
+        "selectBoard": "Wybierz tablicę",
+        "downloadBoard": "Pobierz tablice",
+        "loading": "Ładowanie...",
+        "move": "Przenieś",
+        "noMatching": "Brak pasujących tablic"
+    },
+    "accordions": {
+        "compositing": {
+            "title": "Kompozycja",
+            "infillTab": "Inskrypcja",
+            "coherenceTab": "Przebieg Koherencji"
+        },
+        "generation": {
+            "title": "Generowanie"
+        },
+        "image": {
+            "title": "Zdjęcie"
+        },
+        "advanced": {
+            "options": "$t(accordions.advanced.title) Opcje",
+            "title": "Zaawansowane"
+        },
+        "control": {
+            "title": "Kontrola"
+        }
     }
 }

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

@@ -652,7 +652,6 @@
                 "ipAdapterNoModelSelected": "IP адаптер не выбран",
                 "controlAdapterNoModelSelected": "не выбрана модель адаптера контроля",
                 "controlAdapterIncompatibleBaseModel": "несовместимая базовая модель адаптера контроля",
-                "rgNoRegion": "регион не выбран",
                 "rgNoPromptsOrIPAdapters": "нет текстовых запросов или IP-адаптеров",
                 "ipAdapterIncompatibleBaseModel": "несовместимая базовая модель IP-адаптера",
                 "ipAdapterNoImageSelected": "изображение IP-адаптера не выбрано",
@@ -1660,7 +1659,6 @@
         "clearCaches": "Очистить кэши",
         "recalculateRects": "Пересчитать прямоугольники",
         "saveBboxToGallery": "Сохранить рамку в галерею",
-        "resetCanvas": "Сбросить холст",
         "canvas": "Холст",
         "global": "Глобальный",
         "newGlobalReferenceImageError": "Проблема с созданием глобального эталонного изображения",

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

@@ -96,7 +96,9 @@
         "view": "视图",
         "alpha": "透明度通道",
         "openInViewer": "在查看器中打开",
-        "clipboard": "剪贴板"
+        "clipboard": "剪贴板",
+        "loadingModel": "加载模型",
+        "generating": "生成中"
     },
     "gallery": {
         "galleryImageSize": "预览大小",
@@ -587,7 +589,27 @@
         "huggingFace": "HuggingFace",
         "hfTokenInvalid": "HF 令牌无效或缺失",
         "hfTokenLabel": "HuggingFace 令牌（某些模型所需）",
-        "hfTokenHelperText": "使用某些模型需要 HF 令牌。点击这里创建或获取你的令牌。"
+        "hfTokenHelperText": "使用某些模型需要 HF 令牌。点击这里创建或获取你的令牌。",
+        "includesNModels": "包括 {{n}} 个模型及其依赖项",
+        "starterBundles": "启动器包",
+        "learnMoreAboutSupportedModels": "了解更多关于我们支持的模型的信息",
+        "hfForbidden": "您没有权限访问这个 HF 模型",
+        "hfTokenInvalidErrorMessage": "无效或缺失 HuggingFace 令牌。",
+        "hfTokenRequired": "您正在尝试下载一个需要有效 HuggingFace 令牌的模型。",
+        "hfTokenSaved": "HF 令牌已保存",
+        "hfForbiddenErrorMessage": "我们建议访问 HuggingFace.com 上的仓库页面。所有者可能要求您接受条款才能下载。",
+        "hfTokenUnableToVerifyErrorMessage": "无法验证 HuggingFace 令牌。这可能是由于网络错误导致的。请稍后再试。",
+        "hfTokenInvalidErrorMessage2": "在这里更新它。 ",
+        "hfTokenUnableToVerify": "无法验证 HF 令牌",
+        "skippingXDuplicates_other": "跳过 {{count}} 个重复项",
+        "starterBundleHelpText": "轻松安装所有用于启动基础模型所需的模型，包括主模型、ControlNets、IP适配器等。选择一个安装包时，会跳过已安装的模型。",
+        "installingBundle": "正在安装模型包",
+        "installingModel": "正在安装模型",
+        "installingXModels_other": "正在安装 {{count}} 个模型",
+        "t5Encoder": "T5 编码器",
+        "clipLEmbed": "CLIP-L 嵌入",
+        "clipGEmbed": "CLIP-G 嵌入",
+        "loraModels": "LoRAs（低秩适配）"
     },
     "parameters": {
         "images": "图像",
@@ -646,8 +668,22 @@
                 "controlAdapterIncompatibleBaseModel": "Control Adapter的基础模型不兼容",
                 "ipAdapterIncompatibleBaseModel": "IP Adapter的基础模型不兼容",
                 "ipAdapterNoImageSelected": "未选择IP Adapter图像",
-                "rgNoRegion": "未选择区域"
-            }
+                "t2iAdapterIncompatibleBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}，边界框宽度为 {{width}}",
+                "t2iAdapterIncompatibleScaledBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}，缩放后的边界框高度为 {{height}}",
+                "t2iAdapterIncompatibleBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}，边界框高度为 {{height}}",
+                "t2iAdapterIncompatibleScaledBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}，缩放后的边界框宽度为 {{width}}"
+            },
+            "canvasIsFiltering": "画布正在过滤",
+            "fluxModelIncompatibleScaledBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16)，缩放后的边界框高度为 {{height}}",
+            "noCLIPEmbedModelSelected": "未为FLUX生成选择CLIP嵌入模型",
+            "noFLUXVAEModelSelected": "未为FLUX生成选择VAE模型",
+            "canvasIsRasterizing": "画布正在栅格化",
+            "canvasIsCompositing": "画布正在合成",
+            "fluxModelIncompatibleBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16)，边界框宽度为 {{width}}",
+            "fluxModelIncompatibleScaledBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16)，缩放后的边界框宽度为 {{width}}",
+            "noT5EncoderModelSelected": "未为FLUX生成选择T5编码器模型",
+            "fluxModelIncompatibleBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16)，边界框高度为 {{height}}",
+            "canvasIsTransforming": "画布正在变换"
         },
         "patchmatchDownScaleSize": "缩小",
         "clipSkip": "CLIP 跳过层",
@@ -669,7 +705,15 @@
         "sendToUpscale": "发送到放大",
         "processImage": "处理图像",
         "infillColorValue": "填充颜色",
-        "coherenceMinDenoise": "最小去噪"
+        "coherenceMinDenoise": "最小去噪",
+        "sendToCanvas": "发送到画布",
+        "disabledNoRasterContent": "已禁用（无栅格内容）",
+        "optimizedImageToImage": "优化的图生图",
+        "guidance": "引导",
+        "gaussianBlur": "高斯模糊",
+        "recallMetadata": "调用元数据",
+        "boxBlur": "方框模糊",
+        "staged": "已分阶段处理"
     },
     "settings": {
         "models": "模型",
@@ -699,7 +743,12 @@
         "enableInformationalPopovers": "启用信息弹窗",
         "reloadingIn": "重新加载中",
         "informationalPopoversDisabled": "信息提示框已禁用",
-        "informationalPopoversDisabledDesc": "信息提示框已被禁用.请在设置中重新启用."
+        "informationalPopoversDisabledDesc": "信息提示框已被禁用.请在设置中重新启用.",
+        "enableModelDescriptions": "在下拉菜单中启用模型描述",
+        "confirmOnNewSession": "新会话时确认",
+        "modelDescriptionsDisabledDesc": "下拉菜单中的模型描述已被禁用。可在设置中启用。",
+        "modelDescriptionsDisabled": "下拉菜单中的模型描述已禁用",
+        "showDetailedInvocationProgress": "显示进度详情"
     },
     "toast": {
         "uploadFailed": "上传失败",
@@ -740,7 +789,24 @@
         "errorCopied": "错误信息已复制",
         "modelImportCanceled": "模型导入已取消",
         "importFailed": "导入失败",
-        "importSuccessful": "导入成功"
+        "importSuccessful": "导入成功",
+        "layerSavedToAssets": "图层已保存到资产",
+        "sentToUpscale": "已发送到放大处理",
+        "addedToUncategorized": "已添加到看板 $t(boards.uncategorized) 的资产中",
+        "linkCopied": "链接已复制",
+        "uploadFailedInvalidUploadDesc_withCount_other": "最多只能上传 {{count}} 张 PNG 或 JPEG 图像。",
+        "problemSavingLayer": "无法保存图层",
+        "unableToLoadImage": "无法加载图像",
+        "imageNotLoadedDesc": "无法找到图像",
+        "unableToLoadStylePreset": "无法加载样式预设",
+        "stylePresetLoaded": "样式预设已加载",
+        "problemCopyingLayer": "无法复制图层",
+        "sentToCanvas": "已发送到画布",
+        "unableToLoadImageMetadata": "无法加载图像元数据",
+        "imageSaved": "图像已保存",
+        "imageSavingFailed": "图像保存失败",
+        "layerCopiedToClipboard": "图层已复制到剪贴板",
+        "imagesWillBeAddedTo": "上传的图像将添加到看板 {{boardName}} 的资产中。"
     },
     "accessibility": {
         "invokeProgressBar": "Invoke 进度条",
@@ -890,7 +956,12 @@
         "clearWorkflow": "清除工作流",
         "imageAccessError": "无法找到图像 {{image_name}}，正在恢复默认设置",
         "boardAccessError": "无法找到面板 {{board_id}}，正在恢复默认设置",
-        "modelAccessError": "无法找到模型 {{key}}，正在恢复默认设置"
+        "modelAccessError": "无法找到模型 {{key}}，正在恢复默认设置",
+        "noWorkflows": "无工作流程",
+        "workflowHelpText": "需要帮助？请查看我们的《<LinkComponent>工作流程入门指南</LinkComponent>》。",
+        "noMatchingWorkflows": "无匹配的工作流程",
+        "saveToGallery": "保存到图库",
+        "singleFieldType": "{{name}}（单一模型）"
     },
     "queue": {
         "status": "状态",
@@ -1221,7 +1292,8 @@
             "heading": "去噪强度",
             "paragraphs": [
                 "为输入图像添加的噪声量。",
-                "输入 0 会导致结果图像和输入完全相同，输入 1 则会生成全新的图像。"
+                "输入 0 会导致结果图像和输入完全相同，输入 1 则会生成全新的图像。",
+                "当没有具有可见内容的栅格图层时，此设置将被忽略。"
             ]
         },
         "paramSeed": {
@@ -1410,7 +1482,8 @@
             "paragraphs": [
                 "控制提示对生成过程的影响程度.",
                 "与生成CFG Scale相似."
-            ]
+            ],
+            "heading": "CFG比例"
         },
         "structure": {
             "heading": "结构",
@@ -1441,6 +1514,62 @@
             "paragraphs": [
                 "比例控制决定了输出图像的大小,它是基于输入图像分辨率的倍数来计算的.例如对一张1024x1024的图像进行2倍上采样，将会得到一张2048x2048的输出图像."
             ]
+        },
+        "globalReferenceImage": {
+            "heading": "全局参考图像",
+            "paragraphs": [
+                "应用参考图像以影响整个生成过程。"
+            ]
+        },
+        "rasterLayer": {
+            "paragraphs": [
+                "画布的基于像素的内容，用于图像生成过程。"
+            ],
+            "heading": "栅格图层"
+        },
+        "regionalGuidanceAndReferenceImage": {
+            "paragraphs": [
+                "对于区域引导，使用画笔引导全局提示中的元素应出现的位置。",
+                "对于区域参考图像，使用画笔将参考图像应用到特定区域。"
+            ],
+            "heading": "区域引导与区域参考图像"
+        },
+        "regionalReferenceImage": {
+            "heading": "区域参考图像",
+            "paragraphs": [
+                "使用画笔将参考图像应用到特定区域。"
+            ]
+        },
+        "optimizedDenoising": {
+            "heading": "优化的图生图",
+            "paragraphs": [
+                "启用‘优化的图生图’功能，可在使用 Flux 模型进行图生图和图像修复转换时提供更平滑的降噪强度调节。此设置可以提高对图像变化程度的控制能力，但如果您更倾向于使用标准的降噪强度调节方式，也可以关闭此功能。该设置仍在优化中，目前处于测试阶段。"
+            ]
+        },
+        "inpainting": {
+            "paragraphs": [
+                "控制由降噪强度引导的修改区域。"
+            ],
+            "heading": "图像重绘"
+        },
+        "regionalGuidance": {
+            "heading": "区域引导",
+            "paragraphs": [
+                "使用画笔引导全局提示中的元素应出现的位置。"
+            ]
+        },
+        "fluxDevLicense": {
+            "heading": "非商业许可",
+            "paragraphs": [
+                "FLUX.1 [dev] 模型受 FLUX [dev] 非商业许可协议的约束。如需在 Invoke 中将此模型类型用于商业目的，请访问我们的网站了解更多信息。"
+            ]
+        },
+        "paramGuidance": {
+            "paragraphs": [
+                "控制提示对生成过程的影响程度。",
+                "较高的引导值可能导致过度饱和，而过高或过低的引导值可能导致生成结果失真。引导仅适用于FLUX DEV模型。"
+            ],
+            "heading": "引导"
         }
     },
     "invocationCache": {
@@ -1503,7 +1632,18 @@
         "convertGraph": "转换图表",
         "loadWorkflow": "$t(common.load) 工作流",
         "loadFromGraph": "从图表加载工作流",
-        "autoLayout": "自动布局"
+        "autoLayout": "自动布局",
+        "edit": "编辑",
+        "copyShareLinkForWorkflow": "复制工作流程的分享链接",
+        "delete": "删除",
+        "download": "下载",
+        "defaultWorkflows": "默认工作流程",
+        "userWorkflows": "用户工作流程",
+        "projectWorkflows": "项目工作流程",
+        "copyShareLink": "复制分享链接",
+        "chooseWorkflowFromLibrary": "从库中选择工作流程",
+        "uploadAndSaveWorkflow": "上传到库",
+        "deleteWorkflow2": "您确定要删除此工作流程吗？此操作无法撤销。"
     },
     "accordions": {
         "compositing": {
@@ -1542,7 +1682,108 @@
         "addPositivePrompt": "添加 $t(controlLayers.prompt)",
         "addNegativePrompt": "添加 $t(controlLayers.negativePrompt)",
         "rectangle": "矩形",
-        "opacity": "透明度"
+        "opacity": "透明度",
+        "canvas": "画布",
+        "fitBboxToLayers": "将边界框适配到图层",
+        "cropLayerToBbox": "将图层裁剪到边界框",
+        "saveBboxToGallery": "将边界框保存到图库",
+        "savedToGalleryOk": "已保存到图库",
+        "saveLayerToAssets": "将图层保存到资产",
+        "removeBookmark": "移除书签",
+        "regional": "区域",
+        "saveCanvasToGallery": "将画布保存到图库",
+        "global": "全局",
+        "bookmark": "添加书签以快速切换",
+        "regionalReferenceImage": "局部参考图像",
+        "mergingLayers": "正在合并图层",
+        "newControlLayerError": "创建控制层时出现问题",
+        "pullBboxIntoReferenceImageError": "将边界框导入参考图像时出现问题",
+        "mergeVisibleOk": "已合并图层",
+        "maskFill": "遮罩填充",
+        "newCanvasFromImage": "从图像创建新画布",
+        "pullBboxIntoReferenceImageOk": "边界框已导入到参考图像",
+        "globalReferenceImage_withCount_other": "全局参考图像",
+        "addInpaintMask": "添加 $t(controlLayers.inpaintMask)",
+        "referenceImage": "参考图像",
+        "globalReferenceImage": "全局参考图像",
+        "newRegionalGuidance": "新建 $t(controlLayers.regionalGuidance)",
+        "savedToGalleryError": "保存到图库时出错",
+        "copyRasterLayerTo": "复制 $t(controlLayers.rasterLayer) 到",
+        "clearHistory": "清除历史记录",
+        "inpaintMask": "修复遮罩",
+        "regionalGuidance_withCount_visible": "区域引导（{{count}} 个）",
+        "inpaintMasks_withCount_hidden": "修复遮罩（{{count}} 个已隐藏）",
+        "enableAutoNegative": "启用自动负面提示",
+        "disableAutoNegative": "禁用自动负面提示",
+        "deleteReferenceImage": "删除参考图像",
+        "sendToCanvas": "发送到画布",
+        "controlLayers_withCount_visible": "控制图层（{{count}} 个）",
+        "rasterLayers_withCount_visible": "栅格图层（{{count}} 个）",
+        "convertRegionalGuidanceTo": "将 $t(controlLayers.regionalGuidance) 转换为",
+        "newInpaintMask": "新建 $t(controlLayers.inpaintMask)",
+        "regionIsEmpty": "选定区域为空",
+        "mergeVisible": "合并可见图层",
+        "showHUD": "显示 HUD（抬头显示）",
+        "newLayerFromImage": "从图像创建新图层",
+        "layer_other": "图层",
+        "transparency": "透明度",
+        "addRasterLayer": "添加 $t(controlLayers.rasterLayer)",
+        "newRasterLayerOk": "已创建栅格层",
+        "newRasterLayerError": "创建栅格层时出现问题",
+        "inpaintMasks_withCount_visible": "修复遮罩（{{count}} 个）",
+        "convertRasterLayerTo": "将 $t(controlLayers.rasterLayer) 转换为",
+        "copyControlLayerTo": "复制 $t(controlLayers.controlLayer) 到",
+        "copyInpaintMaskTo": "复制 $t(controlLayers.inpaintMask) 到",
+        "copyRegionalGuidanceTo": "复制 $t(controlLayers.regionalGuidance) 到",
+        "newRasterLayer": "新建 $t(controlLayers.rasterLayer)",
+        "newControlLayer": "新建 $t(controlLayers.controlLayer)",
+        "newImg2ImgCanvasFromImage": "从图像创建新的图生图",
+        "rasterLayer": "栅格层",
+        "controlLayer": "控制层",
+        "outputOnlyMaskedRegions": "仅输出生成的区域",
+        "addControlLayer": "添加 $t(controlLayers.controlLayer)",
+        "newGlobalReferenceImageOk": "已创建全局参考图像",
+        "newGlobalReferenceImageError": "创建全局参考图像时出现问题",
+        "newRegionalReferenceImageOk": "已创建局部参考图像",
+        "newControlLayerOk": "已创建控制层",
+        "mergeVisibleError": "合并图层时出错",
+        "bboxOverlay": "显示边界框覆盖层",
+        "clipToBbox": "将Clip限制到边界框",
+        "width": "宽度",
+        "addGlobalReferenceImage": "添加 $t(controlLayers.globalReferenceImage)",
+        "inpaintMask_withCount_other": "修复遮罩",
+        "regionalGuidance_withCount_other": "区域引导",
+        "newRegionalReferenceImageError": "创建局部参考图像时出现问题",
+        "pullBboxIntoLayerError": "将边界框导入图层时出现问题",
+        "pullBboxIntoLayerOk": "边界框已导入到图层",
+        "sendToCanvasDesc": "按下“Invoke”按钮会将您的工作进度暂存到画布上。",
+        "sendToGallery": "发送到图库",
+        "sendToGalleryDesc": "按下“Invoke”键会生成并保存一张唯一的图像到您的图库中。",
+        "rasterLayer_withCount_other": "栅格图层",
+        "mergeDown": "向下合并",
+        "clearCaches": "清除缓存",
+        "recalculateRects": "重新计算矩形",
+        "duplicate": "复制",
+        "regionalGuidance_withCount_hidden": "区域引导（{{count}} 个已隐藏）",
+        "convertControlLayerTo": "将 $t(controlLayers.controlLayer) 转换为",
+        "convertInpaintMaskTo": "将 $t(controlLayers.inpaintMask) 转换为",
+        "viewProgressInViewer": "在 <Btn>图像查看器</Btn> 中查看进度和输出结果。",
+        "viewProgressOnCanvas": "在 <Btn>画布</Btn> 上查看进度和暂存的输出内容。",
+        "sendingToGallery": "将生成内容发送到图库",
+        "copyToClipboard": "复制到剪贴板",
+        "controlLayer_withCount_other": "控制图层",
+        "sendingToCanvas": "在画布上准备生成",
+        "addReferenceImage": "添加 $t(controlLayers.referenceImage)",
+        "addRegionalGuidance": "添加 $t(controlLayers.regionalGuidance)",
+        "controlLayers_withCount_hidden": "控制图层（{{count}} 个已隐藏）",
+        "rasterLayers_withCount_hidden": "栅格图层（{{count}} 个已隐藏）",
+        "globalReferenceImages_withCount_hidden": "全局参考图像（{{count}} 个已隐藏）",
+        "globalReferenceImages_withCount_visible": "全局参考图像（{{count}} 个）",
+        "layer_withCount_other": "图层（{{count}} 个）",
+        "enableTransparencyEffect": "启用透明效果",
+        "disableTransparencyEffect": "禁用透明效果",
+        "hidingType": "隐藏 {{type}}",
+        "showingType": "显示 {{type}}"
     },
     "ui": {
         "tabs": {

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

@@ -27,6 +27,7 @@ import { ClearQueueConfirmationsAlertDialog } from 'features/queue/components/Cl
 import { DeleteStylePresetDialog } from 'features/stylePresets/components/DeleteStylePresetDialog';
 import { StylePresetModal } from 'features/stylePresets/components/StylePresetForm/StylePresetModal';
 import RefreshAfterResetModal from 'features/system/components/SettingsModal/RefreshAfterResetModal';
+import { VideosModal } from 'features/system/components/VideosModal/VideosModal';
 import { configChanged } from 'features/system/store/configSlice';
 import { selectLanguage } from 'features/system/store/systemSelectors';
 import { AppContent } from 'features/ui/components/AppContent';
@@ -108,6 +109,7 @@ const App = ({ config = DEFAULT_CONFIG, studioInitAction }: Props) => {
       <NewCanvasSessionDialog />
       <ImageContextMenu />
       <FullscreenDropzone />
+      <VideosModal />
     </ErrorBoundary>
   );
 };

invokeai/frontend/web/src/app/hooks/useStudioInitAction.ts

@@ -1,3 +1,4 @@
+import { useStore } from '@nanostores/react';
 import { useAppStore } from 'app/store/storeHooks';
 import { useAssertSingleton } from 'common/hooks/useAssertSingleton';
 import { withResultAsync } from 'common/util/result';
@@ -9,6 +10,7 @@ import { imageDTOToImageObject } from 'features/controlLayers/store/util';
 import { $imageViewer } from 'features/gallery/components/ImageViewer/useImageViewer';
 import { sentImageToCanvas } from 'features/gallery/store/actions';
 import { parseAndRecallAllMetadata } from 'features/metadata/util/handlers';
+import { $hasTemplates } from 'features/nodes/store/nodesSlice';
 import { $isWorkflowListMenuIsOpen } from 'features/nodes/store/workflowListMenu';
 import { $isStylePresetsMenuOpen, activeStylePresetIdChanged } from 'features/stylePresets/store/stylePresetSlice';
 import { toast } from 'features/toast/toast';
@@ -51,6 +53,7 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
   const { t } = useTranslation();
   // Use a ref to ensure that we only perform the action once
   const didInit = useRef(false);
+  const didParseOpenAPISchema = useStore($hasTemplates);
   const store = useAppStore();
   const { getAndLoadWorkflow } = useGetAndLoadLibraryWorkflow();
 
@@ -174,7 +177,7 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
   );
 
   useEffect(() => {
-    if (didInit.current || !action) {
+    if (didInit.current || !action || !didParseOpenAPISchema) {
       return;
     }
 
@@ -187,22 +190,29 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
       case 'selectStylePreset':
         handleSelectStylePreset(action.data.stylePresetId);
         break;
+
       case 'sendToCanvas':
         handleSendToCanvas(action.data.imageName);
         break;
+
       case 'useAllParameters':
         handleUseAllMetadata(action.data.imageName);
         break;
+
       case 'goToDestination':
         handleGoToDestination(action.data.destination);
         break;
+
+      default:
+        break;
     }
   }, [
     handleSendToCanvas,
     handleUseAllMetadata,
     action,
-    handleLoadWorkflow,
     handleSelectStylePreset,
     handleGoToDestination,
+    handleLoadWorkflow,
+    didParseOpenAPISchema,
   ]);
 };

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

@@ -4,7 +4,7 @@ import type { AppStartListening } from 'app/store/middleware/listenerMiddleware'
 import { buildAdHocPostProcessingGraph } from 'features/nodes/util/graph/buildAdHocPostProcessingGraph';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
-import { queueApi } from 'services/api/endpoints/queue';
+import { enqueueMutationFixedCacheKeyOptions, queueApi } from 'services/api/endpoints/queue';
 import type { BatchConfig, ImageDTO } from 'services/api/types';
 import type { JsonObject } from 'type-fest';
 
@@ -32,9 +32,7 @@ export const addAdHocPostProcessingRequestedListener = (startAppListening: AppSt
 
       try {
         const req = dispatch(
-          queueApi.endpoints.enqueueBatch.initiate(enqueueBatchArg, {
-            fixedCacheKey: 'enqueueBatch',
-          })
+          queueApi.endpoints.enqueueBatch.initiate(enqueueBatchArg, enqueueMutationFixedCacheKeyOptions)
         );
 
         const enqueueResult = await req.unwrap();

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

@@ -13,7 +13,7 @@ import { buildSDXLGraph } from 'features/nodes/util/graph/generation/buildSDXLGr
 import type { Graph } from 'features/nodes/util/graph/generation/Graph';
 import { toast } from 'features/toast/toast';
 import { serializeError } from 'serialize-error';
-import { queueApi } from 'services/api/endpoints/queue';
+import { enqueueMutationFixedCacheKeyOptions, queueApi } from 'services/api/endpoints/queue';
 import type { Invocation } from 'services/api/types';
 import { assert, AssertionError } from 'tsafe';
 import type { JsonObject } from 'type-fest';
@@ -91,9 +91,7 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
       }
 
       const req = dispatch(
-        queueApi.endpoints.enqueueBatch.initiate(prepareBatchResult.value, {
-          fixedCacheKey: 'enqueueBatch',
-        })
+        queueApi.endpoints.enqueueBatch.initiate(prepareBatchResult.value, enqueueMutationFixedCacheKeyOptions)
       );
       req.reset();
 

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

@@ -1,10 +1,15 @@
+import { logger } from 'app/logging/logger';
 import { enqueueRequested } from 'app/store/actions';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { selectNodesSlice } from 'features/nodes/store/selectors';
+import { isImageFieldCollectionInputInstance } from 'features/nodes/types/field';
+import { isInvocationNode } from 'features/nodes/types/invocation';
 import { buildNodesGraph } from 'features/nodes/util/graph/buildNodesGraph';
 import { buildWorkflowWithValidation } from 'features/nodes/util/workflow/buildWorkflow';
-import { queueApi } from 'services/api/endpoints/queue';
-import type { BatchConfig } from 'services/api/types';
+import { enqueueMutationFixedCacheKeyOptions, queueApi } from 'services/api/endpoints/queue';
+import type { Batch, BatchConfig } from 'services/api/types';
+
+const log = logger('workflows');
 
 export const addEnqueueRequestedNodes = (startAppListening: AppStartListening) => {
   startAppListening({
@@ -26,6 +31,33 @@ export const addEnqueueRequestedNodes = (startAppListening: AppStartListening) =
         delete builtWorkflow.id;
       }
 
+      const data: Batch['data'] = [];
+
+      // Skip edges from batch nodes - these should not be in the graph, they exist only in the UI
+      const imageBatchNodes = nodes.nodes.filter(isInvocationNode).filter((node) => node.data.type === 'image_batch');
+      for (const node of imageBatchNodes) {
+        const images = node.data.inputs['images'];
+        if (!isImageFieldCollectionInputInstance(images)) {
+          log.warn({ nodeId: node.id }, 'Image batch images field is not an image collection');
+          break;
+        }
+        const edgesFromImageBatch = nodes.edges.filter((e) => e.source === node.id && e.sourceHandle === 'image');
+        const batchDataCollectionItem: NonNullable<Batch['data']>[number] = [];
+        for (const edge of edgesFromImageBatch) {
+          if (!edge.targetHandle) {
+            break;
+          }
+          batchDataCollectionItem.push({
+            node_path: edge.target,
+            field_name: edge.targetHandle,
+            items: images.value,
+          });
+        }
+        if (batchDataCollectionItem.length > 0) {
+          data.push(batchDataCollectionItem);
+        }
+      }
+
       const batchConfig: BatchConfig = {
         batch: {
           graph,
@@ -33,15 +65,12 @@ export const addEnqueueRequestedNodes = (startAppListening: AppStartListening) =
           runs: state.params.iterations,
           origin: 'workflows',
           destination: 'gallery',
+          data,
         },
         prepend: action.payload.prepend,
       };
 
-      const req = dispatch(
-        queueApi.endpoints.enqueueBatch.initiate(batchConfig, {
-          fixedCacheKey: 'enqueueBatch',
-        })
-      );
+      const req = dispatch(queueApi.endpoints.enqueueBatch.initiate(batchConfig, enqueueMutationFixedCacheKeyOptions));
       try {
         await req.unwrap();
       } finally {

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

@@ -2,7 +2,7 @@ import { enqueueRequested } from 'app/store/actions';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
 import { buildMultidiffusionUpscaleGraph } from 'features/nodes/util/graph/buildMultidiffusionUpscaleGraph';
-import { queueApi } from 'services/api/endpoints/queue';
+import { enqueueMutationFixedCacheKeyOptions, queueApi } from 'services/api/endpoints/queue';
 
 export const addEnqueueRequestedUpscale = (startAppListening: AppStartListening) => {
   startAppListening({
@@ -16,11 +16,7 @@ export const addEnqueueRequestedUpscale = (startAppListening: AppStartListening)
 
       const batchConfig = prepareLinearUIBatch(state, g, prepend, noise, posCond, 'upscaling', 'gallery');
 
-      const req = dispatch(
-        queueApi.endpoints.enqueueBatch.initiate(batchConfig, {
-          fixedCacheKey: 'enqueueBatch',
-        })
-      );
+      const req = dispatch(queueApi.endpoints.enqueueBatch.initiate(batchConfig, enqueueMutationFixedCacheKeyOptions));
       try {
         await req.unwrap();
       } finally {

invokeai/frontend/web/src/app/types/invokeai.ts

@@ -25,9 +25,7 @@ export type AppFeature =
   | 'invocationCache'
   | 'bulkDownload'
   | 'starterModels'
-  | 'hfToken'
-  | 'invocationProgressAlert';
-
+  | 'hfToken';
 /**
  * A disable-able Stable Diffusion feature
  */

invokeai/frontend/web/src/common/components/OverlayScrollbars/constants.ts

@@ -2,6 +2,7 @@ import { deepClone } from 'common/util/deepClone';
 import { merge } from 'lodash-es';
 import { ClickScrollPlugin, OverlayScrollbars } from 'overlayscrollbars';
 import type { UseOverlayScrollbarsParams } from 'overlayscrollbars-react';
+import type { CSSProperties } from 'react';
 
 OverlayScrollbars.plugin(ClickScrollPlugin);
 
@@ -27,3 +28,8 @@ export const getOverlayScrollbarsParams = (
   merge(params, { options: { overflow: { y: overflowY, x: overflowX } } });
   return params;
 };
+
+export const overlayScrollbarsStyles: CSSProperties = {
+  height: '100%',
+  width: '100%',
+};

invokeai/frontend/web/src/common/components/SessionMenuItems.tsx

@@ -0,0 +1,42 @@
+import { MenuItem } from '@invoke-ai/ui-library';
+import { useAppDispatch } from 'app/store/storeHooks';
+import {
+  useNewCanvasSession,
+  useNewGallerySession,
+} from 'features/controlLayers/components/NewSessionConfirmationAlertDialog';
+import { canvasReset } from 'features/controlLayers/store/actions';
+import { paramsReset } from 'features/controlLayers/store/paramsSlice';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiArrowsCounterClockwiseBold, PiFilePlusBold } from 'react-icons/pi';
+
+export const SessionMenuItems = memo(() => {
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+  const { newGallerySessionWithDialog } = useNewGallerySession();
+  const { newCanvasSessionWithDialog } = useNewCanvasSession();
+  const resetCanvasLayers = useCallback(() => {
+    dispatch(canvasReset());
+  }, [dispatch]);
+  const resetGenerationSettings = useCallback(() => {
+    dispatch(paramsReset());
+  }, [dispatch]);
+  return (
+    <>
+      <MenuItem icon={<PiFilePlusBold />} onClick={newGallerySessionWithDialog}>
+        {t('controlLayers.newGallerySession')}
+      </MenuItem>
+      <MenuItem icon={<PiFilePlusBold />} onClick={newCanvasSessionWithDialog}>
+        {t('controlLayers.newCanvasSession')}
+      </MenuItem>
+      <MenuItem icon={<PiArrowsCounterClockwiseBold />} onClick={resetCanvasLayers}>
+        {t('controlLayers.resetCanvasLayers')}
+      </MenuItem>
+      <MenuItem icon={<PiArrowsCounterClockwiseBold />} onClick={resetGenerationSettings}>
+        {t('controlLayers.resetGenerationSettings')}
+      </MenuItem>
+    </>
+  );
+});
+
+SessionMenuItems.displayName = 'SessionMenuItems';

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

@@ -46,7 +46,7 @@ const REGION_TARGETS: Record<FocusRegionName, Set<HTMLElement>> = {
 /**
  * The currently-focused region or `null` if no region is focused.
  */
-const $focusedRegion = atom<FocusRegionName | null>(null);
+export const $focusedRegion = atom<FocusRegionName | null>(null);
 
 /**
  * A map of focus regions to atoms that indicate if that region is focused.

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

@@ -1,6 +1,6 @@
 import { useAppDispatch } from 'app/store/storeHooks';
-import { useClearQueue } from 'features/queue/components/ClearQueueConfirmationAlertDialog';
 import { useCancelCurrentQueueItem } from 'features/queue/hooks/useCancelCurrentQueueItem';
+import { useClearQueue } from 'features/queue/hooks/useClearQueue';
 import { useInvoke } from 'features/queue/hooks/useInvoke';
 import { useRegisteredHotkeys } from 'features/system/components/HotkeysModal/useHotkeyData';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';

invokeai/frontend/web/src/common/hooks/useImageUploadButton.tsx

@@ -141,11 +141,9 @@ export const useImageUploadButton = ({ onUpload, isDisabled, allowMultiple }: Us
 };
 
 const sx = {
-  borderColor: 'error.500',
-  borderStyle: 'solid',
-  borderWidth: 0,
-  borderRadius: 'base',
   '&[data-error=true]': {
+    borderColor: 'error.500',
+    borderStyle: 'solid',
     borderWidth: 1,
   },
 } satisfies SystemStyleObject;
@@ -164,7 +162,34 @@ export const UploadImageButton = ({
     <>
       <IconButton
         aria-label="Upload image"
-        variant="ghost"
+        variant="outline"
+        sx={sx}
+        data-error={isError}
+        icon={<PiUploadBold />}
+        isLoading={uploadApi.request.isLoading}
+        {...rest}
+        {...uploadApi.getUploadButtonProps()}
+      />
+      <input {...uploadApi.getUploadInputProps()} />
+    </>
+  );
+};
+
+export const UploadMultipleImageButton = ({
+  isDisabled = false,
+  onUpload,
+  isError = false,
+  ...rest
+}: {
+  onUpload?: (imageDTOs: ImageDTO[]) => void;
+  isError?: boolean;
+} & SetOptional<IconButtonProps, 'aria-label'>) => {
+  const uploadApi = useImageUploadButton({ isDisabled, allowMultiple: true, onUpload });
+  return (
+    <>
+      <IconButton
+        aria-label="Upload image"
+        variant="outline"
         sx={sx}
         data-error={isError}
         icon={<PiUploadBold />}

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

@@ -1,331 +0,0 @@
-import { useStore } from '@nanostores/react';
-import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
-import { $true } from 'app/store/nanostores/util';
-import { useAppSelector } from 'app/store/storeHooks';
-import { useCanvasManagerSafe } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
-import { selectParamsSlice } from 'features/controlLayers/store/paramsSlice';
-import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
-import { selectDynamicPromptsSlice } from 'features/dynamicPrompts/store/dynamicPromptsSlice';
-import { getShouldProcessPrompt } from 'features/dynamicPrompts/util/getShouldProcessPrompt';
-import { $templates } from 'features/nodes/store/nodesSlice';
-import { selectNodesSlice } from 'features/nodes/store/selectors';
-import type { Templates } from 'features/nodes/store/types';
-import { selectWorkflowSettingsSlice } from 'features/nodes/store/workflowSettingsSlice';
-import { isInvocationNode } from 'features/nodes/types/invocation';
-import { selectUpscaleSlice } from 'features/parameters/store/upscaleSlice';
-import { selectConfigSlice } from 'features/system/store/configSlice';
-import { selectSystemSlice } from 'features/system/store/systemSlice';
-import { selectActiveTab } from 'features/ui/store/uiSelectors';
-import i18n from 'i18next';
-import { forEach, upperFirst } from 'lodash-es';
-import { useMemo } from 'react';
-import { getConnectedEdges } from 'reactflow';
-import { $isConnected } from 'services/events/stores';
-
-const LAYER_TYPE_TO_TKEY = {
-  reference_image: 'controlLayers.referenceImage',
-  inpaint_mask: 'controlLayers.inpaintMask',
-  regional_guidance: 'controlLayers.regionalGuidance',
-  raster_layer: 'controlLayers.rasterLayer',
-  control_layer: 'controlLayers.controlLayer',
-} as const;
-
-const createSelector = (
-  templates: Templates,
-  isConnected: boolean,
-  canvasIsFiltering: boolean,
-  canvasIsTransforming: boolean,
-  canvasIsRasterizing: boolean,
-  canvasIsCompositing: boolean
-) =>
-  createMemoizedSelector(
-    [
-      selectSystemSlice,
-      selectNodesSlice,
-      selectWorkflowSettingsSlice,
-      selectDynamicPromptsSlice,
-      selectCanvasSlice,
-      selectParamsSlice,
-      selectUpscaleSlice,
-      selectConfigSlice,
-      selectActiveTab,
-    ],
-    (system, nodes, workflowSettings, dynamicPrompts, canvas, params, upscale, config, activeTabName) => {
-      const { bbox } = canvas;
-      const { model, positivePrompt } = params;
-
-      const reasons: { prefix?: string; content: string }[] = [];
-
-      // Cannot generate if not connected
-      if (!isConnected) {
-        reasons.push({ content: i18n.t('parameters.invoke.systemDisconnected') });
-      }
-
-      if (activeTabName === 'workflows') {
-        if (workflowSettings.shouldValidateGraph) {
-          if (!nodes.nodes.length) {
-            reasons.push({ content: i18n.t('parameters.invoke.noNodesInGraph') });
-          }
-
-          nodes.nodes.forEach((node) => {
-            if (!isInvocationNode(node)) {
-              return;
-            }
-
-            const nodeTemplate = templates[node.data.type];
-
-            if (!nodeTemplate) {
-              // Node type not found
-              reasons.push({ content: i18n.t('parameters.invoke.missingNodeTemplate') });
-              return;
-            }
-
-            const connectedEdges = getConnectedEdges([node], nodes.edges);
-
-            forEach(node.data.inputs, (field) => {
-              const fieldTemplate = nodeTemplate.inputs[field.name];
-              const hasConnection = connectedEdges.some(
-                (edge) => edge.target === node.id && edge.targetHandle === field.name
-              );
-
-              if (!fieldTemplate) {
-                reasons.push({ content: i18n.t('parameters.invoke.missingFieldTemplate') });
-                return;
-              }
-
-              if (fieldTemplate.required && field.value === undefined && !hasConnection) {
-                reasons.push({
-                  content: i18n.t('parameters.invoke.missingInputForField', {
-                    nodeLabel: node.data.label || nodeTemplate.title,
-                    fieldLabel: field.label || fieldTemplate.title,
-                  }),
-                });
-                return;
-              }
-            });
-          });
-        }
-      } else if (activeTabName === 'upscaling') {
-        if (!upscale.upscaleInitialImage) {
-          reasons.push({ content: i18n.t('upscaling.missingUpscaleInitialImage') });
-        } else if (config.maxUpscaleDimension) {
-          const { width, height } = upscale.upscaleInitialImage;
-          const { scale } = upscale;
-
-          const maxPixels = config.maxUpscaleDimension ** 2;
-          const upscaledPixels = width * scale * height * scale;
-
-          if (upscaledPixels > maxPixels) {
-            reasons.push({ content: i18n.t('upscaling.exceedsMaxSize') });
-          }
-        }
-        if (model && !['sd-1', 'sdxl'].includes(model.base)) {
-          // When we are using an upsupported model, do not add the other warnings
-          reasons.push({ content: i18n.t('upscaling.incompatibleBaseModel') });
-        } else {
-          // Using a compatible model, add all warnings
-          if (!model) {
-            reasons.push({ content: i18n.t('parameters.invoke.noModelSelected') });
-          }
-          if (!upscale.upscaleModel) {
-            reasons.push({ content: i18n.t('upscaling.missingUpscaleModel') });
-          }
-          if (!upscale.tileControlnetModel) {
-            reasons.push({ content: i18n.t('upscaling.missingTileControlNetModel') });
-          }
-        }
-      } else {
-        if (canvasIsFiltering) {
-          reasons.push({ content: i18n.t('parameters.invoke.canvasIsFiltering') });
-        }
-        if (canvasIsTransforming) {
-          reasons.push({ content: i18n.t('parameters.invoke.canvasIsTransforming') });
-        }
-        if (canvasIsRasterizing) {
-          reasons.push({ content: i18n.t('parameters.invoke.canvasIsRasterizing') });
-        }
-        if (canvasIsCompositing) {
-          reasons.push({ content: i18n.t('parameters.invoke.canvasIsCompositing') });
-        }
-
-        if (dynamicPrompts.prompts.length === 0 && getShouldProcessPrompt(positivePrompt)) {
-          reasons.push({ content: i18n.t('parameters.invoke.noPrompts') });
-        }
-
-        if (!model) {
-          reasons.push({ content: i18n.t('parameters.invoke.noModelSelected') });
-        }
-
-        if (model?.base === 'flux') {
-          if (!params.t5EncoderModel) {
-            reasons.push({ content: i18n.t('parameters.invoke.noT5EncoderModelSelected') });
-          }
-          if (!params.clipEmbedModel) {
-            reasons.push({ content: i18n.t('parameters.invoke.noCLIPEmbedModelSelected') });
-          }
-          if (!params.fluxVAE) {
-            reasons.push({ content: i18n.t('parameters.invoke.noFLUXVAEModelSelected') });
-          }
-          if (bbox.scaleMethod === 'none') {
-            if (bbox.rect.width % 16 !== 0) {
-              reasons.push({
-                content: i18n.t('parameters.invoke.fluxModelIncompatibleBboxWidth', { width: bbox.rect.width }),
-              });
-            }
-            if (bbox.rect.height % 16 !== 0) {
-              reasons.push({
-                content: i18n.t('parameters.invoke.fluxModelIncompatibleBboxHeight', { height: bbox.rect.height }),
-              });
-            }
-          } else {
-            if (bbox.scaledSize.width % 16 !== 0) {
-              reasons.push({
-                content: i18n.t('parameters.invoke.fluxModelIncompatibleScaledBboxWidth', {
-                  width: bbox.scaledSize.width,
-                }),
-              });
-            }
-            if (bbox.scaledSize.height % 16 !== 0) {
-              reasons.push({
-                content: i18n.t('parameters.invoke.fluxModelIncompatibleScaledBboxHeight', {
-                  height: bbox.scaledSize.height,
-                }),
-              });
-            }
-          }
-        }
-
-        canvas.controlLayers.entities
-          .filter((controlLayer) => controlLayer.isEnabled)
-          .forEach((controlLayer, i) => {
-            const layerLiteral = i18n.t('controlLayers.layer_one');
-            const layerNumber = i + 1;
-            const layerType = i18n.t(LAYER_TYPE_TO_TKEY['control_layer']);
-            const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;
-            const problems: string[] = [];
-            // Must have model
-            if (!controlLayer.controlAdapter.model) {
-              problems.push(i18n.t('parameters.invoke.layer.controlAdapterNoModelSelected'));
-            }
-            // Model base must match
-            if (controlLayer.controlAdapter.model?.base !== model?.base) {
-              problems.push(i18n.t('parameters.invoke.layer.controlAdapterIncompatibleBaseModel'));
-            }
-            if (problems.length) {
-              const content = upperFirst(problems.join(', '));
-              reasons.push({ prefix, content });
-            }
-          });
-
-        canvas.referenceImages.entities
-          .filter((entity) => entity.isEnabled)
-          .forEach((entity, i) => {
-            const layerLiteral = i18n.t('controlLayers.layer_one');
-            const layerNumber = i + 1;
-            const layerType = i18n.t(LAYER_TYPE_TO_TKEY[entity.type]);
-            const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;
-            const problems: string[] = [];
-
-            // Must have model
-            if (!entity.ipAdapter.model) {
-              problems.push(i18n.t('parameters.invoke.layer.ipAdapterNoModelSelected'));
-            }
-            // Model base must match
-            if (entity.ipAdapter.model?.base !== model?.base) {
-              problems.push(i18n.t('parameters.invoke.layer.ipAdapterIncompatibleBaseModel'));
-            }
-            // Must have an image
-            if (!entity.ipAdapter.image) {
-              problems.push(i18n.t('parameters.invoke.layer.ipAdapterNoImageSelected'));
-            }
-
-            if (problems.length) {
-              const content = upperFirst(problems.join(', '));
-              reasons.push({ prefix, content });
-            }
-          });
-
-        canvas.regionalGuidance.entities
-          .filter((entity) => entity.isEnabled)
-          .forEach((entity, i) => {
-            const layerLiteral = i18n.t('controlLayers.layer_one');
-            const layerNumber = i + 1;
-            const layerType = i18n.t(LAYER_TYPE_TO_TKEY[entity.type]);
-            const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;
-            const problems: string[] = [];
-            // Must have a region
-            if (entity.objects.length === 0) {
-              problems.push(i18n.t('parameters.invoke.layer.rgNoRegion'));
-            }
-            // Must have at least 1 prompt or IP Adapter
-            if (
-              entity.positivePrompt === null &&
-              entity.negativePrompt === null &&
-              entity.referenceImages.length === 0
-            ) {
-              problems.push(i18n.t('parameters.invoke.layer.rgNoPromptsOrIPAdapters'));
-            }
-            entity.referenceImages.forEach(({ ipAdapter }) => {
-              // Must have model
-              if (!ipAdapter.model) {
-                problems.push(i18n.t('parameters.invoke.layer.ipAdapterNoModelSelected'));
-              }
-              // Model base must match
-              if (ipAdapter.model?.base !== model?.base) {
-                problems.push(i18n.t('parameters.invoke.layer.ipAdapterIncompatibleBaseModel'));
-              }
-              // Must have an image
-              if (!ipAdapter.image) {
-                problems.push(i18n.t('parameters.invoke.layer.ipAdapterNoImageSelected'));
-              }
-            });
-
-            if (problems.length) {
-              const content = upperFirst(problems.join(', '));
-              reasons.push({ prefix, content });
-            }
-          });
-
-        canvas.rasterLayers.entities
-          .filter((entity) => entity.isEnabled)
-          .forEach((entity, i) => {
-            const layerLiteral = i18n.t('controlLayers.layer_one');
-            const layerNumber = i + 1;
-            const layerType = i18n.t(LAYER_TYPE_TO_TKEY[entity.type]);
-            const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;
-            const problems: string[] = [];
-
-            if (problems.length) {
-              const content = upperFirst(problems.join(', '));
-              reasons.push({ prefix, content });
-            }
-          });
-      }
-
-      return { isReady: !reasons.length, reasons };
-    }
-  );
-
-export const useIsReadyToEnqueue = () => {
-  const templates = useStore($templates);
-  const isConnected = useStore($isConnected);
-  const canvasManager = useCanvasManagerSafe();
-  const canvasIsFiltering = useStore(canvasManager?.stateApi.$isFiltering ?? $true);
-  const canvasIsTransforming = useStore(canvasManager?.stateApi.$isTransforming ?? $true);
-  const canvasIsRasterizing = useStore(canvasManager?.stateApi.$isRasterizing ?? $true);
-  const canvasIsCompositing = useStore(canvasManager?.compositor.$isBusy ?? $true);
-  const selector = useMemo(
-    () =>
-      createSelector(
-        templates,
-        isConnected,
-        canvasIsFiltering,
-        canvasIsTransforming,
-        canvasIsRasterizing,
-        canvasIsCompositing
-      ),
-    [templates, isConnected, canvasIsFiltering, canvasIsTransforming, canvasIsRasterizing, canvasIsCompositing]
-  );
-  const value = useAppSelector(selector);
-  return value;
-};

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

@@ -63,7 +63,7 @@ export const CanvasAddEntityButtons = memo(() => {
               justifyContent="flex-start"
               leftIcon={<PiPlusBold />}
               onClick={addRegionalGuidance}
-              isDisabled={isFLUX || isSD3}
+              isDisabled={isSD3}
             >
               {t('controlLayers.regionalGuidance')}
             </Button>

invokeai/frontend/web/src/features/controlLayers/components/CanvasAlerts/CanvasAlertsInvocationProgress.tsx

@@ -1,13 +1,14 @@
 import { Alert, AlertDescription, AlertIcon, AlertTitle } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { useAppSelector } from 'app/store/storeHooks';
-import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
+import { useDeferredModelLoadingInvocationProgressMessage } from 'features/controlLayers/hooks/useDeferredModelLoadingInvocationProgressMessage';
+import { selectIsLocal } from 'features/system/store/configSlice';
 import { selectSystemShouldShowInvocationProgressDetail } from 'features/system/store/systemSlice';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { $invocationProgressMessage } from 'services/events/stores';
 
-const CanvasAlertsInvocationProgressContent = memo(() => {
+const CanvasAlertsInvocationProgressContentLocal = memo(() => {
   const { t } = useTranslation();
   const invocationProgressMessage = useStore($invocationProgressMessage);
 
@@ -23,23 +24,38 @@ const CanvasAlertsInvocationProgressContent = memo(() => {
     </Alert>
   );
 });
-CanvasAlertsInvocationProgressContent.displayName = 'CanvasAlertsInvocationProgressContent';
+CanvasAlertsInvocationProgressContentLocal.displayName = 'CanvasAlertsInvocationProgressContentLocal';
 
-export const CanvasAlertsInvocationProgress = memo(() => {
-  const isProgressMessageAlertEnabled = useFeatureStatus('invocationProgressAlert');
-  const shouldShowInvocationProgressDetail = useAppSelector(selectSystemShouldShowInvocationProgressDetail);
+const CanvasAlertsInvocationProgressContentCommercial = memo(() => {
+  const message = useDeferredModelLoadingInvocationProgressMessage();
 
-  // The alert is disabled at the system level
-  if (!isProgressMessageAlertEnabled) {
+  if (!message) {
     return null;
   }
 
-  // The alert is disabled at the user level
+  return (
+    <Alert status="loading" borderRadius="base" fontSize="sm" shadow="md" w="fit-content">
+      <AlertIcon />
+      <AlertDescription>{message}</AlertDescription>
+    </Alert>
+  );
+});
+CanvasAlertsInvocationProgressContentCommercial.displayName = 'CanvasAlertsInvocationProgressContentCommercial';
+
+export const CanvasAlertsInvocationProgress = memo(() => {
+  const shouldShowInvocationProgressDetail = useAppSelector(selectSystemShouldShowInvocationProgressDetail);
+  const isLocal = useAppSelector(selectIsLocal);
+
+  if (!isLocal) {
+    return <CanvasAlertsInvocationProgressContentCommercial />;
+  }
+
+  // OSS user setting
   if (!shouldShowInvocationProgressDetail) {
     return null;
   }
 
-  return <CanvasAlertsInvocationProgressContent />;
+  return <CanvasAlertsInvocationProgressContentLocal />;
 });
 
 CanvasAlertsInvocationProgress.displayName = 'CanvasAlertsInvocationProgress';

invokeai/frontend/web/src/features/controlLayers/components/CanvasAlerts/CanvasAlertsSelectedEntityStatus.tsx

@@ -3,6 +3,7 @@ import { Alert, AlertIcon, AlertTitle } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppSelector } from 'app/store/storeHooks';
+import { CanvasEntityStateGate } from 'features/controlLayers/contexts/CanvasEntityStateGate';
 import { useEntityAdapterSafe } from 'features/controlLayers/contexts/EntityAdapterContext';
 import { useEntityTitle } from 'features/controlLayers/hooks/useEntityTitle';
 import { useEntityTypeIsHidden } from 'features/controlLayers/hooks/useEntityTypeIsHidden';
@@ -29,17 +30,23 @@ type AlertData = {
   title: string;
 };
 
+const buildSelectIsEnabled = (entityIdentifier: CanvasEntityIdentifier) =>
+  createSelector(
+    selectCanvasSlice,
+    (canvas) => selectEntityOrThrow(canvas, entityIdentifier, 'CanvasAlertsSelectedEntityStatusContent').isEnabled
+  );
+
+const buildSelectIsLocked = (entityIdentifier: CanvasEntityIdentifier) =>
+  createSelector(
+    selectCanvasSlice,
+    (canvas) => selectEntityOrThrow(canvas, entityIdentifier, 'CanvasAlertsSelectedEntityStatusContent').isLocked
+  );
+
 const CanvasAlertsSelectedEntityStatusContent = memo(({ entityIdentifier, adapter }: ContentProps) => {
   const { t } = useTranslation();
   const title = useEntityTitle(entityIdentifier);
-  const selectIsEnabled = useMemo(
-    () => createSelector(selectCanvasSlice, (canvas) => selectEntityOrThrow(canvas, entityIdentifier).isEnabled),
-    [entityIdentifier]
-  );
-  const selectIsLocked = useMemo(
-    () => createSelector(selectCanvasSlice, (canvas) => selectEntityOrThrow(canvas, entityIdentifier).isLocked),
-    [entityIdentifier]
-  );
+  const selectIsEnabled = useMemo(() => buildSelectIsEnabled(entityIdentifier), [entityIdentifier]);
+  const selectIsLocked = useMemo(() => buildSelectIsLocked(entityIdentifier), [entityIdentifier]);
   const isEnabled = useAppSelector(selectIsEnabled);
   const isLocked = useAppSelector(selectIsLocked);
   const isHidden = useEntityTypeIsHidden(entityIdentifier.type);
@@ -115,7 +122,11 @@ export const CanvasAlertsSelectedEntityStatus = memo(() => {
     return null;
   }
 
-  return <CanvasAlertsSelectedEntityStatusContent entityIdentifier={selectedEntityIdentifier} adapter={adapter} />;
+  return (
+    <CanvasEntityStateGate entityIdentifier={selectedEntityIdentifier}>
+      <CanvasAlertsSelectedEntityStatusContent entityIdentifier={selectedEntityIdentifier} adapter={adapter} />
+    </CanvasEntityStateGate>
+  );
 });
 
 CanvasAlertsSelectedEntityStatus.displayName = 'CanvasAlertsSelectedEntityStatus';

invokeai/frontend/web/src/features/controlLayers/components/CanvasContextMenu/CanvasContextMenuSelectedEntityMenuItems.tsx

@@ -5,6 +5,7 @@ import { InpaintMaskMenuItems } from 'features/controlLayers/components/InpaintM
 import { IPAdapterMenuItems } from 'features/controlLayers/components/IPAdapter/IPAdapterMenuItems';
 import { RasterLayerMenuItems } from 'features/controlLayers/components/RasterLayer/RasterLayerMenuItems';
 import { RegionalGuidanceMenuItems } from 'features/controlLayers/components/RegionalGuidance/RegionalGuidanceMenuItems';
+import { CanvasEntityStateGate } from 'features/controlLayers/contexts/CanvasEntityStateGate';
 import {
   EntityIdentifierContext,
   useEntityIdentifierContext,
@@ -40,6 +41,15 @@ const CanvasContextMenuSelectedEntityMenuItemsContent = memo(() => {
 
 CanvasContextMenuSelectedEntityMenuItemsContent.displayName = 'CanvasContextMenuSelectedEntityMenuItemsContent';
 
+const CanvasContextMenuSelectedEntityMenuGroup = memo((props: PropsWithChildren) => {
+  const entityIdentifier = useEntityIdentifierContext();
+  const title = useEntityTypeString(entityIdentifier.type);
+
+  return <MenuGroup title={title}>{props.children}</MenuGroup>;
+});
+
+CanvasContextMenuSelectedEntityMenuGroup.displayName = 'CanvasContextMenuSelectedEntityMenuGroup';
+
 export const CanvasContextMenuSelectedEntityMenuItems = memo(() => {
   const selectedEntityIdentifier = useAppSelector(selectSelectedEntityIdentifier);
 
@@ -49,20 +59,13 @@ export const CanvasContextMenuSelectedEntityMenuItems = memo(() => {
 
   return (
     <EntityIdentifierContext.Provider value={selectedEntityIdentifier}>
-      <CanvasContextMenuSelectedEntityMenuGroup>
-        <CanvasContextMenuSelectedEntityMenuItemsContent />
-      </CanvasContextMenuSelectedEntityMenuGroup>
+      <CanvasEntityStateGate entityIdentifier={selectedEntityIdentifier}>
+        <CanvasContextMenuSelectedEntityMenuGroup>
+          <CanvasContextMenuSelectedEntityMenuItemsContent />
+        </CanvasContextMenuSelectedEntityMenuGroup>
+      </CanvasEntityStateGate>
     </EntityIdentifierContext.Provider>
   );
 });
 
 CanvasContextMenuSelectedEntityMenuItems.displayName = 'CanvasContextMenuSelectedEntityMenuItems';
-
-const CanvasContextMenuSelectedEntityMenuGroup = memo((props: PropsWithChildren) => {
-  const entityIdentifier = useEntityIdentifierContext();
-  const title = useEntityTypeString(entityIdentifier.type);
-
-  return <MenuGroup title={title}>{props.children}</MenuGroup>;
-});
-
-CanvasContextMenuSelectedEntityMenuGroup.displayName = 'CanvasContextMenuSelectedEntityMenuGroup';

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

@@ -49,7 +49,7 @@ export const EntityListGlobalActionBarAddLayerMenu = memo(() => {
           <MenuItem icon={<PiPlusBold />} onClick={addInpaintMask}>
             {t('controlLayers.inpaintMask')}
           </MenuItem>
-          <MenuItem icon={<PiPlusBold />} onClick={addRegionalGuidance} isDisabled={isFLUX || isSD3}>
+          <MenuItem icon={<PiPlusBold />} onClick={addRegionalGuidance} isDisabled={isSD3}>
             {t('controlLayers.regionalGuidance')}
           </MenuItem>
           <MenuItem icon={<PiPlusBold />} onClick={addRegionalReferenceImage} isDisabled={isFLUX || isSD3}>

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

@@ -7,6 +7,7 @@ import { CanvasEntitySettingsWrapper } from 'features/controlLayers/components/c
 import { CanvasEntityEditableTitle } from 'features/controlLayers/components/common/CanvasEntityTitleEdit';
 import { ControlLayerBadges } from 'features/controlLayers/components/ControlLayer/ControlLayerBadges';
 import { ControlLayerSettings } from 'features/controlLayers/components/ControlLayer/ControlLayerSettings';
+import { CanvasEntityStateGate } from 'features/controlLayers/contexts/CanvasEntityStateGate';
 import { ControlLayerAdapterGate } from 'features/controlLayers/contexts/EntityAdapterContext';
 import { EntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
 import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
@@ -36,24 +37,26 @@ export const ControlLayer = memo(({ id }: Props) => {
   return (
     <EntityIdentifierContext.Provider value={entityIdentifier}>
       <ControlLayerAdapterGate>
-        <CanvasEntityContainer>
-          <CanvasEntityHeader>
-            <CanvasEntityPreviewImage />
-            <CanvasEntityEditableTitle />
-            <Spacer />
-            <ControlLayerBadges />
-            <CanvasEntityHeaderCommonActions />
-          </CanvasEntityHeader>
-          <CanvasEntitySettingsWrapper>
-            <ControlLayerSettings />
-          </CanvasEntitySettingsWrapper>
-          <DndDropTarget
-            dndTarget={replaceCanvasEntityObjectsWithImageDndTarget}
-            dndTargetData={dndTargetData}
-            label={t('controlLayers.replaceLayer')}
-            isDisabled={isBusy}
-          />
-        </CanvasEntityContainer>
+        <CanvasEntityStateGate entityIdentifier={entityIdentifier}>
+          <CanvasEntityContainer>
+            <CanvasEntityHeader>
+              <CanvasEntityPreviewImage />
+              <CanvasEntityEditableTitle />
+              <Spacer />
+              <ControlLayerBadges />
+              <CanvasEntityHeaderCommonActions />
+            </CanvasEntityHeader>
+            <CanvasEntitySettingsWrapper>
+              <ControlLayerSettings />
+            </CanvasEntitySettingsWrapper>
+            <DndDropTarget
+              dndTarget={replaceCanvasEntityObjectsWithImageDndTarget}
+              dndTargetData={dndTargetData}
+              label={t('controlLayers.replaceLayer')}
+              isDisabled={isBusy}
+            />
+          </CanvasEntityContainer>
+        </CanvasEntityStateGate>
       </ControlLayerAdapterGate>
     </EntityIdentifierContext.Provider>
   );

invokeai/frontend/web/src/features/controlLayers/components/ControlLayer/ControlLayerBadges.tsx

@@ -1,26 +1,47 @@
 import { Badge } from '@invoke-ai/ui-library';
+import { createSelector } from '@reduxjs/toolkit';
 import { useAppSelector } from 'app/store/storeHooks';
+import { CanvasEntityStateGate } from 'features/controlLayers/contexts/CanvasEntityStateGate';
 import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
 import { selectCanvasSlice, selectEntityOrThrow } from 'features/controlLayers/store/selectors';
-import { memo } from 'react';
+import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
+import { memo, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 
-export const ControlLayerBadges = memo(() => {
-  const entityIdentifier = useEntityIdentifierContext('control_layer');
-  const { t } = useTranslation();
-  const withTransparencyEffect = useAppSelector(
-    (s) => selectEntityOrThrow(selectCanvasSlice(s), entityIdentifier).withTransparencyEffect
+const buildSelectWithTransparencyEffect = (entityIdentifier: CanvasEntityIdentifier<'control_layer'>) =>
+  createSelector(
+    selectCanvasSlice,
+    (canvas) => selectEntityOrThrow(canvas, entityIdentifier, 'ControlLayerBadgesContent').withTransparencyEffect
   );
 
+const ControlLayerBadgesContent = memo(() => {
+  const entityIdentifier = useEntityIdentifierContext('control_layer');
+  const { t } = useTranslation();
+  const selectWithTransparencyEffect = useMemo(
+    () => buildSelectWithTransparencyEffect(entityIdentifier),
+    [entityIdentifier]
+  );
+  const withTransparencyEffect = useAppSelector(selectWithTransparencyEffect);
+
+  if (!withTransparencyEffect) {
+    return null;
+  }
+
   return (
-    <>
-      {withTransparencyEffect && (
-        <Badge color="base.300" bg="transparent" borderWidth={1} userSelect="none">
-          {t('controlLayers.transparency')}
-        </Badge>
-      )}
-    </>
+    <Badge color="base.300" bg="transparent" borderWidth={1} userSelect="none">
+      {t('controlLayers.transparency')}
+    </Badge>
   );
 });
 
+ControlLayerBadgesContent.displayName = 'ControlLayerBadgesContent';
+
+export const ControlLayerBadges = memo(() => {
+  const entityIdentifier = useEntityIdentifierContext('control_layer');
+  return (
+    <CanvasEntityStateGate entityIdentifier={entityIdentifier}>
+      <ControlLayerBadgesContent />
+    </CanvasEntityStateGate>
+  );
+});
 ControlLayerBadges.displayName = 'ControlLayerBadges';

invokeai/frontend/web/src/features/controlLayers/components/ControlLayer/ControlLayerControlAdapter.tsx

@@ -28,24 +28,18 @@ import { useTranslation } from 'react-i18next';
 import { PiBoundingBoxBold, PiShootingStarFill, PiUploadBold } from 'react-icons/pi';
 import type { ControlNetModelConfig, ImageDTO, T2IAdapterModelConfig } from 'services/api/types';
 
-const useControlLayerControlAdapter = (entityIdentifier: CanvasEntityIdentifier<'control_layer'>) => {
-  const selectControlAdapter = useMemo(
-    () =>
-      createMemoizedAppSelector(selectCanvasSlice, (canvas) => {
-        const layer = selectEntityOrThrow(canvas, entityIdentifier);
-        return layer.controlAdapter;
-      }),
-    [entityIdentifier]
-  );
-  const controlAdapter = useAppSelector(selectControlAdapter);
-  return controlAdapter;
-};
+const buildSelectControlAdapter = (entityIdentifier: CanvasEntityIdentifier<'control_layer'>) =>
+  createMemoizedAppSelector(selectCanvasSlice, (canvas) => {
+    const layer = selectEntityOrThrow(canvas, entityIdentifier, 'ControlLayerControlAdapter');
+    return layer.controlAdapter;
+  });
 
 export const ControlLayerControlAdapter = memo(() => {
   const { t } = useTranslation();
   const { dispatch, getState } = useAppStore();
   const entityIdentifier = useEntityIdentifierContext('control_layer');
-  const controlAdapter = useControlLayerControlAdapter(entityIdentifier);
+  const selectControlAdapter = useMemo(() => buildSelectControlAdapter(entityIdentifier), [entityIdentifier]);
+  const controlAdapter = useAppSelector(selectControlAdapter);
   const filter = useEntityFilter(entityIdentifier);
   const isFLUX = useAppSelector(selectIsFLUX);
   const adapter = useEntityAdapterContext('control_layer');

invokeai/frontend/web/src/features/controlLayers/components/ControlLayer/ControlLayerMenuItemsTransparencyEffect.tsx

@@ -5,21 +5,25 @@ import { useEntityIdentifierContext } from 'features/controlLayers/contexts/Enti
 import { useEntityIsLocked } from 'features/controlLayers/hooks/useEntityIsLocked';
 import { controlLayerWithTransparencyEffectToggled } from 'features/controlLayers/store/canvasSlice';
 import { selectCanvasSlice, selectEntityOrThrow } from 'features/controlLayers/store/selectors';
+import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiDropHalfBold } from 'react-icons/pi';
 
+const buildSelectWithTransparencyEffect = (entityIdentifier: CanvasEntityIdentifier<'control_layer'>) =>
+  createSelector(
+    selectCanvasSlice,
+    (canvas) =>
+      selectEntityOrThrow(canvas, entityIdentifier, 'ControlLayerMenuItemsTransparencyEffect').withTransparencyEffect
+  );
+
 export const ControlLayerMenuItemsTransparencyEffect = memo(() => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
   const entityIdentifier = useEntityIdentifierContext('control_layer');
   const isLocked = useEntityIsLocked(entityIdentifier);
   const selectWithTransparencyEffect = useMemo(
-    () =>
-      createSelector(selectCanvasSlice, (canvas) => {
-        const entity = selectEntityOrThrow(canvas, entityIdentifier);
-        return entity.withTransparencyEffect;
-      }),
+    () => buildSelectWithTransparencyEffect(entityIdentifier),
     [entityIdentifier]
   );
   const withTransparencyEffect = useAppSelector(selectWithTransparencyEffect);

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

@@ -4,6 +4,7 @@ import { CanvasEntityHeader } from 'features/controlLayers/components/common/Can
 import { CanvasEntityHeaderCommonActions } from 'features/controlLayers/components/common/CanvasEntityHeaderCommonActions';
 import { CanvasEntityEditableTitle } from 'features/controlLayers/components/common/CanvasEntityTitleEdit';
 import { IPAdapterSettings } from 'features/controlLayers/components/IPAdapter/IPAdapterSettings';
+import { CanvasEntityStateGate } from 'features/controlLayers/contexts/CanvasEntityStateGate';
 import { EntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
 import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
 import { memo, useMemo } from 'react';
@@ -17,14 +18,16 @@ export const IPAdapter = memo(({ id }: Props) => {
 
   return (
     <EntityIdentifierContext.Provider value={entityIdentifier}>
-      <CanvasEntityContainer>
-        <CanvasEntityHeader ps={4} py={5}>
-          <CanvasEntityEditableTitle />
-          <Spacer />
-          <CanvasEntityHeaderCommonActions />
-        </CanvasEntityHeader>
-        <IPAdapterSettings />
-      </CanvasEntityContainer>
+      <CanvasEntityStateGate entityIdentifier={entityIdentifier}>
+        <CanvasEntityContainer>
+          <CanvasEntityHeader ps={4} py={5}>
+            <CanvasEntityEditableTitle />
+            <Spacer />
+            <CanvasEntityHeaderCommonActions />
+          </CanvasEntityHeader>
+          <IPAdapterSettings />
+        </CanvasEntityContainer>
+      </CanvasEntityStateGate>
     </EntityIdentifierContext.Provider>
   );
 });

invokeai/frontend/web/src/features/controlLayers/components/IPAdapter/IPAdapterMethod.tsx

@@ -1,8 +1,10 @@
 import type { ComboboxOnChange } from '@invoke-ai/ui-library';
 import { Combobox, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
 import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
 import type { IPMethodV2 } from 'features/controlLayers/store/types';
 import { isIPMethodV2 } from 'features/controlLayers/store/types';
+import { selectSystemShouldEnableModelDescriptions } from 'features/system/store/systemSlice';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { assert } from 'tsafe';
@@ -14,13 +16,27 @@ type Props = {
 
 export const IPAdapterMethod = memo(({ method, onChange }: Props) => {
   const { t } = useTranslation();
+  const shouldShowModelDescriptions = useAppSelector(selectSystemShouldEnableModelDescriptions);
+
   const options: { label: string; value: IPMethodV2 }[] = useMemo(
     () => [
-      { label: t('controlLayers.ipAdapterMethod.full'), value: 'full' },
-      { label: t('controlLayers.ipAdapterMethod.style'), value: 'style' },
-      { label: t('controlLayers.ipAdapterMethod.composition'), value: 'composition' },
+      {
+        label: t('controlLayers.ipAdapterMethod.full'),
+        value: 'full',
+        description: shouldShowModelDescriptions ? t('controlLayers.ipAdapterMethod.fullDesc') : undefined,
+      },
+      {
+        label: t('controlLayers.ipAdapterMethod.style'),
+        value: 'style',
+        description: shouldShowModelDescriptions ? t('controlLayers.ipAdapterMethod.styleDesc') : undefined,
+      },
+      {
+        label: t('controlLayers.ipAdapterMethod.composition'),
+        value: 'composition',
+        description: shouldShowModelDescriptions ? t('controlLayers.ipAdapterMethod.compositionDesc') : undefined,
+      },
     ],
-    [t]
+    [t, shouldShowModelDescriptions]
   );
   const _onChange = useCallback<ComboboxOnChange>(
     (v) => {