chore(ui): lint

Updated by "Remove blank strings" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI

invokeai/app/invocations/compel.py

@@ -95,6 +95,7 @@ class CompelInvocation(BaseInvocation):
                 ti_manager,
             ),
         ):
+            context.util.signal_progress("Building conditioning")
             assert isinstance(text_encoder, CLIPTextModel)
             assert isinstance(tokenizer, CLIPTokenizer)
             compel = Compel(
@@ -191,6 +192,7 @@ class SDXLPromptInvocationBase:
                 ti_manager,
             ),
         ):
+            context.util.signal_progress("Building conditioning")
             assert isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection))
             assert isinstance(tokenizer, CLIPTokenizer)
 

invokeai/app/invocations/create_denoise_mask.py

@@ -65,6 +65,7 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
             img_mask = tv_resize(mask, image_tensor.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
             masked_image = image_tensor * torch.where(img_mask < 0.5, 0.0, 1.0)
             # TODO:
+            context.util.signal_progress("Running VAE encoder")
             masked_latents = ImageToLatentsInvocation.vae_encode(vae_info, self.fp32, self.tiled, masked_image.clone())
 
             masked_latents_name = context.tensors.save(tensor=masked_latents)

invokeai/app/invocations/create_gradient_mask.py

@@ -131,6 +131,7 @@ class CreateGradientMaskInvocation(BaseInvocation):
                     image_tensor = image_tensor.unsqueeze(0)
                 img_mask = tv_resize(mask, image_tensor.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
                 masked_image = image_tensor * torch.where(img_mask < 0.5, 0.0, 1.0)
+                context.util.signal_progress("Running VAE encoder")
                 masked_latents = ImageToLatentsInvocation.vae_encode(
                     vae_info, self.fp32, self.tiled, masked_image.clone()
                 )

invokeai/app/invocations/flux_text_encoder.py

@@ -71,6 +71,7 @@ class FluxTextEncoderInvocation(BaseInvocation):
 
             t5_encoder = HFEncoder(t5_text_encoder, t5_tokenizer, False, self.t5_max_seq_len)
 
+            context.util.signal_progress("Running T5 encoder")
             prompt_embeds = t5_encoder(prompt)
 
         assert isinstance(prompt_embeds, torch.Tensor)
@@ -111,6 +112,7 @@ class FluxTextEncoderInvocation(BaseInvocation):
 
             clip_encoder = HFEncoder(clip_text_encoder, clip_tokenizer, True, 77)
 
+            context.util.signal_progress("Running CLIP encoder")
             pooled_prompt_embeds = clip_encoder(prompt)
 
         assert isinstance(pooled_prompt_embeds, torch.Tensor)

invokeai/app/invocations/flux_vae_decode.py

@@ -41,7 +41,8 @@ class FluxVaeDecodeInvocation(BaseInvocation, WithMetadata, WithBoard):
     def _vae_decode(self, vae_info: LoadedModel, latents: torch.Tensor) -> Image.Image:
         with vae_info as vae:
             assert isinstance(vae, AutoEncoder)
-            latents = latents.to(device=TorchDevice.choose_torch_device(), dtype=TorchDevice.choose_torch_dtype())
+            vae_dtype = next(iter(vae.parameters())).dtype
+            latents = latents.to(device=TorchDevice.choose_torch_device(), dtype=vae_dtype)
             img = vae.decode(latents)
 
         img = img.clamp(-1, 1)
@@ -53,6 +54,7 @@ class FluxVaeDecodeInvocation(BaseInvocation, WithMetadata, WithBoard):
     def invoke(self, context: InvocationContext) -> ImageOutput:
         latents = context.tensors.load(self.latents.latents_name)
         vae_info = context.models.load(self.vae.vae)
+        context.util.signal_progress("Running VAE")
         image = self._vae_decode(vae_info=vae_info, latents=latents)
 
         TorchDevice.empty_cache()

invokeai/app/invocations/flux_vae_encode.py

@@ -44,9 +44,8 @@ class FluxVaeEncodeInvocation(BaseInvocation):
         generator = torch.Generator(device=TorchDevice.choose_torch_device()).manual_seed(0)
         with vae_info as vae:
             assert isinstance(vae, AutoEncoder)
-            image_tensor = image_tensor.to(
-                device=TorchDevice.choose_torch_device(), dtype=TorchDevice.choose_torch_dtype()
-            )
+            vae_dtype = next(iter(vae.parameters())).dtype
+            image_tensor = image_tensor.to(device=TorchDevice.choose_torch_device(), dtype=vae_dtype)
             latents = vae.encode(image_tensor, sample=True, generator=generator)
             return latents
 
@@ -60,6 +59,7 @@ class FluxVaeEncodeInvocation(BaseInvocation):
         if image_tensor.dim() == 3:
             image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
 
+        context.util.signal_progress("Running VAE")
         latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
 
         latents = latents.to("cpu")

invokeai/app/invocations/image_to_latents.py

@@ -117,6 +117,7 @@ class ImageToLatentsInvocation(BaseInvocation):
         if image_tensor.dim() == 3:
             image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
 
+        context.util.signal_progress("Running VAE encoder")
         latents = self.vae_encode(
             vae_info=vae_info, upcast=self.fp32, tiled=self.tiled, image_tensor=image_tensor, tile_size=self.tile_size
         )

invokeai/app/invocations/latents_to_image.py

@@ -60,6 +60,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         vae_info = context.models.load(self.vae.vae)
         assert isinstance(vae_info.model, (AutoencoderKL, AutoencoderTiny))
         with SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes), vae_info as vae:
+            context.util.signal_progress("Running VAE decoder")
             assert isinstance(vae, (AutoencoderKL, AutoencoderTiny))
             latents = latents.to(vae.device)
             if self.fp32:

invokeai/app/invocations/metadata.py

@@ -147,6 +147,10 @@ GENERATION_MODES = Literal[
     "flux_img2img",
     "flux_inpaint",
     "flux_outpaint",
+    "sd3_txt2img",
+    "sd3_img2img",
+    "sd3_inpaint",
+    "sd3_outpaint",
 ]
 
 

invokeai/app/invocations/sd3_denoise.py

@@ -1,16 +1,19 @@
-from typing import Callable, Tuple
+from typing import Callable, Optional, Tuple
 
 import torch
+import torchvision.transforms as tv_transforms
 from diffusers.models.transformers.transformer_sd3 import SD3Transformer2DModel
-from diffusers.schedulers.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
+from torchvision.transforms.functional import resize as tv_resize
 from tqdm import tqdm
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
 from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
+    DenoiseMaskField,
     FieldDescriptions,
     Input,
     InputField,
+    LatentsField,
     SD3ConditioningField,
     WithBoard,
     WithMetadata,
@@ -19,7 +22,9 @@ from invokeai.app.invocations.model import TransformerField
 from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.invocations.sd3_text_encoder import SD3_T5_MAX_SEQ_LEN
 from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.flux.sampling_utils import clip_timestep_schedule_fractional
 from invokeai.backend.model_manager.config import BaseModelType
+from invokeai.backend.sd3.extensions.inpaint_extension import InpaintExtension
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import SD3ConditioningInfo
 from invokeai.backend.util.devices import TorchDevice
@@ -30,16 +35,24 @@ from invokeai.backend.util.devices import TorchDevice
     title="SD3 Denoise",
     tags=["image", "sd3"],
     category="image",
-    version="1.0.0",
+    version="1.1.0",
     classification=Classification.Prototype,
 )
 class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Run denoising process with a SD3 model."""
 
+    # If latents is provided, this means we are doing image-to-image.
+    latents: Optional[LatentsField] = InputField(
+        default=None, description=FieldDescriptions.latents, input=Input.Connection
+    )
+    # denoise_mask is used for image-to-image inpainting. Only the masked region is modified.
+    denoise_mask: Optional[DenoiseMaskField] = InputField(
+        default=None, description=FieldDescriptions.denoise_mask, input=Input.Connection
+    )
+    denoising_start: float = InputField(default=0.0, ge=0, le=1, description=FieldDescriptions.denoising_start)
+    denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
     transformer: TransformerField = InputField(
-        description=FieldDescriptions.sd3_model,
-        input=Input.Connection,
-        title="Transformer",
+        description=FieldDescriptions.sd3_model, input=Input.Connection, title="Transformer"
     )
     positive_conditioning: SD3ConditioningField = InputField(
         description=FieldDescriptions.positive_cond, input=Input.Connection
@@ -61,6 +74,41 @@ class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         name = context.tensors.save(tensor=latents)
         return LatentsOutput.build(latents_name=name, latents=latents, seed=None)
 
+    def _prep_inpaint_mask(self, context: InvocationContext, latents: torch.Tensor) -> torch.Tensor | None:
+        """Prepare the inpaint mask.
+        - Loads the mask
+        - Resizes if necessary
+        - Casts to same device/dtype as latents
+
+        Args:
+            context (InvocationContext): The invocation context, for loading the inpaint mask.
+            latents (torch.Tensor): A latent image tensor. Used to determine the target shape, device, and dtype for the
+                inpaint mask.
+
+        Returns:
+            torch.Tensor | None: Inpaint mask. Values of 0.0 represent the regions to be fully denoised, and 1.0
+                represent the regions to be preserved.
+        """
+        if self.denoise_mask is None:
+            return None
+        mask = context.tensors.load(self.denoise_mask.mask_name)
+
+        # The input denoise_mask contains values in [0, 1], where 0.0 represents the regions to be fully denoised, and
+        # 1.0 represents the regions to be preserved.
+        # We invert the mask so that the regions to be preserved are 0.0 and the regions to be denoised are 1.0.
+        mask = 1.0 - mask
+
+        _, _, latent_height, latent_width = latents.shape
+        mask = tv_resize(
+            img=mask,
+            size=[latent_height, latent_width],
+            interpolation=tv_transforms.InterpolationMode.BILINEAR,
+            antialias=False,
+        )
+
+        mask = mask.to(device=latents.device, dtype=latents.dtype)
+        return mask
+
     def _load_text_conditioning(
         self,
         context: InvocationContext,
@@ -170,14 +218,20 @@ class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         prompt_embeds = torch.cat([neg_prompt_embeds, pos_prompt_embeds], dim=0)
         pooled_prompt_embeds = torch.cat([neg_pooled_prompt_embeds, pos_pooled_prompt_embeds], dim=0)
 
-        # Prepare the scheduler.
-        scheduler = FlowMatchEulerDiscreteScheduler()
-        scheduler.set_timesteps(num_inference_steps=self.steps, device=device)
-        timesteps = scheduler.timesteps
-        assert isinstance(timesteps, torch.Tensor)
+        # Prepare the timestep schedule.
+        # We add an extra step to the end to account for the final timestep of 0.0.
+        timesteps: list[float] = torch.linspace(1, 0, self.steps + 1).tolist()
+        # Clip the timesteps schedule based on denoising_start and denoising_end.
+        timesteps = clip_timestep_schedule_fractional(timesteps, self.denoising_start, self.denoising_end)
+        total_steps = len(timesteps) - 1
 
         # Prepare the CFG scale list.
-        cfg_scale = self._prepare_cfg_scale(len(timesteps))
+        cfg_scale = self._prepare_cfg_scale(total_steps)
+
+        # 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:
+            init_latents = init_latents.to(device=device, dtype=inference_dtype)
 
         # Generate initial latent noise.
         num_channels_latents = transformer_info.model.config.in_channels
@@ -191,9 +245,34 @@ class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
             device=device,
             seed=self.seed,
         )
-        latents: torch.Tensor = noise
 
-        total_steps = len(timesteps)
+        # Prepare input latent image.
+        if init_latents is not None:
+            # Noise the init_latents by the appropriate amount for the first timestep.
+            t_0 = timesteps[0]
+            latents = t_0 * noise + (1.0 - t_0) * init_latents
+        else:
+            # init_latents are not provided, so we are not doing image-to-image (i.e. we are starting from pure noise).
+            if self.denoising_start > 1e-5:
+                raise ValueError("denoising_start should be 0 when initial latents are not provided.")
+            latents = noise
+
+        # If len(timesteps) == 1, then short-circuit. We are just noising the input latents, but not taking any
+        # denoising steps.
+        if len(timesteps) <= 1:
+            return latents
+
+        # Prepare inpaint extension.
+        inpaint_mask = self._prep_inpaint_mask(context, latents)
+        inpaint_extension: InpaintExtension | None = None
+        if inpaint_mask is not None:
+            assert init_latents is not None
+            inpaint_extension = InpaintExtension(
+                init_latents=init_latents,
+                inpaint_mask=inpaint_mask,
+                noise=noise,
+            )
+
         step_callback = self._build_step_callback(context)
 
         step_callback(
@@ -210,11 +289,12 @@ class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
             assert isinstance(transformer, SD3Transformer2DModel)
 
             # 6. Denoising loop
-            for step_idx, t in tqdm(list(enumerate(timesteps))):
+            for step_idx, (t_curr, t_prev) in tqdm(list(enumerate(zip(timesteps[:-1], timesteps[1:], strict=True)))):
                 # Expand the latents if we are doing CFG.
                 latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                 # Expand the timestep to match the latent model input.
-                timestep = t.expand(latent_model_input.shape[0])
+                # Multiply by 1000 to match the default FlowMatchEulerDiscreteScheduler num_train_timesteps.
+                timestep = torch.tensor([t_curr * 1000], device=device).expand(latent_model_input.shape[0])
 
                 noise_pred = transformer(
                     hidden_states=latent_model_input,
@@ -232,21 +312,19 @@ class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
 
                 # Compute the previous noisy sample x_t -> x_t-1.
                 latents_dtype = latents.dtype
-                latents = scheduler.step(model_output=noise_pred, timestep=t, sample=latents, return_dict=False)[0]
+                latents = latents.to(dtype=torch.float32)
+                latents = latents + (t_prev - t_curr) * noise_pred
+                latents = latents.to(dtype=latents_dtype)
 
-                # TODO(ryand): This MPS dtype handling was copied from diffusers, I haven't tested to see if it's
-                # needed.
-                if latents.dtype != latents_dtype:
-                    if torch.backends.mps.is_available():
-                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
-                        latents = latents.to(latents_dtype)
+                if inpaint_extension is not None:
+                    latents = inpaint_extension.merge_intermediate_latents_with_init_latents(latents, t_prev)
 
                 step_callback(
                     PipelineIntermediateState(
                         step=step_idx + 1,
                         order=1,
                         total_steps=total_steps,
-                        timestep=int(t),
+                        timestep=int(t_curr),
                         latents=latents,
                     ),
                 )

invokeai/app/invocations/sd3_image_to_latents.py

@@ -0,0 +1,65 @@
+import einops
+import torch
+from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    ImageField,
+    Input,
+    InputField,
+    WithBoard,
+    WithMetadata,
+)
+from invokeai.app.invocations.model import VAEField
+from invokeai.app.invocations.primitives import LatentsOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.model_manager.load.load_base import LoadedModel
+from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
+
+
+@invocation(
+    "sd3_i2l",
+    title="SD3 Image to Latents",
+    tags=["image", "latents", "vae", "i2l", "sd3"],
+    category="image",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class SD3ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Generates latents from an image."""
+
+    image: ImageField = InputField(description="The image to encode")
+    vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection)
+
+    @staticmethod
+    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor) -> torch.Tensor:
+        with vae_info as vae:
+            assert isinstance(vae, AutoencoderKL)
+
+            vae.disable_tiling()
+
+            image_tensor = image_tensor.to(device=vae.device, dtype=vae.dtype)
+            with torch.inference_mode():
+                image_tensor_dist = vae.encode(image_tensor).latent_dist
+                # TODO: Use seed to make sampling reproducible.
+                latents: torch.Tensor = image_tensor_dist.sample().to(dtype=vae.dtype)
+
+            latents = vae.config.scaling_factor * latents
+
+        return latents
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        image = context.images.get_pil(self.image.image_name)
+
+        image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
+        if image_tensor.dim() == 3:
+            image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
+
+        vae_info = context.models.load(self.vae.vae)
+        latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
+
+        latents = latents.to("cpu")
+        name = context.tensors.save(tensor=latents)
+        return LatentsOutput.build(latents_name=name, latents=latents, seed=None)

invokeai/app/invocations/sd3_latents_to_image.py

@@ -47,6 +47,7 @@ class SD3LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         vae_info = context.models.load(self.vae.vae)
         assert isinstance(vae_info.model, (AutoencoderKL))
         with SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes), vae_info as vae:
+            context.util.signal_progress("Running VAE")
             assert isinstance(vae, (AutoencoderKL))
             latents = latents.to(vae.device)
 

invokeai/app/invocations/sd3_text_encoder.py

@@ -95,6 +95,7 @@ class Sd3TextEncoderInvocation(BaseInvocation):
             t5_text_encoder_info as t5_text_encoder,
             t5_tokenizer_info as t5_tokenizer,
         ):
+            context.util.signal_progress("Running T5 encoder")
             assert isinstance(t5_text_encoder, T5EncoderModel)
             assert isinstance(t5_tokenizer, (T5Tokenizer, T5TokenizerFast))
 
@@ -137,6 +138,7 @@ class Sd3TextEncoderInvocation(BaseInvocation):
             clip_tokenizer_info as clip_tokenizer,
             ExitStack() as exit_stack,
         ):
+            context.util.signal_progress("Running CLIP encoder")
             assert isinstance(clip_text_encoder, (CLIPTextModel, CLIPTextModelWithProjection))
             assert isinstance(clip_tokenizer, CLIPTokenizer)
 

invokeai/app/services/shared/invocation_context.py

@@ -160,6 +160,10 @@ class LoggerInterface(InvocationContextInterface):
 
 
 class ImagesInterface(InvocationContextInterface):
+    def __init__(self, services: InvocationServices, data: InvocationContextData, util: "UtilInterface") -> None:
+        super().__init__(services, data)
+        self._util = util
+
     def save(
         self,
         image: Image,
@@ -186,6 +190,8 @@ class ImagesInterface(InvocationContextInterface):
             The saved image DTO.
         """
 
+        self._util.signal_progress("Saving image")
+
         # If `metadata` is provided directly, use that. Else, use the metadata provided by `WithMetadata`, falling back to None.
         metadata_ = None
         if metadata:
@@ -336,6 +342,10 @@ class ConditioningInterface(InvocationContextInterface):
 class ModelsInterface(InvocationContextInterface):
     """Common API for loading, downloading and managing models."""
 
+    def __init__(self, services: InvocationServices, data: InvocationContextData, util: "UtilInterface") -> None:
+        super().__init__(services, data)
+        self._util = util
+
     def exists(self, identifier: Union[str, "ModelIdentifierField"]) -> bool:
         """Check if a model exists.
 
@@ -368,11 +378,15 @@ class ModelsInterface(InvocationContextInterface):
 
         if isinstance(identifier, str):
             model = self._services.model_manager.store.get_model(identifier)
-            return self._services.model_manager.load.load_model(model, submodel_type)
         else:
-            _submodel_type = submodel_type or identifier.submodel_type
+            submodel_type = submodel_type or identifier.submodel_type
             model = self._services.model_manager.store.get_model(identifier.key)
-            return self._services.model_manager.load.load_model(model, _submodel_type)
+
+        message = f"Loading model {model.name}"
+        if submodel_type:
+            message += f" ({submodel_type.value})"
+        self._util.signal_progress(message)
+        return self._services.model_manager.load.load_model(model, submodel_type)
 
     def load_by_attrs(
         self, name: str, base: BaseModelType, type: ModelType, submodel_type: Optional[SubModelType] = None
@@ -397,6 +411,10 @@ class ModelsInterface(InvocationContextInterface):
         if len(configs) > 1:
             raise ValueError(f"More than one model found with name {name}, base {base}, and type {type}")
 
+        message = f"Loading model {name}"
+        if submodel_type:
+            message += f" ({submodel_type.value})"
+        self._util.signal_progress(message)
         return self._services.model_manager.load.load_model(configs[0], submodel_type)
 
     def get_config(self, identifier: Union[str, "ModelIdentifierField"]) -> AnyModelConfig:
@@ -467,6 +485,7 @@ class ModelsInterface(InvocationContextInterface):
         Returns:
             Path to the downloaded model
         """
+        self._util.signal_progress(f"Downloading model {source}")
         return self._services.model_manager.install.download_and_cache_model(source=source)
 
     def load_local_model(
@@ -489,6 +508,8 @@ class ModelsInterface(InvocationContextInterface):
         Returns:
             A LoadedModelWithoutConfig object.
         """
+
+        self._util.signal_progress(f"Loading model {model_path.name}")
         return self._services.model_manager.load.load_model_from_path(model_path=model_path, loader=loader)
 
     def load_remote_model(
@@ -514,6 +535,8 @@ class ModelsInterface(InvocationContextInterface):
             A LoadedModelWithoutConfig object.
         """
         model_path = self._services.model_manager.install.download_and_cache_model(source=str(source))
+
+        self._util.signal_progress(f"Loading model {source}")
         return self._services.model_manager.load.load_model_from_path(model_path=model_path, loader=loader)
 
 
@@ -707,12 +730,12 @@ def build_invocation_context(
     """
 
     logger = LoggerInterface(services=services, data=data)
-    images = ImagesInterface(services=services, data=data)
     tensors = TensorsInterface(services=services, data=data)
-    models = ModelsInterface(services=services, data=data)
     config = ConfigInterface(services=services, data=data)
     util = UtilInterface(services=services, data=data, is_canceled=is_canceled)
     conditioning = ConditioningInterface(services=services, data=data)
+    models = ModelsInterface(services=services, data=data, util=util)
+    images = ImagesInterface(services=services, data=data, util=util)
     boards = BoardsInterface(services=services, data=data)
 
     ctx = InvocationContext(

invokeai/backend/lora/conversions/flux_diffusers_lora_conversion_utils.py

@@ -45,8 +45,9 @@ def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor
     # Constants for FLUX.1
     num_double_layers = 19
     num_single_layers = 38
-    # inner_dim = 3072
-    # mlp_ratio = 4.0
+    hidden_size = 3072
+    mlp_ratio = 4.0
+    mlp_hidden_dim = int(hidden_size * mlp_ratio)
 
     layers: dict[str, AnyLoRALayer] = {}
 
@@ -62,30 +63,43 @@ def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor
             layers[dst_key] = LoRALayer.from_state_dict_values(values=value)
             assert len(src_layer_dict) == 0
 
-    def add_qkv_lora_layer_if_present(src_keys: list[str], dst_qkv_key: str) -> None:
+    def add_qkv_lora_layer_if_present(
+        src_keys: list[str],
+        src_weight_shapes: list[tuple[int, int]],
+        dst_qkv_key: str,
+        allow_missing_keys: bool = False,
+    ) -> None:
         """Handle the Q, K, V matrices for a transformer block. We need special handling because the diffusers format
         stores them in separate matrices, whereas the BFL format used internally by InvokeAI concatenates them.
         """
-        # We expect that either all src keys are present or none of them are. Verify this.
-        keys_present = [key in grouped_state_dict for key in src_keys]
-        assert all(keys_present) or not any(keys_present)
-
         # If none of the keys are present, return early.
+        keys_present = [key in grouped_state_dict for key in src_keys]
         if not any(keys_present):
             return
 
-        src_layer_dicts = [grouped_state_dict.pop(key) for key in src_keys]
         sub_layers: list[LoRALayer] = []
-        for src_layer_dict in src_layer_dicts:
-            values = {
-                "lora_down.weight": src_layer_dict.pop("lora_A.weight"),
-                "lora_up.weight": src_layer_dict.pop("lora_B.weight"),
-            }
-            if alpha is not None:
-                values["alpha"] = torch.tensor(alpha)
-            sub_layers.append(LoRALayer.from_state_dict_values(values=values))
-            assert len(src_layer_dict) == 0
-        layers[dst_qkv_key] = ConcatenatedLoRALayer(lora_layers=sub_layers, concat_axis=0)
+        for src_key, src_weight_shape in zip(src_keys, src_weight_shapes, strict=True):
+            src_layer_dict = grouped_state_dict.pop(src_key, None)
+            if src_layer_dict is not None:
+                values = {
+                    "lora_down.weight": src_layer_dict.pop("lora_A.weight"),
+                    "lora_up.weight": src_layer_dict.pop("lora_B.weight"),
+                }
+                if alpha is not None:
+                    values["alpha"] = torch.tensor(alpha)
+                assert values["lora_down.weight"].shape[1] == src_weight_shape[1]
+                assert values["lora_up.weight"].shape[0] == src_weight_shape[0]
+                sub_layers.append(LoRALayer.from_state_dict_values(values=values))
+                assert len(src_layer_dict) == 0
+            else:
+                if not allow_missing_keys:
+                    raise ValueError(f"Missing LoRA layer: '{src_key}'.")
+                values = {
+                    "lora_up.weight": torch.zeros((src_weight_shape[0], 1)),
+                    "lora_down.weight": torch.zeros((1, src_weight_shape[1])),
+                }
+                sub_layers.append(LoRALayer.from_state_dict_values(values=values))
+        layers[dst_qkv_key] = ConcatenatedLoRALayer(lora_layers=sub_layers)
 
     # time_text_embed.timestep_embedder -> time_in.
     add_lora_layer_if_present("time_text_embed.timestep_embedder.linear_1", "time_in.in_layer")
@@ -118,6 +132,7 @@ def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor
                 f"transformer_blocks.{i}.attn.to_k",
                 f"transformer_blocks.{i}.attn.to_v",
             ],
+            [(hidden_size, hidden_size), (hidden_size, hidden_size), (hidden_size, hidden_size)],
             f"double_blocks.{i}.img_attn.qkv",
         )
         add_qkv_lora_layer_if_present(
@@ -126,6 +141,7 @@ def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor
                 f"transformer_blocks.{i}.attn.add_k_proj",
                 f"transformer_blocks.{i}.attn.add_v_proj",
             ],
+            [(hidden_size, hidden_size), (hidden_size, hidden_size), (hidden_size, hidden_size)],
             f"double_blocks.{i}.txt_attn.qkv",
         )
 
@@ -175,7 +191,14 @@ def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor
                 f"single_transformer_blocks.{i}.attn.to_v",
                 f"single_transformer_blocks.{i}.proj_mlp",
             ],
+            [
+                (hidden_size, hidden_size),
+                (hidden_size, hidden_size),
+                (hidden_size, hidden_size),
+                (mlp_hidden_dim, hidden_size),
+            ],
             f"single_blocks.{i}.linear1",
+            allow_missing_keys=True,
         )
 
         # Output projections.

invokeai/backend/model_manager/load/load_default.py

@@ -35,6 +35,7 @@ class ModelLoader(ModelLoaderBase):
         self._logger = logger
         self._ram_cache = ram_cache
         self._torch_dtype = TorchDevice.choose_torch_dtype()
+        self._torch_device = TorchDevice.choose_torch_device()
 
     def load_model(self, model_config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
         """

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

@@ -84,7 +84,15 @@ class FluxVAELoader(ModelLoader):
             model = AutoEncoder(ae_params[config.config_path])
             sd = load_file(model_path)
             model.load_state_dict(sd, assign=True)
-            model.to(dtype=self._torch_dtype)
+            # VAE is broken in float16, which mps defaults to
+            if self._torch_dtype == torch.float16:
+                try:
+                    vae_dtype = torch.tensor([1.0], dtype=torch.bfloat16, device=self._torch_device).dtype
+                except TypeError:
+                    vae_dtype = torch.float32
+            else:
+                vae_dtype = self._torch_dtype
+            model.to(vae_dtype)
 
         return model
 

invokeai/backend/model_manager/starter_models.py

@@ -300,7 +300,7 @@ ip_adapter_sdxl = StarterModel(
 ip_adapter_flux = StarterModel(
     name="Standard Reference (XLabs FLUX IP-Adapter)",
     base=BaseModelType.Flux,
-    source="https://huggingface.co/XLabs-AI/flux-ip-adapter/resolve/main/flux-ip-adapter.safetensors",
+    source="https://huggingface.co/XLabs-AI/flux-ip-adapter/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],

invokeai/backend/sd3/extensions/inpaint_extension.py

@@ -0,0 +1,58 @@
+import torch
+
+
+class InpaintExtension:
+    """A class for managing inpainting with SD3."""
+
+    def __init__(self, init_latents: torch.Tensor, inpaint_mask: torch.Tensor, noise: torch.Tensor):
+        """Initialize InpaintExtension.
+
+        Args:
+            init_latents (torch.Tensor): The initial latents (i.e. un-noised at timestep 0).
+            inpaint_mask (torch.Tensor): A mask specifying which elements to inpaint. Range [0, 1]. Values of 1 will be
+                re-generated. Values of 0 will remain unchanged. Values between 0 and 1 can be used to blend the
+                inpainted region with the background.
+            noise (torch.Tensor): The noise tensor used to noise the init_latents.
+        """
+        assert init_latents.dim() == inpaint_mask.dim() == noise.dim() == 4
+        assert init_latents.shape[-2:] == inpaint_mask.shape[-2:] == noise.shape[-2:]
+
+        self._init_latents = init_latents
+        self._inpaint_mask = inpaint_mask
+        self._noise = noise
+
+    def _apply_mask_gradient_adjustment(self, t_prev: float) -> torch.Tensor:
+        """Applies inpaint mask gradient adjustment and returns the inpaint mask to be used at the current timestep."""
+        # As we progress through the denoising process, we promote gradient regions of the mask to have a full weight of
+        # 1.0. This helps to produce more coherent seams around the inpainted region. We experimented with a (small)
+        # number of promotion strategies (e.g. gradual promotion based on timestep), but found that a simple cutoff
+        # threshold worked well.
+        # We use a small epsilon to avoid any potential issues with floating point precision.
+        eps = 1e-4
+        mask_gradient_t_cutoff = 0.5
+        if t_prev > mask_gradient_t_cutoff:
+            # Early in the denoising process, use the inpaint mask as-is.
+            return self._inpaint_mask
+        else:
+            # After the cut-off, promote all non-zero mask values to 1.0.
+            mask = self._inpaint_mask.where(self._inpaint_mask <= (0.0 + eps), 1.0)
+
+        return mask
+
+    def merge_intermediate_latents_with_init_latents(
+        self, intermediate_latents: torch.Tensor, t_prev: float
+    ) -> torch.Tensor:
+        """Merge the intermediate latents with the initial latents for the current timestep using the inpaint mask. I.e.
+        update the intermediate latents to keep the regions that are not being inpainted on the correct noise
+        trajectory.
+
+        This function should be called after each denoising step.
+        """
+
+        mask = self._apply_mask_gradient_adjustment(t_prev)
+
+        # Noise the init latents for the current timestep.
+        noised_init_latents = self._noise * t_prev + (1.0 - t_prev) * self._init_latents
+
+        # Merge the intermediate latents with the noised_init_latents using the inpaint_mask.
+        return intermediate_latents * mask + noised_init_latents * (1.0 - mask)

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

@@ -768,7 +768,8 @@
         "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",
@@ -871,7 +872,9 @@
         "recallParameter": "{{label}} Abrufen",
         "parsingFailed": "Parsing Fehlgeschlagen",
         "canvasV2Metadata": "Leinwand",
-        "guidance": "Führung"
+        "guidance": "Führung",
+        "seamlessXAxis": "Nahtlose X Achse",
+        "seamlessYAxis": "Nahtlose Y Achse"
     },
     "popovers": {
         "noiseUseCPU": {
@@ -1392,7 +1395,13 @@
         "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",
@@ -1511,7 +1520,30 @@
         "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

@@ -174,7 +174,8 @@
         "placeholderSelectAModel": "Select a model",
         "reset": "Reset",
         "none": "None",
-        "new": "New"
+        "new": "New",
+        "generating": "Generating"
     },
     "hrf": {
         "hrf": "High Resolution Fix",
@@ -704,6 +705,8 @@
         "baseModel": "Base Model",
         "cancel": "Cancel",
         "clipEmbed": "CLIP Embed",
+        "clipLEmbed": "CLIP-L Embed",
+        "clipGEmbed": "CLIP-G Embed",
         "config": "Config",
         "convert": "Convert",
         "convertingModelBegin": "Converting Model. Please wait.",
@@ -997,7 +1000,7 @@
         "controlNetControlMode": "Control Mode",
         "copyImage": "Copy Image",
         "denoisingStrength": "Denoising Strength",
-        "noRasterLayers": "No Raster Layers",
+        "disabledNoRasterContent": "Disabled (No Raster Content)",
         "downloadImage": "Download Image",
         "general": "General",
         "guidance": "Guidance",
@@ -1137,6 +1140,7 @@
         "resetWebUI": "Reset Web UI",
         "resetWebUIDesc1": "Resetting the web UI only resets the browser's local cache of your images and remembered settings. It does not delete any images from disk.",
         "resetWebUIDesc2": "If images aren't showing up in the gallery or something else isn't working, please try resetting before submitting an issue on GitHub.",
+        "showDetailedInvocationProgress": "Show Progress Details",
         "showProgressInViewer": "Show Progress Images in Viewer",
         "ui": "User Interface",
         "clearIntermediatesDisabled": "Queue must be empty to clear intermediates",
@@ -1318,7 +1322,7 @@
         "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": {
@@ -1671,7 +1675,7 @@
         "clearCaches": "Clear Caches",
         "recalculateRects": "Recalculate Rects",
         "clipToBbox": "Clip Strokes to Bbox",
-        "outputOnlyMaskedRegions": "Output Only Masked Regions",
+        "outputOnlyMaskedRegions": "Output Only Generated Regions",
         "addLayer": "Add Layer",
         "duplicate": "Duplicate",
         "moveToFront": "Move to Front",
@@ -1787,7 +1791,7 @@
         },
         "ipAdapterMethod": {
             "ipAdapterMethod": "IP Adapter Method",
-            "full": "Full",
+            "full": "Style and Composition",
             "style": "Style Only",
             "composition": "Composition Only"
         },
@@ -1999,7 +2003,9 @@
         "upscaleModelDesc": "Upscale (image to image) model",
         "missingUpscaleInitialImage": "Missing initial image for upscaling",
         "missingUpscaleModel": "Missing upscale model",
-        "missingTileControlNetModel": "No valid tile ControlNet models installed"
+        "missingTileControlNetModel": "No valid tile ControlNet models installed",
+        "incompatibleBaseModel": "Unsupported main model architecture for upscaling",
+        "incompatibleBaseModelDesc": "Upscaling is supported for SD1.5 and SDXL architecture models only. Change the main model to enable upscaling."
     },
     "stylePresets": {
         "active": "Active",
@@ -2103,7 +2109,7 @@
     "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>SD 3.5</StrongComponent>: Support for SD 3.5 Medium and Large.",
             "<StrongComponent>Canvas</StrongComponent>: Streamlined Control Layer processing and improved default Control settings."
         ],
         "readReleaseNotes": "Read Release Notes",

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

@@ -158,7 +158,7 @@
         "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",
@@ -170,7 +170,7 @@
         "negativePromptPlaceholder": "Prompt Negativo",
         "controlNetControlMode": "Modo de control",
         "clipSkip": "Omitir el CLIP",
-        "maskBlur": "Difuminar",
+        "maskBlur": "Desenfoque de máscara",
         "patchmatchDownScaleSize": "Reducir a escala",
         "coherenceMode": "Modo"
     },
@@ -202,13 +202,13 @@
         "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 tablero {{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"
@@ -322,7 +322,7 @@
     "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",
@@ -331,5 +331,10 @@
         "layer_withCount_one": "({{count}}) capa",
         "layer_withCount_many": "({{count}}) capas",
         "layer_withCount_other": "({{count}}) capas"
+    },
+    "whatsNew": {
+        "readReleaseNotes": "Leer las notas de la versión",
+        "watchRecentReleaseVideos": "Ver videos de lanzamientos recientes",
+        "watchUiUpdatesOverview": "Descripción general de las actualizaciones de la interfaz de usuario de Watch"
     }
 }

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

@@ -597,7 +597,18 @@
         "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",
@@ -699,7 +710,9 @@
         "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",
@@ -1263,8 +1276,9 @@
         },
         "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"
         },
@@ -1276,7 +1290,7 @@
         },
         "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"
         },
@@ -1444,7 +1458,7 @@
             "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 +1512,7 @@
         "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": {
@@ -1771,7 +1785,7 @@
         "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",
@@ -1881,7 +1895,10 @@
             "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)",
@@ -2025,7 +2042,10 @@
         "newControlLayer": "Nuovo $t(controlLayers.controlLayer)",
         "newInpaintMask": "Nuova $t(controlLayers.inpaintMask)",
         "replaceCurrent": "Sostituisci corrente",
-        "mergeDown": "Unire in basso"
+        "mergeDown": "Unire in basso",
+        "newFromImage": "Nuovo da Immagine",
+        "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."
     },
     "ui": {
         "tabs": {
@@ -2057,7 +2077,9 @@
         "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 +2141,13 @@
     },
     "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>SD 3.5</StrongComponent>: supporto per la generazione da testo a immagine nei flussi di lavoro con SD 3.5 Medium e Large.",
+            "<StrongComponent>Tela</StrongComponent>: elaborazione semplificata del livello di controllo e impostazioni di controllo predefinite migliorate."
+        ]
     },
     "system": {
         "logLevel": {

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 +611,80 @@
         "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"
+        },
+        "resetCanvas": "キャンバスをリセット",
+        "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/zh_CN.json

@@ -587,7 +587,23 @@
         "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"
     },
     "parameters": {
         "images": "图像",
@@ -646,8 +662,23 @@
                 "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"
-            }
+                "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 +700,15 @@
         "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 +738,11 @@
         "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"
     },
     "toast": {
         "uploadFailed": "上传失败",
@@ -740,7 +783,24 @@
         "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 +950,12 @@
         "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 +1286,8 @@
             "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 +1476,8 @@
             "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 +1508,62 @@
             "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 +1626,18 @@
         "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 +1676,18 @@
         "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"
     },
     "ui": {
         "tabs": {

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

@@ -8,6 +8,7 @@ import { $canvasManager } from 'features/controlLayers/store/ephemeral';
 import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
 import { buildFLUXGraph } from 'features/nodes/util/graph/generation/buildFLUXGraph';
 import { buildSD1Graph } from 'features/nodes/util/graph/generation/buildSD1Graph';
+import { buildSD3Graph } from 'features/nodes/util/graph/generation/buildSD3Graph';
 import { buildSDXLGraph } from 'features/nodes/util/graph/generation/buildSDXLGraph';
 import type { Graph } from 'features/nodes/util/graph/generation/Graph';
 import { toast } from 'features/toast/toast';
@@ -34,8 +35,8 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
       let buildGraphResult: Result<
         {
           g: Graph;
-          noise: Invocation<'noise' | 'flux_denoise'>;
-          posCond: Invocation<'compel' | 'sdxl_compel_prompt' | 'flux_text_encoder'>;
+          noise: Invocation<'noise' | 'flux_denoise' | 'sd3_denoise'>;
+          posCond: Invocation<'compel' | 'sdxl_compel_prompt' | 'flux_text_encoder' | 'sd3_text_encoder'>;
         },
         Error
       >;
@@ -51,6 +52,9 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
         case `sd-2`:
           buildGraphResult = await withResultAsync(() => buildSD1Graph(state, manager));
           break;
+        case `sd-3`:
+          buildGraphResult = await withResultAsync(() => buildSD3Graph(state, manager));
+          break;
         case `flux`:
           buildGraphResult = await withResultAsync(() => buildFLUXGraph(state, manager));
           break;

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

@@ -41,29 +41,33 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
 
       log.debug({ imageDTO }, 'Image uploaded');
 
+      if (action.meta.arg.originalArgs.silent || imageDTO.is_intermediate) {
+        // When a "silent" upload is requested, or the image is intermediate, we can skip all post-upload actions,
+        // like toasts and switching the gallery view
+        return;
+      }
+
       const boardId = imageDTO.board_id ?? 'none';
 
-      if (action.meta.arg.originalArgs.withToast) {
-        const DEFAULT_UPLOADED_TOAST = {
-          id: 'IMAGE_UPLOADED',
-          title: t('toast.imageUploaded'),
-          status: 'success',
-        } as const;
+      const DEFAULT_UPLOADED_TOAST = {
+        id: 'IMAGE_UPLOADED',
+        title: t('toast.imageUploaded'),
+        status: 'success',
+      } as const;
 
-        // default action - just upload and alert user
-        if (lastUploadedToastTimeout !== null) {
-          window.clearTimeout(lastUploadedToastTimeout);
-        }
-        const toastApi = toast({
-          ...DEFAULT_UPLOADED_TOAST,
-          title: DEFAULT_UPLOADED_TOAST.title,
-          description: getUploadedToastDescription(boardId, state),
-          duration: null, // we will close the toast manually
-        });
-        lastUploadedToastTimeout = window.setTimeout(() => {
-          toastApi.close();
-        }, 3000);
+      // default action - just upload and alert user
+      if (lastUploadedToastTimeout !== null) {
+        window.clearTimeout(lastUploadedToastTimeout);
       }
+      const toastApi = toast({
+        ...DEFAULT_UPLOADED_TOAST,
+        title: DEFAULT_UPLOADED_TOAST.title,
+        description: getUploadedToastDescription(boardId, state),
+        duration: null, // we will close the toast manually
+      });
+      lastUploadedToastTimeout = window.setTimeout(() => {
+        toastApi.close();
+      }, 3000);
 
       /**
        * We only want to change the board and view if this is the first upload of a batch, else we end up hijacking

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

@@ -25,7 +25,8 @@ export type AppFeature =
   | 'invocationCache'
   | 'bulkDownload'
   | 'starterModels'
-  | 'hfToken';
+  | 'hfToken'
+  | 'invocationProgressAlert';
 
 /**
  * A disable-able Stable Diffusion feature

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

@@ -61,6 +61,11 @@ export const useImageUploadButton = ({ onUpload, isDisabled, allowMultiple }: Us
           log.warn('Multiple files dropped but only one allowed');
           return;
         }
+        if (files.length === 0) {
+          // Should never happen
+          log.warn('No files dropped');
+          return;
+        }
         const file = files[0];
         assert(file !== undefined); // should never happen
         const imageDTO = await uploadImage({
@@ -68,18 +73,20 @@ export const useImageUploadButton = ({ onUpload, isDisabled, allowMultiple }: Us
           image_category: 'user',
           is_intermediate: false,
           board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
+          silent: true,
         }).unwrap();
         if (onUpload) {
           onUpload(imageDTO);
         }
       } else {
-        //
         const imageDTOs = await uploadImages(
-          files.map((file) => ({
+          files.map((file, i) => ({
             file,
             image_category: 'user',
             is_intermediate: false,
             board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
+            silent: false,
+            isFirstUploadOfBatch: i === 0,
           }))
         );
         if (onUpload) {

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

@@ -119,11 +119,20 @@ const createSelector = (
             reasons.push({ content: i18n.t('upscaling.exceedsMaxSize') });
           }
         }
-        if (!upscale.upscaleModel) {
-          reasons.push({ content: i18n.t('upscaling.missingUpscaleModel') });
-        }
-        if (!upscale.tileControlnetModel) {
-          reasons.push({ content: i18n.t('upscaling.missingTileControlNetModel') });
+        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) {

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

@@ -9,7 +9,7 @@ import {
   useAddRegionalGuidance,
   useAddRegionalReferenceImage,
 } from 'features/controlLayers/hooks/addLayerHooks';
-import { selectIsFLUX } from 'features/controlLayers/store/paramsSlice';
+import { selectIsFLUX, selectIsSD3 } from 'features/controlLayers/store/paramsSlice';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiPlusBold } from 'react-icons/pi';
@@ -23,6 +23,7 @@ export const CanvasAddEntityButtons = memo(() => {
   const addGlobalReferenceImage = useAddGlobalReferenceImage();
   const addRegionalReferenceImage = useAddRegionalReferenceImage();
   const isFLUX = useAppSelector(selectIsFLUX);
+  const isSD3 = useAppSelector(selectIsSD3);
 
   return (
     <Flex w="full" h="full" justifyContent="center" gap={4}>
@@ -36,6 +37,7 @@ export const CanvasAddEntityButtons = memo(() => {
               justifyContent="flex-start"
               leftIcon={<PiPlusBold />}
               onClick={addGlobalReferenceImage}
+              isDisabled={isSD3}
             >
               {t('controlLayers.globalReferenceImage')}
             </Button>
@@ -61,7 +63,7 @@ export const CanvasAddEntityButtons = memo(() => {
               justifyContent="flex-start"
               leftIcon={<PiPlusBold />}
               onClick={addRegionalGuidance}
-              isDisabled={isFLUX}
+              isDisabled={isFLUX || isSD3}
             >
               {t('controlLayers.regionalGuidance')}
             </Button>
@@ -73,7 +75,7 @@ export const CanvasAddEntityButtons = memo(() => {
               justifyContent="flex-start"
               leftIcon={<PiPlusBold />}
               onClick={addRegionalReferenceImage}
-              isDisabled={isFLUX}
+              isDisabled={isFLUX || isSD3}
             >
               {t('controlLayers.regionalReferenceImage')}
             </Button>
@@ -88,6 +90,7 @@ export const CanvasAddEntityButtons = memo(() => {
               justifyContent="flex-start"
               leftIcon={<PiPlusBold />}
               onClick={addControlLayer}
+              isDisabled={isSD3}
             >
               {t('controlLayers.controlLayer')}
             </Button>

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

@@ -0,0 +1,45 @@
+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 { selectSystemShouldShowInvocationProgressDetail } from 'features/system/store/systemSlice';
+import { memo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { $invocationProgressMessage } from 'services/events/stores';
+
+const CanvasAlertsInvocationProgressContent = memo(() => {
+  const { t } = useTranslation();
+  const invocationProgressMessage = useStore($invocationProgressMessage);
+
+  if (!invocationProgressMessage) {
+    return null;
+  }
+
+  return (
+    <Alert status="loading" borderRadius="base" fontSize="sm" shadow="md" w="fit-content">
+      <AlertIcon />
+      <AlertTitle>{t('common.generating')}</AlertTitle>
+      <AlertDescription>{invocationProgressMessage}</AlertDescription>
+    </Alert>
+  );
+});
+CanvasAlertsInvocationProgressContent.displayName = 'CanvasAlertsInvocationProgressContent';
+
+export const CanvasAlertsInvocationProgress = memo(() => {
+  const isProgressMessageAlertEnabled = useFeatureStatus('invocationProgressAlert');
+  const shouldShowInvocationProgressDetail = useAppSelector(selectSystemShouldShowInvocationProgressDetail);
+
+  // The alert is disabled at the system level
+  if (!isProgressMessageAlertEnabled) {
+    return null;
+  }
+
+  // The alert is disabled at the user level
+  if (!shouldShowInvocationProgressDetail) {
+    return null;
+  }
+
+  return <CanvasAlertsInvocationProgressContent />;
+});
+
+CanvasAlertsInvocationProgress.displayName = 'CanvasAlertsInvocationProgress';

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

@@ -9,7 +9,7 @@ import {
   useAddRegionalReferenceImage,
 } from 'features/controlLayers/hooks/addLayerHooks';
 import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
-import { selectIsFLUX } from 'features/controlLayers/store/paramsSlice';
+import { selectIsFLUX, selectIsSD3 } from 'features/controlLayers/store/paramsSlice';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiPlusBold } from 'react-icons/pi';
@@ -24,6 +24,7 @@ export const EntityListGlobalActionBarAddLayerMenu = memo(() => {
   const addRasterLayer = useAddRasterLayer();
   const addControlLayer = useAddControlLayer();
   const isFLUX = useAppSelector(selectIsFLUX);
+  const isSD3 = useAppSelector(selectIsSD3);
 
   return (
     <Menu>
@@ -40,7 +41,7 @@ export const EntityListGlobalActionBarAddLayerMenu = memo(() => {
       />
       <MenuList>
         <MenuGroup title={t('controlLayers.global')}>
-          <MenuItem icon={<PiPlusBold />} onClick={addGlobalReferenceImage}>
+          <MenuItem icon={<PiPlusBold />} onClick={addGlobalReferenceImage} isDisabled={isSD3}>
             {t('controlLayers.globalReferenceImage')}
           </MenuItem>
         </MenuGroup>
@@ -48,15 +49,15 @@ export const EntityListGlobalActionBarAddLayerMenu = memo(() => {
           <MenuItem icon={<PiPlusBold />} onClick={addInpaintMask}>
             {t('controlLayers.inpaintMask')}
           </MenuItem>
-          <MenuItem icon={<PiPlusBold />} onClick={addRegionalGuidance} isDisabled={isFLUX}>
+          <MenuItem icon={<PiPlusBold />} onClick={addRegionalGuidance} isDisabled={isFLUX || isSD3}>
             {t('controlLayers.regionalGuidance')}
           </MenuItem>
-          <MenuItem icon={<PiPlusBold />} onClick={addRegionalReferenceImage} isDisabled={isFLUX}>
+          <MenuItem icon={<PiPlusBold />} onClick={addRegionalReferenceImage} isDisabled={isFLUX || isSD3}>
             {t('controlLayers.regionalReferenceImage')}
           </MenuItem>
         </MenuGroup>
         <MenuGroup title={t('controlLayers.layer_other')}>
-          <MenuItem icon={<PiPlusBold />} onClick={addControlLayer}>
+          <MenuItem icon={<PiPlusBold />} onClick={addControlLayer} isDisabled={isSD3}>
             {t('controlLayers.controlLayer')}
           </MenuItem>
           <MenuItem icon={<PiPlusBold />} onClick={addRasterLayer}>

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/useCanvasEntityListDnd.ts

@@ -4,6 +4,7 @@ import { attachClosestEdge, extractClosestEdge } from '@atlaskit/pragmatic-drag-
 import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
 import { singleCanvasEntityDndSource } from 'features/dnd/dnd';
 import { type DndListTargetState, idle } from 'features/dnd/types';
+import { firefoxDndFix } from 'features/dnd/util';
 import type { RefObject } from 'react';
 import { useEffect, useState } from 'react';
 
@@ -17,6 +18,7 @@ export const useCanvasEntityListDnd = (ref: RefObject<HTMLElement>, entityIdenti
       return;
     }
     return combine(
+      firefoxDndFix(element),
       draggable({
         element,
         getInitialData() {

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

@@ -21,6 +21,8 @@ import { GatedImageViewer } from 'features/gallery/components/ImageViewer/ImageV
 import { memo, useCallback, useRef } from 'react';
 import { PiDotsThreeOutlineVerticalFill } from 'react-icons/pi';
 
+import { CanvasAlertsInvocationProgress } from './CanvasAlerts/CanvasAlertsInvocationProgress';
+
 const MenuContent = () => {
   return (
     <CanvasManagerProviderGate>
@@ -84,6 +86,7 @@ export const CanvasMainPanelContent = memo(() => {
                 <CanvasAlertsSelectedEntityStatus />
                 <CanvasAlertsPreserveMask />
                 <CanvasAlertsSendingToGallery />
+                <CanvasAlertsInvocationProgress />
               </Flex>
               <Flex position="absolute" top={1} insetInlineEnd={1}>
                 <Menu>

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

@@ -17,12 +17,15 @@ import { selectImg2imgStrengthConfig } from 'features/system/store/configSlice';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 
-const selectIsEnabled = createSelector(selectActiveRasterLayerEntities, (entities) => entities.length > 0);
+const selectHasRasterLayersWithContent = createSelector(
+  selectActiveRasterLayerEntities,
+  (entities) => entities.length > 0
+);
 
 export const ParamDenoisingStrength = memo(() => {
   const img2imgStrength = useAppSelector(selectImg2imgStrength);
   const dispatch = useAppDispatch();
-  const isEnabled = useAppSelector(selectIsEnabled);
+  const hasRasterLayersWithContent = useAppSelector(selectHasRasterLayersWithContent);
 
   const onChange = useCallback(
     (v: number) => {
@@ -37,16 +40,16 @@ export const ParamDenoisingStrength = memo(() => {
   const [invokeBlue300] = useToken('colors', ['invokeBlue.300']);
 
   return (
-    <FormControl isDisabled={!isEnabled} p={1} justifyContent="space-between" h={8}>
+    <FormControl isDisabled={!hasRasterLayersWithContent} p={1} justifyContent="space-between" h={8}>
       <Flex gap={3} alignItems="center">
         <InformationalPopover feature="paramDenoisingStrength">
           <FormLabel mr={0}>{`${t('parameters.denoisingStrength')}`}</FormLabel>
         </InformationalPopover>
-        {isEnabled && (
+        {hasRasterLayersWithContent && (
           <WavyLine amplitude={img2imgStrength * 10} stroke={invokeBlue300} strokeWidth={1} width={40} height={14} />
         )}
       </Flex>
-      {isEnabled ? (
+      {hasRasterLayersWithContent ? (
         <>
           <CompositeSlider
             step={config.coarseStep}
@@ -70,9 +73,7 @@ export const ParamDenoisingStrength = memo(() => {
         </>
       ) : (
         <Flex alignItems="center">
-          <Badge opacity="0.6">
-            {t('common.disabled')} - {t('parameters.noRasterLayers')}
-          </Badge>
+          <Badge opacity="0.6">{t('parameters.disabledNoRasterContent')}</Badge>
         </Flex>
       )}
     </FormControl>

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/StagingAreaToolbarSaveSelectedToGalleryButton.tsx

@@ -1,17 +1,19 @@
 import { IconButton } from '@invoke-ai/ui-library';
 import { useAppSelector } from 'app/store/storeHooks';
+import { withResultAsync } from 'common/util/result';
 import { selectSelectedImage } from 'features/controlLayers/store/canvasStagingAreaSlice';
 import { selectAutoAddBoardId } from 'features/gallery/store/gallerySelectors';
+import { toast } from 'features/toast/toast';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiFloppyDiskBold } from 'react-icons/pi';
-import { useAddImagesToBoardMutation, useChangeImageIsIntermediateMutation } from 'services/api/endpoints/images';
+import { uploadImage } from 'services/api/endpoints/images';
+
+const TOAST_ID = 'SAVE_STAGING_AREA_IMAGE_TO_GALLERY';
 
 export const StagingAreaToolbarSaveSelectedToGalleryButton = memo(() => {
   const autoAddBoardId = useAppSelector(selectAutoAddBoardId);
   const selectedImage = useAppSelector(selectSelectedImage);
-  const [addImageToBoard] = useAddImagesToBoardMutation();
-  const [changeIsImageIntermediate] = useChangeImageIsIntermediateMutation();
 
   const { t } = useTranslation();
 
@@ -19,21 +21,42 @@ export const StagingAreaToolbarSaveSelectedToGalleryButton = memo(() => {
     if (!selectedImage) {
       return;
     }
-    if (autoAddBoardId !== 'none') {
-      await addImageToBoard({ imageDTOs: [selectedImage.imageDTO], board_id: autoAddBoardId }).unwrap();
-      // The changeIsImageIntermediate request will use the board_id on this specific imageDTO object, so we need to
-      // update it before making the request - else the optimistic board updates will get out of whack.
-      changeIsImageIntermediate({
-        imageDTO: { ...selectedImage.imageDTO, board_id: autoAddBoardId },
+
+    // To save the image to gallery, we will download it and re-upload it. This allows the user to delete the image
+    // the gallery without borking the canvas, which may need this image to exist.
+    const result = await withResultAsync(async () => {
+      // Download the image
+      const res = await fetch(selectedImage.imageDTO.image_url);
+      const blob = await res.blob();
+      // Create a new file with the same name, which we will upload
+      const file = new File([blob], `copy_of_${selectedImage.imageDTO.image_name}`, { type: 'image/png' });
+
+      await uploadImage({
+        file,
+        // Image should show up in the Images tab
+        image_category: 'general',
         is_intermediate: false,
+        // TODO(psyche): Maybe this should just save to the currently-selected board?
+        board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
+        // We will do our own toast - opt out of the default handling
+        silent: true,
+      });
+    });
+
+    if (result.isOk()) {
+      toast({
+        id: TOAST_ID,
+        title: t('controlLayers.savedToGalleryOk'),
+        status: 'success',
       });
     } else {
-      changeIsImageIntermediate({
-        imageDTO: selectedImage.imageDTO,
-        is_intermediate: false,
+      toast({
+        id: TOAST_ID,
+        title: t('controlLayers.savedToGalleryError'),
+        status: 'error',
       });
     }
-  }, [addImageToBoard, autoAddBoardId, changeIsImageIntermediate, selectedImage]);
+  }, [autoAddBoardId, selectedImage, t]);
 
   return (
     <IconButton
@@ -42,7 +65,7 @@ export const StagingAreaToolbarSaveSelectedToGalleryButton = memo(() => {
       icon={<PiFloppyDiskBold />}
       onClick={saveSelectedImageToGallery}
       colorScheme="invokeBlue"
-      isDisabled={!selectedImage || !selectedImage.imageDTO.is_intermediate}
+      isDisabled={!selectedImage}
     />
   );
 });

invokeai/frontend/web/src/features/controlLayers/hooks/saveCanvasHooks.ts

@@ -25,6 +25,8 @@ import type {
   RegionalGuidanceReferenceImageState,
 } from 'features/controlLayers/store/types';
 import { imageDTOToImageObject, imageDTOToImageWithDims, initialControlNet } from 'features/controlLayers/store/util';
+import { selectAutoAddBoardId } from 'features/gallery/store/gallerySelectors';
+import type { BoardId } from 'features/gallery/store/types';
 import { toast } from 'features/toast/toast';
 import { useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -70,6 +72,11 @@ const useSaveCanvas = ({ region, saveToGallery, toastOk, toastError, onSave, wit
       metadata = selectCanvasMetadata(store.getState());
     }
 
+    let boardId: BoardId | undefined = undefined;
+    if (saveToGallery) {
+      boardId = selectAutoAddBoardId(store.getState());
+    }
+
     const result = await withResultAsync(() => {
       const rasterAdapters = canvasManager.compositor.getVisibleAdaptersOfType('raster_layer');
       return canvasManager.compositor.getCompositeImageDTO(
@@ -78,6 +85,8 @@ const useSaveCanvas = ({ region, saveToGallery, toastOk, toastError, onSave, wit
         {
           is_intermediate: !saveToGallery,
           metadata,
+          board_id: boardId,
+          silent: true,
         },
         undefined,
         true // force upload the image to ensure it gets added to the gallery
@@ -222,8 +231,8 @@ export const useNewRasterLayerFromBbox = () => {
       toastError: t('controlLayers.newRasterLayerError'),
     };
   }, [dispatch, t]);
-  const newRasterLayerFromBbox = useSaveCanvas(arg);
-  return newRasterLayerFromBbox;
+  const func = useSaveCanvas(arg);
+  return func;
 };
 
 export const useNewControlLayerFromBbox = () => {

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

@@ -28,19 +28,17 @@ import type {
 } from 'features/controlLayers/store/types';
 import { getEntityIdentifier } from 'features/controlLayers/store/types';
 import { imageDTOToImageObject } from 'features/controlLayers/store/util';
-import { selectAutoAddBoardId } from 'features/gallery/store/gallerySelectors';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { atom, computed } from 'nanostores';
 import type { Logger } from 'roarr';
 import { serializeError } from 'serialize-error';
-import type { UploadImageArg } from 'services/api/endpoints/images';
 import { getImageDTOSafe, uploadImage } from 'services/api/endpoints/images';
-import type { ImageDTO } from 'services/api/types';
+import type { ImageDTO, UploadImageArg } from 'services/api/types';
 import stableHash from 'stable-hash';
 import type { Equals } from 'tsafe';
 import { assert } from 'tsafe';
-import type { JsonObject } from 'type-fest';
+import type { JsonObject, SetOptional } from 'type-fest';
 
 type CompositingOptions = {
   /**
@@ -259,7 +257,7 @@ export class CanvasCompositorModule extends CanvasModuleBase {
   getCompositeImageDTO = async (
     adapters: CanvasEntityAdapter[],
     rect: Rect,
-    uploadOptions: Pick<UploadImageArg, 'is_intermediate' | 'metadata'>,
+    uploadOptions: SetOptional<Omit<UploadImageArg, 'file'>, 'image_category'>,
     compositingOptions?: CompositingOptions,
     forceUpload?: boolean
   ): Promise<ImageDTO> => {
@@ -299,10 +297,7 @@ export class CanvasCompositorModule extends CanvasModuleBase {
       uploadImage({
         file: new File([blob], 'canvas-composite.png', { type: 'image/png' }),
         image_category: 'general',
-        is_intermediate: uploadOptions.is_intermediate,
-        board_id: uploadOptions.is_intermediate ? undefined : selectAutoAddBoardId(this.manager.store.getState()),
-        metadata: uploadOptions.metadata,
-        withToast: false,
+        ...uploadOptions,
       })
     );
     this.$isUploading.set(false);

invokeai/frontend/web/src/features/controlLayers/konva/CanvasEntity/CanvasEntityObjectRenderer.ts

@@ -493,7 +493,7 @@ export class CanvasEntityObjectRenderer extends CanvasModuleBase {
       file: new File([blob], `${this.id}_rasterized.png`, { type: 'image/png' }),
       image_category: 'other',
       is_intermediate: true,
-      withToast: false,
+      silent: true,
     });
     const imageObject = imageDTOToImageObject(imageDTO);
     if (replaceObjects) {

invokeai/frontend/web/src/features/controlLayers/konva/CanvasTool/CanvasBboxToolModule.ts

@@ -1,13 +1,8 @@
-import {
-  roundDownToMultiple,
-  roundToMultiple,
-  roundToMultipleMin,
-  roundUpToMultiple,
-} from 'common/util/roundDownToMultiple';
+import { roundToMultiple, roundToMultipleMin } from 'common/util/roundDownToMultiple';
 import type { CanvasManager } from 'features/controlLayers/konva/CanvasManager';
 import { CanvasModuleBase } from 'features/controlLayers/konva/CanvasModuleBase';
 import type { CanvasToolModule } from 'features/controlLayers/konva/CanvasTool/CanvasToolModule';
-import { getKonvaNodeDebugAttrs, getPrefixedId } from 'features/controlLayers/konva/util';
+import { fitRectToGrid, getKonvaNodeDebugAttrs, getPrefixedId } from 'features/controlLayers/konva/util';
 import { selectBboxOverlay } from 'features/controlLayers/store/canvasSettingsSlice';
 import { selectBbox } from 'features/controlLayers/store/selectors';
 import type { Coordinate, Rect } from 'features/controlLayers/store/types';
@@ -398,18 +393,12 @@ export class CanvasBboxToolModule extends CanvasModuleBase {
     }
 
     // Determine the bbox size that fits within the visible rect. The bbox must be at least 64px in width and height,
-    // and its width and height must be multiples of 8px.
+    // and its width and height must be multiples of the bbox grid size.
     const gridSize = this.manager.stateApi.getBboxGridSize();
 
-    // To be conservative, we will round up the x and y to the nearest grid size, and round down the width and height.
-    // This ensures the bbox is never _larger_ than the visible rect. If the bbox is larger than the visible, we
-    // will always trigger the outpainting workflow, which is not what the user wants.
-    const x = roundUpToMultiple(visibleRect.x, gridSize);
-    const y = roundUpToMultiple(visibleRect.y, gridSize);
-    const width = roundDownToMultiple(visibleRect.width, gridSize);
-    const height = roundDownToMultiple(visibleRect.height, gridSize);
+    const rect = fitRectToGrid(visibleRect, gridSize);
 
-    this.manager.stateApi.setGenerationBbox({ x, y, width, height });
+    this.manager.stateApi.setGenerationBbox(rect);
   };
 
   /**

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

@@ -1,4 +1,6 @@
-import { getPrefixedId, getRectUnion } from 'features/controlLayers/konva/util';
+import { roundUpToMultiple } from 'common/util/roundDownToMultiple';
+import { fitRectToGrid, getPrefixedId, getRectUnion } from 'features/controlLayers/konva/util';
+import type { Rect } from 'features/controlLayers/store/types';
 import { describe, expect, it } from 'vitest';
 
 describe('util', () => {
@@ -44,4 +46,74 @@ describe('util', () => {
       expect(union).toEqual({ x: 0, y: 0, width: 0, height: 0 });
     });
   });
+
+  describe('fitRectToGrid', () => {
+    it('should fit rect within grid without exceeding bounds', () => {
+      const rect: Rect = { x: 0, y: 0, width: 1047, height: 1758 };
+      const gridSize = 50;
+      const result = fitRectToGrid(rect, gridSize);
+
+      expect(result.x).toBe(roundUpToMultiple(rect.x, gridSize));
+      expect(result.y).toBe(roundUpToMultiple(rect.y, gridSize));
+      expect(result.width).toBeLessThanOrEqual(rect.width);
+      expect(result.height).toBeLessThanOrEqual(rect.height);
+      expect(result.width % gridSize).toBe(0);
+      expect(result.height % gridSize).toBe(0);
+    });
+
+    it('should handle small rect within grid bounds', () => {
+      const rect: Rect = { x: 20, y: 30, width: 80, height: 90 };
+      const gridSize = 25;
+      const result = fitRectToGrid(rect, gridSize);
+
+      expect(result.x).toBe(25);
+      expect(result.y).toBe(50);
+      expect(result.width % gridSize).toBe(0);
+      expect(result.height % gridSize).toBe(0);
+      expect(result.width).toBeLessThanOrEqual(rect.width);
+      expect(result.height).toBeLessThanOrEqual(rect.height);
+    });
+
+    it('should handle rect starting outside of grid alignment', () => {
+      const rect: Rect = { x: 13, y: 27, width: 94, height: 112 };
+      const gridSize = 20;
+      const result = fitRectToGrid(rect, gridSize);
+
+      expect(result.x).toBe(20);
+      expect(result.y).toBe(40);
+      expect(result.width % gridSize).toBe(0);
+      expect(result.height % gridSize).toBe(0);
+      expect(result.width).toBeLessThanOrEqual(rect.width);
+      expect(result.height).toBeLessThanOrEqual(rect.height);
+    });
+
+    it('should return the same rect if already aligned to grid', () => {
+      const rect: Rect = { x: 100, y: 100, width: 200, height: 300 };
+      const gridSize = 50;
+      const result = fitRectToGrid(rect, gridSize);
+
+      expect(result).toEqual(rect);
+    });
+
+    it('should handle large grid sizes relative to rect dimensions', () => {
+      const rect: Rect = { x: 250, y: 300, width: 400, height: 500 };
+      const gridSize = 100;
+      const result = fitRectToGrid(rect, gridSize);
+
+      expect(result.x).toBe(300);
+      expect(result.y).toBe(300);
+      expect(result.width % gridSize).toBe(0);
+      expect(result.height % gridSize).toBe(0);
+      expect(result.width).toBeLessThanOrEqual(rect.width);
+      expect(result.height).toBeLessThanOrEqual(rect.height);
+    });
+
+    it('should handle rect with zero width and height', () => {
+      const rect: Rect = { x: 40, y: 60, width: 100, height: 200 };
+      const gridSize = 20;
+      const result = fitRectToGrid(rect, gridSize);
+
+      expect(result).toEqual({ x: 40, y: 60, width: 100, height: 200 });
+    });
+  });
 });

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

@@ -1,5 +1,6 @@
 import type { Selector, Store } from '@reduxjs/toolkit';
 import { $authToken } from 'app/store/nanostores/authToken';
+import { roundDownToMultiple, roundUpToMultiple } from 'common/util/roundDownToMultiple';
 import type {
   CanvasEntityIdentifier,
   CanvasObjectState,
@@ -560,6 +561,33 @@ export const getRectIntersection = (...rects: Rect[]): Rect => {
   return rect || getEmptyRect();
 };
 
+/**
+ * Fits a rect to the nearest multiple of the grid size, rounding down. The returned rect will be smaller than or equal
+ * to the input rect, and will be aligned to the grid.
+ *
+ * In other words, shrink the rect inwards on each size until it fits within the visible rect and aligns to the grid.
+ *
+ * @param rect The rect to fit
+ * @param gridSize The size of the grid
+ * @returns The fitted rect
+ */
+export const fitRectToGrid = (rect: Rect, gridSize: number): Rect => {
+  // Rounding x and y up effectively shrinks the left and top edges of the rect, and rounding width and height down
+  // effectively shrinks the right and bottom edges.
+  const x = roundUpToMultiple(rect.x, gridSize);
+  const y = roundUpToMultiple(rect.y, gridSize);
+
+  // Because we've just shifted the rect's x and y, we need to adjust the width and height by the same amount before
+  // we round those values down.
+  const offsetX = x - rect.x;
+  const offsetY = y - rect.y;
+
+  const width = roundDownToMultiple(rect.width - offsetX, gridSize);
+  const height = roundDownToMultiple(rect.height - offsetY, gridSize);
+
+  return { x, y, width, height };
+};
+
 /**
  * Asserts that the value is never reached. Used for exhaustive checks in switch statements or conditional logic to ensure
  * that all possible values are handled.

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

@@ -90,7 +90,7 @@ const initialState: CanvasSettingsState = {
   invertScrollForToolWidth: false,
   color: { r: 31, g: 160, b: 224, a: 1 }, // invokeBlue.500
   sendToCanvas: false,
-  outputOnlyMaskedRegions: false,
+  outputOnlyMaskedRegions: true,
   autoProcess: true,
   snapToGrid: true,
   showProgressOnCanvas: true,

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

@@ -9,6 +9,8 @@ import type {
   ParameterCFGRescaleMultiplier,
   ParameterCFGScale,
   ParameterCLIPEmbedModel,
+  ParameterCLIPGEmbedModel,
+  ParameterCLIPLEmbedModel,
   ParameterGuidance,
   ParameterMaskBlurMethod,
   ParameterModel,
@@ -71,6 +73,8 @@ export type ParamsState = {
   refinerStart: number;
   t5EncoderModel: ParameterT5EncoderModel | null;
   clipEmbedModel: ParameterCLIPEmbedModel | null;
+  clipLEmbedModel: ParameterCLIPLEmbedModel | null;
+  clipGEmbedModel: ParameterCLIPGEmbedModel | null;
 };
 
 const initialState: ParamsState = {
@@ -115,6 +119,8 @@ const initialState: ParamsState = {
   refinerStart: 0.8,
   t5EncoderModel: null,
   clipEmbedModel: null,
+  clipLEmbedModel: null,
+  clipGEmbedModel: null,
 };
 
 export const paramsSlice = createSlice({
@@ -192,6 +198,12 @@ export const paramsSlice = createSlice({
     clipEmbedModelSelected: (state, action: PayloadAction<ParameterCLIPEmbedModel | null>) => {
       state.clipEmbedModel = action.payload;
     },
+    clipLEmbedModelSelected: (state, action: PayloadAction<ParameterCLIPLEmbedModel | null>) => {
+      state.clipLEmbedModel = action.payload;
+    },
+    clipGEmbedModelSelected: (state, action: PayloadAction<ParameterCLIPGEmbedModel | null>) => {
+      state.clipGEmbedModel = action.payload;
+    },
     vaePrecisionChanged: (state, action: PayloadAction<ParameterPrecision>) => {
       state.vaePrecision = action.payload;
     },
@@ -305,6 +317,8 @@ export const {
   vaePrecisionChanged,
   t5EncoderModelSelected,
   clipEmbedModelSelected,
+  clipLEmbedModelSelected,
+  clipGEmbedModelSelected,
   setClipSkip,
   shouldUseCpuNoiseChanged,
   positivePromptChanged,
@@ -341,6 +355,7 @@ export const createParamsSelector = <T>(selector: Selector<ParamsState, T>) =>
 export const selectBase = createParamsSelector((params) => params.model?.base);
 export const selectIsSDXL = createParamsSelector((params) => params.model?.base === 'sdxl');
 export const selectIsFLUX = createParamsSelector((params) => params.model?.base === 'flux');
+export const selectIsSD3 = createParamsSelector((params) => params.model?.base === 'sd-3');
 
 export const selectModel = createParamsSelector((params) => params.model);
 export const selectModelKey = createParamsSelector((params) => params.model?.key);
@@ -349,6 +364,10 @@ export const selectFLUXVAE = createParamsSelector((params) => params.fluxVAE);
 export const selectVAEKey = createParamsSelector((params) => params.vae?.key);
 export const selectT5EncoderModel = createParamsSelector((params) => params.t5EncoderModel);
 export const selectCLIPEmbedModel = createParamsSelector((params) => params.clipEmbedModel);
+export const selectCLIPLEmbedModel = createParamsSelector((params) => params.clipLEmbedModel);
+
+export const selectCLIPGEmbedModel = createParamsSelector((params) => params.clipGEmbedModel);
+
 export const selectCFGScale = createParamsSelector((params) => params.cfgScale);
 export const selectGuidance = createParamsSelector((params) => params.guidance);
 export const selectSteps = createParamsSelector((params) => params.steps);

invokeai/frontend/web/src/features/dnd/DndImage.tsx

@@ -1,3 +1,4 @@
+import { combine } from '@atlaskit/pragmatic-drag-and-drop/combine';
 import { draggable } from '@atlaskit/pragmatic-drag-and-drop/element/adapter';
 import type { ImageProps, SystemStyleObject } from '@invoke-ai/ui-library';
 import { Image } from '@invoke-ai/ui-library';
@@ -5,6 +6,7 @@ import { useAppStore } from 'app/store/nanostores/store';
 import { singleImageDndSource } from 'features/dnd/dnd';
 import type { DndDragPreviewSingleImageState } from 'features/dnd/DndDragPreviewSingleImage';
 import { createSingleImageDragPreview, setSingleImageDragPreview } from 'features/dnd/DndDragPreviewSingleImage';
+import { firefoxDndFix } from 'features/dnd/util';
 import { useImageContextMenu } from 'features/gallery/components/ImageContextMenu/ImageContextMenu';
 import { memo, useEffect, useState } from 'react';
 import type { ImageDTO } from 'services/api/types';
@@ -35,25 +37,28 @@ export const DndImage = memo(({ imageDTO, asThumbnail, ...rest }: Props) => {
     if (!element) {
       return;
     }
-    return draggable({
-      element,
-      getInitialData: () => singleImageDndSource.getData({ imageDTO }, imageDTO.image_name),
-      onDragStart: () => {
-        setIsDragging(true);
-      },
-      onDrop: () => {
-        setIsDragging(false);
-      },
-      onGenerateDragPreview: (args) => {
-        if (singleImageDndSource.typeGuard(args.source.data)) {
-          setSingleImageDragPreview({
-            singleImageDndData: args.source.data,
-            onGenerateDragPreviewArgs: args,
-            setDragPreviewState,
-          });
-        }
-      },
-    });
+    return combine(
+      firefoxDndFix(element),
+      draggable({
+        element,
+        getInitialData: () => singleImageDndSource.getData({ imageDTO }, imageDTO.image_name),
+        onDragStart: () => {
+          setIsDragging(true);
+        },
+        onDrop: () => {
+          setIsDragging(false);
+        },
+        onGenerateDragPreview: (args) => {
+          if (singleImageDndSource.typeGuard(args.source.data)) {
+            setSingleImageDragPreview({
+              singleImageDndData: args.source.data,
+              onGenerateDragPreviewArgs: args,
+              setDragPreviewState,
+            });
+          }
+        },
+      })
+    );
   }, [imageDTO, element, store]);
 
   useImageContextMenu(imageDTO, element);

invokeai/frontend/web/src/features/dnd/FullscreenDropzone.tsx

@@ -11,10 +11,11 @@ import type { DndTargetState } from 'features/dnd/types';
 import { selectAutoAddBoardId } from 'features/gallery/store/gallerySelectors';
 import { selectMaxImageUploadCount } from 'features/system/store/configSlice';
 import { toast } from 'features/toast/toast';
-import { memo, useEffect, useRef, useState } from 'react';
+import { memo, useCallback, useEffect, useMemo, useRef, useState } from 'react';
 import { useTranslation } from 'react-i18next';
-import { type UploadImageArg, uploadImages } from 'services/api/endpoints/images';
+import { uploadImages } from 'services/api/endpoints/images';
 import { useBoardName } from 'services/api/hooks/useBoardName';
+import type { UploadImageArg } from 'services/api/types';
 import { z } from 'zod';
 
 const ACCEPTED_IMAGE_TYPES = ['image/png', 'image/jpg', 'image/jpeg'];
@@ -71,13 +72,47 @@ export const FullscreenDropzone = memo(() => {
   const maxImageUploadCount = useAppSelector(selectMaxImageUploadCount);
   const [dndState, setDndState] = useState<DndTargetState>('idle');
 
+  const uploadFilesSchema = useMemo(() => getFilesSchema(maxImageUploadCount), [maxImageUploadCount]);
+
+  const validateAndUploadFiles = useCallback(
+    (files: File[]) => {
+      const { getState } = getStore();
+      const parseResult = uploadFilesSchema.safeParse(files);
+
+      if (!parseResult.success) {
+        const description =
+          maxImageUploadCount === undefined
+            ? t('toast.uploadFailedInvalidUploadDesc')
+            : t('toast.uploadFailedInvalidUploadDesc_withCount', { count: maxImageUploadCount });
+
+        toast({
+          id: 'UPLOAD_FAILED',
+          title: t('toast.uploadFailed'),
+          description,
+          status: 'error',
+        });
+        return;
+      }
+      const autoAddBoardId = selectAutoAddBoardId(getState());
+
+      const uploadArgs: UploadImageArg[] = files.map((file, i) => ({
+        file,
+        image_category: 'user',
+        is_intermediate: false,
+        board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
+        isFirstUploadOfBatch: i === 0,
+      }));
+
+      uploadImages(uploadArgs);
+    },
+    [maxImageUploadCount, t, uploadFilesSchema]
+  );
+
   useEffect(() => {
     const element = ref.current;
     if (!element) {
       return;
     }
-    const { getState } = getStore();
-    const uploadFilesSchema = getFilesSchema(maxImageUploadCount);
 
     return combine(
       dropTargetForExternal({
@@ -85,32 +120,7 @@ export const FullscreenDropzone = memo(() => {
         canDrop: containsFiles,
         onDrop: ({ source }) => {
           const files = getFiles({ source });
-          const parseResult = uploadFilesSchema.safeParse(files);
-
-          if (!parseResult.success) {
-            const description =
-              maxImageUploadCount === undefined
-                ? t('toast.uploadFailedInvalidUploadDesc')
-                : t('toast.uploadFailedInvalidUploadDesc_withCount', { count: maxImageUploadCount });
-
-            toast({
-              id: 'UPLOAD_FAILED',
-              title: t('toast.uploadFailed'),
-              description,
-              status: 'error',
-            });
-            return;
-          }
-          const autoAddBoardId = selectAutoAddBoardId(getState());
-
-          const uploadArgs: UploadImageArg[] = files.map((file) => ({
-            file,
-            image_category: 'user',
-            is_intermediate: false,
-            board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
-          }));
-
-          uploadImages(uploadArgs);
+          validateAndUploadFiles(files);
         },
         onDragEnter: () => {
           setDndState('over');
@@ -131,7 +141,27 @@ export const FullscreenDropzone = memo(() => {
         },
       })
     );
-  }, [maxImageUploadCount, t]);
+  }, [validateAndUploadFiles]);
+
+  useEffect(() => {
+    const controller = new AbortController();
+
+    document.addEventListener(
+      'paste',
+      (e) => {
+        if (!e.clipboardData?.files) {
+          return;
+        }
+        const files = Array.from(e.clipboardData.files);
+        validateAndUploadFiles(files);
+      },
+      { signal: controller.signal }
+    );
+
+    return () => {
+      controller.abort();
+    };
+  }, [validateAndUploadFiles]);
 
   return (
     <Box ref={ref} data-dnd-state={dndState} sx={sx}>

invokeai/frontend/web/src/features/dnd/util.ts

@@ -1,6 +1,7 @@
 import type { GetOffsetFn } from '@atlaskit/pragmatic-drag-and-drop/dist/types/public-utils/element/custom-native-drag-preview/types';
 import type { Input } from '@atlaskit/pragmatic-drag-and-drop/types';
 import type { SystemStyleObject } from '@invoke-ai/ui-library';
+import { noop } from 'lodash-es';
 import type { CSSProperties } from 'react';
 
 /**
@@ -44,3 +45,67 @@ export function triggerPostMoveFlash(element: HTMLElement, backgroundColor: CSSP
     iterations: 1,
   });
 }
+
+/**
+ * Firefox has a bug where input or textarea elements with draggable parents do not allow selection of their text.
+ *
+ * This helper function implements a workaround by setting the draggable attribute to false when the mouse is over a
+ * input or textarea child of the draggable. It reverts the attribute on mouse out.
+ *
+ * The fix is only applied for Firefox, and should be used in every `pragmatic-drag-and-drop` `draggable`.
+ *
+ * See:
+ * - https://github.com/atlassian/pragmatic-drag-and-drop/issues/111
+ * - https://bugzilla.mozilla.org/show_bug.cgi?id=1853069
+ *
+ * @example
+ * ```tsx
+ * useEffect(() => {
+ *   const element = ref.current;
+ *   if (!element) {
+ *     return;
+ *   }
+ *   return combine(
+ *     firefoxDndFix(element),
+ *     // The rest of the draggable setup is the same
+ *     draggable({
+ *       element,
+ *       // ...
+ *     }),
+ *   );
+ *```
+ * @param element The draggable element
+ * @returns A cleanup function that removes the event listeners
+ */
+export const firefoxDndFix = (element: HTMLElement): (() => void) => {
+  if (!navigator.userAgent.includes('Firefox')) {
+    return noop;
+  }
+
+  const abortController = new AbortController();
+
+  element.addEventListener(
+    'mouseover',
+    (event) => {
+      if (event.target instanceof HTMLTextAreaElement || event.target instanceof HTMLInputElement) {
+        element.setAttribute('draggable', 'false');
+      }
+    },
+    { signal: abortController.signal }
+  );
+
+  element.addEventListener(
+    'mouseout',
+    (event) => {
+      if (event.target instanceof HTMLTextAreaElement || event.target instanceof HTMLInputElement) {
+        element.setAttribute('draggable', 'true');
+      }
+    },
+    { signal: abortController.signal }
+  );
+
+  return () => {
+    element.setAttribute('draggable', 'true');
+    abortController.abort();
+  };
+};

invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/ImageMenuItemNewFromImageSubMenu.tsx

@@ -1,8 +1,10 @@
 import { Menu, MenuButton, MenuItem, MenuList } from '@invoke-ai/ui-library';
 import { useAppStore } from 'app/store/nanostores/store';
+import { useAppSelector } from 'app/store/storeHooks';
 import { SubMenuButtonContent, useSubMenu } from 'common/hooks/useSubMenu';
 import { NewLayerIcon } from 'features/controlLayers/components/common/icons';
 import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
+import { selectIsFLUX, selectIsSD3 } from 'features/controlLayers/store/paramsSlice';
 import { useImageViewer } from 'features/gallery/components/ImageViewer/useImageViewer';
 import { useImageDTOContext } from 'features/gallery/contexts/ImageDTOContext';
 import { sentImageToCanvas } from 'features/gallery/store/actions';
@@ -20,6 +22,8 @@ export const ImageMenuItemNewFromImageSubMenu = memo(() => {
   const imageDTO = useImageDTOContext();
   const imageViewer = useImageViewer();
   const isBusy = useCanvasIsBusy();
+  const isFLUX = useAppSelector(selectIsFLUX);
+  const isSD3 = useAppSelector(selectIsSD3);
 
   const onClickNewCanvasWithRasterLayerFromImage = useCallback(() => {
     const { dispatch, getState } = store;
@@ -110,17 +114,25 @@ export const ImageMenuItemNewFromImageSubMenu = memo(() => {
           <MenuItem
             icon={<PiFileBold />}
             onClickCapture={onClickNewCanvasWithControlLayerFromImage}
-            isDisabled={isBusy}
+            isDisabled={isBusy || isSD3}
           >
             {t('controlLayers.canvasAsControlLayer')}
           </MenuItem>
           <MenuItem icon={<NewLayerIcon />} onClickCapture={onClickNewInpaintMaskFromImage} isDisabled={isBusy}>
             {t('controlLayers.inpaintMask')}
           </MenuItem>
-          <MenuItem icon={<NewLayerIcon />} onClickCapture={onClickNewRegionalGuidanceFromImage} isDisabled={isBusy}>
+          <MenuItem
+            icon={<NewLayerIcon />}
+            onClickCapture={onClickNewRegionalGuidanceFromImage}
+            isDisabled={isBusy || isFLUX || isSD3}
+          >
             {t('controlLayers.regionalGuidance')}
           </MenuItem>
-          <MenuItem icon={<NewLayerIcon />} onClickCapture={onClickNewControlLayerFromImage} isDisabled={isBusy}>
+          <MenuItem
+            icon={<NewLayerIcon />}
+            onClickCapture={onClickNewControlLayerFromImage}
+            isDisabled={isBusy || isSD3}
+          >
             {t('controlLayers.controlLayer')}
           </MenuItem>
           <MenuItem icon={<NewLayerIcon />} onClickCapture={onClickNewRasterLayerFromImage} isDisabled={isBusy}>

invokeai/frontend/web/src/features/gallery/components/ImageGrid/GalleryImage.tsx

@@ -12,6 +12,7 @@ import type { DndDragPreviewMultipleImageState } from 'features/dnd/DndDragPrevi
 import { createMultipleImageDragPreview, setMultipleImageDragPreview } from 'features/dnd/DndDragPreviewMultipleImage';
 import type { DndDragPreviewSingleImageState } from 'features/dnd/DndDragPreviewSingleImage';
 import { createSingleImageDragPreview, setSingleImageDragPreview } from 'features/dnd/DndDragPreviewSingleImage';
+import { firefoxDndFix } from 'features/dnd/util';
 import { useImageContextMenu } from 'features/gallery/components/ImageContextMenu/ImageContextMenu';
 import { GalleryImageHoverIcons } from 'features/gallery/components/ImageGrid/GalleryImageHoverIcons';
 import { getGalleryImageDataTestId } from 'features/gallery/components/ImageGrid/getGalleryImageDataTestId';
@@ -66,7 +67,7 @@ const galleryImageContainerSX = {
     },
     '&:hover::before': {
       boxShadow:
-        'inset 0px 0px 0px 2px var(--invoke-colors-invokeBlue-300), inset 0px 0px 0px 3px var(--invoke-colors-invokeBlue-800)',
+        'inset 0px 0px 0px 1px var(--invoke-colors-invokeBlue-300), inset 0px 0px 0px 2px var(--invoke-colors-invokeBlue-800)',
     },
     '&:hover[data-selected=true]::before': {
       boxShadow:
@@ -115,13 +116,17 @@ export const GalleryImage = memo(({ imageDTO }: Props) => {
       return;
     }
     return combine(
+      firefoxDndFix(element),
       draggable({
         element,
         getInitialData: () => {
           const { gallery } = store.getState();
           // When we have multiple images selected, and the dragged image is part of the selection, initiate a
           // multi-image drag.
-          if (gallery.selection.length > 1 && gallery.selection.includes(imageDTO)) {
+          if (
+            gallery.selection.length > 1 &&
+            gallery.selection.find(({ image_name }) => image_name === imageDTO.image_name) !== undefined
+          ) {
             return multipleImageDndSource.getData({
               imageDTOs: gallery.selection,
               boardId: gallery.selectedBoardId,

invokeai/frontend/web/src/features/gallery/components/ImageViewer/CurrentImagePreview.tsx

@@ -2,6 +2,7 @@ import { Box, Flex } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { skipToken } from '@reduxjs/toolkit/query';
 import { useAppSelector } from 'app/store/storeHooks';
+import { CanvasAlertsInvocationProgress } from 'features/controlLayers/components/CanvasAlerts/CanvasAlertsInvocationProgress';
 import { CanvasAlertsSendingToCanvas } from 'features/controlLayers/components/CanvasAlerts/CanvasAlertsSendingTo';
 import { DndImage } from 'features/dnd/DndImage';
 import ImageMetadataViewer from 'features/gallery/components/ImageMetadataViewer/ImageMetadataViewer';
@@ -48,9 +49,18 @@ const CurrentImagePreview = () => {
       position="relative"
     >
       <ImageContent imageDTO={imageDTO} />
-      <Box position="absolute" top={0} insetInlineStart={0}>
+      <Flex
+        flexDir="column"
+        gap={2}
+        position="absolute"
+        top={0}
+        insetInlineStart={0}
+        pointerEvents="none"
+        alignItems="flex-start"
+      >
         <CanvasAlertsSendingToCanvas />
-      </Box>
+        <CanvasAlertsInvocationProgress />
+      </Flex>
       {shouldShowImageDetails && imageDTO && (
         <Box position="absolute" opacity={0.8} top={0} width="full" height="full" borderRadius="base">
           <ImageMetadataViewer image={imageDTO} />

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

@@ -44,7 +44,6 @@ export const ImageViewer = memo(({ closeButton }: Props) => {
       right={0}
       bottom={0}
       left={0}
-      rowGap={2}
       alignItems="center"
       justifyContent="center"
     >

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/Fields/BaseModelSelect.tsx

@@ -10,6 +10,8 @@ import type { UpdateModelArg } from 'services/api/endpoints/models';
 const options: ComboboxOption[] = [
   { value: 'sd-1', label: MODEL_TYPE_MAP['sd-1'] },
   { value: 'sd-2', label: MODEL_TYPE_MAP['sd-2'] },
+  { value: 'sd-3', label: MODEL_TYPE_MAP['sd-3'] },
+  { value: 'flux', label: MODEL_TYPE_MAP['flux'] },
   { value: 'sdxl', label: MODEL_TYPE_MAP['sdxl'] },
   { value: 'sdxl-refiner', label: MODEL_TYPE_MAP['sdxl-refiner'] },
 ];

invokeai/frontend/web/src/features/nodes/components/sidePanel/workflow/useLinearViewFieldDnd.ts

@@ -4,6 +4,7 @@ import { attachClosestEdge, extractClosestEdge } from '@atlaskit/pragmatic-drag-
 import { singleWorkflowFieldDndSource } from 'features/dnd/dnd';
 import type { DndListTargetState } from 'features/dnd/types';
 import { idle } from 'features/dnd/types';
+import { firefoxDndFix } from 'features/dnd/util';
 import type { FieldIdentifier } from 'features/nodes/types/field';
 import type { RefObject } from 'react';
 import { useEffect, useState } from 'react';
@@ -18,6 +19,7 @@ export const useLinearViewFieldDnd = (ref: RefObject<HTMLElement>, fieldIdentifi
       return;
     }
     return combine(
+      firefoxDndFix(element),
       draggable({
         element,
         getInitialData() {

invokeai/frontend/web/src/features/nodes/util/graph/buildLinearBatchConfig.ts

@@ -9,8 +9,8 @@ export const prepareLinearUIBatch = (
   state: RootState,
   g: Graph,
   prepend: boolean,
-  noise: Invocation<'noise' | 'flux_denoise'>,
-  posCond: Invocation<'compel' | 'sdxl_compel_prompt' | 'flux_text_encoder'>,
+  noise: Invocation<'noise' | 'flux_denoise' | 'sd3_denoise'>,
+  posCond: Invocation<'compel' | 'sdxl_compel_prompt' | 'flux_text_encoder' | 'sd3_text_encoder'>,
   origin: 'canvas' | 'workflows' | 'upscaling',
   destination: 'canvas' | 'gallery'
 ): BatchConfig => {

invokeai/frontend/web/src/features/nodes/util/graph/generation/addImageToImage.ts

@@ -2,16 +2,18 @@ import type { CanvasManager } from 'features/controlLayers/konva/CanvasManager';
 import { getPrefixedId } from 'features/controlLayers/konva/util';
 import type { CanvasState, Dimensions } from 'features/controlLayers/store/types';
 import type { Graph } from 'features/nodes/util/graph/generation/Graph';
-import { addImageToLatents } from 'features/nodes/util/graph/graphBuilderUtils';
 import { isEqual } from 'lodash-es';
 import type { Invocation } from 'services/api/types';
 
 type AddImageToImageArg = {
   g: Graph;
   manager: CanvasManager;
-  l2i: Invocation<'l2i' | 'flux_vae_decode'>;
-  denoise: Invocation<'denoise_latents' | 'flux_denoise'>;
-  vaeSource: Invocation<'main_model_loader' | 'sdxl_model_loader' | 'flux_model_loader' | 'seamless' | 'vae_loader'>;
+  l2i: Invocation<'l2i' | 'flux_vae_decode' | 'sd3_l2i'>;
+  i2lNodeType: 'i2l' | 'flux_vae_encode' | 'sd3_i2l';
+  denoise: Invocation<'denoise_latents' | 'flux_denoise' | 'sd3_denoise'>;
+  vaeSource: Invocation<
+    'main_model_loader' | 'sdxl_model_loader' | 'flux_model_loader' | 'seamless' | 'vae_loader' | 'sd3_model_loader'
+  >;
   originalSize: Dimensions;
   scaledSize: Dimensions;
   bbox: CanvasState['bbox'];
@@ -23,6 +25,7 @@ export const addImageToImage = async ({
   g,
   manager,
   l2i,
+  i2lNodeType,
   denoise,
   vaeSource,
   originalSize,
@@ -30,10 +33,13 @@ export const addImageToImage = async ({
   bbox,
   denoising_start,
   fp32,
-}: AddImageToImageArg): Promise<Invocation<'img_resize' | 'l2i' | 'flux_vae_decode'>> => {
+}: AddImageToImageArg): Promise<Invocation<'img_resize' | 'l2i' | 'flux_vae_decode' | 'sd3_l2i'>> => {
   denoise.denoising_start = denoising_start;
   const adapters = manager.compositor.getVisibleAdaptersOfType('raster_layer');
-  const { image_name } = await manager.compositor.getCompositeImageDTO(adapters, bbox.rect, { is_intermediate: true });
+  const { image_name } = await manager.compositor.getCompositeImageDTO(adapters, bbox.rect, {
+    is_intermediate: true,
+    silent: true,
+  });
 
   if (!isEqual(scaledSize, originalSize)) {
     // Resize the initial image to the scaled size, denoise, then resize back to the original size
@@ -44,7 +50,12 @@ export const addImageToImage = async ({
       ...scaledSize,
     });
 
-    const i2l = addImageToLatents(g, l2i.type === 'flux_vae_decode', fp32);
+    const i2l = g.addNode({
+      id: i2lNodeType,
+      type: i2lNodeType,
+      image: image_name ? { image_name } : undefined,
+      ...(i2lNodeType === 'i2l' ? { fp32 } : {}),
+    });
 
     const resizeImageToOriginalSize = g.addNode({
       type: 'img_resize',
@@ -61,7 +72,12 @@ export const addImageToImage = async ({
     return resizeImageToOriginalSize;
   } else {
     // No need to resize, just decode
-    const i2l = addImageToLatents(g, l2i.type === 'flux_vae_decode', fp32, image_name);
+    const i2l = g.addNode({
+      id: i2lNodeType,
+      type: i2lNodeType,
+      image: image_name ? { image_name } : undefined,
+      ...(i2lNodeType === 'i2l' ? { fp32 } : {}),
+    });
     g.addEdge(vaeSource, 'vae', i2l, 'vae');
     g.addEdge(i2l, 'latents', denoise, 'latents');
     return l2i;

invokeai/frontend/web/src/features/nodes/util/graph/generation/addInpaint.ts

@@ -6,7 +6,6 @@ import { selectParamsSlice } from 'features/controlLayers/store/paramsSlice';
 import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
 import type { Dimensions } from 'features/controlLayers/store/types';
 import type { Graph } from 'features/nodes/util/graph/generation/Graph';
-import { addImageToLatents } from 'features/nodes/util/graph/graphBuilderUtils';
 import { isEqual } from 'lodash-es';
 import type { Invocation } from 'services/api/types';
 
@@ -14,10 +13,13 @@ type AddInpaintArg = {
   state: RootState;
   g: Graph;
   manager: CanvasManager;
-  l2i: Invocation<'l2i' | 'flux_vae_decode'>;
-  denoise: Invocation<'denoise_latents' | 'flux_denoise'>;
-  vaeSource: Invocation<'main_model_loader' | 'sdxl_model_loader' | 'flux_model_loader' | 'seamless' | 'vae_loader'>;
-  modelLoader: Invocation<'main_model_loader' | 'sdxl_model_loader' | 'flux_model_loader'>;
+  l2i: Invocation<'l2i' | 'flux_vae_decode' | 'sd3_l2i'>;
+  i2lNodeType: 'i2l' | 'flux_vae_encode' | 'sd3_i2l';
+  denoise: Invocation<'denoise_latents' | 'flux_denoise' | 'sd3_denoise'>;
+  vaeSource: Invocation<
+    'main_model_loader' | 'sdxl_model_loader' | 'flux_model_loader' | 'seamless' | 'vae_loader' | 'sd3_model_loader'
+  >;
+  modelLoader: Invocation<'main_model_loader' | 'sdxl_model_loader' | 'flux_model_loader' | 'sd3_model_loader'>;
   originalSize: Dimensions;
   scaledSize: Dimensions;
   denoising_start: number;
@@ -29,6 +31,7 @@ export const addInpaint = async ({
   g,
   manager,
   l2i,
+  i2lNodeType,
   denoise,
   vaeSource,
   modelLoader,
@@ -48,16 +51,23 @@ export const addInpaint = async ({
   const rasterAdapters = manager.compositor.getVisibleAdaptersOfType('raster_layer');
   const initialImage = await manager.compositor.getCompositeImageDTO(rasterAdapters, bbox.rect, {
     is_intermediate: true,
+    silent: true,
   });
 
   const inpaintMaskAdapters = manager.compositor.getVisibleAdaptersOfType('inpaint_mask');
   const maskImage = await manager.compositor.getCompositeImageDTO(inpaintMaskAdapters, bbox.rect, {
     is_intermediate: true,
+    silent: true,
   });
 
   if (!isEqual(scaledSize, originalSize)) {
     // Scale before processing requires some resizing
-    const i2l = addImageToLatents(g, modelLoader.type === 'flux_model_loader', fp32, initialImage.image_name);
+    const i2l = g.addNode({
+      id: i2lNodeType,
+      type: i2lNodeType,
+      image: initialImage.image_name ? { image_name: initialImage.image_name } : undefined,
+      ...(i2lNodeType === 'i2l' ? { fp32 } : {}),
+    });
 
     const resizeImageToScaledSize = g.addNode({
       type: 'img_resize',
@@ -102,7 +112,7 @@ export const addInpaint = async ({
     g.addEdge(vaeSource, 'vae', i2l, 'vae');
 
     g.addEdge(vaeSource, 'vae', createGradientMask, 'vae');
-    if (modelLoader.type !== 'flux_model_loader') {
+    if (modelLoader.type !== 'flux_model_loader' && modelLoader.type !== 'sd3_model_loader') {
       g.addEdge(modelLoader, 'unet', createGradientMask, 'unet');
     }
     g.addEdge(resizeImageToScaledSize, 'image', createGradientMask, 'image');
@@ -126,7 +136,12 @@ export const addInpaint = async ({
     return resizeOutput;
   } else {
     // No scale before processing, much simpler
-    const i2l = addImageToLatents(g, modelLoader.type === 'flux_model_loader', fp32, initialImage.image_name);
+    const i2l = g.addNode({
+      id: i2lNodeType,
+      type: i2lNodeType,
+      image: initialImage.image_name ? { image_name: initialImage.image_name } : undefined,
+      ...(i2lNodeType === 'i2l' ? { fp32 } : {}),
+    });
 
     const alphaToMask = g.addNode({
       id: getPrefixedId('alpha_to_mask'),
@@ -153,7 +168,7 @@ export const addInpaint = async ({
     g.addEdge(i2l, 'latents', denoise, 'latents');
     g.addEdge(vaeSource, 'vae', i2l, 'vae');
     g.addEdge(vaeSource, 'vae', createGradientMask, 'vae');
-    if (modelLoader.type !== 'flux_model_loader') {
+    if (modelLoader.type !== 'flux_model_loader' && modelLoader.type !== 'sd3_model_loader') {
       g.addEdge(modelLoader, 'unet', createGradientMask, 'unet');
     }
     g.addEdge(createGradientMask, 'denoise_mask', denoise, 'denoise_mask');

invokeai/frontend/web/src/features/nodes/util/graph/generation/addNSFWChecker.ts

@@ -11,7 +11,7 @@ import type { Invocation } from 'services/api/types';
 export const addNSFWChecker = (
   g: Graph,
   imageOutput: Invocation<
-    'l2i' | 'img_nsfw' | 'img_watermark' | 'img_resize' | 'canvas_v2_mask_and_crop' | 'flux_vae_decode'
+    'l2i' | 'img_nsfw' | 'img_watermark' | 'img_resize' | 'canvas_v2_mask_and_crop' | 'flux_vae_decode' | 'sd3_l2i'
   >
 ): Invocation<'img_nsfw'> => {
   const nsfw = g.addNode({

invokeai/frontend/web/src/features/nodes/util/graph/generation/addOutpaint.ts

@@ -6,7 +6,7 @@ import { selectParamsSlice } from 'features/controlLayers/store/paramsSlice';
 import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
 import type { Dimensions } from 'features/controlLayers/store/types';
 import type { Graph } from 'features/nodes/util/graph/generation/Graph';
-import { addImageToLatents, getInfill } from 'features/nodes/util/graph/graphBuilderUtils';
+import { getInfill } from 'features/nodes/util/graph/graphBuilderUtils';
 import { isEqual } from 'lodash-es';
 import type { Invocation } from 'services/api/types';
 
@@ -14,10 +14,13 @@ type AddOutpaintArg = {
   state: RootState;
   g: Graph;
   manager: CanvasManager;
-  l2i: Invocation<'l2i' | 'flux_vae_decode'>;
-  denoise: Invocation<'denoise_latents' | 'flux_denoise'>;
-  vaeSource: Invocation<'main_model_loader' | 'sdxl_model_loader' | 'flux_model_loader' | 'seamless' | 'vae_loader'>;
-  modelLoader: Invocation<'main_model_loader' | 'sdxl_model_loader' | 'flux_model_loader'>;
+  l2i: Invocation<'l2i' | 'flux_vae_decode' | 'sd3_l2i'>;
+  i2lNodeType: 'i2l' | 'flux_vae_encode' | 'sd3_i2l';
+  denoise: Invocation<'denoise_latents' | 'flux_denoise' | 'sd3_denoise'>;
+  vaeSource: Invocation<
+    'main_model_loader' | 'sdxl_model_loader' | 'flux_model_loader' | 'seamless' | 'vae_loader' | 'sd3_model_loader'
+  >;
+  modelLoader: Invocation<'main_model_loader' | 'sdxl_model_loader' | 'flux_model_loader' | 'sd3_model_loader'>;
   originalSize: Dimensions;
   scaledSize: Dimensions;
   denoising_start: number;
@@ -29,6 +32,7 @@ export const addOutpaint = async ({
   g,
   manager,
   l2i,
+  i2lNodeType,
   denoise,
   vaeSource,
   modelLoader,
@@ -48,11 +52,13 @@ export const addOutpaint = async ({
   const rasterAdapters = manager.compositor.getVisibleAdaptersOfType('raster_layer');
   const initialImage = await manager.compositor.getCompositeImageDTO(rasterAdapters, bbox.rect, {
     is_intermediate: true,
+    silent: true,
   });
 
   const inpaintMaskAdapters = manager.compositor.getVisibleAdaptersOfType('inpaint_mask');
   const maskImage = await manager.compositor.getCompositeImageDTO(inpaintMaskAdapters, bbox.rect, {
     is_intermediate: true,
+    silent: true,
   });
 
   const infill = getInfill(g, params);
@@ -108,14 +114,19 @@ export const addOutpaint = async ({
     g.addEdge(infill, 'image', createGradientMask, 'image');
     g.addEdge(resizeInputMaskToScaledSize, 'image', createGradientMask, 'mask');
     g.addEdge(vaeSource, 'vae', createGradientMask, 'vae');
-    if (modelLoader.type !== 'flux_model_loader') {
+    if (modelLoader.type !== 'flux_model_loader' && modelLoader.type !== 'sd3_model_loader') {
       g.addEdge(modelLoader, 'unet', createGradientMask, 'unet');
     }
 
     g.addEdge(createGradientMask, 'denoise_mask', denoise, 'denoise_mask');
 
     // Decode infilled image and connect to denoise
-    const i2l = addImageToLatents(g, modelLoader.type === 'flux_model_loader', fp32);
+    const i2l = g.addNode({
+      id: i2lNodeType,
+      type: i2lNodeType,
+      ...(i2lNodeType === 'i2l' ? { fp32 } : {}),
+    });
+
     g.addEdge(infill, 'image', i2l, 'image');
     g.addEdge(vaeSource, 'vae', i2l, 'vae');
     g.addEdge(i2l, 'latents', denoise, 'latents');
@@ -150,7 +161,11 @@ export const addOutpaint = async ({
   } else {
     infill.image = { image_name: initialImage.image_name };
     // No scale before processing, much simpler
-    const i2l = addImageToLatents(g, modelLoader.type === 'flux_model_loader', fp32);
+    const i2l = g.addNode({
+      id: i2lNodeType,
+      type: i2lNodeType,
+      ...(i2lNodeType === 'i2l' ? { fp32 } : {}),
+    });
     const maskAlphaToMask = g.addNode({
       id: getPrefixedId('mask_alpha_to_mask'),
       type: 'tomask',
@@ -187,7 +202,7 @@ export const addOutpaint = async ({
     g.addEdge(i2l, 'latents', denoise, 'latents');
     g.addEdge(vaeSource, 'vae', i2l, 'vae');
     g.addEdge(vaeSource, 'vae', createGradientMask, 'vae');
-    if (modelLoader.type !== 'flux_model_loader') {
+    if (modelLoader.type !== 'flux_model_loader' && modelLoader.type !== 'sd3_model_loader') {
       g.addEdge(modelLoader, 'unet', createGradientMask, 'unet');
     }
 

invokeai/frontend/web/src/features/nodes/util/graph/generation/addTextToImage.ts

@@ -6,7 +6,7 @@ import type { Invocation } from 'services/api/types';
 
 type AddTextToImageArg = {
   g: Graph;
-  l2i: Invocation<'l2i' | 'flux_vae_decode'>;
+  l2i: Invocation<'l2i' | 'flux_vae_decode' | 'sd3_l2i'>;
   originalSize: Dimensions;
   scaledSize: Dimensions;
 };
@@ -16,7 +16,7 @@ export const addTextToImage = ({
   l2i,
   originalSize,
   scaledSize,
-}: AddTextToImageArg): Invocation<'img_resize' | 'l2i' | 'flux_vae_decode'> => {
+}: AddTextToImageArg): Invocation<'img_resize' | 'l2i' | 'flux_vae_decode' | 'sd3_l2i'> => {
   if (!isEqual(scaledSize, originalSize)) {
     // We need to resize the output image back to the original size
     const resizeImageToOriginalSize = g.addNode({

invokeai/frontend/web/src/features/nodes/util/graph/generation/addWatermarker.ts

@@ -11,7 +11,7 @@ import type { Invocation } from 'services/api/types';
 export const addWatermarker = (
   g: Graph,
   imageOutput: Invocation<
-    'l2i' | 'img_nsfw' | 'img_watermark' | 'img_resize' | 'canvas_v2_mask_and_crop' | 'flux_vae_decode'
+    'l2i' | 'img_nsfw' | 'img_watermark' | 'img_resize' | 'canvas_v2_mask_and_crop' | 'flux_vae_decode' | 'sd3_l2i'
   >
 ): Invocation<'img_watermark'> => {
   const watermark = g.addNode({

invokeai/frontend/web/src/features/nodes/util/graph/generation/buildFLUXGraph.ts

@@ -139,7 +139,7 @@ export const buildFLUXGraph = async (
   }
 
   let canvasOutput: Invocation<
-    'l2i' | 'img_nsfw' | 'img_watermark' | 'img_resize' | 'canvas_v2_mask_and_crop' | 'flux_vae_decode'
+    'l2i' | 'img_nsfw' | 'img_watermark' | 'img_resize' | 'canvas_v2_mask_and_crop' | 'flux_vae_decode' | 'sd3_l2i'
   > = l2i;
 
   if (generationMode === 'txt2img') {
@@ -149,6 +149,7 @@ export const buildFLUXGraph = async (
       g,
       manager,
       l2i,
+      i2lNodeType: 'flux_vae_encode',
       denoise,
       vaeSource: modelLoader,
       originalSize,
@@ -163,6 +164,7 @@ export const buildFLUXGraph = async (
       g,
       manager,
       l2i,
+      i2lNodeType: 'flux_vae_encode',
       denoise,
       vaeSource: modelLoader,
       modelLoader,
@@ -177,6 +179,7 @@ export const buildFLUXGraph = async (
       g,
       manager,
       l2i,
+      i2lNodeType: 'flux_vae_encode',
       denoise,
       vaeSource: modelLoader,
       modelLoader,

invokeai/frontend/web/src/features/nodes/util/graph/generation/buildSD1Graph.ts

@@ -170,7 +170,7 @@ export const buildSD1Graph = async (
   const denoising_start = 1 - params.img2imgStrength;
 
   let canvasOutput: Invocation<
-    'l2i' | 'img_nsfw' | 'img_watermark' | 'img_resize' | 'canvas_v2_mask_and_crop' | 'flux_vae_decode'
+    'l2i' | 'img_nsfw' | 'img_watermark' | 'img_resize' | 'canvas_v2_mask_and_crop' | 'flux_vae_decode' | 'sd3_l2i'
   > = l2i;
 
   if (generationMode === 'txt2img') {
@@ -180,6 +180,7 @@ export const buildSD1Graph = async (
       g,
       manager,
       l2i,
+      i2lNodeType: 'i2l',
       denoise,
       vaeSource,
       originalSize,
@@ -194,6 +195,7 @@ export const buildSD1Graph = async (
       g,
       manager,
       l2i,
+      i2lNodeType: 'i2l',
       denoise,
       vaeSource,
       modelLoader,
@@ -208,6 +210,7 @@ export const buildSD1Graph = async (
       g,
       manager,
       l2i,
+      i2lNodeType: 'i2l',
       denoise,
       vaeSource,
       modelLoader,

invokeai/frontend/web/src/features/nodes/util/graph/generation/buildSD3Graph.ts

@@ -0,0 +1,216 @@
+import { logger } from 'app/logging/logger';
+import type { RootState } from 'app/store/store';
+import type { CanvasManager } from 'features/controlLayers/konva/CanvasManager';
+import { getPrefixedId } from 'features/controlLayers/konva/util';
+import { selectCanvasSettingsSlice } from 'features/controlLayers/store/canvasSettingsSlice';
+import { selectParamsSlice } from 'features/controlLayers/store/paramsSlice';
+import { selectCanvasMetadata, selectCanvasSlice } from 'features/controlLayers/store/selectors';
+import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
+import { addImageToImage } from 'features/nodes/util/graph/generation/addImageToImage';
+import { addInpaint } from 'features/nodes/util/graph/generation/addInpaint';
+import { addNSFWChecker } from 'features/nodes/util/graph/generation/addNSFWChecker';
+import { addOutpaint } from 'features/nodes/util/graph/generation/addOutpaint';
+import { addTextToImage } from 'features/nodes/util/graph/generation/addTextToImage';
+import { addWatermarker } from 'features/nodes/util/graph/generation/addWatermarker';
+import { Graph } from 'features/nodes/util/graph/generation/Graph';
+import {
+  CANVAS_OUTPUT_PREFIX,
+  getBoardField,
+  getPresetModifiedPrompts,
+  getSizes,
+} from 'features/nodes/util/graph/graphBuilderUtils';
+import type { Invocation } from 'services/api/types';
+import { isNonRefinerMainModelConfig } from 'services/api/types';
+import type { Equals } from 'tsafe';
+import { assert } from 'tsafe';
+
+const log = logger('system');
+
+export const buildSD3Graph = async (
+  state: RootState,
+  manager: CanvasManager
+): Promise<{ g: Graph; noise: Invocation<'sd3_denoise'>; posCond: Invocation<'sd3_text_encoder'> }> => {
+  const generationMode = await manager.compositor.getGenerationMode();
+  log.debug({ generationMode }, 'Building SD3 graph');
+
+  const params = selectParamsSlice(state);
+  const canvasSettings = selectCanvasSettingsSlice(state);
+  const canvas = selectCanvasSlice(state);
+
+  const { bbox } = canvas;
+
+  const {
+    model,
+    cfgScale: cfg_scale,
+    seed,
+    steps,
+    vae,
+    t5EncoderModel,
+    clipLEmbedModel,
+    clipGEmbedModel,
+    optimizedDenoisingEnabled,
+    img2imgStrength,
+  } = params;
+
+  assert(model, 'No model found in state');
+
+  const { originalSize, scaledSize } = getSizes(bbox);
+  const { positivePrompt, negativePrompt } = getPresetModifiedPrompts(state);
+
+  const g = new Graph(getPrefixedId('sd3_graph'));
+  const modelLoader = g.addNode({
+    type: 'sd3_model_loader',
+    id: getPrefixedId('sd3_model_loader'),
+    model,
+    t5_encoder_model: t5EncoderModel,
+    clip_l_model: clipLEmbedModel,
+    clip_g_model: clipGEmbedModel,
+    vae_model: vae,
+  });
+  const posCond = g.addNode({
+    type: 'sd3_text_encoder',
+    id: getPrefixedId('pos_cond'),
+    prompt: positivePrompt,
+  });
+
+  const negCond = g.addNode({
+    type: 'sd3_text_encoder',
+    id: getPrefixedId('neg_cond'),
+    prompt: negativePrompt,
+  });
+
+  const denoise = g.addNode({
+    type: 'sd3_denoise',
+    id: getPrefixedId('sd3_denoise'),
+    cfg_scale,
+    steps,
+    denoising_start: 0,
+    denoising_end: 1,
+    width: scaledSize.width,
+    height: scaledSize.height,
+  });
+  const l2i = g.addNode({
+    type: 'sd3_l2i',
+    id: getPrefixedId('l2i'),
+  });
+
+  g.addEdge(modelLoader, 'transformer', denoise, 'transformer');
+  g.addEdge(modelLoader, 'clip_l', posCond, 'clip_l');
+  g.addEdge(modelLoader, 'clip_l', negCond, 'clip_l');
+  g.addEdge(modelLoader, 'clip_g', posCond, 'clip_g');
+  g.addEdge(modelLoader, 'clip_g', negCond, 'clip_g');
+  g.addEdge(modelLoader, 't5_encoder', posCond, 't5_encoder');
+  g.addEdge(modelLoader, 't5_encoder', negCond, 't5_encoder');
+
+  g.addEdge(posCond, 'conditioning', denoise, 'positive_conditioning');
+  g.addEdge(negCond, 'conditioning', denoise, 'negative_conditioning');
+
+  g.addEdge(denoise, 'latents', l2i, 'latents');
+
+  const modelConfig = await fetchModelConfigWithTypeGuard(model.key, isNonRefinerMainModelConfig);
+  assert(modelConfig.base === 'sd-3');
+
+  g.upsertMetadata({
+    generation_mode: 'sd3_txt2img',
+    cfg_scale,
+    width: originalSize.width,
+    height: originalSize.height,
+    positive_prompt: positivePrompt,
+    negative_prompt: negativePrompt,
+    model: Graph.getModelMetadataField(modelConfig),
+    seed,
+    steps,
+    vae: vae ?? undefined,
+  });
+  g.addEdge(modelLoader, 'vae', l2i, 'vae');
+
+  let denoising_start: number;
+  if (optimizedDenoisingEnabled) {
+    // We rescale the img2imgStrength (with exponent 0.2) to effectively use the entire range [0, 1] and make the scale
+    // more user-friendly for SD3.5. Without this, most of the 'change' is concentrated in the high denoise strength
+    // range (>0.9).
+    denoising_start = 1 - img2imgStrength ** 0.2;
+  } else {
+    denoising_start = 1 - img2imgStrength;
+  }
+
+  let canvasOutput: Invocation<
+    'l2i' | 'img_nsfw' | 'img_watermark' | 'img_resize' | 'canvas_v2_mask_and_crop' | 'flux_vae_decode' | 'sd3_l2i'
+  > = l2i;
+
+  if (generationMode === 'txt2img') {
+    canvasOutput = addTextToImage({ g, l2i, originalSize, scaledSize });
+  } else if (generationMode === 'img2img') {
+    canvasOutput = await addImageToImage({
+      g,
+      manager,
+      l2i,
+      i2lNodeType: 'sd3_i2l',
+      denoise,
+      vaeSource: modelLoader,
+      originalSize,
+      scaledSize,
+      bbox,
+      denoising_start,
+      fp32: false,
+    });
+  } else if (generationMode === 'inpaint') {
+    canvasOutput = await addInpaint({
+      state,
+      g,
+      manager,
+      l2i,
+      i2lNodeType: 'sd3_i2l',
+      denoise,
+      vaeSource: modelLoader,
+      modelLoader,
+      originalSize,
+      scaledSize,
+      denoising_start,
+      fp32: false,
+    });
+  } else if (generationMode === 'outpaint') {
+    canvasOutput = await addOutpaint({
+      state,
+      g,
+      manager,
+      l2i,
+      i2lNodeType: 'sd3_i2l',
+      denoise,
+      vaeSource: modelLoader,
+      modelLoader,
+      originalSize,
+      scaledSize,
+      denoising_start,
+      fp32: false,
+    });
+  } else {
+    assert<Equals<typeof generationMode, never>>(false);
+  }
+
+  if (state.system.shouldUseNSFWChecker) {
+    canvasOutput = addNSFWChecker(g, canvasOutput);
+  }
+
+  if (state.system.shouldUseWatermarker) {
+    canvasOutput = addWatermarker(g, canvasOutput);
+  }
+
+  // This image will be staged, should not be saved to the gallery or added to a board.
+  const is_intermediate = canvasSettings.sendToCanvas;
+  const board = canvasSettings.sendToCanvas ? undefined : getBoardField(state);
+
+  if (!canvasSettings.sendToCanvas) {
+    g.upsertMetadata(selectCanvasMetadata(state));
+  }
+
+  g.updateNode(canvasOutput, {
+    id: getPrefixedId(CANVAS_OUTPUT_PREFIX),
+    is_intermediate,
+    use_cache: false,
+    board,
+  });
+
+  g.setMetadataReceivingNode(canvasOutput);
+  return { g, noise: denoise, posCond };
+};

invokeai/frontend/web/src/features/nodes/util/graph/generation/buildSDXLGraph.ts

@@ -175,7 +175,7 @@ export const buildSDXLGraph = async (
     : 1 - params.img2imgStrength;
 
   let canvasOutput: Invocation<
-    'l2i' | 'img_nsfw' | 'img_watermark' | 'img_resize' | 'canvas_v2_mask_and_crop' | 'flux_vae_decode'
+    'l2i' | 'img_nsfw' | 'img_watermark' | 'img_resize' | 'canvas_v2_mask_and_crop' | 'flux_vae_decode' | 'sd3_l2i'
   > = l2i;
 
   if (generationMode === 'txt2img') {
@@ -185,6 +185,7 @@ export const buildSDXLGraph = async (
       g,
       manager,
       l2i,
+      i2lNodeType: 'i2l',
       denoise,
       vaeSource,
       originalSize,
@@ -199,6 +200,7 @@ export const buildSDXLGraph = async (
       g,
       manager,
       l2i,
+      i2lNodeType: 'i2l',
       denoise,
       vaeSource,
       modelLoader,
@@ -213,6 +215,7 @@ export const buildSDXLGraph = async (
       g,
       manager,
       l2i,
+      i2lNodeType: 'i2l',
       denoise,
       vaeSource,
       modelLoader,

invokeai/frontend/web/src/features/nodes/util/graph/graphBuilderUtils.ts

@@ -118,16 +118,4 @@ export const getInfill = (
   assert(false, 'Unknown infill method');
 };
 
-export const addImageToLatents = (g: Graph, isFlux: boolean, fp32: boolean, image_name?: string) => {
-  if (isFlux) {
-    return g.addNode({
-      id: 'flux_vae_encode',
-      type: 'flux_vae_encode',
-      image: image_name ? { image_name } : undefined,
-    });
-  } else {
-    return g.addNode({ id: 'i2l', type: 'i2l', fp32, image: image_name ? { image_name } : undefined });
-  }
-};
-
 export const CANVAS_OUTPUT_PREFIX = 'canvas_output';

invokeai/frontend/web/src/features/parameters/components/Advanced/ParamCLIPGEmbedModelSelect.tsx

@@ -0,0 +1,42 @@
+import { Combobox, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useModelCombobox } from 'common/hooks/useModelCombobox';
+import { clipGEmbedModelSelected, selectCLIPGEmbedModel } from 'features/controlLayers/store/paramsSlice';
+import { zModelIdentifierField } from 'features/nodes/types/common';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { useCLIPEmbedModels } from 'services/api/hooks/modelsByType';
+import type { CLIPGEmbedModelConfig } from 'services/api/types';
+import { isCLIPGEmbedModelConfig } from 'services/api/types';
+
+const ParamCLIPEmbedModelSelect = () => {
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+  const clipEmbedModel = useAppSelector(selectCLIPGEmbedModel);
+  const [modelConfigs, { isLoading }] = useCLIPEmbedModels();
+
+  const _onChange = useCallback(
+    (clipEmbedModel: CLIPGEmbedModelConfig | null) => {
+      if (clipEmbedModel) {
+        dispatch(clipGEmbedModelSelected(zModelIdentifierField.parse(clipEmbedModel)));
+      }
+    },
+    [dispatch]
+  );
+
+  const { options, value, onChange, noOptionsMessage } = useModelCombobox({
+    modelConfigs: modelConfigs.filter((config) => isCLIPGEmbedModelConfig(config)),
+    onChange: _onChange,
+    selectedModel: clipEmbedModel,
+    isLoading,
+  });
+
+  return (
+    <FormControl isDisabled={!options.length} isInvalid={!options.length} minW={0} flexGrow={1}>
+      <FormLabel m={0}>{t('modelManager.clipGEmbed')}</FormLabel>
+      <Combobox value={value} options={options} onChange={onChange} noOptionsMessage={noOptionsMessage} />
+    </FormControl>
+  );
+};
+
+export default memo(ParamCLIPEmbedModelSelect);

invokeai/frontend/web/src/features/parameters/components/Advanced/ParamCLIPLEmbedModelSelect.tsx

@@ -0,0 +1,42 @@
+import { Combobox, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useModelCombobox } from 'common/hooks/useModelCombobox';
+import { clipLEmbedModelSelected, selectCLIPLEmbedModel } from 'features/controlLayers/store/paramsSlice';
+import { zModelIdentifierField } from 'features/nodes/types/common';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { useCLIPEmbedModels } from 'services/api/hooks/modelsByType';
+import type { CLIPLEmbedModelConfig } from 'services/api/types';
+import { isCLIPLEmbedModelConfig } from 'services/api/types';
+
+const ParamCLIPEmbedModelSelect = () => {
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+  const clipEmbedModel = useAppSelector(selectCLIPLEmbedModel);
+  const [modelConfigs, { isLoading }] = useCLIPEmbedModels();
+
+  const _onChange = useCallback(
+    (clipEmbedModel: CLIPLEmbedModelConfig | null) => {
+      if (clipEmbedModel) {
+        dispatch(clipLEmbedModelSelected(zModelIdentifierField.parse(clipEmbedModel)));
+      }
+    },
+    [dispatch]
+  );
+
+  const { options, value, onChange, noOptionsMessage } = useModelCombobox({
+    modelConfigs: modelConfigs.filter((config) => isCLIPLEmbedModelConfig(config)),
+    onChange: _onChange,
+    selectedModel: clipEmbedModel,
+    isLoading,
+  });
+
+  return (
+    <FormControl isDisabled={!options.length} isInvalid={!options.length} minW={0} flexGrow={1}>
+      <FormLabel m={0}>{t('modelManager.clipLEmbed')}</FormLabel>
+      <Combobox value={value} options={options} onChange={onChange} noOptionsMessage={noOptionsMessage} />
+    </FormControl>
+  );
+};
+
+export default memo(ParamCLIPEmbedModelSelect);

invokeai/frontend/web/src/features/parameters/components/MainModel/ParamMainModelSelect.tsx

@@ -9,7 +9,7 @@ import { selectActiveTab } from 'features/ui/store/uiSelectors';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { MdMoneyOff } from 'react-icons/md';
-import { useNonSD3MainModels } from 'services/api/hooks/modelsByType';
+import { useMainModels } from 'services/api/hooks/modelsByType';
 import { type AnyModelConfig, isCheckpointMainModelConfig, type MainModelConfig } from 'services/api/types';
 
 const ParamMainModelSelect = () => {
@@ -18,7 +18,7 @@ const ParamMainModelSelect = () => {
   const activeTabName = useAppSelector(selectActiveTab);
   const selectedModelKey = useAppSelector(selectModelKey);
   // const selectedModel = useAppSelector(selectModel);
-  const [modelConfigs, { isLoading }] = useNonSD3MainModels();
+  const [modelConfigs, { isLoading }] = useMainModels();
 
   const selectedModel = useMemo(() => {
     if (!modelConfigs) {

invokeai/frontend/web/src/features/parameters/types/constants.ts

@@ -7,9 +7,10 @@ export const MODEL_TYPE_MAP = {
   any: 'Any',
   'sd-1': 'Stable Diffusion 1.x',
   'sd-2': 'Stable Diffusion 2.x',
+  'sd-3': 'Stable Diffusion 3.x',
   sdxl: 'Stable Diffusion XL',
   'sdxl-refiner': 'Stable Diffusion XL Refiner',
-  flux: 'Flux',
+  flux: 'FLUX',
 };
 
 /**

invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts

@@ -123,6 +123,16 @@ export const zParameterCLIPEmbedModel = zModelIdentifierField;
 export type ParameterCLIPEmbedModel = z.infer<typeof zParameterCLIPEmbedModel>;
 // #endregion
 
+// #region CLIP embed Model
+export const zParameterCLIPLEmbedModel = zModelIdentifierField;
+export type ParameterCLIPLEmbedModel = z.infer<typeof zParameterCLIPLEmbedModel>;
+// #endregion
+
+// #region CLIP embed Model
+export const zParameterCLIPGEmbedModel = zModelIdentifierField;
+export type ParameterCLIPGEmbedModel = z.infer<typeof zParameterCLIPGEmbedModel>;
+// #endregion
+
 // #region LoRA Model
 const zParameterLoRAModel = zModelIdentifierField;
 export type ParameterLoRAModel = z.infer<typeof zParameterLoRAModel>;

invokeai/frontend/web/src/features/settingsAccordions/components/AdvancedSettingsAccordion/AdvancedSettingsAccordion.tsx

@@ -3,9 +3,11 @@ import { Flex, FormControlGroup, StandaloneAccordion } from '@invoke-ai/ui-libra
 import { skipToken } from '@reduxjs/toolkit/query';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
-import { selectIsFLUX, selectParamsSlice, selectVAEKey } from 'features/controlLayers/store/paramsSlice';
+import { selectIsFLUX, selectIsSD3, selectParamsSlice, selectVAEKey } from 'features/controlLayers/store/paramsSlice';
 import ParamCFGRescaleMultiplier from 'features/parameters/components/Advanced/ParamCFGRescaleMultiplier';
 import ParamCLIPEmbedModelSelect from 'features/parameters/components/Advanced/ParamCLIPEmbedModelSelect';
+import ParamCLIPGEmbedModelSelect from 'features/parameters/components/Advanced/ParamCLIPGEmbedModelSelect';
+import ParamCLIPLEmbedModelSelect from 'features/parameters/components/Advanced/ParamCLIPLEmbedModelSelect';
 import ParamClipSkip from 'features/parameters/components/Advanced/ParamClipSkip';
 import ParamT5EncoderModelSelect from 'features/parameters/components/Advanced/ParamT5EncoderModelSelect';
 import ParamSeamlessXAxis from 'features/parameters/components/Seamless/ParamSeamlessXAxis';
@@ -35,6 +37,7 @@ export const AdvancedSettingsAccordion = memo(() => {
   const { currentData: vaeConfig } = useGetModelConfigQuery(vaeKey ?? skipToken);
   const activeTabName = useAppSelector(selectActiveTab);
   const isFLUX = useAppSelector(selectIsFLUX);
+  const isSD3 = useAppSelector(selectIsSD3);
 
   const selectBadges = useMemo(
     () =>
@@ -88,7 +91,7 @@ export const AdvancedSettingsAccordion = memo(() => {
       <Flex gap={4} alignItems="center" p={4} flexDir="column" data-testid="advanced-settings-accordion">
         <Flex gap={4} w="full">
           {isFLUX ? <ParamFLUXVAEModelSelect /> : <ParamVAEModelSelect />}
-          {!isFLUX && <ParamVAEPrecision />}
+          {!isFLUX && !isSD3 && <ParamVAEPrecision />}
         </Flex>
         {activeTabName === 'upscaling' ? (
           <Flex gap={4} alignItems="center">
@@ -98,7 +101,7 @@ export const AdvancedSettingsAccordion = memo(() => {
           </Flex>
         ) : (
           <>
-            {!isFLUX && (
+            {!isFLUX && !isSD3 && (
               <>
                 <FormControlGroup formLabelProps={formLabelProps}>
                   <ParamClipSkip />
@@ -118,6 +121,13 @@ export const AdvancedSettingsAccordion = memo(() => {
                 <ParamCLIPEmbedModelSelect />
               </FormControlGroup>
             )}
+            {isSD3 && (
+              <FormControlGroup>
+                <ParamT5EncoderModelSelect />
+                <ParamCLIPLEmbedModelSelect />
+                <ParamCLIPGEmbedModelSelect />
+              </FormControlGroup>
+            )}
           </>
         )}
       </Flex>

invokeai/frontend/web/src/features/settingsAccordions/components/GenerationSettingsAccordion/GenerationSettingsAccordion.tsx

@@ -4,7 +4,7 @@ import { EMPTY_ARRAY } from 'app/store/constants';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
 import { selectLoRAsSlice } from 'features/controlLayers/store/lorasSlice';
-import { selectIsFLUX } from 'features/controlLayers/store/paramsSlice';
+import { selectIsFLUX, selectIsSD3 } from 'features/controlLayers/store/paramsSlice';
 import { LoRAList } from 'features/lora/components/LoRAList';
 import LoRASelect from 'features/lora/components/LoRASelect';
 import ParamCFGScale from 'features/parameters/components/Core/ParamCFGScale';
@@ -30,6 +30,7 @@ export const GenerationSettingsAccordion = memo(() => {
   const modelConfig = useSelectedModelConfig();
   const activeTabName = useAppSelector(selectActiveTab);
   const isFLUX = useAppSelector(selectIsFLUX);
+  const isSD3 = useAppSelector(selectIsSD3);
   const selectBadges = useMemo(
     () =>
       createMemoizedSelector(selectLoRAsSlice, (loras) => {
@@ -74,7 +75,7 @@ export const GenerationSettingsAccordion = memo(() => {
         <Expander label={t('accordions.advanced.options')} isOpen={isOpenExpander} onToggle={onToggleExpander}>
           <Flex gap={4} flexDir="column" pb={4}>
             <FormControlGroup formLabelProps={formLabelProps}>
-              {!isFLUX && <ParamScheduler />}
+              {!isFLUX && !isSD3 && <ParamScheduler />}
               <ParamSteps />
               {isFLUX ? <ParamGuidance /> : <ParamCFGScale />}
             </FormControlGroup>

invokeai/frontend/web/src/features/settingsAccordions/components/ImageSettingsAccordion/ImageSettingsAccordion.tsx

@@ -3,7 +3,7 @@ import { Expander, Flex, FormControlGroup, StandaloneAccordion } from '@invoke-a
 import { EMPTY_ARRAY } from 'app/store/constants';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
-import { selectIsFLUX, selectParamsSlice } from 'features/controlLayers/store/paramsSlice';
+import { selectIsFLUX, selectIsSD3, selectParamsSlice } from 'features/controlLayers/store/paramsSlice';
 import { selectCanvasSlice, selectScaleMethod } from 'features/controlLayers/store/selectors';
 import { ParamOptimizedDenoisingToggle } from 'features/parameters/components/Advanced/ParamOptimizedDenoisingToggle';
 import BboxScaledHeight from 'features/parameters/components/Bbox/BboxScaledHeight';
@@ -60,6 +60,7 @@ export const ImageSettingsAccordion = memo(() => {
     defaultIsOpen: false,
   });
   const isFLUX = useAppSelector(selectIsFLUX);
+  const isSD3 = useAppSelector(selectIsSD3);
 
   return (
     <StandaloneAccordion
@@ -77,7 +78,7 @@ export const ImageSettingsAccordion = memo(() => {
         </Flex>
         <Expander label={t('accordions.advanced.options')} isOpen={isOpenExpander} onToggle={onToggleExpander}>
           <Flex gap={4} pb={4} flexDir="column">
-            {isFLUX && <ParamOptimizedDenoisingToggle />}
+            {(isFLUX || isSD3) && <ParamOptimizedDenoisingToggle />}
             <BboxScaleMethod />
             {scaleMethod !== 'none' && (
               <FormControlGroup formLabelProps={scalingLabelProps}>

invokeai/frontend/web/src/features/settingsAccordions/components/UpscaleSettingsAccordion/UpscaleWarning.tsx

@@ -34,8 +34,15 @@ export const UpscaleWarning = () => {
     dispatch(tileControlnetModelChanged(validModel || null));
   }, [model?.base, modelConfigs, dispatch]);
 
+  const isBaseModelCompatible = useMemo(() => {
+    return model && ['sd-1', 'sdxl'].includes(model.base);
+  }, [model]);
+
   const modelWarnings = useMemo(() => {
     const _warnings: string[] = [];
+    if (!isBaseModelCompatible) {
+      return _warnings;
+    }
     if (!model) {
       _warnings.push(t('upscaling.mainModelDesc'));
     }
@@ -46,7 +53,7 @@ export const UpscaleWarning = () => {
       _warnings.push(t('upscaling.upscaleModelDesc'));
     }
     return _warnings;
-  }, [model, tileControlnetModel, upscaleModel, t]);
+  }, [isBaseModelCompatible, model, tileControlnetModel, upscaleModel, t]);
 
   const otherWarnings = useMemo(() => {
     const _warnings: string[] = [];
@@ -58,22 +65,25 @@ export const UpscaleWarning = () => {
     return _warnings;
   }, [isTooLargeToUpscale, t, maxUpscaleDimension]);
 
+  const allWarnings = useMemo(() => [...modelWarnings, ...otherWarnings], [modelWarnings, otherWarnings]);
+
   const handleGoToModelManager = useCallback(() => {
     dispatch(setActiveTab('models'));
     $installModelsTab.set(3);
   }, [dispatch]);
 
-  if (modelWarnings.length && isModelsTabDisabled) {
+  if (isBaseModelCompatible && modelWarnings.length > 0 && isModelsTabDisabled) {
     return null;
   }
 
-  if ((!modelWarnings.length && !otherWarnings.length) || isLoading) {
+  if ((isBaseModelCompatible && allWarnings.length === 0) || isLoading) {
     return null;
   }
 
   return (
     <Flex bg="error.500" borderRadius="base" padding={4} direction="column" fontSize="sm" gap={2}>
-      {!!modelWarnings.length && (
+      {!isBaseModelCompatible && <Text>{t('upscaling.incompatibleBaseModelDesc')}</Text>}
+      {modelWarnings.length > 0 && (
         <Text>
           <Trans
             i18nKey="upscaling.missingModelsWarning"
@@ -85,11 +95,13 @@ export const UpscaleWarning = () => {
           />
         </Text>
       )}
-      <UnorderedList>
-        {[...modelWarnings, ...otherWarnings].map((warning) => (
-          <ListItem key={warning}>{warning}</ListItem>
-        ))}
-      </UnorderedList>
+      {allWarnings.length > 0 && (
+        <UnorderedList>
+          {allWarnings.map((warning) => (
+            <ListItem key={warning}>{warning}</ListItem>
+          ))}
+        </UnorderedList>
+      )}
     </Flex>
   );
 };

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

@@ -26,17 +26,20 @@ import { SettingsDeveloperLogLevel } from 'features/system/components/SettingsMo
 import { SettingsDeveloperLogNamespaces } from 'features/system/components/SettingsModal/SettingsDeveloperLogNamespaces';
 import { useClearIntermediates } from 'features/system/components/SettingsModal/useClearIntermediates';
 import { StickyScrollable } from 'features/system/components/StickyScrollable';
+import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
 import {
   selectSystemShouldAntialiasProgressImage,
   selectSystemShouldConfirmOnDelete,
   selectSystemShouldConfirmOnNewSession,
   selectSystemShouldEnableInformationalPopovers,
   selectSystemShouldEnableModelDescriptions,
+  selectSystemShouldShowInvocationProgressDetail,
   selectSystemShouldUseNSFWChecker,
   selectSystemShouldUseWatermarker,
   setShouldConfirmOnDelete,
   setShouldEnableInformationalPopovers,
   setShouldEnableModelDescriptions,
+  setShouldShowInvocationProgressDetail,
   shouldAntialiasProgressImageChanged,
   shouldConfirmOnNewSessionToggled,
   shouldUseNSFWCheckerChanged,
@@ -103,6 +106,8 @@ const SettingsModal = ({ config = defaultConfig, children }: SettingsModalProps)
   const shouldEnableInformationalPopovers = useAppSelector(selectSystemShouldEnableInformationalPopovers);
   const shouldEnableModelDescriptions = useAppSelector(selectSystemShouldEnableModelDescriptions);
   const shouldConfirmOnNewSession = useAppSelector(selectSystemShouldConfirmOnNewSession);
+  const shouldShowInvocationProgressDetail = useAppSelector(selectSystemShouldShowInvocationProgressDetail);
+  const isInvocationProgressAlertEnabled = useFeatureStatus('invocationProgressAlert');
   const onToggleConfirmOnNewSession = useCallback(() => {
     dispatch(shouldConfirmOnNewSessionToggled());
   }, [dispatch]);
@@ -170,6 +175,13 @@ const SettingsModal = ({ config = defaultConfig, children }: SettingsModalProps)
     [dispatch]
   );
 
+  const handleChangeShouldShowInvocationProgressDetail = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => {
+      dispatch(setShouldShowInvocationProgressDetail(e.target.checked));
+    },
+    [dispatch]
+  );
+
   return (
     <>
       {cloneElement(children, {
@@ -221,6 +233,15 @@ const SettingsModal = ({ config = defaultConfig, children }: SettingsModalProps)
                         onChange={handleChangeShouldAntialiasProgressImage}
                       />
                     </FormControl>
+                    {isInvocationProgressAlertEnabled && (
+                      <FormControl>
+                        <FormLabel>{t('settings.showDetailedInvocationProgress')}</FormLabel>
+                        <Switch
+                          isChecked={shouldShowInvocationProgressDetail}
+                          onChange={handleChangeShouldShowInvocationProgressDetail}
+                        />
+                      </FormControl>
+                    )}
                     <FormControl>
                       <InformationalPopover feature="noiseUseCPU" inPortal={false}>
                         <FormLabel>{t('parameters.useCpuNoise')}</FormLabel>

invokeai/frontend/web/src/features/system/store/systemSlice.ts

@@ -21,6 +21,7 @@ const initialSystemState: SystemState = {
   logIsEnabled: true,
   logLevel: 'debug',
   logNamespaces: [...zLogNamespace.options],
+  shouldShowInvocationProgressDetail: false,
 };
 
 export const systemSlice = createSlice({
@@ -64,6 +65,9 @@ export const systemSlice = createSlice({
     shouldConfirmOnNewSessionToggled(state) {
       state.shouldConfirmOnNewSession = !state.shouldConfirmOnNewSession;
     },
+    setShouldShowInvocationProgressDetail(state, action: PayloadAction<boolean>) {
+      state.shouldShowInvocationProgressDetail = action.payload;
+    },
   },
 });
 
@@ -79,6 +83,7 @@ export const {
   setShouldEnableInformationalPopovers,
   setShouldEnableModelDescriptions,
   shouldConfirmOnNewSessionToggled,
+  setShouldShowInvocationProgressDetail,
 } = systemSlice.actions;
 
 /* eslint-disable-next-line @typescript-eslint/no-explicit-any */
@@ -117,3 +122,6 @@ export const selectSystemShouldEnableModelDescriptions = createSystemSelector(
   (system) => system.shouldEnableModelDescriptions
 );
 export const selectSystemShouldConfirmOnNewSession = createSystemSelector((system) => system.shouldConfirmOnNewSession);
+export const selectSystemShouldShowInvocationProgressDetail = createSystemSelector(
+  (system) => system.shouldShowInvocationProgressDetail
+);

invokeai/frontend/web/src/features/system/store/types.ts

@@ -41,4 +41,5 @@ export interface SystemState {
   logIsEnabled: boolean;
   logLevel: LogLevel;
   logNamespaces: LogNamespace[];
+  shouldShowInvocationProgressDetail: boolean;
 }

invokeai/frontend/web/src/services/api/endpoints/images.ts

@@ -6,10 +6,10 @@ import type { components, paths } from 'services/api/schema';
 import type {
   DeleteBoardResult,
   GraphAndWorkflowResponse,
-  ImageCategory,
   ImageDTO,
   ListImagesArgs,
   ListImagesResponse,
+  UploadImageArg,
 } from 'services/api/types';
 import { getCategories, getListImagesUrl } from 'services/api/util';
 import type { JsonObject } from 'type-fest';
@@ -260,20 +260,7 @@ export const imagesApi = api.injectEndpoints({
         return [];
       },
     }),
-    uploadImage: build.mutation<
-      ImageDTO,
-      {
-        file: File;
-        image_category: ImageCategory;
-        is_intermediate: boolean;
-        session_id?: string;
-        board_id?: string;
-        crop_visible?: boolean;
-        metadata?: JsonObject;
-        isFirstUploadOfBatch?: boolean;
-        withToast?: boolean;
-      }
-    >({
+    uploadImage: build.mutation<ImageDTO, UploadImageArg>({
       query: ({ file, image_category, is_intermediate, session_id, board_id, crop_visible, metadata }) => {
         const formData = new FormData();
         formData.append('file', file);
@@ -558,7 +545,6 @@ export const {
   useClearIntermediatesMutation,
   useAddImagesToBoardMutation,
   useRemoveImagesFromBoardMutation,
-  useChangeImageIsIntermediateMutation,
   useDeleteBoardAndImagesMutation,
   useDeleteBoardMutation,
   useStarImagesMutation,
@@ -622,79 +608,17 @@ export const getImageMetadata = (
   return req.unwrap();
 };
 
-export type UploadImageArg = {
-  file: File;
-  image_category: ImageCategory;
-  is_intermediate: boolean;
-  session_id?: string;
-  board_id?: string;
-  crop_visible?: boolean;
-  metadata?: JsonObject;
-  withToast?: boolean;
-};
-
 export const uploadImage = (arg: UploadImageArg): Promise<ImageDTO> => {
-  const {
-    file,
-    image_category,
-    is_intermediate,
-    crop_visible = false,
-    board_id,
-    metadata,
-    session_id,
-    withToast = true,
-  } = arg;
-
   const { dispatch } = getStore();
-
-  const req = dispatch(
-    imagesApi.endpoints.uploadImage.initiate(
-      {
-        file,
-        image_category,
-        is_intermediate,
-        crop_visible,
-        board_id,
-        metadata,
-        session_id,
-        withToast,
-      },
-      { track: false }
-    )
-  );
+  const req = dispatch(imagesApi.endpoints.uploadImage.initiate(arg, { track: false }));
   return req.unwrap();
 };
 
 export const uploadImages = async (args: UploadImageArg[]): Promise<ImageDTO[]> => {
   const { dispatch } = getStore();
   const results = await Promise.allSettled(
-    args.map((arg, i) => {
-      const {
-        file,
-        image_category,
-        is_intermediate,
-        crop_visible = false,
-        board_id,
-        metadata,
-        session_id,
-        withToast = true,
-      } = arg;
-      const req = dispatch(
-        imagesApi.endpoints.uploadImage.initiate(
-          {
-            file,
-            image_category,
-            is_intermediate,
-            crop_visible,
-            board_id,
-            metadata,
-            session_id,
-            isFirstUploadOfBatch: i === 0,
-            withToast,
-          },
-          { track: false }
-        )
-      );
+    args.map((arg) => {
+      const req = dispatch(imagesApi.endpoints.uploadImage.initiate(arg, { track: false }));
       return req.unwrap();
     })
   );

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

@@ -18,7 +18,6 @@ import {
   isIPAdapterModelConfig,
   isLoRAModelConfig,
   isNonRefinerMainModelConfig,
-  isNonSD3MainModelModelConfig,
   isNonSDXLMainModelConfig,
   isRefinerMainModelModelConfig,
   isSD3MainModelModelConfig,
@@ -53,7 +52,6 @@ const buildModelsHook =
   };
 
 export const useMainModels = buildModelsHook(isNonRefinerMainModelConfig);
-export const useNonSD3MainModels = buildModelsHook(isNonSD3MainModelModelConfig);
 export const useNonSDXLMainModels = buildModelsHook(isNonSDXLMainModelConfig);
 export const useRefinerModels = buildModelsHook(isRefinerMainModelModelConfig);
 export const useFluxModels = buildModelsHook(isFluxMainModelModelConfig);

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

@@ -1,5 +1,5 @@
 import type { components, paths } from 'services/api/schema';
-import type { SetRequired } from 'type-fest';
+import type { JsonObject, SetRequired } from 'type-fest';
 
 export type S = components['schemas'];
 
@@ -223,10 +223,6 @@ export const isSD3MainModelModelConfig = (config: AnyModelConfig): config is Mai
   return config.type === 'main' && config.base === 'sd-3';
 };
 
-export const isNonSD3MainModelModelConfig = (config: AnyModelConfig): config is MainModelConfig => {
-  return config.type === 'main' && config.base !== 'sd-3' && config.base !== 'sdxl-refiner';
-};
-
 export const isFluxMainModelModelConfig = (config: AnyModelConfig): config is MainModelConfig => {
   return config.type === 'main' && config.base === 'flux';
 };
@@ -291,3 +287,42 @@ export type SetHFTokenResponse = NonNullable<
 export type SetHFTokenArg = NonNullable<
   paths['/api/v2/models/hf_login']['post']['requestBody']['content']['application/json']
 >;
+
+export type UploadImageArg = {
+  /**
+   * The file object to upload
+   */
+  file: File;
+  /**
+   * THe category of image to upload
+   */
+  image_category: ImageCategory;
+  /**
+   * Whether the uploaded image is an intermediate image (intermediate images are not shown int he gallery)
+   */
+  is_intermediate: boolean;
+  /**
+   * The session with which to associate the uploaded image
+   */
+  session_id?: string;
+  /**
+   * The board id to add the image to
+   */
+  board_id?: string;
+  /**
+   * Whether or not to crop the image to its bounding box before saving
+   */
+  crop_visible?: boolean;
+  /**
+   * Metadata to embed in the image when saving it
+   */
+  metadata?: JsonObject;
+  /**
+   * Whether this upload should be "silent" (no toast on upload, no changing of gallery view)
+   */
+  silent?: boolean;
+  /**
+   * Whether this is the first upload of a batch (used when displaying user feedback with toasts - ignored if the upload is silent)
+   */
+  isFirstUploadOfBatch?: boolean;
+};

invokeai/frontend/web/src/services/events/stores.ts

@@ -1,3 +1,4 @@
+import { round } from 'lodash-es';
 import { atom, computed, map } from 'nanostores';
 import type { S } from 'services/api/types';
 import type { AppSocket } from 'services/events/types';
@@ -10,3 +11,13 @@ export const $lastProgressEvent = atom<S['InvocationProgressEvent'] | null>(null
 export const $progressImage = computed($lastProgressEvent, (val) => val?.image ?? null);
 export const $hasProgressImage = computed($lastProgressEvent, (val) => Boolean(val?.image));
 export const $isProgressFromCanvas = computed($lastProgressEvent, (val) => val?.destination === 'canvas');
+export const $invocationProgressMessage = computed($lastProgressEvent, (val) => {
+  if (!val) {
+    return null;
+  }
+  let message = val.message;
+  if (val.percentage) {
+    message += ` (${round(val.percentage * 100)}%)`;
+  }
+  return message;
+});

invokeai/version/invokeai_version.py

@@ -1 +1 @@
-__version__ = "5.4.1rc2"
+__version__ = "5.4.1"

tests/app/services/model_load/test_load_api.py

@@ -6,7 +6,11 @@ from diffusers import AutoencoderTiny
 
 from invokeai.app.services.invocation_services import InvocationServices
 from invokeai.app.services.model_manager import ModelManagerServiceBase
-from invokeai.app.services.shared.invocation_context import InvocationContext, build_invocation_context
+from invokeai.app.services.shared.invocation_context import (
+    InvocationContext,
+    InvocationContextData,
+    build_invocation_context,
+)
 from invokeai.backend.model_manager.load.load_base import LoadedModelWithoutConfig
 from tests.backend.model_manager.model_manager_fixtures import *  # noqa F403
 
@@ -19,7 +23,7 @@ def mock_context(
     mock_services.model_manager = mm2_model_manager
     return build_invocation_context(
         services=mock_services,
-        data=None,  # type: ignore
+        data=InvocationContextData(queue_item=None, invocation=None, source_invocation_id=None),  # type: ignore
         is_canceled=None,  # type: ignore
     )
 

tests/backend/flux/ip_adapter/xlabs_flux_ip_adapter_state_dict.py

@@ -1,6 +1,6 @@
 # State dict keys and shapes for an XLabs FLUX IP-Adapter model. Intended to be used for unit tests.
 # These keys were extracted from:
-# https://huggingface.co/XLabs-AI/flux-ip-adapter/blob/ad16be50d78a07ea83d8c4bde44ff9753235182e/flux-ip-adapter.safetensors
+# https://huggingface.co/XLabs-AI/flux-ip-adapter/resolve/main/ip_adapter.safetensors
 xlabs_sd_shapes = {
     "double_blocks.0.processor.ip_adapter_double_stream_k_proj.bias": [3072],
     "double_blocks.0.processor.ip_adapter_double_stream_k_proj.weight": [3072, 4096],

tests/backend/lora/conversions/lora_state_dicts/flux_lora_diffusers_no_proj_mlp_format.py

@@ -0,0 +1,688 @@
+# A sample state dict in the Diffusers FLUX LoRA format without .proj_mlp layers.
+# This format was added in response to https://github.com/invoke-ai/InvokeAI/issues/7129
+state_dict_keys = {
+    "transformer.single_transformer_blocks.0.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.0.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.0.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.0.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.0.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.0.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.1.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.1.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.1.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.1.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.1.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.1.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.10.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.10.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.10.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.10.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.10.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.10.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.11.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.11.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.11.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.11.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.11.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.11.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.12.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.12.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.12.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.12.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.12.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.12.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.13.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.13.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.13.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.13.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.13.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.13.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.14.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.14.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.14.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.14.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.14.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.14.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.15.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.15.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.15.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.15.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.15.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.15.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.16.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.16.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.16.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.16.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.16.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.16.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.17.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.17.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.17.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.17.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.17.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.17.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.18.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.18.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.18.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.18.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.18.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.18.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.19.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.19.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.19.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.19.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.19.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.19.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.2.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.2.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.2.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.2.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.2.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.2.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.20.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.20.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.20.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.20.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.20.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.20.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.21.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.21.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.21.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.21.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.21.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.21.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.22.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.22.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.22.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.22.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.22.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.22.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.23.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.23.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.23.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.23.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.23.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.23.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.24.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.24.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.24.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.24.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.24.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.24.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.25.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.25.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.25.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.25.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.25.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.25.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.26.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.26.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.26.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.26.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.26.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.26.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.27.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.27.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.27.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.27.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.27.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.27.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.28.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.28.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.28.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.28.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.28.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.28.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.29.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.29.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.29.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.29.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.29.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.29.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.3.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.3.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.3.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.3.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.3.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.3.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.30.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.30.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.30.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.30.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.30.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.30.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.31.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.31.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.31.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.31.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.31.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.31.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.single_transformer_blocks.32.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.single_transformer_blocks.32.attn.to_k.lora_B.weight": [3072, 16],
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+    "transformer.single_transformer_blocks.33.attn.to_k.lora_A.weight": [16, 3072],
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+    "transformer.single_transformer_blocks.6.attn.to_k.lora_A.weight": [16, 3072],
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+    "transformer.transformer_blocks.9.attn.add_k_proj.lora_B.weight": [3072, 16],
+    "transformer.transformer_blocks.9.attn.add_q_proj.lora_A.weight": [16, 3072],
+    "transformer.transformer_blocks.9.attn.add_q_proj.lora_B.weight": [3072, 16],
+    "transformer.transformer_blocks.9.attn.add_v_proj.lora_A.weight": [16, 3072],
+    "transformer.transformer_blocks.9.attn.add_v_proj.lora_B.weight": [3072, 16],
+    "transformer.transformer_blocks.9.attn.to_add_out.lora_A.weight": [16, 3072],
+    "transformer.transformer_blocks.9.attn.to_add_out.lora_B.weight": [3072, 16],
+    "transformer.transformer_blocks.9.attn.to_k.lora_A.weight": [16, 3072],
+    "transformer.transformer_blocks.9.attn.to_k.lora_B.weight": [3072, 16],
+    "transformer.transformer_blocks.9.attn.to_out.0.lora_A.weight": [16, 3072],
+    "transformer.transformer_blocks.9.attn.to_out.0.lora_B.weight": [3072, 16],
+    "transformer.transformer_blocks.9.attn.to_q.lora_A.weight": [16, 3072],
+    "transformer.transformer_blocks.9.attn.to_q.lora_B.weight": [3072, 16],
+    "transformer.transformer_blocks.9.attn.to_v.lora_A.weight": [16, 3072],
+    "transformer.transformer_blocks.9.attn.to_v.lora_B.weight": [3072, 16],
+    "transformer.transformer_blocks.9.ff.net.0.proj.lora_A.weight": [16, 3072],
+    "transformer.transformer_blocks.9.ff.net.0.proj.lora_B.weight": [12288, 16],
+    "transformer.transformer_blocks.9.ff.net.2.lora_A.weight": [16, 12288],
+    "transformer.transformer_blocks.9.ff.net.2.lora_B.weight": [3072, 16],
+    "transformer.transformer_blocks.9.ff_context.net.0.proj.lora_A.weight": [16, 3072],
+    "transformer.transformer_blocks.9.ff_context.net.0.proj.lora_B.weight": [12288, 16],
+    "transformer.transformer_blocks.9.ff_context.net.2.lora_A.weight": [16, 12288],
+    "transformer.transformer_blocks.9.ff_context.net.2.lora_B.weight": [3072, 16],
+}

tests/backend/lora/conversions/lora_state_dicts/utils.py

@@ -1,8 +1,8 @@
 import torch
 
 
-def keys_to_mock_state_dict(keys: list[str]) -> dict[str, torch.Tensor]:
+def keys_to_mock_state_dict(keys: dict[str, list[int]]) -> dict[str, torch.Tensor]:
     state_dict: dict[str, torch.Tensor] = {}
-    for k in keys:
-        state_dict[k] = torch.empty(1)
+    for k, shape in keys.items():
+        state_dict[k] = torch.empty(shape)
     return state_dict

tests/backend/lora/conversions/test_flux_diffusers_lora_conversion_utils.py

@@ -9,22 +9,26 @@ from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_TRAN
 from tests.backend.lora.conversions.lora_state_dicts.flux_lora_diffusers_format import (
     state_dict_keys as flux_diffusers_state_dict_keys,
 )
+from tests.backend.lora.conversions.lora_state_dicts.flux_lora_diffusers_no_proj_mlp_format import (
+    state_dict_keys as flux_diffusers_no_proj_mlp_state_dict_keys,
+)
 from tests.backend.lora.conversions.lora_state_dicts.flux_lora_kohya_format import (
     state_dict_keys as flux_kohya_state_dict_keys,
 )
 from tests.backend.lora.conversions.lora_state_dicts.utils import keys_to_mock_state_dict
 
 
-def test_is_state_dict_likely_in_flux_diffusers_format_true():
+@pytest.mark.parametrize("sd_keys", [flux_diffusers_state_dict_keys, flux_diffusers_no_proj_mlp_state_dict_keys])
+def test_is_state_dict_likely_in_flux_diffusers_format_true(sd_keys: dict[str, list[int]]):
     """Test that is_state_dict_likely_in_flux_diffusers_format() can identify a state dict in the Diffusers FLUX LoRA format."""
     # Construct a state dict that is in the Diffusers FLUX LoRA format.
-    state_dict = keys_to_mock_state_dict(flux_diffusers_state_dict_keys)
+    state_dict = keys_to_mock_state_dict(sd_keys)
 
     assert is_state_dict_likely_in_flux_diffusers_format(state_dict)
 
 
 def test_is_state_dict_likely_in_flux_diffusers_format_false():
-    """Test that is_state_dict_likely_in_flux_diffusers_format() returns False for a state dict that is not in the Kohya
+    """Test that is_state_dict_likely_in_flux_diffusers_format() returns False for a state dict that is in the Kohya
     FLUX LoRA format.
     """
     # Construct a state dict that is not in the Kohya FLUX LoRA format.
@@ -33,16 +37,17 @@ def test_is_state_dict_likely_in_flux_diffusers_format_false():
     assert not is_state_dict_likely_in_flux_diffusers_format(state_dict)
 
 
-def test_lora_model_from_flux_diffusers_state_dict():
+@pytest.mark.parametrize("sd_keys", [flux_diffusers_state_dict_keys, flux_diffusers_no_proj_mlp_state_dict_keys])
+def test_lora_model_from_flux_diffusers_state_dict(sd_keys: dict[str, list[int]]):
     """Test that lora_model_from_flux_diffusers_state_dict() can load a state dict in the Diffusers FLUX LoRA format."""
     # Construct a state dict that is in the Diffusers FLUX LoRA format.
-    state_dict = keys_to_mock_state_dict(flux_diffusers_state_dict_keys)
+    state_dict = keys_to_mock_state_dict(sd_keys)
     # Load the state dict into a LoRAModelRaw object.
     model = lora_model_from_flux_diffusers_state_dict(state_dict, alpha=8.0)
 
     # Check that the model has the correct number of LoRA layers.
     expected_lora_layers: set[str] = set()
-    for k in flux_diffusers_state_dict_keys:
+    for k in sd_keys:
         k = k.replace("lora_A.weight", "")
         k = k.replace("lora_B.weight", "")
         expected_lora_layers.add(k)

tests/backend/lora/conversions/test_flux_kohya_lora_conversion_utils.py

@@ -23,7 +23,7 @@ from tests.backend.lora.conversions.lora_state_dicts.utils import keys_to_mock_s
 
 
 @pytest.mark.parametrize("sd_keys", [flux_kohya_state_dict_keys, flux_kohya_te1_state_dict_keys])
-def test_is_state_dict_likely_in_flux_kohya_format_true(sd_keys: list[str]):
+def test_is_state_dict_likely_in_flux_kohya_format_true(sd_keys: dict[str, list[int]]):
     """Test that is_state_dict_likely_in_flux_kohya_format() can identify a state dict in the Kohya FLUX LoRA format."""
     # Construct a state dict that is in the Kohya FLUX LoRA format.
     state_dict = keys_to_mock_state_dict(sd_keys)
@@ -83,7 +83,7 @@ def test_convert_flux_transformer_kohya_state_dict_to_invoke_format_error():
 
 
 @pytest.mark.parametrize("sd_keys", [flux_kohya_state_dict_keys, flux_kohya_te1_state_dict_keys])
-def test_lora_model_from_flux_kohya_state_dict(sd_keys: list[str]):
+def test_lora_model_from_flux_kohya_state_dict(sd_keys: dict[str, list[int]]):
     """Test that a LoRAModelRaw can be created from a state dict in the Kohya FLUX LoRA format."""
     # Construct a state dict that is in the Kohya FLUX LoRA format.
     state_dict = keys_to_mock_state_dict(sd_keys)

tests/test_nodes.py

@@ -9,6 +9,8 @@ from invokeai.app.invocations.baseinvocation import (
 from invokeai.app.invocations.fields import InputField, OutputField
 from invokeai.app.invocations.image import ImageField
 from invokeai.app.services.events.events_common import EventBase
+from invokeai.app.services.session_processor.session_processor_common import ProgressImage
+from invokeai.app.services.session_queue.session_queue_common import SessionQueueItem
 from invokeai.app.services.shared.invocation_context import InvocationContext
 
 
@@ -133,6 +135,16 @@ class TestEventService(EventServiceBase):
         self.events.append(event)
         pass
 
+    def emit_invocation_progress(
+        self,
+        queue_item: "SessionQueueItem",
+        invocation: "BaseInvocation",
+        message: str,
+        percentage: float | None = None,
+        image: "ProgressImage | None" = None,
+    ) -> None:
+        pass
+
 
 def wait_until(condition: Callable[[], bool], timeout: int = 10, interval: float = 0.1) -> None:
     import time