chore: bump version to v5.4.0

## Summary

Nodes to support SD3.5 txt2img generations
* adds SD3.5 to starter models
* adds default workflow for SD3.5 txt2img

## Related Issues / Discussions

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## QA Instructions

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## Merge Plan

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## Checklist

- [ ] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_

invokeai/app/invocations/fields.py

@@ -41,6 +41,7 @@ class UIType(str, Enum, metaclass=MetaEnum):
     # region Model Field Types
     MainModel = "MainModelField"
     FluxMainModel = "FluxMainModelField"
+    SD3MainModel = "SD3MainModelField"
     SDXLMainModel = "SDXLMainModelField"
     SDXLRefinerModel = "SDXLRefinerModelField"
     ONNXModel = "ONNXModelField"
@@ -52,6 +53,8 @@ class UIType(str, Enum, metaclass=MetaEnum):
     T2IAdapterModel = "T2IAdapterModelField"
     T5EncoderModel = "T5EncoderModelField"
     CLIPEmbedModel = "CLIPEmbedModelField"
+    CLIPLEmbedModel = "CLIPLEmbedModelField"
+    CLIPGEmbedModel = "CLIPGEmbedModelField"
     SpandrelImageToImageModel = "SpandrelImageToImageModelField"
     # endregion
 
@@ -131,8 +134,10 @@ class FieldDescriptions:
     clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
     t5_encoder = "T5 tokenizer and text encoder"
     clip_embed_model = "CLIP Embed loader"
+    clip_g_model = "CLIP-G Embed loader"
     unet = "UNet (scheduler, LoRAs)"
     transformer = "Transformer"
+    mmditx = "MMDiTX"
     vae = "VAE"
     cond = "Conditioning tensor"
     controlnet_model = "ControlNet model to load"
@@ -140,6 +145,7 @@ class FieldDescriptions:
     lora_model = "LoRA model to load"
     main_model = "Main model (UNet, VAE, CLIP) to load"
     flux_model = "Flux model (Transformer) to load"
+    sd3_model = "SD3 model (MMDiTX) to load"
     sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
     sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
     onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
@@ -246,6 +252,12 @@ class FluxConditioningField(BaseModel):
     conditioning_name: str = Field(description="The name of conditioning tensor")
 
 
+class SD3ConditioningField(BaseModel):
+    """A conditioning tensor primitive value"""
+
+    conditioning_name: str = Field(description="The name of conditioning tensor")
+
+
 class ConditioningField(BaseModel):
     """A conditioning tensor primitive value"""
 

invokeai/app/invocations/flux_model_loader.py

@@ -0,0 +1,89 @@
+from typing import Literal
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
+from invokeai.app.invocations.model import CLIPField, ModelIdentifierField, T5EncoderField, TransformerField, VAEField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.flux.util import max_seq_lengths
+from invokeai.backend.model_manager.config import (
+    CheckpointConfigBase,
+    SubModelType,
+)
+
+
+@invocation_output("flux_model_loader_output")
+class FluxModelLoaderOutput(BaseInvocationOutput):
+    """Flux base model loader output"""
+
+    transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer")
+    clip: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP")
+    t5_encoder: T5EncoderField = OutputField(description=FieldDescriptions.t5_encoder, title="T5 Encoder")
+    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
+    max_seq_len: Literal[256, 512] = OutputField(
+        description="The max sequence length to used for the T5 encoder. (256 for schnell transformer, 512 for dev transformer)",
+        title="Max Seq Length",
+    )
+
+
+@invocation(
+    "flux_model_loader",
+    title="Flux Main Model",
+    tags=["model", "flux"],
+    category="model",
+    version="1.0.4",
+    classification=Classification.Prototype,
+)
+class FluxModelLoaderInvocation(BaseInvocation):
+    """Loads a flux base model, outputting its submodels."""
+
+    model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.flux_model,
+        ui_type=UIType.FluxMainModel,
+        input=Input.Direct,
+    )
+
+    t5_encoder_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.t5_encoder, ui_type=UIType.T5EncoderModel, input=Input.Direct, title="T5 Encoder"
+    )
+
+    clip_embed_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.clip_embed_model,
+        ui_type=UIType.CLIPEmbedModel,
+        input=Input.Direct,
+        title="CLIP Embed",
+    )
+
+    vae_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.vae_model, ui_type=UIType.FluxVAEModel, title="VAE"
+    )
+
+    def invoke(self, context: InvocationContext) -> FluxModelLoaderOutput:
+        for key in [self.model.key, self.t5_encoder_model.key, self.clip_embed_model.key, self.vae_model.key]:
+            if not context.models.exists(key):
+                raise ValueError(f"Unknown model: {key}")
+
+        transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
+        vae = self.vae_model.model_copy(update={"submodel_type": SubModelType.VAE})
+
+        tokenizer = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
+        clip_encoder = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
+
+        tokenizer2 = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
+        t5_encoder = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
+
+        transformer_config = context.models.get_config(transformer)
+        assert isinstance(transformer_config, CheckpointConfigBase)
+
+        return FluxModelLoaderOutput(
+            transformer=TransformerField(transformer=transformer, loras=[]),
+            clip=CLIPField(tokenizer=tokenizer, text_encoder=clip_encoder, loras=[], skipped_layers=0),
+            t5_encoder=T5EncoderField(tokenizer=tokenizer2, text_encoder=t5_encoder),
+            vae=VAEField(vae=vae),
+            max_seq_len=max_seq_lengths[transformer_config.config_path],
+        )

invokeai/app/invocations/model.py

@@ -1,5 +1,5 @@
 import copy
-from typing import List, Literal, Optional
+from typing import List, Optional
 
 from pydantic import BaseModel, Field
 
@@ -13,11 +13,9 @@ from invokeai.app.invocations.baseinvocation import (
 from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.shared.models import FreeUConfig
-from invokeai.backend.flux.util import max_seq_lengths
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
-    CheckpointConfigBase,
     ModelType,
     SubModelType,
 )
@@ -139,78 +137,6 @@ class ModelIdentifierInvocation(BaseInvocation):
         return ModelIdentifierOutput(model=self.model)
 
 
-@invocation_output("flux_model_loader_output")
-class FluxModelLoaderOutput(BaseInvocationOutput):
-    """Flux base model loader output"""
-
-    transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer")
-    clip: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP")
-    t5_encoder: T5EncoderField = OutputField(description=FieldDescriptions.t5_encoder, title="T5 Encoder")
-    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
-    max_seq_len: Literal[256, 512] = OutputField(
-        description="The max sequence length to used for the T5 encoder. (256 for schnell transformer, 512 for dev transformer)",
-        title="Max Seq Length",
-    )
-
-
-@invocation(
-    "flux_model_loader",
-    title="Flux Main Model",
-    tags=["model", "flux"],
-    category="model",
-    version="1.0.4",
-    classification=Classification.Prototype,
-)
-class FluxModelLoaderInvocation(BaseInvocation):
-    """Loads a flux base model, outputting its submodels."""
-
-    model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.flux_model,
-        ui_type=UIType.FluxMainModel,
-        input=Input.Direct,
-    )
-
-    t5_encoder_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.t5_encoder, ui_type=UIType.T5EncoderModel, input=Input.Direct, title="T5 Encoder"
-    )
-
-    clip_embed_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.clip_embed_model,
-        ui_type=UIType.CLIPEmbedModel,
-        input=Input.Direct,
-        title="CLIP Embed",
-    )
-
-    vae_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.vae_model, ui_type=UIType.FluxVAEModel, title="VAE"
-    )
-
-    def invoke(self, context: InvocationContext) -> FluxModelLoaderOutput:
-        for key in [self.model.key, self.t5_encoder_model.key, self.clip_embed_model.key, self.vae_model.key]:
-            if not context.models.exists(key):
-                raise ValueError(f"Unknown model: {key}")
-
-        transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
-        vae = self.vae_model.model_copy(update={"submodel_type": SubModelType.VAE})
-
-        tokenizer = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
-        clip_encoder = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
-
-        tokenizer2 = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
-        t5_encoder = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
-
-        transformer_config = context.models.get_config(transformer)
-        assert isinstance(transformer_config, CheckpointConfigBase)
-
-        return FluxModelLoaderOutput(
-            transformer=TransformerField(transformer=transformer, loras=[]),
-            clip=CLIPField(tokenizer=tokenizer, text_encoder=clip_encoder, loras=[], skipped_layers=0),
-            t5_encoder=T5EncoderField(tokenizer=tokenizer2, text_encoder=t5_encoder),
-            vae=VAEField(vae=vae),
-            max_seq_len=max_seq_lengths[transformer_config.config_path],
-        )
-
-
 @invocation(
     "main_model_loader",
     title="Main Model",

invokeai/app/invocations/primitives.py

@@ -18,6 +18,7 @@ from invokeai.app.invocations.fields import (
     InputField,
     LatentsField,
     OutputField,
+    SD3ConditioningField,
     TensorField,
     UIComponent,
 )
@@ -426,6 +427,17 @@ class FluxConditioningOutput(BaseInvocationOutput):
         return cls(conditioning=FluxConditioningField(conditioning_name=conditioning_name))
 
 
+@invocation_output("sd3_conditioning_output")
+class SD3ConditioningOutput(BaseInvocationOutput):
+    """Base class for nodes that output a single SD3 conditioning tensor"""
+
+    conditioning: SD3ConditioningField = OutputField(description=FieldDescriptions.cond)
+
+    @classmethod
+    def build(cls, conditioning_name: str) -> "SD3ConditioningOutput":
+        return cls(conditioning=SD3ConditioningField(conditioning_name=conditioning_name))
+
+
 @invocation_output("conditioning_output")
 class ConditioningOutput(BaseInvocationOutput):
     """Base class for nodes that output a single conditioning tensor"""

invokeai/app/invocations/sd3_denoise.py

@@ -0,0 +1,260 @@
+from typing import Callable, Tuple
+
+import torch
+from diffusers.models.transformers.transformer_sd3 import SD3Transformer2DModel
+from diffusers.schedulers.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
+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 (
+    FieldDescriptions,
+    Input,
+    InputField,
+    SD3ConditioningField,
+    WithBoard,
+    WithMetadata,
+)
+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.model_manager.config import BaseModelType
+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
+
+
+@invocation(
+    "sd3_denoise",
+    title="SD3 Denoise",
+    tags=["image", "sd3"],
+    category="image",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Run denoising process with a SD3 model."""
+
+    transformer: TransformerField = InputField(
+        description=FieldDescriptions.sd3_model,
+        input=Input.Connection,
+        title="Transformer",
+    )
+    positive_conditioning: SD3ConditioningField = InputField(
+        description=FieldDescriptions.positive_cond, input=Input.Connection
+    )
+    negative_conditioning: SD3ConditioningField = InputField(
+        description=FieldDescriptions.negative_cond, input=Input.Connection
+    )
+    cfg_scale: float | list[float] = InputField(default=3.5, description=FieldDescriptions.cfg_scale, title="CFG Scale")
+    width: int = InputField(default=1024, multiple_of=16, description="Width of the generated image.")
+    height: int = InputField(default=1024, multiple_of=16, description="Height of the generated image.")
+    steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
+    seed: int = InputField(default=0, description="Randomness seed for reproducibility.")
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        latents = self._run_diffusion(context)
+        latents = latents.detach().to("cpu")
+
+        name = context.tensors.save(tensor=latents)
+        return LatentsOutput.build(latents_name=name, latents=latents, seed=None)
+
+    def _load_text_conditioning(
+        self,
+        context: InvocationContext,
+        conditioning_name: str,
+        joint_attention_dim: int,
+        dtype: torch.dtype,
+        device: torch.device,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Load the conditioning data.
+        cond_data = context.conditioning.load(conditioning_name)
+        assert len(cond_data.conditionings) == 1
+        sd3_conditioning = cond_data.conditionings[0]
+        assert isinstance(sd3_conditioning, SD3ConditioningInfo)
+        sd3_conditioning = sd3_conditioning.to(dtype=dtype, device=device)
+
+        t5_embeds = sd3_conditioning.t5_embeds
+        if t5_embeds is None:
+            t5_embeds = torch.zeros(
+                (1, SD3_T5_MAX_SEQ_LEN, joint_attention_dim),
+                device=device,
+                dtype=dtype,
+            )
+
+        clip_prompt_embeds = torch.cat([sd3_conditioning.clip_l_embeds, sd3_conditioning.clip_g_embeds], dim=-1)
+        clip_prompt_embeds = torch.nn.functional.pad(
+            clip_prompt_embeds, (0, t5_embeds.shape[-1] - clip_prompt_embeds.shape[-1])
+        )
+
+        prompt_embeds = torch.cat([clip_prompt_embeds, t5_embeds], dim=-2)
+        pooled_prompt_embeds = torch.cat(
+            [sd3_conditioning.clip_l_pooled_embeds, sd3_conditioning.clip_g_pooled_embeds], dim=-1
+        )
+
+        return prompt_embeds, pooled_prompt_embeds
+
+    def _get_noise(
+        self,
+        num_samples: int,
+        num_channels_latents: int,
+        height: int,
+        width: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        seed: int,
+    ) -> torch.Tensor:
+        # We always generate noise on the same device and dtype then cast to ensure consistency across devices/dtypes.
+        rand_device = "cpu"
+        rand_dtype = torch.float16
+
+        return torch.randn(
+            num_samples,
+            num_channels_latents,
+            int(height) // LATENT_SCALE_FACTOR,
+            int(width) // LATENT_SCALE_FACTOR,
+            device=rand_device,
+            dtype=rand_dtype,
+            generator=torch.Generator(device=rand_device).manual_seed(seed),
+        ).to(device=device, dtype=dtype)
+
+    def _prepare_cfg_scale(self, num_timesteps: int) -> list[float]:
+        """Prepare the CFG scale list.
+
+        Args:
+            num_timesteps (int): The number of timesteps in the scheduler. Could be different from num_steps depending
+            on the scheduler used (e.g. higher order schedulers).
+
+        Returns:
+            list[float]: _description_
+        """
+        if isinstance(self.cfg_scale, float):
+            cfg_scale = [self.cfg_scale] * num_timesteps
+        elif isinstance(self.cfg_scale, list):
+            assert len(self.cfg_scale) == num_timesteps
+            cfg_scale = self.cfg_scale
+        else:
+            raise ValueError(f"Invalid CFG scale type: {type(self.cfg_scale)}")
+
+        return cfg_scale
+
+    def _run_diffusion(
+        self,
+        context: InvocationContext,
+    ):
+        inference_dtype = TorchDevice.choose_torch_dtype()
+        device = TorchDevice.choose_torch_device()
+
+        transformer_info = context.models.load(self.transformer.transformer)
+
+        # Load/process the conditioning data.
+        # TODO(ryand): Make CFG optional.
+        do_classifier_free_guidance = True
+        pos_prompt_embeds, pos_pooled_prompt_embeds = self._load_text_conditioning(
+            context=context,
+            conditioning_name=self.positive_conditioning.conditioning_name,
+            joint_attention_dim=transformer_info.model.config.joint_attention_dim,
+            dtype=inference_dtype,
+            device=device,
+        )
+        neg_prompt_embeds, neg_pooled_prompt_embeds = self._load_text_conditioning(
+            context=context,
+            conditioning_name=self.negative_conditioning.conditioning_name,
+            joint_attention_dim=transformer_info.model.config.joint_attention_dim,
+            dtype=inference_dtype,
+            device=device,
+        )
+        # TODO(ryand): Support both sequential and batched CFG inference.
+        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 CFG scale list.
+        cfg_scale = self._prepare_cfg_scale(len(timesteps))
+
+        # Generate initial latent noise.
+        num_channels_latents = transformer_info.model.config.in_channels
+        assert isinstance(num_channels_latents, int)
+        noise = self._get_noise(
+            num_samples=1,
+            num_channels_latents=num_channels_latents,
+            height=self.height,
+            width=self.width,
+            dtype=inference_dtype,
+            device=device,
+            seed=self.seed,
+        )
+        latents: torch.Tensor = noise
+
+        total_steps = len(timesteps)
+        step_callback = self._build_step_callback(context)
+
+        step_callback(
+            PipelineIntermediateState(
+                step=0,
+                order=1,
+                total_steps=total_steps,
+                timestep=int(timesteps[0]),
+                latents=latents,
+            ),
+        )
+
+        with transformer_info.model_on_device() as (cached_weights, transformer):
+            assert isinstance(transformer, SD3Transformer2DModel)
+
+            # 6. Denoising loop
+            for step_idx, t in tqdm(list(enumerate(timesteps))):
+                # 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])
+
+                noise_pred = transformer(
+                    hidden_states=latent_model_input,
+                    timestep=timestep,
+                    encoder_hidden_states=prompt_embeds,
+                    pooled_projections=pooled_prompt_embeds,
+                    joint_attention_kwargs=None,
+                    return_dict=False,
+                )[0]
+
+                # Apply CFG.
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + cfg_scale[step_idx] * (noise_pred_cond - noise_pred_uncond)
+
+                # 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]
+
+                # 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)
+
+                step_callback(
+                    PipelineIntermediateState(
+                        step=step_idx + 1,
+                        order=1,
+                        total_steps=total_steps,
+                        timestep=int(t),
+                        latents=latents,
+                    ),
+                )
+
+        return latents
+
+    def _build_step_callback(self, context: InvocationContext) -> Callable[[PipelineIntermediateState], None]:
+        def step_callback(state: PipelineIntermediateState) -> None:
+            context.util.sd_step_callback(state, BaseModelType.StableDiffusion3)
+
+        return step_callback

invokeai/app/invocations/sd3_latents_to_image.py

@@ -0,0 +1,73 @@
+from contextlib import nullcontext
+
+import torch
+from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
+from einops import rearrange
+from PIL import Image
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    Input,
+    InputField,
+    LatentsField,
+    WithBoard,
+    WithMetadata,
+)
+from invokeai.app.invocations.model import VAEField
+from invokeai.app.invocations.primitives import ImageOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.stable_diffusion.extensions.seamless import SeamlessExt
+from invokeai.backend.util.devices import TorchDevice
+
+
+@invocation(
+    "sd3_l2i",
+    title="SD3 Latents to Image",
+    tags=["latents", "image", "vae", "l2i", "sd3"],
+    category="latents",
+    version="1.3.0",
+)
+class SD3LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Generates an image from latents."""
+
+    latents: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    vae: VAEField = InputField(
+        description=FieldDescriptions.vae,
+        input=Input.Connection,
+    )
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        latents = context.tensors.load(self.latents.latents_name)
+
+        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:
+            assert isinstance(vae, (AutoencoderKL))
+            latents = latents.to(vae.device)
+
+            vae.disable_tiling()
+
+            tiling_context = nullcontext()
+
+            # clear memory as vae decode can request a lot
+            TorchDevice.empty_cache()
+
+            with torch.inference_mode(), tiling_context:
+                # copied from diffusers pipeline
+                latents = latents / vae.config.scaling_factor
+                img = vae.decode(latents, return_dict=False)[0]
+
+            img = img.clamp(-1, 1)
+            img = rearrange(img[0], "c h w -> h w c")  # noqa: F821
+            img_pil = Image.fromarray((127.5 * (img + 1.0)).byte().cpu().numpy())
+
+        TorchDevice.empty_cache()
+
+        image_dto = context.images.save(image=img_pil)
+
+        return ImageOutput.build(image_dto)

invokeai/app/invocations/sd3_model_loader.py

@@ -0,0 +1,108 @@
+from typing import Optional
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
+from invokeai.app.invocations.model import CLIPField, ModelIdentifierField, T5EncoderField, TransformerField, VAEField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.model_manager.config import SubModelType
+
+
+@invocation_output("sd3_model_loader_output")
+class Sd3ModelLoaderOutput(BaseInvocationOutput):
+    """SD3 base model loader output."""
+
+    transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer")
+    clip_l: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP L")
+    clip_g: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP G")
+    t5_encoder: T5EncoderField = OutputField(description=FieldDescriptions.t5_encoder, title="T5 Encoder")
+    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
+
+
+@invocation(
+    "sd3_model_loader",
+    title="SD3 Main Model",
+    tags=["model", "sd3"],
+    category="model",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class Sd3ModelLoaderInvocation(BaseInvocation):
+    """Loads a SD3 base model, outputting its submodels."""
+
+    model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.sd3_model,
+        ui_type=UIType.SD3MainModel,
+        input=Input.Direct,
+    )
+
+    t5_encoder_model: Optional[ModelIdentifierField] = InputField(
+        description=FieldDescriptions.t5_encoder,
+        ui_type=UIType.T5EncoderModel,
+        input=Input.Direct,
+        title="T5 Encoder",
+        default=None,
+    )
+
+    clip_l_model: Optional[ModelIdentifierField] = InputField(
+        description=FieldDescriptions.clip_embed_model,
+        ui_type=UIType.CLIPLEmbedModel,
+        input=Input.Direct,
+        title="CLIP L Encoder",
+        default=None,
+    )
+
+    clip_g_model: Optional[ModelIdentifierField] = InputField(
+        description=FieldDescriptions.clip_g_model,
+        ui_type=UIType.CLIPGEmbedModel,
+        input=Input.Direct,
+        title="CLIP G Encoder",
+        default=None,
+    )
+
+    vae_model: Optional[ModelIdentifierField] = InputField(
+        description=FieldDescriptions.vae_model, ui_type=UIType.VAEModel, title="VAE", default=None
+    )
+
+    def invoke(self, context: InvocationContext) -> Sd3ModelLoaderOutput:
+        transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
+        vae = (
+            self.vae_model.model_copy(update={"submodel_type": SubModelType.VAE})
+            if self.vae_model
+            else self.model.model_copy(update={"submodel_type": SubModelType.VAE})
+        )
+        tokenizer_l = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
+        clip_encoder_l = (
+            self.clip_l_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
+            if self.clip_l_model
+            else self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
+        )
+        tokenizer_g = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
+        clip_encoder_g = (
+            self.clip_g_model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
+            if self.clip_g_model
+            else self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
+        )
+        tokenizer_t5 = (
+            self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer3})
+            if self.t5_encoder_model
+            else self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer3})
+        )
+        t5_encoder = (
+            self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder3})
+            if self.t5_encoder_model
+            else self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder3})
+        )
+
+        return Sd3ModelLoaderOutput(
+            transformer=TransformerField(transformer=transformer, loras=[]),
+            clip_l=CLIPField(tokenizer=tokenizer_l, text_encoder=clip_encoder_l, loras=[], skipped_layers=0),
+            clip_g=CLIPField(tokenizer=tokenizer_g, text_encoder=clip_encoder_g, loras=[], skipped_layers=0),
+            t5_encoder=T5EncoderField(tokenizer=tokenizer_t5, text_encoder=t5_encoder),
+            vae=VAEField(vae=vae),
+        )

invokeai/app/invocations/sd3_text_encoder.py

@@ -0,0 +1,199 @@
+from contextlib import ExitStack
+from typing import Iterator, Tuple
+
+import torch
+from transformers import (
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    T5EncoderModel,
+    T5Tokenizer,
+    T5TokenizerFast,
+)
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField
+from invokeai.app.invocations.model import CLIPField, T5EncoderField
+from invokeai.app.invocations.primitives import SD3ConditioningOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_CLIP_PREFIX
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+from invokeai.backend.lora.lora_patcher import LoRAPatcher
+from invokeai.backend.model_manager.config import ModelFormat
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData, SD3ConditioningInfo
+
+# The SD3 T5 Max Sequence Length set based on the default in diffusers.
+SD3_T5_MAX_SEQ_LEN = 256
+
+
+@invocation(
+    "sd3_text_encoder",
+    title="SD3 Text Encoding",
+    tags=["prompt", "conditioning", "sd3"],
+    category="conditioning",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class Sd3TextEncoderInvocation(BaseInvocation):
+    """Encodes and preps a prompt for a SD3 image."""
+
+    clip_l: CLIPField = InputField(
+        title="CLIP L",
+        description=FieldDescriptions.clip,
+        input=Input.Connection,
+    )
+    clip_g: CLIPField = InputField(
+        title="CLIP G",
+        description=FieldDescriptions.clip,
+        input=Input.Connection,
+    )
+
+    # The SD3 models were trained with text encoder dropout, so the T5 encoder can be omitted to save time/memory.
+    t5_encoder: T5EncoderField | None = InputField(
+        title="T5Encoder",
+        default=None,
+        description=FieldDescriptions.t5_encoder,
+        input=Input.Connection,
+    )
+    prompt: str = InputField(description="Text prompt to encode.")
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> SD3ConditioningOutput:
+        # Note: The text encoding model are run in separate functions to ensure that all model references are locally
+        # scoped. This ensures that earlier models can be freed and gc'd before loading later models (if necessary).
+
+        clip_l_embeddings, clip_l_pooled_embeddings = self._clip_encode(context, self.clip_l)
+        clip_g_embeddings, clip_g_pooled_embeddings = self._clip_encode(context, self.clip_g)
+
+        t5_embeddings: torch.Tensor | None = None
+        if self.t5_encoder is not None:
+            t5_embeddings = self._t5_encode(context, SD3_T5_MAX_SEQ_LEN)
+
+        conditioning_data = ConditioningFieldData(
+            conditionings=[
+                SD3ConditioningInfo(
+                    clip_l_embeds=clip_l_embeddings,
+                    clip_l_pooled_embeds=clip_l_pooled_embeddings,
+                    clip_g_embeds=clip_g_embeddings,
+                    clip_g_pooled_embeds=clip_g_pooled_embeddings,
+                    t5_embeds=t5_embeddings,
+                )
+            ]
+        )
+
+        conditioning_name = context.conditioning.save(conditioning_data)
+        return SD3ConditioningOutput.build(conditioning_name)
+
+    def _t5_encode(self, context: InvocationContext, max_seq_len: int) -> torch.Tensor:
+        assert self.t5_encoder is not None
+        t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer)
+        t5_text_encoder_info = context.models.load(self.t5_encoder.text_encoder)
+
+        prompt = [self.prompt]
+
+        with (
+            t5_text_encoder_info as t5_text_encoder,
+            t5_tokenizer_info as t5_tokenizer,
+        ):
+            assert isinstance(t5_text_encoder, T5EncoderModel)
+            assert isinstance(t5_tokenizer, (T5Tokenizer, T5TokenizerFast))
+
+            text_inputs = t5_tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=max_seq_len,
+                truncation=True,
+                add_special_tokens=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = t5_tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+            assert isinstance(text_input_ids, torch.Tensor)
+            assert isinstance(untruncated_ids, torch.Tensor)
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = t5_tokenizer.batch_decode(untruncated_ids[:, max_seq_len - 1 : -1])
+                context.logger.warning(
+                    "The following part of your input was truncated because `max_sequence_length` is set to "
+                    f" {max_seq_len} tokens: {removed_text}"
+                )
+
+            prompt_embeds = t5_text_encoder(text_input_ids.to(t5_text_encoder.device))[0]
+
+        assert isinstance(prompt_embeds, torch.Tensor)
+        return prompt_embeds
+
+    def _clip_encode(
+        self, context: InvocationContext, clip_model: CLIPField, tokenizer_max_length: int = 77
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        clip_tokenizer_info = context.models.load(clip_model.tokenizer)
+        clip_text_encoder_info = context.models.load(clip_model.text_encoder)
+
+        prompt = [self.prompt]
+
+        with (
+            clip_text_encoder_info.model_on_device() as (cached_weights, clip_text_encoder),
+            clip_tokenizer_info as clip_tokenizer,
+            ExitStack() as exit_stack,
+        ):
+            assert isinstance(clip_text_encoder, (CLIPTextModel, CLIPTextModelWithProjection))
+            assert isinstance(clip_tokenizer, CLIPTokenizer)
+
+            clip_text_encoder_config = clip_text_encoder_info.config
+            assert clip_text_encoder_config is not None
+
+            # Apply LoRA models to the CLIP encoder.
+            # Note: We apply the LoRA after the transformer has been moved to its target device for faster patching.
+            if clip_text_encoder_config.format in [ModelFormat.Diffusers]:
+                # The model is non-quantized, so we can apply the LoRA weights directly into the model.
+                exit_stack.enter_context(
+                    LoRAPatcher.apply_lora_patches(
+                        model=clip_text_encoder,
+                        patches=self._clip_lora_iterator(context, clip_model),
+                        prefix=FLUX_LORA_CLIP_PREFIX,
+                        cached_weights=cached_weights,
+                    )
+                )
+            else:
+                # There are currently no supported CLIP quantized models. Add support here if needed.
+                raise ValueError(f"Unsupported model format: {clip_text_encoder_config.format}")
+
+            clip_text_encoder = clip_text_encoder.eval().requires_grad_(False)
+
+            text_inputs = clip_tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=tokenizer_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = clip_tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+            assert isinstance(text_input_ids, torch.Tensor)
+            assert isinstance(untruncated_ids, torch.Tensor)
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = clip_tokenizer.batch_decode(untruncated_ids[:, tokenizer_max_length - 1 : -1])
+                context.logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {tokenizer_max_length} tokens: {removed_text}"
+                )
+            prompt_embeds = clip_text_encoder(
+                input_ids=text_input_ids.to(clip_text_encoder.device), output_hidden_states=True
+            )
+            pooled_prompt_embeds = prompt_embeds[0]
+            prompt_embeds = prompt_embeds.hidden_states[-2]
+
+            return prompt_embeds, pooled_prompt_embeds
+
+    def _clip_lora_iterator(
+        self, context: InvocationContext, clip_model: CLIPField
+    ) -> Iterator[Tuple[LoRAModelRaw, float]]:
+        for lora in clip_model.loras:
+            lora_info = context.models.load(lora.lora)
+            assert isinstance(lora_info.model, LoRAModelRaw)
+            yield (lora_info.model, lora.weight)
+            del lora_info

invokeai/app/invocations/segment_anything.py

@@ -5,7 +5,7 @@ from typing import Literal
 import numpy as np
 import torch
 from PIL import Image
-from pydantic import BaseModel, Field, model_validator
+from pydantic import BaseModel, Field
 from transformers import AutoModelForMaskGeneration, AutoProcessor
 from transformers.models.sam import SamModel
 from transformers.models.sam.processing_sam import SamProcessor
@@ -77,19 +77,14 @@ class SegmentAnythingInvocation(BaseInvocation):
         default="all",
     )
 
-    @model_validator(mode="after")
-    def check_point_lists_or_bounding_box(self):
-        if self.point_lists is None and self.bounding_boxes is None:
-            raise ValueError("Either point_lists or bounding_box must be provided.")
-        elif self.point_lists is not None and self.bounding_boxes is not None:
-            raise ValueError("Only one of point_lists or bounding_box can be provided.")
-        return self
-
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> MaskOutput:
         # The models expect a 3-channel RGB image.
         image_pil = context.images.get_pil(self.image.image_name, mode="RGB")
 
+        if self.point_lists is not None and self.bounding_boxes is not None:
+            raise ValueError("Only one of point_lists or bounding_box can be provided.")
+
         if (not self.bounding_boxes or len(self.bounding_boxes) == 0) and (
             not self.point_lists or len(self.point_lists) == 0
         ):

invokeai/app/services/model_records/model_records_base.py

@@ -15,6 +15,7 @@ from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
+    ClipVariantType,
     ControlAdapterDefaultSettings,
     MainModelDefaultSettings,
     ModelFormat,
@@ -85,7 +86,7 @@ class ModelRecordChanges(BaseModelExcludeNull):
 
     # Checkpoint-specific changes
     # TODO(MM2): Should we expose these? Feels footgun-y...
-    variant: Optional[ModelVariantType] = Field(description="The variant of the model.", default=None)
+    variant: Optional[ModelVariantType | ClipVariantType] = Field(description="The variant of the model.", default=None)
     prediction_type: Optional[SchedulerPredictionType] = Field(
         description="The prediction type of the model.", default=None
     )

invokeai/app/services/workflow_records/default_workflows/SD3.5 Text to Image.json

@@ -0,0 +1,382 @@
+{
+    "name": "SD3.5 Text to Image",
+   "author": "InvokeAI",
+    "description": "Sample text to image workflow for Stable Diffusion 3.5",
+    "version": "1.0.0",
+    "contact": "invoke@invoke.ai",
+    "tags": "text2image, SD3.5, default",
+  "notes": "",
+    "exposedFields": [
+      {
+        "nodeId": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "fieldName": "model"
+      },
+      {
+        "nodeId": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
+        "fieldName": "prompt"
+      }
+    ],
+    "meta": {
+      "version": "3.0.0",
+      "category": "default"
+    },
+    "id": "e3a51d6b-8208-4d6d-b187-fcfe8b32934c",
+    "nodes": [
+      {
+        "id": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "type": "invocation",
+        "data": {
+          "id": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+          "type": "sd3_model_loader",
+          "version": "1.0.0",
+          "label": "",
+          "notes": "",
+          "isOpen": true,
+          "isIntermediate": true,
+          "useCache": true,
+          "nodePack": "invokeai",
+          "inputs": {
+            "model": {
+              "name": "model",
+              "label": "",
+              "value": {
+                "key": "f7b20be9-92a8-4cfb-bca4-6c3b5535c10b",
+                "hash": "placeholder",
+                "name": "stable-diffusion-3.5-medium",
+                "base": "sd-3",
+                "type": "main"
+              }
+            },
+            "t5_encoder_model": {
+              "name": "t5_encoder_model",
+              "label": ""
+            },
+            "clip_l_model": {
+              "name": "clip_l_model",
+              "label": ""
+            },
+            "clip_g_model": {
+              "name": "clip_g_model",
+              "label": ""
+            },
+            "vae_model": {
+              "name": "vae_model",
+              "label": ""
+            }
+          }
+        },
+        "position": {
+          "x": -55.58689609637031,
+          "y": -111.53602444662268
+        }
+      },
+      {
+        "id": "f7e394ac-6394-4096-abcb-de0d346506b3",
+        "type": "invocation",
+        "data": {
+          "id": "f7e394ac-6394-4096-abcb-de0d346506b3",
+          "type": "rand_int",
+          "version": "1.0.1",
+          "label": "",
+          "notes": "",
+          "isOpen": true,
+          "isIntermediate": true,
+          "useCache": false,
+          "nodePack": "invokeai",
+          "inputs": {
+            "low": {
+              "name": "low",
+              "label": "",
+              "value": 0
+            },
+            "high": {
+              "name": "high",
+              "label": "",
+              "value": 2147483647
+            }
+          }
+        },
+        "position": {
+          "x": 470.45870147220353,
+          "y": 350.3141781644303
+        }
+      },
+      {
+        "id": "9eb72af0-dd9e-4ec5-ad87-d65e3c01f48b",
+        "type": "invocation",
+        "data": {
+          "id": "9eb72af0-dd9e-4ec5-ad87-d65e3c01f48b",
+          "type": "sd3_l2i",
+          "version": "1.3.0",
+          "label": "",
+          "notes": "",
+          "isOpen": true,
+          "isIntermediate": false,
+          "useCache": true,
+          "nodePack": "invokeai",
+          "inputs": {
+            "board": {
+              "name": "board",
+              "label": ""
+            },
+            "metadata": {
+              "name": "metadata",
+              "label": ""
+            },
+            "latents": {
+              "name": "latents",
+              "label": ""
+            },
+            "vae": {
+              "name": "vae",
+              "label": ""
+            }
+          }
+        },
+        "position": {
+          "x": 1192.3097009334897,
+          "y": -366.0994675072209
+        }
+      },
+      {
+        "id": "3b4f7f27-cfc0-4373-a009-99c5290d0cd6",
+        "type": "invocation",
+        "data": {
+          "id": "3b4f7f27-cfc0-4373-a009-99c5290d0cd6",
+          "type": "sd3_text_encoder",
+          "version": "1.0.0",
+          "label": "",
+          "notes": "",
+          "isOpen": true,
+          "isIntermediate": true,
+          "useCache": true,
+          "nodePack": "invokeai",
+          "inputs": {
+            "clip_l": {
+              "name": "clip_l",
+              "label": ""
+            },
+            "clip_g": {
+              "name": "clip_g",
+              "label": ""
+            },
+            "t5_encoder": {
+              "name": "t5_encoder",
+              "label": ""
+            },
+            "prompt": {
+              "name": "prompt",
+              "label": "",
+              "value": ""
+            }
+          }
+        },
+        "position": {
+          "x": 408.16054647924784,
+          "y": 65.06415352118786
+        }
+      },
+      {
+        "id": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
+        "type": "invocation",
+        "data": {
+          "id": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
+          "type": "sd3_text_encoder",
+          "version": "1.0.0",
+          "label": "",
+          "notes": "",
+          "isOpen": true,
+          "isIntermediate": true,
+          "useCache": true,
+          "nodePack": "invokeai",
+          "inputs": {
+            "clip_l": {
+              "name": "clip_l",
+              "label": ""
+            },
+            "clip_g": {
+              "name": "clip_g",
+              "label": ""
+            },
+            "t5_encoder": {
+              "name": "t5_encoder",
+              "label": ""
+            },
+            "prompt": {
+              "name": "prompt",
+              "label": "",
+              "value": ""
+            }
+          }
+        },
+        "position": {
+          "x": 378.9283412440941,
+          "y": -302.65777497352553
+        }
+      },
+      {
+        "id": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+        "type": "invocation",
+        "data": {
+          "id": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+          "type": "sd3_denoise",
+          "version": "1.0.0",
+          "label": "",
+          "notes": "",
+          "isOpen": true,
+          "isIntermediate": true,
+          "useCache": true,
+          "nodePack": "invokeai",
+          "inputs": {
+            "board": {
+              "name": "board",
+              "label": ""
+            },
+            "metadata": {
+              "name": "metadata",
+              "label": ""
+            },
+            "transformer": {
+              "name": "transformer",
+              "label": ""
+            },
+            "positive_conditioning": {
+              "name": "positive_conditioning",
+              "label": ""
+            },
+            "negative_conditioning": {
+              "name": "negative_conditioning",
+              "label": ""
+            },
+            "cfg_scale": {
+              "name": "cfg_scale",
+              "label": "",
+              "value": 3.5
+            },
+            "width": {
+              "name": "width",
+              "label": "",
+              "value": 1024
+            },
+            "height": {
+              "name": "height",
+              "label": "",
+              "value": 1024
+            },
+            "steps": {
+              "name": "steps",
+              "label": "",
+              "value": 30
+            },
+            "seed": {
+              "name": "seed",
+              "label": "",
+              "value": 0
+            }
+          }
+        },
+        "position": {
+          "x": 813.7814762740603,
+          "y": -142.20529727605867
+        }
+      }
+    ],
+    "edges": [
+      {
+        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4cvae-9eb72af0-dd9e-4ec5-ad87-d65e3c01f48bvae",
+        "type": "default",
+        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "target": "9eb72af0-dd9e-4ec5-ad87-d65e3c01f48b",
+        "sourceHandle": "vae",
+        "targetHandle": "vae"
+      },
+      {
+        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4ct5_encoder-3b4f7f27-cfc0-4373-a009-99c5290d0cd6t5_encoder",
+        "type": "default",
+        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "target": "3b4f7f27-cfc0-4373-a009-99c5290d0cd6",
+        "sourceHandle": "t5_encoder",
+        "targetHandle": "t5_encoder"
+      },
+      {
+        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4ct5_encoder-e17d34e7-6ed1-493c-9a85-4fcd291cb084t5_encoder",
+        "type": "default",
+        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "target": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
+        "sourceHandle": "t5_encoder",
+        "targetHandle": "t5_encoder"
+      },
+      {
+        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4cclip_g-3b4f7f27-cfc0-4373-a009-99c5290d0cd6clip_g",
+        "type": "default",
+        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "target": "3b4f7f27-cfc0-4373-a009-99c5290d0cd6",
+        "sourceHandle": "clip_g",
+        "targetHandle": "clip_g"
+      },
+      {
+        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4cclip_g-e17d34e7-6ed1-493c-9a85-4fcd291cb084clip_g",
+        "type": "default",
+        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "target": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
+        "sourceHandle": "clip_g",
+        "targetHandle": "clip_g"
+      },
+      {
+        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4cclip_l-3b4f7f27-cfc0-4373-a009-99c5290d0cd6clip_l",
+        "type": "default",
+        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "target": "3b4f7f27-cfc0-4373-a009-99c5290d0cd6",
+        "sourceHandle": "clip_l",
+        "targetHandle": "clip_l"
+      },
+      {
+        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4cclip_l-e17d34e7-6ed1-493c-9a85-4fcd291cb084clip_l",
+        "type": "default",
+        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "target": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
+        "sourceHandle": "clip_l",
+        "targetHandle": "clip_l"
+      },
+      {
+        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4ctransformer-c7539f7b-7ac5-49b9-93eb-87ede611409ftransformer",
+        "type": "default",
+        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "target": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+        "sourceHandle": "transformer",
+        "targetHandle": "transformer"
+      },
+      {
+        "id": "reactflow__edge-f7e394ac-6394-4096-abcb-de0d346506b3value-c7539f7b-7ac5-49b9-93eb-87ede611409fseed",
+        "type": "default",
+        "source": "f7e394ac-6394-4096-abcb-de0d346506b3",
+        "target": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+        "sourceHandle": "value",
+        "targetHandle": "seed"
+      },
+      {
+        "id": "reactflow__edge-c7539f7b-7ac5-49b9-93eb-87ede611409flatents-9eb72af0-dd9e-4ec5-ad87-d65e3c01f48blatents",
+        "type": "default",
+        "source": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+        "target": "9eb72af0-dd9e-4ec5-ad87-d65e3c01f48b",
+        "sourceHandle": "latents",
+        "targetHandle": "latents"
+      },
+      {
+        "id": "reactflow__edge-e17d34e7-6ed1-493c-9a85-4fcd291cb084conditioning-c7539f7b-7ac5-49b9-93eb-87ede611409fpositive_conditioning",
+        "type": "default",
+        "source": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
+        "target": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+        "sourceHandle": "conditioning",
+        "targetHandle": "positive_conditioning"
+      },
+      {
+        "id": "reactflow__edge-3b4f7f27-cfc0-4373-a009-99c5290d0cd6conditioning-c7539f7b-7ac5-49b9-93eb-87ede611409fnegative_conditioning",
+        "type": "default",
+        "source": "3b4f7f27-cfc0-4373-a009-99c5290d0cd6",
+        "target": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+        "sourceHandle": "conditioning",
+        "targetHandle": "negative_conditioning"
+      }
+    ]
+  }

invokeai/app/util/step_callback.py

@@ -34,6 +34,25 @@ SD1_5_LATENT_RGB_FACTORS = [
     [-0.1307, -0.1874, -0.7445],  # L4
 ]
 
+SD3_5_LATENT_RGB_FACTORS = [
+    [-0.05240681, 0.03251581, 0.0749016],
+    [-0.0580572, 0.00759826, 0.05729818],
+    [0.16144888, 0.01270368, -0.03768577],
+    [0.14418615, 0.08460266, 0.15941818],
+    [0.04894035, 0.0056485, -0.06686988],
+    [0.05187166, 0.19222395, 0.06261094],
+    [0.1539433, 0.04818359, 0.07103094],
+    [-0.08601796, 0.09013458, 0.10893912],
+    [-0.12398469, -0.06766567, 0.0033688],
+    [-0.0439737, 0.07825329, 0.02258823],
+    [0.03101129, 0.06382551, 0.07753657],
+    [-0.01315361, 0.08554491, -0.08772475],
+    [0.06464487, 0.05914605, 0.13262741],
+    [-0.07863674, -0.02261737, -0.12761454],
+    [-0.09923835, -0.08010759, -0.06264447],
+    [-0.03392309, -0.0804029, -0.06078822],
+]
+
 FLUX_LATENT_RGB_FACTORS = [
     [-0.0412, 0.0149, 0.0521],
     [0.0056, 0.0291, 0.0768],
@@ -110,6 +129,9 @@ def stable_diffusion_step_callback(
         sdxl_latent_rgb_factors = torch.tensor(SDXL_LATENT_RGB_FACTORS, dtype=sample.dtype, device=sample.device)
         sdxl_smooth_matrix = torch.tensor(SDXL_SMOOTH_MATRIX, dtype=sample.dtype, device=sample.device)
         image = sample_to_lowres_estimated_image(sample, sdxl_latent_rgb_factors, sdxl_smooth_matrix)
+    elif base_model == BaseModelType.StableDiffusion3:
+        sd3_latent_rgb_factors = torch.tensor(SD3_5_LATENT_RGB_FACTORS, dtype=sample.dtype, device=sample.device)
+        image = sample_to_lowres_estimated_image(sample, sd3_latent_rgb_factors)
     else:
         v1_5_latent_rgb_factors = torch.tensor(SD1_5_LATENT_RGB_FACTORS, dtype=sample.dtype, device=sample.device)
         image = sample_to_lowres_estimated_image(sample, v1_5_latent_rgb_factors)

invokeai/backend/model_manager/config.py

@@ -53,6 +53,7 @@ class BaseModelType(str, Enum):
     Any = "any"
     StableDiffusion1 = "sd-1"
     StableDiffusion2 = "sd-2"
+    StableDiffusion3 = "sd-3"
     StableDiffusionXL = "sdxl"
     StableDiffusionXLRefiner = "sdxl-refiner"
     Flux = "flux"
@@ -83,8 +84,10 @@ class SubModelType(str, Enum):
     Transformer = "transformer"
     TextEncoder = "text_encoder"
     TextEncoder2 = "text_encoder_2"
+    TextEncoder3 = "text_encoder_3"
     Tokenizer = "tokenizer"
     Tokenizer2 = "tokenizer_2"
+    Tokenizer3 = "tokenizer_3"
     VAE = "vae"
     VAEDecoder = "vae_decoder"
     VAEEncoder = "vae_encoder"
@@ -92,6 +95,13 @@ class SubModelType(str, Enum):
     SafetyChecker = "safety_checker"
 
 
+class ClipVariantType(str, Enum):
+    """Variant type."""
+
+    L = "large"
+    G = "gigantic"
+
+
 class ModelVariantType(str, Enum):
     """Variant type."""
 
@@ -147,6 +157,15 @@ class ModelSourceType(str, Enum):
 DEFAULTS_PRECISION = Literal["fp16", "fp32"]
 
 
+AnyVariant: TypeAlias = Union[ModelVariantType, ClipVariantType, None]
+
+
+class SubmodelDefinition(BaseModel):
+    path_or_prefix: str
+    model_type: ModelType
+    variant: AnyVariant = None
+
+
 class MainModelDefaultSettings(BaseModel):
     vae: str | None = Field(default=None, description="Default VAE for this model (model key)")
     vae_precision: DEFAULTS_PRECISION | None = Field(default=None, description="Default VAE precision for this model")
@@ -193,6 +212,9 @@ class ModelConfigBase(BaseModel):
         schema["required"].extend(["key", "type", "format"])
 
     model_config = ConfigDict(validate_assignment=True, json_schema_extra=json_schema_extra)
+    submodels: Optional[Dict[SubModelType, SubmodelDefinition]] = Field(
+        description="Loadable submodels in this model", default=None
+    )
 
 
 class CheckpointConfigBase(ModelConfigBase):
@@ -335,7 +357,7 @@ class MainConfigBase(ModelConfigBase):
     default_settings: Optional[MainModelDefaultSettings] = Field(
         description="Default settings for this model", default=None
     )
-    variant: ModelVariantType = ModelVariantType.Normal
+    variant: AnyVariant = ModelVariantType.Normal
 
 
 class MainCheckpointConfig(CheckpointConfigBase, MainConfigBase):
@@ -419,12 +441,33 @@ class CLIPEmbedDiffusersConfig(DiffusersConfigBase):
 
     type: Literal[ModelType.CLIPEmbed] = ModelType.CLIPEmbed
     format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
+    variant: ClipVariantType = ClipVariantType.L
 
     @staticmethod
     def get_tag() -> Tag:
         return Tag(f"{ModelType.CLIPEmbed.value}.{ModelFormat.Diffusers.value}")
 
 
+class CLIPGEmbedDiffusersConfig(CLIPEmbedDiffusersConfig):
+    """Model config for CLIP-G Embeddings."""
+
+    variant: ClipVariantType = ClipVariantType.G
+
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.CLIPEmbed.value}.{ModelFormat.Diffusers.value}.{ClipVariantType.G}")
+
+
+class CLIPLEmbedDiffusersConfig(CLIPEmbedDiffusersConfig):
+    """Model config for CLIP-L Embeddings."""
+
+    variant: ClipVariantType = ClipVariantType.L
+
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.CLIPEmbed.value}.{ModelFormat.Diffusers.value}.{ClipVariantType.L}")
+
+
 class CLIPVisionDiffusersConfig(DiffusersConfigBase):
     """Model config for CLIPVision."""
 
@@ -501,6 +544,8 @@ AnyModelConfig = Annotated[
         Annotated[SpandrelImageToImageConfig, SpandrelImageToImageConfig.get_tag()],
         Annotated[CLIPVisionDiffusersConfig, CLIPVisionDiffusersConfig.get_tag()],
         Annotated[CLIPEmbedDiffusersConfig, CLIPEmbedDiffusersConfig.get_tag()],
+        Annotated[CLIPLEmbedDiffusersConfig, CLIPLEmbedDiffusersConfig.get_tag()],
+        Annotated[CLIPGEmbedDiffusersConfig, CLIPGEmbedDiffusersConfig.get_tag()],
     ],
     Discriminator(get_model_discriminator_value),
 ]

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

@@ -128,9 +128,9 @@ class BnbQuantizedLlmInt8bCheckpointModel(ModelLoader):
                 "The bnb modules are not available. Please install bitsandbytes if available on your platform."
             )
         match submodel_type:
-            case SubModelType.Tokenizer2:
+            case SubModelType.Tokenizer2 | SubModelType.Tokenizer3:
                 return T5Tokenizer.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
-            case SubModelType.TextEncoder2:
+            case SubModelType.TextEncoder2 | SubModelType.TextEncoder3:
                 te2_model_path = Path(config.path) / "text_encoder_2"
                 model_config = AutoConfig.from_pretrained(te2_model_path)
                 with accelerate.init_empty_weights():
@@ -172,9 +172,9 @@ class T5EncoderCheckpointModel(ModelLoader):
             raise ValueError("Only T5EncoderConfig models are currently supported here.")
 
         match submodel_type:
-            case SubModelType.Tokenizer2:
+            case SubModelType.Tokenizer2 | SubModelType.Tokenizer3:
                 return T5Tokenizer.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
-            case SubModelType.TextEncoder2:
+            case SubModelType.TextEncoder2 | SubModelType.TextEncoder3:
                 return T5EncoderModel.from_pretrained(Path(config.path) / "text_encoder_2", torch_dtype="auto")
 
         raise ValueError(

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

@@ -42,6 +42,7 @@ VARIANT_TO_IN_CHANNEL_MAP = {
 @ModelLoaderRegistry.register(
     base=BaseModelType.StableDiffusionXLRefiner, type=ModelType.Main, format=ModelFormat.Diffusers
 )
+@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion3, type=ModelType.Main, format=ModelFormat.Diffusers)
 @ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion1, type=ModelType.Main, format=ModelFormat.Checkpoint)
 @ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion2, type=ModelType.Main, format=ModelFormat.Checkpoint)
 @ModelLoaderRegistry.register(base=BaseModelType.StableDiffusionXL, type=ModelType.Main, format=ModelFormat.Checkpoint)
@@ -51,13 +52,6 @@ VARIANT_TO_IN_CHANNEL_MAP = {
 class StableDiffusionDiffusersModel(GenericDiffusersLoader):
     """Class to load main models."""
 
-    model_base_to_model_type = {
-        BaseModelType.StableDiffusion1: "FrozenCLIPEmbedder",
-        BaseModelType.StableDiffusion2: "FrozenOpenCLIPEmbedder",
-        BaseModelType.StableDiffusionXL: "SDXL",
-        BaseModelType.StableDiffusionXLRefiner: "SDXL-Refiner",
-    }
-
     def _load_model(
         self,
         config: AnyModelConfig,

invokeai/backend/model_manager/probe.py

@@ -1,7 +1,7 @@
 import json
 import re
 from pathlib import Path
-from typing import Any, Dict, Literal, Optional, Union
+from typing import Any, Callable, Dict, Literal, Optional, Union
 
 import safetensors.torch
 import spandrel
@@ -22,6 +22,7 @@ from invokeai.backend.lora.conversions.flux_kohya_lora_conversion_utils import i
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
+    AnyVariant,
     BaseModelType,
     ControlAdapterDefaultSettings,
     InvalidModelConfigException,
@@ -33,8 +34,15 @@ from invokeai.backend.model_manager.config import (
     ModelType,
     ModelVariantType,
     SchedulerPredictionType,
+    SubmodelDefinition,
+    SubModelType,
+)
+from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import ConfigLoader
+from invokeai.backend.model_manager.util.model_util import (
+    get_clip_variant_type,
+    lora_token_vector_length,
+    read_checkpoint_meta,
 )
-from invokeai.backend.model_manager.util.model_util import lora_token_vector_length, read_checkpoint_meta
 from invokeai.backend.quantization.gguf.ggml_tensor import GGMLTensor
 from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
 from invokeai.backend.spandrel_image_to_image_model import SpandrelImageToImageModel
@@ -112,6 +120,7 @@ class ModelProbe(object):
         "StableDiffusionXLPipeline": ModelType.Main,
         "StableDiffusionXLImg2ImgPipeline": ModelType.Main,
         "StableDiffusionXLInpaintPipeline": ModelType.Main,
+        "StableDiffusion3Pipeline": ModelType.Main,
         "LatentConsistencyModelPipeline": ModelType.Main,
         "AutoencoderKL": ModelType.VAE,
         "AutoencoderTiny": ModelType.VAE,
@@ -122,8 +131,12 @@ class ModelProbe(object):
         "CLIPTextModel": ModelType.CLIPEmbed,
         "T5EncoderModel": ModelType.T5Encoder,
         "FluxControlNetModel": ModelType.ControlNet,
+        "SD3Transformer2DModel": ModelType.Main,
+        "CLIPTextModelWithProjection": ModelType.CLIPEmbed,
     }
 
+    TYPE2VARIANT: Dict[ModelType, Callable[[str], Optional[AnyVariant]]] = {ModelType.CLIPEmbed: get_clip_variant_type}
+
     @classmethod
     def register_probe(
         cls, format: Literal["diffusers", "checkpoint", "onnx"], model_type: ModelType, probe_class: type[ProbeBase]
@@ -170,7 +183,10 @@ class ModelProbe(object):
         fields["path"] = model_path.as_posix()
         fields["type"] = fields.get("type") or model_type
         fields["base"] = fields.get("base") or probe.get_base_type()
-        fields["variant"] = fields.get("variant") or probe.get_variant_type()
+        variant_func = cls.TYPE2VARIANT.get(fields["type"], None)
+        fields["variant"] = (
+            fields.get("variant") or (variant_func and variant_func(model_path.as_posix())) or probe.get_variant_type()
+        )
         fields["prediction_type"] = fields.get("prediction_type") or probe.get_scheduler_prediction_type()
         fields["image_encoder_model_id"] = fields.get("image_encoder_model_id") or probe.get_image_encoder_model_id()
         fields["name"] = fields.get("name") or cls.get_model_name(model_path)
@@ -217,6 +233,10 @@ class ModelProbe(object):
                 and fields["prediction_type"] == SchedulerPredictionType.VPrediction
             )
 
+        get_submodels = getattr(probe, "get_submodels", None)
+        if fields["base"] == BaseModelType.StableDiffusion3 and callable(get_submodels):
+            fields["submodels"] = get_submodels()
+
         model_info = ModelConfigFactory.make_config(fields)  # , key=fields.get("key", None))
         return model_info
 
@@ -747,18 +767,33 @@ class FolderProbeBase(ProbeBase):
 
 class PipelineFolderProbe(FolderProbeBase):
     def get_base_type(self) -> BaseModelType:
-        with open(self.model_path / "unet" / "config.json", "r") as file:
-            unet_conf = json.load(file)
-        if unet_conf["cross_attention_dim"] == 768:
-            return BaseModelType.StableDiffusion1
-        elif unet_conf["cross_attention_dim"] == 1024:
-            return BaseModelType.StableDiffusion2
-        elif unet_conf["cross_attention_dim"] == 1280:
-            return BaseModelType.StableDiffusionXLRefiner
-        elif unet_conf["cross_attention_dim"] == 2048:
-            return BaseModelType.StableDiffusionXL
-        else:
-            raise InvalidModelConfigException(f"Unknown base model for {self.model_path}")
+        # Handle pipelines with a UNet (i.e SD 1.x, SD2, SDXL).
+        config_path = self.model_path / "unet" / "config.json"
+        if config_path.exists():
+            with open(config_path) as file:
+                unet_conf = json.load(file)
+            if unet_conf["cross_attention_dim"] == 768:
+                return BaseModelType.StableDiffusion1
+            elif unet_conf["cross_attention_dim"] == 1024:
+                return BaseModelType.StableDiffusion2
+            elif unet_conf["cross_attention_dim"] == 1280:
+                return BaseModelType.StableDiffusionXLRefiner
+            elif unet_conf["cross_attention_dim"] == 2048:
+                return BaseModelType.StableDiffusionXL
+            else:
+                raise InvalidModelConfigException(f"Unknown base model for {self.model_path}")
+
+        # Handle pipelines with a transformer (i.e. SD3).
+        config_path = self.model_path / "transformer" / "config.json"
+        if config_path.exists():
+            with open(config_path) as file:
+                transformer_conf = json.load(file)
+            if transformer_conf["_class_name"] == "SD3Transformer2DModel":
+                return BaseModelType.StableDiffusion3
+            else:
+                raise InvalidModelConfigException(f"Unknown base model for {self.model_path}")
+
+        raise InvalidModelConfigException(f"Unknown base model for {self.model_path}")
 
     def get_scheduler_prediction_type(self) -> SchedulerPredictionType:
         with open(self.model_path / "scheduler" / "scheduler_config.json", "r") as file:
@@ -770,6 +805,23 @@ class PipelineFolderProbe(FolderProbeBase):
         else:
             raise InvalidModelConfigException("Unknown scheduler prediction type: {scheduler_conf['prediction_type']}")
 
+    def get_submodels(self) -> Dict[SubModelType, SubmodelDefinition]:
+        config = ConfigLoader.load_config(self.model_path, config_name="model_index.json")
+        submodels: Dict[SubModelType, SubmodelDefinition] = {}
+        for key, value in config.items():
+            if key.startswith("_") or not (isinstance(value, list) and len(value) == 2):
+                continue
+            model_loader = str(value[1])
+            if model_type := ModelProbe.CLASS2TYPE.get(model_loader):
+                variant_func = ModelProbe.TYPE2VARIANT.get(model_type, None)
+                submodels[SubModelType(key)] = SubmodelDefinition(
+                    path_or_prefix=(self.model_path / key).resolve().as_posix(),
+                    model_type=model_type,
+                    variant=variant_func and variant_func((self.model_path / key).as_posix()),
+                )
+
+        return submodels
+
     def get_variant_type(self) -> ModelVariantType:
         # This only works for pipelines! Any kind of
         # exception results in our returning the

invokeai/backend/model_manager/starter_models.py

@@ -140,6 +140,22 @@ flux_dev = StarterModel(
     type=ModelType.Main,
     dependencies=[t5_base_encoder, flux_vae, clip_l_encoder],
 )
+sd35_medium = StarterModel(
+    name="SD3.5 Medium",
+    base=BaseModelType.StableDiffusion3,
+    source="stabilityai/stable-diffusion-3.5-medium",
+    description="Medium SD3.5 Model: ~15GB",
+    type=ModelType.Main,
+    dependencies=[],
+)
+sd35_large = StarterModel(
+    name="SD3.5 Large",
+    base=BaseModelType.StableDiffusion3,
+    source="stabilityai/stable-diffusion-3.5-large",
+    description="Large SD3.5 Model: ~19G",
+    type=ModelType.Main,
+    dependencies=[],
+)
 cyberrealistic_sd1 = StarterModel(
     name="CyberRealistic v4.1",
     base=BaseModelType.StableDiffusion1,
@@ -570,6 +586,8 @@ STARTER_MODELS: list[StarterModel] = [
     flux_dev_quantized,
     flux_schnell,
     flux_dev,
+    sd35_medium,
+    sd35_large,
     cyberrealistic_sd1,
     rev_animated_sd1,
     dreamshaper_8_sd1,

invokeai/backend/model_manager/util/model_util.py

@@ -8,6 +8,7 @@ import safetensors
 import torch
 from picklescan.scanner import scan_file_path
 
+from invokeai.backend.model_manager.config import ClipVariantType
 from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
 
 
@@ -165,3 +166,20 @@ def convert_bundle_to_flux_transformer_checkpoint(
         del transformer_state_dict[k]
 
     return original_state_dict
+
+
+def get_clip_variant_type(location: str) -> Optional[ClipVariantType]:
+    path = Path(location)
+    config_path = path / "config.json"
+    if not config_path.exists():
+        return None
+    with open(config_path) as file:
+        clip_conf = json.load(file)
+        hidden_size = clip_conf.get("hidden_size", -1)
+        match hidden_size:
+            case 1280:
+                return ClipVariantType.G
+            case 768:
+                return ClipVariantType.L
+            case _:
+                return None

invokeai/backend/model_manager/util/select_hf_files.py

@@ -129,9 +129,11 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
 
             # Some special handling is needed here if there is not an exact match and if we cannot infer the variant
             # from the file name. In this case, we only give this file a point if the requested variant is FP32 or DEFAULT.
-            if candidate_variant_label == f".{variant}" or (
-                not candidate_variant_label and variant in [ModelRepoVariant.FP32, ModelRepoVariant.Default]
-            ):
+            if (
+                variant is not ModelRepoVariant.Default
+                and candidate_variant_label
+                and candidate_variant_label.startswith(f".{variant.value}")
+            ) or (not candidate_variant_label and variant in [ModelRepoVariant.FP32, ModelRepoVariant.Default]):
                 score += 1
 
             if parent not in subfolder_weights:
@@ -146,7 +148,7 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
         # Check if at least one of the files has the explicit fp16 variant.
         at_least_one_fp16 = False
         for candidate in candidate_list:
-            if len(candidate.path.suffixes) == 2 and candidate.path.suffixes[0] == ".fp16":
+            if len(candidate.path.suffixes) == 2 and candidate.path.suffixes[0].startswith(".fp16"):
                 at_least_one_fp16 = True
                 break
 
@@ -162,7 +164,16 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
         # candidate.
         highest_score_candidate = max(candidate_list, key=lambda candidate: candidate.score)
         if highest_score_candidate:
-            result.add(highest_score_candidate.path)
+            pattern = r"^(.*?)-\d+-of-\d+(\.\w+)$"
+            match = re.match(pattern, highest_score_candidate.path.as_posix())
+            if match:
+                for candidate in candidate_list:
+                    if candidate.path.as_posix().startswith(match.group(1)) and candidate.path.as_posix().endswith(
+                        match.group(2)
+                    ):
+                        result.add(candidate.path)
+            else:
+                result.add(highest_score_candidate.path)
 
     # If one of the architecture-related variants was specified and no files matched other than
     # config and text files then we return an empty list

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -49,9 +49,32 @@ class FLUXConditioningInfo:
         return self
 
 
+@dataclass
+class SD3ConditioningInfo:
+    clip_l_pooled_embeds: torch.Tensor
+    clip_l_embeds: torch.Tensor
+    clip_g_pooled_embeds: torch.Tensor
+    clip_g_embeds: torch.Tensor
+    t5_embeds: torch.Tensor | None
+
+    def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
+        self.clip_l_pooled_embeds = self.clip_l_pooled_embeds.to(device=device, dtype=dtype)
+        self.clip_l_embeds = self.clip_l_embeds.to(device=device, dtype=dtype)
+        self.clip_g_pooled_embeds = self.clip_g_pooled_embeds.to(device=device, dtype=dtype)
+        self.clip_g_embeds = self.clip_g_embeds.to(device=device, dtype=dtype)
+        if self.t5_embeds is not None:
+            self.t5_embeds = self.t5_embeds.to(device=device, dtype=dtype)
+        return self
+
+
 @dataclass
 class ConditioningFieldData:
-    conditionings: List[BasicConditioningInfo] | List[SDXLConditioningInfo] | List[FLUXConditioningInfo]
+    conditionings: (
+        List[BasicConditioningInfo]
+        | List[SDXLConditioningInfo]
+        | List[FLUXConditioningInfo]
+        | List[SD3ConditioningInfo]
+    )
 
 
 @dataclass

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

@@ -164,7 +164,7 @@ const handleVAEModels: ModelHandler = (models, state, dispatch, log) => {
   // We have a VAE selected, need to check if it is available
 
   // Grab just the VAE models
-  const vaeModels = models.filter(isNonFluxVAEModelConfig);
+  const vaeModels = models.filter((m) => isNonFluxVAEModelConfig(m));
 
   // If the current VAE model is available, we don't need to do anything
   if (vaeModels.some((m) => m.key === selectedVAEModel.key)) {
@@ -297,7 +297,7 @@ const handleUpscaleModel: ModelHandler = (models, state, dispatch, log) => {
 
 const handleT5EncoderModels: ModelHandler = (models, state, dispatch, log) => {
   const selectedT5EncoderModel = state.params.t5EncoderModel;
-  const t5EncoderModels = models.filter(isT5EncoderModelConfig);
+  const t5EncoderModels = models.filter((m) => isT5EncoderModelConfig(m));
 
   // If the currently selected model is available, we don't need to do anything
   if (selectedT5EncoderModel && t5EncoderModels.some((m) => m.key === selectedT5EncoderModel.key)) {
@@ -325,7 +325,7 @@ const handleT5EncoderModels: ModelHandler = (models, state, dispatch, log) => {
 
 const handleCLIPEmbedModels: ModelHandler = (models, state, dispatch, log) => {
   const selectedCLIPEmbedModel = state.params.clipEmbedModel;
-  const CLIPEmbedModels = models.filter(isCLIPEmbedModelConfig);
+  const CLIPEmbedModels = models.filter((m) => isCLIPEmbedModelConfig(m));
 
   // If the currently selected model is available, we don't need to do anything
   if (selectedCLIPEmbedModel && CLIPEmbedModels.some((m) => m.key === selectedCLIPEmbedModel.key)) {
@@ -353,7 +353,7 @@ const handleCLIPEmbedModels: ModelHandler = (models, state, dispatch, log) => {
 
 const handleFLUXVAEModels: ModelHandler = (models, state, dispatch, log) => {
   const selectedFLUXVAEModel = state.params.fluxVAE;
-  const fluxVAEModels = models.filter(isFluxVAEModelConfig);
+  const fluxVAEModels = models.filter((m) => isFluxVAEModelConfig(m));
 
   // If the currently selected model is available, we don't need to do anything
   if (selectedFLUXVAEModel && fluxVAEModels.some((m) => m.key === selectedFLUXVAEModel.key)) {

invokeai/frontend/web/src/common/util/fixTooltipCloseOnScrollStyles.ts

@@ -0,0 +1,15 @@
+import type { CSSProperties } from 'react';
+
+/**
+ * Chakra's Tooltip's method of finding the nearest scroll parent has a problem - it assumes the first parent with
+ * `overflow: hidden` is the scroll parent. In this case, the Collapse component has that style, but isn't scrollable
+ * itself. The result is that the tooltip does not close on scroll, because the scrolling happens higher up in the DOM.
+ *
+ * As a hacky workaround, we can set the overflow to `visible`, which allows the scroll parent search to continue up to
+ * the actual scroll parent (in this case, the OverlayScrollbarsComponent in BoardsListWrapper).
+ *
+ * See: https://github.com/chakra-ui/chakra-ui/issues/7871#issuecomment-2453780958
+ */
+export const fixTooltipCloseOnScrollStyles: CSSProperties = {
+  overflow: 'visible',
+};

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

@@ -72,10 +72,16 @@ const useSaveCanvas = ({ region, saveToGallery, toastOk, toastError, onSave, wit
 
     const result = await withResultAsync(() => {
       const rasterAdapters = canvasManager.compositor.getVisibleAdaptersOfType('raster_layer');
-      return canvasManager.compositor.getCompositeImageDTO(rasterAdapters, rect, {
-        is_intermediate: !saveToGallery,
-        metadata,
-      });
+      return canvasManager.compositor.getCompositeImageDTO(
+        rasterAdapters,
+        rect,
+        {
+          is_intermediate: !saveToGallery,
+          metadata,
+        },
+        undefined,
+        true // force upload the image to ensure it gets added to the gallery
+      );
     });
 
     if (result.isOk()) {

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

@@ -253,18 +253,20 @@ export class CanvasCompositorModule extends CanvasModuleBase {
    * @param rect The region to include in the rasterized image
    * @param uploadOptions Options for uploading the image
    * @param compositingOptions Options for compositing the entities
+   * @param forceUpload If true, the image is always re-uploaded, returning a new image DTO
    * @returns A promise that resolves to the image DTO
    */
   getCompositeImageDTO = async (
     adapters: CanvasEntityAdapter[],
     rect: Rect,
     uploadOptions: Pick<UploadOptions, 'is_intermediate' | 'metadata'>,
-    compositingOptions?: CompositingOptions
+    compositingOptions?: CompositingOptions,
+    forceUpload?: boolean
   ): Promise<ImageDTO> => {
     assert(rect.width > 0 && rect.height > 0, 'Unable to rasterize empty rect');
 
     const hash = this.getCompositeHash(adapters, { rect });
-    const cachedImageName = this.manager.cache.imageNameCache.get(hash);
+    const cachedImageName = forceUpload ? undefined : this.manager.cache.imageNameCache.get(hash);
 
     let imageDTO: ImageDTO | null = null;
 

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

@@ -210,6 +210,10 @@ export class CanvasEntityFilterer extends CanvasModuleBase {
    * Processes the filter, updating the module's state and rendering the filtered image.
    */
   processImmediate = async () => {
+    if (!this.$isFiltering.get()) {
+      this.log.warn('Cannot process filter when not initialized');
+      return;
+    }
     const config = this.$filterConfig.get();
     const filterData = IMAGE_FILTERS[config.type];
 
@@ -342,7 +346,6 @@ export class CanvasEntityFilterer extends CanvasModuleBase {
     });
 
     // Final cleanup and teardown, returning user to main canvas UI
-    this.resetEphemeralState();
     this.teardown();
   };
 
@@ -409,9 +412,11 @@ export class CanvasEntityFilterer extends CanvasModuleBase {
   };
 
   teardown = () => {
-    this.$initialFilterConfig.set(null);
-    this.konva.group.remove();
     this.unsubscribe();
+    this.konva.group.remove();
+    // The reset must be done _after_ unsubscribing from listeners, in case the listeners would otherwise react to
+    // the reset. For example, if auto-processing is enabled and we reset the state, it may trigger processing.
+    this.resetEphemeralState();
     this.$isFiltering.set(false);
     this.manager.stateApi.$filteringAdapter.set(null);
   };
@@ -428,7 +433,6 @@ export class CanvasEntityFilterer extends CanvasModuleBase {
 
   cancel = () => {
     this.log.trace('Canceling');
-    this.resetEphemeralState();
     this.teardown();
   };
 

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

@@ -1,4 +1,5 @@
 import { Mutex } from 'async-mutex';
+import { parseify } from 'common/util/serialize';
 import type { CanvasManager } from 'features/controlLayers/konva/CanvasManager';
 import { CanvasModuleBase } from 'features/controlLayers/konva/CanvasModuleBase';
 import { getPrefixedId, loadImage } from 'features/controlLayers/konva/util';
@@ -26,13 +27,13 @@ export class CanvasProgressImageModule extends CanvasModuleBase {
     group: Konva.Group;
     image: Konva.Image | null; // The image is loaded asynchronously, so it may not be available immediately
   };
-  isLoading: boolean = false;
-  isError: boolean = false;
+  $isLoading = atom<boolean>(false);
+  $isError = atom<boolean>(false);
   imageElement: HTMLImageElement | null = null;
 
   subscriptions = new Set<() => void>();
   $lastProgressEvent = atom<ProgressEventWithImage | null>(null);
-  hasActiveGeneration: boolean = false;
+  $hasActiveGeneration = atom<boolean>(false);
   mutex: Mutex = new Mutex();
 
   constructor(manager: CanvasManager) {
@@ -56,12 +57,9 @@ export class CanvasProgressImageModule extends CanvasModuleBase {
     this.subscriptions.add(
       this.manager.stateApi.createStoreSubscription(selectCanvasQueueCounts, ({ data }) => {
         if (data && (data.in_progress > 0 || data.pending > 0)) {
-          this.hasActiveGeneration = true;
+          this.$hasActiveGeneration.set(true);
         } else {
-          this.hasActiveGeneration = false;
-          if (!this.manager.stagingArea.$isStaging.get()) {
-            this.$lastProgressEvent.set(null);
-          }
+          this.$hasActiveGeneration.set(false);
         }
       })
     );
@@ -76,23 +74,36 @@ export class CanvasProgressImageModule extends CanvasModuleBase {
       if (!isProgressEventWithImage(data)) {
         return;
       }
-      if (!this.hasActiveGeneration) {
+      if (!this.$hasActiveGeneration.get()) {
         return;
       }
       this.$lastProgressEvent.set(data);
     };
 
+    // Handle a canceled or failed canvas generation. We should clear the progress image in this case.
+    const queueItemStatusChangedListener = (data: S['QueueItemStatusChangedEvent']) => {
+      if (data.destination !== 'canvas') {
+        return;
+      }
+      if (data.status === 'failed' || data.status === 'canceled') {
+        this.$lastProgressEvent.set(null);
+        this.$hasActiveGeneration.set(false);
+      }
+    };
+
     const clearProgress = () => {
       this.$lastProgressEvent.set(null);
     };
 
     this.manager.socket.on('invocation_progress', progressListener);
+    this.manager.socket.on('queue_item_status_changed', queueItemStatusChangedListener);
     this.manager.socket.on('connect', clearProgress);
     this.manager.socket.on('connect_error', clearProgress);
     this.manager.socket.on('disconnect', clearProgress);
 
     return () => {
       this.manager.socket.off('invocation_progress', progressListener);
+      this.manager.socket.off('queue_item_status_changed', queueItemStatusChangedListener);
       this.manager.socket.off('connect', clearProgress);
       this.manager.socket.off('connect_error', clearProgress);
       this.manager.socket.off('disconnect', clearProgress);
@@ -114,13 +125,13 @@ export class CanvasProgressImageModule extends CanvasModuleBase {
       this.konva.image?.destroy();
       this.konva.image = null;
       this.imageElement = null;
-      this.isLoading = false;
-      this.isError = false;
+      this.$isLoading.set(false);
+      this.$isError.set(false);
       release();
       return;
     }
 
-    this.isLoading = true;
+    this.$isLoading.set(true);
 
     const { x, y, width, height } = this.manager.stateApi.getBbox().rect;
     try {
@@ -149,9 +160,9 @@ export class CanvasProgressImageModule extends CanvasModuleBase {
       // Should not be visible if the user has disabled showing staging images
       this.konva.group.visible(this.manager.stagingArea.$shouldShowStagedImage.get());
     } catch {
-      this.isError = true;
+      this.$isError.set(true);
     } finally {
-      this.isLoading = false;
+      this.$isLoading.set(false);
       release();
     }
   };
@@ -162,4 +173,16 @@ export class CanvasProgressImageModule extends CanvasModuleBase {
     this.subscriptions.clear();
     this.konva.group.destroy();
   };
+
+  repr = () => {
+    return {
+      id: this.id,
+      type: this.type,
+      path: this.path,
+      $lastProgressEvent: parseify(this.$lastProgressEvent.get()),
+      $hasActiveGeneration: this.$hasActiveGeneration.get(),
+      $isError: this.$isError.get(),
+      $isLoading: this.$isLoading.get(),
+    };
+  };
 }

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

@@ -535,6 +535,11 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
    * Processes the SAM points to segment the entity, updating the module's state and rendering the mask.
    */
   processImmediate = async () => {
+    if (!this.$isSegmenting.get()) {
+      this.log.warn('Cannot process segmentation when not initialized');
+      return;
+    }
+
     if (this.$isProcessing.get()) {
       this.log.warn('Already processing');
       return;
@@ -689,7 +694,6 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
     });
 
     // Final cleanup and teardown, returning user to main canvas UI
-    this.resetEphemeralState();
     this.teardown();
   };
 
@@ -758,7 +762,6 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
   cancel = () => {
     this.log.trace('Canceling');
     // Reset the module's state and tear down, returning user to main canvas UI
-    this.resetEphemeralState();
     this.teardown();
   };
 
@@ -773,8 +776,11 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
    * - Resets the global segmenting adapter
    */
   teardown = () => {
-    this.konva.group.remove();
     this.unsubscribe();
+    this.konva.group.remove();
+    // The reset must be done _after_ unsubscribing from listeners, in case the listeners would otherwise react to
+    // the reset. For example, if auto-processing is enabled and we reset the state, it may trigger processing.
+    this.resetEphemeralState();
     this.$isSegmenting.set(false);
     this.manager.stateApi.$segmentingAdapter.set(null);
   };

invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/BoardsList.tsx

@@ -1,6 +1,7 @@
 import { Button, Collapse, Flex, Icon, Text, useDisclosure } from '@invoke-ai/ui-library';
 import { EMPTY_ARRAY } from 'app/store/constants';
 import { useAppSelector } from 'app/store/storeHooks';
+import { fixTooltipCloseOnScrollStyles } from 'common/util/fixTooltipCloseOnScrollStyles';
 import {
   selectBoardSearchText,
   selectListBoardsQueryArgs,
@@ -104,7 +105,7 @@ export const BoardsList = ({ isPrivate }: Props) => {
         )}
         <AddBoardButton isPrivateBoard={isPrivate} />
       </Flex>
-      <Collapse in={isOpen}>
+      <Collapse in={isOpen} style={fixTooltipCloseOnScrollStyles}>
         <Flex direction="column" gap={1}>
           {boardElements.length ? (
             boardElements

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

@@ -11,6 +11,7 @@ const BASE_COLOR_MAP: Record<BaseModelType, string> = {
   any: 'base',
   'sd-1': 'green',
   'sd-2': 'teal',
+  'sd-3': 'purple',
   sdxl: 'invokeBlue',
   'sdxl-refiner': 'invokeBlue',
   flux: 'gold',

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

@@ -80,19 +80,19 @@ const ModelList = () => {
     [clipVisionModels, searchTerm, filteredModelType]
   );
 
-  const [vaeModels, { isLoading: isLoadingVAEModels }] = useVAEModels();
+  const [vaeModels, { isLoading: isLoadingVAEModels }] = useVAEModels({ excludeSubmodels: true });
   const filteredVAEModels = useMemo(
     () => modelsFilter(vaeModels, searchTerm, filteredModelType),
     [vaeModels, searchTerm, filteredModelType]
   );
 
-  const [t5EncoderModels, { isLoading: isLoadingT5EncoderModels }] = useT5EncoderModels();
+  const [t5EncoderModels, { isLoading: isLoadingT5EncoderModels }] = useT5EncoderModels({ excludeSubmodels: true });
   const filteredT5EncoderModels = useMemo(
     () => modelsFilter(t5EncoderModels, searchTerm, filteredModelType),
     [t5EncoderModels, searchTerm, filteredModelType]
   );
 
-  const [clipEmbedModels, { isLoading: isLoadingClipEmbedModels }] = useCLIPEmbedModels();
+  const [clipEmbedModels, { isLoading: isLoadingClipEmbedModels }] = useCLIPEmbedModels({ excludeSubmodels: true });
   const filteredClipEmbedModels = useMemo(
     () => modelsFilter(clipEmbedModels, searchTerm, filteredModelType),
     [clipEmbedModels, searchTerm, filteredModelType]

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

@@ -8,6 +8,10 @@ import {
   isBooleanFieldInputTemplate,
   isCLIPEmbedModelFieldInputInstance,
   isCLIPEmbedModelFieldInputTemplate,
+  isCLIPGEmbedModelFieldInputInstance,
+  isCLIPGEmbedModelFieldInputTemplate,
+  isCLIPLEmbedModelFieldInputInstance,
+  isCLIPLEmbedModelFieldInputTemplate,
   isColorFieldInputInstance,
   isColorFieldInputTemplate,
   isControlNetModelFieldInputInstance,
@@ -34,6 +38,8 @@ import {
   isModelIdentifierFieldInputTemplate,
   isSchedulerFieldInputInstance,
   isSchedulerFieldInputTemplate,
+  isSD3MainModelFieldInputInstance,
+  isSD3MainModelFieldInputTemplate,
   isSDXLMainModelFieldInputInstance,
   isSDXLMainModelFieldInputTemplate,
   isSDXLRefinerModelFieldInputInstance,
@@ -54,6 +60,8 @@ import { memo } from 'react';
 import BoardFieldInputComponent from './inputs/BoardFieldInputComponent';
 import BooleanFieldInputComponent from './inputs/BooleanFieldInputComponent';
 import CLIPEmbedModelFieldInputComponent from './inputs/CLIPEmbedModelFieldInputComponent';
+import CLIPGEmbedModelFieldInputComponent from './inputs/CLIPGEmbedModelFieldInputComponent';
+import CLIPLEmbedModelFieldInputComponent from './inputs/CLIPLEmbedModelFieldInputComponent';
 import ColorFieldInputComponent from './inputs/ColorFieldInputComponent';
 import ControlNetModelFieldInputComponent from './inputs/ControlNetModelFieldInputComponent';
 import EnumFieldInputComponent from './inputs/EnumFieldInputComponent';
@@ -66,6 +74,7 @@ import MainModelFieldInputComponent from './inputs/MainModelFieldInputComponent'
 import NumberFieldInputComponent from './inputs/NumberFieldInputComponent';
 import RefinerModelFieldInputComponent from './inputs/RefinerModelFieldInputComponent';
 import SchedulerFieldInputComponent from './inputs/SchedulerFieldInputComponent';
+import SD3MainModelFieldInputComponent from './inputs/SD3MainModelFieldInputComponent';
 import SDXLMainModelFieldInputComponent from './inputs/SDXLMainModelFieldInputComponent';
 import SpandrelImageToImageModelFieldInputComponent from './inputs/SpandrelImageToImageModelFieldInputComponent';
 import StringFieldInputComponent from './inputs/StringFieldInputComponent';
@@ -132,6 +141,14 @@ const InputFieldRenderer = ({ nodeId, fieldName }: InputFieldProps) => {
     return <CLIPEmbedModelFieldInputComponent nodeId={nodeId} field={fieldInstance} fieldTemplate={fieldTemplate} />;
   }
 
+  if (isCLIPLEmbedModelFieldInputInstance(fieldInstance) && isCLIPLEmbedModelFieldInputTemplate(fieldTemplate)) {
+    return <CLIPLEmbedModelFieldInputComponent nodeId={nodeId} field={fieldInstance} fieldTemplate={fieldTemplate} />;
+  }
+
+  if (isCLIPGEmbedModelFieldInputInstance(fieldInstance) && isCLIPGEmbedModelFieldInputTemplate(fieldTemplate)) {
+    return <CLIPGEmbedModelFieldInputComponent nodeId={nodeId} field={fieldInstance} fieldTemplate={fieldTemplate} />;
+  }
+
   if (isFluxVAEModelFieldInputInstance(fieldInstance) && isFluxVAEModelFieldInputTemplate(fieldTemplate)) {
     return <FluxVAEModelFieldInputComponent nodeId={nodeId} field={fieldInstance} fieldTemplate={fieldTemplate} />;
   }
@@ -168,10 +185,15 @@ const InputFieldRenderer = ({ nodeId, fieldName }: InputFieldProps) => {
   if (isColorFieldInputInstance(fieldInstance) && isColorFieldInputTemplate(fieldTemplate)) {
     return <ColorFieldInputComponent nodeId={nodeId} field={fieldInstance} fieldTemplate={fieldTemplate} />;
   }
+
   if (isFluxMainModelFieldInputInstance(fieldInstance) && isFluxMainModelFieldInputTemplate(fieldTemplate)) {
     return <FluxMainModelFieldInputComponent nodeId={nodeId} field={fieldInstance} fieldTemplate={fieldTemplate} />;
   }
 
+  if (isSD3MainModelFieldInputInstance(fieldInstance) && isSD3MainModelFieldInputTemplate(fieldTemplate)) {
+    return <SD3MainModelFieldInputComponent nodeId={nodeId} field={fieldInstance} fieldTemplate={fieldTemplate} />;
+  }
+
   if (isSDXLMainModelFieldInputInstance(fieldInstance) && isSDXLMainModelFieldInputTemplate(fieldTemplate)) {
     return <SDXLMainModelFieldInputComponent nodeId={nodeId} field={fieldInstance} fieldTemplate={fieldTemplate} />;
   }

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

@@ -39,14 +39,15 @@ const CLIPEmbedModelFieldInputComponent = (props: Props) => {
     isLoading,
     selectedModel: field.value,
   });
+  const required = props.fieldTemplate.required;
 
   return (
     <Flex w="full" alignItems="center" gap={2}>
       <Tooltip label={!disabledTabs.includes('models') && t('modelManager.starterModelsInModelManager')}>
-        <FormControl className="nowheel nodrag" isDisabled={!options.length} isInvalid={!value}>
+        <FormControl className="nowheel nodrag" isDisabled={!options.length} isInvalid={!value && required}>
           <Combobox
             value={value}
-            placeholder={placeholder}
+            placeholder={required ? placeholder : `(Optional) ${placeholder}`}
             options={options}
             onChange={onChange}
             noOptionsMessage={noOptionsMessage}

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

@@ -0,0 +1,62 @@
+import { Combobox, Flex, FormControl, Tooltip } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useGroupedModelCombobox } from 'common/hooks/useGroupedModelCombobox';
+import { fieldCLIPGEmbedValueChanged } from 'features/nodes/store/nodesSlice';
+import type { CLIPGEmbedModelFieldInputInstance, CLIPGEmbedModelFieldInputTemplate } from 'features/nodes/types/field';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { useCLIPEmbedModels } from 'services/api/hooks/modelsByType';
+import { type CLIPGEmbedModelConfig, isCLIPGEmbedModelConfig } from 'services/api/types';
+
+import type { FieldComponentProps } from './types';
+
+type Props = FieldComponentProps<CLIPGEmbedModelFieldInputInstance, CLIPGEmbedModelFieldInputTemplate>;
+
+const CLIPGEmbedModelFieldInputComponent = (props: Props) => {
+  const { nodeId, field } = props;
+  const { t } = useTranslation();
+  const disabledTabs = useAppSelector((s) => s.config.disabledTabs);
+  const dispatch = useAppDispatch();
+  const [modelConfigs, { isLoading }] = useCLIPEmbedModels();
+
+  const _onChange = useCallback(
+    (value: CLIPGEmbedModelConfig | null) => {
+      if (!value) {
+        return;
+      }
+      dispatch(
+        fieldCLIPGEmbedValueChanged({
+          nodeId,
+          fieldName: field.name,
+          value,
+        })
+      );
+    },
+    [dispatch, field.name, nodeId]
+  );
+  const { options, value, onChange, placeholder, noOptionsMessage } = useGroupedModelCombobox({
+    modelConfigs: modelConfigs.filter((config) => isCLIPGEmbedModelConfig(config)),
+    onChange: _onChange,
+    isLoading,
+    selectedModel: field.value,
+  });
+  const required = props.fieldTemplate.required;
+
+  return (
+    <Flex w="full" alignItems="center" gap={2}>
+      <Tooltip label={!disabledTabs.includes('models') && t('modelManager.starterModelsInModelManager')}>
+        <FormControl className="nowheel nodrag" isDisabled={!options.length} isInvalid={!value && required}>
+          <Combobox
+            value={value}
+            placeholder={required ? placeholder : `(Optional) ${placeholder}`}
+            options={options}
+            onChange={onChange}
+            noOptionsMessage={noOptionsMessage}
+          />
+        </FormControl>
+      </Tooltip>
+    </Flex>
+  );
+};
+
+export default memo(CLIPGEmbedModelFieldInputComponent);

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

@@ -0,0 +1,62 @@
+import { Combobox, Flex, FormControl, Tooltip } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useGroupedModelCombobox } from 'common/hooks/useGroupedModelCombobox';
+import { fieldCLIPLEmbedValueChanged } from 'features/nodes/store/nodesSlice';
+import type { CLIPLEmbedModelFieldInputInstance, CLIPLEmbedModelFieldInputTemplate } from 'features/nodes/types/field';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { useCLIPEmbedModels } from 'services/api/hooks/modelsByType';
+import { type CLIPLEmbedModelConfig, isCLIPLEmbedModelConfig } from 'services/api/types';
+
+import type { FieldComponentProps } from './types';
+
+type Props = FieldComponentProps<CLIPLEmbedModelFieldInputInstance, CLIPLEmbedModelFieldInputTemplate>;
+
+const CLIPLEmbedModelFieldInputComponent = (props: Props) => {
+  const { nodeId, field } = props;
+  const { t } = useTranslation();
+  const disabledTabs = useAppSelector((s) => s.config.disabledTabs);
+  const dispatch = useAppDispatch();
+  const [modelConfigs, { isLoading }] = useCLIPEmbedModels();
+
+  const _onChange = useCallback(
+    (value: CLIPLEmbedModelConfig | null) => {
+      if (!value) {
+        return;
+      }
+      dispatch(
+        fieldCLIPLEmbedValueChanged({
+          nodeId,
+          fieldName: field.name,
+          value,
+        })
+      );
+    },
+    [dispatch, field.name, nodeId]
+  );
+  const { options, value, onChange, placeholder, noOptionsMessage } = useGroupedModelCombobox({
+    modelConfigs: modelConfigs.filter((config) => isCLIPLEmbedModelConfig(config)),
+    onChange: _onChange,
+    isLoading,
+    selectedModel: field.value,
+  });
+  const required = props.fieldTemplate.required;
+
+  return (
+    <Flex w="full" alignItems="center" gap={2}>
+      <Tooltip label={!disabledTabs.includes('models') && t('modelManager.starterModelsInModelManager')}>
+        <FormControl className="nowheel nodrag" isDisabled={!options.length} isInvalid={!value && required}>
+          <Combobox
+            value={value}
+            placeholder={required ? placeholder : `(Optional) ${placeholder}`}
+            options={options}
+            onChange={onChange}
+            noOptionsMessage={noOptionsMessage}
+          />
+        </FormControl>
+      </Tooltip>
+    </Flex>
+  );
+};
+
+export default memo(CLIPLEmbedModelFieldInputComponent);

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/inputs/SD3MainModelFieldInputComponent.tsx

@@ -0,0 +1,59 @@
+import { Combobox, Flex, FormControl } from '@invoke-ai/ui-library';
+import { useAppDispatch } from 'app/store/storeHooks';
+import { useGroupedModelCombobox } from 'common/hooks/useGroupedModelCombobox';
+import { fieldMainModelValueChanged } from 'features/nodes/store/nodesSlice';
+import type { SD3MainModelFieldInputInstance, SD3MainModelFieldInputTemplate } from 'features/nodes/types/field';
+import { memo, useCallback } from 'react';
+import { useSD3Models } from 'services/api/hooks/modelsByType';
+import type { MainModelConfig } from 'services/api/types';
+
+import type { FieldComponentProps } from './types';
+
+type Props = FieldComponentProps<SD3MainModelFieldInputInstance, SD3MainModelFieldInputTemplate>;
+
+const SD3MainModelFieldInputComponent = (props: Props) => {
+  const { nodeId, field } = props;
+  const dispatch = useAppDispatch();
+  const [modelConfigs, { isLoading }] = useSD3Models();
+  const _onChange = useCallback(
+    (value: MainModelConfig | null) => {
+      if (!value) {
+        return;
+      }
+      dispatch(
+        fieldMainModelValueChanged({
+          nodeId,
+          fieldName: field.name,
+          value,
+        })
+      );
+    },
+    [dispatch, field.name, nodeId]
+  );
+  const { options, value, onChange, placeholder, noOptionsMessage } = useGroupedModelCombobox({
+    modelConfigs,
+    onChange: _onChange,
+    isLoading,
+    selectedModel: field.value,
+  });
+
+  return (
+    <Flex w="full" alignItems="center" gap={2}>
+      <FormControl
+        className="nowheel nodrag"
+        isDisabled={!options.length}
+        isInvalid={!value && props.fieldTemplate.required}
+      >
+        <Combobox
+          value={value}
+          placeholder={placeholder}
+          options={options}
+          onChange={onChange}
+          noOptionsMessage={noOptionsMessage}
+        />
+      </FormControl>
+    </Flex>
+  );
+};
+
+export default memo(SD3MainModelFieldInputComponent);

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/inputs/T5EncoderModelFieldInputComponent.tsx

@@ -40,14 +40,14 @@ const T5EncoderModelFieldInputComponent = (props: Props) => {
     isLoading,
     selectedModel: field.value,
   });
-
+  const required = props.fieldTemplate.required;
   return (
     <Flex w="full" alignItems="center" gap={2}>
       <Tooltip label={!isModelsTabDisabled && t('modelManager.starterModelsInModelManager')}>
-        <FormControl className="nowheel nodrag" isDisabled={!options.length} isInvalid={!value}>
+        <FormControl className="nowheel nodrag" isDisabled={!options.length} isInvalid={!value && required}>
           <Combobox
             value={value}
-            placeholder={placeholder}
+            placeholder={required ? placeholder : `(Optional) ${placeholder}`}
             options={options}
             onChange={onChange}
             noOptionsMessage={noOptionsMessage}

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

@@ -36,13 +36,14 @@ const VAEModelFieldInputComponent = (props: Props) => {
     selectedModel: field.value,
     isLoading,
   });
+  const required = props.fieldTemplate.required;
 
   return (
     <Flex w="full" alignItems="center" gap={2}>
-      <FormControl className="nowheel nodrag" isDisabled={!options.length} isInvalid={!value}>
+      <FormControl className="nowheel nodrag" isDisabled={!options.length} isInvalid={!value && required}>
         <Combobox
           value={value}
-          placeholder={placeholder}
+          placeholder={required ? placeholder : `(Optional) ${placeholder}`}
           options={options}
           onChange={onChange}
           noOptionsMessage={noOptionsMessage}

invokeai/frontend/web/src/features/nodes/components/sidePanel/WorkflowListMenu/WorkflowList.tsx

@@ -2,6 +2,7 @@ import { Button, Collapse, Flex, Icon, Spinner, Text } from '@invoke-ai/ui-libra
 import { EMPTY_ARRAY } from 'app/store/constants';
 import { useAppSelector } from 'app/store/storeHooks';
 import { IAINoContentFallback } from 'common/components/IAIImageFallback';
+import { fixTooltipCloseOnScrollStyles } from 'common/util/fixTooltipCloseOnScrollStyles';
 import { useCategorySections } from 'features/nodes/hooks/useCategorySections';
 import {
   selectWorkflowOrderBy,
@@ -61,7 +62,7 @@ export const WorkflowList = ({ category }: { category: WorkflowCategory }) => {
           </Text>
         </Flex>
       </Button>
-      <Collapse in={isOpen}>
+      <Collapse in={isOpen} style={fixTooltipCloseOnScrollStyles}>
         {isLoading ? (
           <Flex alignItems="center" justifyContent="center" p={20}>
             <Spinner />

invokeai/frontend/web/src/features/nodes/components/sidePanel/WorkflowListMenu/WorkflowListItem.tsx

@@ -105,7 +105,7 @@ export const WorkflowListItem = ({ workflow }: { workflow: WorkflowRecordListIte
       onMouseOut={handleMouseOut}
       alignItems="center"
     >
-      <Tooltip label={<WorkflowListItemTooltip workflow={workflow} />}>
+      <Tooltip label={<WorkflowListItemTooltip workflow={workflow} />} closeOnScroll>
         <Flex flexDir="column" gap={1}>
           <Flex gap={4} alignItems="center">
             <Text noOfLines={2}>{workflow.name}</Text>
@@ -137,6 +137,7 @@ export const WorkflowListItem = ({ workflow }: { workflow: WorkflowRecordListIte
           label={t('workflows.edit')}
           // This prevents an issue where the tooltip isn't closed after the modal is opened
           isOpen={!isHovered ? false : undefined}
+          closeOnScroll
         >
           <IconButton
             size="sm"
@@ -150,6 +151,7 @@ export const WorkflowListItem = ({ workflow }: { workflow: WorkflowRecordListIte
           label={t('workflows.download')}
           // This prevents an issue where the tooltip isn't closed after the modal is opened
           isOpen={!isHovered ? false : undefined}
+          closeOnScroll
         >
           <IconButton
             size="sm"
@@ -164,6 +166,7 @@ export const WorkflowListItem = ({ workflow }: { workflow: WorkflowRecordListIte
             label={t('workflows.copyShareLink')}
             // This prevents an issue where the tooltip isn't closed after the modal is opened
             isOpen={!isHovered ? false : undefined}
+            closeOnScroll
           >
             <IconButton
               size="sm"
@@ -179,6 +182,7 @@ export const WorkflowListItem = ({ workflow }: { workflow: WorkflowRecordListIte
             label={t('workflows.delete')}
             // This prevents an issue where the tooltip isn't closed after the modal is opened
             isOpen={!isHovered ? false : undefined}
+            closeOnScroll
           >
             <IconButton
               size="sm"

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

@@ -8,6 +8,8 @@ import type {
   BoardFieldValue,
   BooleanFieldValue,
   CLIPEmbedModelFieldValue,
+  CLIPGEmbedModelFieldValue,
+  CLIPLEmbedModelFieldValue,
   ColorFieldValue,
   ControlNetModelFieldValue,
   EnumFieldValue,
@@ -33,6 +35,8 @@ import {
   zBoardFieldValue,
   zBooleanFieldValue,
   zCLIPEmbedModelFieldValue,
+  zCLIPGEmbedModelFieldValue,
+  zCLIPLEmbedModelFieldValue,
   zColorFieldValue,
   zControlNetModelFieldValue,
   zEnumFieldValue,
@@ -354,6 +358,12 @@ export const nodesSlice = createSlice({
     fieldCLIPEmbedValueChanged: (state, action: FieldValueAction<CLIPEmbedModelFieldValue>) => {
       fieldValueReducer(state, action, zCLIPEmbedModelFieldValue);
     },
+    fieldCLIPLEmbedValueChanged: (state, action: FieldValueAction<CLIPLEmbedModelFieldValue>) => {
+      fieldValueReducer(state, action, zCLIPLEmbedModelFieldValue);
+    },
+    fieldCLIPGEmbedValueChanged: (state, action: FieldValueAction<CLIPGEmbedModelFieldValue>) => {
+      fieldValueReducer(state, action, zCLIPGEmbedModelFieldValue);
+    },
     fieldFluxVAEModelValueChanged: (state, action: FieldValueAction<FluxVAEModelFieldValue>) => {
       fieldValueReducer(state, action, zFluxVAEModelFieldValue);
     },
@@ -420,6 +430,8 @@ export const {
   fieldVaeModelValueChanged,
   fieldT5EncoderValueChanged,
   fieldCLIPEmbedValueChanged,
+  fieldCLIPLEmbedValueChanged,
+  fieldCLIPGEmbedValueChanged,
   fieldFluxVAEModelValueChanged,
   nodeEditorReset,
   nodeIsIntermediateChanged,
@@ -527,6 +539,8 @@ export const isAnyNodeOrEdgeMutation = isAnyOf(
   fieldVaeModelValueChanged,
   fieldT5EncoderValueChanged,
   fieldCLIPEmbedValueChanged,
+  fieldCLIPLEmbedValueChanged,
+  fieldCLIPGEmbedValueChanged,
   fieldFluxVAEModelValueChanged,
   // The `nodesChanged` has extra logic and is handled in its own extra reducer
   // nodesChanged,

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

@@ -61,8 +61,8 @@ export type SchedulerField = z.infer<typeof zSchedulerField>;
 // #endregion
 
 // #region Model-related schemas
-const zBaseModel = z.enum(['any', 'sd-1', 'sd-2', 'sdxl', 'sdxl-refiner', 'flux']);
-export const zMainModelBase = z.enum(['sd-1', 'sd-2', 'sdxl', 'flux']);
+const zBaseModel = z.enum(['any', 'sd-1', 'sd-2', 'sd-3', 'sdxl', 'sdxl-refiner', 'flux']);
+export const zMainModelBase = z.enum(['sd-1', 'sd-2', 'sd-3', 'sdxl', 'flux']);
 export type MainModelBase = z.infer<typeof zMainModelBase>;
 export const isMainModelBase = (base: unknown): base is MainModelBase => zMainModelBase.safeParse(base).success;
 const zModelType = z.enum([
@@ -84,8 +84,10 @@ const zSubModelType = z.enum([
   'transformer',
   'text_encoder',
   'text_encoder_2',
+  'text_encoder_3',
   'tokenizer',
   'tokenizer_2',
+  'tokenizer_3',
   'vae',
   'vae_decoder',
   'vae_encoder',

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

@@ -32,6 +32,7 @@ export const MODEL_TYPES = [
   'LoRAModelField',
   'MainModelField',
   'FluxMainModelField',
+  'SD3MainModelField',
   'SDXLMainModelField',
   'SDXLRefinerModelField',
   'VaeModelField',
@@ -65,6 +66,7 @@ export const FIELD_COLORS: { [key: string]: string } = {
   LoRAModelField: 'teal.500',
   MainModelField: 'teal.500',
   FluxMainModelField: 'teal.500',
+  SD3MainModelField: 'teal.500',
   SDXLMainModelField: 'teal.500',
   SDXLRefinerModelField: 'teal.500',
   SpandrelImageToImageModelField: 'teal.500',

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

@@ -115,6 +115,10 @@ const zSDXLMainModelFieldType = zFieldTypeBase.extend({
   name: z.literal('SDXLMainModelField'),
   originalType: zStatelessFieldType.optional(),
 });
+const zSD3MainModelFieldType = zFieldTypeBase.extend({
+  name: z.literal('SD3MainModelField'),
+  originalType: zStatelessFieldType.optional(),
+});
 const zFluxMainModelFieldType = zFieldTypeBase.extend({
   name: z.literal('FluxMainModelField'),
   originalType: zStatelessFieldType.optional(),
@@ -155,6 +159,14 @@ const zCLIPEmbedModelFieldType = zFieldTypeBase.extend({
   name: z.literal('CLIPEmbedModelField'),
   originalType: zStatelessFieldType.optional(),
 });
+const zCLIPLEmbedModelFieldType = zFieldTypeBase.extend({
+  name: z.literal('CLIPLEmbedModelField'),
+  originalType: zStatelessFieldType.optional(),
+});
+const zCLIPGEmbedModelFieldType = zFieldTypeBase.extend({
+  name: z.literal('CLIPGEmbedModelField'),
+  originalType: zStatelessFieldType.optional(),
+});
 const zFluxVAEModelFieldType = zFieldTypeBase.extend({
   name: z.literal('FluxVAEModelField'),
   originalType: zStatelessFieldType.optional(),
@@ -174,6 +186,7 @@ const zStatefulFieldType = z.union([
   zModelIdentifierFieldType,
   zMainModelFieldType,
   zSDXLMainModelFieldType,
+  zSD3MainModelFieldType,
   zFluxMainModelFieldType,
   zSDXLRefinerModelFieldType,
   zVAEModelFieldType,
@@ -184,6 +197,8 @@ const zStatefulFieldType = z.union([
   zSpandrelImageToImageModelFieldType,
   zT5EncoderModelFieldType,
   zCLIPEmbedModelFieldType,
+  zCLIPLEmbedModelFieldType,
+  zCLIPGEmbedModelFieldType,
   zFluxVAEModelFieldType,
   zColorFieldType,
   zSchedulerFieldType,
@@ -467,6 +482,29 @@ export const isSDXLMainModelFieldInputTemplate = (val: unknown): val is SDXLMain
   zSDXLMainModelFieldInputTemplate.safeParse(val).success;
 // #endregion
 
+// #region SD3MainModelField
+
+const zSD3MainModelFieldValue = zMainModelFieldValue; // TODO: Narrow to SDXL models only.
+const zSD3MainModelFieldInputInstance = zFieldInputInstanceBase.extend({
+  value: zSD3MainModelFieldValue,
+});
+const zSD3MainModelFieldInputTemplate = zFieldInputTemplateBase.extend({
+  type: zSD3MainModelFieldType,
+  originalType: zFieldType.optional(),
+  default: zSD3MainModelFieldValue,
+});
+const zSD3MainModelFieldOutputTemplate = zFieldOutputTemplateBase.extend({
+  type: zSD3MainModelFieldType,
+});
+export type SD3MainModelFieldInputInstance = z.infer<typeof zSD3MainModelFieldInputInstance>;
+export type SD3MainModelFieldInputTemplate = z.infer<typeof zSD3MainModelFieldInputTemplate>;
+export const isSD3MainModelFieldInputInstance = (val: unknown): val is SD3MainModelFieldInputInstance =>
+  zSD3MainModelFieldInputInstance.safeParse(val).success;
+export const isSD3MainModelFieldInputTemplate = (val: unknown): val is SD3MainModelFieldInputTemplate =>
+  zSD3MainModelFieldInputTemplate.safeParse(val).success;
+
+// #endregion
+
 // #region FluxMainModelField
 
 const zFluxMainModelFieldValue = zMainModelFieldValue; // TODO: Narrow to SDXL models only.
@@ -725,6 +763,52 @@ export const isCLIPEmbedModelFieldInputTemplate = (val: unknown): val is CLIPEmb
 
 // #endregion
 
+// #region CLIPLEmbedModelField
+
+export const zCLIPLEmbedModelFieldValue = zModelIdentifierField.optional();
+const zCLIPLEmbedModelFieldInputInstance = zFieldInputInstanceBase.extend({
+  value: zCLIPLEmbedModelFieldValue,
+});
+const zCLIPLEmbedModelFieldInputTemplate = zFieldInputTemplateBase.extend({
+  type: zCLIPLEmbedModelFieldType,
+  originalType: zFieldType.optional(),
+  default: zCLIPLEmbedModelFieldValue,
+});
+
+export type CLIPLEmbedModelFieldValue = z.infer<typeof zCLIPLEmbedModelFieldValue>;
+
+export type CLIPLEmbedModelFieldInputInstance = z.infer<typeof zCLIPLEmbedModelFieldInputInstance>;
+export type CLIPLEmbedModelFieldInputTemplate = z.infer<typeof zCLIPLEmbedModelFieldInputTemplate>;
+export const isCLIPLEmbedModelFieldInputInstance = (val: unknown): val is CLIPLEmbedModelFieldInputInstance =>
+  zCLIPLEmbedModelFieldInputInstance.safeParse(val).success;
+export const isCLIPLEmbedModelFieldInputTemplate = (val: unknown): val is CLIPLEmbedModelFieldInputTemplate =>
+  zCLIPLEmbedModelFieldInputTemplate.safeParse(val).success;
+
+// #endregion
+
+// #region CLIPGEmbedModelField
+
+export const zCLIPGEmbedModelFieldValue = zModelIdentifierField.optional();
+const zCLIPGEmbedModelFieldInputInstance = zFieldInputInstanceBase.extend({
+  value: zCLIPGEmbedModelFieldValue,
+});
+const zCLIPGEmbedModelFieldInputTemplate = zFieldInputTemplateBase.extend({
+  type: zCLIPGEmbedModelFieldType,
+  originalType: zFieldType.optional(),
+  default: zCLIPGEmbedModelFieldValue,
+});
+
+export type CLIPGEmbedModelFieldValue = z.infer<typeof zCLIPLEmbedModelFieldValue>;
+
+export type CLIPGEmbedModelFieldInputInstance = z.infer<typeof zCLIPGEmbedModelFieldInputInstance>;
+export type CLIPGEmbedModelFieldInputTemplate = z.infer<typeof zCLIPGEmbedModelFieldInputTemplate>;
+export const isCLIPGEmbedModelFieldInputInstance = (val: unknown): val is CLIPGEmbedModelFieldInputInstance =>
+  zCLIPGEmbedModelFieldInputInstance.safeParse(val).success;
+export const isCLIPGEmbedModelFieldInputTemplate = (val: unknown): val is CLIPGEmbedModelFieldInputTemplate =>
+  zCLIPGEmbedModelFieldInputTemplate.safeParse(val).success;
+
+// #endregion
+
 // #region SchedulerField
 
 export const zSchedulerFieldValue = zSchedulerField.optional();
@@ -806,6 +890,7 @@ export const zStatefulFieldValue = z.union([
   zMainModelFieldValue,
   zSDXLMainModelFieldValue,
   zFluxMainModelFieldValue,
+  zSD3MainModelFieldValue,
   zSDXLRefinerModelFieldValue,
   zVAEModelFieldValue,
   zLoRAModelFieldValue,
@@ -816,6 +901,8 @@ export const zStatefulFieldValue = z.union([
   zT5EncoderModelFieldValue,
   zFluxVAEModelFieldValue,
   zCLIPEmbedModelFieldValue,
+  zCLIPLEmbedModelFieldValue,
+  zCLIPGEmbedModelFieldValue,
   zColorFieldValue,
   zSchedulerFieldValue,
 ]);
@@ -837,6 +924,7 @@ const zStatefulFieldInputInstance = z.union([
   zModelIdentifierFieldInputInstance,
   zMainModelFieldInputInstance,
   zFluxMainModelFieldInputInstance,
+  zSD3MainModelFieldInputInstance,
   zSDXLMainModelFieldInputInstance,
   zSDXLRefinerModelFieldInputInstance,
   zVAEModelFieldInputInstance,
@@ -870,6 +958,7 @@ const zStatefulFieldInputTemplate = z.union([
   zModelIdentifierFieldInputTemplate,
   zMainModelFieldInputTemplate,
   zFluxMainModelFieldInputTemplate,
+  zSD3MainModelFieldInputTemplate,
   zSDXLMainModelFieldInputTemplate,
   zSDXLRefinerModelFieldInputTemplate,
   zVAEModelFieldInputTemplate,
@@ -881,6 +970,8 @@ const zStatefulFieldInputTemplate = z.union([
   zT5EncoderModelFieldInputTemplate,
   zFluxVAEModelFieldInputTemplate,
   zCLIPEmbedModelFieldInputTemplate,
+  zCLIPLEmbedModelFieldInputTemplate,
+  zCLIPGEmbedModelFieldInputTemplate,
   zColorFieldInputTemplate,
   zSchedulerFieldInputTemplate,
   zStatelessFieldInputTemplate,
@@ -904,6 +995,7 @@ const zStatefulFieldOutputTemplate = z.union([
   zModelIdentifierFieldOutputTemplate,
   zMainModelFieldOutputTemplate,
   zFluxMainModelFieldOutputTemplate,
+  zSD3MainModelFieldOutputTemplate,
   zSDXLMainModelFieldOutputTemplate,
   zSDXLRefinerModelFieldOutputTemplate,
   zVAEModelFieldOutputTemplate,

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

@@ -16,6 +16,7 @@ const FIELD_VALUE_FALLBACK_MAP: Record<StatefulFieldType['name'], FieldValue> =
   SchedulerField: 'dpmpp_3m_k',
   SDXLMainModelField: undefined,
   FluxMainModelField: undefined,
+  SD3MainModelField: undefined,
   SDXLRefinerModelField: undefined,
   StringField: '',
   T2IAdapterModelField: undefined,
@@ -25,6 +26,8 @@ const FIELD_VALUE_FALLBACK_MAP: Record<StatefulFieldType['name'], FieldValue> =
   T5EncoderModelField: undefined,
   FluxVAEModelField: undefined,
   CLIPEmbedModelField: undefined,
+  CLIPLEmbedModelField: undefined,
+  CLIPGEmbedModelField: undefined,
 };
 
 export const buildFieldInputInstance = (id: string, template: FieldInputTemplate): FieldInputInstance => {

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

@@ -3,6 +3,8 @@ import type {
   BoardFieldInputTemplate,
   BooleanFieldInputTemplate,
   CLIPEmbedModelFieldInputTemplate,
+  CLIPGEmbedModelFieldInputTemplate,
+  CLIPLEmbedModelFieldInputTemplate,
   ColorFieldInputTemplate,
   ControlNetModelFieldInputTemplate,
   EnumFieldInputTemplate,
@@ -18,6 +20,7 @@ import type {
   MainModelFieldInputTemplate,
   ModelIdentifierFieldInputTemplate,
   SchedulerFieldInputTemplate,
+  SD3MainModelFieldInputTemplate,
   SDXLMainModelFieldInputTemplate,
   SDXLRefinerModelFieldInputTemplate,
   SpandrelImageToImageModelFieldInputTemplate,
@@ -198,6 +201,20 @@ const buildFluxMainModelFieldInputTemplate: FieldInputTemplateBuilder<FluxMainMo
   return template;
 };
 
+const buildSD3MainModelFieldInputTemplate: FieldInputTemplateBuilder<SD3MainModelFieldInputTemplate> = ({
+  schemaObject,
+  baseField,
+  fieldType,
+}) => {
+  const template: SD3MainModelFieldInputTemplate = {
+    ...baseField,
+    type: fieldType,
+    default: schemaObject.default ?? undefined,
+  };
+
+  return template;
+};
+
 const buildRefinerModelFieldInputTemplate: FieldInputTemplateBuilder<SDXLRefinerModelFieldInputTemplate> = ({
   schemaObject,
   baseField,
@@ -254,6 +271,34 @@ const buildCLIPEmbedModelFieldInputTemplate: FieldInputTemplateBuilder<CLIPEmbed
   return template;
 };
 
+const buildCLIPLEmbedModelFieldInputTemplate: FieldInputTemplateBuilder<CLIPLEmbedModelFieldInputTemplate> = ({
+  schemaObject,
+  baseField,
+  fieldType,
+}) => {
+  const template: CLIPLEmbedModelFieldInputTemplate = {
+    ...baseField,
+    type: fieldType,
+    default: schemaObject.default ?? undefined,
+  };
+
+  return template;
+};
+
+const buildCLIPGEmbedModelFieldInputTemplate: FieldInputTemplateBuilder<CLIPGEmbedModelFieldInputTemplate> = ({
+  schemaObject,
+  baseField,
+  fieldType,
+}) => {
+  const template: CLIPGEmbedModelFieldInputTemplate = {
+    ...baseField,
+    type: fieldType,
+    default: schemaObject.default ?? undefined,
+  };
+
+  return template;
+};
+
 const buildFluxVAEModelFieldInputTemplate: FieldInputTemplateBuilder<FluxVAEModelFieldInputTemplate> = ({
   schemaObject,
   baseField,
@@ -446,6 +491,7 @@ export const TEMPLATE_BUILDER_MAP: Record<StatefulFieldType['name'], FieldInputT
   MainModelField: buildMainModelFieldInputTemplate,
   SchedulerField: buildSchedulerFieldInputTemplate,
   SDXLMainModelField: buildSDXLMainModelFieldInputTemplate,
+  SD3MainModelField: buildSD3MainModelFieldInputTemplate,
   FluxMainModelField: buildFluxMainModelFieldInputTemplate,
   SDXLRefinerModelField: buildRefinerModelFieldInputTemplate,
   StringField: buildStringFieldInputTemplate,
@@ -454,6 +500,8 @@ export const TEMPLATE_BUILDER_MAP: Record<StatefulFieldType['name'], FieldInputT
   VAEModelField: buildVAEModelFieldInputTemplate,
   T5EncoderModelField: buildT5EncoderModelFieldInputTemplate,
   CLIPEmbedModelField: buildCLIPEmbedModelFieldInputTemplate,
+  CLIPLEmbedModelField: buildCLIPLEmbedModelFieldInputTemplate,
+  CLIPGEmbedModelField: buildCLIPGEmbedModelFieldInputTemplate,
   FluxVAEModelField: buildFluxVAEModelFieldInputTemplate,
 } as const;
 

invokeai/frontend/web/src/features/nodes/util/workflow/validateWorkflow.ts

@@ -30,6 +30,7 @@ const MODEL_FIELD_TYPES = [
   'MainModelField',
   'SDXLMainModelField',
   'FluxMainModelField',
+  'SD3MainModelField',
   'SDXLRefinerModelField',
   'VAEModelField',
   'LoRAModelField',

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 { useMainModels } from 'services/api/hooks/modelsByType';
+import { useNonSD3MainModels } 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 }] = useMainModels();
+  const [modelConfigs, { isLoading }] = useNonSD3MainModels();
 
   const selectedModel = useMemo(() => {
     if (!modelConfigs) {

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

@@ -19,6 +19,7 @@ export const MODEL_TYPE_SHORT_MAP = {
   any: 'Any',
   'sd-1': 'SD1.X',
   'sd-2': 'SD2.X',
+  'sd-3': 'SD3.X',
   sdxl: 'SDXL',
   'sdxl-refiner': 'SDXLR',
   flux: 'FLUX',
@@ -40,6 +41,10 @@ export const CLIP_SKIP_MAP = {
     maxClip: 24,
     markers: [0, 1, 2, 3, 5, 10, 15, 20, 24],
   },
+  'sd-3': {
+    maxClip: 0,
+    markers: [],
+  },
   sdxl: {
     maxClip: 24,
     markers: [0, 1, 2, 3, 5, 10, 15, 20, 24],

invokeai/frontend/web/src/features/stylePresets/components/StylePresetImage.tsx

@@ -8,6 +8,7 @@ const FALLBACK_ICON_SIZE = '24px';
 const StylePresetImage = ({ presetImageUrl, imageWidth }: { presetImageUrl: string | null; imageWidth?: number }) => {
   return (
     <Tooltip
+      closeOnScroll
       label={
         presetImageUrl && (
           <Image

invokeai/frontend/web/src/features/stylePresets/components/StylePresetList.tsx

@@ -1,6 +1,7 @@
 import { Button, Collapse, Flex, Icon, Text, useDisclosure } from '@invoke-ai/ui-library';
 import { useAppSelector } from 'app/store/storeHooks';
 import { IAINoContentFallback } from 'common/components/IAIImageFallback';
+import { fixTooltipCloseOnScrollStyles } from 'common/util/fixTooltipCloseOnScrollStyles';
 import { selectStylePresetSearchTerm } from 'features/stylePresets/store/stylePresetSlice';
 import { useTranslation } from 'react-i18next';
 import { PiCaretDownBold } from 'react-icons/pi';
@@ -23,7 +24,7 @@ export const StylePresetList = ({ title, data }: { title: string; data: StylePre
           </Text>
         </Flex>
       </Button>
-      <Collapse in={isOpen}>
+      <Collapse in={isOpen} style={fixTooltipCloseOnScrollStyles}>
         {data.length ? (
           data.map((preset) => <StylePresetListItem preset={preset} key={preset.id} />)
         ) : (

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

@@ -25,7 +25,7 @@ const WorkflowLibraryMenu = () => {
   const shift = useShiftModifier();
   useGlobalMenuClose(onClose);
   return (
-    <Menu isOpen={isOpen} onOpen={onOpen} onClose={onClose} isLazy>
+    <Menu isOpen={isOpen} onOpen={onOpen} onClose={onClose}>
       <MenuButton
         as={IconButton}
         aria-label={t('workflows.workflowEditorMenu')}

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

@@ -18,8 +18,10 @@ import {
   isIPAdapterModelConfig,
   isLoRAModelConfig,
   isNonRefinerMainModelConfig,
+  isNonSD3MainModelModelConfig,
   isNonSDXLMainModelConfig,
   isRefinerMainModelModelConfig,
+  isSD3MainModelModelConfig,
   isSDXLMainModelModelConfig,
   isSpandrelImageToImageModelConfig,
   isT2IAdapterModelConfig,
@@ -28,8 +30,13 @@ import {
   isVAEModelConfig,
 } from 'services/api/types';
 
+type ModelHookArgs = { excludeSubmodels?: boolean };
+
 const buildModelsHook =
-  <T extends AnyModelConfig>(typeGuard: (config: AnyModelConfig) => config is T) =>
+  <T extends AnyModelConfig>(
+    typeGuard: (config: AnyModelConfig, excludeSubmodels?: boolean) => config is T,
+    excludeSubmodels?: boolean
+  ) =>
   () => {
     const result = useGetModelConfigsQuery(undefined);
     const modelConfigs = useMemo(() => {
@@ -37,28 +44,35 @@ const buildModelsHook =
         return EMPTY_ARRAY;
       }
 
-      return modelConfigsAdapterSelectors.selectAll(result.data).filter(typeGuard);
+      return modelConfigsAdapterSelectors
+        .selectAll(result.data)
+        .filter((config) => typeGuard(config, excludeSubmodels));
     }, [result]);
 
     return [modelConfigs, result] as const;
   };
 
 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);
+export const useSD3Models = buildModelsHook(isSD3MainModelModelConfig);
 export const useSDXLModels = buildModelsHook(isSDXLMainModelModelConfig);
 export const useLoRAModels = buildModelsHook(isLoRAModelConfig);
 export const useControlNetAndT2IAdapterModels = buildModelsHook(isControlNetOrT2IAdapterModelConfig);
 export const useControlNetModels = buildModelsHook(isControlNetModelConfig);
 export const useT2IAdapterModels = buildModelsHook(isT2IAdapterModelConfig);
-export const useT5EncoderModels = buildModelsHook(isT5EncoderModelConfig);
-export const useCLIPEmbedModels = buildModelsHook(isCLIPEmbedModelConfig);
+export const useT5EncoderModels = (args?: ModelHookArgs) =>
+  buildModelsHook(isT5EncoderModelConfig, args?.excludeSubmodels)();
+export const useCLIPEmbedModels = (args?: ModelHookArgs) =>
+  buildModelsHook(isCLIPEmbedModelConfig, args?.excludeSubmodels)();
 export const useSpandrelImageToImageModels = buildModelsHook(isSpandrelImageToImageModelConfig);
 export const useIPAdapterModels = buildModelsHook(isIPAdapterModelConfig);
 export const useEmbeddingModels = buildModelsHook(isTIModelConfig);
-export const useVAEModels = buildModelsHook(isVAEModelConfig);
-export const useFluxVAEModels = buildModelsHook(isFluxVAEModelConfig);
+export const useVAEModels = (args?: ModelHookArgs) => buildModelsHook(isVAEModelConfig, args?.excludeSubmodels)();
+export const useFluxVAEModels = (args?: ModelHookArgs) =>
+  buildModelsHook(isFluxVAEModelConfig, args?.excludeSubmodels)();
 export const useCLIPVisionModels = buildModelsHook(isCLIPVisionModelConfig);
 
 // const buildModelsSelector =

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

@@ -53,6 +53,8 @@ export type ControlNetModelConfig = S['ControlNetDiffusersConfig'] | S['ControlN
 export type IPAdapterModelConfig = S['IPAdapterInvokeAIConfig'] | S['IPAdapterCheckpointConfig'];
 export type T2IAdapterModelConfig = S['T2IAdapterConfig'];
 export type CLIPEmbedModelConfig = S['CLIPEmbedDiffusersConfig'];
+export type CLIPLEmbedModelConfig = S['CLIPLEmbedDiffusersConfig'];
+export type CLIPGEmbedModelConfig = S['CLIPGEmbedDiffusersConfig'];
 export type T5EncoderModelConfig = S['T5EncoderConfig'];
 export type T5EncoderBnbQuantizedLlmInt8bModelConfig = S['T5EncoderBnbQuantizedLlmInt8bConfig'];
 export type SpandrelImageToImageModelConfig = S['SpandrelImageToImageConfig'];
@@ -75,20 +77,63 @@ export type AnyModelConfig =
   | MainModelConfig
   | CLIPVisionDiffusersConfig;
 
+/**
+ * Checks if a list of submodels contains any that match a given variant or type
+ * @param submodels The list of submodels to check
+ * @param checkStr The string to check against for variant or type
+ * @returns A boolean
+ */
+const checkSubmodel = (submodels: AnyModelConfig['submodels'], checkStr: string): boolean => {
+  for (const submodel in submodels) {
+    if (
+      submodel &&
+      submodels[submodel] &&
+      (submodels[submodel].model_type === checkStr || submodels[submodel].variant === checkStr)
+    ) {
+      return true;
+    }
+  }
+  return false;
+};
+
+/**
+ * Checks if a main model config has submodels that match a given variant or type
+ * @param identifiers A list of strings to check against for variant or type in submodels
+ * @param config The model config
+ * @returns A boolean
+ */
+const checkSubmodels = (identifiers: string[], config: AnyModelConfig): boolean => {
+  return identifiers.every(
+    (identifier) =>
+      config.type === 'main' &&
+      config.submodels &&
+      (identifier in config.submodels || checkSubmodel(config.submodels, identifier))
+  );
+};
+
 export const isLoRAModelConfig = (config: AnyModelConfig): config is LoRAModelConfig => {
   return config.type === 'lora';
 };
 
-export const isVAEModelConfig = (config: AnyModelConfig): config is VAEModelConfig => {
-  return config.type === 'vae';
+export const isVAEModelConfig = (config: AnyModelConfig, excludeSubmodels?: boolean): config is VAEModelConfig => {
+  return config.type === 'vae' || (!excludeSubmodels && config.type === 'main' && checkSubmodels(['vae'], config));
 };
 
-export const isNonFluxVAEModelConfig = (config: AnyModelConfig): config is VAEModelConfig => {
-  return config.type === 'vae' && config.base !== 'flux';
+export const isNonFluxVAEModelConfig = (
+  config: AnyModelConfig,
+  excludeSubmodels?: boolean
+): config is VAEModelConfig => {
+  return (
+    (config.type === 'vae' || (!excludeSubmodels && config.type === 'main' && checkSubmodels(['vae'], config))) &&
+    config.base !== 'flux'
+  );
 };
 
-export const isFluxVAEModelConfig = (config: AnyModelConfig): config is VAEModelConfig => {
-  return config.type === 'vae' && config.base === 'flux';
+export const isFluxVAEModelConfig = (config: AnyModelConfig, excludeSubmodels?: boolean): config is VAEModelConfig => {
+  return (
+    (config.type === 'vae' || (!excludeSubmodels && config.type === 'main' && checkSubmodels(['vae'], config))) &&
+    config.base === 'flux'
+  );
 };
 
 export const isControlNetModelConfig = (config: AnyModelConfig): config is ControlNetModelConfig => {
@@ -108,13 +153,43 @@ export const isT2IAdapterModelConfig = (config: AnyModelConfig): config is T2IAd
 };
 
 export const isT5EncoderModelConfig = (
-  config: AnyModelConfig
+  config: AnyModelConfig,
+  excludeSubmodels?: boolean
 ): config is T5EncoderModelConfig | T5EncoderBnbQuantizedLlmInt8bModelConfig => {
-  return config.type === 't5_encoder';
+  return (
+    config.type === 't5_encoder' ||
+    (!excludeSubmodels && config.type === 'main' && checkSubmodels(['t5_encoder'], config))
+  );
 };
 
-export const isCLIPEmbedModelConfig = (config: AnyModelConfig): config is CLIPEmbedModelConfig => {
-  return config.type === 'clip_embed';
+export const isCLIPEmbedModelConfig = (
+  config: AnyModelConfig,
+  excludeSubmodels?: boolean
+): config is CLIPEmbedModelConfig => {
+  return (
+    config.type === 'clip_embed' ||
+    (!excludeSubmodels && config.type === 'main' && checkSubmodels(['clip_embed'], config))
+  );
+};
+
+export const isCLIPLEmbedModelConfig = (
+  config: AnyModelConfig,
+  excludeSubmodels?: boolean
+): config is CLIPLEmbedModelConfig => {
+  return (
+    (config.type === 'clip_embed' && config.variant === 'large') ||
+    (!excludeSubmodels && config.type === 'main' && checkSubmodels(['clip_embed', 'large'], config))
+  );
+};
+
+export const isCLIPGEmbedModelConfig = (
+  config: AnyModelConfig,
+  excludeSubmodels?: boolean
+): config is CLIPGEmbedModelConfig => {
+  return (
+    (config.type === 'clip_embed' && config.variant === 'gigantic') ||
+    (!excludeSubmodels && config.type === 'main' && checkSubmodels(['clip_embed', 'gigantic'], config))
+  );
 };
 
 export const isSpandrelImageToImageModelConfig = (
@@ -145,6 +220,14 @@ export const isSDXLMainModelModelConfig = (config: AnyModelConfig): config is Ma
   return config.type === 'main' && config.base === 'sdxl';
 };
 
+export const isSD3MainModelModelConfig = (config: AnyModelConfig): config is MainModelConfig => {
+  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';
 };

invokeai/version/invokeai_version.py

@@ -1 +1 @@
-__version__ = "5.3.1"
+__version__ = "5.4.0"

pyproject.toml

@@ -41,7 +41,7 @@ dependencies = [
   "diffusers[torch]==0.31.0",
   "gguf==0.10.0",
   "invisible-watermark==0.2.0", # needed to install SDXL base and refiner using their repo_ids
-  "mediapipe>=0.10.7",          # needed for "mediapipeface" controlnet model
+  "mediapipe==0.10.14",         # needed for "mediapipeface" controlnet model
   "numpy<2.0.0",
   "onnx==1.16.1",
   "onnxruntime==1.19.2",
@@ -52,7 +52,7 @@ dependencies = [
   "sentencepiece==0.2.0",
   "spandrel==0.3.4",
   "timm==0.6.13",               # needed to override timm latest in controlnet_aux, see  https://github.com/isl-org/ZoeDepth/issues/26
-  "torch",                      # torch and related dependencies are not pinned, resolved as dependency of `diffusers[torch]` and so forth
+  "torch<2.5.0",                # torch and related dependencies are loosely pinned, will respect requirement of `diffusers[torch]`
   "torchmetrics",
   "torchsde",
   "torchvision",