chore: 4.2.9 version bump

Currently translated at 97.6% (1342 of 1374 strings)

Co-authored-by: Phrixus2023 <920414016@qq.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hans/
Translation: InvokeAI/Web UI

.github/workflows/build-container.yml

@@ -13,6 +13,12 @@ on:
     tags:
       - 'v*.*.*'
   workflow_dispatch:
+    inputs:
+      push-to-registry:
+        description: Push the built image to the container registry
+        required: false
+        type: boolean
+        default: false
 
 permissions:
   contents: write
@@ -50,16 +56,15 @@ jobs:
           df -h
 
       - name: Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Docker meta
         id: meta
-        uses: docker/metadata-action@v4
+        uses: docker/metadata-action@v5
         with:
           github-token: ${{ secrets.GITHUB_TOKEN }}
           images: |
             ghcr.io/${{ github.repository }}
-            ${{ env.DOCKERHUB_REPOSITORY }}
           tags: |
             type=ref,event=branch
             type=ref,event=tag
@@ -72,49 +77,33 @@ jobs:
             suffix=-${{ matrix.gpu-driver }},onlatest=false
 
       - name: Set up QEMU
-        uses: docker/setup-qemu-action@v2
+        uses: docker/setup-qemu-action@v3
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
+        uses: docker/setup-buildx-action@v3
         with:
           platforms: ${{ env.PLATFORMS }}
 
       - name: Login to GitHub Container Registry
         if: github.event_name != 'pull_request'
-        uses: docker/login-action@v2
+        uses: docker/login-action@v3
         with:
           registry: ghcr.io
           username: ${{ github.repository_owner }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
-      # - name: Login to Docker Hub
-      #   if: github.event_name != 'pull_request' && vars.DOCKERHUB_REPOSITORY != ''
-      #   uses: docker/login-action@v2
-      #   with:
-      #     username: ${{ secrets.DOCKERHUB_USERNAME }}
-      #     password: ${{ secrets.DOCKERHUB_TOKEN }}
-
       - name: Build container
         timeout-minutes: 40
         id: docker_build
-        uses: docker/build-push-action@v4
+        uses: docker/build-push-action@v6
         with:
           context: .
           file: docker/Dockerfile
           platforms: ${{ env.PLATFORMS }}
-          push: ${{ github.ref == 'refs/heads/main' || github.ref_type == 'tag' }}
+          push: ${{ github.ref == 'refs/heads/main' || github.ref_type == 'tag' || github.event.inputs.push-to-registry }}
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}
           cache-from: |
             type=gha,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
             type=gha,scope=main-${{ matrix.gpu-driver }}
           cache-to: type=gha,mode=max,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
-
-      # - name: Docker Hub Description
-      #   if: github.ref == 'refs/heads/main' || github.ref == 'refs/tags/*' && vars.DOCKERHUB_REPOSITORY != ''
-      #   uses: peter-evans/dockerhub-description@v3
-      #   with:
-      #     username: ${{ secrets.DOCKERHUB_USERNAME }}
-      #     password: ${{ secrets.DOCKERHUB_TOKEN }}
-      #     repository: ${{ vars.DOCKERHUB_REPOSITORY }}
-      #     short-description: ${{ github.event.repository.description }}

.github/workflows/python-tests.yml

@@ -60,7 +60,7 @@ jobs:
             extra-index-url: 'https://download.pytorch.org/whl/cpu'
             github-env: $GITHUB_ENV
           - platform: macos-default
-            os: macOS-12
+            os: macOS-14
             github-env: $GITHUB_ENV
           - platform: windows-cpu
             os: windows-2022

docker/README.md

@@ -1,20 +1,22 @@
 # Invoke in Docker
 
-- Ensure that Docker can use the GPU on your system
-- This documentation assumes Linux, but should work similarly under Windows with WSL2
+First things first:
+
+- Ensure that Docker can use your [NVIDIA][nvidia docker docs] or [AMD][amd docker docs] GPU.
+- This document assumes a Linux system, but should work similarly under Windows with WSL2.
 - We don't recommend running Invoke in Docker on macOS at this time. It works, but very slowly.
 
-## Quickstart :lightning:
+## Quickstart
 
-No `docker compose`, no persistence, just a simple one-liner using the official images:
+No `docker compose`, no persistence, single command, using the official images:
 
-**CUDA:**
+**CUDA (NVIDIA GPU):**
 
 ```bash
 docker run --runtime=nvidia --gpus=all --publish 9090:9090 ghcr.io/invoke-ai/invokeai
 ```
 
-**ROCm:**
+**ROCm (AMD GPU):**
 
 ```bash
 docker run --device /dev/kfd --device /dev/dri --publish 9090:9090 ghcr.io/invoke-ai/invokeai:main-rocm
@@ -22,12 +24,20 @@ docker run --device /dev/kfd --device /dev/dri --publish 9090:9090 ghcr.io/invok
 
 Open `http://localhost:9090` in your browser once the container finishes booting, install some models, and generate away!
 
-> [!TIP]
-> To persist your data (including downloaded models) outside of the container, add a `--volume/-v` flag to the above command, e.g.: `docker run --volume /some/local/path:/invokeai <...the rest of the command>`
+### Data persistence
+
+To persist your generated images and downloaded models outside of the container, add a `--volume/-v` flag to the above command, e.g.:
+
+```bash
+docker run --volume /some/local/path:/invokeai {...etc...}
+```
+
+`/some/local/path/invokeai` will contain all your data.
+It can *usually* be reused between different installs of Invoke. Tread with caution and read the release notes!
 
 ## Customize the container
 
-We ship the `run.sh` script, which is a convenient wrapper around `docker compose` for cases where custom image build args are needed. Alternatively, the familiar `docker compose` commands work just as well.
+The included `run.sh` script is a convenience wrapper around `docker compose`. It can be helpful for passing additional build arguments to `docker compose`. Alternatively, the familiar `docker compose` commands work just as well.
 
 ```bash
 cd docker
@@ -38,11 +48,14 @@ cp .env.sample .env
 
 It will take a few minutes to build the image the first time. Once the application starts up, open `http://localhost:9090` in your browser to invoke!
 
+>[!TIP]
+>When using the `run.sh` script, the container will continue running after Ctrl+C. To shut it down, use the `docker compose down` command.
+
 ## Docker setup in detail
 
 #### Linux
 
-1. Ensure builkit is enabled in the Docker daemon settings (`/etc/docker/daemon.json`)
+1. Ensure buildkit is enabled in the Docker daemon settings (`/etc/docker/daemon.json`)
 2. Install the `docker compose` plugin using your package manager, or follow a [tutorial](https://docs.docker.com/compose/install/linux/#install-using-the-repository).
     - The deprecated `docker-compose` (hyphenated) CLI probably won't work. Update to a recent version.
 3. Ensure docker daemon is able to access the GPU.
@@ -98,25 +111,7 @@ GPU_DRIVER=cuda
 
 Any environment variables supported by InvokeAI can be set here. See the [Configuration docs](https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/) for further detail.
 
-## Even More Customizing!
+---
 
-See the `docker-compose.yml` file. The `command` instruction can be uncommented and used to run arbitrary startup commands. Some examples below.
-
-### Reconfigure the runtime directory
-
-Can be used to download additional models from the supported model list
-
-In conjunction with `INVOKEAI_ROOT` can be also used to initialize a runtime directory
-
-```yaml
-command:
-  - invokeai-configure
-  - --yes
-```
-
-Or install models:
-
-```yaml
-command:
-  - invokeai-model-install
-```
+[nvidia docker docs]: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html
+[amd docker docs]: https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/docker.html

docs/help/FAQ.md

@@ -196,6 +196,22 @@ tips to reduce the problem:
     === "12GB VRAM GPU"
 
         This should be sufficient to generate larger images up to about 1280x1280.
+		
+## Checkpoint Models Load Slowly or Use Too Much RAM
+
+The difference between diffusers models (a folder containing multiple
+subfolders) and checkpoint models (a file ending with .safetensors or
+.ckpt) is that InvokeAI is able to load diffusers models into memory
+incrementally, while checkpoint models must be loaded all at
+once. With very large models, or systems with limited RAM, you may
+experience slowdowns and other memory-related issues when loading
+checkpoint models.
+
+To solve this, go to the Model Manager tab (the cube), select the
+checkpoint model that's giving you trouble, and press the "Convert"
+button in the upper right of your browser window. This will conver the
+checkpoint into a diffusers model, after which loading should be
+faster and less memory-intensive.
 
 ## Memory Leak (Linux)
 

invokeai/app/api/routers/model_manager.py

@@ -3,8 +3,10 @@
 
 import io
 import pathlib
+import shutil
 import traceback
 from copy import deepcopy
+from enum import Enum
 from tempfile import TemporaryDirectory
 from typing import List, Optional, Type
 
@@ -17,6 +19,7 @@ from starlette.exceptions import HTTPException
 from typing_extensions import Annotated
 
 from invokeai.app.api.dependencies import ApiDependencies
+from invokeai.app.services.config import get_config
 from invokeai.app.services.model_images.model_images_common import ModelImageFileNotFoundException
 from invokeai.app.services.model_install.model_install_common import ModelInstallJob
 from invokeai.app.services.model_records import (
@@ -31,6 +34,7 @@ from invokeai.backend.model_manager.config import (
     ModelFormat,
     ModelType,
 )
+from invokeai.backend.model_manager.load.model_cache.model_cache_base import CacheStats
 from invokeai.backend.model_manager.metadata.fetch.huggingface import HuggingFaceMetadataFetch
 from invokeai.backend.model_manager.metadata.metadata_base import ModelMetadataWithFiles, UnknownMetadataException
 from invokeai.backend.model_manager.search import ModelSearch
@@ -50,6 +54,13 @@ class ModelsList(BaseModel):
     model_config = ConfigDict(use_enum_values=True)
 
 
+class CacheType(str, Enum):
+    """Cache type - one of vram or ram."""
+
+    RAM = "RAM"
+    VRAM = "VRAM"
+
+
 def add_cover_image_to_model_config(config: AnyModelConfig, dependencies: Type[ApiDependencies]) -> AnyModelConfig:
     """Add a cover image URL to a model configuration."""
     cover_image = dependencies.invoker.services.model_images.get_url(config.key)
@@ -797,3 +808,83 @@ async def get_starter_models() -> list[StarterModel]:
         model.dependencies = missing_deps
 
     return starter_models
+
+
+@model_manager_router.get(
+    "/model_cache",
+    operation_id="get_cache_size",
+    response_model=float,
+    summary="Get maximum size of model manager RAM or VRAM cache.",
+)
+async def get_cache_size(cache_type: CacheType = Query(description="The cache type", default=CacheType.RAM)) -> float:
+    """Return the current RAM or VRAM cache size setting (in GB)."""
+    cache = ApiDependencies.invoker.services.model_manager.load.ram_cache
+    value = 0.0
+    if cache_type == CacheType.RAM:
+        value = cache.max_cache_size
+    elif cache_type == CacheType.VRAM:
+        value = cache.max_vram_cache_size
+    return value
+
+
+@model_manager_router.put(
+    "/model_cache",
+    operation_id="set_cache_size",
+    response_model=float,
+    summary="Set maximum size of model manager RAM or VRAM cache, optionally writing new value out to invokeai.yaml config file.",
+)
+async def set_cache_size(
+    value: float = Query(description="The new value for the maximum cache size"),
+    cache_type: CacheType = Query(description="The cache type", default=CacheType.RAM),
+    persist: bool = Query(description="Write new value out to invokeai.yaml", default=False),
+) -> float:
+    """Set the current RAM or VRAM cache size setting (in GB). ."""
+    cache = ApiDependencies.invoker.services.model_manager.load.ram_cache
+    app_config = get_config()
+    # Record initial state.
+    vram_old = app_config.vram
+    ram_old = app_config.ram
+
+    # Prepare target state.
+    vram_new = vram_old
+    ram_new = ram_old
+    if cache_type == CacheType.RAM:
+        ram_new = value
+    elif cache_type == CacheType.VRAM:
+        vram_new = value
+    else:
+        raise ValueError(f"Unexpected {cache_type=}.")
+
+    config_path = app_config.config_file_path
+    new_config_path = config_path.with_suffix(".yaml.new")
+
+    try:
+        # Try to apply the target state.
+        cache.max_vram_cache_size = vram_new
+        cache.max_cache_size = ram_new
+        app_config.ram = ram_new
+        app_config.vram = vram_new
+        if persist:
+            app_config.write_file(new_config_path)
+            shutil.move(new_config_path, config_path)
+    except Exception as e:
+        # If there was a failure, restore the initial state.
+        cache.max_cache_size = ram_old
+        cache.max_vram_cache_size = vram_old
+        app_config.ram = ram_old
+        app_config.vram = vram_old
+
+        raise RuntimeError("Failed to update cache size") from e
+    return value
+
+
+@model_manager_router.get(
+    "/stats",
+    operation_id="get_stats",
+    response_model=Optional[CacheStats],
+    summary="Get model manager RAM cache performance statistics.",
+)
+async def get_stats() -> Optional[CacheStats]:
+    """Return performance statistics on the model manager's RAM cache. Will return null if no models have been loaded."""
+
+    return ApiDependencies.invoker.services.model_manager.load.ram_cache.stats

invokeai/app/api/routers/style_presets.py

@@ -26,13 +26,10 @@ from invokeai.app.services.style_preset_records.style_preset_records_common impo
 )
 
 
-class StylePresetUpdateFormData(BaseModel):
+class StylePresetFormData(BaseModel):
     name: str = Field(description="Preset name")
     positive_prompt: str = Field(description="Positive prompt")
     negative_prompt: str = Field(description="Negative prompt")
-
-
-class StylePresetCreateFormData(StylePresetUpdateFormData):
     type: PresetType = Field(description="Preset type")
 
 
@@ -95,9 +92,10 @@ async def update_style_preset(
 
     try:
         parsed_data = json.loads(data)
-        validated_data = StylePresetUpdateFormData(**parsed_data)
+        validated_data = StylePresetFormData(**parsed_data)
 
         name = validated_data.name
+        type = validated_data.type
         positive_prompt = validated_data.positive_prompt
         negative_prompt = validated_data.negative_prompt
 
@@ -105,7 +103,7 @@ async def update_style_preset(
         raise HTTPException(status_code=400, detail="Invalid preset data")
 
     preset_data = PresetData(positive_prompt=positive_prompt, negative_prompt=negative_prompt)
-    changes = StylePresetChanges(name=name, preset_data=preset_data)
+    changes = StylePresetChanges(name=name, preset_data=preset_data, type=type)
 
     style_preset_image = ApiDependencies.invoker.services.style_preset_image_files.get_url(style_preset_id)
     style_preset = ApiDependencies.invoker.services.style_preset_records.update(
@@ -145,7 +143,7 @@ async def create_style_preset(
 
     try:
         parsed_data = json.loads(data)
-        validated_data = StylePresetCreateFormData(**parsed_data)
+        validated_data = StylePresetFormData(**parsed_data)
 
         name = validated_data.name
         type = validated_data.type

invokeai/app/invocations/denoise_latents.py

@@ -185,7 +185,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
     )
     denoise_mask: Optional[DenoiseMaskField] = InputField(
         default=None,
-        description=FieldDescriptions.mask,
+        description=FieldDescriptions.denoise_mask,
         input=Input.Connection,
         ui_order=8,
     )

invokeai/app/invocations/fields.py

@@ -40,14 +40,18 @@ class UIType(str, Enum, metaclass=MetaEnum):
 
     # region Model Field Types
     MainModel = "MainModelField"
+    FluxMainModel = "FluxMainModelField"
     SDXLMainModel = "SDXLMainModelField"
     SDXLRefinerModel = "SDXLRefinerModelField"
     ONNXModel = "ONNXModelField"
     VAEModel = "VAEModelField"
+    FluxVAEModel = "FluxVAEModelField"
     LoRAModel = "LoRAModelField"
     ControlNetModel = "ControlNetModelField"
     IPAdapterModel = "IPAdapterModelField"
     T2IAdapterModel = "T2IAdapterModelField"
+    T5EncoderModel = "T5EncoderModelField"
+    CLIPEmbedModel = "CLIPEmbedModelField"
     SpandrelImageToImageModel = "SpandrelImageToImageModelField"
     # endregion
 
@@ -125,13 +129,17 @@ class FieldDescriptions:
     negative_cond = "Negative conditioning tensor"
     noise = "Noise tensor"
     clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
+    t5_encoder = "T5 tokenizer and text encoder"
+    clip_embed_model = "CLIP Embed loader"
     unet = "UNet (scheduler, LoRAs)"
+    transformer = "Transformer"
     vae = "VAE"
     cond = "Conditioning tensor"
     controlnet_model = "ControlNet model to load"
     vae_model = "VAE model to load"
     lora_model = "LoRA model to load"
     main_model = "Main model (UNet, VAE, CLIP) to load"
+    flux_model = "Flux model (Transformer) 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"
@@ -173,7 +181,7 @@ class FieldDescriptions:
     )
     num_1 = "The first number"
     num_2 = "The second number"
-    mask = "The mask to use for the operation"
+    denoise_mask = "A mask of the region to apply the denoising process to."
     board = "The board to save the image to"
     image = "The image to process"
     tile_size = "Tile size"
@@ -231,6 +239,12 @@ class ColorField(BaseModel):
         return (self.r, self.g, self.b, self.a)
 
 
+class FluxConditioningField(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_denoise.py

@@ -0,0 +1,249 @@
+from typing import Callable, Optional
+
+import torch
+import torchvision.transforms as tv_transforms
+from torchvision.transforms.functional import resize as tv_resize
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.fields import (
+    DenoiseMaskField,
+    FieldDescriptions,
+    FluxConditioningField,
+    Input,
+    InputField,
+    LatentsField,
+    WithBoard,
+    WithMetadata,
+)
+from invokeai.app.invocations.model import TransformerField
+from invokeai.app.invocations.primitives import LatentsOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.flux.denoise import denoise
+from invokeai.backend.flux.inpaint_extension import InpaintExtension
+from invokeai.backend.flux.model import Flux
+from invokeai.backend.flux.sampling_utils import (
+    clip_timestep_schedule,
+    generate_img_ids,
+    get_noise,
+    get_schedule,
+    pack,
+    unpack,
+)
+from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import FLUXConditioningInfo
+from invokeai.backend.util.devices import TorchDevice
+
+
+@invocation(
+    "flux_denoise",
+    title="FLUX Denoise",
+    tags=["image", "flux"],
+    category="image",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Run denoising process with a FLUX transformer model."""
+
+    # If latents is provided, this means we are doing image-to-image.
+    latents: Optional[LatentsField] = InputField(
+        default=None,
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    # denoise_mask is used for image-to-image inpainting. Only the masked region is modified.
+    denoise_mask: Optional[DenoiseMaskField] = InputField(
+        default=None,
+        description=FieldDescriptions.denoise_mask,
+        input=Input.Connection,
+    )
+    denoising_start: float = InputField(
+        default=0.0,
+        ge=0,
+        le=1,
+        description=FieldDescriptions.denoising_start,
+    )
+    denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
+    transformer: TransformerField = InputField(
+        description=FieldDescriptions.flux_model,
+        input=Input.Connection,
+        title="Transformer",
+    )
+    positive_text_conditioning: FluxConditioningField = InputField(
+        description=FieldDescriptions.positive_cond, input=Input.Connection
+    )
+    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.")
+    num_steps: int = InputField(
+        default=4, description="Number of diffusion steps. Recommended values are schnell: 4, dev: 50."
+    )
+    guidance: float = InputField(
+        default=4.0,
+        description="The guidance strength. Higher values adhere more strictly to the prompt, and will produce less diverse images. FLUX dev only, ignored for schnell.",
+    )
+    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 _run_diffusion(
+        self,
+        context: InvocationContext,
+    ):
+        inference_dtype = torch.bfloat16
+
+        # Load the conditioning data.
+        cond_data = context.conditioning.load(self.positive_text_conditioning.conditioning_name)
+        assert len(cond_data.conditionings) == 1
+        flux_conditioning = cond_data.conditionings[0]
+        assert isinstance(flux_conditioning, FLUXConditioningInfo)
+        flux_conditioning = flux_conditioning.to(dtype=inference_dtype)
+        t5_embeddings = flux_conditioning.t5_embeds
+        clip_embeddings = flux_conditioning.clip_embeds
+
+        # Load the input latents, if provided.
+        init_latents = context.tensors.load(self.latents.latents_name) if self.latents else None
+        if init_latents is not None:
+            init_latents = init_latents.to(device=TorchDevice.choose_torch_device(), dtype=inference_dtype)
+
+        # Prepare input noise.
+        noise = get_noise(
+            num_samples=1,
+            height=self.height,
+            width=self.width,
+            device=TorchDevice.choose_torch_device(),
+            dtype=inference_dtype,
+            seed=self.seed,
+        )
+
+        transformer_info = context.models.load(self.transformer.transformer)
+        is_schnell = "schnell" in transformer_info.config.config_path
+
+        # Calculate the timestep schedule.
+        image_seq_len = noise.shape[-1] * noise.shape[-2] // 4
+        timesteps = get_schedule(
+            num_steps=self.num_steps,
+            image_seq_len=image_seq_len,
+            shift=not is_schnell,
+        )
+
+        # Clip the timesteps schedule based on denoising_start and denoising_end.
+        timesteps = clip_timestep_schedule(timesteps, self.denoising_start, self.denoising_end)
+
+        # Prepare input latent image.
+        if init_latents is not None:
+            # If init_latents is provided, we are doing image-to-image.
+
+            if is_schnell:
+                context.logger.warning(
+                    "Running image-to-image with a FLUX schnell model. This is not recommended. The results are likely "
+                    "to be poor. Consider using a FLUX dev model instead."
+                )
+
+            # Noise the orig_latents by the appropriate amount for the first timestep.
+            t_0 = timesteps[0]
+            x = t_0 * noise + (1.0 - t_0) * init_latents
+        else:
+            # init_latents are not provided, so we are not doing image-to-image (i.e. we are starting from pure noise).
+            if self.denoising_start > 1e-5:
+                raise ValueError("denoising_start should be 0 when initial latents are not provided.")
+
+            x = noise
+
+        # If len(timesteps) == 1, then short-circuit. We are just noising the input latents, but not taking any
+        # denoising steps.
+        if len(timesteps) <= 1:
+            return x
+
+        inpaint_mask = self._prep_inpaint_mask(context, x)
+
+        b, _c, h, w = x.shape
+        img_ids = generate_img_ids(h=h, w=w, batch_size=b, device=x.device, dtype=x.dtype)
+
+        bs, t5_seq_len, _ = t5_embeddings.shape
+        txt_ids = torch.zeros(bs, t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device())
+
+        # Pack all latent tensors.
+        init_latents = pack(init_latents) if init_latents is not None else None
+        inpaint_mask = pack(inpaint_mask) if inpaint_mask is not None else None
+        noise = pack(noise)
+        x = pack(x)
+
+        # Now that we have 'packed' the latent tensors, verify that we calculated the image_seq_len correctly.
+        assert image_seq_len == x.shape[1]
+
+        # Prepare inpaint extension.
+        inpaint_extension: InpaintExtension | None = None
+        if inpaint_mask is not None:
+            assert init_latents is not None
+            inpaint_extension = InpaintExtension(
+                init_latents=init_latents,
+                inpaint_mask=inpaint_mask,
+                noise=noise,
+            )
+
+        with transformer_info as transformer:
+            assert isinstance(transformer, Flux)
+
+            x = denoise(
+                model=transformer,
+                img=x,
+                img_ids=img_ids,
+                txt=t5_embeddings,
+                txt_ids=txt_ids,
+                vec=clip_embeddings,
+                timesteps=timesteps,
+                step_callback=self._build_step_callback(context),
+                guidance=self.guidance,
+                inpaint_extension=inpaint_extension,
+            )
+
+        x = unpack(x.float(), self.height, self.width)
+        return x
+
+    def _prep_inpaint_mask(self, context: InvocationContext, latents: torch.Tensor) -> torch.Tensor | None:
+        """Prepare the inpaint mask.
+
+        - Loads the mask
+        - Resizes if necessary
+        - Casts to same device/dtype as latents
+        - Expands mask to the same shape as latents so that they line up after 'packing'
+
+        Args:
+            context (InvocationContext): The invocation context, for loading the inpaint mask.
+            latents (torch.Tensor): A latent image tensor. In 'unpacked' format. Used to determine the target shape,
+                device, and dtype for the inpaint mask.
+
+        Returns:
+            torch.Tensor | None: Inpaint mask.
+        """
+        if self.denoise_mask is None:
+            return None
+
+        mask = context.tensors.load(self.denoise_mask.mask_name)
+
+        _, _, latent_height, latent_width = latents.shape
+        mask = tv_resize(
+            img=mask,
+            size=[latent_height, latent_width],
+            interpolation=tv_transforms.InterpolationMode.BILINEAR,
+            antialias=False,
+        )
+
+        mask = mask.to(device=latents.device, dtype=latents.dtype)
+
+        # Expand the inpaint mask to the same shape as `latents` so that when we 'pack' `mask` it lines up with
+        # `latents`.
+        return mask.expand_as(latents)
+
+    def _build_step_callback(self, context: InvocationContext) -> Callable[[PipelineIntermediateState], None]:
+        def step_callback(state: PipelineIntermediateState) -> None:
+            state.latents = unpack(state.latents.float(), self.height, self.width).squeeze()
+            context.util.flux_step_callback(state)
+
+        return step_callback

invokeai/app/invocations/flux_text_encoder.py

@@ -0,0 +1,92 @@
+from typing import Literal
+
+import torch
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField
+from invokeai.app.invocations.model import CLIPField, T5EncoderField
+from invokeai.app.invocations.primitives import FluxConditioningOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.flux.modules.conditioner import HFEncoder
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData, FLUXConditioningInfo
+
+
+@invocation(
+    "flux_text_encoder",
+    title="FLUX Text Encoding",
+    tags=["prompt", "conditioning", "flux"],
+    category="conditioning",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class FluxTextEncoderInvocation(BaseInvocation):
+    """Encodes and preps a prompt for a flux image."""
+
+    clip: CLIPField = InputField(
+        title="CLIP",
+        description=FieldDescriptions.clip,
+        input=Input.Connection,
+    )
+    t5_encoder: T5EncoderField = InputField(
+        title="T5Encoder",
+        description=FieldDescriptions.t5_encoder,
+        input=Input.Connection,
+    )
+    t5_max_seq_len: Literal[256, 512] = InputField(
+        description="Max sequence length for the T5 encoder. Expected to be 256 for FLUX schnell models and 512 for FLUX dev models."
+    )
+    prompt: str = InputField(description="Text prompt to encode.")
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> FluxConditioningOutput:
+        # Note: The T5 and CLIP encoding are done in separate functions to ensure that all model references are locally
+        # scoped. This ensures that the T5 model can be freed and gc'd before loading the CLIP model (if necessary).
+        t5_embeddings = self._t5_encode(context)
+        clip_embeddings = self._clip_encode(context)
+        conditioning_data = ConditioningFieldData(
+            conditionings=[FLUXConditioningInfo(clip_embeds=clip_embeddings, t5_embeds=t5_embeddings)]
+        )
+
+        conditioning_name = context.conditioning.save(conditioning_data)
+        return FluxConditioningOutput.build(conditioning_name)
+
+    def _t5_encode(self, context: InvocationContext) -> torch.Tensor:
+        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)
+
+            t5_encoder = HFEncoder(t5_text_encoder, t5_tokenizer, False, self.t5_max_seq_len)
+
+            prompt_embeds = t5_encoder(prompt)
+
+        assert isinstance(prompt_embeds, torch.Tensor)
+        return prompt_embeds
+
+    def _clip_encode(self, context: InvocationContext) -> torch.Tensor:
+        clip_tokenizer_info = context.models.load(self.clip.tokenizer)
+        clip_text_encoder_info = context.models.load(self.clip.text_encoder)
+
+        prompt = [self.prompt]
+
+        with (
+            clip_text_encoder_info as clip_text_encoder,
+            clip_tokenizer_info as clip_tokenizer,
+        ):
+            assert isinstance(clip_text_encoder, CLIPTextModel)
+            assert isinstance(clip_tokenizer, CLIPTokenizer)
+
+            clip_encoder = HFEncoder(clip_text_encoder, clip_tokenizer, True, 77)
+
+            pooled_prompt_embeds = clip_encoder(prompt)
+
+        assert isinstance(pooled_prompt_embeds, torch.Tensor)
+        return pooled_prompt_embeds

invokeai/app/invocations/flux_vae_decode.py

@@ -0,0 +1,60 @@
+import torch
+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.flux.modules.autoencoder import AutoEncoder
+from invokeai.backend.model_manager.load.load_base import LoadedModel
+from invokeai.backend.util.devices import TorchDevice
+
+
+@invocation(
+    "flux_vae_decode",
+    title="FLUX Latents to Image",
+    tags=["latents", "image", "vae", "l2i", "flux"],
+    category="latents",
+    version="1.0.0",
+)
+class FluxVaeDecodeInvocation(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,
+    )
+
+    def _vae_decode(self, vae_info: LoadedModel, latents: torch.Tensor) -> Image.Image:
+        with vae_info as vae:
+            assert isinstance(vae, AutoEncoder)
+            latents = latents.to(device=TorchDevice.choose_torch_device(), dtype=TorchDevice.choose_torch_dtype())
+            img = vae.decode(latents)
+
+        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())
+        return img_pil
+
+    @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)
+        image = self._vae_decode(vae_info=vae_info, latents=latents)
+
+        TorchDevice.empty_cache()
+        image_dto = context.images.save(image=image)
+        return ImageOutput.build(image_dto)

invokeai/app/invocations/flux_vae_encode.py

@@ -0,0 +1,67 @@
+import einops
+import torch
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    ImageField,
+    Input,
+    InputField,
+)
+from invokeai.app.invocations.model import VAEField
+from invokeai.app.invocations.primitives import LatentsOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.flux.modules.autoencoder import AutoEncoder
+from invokeai.backend.model_manager import LoadedModel
+from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
+from invokeai.backend.util.devices import TorchDevice
+
+
+@invocation(
+    "flux_vae_encode",
+    title="FLUX Image to Latents",
+    tags=["latents", "image", "vae", "i2l", "flux"],
+    category="latents",
+    version="1.0.0",
+)
+class FluxVaeEncodeInvocation(BaseInvocation):
+    """Encodes an image into latents."""
+
+    image: ImageField = InputField(
+        description="The image to encode.",
+    )
+    vae: VAEField = InputField(
+        description=FieldDescriptions.vae,
+        input=Input.Connection,
+    )
+
+    @staticmethod
+    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor) -> torch.Tensor:
+        # TODO(ryand): Expose seed parameter at the invocation level.
+        # TODO(ryand): Write a util function for generating random tensors that is consistent across devices / dtypes.
+        # There's a starting point in get_noise(...), but it needs to be extracted and generalized. This function
+        # should be used for VAE encode sampling.
+        generator = torch.Generator(device=TorchDevice.choose_torch_device()).manual_seed(0)
+        with vae_info as vae:
+            assert isinstance(vae, AutoEncoder)
+            image_tensor = image_tensor.to(
+                device=TorchDevice.choose_torch_device(), dtype=TorchDevice.choose_torch_dtype()
+            )
+            latents = vae.encode(image_tensor, sample=True, generator=generator)
+            return latents
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        image = context.images.get_pil(self.image.image_name)
+
+        vae_info = context.models.load(self.vae.vae)
+
+        image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
+        if image_tensor.dim() == 3:
+            image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
+
+        latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
+
+        latents = latents.to("cpu")
+        name = context.tensors.save(tensor=latents)
+        return LatentsOutput.build(latents_name=name, latents=latents, seed=None)

invokeai/app/invocations/mask.py

@@ -126,7 +126,7 @@ class ImageMaskToTensorInvocation(BaseInvocation, WithMetadata):
     title="Tensor Mask to Image",
     tags=["mask"],
     category="mask",
-    version="1.0.0",
+    version="1.1.0",
 )
 class MaskTensorToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Convert a mask tensor to an image."""
@@ -135,6 +135,11 @@ class MaskTensorToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         mask = context.tensors.load(self.mask.tensor_name)
+
+        # Squeeze the channel dimension if it exists.
+        if mask.dim() == 3:
+            mask = mask.squeeze(0)
+
         # Ensure that the mask is binary.
         if mask.dtype != torch.bool:
             mask = mask > 0.5

invokeai/app/invocations/model.py

@@ -1,5 +1,5 @@
 import copy
-from typing import List, Optional
+from typing import List, Literal, Optional
 
 from pydantic import BaseModel, Field
 
@@ -13,7 +13,14 @@ 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.model_manager.config import AnyModelConfig, BaseModelType, ModelType, SubModelType
+from invokeai.backend.flux.util import max_seq_lengths
+from invokeai.backend.model_manager.config import (
+    AnyModelConfig,
+    BaseModelType,
+    CheckpointConfigBase,
+    ModelType,
+    SubModelType,
+)
 
 
 class ModelIdentifierField(BaseModel):
@@ -60,6 +67,15 @@ class CLIPField(BaseModel):
     loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
 
 
+class TransformerField(BaseModel):
+    transformer: ModelIdentifierField = Field(description="Info to load Transformer submodel")
+
+
+class T5EncoderField(BaseModel):
+    tokenizer: ModelIdentifierField = Field(description="Info to load tokenizer submodel")
+    text_encoder: ModelIdentifierField = Field(description="Info to load text_encoder submodel")
+
+
 class VAEField(BaseModel):
     vae: ModelIdentifierField = Field(description="Info to load vae submodel")
     seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
@@ -122,6 +138,78 @@ 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),
+            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

@@ -12,6 +12,7 @@ from invokeai.app.invocations.fields import (
     ConditioningField,
     DenoiseMaskField,
     FieldDescriptions,
+    FluxConditioningField,
     ImageField,
     Input,
     InputField,
@@ -414,6 +415,17 @@ class MaskOutput(BaseInvocationOutput):
     height: int = OutputField(description="The height of the mask in pixels.")
 
 
+@invocation_output("flux_conditioning_output")
+class FluxConditioningOutput(BaseInvocationOutput):
+    """Base class for nodes that output a single conditioning tensor"""
+
+    conditioning: FluxConditioningField = OutputField(description=FieldDescriptions.cond)
+
+    @classmethod
+    def build(cls, conditioning_name: str) -> "FluxConditioningOutput":
+        return cls(conditioning=FluxConditioningField(conditioning_name=conditioning_name))
+
+
 @invocation_output("conditioning_output")
 class ConditioningOutput(BaseInvocationOutput):
     """Base class for nodes that output a single conditioning tensor"""

invokeai/app/services/model_install/model_install_common.py

@@ -103,7 +103,7 @@ class HFModelSource(StringLikeSource):
         if self.variant:
             base += f":{self.variant or ''}"
         if self.subfolder:
-            base += f":{self.subfolder}"
+            base += f"::{self.subfolder.as_posix()}"
         return base
 
 

invokeai/app/services/model_install/model_install_default.py

@@ -783,8 +783,9 @@ class ModelInstallService(ModelInstallServiceBase):
         # So what we do is to synthesize a folder named "sdxl-turbo_vae" here.
         if subfolder:
             top = Path(remote_files[0].path.parts[0])  # e.g. "sdxl-turbo/"
-            path_to_remove = top / subfolder.parts[-1]  # sdxl-turbo/vae/
-            path_to_add = Path(f"{top}_{subfolder}")
+            path_to_remove = top / subfolder  # sdxl-turbo/vae/
+            subfolder_rename = subfolder.name.replace("/", "_").replace("\\", "_")
+            path_to_add = Path(f"{top}_{subfolder_rename}")
         else:
             path_to_remove = Path(".")
             path_to_add = Path(".")

invokeai/app/services/model_records/model_records_base.py

@@ -77,6 +77,7 @@ class ModelRecordChanges(BaseModelExcludeNull):
     type: Optional[ModelType] = Field(description="Type of model", default=None)
     key: Optional[str] = Field(description="Database ID for this model", default=None)
     hash: Optional[str] = Field(description="hash of model file", default=None)
+    format: Optional[str] = Field(description="format of model file", default=None)
     trigger_phrases: Optional[set[str]] = Field(description="Set of trigger phrases for this model", default=None)
     default_settings: Optional[MainModelDefaultSettings | ControlAdapterDefaultSettings] = Field(
         description="Default settings for this model", default=None

invokeai/app/services/shared/invocation_context.py

@@ -14,7 +14,7 @@ from invokeai.app.services.image_records.image_records_common import ImageCatego
 from invokeai.app.services.images.images_common import ImageDTO
 from invokeai.app.services.invocation_services import InvocationServices
 from invokeai.app.services.model_records.model_records_base import UnknownModelException
-from invokeai.app.util.step_callback import stable_diffusion_step_callback
+from invokeai.app.util.step_callback import flux_step_callback, stable_diffusion_step_callback
 from invokeai.backend.model_manager.config import (
     AnyModel,
     AnyModelConfig,
@@ -557,6 +557,24 @@ class UtilInterface(InvocationContextInterface):
             is_canceled=self.is_canceled,
         )
 
+    def flux_step_callback(self, intermediate_state: PipelineIntermediateState) -> None:
+        """
+        The step callback emits a progress event with the current step, the total number of
+        steps, a preview image, and some other internal metadata.
+
+        This should be called after each denoising step.
+
+        Args:
+            intermediate_state: The intermediate state of the diffusion pipeline.
+        """
+
+        flux_step_callback(
+            context_data=self._data,
+            intermediate_state=intermediate_state,
+            events=self._services.events,
+            is_canceled=self.is_canceled,
+        )
+
 
 class InvocationContext:
     """Provides access to various services and data for the current invocation.

invokeai/app/services/style_preset_records/style_preset_records_common.py

@@ -32,6 +32,7 @@ class PresetType(str, Enum, metaclass=MetaEnum):
 class StylePresetChanges(BaseModel, extra="forbid"):
     name: Optional[str] = Field(default=None, description="The style preset's new name.")
     preset_data: Optional[PresetData] = Field(default=None, description="The updated data for style preset.")
+    type: Optional[PresetType] = Field(description="The updated type of the style preset")
 
 
 class StylePresetWithoutId(BaseModel):

invokeai/app/services/workflow_records/default_workflows/FLUX Image to Image.json

@@ -0,0 +1,407 @@
+{
+  "name": "FLUX Image to Image",
+  "author": "InvokeAI",
+  "description": "A simple image-to-image workflow using a FLUX dev model. ",
+  "version": "1.0.4",
+  "contact": "",
+  "tags": "image2image, flux, image-to-image",
+  "notes": "Prerequisite model downloads: T5 Encoder, CLIP-L Encoder, and FLUX VAE. Quantized and un-quantized versions can be found in the starter models tab within your Model Manager. We recommend using FLUX dev models for image-to-image workflows. The image-to-image performance with FLUX schnell models is poor.",
+  "exposedFields": [
+    {
+      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "fieldName": "model"
+    },
+    {
+      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "fieldName": "t5_encoder_model"
+    },
+    {
+      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "fieldName": "clip_embed_model"
+    },
+    {
+      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "fieldName": "vae_model"
+    },
+    {
+      "nodeId": "ace0258f-67d7-4eee-a218-6fff27065214",
+      "fieldName": "denoising_start"
+    },
+    {
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invokeai/app/services/workflow_records/default_workflows/Flux Text to Image.json

@@ -0,0 +1,326 @@
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+      "sourceHandle": "transformer",
+      "targetHandle": "transformer"
+    },
+    {
+      "id": "reactflow__edge-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cconditioning-4fe24f07-f906-4f55-ab2c-9beee56ef5bdpositive_text_conditioning",
+      "type": "default",
+      "source": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
+      "target": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
+      "sourceHandle": "conditioning",
+      "targetHandle": "positive_text_conditioning"
+    },
+    {
+      "id": "reactflow__edge-4754c534-a5f3-4ad0-9382-7887985e668cvalue-4fe24f07-f906-4f55-ab2c-9beee56ef5bdseed",
+      "type": "default",
+      "source": "4754c534-a5f3-4ad0-9382-7887985e668c",
+      "target": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
+      "sourceHandle": "value",
+      "targetHandle": "seed"
+    },
+    {
+      "id": "reactflow__edge-4fe24f07-f906-4f55-ab2c-9beee56ef5bdlatents-7e5172eb-48c1-44db-a770-8fd83e1435d1latents",
+      "type": "default",
+      "source": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
+      "target": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
+      "sourceHandle": "latents",
+      "targetHandle": "latents"
+    },
+    {
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90vae-7e5172eb-48c1-44db-a770-8fd83e1435d1vae",
+      "type": "default",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "target": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
+      "sourceHandle": "vae",
+      "targetHandle": "vae"
+    },
+    {
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90max_seq_len-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_max_seq_len",
+      "type": "default",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
+      "sourceHandle": "max_seq_len",
+      "targetHandle": "t5_max_seq_len"
+    },
+    {
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90t5_encoder-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_encoder",
+      "type": "default",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
+      "sourceHandle": "t5_encoder",
+      "targetHandle": "t5_encoder"
+    },
+    {
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90clip-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cclip",
+      "type": "default",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
+      "sourceHandle": "clip",
+      "targetHandle": "clip"
+    }
+  ]
+}

invokeai/app/util/step_callback.py

@@ -38,6 +38,25 @@ SD1_5_LATENT_RGB_FACTORS = [
     [-0.1307, -0.1874, -0.7445],  # L4
 ]
 
+FLUX_LATENT_RGB_FACTORS = [
+    [-0.0412, 0.0149, 0.0521],
+    [0.0056, 0.0291, 0.0768],
+    [0.0342, -0.0681, -0.0427],
+    [-0.0258, 0.0092, 0.0463],
+    [0.0863, 0.0784, 0.0547],
+    [-0.0017, 0.0402, 0.0158],
+    [0.0501, 0.1058, 0.1152],
+    [-0.0209, -0.0218, -0.0329],
+    [-0.0314, 0.0083, 0.0896],
+    [0.0851, 0.0665, -0.0472],
+    [-0.0534, 0.0238, -0.0024],
+    [0.0452, -0.0026, 0.0048],
+    [0.0892, 0.0831, 0.0881],
+    [-0.1117, -0.0304, -0.0789],
+    [0.0027, -0.0479, -0.0043],
+    [-0.1146, -0.0827, -0.0598],
+]
+
 
 def sample_to_lowres_estimated_image(
     samples: torch.Tensor, latent_rgb_factors: torch.Tensor, smooth_matrix: Optional[torch.Tensor] = None
@@ -94,3 +113,32 @@ def stable_diffusion_step_callback(
         intermediate_state,
         ProgressImage(dataURL=dataURL, width=width, height=height),
     )
+
+
+def flux_step_callback(
+    context_data: "InvocationContextData",
+    intermediate_state: PipelineIntermediateState,
+    events: "EventServiceBase",
+    is_canceled: Callable[[], bool],
+) -> None:
+    if is_canceled():
+        raise CanceledException
+    sample = intermediate_state.latents
+    latent_rgb_factors = torch.tensor(FLUX_LATENT_RGB_FACTORS, dtype=sample.dtype, device=sample.device)
+    latent_image_perm = sample.permute(1, 2, 0).to(dtype=sample.dtype, device=sample.device)
+    latent_image = latent_image_perm @ latent_rgb_factors
+    latents_ubyte = (
+        ((latent_image + 1) / 2).clamp(0, 1).mul(0xFF)  # change scale from -1..1 to 0..1  # to 0..255
+    ).to(device="cpu", dtype=torch.uint8)
+    image = Image.fromarray(latents_ubyte.cpu().numpy())
+    (width, height) = image.size
+    width *= 8
+    height *= 8
+    dataURL = image_to_dataURL(image, image_format="JPEG")
+
+    events.emit_invocation_denoise_progress(
+        context_data.queue_item,
+        context_data.invocation,
+        intermediate_state,
+        ProgressImage(dataURL=dataURL, width=width, height=height),
+    )

invokeai/backend/flux/denoise.py

@@ -0,0 +1,56 @@
+from typing import Callable
+
+import torch
+from tqdm import tqdm
+
+from invokeai.backend.flux.inpaint_extension import InpaintExtension
+from invokeai.backend.flux.model import Flux
+from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
+
+
+def denoise(
+    model: Flux,
+    # model input
+    img: torch.Tensor,
+    img_ids: torch.Tensor,
+    txt: torch.Tensor,
+    txt_ids: torch.Tensor,
+    vec: torch.Tensor,
+    # sampling parameters
+    timesteps: list[float],
+    step_callback: Callable[[PipelineIntermediateState], None],
+    guidance: float,
+    inpaint_extension: InpaintExtension | None,
+):
+    step = 0
+    # guidance_vec is ignored for schnell.
+    guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
+    for t_curr, t_prev in tqdm(list(zip(timesteps[:-1], timesteps[1:], strict=True))):
+        t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
+        pred = model(
+            img=img,
+            img_ids=img_ids,
+            txt=txt,
+            txt_ids=txt_ids,
+            y=vec,
+            timesteps=t_vec,
+            guidance=guidance_vec,
+        )
+        preview_img = img - t_curr * pred
+        img = img + (t_prev - t_curr) * pred
+
+        if inpaint_extension is not None:
+            img = inpaint_extension.merge_intermediate_latents_with_init_latents(img, t_prev)
+
+        step_callback(
+            PipelineIntermediateState(
+                step=step,
+                order=1,
+                total_steps=len(timesteps),
+                timestep=int(t_curr),
+                latents=preview_img,
+            ),
+        )
+        step += 1
+
+    return img

invokeai/backend/flux/inpaint_extension.py

@@ -0,0 +1,35 @@
+import torch
+
+
+class InpaintExtension:
+    """A class for managing inpainting with FLUX."""
+
+    def __init__(self, init_latents: torch.Tensor, inpaint_mask: torch.Tensor, noise: torch.Tensor):
+        """Initialize InpaintExtension.
+
+        Args:
+            init_latents (torch.Tensor): The initial latents (i.e. un-noised at timestep 0). In 'packed' format.
+            inpaint_mask (torch.Tensor): A mask specifying which elements to inpaint. Range [0, 1]. Values of 1 will be
+                re-generated. Values of 0 will remain unchanged. Values between 0 and 1 can be used to blend the
+                inpainted region with the background. In 'packed' format.
+            noise (torch.Tensor): The noise tensor used to noise the init_latents. In 'packed' format.
+        """
+        assert init_latents.shape == inpaint_mask.shape == noise.shape
+        self._init_latents = init_latents
+        self._inpaint_mask = inpaint_mask
+        self._noise = noise
+
+    def merge_intermediate_latents_with_init_latents(
+        self, intermediate_latents: torch.Tensor, timestep: float
+    ) -> torch.Tensor:
+        """Merge the intermediate latents with the initial latents for the current timestep using the inpaint mask. I.e.
+        update the intermediate latents to keep the regions that are not being inpainted on the correct noise
+        trajectory.
+
+        This function should be called after each denoising step.
+        """
+        # Noise the init latents for the current timestep.
+        noised_init_latents = self._noise * timestep + (1.0 - timestep) * self._init_latents
+
+        # Merge the intermediate latents with the noised_init_latents using the inpaint_mask.
+        return intermediate_latents * self._inpaint_mask + noised_init_latents * (1.0 - self._inpaint_mask)

invokeai/backend/flux/math.py

@@ -0,0 +1,32 @@
+# Initially pulled from https://github.com/black-forest-labs/flux
+
+import torch
+from einops import rearrange
+from torch import Tensor
+
+
+def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
+    q, k = apply_rope(q, k, pe)
+
+    x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+    x = rearrange(x, "B H L D -> B L (H D)")
+
+    return x
+
+
+def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
+    assert dim % 2 == 0
+    scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
+    omega = 1.0 / (theta**scale)
+    out = torch.einsum("...n,d->...nd", pos, omega)
+    out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
+    out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
+    return out.float()
+
+
+def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
+    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+    xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+    xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+    xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+    return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)

invokeai/backend/flux/model.py

@@ -0,0 +1,117 @@
+# Initially pulled from https://github.com/black-forest-labs/flux
+
+from dataclasses import dataclass
+
+import torch
+from torch import Tensor, nn
+
+from invokeai.backend.flux.modules.layers import (
+    DoubleStreamBlock,
+    EmbedND,
+    LastLayer,
+    MLPEmbedder,
+    SingleStreamBlock,
+    timestep_embedding,
+)
+
+
+@dataclass
+class FluxParams:
+    in_channels: int
+    vec_in_dim: int
+    context_in_dim: int
+    hidden_size: int
+    mlp_ratio: float
+    num_heads: int
+    depth: int
+    depth_single_blocks: int
+    axes_dim: list[int]
+    theta: int
+    qkv_bias: bool
+    guidance_embed: bool
+
+
+class Flux(nn.Module):
+    """
+    Transformer model for flow matching on sequences.
+    """
+
+    def __init__(self, params: FluxParams):
+        super().__init__()
+
+        self.params = params
+        self.in_channels = params.in_channels
+        self.out_channels = self.in_channels
+        if params.hidden_size % params.num_heads != 0:
+            raise ValueError(f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}")
+        pe_dim = params.hidden_size // params.num_heads
+        if sum(params.axes_dim) != pe_dim:
+            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
+        self.hidden_size = params.hidden_size
+        self.num_heads = params.num_heads
+        self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
+        self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
+        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
+        self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
+        self.guidance_in = (
+            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity()
+        )
+        self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
+
+        self.double_blocks = nn.ModuleList(
+            [
+                DoubleStreamBlock(
+                    self.hidden_size,
+                    self.num_heads,
+                    mlp_ratio=params.mlp_ratio,
+                    qkv_bias=params.qkv_bias,
+                )
+                for _ in range(params.depth)
+            ]
+        )
+
+        self.single_blocks = nn.ModuleList(
+            [
+                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio)
+                for _ in range(params.depth_single_blocks)
+            ]
+        )
+
+        self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels)
+
+    def forward(
+        self,
+        img: Tensor,
+        img_ids: Tensor,
+        txt: Tensor,
+        txt_ids: Tensor,
+        timesteps: Tensor,
+        y: Tensor,
+        guidance: Tensor | None = None,
+    ) -> Tensor:
+        if img.ndim != 3 or txt.ndim != 3:
+            raise ValueError("Input img and txt tensors must have 3 dimensions.")
+
+        # running on sequences img
+        img = self.img_in(img)
+        vec = self.time_in(timestep_embedding(timesteps, 256))
+        if self.params.guidance_embed:
+            if guidance is None:
+                raise ValueError("Didn't get guidance strength for guidance distilled model.")
+            vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
+        vec = vec + self.vector_in(y)
+        txt = self.txt_in(txt)
+
+        ids = torch.cat((txt_ids, img_ids), dim=1)
+        pe = self.pe_embedder(ids)
+
+        for block in self.double_blocks:
+            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
+
+        img = torch.cat((txt, img), 1)
+        for block in self.single_blocks:
+            img = block(img, vec=vec, pe=pe)
+        img = img[:, txt.shape[1] :, ...]
+
+        img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)
+        return img

invokeai/backend/flux/modules/autoencoder.py

@@ -0,0 +1,324 @@
+# Initially pulled from https://github.com/black-forest-labs/flux
+
+from dataclasses import dataclass
+
+import torch
+from einops import rearrange
+from torch import Tensor, nn
+
+
+@dataclass
+class AutoEncoderParams:
+    resolution: int
+    in_channels: int
+    ch: int
+    out_ch: int
+    ch_mult: list[int]
+    num_res_blocks: int
+    z_channels: int
+    scale_factor: float
+    shift_factor: float
+
+
+class AttnBlock(nn.Module):
+    def __init__(self, in_channels: int):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+
+        self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1)
+        self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1)
+        self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1)
+        self.proj_out = nn.Conv2d(in_channels, in_channels, kernel_size=1)
+
+    def attention(self, h_: Tensor) -> Tensor:
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        b, c, h, w = q.shape
+        q = rearrange(q, "b c h w -> b 1 (h w) c").contiguous()
+        k = rearrange(k, "b c h w -> b 1 (h w) c").contiguous()
+        v = rearrange(v, "b c h w -> b 1 (h w) c").contiguous()
+        h_ = nn.functional.scaled_dot_product_attention(q, k, v)
+
+        return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return x + self.proj_out(self.attention(x))
+
+
+class ResnetBlock(nn.Module):
+    def __init__(self, in_channels: int, out_channels: int):
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+
+        self.norm1 = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.norm2 = nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True)
+        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        if self.in_channels != self.out_channels:
+            self.nin_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, x):
+        h = x
+        h = self.norm1(h)
+        h = torch.nn.functional.silu(h)
+        h = self.conv1(h)
+
+        h = self.norm2(h)
+        h = torch.nn.functional.silu(h)
+        h = self.conv2(h)
+
+        if self.in_channels != self.out_channels:
+            x = self.nin_shortcut(x)
+
+        return x + h
+
+
+class Downsample(nn.Module):
+    def __init__(self, in_channels: int):
+        super().__init__()
+        # no asymmetric padding in torch conv, must do it ourselves
+        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
+
+    def forward(self, x: Tensor):
+        pad = (0, 1, 0, 1)
+        x = nn.functional.pad(x, pad, mode="constant", value=0)
+        x = self.conv(x)
+        return x
+
+
+class Upsample(nn.Module):
+    def __init__(self, in_channels: int):
+        super().__init__()
+        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x: Tensor):
+        x = nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+        x = self.conv(x)
+        return x
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        resolution: int,
+        in_channels: int,
+        ch: int,
+        ch_mult: list[int],
+        num_res_blocks: int,
+        z_channels: int,
+    ):
+        super().__init__()
+        self.ch = ch
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        # downsampling
+        self.conv_in = nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1)
+
+        curr_res = resolution
+        in_ch_mult = (1,) + tuple(ch_mult)
+        self.in_ch_mult = in_ch_mult
+        self.down = nn.ModuleList()
+        block_in = self.ch
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch * in_ch_mult[i_level]
+            block_out = ch * ch_mult[i_level]
+            for _ in range(self.num_res_blocks):
+                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
+                block_in = block_out
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions - 1:
+                down.downsample = Downsample(block_in)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
+        self.mid.attn_1 = AttnBlock(block_in)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
+
+        # end
+        self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
+        self.conv_out = nn.Conv2d(block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x: Tensor) -> Tensor:
+        # downsampling
+        hs = [self.conv_in(x)]
+        for i_level in range(self.num_resolutions):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level].block[i_block](hs[-1])
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+                hs.append(h)
+            if i_level != self.num_resolutions - 1:
+                hs.append(self.down[i_level].downsample(hs[-1]))
+
+        # middle
+        h = hs[-1]
+        h = self.mid.block_1(h)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h)
+        # end
+        h = self.norm_out(h)
+        h = torch.nn.functional.silu(h)
+        h = self.conv_out(h)
+        return h
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        ch: int,
+        out_ch: int,
+        ch_mult: list[int],
+        num_res_blocks: int,
+        in_channels: int,
+        resolution: int,
+        z_channels: int,
+    ):
+        super().__init__()
+        self.ch = ch
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.ffactor = 2 ** (self.num_resolutions - 1)
+
+        # compute in_ch_mult, block_in and curr_res at lowest res
+        block_in = ch * ch_mult[self.num_resolutions - 1]
+        curr_res = resolution // 2 ** (self.num_resolutions - 1)
+        self.z_shape = (1, z_channels, curr_res, curr_res)
+
+        # z to block_in
+        self.conv_in = nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
+        self.mid.attn_1 = AttnBlock(block_in)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch * ch_mult[i_level]
+            for _ in range(self.num_res_blocks + 1):
+                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
+                block_in = block_out
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = Upsample(block_in)
+                curr_res = curr_res * 2
+            self.up.insert(0, up)  # prepend to get consistent order
+
+        # end
+        self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
+        self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, z: Tensor) -> Tensor:
+        # z to block_in
+        h = self.conv_in(z)
+
+        # middle
+        h = self.mid.block_1(h)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h)
+
+        # upsampling
+        for i_level in reversed(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks + 1):
+                h = self.up[i_level].block[i_block](h)
+                if len(self.up[i_level].attn) > 0:
+                    h = self.up[i_level].attn[i_block](h)
+            if i_level != 0:
+                h = self.up[i_level].upsample(h)
+
+        # end
+        h = self.norm_out(h)
+        h = torch.nn.functional.silu(h)
+        h = self.conv_out(h)
+        return h
+
+
+class DiagonalGaussian(nn.Module):
+    def __init__(self, chunk_dim: int = 1):
+        super().__init__()
+        self.chunk_dim = chunk_dim
+
+    def forward(self, z: Tensor, sample: bool = True, generator: torch.Generator | None = None) -> Tensor:
+        mean, logvar = torch.chunk(z, 2, dim=self.chunk_dim)
+        if sample:
+            std = torch.exp(0.5 * logvar)
+            # Unfortunately, torch.randn_like(...) does not accept a generator argument at the time of writing, so we
+            # have to use torch.randn(...) instead.
+            return mean + std * torch.randn(size=mean.size(), generator=generator, dtype=mean.dtype, device=mean.device)
+        else:
+            return mean
+
+
+class AutoEncoder(nn.Module):
+    def __init__(self, params: AutoEncoderParams):
+        super().__init__()
+        self.encoder = Encoder(
+            resolution=params.resolution,
+            in_channels=params.in_channels,
+            ch=params.ch,
+            ch_mult=params.ch_mult,
+            num_res_blocks=params.num_res_blocks,
+            z_channels=params.z_channels,
+        )
+        self.decoder = Decoder(
+            resolution=params.resolution,
+            in_channels=params.in_channels,
+            ch=params.ch,
+            out_ch=params.out_ch,
+            ch_mult=params.ch_mult,
+            num_res_blocks=params.num_res_blocks,
+            z_channels=params.z_channels,
+        )
+        self.reg = DiagonalGaussian()
+
+        self.scale_factor = params.scale_factor
+        self.shift_factor = params.shift_factor
+
+    def encode(self, x: Tensor, sample: bool = True, generator: torch.Generator | None = None) -> Tensor:
+        """Run VAE encoding on input tensor x.
+
+        Args:
+            x (Tensor): Input image tensor. Shape: (batch_size, in_channels, height, width).
+            sample (bool, optional): If True, sample from the encoded distribution, else, return the distribution mean.
+                Defaults to True.
+            generator (torch.Generator | None, optional): Optional random number generator for reproducibility.
+                Defaults to None.
+
+        Returns:
+            Tensor: Encoded latent tensor. Shape: (batch_size, z_channels, latent_height, latent_width).
+        """
+
+        z = self.reg(self.encoder(x), sample=sample, generator=generator)
+        z = self.scale_factor * (z - self.shift_factor)
+        return z
+
+    def decode(self, z: Tensor) -> Tensor:
+        z = z / self.scale_factor + self.shift_factor
+        return self.decoder(z)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.decode(self.encode(x))

invokeai/backend/flux/modules/conditioner.py

@@ -0,0 +1,33 @@
+# Initially pulled from https://github.com/black-forest-labs/flux
+
+from torch import Tensor, nn
+from transformers import PreTrainedModel, PreTrainedTokenizer
+
+
+class HFEncoder(nn.Module):
+    def __init__(self, encoder: PreTrainedModel, tokenizer: PreTrainedTokenizer, is_clip: bool, max_length: int):
+        super().__init__()
+        self.max_length = max_length
+        self.is_clip = is_clip
+        self.output_key = "pooler_output" if self.is_clip else "last_hidden_state"
+        self.tokenizer = tokenizer
+        self.hf_module = encoder
+        self.hf_module = self.hf_module.eval().requires_grad_(False)
+
+    def forward(self, text: list[str]) -> Tensor:
+        batch_encoding = self.tokenizer(
+            text,
+            truncation=True,
+            max_length=self.max_length,
+            return_length=False,
+            return_overflowing_tokens=False,
+            padding="max_length",
+            return_tensors="pt",
+        )
+
+        outputs = self.hf_module(
+            input_ids=batch_encoding["input_ids"].to(self.hf_module.device),
+            attention_mask=None,
+            output_hidden_states=False,
+        )
+        return outputs[self.output_key]

invokeai/backend/flux/modules/layers.py

@@ -0,0 +1,253 @@
+# Initially pulled from https://github.com/black-forest-labs/flux
+
+import math
+from dataclasses import dataclass
+
+import torch
+from einops import rearrange
+from torch import Tensor, nn
+
+from invokeai.backend.flux.math import attention, rope
+
+
+class EmbedND(nn.Module):
+    def __init__(self, dim: int, theta: int, axes_dim: list[int]):
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        self.axes_dim = axes_dim
+
+    def forward(self, ids: Tensor) -> Tensor:
+        n_axes = ids.shape[-1]
+        emb = torch.cat(
+            [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
+            dim=-3,
+        )
+
+        return emb.unsqueeze(1)
+
+
+def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
+    """
+    Create sinusoidal timestep embeddings.
+    :param t: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an (N, D) Tensor of positional embeddings.
+    """
+    t = time_factor * t
+    half = dim // 2
+    freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(t.device)
+
+    args = t[:, None].float() * freqs[None]
+    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+    if dim % 2:
+        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+    if torch.is_floating_point(t):
+        embedding = embedding.to(t)
+    return embedding
+
+
+class MLPEmbedder(nn.Module):
+    def __init__(self, in_dim: int, hidden_dim: int):
+        super().__init__()
+        self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
+        self.silu = nn.SiLU()
+        self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.out_layer(self.silu(self.in_layer(x)))
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+        self.scale = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x: Tensor):
+        x_dtype = x.dtype
+        x = x.float()
+        rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
+        return (x * rrms).to(dtype=x_dtype) * self.scale
+
+
+class QKNorm(torch.nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+        self.query_norm = RMSNorm(dim)
+        self.key_norm = RMSNorm(dim)
+
+    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
+        q = self.query_norm(q)
+        k = self.key_norm(k)
+        return q.to(v), k.to(v)
+
+
+class SelfAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.norm = QKNorm(head_dim)
+        self.proj = nn.Linear(dim, dim)
+
+    def forward(self, x: Tensor, pe: Tensor) -> Tensor:
+        qkv = self.qkv(x)
+        q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        q, k = self.norm(q, k, v)
+        x = attention(q, k, v, pe=pe)
+        x = self.proj(x)
+        return x
+
+
+@dataclass
+class ModulationOut:
+    shift: Tensor
+    scale: Tensor
+    gate: Tensor
+
+
+class Modulation(nn.Module):
+    def __init__(self, dim: int, double: bool):
+        super().__init__()
+        self.is_double = double
+        self.multiplier = 6 if double else 3
+        self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)
+
+    def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
+        out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
+
+        return (
+            ModulationOut(*out[:3]),
+            ModulationOut(*out[3:]) if self.is_double else None,
+        )
+
+
+class DoubleStreamBlock(nn.Module):
+    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False):
+        super().__init__()
+
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.img_mod = Modulation(hidden_size, double=True)
+        self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
+
+        self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.img_mlp = nn.Sequential(
+            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
+            nn.GELU(approximate="tanh"),
+            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
+        )
+
+        self.txt_mod = Modulation(hidden_size, double=True)
+        self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
+
+        self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.txt_mlp = nn.Sequential(
+            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
+            nn.GELU(approximate="tanh"),
+            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
+        )
+
+    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor) -> tuple[Tensor, Tensor]:
+        img_mod1, img_mod2 = self.img_mod(vec)
+        txt_mod1, txt_mod2 = self.txt_mod(vec)
+
+        # prepare image for attention
+        img_modulated = self.img_norm1(img)
+        img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
+        img_qkv = self.img_attn.qkv(img_modulated)
+        img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
+
+        # prepare txt for attention
+        txt_modulated = self.txt_norm1(txt)
+        txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
+        txt_qkv = self.txt_attn.qkv(txt_modulated)
+        txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
+
+        # run actual attention
+        q = torch.cat((txt_q, img_q), dim=2)
+        k = torch.cat((txt_k, img_k), dim=2)
+        v = torch.cat((txt_v, img_v), dim=2)
+
+        attn = attention(q, k, v, pe=pe)
+        txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
+
+        # calculate the img bloks
+        img = img + img_mod1.gate * self.img_attn.proj(img_attn)
+        img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
+
+        # calculate the txt bloks
+        txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
+        txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
+        return img, txt
+
+
+class SingleStreamBlock(nn.Module):
+    """
+    A DiT block with parallel linear layers as described in
+    https://arxiv.org/abs/2302.05442 and adapted modulation interface.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qk_scale: float | None = None,
+    ):
+        super().__init__()
+        self.hidden_dim = hidden_size
+        self.num_heads = num_heads
+        head_dim = hidden_size // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        # qkv and mlp_in
+        self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
+        # proj and mlp_out
+        self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
+
+        self.norm = QKNorm(head_dim)
+
+        self.hidden_size = hidden_size
+        self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+
+        self.mlp_act = nn.GELU(approximate="tanh")
+        self.modulation = Modulation(hidden_size, double=False)
+
+    def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
+        mod, _ = self.modulation(vec)
+        x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
+        qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
+
+        q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        q, k = self.norm(q, k, v)
+
+        # compute attention
+        attn = attention(q, k, v, pe=pe)
+        # compute activation in mlp stream, cat again and run second linear layer
+        output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
+        return x + mod.gate * output
+
+
+class LastLayer(nn.Module):
+    def __init__(self, hidden_size: int, patch_size: int, out_channels: int):
+        super().__init__()
+        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
+        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
+
+    def forward(self, x: Tensor, vec: Tensor) -> Tensor:
+        shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
+        x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
+        x = self.linear(x)
+        return x

invokeai/backend/flux/sampling_utils.py

@@ -0,0 +1,135 @@
+# Initially pulled from https://github.com/black-forest-labs/flux
+
+import math
+from typing import Callable
+
+import torch
+from einops import rearrange, repeat
+
+
+def get_noise(
+    num_samples: int,
+    height: int,
+    width: int,
+    device: torch.device,
+    dtype: torch.dtype,
+    seed: int,
+):
+    # 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,
+        16,
+        # allow for packing
+        2 * math.ceil(height / 16),
+        2 * math.ceil(width / 16),
+        device=rand_device,
+        dtype=rand_dtype,
+        generator=torch.Generator(device=rand_device).manual_seed(seed),
+    ).to(device=device, dtype=dtype)
+
+
+def time_shift(mu: float, sigma: float, t: torch.Tensor) -> torch.Tensor:
+    return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+
+def get_lin_function(x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15) -> Callable[[float], float]:
+    m = (y2 - y1) / (x2 - x1)
+    b = y1 - m * x1
+    return lambda x: m * x + b
+
+
+def get_schedule(
+    num_steps: int,
+    image_seq_len: int,
+    base_shift: float = 0.5,
+    max_shift: float = 1.15,
+    shift: bool = True,
+) -> list[float]:
+    # extra step for zero
+    timesteps = torch.linspace(1, 0, num_steps + 1)
+
+    # shifting the schedule to favor high timesteps for higher signal images
+    if shift:
+        # estimate mu based on linear estimation between two points
+        mu = get_lin_function(y1=base_shift, y2=max_shift)(image_seq_len)
+        timesteps = time_shift(mu, 1.0, timesteps)
+
+    return timesteps.tolist()
+
+
+def _find_last_index_ge_val(timesteps: list[float], val: float, eps: float = 1e-6) -> int:
+    """Find the last index in timesteps that is >= val.
+
+    We use epsilon-close equality to avoid potential floating point errors.
+    """
+    idx = len(list(filter(lambda t: t >= (val - eps), timesteps))) - 1
+    assert idx >= 0
+    return idx
+
+
+def clip_timestep_schedule(timesteps: list[float], denoising_start: float, denoising_end: float) -> list[float]:
+    """Clip the timestep schedule to the denoising range.
+
+    Args:
+        timesteps (list[float]): The original timestep schedule: [1.0, ..., 0.0].
+        denoising_start (float): A value in [0, 1] specifying the start of the denoising process. E.g. a value of 0.2
+            would mean that the denoising process start at the last timestep in the schedule >= 0.8.
+        denoising_end (float): A value in [0, 1] specifying the end of the denoising process. E.g. a value of 0.8 would
+            mean that the denoising process end at the last timestep in the schedule >= 0.2.
+
+    Returns:
+        list[float]: The clipped timestep schedule.
+    """
+    assert 0.0 <= denoising_start <= 1.0
+    assert 0.0 <= denoising_end <= 1.0
+    assert denoising_start <= denoising_end
+
+    t_start_val = 1.0 - denoising_start
+    t_end_val = 1.0 - denoising_end
+
+    t_start_idx = _find_last_index_ge_val(timesteps, t_start_val)
+    t_end_idx = _find_last_index_ge_val(timesteps, t_end_val)
+
+    clipped_timesteps = timesteps[t_start_idx : t_end_idx + 1]
+
+    return clipped_timesteps
+
+
+def unpack(x: torch.Tensor, height: int, width: int) -> torch.Tensor:
+    """Unpack flat array of patch embeddings to latent image."""
+    return rearrange(
+        x,
+        "b (h w) (c ph pw) -> b c (h ph) (w pw)",
+        h=math.ceil(height / 16),
+        w=math.ceil(width / 16),
+        ph=2,
+        pw=2,
+    )
+
+
+def pack(x: torch.Tensor) -> torch.Tensor:
+    """Pack latent image to flattented array of patch embeddings."""
+    # Pixel unshuffle with a scale of 2, and flatten the height/width dimensions to get an array of patches.
+    return rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+
+
+def generate_img_ids(h: int, w: int, batch_size: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor:
+    """Generate tensor of image position ids.
+
+    Args:
+        h (int): Height of image in latent space.
+        w (int): Width of image in latent space.
+        batch_size (int): Batch size.
+        device (torch.device): Device.
+        dtype (torch.dtype): dtype.
+
+    Returns:
+        torch.Tensor: Image position ids.
+    """
+    img_ids = torch.zeros(h // 2, w // 2, 3, device=device, dtype=dtype)
+    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=device, dtype=dtype)[:, None]
+    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=device, dtype=dtype)[None, :]
+    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=batch_size)
+    return img_ids

invokeai/backend/flux/util.py

@@ -0,0 +1,71 @@
+# Initially pulled from https://github.com/black-forest-labs/flux
+
+from dataclasses import dataclass
+from typing import Dict, Literal
+
+from invokeai.backend.flux.model import FluxParams
+from invokeai.backend.flux.modules.autoencoder import AutoEncoderParams
+
+
+@dataclass
+class ModelSpec:
+    params: FluxParams
+    ae_params: AutoEncoderParams
+    ckpt_path: str | None
+    ae_path: str | None
+    repo_id: str | None
+    repo_flow: str | None
+    repo_ae: str | None
+
+
+max_seq_lengths: Dict[str, Literal[256, 512]] = {
+    "flux-dev": 512,
+    "flux-schnell": 256,
+}
+
+
+ae_params = {
+    "flux": AutoEncoderParams(
+        resolution=256,
+        in_channels=3,
+        ch=128,
+        out_ch=3,
+        ch_mult=[1, 2, 4, 4],
+        num_res_blocks=2,
+        z_channels=16,
+        scale_factor=0.3611,
+        shift_factor=0.1159,
+    )
+}
+
+
+params = {
+    "flux-dev": FluxParams(
+        in_channels=64,
+        vec_in_dim=768,
+        context_in_dim=4096,
+        hidden_size=3072,
+        mlp_ratio=4.0,
+        num_heads=24,
+        depth=19,
+        depth_single_blocks=38,
+        axes_dim=[16, 56, 56],
+        theta=10_000,
+        qkv_bias=True,
+        guidance_embed=True,
+    ),
+    "flux-schnell": FluxParams(
+        in_channels=64,
+        vec_in_dim=768,
+        context_in_dim=4096,
+        hidden_size=3072,
+        mlp_ratio=4.0,
+        num_heads=24,
+        depth=19,
+        depth_single_blocks=38,
+        axes_dim=[16, 56, 56],
+        theta=10_000,
+        qkv_bias=True,
+        guidance_embed=False,
+    ),
+}

invokeai/backend/model_manager/config.py

@@ -52,6 +52,7 @@ class BaseModelType(str, Enum):
     StableDiffusion2 = "sd-2"
     StableDiffusionXL = "sdxl"
     StableDiffusionXLRefiner = "sdxl-refiner"
+    Flux = "flux"
     # Kandinsky2_1 = "kandinsky-2.1"
 
 
@@ -66,7 +67,9 @@ class ModelType(str, Enum):
     TextualInversion = "embedding"
     IPAdapter = "ip_adapter"
     CLIPVision = "clip_vision"
+    CLIPEmbed = "clip_embed"
     T2IAdapter = "t2i_adapter"
+    T5Encoder = "t5_encoder"
     SpandrelImageToImage = "spandrel_image_to_image"
 
 
@@ -74,6 +77,7 @@ class SubModelType(str, Enum):
     """Submodel type."""
 
     UNet = "unet"
+    Transformer = "transformer"
     TextEncoder = "text_encoder"
     TextEncoder2 = "text_encoder_2"
     Tokenizer = "tokenizer"
@@ -104,6 +108,9 @@ class ModelFormat(str, Enum):
     EmbeddingFile = "embedding_file"
     EmbeddingFolder = "embedding_folder"
     InvokeAI = "invokeai"
+    T5Encoder = "t5_encoder"
+    BnbQuantizedLlmInt8b = "bnb_quantized_int8b"
+    BnbQuantizednf4b = "bnb_quantized_nf4b"
 
 
 class SchedulerPredictionType(str, Enum):
@@ -186,7 +193,9 @@ class ModelConfigBase(BaseModel):
 class CheckpointConfigBase(ModelConfigBase):
     """Model config for checkpoint-style models."""
 
-    format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
+    format: Literal[ModelFormat.Checkpoint, ModelFormat.BnbQuantizednf4b] = Field(
+        description="Format of the provided checkpoint model", default=ModelFormat.Checkpoint
+    )
     config_path: str = Field(description="path to the checkpoint model config file")
     converted_at: Optional[float] = Field(
         description="When this model was last converted to diffusers", default_factory=time.time
@@ -205,6 +214,26 @@ class LoRAConfigBase(ModelConfigBase):
     trigger_phrases: Optional[set[str]] = Field(description="Set of trigger phrases for this model", default=None)
 
 
+class T5EncoderConfigBase(ModelConfigBase):
+    type: Literal[ModelType.T5Encoder] = ModelType.T5Encoder
+
+
+class T5EncoderConfig(T5EncoderConfigBase):
+    format: Literal[ModelFormat.T5Encoder] = ModelFormat.T5Encoder
+
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.T5Encoder.value}.{ModelFormat.T5Encoder.value}")
+
+
+class T5EncoderBnbQuantizedLlmInt8bConfig(T5EncoderConfigBase):
+    format: Literal[ModelFormat.BnbQuantizedLlmInt8b] = ModelFormat.BnbQuantizedLlmInt8b
+
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.T5Encoder.value}.{ModelFormat.BnbQuantizedLlmInt8b.value}")
+
+
 class LoRALyCORISConfig(LoRAConfigBase):
     """Model config for LoRA/Lycoris models."""
 
@@ -229,7 +258,6 @@ class VAECheckpointConfig(CheckpointConfigBase):
     """Model config for standalone VAE models."""
 
     type: Literal[ModelType.VAE] = ModelType.VAE
-    format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
 
     @staticmethod
     def get_tag() -> Tag:
@@ -268,7 +296,6 @@ class ControlNetCheckpointConfig(CheckpointConfigBase, ControlAdapterConfigBase)
     """Model config for ControlNet models (diffusers version)."""
 
     type: Literal[ModelType.ControlNet] = ModelType.ControlNet
-    format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
 
     @staticmethod
     def get_tag() -> Tag:
@@ -317,6 +344,21 @@ class MainCheckpointConfig(CheckpointConfigBase, MainConfigBase):
         return Tag(f"{ModelType.Main.value}.{ModelFormat.Checkpoint.value}")
 
 
+class MainBnbQuantized4bCheckpointConfig(CheckpointConfigBase, MainConfigBase):
+    """Model config for main checkpoint models."""
+
+    prediction_type: SchedulerPredictionType = SchedulerPredictionType.Epsilon
+    upcast_attention: bool = False
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.format = ModelFormat.BnbQuantizednf4b
+
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.Main.value}.{ModelFormat.BnbQuantizednf4b.value}")
+
+
 class MainDiffusersConfig(DiffusersConfigBase, MainConfigBase):
     """Model config for main diffusers models."""
 
@@ -350,6 +392,17 @@ class IPAdapterCheckpointConfig(IPAdapterBaseConfig):
         return Tag(f"{ModelType.IPAdapter.value}.{ModelFormat.Checkpoint.value}")
 
 
+class CLIPEmbedDiffusersConfig(DiffusersConfigBase):
+    """Model config for Clip Embeddings."""
+
+    type: Literal[ModelType.CLIPEmbed] = ModelType.CLIPEmbed
+    format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
+
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.CLIPEmbed.value}.{ModelFormat.Diffusers.value}")
+
+
 class CLIPVisionDiffusersConfig(DiffusersConfigBase):
     """Model config for CLIPVision."""
 
@@ -408,12 +461,15 @@ AnyModelConfig = Annotated[
     Union[
         Annotated[MainDiffusersConfig, MainDiffusersConfig.get_tag()],
         Annotated[MainCheckpointConfig, MainCheckpointConfig.get_tag()],
+        Annotated[MainBnbQuantized4bCheckpointConfig, MainBnbQuantized4bCheckpointConfig.get_tag()],
         Annotated[VAEDiffusersConfig, VAEDiffusersConfig.get_tag()],
         Annotated[VAECheckpointConfig, VAECheckpointConfig.get_tag()],
         Annotated[ControlNetDiffusersConfig, ControlNetDiffusersConfig.get_tag()],
         Annotated[ControlNetCheckpointConfig, ControlNetCheckpointConfig.get_tag()],
         Annotated[LoRALyCORISConfig, LoRALyCORISConfig.get_tag()],
         Annotated[LoRADiffusersConfig, LoRADiffusersConfig.get_tag()],
+        Annotated[T5EncoderConfig, T5EncoderConfig.get_tag()],
+        Annotated[T5EncoderBnbQuantizedLlmInt8bConfig, T5EncoderBnbQuantizedLlmInt8bConfig.get_tag()],
         Annotated[TextualInversionFileConfig, TextualInversionFileConfig.get_tag()],
         Annotated[TextualInversionFolderConfig, TextualInversionFolderConfig.get_tag()],
         Annotated[IPAdapterInvokeAIConfig, IPAdapterInvokeAIConfig.get_tag()],
@@ -421,6 +477,7 @@ AnyModelConfig = Annotated[
         Annotated[T2IAdapterConfig, T2IAdapterConfig.get_tag()],
         Annotated[SpandrelImageToImageConfig, SpandrelImageToImageConfig.get_tag()],
         Annotated[CLIPVisionDiffusersConfig, CLIPVisionDiffusersConfig.get_tag()],
+        Annotated[CLIPEmbedDiffusersConfig, CLIPEmbedDiffusersConfig.get_tag()],
     ],
     Discriminator(get_model_discriminator_value),
 ]

invokeai/backend/model_manager/load/load_default.py

@@ -66,12 +66,14 @@ class ModelLoader(ModelLoaderBase):
         return (model_base / config.path).resolve()
 
     def _load_and_cache(self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> ModelLockerBase:
+        stats_name = ":".join([config.base, config.type, config.name, (submodel_type or "")])
         try:
-            return self._ram_cache.get(config.key, submodel_type)
+            return self._ram_cache.get(config.key, submodel_type, stats_name=stats_name)
         except IndexError:
             pass
 
         config.path = str(self._get_model_path(config))
+        self._ram_cache.make_room(self.get_size_fs(config, Path(config.path), submodel_type))
         loaded_model = self._load_model(config, submodel_type)
 
         self._ram_cache.put(
@@ -83,7 +85,7 @@ class ModelLoader(ModelLoaderBase):
         return self._ram_cache.get(
             key=config.key,
             submodel_type=submodel_type,
-            stats_name=":".join([config.base, config.type, config.name, (submodel_type or "")]),
+            stats_name=stats_name,
         )
 
     def get_size_fs(

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

@@ -128,7 +128,24 @@ class ModelCacheBase(ABC, Generic[T]):
     @property
     @abstractmethod
     def max_cache_size(self) -> float:
-        """Return true if the cache is configured to lazily offload models in VRAM."""
+        """Return the maximum size the RAM cache can grow to."""
+        pass
+
+    @max_cache_size.setter
+    @abstractmethod
+    def max_cache_size(self, value: float) -> None:
+        """Set the cap on vram cache size."""
+
+    @property
+    @abstractmethod
+    def max_vram_cache_size(self) -> float:
+        """Return the maximum size the VRAM cache can grow to."""
+        pass
+
+    @max_vram_cache_size.setter
+    @abstractmethod
+    def max_vram_cache_size(self, value: float) -> float:
+        """Set the maximum size the VRAM cache can grow to."""
         pass
 
     @abstractmethod
@@ -193,15 +210,6 @@ class ModelCacheBase(ABC, Generic[T]):
         """
         pass
 
-    @abstractmethod
-    def exists(
-        self,
-        key: str,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> bool:
-        """Return true if the model identified by key and submodel_type is in the cache."""
-        pass
-
     @abstractmethod
     def cache_size(self) -> int:
         """Get the total size of the models currently cached."""

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

@@ -1,22 +1,6 @@
 # Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Development team
 # TODO: Add Stalker's proper name to copyright
-"""
-Manage a RAM cache of diffusion/transformer models for fast switching.
-They are moved between GPU VRAM and CPU RAM as necessary. If the cache
-grows larger than a preset maximum, then the least recently used
-model will be cleared and (re)loaded from disk when next needed.
-
-The cache returns context manager generators designed to load the
-model into the GPU within the context, and unload outside the
-context. Use like this:
-
-   cache = ModelCache(max_cache_size=7.5)
-   with cache.get_model('runwayml/stable-diffusion-1-5') as SD1,
-          cache.get_model('stabilityai/stable-diffusion-2') as SD2:
-       do_something_in_GPU(SD1,SD2)
-
-
-"""
+""" """
 
 import gc
 import math
@@ -40,53 +24,74 @@ from invokeai.backend.model_manager.load.model_util import calc_model_size_by_da
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
-# Maximum size of the cache, in gigs
-# Default is roughly enough to hold three fp16 diffusers models in RAM simultaneously
-DEFAULT_MAX_CACHE_SIZE = 6.0
-
-# amount of GPU memory to hold in reserve for use by generations (GB)
-DEFAULT_MAX_VRAM_CACHE_SIZE = 2.75
-
-# actual size of a gig
-GIG = 1073741824
+# Size of a GB in bytes.
+GB = 2**30
 
 # Size of a MB in bytes.
 MB = 2**20
 
 
 class ModelCache(ModelCacheBase[AnyModel]):
-    """Implementation of ModelCacheBase."""
+    """A cache for managing models in memory.
+
+    The cache is based on two levels of model storage:
+    - execution_device: The device where most models are executed (typically "cuda", "mps", or "cpu").
+    - storage_device: The device where models are offloaded when not in active use (typically "cpu").
+
+    The model cache is based on the following assumptions:
+    - storage_device_mem_size > execution_device_mem_size
+    - disk_to_storage_device_transfer_time >> storage_device_to_execution_device_transfer_time
+
+    A copy of all models in the cache is always kept on the storage_device. A subset of the models also have a copy on
+    the execution_device.
+
+    Models are moved between the storage_device and the execution_device as necessary. Cache size limits are enforced
+    on both the storage_device and the execution_device. The execution_device cache uses a smallest-first offload
+    policy. The storage_device cache uses a least-recently-used (LRU) offload policy.
+
+    Note: Neither of these offload policies has really been compared against alternatives. It's likely that different
+    policies would be better, although the optimal policies are likely heavily dependent on usage patterns and HW
+    configuration.
+
+    The cache returns context manager generators designed to load the model into the execution device (often GPU) within
+    the context, and unload outside the context.
+
+    Example usage:
+    ```
+    cache = ModelCache(max_cache_size=7.5, max_vram_cache_size=6.0)
+    with cache.get_model('runwayml/stable-diffusion-1-5') as SD1:
+        do_something_on_gpu(SD1)
+    ```
+    """
 
     def __init__(
         self,
-        max_cache_size: float = DEFAULT_MAX_CACHE_SIZE,
-        max_vram_cache_size: float = DEFAULT_MAX_VRAM_CACHE_SIZE,
+        max_cache_size: float,
+        max_vram_cache_size: float,
         execution_device: torch.device = torch.device("cuda"),
         storage_device: torch.device = torch.device("cpu"),
         precision: torch.dtype = torch.float16,
-        sequential_offload: bool = False,
         lazy_offloading: bool = True,
-        sha_chunksize: int = 16777216,
         log_memory_usage: bool = False,
         logger: Optional[Logger] = None,
     ):
         """
         Initialize the model RAM cache.
 
-        :param max_cache_size: Maximum size of the RAM cache [6.0 GB]
+        :param max_cache_size: Maximum size of the storage_device cache in GBs.
+        :param max_vram_cache_size: Maximum size of the execution_device cache in GBs.
         :param execution_device: Torch device to load active model into [torch.device('cuda')]
         :param storage_device: Torch device to save inactive model in [torch.device('cpu')]
         :param precision: Precision for loaded models [torch.float16]
         :param lazy_offloading: Keep model in VRAM until another model needs to be loaded
-        :param sequential_offload: Conserve VRAM by loading and unloading each stage of the pipeline sequentially
         :param log_memory_usage: If True, a memory snapshot will be captured before and after every model cache
             operation, and the result will be logged (at debug level). There is a time cost to capturing the memory
             snapshots, so it is recommended to disable this feature unless you are actively inspecting the model cache's
             behaviour.
+        :param logger: InvokeAILogger to use (otherwise creates one)
         """
         # allow lazy offloading only when vram cache enabled
         self._lazy_offloading = lazy_offloading and max_vram_cache_size > 0
-        self._precision: torch.dtype = precision
         self._max_cache_size: float = max_cache_size
         self._max_vram_cache_size: float = max_vram_cache_size
         self._execution_device: torch.device = execution_device
@@ -128,6 +133,16 @@ class ModelCache(ModelCacheBase[AnyModel]):
         """Set the cap on cache size."""
         self._max_cache_size = value
 
+    @property
+    def max_vram_cache_size(self) -> float:
+        """Return the cap on vram cache size."""
+        return self._max_vram_cache_size
+
+    @max_vram_cache_size.setter
+    def max_vram_cache_size(self, value: float) -> None:
+        """Set the cap on vram cache size."""
+        self._max_vram_cache_size = value
+
     @property
     def stats(self) -> Optional[CacheStats]:
         """Return collected CacheStats object."""
@@ -145,15 +160,6 @@ class ModelCache(ModelCacheBase[AnyModel]):
             total += cache_record.size
         return total
 
-    def exists(
-        self,
-        key: str,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> bool:
-        """Return true if the model identified by key and submodel_type is in the cache."""
-        key = self._make_cache_key(key, submodel_type)
-        return key in self._cached_models
-
     def put(
         self,
         key: str,
@@ -203,7 +209,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
         # more stats
         if self.stats:
             stats_name = stats_name or key
-            self.stats.cache_size = int(self._max_cache_size * GIG)
+            self.stats.cache_size = int(self._max_cache_size * GB)
             self.stats.high_watermark = max(self.stats.high_watermark, self.cache_size())
             self.stats.in_cache = len(self._cached_models)
             self.stats.loaded_model_sizes[stats_name] = max(
@@ -231,10 +237,13 @@ class ModelCache(ModelCacheBase[AnyModel]):
             return model_key
 
     def offload_unlocked_models(self, size_required: int) -> None:
-        """Move any unused models from VRAM."""
-        reserved = self._max_vram_cache_size * GIG
+        """Offload models from the execution_device to make room for size_required.
+
+        :param size_required: The amount of space to clear in the execution_device cache, in bytes.
+        """
+        reserved = self._max_vram_cache_size * GB
         vram_in_use = torch.cuda.memory_allocated() + size_required
-        self.logger.debug(f"{(vram_in_use/GIG):.2f}GB VRAM needed for models; max allowed={(reserved/GIG):.2f}GB")
+        self.logger.debug(f"{(vram_in_use/GB):.2f}GB VRAM needed for models; max allowed={(reserved/GB):.2f}GB")
         for _, cache_entry in sorted(self._cached_models.items(), key=lambda x: x[1].size):
             if vram_in_use <= reserved:
                 break
@@ -245,7 +254,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
                 cache_entry.loaded = False
                 vram_in_use = torch.cuda.memory_allocated() + size_required
                 self.logger.debug(
-                    f"Removing {cache_entry.key} from VRAM to free {(cache_entry.size/GIG):.2f}GB; vram free = {(torch.cuda.memory_allocated()/GIG):.2f}GB"
+                    f"Removing {cache_entry.key} from VRAM to free {(cache_entry.size/GB):.2f}GB; vram free = {(torch.cuda.memory_allocated()/GB):.2f}GB"
                 )
 
         TorchDevice.empty_cache()
@@ -303,7 +312,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
         self.logger.debug(
             f"Moved model '{cache_entry.key}' from {source_device} to"
             f" {target_device} in {(end_model_to_time-start_model_to_time):.2f}s."
-            f"Estimated model size: {(cache_entry.size/GIG):.3f} GB."
+            f"Estimated model size: {(cache_entry.size/GB):.3f} GB."
             f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
         )
 
@@ -326,14 +335,14 @@ class ModelCache(ModelCacheBase[AnyModel]):
                     f"Moving model '{cache_entry.key}' from {source_device} to"
                     f" {target_device} caused an unexpected change in VRAM usage. The model's"
                     " estimated size may be incorrect. Estimated model size:"
-                    f" {(cache_entry.size/GIG):.3f} GB.\n"
+                    f" {(cache_entry.size/GB):.3f} GB.\n"
                     f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
                 )
 
     def print_cuda_stats(self) -> None:
         """Log CUDA diagnostics."""
-        vram = "%4.2fG" % (torch.cuda.memory_allocated() / GIG)
-        ram = "%4.2fG" % (self.cache_size() / GIG)
+        vram = "%4.2fG" % (torch.cuda.memory_allocated() / GB)
+        ram = "%4.2fG" % (self.cache_size() / GB)
 
         in_ram_models = 0
         in_vram_models = 0
@@ -353,17 +362,20 @@ class ModelCache(ModelCacheBase[AnyModel]):
                 )
 
     def make_room(self, size: int) -> None:
-        """Make enough room in the cache to accommodate a new model of indicated size."""
-        # calculate how much memory this model will require
-        # multiplier = 2 if self.precision==torch.float32 else 1
+        """Make enough room in the cache to accommodate a new model of indicated size.
+
+        Note: This function deletes all of the cache's internal references to a model in order to free it. If there are
+        external references to the model, there's nothing that the cache can do about it, and those models will not be
+        garbage-collected.
+        """
         bytes_needed = size
-        maximum_size = self.max_cache_size * GIG  # stored in GB, convert to bytes
+        maximum_size = self.max_cache_size * GB  # stored in GB, convert to bytes
         current_size = self.cache_size()
 
         if current_size + bytes_needed > maximum_size:
             self.logger.debug(
-                f"Max cache size exceeded: {(current_size/GIG):.2f}/{self.max_cache_size:.2f} GB, need an additional"
-                f" {(bytes_needed/GIG):.2f} GB"
+                f"Max cache size exceeded: {(current_size/GB):.2f}/{self.max_cache_size:.2f} GB, need an additional"
+                f" {(bytes_needed/GB):.2f} GB"
             )
 
         self.logger.debug(f"Before making_room: cached_models={len(self._cached_models)}")
@@ -380,7 +392,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
 
             if not cache_entry.locked:
                 self.logger.debug(
-                    f"Removing {model_key} from RAM cache to free at least {(size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)"
+                    f"Removing {model_key} from RAM cache to free at least {(size/GB):.2f} GB (-{(cache_entry.size/GB):.2f} GB)"
                 )
                 current_size -= cache_entry.size
                 models_cleared += 1

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

@@ -0,0 +1,246 @@
+# Copyright (c) 2024, Brandon W. Rising and the InvokeAI Development Team
+"""Class for Flux model loading in InvokeAI."""
+
+from pathlib import Path
+from typing import Optional
+
+import accelerate
+import torch
+from safetensors.torch import load_file
+from transformers import AutoConfig, AutoModelForTextEncoding, CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
+
+from invokeai.app.services.config.config_default import get_config
+from invokeai.backend.flux.model import Flux
+from invokeai.backend.flux.modules.autoencoder import AutoEncoder
+from invokeai.backend.flux.util import ae_params, params
+from invokeai.backend.model_manager import (
+    AnyModel,
+    AnyModelConfig,
+    BaseModelType,
+    ModelFormat,
+    ModelType,
+    SubModelType,
+)
+from invokeai.backend.model_manager.config import (
+    CheckpointConfigBase,
+    CLIPEmbedDiffusersConfig,
+    MainBnbQuantized4bCheckpointConfig,
+    MainCheckpointConfig,
+    T5EncoderBnbQuantizedLlmInt8bConfig,
+    T5EncoderConfig,
+    VAECheckpointConfig,
+)
+from invokeai.backend.model_manager.load.load_default import ModelLoader
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.util.model_util import (
+    convert_bundle_to_flux_transformer_checkpoint,
+)
+from invokeai.backend.util.silence_warnings import SilenceWarnings
+
+try:
+    from invokeai.backend.quantization.bnb_llm_int8 import quantize_model_llm_int8
+    from invokeai.backend.quantization.bnb_nf4 import quantize_model_nf4
+
+    bnb_available = True
+except ImportError:
+    bnb_available = False
+
+app_config = get_config()
+
+
+@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.VAE, format=ModelFormat.Checkpoint)
+class FluxVAELoader(ModelLoader):
+    """Class to load VAE models."""
+
+    def _load_model(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        if not isinstance(config, VAECheckpointConfig):
+            raise ValueError("Only VAECheckpointConfig models are currently supported here.")
+        model_path = Path(config.path)
+
+        with SilenceWarnings():
+            model = AutoEncoder(ae_params[config.config_path])
+            sd = load_file(model_path)
+            model.load_state_dict(sd, assign=True)
+            model.to(dtype=self._torch_dtype)
+
+        return model
+
+
+@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.CLIPEmbed, format=ModelFormat.Diffusers)
+class ClipCheckpointModel(ModelLoader):
+    """Class to load main models."""
+
+    def _load_model(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        if not isinstance(config, CLIPEmbedDiffusersConfig):
+            raise ValueError("Only CLIPEmbedDiffusersConfig models are currently supported here.")
+
+        match submodel_type:
+            case SubModelType.Tokenizer:
+                return CLIPTokenizer.from_pretrained(Path(config.path) / "tokenizer")
+            case SubModelType.TextEncoder:
+                return CLIPTextModel.from_pretrained(Path(config.path) / "text_encoder")
+
+        raise ValueError(
+            f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
+        )
+
+
+@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.T5Encoder, format=ModelFormat.BnbQuantizedLlmInt8b)
+class BnbQuantizedLlmInt8bCheckpointModel(ModelLoader):
+    """Class to load main models."""
+
+    def _load_model(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        if not isinstance(config, T5EncoderBnbQuantizedLlmInt8bConfig):
+            raise ValueError("Only T5EncoderBnbQuantizedLlmInt8bConfig models are currently supported here.")
+        if not bnb_available:
+            raise ImportError(
+                "The bnb modules are not available. Please install bitsandbytes if available on your platform."
+            )
+        match submodel_type:
+            case SubModelType.Tokenizer2:
+                return T5Tokenizer.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
+            case SubModelType.TextEncoder2:
+                te2_model_path = Path(config.path) / "text_encoder_2"
+                model_config = AutoConfig.from_pretrained(te2_model_path)
+                with accelerate.init_empty_weights():
+                    model = AutoModelForTextEncoding.from_config(model_config)
+                    model = quantize_model_llm_int8(model, modules_to_not_convert=set())
+
+                state_dict_path = te2_model_path / "bnb_llm_int8_model.safetensors"
+                state_dict = load_file(state_dict_path)
+                self._load_state_dict_into_t5(model, state_dict)
+
+                return model
+
+        raise ValueError(
+            f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
+        )
+
+    @classmethod
+    def _load_state_dict_into_t5(cls, model: T5EncoderModel, state_dict: dict[str, torch.Tensor]):
+        # There is a shared reference to a single weight tensor in the model.
+        # Both "encoder.embed_tokens.weight" and "shared.weight" refer to the same tensor, so only the latter should
+        # be present in the state_dict.
+        missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False, assign=True)
+        assert len(unexpected_keys) == 0
+        assert set(missing_keys) == {"encoder.embed_tokens.weight"}
+        # Assert that the layers we expect to be shared are actually shared.
+        assert model.encoder.embed_tokens.weight is model.shared.weight
+
+
+@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.T5Encoder, format=ModelFormat.T5Encoder)
+class T5EncoderCheckpointModel(ModelLoader):
+    """Class to load main models."""
+
+    def _load_model(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        if not isinstance(config, T5EncoderConfig):
+            raise ValueError("Only T5EncoderConfig models are currently supported here.")
+
+        match submodel_type:
+            case SubModelType.Tokenizer2:
+                return T5Tokenizer.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
+            case SubModelType.TextEncoder2:
+                return T5EncoderModel.from_pretrained(Path(config.path) / "text_encoder_2")
+
+        raise ValueError(
+            f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
+        )
+
+
+@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.Main, format=ModelFormat.Checkpoint)
+class FluxCheckpointModel(ModelLoader):
+    """Class to load main models."""
+
+    def _load_model(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        if not isinstance(config, CheckpointConfigBase):
+            raise ValueError("Only CheckpointConfigBase models are currently supported here.")
+
+        match submodel_type:
+            case SubModelType.Transformer:
+                return self._load_from_singlefile(config)
+
+        raise ValueError(
+            f"Only Transformer submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
+        )
+
+    def _load_from_singlefile(
+        self,
+        config: AnyModelConfig,
+    ) -> AnyModel:
+        assert isinstance(config, MainCheckpointConfig)
+        model_path = Path(config.path)
+
+        with SilenceWarnings():
+            model = Flux(params[config.config_path])
+            sd = load_file(model_path)
+            if "model.diffusion_model.double_blocks.0.img_attn.norm.key_norm.scale" in sd:
+                sd = convert_bundle_to_flux_transformer_checkpoint(sd)
+            new_sd_size = sum([ten.nelement() * torch.bfloat16.itemsize for ten in sd.values()])
+            self._ram_cache.make_room(new_sd_size)
+            for k in sd.keys():
+                # We need to cast to bfloat16 due to it being the only currently supported dtype for inference
+                sd[k] = sd[k].to(torch.bfloat16)
+            model.load_state_dict(sd, assign=True)
+        return model
+
+
+@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.Main, format=ModelFormat.BnbQuantizednf4b)
+class FluxBnbQuantizednf4bCheckpointModel(ModelLoader):
+    """Class to load main models."""
+
+    def _load_model(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        if not isinstance(config, CheckpointConfigBase):
+            raise ValueError("Only CheckpointConfigBase models are currently supported here.")
+
+        match submodel_type:
+            case SubModelType.Transformer:
+                return self._load_from_singlefile(config)
+
+        raise ValueError(
+            f"Only Transformer submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
+        )
+
+    def _load_from_singlefile(
+        self,
+        config: AnyModelConfig,
+    ) -> AnyModel:
+        assert isinstance(config, MainBnbQuantized4bCheckpointConfig)
+        if not bnb_available:
+            raise ImportError(
+                "The bnb modules are not available. Please install bitsandbytes if available on your platform."
+            )
+        model_path = Path(config.path)
+
+        with SilenceWarnings():
+            with accelerate.init_empty_weights():
+                model = Flux(params[config.config_path])
+                model = quantize_model_nf4(model, modules_to_not_convert=set(), compute_dtype=torch.bfloat16)
+            sd = load_file(model_path)
+            if "model.diffusion_model.double_blocks.0.img_attn.norm.key_norm.scale" in sd:
+                sd = convert_bundle_to_flux_transformer_checkpoint(sd)
+            model.load_state_dict(sd, assign=True)
+        return model

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

@@ -78,7 +78,12 @@ class GenericDiffusersLoader(ModelLoader):
 
     # TO DO: Add exception handling
     def _hf_definition_to_type(self, module: str, class_name: str) -> ModelMixin:  # fix with correct type
-        if module in ["diffusers", "transformers"]:
+        if module in [
+            "diffusers",
+            "transformers",
+            "invokeai.backend.quantization.fast_quantized_transformers_model",
+            "invokeai.backend.quantization.fast_quantized_diffusion_model",
+        ]:
             res_type = sys.modules[module]
         else:
             res_type = sys.modules["diffusers"].pipelines

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

@@ -36,8 +36,18 @@ VARIANT_TO_IN_CHANNEL_MAP = {
 }
 
 
-@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.Main, format=ModelFormat.Diffusers)
-@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.Main, format=ModelFormat.Checkpoint)
+@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion1, type=ModelType.Main, format=ModelFormat.Diffusers)
+@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion2, type=ModelType.Main, format=ModelFormat.Diffusers)
+@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusionXL, type=ModelType.Main, format=ModelFormat.Diffusers)
+@ModelLoaderRegistry.register(
+    base=BaseModelType.StableDiffusionXLRefiner, 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)
+@ModelLoaderRegistry.register(
+    base=BaseModelType.StableDiffusionXLRefiner, type=ModelType.Main, format=ModelFormat.Checkpoint
+)
 class StableDiffusionDiffusersModel(GenericDiffusersLoader):
     """Class to load main models."""
 

invokeai/backend/model_manager/load/model_util.py

@@ -9,7 +9,7 @@ from typing import Optional
 import torch
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from diffusers.schedulers.scheduling_utils import SchedulerMixin
-from transformers import CLIPTokenizer
+from transformers import CLIPTokenizer, T5Tokenizer, T5TokenizerFast
 
 from invokeai.backend.image_util.depth_anything.depth_anything_pipeline import DepthAnythingPipeline
 from invokeai.backend.image_util.grounding_dino.grounding_dino_pipeline import GroundingDinoPipeline
@@ -50,6 +50,17 @@ def calc_model_size_by_data(logger: logging.Logger, model: AnyModel) -> int:
         ),
     ):
         return model.calc_size()
+    elif isinstance(
+        model,
+        (
+            T5TokenizerFast,
+            T5Tokenizer,
+        ),
+    ):
+        # HACK(ryand): len(model) just returns the vocabulary size, so this is blatantly wrong. It should be small
+        # relative to the text encoder that it's used with, so shouldn't matter too much, but we should fix this at some
+        # point.
+        return len(model)
     else:
         # TODO(ryand): Promote this from a log to an exception once we are confident that we are handling all of the
         # supported model types.

invokeai/backend/model_manager/probe.py

@@ -95,6 +95,7 @@ class ModelProbe(object):
     }
 
     CLASS2TYPE = {
+        "FluxPipeline": ModelType.Main,
         "StableDiffusionPipeline": ModelType.Main,
         "StableDiffusionInpaintPipeline": ModelType.Main,
         "StableDiffusionXLPipeline": ModelType.Main,
@@ -106,6 +107,9 @@ class ModelProbe(object):
         "ControlNetModel": ModelType.ControlNet,
         "CLIPVisionModelWithProjection": ModelType.CLIPVision,
         "T2IAdapter": ModelType.T2IAdapter,
+        "CLIPModel": ModelType.CLIPEmbed,
+        "CLIPTextModel": ModelType.CLIPEmbed,
+        "T5EncoderModel": ModelType.T5Encoder,
     }
 
     @classmethod
@@ -161,7 +165,7 @@ class ModelProbe(object):
         fields["description"] = (
             fields.get("description") or f"{fields['base'].value} {model_type.value} model {fields['name']}"
         )
-        fields["format"] = fields.get("format") or probe.get_format()
+        fields["format"] = ModelFormat(fields.get("format")) if "format" in fields else probe.get_format()
         fields["hash"] = fields.get("hash") or ModelHash(algorithm=hash_algo).hash(model_path)
 
         fields["default_settings"] = fields.get("default_settings")
@@ -176,10 +180,10 @@ class ModelProbe(object):
             fields["repo_variant"] = fields.get("repo_variant") or probe.get_repo_variant()
 
         # additional fields needed for main and controlnet models
-        if (
-            fields["type"] in [ModelType.Main, ModelType.ControlNet, ModelType.VAE]
-            and fields["format"] is ModelFormat.Checkpoint
-        ):
+        if fields["type"] in [ModelType.Main, ModelType.ControlNet, ModelType.VAE] and fields["format"] in [
+            ModelFormat.Checkpoint,
+            ModelFormat.BnbQuantizednf4b,
+        ]:
             ckpt_config_path = cls._get_checkpoint_config_path(
                 model_path,
                 model_type=fields["type"],
@@ -222,7 +226,19 @@ class ModelProbe(object):
         ckpt = ckpt.get("state_dict", ckpt)
 
         for key in [str(k) for k in ckpt.keys()]:
-            if key.startswith(("cond_stage_model.", "first_stage_model.", "model.diffusion_model.")):
+            if key.startswith(
+                (
+                    "cond_stage_model.",
+                    "first_stage_model.",
+                    "model.diffusion_model.",
+                    # FLUX models in the official BFL format contain keys with the "double_blocks." prefix.
+                    "double_blocks.",
+                    # Some FLUX checkpoint files contain transformer keys prefixed with "model.diffusion_model".
+                    # This prefix is typically used to distinguish between multiple models bundled in a single file.
+                    "model.diffusion_model.double_blocks.",
+                )
+            ):
+                # Keys starting with double_blocks are associated with Flux models
                 return ModelType.Main
             elif key.startswith(("encoder.conv_in", "decoder.conv_in")):
                 return ModelType.VAE
@@ -280,9 +296,16 @@ class ModelProbe(object):
         if (folder_path / "image_encoder.txt").exists():
             return ModelType.IPAdapter
 
-        i = folder_path / "model_index.json"
-        c = folder_path / "config.json"
-        config_path = i if i.exists() else c if c.exists() else None
+        config_path = None
+        for p in [
+            folder_path / "model_index.json",  # pipeline
+            folder_path / "config.json",  # most diffusers
+            folder_path / "text_encoder_2" / "config.json",  # T5 text encoder
+            folder_path / "text_encoder" / "config.json",  # T5 CLIP
+        ]:
+            if p.exists():
+                config_path = p
+                break
 
         if config_path:
             with open(config_path, "r") as file:
@@ -321,10 +344,30 @@ class ModelProbe(object):
             return possible_conf.absolute()
 
         if model_type is ModelType.Main:
-            config_file = LEGACY_CONFIGS[base_type][variant_type]
-            if isinstance(config_file, dict):  # need another tier for sd-2.x models
-                config_file = config_file[prediction_type]
-            config_file = f"stable-diffusion/{config_file}"
+            if base_type == BaseModelType.Flux:
+                # TODO: Decide between dev/schnell
+                checkpoint = ModelProbe._scan_and_load_checkpoint(model_path)
+                state_dict = checkpoint.get("state_dict") or checkpoint
+                if (
+                    "guidance_in.out_layer.weight" in state_dict
+                    or "model.diffusion_model.guidance_in.out_layer.weight" in state_dict
+                ):
+                    # For flux, this is a key in invokeai.backend.flux.util.params
+                    #   Due to model type and format being the descriminator for model configs this
+                    #   is used rather than attempting to support flux with separate model types and format
+                    #   If changed in the future, please fix me
+                    config_file = "flux-dev"
+                else:
+                    # For flux, this is a key in invokeai.backend.flux.util.params
+                    #   Due to model type and format being the discriminator for model configs this
+                    #   is used rather than attempting to support flux with separate model types and format
+                    #   If changed in the future, please fix me
+                    config_file = "flux-schnell"
+            else:
+                config_file = LEGACY_CONFIGS[base_type][variant_type]
+                if isinstance(config_file, dict):  # need another tier for sd-2.x models
+                    config_file = config_file[prediction_type]
+                config_file = f"stable-diffusion/{config_file}"
         elif model_type is ModelType.ControlNet:
             config_file = (
                 "controlnet/cldm_v15.yaml"
@@ -333,7 +376,13 @@ class ModelProbe(object):
             )
         elif model_type is ModelType.VAE:
             config_file = (
-                "stable-diffusion/v1-inference.yaml"
+                # For flux, this is a key in invokeai.backend.flux.util.ae_params
+                #   Due to model type and format being the descriminator for model configs this
+                #   is used rather than attempting to support flux with separate model types and format
+                #   If changed in the future, please fix me
+                "flux"
+                if base_type is BaseModelType.Flux
+                else "stable-diffusion/v1-inference.yaml"
                 if base_type is BaseModelType.StableDiffusion1
                 else "stable-diffusion/sd_xl_base.yaml"
                 if base_type is BaseModelType.StableDiffusionXL
@@ -416,11 +465,18 @@ class CheckpointProbeBase(ProbeBase):
         self.checkpoint = ModelProbe._scan_and_load_checkpoint(model_path)
 
     def get_format(self) -> ModelFormat:
+        state_dict = self.checkpoint.get("state_dict") or self.checkpoint
+        if (
+            "double_blocks.0.img_attn.proj.weight.quant_state.bitsandbytes__nf4" in state_dict
+            or "model.diffusion_model.double_blocks.0.img_attn.proj.weight.quant_state.bitsandbytes__nf4" in state_dict
+        ):
+            return ModelFormat.BnbQuantizednf4b
         return ModelFormat("checkpoint")
 
     def get_variant_type(self) -> ModelVariantType:
         model_type = ModelProbe.get_model_type_from_checkpoint(self.model_path, self.checkpoint)
-        if model_type != ModelType.Main:
+        base_type = self.get_base_type()
+        if model_type != ModelType.Main or base_type == BaseModelType.Flux:
             return ModelVariantType.Normal
         state_dict = self.checkpoint.get("state_dict") or self.checkpoint
         in_channels = state_dict["model.diffusion_model.input_blocks.0.0.weight"].shape[1]
@@ -440,6 +496,11 @@ class PipelineCheckpointProbe(CheckpointProbeBase):
     def get_base_type(self) -> BaseModelType:
         checkpoint = self.checkpoint
         state_dict = self.checkpoint.get("state_dict") or checkpoint
+        if (
+            "double_blocks.0.img_attn.norm.key_norm.scale" in state_dict
+            or "model.diffusion_model.double_blocks.0.img_attn.norm.key_norm.scale" in state_dict
+        ):
+            return BaseModelType.Flux
         key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
         if key_name in state_dict and state_dict[key_name].shape[-1] == 768:
             return BaseModelType.StableDiffusion1
@@ -482,6 +543,7 @@ class VaeCheckpointProbe(CheckpointProbeBase):
             (r"xl", BaseModelType.StableDiffusionXL),
             (r"sd2", BaseModelType.StableDiffusion2),
             (r"vae", BaseModelType.StableDiffusion1),
+            (r"FLUX.1-schnell_ae", BaseModelType.Flux),
         ]:
             if re.search(regexp, self.model_path.name, re.IGNORECASE):
                 return basetype
@@ -713,6 +775,30 @@ class TextualInversionFolderProbe(FolderProbeBase):
         return TextualInversionCheckpointProbe(path).get_base_type()
 
 
+class T5EncoderFolderProbe(FolderProbeBase):
+    def get_base_type(self) -> BaseModelType:
+        return BaseModelType.Any
+
+    def get_format(self) -> ModelFormat:
+        path = self.model_path / "text_encoder_2"
+        if (path / "model.safetensors.index.json").exists():
+            return ModelFormat.T5Encoder
+        files = list(path.glob("*.safetensors"))
+        if len(files) == 0:
+            raise InvalidModelConfigException(f"{self.model_path.as_posix()}: no .safetensors files found")
+
+        # shortcut: look for the quantization in the name
+        if any(x for x in files if "llm_int8" in x.as_posix()):
+            return ModelFormat.BnbQuantizedLlmInt8b
+
+        # more reliable path: probe contents for a 'SCB' key
+        ckpt = read_checkpoint_meta(files[0], scan=True)
+        if any("SCB" in x for x in ckpt.keys()):
+            return ModelFormat.BnbQuantizedLlmInt8b
+
+        raise InvalidModelConfigException(f"{self.model_path.as_posix()}: unknown model format")
+
+
 class ONNXFolderProbe(PipelineFolderProbe):
     def get_base_type(self) -> BaseModelType:
         # Due to the way the installer is set up, the configuration file for safetensors
@@ -805,6 +891,11 @@ class CLIPVisionFolderProbe(FolderProbeBase):
         return BaseModelType.Any
 
 
+class CLIPEmbedFolderProbe(FolderProbeBase):
+    def get_base_type(self) -> BaseModelType:
+        return BaseModelType.Any
+
+
 class SpandrelImageToImageFolderProbe(FolderProbeBase):
     def get_base_type(self) -> BaseModelType:
         raise NotImplementedError()
@@ -835,8 +926,10 @@ ModelProbe.register_probe("diffusers", ModelType.Main, PipelineFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.VAE, VaeFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.LoRA, LoRAFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.TextualInversion, TextualInversionFolderProbe)
+ModelProbe.register_probe("diffusers", ModelType.T5Encoder, T5EncoderFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.ControlNet, ControlNetFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.IPAdapter, IPAdapterFolderProbe)
+ModelProbe.register_probe("diffusers", ModelType.CLIPEmbed, CLIPEmbedFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.CLIPVision, CLIPVisionFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.T2IAdapter, T2IAdapterFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.SpandrelImageToImage, SpandrelImageToImageFolderProbe)

invokeai/backend/model_manager/starter_models.py

@@ -2,7 +2,7 @@ from typing import Optional
 
 from pydantic import BaseModel
 
-from invokeai.backend.model_manager.config import BaseModelType, ModelType
+from invokeai.backend.model_manager.config import BaseModelType, ModelFormat, ModelType
 
 
 class StarterModelWithoutDependencies(BaseModel):
@@ -11,6 +11,7 @@ class StarterModelWithoutDependencies(BaseModel):
     name: str
     base: BaseModelType
     type: ModelType
+    format: Optional[ModelFormat] = None
     is_installed: bool = False
 
 
@@ -51,10 +52,76 @@ cyberrealistic_negative = StarterModel(
     type=ModelType.TextualInversion,
 )
 
+t5_base_encoder = StarterModel(
+    name="t5_base_encoder",
+    base=BaseModelType.Any,
+    source="InvokeAI/t5-v1_1-xxl::bfloat16",
+    description="T5-XXL text encoder (used in FLUX pipelines). ~8GB",
+    type=ModelType.T5Encoder,
+)
+
+t5_8b_quantized_encoder = StarterModel(
+    name="t5_bnb_int8_quantized_encoder",
+    base=BaseModelType.Any,
+    source="InvokeAI/t5-v1_1-xxl::bnb_llm_int8",
+    description="T5-XXL text encoder with bitsandbytes LLM.int8() quantization (used in FLUX pipelines). ~5GB",
+    type=ModelType.T5Encoder,
+    format=ModelFormat.BnbQuantizedLlmInt8b,
+)
+
+clip_l_encoder = StarterModel(
+    name="clip-vit-large-patch14",
+    base=BaseModelType.Any,
+    source="InvokeAI/clip-vit-large-patch14-text-encoder::bfloat16",
+    description="CLIP-L text encoder (used in FLUX pipelines). ~250MB",
+    type=ModelType.CLIPEmbed,
+)
+
+flux_vae = StarterModel(
+    name="FLUX.1-schnell_ae",
+    base=BaseModelType.Flux,
+    source="black-forest-labs/FLUX.1-schnell::ae.safetensors",
+    description="FLUX VAE compatible with both schnell and dev variants.",
+    type=ModelType.VAE,
+)
+
+
 # List of starter models, displayed on the frontend.
 # The order/sort of this list is not changed by the frontend - set it how you want it here.
 STARTER_MODELS: list[StarterModel] = [
     # region: Main
+    StarterModel(
+        name="FLUX Schnell (Quantized)",
+        base=BaseModelType.Flux,
+        source="InvokeAI/flux_schnell::transformer/bnb_nf4/flux1-schnell-bnb_nf4.safetensors",
+        description="FLUX schnell transformer quantized to bitsandbytes NF4 format. Total size with dependencies: ~12GB",
+        type=ModelType.Main,
+        dependencies=[t5_8b_quantized_encoder, flux_vae, clip_l_encoder],
+    ),
+    StarterModel(
+        name="FLUX Dev (Quantized)",
+        base=BaseModelType.Flux,
+        source="InvokeAI/flux_dev::transformer/bnb_nf4/flux1-dev-bnb_nf4.safetensors",
+        description="FLUX dev transformer quantized to bitsandbytes NF4 format. Total size with dependencies: ~12GB",
+        type=ModelType.Main,
+        dependencies=[t5_8b_quantized_encoder, flux_vae, clip_l_encoder],
+    ),
+    StarterModel(
+        name="FLUX Schnell",
+        base=BaseModelType.Flux,
+        source="InvokeAI/flux_schnell::transformer/base/flux1-schnell.safetensors",
+        description="FLUX schnell transformer in bfloat16. Total size with dependencies: ~33GB",
+        type=ModelType.Main,
+        dependencies=[t5_base_encoder, flux_vae, clip_l_encoder],
+    ),
+    StarterModel(
+        name="FLUX Dev",
+        base=BaseModelType.Flux,
+        source="InvokeAI/flux_dev::transformer/base/flux1-dev.safetensors",
+        description="FLUX dev transformer in bfloat16. Total size with dependencies: ~33GB",
+        type=ModelType.Main,
+        dependencies=[t5_base_encoder, flux_vae, clip_l_encoder],
+    ),
     StarterModel(
         name="CyberRealistic v4.1",
         base=BaseModelType.StableDiffusion1,
@@ -125,6 +192,7 @@ STARTER_MODELS: list[StarterModel] = [
     # endregion
     # region VAE
     sdxl_fp16_vae_fix,
+    flux_vae,
     # endregion
     # region LoRA
     StarterModel(
@@ -450,6 +518,11 @@ STARTER_MODELS: list[StarterModel] = [
         type=ModelType.SpandrelImageToImage,
     ),
     # endregion
+    # region TextEncoders
+    t5_base_encoder,
+    t5_8b_quantized_encoder,
+    clip_l_encoder,
+    # endregion
 ]
 
 assert len(STARTER_MODELS) == len({m.source for m in STARTER_MODELS}), "Duplicate starter models"

invokeai/backend/model_manager/util/model_util.py

@@ -133,3 +133,29 @@ def lora_token_vector_length(checkpoint: Dict[str, torch.Tensor]) -> Optional[in
             break
 
     return lora_token_vector_length
+
+
+def convert_bundle_to_flux_transformer_checkpoint(
+    transformer_state_dict: dict[str, torch.Tensor],
+) -> dict[str, torch.Tensor]:
+    original_state_dict: dict[str, torch.Tensor] = {}
+    keys_to_remove: list[str] = []
+
+    for k, v in transformer_state_dict.items():
+        if not k.startswith("model.diffusion_model"):
+            keys_to_remove.append(k)  # This can be removed in the future if we only want to delete transformer keys
+            continue
+        if k.endswith("scale"):
+            # Scale math must be done at bfloat16 due to our current flux model
+            # support limitations at inference time
+            v = v.to(dtype=torch.bfloat16)
+        new_key = k.replace("model.diffusion_model.", "")
+        original_state_dict[new_key] = v
+        keys_to_remove.append(k)
+
+    # Remove processed keys from the original dictionary, leaving others in case
+    # other model state dicts need to be pulled
+    for k in keys_to_remove:
+        del transformer_state_dict[k]
+
+    return original_state_dict

invokeai/backend/model_manager/util/select_hf_files.py

@@ -54,6 +54,7 @@ def filter_files(
                 "lora_weights.safetensors",
                 "weights.pb",
                 "onnx_data",
+                "spiece.model",  # Added for `black-forest-labs/FLUX.1-schnell`.
             )
         ):
             paths.append(file)
@@ -62,13 +63,13 @@ def filter_files(
         # downloading random checkpoints that might also be in the repo. However there is no guarantee
         # that a checkpoint doesn't contain "model" in its name, and no guarantee that future diffusers models
         # will adhere to this naming convention, so this is an area to be careful of.
-        elif re.search(r"model(\.[^.]+)?\.(safetensors|bin|onnx|xml|pth|pt|ckpt|msgpack)$", file.name):
+        elif re.search(r"model.*\.(safetensors|bin|onnx|xml|pth|pt|ckpt|msgpack)$", file.name):
             paths.append(file)
 
     # limit search to subfolder if requested
     if subfolder:
         subfolder = root / subfolder
-        paths = [x for x in paths if x.parent == Path(subfolder)]
+        paths = [x for x in paths if Path(subfolder) in x.parents]
 
     # _filter_by_variant uniquifies the paths and returns a set
     return sorted(_filter_by_variant(paths, variant))
@@ -97,7 +98,9 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
             if variant == ModelRepoVariant.Flax:
                 result.add(path)
 
-        elif path.suffix in [".json", ".txt"]:
+        # Note: '.model' was added to support:
+        # https://huggingface.co/black-forest-labs/FLUX.1-schnell/blob/768d12a373ed5cc9ef9a9dea7504dc09fcc14842/tokenizer_2/spiece.model
+        elif path.suffix in [".json", ".txt", ".model"]:
             result.add(path)
 
         elif variant in [
@@ -140,6 +143,23 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
             continue
 
     for candidate_list in subfolder_weights.values():
+        # 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":
+                at_least_one_fp16 = True
+                break
+
+        if not at_least_one_fp16:
+            # If none of the candidates in this candidate_list have the explicit fp16 variant label, then this
+            # candidate_list probably doesn't adhere to the variant naming convention that we expected. In this case,
+            # we'll simply keep all the candidates. An example of a model that hits this case is
+            # `black-forest-labs/FLUX.1-schnell` (as of commit 012d2fd).
+            for candidate in candidate_list:
+                result.add(candidate.path)
+
+        # The candidate_list seems to have the expected variant naming convention. We'll select the highest scoring
+        # candidate.
         highest_score_candidate = max(candidate_list, key=lambda candidate: candidate.score)
         if highest_score_candidate:
             result.add(highest_score_candidate.path)

invokeai/backend/quantization/bnb_llm_int8.py

@@ -0,0 +1,135 @@
+import bitsandbytes as bnb
+import torch
+
+# This file contains utils for working with models that use bitsandbytes LLM.int8() quantization.
+# The utils in this file are partially inspired by:
+# https://github.com/Lightning-AI/pytorch-lightning/blob/1551a16b94f5234a4a78801098f64d0732ef5cb5/src/lightning/fabric/plugins/precision/bitsandbytes.py
+
+
+# NOTE(ryand): All of the custom state_dict manipulation logic in this file is pretty hacky. This could be made much
+# cleaner by re-implementing bnb.nn.Linear8bitLt with proper use of buffers and less magic. But, for now, we try to
+# stick close to the bitsandbytes classes to make interoperability easier with other models that might use bitsandbytes.
+
+
+class InvokeInt8Params(bnb.nn.Int8Params):
+    """We override cuda() to avoid re-quantizing the weights in the following cases:
+    - We loaded quantized weights from a state_dict on the cpu, and then moved the model to the gpu.
+    - We are moving the model back-and-forth between the cpu and gpu.
+    """
+
+    def cuda(self, device):
+        if self.has_fp16_weights:
+            return super().cuda(device)
+        elif self.CB is not None and self.SCB is not None:
+            self.data = self.data.cuda()
+            self.CB = self.data
+            self.SCB = self.SCB.cuda()
+        else:
+            # we store the 8-bit rows-major weight
+            # we convert this weight to the turning/ampere weight during the first inference pass
+            B = self.data.contiguous().half().cuda(device)
+            CB, CBt, SCB, SCBt, coo_tensorB = bnb.functional.double_quant(B)
+            del CBt
+            del SCBt
+            self.data = CB
+            self.CB = CB
+            self.SCB = SCB
+
+        return self
+
+
+class InvokeLinear8bitLt(bnb.nn.Linear8bitLt):
+    def _load_from_state_dict(
+        self,
+        state_dict: dict[str, torch.Tensor],
+        prefix: str,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        weight = state_dict.pop(prefix + "weight")
+        bias = state_dict.pop(prefix + "bias", None)
+
+        # See `bnb.nn.Linear8bitLt._save_to_state_dict()` for the serialization logic of SCB and weight_format.
+        scb = state_dict.pop(prefix + "SCB", None)
+
+        # Currently, we only support weight_format=0.
+        weight_format = state_dict.pop(prefix + "weight_format", None)
+        assert weight_format == 0
+
+        # TODO(ryand): Technically, we should be using `strict`, `missing_keys`, `unexpected_keys`, and `error_msgs`
+        # rather than raising an exception to correctly implement this API.
+        assert len(state_dict) == 0
+
+        if scb is not None:
+            # We are loading a pre-quantized state dict.
+            self.weight = InvokeInt8Params(
+                data=weight,
+                requires_grad=self.weight.requires_grad,
+                has_fp16_weights=False,
+                # Note: After quantization, CB is the same as weight.
+                CB=weight,
+                SCB=scb,
+            )
+            self.bias = bias if bias is None else torch.nn.Parameter(bias)
+        else:
+            # We are loading a non-quantized state dict.
+
+            # We could simply call the `super()._load_from_state_dict()` method here, but then we wouldn't be able to
+            # load from a state_dict into a model on the "meta" device. Attempting to load into a model on the "meta"
+            # device requires setting `assign=True`, doing this with the default `super()._load_from_state_dict()`
+            # implementation causes `Params4Bit` to be replaced by a `torch.nn.Parameter`. By initializing a new
+            # `Params4bit` object, we work around this issue. It's a bit hacky, but it gets the job done.
+            self.weight = InvokeInt8Params(
+                data=weight,
+                requires_grad=self.weight.requires_grad,
+                has_fp16_weights=False,
+                CB=None,
+                SCB=None,
+            )
+            self.bias = bias if bias is None else torch.nn.Parameter(bias)
+
+        # Reset the state. The persisted fields are based on the initialization behaviour in
+        # `bnb.nn.Linear8bitLt.__init__()`.
+        new_state = bnb.MatmulLtState()
+        new_state.threshold = self.state.threshold
+        new_state.has_fp16_weights = False
+        new_state.use_pool = self.state.use_pool
+        self.state = new_state
+
+
+def _convert_linear_layers_to_llm_8bit(
+    module: torch.nn.Module, ignore_modules: set[str], outlier_threshold: float, prefix: str = ""
+) -> None:
+    """Convert all linear layers in the module to bnb.nn.Linear8bitLt layers."""
+    for name, child in module.named_children():
+        fullname = f"{prefix}.{name}" if prefix else name
+        if isinstance(child, torch.nn.Linear) and not any(fullname.startswith(s) for s in ignore_modules):
+            has_bias = child.bias is not None
+            replacement = InvokeLinear8bitLt(
+                child.in_features,
+                child.out_features,
+                bias=has_bias,
+                has_fp16_weights=False,
+                threshold=outlier_threshold,
+            )
+            replacement.weight.data = child.weight.data
+            if has_bias:
+                replacement.bias.data = child.bias.data
+            replacement.requires_grad_(False)
+            module.__setattr__(name, replacement)
+        else:
+            _convert_linear_layers_to_llm_8bit(
+                child, ignore_modules, outlier_threshold=outlier_threshold, prefix=fullname
+            )
+
+
+def quantize_model_llm_int8(model: torch.nn.Module, modules_to_not_convert: set[str], outlier_threshold: float = 6.0):
+    """Apply bitsandbytes LLM.8bit() quantization to the model."""
+    _convert_linear_layers_to_llm_8bit(
+        module=model, ignore_modules=modules_to_not_convert, outlier_threshold=outlier_threshold
+    )
+
+    return model

invokeai/backend/quantization/bnb_nf4.py

@@ -0,0 +1,156 @@
+import bitsandbytes as bnb
+import torch
+
+# This file contains utils for working with models that use bitsandbytes NF4 quantization.
+# The utils in this file are partially inspired by:
+# https://github.com/Lightning-AI/pytorch-lightning/blob/1551a16b94f5234a4a78801098f64d0732ef5cb5/src/lightning/fabric/plugins/precision/bitsandbytes.py
+
+# NOTE(ryand): All of the custom state_dict manipulation logic in this file is pretty hacky. This could be made much
+# cleaner by re-implementing bnb.nn.LinearNF4 with proper use of buffers and less magic. But, for now, we try to stick
+# close to the bitsandbytes classes to make interoperability easier with other models that might use bitsandbytes.
+
+
+class InvokeLinearNF4(bnb.nn.LinearNF4):
+    """A class that extends `bnb.nn.LinearNF4` to add the following functionality:
+    - Ability to load Linear NF4 layers from a pre-quantized state_dict.
+    - Ability to load Linear NF4 layers from a state_dict when the model is on the "meta" device.
+    """
+
+    def _load_from_state_dict(
+        self,
+        state_dict: dict[str, torch.Tensor],
+        prefix: str,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        """This method is based on the logic in the bitsandbytes serialization unit tests for `Linear4bit`:
+        https://github.com/bitsandbytes-foundation/bitsandbytes/blob/6d714a5cce3db5bd7f577bc447becc7a92d5ccc7/tests/test_linear4bit.py#L52-L71
+        """
+        weight = state_dict.pop(prefix + "weight")
+        bias = state_dict.pop(prefix + "bias", None)
+        # We expect the remaining keys to be quant_state keys.
+        quant_state_sd = state_dict
+
+        # During serialization, the quant_state is stored as subkeys of "weight." (See
+        # `bnb.nn.LinearNF4._save_to_state_dict()`). We validate that they at least have the correct prefix.
+        # TODO(ryand): Technically, we should be using `strict`, `missing_keys`, `unexpected_keys`, and `error_msgs`
+        # rather than raising an exception to correctly implement this API.
+        assert all(k.startswith(prefix + "weight.") for k in quant_state_sd.keys())
+
+        if len(quant_state_sd) > 0:
+            # We are loading a pre-quantized state dict.
+            self.weight = bnb.nn.Params4bit.from_prequantized(
+                data=weight, quantized_stats=quant_state_sd, device=weight.device
+            )
+            self.bias = bias if bias is None else torch.nn.Parameter(bias, requires_grad=False)
+        else:
+            # We are loading a non-quantized state dict.
+
+            # We could simply call the `super()._load_from_state_dict()` method here, but then we wouldn't be able to
+            # load from a state_dict into a model on the "meta" device. Attempting to load into a model on the "meta"
+            # device requires setting `assign=True`, doing this with the default `super()._load_from_state_dict()`
+            # implementation causes `Params4Bit` to be replaced by a `torch.nn.Parameter`. By initializing a new
+            # `Params4bit` object, we work around this issue. It's a bit hacky, but it gets the job done.
+            self.weight = bnb.nn.Params4bit(
+                data=weight,
+                requires_grad=self.weight.requires_grad,
+                compress_statistics=self.weight.compress_statistics,
+                quant_type=self.weight.quant_type,
+                quant_storage=self.weight.quant_storage,
+                module=self,
+            )
+            self.bias = bias if bias is None else torch.nn.Parameter(bias)
+
+
+def _replace_param(
+    param: torch.nn.Parameter | bnb.nn.Params4bit,
+    data: torch.Tensor,
+) -> torch.nn.Parameter:
+    """A helper function to replace the data of a model parameter with new data in a way that allows replacing params on
+    the "meta" device.
+
+    Supports both `torch.nn.Parameter` and `bnb.nn.Params4bit` parameters.
+    """
+    if param.device.type == "meta":
+        # Doing `param.data = data` raises a RuntimeError if param.data was on the "meta" device, so we need to
+        # re-create the param instead of overwriting the data.
+        if isinstance(param, bnb.nn.Params4bit):
+            return bnb.nn.Params4bit(
+                data,
+                requires_grad=data.requires_grad,
+                quant_state=param.quant_state,
+                compress_statistics=param.compress_statistics,
+                quant_type=param.quant_type,
+            )
+        return torch.nn.Parameter(data, requires_grad=data.requires_grad)
+
+    param.data = data
+    return param
+
+
+def _convert_linear_layers_to_nf4(
+    module: torch.nn.Module,
+    ignore_modules: set[str],
+    compute_dtype: torch.dtype,
+    compress_statistics: bool = False,
+    prefix: str = "",
+) -> None:
+    """Convert all linear layers in the model to NF4 quantized linear layers.
+
+    Args:
+        module: All linear layers in this module will be converted.
+        ignore_modules: A set of module prefixes to ignore when converting linear layers.
+        compute_dtype: The dtype to use for computation in the quantized linear layers.
+        compress_statistics: Whether to enable nested quantization (aka double quantization) where the quantization
+           constants from the first quantization are quantized again.
+        prefix: The prefix of the current module in the model. Used to call this function recursively.
+    """
+    for name, child in module.named_children():
+        fullname = f"{prefix}.{name}" if prefix else name
+        if isinstance(child, torch.nn.Linear) and not any(fullname.startswith(s) for s in ignore_modules):
+            has_bias = child.bias is not None
+            replacement = InvokeLinearNF4(
+                child.in_features,
+                child.out_features,
+                bias=has_bias,
+                compute_dtype=compute_dtype,
+                compress_statistics=compress_statistics,
+            )
+            if has_bias:
+                replacement.bias = _replace_param(replacement.bias, child.bias.data)
+            replacement.weight = _replace_param(replacement.weight, child.weight.data)
+            replacement.requires_grad_(False)
+            module.__setattr__(name, replacement)
+        else:
+            _convert_linear_layers_to_nf4(child, ignore_modules, compute_dtype=compute_dtype, prefix=fullname)
+
+
+def quantize_model_nf4(model: torch.nn.Module, modules_to_not_convert: set[str], compute_dtype: torch.dtype):
+    """Apply bitsandbytes nf4 quantization to the model.
+
+    You likely want to call this function inside a `accelerate.init_empty_weights()` context.
+
+    Example usage:
+    ```
+    # Initialize the model from a config on the meta device.
+    with accelerate.init_empty_weights():
+        model = ModelClass.from_config(...)
+
+    # Add NF4 quantization linear layers to the model - still on the meta device.
+    with accelerate.init_empty_weights():
+        model = quantize_model_nf4(model, modules_to_not_convert=set(), compute_dtype=torch.float16)
+
+    # Load a state_dict into the model. (Could be either a prequantized or non-quantized state_dict.)
+    model.load_state_dict(state_dict, strict=True, assign=True)
+
+    # Move the model to the "cuda" device. If the model was non-quantized, this is where the weight quantization takes
+    # place.
+    model.to("cuda")
+    ```
+    """
+    _convert_linear_layers_to_nf4(module=model, ignore_modules=modules_to_not_convert, compute_dtype=compute_dtype)
+
+    return model

invokeai/backend/quantization/scripts/load_flux_model_bnb_llm_int8.py

@@ -0,0 +1,79 @@
+from pathlib import Path
+
+import accelerate
+from safetensors.torch import load_file, save_file
+
+from invokeai.backend.flux.model import Flux
+from invokeai.backend.flux.util import params
+from invokeai.backend.quantization.bnb_llm_int8 import quantize_model_llm_int8
+from invokeai.backend.quantization.scripts.load_flux_model_bnb_nf4 import log_time
+
+
+def main():
+    """A script for quantizing a FLUX transformer model using the bitsandbytes LLM.int8() quantization method.
+
+    This script is primarily intended for reference. The script params (e.g. the model_path, modules_to_not_convert,
+    etc.) are hardcoded and would need to be modified for other use cases.
+    """
+    # Load the FLUX transformer model onto the meta device.
+    model_path = Path(
+        "/data/invokeai/models/.download_cache/https__huggingface.co_black-forest-labs_flux.1-schnell_resolve_main_flux1-schnell.safetensors/flux1-schnell.safetensors"
+    )
+
+    with log_time("Intialize FLUX transformer on meta device"):
+        # TODO(ryand): Determine if this is a schnell model or a dev model and load the appropriate config.
+        p = params["flux-schnell"]
+
+        # Initialize the model on the "meta" device.
+        with accelerate.init_empty_weights():
+            model = Flux(p)
+
+    # TODO(ryand): We may want to add some modules to not quantize here (e.g. the proj_out layer). See the accelerate
+    # `get_keys_to_not_convert(...)` function for a heuristic to determine which modules to not quantize.
+    modules_to_not_convert: set[str] = set()
+
+    model_int8_path = model_path.parent / "bnb_llm_int8.safetensors"
+    if model_int8_path.exists():
+        # The quantized model already exists, load it and return it.
+        print(f"A pre-quantized model already exists at '{model_int8_path}'. Attempting to load it...")
+
+        # Replace the linear layers with LLM.int8() quantized linear layers (still on the meta device).
+        with log_time("Replace linear layers with LLM.int8() layers"), accelerate.init_empty_weights():
+            model = quantize_model_llm_int8(model, modules_to_not_convert=modules_to_not_convert)
+
+        with log_time("Load state dict into model"):
+            sd = load_file(model_int8_path)
+            model.load_state_dict(sd, strict=True, assign=True)
+
+        with log_time("Move model to cuda"):
+            model = model.to("cuda")
+
+        print(f"Successfully loaded pre-quantized model from '{model_int8_path}'.")
+
+    else:
+        # The quantized model does not exist, quantize the model and save it.
+        print(f"No pre-quantized model found at '{model_int8_path}'. Quantizing the model...")
+
+        with log_time("Replace linear layers with LLM.int8() layers"), accelerate.init_empty_weights():
+            model = quantize_model_llm_int8(model, modules_to_not_convert=modules_to_not_convert)
+
+        with log_time("Load state dict into model"):
+            state_dict = load_file(model_path)
+            # TODO(ryand): Cast the state_dict to the appropriate dtype?
+            model.load_state_dict(state_dict, strict=True, assign=True)
+
+        with log_time("Move model to cuda and quantize"):
+            model = model.to("cuda")
+
+        with log_time("Save quantized model"):
+            model_int8_path.parent.mkdir(parents=True, exist_ok=True)
+            save_file(model.state_dict(), model_int8_path)
+
+        print(f"Successfully quantized and saved model to '{model_int8_path}'.")
+
+    assert isinstance(model, Flux)
+    return model
+
+
+if __name__ == "__main__":
+    main()

invokeai/backend/quantization/scripts/load_flux_model_bnb_nf4.py

@@ -0,0 +1,96 @@
+import time
+from contextlib import contextmanager
+from pathlib import Path
+
+import accelerate
+import torch
+from safetensors.torch import load_file, save_file
+
+from invokeai.backend.flux.model import Flux
+from invokeai.backend.flux.util import params
+from invokeai.backend.quantization.bnb_nf4 import quantize_model_nf4
+
+
+@contextmanager
+def log_time(name: str):
+    """Helper context manager to log the time taken by a block of code."""
+    start = time.time()
+    try:
+        yield None
+    finally:
+        end = time.time()
+        print(f"'{name}' took {end - start:.4f} secs")
+
+
+def main():
+    """A script for quantizing a FLUX transformer model using the bitsandbytes NF4 quantization method.
+
+    This script is primarily intended for reference. The script params (e.g. the model_path, modules_to_not_convert,
+    etc.) are hardcoded and would need to be modified for other use cases.
+    """
+    model_path = Path(
+        "/data/invokeai/models/.download_cache/https__huggingface.co_black-forest-labs_flux.1-schnell_resolve_main_flux1-schnell.safetensors/flux1-schnell.safetensors"
+    )
+
+    # inference_dtype = torch.bfloat16
+    with log_time("Intialize FLUX transformer on meta device"):
+        # TODO(ryand): Determine if this is a schnell model or a dev model and load the appropriate config.
+        p = params["flux-schnell"]
+
+        # Initialize the model on the "meta" device.
+        with accelerate.init_empty_weights():
+            model = Flux(p)
+
+    # TODO(ryand): We may want to add some modules to not quantize here (e.g. the proj_out layer). See the accelerate
+    # `get_keys_to_not_convert(...)` function for a heuristic to determine which modules to not quantize.
+    modules_to_not_convert: set[str] = set()
+
+    model_nf4_path = model_path.parent / "bnb_nf4.safetensors"
+    if model_nf4_path.exists():
+        # The quantized model already exists, load it and return it.
+        print(f"A pre-quantized model already exists at '{model_nf4_path}'. Attempting to load it...")
+
+        # Replace the linear layers with NF4 quantized linear layers (still on the meta device).
+        with log_time("Replace linear layers with NF4 layers"), accelerate.init_empty_weights():
+            model = quantize_model_nf4(
+                model, modules_to_not_convert=modules_to_not_convert, compute_dtype=torch.bfloat16
+            )
+
+        with log_time("Load state dict into model"):
+            state_dict = load_file(model_nf4_path)
+            model.load_state_dict(state_dict, strict=True, assign=True)
+
+        with log_time("Move model to cuda"):
+            model = model.to("cuda")
+
+        print(f"Successfully loaded pre-quantized model from '{model_nf4_path}'.")
+
+    else:
+        # The quantized model does not exist, quantize the model and save it.
+        print(f"No pre-quantized model found at '{model_nf4_path}'. Quantizing the model...")
+
+        with log_time("Replace linear layers with NF4 layers"), accelerate.init_empty_weights():
+            model = quantize_model_nf4(
+                model, modules_to_not_convert=modules_to_not_convert, compute_dtype=torch.bfloat16
+            )
+
+        with log_time("Load state dict into model"):
+            state_dict = load_file(model_path)
+            # TODO(ryand): Cast the state_dict to the appropriate dtype?
+            model.load_state_dict(state_dict, strict=True, assign=True)
+
+        with log_time("Move model to cuda and quantize"):
+            model = model.to("cuda")
+
+        with log_time("Save quantized model"):
+            model_nf4_path.parent.mkdir(parents=True, exist_ok=True)
+            save_file(model.state_dict(), model_nf4_path)
+
+        print(f"Successfully quantized and saved model to '{model_nf4_path}'.")
+
+    assert isinstance(model, Flux)
+    return model
+
+
+if __name__ == "__main__":
+    main()

invokeai/backend/quantization/scripts/quantize_t5_xxl_bnb_llm_int8.py

@@ -0,0 +1,92 @@
+from pathlib import Path
+
+import accelerate
+from safetensors.torch import load_file, save_file
+from transformers import AutoConfig, AutoModelForTextEncoding, T5EncoderModel
+
+from invokeai.backend.quantization.bnb_llm_int8 import quantize_model_llm_int8
+from invokeai.backend.quantization.scripts.load_flux_model_bnb_nf4 import log_time
+
+
+def load_state_dict_into_t5(model: T5EncoderModel, state_dict: dict):
+    # There is a shared reference to a single weight tensor in the model.
+    # Both "encoder.embed_tokens.weight" and "shared.weight" refer to the same tensor, so only the latter should
+    # be present in the state_dict.
+    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False, assign=True)
+    assert len(unexpected_keys) == 0
+    assert set(missing_keys) == {"encoder.embed_tokens.weight"}
+    # Assert that the layers we expect to be shared are actually shared.
+    assert model.encoder.embed_tokens.weight is model.shared.weight
+
+
+def main():
+    """A script for quantizing a T5 text encoder model using the bitsandbytes LLM.int8() quantization method.
+
+    This script is primarily intended for reference. The script params (e.g. the model_path, modules_to_not_convert,
+    etc.) are hardcoded and would need to be modified for other use cases.
+    """
+    model_path = Path("/data/misc/text_encoder_2")
+
+    with log_time("Intialize T5 on meta device"):
+        model_config = AutoConfig.from_pretrained(model_path)
+        with accelerate.init_empty_weights():
+            model = AutoModelForTextEncoding.from_config(model_config)
+
+    # TODO(ryand): We may want to add some modules to not quantize here (e.g. the proj_out layer). See the accelerate
+    # `get_keys_to_not_convert(...)` function for a heuristic to determine which modules to not quantize.
+    modules_to_not_convert: set[str] = set()
+
+    model_int8_path = model_path / "bnb_llm_int8.safetensors"
+    if model_int8_path.exists():
+        # The quantized model already exists, load it and return it.
+        print(f"A pre-quantized model already exists at '{model_int8_path}'. Attempting to load it...")
+
+        # Replace the linear layers with LLM.int8() quantized linear layers (still on the meta device).
+        with log_time("Replace linear layers with LLM.int8() layers"), accelerate.init_empty_weights():
+            model = quantize_model_llm_int8(model, modules_to_not_convert=modules_to_not_convert)
+
+        with log_time("Load state dict into model"):
+            sd = load_file(model_int8_path)
+            load_state_dict_into_t5(model, sd)
+
+        with log_time("Move model to cuda"):
+            model = model.to("cuda")
+
+        print(f"Successfully loaded pre-quantized model from '{model_int8_path}'.")
+
+    else:
+        # The quantized model does not exist, quantize the model and save it.
+        print(f"No pre-quantized model found at '{model_int8_path}'. Quantizing the model...")
+
+        with log_time("Replace linear layers with LLM.int8() layers"), accelerate.init_empty_weights():
+            model = quantize_model_llm_int8(model, modules_to_not_convert=modules_to_not_convert)
+
+        with log_time("Load state dict into model"):
+            # Load sharded state dict.
+            files = list(model_path.glob("*.safetensors"))
+            state_dict = {}
+            for file in files:
+                sd = load_file(file)
+                state_dict.update(sd)
+            load_state_dict_into_t5(model, state_dict)
+
+        with log_time("Move model to cuda and quantize"):
+            model = model.to("cuda")
+
+        with log_time("Save quantized model"):
+            model_int8_path.parent.mkdir(parents=True, exist_ok=True)
+            state_dict = model.state_dict()
+            state_dict.pop("encoder.embed_tokens.weight")
+            save_file(state_dict, model_int8_path)
+            # This handling of shared weights could also be achieved with save_model(...), but then we'd lose control
+            # over which keys are kept. And, the corresponding load_model(...) function does not support assign=True.
+            # save_model(model, model_int8_path)
+
+        print(f"Successfully quantized and saved model to '{model_int8_path}'.")
+
+    assert isinstance(model, T5EncoderModel)
+    return model
+
+
+if __name__ == "__main__":
+    main()

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -25,11 +25,6 @@ class BasicConditioningInfo:
         return self
 
 
-@dataclass
-class ConditioningFieldData:
-    conditionings: List[BasicConditioningInfo]
-
-
 @dataclass
 class SDXLConditioningInfo(BasicConditioningInfo):
     """SDXL text conditioning information produced by Compel."""
@@ -43,6 +38,22 @@ class SDXLConditioningInfo(BasicConditioningInfo):
         return super().to(device=device, dtype=dtype)
 
 
+@dataclass
+class FLUXConditioningInfo:
+    clip_embeds: torch.Tensor
+    t5_embeds: torch.Tensor
+
+    def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
+        self.clip_embeds = self.clip_embeds.to(device=device, dtype=dtype)
+        self.t5_embeds = self.t5_embeds.to(device=device, dtype=dtype)
+        return self
+
+
+@dataclass
+class ConditioningFieldData:
+    conditionings: List[BasicConditioningInfo] | List[SDXLConditioningInfo] | List[FLUXConditioningInfo]
+
+
 @dataclass
 class IPAdapterConditioningInfo:
     cond_image_prompt_embeds: torch.Tensor

invokeai/backend/util/__init__.py

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

invokeai/backend/util/util.py

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

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

@@ -127,7 +127,14 @@
         "bulkDownloadRequestedDesc": "Dein Download wird vorbereitet. Dies kann ein paar Momente dauern.",
         "bulkDownloadRequestFailed": "Problem beim Download vorbereiten",
         "bulkDownloadFailed": "Download fehlgeschlagen",
-        "alwaysShowImageSizeBadge": "Zeige immer Bilder Größe Abzeichen"
+        "alwaysShowImageSizeBadge": "Zeige immer Bilder Größe Abzeichen",
+        "selectForCompare": "Zum Vergleichen auswählen",
+        "compareImage": "Bilder vergleichen",
+        "exitSearch": "Suche beenden",
+        "newestFirst": "Neueste zuerst",
+        "oldestFirst": "Älteste zuerst",
+        "openInViewer": "Im Viewer öffnen",
+        "swapImages": "Bilder tauschen"
     },
     "hotkeys": {
         "keyboardShortcuts": "Tastenkürzel",
@@ -631,7 +638,8 @@
         "archived": "Archiviert",
         "noBoards": "Kein {boardType}} Ordner",
         "hideBoards": "Ordner verstecken",
-        "viewBoards": "Ordner ansehen"
+        "viewBoards": "Ordner ansehen",
+        "deletedPrivateBoardsCannotbeRestored": "Gelöschte Boards können nicht wiederhergestellt werden. Wenn Sie „Nur Board löschen“ wählen, werden die Bilder in einen privaten, nicht kategorisierten Status für den Ersteller des Bildes versetzt."
     },
     "controlnet": {
         "showAdvanced": "Zeige Erweitert",
@@ -781,7 +789,9 @@
         "batchFieldValues": "Stapelverarbeitungswerte",
         "batchQueued": "Stapelverarbeitung eingereiht",
         "graphQueued": "Graph eingereiht",
-        "graphFailedToQueue": "Fehler beim Einreihen des Graphen"
+        "graphFailedToQueue": "Fehler beim Einreihen des Graphen",
+        "generations_one": "Generation",
+        "generations_other": "Generationen"
     },
     "metadata": {
         "negativePrompt": "Negativ Beschreibung",
@@ -1146,5 +1156,10 @@
         "noMatchingTriggers": "Keine passenden Trigger",
         "addPromptTrigger": "Prompt-Trigger hinzufügen",
         "compatibleEmbeddings": "Kompatible Einbettungen"
+    },
+    "ui": {
+        "tabs": {
+            "queue": "Warteschlange"
+        }
     }
 }

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

@@ -696,6 +696,8 @@
         "availableModels": "Available Models",
         "baseModel": "Base Model",
         "cancel": "Cancel",
+        "clipEmbed": "CLIP Embed",
+        "clipVision": "CLIP Vision",
         "config": "Config",
         "convert": "Convert",
         "convertingModelBegin": "Converting Model. Please wait.",
@@ -783,13 +785,16 @@
         "settings": "Settings",
         "simpleModelPlaceholder": "URL or path to a local file or diffusers folder",
         "source": "Source",
+        "spandrelImageToImage": "Image to Image (Spandrel)",
         "starterModels": "Starter Models",
+        "starterModelsInModelManager": "Starter Models can be found in Model Manager",
         "syncModels": "Sync Models",
         "textualInversions": "Textual Inversions",
         "triggerPhrases": "Trigger Phrases",
         "loraTriggerPhrases": "LoRA Trigger Phrases",
         "mainModelTriggerPhrases": "Main Model Trigger Phrases",
         "typePhraseHere": "Type phrase here",
+        "t5Encoder": "T5 Encoder",
         "upcastAttention": "Upcast Attention",
         "uploadImage": "Upload Image",
         "urlOrLocalPath": "URL or Local Path",
@@ -1675,6 +1680,7 @@
         "layers_other": "Layers"
     },
     "upscaling": {
+        "upscale": "Upscale",
         "creativity": "Creativity",
         "exceedsMaxSize": "Upscale settings exceed max size limit",
         "exceedsMaxSizeDetails": "Max upscale limit is {{maxUpscaleDimension}}x{{maxUpscaleDimension}} pixels. Please try a smaller image or decrease your scale selection.",
@@ -1723,6 +1729,7 @@
         "positivePrompt": "Positive Prompt",
         "preview": "Preview",
         "private": "Private",
+        "promptTemplateCleared": "Prompt Template Cleared",
         "searchByName": "Search by name",
         "shared": "Shared",
         "sharedTemplates": "Shared Templates",

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

@@ -86,15 +86,15 @@
         "loadMore": "Cargar más",
         "noImagesInGallery": "No hay imágenes para mostrar",
         "deleteImage_one": "Eliminar Imagen",
-        "deleteImage_many": "",
-        "deleteImage_other": "",
+        "deleteImage_many": "Eliminar {{count}} Imágenes",
+        "deleteImage_other": "Eliminar {{count}} Imágenes",
         "deleteImagePermanent": "Las imágenes eliminadas no se pueden restaurar.",
         "assets": "Activos",
         "autoAssignBoardOnClick": "Asignación automática de tableros al hacer clic"
     },
     "hotkeys": {
         "keyboardShortcuts": "Atajos de teclado",
-        "appHotkeys": "Atajos de applicación",
+        "appHotkeys": "Atajos de aplicación",
         "generalHotkeys": "Atajos generales",
         "galleryHotkeys": "Atajos de galería",
         "unifiedCanvasHotkeys": "Atajos de lienzo unificado",
@@ -535,7 +535,7 @@
         "bottomMessage": "Al eliminar este panel y las imágenes que contiene, se restablecerán las funciones que los estén utilizando actualmente.",
         "deleteBoardAndImages": "Borrar el panel y las imágenes",
         "loading": "Cargando...",
-        "deletedBoardsCannotbeRestored": "Los paneles eliminados no se pueden restaurar",
+        "deletedBoardsCannotbeRestored": "Los paneles eliminados no se pueden restaurar. Al Seleccionar 'Borrar Solo el Panel' transferirá las imágenes a un estado sin categorizar.",
         "move": "Mover",
         "menuItemAutoAdd": "Agregar automáticamente a este panel",
         "searchBoard": "Buscando paneles…",
@@ -549,7 +549,13 @@
         "imagesWithCount_other": "{{count}} imágenes",
         "assetsWithCount_one": "{{count}} activo",
         "assetsWithCount_many": "{{count}} activos",
-        "assetsWithCount_other": "{{count}} activos"
+        "assetsWithCount_other": "{{count}} activos",
+        "hideBoards": "Ocultar Paneles",
+        "addPrivateBoard": "Agregar un tablero privado",
+        "addSharedBoard": "Agregar Panel Compartido",
+        "boards": "Paneles",
+        "archiveBoard": "Archivar Panel",
+        "archived": "Archivado"
     },
     "accordions": {
         "compositing": {

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

@@ -496,7 +496,9 @@
         "main": "Principali",
         "noModelsInstalledDesc1": "Installa i modelli con",
         "ipAdapters": "Adattatori IP",
-        "noMatchingModels": "Nessun modello corrispondente"
+        "noMatchingModels": "Nessun modello corrispondente",
+        "starterModelsInModelManager": "I modelli iniziali possono essere trovati in Gestione Modelli",
+        "spandrelImageToImage": "Immagine a immagine (Spandrel)"
     },
     "parameters": {
         "images": "Immagini",
@@ -510,7 +512,7 @@
         "perlinNoise": "Rumore Perlin",
         "type": "Tipo",
         "strength": "Forza",
-        "upscaling": "Ampliamento",
+        "upscaling": "Amplia",
         "scale": "Scala",
         "imageFit": "Adatta l'immagine iniziale alle dimensioni di output",
         "scaleBeforeProcessing": "Scala prima dell'elaborazione",
@@ -593,7 +595,7 @@
         "globalPositivePromptPlaceholder": "Prompt positivo globale",
         "globalNegativePromptPlaceholder": "Prompt negativo globale",
         "processImage": "Elabora Immagine",
-        "sendToUpscale": "Invia a Ampliare",
+        "sendToUpscale": "Invia a Amplia",
         "postProcessing": "Post-elaborazione (Shift + U)"
     },
     "settings": {
@@ -929,7 +931,7 @@
         "missingInvocationTemplate": "Modello di invocazione mancante",
         "missingFieldTemplate": "Modello di campo mancante",
         "singleFieldType": "{{name}} (Singola)",
-        "imageAccessError": "Impossibile trovare l'immagine {{image_name}}, ripristino delle impostazioni predefinite",
+        "imageAccessError": "Impossibile trovare l'immagine {{image_name}}, ripristino ai valori predefiniti",
         "boardAccessError": "Impossibile trovare la bacheca {{board_id}}, ripristino ai valori predefiniti",
         "modelAccessError": "Impossibile trovare il modello {{key}}, ripristino ai valori predefiniti"
     },
@@ -1420,7 +1422,7 @@
         "paramUpscaleMethod": {
             "heading": "Metodo di ampliamento",
             "paragraphs": [
-                "Metodo utilizzato per eseguire l'ampliamento dell'immagine per la correzione ad alta risoluzione."
+                "Metodo utilizzato per ampliare l'immagine per la correzione ad alta risoluzione."
             ]
         },
         "patchmatchDownScaleSize": {
@@ -1528,7 +1530,7 @@
         },
         "upscaleModel": {
             "paragraphs": [
-                "Il modello di ampliamento (Upscale), scala l'immagine alle dimensioni di uscita prima di aggiungere i dettagli. È possibile utilizzare qualsiasi modello di ampliamento supportato, ma alcuni sono specializzati per diversi tipi di immagini, come foto o disegni al tratto."
+                "Il modello di ampliamento, scala l'immagine alle dimensioni di uscita prima di aggiungere i dettagli. È possibile utilizzare qualsiasi modello di ampliamento supportato, ma alcuni sono specializzati per diversi tipi di immagini, come foto o disegni al tratto."
             ],
             "heading": "Modello di ampliamento"
         },
@@ -1720,26 +1722,27 @@
             "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
             "queue": "Coda",
             "queueTab": "$t(ui.tabs.queue) $t(common.tab)",
-            "upscaling": "Ampliamento",
+            "upscaling": "Amplia",
             "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)"
         }
     },
     "upscaling": {
         "creativity": "Creatività",
         "structure": "Struttura",
-        "upscaleModel": "Modello di Ampliamento",
+        "upscaleModel": "Modello di ampliamento",
         "scale": "Scala",
         "missingModelsWarning": "Visita <LinkComponent>Gestione modelli</LinkComponent> per installare i modelli richiesti:",
         "mainModelDesc": "Modello principale (architettura SD1.5 o SDXL)",
         "tileControlNetModelDesc": "Modello Tile ControlNet per l'architettura del modello principale scelto",
-        "upscaleModelDesc": "Modello per l'ampliamento (da immagine a immagine)",
+        "upscaleModelDesc": "Modello per l'ampliamento (immagine a immagine)",
         "missingUpscaleInitialImage": "Immagine iniziale mancante per l'ampliamento",
         "missingUpscaleModel": "Modello per l’ampliamento mancante",
         "missingTileControlNetModel": "Nessun modello ControlNet Tile valido installato",
         "postProcessingModel": "Modello di post-elaborazione",
         "postProcessingMissingModelWarning": "Visita <LinkComponent>Gestione modelli</LinkComponent> per installare un modello di post-elaborazione (da immagine a immagine).",
         "exceedsMaxSize": "Le impostazioni di ampliamento superano il limite massimo delle dimensioni",
-        "exceedsMaxSizeDetails": "Il limite massimo di ampliamento è {{maxUpscaleDimension}}x{{maxUpscaleDimension}} pixel. Prova un'immagine più piccola o diminuisci la scala selezionata."
+        "exceedsMaxSizeDetails": "Il limite massimo di ampliamento è {{maxUpscaleDimension}}x{{maxUpscaleDimension}} pixel. Prova un'immagine più piccola o diminuisci la scala selezionata.",
+        "upscale": "Amplia"
     },
     "upsell": {
         "inviteTeammates": "Invita collaboratori",
@@ -1782,7 +1785,14 @@
         "updatePromptTemplate": "Aggiorna il modello di prompt",
         "type": "Tipo",
         "promptTemplatesDesc2": "Utilizza la stringa segnaposto <Pre>{{placeholder}}</Pre> per specificare dove inserire il tuo prompt nel modello.",
-        "importTemplates": "Importa modelli di prompt",
-        "importTemplatesDesc": "Il formato deve essere un CSV con colonne 'name' e 'prompt' o 'positive_prompt' e 'negative_prompt' incluse, oppure un file JSON con chiavi 'name' e 'prompt' o 'positive_prompt' e 'negative_prompt"
+        "importTemplates": "Importa modelli di prompt (CSV/JSON)",
+        "exportDownloaded": "Esportazione completata",
+        "exportFailed": "Impossibile generare e scaricare il file CSV",
+        "exportPromptTemplates": "Esporta i miei modelli di prompt (CSV)",
+        "positivePromptColumn": "'prompt' o 'positive_prompt'",
+        "noTemplates": "Nessun modello",
+        "acceptedColumnsKeys": "Colonne/chiavi accettate:",
+        "templateActions": "Azioni modello",
+        "promptTemplateCleared": "Modello di prompt cancellato"
     }
 }

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

@@ -91,7 +91,8 @@
         "enabled": "Включено",
         "disabled": "Отключено",
         "comparingDesc": "Сравнение двух изображений",
-        "comparing": "Сравнение"
+        "comparing": "Сравнение",
+        "dontShowMeThese": "Не показывай мне это"
     },
     "gallery": {
         "galleryImageSize": "Размер изображений",
@@ -153,7 +154,11 @@
         "showArchivedBoards": "Показать архивированные доски",
         "searchImages": "Поиск по метаданным",
         "displayBoardSearch": "Отобразить поиск досок",
-        "displaySearch": "Отобразить поиск"
+        "displaySearch": "Отобразить поиск",
+        "exitBoardSearch": "Выйти из поиска досок",
+        "go": "Перейти",
+        "exitSearch": "Выйти из поиска",
+        "jump": "Пыгнуть"
     },
     "hotkeys": {
         "keyboardShortcuts": "Горячие клавиши",
@@ -376,6 +381,10 @@
         "toggleViewer": {
             "title": "Переключить просмотр изображений",
             "desc": "Переключение между средством просмотра изображений и рабочей областью для текущей вкладки."
+        },
+        "postProcess": {
+            "desc": "Обработайте текущее изображение с помощью выбранной модели постобработки",
+            "title": "Обработать изображение"
         }
     },
     "modelManager": {
@@ -492,7 +501,8 @@
         "noModelsInstalled": "Нет установленных моделей",
         "noModelsInstalledDesc1": "Установите модели с помощью",
         "noMatchingModels": "Нет подходящих моделей",
-        "ipAdapters": "IP адаптеры"
+        "ipAdapters": "IP адаптеры",
+        "starterModelsInModelManager": "Стартовые модели можно найти в Менеджере моделей"
     },
     "parameters": {
         "images": "Изображения",
@@ -589,7 +599,10 @@
         "infillColorValue": "Цвет заливки",
         "globalSettings": "Глобальные настройки",
         "globalNegativePromptPlaceholder": "Глобальный негативный запрос",
-        "globalPositivePromptPlaceholder": "Глобальный запрос"
+        "globalPositivePromptPlaceholder": "Глобальный запрос",
+        "postProcessing": "Постобработка (Shift + U)",
+        "processImage": "Обработка изображения",
+        "sendToUpscale": "Отправить на увеличение"
     },
     "settings": {
         "models": "Модели",
@@ -623,7 +636,9 @@
         "intermediatesCleared_many": "Очищено {{count}} промежуточных",
         "clearIntermediatesDesc1": "Очистка промежуточных элементов приведет к сбросу состояния Canvas и ControlNet.",
         "intermediatesClearedFailed": "Проблема очистки промежуточных",
-        "reloadingIn": "Перезагрузка через"
+        "reloadingIn": "Перезагрузка через",
+        "informationalPopoversDisabled": "Информационные всплывающие окна отключены",
+        "informationalPopoversDisabledDesc": "Информационные всплывающие окна были отключены. Включите их в Настройках."
     },
     "toast": {
         "uploadFailed": "Загрузка не удалась",
@@ -694,7 +709,9 @@
         "sessionRef": "Сессия: {{sessionId}}",
         "outOfMemoryError": "Ошибка нехватки памяти",
         "outOfMemoryErrorDesc": "Ваши текущие настройки генерации превышают возможности системы. Пожалуйста, измените настройки и повторите попытку.",
-        "somethingWentWrong": "Что-то пошло не так"
+        "somethingWentWrong": "Что-то пошло не так",
+        "importFailed": "Импорт неудачен",
+        "importSuccessful": "Импорт успешен"
     },
     "tooltip": {
         "feature": {
@@ -1017,7 +1034,8 @@
         "composition": "Только композиция",
         "hed": "HED",
         "beginEndStepPercentShort": "Начало/конец %",
-        "setControlImageDimensionsForce": "Скопируйте размер в Ш/В (игнорируйте модель)"
+        "setControlImageDimensionsForce": "Скопируйте размер в Ш/В (игнорируйте модель)",
+        "depthAnythingSmallV2": "Small V2"
     },
     "boards": {
         "autoAddBoard": "Авто добавление Доски",
@@ -1042,7 +1060,7 @@
         "downloadBoard": "Скачать доску",
         "deleteBoard": "Удалить доску",
         "deleteBoardAndImages": "Удалить доску и изображения",
-        "deletedBoardsCannotbeRestored": "Удаленные доски не подлежат восстановлению",
+        "deletedBoardsCannotbeRestored": "Удаленные доски не могут быть восстановлены. Выбор «Удалить только доску» переведет изображения в состояние без категории.",
         "assetsWithCount_one": "{{count}} ассет",
         "assetsWithCount_few": "{{count}} ассета",
         "assetsWithCount_many": "{{count}} ассетов",
@@ -1057,7 +1075,11 @@
         "boards": "Доски",
         "addPrivateBoard": "Добавить личную доску",
         "private": "Личные доски",
-        "shared": "Общие доски"
+        "shared": "Общие доски",
+        "hideBoards": "Скрыть доски",
+        "viewBoards": "Просмотреть доски",
+        "noBoards": "Нет досок {{boardType}}",
+        "deletedPrivateBoardsCannotbeRestored": "Удаленные доски не могут быть восстановлены. Выбор «Удалить только доску» переведет изображения в приватное состояние без категории для создателя изображения."
     },
     "dynamicPrompts": {
         "seedBehaviour": {
@@ -1417,6 +1439,30 @@
             "paragraphs": [
                 "Метод, с помощью которого применяется текущий IP-адаптер."
             ]
+        },
+        "structure": {
+            "paragraphs": [
+                "Структура контролирует, насколько точно выходное изображение будет соответствовать макету оригинала. Низкая структура допускает значительные изменения, в то время как высокая структура строго сохраняет исходную композицию и макет."
+            ],
+            "heading": "Структура"
+        },
+        "scale": {
+            "paragraphs": [
+                "Масштаб управляет размером выходного изображения и основывается на кратном разрешении входного изображения. Например, при увеличении в 2 раза изображения 1024x1024 на выходе получится 2048 x 2048."
+            ],
+            "heading": "Масштаб"
+        },
+        "creativity": {
+            "paragraphs": [
+                "Креативность контролирует степень свободы, предоставляемой модели при добавлении деталей. При низкой креативности модель остается близкой к оригинальному изображению, в то время как высокая креативность позволяет вносить больше изменений. При использовании подсказки высокая креативность увеличивает влияние подсказки."
+            ],
+            "heading": "Креативность"
+        },
+        "upscaleModel": {
+            "heading": "Модель увеличения",
+            "paragraphs": [
+                "Модель увеличения масштаба масштабирует изображение до выходного размера перед добавлением деталей. Можно использовать любую поддерживаемую модель масштабирования, но некоторые из них специализированы для различных видов изображений, например фотографий или линейных рисунков."
+            ]
         }
     },
     "metadata": {
@@ -1693,7 +1739,80 @@
             "canvasTab": "$t(ui.tabs.canvas) $t(common.tab)",
             "queueTab": "$t(ui.tabs.queue) $t(common.tab)",
             "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
-            "queue": "Очередь"
+            "queue": "Очередь",
+            "upscaling": "Увеличение",
+            "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)"
         }
+    },
+    "upscaling": {
+        "exceedsMaxSize": "Параметры масштабирования превышают максимальный размер",
+        "exceedsMaxSizeDetails": "Максимальный предел масштабирования составляет {{maxUpscaleDimension}}x{{maxUpscaleDimension}} пикселей. Пожалуйста, попробуйте использовать меньшее изображение или уменьшите масштаб.",
+        "structure": "Структура",
+        "missingTileControlNetModel": "Не установлены подходящие модели ControlNet",
+        "missingUpscaleInitialImage": "Отсутствует увеличиваемое изображение",
+        "missingUpscaleModel": "Отсутствует увеличивающая модель",
+        "creativity": "Креативность",
+        "upscaleModel": "Модель увеличения",
+        "scale": "Масштаб",
+        "mainModelDesc": "Основная модель (архитектура SD1.5 или SDXL)",
+        "upscaleModelDesc": "Модель увеличения (img2img)",
+        "postProcessingModel": "Модель постобработки",
+        "tileControlNetModelDesc": "Модель ControlNet для выбранной архитектуры основной модели",
+        "missingModelsWarning": "Зайдите в <LinkComponent>Менеджер моделей</LinkComponent> чтоб установить необходимые модели:",
+        "postProcessingMissingModelWarning": "Посетите <LinkComponent>Менеджер моделей</LinkComponent>, чтобы установить модель постобработки (img2img).",
+        "upscale": "Увеличить"
+    },
+    "stylePresets": {
+        "noMatchingTemplates": "Нет подходящих шаблонов",
+        "promptTemplatesDesc1": "Шаблоны подсказок добавляют текст к подсказкам, которые вы пишете в окне подсказок.",
+        "sharedTemplates": "Общие шаблоны",
+        "templateDeleted": "Шаблон запроса удален",
+        "toggleViewMode": "Переключить режим просмотра",
+        "type": "Тип",
+        "unableToDeleteTemplate": "Не получилось удалить шаблон запроса",
+        "viewModeTooltip": "Вот как будет выглядеть ваш запрос с выбранным шаблоном. Чтобы его отредактировать, щелкните в любом месте текстового поля.",
+        "viewList": "Просмотреть список шаблонов",
+        "active": "Активно",
+        "choosePromptTemplate": "Выберите шаблон запроса",
+        "defaultTemplates": "Стандартные шаблоны",
+        "deleteImage": "Удалить изображение",
+        "deleteTemplate": "Удалить шаблон",
+        "deleteTemplate2": "Вы уверены, что хотите удалить этот шаблон? Это нельзя отменить.",
+        "editTemplate": "Редактировать шаблон",
+        "exportPromptTemplates": "Экспорт моих шаблонов запроса (CSV)",
+        "exportDownloaded": "Экспорт скачан",
+        "exportFailed": "Невозможно сгенерировать и загрузить CSV",
+        "flatten": "Объединить выбранный шаблон с текущим запросом",
+        "acceptedColumnsKeys": "Принимаемые столбцы/ключи:",
+        "positivePromptColumn": "'prompt' или 'positive_prompt'",
+        "insertPlaceholder": "Вставить заполнитель",
+        "name": "Имя",
+        "negativePrompt": "Негативный запрос",
+        "promptTemplatesDesc3": "Если вы не используете заполнитель, шаблон будет добавлен в конец запроса.",
+        "positivePrompt": "Позитивный запрос",
+        "preview": "Предпросмотр",
+        "private": "Приватный",
+        "templateActions": "Действия с шаблоном",
+        "updatePromptTemplate": "Обновить шаблон запроса",
+        "uploadImage": "Загрузить изображение",
+        "useForTemplate": "Использовать для шаблона запроса",
+        "clearTemplateSelection": "Очистить выбор шаблона",
+        "copyTemplate": "Копировать шаблон",
+        "createPromptTemplate": "Создать шаблон запроса",
+        "importTemplates": "Импортировать шаблоны запроса (CSV/JSON)",
+        "nameColumn": "'name'",
+        "negativePromptColumn": "'negative_prompt'",
+        "myTemplates": "Мои шаблоны",
+        "noTemplates": "Нет шаблонов",
+        "promptTemplatesDesc2": "Используйте строку-заполнитель <Pre>{{placeholder}}</Pre>, чтобы указать место, куда должен быть включен ваш запрос в шаблоне.",
+        "searchByName": "Поиск по имени",
+        "shared": "Общий",
+        "promptTemplateCleared": "Шаблон запроса создан"
+    },
+    "upsell": {
+        "inviteTeammates": "Пригласите членов команды",
+        "professional": "Профессионал",
+        "professionalUpsell": "Доступно в профессиональной версии Invoke. Нажмите здесь или посетите invoke.com/pricing для получения более подробной информации.",
+        "shareAccess": "Поделиться доступом"
     }
 }

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

@@ -154,7 +154,8 @@
         "displaySearch": "显示搜索",
         "stretchToFit": "拉伸以适应",
         "exitCompare": "退出对比",
-        "compareHelp1": "在点击图库中的图片或使用箭头键切换比较图片时，请按住<Kbd>Alt</Kbd> 键。"
+        "compareHelp1": "在点击图库中的图片或使用箭头键切换比较图片时，请按住<Kbd>Alt</Kbd> 键。",
+        "go": "运行"
     },
     "hotkeys": {
         "keyboardShortcuts": "快捷键",
@@ -494,7 +495,9 @@
         "huggingFacePlaceholder": "所有者或模型名称",
         "huggingFaceRepoID": "HuggingFace仓库ID",
         "loraTriggerPhrases": "LoRA 触发词",
-        "ipAdapters": "IP适配器"
+        "ipAdapters": "IP适配器",
+        "spandrelImageToImage": "图生图(Spandrel)",
+        "starterModelsInModelManager": "您可以在模型管理器中找到初始模型"
     },
     "parameters": {
         "images": "图像",
@@ -695,7 +698,9 @@
         "outOfMemoryErrorDesc": "您当前的生成设置已超出系统处理能力.请调整设置后再次尝试.",
         "parametersSet": "参数已恢复",
         "errorCopied": "错误信息已复制",
-        "modelImportCanceled": "模型导入已取消"
+        "modelImportCanceled": "模型导入已取消",
+        "importFailed": "导入失败",
+        "importSuccessful": "导入成功"
     },
     "unifiedCanvas": {
         "layer": "图层",
@@ -1705,12 +1710,55 @@
         "missingModelsWarning": "请访问<LinkComponent>模型管理器</LinkComponent> 安装所需的模型：",
         "mainModelDesc": "主模型（SD1.5或SDXL架构）",
         "exceedsMaxSize": "放大设置超出了最大尺寸限制",
-        "exceedsMaxSizeDetails": "最大放大限制是 {{maxUpscaleDimension}}x{{maxUpscaleDimension}} 像素.请尝试一个较小的图像或减少您的缩放选择."
+        "exceedsMaxSizeDetails": "最大放大限制是 {{maxUpscaleDimension}}x{{maxUpscaleDimension}} 像素.请尝试一个较小的图像或减少您的缩放选择.",
+        "upscale": "放大"
     },
     "upsell": {
         "inviteTeammates": "邀请团队成员",
         "professional": "专业",
         "professionalUpsell": "可在 Invoke 的专业版中使用.点击此处或访问 invoke.com/pricing 了解更多详情.",
         "shareAccess": "共享访问权限"
+    },
+    "stylePresets": {
+        "positivePrompt": "正向提示词",
+        "preview": "预览",
+        "deleteImage": "删除图像",
+        "deleteTemplate": "删除模版",
+        "deleteTemplate2": "您确定要删除这个模板吗？请注意，删除后无法恢复.",
+        "importTemplates": "导入提示模板，支持CSV或JSON格式",
+        "insertPlaceholder": "插入一个占位符",
+        "myTemplates": "我的模版",
+        "name": "名称",
+        "type": "类型",
+        "unableToDeleteTemplate": "无法删除提示模板",
+        "updatePromptTemplate": "更新提示词模版",
+        "exportPromptTemplates": "导出我的提示模板为CSV格式",
+        "exportDownloaded": "导出已下载",
+        "noMatchingTemplates": "无匹配的模版",
+        "promptTemplatesDesc1": "提示模板可以帮助您在编写提示时添加预设的文本内容.",
+        "promptTemplatesDesc3": "如果您没有使用占位符，那么模板的内容将会被添加到您提示的末尾.",
+        "searchByName": "按名称搜索",
+        "shared": "已分享",
+        "sharedTemplates": "已分享的模版",
+        "templateActions": "模版操作",
+        "templateDeleted": "提示模版已删除",
+        "toggleViewMode": "切换显示模式",
+        "uploadImage": "上传图像",
+        "active": "激活",
+        "choosePromptTemplate": "选择提示词模板",
+        "clearTemplateSelection": "清除模版选择",
+        "copyTemplate": "拷贝模版",
+        "createPromptTemplate": "创建提示词模版",
+        "defaultTemplates": "默认模版",
+        "editTemplate": "编辑模版",
+        "exportFailed": "无法生成并下载CSV文件",
+        "flatten": "将选定的模板内容合并到当前提示中",
+        "negativePrompt": "反向提示词",
+        "promptTemplateCleared": "提示模板已清除",
+        "useForTemplate": "用于提示词模版",
+        "viewList": "预览模版列表",
+        "viewModeTooltip": "这是您的提示在当前选定的模板下的预览效果。如需编辑提示，请直接在文本框中点击进行修改.",
+        "noTemplates": "无模版",
+        "private": "私密"
     }
 }

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

@@ -14,6 +14,7 @@ import DeleteImageModal from 'features/deleteImageModal/components/DeleteImageMo
 import { DynamicPromptsModal } from 'features/dynamicPrompts/components/DynamicPromptsPreviewModal';
 import { useStarterModelsToast } from 'features/modelManagerV2/hooks/useStarterModelsToast';
 import { StylePresetModal } from 'features/stylePresets/components/StylePresetForm/StylePresetModal';
+import { activeStylePresetIdChanged } from 'features/stylePresets/store/stylePresetSlice';
 import { configChanged } from 'features/system/store/configSlice';
 import { languageSelector } from 'features/system/store/systemSelectors';
 import InvokeTabs from 'features/ui/components/InvokeTabs';
@@ -39,10 +40,17 @@ interface Props {
     action: 'sendToImg2Img' | 'sendToCanvas' | 'useAllParameters';
   };
   selectedWorkflowId?: string;
+  selectedStylePresetId?: string;
   destination?: InvokeTabName | undefined;
 }
 
-const App = ({ config = DEFAULT_CONFIG, selectedImage, selectedWorkflowId, destination }: Props) => {
+const App = ({
+  config = DEFAULT_CONFIG,
+  selectedImage,
+  selectedWorkflowId,
+  selectedStylePresetId,
+  destination,
+}: Props) => {
   const language = useAppSelector(languageSelector);
   const logger = useLogger('system');
   const dispatch = useAppDispatch();
@@ -81,6 +89,12 @@ const App = ({ config = DEFAULT_CONFIG, selectedImage, selectedWorkflowId, desti
     }
   }, [selectedWorkflowId, getAndLoadWorkflow]);
 
+  useEffect(() => {
+    if (selectedStylePresetId) {
+      dispatch(activeStylePresetIdChanged(selectedStylePresetId));
+    }
+  }, [dispatch, selectedStylePresetId]);
+
   useEffect(() => {
     if (destination) {
       dispatch(setActiveTab(destination));

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

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

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

@@ -13,6 +13,7 @@ import {
 import { getShouldProcessPrompt } from 'features/dynamicPrompts/util/getShouldProcessPrompt';
 import { getPresetModifiedPrompts } from 'features/nodes/util/graph/graphBuilderUtils';
 import { activeStylePresetIdChanged } from 'features/stylePresets/store/stylePresetSlice';
+import { stylePresetsApi } from 'services/api/endpoints/stylePresets';
 import { utilitiesApi } from 'services/api/endpoints/utilities';
 import { socketConnected } from 'services/events/actions';
 
@@ -22,7 +23,10 @@ const matcher = isAnyOf(
   maxPromptsChanged,
   maxPromptsReset,
   socketConnected,
-  activeStylePresetIdChanged
+  activeStylePresetIdChanged,
+  stylePresetsApi.endpoints.deleteStylePreset.matchFulfilled,
+  stylePresetsApi.endpoints.updateStylePreset.matchFulfilled,
+  stylePresetsApi.endpoints.listStylePresets.matchFulfilled
 );
 
 export const addDynamicPromptsListener = (startAppListening: AppStartListening) => {

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

@@ -8,7 +8,7 @@ import { $authToken } from 'app/store/nanostores/authToken';
  */
 
 export const convertImageUrlToBlob = async (url: string) =>
-  new Promise<Blob | null>((resolve) => {
+  new Promise<Blob | null>((resolve, reject) => {
     const img = new Image();
     img.onload = () => {
       const canvas = document.createElement('canvas');
@@ -17,17 +17,23 @@ export const convertImageUrlToBlob = async (url: string) =>
 
       const context = canvas.getContext('2d');
       if (!context) {
+        reject(new Error('Failed to get canvas context'));
         return;
       }
       context.drawImage(img, 0, 0);
-      resolve(
-        new Promise<Blob | null>((resolve) => {
-          canvas.toBlob(function (blob) {
-            resolve(blob);
-          }, 'image/png');
-        })
-      );
+      canvas.toBlob((blob) => {
+        if (blob) {
+          resolve(blob);
+        } else {
+          reject(new Error('Failed to convert image to blob'));
+        }
+      }, 'image/png');
     };
+
+    img.onerror = () => {
+      reject(new Error('Image failed to load. The URL may be invalid or the object may not exist.'));
+    };
+
     img.crossOrigin = $authToken.get() ? 'use-credentials' : 'anonymous';
     img.src = url;
   });

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

@@ -66,7 +66,7 @@ export const Gallery = () => {
     <Flex flexDirection="column" alignItems="center" justifyContent="space-between" h="full" w="full" pt={1}>
       <Tabs index={galleryView === 'images' ? 0 : 1} variant="enclosed" display="flex" flexDir="column" w="full">
         <TabList gap={2} fontSize="sm" borderColor="base.800" alignItems="center" w="full">
-          <Text fontSize="sm" fontWeight="semibold" noOfLines={1} px="2">
+          <Text fontSize="sm" fontWeight="semibold" noOfLines={1} px="2" wordBreak="break-all">
             {boardName}
           </Text>
           <Spacer />

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

@@ -60,7 +60,6 @@ const ImageGalleryContent = () => {
         <GalleryHeader />
         <Flex alignItems="center" justifyContent="space-between" w="full">
           <Button
-            w={112}
             size="sm"
             variant="ghost"
             onClick={handleToggleBoardPanel}

invokeai/frontend/web/src/features/gallery/hooks/useImageActions.ts

@@ -1,14 +1,20 @@
 import { skipToken } from '@reduxjs/toolkit/query';
-import { useAppSelector } from 'app/store/storeHooks';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { handlers, parseAndRecallAllMetadata, parseAndRecallPrompts } from 'features/metadata/util/handlers';
 import { $stylePresetModalState } from 'features/stylePresets/store/stylePresetModal';
+import { activeStylePresetIdChanged } from 'features/stylePresets/store/stylePresetSlice';
+import { toast } from 'features/toast/toast';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
 import { useCallback, useEffect, useState } from 'react';
+import { useTranslation } from 'react-i18next';
 import { useGetImageDTOQuery } from 'services/api/endpoints/images';
 import { useDebouncedMetadata } from 'services/api/hooks/useDebouncedMetadata';
 
 export const useImageActions = (image_name?: string) => {
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
   const activeTabName = useAppSelector(activeTabNameSelector);
+  const activeStylePresetId = useAppSelector((s) => s.stylePreset.activeStylePresetId);
   const { metadata, isLoading: isLoadingMetadata } = useDebouncedMetadata(image_name);
   const [hasMetadata, setHasMetadata] = useState(false);
   const [hasSeed, setHasSeed] = useState(false);
@@ -46,14 +52,26 @@ export const useImageActions = (image_name?: string) => {
     parseMetadata();
   }, [metadata]);
 
+  const clearStylePreset = useCallback(() => {
+    if (activeStylePresetId) {
+      dispatch(activeStylePresetIdChanged(null));
+      toast({
+        status: 'info',
+        title: t('stylePresets.promptTemplateCleared'),
+      });
+    }
+  }, [dispatch, activeStylePresetId, t]);
+
   const recallAll = useCallback(() => {
     parseAndRecallAllMetadata(metadata, activeTabName === 'generation');
-  }, [activeTabName, metadata]);
+    clearStylePreset();
+  }, [activeTabName, metadata, clearStylePreset]);
 
   const remix = useCallback(() => {
     // Recalls all metadata parameters except seed
     parseAndRecallAllMetadata(metadata, activeTabName === 'generation', ['seed']);
-  }, [activeTabName, metadata]);
+    clearStylePreset();
+  }, [activeTabName, metadata, clearStylePreset]);
 
   const recallSeed = useCallback(() => {
     handlers.seed.parse(metadata).then((seed) => {
@@ -63,12 +81,24 @@ export const useImageActions = (image_name?: string) => {
 
   const recallPrompts = useCallback(() => {
     parseAndRecallPrompts(metadata);
-  }, [metadata]);
+    clearStylePreset();
+  }, [metadata, clearStylePreset]);
 
   const createAsPreset = useCallback(async () => {
     if (image_name && metadata && imageDTO) {
-      const positivePrompt = await handlers.positivePrompt.parse(metadata);
-      const negativePrompt = await handlers.negativePrompt.parse(metadata);
+      let positivePrompt;
+      let negativePrompt;
+
+      try {
+        positivePrompt = await handlers.positivePrompt.parse(metadata);
+      } catch (error) {
+        positivePrompt = '';
+      }
+      try {
+        negativePrompt = await handlers.negativePrompt.parse(metadata);
+      } catch (error) {
+        negativePrompt = '';
+      }
 
       $stylePresetModalState.set({
         prefilledFormData: {

invokeai/frontend/web/src/features/modelManagerV2/subpanels/AddModelPanel/StarterModels/StartModelsResultItem.tsx

@@ -5,17 +5,33 @@ import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiPlusBold } from 'react-icons/pi';
 import type { GetStarterModelsResponse } from 'services/api/endpoints/models';
+import type { AnyModelConfig } from 'services/api/types';
 
 type Props = {
   result: GetStarterModelsResponse[number];
+  modelList: AnyModelConfig[];
 };
-export const StarterModelsResultItem = memo(({ result }: Props) => {
+export const StarterModelsResultItem = memo(({ result, modelList }: Props) => {
   const { t } = useTranslation();
   const allSources = useMemo(() => {
-    const _allSources = [{ source: result.source, config: { name: result.name, description: result.description } }];
+    const _allSources = [
+      {
+        source: result.source,
+        config: {
+          name: result.name,
+          description: result.description,
+          type: result.type,
+          base: result.base,
+          format: result.format,
+        },
+      },
+    ];
     if (result.dependencies) {
       for (const d of result.dependencies) {
-        _allSources.push({ source: d.source, config: { name: d.name, description: d.description } });
+        _allSources.push({
+          source: d.source,
+          config: { name: d.name, description: d.description, type: d.type, base: d.base, format: d.format },
+        });
       }
     }
     return _allSources;
@@ -24,9 +40,12 @@ export const StarterModelsResultItem = memo(({ result }: Props) => {
 
   const onClick = useCallback(() => {
     for (const { config, source } of allSources) {
+      if (modelList.some((mc) => config.base === mc.base && config.name === mc.name && config.type === mc.type)) {
+        continue;
+      }
       installModel({ config, source });
     }
-  }, [allSources, installModel]);
+  }, [modelList, allSources, installModel]);
 
   return (
     <Flex alignItems="center" justifyContent="space-between" w="100%" gap={3}>

invokeai/frontend/web/src/features/modelManagerV2/subpanels/AddModelPanel/StarterModels/StarterModelsForm.tsx

@@ -1,17 +1,31 @@
 import { Flex } from '@invoke-ai/ui-library';
+import { EMPTY_ARRAY } from 'app/store/constants';
 import { FetchingModelsLoader } from 'features/modelManagerV2/subpanels/ModelManagerPanel/FetchingModelsLoader';
-import { memo } from 'react';
-import { useGetStarterModelsQuery } from 'services/api/endpoints/models';
+import { memo, useMemo } from 'react';
+import {
+  modelConfigsAdapterSelectors,
+  useGetModelConfigsQuery,
+  useGetStarterModelsQuery,
+} from 'services/api/endpoints/models';
 
 import { StarterModelsResults } from './StarterModelsResults';
 
 export const StarterModelsForm = memo(() => {
   const { isLoading, data } = useGetStarterModelsQuery();
+  const { data: modelListRes } = useGetModelConfigsQuery();
+
+  const modelList = useMemo(() => {
+    if (!modelListRes) {
+      return EMPTY_ARRAY;
+    }
+
+    return modelConfigsAdapterSelectors.selectAll(modelListRes);
+  }, [modelListRes]);
 
   return (
     <Flex flexDir="column" height="100%" gap={3}>
       {isLoading && <FetchingModelsLoader loadingMessage="Loading Embeddings..." />}
-      {data && <StarterModelsResults results={data} />}
+      {data && <StarterModelsResults results={data} modelList={modelList} />}
     </Flex>
   );
 });

invokeai/frontend/web/src/features/modelManagerV2/subpanels/AddModelPanel/StarterModels/StarterModelsResults.tsx

@@ -5,14 +5,16 @@ import { memo, useCallback, useMemo, useState } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiXBold } from 'react-icons/pi';
 import type { GetStarterModelsResponse } from 'services/api/endpoints/models';
+import type { AnyModelConfig } from 'services/api/types';
 
 import { StarterModelsResultItem } from './StartModelsResultItem';
 
 type StarterModelsResultsProps = {
   results: NonNullable<GetStarterModelsResponse>;
+  modelList: AnyModelConfig[];
 };
 
-export const StarterModelsResults = memo(({ results }: StarterModelsResultsProps) => {
+export const StarterModelsResults = memo(({ results, modelList }: StarterModelsResultsProps) => {
   const { t } = useTranslation();
   const [searchTerm, setSearchTerm] = useState('');
 
@@ -72,7 +74,7 @@ export const StarterModelsResults = memo(({ results }: StarterModelsResultsProps
         <ScrollableContent>
           <Flex flexDir="column" gap={3}>
             {filteredResults.map((result) => (
-              <StarterModelsResultItem key={result.source} result={result} />
+              <StarterModelsResultItem key={result.source} result={result} modelList={modelList} />
             ))}
           </Flex>
         </ScrollableContent>

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

@@ -13,6 +13,7 @@ const BASE_COLOR_MAP: Record<BaseModelType, string> = {
   'sd-2': 'teal',
   sdxl: 'invokeBlue',
   'sdxl-refiner': 'invokeBlue',
+  flux: 'gold',
 };
 
 const ModelBaseBadge = ({ base }: Props) => {

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

@@ -13,6 +13,9 @@ const FORMAT_NAME_MAP: Record<AnyModelConfig['format'], string> = {
   invokeai: 'internal',
   embedding_file: 'embedding',
   embedding_folder: 'embedding',
+  t5_encoder: 't5_encoder',
+  bnb_quantized_int8b: 'bnb_quantized_int8b',
+  bnb_quantized_nf4b: 'quantized',
 };
 
 const FORMAT_COLOR_MAP: Record<AnyModelConfig['format'], string> = {
@@ -22,6 +25,9 @@ const FORMAT_COLOR_MAP: Record<AnyModelConfig['format'], string> = {
   invokeai: 'base',
   embedding_file: 'base',
   embedding_folder: 'base',
+  t5_encoder: 'base',
+  bnb_quantized_int8b: 'base',
+  bnb_quantized_nf4b: 'base',
 };
 
 const ModelFormatBadge = ({ format }: Props) => {

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

@@ -5,6 +5,7 @@ import type { FilterableModelType } from 'features/modelManagerV2/store/modelMan
 import { memo, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import {
+  useClipEmbedModels,
   useControlNetModels,
   useEmbeddingModels,
   useIPAdapterModels,
@@ -13,6 +14,7 @@ import {
   useRefinerModels,
   useSpandrelImageToImageModels,
   useT2IAdapterModels,
+  useT5EncoderModels,
   useVAEModels,
 } from 'services/api/hooks/modelsByType';
 import type { AnyModelConfig } from 'services/api/types';
@@ -73,6 +75,18 @@ const ModelList = () => {
     [vaeModels, searchTerm, filteredModelType]
   );
 
+  const [t5EncoderModels, { isLoading: isLoadingT5EncoderModels }] = useT5EncoderModels();
+  const filteredT5EncoderModels = useMemo(
+    () => modelsFilter(t5EncoderModels, searchTerm, filteredModelType),
+    [t5EncoderModels, searchTerm, filteredModelType]
+  );
+
+  const [clipEmbedModels, { isLoading: isLoadingClipEmbedModels }] = useClipEmbedModels();
+  const filteredClipEmbedModels = useMemo(
+    () => modelsFilter(clipEmbedModels, searchTerm, filteredModelType),
+    [clipEmbedModels, searchTerm, filteredModelType]
+  );
+
   const [spandrelImageToImageModels, { isLoading: isLoadingSpandrelImageToImageModels }] =
     useSpandrelImageToImageModels();
   const filteredSpandrelImageToImageModels = useMemo(
@@ -90,7 +104,9 @@ const ModelList = () => {
       filteredT2IAdapterModels.length +
       filteredIPAdapterModels.length +
       filteredVAEModels.length +
-      filteredSpandrelImageToImageModels.length
+      filteredSpandrelImageToImageModels.length +
+      t5EncoderModels.length +
+      clipEmbedModels.length
     );
   }, [
     filteredControlNetModels.length,
@@ -102,6 +118,8 @@ const ModelList = () => {
     filteredT2IAdapterModels.length,
     filteredVAEModels.length,
     filteredSpandrelImageToImageModels.length,
+    t5EncoderModels.length,
+    clipEmbedModels.length,
   ]);
 
   return (
@@ -154,13 +172,23 @@ const ModelList = () => {
         {!isLoadingT2IAdapterModels && filteredT2IAdapterModels.length > 0 && (
           <ModelListWrapper title={t('common.t2iAdapter')} modelList={filteredT2IAdapterModels} key="t2i-adapters" />
         )}
+        {/* T5 Encoders List */}
+        {isLoadingT5EncoderModels && <FetchingModelsLoader loadingMessage="Loading T5 Encoder Models..." />}
+        {!isLoadingT5EncoderModels && filteredT5EncoderModels.length > 0 && (
+          <ModelListWrapper title={t('modelManager.t5Encoder')} modelList={filteredT5EncoderModels} key="t5-encoder" />
+        )}
+        {/* Clip Embed List */}
+        {isLoadingClipEmbedModels && <FetchingModelsLoader loadingMessage="Loading Clip Embed Models..." />}
+        {!isLoadingClipEmbedModels && filteredClipEmbedModels.length > 0 && (
+          <ModelListWrapper title={t('modelManager.clipEmbed')} modelList={filteredClipEmbedModels} key="clip-embed" />
+        )}
         {/* Spandrel Image to Image List */}
         {isLoadingSpandrelImageToImageModels && (
           <FetchingModelsLoader loadingMessage="Loading Image-to-Image Models..." />
         )}
         {!isLoadingSpandrelImageToImageModels && filteredSpandrelImageToImageModels.length > 0 && (
           <ModelListWrapper
-            title="Image-to-Image"
+            title={t('modelManager.spandrelImageToImage')}
             modelList={filteredSpandrelImageToImageModels}
             key="spandrel-image-to-image"
           />

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

@@ -19,9 +19,10 @@ export const ModelTypeFilter = memo(() => {
       controlnet: 'ControlNet',
       vae: 'VAE',
       t2i_adapter: t('common.t2iAdapter'),
+      t5_encoder: t('modelManager.t5Encoder'),
+      clip_embed: t('modelManager.clipEmbed'),
       ip_adapter: t('common.ipAdapter'),
-      clip_vision: 'Clip Vision',
-      spandrel_image_to_image: 'Image-to-Image',
+      spandrel_image_to_image: t('modelManager.spandrelImageToImage'),
     }),
     [t]
   );

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

@@ -6,6 +6,8 @@ import {
   isBoardFieldInputTemplate,
   isBooleanFieldInputInstance,
   isBooleanFieldInputTemplate,
+  isCLIPEmbedModelFieldInputInstance,
+  isCLIPEmbedModelFieldInputTemplate,
   isColorFieldInputInstance,
   isColorFieldInputTemplate,
   isControlNetModelFieldInputInstance,
@@ -14,6 +16,10 @@ import {
   isEnumFieldInputTemplate,
   isFloatFieldInputInstance,
   isFloatFieldInputTemplate,
+  isFluxMainModelFieldInputInstance,
+  isFluxMainModelFieldInputTemplate,
+  isFluxVAEModelFieldInputInstance,
+  isFluxVAEModelFieldInputTemplate,
   isImageFieldInputInstance,
   isImageFieldInputTemplate,
   isIntegerFieldInputInstance,
@@ -38,6 +44,8 @@ import {
   isStringFieldInputTemplate,
   isT2IAdapterModelFieldInputInstance,
   isT2IAdapterModelFieldInputTemplate,
+  isT5EncoderModelFieldInputInstance,
+  isT5EncoderModelFieldInputTemplate,
   isVAEModelFieldInputInstance,
   isVAEModelFieldInputTemplate,
 } from 'features/nodes/types/field';
@@ -45,9 +53,12 @@ import { memo } from 'react';
 
 import BoardFieldInputComponent from './inputs/BoardFieldInputComponent';
 import BooleanFieldInputComponent from './inputs/BooleanFieldInputComponent';
+import CLIPEmbedModelFieldInputComponent from './inputs/CLIPEmbedModelFieldInputComponent';
 import ColorFieldInputComponent from './inputs/ColorFieldInputComponent';
 import ControlNetModelFieldInputComponent from './inputs/ControlNetModelFieldInputComponent';
 import EnumFieldInputComponent from './inputs/EnumFieldInputComponent';
+import FluxMainModelFieldInputComponent from './inputs/FluxMainModelFieldInputComponent';
+import FluxVAEModelFieldInputComponent from './inputs/FluxVAEModelFieldInputComponent';
 import ImageFieldInputComponent from './inputs/ImageFieldInputComponent';
 import IPAdapterModelFieldInputComponent from './inputs/IPAdapterModelFieldInputComponent';
 import LoRAModelFieldInputComponent from './inputs/LoRAModelFieldInputComponent';
@@ -59,6 +70,7 @@ import SDXLMainModelFieldInputComponent from './inputs/SDXLMainModelFieldInputCo
 import SpandrelImageToImageModelFieldInputComponent from './inputs/SpandrelImageToImageModelFieldInputComponent';
 import StringFieldInputComponent from './inputs/StringFieldInputComponent';
 import T2IAdapterModelFieldInputComponent from './inputs/T2IAdapterModelFieldInputComponent';
+import T5EncoderModelFieldInputComponent from './inputs/T5EncoderModelFieldInputComponent';
 import VAEModelFieldInputComponent from './inputs/VAEModelFieldInputComponent';
 
 type InputFieldProps = {
@@ -113,6 +125,17 @@ const InputFieldRenderer = ({ nodeId, fieldName }: InputFieldProps) => {
     return <VAEModelFieldInputComponent nodeId={nodeId} field={fieldInstance} fieldTemplate={fieldTemplate} />;
   }
 
+  if (isT5EncoderModelFieldInputInstance(fieldInstance) && isT5EncoderModelFieldInputTemplate(fieldTemplate)) {
+    return <T5EncoderModelFieldInputComponent nodeId={nodeId} field={fieldInstance} fieldTemplate={fieldTemplate} />;
+  }
+  if (isCLIPEmbedModelFieldInputInstance(fieldInstance) && isCLIPEmbedModelFieldInputTemplate(fieldTemplate)) {
+    return <CLIPEmbedModelFieldInputComponent nodeId={nodeId} field={fieldInstance} fieldTemplate={fieldTemplate} />;
+  }
+
+  if (isFluxVAEModelFieldInputInstance(fieldInstance) && isFluxVAEModelFieldInputTemplate(fieldTemplate)) {
+    return <FluxVAEModelFieldInputComponent nodeId={nodeId} field={fieldInstance} fieldTemplate={fieldTemplate} />;
+  }
+
   if (isLoRAModelFieldInputInstance(fieldInstance) && isLoRAModelFieldInputTemplate(fieldTemplate)) {
     return <LoRAModelFieldInputComponent nodeId={nodeId} field={fieldInstance} fieldTemplate={fieldTemplate} />;
   }
@@ -145,6 +168,9 @@ 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 (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

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

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

@@ -0,0 +1,55 @@
+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 { FluxMainModelFieldInputInstance, FluxMainModelFieldInputTemplate } from 'features/nodes/types/field';
+import { memo, useCallback } from 'react';
+import { useFluxModels } from 'services/api/hooks/modelsByType';
+import type { MainModelConfig } from 'services/api/types';
+
+import type { FieldComponentProps } from './types';
+
+type Props = FieldComponentProps<FluxMainModelFieldInputInstance, FluxMainModelFieldInputTemplate>;
+
+const FluxMainModelFieldInputComponent = (props: Props) => {
+  const { nodeId, field } = props;
+  const dispatch = useAppDispatch();
+  const [modelConfigs, { isLoading }] = useFluxModels();
+  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}>
+        <Combobox
+          value={value}
+          placeholder={placeholder}
+          options={options}
+          onChange={onChange}
+          noOptionsMessage={noOptionsMessage}
+        />
+      </FormControl>
+    </Flex>
+  );
+};
+
+export default memo(FluxMainModelFieldInputComponent);

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

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

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

@@ -0,0 +1,60 @@
+import { Combobox, Flex, FormControl, Tooltip } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useGroupedModelCombobox } from 'common/hooks/useGroupedModelCombobox';
+import { fieldT5EncoderValueChanged } from 'features/nodes/store/nodesSlice';
+import type { T5EncoderModelFieldInputInstance, T5EncoderModelFieldInputTemplate } from 'features/nodes/types/field';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { useT5EncoderModels } from 'services/api/hooks/modelsByType';
+import type { T5EncoderBnbQuantizedLlmInt8bModelConfig, T5EncoderModelConfig } from 'services/api/types';
+
+import type { FieldComponentProps } from './types';
+
+type Props = FieldComponentProps<T5EncoderModelFieldInputInstance, T5EncoderModelFieldInputTemplate>;
+
+const T5EncoderModelFieldInputComponent = (props: Props) => {
+  const { nodeId, field } = props;
+  const { t } = useTranslation();
+  const disabledTabs = useAppSelector((s) => s.config.disabledTabs);
+  const dispatch = useAppDispatch();
+  const [modelConfigs, { isLoading }] = useT5EncoderModels();
+  const _onChange = useCallback(
+    (value: T5EncoderBnbQuantizedLlmInt8bModelConfig | T5EncoderModelConfig | null) => {
+      if (!value) {
+        return;
+      }
+      dispatch(
+        fieldT5EncoderValueChanged({
+          nodeId,
+          fieldName: field.name,
+          value,
+        })
+      );
+    },
+    [dispatch, field.name, nodeId]
+  );
+  const { options, value, onChange, placeholder, noOptionsMessage } = useGroupedModelCombobox({
+    modelConfigs,
+    onChange: _onChange,
+    isLoading,
+    selectedModel: field.value,
+  });
+
+  return (
+    <Flex w="full" alignItems="center" gap={2}>
+      <Tooltip label={!disabledTabs.includes('models') && t('modelManager.starterModelsInModelManager')}>
+        <FormControl className="nowheel nodrag" isDisabled={!options.length} isInvalid={!value}>
+          <Combobox
+            value={value}
+            placeholder={placeholder}
+            options={options}
+            onChange={onChange}
+            noOptionsMessage={noOptionsMessage}
+          />
+        </FormControl>
+      </Tooltip>
+    </Flex>
+  );
+};
+
+export default memo(T5EncoderModelFieldInputComponent);

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

@@ -6,11 +6,13 @@ import { SHARED_NODE_PROPERTIES } from 'features/nodes/types/constants';
 import type {
   BoardFieldValue,
   BooleanFieldValue,
+  CLIPEmbedModelFieldValue,
   ColorFieldValue,
   ControlNetModelFieldValue,
   EnumFieldValue,
   FieldValue,
   FloatFieldValue,
+  FluxVAEModelFieldValue,
   ImageFieldValue,
   IntegerFieldValue,
   IPAdapterModelFieldValue,
@@ -23,15 +25,18 @@ import type {
   StatefulFieldValue,
   StringFieldValue,
   T2IAdapterModelFieldValue,
+  T5EncoderModelFieldValue,
   VAEModelFieldValue,
 } from 'features/nodes/types/field';
 import {
   zBoardFieldValue,
   zBooleanFieldValue,
+  zCLIPEmbedModelFieldValue,
   zColorFieldValue,
   zControlNetModelFieldValue,
   zEnumFieldValue,
   zFloatFieldValue,
+  zFluxVAEModelFieldValue,
   zImageFieldValue,
   zIntegerFieldValue,
   zIPAdapterModelFieldValue,
@@ -44,6 +49,7 @@ import {
   zStatefulFieldValue,
   zStringFieldValue,
   zT2IAdapterModelFieldValue,
+  zT5EncoderModelFieldValue,
   zVAEModelFieldValue,
 } from 'features/nodes/types/field';
 import type { AnyNode, InvocationNodeEdge } from 'features/nodes/types/invocation';
@@ -341,6 +347,15 @@ export const nodesSlice = createSlice({
     ) => {
       fieldValueReducer(state, action, zSpandrelImageToImageModelFieldValue);
     },
+    fieldT5EncoderValueChanged: (state, action: FieldValueAction<T5EncoderModelFieldValue>) => {
+      fieldValueReducer(state, action, zT5EncoderModelFieldValue);
+    },
+    fieldCLIPEmbedValueChanged: (state, action: FieldValueAction<CLIPEmbedModelFieldValue>) => {
+      fieldValueReducer(state, action, zCLIPEmbedModelFieldValue);
+    },
+    fieldFluxVAEModelValueChanged: (state, action: FieldValueAction<FluxVAEModelFieldValue>) => {
+      fieldValueReducer(state, action, zFluxVAEModelFieldValue);
+    },
     fieldEnumModelValueChanged: (state, action: FieldValueAction<EnumFieldValue>) => {
       fieldValueReducer(state, action, zEnumFieldValue);
     },
@@ -402,6 +417,9 @@ export const {
   fieldSchedulerValueChanged,
   fieldStringValueChanged,
   fieldVaeModelValueChanged,
+  fieldT5EncoderValueChanged,
+  fieldCLIPEmbedValueChanged,
+  fieldFluxVAEModelValueChanged,
   nodeEditorReset,
   nodeIsIntermediateChanged,
   nodeIsOpenChanged,
@@ -514,6 +532,9 @@ export const isAnyNodeOrEdgeMutation = isAnyOf(
   fieldSchedulerValueChanged,
   fieldStringValueChanged,
   fieldVaeModelValueChanged,
+  fieldT5EncoderValueChanged,
+  fieldCLIPEmbedValueChanged,
+  fieldFluxVAEModelValueChanged,
   nodesChanged,
   nodeIsIntermediateChanged,
   nodeIsOpenChanged,

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

@@ -61,7 +61,7 @@ export type SchedulerField = z.infer<typeof zSchedulerField>;
 // #endregion
 
 // #region Model-related schemas
-const zBaseModel = z.enum(['any', 'sd-1', 'sd-2', 'sdxl', 'sdxl-refiner']);
+const zBaseModel = z.enum(['any', 'sd-1', 'sd-2', 'sdxl', 'sdxl-refiner', 'flux']);
 const zModelType = z.enum([
   'main',
   'vae',
@@ -73,9 +73,12 @@ const zModelType = z.enum([
   'onnx',
   'clip_vision',
   'spandrel_image_to_image',
+  't5_encoder',
+  'clip_embed',
 ]);
 const zSubModelType = z.enum([
   'unet',
+  'transformer',
   'text_encoder',
   'text_encoder_2',
   'tokenizer',

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

@@ -31,6 +31,7 @@ export const MODEL_TYPES = [
   'ControlNetModelField',
   'LoRAModelField',
   'MainModelField',
+  'FluxMainModelField',
   'SDXLMainModelField',
   'SDXLRefinerModelField',
   'VaeModelField',
@@ -38,6 +39,7 @@ export const MODEL_TYPES = [
   'VAEField',
   'CLIPField',
   'T2IAdapterModelField',
+  'T5EncoderField',
   'SpandrelImageToImageModelField',
 ];
 
@@ -50,6 +52,7 @@ export const FIELD_COLORS: { [key: string]: string } = {
   CLIPField: 'green.500',
   ColorField: 'pink.300',
   ConditioningField: 'cyan.500',
+  FluxConditioningField: 'cyan.500',
   ControlField: 'teal.500',
   ControlNetModelField: 'teal.500',
   EnumField: 'blue.500',
@@ -61,6 +64,7 @@ export const FIELD_COLORS: { [key: string]: string } = {
   LatentsField: 'pink.500',
   LoRAModelField: 'teal.500',
   MainModelField: 'teal.500',
+  FluxMainModelField: 'teal.500',
   SDXLMainModelField: 'teal.500',
   SDXLRefinerModelField: 'teal.500',
   SpandrelImageToImageModelField: 'teal.500',
@@ -68,6 +72,8 @@ export const FIELD_COLORS: { [key: string]: string } = {
   T2IAdapterField: 'teal.500',
   T2IAdapterModelField: 'teal.500',
   UNetField: 'red.500',
+  T5EncoderField: 'green.500',
+  TransformerField: 'red.500',
   VAEField: 'blue.500',
   VAEModelField: '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 zFluxMainModelFieldType = zFieldTypeBase.extend({
+  name: z.literal('FluxMainModelField'),
+  originalType: zStatelessFieldType.optional(),
+});
 const zSDXLRefinerModelFieldType = zFieldTypeBase.extend({
   name: z.literal('SDXLRefinerModelField'),
   originalType: zStatelessFieldType.optional(),
@@ -143,6 +147,18 @@ const zSpandrelImageToImageModelFieldType = zFieldTypeBase.extend({
   name: z.literal('SpandrelImageToImageModelField'),
   originalType: zStatelessFieldType.optional(),
 });
+const zT5EncoderModelFieldType = zFieldTypeBase.extend({
+  name: z.literal('T5EncoderModelField'),
+  originalType: zStatelessFieldType.optional(),
+});
+const zCLIPEmbedModelFieldType = zFieldTypeBase.extend({
+  name: z.literal('CLIPEmbedModelField'),
+  originalType: zStatelessFieldType.optional(),
+});
+const zFluxVAEModelFieldType = zFieldTypeBase.extend({
+  name: z.literal('FluxVAEModelField'),
+  originalType: zStatelessFieldType.optional(),
+});
 const zSchedulerFieldType = zFieldTypeBase.extend({
   name: z.literal('SchedulerField'),
   originalType: zStatelessFieldType.optional(),
@@ -158,6 +174,7 @@ const zStatefulFieldType = z.union([
   zModelIdentifierFieldType,
   zMainModelFieldType,
   zSDXLMainModelFieldType,
+  zFluxMainModelFieldType,
   zSDXLRefinerModelFieldType,
   zVAEModelFieldType,
   zLoRAModelFieldType,
@@ -165,6 +182,9 @@ const zStatefulFieldType = z.union([
   zIPAdapterModelFieldType,
   zT2IAdapterModelFieldType,
   zSpandrelImageToImageModelFieldType,
+  zT5EncoderModelFieldType,
+  zCLIPEmbedModelFieldType,
+  zFluxVAEModelFieldType,
   zColorFieldType,
   zSchedulerFieldType,
 ]);
@@ -447,6 +467,29 @@ export const isSDXLMainModelFieldInputTemplate = (val: unknown): val is SDXLMain
   zSDXLMainModelFieldInputTemplate.safeParse(val).success;
 // #endregion
 
+// #region FluxMainModelField
+
+const zFluxMainModelFieldValue = zMainModelFieldValue; // TODO: Narrow to SDXL models only.
+const zFluxMainModelFieldInputInstance = zFieldInputInstanceBase.extend({
+  value: zFluxMainModelFieldValue,
+});
+const zFluxMainModelFieldInputTemplate = zFieldInputTemplateBase.extend({
+  type: zFluxMainModelFieldType,
+  originalType: zFieldType.optional(),
+  default: zFluxMainModelFieldValue,
+});
+const zFluxMainModelFieldOutputTemplate = zFieldOutputTemplateBase.extend({
+  type: zFluxMainModelFieldType,
+});
+export type FluxMainModelFieldInputInstance = z.infer<typeof zFluxMainModelFieldInputInstance>;
+export type FluxMainModelFieldInputTemplate = z.infer<typeof zFluxMainModelFieldInputTemplate>;
+export const isFluxMainModelFieldInputInstance = (val: unknown): val is FluxMainModelFieldInputInstance =>
+  zFluxMainModelFieldInputInstance.safeParse(val).success;
+export const isFluxMainModelFieldInputTemplate = (val: unknown): val is FluxMainModelFieldInputTemplate =>
+  zFluxMainModelFieldInputTemplate.safeParse(val).success;
+
+// #endregion
+
 // #region SDXLRefinerModelField
 
 /** @alias */ // tells knip to ignore this duplicate export
@@ -613,6 +656,75 @@ export const isSpandrelImageToImageModelFieldInputTemplate = (
   zSpandrelImageToImageModelFieldInputTemplate.safeParse(val).success;
 // #endregion
 
+// #region T5EncoderModelField
+
+export const zT5EncoderModelFieldValue = zModelIdentifierField.optional();
+const zT5EncoderModelFieldInputInstance = zFieldInputInstanceBase.extend({
+  value: zT5EncoderModelFieldValue,
+});
+const zT5EncoderModelFieldInputTemplate = zFieldInputTemplateBase.extend({
+  type: zT5EncoderModelFieldType,
+  originalType: zFieldType.optional(),
+  default: zT5EncoderModelFieldValue,
+});
+
+export type T5EncoderModelFieldValue = z.infer<typeof zT5EncoderModelFieldValue>;
+
+export type T5EncoderModelFieldInputInstance = z.infer<typeof zT5EncoderModelFieldInputInstance>;
+export type T5EncoderModelFieldInputTemplate = z.infer<typeof zT5EncoderModelFieldInputTemplate>;
+export const isT5EncoderModelFieldInputInstance = (val: unknown): val is T5EncoderModelFieldInputInstance =>
+  zT5EncoderModelFieldInputInstance.safeParse(val).success;
+export const isT5EncoderModelFieldInputTemplate = (val: unknown): val is T5EncoderModelFieldInputTemplate =>
+  zT5EncoderModelFieldInputTemplate.safeParse(val).success;
+
+// #endregion
+
+// #region FluxVAEModelField
+
+export const zFluxVAEModelFieldValue = zModelIdentifierField.optional();
+const zFluxVAEModelFieldInputInstance = zFieldInputInstanceBase.extend({
+  value: zFluxVAEModelFieldValue,
+});
+const zFluxVAEModelFieldInputTemplate = zFieldInputTemplateBase.extend({
+  type: zFluxVAEModelFieldType,
+  originalType: zFieldType.optional(),
+  default: zFluxVAEModelFieldValue,
+});
+
+export type FluxVAEModelFieldValue = z.infer<typeof zFluxVAEModelFieldValue>;
+
+export type FluxVAEModelFieldInputInstance = z.infer<typeof zFluxVAEModelFieldInputInstance>;
+export type FluxVAEModelFieldInputTemplate = z.infer<typeof zFluxVAEModelFieldInputTemplate>;
+export const isFluxVAEModelFieldInputInstance = (val: unknown): val is FluxVAEModelFieldInputInstance =>
+  zFluxVAEModelFieldInputInstance.safeParse(val).success;
+export const isFluxVAEModelFieldInputTemplate = (val: unknown): val is FluxVAEModelFieldInputTemplate =>
+  zFluxVAEModelFieldInputTemplate.safeParse(val).success;
+
+// #endregion
+
+// #region CLIPEmbedModelField
+
+export const zCLIPEmbedModelFieldValue = zModelIdentifierField.optional();
+const zCLIPEmbedModelFieldInputInstance = zFieldInputInstanceBase.extend({
+  value: zCLIPEmbedModelFieldValue,
+});
+const zCLIPEmbedModelFieldInputTemplate = zFieldInputTemplateBase.extend({
+  type: zCLIPEmbedModelFieldType,
+  originalType: zFieldType.optional(),
+  default: zCLIPEmbedModelFieldValue,
+});
+
+export type CLIPEmbedModelFieldValue = z.infer<typeof zCLIPEmbedModelFieldValue>;
+
+export type CLIPEmbedModelFieldInputInstance = z.infer<typeof zCLIPEmbedModelFieldInputInstance>;
+export type CLIPEmbedModelFieldInputTemplate = z.infer<typeof zCLIPEmbedModelFieldInputTemplate>;
+export const isCLIPEmbedModelFieldInputInstance = (val: unknown): val is CLIPEmbedModelFieldInputInstance =>
+  zCLIPEmbedModelFieldInputInstance.safeParse(val).success;
+export const isCLIPEmbedModelFieldInputTemplate = (val: unknown): val is CLIPEmbedModelFieldInputTemplate =>
+  zCLIPEmbedModelFieldInputTemplate.safeParse(val).success;
+
+// #endregion
+
 // #region SchedulerField
 
 export const zSchedulerFieldValue = zSchedulerField.optional();
@@ -693,6 +805,7 @@ export const zStatefulFieldValue = z.union([
   zModelIdentifierFieldValue,
   zMainModelFieldValue,
   zSDXLMainModelFieldValue,
+  zFluxMainModelFieldValue,
   zSDXLRefinerModelFieldValue,
   zVAEModelFieldValue,
   zLoRAModelFieldValue,
@@ -700,6 +813,9 @@ export const zStatefulFieldValue = z.union([
   zIPAdapterModelFieldValue,
   zT2IAdapterModelFieldValue,
   zSpandrelImageToImageModelFieldValue,
+  zT5EncoderModelFieldValue,
+  zFluxVAEModelFieldValue,
+  zCLIPEmbedModelFieldValue,
   zColorFieldValue,
   zSchedulerFieldValue,
 ]);
@@ -720,6 +836,7 @@ const zStatefulFieldInputInstance = z.union([
   zBoardFieldInputInstance,
   zModelIdentifierFieldInputInstance,
   zMainModelFieldInputInstance,
+  zFluxMainModelFieldInputInstance,
   zSDXLMainModelFieldInputInstance,
   zSDXLRefinerModelFieldInputInstance,
   zVAEModelFieldInputInstance,
@@ -728,6 +845,9 @@ const zStatefulFieldInputInstance = z.union([
   zIPAdapterModelFieldInputInstance,
   zT2IAdapterModelFieldInputInstance,
   zSpandrelImageToImageModelFieldInputInstance,
+  zT5EncoderModelFieldInputInstance,
+  zFluxVAEModelFieldInputInstance,
+  zCLIPEmbedModelFieldInputInstance,
   zColorFieldInputInstance,
   zSchedulerFieldInputInstance,
 ]);
@@ -749,6 +869,7 @@ const zStatefulFieldInputTemplate = z.union([
   zBoardFieldInputTemplate,
   zModelIdentifierFieldInputTemplate,
   zMainModelFieldInputTemplate,
+  zFluxMainModelFieldInputTemplate,
   zSDXLMainModelFieldInputTemplate,
   zSDXLRefinerModelFieldInputTemplate,
   zVAEModelFieldInputTemplate,
@@ -757,6 +878,9 @@ const zStatefulFieldInputTemplate = z.union([
   zIPAdapterModelFieldInputTemplate,
   zT2IAdapterModelFieldInputTemplate,
   zSpandrelImageToImageModelFieldInputTemplate,
+  zT5EncoderModelFieldInputTemplate,
+  zFluxVAEModelFieldInputTemplate,
+  zCLIPEmbedModelFieldInputTemplate,
   zColorFieldInputTemplate,
   zSchedulerFieldInputTemplate,
   zStatelessFieldInputTemplate,
@@ -779,6 +903,7 @@ const zStatefulFieldOutputTemplate = z.union([
   zBoardFieldOutputTemplate,
   zModelIdentifierFieldOutputTemplate,
   zMainModelFieldOutputTemplate,
+  zFluxMainModelFieldOutputTemplate,
   zSDXLMainModelFieldOutputTemplate,
   zSDXLRefinerModelFieldOutputTemplate,
   zVAEModelFieldOutputTemplate,

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

@@ -15,12 +15,16 @@ const FIELD_VALUE_FALLBACK_MAP: Record<StatefulFieldType['name'], FieldValue> =
   MainModelField: undefined,
   SchedulerField: 'euler',
   SDXLMainModelField: undefined,
+  FluxMainModelField: undefined,
   SDXLRefinerModelField: undefined,
   StringField: '',
   T2IAdapterModelField: undefined,
   SpandrelImageToImageModelField: undefined,
   VAEModelField: undefined,
   ControlNetModelField: undefined,
+  T5EncoderModelField: undefined,
+  FluxVAEModelField: undefined,
+  CLIPEmbedModelField: undefined,
 };
 
 export const buildFieldInputInstance = (id: string, template: FieldInputTemplate): FieldInputInstance => {

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

@@ -2,12 +2,15 @@ import { FieldParseError } from 'features/nodes/types/error';
 import type {
   BoardFieldInputTemplate,
   BooleanFieldInputTemplate,
+  CLIPEmbedModelFieldInputTemplate,
   ColorFieldInputTemplate,
   ControlNetModelFieldInputTemplate,
   EnumFieldInputTemplate,
   FieldInputTemplate,
   FieldType,
   FloatFieldInputTemplate,
+  FluxMainModelFieldInputTemplate,
+  FluxVAEModelFieldInputTemplate,
   ImageFieldInputTemplate,
   IntegerFieldInputTemplate,
   IPAdapterModelFieldInputTemplate,
@@ -22,6 +25,7 @@ import type {
   StatelessFieldInputTemplate,
   StringFieldInputTemplate,
   T2IAdapterModelFieldInputTemplate,
+  T5EncoderModelFieldInputTemplate,
   VAEModelFieldInputTemplate,
 } from 'features/nodes/types/field';
 import { isStatefulFieldType } from 'features/nodes/types/field';
@@ -180,6 +184,20 @@ const buildSDXLMainModelFieldInputTemplate: FieldInputTemplateBuilder<SDXLMainMo
   return template;
 };
 
+const buildFluxMainModelFieldInputTemplate: FieldInputTemplateBuilder<FluxMainModelFieldInputTemplate> = ({
+  schemaObject,
+  baseField,
+  fieldType,
+}) => {
+  const template: FluxMainModelFieldInputTemplate = {
+    ...baseField,
+    type: fieldType,
+    default: schemaObject.default ?? undefined,
+  };
+
+  return template;
+};
+
 const buildRefinerModelFieldInputTemplate: FieldInputTemplateBuilder<SDXLRefinerModelFieldInputTemplate> = ({
   schemaObject,
   baseField,
@@ -208,6 +226,48 @@ const buildVAEModelFieldInputTemplate: FieldInputTemplateBuilder<VAEModelFieldIn
   return template;
 };
 
+const buildT5EncoderModelFieldInputTemplate: FieldInputTemplateBuilder<T5EncoderModelFieldInputTemplate> = ({
+  schemaObject,
+  baseField,
+  fieldType,
+}) => {
+  const template: T5EncoderModelFieldInputTemplate = {
+    ...baseField,
+    type: fieldType,
+    default: schemaObject.default ?? undefined,
+  };
+
+  return template;
+};
+
+const buildCLIPEmbedModelFieldInputTemplate: FieldInputTemplateBuilder<CLIPEmbedModelFieldInputTemplate> = ({
+  schemaObject,
+  baseField,
+  fieldType,
+}) => {
+  const template: CLIPEmbedModelFieldInputTemplate = {
+    ...baseField,
+    type: fieldType,
+    default: schemaObject.default ?? undefined,
+  };
+
+  return template;
+};
+
+const buildFluxVAEModelFieldInputTemplate: FieldInputTemplateBuilder<FluxVAEModelFieldInputTemplate> = ({
+  schemaObject,
+  baseField,
+  fieldType,
+}) => {
+  const template: FluxVAEModelFieldInputTemplate = {
+    ...baseField,
+    type: fieldType,
+    default: schemaObject.default ?? undefined,
+  };
+
+  return template;
+};
+
 const buildLoRAModelFieldInputTemplate: FieldInputTemplateBuilder<LoRAModelFieldInputTemplate> = ({
   schemaObject,
   baseField,
@@ -386,11 +446,15 @@ export const TEMPLATE_BUILDER_MAP: Record<StatefulFieldType['name'], FieldInputT
   MainModelField: buildMainModelFieldInputTemplate,
   SchedulerField: buildSchedulerFieldInputTemplate,
   SDXLMainModelField: buildSDXLMainModelFieldInputTemplate,
+  FluxMainModelField: buildFluxMainModelFieldInputTemplate,
   SDXLRefinerModelField: buildRefinerModelFieldInputTemplate,
   StringField: buildStringFieldInputTemplate,
   T2IAdapterModelField: buildT2IAdapterModelFieldInputTemplate,
   SpandrelImageToImageModelField: buildSpandrelImageToImageModelFieldInputTemplate,
   VAEModelField: buildVAEModelFieldInputTemplate,
+  T5EncoderModelField: buildT5EncoderModelFieldInputTemplate,
+  CLIPEmbedModelField: buildCLIPEmbedModelFieldInputTemplate,
+  FluxVAEModelField: buildFluxVAEModelFieldInputTemplate,
 } as const;
 
 export const buildFieldInputTemplate = (

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

@@ -29,6 +29,7 @@ const MODEL_FIELD_TYPES = [
   'ModelIdentifier',
   'MainModelField',
   'SDXLMainModelField',
+  'FluxMainModelField',
   'SDXLRefinerModelField',
   'VAEModelField',
   'LoRAModelField',

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

@@ -8,7 +8,7 @@ import { modelSelected } from 'features/parameters/store/actions';
 import { selectGenerationSlice } from 'features/parameters/store/generationSlice';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
-import { useMainModels } from 'services/api/hooks/modelsByType';
+import { useSDMainModels } from 'services/api/hooks/modelsByType';
 import type { MainModelConfig } from 'services/api/types';
 
 const selectModel = createMemoizedSelector(selectGenerationSlice, (generation) => generation.model);
@@ -17,7 +17,7 @@ const ParamMainModelSelect = () => {
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
   const selectedModel = useAppSelector(selectModel);
-  const [modelConfigs, { isLoading }] = useMainModels();
+  const [modelConfigs, { isLoading }] = useSDMainModels();
   const tooltipLabel = useMemo(() => {
     if (!modelConfigs.length || !selectedModel) {
       return;

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

@@ -9,6 +9,7 @@ export const MODEL_TYPE_MAP = {
   'sd-2': 'Stable Diffusion 2.x',
   sdxl: 'Stable Diffusion XL',
   'sdxl-refiner': 'Stable Diffusion XL Refiner',
+  flux: 'Flux',
 };
 
 /**
@@ -20,6 +21,7 @@ export const MODEL_TYPE_SHORT_MAP = {
   'sd-2': 'SD2.X',
   sdxl: 'SDXL',
   'sdxl-refiner': 'SDXLR',
+  flux: 'FLUX',
 };
 
 /**
@@ -46,6 +48,10 @@ export const CLIP_SKIP_MAP = {
     maxClip: 24,
     markers: [0, 1, 2, 3, 5, 10, 15, 20, 24],
   },
+  flux: {
+    maxClip: 0,
+    markers: [],
+  },
 };
 
 /**

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

@@ -48,7 +48,12 @@ export const UpscaleSettingsAccordion = memo(() => {
   });
 
   return (
-    <StandaloneAccordion label="Upscale" badges={badges} isOpen={isOpenAccordion} onToggle={onToggleAccordion}>
+    <StandaloneAccordion
+      label={t('upscaling.upscale')}
+      badges={badges}
+      isOpen={isOpenAccordion}
+      onToggle={onToggleAccordion}
+    >
       <Flex pt={4} px={4} w="full" h="full" flexDir="column" data-testid="upscale-settings-accordion">
         <Flex flexDir="column" gap={4}>
           <Flex gap={4}>

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

@@ -1,4 +1,4 @@
-import { Badge, Flex, IconButton, Text, Tooltip } from '@invoke-ai/ui-library';
+import { Badge, Flex, IconButton, Spacer, Text, Tooltip } from '@invoke-ai/ui-library';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { negativePromptChanged, positivePromptChanged } from 'features/controlLayers/store/controlLayersSlice';
 import { usePresetModifiedPrompts } from 'features/stylePresets/hooks/usePresetModifiedPrompts';
@@ -69,45 +69,40 @@ export const ActiveStylePreset = () => {
     );
   }
   return (
-    <Flex justifyContent="space-between" w="full" alignItems="center">
-      <Flex gap={2} alignItems="center">
-        <StylePresetImage imageWidth={25} presetImageUrl={activeStylePreset.image} />
-        <Flex flexDir="column">
-          <Badge colorScheme="invokeBlue" variant="subtle">
-            {activeStylePreset.name}
-          </Badge>
-        </Flex>
-      </Flex>
-      <Flex gap={1}>
-        <Tooltip label={t('stylePresets.toggleViewMode')}>
-          <IconButton
-            onClick={handleToggleViewMode}
-            variant="outline"
-            size="sm"
-            aria-label={t('stylePresets.toggleViewMode')}
-            colorScheme={viewMode ? 'invokeBlue' : 'base'}
-            icon={<PiEyeBold />}
-          />
-        </Tooltip>
-        <Tooltip label={t('stylePresets.flatten')}>
-          <IconButton
-            onClick={handleFlattenPrompts}
-            variant="outline"
-            size="sm"
-            aria-label={t('stylePresets.flatten')}
-            icon={<PiStackSimpleBold />}
-          />
-        </Tooltip>
-        <Tooltip label={t('stylePresets.clearTemplateSelection')}>
-          <IconButton
-            onClick={handleClearActiveStylePreset}
-            variant="outline"
-            size="sm"
-            aria-label={t('stylePresets.clearTemplateSelection')}
-            icon={<PiXBold />}
-          />
-        </Tooltip>
-      </Flex>
+    <Flex w="full" alignItems="center" gap={2} minW={0}>
+      <StylePresetImage imageWidth={25} presetImageUrl={activeStylePreset.image} />
+      <Badge colorScheme="invokeBlue" variant="subtle" justifySelf="flex-start">
+        {activeStylePreset.name}
+      </Badge>
+      <Spacer />
+      <Tooltip label={t('stylePresets.toggleViewMode')}>
+        <IconButton
+          onClick={handleToggleViewMode}
+          variant="outline"
+          size="sm"
+          aria-label={t('stylePresets.toggleViewMode')}
+          colorScheme={viewMode ? 'invokeBlue' : 'base'}
+          icon={<PiEyeBold />}
+        />
+      </Tooltip>
+      <Tooltip label={t('stylePresets.flatten')}>
+        <IconButton
+          onClick={handleFlattenPrompts}
+          variant="outline"
+          size="sm"
+          aria-label={t('stylePresets.flatten')}
+          icon={<PiStackSimpleBold />}
+        />
+      </Tooltip>
+      <Tooltip label={t('stylePresets.clearTemplateSelection')}>
+        <IconButton
+          onClick={handleClearActiveStylePreset}
+          variant="outline"
+          size="sm"
+          aria-label={t('stylePresets.clearTemplateSelection')}
+          icon={<PiXBold />}
+        />
+      </Tooltip>
     </Flex>
   );
 };

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

@@ -16,9 +16,9 @@ export const StylePresetExportButton = () => {
   const { t } = useTranslation();
   const { presetCount } = useListStylePresetsQuery(undefined, {
     selectFromResult: ({ data }) => {
-      const userPresets = data?.filter((preset) => preset.type === 'user') ?? EMPTY_ARRAY;
+      const presetsToExport = data?.filter((preset) => preset.type !== 'default') ?? EMPTY_ARRAY;
       return {
-        presetCount: userPresets.length,
+        presetCount: presetsToExport.length,
       };
     },
   });

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

@@ -30,8 +30,8 @@ export const StylePresetForm = ({
   updatingStylePresetId: string | null;
   formData: StylePresetFormData | null;
 }) => {
-  const [createStylePreset] = useCreateStylePresetMutation();
-  const [updateStylePreset] = useUpdateStylePresetMutation();
+  const [createStylePreset, { isLoading: isCreating }] = useCreateStylePresetMutation();
+  const [updateStylePreset, { isLoading: isUpdating }] = useUpdateStylePresetMutation();
   const { t } = useTranslation();
   const allowPrivateStylePresets = useAppSelector((s) => s.config.allowPrivateStylePresets);
 
@@ -93,8 +93,8 @@ export const StylePresetForm = ({
         </FormControl>
       </Flex>
 
-      <StylePresetPromptField label="Positive Prompt" control={control} name="positivePrompt" />
-      <StylePresetPromptField label="Negative Prompt" control={control} name="negativePrompt" />
+      <StylePresetPromptField label={t('stylePresets.positivePrompt')} control={control} name="positivePrompt" />
+      <StylePresetPromptField label={t('stylePresets.negativePrompt')} control={control} name="negativePrompt" />
       <Box>
         <Text variant="subtext">{t('stylePresets.promptTemplatesDesc1')}</Text>
         <Text variant="subtext">
@@ -109,7 +109,11 @@ export const StylePresetForm = ({
 
       <Flex justifyContent="space-between" alignItems="flex-end" gap={10}>
         {allowPrivateStylePresets ? <StylePresetTypeField control={control} name="type" /> : <Spacer />}
-        <Button onClick={handleSubmit(handleClickSave)} isDisabled={!formState.isValid}>
+        <Button
+          onClick={handleSubmit(handleClickSave)}
+          isDisabled={!formState.isValid}
+          isLoading={isCreating || isUpdating}
+        >
           {t('common.save')}
         </Button>
       </Flex>

invokeai/frontend/web/src/features/stylePresets/components/StylePresetForm/StylePresetModal.tsx

@@ -48,9 +48,13 @@ export const StylePresetModal = () => {
       } else {
         let file = null;
         if (data.imageUrl) {
-          const blob = await convertImageUrlToBlob(data.imageUrl);
-          if (blob) {
-            file = new File([blob], 'style_preset.png', { type: 'image/png' });
+          try {
+            const blob = await convertImageUrlToBlob(data.imageUrl);
+            if (blob) {
+              file = new File([blob], 'style_preset.png', { type: 'image/png' });
+            }
+          } catch (error) {
+            // do nothing
           }
         }
         setFormData({

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

@@ -21,6 +21,7 @@ const StylePresetImage = ({ presetImageUrl, imageWidth }: { presetImageUrl: stri
           />
         )
       }
+      p={2}
     >
       <Image
         src={presetImageUrl || ''}

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

@@ -77,7 +77,7 @@ export const StylePresetListItem = ({ preset }: { preset: StylePresetRecordWithI
 
   const handleDeletePreset = useCallback(async () => {
     try {
-      await deleteStylePreset(preset.id);
+      await deleteStylePreset(preset.id).unwrap();
       toast({
         status: 'success',
         title: t('stylePresets.templateDeleted'),
@@ -174,7 +174,8 @@ export const StylePresetListItem = ({ preset }: { preset: StylePresetRecordWithI
         onClose={onClose}
         title={t('stylePresets.deleteTemplate')}
         acceptCallback={handleDeletePreset}
-        acceptButtonText="Delete"
+        acceptButtonText={t('common.delete')}
+        cancelButtonText={t('common.cancel')}
       >
         <p>{t('stylePresets.deleteTemplate2')}</p>
       </ConfirmationAlertDialog>

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

@@ -29,14 +29,14 @@ export const StylePresetMenuTrigger = () => {
       py={2}
       px={3}
       borderRadius="base"
-      gap={1}
+      gap={2}
       role="button"
       _hover={_hover}
       transitionProperty="background-color"
       transitionDuration="normal"
+      w="full"
     >
       <ActiveStylePreset />
-
       <IconButton aria-label={t('stylePresets.viewList')} variant="ghost" icon={<PiCaretDownBold />} size="sm" />
     </Flex>
   );

invokeai/frontend/web/src/features/stylePresets/store/stylePresetSlice.ts

@@ -1,6 +1,7 @@
 import type { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice } from '@reduxjs/toolkit';
 import type { PersistConfig } from 'app/store/store';
+import { stylePresetsApi } from 'services/api/endpoints/stylePresets';
 
 import type { StylePresetState } from './types';
 
@@ -24,6 +25,26 @@ export const stylePresetSlice = createSlice({
       state.viewMode = action.payload;
     },
   },
+  extraReducers(builder) {
+    builder.addMatcher(stylePresetsApi.endpoints.deleteStylePreset.matchFulfilled, (state, action) => {
+      if (state.activeStylePresetId === null) {
+        return;
+      }
+      const deletedId = action.meta.arg.originalArgs;
+      if (state.activeStylePresetId === deletedId) {
+        state.activeStylePresetId = null;
+      }
+    });
+    builder.addMatcher(stylePresetsApi.endpoints.listStylePresets.matchFulfilled, (state, action) => {
+      if (state.activeStylePresetId === null) {
+        return;
+      }
+      const ids = action.payload.map((preset) => preset.id);
+      if (!ids.includes(state.activeStylePresetId)) {
+        state.activeStylePresetId = null;
+      }
+    });
+  },
 });
 
 export const { activeStylePresetIdChanged, searchTermChanged, viewModeChanged } = stylePresetSlice.actions;

invokeai/frontend/web/src/features/stylePresets/util/getViewModeChunks.test.ts

@@ -0,0 +1,53 @@
+import { getViewModeChunks } from 'features/stylePresets/util/getViewModeChunks';
+import { describe, expect, it } from 'vitest';
+
+describe('getViewModeChunks', () => {
+  it('should return empty strings when presetPrompt is not provided', () => {
+    const currentPrompt = 'current prompt';
+    const presetPrompt = undefined;
+    const result = getViewModeChunks(currentPrompt, presetPrompt);
+    expect(result).toEqual(['', currentPrompt, '']);
+  });
+
+  it('should return empty strings when presetPrompt is empty', () => {
+    const currentPrompt = 'current prompt';
+    const presetPrompt = '';
+    const result = getViewModeChunks(currentPrompt, presetPrompt);
+    expect(result).toEqual(['', currentPrompt, '']);
+  });
+
+  it('should append presetPrompt to currentPrompt when presetPrompt does not contain PRESET_PLACEHOLDER', () => {
+    const currentPrompt = 'current prompt';
+    const presetPrompt = 'preset prompt';
+    const result = getViewModeChunks(currentPrompt, presetPrompt);
+    expect(result).toEqual(['', `${currentPrompt} `, presetPrompt]);
+  });
+
+  it('should split presetPrompt into 3 parts when presetPrompt contains PRESET_PLACEHOLDER', () => {
+    const currentPrompt = 'current prompt';
+    const presetPrompt = 'before {prompt} after';
+    const result = getViewModeChunks(currentPrompt, presetPrompt);
+    expect(result).toEqual(['before ', currentPrompt, ' after']);
+  });
+
+  it('should split presetPrompt into 3 parts when presetPrompt contains multiple PRESET_PLACEHOLDER', () => {
+    const currentPrompt = 'current prompt';
+    const presetPrompt = 'before {prompt} middle {prompt} after';
+    const result = getViewModeChunks(currentPrompt, presetPrompt);
+    expect(result).toEqual(['before ', currentPrompt, ' middle {prompt} after']);
+  });
+
+  it('should handle the PRESET_PLACEHOLDER being at the start of the presetPrompt', () => {
+    const currentPrompt = 'current prompt';
+    const presetPrompt = '{prompt} after';
+    const result = getViewModeChunks(currentPrompt, presetPrompt);
+    expect(result).toEqual(['', currentPrompt, ' after']);
+  });
+
+  it('should handle the PRESET_PLACEHOLDER being at the end of the presetPrompt', () => {
+    const currentPrompt = 'current prompt';
+    const presetPrompt = 'before {prompt}';
+    const result = getViewModeChunks(currentPrompt, presetPrompt);
+    expect(result).toEqual(['before ', currentPrompt, '']);
+  });
+});

invokeai/frontend/web/src/features/stylePresets/util/getViewModeChunks.ts

@@ -0,0 +1,17 @@
+import { PRESET_PLACEHOLDER } from 'features/stylePresets/hooks/usePresetModifiedPrompts';
+
+export const getViewModeChunks = (currentPrompt: string, presetPrompt?: string): [string, string, string] => {
+  if (!presetPrompt || !presetPrompt.length) {
+    return ['', currentPrompt, ''];
+  }
+
+  // When preset prompt does not contain the placeholder, we append the preset to the current prompt
+  if (!presetPrompt.includes(PRESET_PLACEHOLDER)) {
+    return ['', `${currentPrompt} `, presetPrompt];
+  }
+
+  // Otherwise, we split the preset prompt into 3 parts: before, current, and after the placeholder
+  const [before, ...after] = presetPrompt.split(PRESET_PLACEHOLDER);
+
+  return [before || '', currentPrompt, after.join(PRESET_PLACEHOLDER) || ''];
+};

invokeai/frontend/web/src/features/stylePresets/util/getViewModeChunks.tsx

@@ -1,15 +0,0 @@
-import { PRESET_PLACEHOLDER } from 'features/stylePresets/hooks/usePresetModifiedPrompts';
-
-export const getViewModeChunks = (currentPrompt: string, presetPrompt?: string): [string, string, string] => {
-  if (!presetPrompt || !presetPrompt.length) {
-    return ['', currentPrompt, ''];
-  }
-
-  const [before, after] = presetPrompt.split(PRESET_PLACEHOLDER, 2);
-
-  if (!before || !after) {
-    return ['', `${currentPrompt} `, before || after || ''];
-  }
-
-  return [before ?? '', currentPrompt, after ?? ''];
-};

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

@@ -94,7 +94,7 @@ export const stylePresetsApi = api.injectEndpoints({
     }),
     exportStylePresets: build.query<string, void>({
       query: () => ({
-        url: buildStylePresetsUrl('/export'),
+        url: buildStylePresetsUrl('export'),
         responseHandler: (response) => response.text(),
       }),
       providesTags: ['FetchOnReconnect', { type: 'StylePreset', id: LIST_TAG }],