Empty out invokeai/backend/model_manager/__init__.py and use full absolute imports instead.

* [MM] add API routes for getting & setting MM cache sizes, and retrieving MM stats

* Update invokeai/app/api/routers/model_manager.py

Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>

* code cleanup after @ryand review

* Update invokeai/app/api/routers/model_manager.py

Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>

* fix merge conflicts; tested and working

---------

Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>

.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/contributing/MODEL_MANAGER.md

@@ -1468,7 +1468,7 @@ to load several different diffusers types:
 from pathlib import Path
 from typing import Optional
 
-from invokeai.backend.model_manager import (
+from invokeai.backend.model_manager.config import (
     AnyModel,
     BaseModelType,
     ModelFormat,

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/invocations/create_gradient_mask.py

@@ -19,8 +19,8 @@ from invokeai.app.invocations.fields import (
 from invokeai.app.invocations.image_to_latents import ImageToLatentsInvocation
 from invokeai.app.invocations.model import UNetField, VAEField
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager import LoadedModel
 from invokeai.backend.model_manager.config import MainConfigBase, ModelVariantType
+from invokeai.backend.model_manager.load.load_base import LoadedModel
 from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
 
 

invokeai/app/invocations/denoise_latents.py

@@ -37,7 +37,7 @@ from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
 from invokeai.backend.lora import LoRAModelRaw
-from invokeai.backend.model_manager import BaseModelType, ModelVariantType
+from invokeai.backend.model_manager.config import BaseModelType, ModelVariantType
 from invokeai.backend.model_patcher import ModelPatcher
 from invokeai.backend.stable_diffusion import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.denoise_context import DenoiseContext, DenoiseInputs
@@ -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.load.load_base 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/image_to_latents.py

@@ -23,7 +23,7 @@ from invokeai.app.invocations.fields import (
 from invokeai.app.invocations.model import VAEField
 from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager import LoadedModel
+from invokeai.backend.model_manager.load.load_base import LoadedModel
 from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
 from invokeai.backend.stable_diffusion.vae_tiling import patch_vae_tiling_params
 

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/invocations/sdxl.py

@@ -2,7 +2,7 @@ from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocati
 from invokeai.app.invocations.fields import FieldDescriptions, InputField, OutputField, UIType
 from invokeai.app.invocations.model import CLIPField, ModelIdentifierField, UNetField, VAEField
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager import SubModelType
+from invokeai.backend.model_manager.config import SubModelType
 
 
 @invocation_output("sdxl_model_loader_output")

invokeai/app/services/model_install/model_install_base.py

@@ -13,7 +13,7 @@ from invokeai.app.services.events.events_base import EventServiceBase
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.model_install.model_install_common import ModelInstallJob, ModelSource
 from invokeai.app.services.model_records import ModelRecordChanges, ModelRecordServiceBase
-from invokeai.backend.model_manager import AnyModelConfig
+from invokeai.backend.model_manager.config import AnyModelConfig
 
 
 class ModelInstallServiceBase(ABC):

invokeai/app/services/model_install/model_install_common.py

@@ -10,8 +10,7 @@ from typing_extensions import Annotated
 
 from invokeai.app.services.download import DownloadJob, MultiFileDownloadJob
 from invokeai.app.services.model_records import ModelRecordChanges
-from invokeai.backend.model_manager import AnyModelConfig, ModelRepoVariant
-from invokeai.backend.model_manager.config import ModelSourceType
+from invokeai.backend.model_manager.config import AnyModelConfig, ModelRepoVariant, ModelSourceType
 from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
 
 
@@ -103,7 +102,7 @@ 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_load/model_load_base.py

@@ -5,7 +5,7 @@ from abc import ABC, abstractmethod
 from pathlib import Path
 from typing import Callable, Optional
 
-from invokeai.backend.model_manager import AnyModel, AnyModelConfig, SubModelType
+from invokeai.backend.model_manager.config import AnyModel, AnyModelConfig, SubModelType
 from invokeai.backend.model_manager.load import LoadedModel, LoadedModelWithoutConfig
 from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
 

invokeai/app/services/model_load/model_load_default.py

@@ -11,7 +11,7 @@ from torch import load as torch_load
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.model_load.model_load_base import ModelLoadServiceBase
-from invokeai.backend.model_manager import AnyModel, AnyModelConfig, SubModelType
+from invokeai.backend.model_manager.config import AnyModel, AnyModelConfig, SubModelType
 from invokeai.backend.model_manager.load import (
     LoadedModel,
     LoadedModelWithoutConfig,

invokeai/app/services/model_manager/__init__.py

@@ -1,16 +1,10 @@
 """Initialization file for model manager service."""
 
 from invokeai.app.services.model_manager.model_manager_default import ModelManagerService, ModelManagerServiceBase
-from invokeai.backend.model_manager import AnyModel, AnyModelConfig, BaseModelType, ModelType, SubModelType
 from invokeai.backend.model_manager.load import LoadedModel
 
 __all__ = [
     "ModelManagerServiceBase",
     "ModelManagerService",
-    "AnyModel",
-    "AnyModelConfig",
-    "BaseModelType",
-    "ModelType",
-    "SubModelType",
     "LoadedModel",
 ]

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/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"
+    },
+    {
+      "nodeId": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
+      "fieldName": "prompt"
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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/lora.py

@@ -10,7 +10,7 @@ from safetensors.torch import load_file
 from typing_extensions import Self
 
 import invokeai.backend.util.logging as logger
-from invokeai.backend.model_manager import BaseModelType
+from invokeai.backend.model_manager.config import BaseModelType
 from invokeai.backend.raw_model import RawModel
 
 

invokeai/backend/model_manager/__init__.py

@@ -1,35 +0,0 @@
-"""Re-export frequently-used symbols from the Model Manager backend."""
-
-from invokeai.backend.model_manager.config import (
-    AnyModel,
-    AnyModelConfig,
-    BaseModelType,
-    InvalidModelConfigException,
-    ModelConfigFactory,
-    ModelFormat,
-    ModelRepoVariant,
-    ModelType,
-    ModelVariantType,
-    SchedulerPredictionType,
-    SubModelType,
-)
-from invokeai.backend.model_manager.load import LoadedModel
-from invokeai.backend.model_manager.probe import ModelProbe
-from invokeai.backend.model_manager.search import ModelSearch
-
-__all__ = [
-    "AnyModel",
-    "AnyModelConfig",
-    "BaseModelType",
-    "ModelRepoVariant",
-    "InvalidModelConfigException",
-    "LoadedModel",
-    "ModelConfigFactory",
-    "ModelFormat",
-    "ModelProbe",
-    "ModelSearch",
-    "ModelType",
-    "ModelVariantType",
-    "SchedulerPredictionType",
-    "SubModelType",
-]

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

@@ -6,13 +6,13 @@ from pathlib import Path
 from typing import Optional
 
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.model_manager import (
+from invokeai.backend.model_manager.config import (
     AnyModel,
     AnyModelConfig,
+    DiffusersConfigBase,
     InvalidModelConfigException,
     SubModelType,
 )
-from invokeai.backend.model_manager.config import DiffusersConfigBase
 from invokeai.backend.model_manager.load.load_base import LoadedModel, ModelLoaderBase
 from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase, ModelLockerBase
 from invokeai.backend.model_manager.load.model_util import calc_model_size_by_fs
@@ -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
@@ -27,7 +11,7 @@ from typing import Dict, List, Optional
 
 import torch
 
-from invokeai.backend.model_manager import AnyModel, SubModelType
+from invokeai.backend.model_manager.config import AnyModel, SubModelType
 from invokeai.backend.model_manager.load.memory_snapshot import MemorySnapshot, get_pretty_snapshot_diff
 from invokeai.backend.model_manager.load.model_cache.model_cache_base import (
     CacheRecord,
@@ -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_cache/model_locker.py

@@ -6,7 +6,7 @@ from typing import Dict, Optional
 
 import torch
 
-from invokeai.backend.model_manager import AnyModel
+from invokeai.backend.model_manager.config import AnyModel
 from invokeai.backend.model_manager.load.model_cache.model_cache_base import (
     CacheRecord,
     ModelCacheBase,

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

@@ -5,14 +5,15 @@ from typing import Optional
 
 from diffusers import ControlNetModel
 
-from invokeai.backend.model_manager import (
+from invokeai.backend.model_manager.config import (
     AnyModel,
     AnyModelConfig,
     BaseModelType,
+    ControlNetCheckpointConfig,
     ModelFormat,
     ModelType,
+    SubModelType,
 )
-from invokeai.backend.model_manager.config import ControlNetCheckpointConfig, SubModelType
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 

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

@@ -0,0 +1,232 @@
+# 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.config import (
+    AnyModel,
+    AnyModelConfig,
+    BaseModelType,
+    CheckpointConfigBase,
+    CLIPEmbedDiffusersConfig,
+    MainBnbQuantized4bCheckpointConfig,
+    MainCheckpointConfig,
+    ModelFormat,
+    ModelType,
+    SubModelType,
+    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.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)
+            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)
+            model.load_state_dict(sd, assign=True)
+        return model

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

@@ -8,16 +8,16 @@ from typing import Any, Optional
 from diffusers.configuration_utils import ConfigMixin
 from diffusers.models.modeling_utils import ModelMixin
 
-from invokeai.backend.model_manager import (
+from invokeai.backend.model_manager.config import (
     AnyModel,
     AnyModelConfig,
     BaseModelType,
+    DiffusersConfigBase,
     InvalidModelConfigException,
     ModelFormat,
     ModelType,
     SubModelType,
 )
-from invokeai.backend.model_manager.config import DiffusersConfigBase
 from invokeai.backend.model_manager.load.load_default import ModelLoader
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 
@@ -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/ip_adapter.py

@@ -7,7 +7,14 @@ from typing import Optional
 import torch
 
 from invokeai.backend.ip_adapter.ip_adapter import build_ip_adapter
-from invokeai.backend.model_manager import AnyModel, AnyModelConfig, BaseModelType, ModelFormat, ModelType, SubModelType
+from invokeai.backend.model_manager.config import (
+    AnyModel,
+    AnyModelConfig,
+    BaseModelType,
+    ModelFormat,
+    ModelType,
+    SubModelType,
+)
 from invokeai.backend.model_manager.load import ModelLoader, ModelLoaderRegistry
 from invokeai.backend.raw_model import RawModel
 

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

@@ -7,7 +7,7 @@ from typing import Optional
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.lora import LoRAModelRaw
-from invokeai.backend.model_manager import (
+from invokeai.backend.model_manager.config import (
     AnyModel,
     AnyModelConfig,
     BaseModelType,

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

@@ -5,7 +5,7 @@
 from pathlib import Path
 from typing import Optional
 
-from invokeai.backend.model_manager import (
+from invokeai.backend.model_manager.config import (
     AnyModel,
     AnyModelConfig,
     BaseModelType,

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

@@ -11,20 +11,18 @@ from diffusers import (
     StableDiffusionXLPipeline,
 )
 
-from invokeai.backend.model_manager import (
+from invokeai.backend.model_manager.config import (
     AnyModel,
     AnyModelConfig,
     BaseModelType,
+    CheckpointConfigBase,
+    DiffusersConfigBase,
+    MainCheckpointConfig,
     ModelFormat,
     ModelType,
     ModelVariantType,
     SubModelType,
 )
-from invokeai.backend.model_manager.config import (
-    CheckpointConfigBase,
-    DiffusersConfigBase,
-    MainCheckpointConfig,
-)
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 from invokeai.backend.util.silence_warnings import SilenceWarnings
@@ -36,8 +34,18 @@ 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_loaders/textual_inversion.py

@@ -4,7 +4,7 @@
 from pathlib import Path
 from typing import Optional
 
-from invokeai.backend.model_manager import (
+from invokeai.backend.model_manager.config import (
     AnyModel,
     AnyModelConfig,
     BaseModelType,

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

@@ -5,13 +5,15 @@ from typing import Optional
 
 from diffusers import AutoencoderKL
 
-from invokeai.backend.model_manager import (
+from invokeai.backend.model_manager.config import (
+    AnyModel,
     AnyModelConfig,
     BaseModelType,
     ModelFormat,
     ModelType,
+    SubModelType,
+    VAECheckpointConfig,
 )
-from invokeai.backend.model_manager.config import AnyModel, SubModelType, VAECheckpointConfig
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 

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/merge.py

@@ -17,8 +17,13 @@ from diffusers.utils import logging as dlogging
 
 from invokeai.app.services.model_install import ModelInstallServiceBase
 from invokeai.app.services.model_records.model_records_base import ModelRecordChanges
-from invokeai.backend.model_manager import AnyModelConfig, BaseModelType, ModelType, ModelVariantType
-from invokeai.backend.model_manager.config import MainDiffusersConfig
+from invokeai.backend.model_manager.config import (
+    AnyModelConfig,
+    BaseModelType,
+    MainDiffusersConfig,
+    ModelType,
+    ModelVariantType,
+)
 from invokeai.backend.util.devices import TorchDevice
 
 

invokeai/backend/model_manager/metadata/fetch/fetch_base.py

@@ -17,7 +17,7 @@ from typing import Optional
 from pydantic.networks import AnyHttpUrl
 from requests.sessions import Session
 
-from invokeai.backend.model_manager import ModelRepoVariant
+from invokeai.backend.model_manager.config import ModelRepoVariant
 from invokeai.backend.model_manager.metadata.metadata_base import (
     AnyModelRepoMetadata,
     AnyModelRepoMetadataValidator,

invokeai/backend/model_manager/metadata/metadata_base.py

@@ -23,7 +23,7 @@ from pydantic.networks import AnyHttpUrl
 from requests.sessions import Session
 from typing_extensions import Annotated
 
-from invokeai.backend.model_manager import ModelRepoVariant
+from invokeai.backend.model_manager.config import ModelRepoVariant
 from invokeai.backend.model_manager.util.select_hf_files import filter_files
 
 

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 +107,7 @@ class ModelProbe(object):
         "ControlNetModel": ModelType.ControlNet,
         "CLIPVisionModelWithProjection": ModelType.CLIPVision,
         "T2IAdapter": ModelType.T2IAdapter,
+        "CLIPModel": ModelType.CLIPEmbed,
     }
 
     @classmethod
@@ -161,7 +163,7 @@ 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 +178,10 @@ 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 +224,8 @@ 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.", "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
@@ -321,10 +324,27 @@ 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:
+                    # 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 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-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 +353,13 @@ 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 +442,15 @@ 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:
+            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 +470,8 @@ 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:
+            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 +514,7 @@ 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 +746,11 @@ class TextualInversionFolderProbe(FolderProbeBase):
         return TextualInversionCheckpointProbe(path).get_base_type()
 
 
+class T5EncoderFolderProbe(FolderProbeBase):
+    def get_format(self) -> ModelFormat:
+        return ModelFormat.T5Encoder
+
+
 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 +843,11 @@ 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 +878,10 @@ 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/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/model_patcher.py

@@ -14,7 +14,7 @@ from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokeniz
 
 from invokeai.app.shared.models import FreeUConfig
 from invokeai.backend.lora import LoRAModelRaw
-from invokeai.backend.model_manager import AnyModel
+from invokeai.backend.model_manager.config import AnyModel
 from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
 from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
 from invokeai.backend.stable_diffusion.extensions.lora import LoRAExt

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/stable_diffusion/extensions/t2i_adapter.py

@@ -8,7 +8,7 @@ from diffusers import T2IAdapter
 from PIL.Image import Image
 
 from invokeai.app.util.controlnet_utils import prepare_control_image
-from invokeai.backend.model_manager import BaseModelType
+from invokeai.backend.model_manager.config import BaseModelType
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningMode
 from invokeai.backend.stable_diffusion.extension_callback_type import ExtensionCallbackType
 from invokeai.backend.stable_diffusion.extensions.base import ExtensionBase, callback

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/test_utils.py

@@ -7,7 +7,8 @@ import torch
 
 from invokeai.app.services.model_manager import ModelManagerServiceBase
 from invokeai.app.services.model_records import UnknownModelException
-from invokeai.backend.model_manager import BaseModelType, LoadedModel, ModelType, SubModelType
+from invokeai.backend.model_manager.config import BaseModelType, ModelType, SubModelType
+from invokeai.backend.model_manager.load.load_base import LoadedModel
 
 
 @pytest.fixture(scope="session")

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/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",

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/features/gallery/hooks/useImageActions.ts

@@ -86,8 +86,19 @@ export const useImageActions = (image_name?: string) => {
 
   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/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/services/api/hooks/modelsByType.ts

@@ -3,16 +3,21 @@ import { useMemo } from 'react';
 import { modelConfigsAdapterSelectors, useGetModelConfigsQuery } from 'services/api/endpoints/models';
 import type { AnyModelConfig } from 'services/api/types';
 import {
+  isClipEmbedModelConfig,
   isControlNetModelConfig,
   isControlNetOrT2IAdapterModelConfig,
+  isFluxMainModelModelConfig,
+  isFluxVAEModelConfig,
   isIPAdapterModelConfig,
   isLoRAModelConfig,
   isNonRefinerMainModelConfig,
+  isNonRefinerNonFluxMainModelConfig,
   isNonSDXLMainModelConfig,
   isRefinerMainModelModelConfig,
   isSDXLMainModelModelConfig,
   isSpandrelImageToImageModelConfig,
   isT2IAdapterModelConfig,
+  isT5EncoderModelConfig,
   isTIModelConfig,
   isVAEModelConfig,
 } from 'services/api/types';
@@ -32,15 +37,20 @@ const buildModelsHook =
     return [modelConfigs, result] as const;
   };
 
+export const useSDMainModels = buildModelsHook(isNonRefinerNonFluxMainModelConfig);
 export const useMainModels = buildModelsHook(isNonRefinerMainModelConfig);
 export const useNonSDXLMainModels = buildModelsHook(isNonSDXLMainModelConfig);
 export const useRefinerModels = buildModelsHook(isRefinerMainModelModelConfig);
+export const useFluxModels = buildModelsHook(isFluxMainModelModelConfig);
 export const useSDXLModels = buildModelsHook(isSDXLMainModelModelConfig);
 export const useLoRAModels = buildModelsHook(isLoRAModelConfig);
 export const useControlNetAndT2IAdapterModels = buildModelsHook(isControlNetOrT2IAdapterModelConfig);
 export const useControlNetModels = buildModelsHook(isControlNetModelConfig);
 export const useT2IAdapterModels = buildModelsHook(isT2IAdapterModelConfig);
+export const useT5EncoderModels = buildModelsHook(isT5EncoderModelConfig);
+export const useClipEmbedModels = buildModelsHook(isClipEmbedModelConfig);
 export const useSpandrelImageToImageModels = buildModelsHook(isSpandrelImageToImageModelConfig);
 export const useIPAdapterModels = buildModelsHook(isIPAdapterModelConfig);
 export const useEmbeddingModels = buildModelsHook(isTIModelConfig);
 export const useVAEModels = buildModelsHook(isVAEModelConfig);
+export const useFluxVAEModels = buildModelsHook(isFluxVAEModelConfig);