FLUX memory management improvements (#6791)

## Summary

This PR contains several improvements to memory management for FLUX
workflows.

It is now possible to achieve better FLUX model caching performance, but
this still requires users to manually configure their `ram`/`vram`
settings. E.g. a `vram` setting of 16.0 should allow for all quantized
FLUX models to be kept in memory on the GPU.

Changes:
- Check the size of a model on disk and free the requisite space in the
model cache before loading it. (This behaviour existed previously, but
was removed in https://github.com/invoke-ai/InvokeAI/pull/6072/files.
The removal did not seem to be intentional).
- Removed the hack to free 24GB of space in the cache before loading the
FLUX model.
- Split the T5 embedding and CLIP embedding steps into separate
functions so that the two models don't both have to be held in RAM at
the same time.
- Fix a bug in `InvokeLinear8bitLt` that was causing some tensors to be
left on the GPU when the model was offloaded to the CPU. (This class is
getting very messy due to the non-standard state_dict handling in
`bnb.nn.Linear8bitLt`. )
- Tidy up some dtype handling in FluxTextToImageInvocation to avoid
situations where we hold references to two copies of the same tensor
unnecessarily.
- (minor) Misc cleanup of ModelCache: improve docs and remove unused
vars.

Future:
We should revisit our default ram/vram configs. The current defaults are
very conservative, and users could see major performance improvements
from tuning these values.

## QA Instructions

I tested the FLUX workflow with the following configurations and
verified that the cache hit rates and memory usage matched the expected
behaviour:
- `ram = 16` and `vram = 16`
- `ram = 16` and `vram = 1`
- `ram = 1` and `vram = 1`

Note that the changes in this PR are not isolated to FLUX. Since we now
check the size of models on disk, we may see slight changes in model
cache offload patterns for other models as well.

## Checklist

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

.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

invokeai/app/api/routers/session_queue.py

@@ -11,7 +11,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
     Batch,
     BatchStatus,
     CancelByBatchIDsResult,
-    CancelByOriginResult,
     ClearResult,
     EnqueueBatchResult,
     PruneResult,
@@ -106,19 +105,6 @@ async def cancel_by_batch_ids(
     return ApiDependencies.invoker.services.session_queue.cancel_by_batch_ids(queue_id=queue_id, batch_ids=batch_ids)
 
 
-@session_queue_router.put(
-    "/{queue_id}/cancel_by_origin",
-    operation_id="cancel_by_origin",
-    responses={200: {"model": CancelByBatchIDsResult}},
-)
-async def cancel_by_origin(
-    queue_id: str = Path(description="The queue id to perform this operation on"),
-    origin: str = Query(description="The origin to cancel all queue items for"),
-) -> CancelByOriginResult:
-    """Immediately cancels all queue items with the given origin"""
-    return ApiDependencies.invoker.services.session_queue.cancel_by_origin(queue_id=queue_id, origin=origin)
-
-
 @session_queue_router.put(
     "/{queue_id}/clear",
     operation_id="clear",

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"
@@ -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_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_text_to_image.py

@@ -0,0 +1,169 @@
+import torch
+from einops import rearrange
+from PIL import Image
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    FluxConditioningField,
+    Input,
+    InputField,
+    WithBoard,
+    WithMetadata,
+)
+from invokeai.app.invocations.model import TransformerField, VAEField
+from invokeai.app.invocations.primitives import ImageOutput
+from invokeai.app.services.session_processor.session_processor_common import CanceledException
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.flux.model import Flux
+from invokeai.backend.flux.modules.autoencoder import AutoEncoder
+from invokeai.backend.flux.sampling import denoise, get_noise, get_schedule, prepare_latent_img_patches, unpack
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import FLUXConditioningInfo
+from invokeai.backend.util.devices import TorchDevice
+
+
+@invocation(
+    "flux_text_to_image",
+    title="FLUX Text to Image",
+    tags=["image", "flux"],
+    category="image",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class FluxTextToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Text-to-image generation using a FLUX model."""
+
+    transformer: TransformerField = InputField(
+        description=FieldDescriptions.flux_model,
+        input=Input.Connection,
+        title="Transformer",
+    )
+    vae: VAEField = InputField(
+        description=FieldDescriptions.vae,
+        input=Input.Connection,
+    )
+    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. Recommend 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) -> ImageOutput:
+        latents = self._run_diffusion(context)
+        image = self._run_vae_decoding(context, latents)
+        image_dto = context.images.save(image=image)
+        return ImageOutput.build(image_dto)
+
+    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
+
+        transformer_info = context.models.load(self.transformer.transformer)
+
+        # Prepare input noise.
+        x = get_noise(
+            num_samples=1,
+            height=self.height,
+            width=self.width,
+            device=TorchDevice.choose_torch_device(),
+            dtype=inference_dtype,
+            seed=self.seed,
+        )
+
+        x, img_ids = prepare_latent_img_patches(x)
+
+        is_schnell = "schnell" in transformer_info.config.config_path
+
+        timesteps = get_schedule(
+            num_steps=self.num_steps,
+            image_seq_len=x.shape[1],
+            shift=not is_schnell,
+        )
+
+        bs, t5_seq_len, _ = t5_embeddings.shape
+        txt_ids = torch.zeros(bs, t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device())
+
+        with transformer_info as transformer:
+            assert isinstance(transformer, Flux)
+
+            def step_callback() -> None:
+                if context.util.is_canceled():
+                    raise CanceledException
+
+                # TODO: Make this look like the image before re-enabling
+                # latent_image = unpack(img.float(), self.height, self.width)
+                # latent_image = latent_image.squeeze()  # Remove unnecessary dimensions
+                # flattened_tensor = latent_image.reshape(-1)  # Flatten to shape [48*128*128]
+
+                # # Create a new tensor of the required shape [255, 255, 3]
+                # latent_image = flattened_tensor[: 255 * 255 * 3].reshape(255, 255, 3)  # Reshape to RGB format
+
+                # # Convert to a NumPy array and then to a PIL Image
+                # image = Image.fromarray(latent_image.cpu().numpy().astype(np.uint8))
+
+                # (width, height) = image.size
+                # width *= 8
+                # height *= 8
+
+                # dataURL = image_to_dataURL(image, image_format="JPEG")
+
+                # # TODO: move this whole function to invocation context to properly reference these variables
+                # context._services.events.emit_invocation_denoise_progress(
+                #     context._data.queue_item,
+                #     context._data.invocation,
+                #     state,
+                #     ProgressImage(dataURL=dataURL, width=width, height=height),
+                # )
+
+            x = denoise(
+                model=transformer,
+                img=x,
+                img_ids=img_ids,
+                txt=t5_embeddings,
+                txt_ids=txt_ids,
+                vec=clip_embeddings,
+                timesteps=timesteps,
+                step_callback=step_callback,
+                guidance=self.guidance,
+            )
+
+        x = unpack(x.float(), self.height, self.width)
+
+        return x
+
+    def _run_vae_decoding(
+        self,
+        context: InvocationContext,
+        latents: torch.Tensor,
+    ) -> Image.Image:
+        vae_info = context.models.load(self.vae.vae)
+        with vae_info as vae:
+            assert isinstance(vae, AutoEncoder)
+            latents = latents.to(dtype=TorchDevice.choose_torch_dtype())
+            img = vae.decode(latents)
+
+        img = img.clamp(-1, 1)
+        img = rearrange(img[0], "c h w -> h w c")
+        img_pil = Image.fromarray((127.5 * (img + 1.0)).byte().cpu().numpy())
+
+        return img_pil

invokeai/app/invocations/image.py

@@ -6,19 +6,13 @@ import cv2
 import numpy
 from PIL import Image, ImageChops, ImageFilter, ImageOps
 
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    Classification,
-    invocation,
-    invocation_output,
-)
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
 from invokeai.app.invocations.constants import IMAGE_MODES
 from invokeai.app.invocations.fields import (
     ColorField,
     FieldDescriptions,
     ImageField,
     InputField,
-    OutputField,
     WithBoard,
     WithMetadata,
 )
@@ -1013,66 +1007,3 @@ class MaskFromIDInvocation(BaseInvocation, WithMetadata, WithBoard):
         image_dto = context.images.save(image=mask, image_category=ImageCategory.MASK)
 
         return ImageOutput.build(image_dto)
-
-
-@invocation_output("canvas_v2_mask_and_crop_output")
-class CanvasV2MaskAndCropOutput(ImageOutput):
-    offset_x: int = OutputField(description="The x offset of the image, after cropping")
-    offset_y: int = OutputField(description="The y offset of the image, after cropping")
-
-
-@invocation(
-    "canvas_v2_mask_and_crop",
-    title="Canvas V2 Mask and Crop",
-    tags=["image", "mask", "id"],
-    category="image",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class CanvasV2MaskAndCropInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """Handles Canvas V2 image output masking and cropping"""
-
-    image: ImageField = InputField(description="The image to apply the mask to")
-    mask: ImageField = InputField(description="The mask to apply")
-    mask_blur: int = InputField(default=0, ge=0, description="The amount to blur the mask by")
-
-    def _prepare_mask(self, mask: Image.Image) -> Image.Image:
-        mask_array = numpy.array(mask)
-        kernel = numpy.ones((self.mask_blur, self.mask_blur), numpy.uint8)
-        dilated_mask_array = cv2.erode(mask_array, kernel, iterations=3)
-        dilated_mask = Image.fromarray(dilated_mask_array)
-        if self.mask_blur > 0:
-            mask = dilated_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
-        return ImageOps.invert(mask.convert("L"))
-
-    def invoke(self, context: InvocationContext) -> CanvasV2MaskAndCropOutput:
-        image = context.images.get_pil(self.image.image_name)
-        mask = self._prepare_mask(context.images.get_pil(self.mask.image_name))
-        image.putalpha(mask)
-        # bbox = image.getbbox()
-        # image = image.crop(bbox)
-        image_dto = context.images.save(image=image)
-
-        return CanvasV2MaskAndCropOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            offset_x=0,
-            offset_y=0,
-            width=image.width,
-            height=image.height,
-        )
-
-    # def invoke(self, context: InvocationContext) -> CanvasV2MaskAndCropOutput:
-    #     image = context.images.get_pil(self.image.image_name)
-    #     mask = self._prepare_mask(context.images.get_pil(self.mask.image_name))
-    #     image.putalpha(mask)
-    #     bbox = image.getbbox()
-    #     image = image.crop(bbox)
-    #     image_dto = context.images.save(image=image)
-
-    #     return CanvasV2MaskAndCropOutput(
-    #         image=ImageField(image_name=image_dto.image_name),
-    #         offset_x=bbox[0],
-    #         offset_y=bbox[1],
-    #         width=image.width,
-    #         height=image.height,
-    #     )

invokeai/app/invocations/model.py

@@ -1,5 +1,5 @@
 import copy
-from typing import List, Optional
+from typing import List, Literal, Optional
 
 from pydantic import BaseModel, Field
 
@@ -13,7 +13,14 @@ from invokeai.app.invocations.baseinvocation import (
 from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.shared.models import FreeUConfig
-from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelType, SubModelType
+from invokeai.backend.flux.util import max_seq_lengths
+from invokeai.backend.model_manager.config import (
+    AnyModelConfig,
+    BaseModelType,
+    CheckpointConfigBase,
+    ModelType,
+    SubModelType,
+)
 
 
 class ModelIdentifierField(BaseModel):
@@ -60,6 +67,15 @@ class CLIPField(BaseModel):
     loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
 
 
+class TransformerField(BaseModel):
+    transformer: ModelIdentifierField = Field(description="Info to load Transformer submodel")
+
+
+class T5EncoderField(BaseModel):
+    tokenizer: ModelIdentifierField = Field(description="Info to load tokenizer submodel")
+    text_encoder: ModelIdentifierField = Field(description="Info to load text_encoder submodel")
+
+
 class VAEField(BaseModel):
     vae: ModelIdentifierField = Field(description="Info to load vae submodel")
     seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
@@ -122,6 +138,78 @@ class ModelIdentifierInvocation(BaseInvocation):
         return ModelIdentifierOutput(model=self.model)
 
 
+@invocation_output("flux_model_loader_output")
+class FluxModelLoaderOutput(BaseInvocationOutput):
+    """Flux base model loader output"""
+
+    transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer")
+    clip: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP")
+    t5_encoder: T5EncoderField = OutputField(description=FieldDescriptions.t5_encoder, title="T5 Encoder")
+    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
+    max_seq_len: Literal[256, 512] = OutputField(
+        description="The max sequence length to used for the T5 encoder. (256 for schnell transformer, 512 for dev transformer)",
+        title="Max Seq Length",
+    )
+
+
+@invocation(
+    "flux_model_loader",
+    title="Flux Main Model",
+    tags=["model", "flux"],
+    category="model",
+    version="1.0.4",
+    classification=Classification.Prototype,
+)
+class FluxModelLoaderInvocation(BaseInvocation):
+    """Loads a flux base model, outputting its submodels."""
+
+    model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.flux_model,
+        ui_type=UIType.FluxMainModel,
+        input=Input.Direct,
+    )
+
+    t5_encoder_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.t5_encoder, ui_type=UIType.T5EncoderModel, input=Input.Direct, title="T5 Encoder"
+    )
+
+    clip_embed_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.clip_embed_model,
+        ui_type=UIType.CLIPEmbedModel,
+        input=Input.Direct,
+        title="CLIP Embed",
+    )
+
+    vae_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.vae_model, ui_type=UIType.FluxVAEModel, title="VAE"
+    )
+
+    def invoke(self, context: InvocationContext) -> FluxModelLoaderOutput:
+        for key in [self.model.key, self.t5_encoder_model.key, self.clip_embed_model.key, self.vae_model.key]:
+            if not context.models.exists(key):
+                raise ValueError(f"Unknown model: {key}")
+
+        transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
+        vae = self.vae_model.model_copy(update={"submodel_type": SubModelType.VAE})
+
+        tokenizer = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
+        clip_encoder = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
+
+        tokenizer2 = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
+        t5_encoder = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
+
+        transformer_config = context.models.get_config(transformer)
+        assert isinstance(transformer_config, CheckpointConfigBase)
+
+        return FluxModelLoaderOutput(
+            transformer=TransformerField(transformer=transformer),
+            clip=CLIPField(tokenizer=tokenizer, text_encoder=clip_encoder, loras=[], skipped_layers=0),
+            t5_encoder=T5EncoderField(tokenizer=tokenizer2, text_encoder=t5_encoder),
+            vae=VAEField(vae=vae),
+            max_seq_len=max_seq_lengths[transformer_config.config_path],
+        )
+
+
 @invocation(
     "main_model_loader",
     title="Main Model",

invokeai/app/invocations/primitives.py

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

invokeai/app/services/events/events_common.py

@@ -88,7 +88,6 @@ class QueueItemEventBase(QueueEventBase):
 
     item_id: int = Field(description="The ID of the queue item")
     batch_id: str = Field(description="The ID of the queue batch")
-    origin: str | None = Field(default=None, description="The origin of the batch")
 
 
 class InvocationEventBase(QueueItemEventBase):
@@ -96,6 +95,8 @@ class InvocationEventBase(QueueItemEventBase):
 
     session_id: str = Field(description="The ID of the session (aka graph execution state)")
     queue_id: str = Field(description="The ID of the queue")
+    item_id: int = Field(description="The ID of the queue item")
+    batch_id: str = Field(description="The ID of the queue batch")
     session_id: str = Field(description="The ID of the session (aka graph execution state)")
     invocation: AnyInvocation = Field(description="The ID of the invocation")
     invocation_source_id: str = Field(description="The ID of the prepared invocation's source node")
@@ -113,7 +114,6 @@ class InvocationStartedEvent(InvocationEventBase):
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
-            origin=queue_item.origin,
             session_id=queue_item.session_id,
             invocation=invocation,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
@@ -147,7 +147,6 @@ class InvocationDenoiseProgressEvent(InvocationEventBase):
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
-            origin=queue_item.origin,
             session_id=queue_item.session_id,
             invocation=invocation,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
@@ -185,7 +184,6 @@ class InvocationCompleteEvent(InvocationEventBase):
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
-            origin=queue_item.origin,
             session_id=queue_item.session_id,
             invocation=invocation,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
@@ -218,7 +216,6 @@ class InvocationErrorEvent(InvocationEventBase):
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
-            origin=queue_item.origin,
             session_id=queue_item.session_id,
             invocation=invocation,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
@@ -256,7 +253,6 @@ class QueueItemStatusChangedEvent(QueueItemEventBase):
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
-            origin=queue_item.origin,
             session_id=queue_item.session_id,
             status=queue_item.status,
             error_type=queue_item.error_type,
@@ -283,14 +279,12 @@ class BatchEnqueuedEvent(QueueEventBase):
         description="The number of invocations initially requested to be enqueued (may be less than enqueued if queue was full)"
     )
     priority: int = Field(description="The priority of the batch")
-    origin: str | None = Field(default=None, description="The origin of the batch")
 
     @classmethod
     def build(cls, enqueue_result: EnqueueBatchResult) -> "BatchEnqueuedEvent":
         return cls(
             queue_id=enqueue_result.queue_id,
             batch_id=enqueue_result.batch.batch_id,
-            origin=enqueue_result.batch.origin,
             enqueued=enqueue_result.enqueued,
             requested=enqueue_result.requested,
             priority=enqueue_result.priority,

invokeai/app/services/model_install/model_install_default.py

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

invokeai/app/services/model_records/model_records_base.py

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

invokeai/app/services/session_queue/session_queue_base.py

@@ -6,7 +6,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
     Batch,
     BatchStatus,
     CancelByBatchIDsResult,
-    CancelByOriginResult,
     CancelByQueueIDResult,
     ClearResult,
     EnqueueBatchResult,
@@ -96,11 +95,6 @@ class SessionQueueBase(ABC):
         """Cancels all queue items with matching batch IDs"""
         pass
 
-    @abstractmethod
-    def cancel_by_origin(self, queue_id: str, origin: str) -> CancelByOriginResult:
-        """Cancels all queue items with the given batch origin"""
-        pass
-
     @abstractmethod
     def cancel_by_queue_id(self, queue_id: str) -> CancelByQueueIDResult:
         """Cancels all queue items with matching queue ID"""

invokeai/app/services/session_queue/session_queue_common.py

@@ -77,7 +77,6 @@ BatchDataCollection: TypeAlias = list[list[BatchDatum]]
 
 class Batch(BaseModel):
     batch_id: str = Field(default_factory=uuid_string, description="The ID of the batch")
-    origin: str | None = Field(default=None, description="The origin of this batch.")
     data: Optional[BatchDataCollection] = Field(default=None, description="The batch data collection.")
     graph: Graph = Field(description="The graph to initialize the session with")
     workflow: Optional[WorkflowWithoutID] = Field(
@@ -196,7 +195,6 @@ class SessionQueueItemWithoutGraph(BaseModel):
     status: QUEUE_ITEM_STATUS = Field(default="pending", description="The status of this queue item")
     priority: int = Field(default=0, description="The priority of this queue item")
     batch_id: str = Field(description="The ID of the batch associated with this queue item")
-    origin: str | None = Field(default=None, description="The origin of this queue item. ")
     session_id: str = Field(
         description="The ID of the session associated with this queue item. The session doesn't exist in graph_executions until the queue item is executed."
     )
@@ -296,7 +294,6 @@ class SessionQueueStatus(BaseModel):
 class BatchStatus(BaseModel):
     queue_id: str = Field(..., description="The ID of the queue")
     batch_id: str = Field(..., description="The ID of the batch")
-    origin: str | None = Field(..., description="The origin of the batch")
     pending: int = Field(..., description="Number of queue items with status 'pending'")
     in_progress: int = Field(..., description="Number of queue items with status 'in_progress'")
     completed: int = Field(..., description="Number of queue items with status 'complete'")
@@ -331,12 +328,6 @@ class CancelByBatchIDsResult(BaseModel):
     canceled: int = Field(..., description="Number of queue items canceled")
 
 
-class CancelByOriginResult(BaseModel):
-    """Result of canceling by list of batch ids"""
-
-    canceled: int = Field(..., description="Number of queue items canceled")
-
-
 class CancelByQueueIDResult(CancelByBatchIDsResult):
     """Result of canceling by queue id"""
 
@@ -442,7 +433,6 @@ class SessionQueueValueToInsert(NamedTuple):
     field_values: Optional[str]  # field_values json
     priority: int  # priority
     workflow: Optional[str]  # workflow json
-    origin: str | None
 
 
 ValuesToInsert: TypeAlias = list[SessionQueueValueToInsert]
@@ -463,7 +453,6 @@ def prepare_values_to_insert(queue_id: str, batch: Batch, priority: int, max_new
                 json.dumps(field_values, default=to_jsonable_python) if field_values else None,  # field_values (json)
                 priority,  # priority
                 json.dumps(workflow, default=to_jsonable_python) if workflow else None,  # workflow (json)
-                batch.origin,  # origin
             )
         )
     return values_to_insert

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -10,7 +10,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
     Batch,
     BatchStatus,
     CancelByBatchIDsResult,
-    CancelByOriginResult,
     CancelByQueueIDResult,
     ClearResult,
     EnqueueBatchResult,
@@ -128,8 +127,8 @@ class SqliteSessionQueue(SessionQueueBase):
 
             self.__cursor.executemany(
                 """--sql
-                INSERT INTO session_queue (queue_id, session, session_id, batch_id, field_values, priority, workflow, origin)
-                VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+                INSERT INTO session_queue (queue_id, session, session_id, batch_id, field_values, priority, workflow)
+                VALUES (?, ?, ?, ?, ?, ?, ?)
                 """,
                 values_to_insert,
             )
@@ -418,7 +417,11 @@ class SqliteSessionQueue(SessionQueueBase):
             )
             self.__conn.commit()
             if current_queue_item is not None and current_queue_item.batch_id in batch_ids:
-                self._set_queue_item_status(current_queue_item.item_id, "canceled")
+                batch_status = self.get_batch_status(queue_id=queue_id, batch_id=current_queue_item.batch_id)
+                queue_status = self.get_queue_status(queue_id=queue_id)
+                self.__invoker.services.events.emit_queue_item_status_changed(
+                    current_queue_item, batch_status, queue_status
+                )
         except Exception:
             self.__conn.rollback()
             raise
@@ -426,46 +429,6 @@ class SqliteSessionQueue(SessionQueueBase):
             self.__lock.release()
         return CancelByBatchIDsResult(canceled=count)
 
-    def cancel_by_origin(self, queue_id: str, origin: str) -> CancelByOriginResult:
-        try:
-            current_queue_item = self.get_current(queue_id)
-            self.__lock.acquire()
-            where = """--sql
-                WHERE
-                  queue_id == ?
-                  AND origin == ?
-                  AND status != 'canceled'
-                  AND status != 'completed'
-                  AND status != 'failed'
-                """
-            params = (queue_id, origin)
-            self.__cursor.execute(
-                f"""--sql
-                SELECT COUNT(*)
-                FROM session_queue
-                {where};
-                """,
-                params,
-            )
-            count = self.__cursor.fetchone()[0]
-            self.__cursor.execute(
-                f"""--sql
-                UPDATE session_queue
-                SET status = 'canceled'
-                {where};
-                """,
-                params,
-            )
-            self.__conn.commit()
-            if current_queue_item is not None and current_queue_item.origin == origin:
-                self._set_queue_item_status(current_queue_item.item_id, "canceled")
-        except Exception:
-            self.__conn.rollback()
-            raise
-        finally:
-            self.__lock.release()
-        return CancelByOriginResult(canceled=count)
-
     def cancel_by_queue_id(self, queue_id: str) -> CancelByQueueIDResult:
         try:
             current_queue_item = self.get_current(queue_id)
@@ -578,8 +541,7 @@ class SqliteSessionQueue(SessionQueueBase):
                     started_at,
                     session_id,
                     batch_id,
-                    queue_id,
-                    origin
+                    queue_id
                 FROM session_queue
                 WHERE queue_id = ?
             """
@@ -659,7 +621,7 @@ class SqliteSessionQueue(SessionQueueBase):
             self.__lock.acquire()
             self.__cursor.execute(
                 """--sql
-                SELECT status, count(*), origin
+                SELECT status, count(*)
                 FROM session_queue
                 WHERE
                   queue_id = ?
@@ -671,7 +633,6 @@ class SqliteSessionQueue(SessionQueueBase):
             result = cast(list[sqlite3.Row], self.__cursor.fetchall())
             total = sum(row[1] for row in result)
             counts: dict[str, int] = {row[0]: row[1] for row in result}
-            origin = result[0]["origin"] if result else None
         except Exception:
             self.__conn.rollback()
             raise
@@ -680,7 +641,6 @@ class SqliteSessionQueue(SessionQueueBase):
 
         return BatchStatus(
             batch_id=batch_id,
-            origin=origin,
             queue_id=queue_id,
             pending=counts.get("pending", 0),
             in_progress=counts.get("in_progress", 0),

invokeai/app/services/shared/sqlite/sqlite_util.py

@@ -17,7 +17,6 @@ from invokeai.app.services.shared.sqlite_migrator.migrations.migration_11 import
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_12 import build_migration_12
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_13 import build_migration_13
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_14 import build_migration_14
-from invokeai.app.services.shared.sqlite_migrator.migrations.migration_15 import build_migration_15
 from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
 
 
@@ -52,7 +51,6 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
     migrator.register_migration(build_migration_12(app_config=config))
     migrator.register_migration(build_migration_13())
     migrator.register_migration(build_migration_14())
-    migrator.register_migration(build_migration_15())
     migrator.run_migrations()
 
     return db

invokeai/app/services/shared/sqlite_migrator/migrations/migration_15.py

@@ -1,31 +0,0 @@
-import sqlite3
-
-from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
-
-
-class Migration15Callback:
-    def __call__(self, cursor: sqlite3.Cursor) -> None:
-        self._add_origin_col(cursor)
-
-    def _add_origin_col(self, cursor: sqlite3.Cursor) -> None:
-        """
-        - Adds `origin` column to the session queue table.
-        """
-
-        cursor.execute("ALTER TABLE session_queue ADD COLUMN origin TEXT;")
-
-
-def build_migration_15() -> Migration:
-    """
-    Build the migration from database version 14 to 15.
-
-    This migration does the following:
-        - Adds `origin` column to the session queue table.
-    """
-    migration_15 = Migration(
-        from_version=14,
-        to_version=15,
-        callback=Migration15Callback(),
-    )
-
-    return migration_15

invokeai/app/services/workflow_records/default_workflows/Flux Text to Image.json

@@ -0,0 +1,260 @@
+{
+  "name": "FLUX Text to Image",
+  "author": "InvokeAI",
+  "description": "A simple text-to-image workflow using FLUX dev or schnell models. 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 4 steps for FLUX schnell models and 30 steps for FLUX dev models.",
+  "version": "1.0.4",
+  "contact": "",
+  "tags": "text2image, flux",
+  "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 4 steps for FLUX schnell models and 30 steps for FLUX dev models.",
+  "exposedFields": [
+    {
+      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "fieldName": "model"
+    },
+    {
+      "nodeId": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
+      "fieldName": "prompt"
+    },
+    {
+      "nodeId": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "fieldName": "num_steps"
+    },
+    {
+      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "fieldName": "t5_encoder_model"
+    }
+  ],
+  "meta": {
+    "version": "3.0.0",
+    "category": "default"
+  },
+  "nodes": [
+    {
+      "id": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "type": "invocation",
+      "data": {
+        "id": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+        "type": "flux_model_loader",
+        "version": "1.0.4",
+        "label": "",
+        "notes": "",
+        "isOpen": true,
+        "isIntermediate": true,
+        "useCache": false,
+        "inputs": {
+          "model": {
+            "name": "model",
+            "label": ""
+          },
+          "t5_encoder_model": {
+            "name": "t5_encoder_model",
+            "label": ""
+          },
+          "clip_embed_model": {
+            "name": "clip_embed_model",
+            "label": ""
+          },
+          "vae_model": {
+            "name": "vae_model",
+            "label": ""
+          }
+        }
+      },
+      "position": {
+        "x": 381.1882713063478,
+        "y": -95.89663532854017
+      }
+    },
+    {
+      "id": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
+      "type": "invocation",
+      "data": {
+        "id": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
+        "type": "flux_text_encoder",
+        "version": "1.0.0",
+        "label": "",
+        "notes": "",
+        "isOpen": true,
+        "isIntermediate": true,
+        "useCache": true,
+        "inputs": {
+          "clip": {
+            "name": "clip",
+            "label": ""
+          },
+          "t5_encoder": {
+            "name": "t5_encoder",
+            "label": ""
+          },
+          "t5_max_seq_len": {
+            "name": "t5_max_seq_len",
+            "label": "T5 Max Seq Len",
+            "value": 256
+          },
+          "prompt": {
+            "name": "prompt",
+            "label": "",
+            "value": "a cat"
+          }
+        }
+      },
+      "position": {
+        "x": 824.1970602278849,
+        "y": 146.98251001061735
+      }
+    },
+    {
+      "id": "4754c534-a5f3-4ad0-9382-7887985e668c",
+      "type": "invocation",
+      "data": {
+        "id": "4754c534-a5f3-4ad0-9382-7887985e668c",
+        "type": "rand_int",
+        "version": "1.0.1",
+        "label": "",
+        "notes": "",
+        "isOpen": true,
+        "isIntermediate": true,
+        "useCache": false,
+        "inputs": {
+          "low": {
+            "name": "low",
+            "label": "",
+            "value": 0
+          },
+          "high": {
+            "name": "high",
+            "label": "",
+            "value": 2147483647
+          }
+        }
+      },
+      "position": {
+        "x": 822.9899179655476,
+        "y": 360.9657214885052
+      }
+    },
+    {
+      "id": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "type": "invocation",
+      "data": {
+        "id": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+        "type": "flux_text_to_image",
+        "version": "1.0.0",
+        "label": "",
+        "notes": "",
+        "isOpen": true,
+        "isIntermediate": false,
+        "useCache": true,
+        "inputs": {
+          "board": {
+            "name": "board",
+            "label": ""
+          },
+          "metadata": {
+            "name": "metadata",
+            "label": ""
+          },
+          "transformer": {
+            "name": "transformer",
+            "label": ""
+          },
+          "vae": {
+            "name": "vae",
+            "label": ""
+          },
+          "positive_text_conditioning": {
+            "name": "positive_text_conditioning",
+            "label": ""
+          },
+          "width": {
+            "name": "width",
+            "label": "",
+            "value": 1024
+          },
+          "height": {
+            "name": "height",
+            "label": "",
+            "value": 1024
+          },
+          "num_steps": {
+            "name": "num_steps",
+            "label": "Steps (Recommend 30 for Dev, 4 for Schnell)",
+            "value": 30
+          },
+          "guidance": {
+            "name": "guidance",
+            "label": "",
+            "value": 4
+          },
+          "seed": {
+            "name": "seed",
+            "label": "",
+            "value": 0
+          }
+        }
+      },
+      "position": {
+        "x": 1216.3900791301849,
+        "y": 5.500841807102248
+      }
+    }
+  ],
+  "edges": [
+    {
+      "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-ab0e89bfac90vae-159bdf1b-79e7-4174-b86e-d40e646964c8vae",
+      "type": "default",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "sourceHandle": "vae",
+      "targetHandle": "vae"
+    },
+    {
+      "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"
+    },
+    {
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90transformer-159bdf1b-79e7-4174-b86e-d40e646964c8transformer",
+      "type": "default",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "sourceHandle": "transformer",
+      "targetHandle": "transformer"
+    },
+    {
+      "id": "reactflow__edge-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cconditioning-159bdf1b-79e7-4174-b86e-d40e646964c8positive_text_conditioning",
+      "type": "default",
+      "source": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
+      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "sourceHandle": "conditioning",
+      "targetHandle": "positive_text_conditioning"
+    },
+    {
+      "id": "reactflow__edge-4754c534-a5f3-4ad0-9382-7887985e668cvalue-159bdf1b-79e7-4174-b86e-d40e646964c8seed",
+      "type": "default",
+      "source": "4754c534-a5f3-4ad0-9382-7887985e668c",
+      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "sourceHandle": "value",
+      "targetHandle": "seed"
+    }
+  ]
+}

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 +1,310 @@
+# 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, sample: bool = True, chunk_dim: int = 1):
+        super().__init__()
+        self.sample = sample
+        self.chunk_dim = chunk_dim
+
+    def forward(self, z: Tensor) -> Tensor:
+        mean, logvar = torch.chunk(z, 2, dim=self.chunk_dim)
+        if self.sample:
+            std = torch.exp(0.5 * logvar)
+            return mean + std * torch.randn_like(mean)
+        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) -> Tensor:
+        z = self.reg(self.encoder(x))
+        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.py

@@ -0,0 +1,167 @@
+# Initially pulled from https://github.com/black-forest-labs/flux
+
+import math
+from typing import Callable
+
+import torch
+from einops import rearrange, repeat
+from torch import Tensor
+from tqdm import tqdm
+
+from invokeai.backend.flux.model import Flux
+from invokeai.backend.flux.modules.conditioner import HFEncoder
+
+
+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 prepare(t5: HFEncoder, clip: HFEncoder, img: Tensor, prompt: str | list[str]) -> dict[str, Tensor]:
+    bs, c, h, w = img.shape
+    if bs == 1 and not isinstance(prompt, str):
+        bs = len(prompt)
+
+    img = rearrange(img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+    if img.shape[0] == 1 and bs > 1:
+        img = repeat(img, "1 ... -> bs ...", bs=bs)
+
+    img_ids = torch.zeros(h // 2, w // 2, 3)
+    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2)[:, None]
+    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2)[None, :]
+    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
+
+    if isinstance(prompt, str):
+        prompt = [prompt]
+    txt = t5(prompt)
+    if txt.shape[0] == 1 and bs > 1:
+        txt = repeat(txt, "1 ... -> bs ...", bs=bs)
+    txt_ids = torch.zeros(bs, txt.shape[1], 3)
+
+    vec = clip(prompt)
+    if vec.shape[0] == 1 and bs > 1:
+        vec = repeat(vec, "1 ... -> bs ...", bs=bs)
+
+    return {
+        "img": img,
+        "img_ids": img_ids.to(img.device),
+        "txt": txt.to(img.device),
+        "txt_ids": txt_ids.to(img.device),
+        "vec": vec.to(img.device),
+    }
+
+
+def time_shift(mu: float, sigma: float, t: 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:
+        # eastimate 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 denoise(
+    model: Flux,
+    # model input
+    img: Tensor,
+    img_ids: Tensor,
+    txt: Tensor,
+    txt_ids: Tensor,
+    vec: Tensor,
+    # sampling parameters
+    timesteps: list[float],
+    step_callback: Callable[[], None],
+    guidance: float = 4.0,
+):
+    # 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,
+        )
+
+        img = img + (t_prev - t_curr) * pred
+        step_callback()
+
+    return img
+
+
+def unpack(x: Tensor, height: int, width: int) -> Tensor:
+    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 prepare_latent_img_patches(latent_img: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    """Convert an input image in latent space to patches for diffusion.
+
+    This implementation was extracted from:
+    https://github.com/black-forest-labs/flux/blob/c00d7c60b085fce8058b9df845e036090873f2ce/src/flux/sampling.py#L32
+
+    Returns:
+        tuple[Tensor, Tensor]: (img, img_ids), as defined in the original flux repo.
+    """
+    bs, c, h, w = latent_img.shape
+
+    # Pixel unshuffle with a scale of 2, and flatten the height/width dimensions to get an array of patches.
+    img = rearrange(latent_img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+    if img.shape[0] == 1 and bs > 1:
+        img = repeat(img, "1 ... -> bs ...", bs=bs)
+
+    # Generate patch position ids.
+    img_ids = torch.zeros(h // 2, w // 2, 3, device=img.device, dtype=img.dtype)
+    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=img.device, dtype=img.dtype)[:, None]
+    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=img.device, dtype=img.dtype)[None, :]
+    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
+
+    return img, img_ids

invokeai/backend/flux/util.py

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

invokeai/backend/model_manager/config.py

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

invokeai/backend/model_manager/load/load_default.py

@@ -72,6 +72,7 @@ class ModelLoader(ModelLoaderBase):
             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(

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

@@ -193,15 +193,6 @@ class ModelCacheBase(ABC, Generic[T]):
         """
         pass
 
-    @abstractmethod
-    def exists(
-        self,
-        key: str,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> bool:
-        """Return true if the model identified by key and submodel_type is in the cache."""
-        pass
-
     @abstractmethod
     def cache_size(self) -> int:
         """Get the total size of the models currently cached."""

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

@@ -1,22 +1,6 @@
 # Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Development team
 # TODO: Add Stalker's proper name to copyright
-"""
-Manage a RAM cache of diffusion/transformer models for fast switching.
-They are moved between GPU VRAM and CPU RAM as necessary. If the cache
-grows larger than a preset maximum, then the least recently used
-model will be cleared and (re)loaded from disk when next needed.
-
-The cache returns context manager generators designed to load the
-model into the GPU within the context, and unload outside the
-context. Use like this:
-
-   cache = ModelCache(max_cache_size=7.5)
-   with cache.get_model('runwayml/stable-diffusion-1-5') as SD1,
-          cache.get_model('stabilityai/stable-diffusion-2') as SD2:
-       do_something_in_GPU(SD1,SD2)
-
-
-"""
+""" """
 
 import gc
 import math
@@ -40,45 +24,64 @@ 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 lazy_offloading: Keep model in VRAM until another model needs to be loaded.
         :param log_memory_usage: If True, a memory snapshot will be captured before and after every model cache
             operation, and the result will be logged (at debug level). There is a time cost to capturing the memory
             snapshots, so it is recommended to disable this feature unless you are actively inspecting the model cache's
@@ -86,7 +89,6 @@ class ModelCache(ModelCacheBase[AnyModel]):
         """
         # 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
@@ -145,15 +147,6 @@ 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 +196,7 @@ 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 +224,13 @@ 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 +241,7 @@ 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 +299,7 @@ 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 +322,14 @@ 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 +349,20 @@ 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 +379,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
 
             if not cache_entry.locked:
                 self.logger.debug(
-                    f"Removing {model_key} from RAM cache to free at least {(size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)"
+                    f"Removing {model_key} from RAM cache to free at least {(size/GB):.2f} GB (-{(cache_entry.size/GB):.2f} GB)"
                 )
                 current_size -= cache_entry.size
                 models_cleared += 1

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

@@ -0,0 +1,234 @@
+# Copyright (c) 2024, Brandon W. Rising and the InvokeAI Development Team
+"""Class for Flux model loading in InvokeAI."""
+
+from pathlib import Path
+from typing import Optional
+
+import accelerate
+import torch
+from safetensors.torch import load_file
+from transformers import AutoConfig, AutoModelForTextEncoding, CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
+
+from invokeai.app.services.config.config_default import get_config
+from invokeai.backend.flux.model import Flux
+from invokeai.backend.flux.modules.autoencoder import AutoEncoder
+from invokeai.backend.flux.util import ae_params, params
+from invokeai.backend.model_manager import (
+    AnyModel,
+    AnyModelConfig,
+    BaseModelType,
+    ModelFormat,
+    ModelType,
+    SubModelType,
+)
+from invokeai.backend.model_manager.config import (
+    CheckpointConfigBase,
+    CLIPEmbedDiffusersConfig,
+    MainBnbQuantized4bCheckpointConfig,
+    MainCheckpointConfig,
+    T5EncoderBnbQuantizedLlmInt8bConfig,
+    T5EncoderConfig,
+    VAECheckpointConfig,
+)
+from invokeai.backend.model_manager.load.load_default import ModelLoader
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.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

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

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

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

invokeai/backend/model_manager/load/model_util.py

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

invokeai/backend/model_manager/probe.py

@@ -95,6 +95,7 @@ class ModelProbe(object):
     }
 
     CLASS2TYPE = {
+        "FluxPipeline": ModelType.Main,
         "StableDiffusionPipeline": ModelType.Main,
         "StableDiffusionInpaintPipeline": ModelType.Main,
         "StableDiffusionXLPipeline": ModelType.Main,
@@ -106,6 +107,7 @@ 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/quantization/bnb_llm_int8.py

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

invokeai/backend/quantization/bnb_nf4.py

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

invokeai/backend/quantization/scripts/load_flux_model_bnb_llm_int8.py

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

invokeai/backend/quantization/scripts/load_flux_model_bnb_nf4.py

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

invokeai/backend/quantization/scripts/quantize_t5_xxl_bnb_llm_int8.py

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

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

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

invokeai/backend/util/__init__.py

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

invokeai/backend/util/util.py

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

invokeai/frontend/web/.eslintrc.js

@@ -12,10 +12,6 @@ module.exports = {
     'i18next/no-literal-string': 'error',
     // https://eslint.org/docs/latest/rules/no-console
     'no-console': 'error',
-    // https://eslint.org/docs/latest/rules/no-promise-executor-return
-    'no-promise-executor-return': 'error',
-    // https://eslint.org/docs/latest/rules/require-await
-    'require-await': 'error',
   },
   overrides: [
     /**

invokeai/frontend/web/.storybook/ReduxInit.tsx

@@ -1,5 +1,5 @@
 import { PropsWithChildren, memo, useEffect } from 'react';
-import { modelChanged } from '../src/features/controlLayers/store/canvasV2Slice';
+import { modelChanged } from '../src/features/parameters/store/generationSlice';
 import { useAppDispatch } from '../src/app/store/storeHooks';
 import { useGlobalModifiersInit } from '@invoke-ai/ui-library';
 /**
@@ -10,9 +10,7 @@ export const ReduxInit = memo((props: PropsWithChildren) => {
   const dispatch = useAppDispatch();
   useGlobalModifiersInit();
   useEffect(() => {
-    dispatch(
-      modelChanged({ model: { key: 'test_model', hash: 'some_hash', name: 'some name', base: 'sd-1', type: 'main' } })
-    );
+    dispatch(modelChanged({ key: 'test_model', hash: 'some_hash', name: 'some name', base: 'sd-1', type: 'main' }));
   }, []);
 
   return props.children;

invokeai/frontend/web/knip.ts

@@ -9,8 +9,6 @@ const config: KnipConfig = {
     'src/services/api/schema.ts',
     'src/features/nodes/types/v1/**',
     'src/features/nodes/types/v2/**',
-    // TODO(psyche): maybe we can clean up these utils after canvas v2 release
-    'src/features/controlLayers/konva/util.ts',
   ],
   ignoreBinaries: ['only-allow'],
   paths: {

invokeai/frontend/web/package.json

@@ -52,17 +52,17 @@
     }
   },
   "dependencies": {
+    "@chakra-ui/react-use-size": "^2.1.0",
     "@dagrejs/dagre": "^1.1.3",
     "@dagrejs/graphlib": "^2.2.3",
     "@dnd-kit/core": "^6.1.0",
     "@dnd-kit/sortable": "^8.0.0",
     "@dnd-kit/utilities": "^3.2.2",
     "@fontsource-variable/inter": "^5.0.20",
-    "@invoke-ai/ui-library": "^0.0.31",
+    "@invoke-ai/ui-library": "^0.0.29",
     "@nanostores/react": "^0.7.3",
     "@reduxjs/toolkit": "2.2.3",
     "@roarr/browser-log-writer": "^1.3.0",
-    "async-mutex": "^0.5.0",
     "chakra-react-select": "^4.9.1",
     "compare-versions": "^6.1.1",
     "dateformat": "^5.0.3",
@@ -74,8 +74,6 @@
     "jsondiffpatch": "^0.6.0",
     "konva": "^9.3.14",
     "lodash-es": "^4.17.21",
-    "lru-cache": "^11.0.0",
-    "nanoid": "^5.0.7",
     "nanostores": "^0.11.2",
     "new-github-issue-url": "^1.0.0",
     "overlayscrollbars": "^2.10.0",
@@ -90,6 +88,7 @@
     "react-hotkeys-hook": "4.5.0",
     "react-i18next": "^14.1.3",
     "react-icons": "^5.2.1",
+    "react-konva": "^18.2.10",
     "react-redux": "9.1.2",
     "react-resizable-panels": "^2.0.23",
     "react-select": "5.8.0",
@@ -103,9 +102,9 @@
     "roarr": "^7.21.1",
     "serialize-error": "^11.0.3",
     "socket.io-client": "^4.7.5",
-    "stable-hash": "^0.0.4",
     "use-debounce": "^10.0.2",
     "use-device-pixel-ratio": "^1.1.2",
+    "use-image": "^1.1.1",
     "uuid": "^10.0.0",
     "zod": "^3.23.8",
     "zod-validation-error": "^3.3.1"

invokeai/frontend/web/pnpm-lock.yaml

@@ -5,6 +5,9 @@ settings:
   excludeLinksFromLockfile: false
 
 dependencies:
+  '@chakra-ui/react-use-size':
+    specifier: ^2.1.0
+    version: 2.1.0(react@18.3.1)
   '@dagrejs/dagre':
     specifier: ^1.1.3
     version: 1.1.3
@@ -24,8 +27,8 @@ dependencies:
     specifier: ^5.0.20
     version: 5.0.20
   '@invoke-ai/ui-library':
-    specifier: ^0.0.31
-    version: 0.0.31(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.0.20)(@types/react@18.3.3)(i18next@23.12.2)(react-dom@18.3.1)(react@18.3.1)
+    specifier: ^0.0.29
+    version: 0.0.29(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.0.20)(@types/react@18.3.3)(i18next@23.12.2)(react-dom@18.3.1)(react@18.3.1)
   '@nanostores/react':
     specifier: ^0.7.3
     version: 0.7.3(nanostores@0.11.2)(react@18.3.1)
@@ -35,9 +38,6 @@ dependencies:
   '@roarr/browser-log-writer':
     specifier: ^1.3.0
     version: 1.3.0
-  async-mutex:
-    specifier: ^0.5.0
-    version: 0.5.0
   chakra-react-select:
     specifier: ^4.9.1
     version: 4.9.1(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/layout@2.3.1)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@emotion/react@11.13.0)(@types/react@18.3.3)(react-dom@18.3.1)(react@18.3.1)
@@ -71,12 +71,6 @@ dependencies:
   lodash-es:
     specifier: ^4.17.21
     version: 4.17.21
-  lru-cache:
-    specifier: ^11.0.0
-    version: 11.0.0
-  nanoid:
-    specifier: ^5.0.7
-    version: 5.0.7
   nanostores:
     specifier: ^0.11.2
     version: 0.11.2
@@ -119,6 +113,9 @@ dependencies:
   react-icons:
     specifier: ^5.2.1
     version: 5.2.1(react@18.3.1)
+  react-konva:
+    specifier: ^18.2.10
+    version: 18.2.10(konva@9.3.14)(react-dom@18.3.1)(react@18.3.1)
   react-redux:
     specifier: 9.1.2
     version: 9.1.2(@types/react@18.3.3)(react@18.3.1)(redux@5.0.1)
@@ -158,15 +155,15 @@ dependencies:
   socket.io-client:
     specifier: ^4.7.5
     version: 4.7.5
-  stable-hash:
-    specifier: ^0.0.4
-    version: 0.0.4
   use-debounce:
     specifier: ^10.0.2
     version: 10.0.2(react@18.3.1)
   use-device-pixel-ratio:
     specifier: ^1.1.2
     version: 1.1.2(react@18.3.1)
+  use-image:
+    specifier: ^1.1.1
+    version: 1.1.1(react-dom@18.3.1)(react@18.3.1)
   uuid:
     specifier: ^10.0.0
     version: 10.0.0
@@ -3564,8 +3561,8 @@ packages:
       prettier: 3.3.3
     dev: true
 
-  /@invoke-ai/ui-library@0.0.31(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.0.20)(@types/react@18.3.3)(i18next@23.12.2)(react-dom@18.3.1)(react@18.3.1):
-    resolution: {integrity: sha512-7LtOUN/bcGHc8jCRd2m22DvP2eeogqwM/shdXQpLH5RY2FzWJNXlWdVT4hIPGDu7znnk3xvXlZvo6tiGSjbnCQ==}
+  /@invoke-ai/ui-library@0.0.29(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.0.20)(@types/react@18.3.3)(i18next@23.12.2)(react-dom@18.3.1)(react@18.3.1):
+    resolution: {integrity: sha512-7SYOaiEEKk9iHk0hg2R2yVxiuV3I1x6bDEv0R3Y2tCH/Aq5XDG2tR+d7SQAPqf5+za3S+qfNFjbjl7GvEMwqmA==}
     peerDependencies:
       '@fontsource-variable/inter': ^5.0.16
       react: ^18.2.0
@@ -5205,6 +5202,12 @@ packages:
       '@types/react': 18.3.3
     dev: true
 
+  /@types/react-reconciler@0.28.8:
+    resolution: {integrity: sha512-SN9c4kxXZonFhbX4hJrZy37yw9e7EIxcpHCxQv5JUS18wDE5ovkQKlqQEkufdJCCMfuI9BnjUJvhYeJ9x5Ra7g==}
+    dependencies:
+      '@types/react': 18.3.3
+    dev: false
+
   /@types/react-transition-group@4.4.10:
     resolution: {integrity: sha512-hT/+s0VQs2ojCX823m60m5f0sL5idt9SO6Tj6Dg+rdphGPIeJbJ6CxvBYkgkGKrYeDjvIpKTR38UzmtHJOGW3Q==}
     dependencies:
@@ -5900,12 +5903,6 @@ packages:
       tslib: 2.6.3
     dev: true
 
-  /async-mutex@0.5.0:
-    resolution: {integrity: sha512-1A94B18jkJ3DYq284ohPxoXbfTA5HsQ7/Mf4DEhcyLx3Bz27Rh59iScbB6EPiP+B+joue6YCxcMXSbFC1tZKwA==}
-    dependencies:
-      tslib: 2.6.3
-    dev: false
-
   /attr-accept@2.2.2:
     resolution: {integrity: sha512-7prDjvt9HmqiZ0cl5CRjtS84sEyhsHP2coDkaZKRKVfCDo9s7iw7ChVmar78Gu9pC4SoR/28wFu/G5JJhTnqEg==}
     engines: {node: '>=4'}
@@ -8387,6 +8384,15 @@ packages:
       set-function-name: 2.0.2
     dev: true
 
+  /its-fine@1.2.5(react@18.3.1):
+    resolution: {integrity: sha512-fXtDA0X0t0eBYAGLVM5YsgJGsJ5jEmqZEPrGbzdf5awjv0xE7nqv3TVnvtUF060Tkes15DbDAKW/I48vsb6SyA==}
+    peerDependencies:
+      react: '>=18.0'
+    dependencies:
+      '@types/react-reconciler': 0.28.8
+      react: 18.3.1
+    dev: false
+
   /jackspeak@2.3.6:
     resolution: {integrity: sha512-N3yCS/NegsOBokc8GAdM8UcmfsKiSS8cipheD/nivzr700H+nsMOxJjQnvwOcRYVuFkdH0wGUvW2WbXGmrZGbQ==}
     engines: {node: '>=14'}
@@ -8704,11 +8710,6 @@ packages:
     engines: {node: 14 || >=16.14}
     dev: true
 
-  /lru-cache@11.0.0:
-    resolution: {integrity: sha512-Qv32eSV1RSCfhY3fpPE2GNZ8jgM9X7rdAfemLWqTUxwiyIC4jJ6Sy0fZ8H+oLWevO6i4/bizg7c8d8i6bxrzbA==}
-    engines: {node: 20 || >=22}
-    dev: false
-
   /lru-cache@5.1.1:
     resolution: {integrity: sha512-KpNARQA3Iwv+jTA0utUVVbrh+Jlrr1Fv0e56GGzAFOXN7dk/FviaDW8LHmK52DlcH4WP2n6gI8vN1aesBFgo9w==}
     dependencies:
@@ -8996,12 +8997,6 @@ packages:
     hasBin: true
     dev: true
 
-  /nanoid@5.0.7:
-    resolution: {integrity: sha512-oLxFY2gd2IqnjcYyOXD8XGCftpGtZP2AbHbOkthDkvRywH5ayNtPVy9YlOPcHckXzbLTCHpkb7FB+yuxKV13pQ==}
-    engines: {node: ^18 || >=20}
-    hasBin: true
-    dev: false
-
   /nanostores@0.11.2:
     resolution: {integrity: sha512-6bucNxMJA5rNV554WQl+MWGng0QVMzlRgpKTHHfIbVLrhQ+yRXBychV9ECGVuuUfCMQPjfIG9bj8oJFZ9hYP/Q==}
     engines: {node: ^18.0.0 || >=20.0.0}
@@ -9806,6 +9801,33 @@ packages:
     resolution: {integrity: sha512-/LLMVyas0ljjAtoYiPqYiL8VWXzUUdThrmU5+n20DZv+a+ClRoevUzw5JxU+Ieh5/c87ytoTBV9G1FiKfNJdmg==}
     dev: true
 
+  /react-konva@18.2.10(konva@9.3.14)(react-dom@18.3.1)(react@18.3.1):
+    resolution: {integrity: sha512-ohcX1BJINL43m4ynjZ24MxFI1syjBdrXhqVxYVDw2rKgr3yuS0x/6m1Y2Z4sl4T/gKhfreBx8KHisd0XC6OT1g==}
+    peerDependencies:
+      konva: ^8.0.1 || ^7.2.5 || ^9.0.0
+      react: '>=18.0.0'
+      react-dom: '>=18.0.0'
+    dependencies:
+      '@types/react-reconciler': 0.28.8
+      its-fine: 1.2.5(react@18.3.1)
+      konva: 9.3.14
+      react: 18.3.1
+      react-dom: 18.3.1(react@18.3.1)
+      react-reconciler: 0.29.2(react@18.3.1)
+      scheduler: 0.23.2
+    dev: false
+
+  /react-reconciler@0.29.2(react@18.3.1):
+    resolution: {integrity: sha512-zZQqIiYgDCTP/f1N/mAR10nJGrPD2ZR+jDSEsKWJHYC7Cm2wodlwbR3upZRdC3cjIjSlTLNVyO7Iu0Yy7t2AYg==}
+    engines: {node: '>=0.10.0'}
+    peerDependencies:
+      react: ^18.3.1
+    dependencies:
+      loose-envify: 1.4.0
+      react: 18.3.1
+      scheduler: 0.23.2
+    dev: false
+
   /react-redux@9.1.2(@types/react@18.3.3)(react@18.3.1)(redux@5.0.1):
     resolution: {integrity: sha512-0OA4dhM1W48l3uzmv6B7TXPCGmokUU4p1M44DGN2/D9a1FjVPukVjER1PcPX97jIg6aUeLq1XJo1IpfbgULn0w==}
     peerDependencies:
@@ -10603,10 +10625,6 @@ packages:
     resolution: {integrity: sha512-D9cPgkvLlV3t3IzL0D0YLvGA9Ahk4PcvVwUbN0dSGr1aP0Nrt4AEnTUbuGvquEC0mA64Gqt1fzirlRs5ibXx8g==}
     dev: true
 
-  /stable-hash@0.0.4:
-    resolution: {integrity: sha512-LjdcbuBeLcdETCrPn9i8AYAZ1eCtu4ECAWtP7UleOiZ9LzVxRzzUZEoZ8zB24nhkQnDWyET0I+3sWokSDS3E7g==}
-    dev: false
-
   /stack-generator@2.0.10:
     resolution: {integrity: sha512-mwnua/hkqM6pF4k8SnmZ2zfETsRUpWXREfA/goT8SLCV4iOFa4bzOX2nDipWAZFPTjLvQB82f5yaodMVhK0yJQ==}
     dependencies:
@@ -11312,6 +11330,16 @@ packages:
       react: 18.3.1
     dev: false
 
+  /use-image@1.1.1(react-dom@18.3.1)(react@18.3.1):
+    resolution: {integrity: sha512-n4YO2k8AJG/BcDtxmBx8Aa+47kxY5m335dJiCQA5tTeVU4XdhrhqR6wT0WISRXwdMEOv5CSjqekDZkEMiiWaYQ==}
+    peerDependencies:
+      react: '>=16.8.0'
+      react-dom: '>=16.8.0'
+    dependencies:
+      react: 18.3.1
+      react-dom: 18.3.1(react@18.3.1)
+    dev: false
+
   /use-isomorphic-layout-effect@1.1.2(@types/react@18.3.3)(react@18.3.1):
     resolution: {integrity: sha512-49L8yCO3iGT/ZF9QttjwLF/ZD9Iwto5LnH5LmEdk/6cFmXddqi2ulF0edxTwjj+7mqvpVVGQWvbXZdn32wRSHA==}
     peerDependencies:

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

@@ -80,7 +80,6 @@
         "aboutDesc": "Using Invoke for work? Check out:",
         "aboutHeading": "Own Your Creative Power",
         "accept": "Accept",
-        "apply": "Apply",
         "add": "Add",
         "advanced": "Advanced",
         "ai": "ai",
@@ -116,7 +115,6 @@
         "githubLabel": "Github",
         "goTo": "Go to",
         "hotkeysLabel": "Hotkeys",
-        "loadingImage": "Loading Image",
         "imageFailedToLoad": "Unable to Load Image",
         "img2img": "Image To Image",
         "inpaint": "inpaint",
@@ -327,10 +325,6 @@
         "canceled": "Canceled",
         "completedIn": "Completed in",
         "batch": "Batch",
-        "origin": "Origin",
-        "originCanvas": "Canvas",
-        "originWorkflows": "Workflows",
-        "originOther": "Other",
         "batchFieldValues": "Batch Field Values",
         "item": "Item",
         "session": "Session",
@@ -702,6 +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.",
@@ -789,13 +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",
@@ -1101,6 +1100,7 @@
         "confirmOnDelete": "Confirm On Delete",
         "developer": "Developer",
         "displayInProgress": "Display Progress Images",
+        "enableImageDebugging": "Enable Image Debugging",
         "enableInformationalPopovers": "Enable Informational Popovers",
         "informationalPopoversDisabled": "Informational Popovers Disabled",
         "informationalPopoversDisabledDesc": "Informational popovers have been disabled. Enable them in Settings.",
@@ -1567,7 +1567,7 @@
         "copyToClipboard": "Copy to Clipboard",
         "cursorPosition": "Cursor Position",
         "darkenOutsideSelection": "Darken Outside Selection",
-        "discardAll": "Discard All & Cancel Pending Generations",
+        "discardAll": "Discard All",
         "discardCurrent": "Discard Current",
         "downloadAsImage": "Download As Image",
         "enableMask": "Enable Mask",
@@ -1645,31 +1645,16 @@
         "storeNotInitialized": "Store is not initialized"
     },
     "controlLayers": {
-        "generateMode": "Generate",
-        "generateModeDesc": "Create individual images. Generated images are added directly to the gallery.",
-        "composeMode": "Compose",
-        "composeModeDesc": "Compose your work iterative. Generated images are added back to the canvas.",
-        "autoSave": "Auto-save to Gallery",
-        "resetCanvas": "Reset Canvas",
-        "resetAll": "Reset All",
         "deleteAll": "Delete All",
-        "clearCaches": "Clear Caches",
-        "recalculateRects": "Recalculate Rects",
-        "clipToBbox": "Clip Strokes to Bbox",
         "addLayer": "Add Layer",
         "moveToFront": "Move to Front",
         "moveToBack": "Move to Back",
         "moveForward": "Move Forward",
         "moveBackward": "Move Backward",
         "brushSize": "Brush Size",
-        "width": "Width",
-        "zoom": "Zoom",
-        "resetView": "Reset View",
         "controlLayers": "Control Layers",
         "globalMaskOpacity": "Global Mask Opacity",
         "autoNegative": "Auto Negative",
-        "enableAutoNegative": "Enable Auto Negative",
-        "disableAutoNegative": "Disable Auto Negative",
         "deletePrompt": "Delete Prompt",
         "resetRegion": "Reset Region",
         "debugLayers": "Debug Layers",
@@ -1678,83 +1663,21 @@
         "addPositivePrompt": "Add $t(common.positivePrompt)",
         "addNegativePrompt": "Add $t(common.negativePrompt)",
         "addIPAdapter": "Add $t(common.ipAdapter)",
+        "regionalGuidance": "Regional Guidance",
         "regionalGuidanceLayer": "$t(controlLayers.regionalGuidance) $t(unifiedCanvas.layer)",
-        "raster": "Raster",
-        "rasterLayer_one": "Raster Layer",
-        "controlLayer_one": "Control Layer",
-        "inpaintMask_one": "Inpaint Mask",
-        "regionalGuidance_one": "Regional Guidance",
-        "ipAdapter_one": "IP Adapter",
-        "rasterLayer_other": "Raster Layers",
-        "controlLayer_other": "Control Layers",
-        "inpaintMask_other": "Inpaint Masks",
-        "regionalGuidance_other": "Regional Guidance",
-        "ipAdapter_other": "IP Adapters",
         "opacity": "Opacity",
-        "regionalGuidance_withCount_hidden": "Regional Guidance ({{count}} hidden)",
-        "controlAdapters_withCount_hidden": "Control Adapters ({{count}} hidden)",
-        "controlLayers_withCount_hidden": "Control Layers ({{count}} hidden)",
-        "rasterLayers_withCount_hidden": "Raster Layers ({{count}} hidden)",
-        "ipAdapters_withCount_hidden": "IP Adapters ({{count}} hidden)",
-        "inpaintMasks_withCount_hidden": "Inpaint Masks ({{count}} hidden)",
-        "regionalGuidance_withCount_visible": "Regional Guidance ({{count}})",
-        "controlAdapters_withCount_visible": "Control Adapters ({{count}})",
-        "controlLayers_withCount_visible": "Control Layers ({{count}})",
-        "rasterLayers_withCount_visible": "Raster Layers ({{count}})",
-        "ipAdapters_withCount_visible": "IP Adapters ({{count}})",
-        "inpaintMasks_withCount_visible": "Inpaint Masks ({{count}})",
         "globalControlAdapter": "Global $t(controlnet.controlAdapter_one)",
         "globalControlAdapterLayer": "Global $t(controlnet.controlAdapter_one) $t(unifiedCanvas.layer)",
         "globalIPAdapter": "Global $t(common.ipAdapter)",
         "globalIPAdapterLayer": "Global $t(common.ipAdapter) $t(unifiedCanvas.layer)",
         "globalInitialImage": "Global Initial Image",
         "globalInitialImageLayer": "$t(controlLayers.globalInitialImage) $t(unifiedCanvas.layer)",
-        "layer": "Layer",
         "opacityFilter": "Opacity Filter",
         "clearProcessor": "Clear Processor",
         "resetProcessor": "Reset Processor to Defaults",
         "noLayersAdded": "No Layers Added",
         "layers_one": "Layer",
-        "layers_other": "Layers",
-        "objects_zero": "empty",
-        "objects_one": "{{count}} object",
-        "objects_other": "{{count}} objects",
-        "convertToControlLayer": "Convert to Control Layer",
-        "convertToRasterLayer": "Convert to Raster Layer",
-        "transparency": "Transparency",
-        "enableTransparencyEffect": "Enable Transparency Effect",
-        "disableTransparencyEffect": "Disable Transparency Effect",
-        "hidingType": "Hiding {{type}}",
-        "showingType": "Showing {{type}}",
-        "dynamicGrid": "Dynamic Grid",
-        "logDebugInfo": "Log Debug Info",
-        "fill": {
-            "fillStyle": "Fill Style",
-            "solid": "Solid",
-            "grid": "Grid",
-            "crosshatch": "Crosshatch",
-            "vertical": "Vertical",
-            "horizontal": "Horizontal",
-            "diagonal": "Diagonal"
-        },
-        "tool": {
-            "brush": "Brush",
-            "eraser": "Eraser",
-            "rectangle": "Rectangle",
-            "bbox": "Bbox",
-            "move": "Move",
-            "view": "View",
-            "transform": "Transform",
-            "colorPicker": "Color Picker"
-        },
-        "filter": {
-            "filter": "Filter",
-            "filters": "Filters",
-            "filterType": "Filter Type",
-            "preview": "Preview",
-            "apply": "Apply",
-            "cancel": "Cancel"
-        }
+        "layers_other": "Layers"
     },
     "upscaling": {
         "upscale": "Upscale",
@@ -1842,30 +1765,5 @@
             "upscaling": "Upscaling",
             "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)"
         }
-    },
-    "system": {
-        "enableLogging": "Enable Logging",
-        "logLevel": {
-            "logLevel": "Log Level",
-            "trace": "Trace",
-            "debug": "Debug",
-            "info": "Info",
-            "warn": "Warn",
-            "error": "Error",
-            "fatal": "Fatal"
-        },
-        "logNamespaces": {
-            "logNamespaces": "Log Namespaces",
-            "gallery": "Gallery",
-            "models": "Models",
-            "config": "Config",
-            "canvas": "Canvas",
-            "generation": "Generation",
-            "workflows": "Workflows",
-            "system": "System",
-            "events": "Events",
-            "queue": "Queue",
-            "metadata": "Metadata"
-        }
     }
 }

invokeai/frontend/web/scripts/typegen.js

@@ -38,7 +38,7 @@ async function generateTypes(schema) {
   process.stdout.write(`\nOK!\r\n`);
 }
 
-function main() {
+async function main() {
   const encoding = 'utf-8';
 
   if (process.stdin.isTTY) {

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

@@ -6,7 +6,6 @@ import { appStarted } from 'app/store/middleware/listenerMiddleware/listeners/ap
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import type { PartialAppConfig } from 'app/types/invokeai';
 import ImageUploadOverlay from 'common/components/ImageUploadOverlay';
-import { useScopeFocusWatcher } from 'common/hooks/interactionScopes';
 import { useClearStorage } from 'common/hooks/useClearStorage';
 import { useFullscreenDropzone } from 'common/hooks/useFullscreenDropzone';
 import { useGlobalHotkeys } from 'common/hooks/useGlobalHotkeys';
@@ -14,13 +13,13 @@ import ChangeBoardModal from 'features/changeBoardModal/components/ChangeBoardMo
 import DeleteImageModal from 'features/deleteImageModal/components/DeleteImageModal';
 import { DynamicPromptsModal } from 'features/dynamicPrompts/components/DynamicPromptsPreviewModal';
 import { useStarterModelsToast } from 'features/modelManagerV2/hooks/useStarterModelsToast';
-import { ClearQueueConfirmationsAlertDialog } from 'features/queue/components/ClearQueueConfirmationAlertDialog';
 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 { AppContent } from 'features/ui/components/AppContent';
+import InvokeTabs from 'features/ui/components/InvokeTabs';
+import type { InvokeTabName } from 'features/ui/store/tabMap';
 import { setActiveTab } from 'features/ui/store/uiSlice';
-import type { TabName } from 'features/ui/store/uiTypes';
 import { useGetAndLoadLibraryWorkflow } from 'features/workflowLibrary/hooks/useGetAndLoadLibraryWorkflow';
 import { AnimatePresence } from 'framer-motion';
 import i18n from 'i18n';
@@ -41,10 +40,17 @@ interface Props {
     action: 'sendToImg2Img' | 'sendToCanvas' | 'useAllParameters';
   };
   selectedWorkflowId?: string;
-  destination?: TabName | undefined;
+  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();
@@ -83,6 +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));
@@ -95,7 +107,6 @@ const App = ({ config = DEFAULT_CONFIG, selectedImage, selectedWorkflowId, desti
 
   useStarterModelsToast();
   useSyncQueueStatus();
-  useScopeFocusWatcher();
 
   return (
     <ErrorBoundary onReset={handleReset} FallbackComponent={AppErrorBoundaryFallback}>
@@ -108,7 +119,7 @@ const App = ({ config = DEFAULT_CONFIG, selectedImage, selectedWorkflowId, desti
         {...dropzone.getRootProps()}
       >
         <input {...dropzone.getInputProps()} />
-        <AppContent />
+        <InvokeTabs />
         <AnimatePresence>
           {dropzone.isDragActive && isHandlingUpload && (
             <ImageUploadOverlay dropzone={dropzone} setIsHandlingUpload={setIsHandlingUpload} />
@@ -119,7 +130,6 @@ const App = ({ config = DEFAULT_CONFIG, selectedImage, selectedWorkflowId, desti
       <ChangeBoardModal />
       <DynamicPromptsModal />
       <StylePresetModal />
-      <ClearQueueConfirmationsAlertDialog />
       <PreselectedImage selectedImage={selectedImage} />
     </ErrorBoundary>
   );

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

@@ -19,7 +19,7 @@ import type { PartialAppConfig } from 'app/types/invokeai';
 import Loading from 'common/components/Loading/Loading';
 import AppDndContext from 'features/dnd/components/AppDndContext';
 import type { WorkflowCategory } from 'features/nodes/types/workflow';
-import type { TabName } from 'features/ui/store/uiTypes';
+import type { InvokeTabName } from 'features/ui/store/tabMap';
 import type { PropsWithChildren, ReactNode } from 'react';
 import React, { lazy, memo, useEffect, useMemo } from 'react';
 import { Provider } from 'react-redux';
@@ -45,7 +45,8 @@ interface Props extends PropsWithChildren {
     action: 'sendToImg2Img' | 'sendToCanvas' | 'useAllParameters';
   };
   selectedWorkflowId?: string;
-  destination?: TabName;
+  selectedStylePresetId?: string;
+  destination?: InvokeTabName;
   customStarUi?: CustomStarUi;
   socketOptions?: Partial<ManagerOptions & SocketOptions>;
   isDebugging?: boolean;
@@ -66,6 +67,7 @@ const InvokeAIUI = ({
   queueId,
   selectedImage,
   selectedWorkflowId,
+  selectedStylePresetId,
   destination,
   customStarUi,
   socketOptions,
@@ -227,6 +229,7 @@ const InvokeAIUI = ({
                 config={config}
                 selectedImage={selectedImage}
                 selectedWorkflowId={selectedWorkflowId}
+                selectedStylePresetId={selectedStylePresetId}
                 destination={destination}
               />
             </AppDndContext>

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

@@ -2,7 +2,7 @@ import { useStore } from '@nanostores/react';
 import { $authToken } from 'app/store/nanostores/authToken';
 import { $baseUrl } from 'app/store/nanostores/baseUrl';
 import { $isDebugging } from 'app/store/nanostores/isDebugging';
-import { useAppStore } from 'app/store/nanostores/store';
+import { useAppDispatch } from 'app/store/storeHooks';
 import type { MapStore } from 'nanostores';
 import { atom, map } from 'nanostores';
 import { useEffect, useMemo } from 'react';
@@ -18,19 +18,14 @@ declare global {
   }
 }
 
-export type AppSocket = Socket<ServerToClientEvents, ClientToServerEvents>;
-
-export const $socket = atom<AppSocket | null>(null);
 export const $socketOptions = map<Partial<ManagerOptions & SocketOptions>>({});
-
 const $isSocketInitialized = atom<boolean>(false);
-export const $isConnected = atom<boolean>(false);
 
 /**
  * Initializes the socket.io connection and sets up event listeners.
  */
 export const useSocketIO = () => {
-  const { dispatch, getState } = useAppStore();
+  const dispatch = useAppDispatch();
   const baseUrl = useStore($baseUrl);
   const authToken = useStore($authToken);
   const addlSocketOptions = useStore($socketOptions);
@@ -66,9 +61,8 @@ export const useSocketIO = () => {
       return;
     }
 
-    const socket: AppSocket = io(socketUrl, socketOptions);
-    $socket.set(socket);
-    setEventListeners({ socket, dispatch, getState, setIsConnected: $isConnected.set });
+    const socket: Socket<ServerToClientEvents, ClientToServerEvents> = io(socketUrl, socketOptions);
+    setEventListeners({ dispatch, socket });
     socket.connect();
 
     if ($isDebugging.get() || import.meta.env.MODE === 'development') {
@@ -90,5 +84,5 @@ export const useSocketIO = () => {
       socket.disconnect();
       $isSocketInitialized.set(false);
     };
-  }, [dispatch, getState, socketOptions, socketUrl]);
+  }, [dispatch, socketOptions, socketUrl]);
 };

invokeai/frontend/web/src/app/logging/logger.ts

@@ -15,21 +15,21 @@ export const BASE_CONTEXT = {};
 
 export const $logger = atom<Logger>(Roarr.child(BASE_CONTEXT));
 
-export const zLogNamespace = z.enum([
-  'canvas',
-  'config',
-  'events',
-  'gallery',
-  'generation',
-  'metadata',
-  'models',
-  'system',
-  'queue',
-  'workflows',
-]);
-export type LogNamespace = z.infer<typeof zLogNamespace>;
+export type LoggerNamespace =
+  | 'images'
+  | 'models'
+  | 'config'
+  | 'canvas'
+  | 'generation'
+  | 'nodes'
+  | 'system'
+  | 'socketio'
+  | 'session'
+  | 'queue'
+  | 'dnd'
+  | 'controlLayers';
 
-export const logger = (namespace: LogNamespace) => $logger.get().child({ namespace });
+export const logger = (namespace: LoggerNamespace) => $logger.get().child({ namespace });
 
 export const zLogLevel = z.enum(['trace', 'debug', 'info', 'warn', 'error', 'fatal']);
 export type LogLevel = z.infer<typeof zLogLevel>;

invokeai/frontend/web/src/app/logging/useLogger.ts

@@ -3,34 +3,27 @@ import { useAppSelector } from 'app/store/storeHooks';
 import { useEffect, useMemo } from 'react';
 import { ROARR, Roarr } from 'roarr';
 
-import type { LogNamespace } from './logger';
+import type { LoggerNamespace } from './logger';
 import { $logger, BASE_CONTEXT, LOG_LEVEL_MAP, logger } from './logger';
 
-export const useLogger = (namespace: LogNamespace) => {
-  const logLevel = useAppSelector((s) => s.system.logLevel);
-  const logNamespaces = useAppSelector((s) => s.system.logNamespaces);
-  const logIsEnabled = useAppSelector((s) => s.system.logIsEnabled);
+export const useLogger = (namespace: LoggerNamespace) => {
+  const consoleLogLevel = useAppSelector((s) => s.system.consoleLogLevel);
+  const shouldLogToConsole = useAppSelector((s) => s.system.shouldLogToConsole);
 
   // The provided Roarr browser log writer uses localStorage to config logging to console
   useEffect(() => {
-    if (logIsEnabled) {
+    if (shouldLogToConsole) {
       // Enable console log output
       localStorage.setItem('ROARR_LOG', 'true');
 
       // Use a filter to show only logs of the given level
-      let filter = `context.logLevel:>=${LOG_LEVEL_MAP[logLevel]}`;
-      if (logNamespaces.length > 0) {
-        filter += ` AND (${logNamespaces.map((ns) => `context.namespace:${ns}`).join(' OR ')})`;
-      } else {
-        filter += ' AND context.namespace:undefined';
-      }
-      localStorage.setItem('ROARR_FILTER', filter);
+      localStorage.setItem('ROARR_FILTER', `context.logLevel:>=${LOG_LEVEL_MAP[consoleLogLevel]}`);
     } else {
       // Disable console log output
       localStorage.setItem('ROARR_LOG', 'false');
     }
     ROARR.write = createLogWriter();
-  }, [logLevel, logIsEnabled, logNamespaces]);
+  }, [consoleLogLevel, shouldLogToConsole]);
 
   // Update the module-scoped logger context as needed
   useEffect(() => {

invokeai/frontend/web/src/app/store/actions.ts

@@ -1,7 +1,7 @@
 import { createAction } from '@reduxjs/toolkit';
-import type { TabName } from 'features/ui/store/uiTypes';
+import type { InvokeTabName } from 'features/ui/store/tabMap';
 
 export const enqueueRequested = createAction<{
-  tabName: TabName;
+  tabName: InvokeTabName;
   prepend: boolean;
 }>('app/enqueueRequested');

invokeai/frontend/web/src/app/store/createMemoizedSelector.ts

@@ -1,6 +1,5 @@
 import { createDraftSafeSelectorCreator, createSelectorCreator, lruMemoize } from '@reduxjs/toolkit';
 import type { GetSelectorsOptions } from '@reduxjs/toolkit/dist/entities/state_selectors';
-import type { RootState } from 'app/store/store';
 import { isEqual } from 'lodash-es';
 
 /**
@@ -20,5 +19,3 @@ export const getSelectorsOptions: GetSelectorsOptions = {
     argsMemoize: lruMemoize,
   }),
 };
-
-export const createMemoizedAppSelector = createMemoizedSelector.withTypes<RootState>();

invokeai/frontend/web/src/app/store/enhancers/reduxRemember/errors.ts

@@ -1,4 +1,5 @@
 import { logger } from 'app/logging/logger';
+import { parseify } from 'common/util/serialize';
 import { PersistError, RehydrateError } from 'redux-remember';
 import { serializeError } from 'serialize-error';
 
@@ -40,6 +41,6 @@ export const errorHandler = (err: PersistError | RehydrateError) => {
   } else if (err instanceof RehydrateError) {
     log.error({ error: serializeError(err) }, 'Problem rehydrating state');
   } else {
-    log.error({ error: serializeError(err) }, 'Problem in persistence layer');
+    log.error({ error: parseify(err) }, 'Problem in persistence layer');
   }
 };

invokeai/frontend/web/src/app/store/middleware/devtools/actionSanitizer.ts

@@ -1,7 +1,9 @@
 import type { UnknownAction } from '@reduxjs/toolkit';
+import { deepClone } from 'common/util/deepClone';
 import { isAnyGraphBuilt } from 'features/nodes/store/actions';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
 import type { Graph } from 'services/api/types';
+import { socketGeneratorProgress } from 'services/events/actions';
 
 export const actionSanitizer = <A extends UnknownAction>(action: A): A => {
   if (isAnyGraphBuilt(action)) {
@@ -22,5 +24,13 @@ export const actionSanitizer = <A extends UnknownAction>(action: A): A => {
     };
   }
 
+  if (socketGeneratorProgress.match(action)) {
+    const sanitized = deepClone(action);
+    if (sanitized.payload.data.progress_image) {
+      sanitized.payload.data.progress_image.dataURL = '<Progress image omitted>';
+    }
+    return sanitized;
+  }
+
   return action;
 };

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts

@@ -1,7 +1,7 @@
 import type { TypedStartListening } from '@reduxjs/toolkit';
 import { createListenerMiddleware } from '@reduxjs/toolkit';
 import { addAdHocPostProcessingRequestedListener } from 'app/store/middleware/listenerMiddleware/listeners/addAdHocPostProcessingRequestedListener';
-import { addStagingListeners } from 'app/store/middleware/listenerMiddleware/listeners/addCommitStagingAreaImageListener';
+import { addCommitStagingAreaImageListener } from 'app/store/middleware/listenerMiddleware/listeners/addCommitStagingAreaImageListener';
 import { addAnyEnqueuedListener } from 'app/store/middleware/listenerMiddleware/listeners/anyEnqueued';
 import { addAppConfigReceivedListener } from 'app/store/middleware/listenerMiddleware/listeners/appConfigReceived';
 import { addAppStartedListener } from 'app/store/middleware/listenerMiddleware/listeners/appStarted';
@@ -9,6 +9,17 @@ import { addBatchEnqueuedListener } from 'app/store/middleware/listenerMiddlewar
 import { addDeleteBoardAndImagesFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/boardAndImagesDeleted';
 import { addBoardIdSelectedListener } from 'app/store/middleware/listenerMiddleware/listeners/boardIdSelected';
 import { addBulkDownloadListeners } from 'app/store/middleware/listenerMiddleware/listeners/bulkDownload';
+import { addCanvasCopiedToClipboardListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasCopiedToClipboard';
+import { addCanvasDownloadedAsImageListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasDownloadedAsImage';
+import { addCanvasImageToControlNetListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasImageToControlNet';
+import { addCanvasMaskSavedToGalleryListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasMaskSavedToGallery';
+import { addCanvasMaskToControlNetListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasMaskToControlNet';
+import { addCanvasMergedListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasMerged';
+import { addCanvasSavedToGalleryListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasSavedToGallery';
+import { addControlAdapterPreprocessor } from 'app/store/middleware/listenerMiddleware/listeners/controlAdapterPreprocessor';
+import { addControlNetAutoProcessListener } from 'app/store/middleware/listenerMiddleware/listeners/controlNetAutoProcess';
+import { addControlNetImageProcessedListener } from 'app/store/middleware/listenerMiddleware/listeners/controlNetImageProcessed';
+import { addEnqueueRequestedCanvasListener } from 'app/store/middleware/listenerMiddleware/listeners/enqueueRequestedCanvas';
 import { addEnqueueRequestedLinear } from 'app/store/middleware/listenerMiddleware/listeners/enqueueRequestedLinear';
 import { addEnqueueRequestedNodes } from 'app/store/middleware/listenerMiddleware/listeners/enqueueRequestedNodes';
 import { addGalleryImageClickedListener } from 'app/store/middleware/listenerMiddleware/listeners/galleryImageClicked';
@@ -26,7 +37,16 @@ import { addModelSelectedListener } from 'app/store/middleware/listenerMiddlewar
 import { addModelsLoadedListener } from 'app/store/middleware/listenerMiddleware/listeners/modelsLoaded';
 import { addDynamicPromptsListener } from 'app/store/middleware/listenerMiddleware/listeners/promptChanged';
 import { addSetDefaultSettingsListener } from 'app/store/middleware/listenerMiddleware/listeners/setDefaultSettings';
-import { addSocketConnectedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketConnected';
+import { addSocketConnectedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketConnected';
+import { addSocketDisconnectedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketDisconnected';
+import { addGeneratorProgressEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketGeneratorProgress';
+import { addInvocationCompleteEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationComplete';
+import { addInvocationErrorEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationError';
+import { addInvocationStartedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationStarted';
+import { addModelInstallEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketModelInstall';
+import { addModelLoadEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketModelLoad';
+import { addSocketQueueItemStatusChangedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketQueueItemStatusChanged';
+import { addStagingAreaImageSavedListener } from 'app/store/middleware/listenerMiddleware/listeners/stagingAreaImageSaved';
 import { addUpdateAllNodesRequestedListener } from 'app/store/middleware/listenerMiddleware/listeners/updateAllNodesRequested';
 import { addWorkflowLoadRequestedListener } from 'app/store/middleware/listenerMiddleware/listeners/workflowLoadRequested';
 import type { AppDispatch, RootState } from 'app/store/store';
@@ -63,6 +83,7 @@ addGalleryImageClickedListener(startAppListening);
 addGalleryOffsetChangedListener(startAppListening);
 
 // User Invoked
+addEnqueueRequestedCanvasListener(startAppListening);
 addEnqueueRequestedNodes(startAppListening);
 addEnqueueRequestedLinear(startAppListening);
 addEnqueueRequestedUpscale(startAppListening);
@@ -70,23 +91,32 @@ addAnyEnqueuedListener(startAppListening);
 addBatchEnqueuedListener(startAppListening);
 
 // Canvas actions
-// addCanvasSavedToGalleryListener(startAppListening);
-// addCanvasMaskSavedToGalleryListener(startAppListening);
-// addCanvasImageToControlNetListener(startAppListening);
-// addCanvasMaskToControlNetListener(startAppListening);
-// addCanvasDownloadedAsImageListener(startAppListening);
-// addCanvasCopiedToClipboardListener(startAppListening);
-// addCanvasMergedListener(startAppListening);
-// addStagingAreaImageSavedListener(startAppListening);
-// addCommitStagingAreaImageListener(startAppListening);
-addStagingListeners(startAppListening);
+addCanvasSavedToGalleryListener(startAppListening);
+addCanvasMaskSavedToGalleryListener(startAppListening);
+addCanvasImageToControlNetListener(startAppListening);
+addCanvasMaskToControlNetListener(startAppListening);
+addCanvasDownloadedAsImageListener(startAppListening);
+addCanvasCopiedToClipboardListener(startAppListening);
+addCanvasMergedListener(startAppListening);
+addStagingAreaImageSavedListener(startAppListening);
+addCommitStagingAreaImageListener(startAppListening);
 
 // Socket.IO
+addGeneratorProgressEventListener(startAppListening);
+addInvocationCompleteEventListener(startAppListening);
+addInvocationErrorEventListener(startAppListening);
+addInvocationStartedEventListener(startAppListening);
 addSocketConnectedEventListener(startAppListening);
-
-// Gallery bulk download
+addSocketDisconnectedEventListener(startAppListening);
+addModelLoadEventListener(startAppListening);
+addModelInstallEventListener(startAppListening);
+addSocketQueueItemStatusChangedEventListener(startAppListening);
 addBulkDownloadListeners(startAppListening);
 
+// ControlNet
+addControlNetImageProcessedListener(startAppListening);
+addControlNetAutoProcessListener(startAppListening);
+
 // Boards
 addImageAddedToBoardFulfilledListener(startAppListening);
 addImageRemovedFromBoardFulfilledListener(startAppListening);
@@ -118,4 +148,4 @@ addAdHocPostProcessingRequestedListener(startAppListening);
 addDynamicPromptsListener(startAppListening);
 
 addSetDefaultSettingsListener(startAppListening);
-// addControlAdapterPreprocessor(startAppListening);
+addControlAdapterPreprocessor(startAppListening);

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

@@ -1,21 +1,21 @@
 import { createAction } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import type { SerializableObject } from 'common/types';
+import { parseify } from 'common/util/serialize';
 import { buildAdHocPostProcessingGraph } from 'features/nodes/util/graph/buildAdHocPostProcessingGraph';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { queueApi } from 'services/api/endpoints/queue';
 import type { BatchConfig, ImageDTO } from 'services/api/types';
 
-const log = logger('queue');
-
 export const adHocPostProcessingRequested = createAction<{ imageDTO: ImageDTO }>(`upscaling/postProcessingRequested`);
 
 export const addAdHocPostProcessingRequestedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: adHocPostProcessingRequested,
     effect: async (action, { dispatch, getState }) => {
+      const log = logger('session');
+
       const { imageDTO } = action.payload;
       const state = getState();
 
@@ -39,9 +39,9 @@ export const addAdHocPostProcessingRequestedListener = (startAppListening: AppSt
 
         const enqueueResult = await req.unwrap();
         req.reset();
-        log.debug({ enqueueResult } as SerializableObject, t('queue.graphQueued'));
+        log.debug({ enqueueResult: parseify(enqueueResult) }, t('queue.graphQueued'));
       } catch (error) {
-        log.error({ enqueueBatchArg } as SerializableObject, t('queue.graphFailedToQueue'));
+        log.error({ enqueueBatchArg: parseify(enqueueBatchArg) }, t('queue.graphFailedToQueue'));
 
         if (error instanceof Object && 'status' in error && error.status === 403) {
           return;

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

@@ -23,7 +23,7 @@ export const addArchivedOrDeletedBoardListener = (startAppListening: AppStartLis
    */
   startAppListening({
     matcher: matchAnyBoardDeleted,
-    effect: (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const state = getState();
       const deletedBoardId = action.meta.arg.originalArgs;
       const { autoAddBoardId, selectedBoardId } = state.gallery;
@@ -44,7 +44,7 @@ export const addArchivedOrDeletedBoardListener = (startAppListening: AppStartLis
   // If we archived a board, it may end up hidden. If it's selected or the auto-add board, we should reset those.
   startAppListening({
     matcher: boardsApi.endpoints.updateBoard.matchFulfilled,
-    effect: (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const state = getState();
       const { shouldShowArchivedBoards } = state.gallery;
 
@@ -61,7 +61,7 @@ export const addArchivedOrDeletedBoardListener = (startAppListening: AppStartLis
   // When we hide archived boards, if the selected or the auto-add board is archived, we should reset those.
   startAppListening({
     actionCreator: shouldShowArchivedBoardsChanged,
-    effect: (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const shouldShowArchivedBoards = action.payload;
 
       // We only need to take action if we have just hidden archived boards.
@@ -100,7 +100,7 @@ export const addArchivedOrDeletedBoardListener = (startAppListening: AppStartLis
    */
   startAppListening({
     matcher: boardsApi.endpoints.listAllBoards.matchFulfilled,
-    effect: (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const boards = action.payload;
       const state = getState();
       const { selectedBoardId, autoAddBoardId } = state.gallery;

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

@@ -1,36 +1,33 @@
+import { isAnyOf } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import {
-  rasterLayerAdded,
-  sessionStagingAreaImageAccepted,
-  sessionStagingAreaReset,
-} from 'features/controlLayers/store/canvasV2Slice';
-import type { CanvasRasterLayerState } from 'features/controlLayers/store/types';
-import { imageDTOToImageObject } from 'features/controlLayers/store/types';
+  canvasBatchIdsReset,
+  commitStagingAreaImage,
+  discardStagedImages,
+  resetCanvas,
+  setInitialCanvasImage,
+} from 'features/canvas/store/canvasSlice';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { queueApi } from 'services/api/endpoints/queue';
-import { $lastCanvasProgressEvent } from 'services/events/setEventListeners';
-import { assert } from 'tsafe';
 
-const log = logger('canvas');
+const matcher = isAnyOf(commitStagingAreaImage, discardStagedImages, resetCanvas, setInitialCanvasImage);
 
-export const addStagingListeners = (startAppListening: AppStartListening) => {
+export const addCommitStagingAreaImageListener = (startAppListening: AppStartListening) => {
   startAppListening({
-    actionCreator: sessionStagingAreaReset,
-    effect: async (_, { dispatch }) => {
+    matcher,
+    effect: async (_, { dispatch, getState }) => {
+      const log = logger('canvas');
+      const state = getState();
+      const { batchIds } = state.canvas;
+
       try {
         const req = dispatch(
-          queueApi.endpoints.cancelByBatchOrigin.initiate(
-            { origin: 'canvas' },
-            { fixedCacheKey: 'cancelByBatchOrigin' }
-          )
+          queueApi.endpoints.cancelByBatchIds.initiate({ batch_ids: batchIds }, { fixedCacheKey: 'cancelByBatchIds' })
         );
         const { canceled } = await req.unwrap();
         req.reset();
-
-        $lastCanvasProgressEvent.set(null);
-
         if (canceled > 0) {
           log.debug(`Canceled ${canceled} canvas batches`);
           toast({
@@ -39,6 +36,7 @@ export const addStagingListeners = (startAppListening: AppStartListening) => {
             status: 'success',
           });
         }
+        dispatch(canvasBatchIdsReset());
       } catch {
         log.error('Failed to cancel canvas batches');
         toast({
@@ -49,26 +47,4 @@ export const addStagingListeners = (startAppListening: AppStartListening) => {
       }
     },
   });
-
-  startAppListening({
-    actionCreator: sessionStagingAreaImageAccepted,
-    effect: (action, api) => {
-      const { index } = action.payload;
-      const state = api.getState();
-      const stagingAreaImage = state.canvasV2.session.stagedImages[index];
-
-      assert(stagingAreaImage, 'No staged image found to accept');
-      const { x, y } = state.canvasV2.bbox.rect;
-
-      const { imageDTO, offsetX, offsetY } = stagingAreaImage;
-      const imageObject = imageDTOToImageObject(imageDTO);
-      const overrides: Partial<CanvasRasterLayerState> = {
-        position: { x: x + offsetX, y: y + offsetY },
-        objects: [imageObject],
-      };
-
-      api.dispatch(rasterLayerAdded({ overrides, isSelected: true }));
-      api.dispatch(sessionStagingAreaReset());
-    },
-  });
 };

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

@@ -4,7 +4,7 @@ import { queueApi, selectQueueStatus } from 'services/api/endpoints/queue';
 export const addAnyEnqueuedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: queueApi.endpoints.enqueueBatch.matchFulfilled,
-    effect: (_, { dispatch, getState }) => {
+    effect: async (_, { dispatch, getState }) => {
       const { data } = selectQueueStatus(getState());
 
       if (!data || data.processor.is_started) {

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

@@ -1,14 +1,14 @@
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { setInfillMethod } from 'features/controlLayers/store/canvasV2Slice';
+import { setInfillMethod } from 'features/parameters/store/generationSlice';
 import { shouldUseNSFWCheckerChanged, shouldUseWatermarkerChanged } from 'features/system/store/systemSlice';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
 
 export const addAppConfigReceivedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: appInfoApi.endpoints.getAppConfig.matchFulfilled,
-    effect: (action, { getState, dispatch }) => {
+    effect: async (action, { getState, dispatch }) => {
       const { infill_methods = [], nsfw_methods = [], watermarking_methods = [] } = action.payload;
-      const infillMethod = getState().canvasV2.compositing.infillMethod;
+      const infillMethod = getState().generation.infillMethod;
 
       if (!infill_methods.includes(infillMethod)) {
         // if there is no infill method, set it to the first one

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

@@ -6,7 +6,7 @@ export const appStarted = createAction('app/appStarted');
 export const addAppStartedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: appStarted,
-    effect: (action, { unsubscribe, cancelActiveListeners }) => {
+    effect: async (action, { unsubscribe, cancelActiveListeners }) => {
       // this should only run once
       cancelActiveListeners();
       unsubscribe();

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

@@ -1,30 +1,27 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import type { SerializableObject } from 'common/types';
+import { parseify } from 'common/util/serialize';
 import { zPydanticValidationError } from 'features/system/store/zodSchemas';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { truncate, upperFirst } from 'lodash-es';
-import { serializeError } from 'serialize-error';
 import { queueApi } from 'services/api/endpoints/queue';
 
-const log = logger('queue');
-
 export const addBatchEnqueuedListener = (startAppListening: AppStartListening) => {
   // success
   startAppListening({
     matcher: queueApi.endpoints.enqueueBatch.matchFulfilled,
-    effect: (action) => {
-      const enqueueResult = action.payload;
+    effect: async (action) => {
+      const response = action.payload;
       const arg = action.meta.arg.originalArgs;
-      log.debug({ enqueueResult } as SerializableObject, 'Batch enqueued');
+      logger('queue').debug({ enqueueResult: parseify(response) }, 'Batch enqueued');
 
       toast({
         id: 'QUEUE_BATCH_SUCCEEDED',
         title: t('queue.batchQueued'),
         status: 'success',
         description: t('queue.batchQueuedDesc', {
-          count: enqueueResult.enqueued,
+          count: response.enqueued,
           direction: arg.prepend ? t('queue.front') : t('queue.back'),
         }),
       });
@@ -34,9 +31,9 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
   // error
   startAppListening({
     matcher: queueApi.endpoints.enqueueBatch.matchRejected,
-    effect: (action) => {
+    effect: async (action) => {
       const response = action.payload;
-      const batchConfig = action.meta.arg.originalArgs;
+      const arg = action.meta.arg.originalArgs;
 
       if (!response) {
         toast({
@@ -45,7 +42,7 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
           status: 'error',
           description: t('common.unknownError'),
         });
-        log.error({ batchConfig } as SerializableObject, t('queue.batchFailedToQueue'));
+        logger('queue').error({ batchConfig: parseify(arg), error: parseify(response) }, t('queue.batchFailedToQueue'));
         return;
       }
 
@@ -71,7 +68,7 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
           description: t('common.unknownError'),
         });
       }
-      log.error({ batchConfig, error: serializeError(response) } as SerializableObject, t('queue.batchFailedToQueue'));
+      logger('queue').error({ batchConfig: parseify(arg), error: parseify(response) }, t('queue.batchFailedToQueue'));
     },
   });
 };

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

@@ -1,4 +1,7 @@
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { resetCanvas } from 'features/canvas/store/canvasSlice';
+import { controlAdaptersReset } from 'features/controlAdapters/store/controlAdaptersSlice';
+import { allLayersDeleted } from 'features/controlLayers/store/controlLayersSlice';
 import { getImageUsage } from 'features/deleteImageModal/store/selectors';
 import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
 import { imagesApi } from 'services/api/endpoints/images';
@@ -6,22 +9,39 @@ import { imagesApi } from 'services/api/endpoints/images';
 export const addDeleteBoardAndImagesFulfilledListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: imagesApi.endpoints.deleteBoardAndImages.matchFulfilled,
-    effect: (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const { deleted_images } = action.payload;
 
       // Remove all deleted images from the UI
 
+      let wasCanvasReset = false;
       let wasNodeEditorReset = false;
+      let wereControlAdaptersReset = false;
+      let wereControlLayersReset = false;
 
-      const { nodes, canvasV2 } = getState();
-
+      const { canvas, nodes, controlAdapters, controlLayers } = getState();
       deleted_images.forEach((image_name) => {
-        const imageUsage = getImageUsage(nodes.present, canvasV2, image_name);
+        const imageUsage = getImageUsage(canvas, nodes.present, controlAdapters, controlLayers.present, image_name);
+
+        if (imageUsage.isCanvasImage && !wasCanvasReset) {
+          dispatch(resetCanvas());
+          wasCanvasReset = true;
+        }
 
         if (imageUsage.isNodesImage && !wasNodeEditorReset) {
           dispatch(nodeEditorReset());
           wasNodeEditorReset = true;
         }
+
+        if (imageUsage.isControlImage && !wereControlAdaptersReset) {
+          dispatch(controlAdaptersReset());
+          wereControlAdaptersReset = true;
+        }
+
+        if (imageUsage.isControlLayerImage && !wereControlLayersReset) {
+          dispatch(allLayersDeleted());
+          wereControlLayersReset = true;
+        }
       });
     },
   });

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/bulkDownload.tsx

@@ -1,15 +1,21 @@
+import { ExternalLink } from '@invoke-ai/ui-library';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
+import {
+  socketBulkDownloadComplete,
+  socketBulkDownloadError,
+  socketBulkDownloadStarted,
+} from 'services/events/actions';
 
-const log = logger('gallery');
+const log = logger('images');
 
 export const addBulkDownloadListeners = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: imagesApi.endpoints.bulkDownloadImages.matchFulfilled,
-    effect: (action) => {
+    effect: async (action) => {
       log.debug(action.payload, 'Bulk download requested');
 
       // If we have an item name, we are processing the bulk download locally and should use it as the toast id to
@@ -27,7 +33,7 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
 
   startAppListening({
     matcher: imagesApi.endpoints.bulkDownloadImages.matchRejected,
-    effect: () => {
+    effect: async () => {
       log.debug('Bulk download request failed');
 
       // There isn't any toast to update if we get this event.
@@ -38,4 +44,55 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
       });
     },
   });
+
+  startAppListening({
+    actionCreator: socketBulkDownloadStarted,
+    effect: async (action) => {
+      // This should always happen immediately after the bulk download request, so we don't need to show a toast here.
+      log.debug(action.payload.data, 'Bulk download preparation started');
+    },
+  });
+
+  startAppListening({
+    actionCreator: socketBulkDownloadComplete,
+    effect: async (action) => {
+      log.debug(action.payload.data, 'Bulk download preparation completed');
+
+      const { bulk_download_item_name } = action.payload.data;
+
+      // TODO(psyche): This URL may break in in some environments (e.g. Nvidia workbench) but we need to test it first
+      const url = `/api/v1/images/download/${bulk_download_item_name}`;
+
+      toast({
+        id: bulk_download_item_name,
+        title: t('gallery.bulkDownloadReady', 'Download ready'),
+        status: 'success',
+        description: (
+          <ExternalLink
+            label={t('gallery.clickToDownload', 'Click here to download')}
+            href={url}
+            download={bulk_download_item_name}
+          />
+        ),
+        duration: null,
+      });
+    },
+  });
+
+  startAppListening({
+    actionCreator: socketBulkDownloadError,
+    effect: async (action) => {
+      log.debug(action.payload.data, 'Bulk download preparation failed');
+
+      const { bulk_download_item_name } = action.payload.data;
+
+      toast({
+        id: bulk_download_item_name,
+        title: t('gallery.bulkDownloadFailed'),
+        status: 'error',
+        description: action.payload.data.error,
+        duration: null,
+      });
+    },
+  });
 };

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

@@ -0,0 +1,38 @@
+import { $logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { canvasCopiedToClipboard } from 'features/canvas/store/actions';
+import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
+import { copyBlobToClipboard } from 'features/system/util/copyBlobToClipboard';
+import { toast } from 'features/toast/toast';
+import { t } from 'i18next';
+
+export const addCanvasCopiedToClipboardListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: canvasCopiedToClipboard,
+    effect: async (action, { getState }) => {
+      const moduleLog = $logger.get().child({ namespace: 'canvasCopiedToClipboardListener' });
+      const state = getState();
+
+      try {
+        const blob = getBaseLayerBlob(state);
+
+        copyBlobToClipboard(blob);
+      } catch (err) {
+        moduleLog.error(String(err));
+        toast({
+          id: 'CANVAS_COPY_FAILED',
+          title: t('toast.problemCopyingCanvas'),
+          description: t('toast.problemCopyingCanvasDesc'),
+          status: 'error',
+        });
+        return;
+      }
+
+      toast({
+        id: 'CANVAS_COPY_SUCCEEDED',
+        title: t('toast.canvasCopiedClipboard'),
+        status: 'success',
+      });
+    },
+  });
+};

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

@@ -0,0 +1,34 @@
+import { $logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { canvasDownloadedAsImage } from 'features/canvas/store/actions';
+import { downloadBlob } from 'features/canvas/util/downloadBlob';
+import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
+import { toast } from 'features/toast/toast';
+import { t } from 'i18next';
+
+export const addCanvasDownloadedAsImageListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: canvasDownloadedAsImage,
+    effect: async (action, { getState }) => {
+      const moduleLog = $logger.get().child({ namespace: 'canvasSavedToGalleryListener' });
+      const state = getState();
+
+      let blob;
+      try {
+        blob = await getBaseLayerBlob(state);
+      } catch (err) {
+        moduleLog.error(String(err));
+        toast({
+          id: 'CANVAS_DOWNLOAD_FAILED',
+          title: t('toast.problemDownloadingCanvas'),
+          description: t('toast.problemDownloadingCanvasDesc'),
+          status: 'error',
+        });
+        return;
+      }
+
+      downloadBlob(blob, 'canvas.png');
+      toast({ id: 'CANVAS_DOWNLOAD_SUCCEEDED', title: t('toast.canvasDownloaded'), status: 'success' });
+    },
+  });
+};

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

@@ -0,0 +1,60 @@
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { canvasImageToControlAdapter } from 'features/canvas/store/actions';
+import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
+import { controlAdapterImageChanged } from 'features/controlAdapters/store/controlAdaptersSlice';
+import { toast } from 'features/toast/toast';
+import { t } from 'i18next';
+import { imagesApi } from 'services/api/endpoints/images';
+
+export const addCanvasImageToControlNetListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: canvasImageToControlAdapter,
+    effect: async (action, { dispatch, getState }) => {
+      const log = logger('canvas');
+      const state = getState();
+      const { id } = action.payload;
+
+      let blob: Blob;
+      try {
+        blob = await getBaseLayerBlob(state, true);
+      } catch (err) {
+        log.error(String(err));
+        toast({
+          id: 'PROBLEM_SAVING_CANVAS',
+          title: t('toast.problemSavingCanvas'),
+          description: t('toast.problemSavingCanvasDesc'),
+          status: 'error',
+        });
+        return;
+      }
+
+      const { autoAddBoardId } = state.gallery;
+
+      const imageDTO = await dispatch(
+        imagesApi.endpoints.uploadImage.initiate({
+          file: new File([blob], 'savedCanvas.png', {
+            type: 'image/png',
+          }),
+          image_category: 'control',
+          is_intermediate: true,
+          board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
+          crop_visible: false,
+          postUploadAction: {
+            type: 'TOAST',
+            title: t('toast.canvasSentControlnetAssets'),
+          },
+        })
+      ).unwrap();
+
+      const { image_name } = imageDTO;
+
+      dispatch(
+        controlAdapterImageChanged({
+          id,
+          controlImage: image_name,
+        })
+      );
+    },
+  });
+};

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

@@ -0,0 +1,60 @@
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { canvasMaskSavedToGallery } from 'features/canvas/store/actions';
+import { getCanvasData } from 'features/canvas/util/getCanvasData';
+import { toast } from 'features/toast/toast';
+import { t } from 'i18next';
+import { imagesApi } from 'services/api/endpoints/images';
+
+export const addCanvasMaskSavedToGalleryListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: canvasMaskSavedToGallery,
+    effect: async (action, { dispatch, getState }) => {
+      const log = logger('canvas');
+      const state = getState();
+
+      const canvasBlobsAndImageData = await getCanvasData(
+        state.canvas.layerState,
+        state.canvas.boundingBoxCoordinates,
+        state.canvas.boundingBoxDimensions,
+        state.canvas.isMaskEnabled,
+        state.canvas.shouldPreserveMaskedArea
+      );
+
+      if (!canvasBlobsAndImageData) {
+        return;
+      }
+
+      const { maskBlob } = canvasBlobsAndImageData;
+
+      if (!maskBlob) {
+        log.error('Problem getting mask layer blob');
+        toast({
+          id: 'PROBLEM_SAVING_MASK',
+          title: t('toast.problemSavingMask'),
+          description: t('toast.problemSavingMaskDesc'),
+          status: 'error',
+        });
+        return;
+      }
+
+      const { autoAddBoardId } = state.gallery;
+
+      dispatch(
+        imagesApi.endpoints.uploadImage.initiate({
+          file: new File([maskBlob], 'canvasMaskImage.png', {
+            type: 'image/png',
+          }),
+          image_category: 'mask',
+          is_intermediate: false,
+          board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
+          crop_visible: true,
+          postUploadAction: {
+            type: 'TOAST',
+            title: t('toast.maskSavedAssets'),
+          },
+        })
+      );
+    },
+  });
+};

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

@@ -0,0 +1,70 @@
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { canvasMaskToControlAdapter } from 'features/canvas/store/actions';
+import { getCanvasData } from 'features/canvas/util/getCanvasData';
+import { controlAdapterImageChanged } from 'features/controlAdapters/store/controlAdaptersSlice';
+import { toast } from 'features/toast/toast';
+import { t } from 'i18next';
+import { imagesApi } from 'services/api/endpoints/images';
+
+export const addCanvasMaskToControlNetListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: canvasMaskToControlAdapter,
+    effect: async (action, { dispatch, getState }) => {
+      const log = logger('canvas');
+      const state = getState();
+      const { id } = action.payload;
+      const canvasBlobsAndImageData = await getCanvasData(
+        state.canvas.layerState,
+        state.canvas.boundingBoxCoordinates,
+        state.canvas.boundingBoxDimensions,
+        state.canvas.isMaskEnabled,
+        state.canvas.shouldPreserveMaskedArea
+      );
+
+      if (!canvasBlobsAndImageData) {
+        return;
+      }
+
+      const { maskBlob } = canvasBlobsAndImageData;
+
+      if (!maskBlob) {
+        log.error('Problem getting mask layer blob');
+        toast({
+          id: 'PROBLEM_IMPORTING_MASK',
+          title: t('toast.problemImportingMask'),
+          description: t('toast.problemImportingMaskDesc'),
+          status: 'error',
+        });
+        return;
+      }
+
+      const { autoAddBoardId } = state.gallery;
+
+      const imageDTO = await dispatch(
+        imagesApi.endpoints.uploadImage.initiate({
+          file: new File([maskBlob], 'canvasMaskImage.png', {
+            type: 'image/png',
+          }),
+          image_category: 'mask',
+          is_intermediate: true,
+          board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
+          crop_visible: false,
+          postUploadAction: {
+            type: 'TOAST',
+            title: t('toast.maskSentControlnetAssets'),
+          },
+        })
+      ).unwrap();
+
+      const { image_name } = imageDTO;
+
+      dispatch(
+        controlAdapterImageChanged({
+          id,
+          controlImage: image_name,
+        })
+      );
+    },
+  });
+};

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

@@ -0,0 +1,73 @@
+import { $logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { canvasMerged } from 'features/canvas/store/actions';
+import { $canvasBaseLayer } from 'features/canvas/store/canvasNanostore';
+import { setMergedCanvas } from 'features/canvas/store/canvasSlice';
+import { getFullBaseLayerBlob } from 'features/canvas/util/getFullBaseLayerBlob';
+import { toast } from 'features/toast/toast';
+import { t } from 'i18next';
+import { imagesApi } from 'services/api/endpoints/images';
+
+export const addCanvasMergedListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: canvasMerged,
+    effect: async (action, { dispatch }) => {
+      const moduleLog = $logger.get().child({ namespace: 'canvasCopiedToClipboardListener' });
+      const blob = await getFullBaseLayerBlob();
+
+      if (!blob) {
+        moduleLog.error('Problem getting base layer blob');
+        toast({
+          id: 'PROBLEM_MERGING_CANVAS',
+          title: t('toast.problemMergingCanvas'),
+          description: t('toast.problemMergingCanvasDesc'),
+          status: 'error',
+        });
+        return;
+      }
+
+      const canvasBaseLayer = $canvasBaseLayer.get();
+
+      if (!canvasBaseLayer) {
+        moduleLog.error('Problem getting canvas base layer');
+        toast({
+          id: 'PROBLEM_MERGING_CANVAS',
+          title: t('toast.problemMergingCanvas'),
+          description: t('toast.problemMergingCanvasDesc'),
+          status: 'error',
+        });
+        return;
+      }
+
+      const baseLayerRect = canvasBaseLayer.getClientRect({
+        relativeTo: canvasBaseLayer.getParent() ?? undefined,
+      });
+
+      const imageDTO = await dispatch(
+        imagesApi.endpoints.uploadImage.initiate({
+          file: new File([blob], 'mergedCanvas.png', {
+            type: 'image/png',
+          }),
+          image_category: 'general',
+          is_intermediate: true,
+          postUploadAction: {
+            type: 'TOAST',
+            title: t('toast.canvasMerged'),
+          },
+        })
+      ).unwrap();
+
+      // TODO: I can't figure out how to do the type narrowing in the `take()` so just brute forcing it here
+      const { image_name } = imageDTO;
+
+      dispatch(
+        setMergedCanvas({
+          kind: 'image',
+          layer: 'base',
+          imageName: image_name,
+          ...baseLayerRect,
+        })
+      );
+    },
+  });
+};

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

@@ -0,0 +1,53 @@
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { parseify } from 'common/util/serialize';
+import { canvasSavedToGallery } from 'features/canvas/store/actions';
+import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
+import { toast } from 'features/toast/toast';
+import { t } from 'i18next';
+import { imagesApi } from 'services/api/endpoints/images';
+
+export const addCanvasSavedToGalleryListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: canvasSavedToGallery,
+    effect: async (action, { dispatch, getState }) => {
+      const log = logger('canvas');
+      const state = getState();
+
+      let blob;
+      try {
+        blob = await getBaseLayerBlob(state);
+      } catch (err) {
+        log.error(String(err));
+        toast({
+          id: 'CANVAS_SAVE_FAILED',
+          title: t('toast.problemSavingCanvas'),
+          description: t('toast.problemSavingCanvasDesc'),
+          status: 'error',
+        });
+        return;
+      }
+
+      const { autoAddBoardId } = state.gallery;
+
+      dispatch(
+        imagesApi.endpoints.uploadImage.initiate({
+          file: new File([blob], 'savedCanvas.png', {
+            type: 'image/png',
+          }),
+          image_category: 'general',
+          is_intermediate: false,
+          board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
+          crop_visible: true,
+          postUploadAction: {
+            type: 'TOAST',
+            title: t('toast.canvasSavedGallery'),
+          },
+          metadata: {
+            _canvas_objects: parseify(state.canvas.layerState.objects),
+          },
+        })
+      );
+    },
+  });
+};

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

@@ -0,0 +1,194 @@
+import { isAnyOf } from '@reduxjs/toolkit';
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { AppDispatch } from 'app/store/store';
+import { parseify } from 'common/util/serialize';
+import {
+  caLayerImageChanged,
+  caLayerModelChanged,
+  caLayerProcessedImageChanged,
+  caLayerProcessorConfigChanged,
+  caLayerProcessorPendingBatchIdChanged,
+  caLayerRecalled,
+  isControlAdapterLayer,
+} from 'features/controlLayers/store/controlLayersSlice';
+import { CA_PROCESSOR_DATA } from 'features/controlLayers/util/controlAdapters';
+import { toast } from 'features/toast/toast';
+import { t } from 'i18next';
+import { isEqual } from 'lodash-es';
+import { getImageDTO } from 'services/api/endpoints/images';
+import { queueApi } from 'services/api/endpoints/queue';
+import type { BatchConfig } from 'services/api/types';
+import { socketInvocationComplete } from 'services/events/actions';
+import { assert } from 'tsafe';
+
+const matcher = isAnyOf(
+  caLayerImageChanged,
+  caLayerProcessedImageChanged,
+  caLayerProcessorConfigChanged,
+  caLayerModelChanged,
+  caLayerRecalled
+);
+
+const DEBOUNCE_MS = 300;
+const log = logger('session');
+
+/**
+ * Simple helper to cancel a batch and reset the pending batch ID
+ */
+const cancelProcessorBatch = async (dispatch: AppDispatch, layerId: string, batchId: string) => {
+  const req = dispatch(queueApi.endpoints.cancelByBatchIds.initiate({ batch_ids: [batchId] }));
+  log.trace({ batchId }, 'Cancelling existing preprocessor batch');
+  try {
+    await req.unwrap();
+  } catch {
+    // no-op
+  } finally {
+    req.reset();
+    // Always reset the pending batch ID - the cancel req could fail if the batch doesn't exist
+    dispatch(caLayerProcessorPendingBatchIdChanged({ layerId, batchId: null }));
+  }
+};
+
+export const addControlAdapterPreprocessor = (startAppListening: AppStartListening) => {
+  startAppListening({
+    matcher,
+    effect: async (action, { dispatch, getState, getOriginalState, cancelActiveListeners, delay, take, signal }) => {
+      const layerId = caLayerRecalled.match(action) ? action.payload.id : action.payload.layerId;
+      const state = getState();
+      const originalState = getOriginalState();
+
+      // Cancel any in-progress instances of this listener
+      cancelActiveListeners();
+      log.trace('Control Layer CA auto-process triggered');
+
+      // Delay before starting actual work
+      await delay(DEBOUNCE_MS);
+
+      const layer = state.controlLayers.present.layers.filter(isControlAdapterLayer).find((l) => l.id === layerId);
+
+      if (!layer) {
+        return;
+      }
+
+      // We should only process if the processor settings or image have changed
+      const originalLayer = originalState.controlLayers.present.layers
+        .filter(isControlAdapterLayer)
+        .find((l) => l.id === layerId);
+      const originalImage = originalLayer?.controlAdapter.image;
+      const originalConfig = originalLayer?.controlAdapter.processorConfig;
+
+      const image = layer.controlAdapter.image;
+      const processedImage = layer.controlAdapter.processedImage;
+      const config = layer.controlAdapter.processorConfig;
+
+      if (isEqual(config, originalConfig) && isEqual(image, originalImage) && processedImage) {
+        // Neither config nor image have changed, we can bail
+        return;
+      }
+
+      if (!image || !config) {
+        // - If we have no image, we have nothing to process
+        // - If we have no processor config, we have nothing to process
+        // Clear the processed image and bail
+        dispatch(caLayerProcessedImageChanged({ layerId, imageDTO: null }));
+        return;
+      }
+
+      // At this point, the user has stopped fiddling with the processor settings and there is a processor selected.
+
+      // If there is a pending processor batch, cancel it.
+      if (layer.controlAdapter.processorPendingBatchId) {
+        cancelProcessorBatch(dispatch, layerId, layer.controlAdapter.processorPendingBatchId);
+      }
+
+      // TODO(psyche): I can't get TS to be happy, it thinkgs `config` is `never` but it should be inferred from the generic... I'll just cast it for now
+      const processorNode = CA_PROCESSOR_DATA[config.type].buildNode(image, config as never);
+      const enqueueBatchArg: BatchConfig = {
+        prepend: true,
+        batch: {
+          graph: {
+            nodes: {
+              [processorNode.id]: {
+                ...processorNode,
+                // Control images are always intermediate - do not save to gallery
+                is_intermediate: true,
+              },
+            },
+            edges: [],
+          },
+          runs: 1,
+        },
+      };
+
+      // Kick off the processor batch
+      const req = dispatch(
+        queueApi.endpoints.enqueueBatch.initiate(enqueueBatchArg, {
+          fixedCacheKey: 'enqueueBatch',
+        })
+      );
+
+      try {
+        const enqueueResult = await req.unwrap();
+        // TODO(psyche): Update the pydantic models, pretty sure we will _always_ have a batch_id here, but the model says it's optional
+        assert(enqueueResult.batch.batch_id, 'Batch ID not returned from queue');
+        dispatch(caLayerProcessorPendingBatchIdChanged({ layerId, batchId: enqueueResult.batch.batch_id }));
+        log.debug({ enqueueResult: parseify(enqueueResult) }, t('queue.graphQueued'));
+
+        // Wait for the processor node to complete
+        const [invocationCompleteAction] = await take(
+          (action): action is ReturnType<typeof socketInvocationComplete> =>
+            socketInvocationComplete.match(action) &&
+            action.payload.data.batch_id === enqueueResult.batch.batch_id &&
+            action.payload.data.invocation_source_id === processorNode.id
+        );
+
+        // We still have to check the output type
+        assert(
+          invocationCompleteAction.payload.data.result.type === 'image_output',
+          `Processor did not return an image output, got: ${invocationCompleteAction.payload.data.result}`
+        );
+        const { image_name } = invocationCompleteAction.payload.data.result.image;
+
+        const imageDTO = await getImageDTO(image_name);
+        assert(imageDTO, "Failed to fetch processor output's image DTO");
+
+        // Whew! We made it. Update the layer with the processed image
+        log.debug({ layerId, imageDTO }, 'ControlNet image processed');
+        dispatch(caLayerProcessedImageChanged({ layerId, imageDTO }));
+        dispatch(caLayerProcessorPendingBatchIdChanged({ layerId, batchId: null }));
+      } catch (error) {
+        if (signal.aborted) {
+          // The listener was canceled - we need to cancel the pending processor batch, if there is one (could have changed by now).
+          const pendingBatchId = getState()
+            .controlLayers.present.layers.filter(isControlAdapterLayer)
+            .find((l) => l.id === layerId)?.controlAdapter.processorPendingBatchId;
+          if (pendingBatchId) {
+            cancelProcessorBatch(dispatch, layerId, pendingBatchId);
+          }
+          log.trace('Control Adapter preprocessor cancelled');
+        } else {
+          // Some other error condition...
+          log.error({ enqueueBatchArg: parseify(enqueueBatchArg) }, t('queue.graphFailedToQueue'));
+
+          if (error instanceof Object) {
+            if ('data' in error && 'status' in error) {
+              if (error.status === 403) {
+                dispatch(caLayerImageChanged({ layerId, imageDTO: null }));
+                return;
+              }
+            }
+          }
+
+          toast({
+            id: 'GRAPH_QUEUE_FAILED',
+            title: t('queue.graphFailedToQueue'),
+            status: 'error',
+          });
+        }
+      } finally {
+        req.reset();
+      }
+    },
+  });
+};

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

@@ -0,0 +1,85 @@
+import type { AnyListenerPredicate } from '@reduxjs/toolkit';
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { RootState } from 'app/store/store';
+import { controlAdapterImageProcessed } from 'features/controlAdapters/store/actions';
+import {
+  controlAdapterAutoConfigToggled,
+  controlAdapterImageChanged,
+  controlAdapterModelChanged,
+  controlAdapterProcessorParamsChanged,
+  controlAdapterProcessortTypeChanged,
+  selectControlAdapterById,
+} from 'features/controlAdapters/store/controlAdaptersSlice';
+import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
+
+type AnyControlAdapterParamChangeAction =
+  | ReturnType<typeof controlAdapterProcessorParamsChanged>
+  | ReturnType<typeof controlAdapterModelChanged>
+  | ReturnType<typeof controlAdapterImageChanged>
+  | ReturnType<typeof controlAdapterProcessortTypeChanged>
+  | ReturnType<typeof controlAdapterAutoConfigToggled>;
+
+const predicate: AnyListenerPredicate<RootState> = (action, state, prevState) => {
+  const isActionMatched =
+    controlAdapterProcessorParamsChanged.match(action) ||
+    controlAdapterModelChanged.match(action) ||
+    controlAdapterImageChanged.match(action) ||
+    controlAdapterProcessortTypeChanged.match(action) ||
+    controlAdapterAutoConfigToggled.match(action);
+
+  if (!isActionMatched) {
+    return false;
+  }
+
+  const { id } = action.payload;
+  const prevCA = selectControlAdapterById(prevState.controlAdapters, id);
+  const ca = selectControlAdapterById(state.controlAdapters, id);
+  if (!prevCA || !isControlNetOrT2IAdapter(prevCA) || !ca || !isControlNetOrT2IAdapter(ca)) {
+    return false;
+  }
+
+  if (controlAdapterAutoConfigToggled.match(action)) {
+    // do not process if the user just disabled auto-config
+    if (prevCA.shouldAutoConfig === true) {
+      return false;
+    }
+  }
+
+  const { controlImage, processorType, shouldAutoConfig } = ca;
+  if (controlAdapterModelChanged.match(action) && !shouldAutoConfig) {
+    // do not process if the action is a model change but the processor settings are dirty
+    return false;
+  }
+
+  const isProcessorSelected = processorType !== 'none';
+
+  const hasControlImage = Boolean(controlImage);
+
+  return isProcessorSelected && hasControlImage;
+};
+
+const DEBOUNCE_MS = 300;
+
+/**
+ * Listener that automatically processes a ControlNet image when its processor parameters are changed.
+ *
+ * The network request is debounced.
+ */
+export const addControlNetAutoProcessListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    predicate,
+    effect: async (action, { dispatch, cancelActiveListeners, delay }) => {
+      const log = logger('session');
+      const { id } = (action as AnyControlAdapterParamChangeAction).payload;
+
+      // Cancel any in-progress instances of this listener
+      cancelActiveListeners();
+      log.trace('ControlNet auto-process triggered');
+      // Delay before starting actual work
+      await delay(DEBOUNCE_MS);
+
+      dispatch(controlAdapterImageProcessed({ id }));
+    },
+  });
+};

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

@@ -0,0 +1,118 @@
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { parseify } from 'common/util/serialize';
+import { controlAdapterImageProcessed } from 'features/controlAdapters/store/actions';
+import {
+  controlAdapterImageChanged,
+  controlAdapterProcessedImageChanged,
+  pendingControlImagesCleared,
+  selectControlAdapterById,
+} from 'features/controlAdapters/store/controlAdaptersSlice';
+import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
+import { toast } from 'features/toast/toast';
+import { t } from 'i18next';
+import { imagesApi } from 'services/api/endpoints/images';
+import { queueApi } from 'services/api/endpoints/queue';
+import type { BatchConfig, ImageDTO } from 'services/api/types';
+import { socketInvocationComplete } from 'services/events/actions';
+
+export const addControlNetImageProcessedListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: controlAdapterImageProcessed,
+    effect: async (action, { dispatch, getState, take }) => {
+      const log = logger('session');
+      const { id } = action.payload;
+      const ca = selectControlAdapterById(getState().controlAdapters, id);
+
+      if (!ca?.controlImage || !isControlNetOrT2IAdapter(ca)) {
+        log.error('Unable to process ControlNet image');
+        return;
+      }
+
+      if (ca.processorType === 'none' || ca.processorNode.type === 'none') {
+        return;
+      }
+
+      // ControlNet one-off procressing graph is just the processor node, no edges.
+      // Also we need to grab the image.
+
+      const nodeId = ca.processorNode.id;
+      const enqueueBatchArg: BatchConfig = {
+        prepend: true,
+        batch: {
+          graph: {
+            nodes: {
+              [ca.processorNode.id]: {
+                ...ca.processorNode,
+                is_intermediate: true,
+                use_cache: false,
+                image: { image_name: ca.controlImage },
+              },
+            },
+            edges: [],
+          },
+          runs: 1,
+        },
+      };
+
+      try {
+        const req = dispatch(
+          queueApi.endpoints.enqueueBatch.initiate(enqueueBatchArg, {
+            fixedCacheKey: 'enqueueBatch',
+          })
+        );
+        const enqueueResult = await req.unwrap();
+        req.reset();
+        log.debug({ enqueueResult: parseify(enqueueResult) }, t('queue.graphQueued'));
+
+        const [invocationCompleteAction] = await take(
+          (action): action is ReturnType<typeof socketInvocationComplete> =>
+            socketInvocationComplete.match(action) &&
+            action.payload.data.batch_id === enqueueResult.batch.batch_id &&
+            action.payload.data.invocation_source_id === nodeId
+        );
+
+        // We still have to check the output type
+        if (invocationCompleteAction.payload.data.result.type === 'image_output') {
+          const { image_name } = invocationCompleteAction.payload.data.result.image;
+
+          // Wait for the ImageDTO to be received
+          const [{ payload }] = await take(
+            (action) =>
+              imagesApi.endpoints.getImageDTO.matchFulfilled(action) && action.payload.image_name === image_name
+          );
+
+          const processedControlImage = payload as ImageDTO;
+
+          log.debug({ controlNetId: action.payload, processedControlImage }, 'ControlNet image processed');
+
+          // Update the processed image in the store
+          dispatch(
+            controlAdapterProcessedImageChanged({
+              id,
+              processedControlImage: processedControlImage.image_name,
+            })
+          );
+        }
+      } catch (error) {
+        log.error({ enqueueBatchArg: parseify(enqueueBatchArg) }, t('queue.graphFailedToQueue'));
+
+        if (error instanceof Object) {
+          if ('data' in error && 'status' in error) {
+            if (error.status === 403) {
+              dispatch(pendingControlImagesCleared());
+              dispatch(controlAdapterImageChanged({ id, controlImage: null }));
+              return;
+            }
+          }
+        }
+
+        toast({
+          id: 'GRAPH_QUEUE_FAILED',
+          title: t('queue.graphFailedToQueue'),
+          status: 'error',
+        });
+      }
+    },
+  });
+};

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

@@ -0,0 +1,144 @@
+import { logger } from 'app/logging/logger';
+import { enqueueRequested } from 'app/store/actions';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import openBase64ImageInTab from 'common/util/openBase64ImageInTab';
+import { parseify } from 'common/util/serialize';
+import { canvasBatchIdAdded, stagingAreaInitialized } from 'features/canvas/store/canvasSlice';
+import { blobToDataURL } from 'features/canvas/util/blobToDataURL';
+import { getCanvasData } from 'features/canvas/util/getCanvasData';
+import { getCanvasGenerationMode } from 'features/canvas/util/getCanvasGenerationMode';
+import { canvasGraphBuilt } from 'features/nodes/store/actions';
+import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
+import { buildCanvasGraph } from 'features/nodes/util/graph/canvas/buildCanvasGraph';
+import { imagesApi } from 'services/api/endpoints/images';
+import { queueApi } from 'services/api/endpoints/queue';
+import type { ImageDTO } from 'services/api/types';
+
+/**
+ * This listener is responsible invoking the canvas. This involves a number of steps:
+ *
+ * 1. Generate image blobs from the canvas layers
+ * 2. Determine the generation mode from the layers (txt2img, img2img, inpaint)
+ * 3. Build the canvas graph
+ * 4. Create the session with the graph
+ * 5. Upload the init image if necessary
+ * 6. Upload the mask image if necessary
+ * 7. Update the init and mask images with the session ID
+ * 8. Initialize the staging area if not yet initialized
+ * 9. Dispatch the sessionReadyToInvoke action to invoke the session
+ */
+export const addEnqueueRequestedCanvasListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    predicate: (action): action is ReturnType<typeof enqueueRequested> =>
+      enqueueRequested.match(action) && action.payload.tabName === 'canvas',
+    effect: async (action, { getState, dispatch }) => {
+      const log = logger('queue');
+      const { prepend } = action.payload;
+      const state = getState();
+
+      const { layerState, boundingBoxCoordinates, boundingBoxDimensions, isMaskEnabled, shouldPreserveMaskedArea } =
+        state.canvas;
+
+      // Build canvas blobs
+      const canvasBlobsAndImageData = await getCanvasData(
+        layerState,
+        boundingBoxCoordinates,
+        boundingBoxDimensions,
+        isMaskEnabled,
+        shouldPreserveMaskedArea
+      );
+
+      if (!canvasBlobsAndImageData) {
+        log.error('Unable to create canvas data');
+        return;
+      }
+
+      const { baseBlob, baseImageData, maskBlob, maskImageData } = canvasBlobsAndImageData;
+
+      // Determine the generation mode
+      const generationMode = getCanvasGenerationMode(baseImageData, maskImageData);
+
+      if (state.system.enableImageDebugging) {
+        const baseDataURL = await blobToDataURL(baseBlob);
+        const maskDataURL = await blobToDataURL(maskBlob);
+        openBase64ImageInTab([
+          { base64: maskDataURL, caption: 'mask b64' },
+          { base64: baseDataURL, caption: 'image b64' },
+        ]);
+      }
+
+      log.debug(`Generation mode: ${generationMode}`);
+
+      // Temp placeholders for the init and mask images
+      let canvasInitImage: ImageDTO | undefined;
+      let canvasMaskImage: ImageDTO | undefined;
+
+      // For img2img and inpaint/outpaint, we need to upload the init images
+      if (['img2img', 'inpaint', 'outpaint'].includes(generationMode)) {
+        // upload the image, saving the request id
+        canvasInitImage = await dispatch(
+          imagesApi.endpoints.uploadImage.initiate({
+            file: new File([baseBlob], 'canvasInitImage.png', {
+              type: 'image/png',
+            }),
+            image_category: 'general',
+            is_intermediate: true,
+          })
+        ).unwrap();
+      }
+
+      // For inpaint/outpaint, we also need to upload the mask layer
+      if (['inpaint', 'outpaint'].includes(generationMode)) {
+        // upload the image, saving the request id
+        canvasMaskImage = await dispatch(
+          imagesApi.endpoints.uploadImage.initiate({
+            file: new File([maskBlob], 'canvasMaskImage.png', {
+              type: 'image/png',
+            }),
+            image_category: 'mask',
+            is_intermediate: true,
+          })
+        ).unwrap();
+      }
+
+      const graph = await buildCanvasGraph(state, generationMode, canvasInitImage, canvasMaskImage);
+
+      log.debug({ graph: parseify(graph) }, `Canvas graph built`);
+
+      // currently this action is just listened to for logging
+      dispatch(canvasGraphBuilt(graph));
+
+      const batchConfig = prepareLinearUIBatch(state, graph, prepend);
+
+      try {
+        const req = dispatch(
+          queueApi.endpoints.enqueueBatch.initiate(batchConfig, {
+            fixedCacheKey: 'enqueueBatch',
+          })
+        );
+
+        const enqueueResult = await req.unwrap();
+        req.reset();
+
+        const batchId = enqueueResult.batch.batch_id as string; // we know the is a string, backend provides it
+
+        // Prep the canvas staging area if it is not yet initialized
+        if (!state.canvas.layerState.stagingArea.boundingBox) {
+          dispatch(
+            stagingAreaInitialized({
+              boundingBox: {
+                ...state.canvas.boundingBoxCoordinates,
+                ...state.canvas.boundingBoxDimensions,
+              },
+            })
+          );
+        }
+
+        // Associate the session with the canvas session ID
+        dispatch(canvasBatchIdAdded(batchId));
+      } catch {
+        // no-op
+      }
+    },
+  });
+};

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

@@ -1,18 +1,10 @@
-import { logger } from 'app/logging/logger';
 import { enqueueRequested } from 'app/store/actions';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { $canvasManager } from 'features/controlLayers/konva/CanvasManager';
-import { sessionStagingAreaReset, sessionStartedStaging } from 'features/controlLayers/store/canvasV2Slice';
+import { isImageViewerOpenChanged } from 'features/gallery/store/gallerySlice';
 import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
-import { buildSD1Graph } from 'features/nodes/util/graph/generation/buildSD1Graph';
-import { buildSDXLGraph } from 'features/nodes/util/graph/generation/buildSDXLGraph';
-import type { Graph } from 'features/nodes/util/graph/generation/Graph';
-import { serializeError } from 'serialize-error';
+import { buildGenerationTabGraph } from 'features/nodes/util/graph/generation/buildGenerationTabGraph';
+import { buildGenerationTabSDXLGraph } from 'features/nodes/util/graph/generation/buildGenerationTabSDXLGraph';
 import { queueApi } from 'services/api/endpoints/queue';
-import type { Invocation } from 'services/api/types';
-import { assert } from 'tsafe';
-
-const log = logger('generation');
 
 export const addEnqueueRequestedLinear = (startAppListening: AppStartListening) => {
   startAppListening({
@@ -20,54 +12,32 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
       enqueueRequested.match(action) && action.payload.tabName === 'generation',
     effect: async (action, { getState, dispatch }) => {
       const state = getState();
-      const model = state.canvasV2.params.model;
+      const { shouldShowProgressInViewer } = state.ui;
+      const model = state.generation.model;
       const { prepend } = action.payload;
 
-      const manager = $canvasManager.get();
-      assert(manager, 'No model found in state');
+      let graph;
 
-      let didStartStaging = false;
-      if (!state.canvasV2.session.isStaging && state.canvasV2.session.mode === 'compose') {
-        dispatch(sessionStartedStaging());
-        didStartStaging = true;
+      if (model?.base === 'sdxl') {
+        graph = await buildGenerationTabSDXLGraph(state);
+      } else {
+        graph = await buildGenerationTabGraph(state);
       }
 
+      const batchConfig = prepareLinearUIBatch(state, graph, prepend);
+
+      const req = dispatch(
+        queueApi.endpoints.enqueueBatch.initiate(batchConfig, {
+          fixedCacheKey: 'enqueueBatch',
+        })
+      );
       try {
-        let g: Graph;
-        let noise: Invocation<'noise'>;
-        let posCond: Invocation<'compel' | 'sdxl_compel_prompt'>;
-
-        assert(model, 'No model found in state');
-        const base = model.base;
-
-        if (base === 'sdxl') {
-          const result = await buildSDXLGraph(state, manager);
-          g = result.g;
-          noise = result.noise;
-          posCond = result.posCond;
-        } else if (base === 'sd-1' || base === 'sd-2') {
-          const result = await buildSD1Graph(state, manager);
-          g = result.g;
-          noise = result.noise;
-          posCond = result.posCond;
-        } else {
-          assert(false, `No graph builders for base ${base}`);
-        }
-
-        const batchConfig = prepareLinearUIBatch(state, g, prepend, noise, posCond);
-
-        const req = dispatch(
-          queueApi.endpoints.enqueueBatch.initiate(batchConfig, {
-            fixedCacheKey: 'enqueueBatch',
-          })
-        );
-        req.reset();
         await req.unwrap();
-      } catch (error) {
-        log.error({ error: serializeError(error) }, 'Failed to enqueue batch');
-        if (didStartStaging && getState().canvasV2.session.isStaging) {
-          dispatch(sessionStagingAreaReset());
+        if (shouldShowProgressInViewer) {
+          dispatch(isImageViewerOpenChanged(true));
         }
+      } finally {
+        req.reset();
       }
     },
   });

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

@@ -29,8 +29,7 @@ export const addEnqueueRequestedNodes = (startAppListening: AppStartListening) =
         batch: {
           graph,
           workflow: builtWorkflow,
-          runs: state.canvasV2.params.iterations,
-          origin: 'workflows',
+          runs: state.generation.iterations,
         },
         prepend: action.payload.prepend,
       };

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

@@ -14,9 +14,9 @@ export const addEnqueueRequestedUpscale = (startAppListening: AppStartListening)
       const { shouldShowProgressInViewer } = state.ui;
       const { prepend } = action.payload;
 
-      const { g, noise, posCond } = await buildMultidiffusionUpscaleGraph(state);
+      const graph = await buildMultidiffusionUpscaleGraph(state);
 
-      const batchConfig = prepareLinearUIBatch(state, g, prepend, noise, posCond);
+      const batchConfig = prepareLinearUIBatch(state, graph, prepend);
 
       const req = dispatch(
         queueApi.endpoints.enqueueBatch.initiate(batchConfig, {

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

@@ -27,7 +27,7 @@ export const galleryImageClicked = createAction<{
 export const addGalleryImageClickedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: galleryImageClicked,
-    effect: (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const { imageDTO, shiftKey, ctrlKey, metaKey, altKey } = action.payload;
       const state = getState();
       const queryArgs = selectListImagesQueryArgs(state);

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

@@ -1,27 +1,24 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import type { SerializableObject } from 'common/types';
 import { parseify } from 'common/util/serialize';
 import { $templates } from 'features/nodes/store/nodesSlice';
 import { parseSchema } from 'features/nodes/util/schema/parseSchema';
 import { size } from 'lodash-es';
-import { serializeError } from 'serialize-error';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
 
-const log = logger('system');
-
 export const addGetOpenAPISchemaListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: appInfoApi.endpoints.getOpenAPISchema.matchFulfilled,
     effect: (action, { getState }) => {
+      const log = logger('system');
       const schemaJSON = action.payload;
 
-      log.debug({ schemaJSON: parseify(schemaJSON) } as SerializableObject, 'Received OpenAPI schema');
+      log.debug({ schemaJSON: parseify(schemaJSON) }, 'Received OpenAPI schema');
       const { nodesAllowlist, nodesDenylist } = getState().config;
 
       const nodeTemplates = parseSchema(schemaJSON, nodesAllowlist, nodesDenylist);
 
-      log.debug({ nodeTemplates } as SerializableObject, `Built ${size(nodeTemplates)} node templates`);
+      log.debug({ nodeTemplates: parseify(nodeTemplates) }, `Built ${size(nodeTemplates)} node templates`);
 
       $templates.set(nodeTemplates);
     },
@@ -33,7 +30,8 @@ export const addGetOpenAPISchemaListener = (startAppListening: AppStartListening
       // If action.meta.condition === true, the request was canceled/skipped because another request was in flight or
       // the value was already in the cache. We don't want to log these errors.
       if (!action.meta.condition) {
-        log.error({ error: serializeError(action.error) }, 'Problem retrieving OpenAPI Schema');
+        const log = logger('system');
+        log.error({ error: parseify(action.error) }, 'Problem retrieving OpenAPI Schema');
       }
     },
   });

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

@@ -2,13 +2,15 @@ import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { imagesApi } from 'services/api/endpoints/images';
 
-const log = logger('gallery');
-
 export const addImageAddedToBoardFulfilledListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: imagesApi.endpoints.addImageToBoard.matchFulfilled,
     effect: (action) => {
+      const log = logger('images');
       const { board_id, imageDTO } = action.meta.arg.originalArgs;
+
+      // TODO: update listImages cache for this board
+
       log.debug({ board_id, imageDTO }, 'Image added to board');
     },
   });
@@ -16,7 +18,9 @@ export const addImageAddedToBoardFulfilledListener = (startAppListening: AppStar
   startAppListening({
     matcher: imagesApi.endpoints.addImageToBoard.matchRejected,
     effect: (action) => {
+      const log = logger('images');
       const { board_id, imageDTO } = action.meta.arg.originalArgs;
+
       log.debug({ board_id, imageDTO }, 'Problem adding image to board');
     },
   });

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

@@ -1,7 +1,20 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import type { AppDispatch, RootState } from 'app/store/store';
-import { entityDeleted, ipaImageChanged } from 'features/controlLayers/store/canvasV2Slice';
+import { resetCanvas } from 'features/canvas/store/canvasSlice';
+import {
+  controlAdapterImageChanged,
+  controlAdapterProcessedImageChanged,
+  selectControlAdapterAll,
+} from 'features/controlAdapters/store/controlAdaptersSlice';
+import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
+import {
+  isControlAdapterLayer,
+  isInitialImageLayer,
+  isIPAdapterLayer,
+  isRegionalGuidanceLayer,
+  layerDeleted,
+} from 'features/controlLayers/store/controlLayersSlice';
 import { imageDeletionConfirmed } from 'features/deleteImageModal/store/actions';
 import { isModalOpenChanged } from 'features/deleteImageModal/store/slice';
 import { selectListImagesQueryArgs } from 'features/gallery/store/gallerySelectors';
@@ -13,10 +26,6 @@ import { forEach, intersectionBy } from 'lodash-es';
 import { imagesApi } from 'services/api/endpoints/images';
 import type { ImageDTO } from 'services/api/types';
 
-const log = logger('gallery');
-
-//TODO(psyche): handle image deletion (canvas sessions?)
-
 // Some utils to delete images from different parts of the app
 const deleteNodesImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
   state.nodes.present.nodes.forEach((node) => {
@@ -38,37 +47,52 @@ const deleteNodesImages = (state: RootState, dispatch: AppDispatch, imageDTO: Im
   });
 };
 
-// const deleteControlAdapterImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
-//   state.canvasV2.controlAdapters.entities.forEach(({ id, imageObject, processedImageObject }) => {
-//     if (
-//       imageObject?.image.image_name === imageDTO.image_name ||
-//       processedImageObject?.image.image_name === imageDTO.image_name
-//     ) {
-//       dispatch(caImageChanged({ id, imageDTO: null }));
-//       dispatch(caProcessedImageChanged({ id, imageDTO: null }));
-//     }
-//   });
-// };
-
-const deleteIPAdapterImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
-  state.canvasV2.ipAdapters.entities.forEach(({ id, ipAdapter }) => {
-    if (ipAdapter.image?.image_name === imageDTO.image_name) {
-      dispatch(ipaImageChanged({ id, imageDTO: null }));
+const deleteControlAdapterImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
+  forEach(selectControlAdapterAll(state.controlAdapters), (ca) => {
+    if (
+      ca.controlImage === imageDTO.image_name ||
+      (isControlNetOrT2IAdapter(ca) && ca.processedControlImage === imageDTO.image_name)
+    ) {
+      dispatch(
+        controlAdapterImageChanged({
+          id: ca.id,
+          controlImage: null,
+        })
+      );
+      dispatch(
+        controlAdapterProcessedImageChanged({
+          id: ca.id,
+          processedControlImage: null,
+        })
+      );
     }
   });
 };
 
-const deleteLayerImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
-  state.canvasV2.rasterLayers.entities.forEach(({ id, objects }) => {
-    let shouldDelete = false;
-    for (const obj of objects) {
-      if (obj.type === 'image' && obj.image.image_name === imageDTO.image_name) {
-        shouldDelete = true;
-        break;
+const deleteControlLayerImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
+  state.controlLayers.present.layers.forEach((l) => {
+    if (isRegionalGuidanceLayer(l)) {
+      if (l.ipAdapters.some((ipa) => ipa.image?.name === imageDTO.image_name)) {
+        dispatch(layerDeleted(l.id));
       }
     }
-    if (shouldDelete) {
-      dispatch(entityDeleted({ entityIdentifier: { id, type: 'raster_layer' } }));
+    if (isControlAdapterLayer(l)) {
+      if (
+        l.controlAdapter.image?.name === imageDTO.image_name ||
+        l.controlAdapter.processedImage?.name === imageDTO.image_name
+      ) {
+        dispatch(layerDeleted(l.id));
+      }
+    }
+    if (isIPAdapterLayer(l)) {
+      if (l.ipAdapter.image?.name === imageDTO.image_name) {
+        dispatch(layerDeleted(l.id));
+      }
+    }
+    if (isInitialImageLayer(l)) {
+      if (l.image?.name === imageDTO.image_name) {
+        dispatch(layerDeleted(l.id));
+      }
     }
   });
 };
@@ -121,10 +145,14 @@ export const addImageDeletionListeners = (startAppListening: AppStartListening)
           }
         }
 
+        // We need to reset the features where the image is in use - none of these work if their image(s) don't exist
+        if (imageUsage.isCanvasImage) {
+          dispatch(resetCanvas());
+        }
+
+        deleteControlAdapterImages(state, dispatch, imageDTO);
         deleteNodesImages(state, dispatch, imageDTO);
-        // deleteControlAdapterImages(state, dispatch, imageDTO);
-        deleteIPAdapterImages(state, dispatch, imageDTO);
-        deleteLayerImages(state, dispatch, imageDTO);
+        deleteControlLayerImages(state, dispatch, imageDTO);
       } catch {
         // no-op
       } finally {
@@ -161,11 +189,14 @@ export const addImageDeletionListeners = (startAppListening: AppStartListening)
 
         // We need to reset the features where the image is in use - none of these work if their image(s) don't exist
 
+        if (imagesUsage.some((i) => i.isCanvasImage)) {
+          dispatch(resetCanvas());
+        }
+
         imageDTOs.forEach((imageDTO) => {
+          deleteControlAdapterImages(state, dispatch, imageDTO);
           deleteNodesImages(state, dispatch, imageDTO);
-          // deleteControlAdapterImages(state, dispatch, imageDTO);
-          deleteIPAdapterImages(state, dispatch, imageDTO);
-          deleteLayerImages(state, dispatch, imageDTO);
+          deleteControlLayerImages(state, dispatch, imageDTO);
         });
       } catch {
         // no-op
@@ -189,6 +220,7 @@ export const addImageDeletionListeners = (startAppListening: AppStartListening)
   startAppListening({
     matcher: imagesApi.endpoints.deleteImage.matchFulfilled,
     effect: (action) => {
+      const log = logger('images');
       log.debug({ imageDTO: action.meta.arg.originalArgs }, 'Image deleted');
     },
   });
@@ -196,6 +228,7 @@ export const addImageDeletionListeners = (startAppListening: AppStartListening)
   startAppListening({
     matcher: imagesApi.endpoints.deleteImage.matchRejected,
     effect: (action) => {
+      const log = logger('images');
       log.debug({ imageDTO: action.meta.arg.originalArgs }, 'Unable to delete image');
     },
   });

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

@@ -1,18 +1,28 @@
 import { createAction } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { parseify } from 'common/util/serialize';
+import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
 import {
-  controlLayerAdded,
-  ipaImageChanged,
-  rasterLayerAdded,
-  rgIPAdapterImageChanged,
-} from 'features/controlLayers/store/canvasV2Slice';
-import type { CanvasControlLayerState, CanvasRasterLayerState } from 'features/controlLayers/store/types';
-import { imageDTOToImageObject } from 'features/controlLayers/store/types';
+  controlAdapterImageChanged,
+  controlAdapterIsEnabledChanged,
+} from 'features/controlAdapters/store/controlAdaptersSlice';
+import {
+  caLayerImageChanged,
+  iiLayerImageChanged,
+  ipaLayerImageChanged,
+  rgLayerIPAdapterImageChanged,
+} from 'features/controlLayers/store/controlLayersSlice';
 import type { TypesafeDraggableData, TypesafeDroppableData } from 'features/dnd/types';
 import { isValidDrop } from 'features/dnd/util/isValidDrop';
-import { imageToCompareChanged, isImageViewerOpenChanged, selectionChanged } from 'features/gallery/store/gallerySlice';
+import {
+  imageSelected,
+  imageToCompareChanged,
+  isImageViewerOpenChanged,
+  selectionChanged,
+} from 'features/gallery/store/gallerySlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
+import { selectOptimalDimension } from 'features/parameters/store/generationSlice';
 import { upscaleInitialImageChanged } from 'features/parameters/store/upscaleSlice';
 import { imagesApi } from 'services/api/endpoints/images';
 
@@ -21,12 +31,11 @@ export const dndDropped = createAction<{
   activeData: TypesafeDraggableData;
 }>('dnd/dndDropped');
 
-const log = logger('system');
-
 export const addImageDroppedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: dndDropped,
-    effect: (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch, getState }) => {
+      const log = logger('dnd');
       const { activeData, overData } = action.payload;
       if (!isValidDrop(overData, activeData)) {
         return;
@@ -37,21 +46,81 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
       } else if (activeData.payloadType === 'GALLERY_SELECTION') {
         log.debug({ activeData, overData }, `Images (${getState().gallery.selection.length}) dropped`);
       } else if (activeData.payloadType === 'NODE_FIELD') {
-        log.debug({ activeData, overData }, 'Node field dropped');
+        log.debug({ activeData: parseify(activeData), overData: parseify(overData) }, 'Node field dropped');
       } else {
         log.debug({ activeData, overData }, `Unknown payload dropped`);
       }
 
+      /**
+       * Image dropped on current image
+       */
+      if (
+        overData.actionType === 'SET_CURRENT_IMAGE' &&
+        activeData.payloadType === 'IMAGE_DTO' &&
+        activeData.payload.imageDTO
+      ) {
+        dispatch(imageSelected(activeData.payload.imageDTO));
+        dispatch(isImageViewerOpenChanged(true));
+        return;
+      }
+
+      /**
+       * Image dropped on ControlNet
+       */
+      if (
+        overData.actionType === 'SET_CONTROL_ADAPTER_IMAGE' &&
+        activeData.payloadType === 'IMAGE_DTO' &&
+        activeData.payload.imageDTO
+      ) {
+        const { id } = overData.context;
+        dispatch(
+          controlAdapterImageChanged({
+            id,
+            controlImage: activeData.payload.imageDTO.image_name,
+          })
+        );
+        dispatch(
+          controlAdapterIsEnabledChanged({
+            id,
+            isEnabled: true,
+          })
+        );
+        return;
+      }
+
+      /**
+       * Image dropped on Control Adapter Layer
+       */
+      if (
+        overData.actionType === 'SET_CA_LAYER_IMAGE' &&
+        activeData.payloadType === 'IMAGE_DTO' &&
+        activeData.payload.imageDTO
+      ) {
+        const { layerId } = overData.context;
+        dispatch(
+          caLayerImageChanged({
+            layerId,
+            imageDTO: activeData.payload.imageDTO,
+          })
+        );
+        return;
+      }
+
       /**
        * Image dropped on IP Adapter Layer
        */
       if (
-        overData.actionType === 'SET_IPA_IMAGE' &&
+        overData.actionType === 'SET_IPA_LAYER_IMAGE' &&
         activeData.payloadType === 'IMAGE_DTO' &&
         activeData.payload.imageDTO
       ) {
-        const { id } = overData.context;
-        dispatch(ipaImageChanged({ id, imageDTO: activeData.payload.imageDTO }));
+        const { layerId } = overData.context;
+        dispatch(
+          ipaLayerImageChanged({
+            layerId,
+            imageDTO: activeData.payload.imageDTO,
+          })
+        );
         return;
       }
 
@@ -59,48 +128,48 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
        * Image dropped on RG Layer IP Adapter
        */
       if (
-        overData.actionType === 'SET_RG_IP_ADAPTER_IMAGE' &&
+        overData.actionType === 'SET_RG_LAYER_IP_ADAPTER_IMAGE' &&
         activeData.payloadType === 'IMAGE_DTO' &&
         activeData.payload.imageDTO
       ) {
-        const { id, ipAdapterId } = overData.context;
-        dispatch(rgIPAdapterImageChanged({ id, ipAdapterId, imageDTO: activeData.payload.imageDTO }));
+        const { layerId, ipAdapterId } = overData.context;
+        dispatch(
+          rgLayerIPAdapterImageChanged({
+            layerId,
+            ipAdapterId,
+            imageDTO: activeData.payload.imageDTO,
+          })
+        );
         return;
       }
 
       /**
-       * Image dropped on Raster layer
+       * Image dropped on II Layer Image
        */
       if (
-        overData.actionType === 'ADD_RASTER_LAYER_FROM_IMAGE' &&
+        overData.actionType === 'SET_II_LAYER_IMAGE' &&
         activeData.payloadType === 'IMAGE_DTO' &&
         activeData.payload.imageDTO
       ) {
-        const imageObject = imageDTOToImageObject(activeData.payload.imageDTO);
-        const { x, y } = getState().canvasV2.bbox.rect;
-        const overrides: Partial<CanvasRasterLayerState> = {
-          objects: [imageObject],
-          position: { x, y },
-        };
-        dispatch(rasterLayerAdded({ overrides, isSelected: true }));
+        const { layerId } = overData.context;
+        dispatch(
+          iiLayerImageChanged({
+            layerId,
+            imageDTO: activeData.payload.imageDTO,
+          })
+        );
         return;
       }
 
       /**
-       * Image dropped on Raster layer
+       * Image dropped on Canvas
        */
       if (
-        overData.actionType === 'ADD_CONTROL_LAYER_FROM_IMAGE' &&
+        overData.actionType === 'SET_CANVAS_INITIAL_IMAGE' &&
         activeData.payloadType === 'IMAGE_DTO' &&
         activeData.payload.imageDTO
       ) {
-        const imageObject = imageDTOToImageObject(activeData.payload.imageDTO);
-        const { x, y } = getState().canvasV2.bbox.rect;
-        const overrides: Partial<CanvasControlLayerState> = {
-          objects: [imageObject],
-          position: { x, y },
-        };
-        dispatch(controlLayerAdded({ overrides, isSelected: true }));
+        dispatch(setInitialCanvasImage(activeData.payload.imageDTO, selectOptimalDimension(getState())));
         return;
       }
 

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

@@ -2,13 +2,13 @@ import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { imagesApi } from 'services/api/endpoints/images';
 
-const log = logger('gallery');
-
 export const addImageRemovedFromBoardFulfilledListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: imagesApi.endpoints.removeImageFromBoard.matchFulfilled,
     effect: (action) => {
+      const log = logger('images');
       const imageDTO = action.meta.arg.originalArgs;
+
       log.debug({ imageDTO }, 'Image removed from board');
     },
   });
@@ -16,7 +16,9 @@ export const addImageRemovedFromBoardFulfilledListener = (startAppListening: App
   startAppListening({
     matcher: imagesApi.endpoints.removeImageFromBoard.matchRejected,
     effect: (action) => {
+      const log = logger('images');
       const imageDTO = action.meta.arg.originalArgs;
+
       log.debug({ imageDTO }, 'Problem removing image from board');
     },
   });

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

@@ -6,17 +6,16 @@ import { imagesToDeleteSelected, isModalOpenChanged } from 'features/deleteImage
 export const addImageToDeleteSelectedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: imagesToDeleteSelected,
-    effect: (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const imageDTOs = action.payload;
       const state = getState();
       const { shouldConfirmOnDelete } = state.system;
       const imagesUsage = selectImageUsage(getState());
 
       const isImageInUse =
-        imagesUsage.some((i) => i.isLayerImage) ||
-        imagesUsage.some((i) => i.isControlAdapterImage) ||
-        imagesUsage.some((i) => i.isIPAdapterImage) ||
-        imagesUsage.some((i) => i.isLayerImage);
+        imagesUsage.some((i) => i.isCanvasImage) ||
+        imagesUsage.some((i) => i.isControlImage) ||
+        imagesUsage.some((i) => i.isNodesImage);
 
       if (shouldConfirmOnDelete || isImageInUse) {
         dispatch(isModalOpenChanged(true));

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

@@ -1,8 +1,19 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { ipaImageChanged, rgIPAdapterImageChanged } from 'features/controlLayers/store/canvasV2Slice';
+import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
+import {
+  controlAdapterImageChanged,
+  controlAdapterIsEnabledChanged,
+} from 'features/controlAdapters/store/controlAdaptersSlice';
+import {
+  caLayerImageChanged,
+  iiLayerImageChanged,
+  ipaLayerImageChanged,
+  rgLayerIPAdapterImageChanged,
+} from 'features/controlLayers/store/controlLayersSlice';
 import { selectListBoardsQueryArgs } from 'features/gallery/store/gallerySelectors';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
+import { selectOptimalDimension } from 'features/parameters/store/generationSlice';
 import { upscaleInitialImageChanged } from 'features/parameters/store/upscaleSlice';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
@@ -10,12 +21,11 @@ import { omit } from 'lodash-es';
 import { boardsApi } from 'services/api/endpoints/boards';
 import { imagesApi } from 'services/api/endpoints/images';
 
-const log = logger('gallery');
-
 export const addImageUploadedFulfilledListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: imagesApi.endpoints.uploadImage.matchFulfilled,
     effect: (action, { dispatch, getState }) => {
+      const log = logger('images');
       const imageDTO = action.payload;
       const state = getState();
       const { autoAddBoardId } = state.gallery;
@@ -71,6 +81,15 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
         return;
       }
 
+      if (postUploadAction?.type === 'SET_CANVAS_INITIAL_IMAGE') {
+        dispatch(setInitialCanvasImage(imageDTO, selectOptimalDimension(state)));
+        toast({
+          ...DEFAULT_UPLOADED_TOAST,
+          description: t('toast.setAsCanvasInitialImage'),
+        });
+        return;
+      }
+
       if (postUploadAction?.type === 'SET_UPSCALE_INITIAL_IMAGE') {
         dispatch(upscaleInitialImageChanged(imageDTO));
         toast({
@@ -80,31 +99,70 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
         return;
       }
 
-      // if (postUploadAction?.type === 'SET_CA_IMAGE') {
-      //   const { id } = postUploadAction;
-      //   dispatch(caImageChanged({ id, imageDTO }));
-      //   toast({ ...DEFAULT_UPLOADED_TOAST, description: t('toast.setControlImage') });
-      //   return;
-      // }
-
-      if (postUploadAction?.type === 'SET_IPA_IMAGE') {
+      if (postUploadAction?.type === 'SET_CONTROL_ADAPTER_IMAGE') {
         const { id } = postUploadAction;
-        dispatch(ipaImageChanged({ id, imageDTO }));
-        toast({ ...DEFAULT_UPLOADED_TOAST, description: t('toast.setControlImage') });
+        dispatch(
+          controlAdapterIsEnabledChanged({
+            id,
+            isEnabled: true,
+          })
+        );
+        dispatch(
+          controlAdapterImageChanged({
+            id,
+            controlImage: imageDTO.image_name,
+          })
+        );
+        toast({
+          ...DEFAULT_UPLOADED_TOAST,
+          description: t('toast.setControlImage'),
+        });
         return;
       }
 
-      if (postUploadAction?.type === 'SET_RG_IP_ADAPTER_IMAGE') {
-        const { id, ipAdapterId } = postUploadAction;
-        dispatch(rgIPAdapterImageChanged({ id, ipAdapterId, imageDTO }));
-        toast({ ...DEFAULT_UPLOADED_TOAST, description: t('toast.setControlImage') });
-        return;
+      if (postUploadAction?.type === 'SET_CA_LAYER_IMAGE') {
+        const { layerId } = postUploadAction;
+        dispatch(caLayerImageChanged({ layerId, imageDTO }));
+        toast({
+          ...DEFAULT_UPLOADED_TOAST,
+          description: t('toast.setControlImage'),
+        });
+      }
+
+      if (postUploadAction?.type === 'SET_IPA_LAYER_IMAGE') {
+        const { layerId } = postUploadAction;
+        dispatch(ipaLayerImageChanged({ layerId, imageDTO }));
+        toast({
+          ...DEFAULT_UPLOADED_TOAST,
+          description: t('toast.setControlImage'),
+        });
+      }
+
+      if (postUploadAction?.type === 'SET_RG_LAYER_IP_ADAPTER_IMAGE') {
+        const { layerId, ipAdapterId } = postUploadAction;
+        dispatch(rgLayerIPAdapterImageChanged({ layerId, ipAdapterId, imageDTO }));
+        toast({
+          ...DEFAULT_UPLOADED_TOAST,
+          description: t('toast.setControlImage'),
+        });
+      }
+
+      if (postUploadAction?.type === 'SET_II_LAYER_IMAGE') {
+        const { layerId } = postUploadAction;
+        dispatch(iiLayerImageChanged({ layerId, imageDTO }));
+        toast({
+          ...DEFAULT_UPLOADED_TOAST,
+          description: t('toast.setControlImage'),
+        });
       }
 
       if (postUploadAction?.type === 'SET_NODES_IMAGE') {
         const { nodeId, fieldName } = postUploadAction;
         dispatch(fieldImageValueChanged({ nodeId, fieldName, value: imageDTO }));
-        toast({ ...DEFAULT_UPLOADED_TOAST, description: `${t('toast.setNodeField')} ${fieldName}` });
+        toast({
+          ...DEFAULT_UPLOADED_TOAST,
+          description: `${t('toast.setNodeField')} ${fieldName}`,
+        });
         return;
       }
     },
@@ -113,6 +171,7 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
   startAppListening({
     matcher: imagesApi.endpoints.uploadImage.matchRejected,
     effect: (action) => {
+      const log = logger('images');
       const sanitizedData = {
         arg: {
           ...omit(action.meta.arg.originalArgs, ['file', 'postUploadAction']),

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

@@ -6,7 +6,7 @@ import type { ImageDTO } from 'services/api/types';
 export const addImagesStarredListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: imagesApi.endpoints.starImages.matchFulfilled,
-    effect: (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const { updated_image_names: starredImages } = action.payload;
 
       const state = getState();

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

@@ -6,7 +6,7 @@ import type { ImageDTO } from 'services/api/types';
 export const addImagesUnstarredListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: imagesApi.endpoints.unstarImages.matchFulfilled,
-    effect: (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const { updated_image_names: unstarredImages } = action.payload;
 
       const state = getState();

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

@@ -1,17 +1,23 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { loraDeleted, modelChanged, vaeSelected } from 'features/controlLayers/store/canvasV2Slice';
+import {
+  controlAdapterIsEnabledChanged,
+  selectControlAdapterAll,
+} from 'features/controlAdapters/store/controlAdaptersSlice';
+import { loraRemoved } from 'features/lora/store/loraSlice';
 import { modelSelected } from 'features/parameters/store/actions';
+import { modelChanged, vaeSelected } from 'features/parameters/store/generationSlice';
 import { zParameterModel } from 'features/parameters/types/parameterSchemas';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
-
-const log = logger('models');
+import { forEach } from 'lodash-es';
 
 export const addModelSelectedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: modelSelected,
     effect: (action, { getState, dispatch }) => {
+      const log = logger('models');
+
       const state = getState();
       const result = zParameterModel.safeParse(action.payload);
 
@@ -23,36 +29,34 @@ export const addModelSelectedListener = (startAppListening: AppStartListening) =
       const newModel = result.data;
 
       const newBaseModel = newModel.base;
-      const didBaseModelChange = state.canvasV2.params.model?.base !== newBaseModel;
+      const didBaseModelChange = state.generation.model?.base !== newBaseModel;
 
       if (didBaseModelChange) {
         // we may need to reset some incompatible submodels
         let modelsCleared = 0;
 
         // handle incompatible loras
-        state.canvasV2.loras.forEach((lora) => {
+        forEach(state.lora.loras, (lora, id) => {
           if (lora.model.base !== newBaseModel) {
-            dispatch(loraDeleted({ id: lora.id }));
+            dispatch(loraRemoved(id));
             modelsCleared += 1;
           }
         });
 
         // handle incompatible vae
-        const { vae } = state.canvasV2.params;
+        const { vae } = state.generation;
         if (vae && vae.base !== newBaseModel) {
           dispatch(vaeSelected(null));
           modelsCleared += 1;
         }
 
         // handle incompatible controlnets
-        // state.canvasV2.controlAdapters.entities.forEach((ca) => {
-        //   if (ca.model?.base !== newBaseModel) {
-        //     modelsCleared += 1;
-        //     if (ca.isEnabled) {
-        //       dispatch(entityIsEnabledToggled({ entityIdentifier: { id: ca.id, type: 'control_adapter' } }));
-        //     }
-        //   }
-        // });
+        selectControlAdapterAll(state.controlAdapters).forEach((ca) => {
+          if (ca.model?.base !== newBaseModel) {
+            dispatch(controlAdapterIsEnabledChanged({ id: ca.id, isEnabled: false }));
+            modelsCleared += 1;
+          }
+        });
 
         if (modelsCleared > 0) {
           toast({
@@ -66,7 +70,7 @@ export const addModelSelectedListener = (startAppListening: AppStartListening) =
         }
       }
 
-      dispatch(modelChanged({ model: newModel, previousModel: state.canvasV2.params.model }));
+      dispatch(modelChanged(newModel, state.generation.model));
     },
   });
 };

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

@@ -1,42 +1,36 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import type { AppDispatch, RootState } from 'app/store/store';
-import type { SerializableObject } from 'common/types';
+import type { JSONObject } from 'common/types';
 import {
-  bboxHeightChanged,
-  bboxWidthChanged,
-  controlLayerModelChanged,
-  ipaModelChanged,
-  loraDeleted,
-  modelChanged,
-  refinerModelChanged,
-  rgIPAdapterModelChanged,
-  vaeSelected,
-} from 'features/controlLayers/store/canvasV2Slice';
-import { calculateNewSize } from 'features/parameters/components/DocumentSize/calculateNewSize';
+  controlAdapterModelCleared,
+  selectControlAdapterAll,
+} from 'features/controlAdapters/store/controlAdaptersSlice';
+import { heightChanged, widthChanged } from 'features/controlLayers/store/controlLayersSlice';
+import { loraRemoved } from 'features/lora/store/loraSlice';
+import { calculateNewSize } from 'features/parameters/components/ImageSize/calculateNewSize';
+import { modelChanged, vaeSelected } from 'features/parameters/store/generationSlice';
 import { postProcessingModelChanged, upscaleModelChanged } from 'features/parameters/store/upscaleSlice';
 import { zParameterModel, zParameterVAEModel } from 'features/parameters/types/parameterSchemas';
 import { getIsSizeOptimal, getOptimalDimension } from 'features/parameters/util/optimalDimension';
+import { refinerModelChanged } from 'features/sdxl/store/sdxlSlice';
+import { forEach } from 'lodash-es';
 import type { Logger } from 'roarr';
 import { modelConfigsAdapterSelectors, modelsApi } from 'services/api/endpoints/models';
 import type { AnyModelConfig } from 'services/api/types';
 import {
-  isControlNetOrT2IAdapterModelConfig,
-  isIPAdapterModelConfig,
-  isLoRAModelConfig,
   isNonRefinerMainModelConfig,
   isRefinerMainModelModelConfig,
   isSpandrelImageToImageModelConfig,
   isVAEModelConfig,
 } from 'services/api/types';
 
-const log = logger('models');
-
 export const addModelsLoadedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     predicate: modelsApi.endpoints.getModelConfigs.matchFulfilled,
-    effect: (action, { getState, dispatch }) => {
+    effect: async (action, { getState, dispatch }) => {
       // models loaded, we need to ensure the selected model is available and if not, select the first one
+      const log = logger('models');
       log.info({ models: action.payload.entities }, `Models loaded (${action.payload.ids.length})`);
 
       const state = getState();
@@ -49,7 +43,6 @@ export const addModelsLoadedListener = (startAppListening: AppStartListening) =>
       handleLoRAModels(models, state, dispatch, log);
       handleControlAdapterModels(models, state, dispatch, log);
       handleSpandrelImageToImageModels(models, state, dispatch, log);
-      handleIPAdapterModels(models, state, dispatch, log);
     },
   });
 };
@@ -58,15 +51,15 @@ type ModelHandler = (
   models: AnyModelConfig[],
   state: RootState,
   dispatch: AppDispatch,
-  log: Logger<SerializableObject>
+  log: Logger<JSONObject>
 ) => undefined;
 
 const handleMainModels: ModelHandler = (models, state, dispatch, log) => {
-  const currentModel = state.canvasV2.params.model;
+  const currentModel = state.generation.model;
   const mainModels = models.filter(isNonRefinerMainModelConfig);
   if (mainModels.length === 0) {
     // No models loaded at all
-    dispatch(modelChanged({ model: null }));
+    dispatch(modelChanged(null));
     return;
   }
 
@@ -81,19 +74,25 @@ const handleMainModels: ModelHandler = (models, state, dispatch, log) => {
   if (defaultModelInList) {
     const result = zParameterModel.safeParse(defaultModelInList);
     if (result.success) {
-      dispatch(modelChanged({ model: defaultModelInList, previousModel: currentModel }));
+      dispatch(modelChanged(defaultModelInList, currentModel));
 
       const optimalDimension = getOptimalDimension(defaultModelInList);
-      if (getIsSizeOptimal(state.canvasV2.bbox.rect.width, state.canvasV2.bbox.rect.height, optimalDimension)) {
+      if (
+        getIsSizeOptimal(
+          state.controlLayers.present.size.width,
+          state.controlLayers.present.size.height,
+          optimalDimension
+        )
+      ) {
         return;
       }
       const { width, height } = calculateNewSize(
-        state.canvasV2.bbox.aspectRatio.value,
+        state.controlLayers.present.size.aspectRatio.value,
         optimalDimension * optimalDimension
       );
 
-      dispatch(bboxWidthChanged({ width }));
-      dispatch(bboxHeightChanged({ height }));
+      dispatch(widthChanged({ width }));
+      dispatch(heightChanged({ height }));
       return;
     }
   }
@@ -105,11 +104,11 @@ const handleMainModels: ModelHandler = (models, state, dispatch, log) => {
     return;
   }
 
-  dispatch(modelChanged({ model: result.data, previousModel: currentModel }));
+  dispatch(modelChanged(result.data, currentModel));
 };
 
 const handleRefinerModels: ModelHandler = (models, state, dispatch, _log) => {
-  const currentRefinerModel = state.canvasV2.params.refinerModel;
+  const currentRefinerModel = state.sdxl.refinerModel;
   const refinerModels = models.filter(isRefinerMainModelModelConfig);
   if (models.length === 0) {
     // No models loaded at all
@@ -128,7 +127,7 @@ const handleRefinerModels: ModelHandler = (models, state, dispatch, _log) => {
 };
 
 const handleVAEModels: ModelHandler = (models, state, dispatch, log) => {
-  const currentVae = state.canvasV2.params.vae;
+  const currentVae = state.generation.vae;
 
   if (currentVae === null) {
     // null is a valid VAE! it means "use the default with the main model"
@@ -161,45 +160,28 @@ const handleVAEModels: ModelHandler = (models, state, dispatch, log) => {
 };
 
 const handleLoRAModels: ModelHandler = (models, state, dispatch, _log) => {
-  const loraModels = models.filter(isLoRAModelConfig);
-  state.canvasV2.loras.forEach((lora) => {
-    const isLoRAAvailable = loraModels.some((m) => m.key === lora.model.key);
+  const loras = state.lora.loras;
+
+  forEach(loras, (lora, id) => {
+    const isLoRAAvailable = models.some((m) => m.key === lora.model.key);
+
     if (isLoRAAvailable) {
       return;
     }
-    dispatch(loraDeleted({ id: lora.id }));
+
+    dispatch(loraRemoved(id));
   });
 };
 
 const handleControlAdapterModels: ModelHandler = (models, state, dispatch, _log) => {
-  const caModels = models.filter(isControlNetOrT2IAdapterModelConfig);
-  state.canvasV2.controlLayers.entities.forEach((entity) => {
-    const isModelAvailable = caModels.some((m) => m.key === entity.controlAdapter.model?.key);
+  selectControlAdapterAll(state.controlAdapters).forEach((ca) => {
+    const isModelAvailable = models.some((m) => m.key === ca.model?.key);
+
     if (isModelAvailable) {
       return;
     }
-    dispatch(controlLayerModelChanged({ id: entity.id, modelConfig: null }));
-  });
-};
 
-const handleIPAdapterModels: ModelHandler = (models, state, dispatch, _log) => {
-  const ipaModels = models.filter(isIPAdapterModelConfig);
-  state.canvasV2.ipAdapters.entities.forEach((entity) => {
-    const isModelAvailable = ipaModels.some((m) => m.key === entity.ipAdapter.model?.key);
-    if (isModelAvailable) {
-      return;
-    }
-    dispatch(ipaModelChanged({ id: entity.id, modelConfig: null }));
-  });
-
-  state.canvasV2.regions.entities.forEach(({ id, ipAdapters }) => {
-    ipAdapters.forEach(({ id: ipAdapterId, model }) => {
-      const isModelAvailable = ipaModels.some((m) => m.key === model?.key);
-      if (isModelAvailable) {
-        return;
-      }
-      dispatch(rgIPAdapterModelChanged({ id, ipAdapterId, modelConfig: null }));
-    });
+    dispatch(controlAdapterModelCleared({ id: ca.id }));
   });
 };
 

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

@@ -1,6 +1,6 @@
 import { isAnyOf } from '@reduxjs/toolkit';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { positivePromptChanged } from 'features/controlLayers/store/canvasV2Slice';
+import { positivePromptChanged } from 'features/controlLayers/store/controlLayersSlice';
 import {
   combinatorialToggled,
   isErrorChanged,
@@ -15,7 +15,7 @@ import { getPresetModifiedPrompts } from 'features/nodes/util/graph/graphBuilder
 import { activeStylePresetIdChanged } from 'features/stylePresets/store/stylePresetSlice';
 import { stylePresetsApi } from 'services/api/endpoints/stylePresets';
 import { utilitiesApi } from 'services/api/endpoints/utilities';
-import { socketConnected } from 'services/events/setEventListeners';
+import { socketConnected } from 'services/events/actions';
 
 const matcher = isAnyOf(
   positivePromptChanged,
@@ -24,6 +24,8 @@ const matcher = isAnyOf(
   maxPromptsReset,
   socketConnected,
   activeStylePresetIdChanged,
+  stylePresetsApi.endpoints.deleteStylePreset.matchFulfilled,
+  stylePresetsApi.endpoints.updateStylePreset.matchFulfilled,
   stylePresetsApi.endpoints.listStylePresets.matchFulfilled
 );
 

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

@@ -1,15 +1,14 @@
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { heightChanged, widthChanged } from 'features/controlLayers/store/controlLayersSlice';
+import { setDefaultSettings } from 'features/parameters/store/actions';
 import {
-  bboxHeightChanged,
-  bboxWidthChanged,
   setCfgRescaleMultiplier,
   setCfgScale,
   setScheduler,
   setSteps,
   vaePrecisionChanged,
   vaeSelected,
-} from 'features/controlLayers/store/canvasV2Slice';
-import { setDefaultSettings } from 'features/parameters/store/actions';
+} from 'features/parameters/store/generationSlice';
 import {
   isParameterCFGRescaleMultiplier,
   isParameterCFGScale,
@@ -31,7 +30,7 @@ export const addSetDefaultSettingsListener = (startAppListening: AppStartListeni
     effect: async (action, { dispatch, getState }) => {
       const state = getState();
 
-      const currentModel = state.canvasV2.params.model;
+      const currentModel = state.generation.model;
 
       if (!currentModel) {
         return;
@@ -99,13 +98,13 @@ export const addSetDefaultSettingsListener = (startAppListening: AppStartListeni
         const setSizeOptions = { updateAspectRatio: true, clamp: true };
         if (width) {
           if (isParameterWidth(width)) {
-            dispatch(bboxWidthChanged({ width, ...setSizeOptions }));
+            dispatch(widthChanged({ width, ...setSizeOptions }));
           }
         }
 
         if (height) {
           if (isParameterHeight(height)) {
-            dispatch(bboxHeightChanged({ height, ...setSizeOptions }));
+            dispatch(heightChanged({ height, ...setSizeOptions }));
           }
         }
 

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

@@ -6,9 +6,9 @@ import { atom } from 'nanostores';
 import { api } from 'services/api';
 import { modelsApi } from 'services/api/endpoints/models';
 import { queueApi, selectQueueStatus } from 'services/api/endpoints/queue';
-import { socketConnected } from 'services/events/setEventListeners';
+import { socketConnected } from 'services/events/actions';
 
-const log = logger('events');
+const log = logger('socketio');
 
 const $isFirstConnection = atom(true);
 

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

@@ -0,0 +1,14 @@
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { socketDisconnected } from 'services/events/actions';
+
+const log = logger('socketio');
+
+export const addSocketDisconnectedEventListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: socketDisconnected,
+    effect: () => {
+      log.debug('Disconnected');
+    },
+  });
+};