ONNX Support (#3562)

Note: this branch based on #3548, not on main

While find out what needs to be done to implement onnx, found that I can
do draft of it pretty quickly, so... here it is)
Supports LoRA and TI.
As example - cat with sadcatmeme lora:

![image](https://github.com/invoke-ai/InvokeAI/assets/7768370/dbd1a5df-0629-4741-94b3-8e09f4b4d5a3)

![image](https://github.com/invoke-ai/InvokeAI/assets/7768370/d918836c-fdc7-43c0-aa81-dde9182f2e0f)

installer/lib/installer.py

@@ -455,7 +455,7 @@ def get_torch_source() -> (Union[str, None], str):
     device = graphical_accelerator()
 
     url = None
-    optional_modules = None
+    optional_modules = "[onnx]"
     if OS == "Linux":
         if device == "rocm":
             url = "https://download.pytorch.org/whl/rocm5.4.2"
@@ -464,7 +464,10 @@ def get_torch_source() -> (Union[str, None], str):
 
     if device == "cuda":
         url = "https://download.pytorch.org/whl/cu117"
-        optional_modules = "[xformers]"
+        optional_modules = "[xformers,onnx-cuda]"
+    if device == "cuda_and_dml":
+        url = "https://download.pytorch.org/whl/cu117"
+        optional_modules = "[xformers,onnx-directml]"
 
     # in all other cases, Torch wheels should be coming from PyPi as of Torch 1.13
 

installer/lib/messages.py

@@ -167,6 +167,10 @@ def graphical_accelerator():
         "an [gold1 b]NVIDIA[/] GPU (using CUDA™)",
         "cuda",
     )
+    nvidia_with_dml = (
+        "an [gold1 b]NVIDIA[/] GPU (using CUDA™, and DirectML™ for ONNX) -- ALPHA",
+        "cuda_and_dml",
+    )
     amd = (
         "an [gold1 b]AMD[/] GPU (using ROCm™)",
         "rocm",
@@ -181,7 +185,7 @@ def graphical_accelerator():
     )
 
     if OS == "Windows":
-        options = [nvidia, cpu]
+        options = [nvidia, nvidia_with_dml, cpu]
     if OS == "Linux":
         options = [nvidia, amd, cpu]
     elif OS == "Darwin":

invokeai/app/invocations/compel.py

@@ -1,6 +1,14 @@
 from typing import Literal, Optional, Union, List, Annotated
 from pydantic import BaseModel, Field
 import re
+
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
+from .model import ClipField
+
+from ...backend.util.devices import torch_dtype
+from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
+from ...backend.model_management import BaseModelType, ModelType, SubModelType, ModelPatcher
+
 import torch
 from compel import Compel, ReturnedEmbeddingsType
 from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment

invokeai/app/invocations/latent.py

@@ -24,6 +24,7 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
 )
 from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
+from ...backend.model_management import ModelPatcher
 from ...backend.util.devices import choose_torch_device, torch_dtype, choose_precision
 from ..models.image import ImageCategory, ImageField, ResourceOrigin
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext

invokeai/app/invocations/model.py

@@ -53,6 +53,7 @@ class MainModelField(BaseModel):
 
     model_name: str = Field(description="Name of the model")
     base_model: BaseModelType = Field(description="Base model")
+    model_type: ModelType = Field(description="Model Type")
 
 
 class LoRAModelField(BaseModel):

invokeai/app/invocations/onnx.py

@@ -0,0 +1,578 @@
+# Copyright (c) 2023 Borisov Sergey (https://github.com/StAlKeR7779)
+
+from contextlib import ExitStack
+from typing import List, Literal, Optional, Union
+
+import re
+import inspect
+
+from pydantic import BaseModel, Field, validator
+import torch
+import numpy as np
+from diffusers import ControlNetModel, DPMSolverMultistepScheduler
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.schedulers import SchedulerMixin as Scheduler
+
+from ..models.image import ImageCategory, ImageField, ResourceOrigin
+from ...backend.model_management import ONNXModelPatcher
+from ...backend.util import choose_torch_device
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
+from .compel import ConditioningField
+from .controlnet_image_processors import ControlField
+from .image import ImageOutput
+from .model import ModelInfo, UNetField, VaeField
+
+from invokeai.app.invocations.metadata import CoreMetadata
+from invokeai.backend import BaseModelType, ModelType, SubModelType
+from invokeai.app.util.step_callback import stable_diffusion_step_callback
+from ...backend.stable_diffusion import PipelineIntermediateState
+
+from tqdm import tqdm
+from .model import ClipField
+from .latent import LatentsField, LatentsOutput, build_latents_output, get_scheduler, SAMPLER_NAME_VALUES
+from .compel import CompelOutput
+
+
+ORT_TO_NP_TYPE = {
+    "tensor(bool)": np.bool_,
+    "tensor(int8)": np.int8,
+    "tensor(uint8)": np.uint8,
+    "tensor(int16)": np.int16,
+    "tensor(uint16)": np.uint16,
+    "tensor(int32)": np.int32,
+    "tensor(uint32)": np.uint32,
+    "tensor(int64)": np.int64,
+    "tensor(uint64)": np.uint64,
+    "tensor(float16)": np.float16,
+    "tensor(float)": np.float32,
+    "tensor(double)": np.float64,
+}
+
+PRECISION_VALUES = Literal[tuple(list(ORT_TO_NP_TYPE.keys()))]
+
+
+class ONNXPromptInvocation(BaseInvocation):
+    type: Literal["prompt_onnx"] = "prompt_onnx"
+
+    prompt: str = Field(default="", description="Prompt")
+    clip: ClipField = Field(None, description="Clip to use")
+
+    def invoke(self, context: InvocationContext) -> CompelOutput:
+        tokenizer_info = context.services.model_manager.get_model(
+            **self.clip.tokenizer.dict(),
+        )
+        text_encoder_info = context.services.model_manager.get_model(
+            **self.clip.text_encoder.dict(),
+        )
+        with tokenizer_info as orig_tokenizer, text_encoder_info as text_encoder, ExitStack() as stack:
+            # loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.clip.loras]
+            loras = [
+                (context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight)
+                for lora in self.clip.loras
+            ]
+
+            ti_list = []
+            for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", self.prompt):
+                name = trigger[1:-1]
+                try:
+                    ti_list.append(
+                        # stack.enter_context(
+                        #    context.services.model_manager.get_model(
+                        #        model_name=name,
+                        #        base_model=self.clip.text_encoder.base_model,
+                        #        model_type=ModelType.TextualInversion,
+                        #    )
+                        # )
+                        context.services.model_manager.get_model(
+                            model_name=name,
+                            base_model=self.clip.text_encoder.base_model,
+                            model_type=ModelType.TextualInversion,
+                        ).context.model
+                    )
+                except Exception:
+                    # print(e)
+                    # import traceback
+                    # print(traceback.format_exc())
+                    print(f'Warn: trigger: "{trigger}" not found')
+            if loras or ti_list:
+                text_encoder.release_session()
+            with ONNXModelPatcher.apply_lora_text_encoder(text_encoder, loras), ONNXModelPatcher.apply_ti(
+                orig_tokenizer, text_encoder, ti_list
+            ) as (tokenizer, ti_manager):
+                text_encoder.create_session()
+
+                # copy from
+                # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L153
+                text_inputs = tokenizer(
+                    self.prompt,
+                    padding="max_length",
+                    max_length=tokenizer.model_max_length,
+                    truncation=True,
+                    return_tensors="np",
+                )
+                text_input_ids = text_inputs.input_ids
+                """
+                untruncated_ids = tokenizer(prompt, padding="max_length", return_tensors="np").input_ids
+
+                if not np.array_equal(text_input_ids, untruncated_ids):
+                    removed_text = self.tokenizer.batch_decode(
+                        untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+                    )
+                    logger.warning(
+                        "The following part of your input was truncated because CLIP can only handle sequences up to"
+                        f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                    )
+                """
+
+                prompt_embeds = text_encoder(input_ids=text_input_ids.astype(np.int32))[0]
+
+        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
+
+        # TODO: hacky but works ;D maybe rename latents somehow?
+        context.services.latents.save(conditioning_name, (prompt_embeds, None))
+
+        return CompelOutput(
+            conditioning=ConditioningField(
+                conditioning_name=conditioning_name,
+            ),
+        )
+
+
+# Text to image
+class ONNXTextToLatentsInvocation(BaseInvocation):
+    """Generates latents from conditionings."""
+
+    type: Literal["t2l_onnx"] = "t2l_onnx"
+
+    # Inputs
+    # fmt: off
+    positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
+    negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
+    noise: Optional[LatentsField] = Field(description="The noise to use")
+    steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
+    cfg_scale: Union[float, List[float]] = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
+    scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
+    precision: PRECISION_VALUES = Field(default = "tensor(float16)", description="The precision to use when generating latents")
+    unet: UNetField = Field(default=None, description="UNet submodel")
+    control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
+    # seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
+    # seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
+    # fmt: on
+
+    @validator("cfg_scale")
+    def ge_one(cls, v):
+        """validate that all cfg_scale values are >= 1"""
+        if isinstance(v, list):
+            for i in v:
+                if i < 1:
+                    raise ValueError("cfg_scale must be greater than 1")
+        else:
+            if v < 1:
+                raise ValueError("cfg_scale must be greater than 1")
+        return v
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["latents"],
+                "type_hints": {
+                    "model": "model",
+                    "control": "control",
+                    # "cfg_scale": "float",
+                    "cfg_scale": "number",
+                },
+            },
+        }
+
+    # based on
+    # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        c, _ = context.services.latents.get(self.positive_conditioning.conditioning_name)
+        uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name)
+        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
+        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
+        if isinstance(c, torch.Tensor):
+            c = c.cpu().numpy()
+        if isinstance(uc, torch.Tensor):
+            uc = uc.cpu().numpy()
+        device = torch.device(choose_torch_device())
+        prompt_embeds = np.concatenate([uc, c])
+
+        latents = context.services.latents.get(self.noise.latents_name)
+        if isinstance(latents, torch.Tensor):
+            latents = latents.cpu().numpy()
+
+        # TODO: better execution device handling
+        latents = latents.astype(ORT_TO_NP_TYPE[self.precision])
+
+        # get the initial random noise unless the user supplied it
+        do_classifier_free_guidance = True
+        # latents_dtype = prompt_embeds.dtype
+        # latents_shape = (batch_size * num_images_per_prompt, 4, height // 8, width // 8)
+        # if latents.shape != latents_shape:
+        #    raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+
+        scheduler = get_scheduler(
+            context=context,
+            scheduler_info=self.unet.scheduler,
+            scheduler_name=self.scheduler,
+        )
+
+        def torch2numpy(latent: torch.Tensor):
+            return latent.cpu().numpy()
+
+        def numpy2torch(latent, device):
+            return torch.from_numpy(latent).to(device)
+
+        def dispatch_progress(
+            self, context: InvocationContext, source_node_id: str, intermediate_state: PipelineIntermediateState
+        ) -> None:
+            stable_diffusion_step_callback(
+                context=context,
+                intermediate_state=intermediate_state,
+                node=self.dict(),
+                source_node_id=source_node_id,
+            )
+
+        scheduler.set_timesteps(self.steps)
+        latents = latents * np.float64(scheduler.init_noise_sigma)
+
+        extra_step_kwargs = dict()
+        if "eta" in set(inspect.signature(scheduler.step).parameters.keys()):
+            extra_step_kwargs.update(
+                eta=0.0,
+            )
+
+        unet_info = context.services.model_manager.get_model(**self.unet.unet.dict())
+
+        with unet_info as unet, ExitStack() as stack:
+            # loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.unet.loras]
+            loras = [
+                (context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight)
+                for lora in self.unet.loras
+            ]
+
+            if loras:
+                unet.release_session()
+            with ONNXModelPatcher.apply_lora_unet(unet, loras):
+                # TODO:
+                _, _, h, w = latents.shape
+                unet.create_session(h, w)
+
+                timestep_dtype = next(
+                    (input.type for input in unet.session.get_inputs() if input.name == "timestep"), "tensor(float16)"
+                )
+                timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
+                for i in tqdm(range(len(scheduler.timesteps))):
+                    t = scheduler.timesteps[i]
+                    # expand the latents if we are doing classifier free guidance
+                    latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
+                    latent_model_input = scheduler.scale_model_input(numpy2torch(latent_model_input, device), t)
+                    latent_model_input = latent_model_input.cpu().numpy()
+
+                    # predict the noise residual
+                    timestep = np.array([t], dtype=timestep_dtype)
+                    noise_pred = unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)
+                    noise_pred = noise_pred[0]
+
+                    # perform guidance
+                    if do_classifier_free_guidance:
+                        noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
+                        noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
+
+                    # compute the previous noisy sample x_t -> x_t-1
+                    scheduler_output = scheduler.step(
+                        numpy2torch(noise_pred, device), t, numpy2torch(latents, device), **extra_step_kwargs
+                    )
+                    latents = torch2numpy(scheduler_output.prev_sample)
+
+                    state = PipelineIntermediateState(
+                        run_id="test", step=i, timestep=timestep, latents=scheduler_output.prev_sample
+                    )
+                    dispatch_progress(self, context=context, source_node_id=source_node_id, intermediate_state=state)
+
+                    # call the callback, if provided
+                    # if callback is not None and i % callback_steps == 0:
+                    #    callback(i, t, latents)
+
+        torch.cuda.empty_cache()
+
+        name = f"{context.graph_execution_state_id}__{self.id}"
+        context.services.latents.save(name, latents)
+        return build_latents_output(latents_name=name, latents=torch.from_numpy(latents))
+
+
+# Latent to image
+class ONNXLatentsToImageInvocation(BaseInvocation):
+    """Generates an image from latents."""
+
+    type: Literal["l2i_onnx"] = "l2i_onnx"
+
+    # Inputs
+    latents: Optional[LatentsField] = Field(description="The latents to generate an image from")
+    vae: VaeField = Field(default=None, description="Vae submodel")
+    metadata: Optional[CoreMetadata] = Field(
+        default=None, description="Optional core metadata to be written to the image"
+    )
+    # tiled: bool = Field(default=False, description="Decode latents by overlaping tiles(less memory consumption)")
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["latents", "image"],
+            },
+        }
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        latents = context.services.latents.get(self.latents.latents_name)
+
+        if self.vae.vae.submodel != SubModelType.VaeDecoder:
+            raise Exception(f"Expected vae_decoder, found: {self.vae.vae.model_type}")
+
+        vae_info = context.services.model_manager.get_model(
+            **self.vae.vae.dict(),
+        )
+
+        # clear memory as vae decode can request a lot
+        torch.cuda.empty_cache()
+
+        with vae_info as vae:
+            vae.create_session()
+
+            # copied from
+            # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L427
+            latents = 1 / 0.18215 * latents
+            # image = self.vae_decoder(latent_sample=latents)[0]
+            # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
+            image = np.concatenate([vae(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])])
+
+            image = np.clip(image / 2 + 0.5, 0, 1)
+            image = image.transpose((0, 2, 3, 1))
+            image = VaeImageProcessor.numpy_to_pil(image)[0]
+
+        torch.cuda.empty_cache()
+
+        image_dto = context.services.images.create(
+            image=image,
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.GENERAL,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+            metadata=self.metadata.dict() if self.metadata else None,
+        )
+
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )
+
+
+class ONNXModelLoaderOutput(BaseInvocationOutput):
+    """Model loader output"""
+
+    # fmt: off
+    type: Literal["model_loader_output_onnx"] = "model_loader_output_onnx"
+
+    unet: UNetField = Field(default=None, description="UNet submodel")
+    clip: ClipField = Field(default=None, description="Tokenizer and text_encoder submodels")
+    vae_decoder: VaeField = Field(default=None, description="Vae submodel")
+    vae_encoder: VaeField = Field(default=None, description="Vae submodel")
+    # fmt: on
+
+
+class ONNXSD1ModelLoaderInvocation(BaseInvocation):
+    """Loading submodels of selected model."""
+
+    type: Literal["sd1_model_loader_onnx"] = "sd1_model_loader_onnx"
+
+    model_name: str = Field(default="", description="Model to load")
+    # TODO: precision?
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {"tags": ["model", "loader"], "type_hints": {"model_name": "model"}},  # TODO: rename to model_name?
+        }
+
+    def invoke(self, context: InvocationContext) -> ONNXModelLoaderOutput:
+        model_name = "stable-diffusion-v1-5"
+        base_model = BaseModelType.StableDiffusion1
+
+        # TODO: not found exceptions
+        if not context.services.model_manager.model_exists(
+            model_name=model_name,
+            base_model=BaseModelType.StableDiffusion1,
+            model_type=ModelType.ONNX,
+        ):
+            raise Exception(f"Unkown model name: {model_name}!")
+
+        return ONNXModelLoaderOutput(
+            unet=UNetField(
+                unet=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=ModelType.ONNX,
+                    submodel=SubModelType.UNet,
+                ),
+                scheduler=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=ModelType.ONNX,
+                    submodel=SubModelType.Scheduler,
+                ),
+                loras=[],
+            ),
+            clip=ClipField(
+                tokenizer=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=ModelType.ONNX,
+                    submodel=SubModelType.Tokenizer,
+                ),
+                text_encoder=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=ModelType.ONNX,
+                    submodel=SubModelType.TextEncoder,
+                ),
+                loras=[],
+            ),
+            vae_decoder=VaeField(
+                vae=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=ModelType.ONNX,
+                    submodel=SubModelType.VaeDecoder,
+                ),
+            ),
+            vae_encoder=VaeField(
+                vae=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=ModelType.ONNX,
+                    submodel=SubModelType.VaeEncoder,
+                ),
+            ),
+        )
+
+
+class OnnxModelField(BaseModel):
+    """Onnx model field"""
+
+    model_name: str = Field(description="Name of the model")
+    base_model: BaseModelType = Field(description="Base model")
+    model_type: ModelType = Field(description="Model Type")
+
+
+class OnnxModelLoaderInvocation(BaseInvocation):
+    """Loads a main model, outputting its submodels."""
+
+    type: Literal["onnx_model_loader"] = "onnx_model_loader"
+
+    model: OnnxModelField = Field(description="The model to load")
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "title": "Onnx Model Loader",
+                "tags": ["model", "loader"],
+                "type_hints": {"model": "model"},
+            },
+        }
+
+    def invoke(self, context: InvocationContext) -> ONNXModelLoaderOutput:
+        base_model = self.model.base_model
+        model_name = self.model.model_name
+        model_type = ModelType.ONNX
+
+        # TODO: not found exceptions
+        if not context.services.model_manager.model_exists(
+            model_name=model_name,
+            base_model=base_model,
+            model_type=model_type,
+        ):
+            raise Exception(f"Unknown {base_model} {model_type} model: {model_name}")
+
+        """
+        if not context.services.model_manager.model_exists(
+            model_name=self.model_name,
+            model_type=SDModelType.Diffusers,
+            submodel=SDModelType.Tokenizer,
+        ):
+            raise Exception(
+                f"Failed to find tokenizer submodel in {self.model_name}! Check if model corrupted"
+            )
+
+        if not context.services.model_manager.model_exists(
+            model_name=self.model_name,
+            model_type=SDModelType.Diffusers,
+            submodel=SDModelType.TextEncoder,
+        ):
+            raise Exception(
+                f"Failed to find text_encoder submodel in {self.model_name}! Check if model corrupted"
+            )
+
+        if not context.services.model_manager.model_exists(
+            model_name=self.model_name,
+            model_type=SDModelType.Diffusers,
+            submodel=SDModelType.UNet,
+        ):
+            raise Exception(
+                f"Failed to find unet submodel from {self.model_name}! Check if model corrupted"
+            )
+        """
+
+        return ONNXModelLoaderOutput(
+            unet=UNetField(
+                unet=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.UNet,
+                ),
+                scheduler=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Scheduler,
+                ),
+                loras=[],
+            ),
+            clip=ClipField(
+                tokenizer=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Tokenizer,
+                ),
+                text_encoder=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.TextEncoder,
+                ),
+                loras=[],
+                skipped_layers=0,
+            ),
+            vae_decoder=VaeField(
+                vae=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.VaeDecoder,
+                ),
+            ),
+            vae_encoder=VaeField(
+                vae=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.VaeEncoder,
+                ),
+            ),
+        )

invokeai/backend/install/model_install_backend.py

@@ -12,6 +12,7 @@ from typing import Optional, List, Dict, Callable, Union, Set
 import requests
 from diffusers import DiffusionPipeline
 from diffusers import logging as dlogging
+import onnx
 from huggingface_hub import hf_hub_url, HfFolder, HfApi
 from omegaconf import OmegaConf
 from tqdm import tqdm
@@ -302,8 +303,10 @@ class ModelInstall(object):
 
         with TemporaryDirectory(dir=self.config.models_path) as staging:
             staging = Path(staging)
-            if "model_index.json" in files:
+            if "model_index.json" in files and "unet/model.onnx" not in files:
                 location = self._download_hf_pipeline(repo_id, staging)  # pipeline
+            elif "unet/model.onnx" in files:
+                location = self._download_hf_model(repo_id, files, staging)
             else:
                 for suffix in ["safetensors", "bin"]:
                     if f"pytorch_lora_weights.{suffix}" in files:
@@ -368,7 +371,7 @@ class ModelInstall(object):
             model_format=info.format,
         )
         legacy_conf = None
-        if info.model_type == ModelType.Main:
+        if info.model_type == ModelType.Main or info.model_type == ModelType.ONNX:
             attributes.update(
                 dict(
                     variant=info.variant_type,
@@ -433,8 +436,13 @@ class ModelInstall(object):
         location = staging / name
         paths = list()
         for filename in files:
+            filePath = Path(filename)
             p = hf_download_with_resume(
-                repo_id, model_dir=location, model_name=filename, access_token=self.access_token
+                repo_id,
+                model_dir=location / filePath.parent,
+                model_name=filePath.name,
+                access_token=self.access_token,
+                subfolder=filePath.parent,
             )
             if p:
                 paths.append(p)
@@ -482,11 +490,12 @@ def hf_download_with_resume(
     model_name: str,
     model_dest: Path = None,
     access_token: str = None,
+    subfolder: str = None,
 ) -> Path:
     model_dest = model_dest or Path(os.path.join(model_dir, model_name))
     os.makedirs(model_dir, exist_ok=True)
 
-    url = hf_hub_url(repo_id, model_name)
+    url = hf_hub_url(repo_id, model_name, subfolder=subfolder)
 
     header = {"Authorization": f"Bearer {access_token}"} if access_token else {}
     open_mode = "wb"

invokeai/backend/model_management/__init__.py

@@ -3,6 +3,7 @@ Initialization file for invokeai.backend.model_management
 """
 from .model_manager import ModelManager, ModelInfo, AddModelResult, SchedulerPredictionType
 from .model_cache import ModelCache
+from .lora import ModelPatcher, ONNXModelPatcher
 from .models import (
     BaseModelType,
     ModelType,

invokeai/backend/model_management/lora.py

@@ -6,11 +6,22 @@ from typing import Optional, Dict, Tuple, Any, Union, List
 from pathlib import Path
 
 import torch
+from safetensors.torch import load_file
+from torch.utils.hooks import RemovableHandle
+
+from diffusers.models import UNet2DConditionModel
+from transformers import CLIPTextModel
+from onnx import numpy_helper
+from onnxruntime import OrtValue
+import numpy as np
+
 from compel.embeddings_provider import BaseTextualInversionManager
 from diffusers.models import UNet2DConditionModel
 from safetensors.torch import load_file
 from transformers import CLIPTextModel, CLIPTokenizer
 
+# TODO: rename and split this file
+
 
 class LoRALayerBase:
     # rank: Optional[int]
@@ -698,3 +709,186 @@ class TextualInversionManager(BaseTextualInversionManager):
                 new_token_ids.extend(self.pad_tokens[token_id])
 
         return new_token_ids
+
+
+class ONNXModelPatcher:
+    from .models.base import IAIOnnxRuntimeModel, OnnxRuntimeModel
+
+    @classmethod
+    @contextmanager
+    def apply_lora_unet(
+        cls,
+        unet: OnnxRuntimeModel,
+        loras: List[Tuple[LoRAModel, float]],
+    ):
+        with cls.apply_lora(unet, loras, "lora_unet_"):
+            yield
+
+    @classmethod
+    @contextmanager
+    def apply_lora_text_encoder(
+        cls,
+        text_encoder: OnnxRuntimeModel,
+        loras: List[Tuple[LoRAModel, float]],
+    ):
+        with cls.apply_lora(text_encoder, loras, "lora_te_"):
+            yield
+
+    # based on
+    # https://github.com/ssube/onnx-web/blob/ca2e436f0623e18b4cfe8a0363fcfcf10508acf7/api/onnx_web/convert/diffusion/lora.py#L323
+    @classmethod
+    @contextmanager
+    def apply_lora(
+        cls,
+        model: IAIOnnxRuntimeModel,
+        loras: List[Tuple[LoraModel, float]],
+        prefix: str,
+    ):
+        from .models.base import IAIOnnxRuntimeModel
+
+        if not isinstance(model, IAIOnnxRuntimeModel):
+            raise Exception("Only IAIOnnxRuntimeModel models supported")
+
+        orig_weights = dict()
+
+        try:
+            blended_loras = dict()
+
+            for lora, lora_weight in loras:
+                for layer_key, layer in lora.layers.items():
+                    if not layer_key.startswith(prefix):
+                        continue
+
+                    layer.to(dtype=torch.float32)
+                    layer_key = layer_key.replace(prefix, "")
+                    layer_weight = layer.get_weight().detach().cpu().numpy() * lora_weight
+                    if layer_key is blended_loras:
+                        blended_loras[layer_key] += layer_weight
+                    else:
+                        blended_loras[layer_key] = layer_weight
+
+            node_names = dict()
+            for node in model.nodes.values():
+                node_names[node.name.replace("/", "_").replace(".", "_").lstrip("_")] = node.name
+
+            for layer_key, lora_weight in blended_loras.items():
+                conv_key = layer_key + "_Conv"
+                gemm_key = layer_key + "_Gemm"
+                matmul_key = layer_key + "_MatMul"
+
+                if conv_key in node_names or gemm_key in node_names:
+                    if conv_key in node_names:
+                        conv_node = model.nodes[node_names[conv_key]]
+                    else:
+                        conv_node = model.nodes[node_names[gemm_key]]
+
+                    weight_name = [n for n in conv_node.input if ".weight" in n][0]
+                    orig_weight = model.tensors[weight_name]
+
+                    if orig_weight.shape[-2:] == (1, 1):
+                        if lora_weight.shape[-2:] == (1, 1):
+                            new_weight = orig_weight.squeeze((3, 2)) + lora_weight.squeeze((3, 2))
+                        else:
+                            new_weight = orig_weight.squeeze((3, 2)) + lora_weight
+
+                        new_weight = np.expand_dims(new_weight, (2, 3))
+                    else:
+                        if orig_weight.shape != lora_weight.shape:
+                            new_weight = orig_weight + lora_weight.reshape(orig_weight.shape)
+                        else:
+                            new_weight = orig_weight + lora_weight
+
+                    orig_weights[weight_name] = orig_weight
+                    model.tensors[weight_name] = new_weight.astype(orig_weight.dtype)
+
+                elif matmul_key in node_names:
+                    weight_node = model.nodes[node_names[matmul_key]]
+                    matmul_name = [n for n in weight_node.input if "MatMul" in n][0]
+
+                    orig_weight = model.tensors[matmul_name]
+                    new_weight = orig_weight + lora_weight.transpose()
+
+                    orig_weights[matmul_name] = orig_weight
+                    model.tensors[matmul_name] = new_weight.astype(orig_weight.dtype)
+
+                else:
+                    # warn? err?
+                    pass
+
+            yield
+
+        finally:
+            # restore original weights
+            for name, orig_weight in orig_weights.items():
+                model.tensors[name] = orig_weight
+
+    @classmethod
+    @contextmanager
+    def apply_ti(
+        cls,
+        tokenizer: CLIPTokenizer,
+        text_encoder: IAIOnnxRuntimeModel,
+        ti_list: List[Any],
+    ) -> Tuple[CLIPTokenizer, TextualInversionManager]:
+        from .models.base import IAIOnnxRuntimeModel
+
+        if not isinstance(text_encoder, IAIOnnxRuntimeModel):
+            raise Exception("Only IAIOnnxRuntimeModel models supported")
+
+        orig_embeddings = None
+
+        try:
+            ti_tokenizer = copy.deepcopy(tokenizer)
+            ti_manager = TextualInversionManager(ti_tokenizer)
+
+            def _get_trigger(ti, index):
+                trigger = ti.name
+                if index > 0:
+                    trigger += f"-!pad-{i}"
+                return f"<{trigger}>"
+
+            # modify tokenizer
+            new_tokens_added = 0
+            for ti in ti_list:
+                for i in range(ti.embedding.shape[0]):
+                    new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti, i))
+
+            # modify text_encoder
+            orig_embeddings = text_encoder.tensors["text_model.embeddings.token_embedding.weight"]
+
+            embeddings = np.concatenate(
+                (np.copy(orig_embeddings), np.zeros((new_tokens_added, orig_embeddings.shape[1]))),
+                axis=0,
+            )
+
+            for ti in ti_list:
+                ti_tokens = []
+                for i in range(ti.embedding.shape[0]):
+                    embedding = ti.embedding[i].detach().numpy()
+                    trigger = _get_trigger(ti, i)
+
+                    token_id = ti_tokenizer.convert_tokens_to_ids(trigger)
+                    if token_id == ti_tokenizer.unk_token_id:
+                        raise RuntimeError(f"Unable to find token id for token '{trigger}'")
+
+                    if embeddings[token_id].shape != embedding.shape:
+                        raise ValueError(
+                            f"Cannot load embedding for {trigger}. It was trained on a model with token dimension {embedding.shape[0]}, but the current model has token dimension {embeddings[token_id].shape[0]}."
+                        )
+
+                    embeddings[token_id] = embedding
+                    ti_tokens.append(token_id)
+
+                if len(ti_tokens) > 1:
+                    ti_manager.pad_tokens[ti_tokens[0]] = ti_tokens[1:]
+
+            text_encoder.tensors["text_model.embeddings.token_embedding.weight"] = embeddings.astype(
+                orig_embeddings.dtype
+            )
+
+            yield ti_tokenizer, ti_manager
+
+        finally:
+            # restore
+            if orig_embeddings is not None:
+                text_encoder.tensors["text_model.embeddings.token_embedding.weight"] = orig_embeddings

invokeai/backend/model_management/model_cache.py

@@ -360,7 +360,8 @@ class ModelCache(object):
             # 2 refs:
             # 1 from cache_entry
             # 1 from getrefcount function
-            if not cache_entry.locked and refs <= 2:
+            # 1 from onnx runtime object
+            if not cache_entry.locked and refs <= 3 if "onnx" in model_key else 2:
                 self.logger.debug(
                     f"Unloading model {model_key} to free {(model_size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)"
                 )

invokeai/backend/model_management/model_probe.py

@@ -27,7 +27,7 @@ class ModelProbeInfo(object):
     variant_type: ModelVariantType
     prediction_type: SchedulerPredictionType
     upcast_attention: bool
-    format: Literal["diffusers", "checkpoint", "lycoris"]
+    format: Literal["diffusers", "checkpoint", "lycoris", "olive", "onnx"]
     image_size: int
 
 
@@ -41,6 +41,7 @@ class ModelProbe(object):
     PROBES = {
         "diffusers": {},
         "checkpoint": {},
+        "onnx": {},
     }
 
     CLASS2TYPE = {
@@ -53,7 +54,9 @@ class ModelProbe(object):
     }
 
     @classmethod
-    def register_probe(cls, format: Literal["diffusers", "checkpoint"], model_type: ModelType, probe_class: ProbeBase):
+    def register_probe(
+        cls, format: Literal["diffusers", "checkpoint", "onnx"], model_type: ModelType, probe_class: ProbeBase
+    ):
         cls.PROBES[format][model_type] = probe_class
 
     @classmethod
@@ -95,6 +98,7 @@ class ModelProbe(object):
                 if format_type == "diffusers"
                 else cls.get_model_type_from_checkpoint(model_path, model)
             )
+            format_type = "onnx" if model_type == ModelType.ONNX else format_type
             probe_class = cls.PROBES[format_type].get(model_type)
             if not probe_class:
                 return None
@@ -168,6 +172,8 @@ class ModelProbe(object):
         if model:
             class_name = model.__class__.__name__
         else:
+            if (folder_path / "unet/model.onnx").exists():
+                return ModelType.ONNX
             if (folder_path / "learned_embeds.bin").exists():
                 return ModelType.TextualInversion
 
@@ -460,6 +466,17 @@ class TextualInversionFolderProbe(FolderProbeBase):
         return TextualInversionCheckpointProbe(None, checkpoint=checkpoint).get_base_type()
 
 
+class ONNXFolderProbe(FolderProbeBase):
+    def get_format(self) -> str:
+        return "onnx"
+
+    def get_base_type(self) -> BaseModelType:
+        return BaseModelType.StableDiffusion1
+
+    def get_variant_type(self) -> ModelVariantType:
+        return ModelVariantType.Normal
+
+
 class ControlNetFolderProbe(FolderProbeBase):
     def get_base_type(self) -> BaseModelType:
         config_file = self.folder_path / "config.json"
@@ -497,3 +514,4 @@ ModelProbe.register_probe("checkpoint", ModelType.Vae, VaeCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.Lora, LoRACheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.TextualInversion, TextualInversionCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.ControlNet, ControlNetCheckpointProbe)
+ModelProbe.register_probe("onnx", ModelType.ONNX, ONNXFolderProbe)

invokeai/backend/model_management/models/__init__.py

@@ -23,8 +23,11 @@ from .lora import LoRAModel
 from .controlnet import ControlNetModel  # TODO:
 from .textual_inversion import TextualInversionModel
 
+from .stable_diffusion_onnx import ONNXStableDiffusion1Model, ONNXStableDiffusion2Model
+
 MODEL_CLASSES = {
     BaseModelType.StableDiffusion1: {
+        ModelType.ONNX: ONNXStableDiffusion1Model,
         ModelType.Main: StableDiffusion1Model,
         ModelType.Vae: VaeModel,
         ModelType.Lora: LoRAModel,
@@ -32,6 +35,7 @@ MODEL_CLASSES = {
         ModelType.TextualInversion: TextualInversionModel,
     },
     BaseModelType.StableDiffusion2: {
+        ModelType.ONNX: ONNXStableDiffusion2Model,
         ModelType.Main: StableDiffusion2Model,
         ModelType.Vae: VaeModel,
         ModelType.Lora: LoRAModel,
@@ -45,6 +49,7 @@ MODEL_CLASSES = {
         ModelType.Lora: LoRAModel,
         ModelType.ControlNet: ControlNetModel,
         ModelType.TextualInversion: TextualInversionModel,
+        ModelType.ONNX: ONNXStableDiffusion2Model,
     },
     BaseModelType.StableDiffusionXLRefiner: {
         ModelType.Main: StableDiffusionXLModel,
@@ -53,6 +58,7 @@ MODEL_CLASSES = {
         ModelType.Lora: LoRAModel,
         ModelType.ControlNet: ControlNetModel,
         ModelType.TextualInversion: TextualInversionModel,
+        ModelType.ONNX: ONNXStableDiffusion2Model,
     },
     # BaseModelType.Kandinsky2_1: {
     #    ModelType.Main: Kandinsky2_1Model,

invokeai/backend/model_management/models/base.py

@@ -8,13 +8,23 @@ from abc import ABCMeta, abstractmethod
 from pathlib import Path
 from picklescan.scanner import scan_file_path
 import torch
+import numpy as np
 import safetensors.torch
-from diffusers import DiffusionPipeline, ConfigMixin
+from pathlib import Path
+from diffusers import DiffusionPipeline, ConfigMixin, OnnxRuntimeModel
 
 from contextlib import suppress
 from pydantic import BaseModel, Field
 from typing import List, Dict, Optional, Type, Literal, TypeVar, Generic, Callable, Any, Union
 
+import onnx
+from onnx import numpy_helper
+from onnxruntime import (
+    InferenceSession,
+    SessionOptions,
+    get_available_providers,
+)
+
 
 class DuplicateModelException(Exception):
     pass
@@ -37,6 +47,7 @@ class BaseModelType(str, Enum):
 
 
 class ModelType(str, Enum):
+    ONNX = "onnx"
     Main = "main"
     Vae = "vae"
     Lora = "lora"
@@ -51,6 +62,8 @@ class SubModelType(str, Enum):
     Tokenizer = "tokenizer"
     Tokenizer2 = "tokenizer_2"
     Vae = "vae"
+    VaeDecoder = "vae_decoder"
+    VaeEncoder = "vae_encoder"
     Scheduler = "scheduler"
     SafetyChecker = "safety_checker"
     # MoVQ = "movq"
@@ -362,6 +375,8 @@ def calc_model_size_by_data(model) -> int:
         return _calc_pipeline_by_data(model)
     elif isinstance(model, torch.nn.Module):
         return _calc_model_by_data(model)
+    elif isinstance(model, IAIOnnxRuntimeModel):
+        return _calc_onnx_model_by_data(model)
     else:
         return 0
 
@@ -382,6 +397,12 @@ def _calc_model_by_data(model) -> int:
     return mem
 
 
+def _calc_onnx_model_by_data(model) -> int:
+    tensor_size = model.tensors.size() * 2  # The session doubles this
+    mem = tensor_size  # in bytes
+    return mem
+
+
 def _fast_safetensors_reader(path: str):
     checkpoint = dict()
     device = torch.device("meta")
@@ -449,3 +470,208 @@ class SilenceWarnings(object):
         transformers_logging.set_verbosity(self.transformers_verbosity)
         diffusers_logging.set_verbosity(self.diffusers_verbosity)
         warnings.simplefilter("default")
+
+
+ONNX_WEIGHTS_NAME = "model.onnx"
+
+
+class IAIOnnxRuntimeModel:
+    class _tensor_access:
+        def __init__(self, model):
+            self.model = model
+            self.indexes = dict()
+            for idx, obj in enumerate(self.model.proto.graph.initializer):
+                self.indexes[obj.name] = idx
+
+        def __getitem__(self, key: str):
+            value = self.model.proto.graph.initializer[self.indexes[key]]
+            return numpy_helper.to_array(value)
+
+        def __setitem__(self, key: str, value: np.ndarray):
+            new_node = numpy_helper.from_array(value)
+            # set_external_data(new_node, location="in-memory-location")
+            new_node.name = key
+            # new_node.ClearField("raw_data")
+            del self.model.proto.graph.initializer[self.indexes[key]]
+            self.model.proto.graph.initializer.insert(self.indexes[key], new_node)
+            # self.model.data[key] = OrtValue.ortvalue_from_numpy(value)
+
+        # __delitem__
+
+        def __contains__(self, key: str):
+            return self.indexes[key] in self.model.proto.graph.initializer
+
+        def items(self):
+            raise NotImplementedError("tensor.items")
+            # return [(obj.name, obj) for obj in self.raw_proto]
+
+        def keys(self):
+            return self.indexes.keys()
+
+        def values(self):
+            raise NotImplementedError("tensor.values")
+            # return [obj for obj in self.raw_proto]
+
+        def size(self):
+            bytesSum = 0
+            for node in self.model.proto.graph.initializer:
+                bytesSum += sys.getsizeof(node.raw_data)
+            return bytesSum
+
+    class _access_helper:
+        def __init__(self, raw_proto):
+            self.indexes = dict()
+            self.raw_proto = raw_proto
+            for idx, obj in enumerate(raw_proto):
+                self.indexes[obj.name] = idx
+
+        def __getitem__(self, key: str):
+            return self.raw_proto[self.indexes[key]]
+
+        def __setitem__(self, key: str, value):
+            index = self.indexes[key]
+            del self.raw_proto[index]
+            self.raw_proto.insert(index, value)
+
+        # __delitem__
+
+        def __contains__(self, key: str):
+            return key in self.indexes
+
+        def items(self):
+            return [(obj.name, obj) for obj in self.raw_proto]
+
+        def keys(self):
+            return self.indexes.keys()
+
+        def values(self):
+            return [obj for obj in self.raw_proto]
+
+    def __init__(self, model_path: str, provider: Optional[str]):
+        self.path = model_path
+        self.session = None
+        self.provider = provider
+        """
+        self.data_path = self.path + "_data"
+        if not os.path.exists(self.data_path):
+            print(f"Moving model tensors to separate file: {self.data_path}")
+            tmp_proto = onnx.load(model_path, load_external_data=True)
+            onnx.save_model(tmp_proto, self.path, save_as_external_data=True, all_tensors_to_one_file=True, location=os.path.basename(self.data_path), size_threshold=1024, convert_attribute=False)
+            del tmp_proto
+            gc.collect()
+
+        self.proto = onnx.load(model_path, load_external_data=False)
+        """
+
+        self.proto = onnx.load(model_path, load_external_data=True)
+        # self.data = dict()
+        # for tensor in self.proto.graph.initializer:
+        #     name = tensor.name
+
+        #     if tensor.HasField("raw_data"):
+        #         npt = numpy_helper.to_array(tensor)
+        #         orv = OrtValue.ortvalue_from_numpy(npt)
+        #         # self.data[name] = orv
+        #         # set_external_data(tensor, location="in-memory-location")
+        #         tensor.name = name
+        #         # tensor.ClearField("raw_data")
+
+        self.nodes = self._access_helper(self.proto.graph.node)
+        # self.initializers = self._access_helper(self.proto.graph.initializer)
+        # print(self.proto.graph.input)
+        # print(self.proto.graph.initializer)
+
+        self.tensors = self._tensor_access(self)
+
+    # TODO: integrate with model manager/cache
+    def create_session(self, height=None, width=None):
+        if self.session is None or self.session_width != width or self.session_height != height:
+            # onnx.save(self.proto, "tmp.onnx")
+            # onnx.save_model(self.proto, "tmp.onnx", save_as_external_data=True, all_tensors_to_one_file=True, location="tmp.onnx_data", size_threshold=1024, convert_attribute=False)
+            # TODO: something to be able to get weight when they already moved outside of model proto
+            # (trimmed_model, external_data) = buffer_external_data_tensors(self.proto)
+            sess = SessionOptions()
+            # self._external_data.update(**external_data)
+            # sess.add_external_initializers(list(self.data.keys()), list(self.data.values()))
+            # sess.enable_profiling = True
+
+            # sess.intra_op_num_threads = 1
+            # sess.inter_op_num_threads = 1
+            # sess.execution_mode = ExecutionMode.ORT_SEQUENTIAL
+            # sess.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL
+            # sess.enable_cpu_mem_arena = True
+            # sess.enable_mem_pattern = True
+            # sess.add_session_config_entry("session.intra_op.use_xnnpack_threadpool", "1") ########### It's the key code
+            self.session_height = height
+            self.session_width = width
+            if height and width:
+                sess.add_free_dimension_override_by_name("unet_sample_batch", 2)
+                sess.add_free_dimension_override_by_name("unet_sample_channels", 4)
+                sess.add_free_dimension_override_by_name("unet_hidden_batch", 2)
+                sess.add_free_dimension_override_by_name("unet_hidden_sequence", 77)
+                sess.add_free_dimension_override_by_name("unet_sample_height", self.session_height)
+                sess.add_free_dimension_override_by_name("unet_sample_width", self.session_width)
+                sess.add_free_dimension_override_by_name("unet_time_batch", 1)
+            providers = []
+            if self.provider:
+                providers.append(self.provider)
+            else:
+                providers = get_available_providers()
+            if "TensorrtExecutionProvider" in providers:
+                providers.remove("TensorrtExecutionProvider")
+            try:
+                self.session = InferenceSession(self.proto.SerializeToString(), providers=providers, sess_options=sess)
+            except Exception as e:
+                raise e
+            # self.session = InferenceSession("tmp.onnx", providers=[self.provider], sess_options=self.sess_options)
+            # self.io_binding = self.session.io_binding()
+
+    def release_session(self):
+        self.session = None
+        import gc
+
+        gc.collect()
+        return
+
+    def __call__(self, **kwargs):
+        if self.session is None:
+            raise Exception("You should call create_session before running model")
+
+        inputs = {k: np.array(v) for k, v in kwargs.items()}
+        output_names = self.session.get_outputs()
+        # for k in inputs:
+        #     self.io_binding.bind_cpu_input(k, inputs[k])
+        # for name in output_names:
+        #     self.io_binding.bind_output(name.name)
+        # self.session.run_with_iobinding(self.io_binding, None)
+        # return self.io_binding.copy_outputs_to_cpu()
+        return self.session.run(None, inputs)
+
+    # compatability with diffusers load code
+    @classmethod
+    def from_pretrained(
+        cls,
+        model_id: Union[str, Path],
+        subfolder: Union[str, Path] = None,
+        file_name: Optional[str] = None,
+        provider: Optional[str] = None,
+        sess_options: Optional["SessionOptions"] = None,
+        **kwargs,
+    ):
+        file_name = file_name or ONNX_WEIGHTS_NAME
+
+        if os.path.isdir(model_id):
+            model_path = model_id
+            if subfolder is not None:
+                model_path = os.path.join(model_path, subfolder)
+            model_path = os.path.join(model_path, file_name)
+
+        else:
+            model_path = model_id
+
+        # load model from local directory
+        if not os.path.isfile(model_path):
+            raise Exception(f"Model not found: {model_path}")
+
+        # TODO: session options
+        return cls(model_path, provider=provider)

invokeai/backend/model_management/models/stable_diffusion_onnx.py

@@ -0,0 +1,157 @@
+import os
+import json
+from enum import Enum
+from pydantic import Field
+from pathlib import Path
+from typing import Literal, Optional, Union
+from .base import (
+    ModelBase,
+    ModelConfigBase,
+    BaseModelType,
+    ModelType,
+    SubModelType,
+    ModelVariantType,
+    DiffusersModel,
+    SchedulerPredictionType,
+    SilenceWarnings,
+    read_checkpoint_meta,
+    classproperty,
+    OnnxRuntimeModel,
+    IAIOnnxRuntimeModel,
+)
+from invokeai.app.services.config import InvokeAIAppConfig
+
+
+class StableDiffusionOnnxModelFormat(str, Enum):
+    Olive = "olive"
+    Onnx = "onnx"
+
+
+class ONNXStableDiffusion1Model(DiffusersModel):
+    class Config(ModelConfigBase):
+        model_format: Literal[StableDiffusionOnnxModelFormat.Onnx]
+        variant: ModelVariantType
+
+    def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
+        assert base_model == BaseModelType.StableDiffusion1
+        assert model_type == ModelType.ONNX
+        super().__init__(
+            model_path=model_path,
+            base_model=BaseModelType.StableDiffusion1,
+            model_type=ModelType.ONNX,
+        )
+
+        for child_name, child_type in self.child_types.items():
+            if child_type is OnnxRuntimeModel:
+                self.child_types[child_name] = IAIOnnxRuntimeModel
+
+            # TODO: check that no optimum models provided
+
+    @classmethod
+    def probe_config(cls, path: str, **kwargs):
+        model_format = cls.detect_format(path)
+        in_channels = 4  # TODO:
+
+        if in_channels == 9:
+            variant = ModelVariantType.Inpaint
+        elif in_channels == 4:
+            variant = ModelVariantType.Normal
+        else:
+            raise Exception("Unkown stable diffusion 1.* model format")
+
+        return cls.create_config(
+            path=path,
+            model_format=model_format,
+            variant=variant,
+        )
+
+    @classproperty
+    def save_to_config(cls) -> bool:
+        return True
+
+    @classmethod
+    def detect_format(cls, model_path: str):
+        # TODO: Detect onnx vs olive
+        return StableDiffusionOnnxModelFormat.Onnx
+
+    @classmethod
+    def convert_if_required(
+        cls,
+        model_path: str,
+        output_path: str,
+        config: ModelConfigBase,
+        base_model: BaseModelType,
+    ) -> str:
+        return model_path
+
+
+class ONNXStableDiffusion2Model(DiffusersModel):
+    # TODO: check that configs overwriten properly
+    class Config(ModelConfigBase):
+        model_format: Literal[StableDiffusionOnnxModelFormat.Onnx]
+        variant: ModelVariantType
+        prediction_type: SchedulerPredictionType
+        upcast_attention: bool
+
+    def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
+        assert base_model == BaseModelType.StableDiffusion2
+        assert model_type == ModelType.ONNX
+        super().__init__(
+            model_path=model_path,
+            base_model=BaseModelType.StableDiffusion2,
+            model_type=ModelType.ONNX,
+        )
+
+        for child_name, child_type in self.child_types.items():
+            if child_type is OnnxRuntimeModel:
+                self.child_types[child_name] = IAIOnnxRuntimeModel
+            # TODO: check that no optimum models provided
+
+    @classmethod
+    def probe_config(cls, path: str, **kwargs):
+        model_format = cls.detect_format(path)
+        in_channels = 4  # TODO:
+
+        if in_channels == 9:
+            variant = ModelVariantType.Inpaint
+        elif in_channels == 5:
+            variant = ModelVariantType.Depth
+        elif in_channels == 4:
+            variant = ModelVariantType.Normal
+        else:
+            raise Exception("Unkown stable diffusion 2.* model format")
+
+        if variant == ModelVariantType.Normal:
+            prediction_type = SchedulerPredictionType.VPrediction
+            upcast_attention = True
+
+        else:
+            prediction_type = SchedulerPredictionType.Epsilon
+            upcast_attention = False
+
+        return cls.create_config(
+            path=path,
+            model_format=model_format,
+            variant=variant,
+            prediction_type=prediction_type,
+            upcast_attention=upcast_attention,
+        )
+
+    @classproperty
+    def save_to_config(cls) -> bool:
+        return True
+
+    @classmethod
+    def detect_format(cls, model_path: str):
+        # TODO: Detect onnx vs olive
+        return StableDiffusionOnnxModelFormat.Onnx
+
+    @classmethod
+    def convert_if_required(
+        cls,
+        model_path: str,
+        output_path: str,
+        config: ModelConfigBase,
+        base_model: BaseModelType,
+    ) -> str:
+        return model_path

invokeai/frontend/web/dist/assets/ThemeLocaleProvider-a4dc3f38.js

@@ -1,4 +1,4 @@
-import{A as m,f_ as Je,z as y,a4 as Ka,f$ as Xa,af as va,aj as d,g0 as b,g1 as t,g2 as Ya,g3 as h,g4 as ua,g5 as Ja,g6 as Qa,aI as Za,g7 as et,ad as rt,g8 as at}from"./index-9bb68e3a.js";import{s as fa,n as o,t as tt,o as ha,p as ot,q as ma,v as ga,w as ya,x as it,y as Sa,z as pa,A as xr,B as nt,D as lt,E as st,F as xa,G as $a,H as ka,J as dt,K as _a,L as ct,M as bt,N as vt,O as ut,Q as wa,R as ft,S as ht,T as mt,U as gt,V as yt,W as St,e as pt,X as xt}from"./MantineProvider-ae002ae6.js";var za=String.raw,Ca=za`
+import{A as m,f$ as Je,z as y,a4 as Ka,g0 as Xa,af as va,aj as d,g1 as b,g2 as t,g3 as Ya,g4 as h,g5 as ua,g6 as Ja,g7 as Qa,aI as Za,g8 as et,ad as rt,g9 as at}from"./index-18f2f740.js";import{s as fa,n as o,t as tt,o as ha,p as ot,q as ma,v as ga,w as ya,x as it,y as Sa,z as pa,A as xr,B as nt,D as lt,E as st,F as xa,G as $a,H as ka,J as dt,K as _a,L as ct,M as bt,N as vt,O as ut,Q as wa,R as ft,S as ht,T as mt,U as gt,V as yt,W as St,e as pt,X as xt}from"./MantineProvider-b20a2267.js";var za=String.raw,Ca=za`
   :root,
   :host {
     --chakra-vh: 100vh;

invokeai/frontend/web/dist/index.html

@@ -12,7 +12,7 @@
         margin: 0;
       }
     </style>
-    <script type="module" crossorigin src="./assets/index-9bb68e3a.js"></script>
+    <script type="module" crossorigin src="./assets/index-18f2f740.js"></script>
   </head>
 
   <body dir="ltr">

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

@@ -342,6 +342,8 @@
         "diffusersModels": "Diffusers",
         "loraModels": "LoRAs",
         "safetensorModels": "SafeTensors",
+        "onnxModels": "Onnx",
+        "oliveModels": "Olives",
         "modelAdded": "Model Added",
         "modelUpdated": "Model Updated",
         "modelUpdateFailed": "Model Update Failed",

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

@@ -342,6 +342,8 @@
         "diffusersModels": "Diffusers",
         "loraModels": "LoRAs",
         "safetensorModels": "SafeTensors",
+        "onnxModels": "Onnx",
+        "oliveModels": "Olives",
         "modelAdded": "Model Added",
         "modelUpdated": "Model Updated",
         "modelUpdateFailed": "Model Update Failed",

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

@@ -36,7 +36,8 @@ export const addModelsLoadedListener = () => {
         action.payload.entities,
         (m) =>
           m?.model_name === currentModel?.model_name &&
-          m?.base_model === currentModel?.base_model
+          m?.base_model === currentModel?.base_model &&
+          m?.model_type === currentModel?.model_type
       );
 
       if (isCurrentModelAvailable) {
@@ -83,7 +84,8 @@ export const addModelsLoadedListener = () => {
         action.payload.entities,
         (m) =>
           m?.model_name === currentModel?.model_name &&
-          m?.base_model === currentModel?.base_model
+          m?.base_model === currentModel?.base_model &&
+          m?.model_type === currentModel?.model_type
       );
 
       if (isCurrentModelAvailable) {

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

@@ -47,9 +47,9 @@ export const addTabChangedListener = () => {
         }
 
         // only store the model name and base model in redux
-        const { base_model, model_name } = firstValidCanvasModel;
+        const { base_model, model_name, model_type } = firstValidCanvasModel;
 
-        dispatch(modelChanged({ base_model, model_name }));
+        dispatch(modelChanged({ base_model, model_name, model_type }));
       }
     },
   });

invokeai/frontend/web/src/features/nodes/components/fields/ModelInputFieldComponent.tsx

@@ -14,8 +14,11 @@ import SyncModelsButton from 'features/ui/components/tabs/ModelManager/subpanels
 import { forEach } from 'lodash-es';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
+import {
+  useGetMainModelsQuery,
+  useGetOnnxModelsQuery,
+} from 'services/api/endpoints/models';
 import { NON_REFINER_BASE_MODELS } from 'services/api/constants';
-import { useGetMainModelsQuery } from 'services/api/endpoints/models';
 import { FieldComponentProps } from './types';
 import { useFeatureStatus } from '../../../system/hooks/useFeatureStatus';
 
@@ -28,6 +31,7 @@ const ModelInputFieldComponent = (
   const { t } = useTranslation();
   const isSyncModelEnabled = useFeatureStatus('syncModels').isFeatureEnabled;
 
+  const { data: onnxModels } = useGetOnnxModelsQuery(NON_REFINER_BASE_MODELS);
   const { data: mainModels, isLoading } = useGetMainModelsQuery(
     NON_REFINER_BASE_MODELS
   );
@@ -51,17 +55,39 @@ const ModelInputFieldComponent = (
       });
     });
 
+    if (onnxModels) {
+      forEach(onnxModels.entities, (model, id) => {
+        if (!model) {
+          return;
+        }
+
+        data.push({
+          value: id,
+          label: model.model_name,
+          group: MODEL_TYPE_MAP[model.base_model],
+        });
+      });
+    }
     return data;
-  }, [mainModels]);
+  }, [mainModels, onnxModels]);
 
   // grab the full model entity from the RTK Query cache
   // TODO: maybe we should just store the full model entity in state?
   const selectedModel = useMemo(
     () =>
-      mainModels?.entities[
+      (mainModels?.entities[
         `${field.value?.base_model}/main/${field.value?.model_name}`
-      ] ?? null,
-    [field.value?.base_model, field.value?.model_name, mainModels?.entities]
+      ] ||
+        onnxModels?.entities[
+          `${field.value?.base_model}/onnx/${field.value?.model_name}`
+        ]) ??
+      null,
+    [
+      field.value?.base_model,
+      field.value?.model_name,
+      mainModels?.entities,
+      onnxModels?.entities,
+    ]
   );
 
   const handleChangeModel = useCallback(

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addLoRAsToGraph.ts

@@ -9,6 +9,7 @@ import {
   CLIP_SKIP,
   LORA_LOADER,
   MAIN_MODEL_LOADER,
+  ONNX_MODEL_LOADER,
   METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   POSITIVE_CONDITIONING,
@@ -17,7 +18,8 @@ import {
 export const addLoRAsToGraph = (
   state: RootState,
   graph: NonNullableGraph,
-  baseNodeId: string
+  baseNodeId: string,
+  modelLoader: string = MAIN_MODEL_LOADER
 ): void => {
   /**
    * LoRA nodes get the UNet and CLIP models from the main model loader and apply the LoRA to them.
@@ -40,6 +42,10 @@ export const addLoRAsToGraph = (
         !(
           e.source.node_id === MAIN_MODEL_LOADER &&
           ['unet'].includes(e.source.field)
+        ) &&
+        !(
+          e.source.node_id === ONNX_MODEL_LOADER &&
+          ['unet'].includes(e.source.field)
         )
     );
     // Remove CLIP_SKIP connections to conditionings to feed it through LoRAs
@@ -75,12 +81,11 @@ export const addLoRAsToGraph = (
 
     // add to graph
     graph.nodes[currentLoraNodeId] = loraLoaderNode;
-
     if (currentLoraIndex === 0) {
       // first lora = start the lora chain, attach directly to model loader
       graph.edges.push({
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: modelLoader,
           field: 'unet',
         },
         destination: {

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addVAEToGraph.ts

@@ -9,13 +9,15 @@ import {
   LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
   METADATA_ACCUMULATOR,
+  ONNX_MODEL_LOADER,
   TEXT_TO_IMAGE_GRAPH,
   VAE_LOADER,
 } from './constants';
 
 export const addVAEToGraph = (
   state: RootState,
-  graph: NonNullableGraph
+  graph: NonNullableGraph,
+  modelLoader: string = MAIN_MODEL_LOADER
 ): void => {
   const { vae } = state.generation;
 
@@ -32,12 +34,12 @@ export const addVAEToGraph = (
       vae_model: vae,
     };
   }
-
+  const isOnnxModel = modelLoader == ONNX_MODEL_LOADER;
   if (graph.id === TEXT_TO_IMAGE_GRAPH || graph.id === IMAGE_TO_IMAGE_GRAPH) {
     graph.edges.push({
       source: {
-        node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
-        field: 'vae',
+        node_id: isAutoVae ? modelLoader : VAE_LOADER,
+        field: isAutoVae && isOnnxModel ? 'vae_decoder' : 'vae',
       },
       destination: {
         node_id: LATENTS_TO_IMAGE,
@@ -49,8 +51,8 @@ export const addVAEToGraph = (
   if (graph.id === IMAGE_TO_IMAGE_GRAPH) {
     graph.edges.push({
       source: {
-        node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
-        field: 'vae',
+        node_id: isAutoVae ? modelLoader : VAE_LOADER,
+        field: isAutoVae && isOnnxModel ? 'vae_decoder' : 'vae',
       },
       destination: {
         node_id: IMAGE_TO_LATENTS,
@@ -62,8 +64,8 @@ export const addVAEToGraph = (
   if (graph.id === INPAINT_GRAPH) {
     graph.edges.push({
       source: {
-        node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
-        field: 'vae',
+        node_id: isAutoVae ? modelLoader : VAE_LOADER,
+        field: isAutoVae && isOnnxModel ? 'vae_decoder' : 'vae',
       },
       destination: {
         node_id: INPAINT,

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts

@@ -12,6 +12,7 @@ import {
   CLIP_SKIP,
   LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
+  ONNX_MODEL_LOADER,
   METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
@@ -52,7 +53,8 @@ export const buildCanvasTextToImageGraph = (
   const use_cpu = shouldUseNoiseSettings
     ? shouldUseCpuNoise
     : initialGenerationState.shouldUseCpuNoise;
-
+  const onnx_model_type = model.model_type.includes('onnx');
+  const model_loader = onnx_model_type ? ONNX_MODEL_LOADER : MAIN_MODEL_LOADER;
   /**
    * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
    * full graph here as a template. Then use the parameters from app state and set friendlier node
@@ -63,17 +65,18 @@ export const buildCanvasTextToImageGraph = (
    */
 
   // copy-pasted graph from node editor, filled in with state values & friendly node ids
+  // TODO: Actually create the graph correctly for ONNX
   const graph: NonNullableGraph = {
     id: TEXT_TO_IMAGE_GRAPH,
     nodes: {
       [POSITIVE_CONDITIONING]: {
-        type: 'compel',
+        type: onnx_model_type ? 'prompt_onnx' : 'compel',
         id: POSITIVE_CONDITIONING,
         is_intermediate: true,
         prompt: positivePrompt,
       },
       [NEGATIVE_CONDITIONING]: {
-        type: 'compel',
+        type: onnx_model_type ? 'prompt_onnx' : 'compel',
         id: NEGATIVE_CONDITIONING,
         is_intermediate: true,
         prompt: negativePrompt,
@@ -87,16 +90,16 @@ export const buildCanvasTextToImageGraph = (
         use_cpu,
       },
       [TEXT_TO_LATENTS]: {
-        type: 't2l',
+        type: onnx_model_type ? 't2l_onnx' : 't2l',
         id: TEXT_TO_LATENTS,
         is_intermediate: true,
         cfg_scale,
         scheduler,
         steps,
       },
-      [MAIN_MODEL_LOADER]: {
-        type: 'main_model_loader',
-        id: MAIN_MODEL_LOADER,
+      [model_loader]: {
+        type: model_loader,
+        id: model_loader,
         is_intermediate: true,
         model,
       },
@@ -107,7 +110,7 @@ export const buildCanvasTextToImageGraph = (
         skipped_layers: clipSkip,
       },
       [LATENTS_TO_IMAGE]: {
-        type: 'l2i',
+        type: onnx_model_type ? 'l2i_onnx' : 'l2i',
         id: LATENTS_TO_IMAGE,
         is_intermediate: !shouldAutoSave,
       },
@@ -135,7 +138,7 @@ export const buildCanvasTextToImageGraph = (
       },
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: model_loader,
           field: 'clip',
         },
         destination: {
@@ -165,7 +168,7 @@ export const buildCanvasTextToImageGraph = (
       },
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: model_loader,
           field: 'unet',
         },
         destination: {
@@ -229,10 +232,10 @@ export const buildCanvasTextToImageGraph = (
   });
 
   // add LoRA support
-  addLoRAsToGraph(state, graph, TEXT_TO_LATENTS);
+  addLoRAsToGraph(state, graph, TEXT_TO_LATENTS, model_loader);
 
   // optionally add custom VAE
-  addVAEToGraph(state, graph);
+  addVAEToGraph(state, graph, model_loader);
 
   // add dynamic prompts - also sets up core iteration and seed
   addDynamicPromptsToGraph(state, graph);

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts

@@ -12,6 +12,7 @@ import {
   CLIP_SKIP,
   LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
+  ONNX_MODEL_LOADER,
   METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
@@ -48,6 +49,8 @@ export const buildLinearTextToImageGraph = (
     throw new Error('No model found in state');
   }
 
+  const onnx_model_type = model.model_type.includes('onnx');
+  const model_loader = onnx_model_type ? ONNX_MODEL_LOADER : MAIN_MODEL_LOADER;
   /**
    * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
    * full graph here as a template. Then use the parameters from app state and set friendlier node
@@ -58,12 +61,14 @@ export const buildLinearTextToImageGraph = (
    */
 
   // copy-pasted graph from node editor, filled in with state values & friendly node ids
+
+  // TODO: Actually create the graph correctly for ONNX
   const graph: NonNullableGraph = {
     id: TEXT_TO_IMAGE_GRAPH,
     nodes: {
-      [MAIN_MODEL_LOADER]: {
-        type: 'main_model_loader',
-        id: MAIN_MODEL_LOADER,
+      [model_loader]: {
+        type: model_loader,
+        id: model_loader,
         model,
       },
       [CLIP_SKIP]: {
@@ -72,12 +77,12 @@ export const buildLinearTextToImageGraph = (
         skipped_layers: clipSkip,
       },
       [POSITIVE_CONDITIONING]: {
-        type: 'compel',
+        type: onnx_model_type ? 'prompt_onnx' : 'compel',
         id: POSITIVE_CONDITIONING,
         prompt: positivePrompt,
       },
       [NEGATIVE_CONDITIONING]: {
-        type: 'compel',
+        type: onnx_model_type ? 'prompt_onnx' : 'compel',
         id: NEGATIVE_CONDITIONING,
         prompt: negativePrompt,
       },
@@ -89,14 +94,14 @@ export const buildLinearTextToImageGraph = (
         use_cpu,
       },
       [TEXT_TO_LATENTS]: {
-        type: 't2l',
+        type: onnx_model_type ? 't2l_onnx' : 't2l',
         id: TEXT_TO_LATENTS,
         cfg_scale,
         scheduler,
         steps,
       },
       [LATENTS_TO_IMAGE]: {
-        type: 'l2i',
+        type: onnx_model_type ? 'l2i_onnx' : 'l2i',
         id: LATENTS_TO_IMAGE,
         fp32: vaePrecision === 'fp32' ? true : false,
       },
@@ -104,7 +109,7 @@ export const buildLinearTextToImageGraph = (
     edges: [
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: model_loader,
           field: 'clip',
         },
         destination: {
@@ -114,7 +119,7 @@ export const buildLinearTextToImageGraph = (
       },
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: model_loader,
           field: 'unet',
         },
         destination: {
@@ -218,10 +223,10 @@ export const buildLinearTextToImageGraph = (
   });
 
   // add LoRA support
-  addLoRAsToGraph(state, graph, TEXT_TO_LATENTS);
+  addLoRAsToGraph(state, graph, TEXT_TO_LATENTS, model_loader);
 
   // optionally add custom VAE
-  addVAEToGraph(state, graph);
+  addVAEToGraph(state, graph, model_loader);
 
   // add dynamic prompts - also sets up core iteration and seed
   addDynamicPromptsToGraph(state, graph);

invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts

@@ -10,6 +10,7 @@ export const RANDOM_INT = 'rand_int';
 export const RANGE_OF_SIZE = 'range_of_size';
 export const ITERATE = 'iterate';
 export const MAIN_MODEL_LOADER = 'main_model_loader';
+export const ONNX_MODEL_LOADER = 'onnx_model_loader';
 export const VAE_LOADER = 'vae_loader';
 export const LORA_LOADER = 'lora_loader';
 export const CLIP_SKIP = 'clip_skip';

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

@@ -15,8 +15,11 @@ import { modelIdToMainModelParam } from 'features/parameters/util/modelIdToMainM
 import SyncModelsButton from 'features/ui/components/tabs/ModelManager/subpanels/ModelManagerSettingsPanel/SyncModelsButton';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
 import { forEach } from 'lodash-es';
+import {
+  useGetMainModelsQuery,
+  useGetOnnxModelsQuery,
+} from 'services/api/endpoints/models';
 import { NON_REFINER_BASE_MODELS } from 'services/api/constants';
-import { useGetMainModelsQuery } from 'services/api/endpoints/models';
 import { useFeatureStatus } from '../../../../system/hooks/useFeatureStatus';
 
 const selector = createSelector(
@@ -35,6 +38,9 @@ const ParamMainModelSelect = () => {
   const { data: mainModels, isLoading } = useGetMainModelsQuery(
     NON_REFINER_BASE_MODELS
   );
+  const { data: onnxModels, isLoading: onnxLoading } = useGetOnnxModelsQuery(
+    NON_REFINER_BASE_MODELS
+  );
 
   const activeTabName = useAppSelector(activeTabNameSelector);
 
@@ -59,17 +65,35 @@ const ParamMainModelSelect = () => {
         group: MODEL_TYPE_MAP[model.base_model],
       });
     });
+    forEach(onnxModels?.entities, (model, id) => {
+      if (
+        !model ||
+        activeTabName === 'unifiedCanvas' ||
+        activeTabName === 'img2img'
+      ) {
+        return;
+      }
+
+      data.push({
+        value: id,
+        label: model.model_name,
+        group: MODEL_TYPE_MAP[model.base_model],
+      });
+    });
 
     return data;
-  }, [mainModels, activeTabName]);
+  }, [mainModels, onnxModels, activeTabName]);
 
   // grab the full model entity from the RTK Query cache
   // TODO: maybe we should just store the full model entity in state?
   const selectedModel = useMemo(
     () =>
-      mainModels?.entities[`${model?.base_model}/main/${model?.model_name}`] ??
+      (mainModels?.entities[`${model?.base_model}/main/${model?.model_name}`] ||
+        onnxModels?.entities[
+          `${model?.base_model}/onnx/${model?.model_name}`
+        ]) ??
       null,
-    [mainModels?.entities, model]
+    [mainModels?.entities, model, onnxModels?.entities]
   );
 
   const handleChangeModel = useCallback(
@@ -89,7 +113,7 @@ const ParamMainModelSelect = () => {
     [dispatch]
   );
 
-  return isLoading ? (
+  return isLoading || onnxLoading ? (
     <IAIMantineSearchableSelect
       label={t('modelManager.model')}
       placeholder="Loading..."

invokeai/frontend/web/src/features/parameters/store/actions.ts

@@ -1,10 +1,10 @@
 import { createAction } from '@reduxjs/toolkit';
-import { ImageDTO, MainModelField } from 'services/api/types';
+import { ImageDTO, MainModelField, OnnxModelField } from 'services/api/types';
 
 export const initialImageSelected = createAction<ImageDTO | undefined>(
   'generation/initialImageSelected'
 );
 
-export const modelSelected = createAction<MainModelField>(
+export const modelSelected = createAction<MainModelField | OnnxModelField>(
   'generation/modelSelected'
 );

invokeai/frontend/web/src/features/parameters/store/generationSlice.ts

@@ -3,7 +3,7 @@ import { createSlice } from '@reduxjs/toolkit';
 import { roundToMultiple } from 'common/util/roundDownToMultiple';
 import { configChanged } from 'features/system/store/configSlice';
 import { clamp } from 'lodash-es';
-import { ImageDTO, MainModelField } from 'services/api/types';
+import { ImageDTO, MainModelField, OnnxModelField } from 'services/api/types';
 import { clipSkipMap } from '../types/constants';
 import {
   CfgScaleParam,
@@ -50,7 +50,7 @@ export interface GenerationState {
   shouldUseSymmetry: boolean;
   horizontalSymmetrySteps: number;
   verticalSymmetrySteps: number;
-  model: MainModelField | null;
+  model: MainModelField | OnnxModelField | null;
   vae: VaeModelParam | null;
   vaePrecision: PrecisionParam;
   seamlessXAxis: boolean;
@@ -272,11 +272,12 @@ export const generationSlice = createSlice({
       const defaultModel = action.payload.sd?.defaultModel;
 
       if (defaultModel && !state.model) {
-        const [base_model, _model_type, model_name] = defaultModel.split('/');
+        const [base_model, model_type, model_name] = defaultModel.split('/');
 
         const result = zMainModel.safeParse({
           model_name,
           base_model,
+          model_type,
         });
 
         if (result.success) {

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

@@ -210,6 +210,14 @@ export type HeightParam = z.infer<typeof zHeight>;
 export const isValidHeight = (val: unknown): val is HeightParam =>
   zHeight.safeParse(val).success;
 
+const zModelType = z.enum([
+  'vae',
+  'lora',
+  'onnx',
+  'main',
+  'controlnet',
+  'embedding',
+]);
 const zBaseModel = z.enum(['sd-1', 'sd-2', 'sdxl', 'sdxl-refiner']);
 
 export type BaseModelParam = z.infer<typeof zBaseModel>;
@@ -221,12 +229,18 @@ export type BaseModelParam = z.infer<typeof zBaseModel>;
 export const zMainModel = z.object({
   model_name: z.string().min(1),
   base_model: zBaseModel,
+  model_type: zModelType,
 });
 
 /**
  * Type alias for model parameter, inferred from its zod schema
  */
 export type MainModelParam = z.infer<typeof zMainModel>;
+
+/**
+ * Type alias for model parameter, inferred from its zod schema
+ */
+export type OnnxModelParam = z.infer<typeof zMainModel>;
 /**
  * Validates/type-guards a value as a model parameter
  */

invokeai/frontend/web/src/features/parameters/util/modelIdToMainModelParam.ts

@@ -8,11 +8,12 @@ export const modelIdToMainModelParam = (
   mainModelId: string
 ): MainModelParam | undefined => {
   const log = logger('models');
-  const [base_model, _model_type, model_name] = mainModelId.split('/');
+  const [base_model, model_type, model_name] = mainModelId.split('/');
 
   const result = zMainModel.safeParse({
     base_model,
     model_name,
+    model_type,
   });
 
   if (!result.success) {

invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/ModelList.tsx

@@ -8,7 +8,9 @@ import { useCallback, useState } from 'react';
 import { useTranslation } from 'react-i18next';
 import {
   MainModelConfigEntity,
+  OnnxModelConfigEntity,
   useGetMainModelsQuery,
+  useGetOnnxModelsQuery,
   useGetLoRAModelsQuery,
   LoRAModelConfigEntity,
 } from 'services/api/endpoints/models';
@@ -20,9 +22,9 @@ type ModelListProps = {
   setSelectedModelId: (name: string | undefined) => void;
 };
 
-type ModelFormat = 'images' | 'checkpoint' | 'diffusers';
+type ModelFormat = 'images' | 'checkpoint' | 'diffusers' | 'olive' | 'onnx';
 
-type ModelType = 'main' | 'lora';
+type ModelType = 'main' | 'lora' | 'onnx';
 
 type CombinedModelFormat = ModelFormat | 'lora';
 
@@ -61,6 +63,18 @@ const ModelList = (props: ModelListProps) => {
     }),
   });
 
+  const { filteredOnnxModels } = useGetOnnxModelsQuery(ALL_BASE_MODELS, {
+    selectFromResult: ({ data }) => ({
+      filteredOnnxModels: modelsFilter(data, 'onnx', 'onnx', nameFilter),
+    }),
+  });
+
+  const { filteredOliveModels } = useGetOnnxModelsQuery(ALL_BASE_MODELS, {
+    selectFromResult: ({ data }) => ({
+      filteredOliveModels: modelsFilter(data, 'onnx', 'olive', nameFilter),
+    }),
+  });
+
   const handleSearchFilter = useCallback((e: ChangeEvent<HTMLInputElement>) => {
     setNameFilter(e.target.value);
   }, []);
@@ -85,10 +99,17 @@ const ModelList = (props: ModelListProps) => {
           </IAIButton>
           <IAIButton
             size="sm"
-            onClick={() => setModelFormatFilter('checkpoint')}
-            isChecked={modelFormatFilter === 'checkpoint'}
+            onClick={() => setModelFormatFilter('onnx')}
+            isChecked={modelFormatFilter === 'onnx'}
           >
-            {t('modelManager.checkpointModels')}
+            {t('modelManager.onnxModels')}
+          </IAIButton>
+          <IAIButton
+            size="sm"
+            onClick={() => setModelFormatFilter('olive')}
+            isChecked={modelFormatFilter === 'olive'}
+          >
+            {t('modelManager.oliveModels')}
           </IAIButton>
           <IAIButton
             size="sm"
@@ -147,6 +168,42 @@ const ModelList = (props: ModelListProps) => {
                 </Flex>
               </StyledModelContainer>
             )}
+          {['images', 'olive'].includes(modelFormatFilter) &&
+            filteredOliveModels.length > 0 && (
+              <StyledModelContainer>
+                <Flex sx={{ gap: 2, flexDir: 'column' }}>
+                  <Text variant="subtext" fontSize="sm">
+                    Olives
+                  </Text>
+                  {filteredOliveModels.map((model) => (
+                    <ModelListItem
+                      key={model.id}
+                      model={model}
+                      isSelected={selectedModelId === model.id}
+                      setSelectedModelId={setSelectedModelId}
+                    />
+                  ))}
+                </Flex>
+              </StyledModelContainer>
+            )}
+          {['images', 'onnx'].includes(modelFormatFilter) &&
+            filteredOnnxModels.length > 0 && (
+              <StyledModelContainer>
+                <Flex sx={{ gap: 2, flexDir: 'column' }}>
+                  <Text variant="subtext" fontSize="sm">
+                    Onnx
+                  </Text>
+                  {filteredOnnxModels.map((model) => (
+                    <ModelListItem
+                      key={model.id}
+                      model={model}
+                      isSelected={selectedModelId === model.id}
+                      setSelectedModelId={setSelectedModelId}
+                    />
+                  ))}
+                </Flex>
+              </StyledModelContainer>
+            )}
           {['images', 'lora'].includes(modelFormatFilter) &&
             filteredLoraModels.length > 0 && (
               <StyledModelContainer>
@@ -173,7 +230,12 @@ const ModelList = (props: ModelListProps) => {
 
 export default ModelList;
 
-const modelsFilter = <T extends MainModelConfigEntity | LoRAModelConfigEntity>(
+const modelsFilter = <
+  T extends
+    | MainModelConfigEntity
+    | LoRAModelConfigEntity
+    | OnnxModelConfigEntity
+>(
   data: EntityState<T> | undefined,
   model_type: ModelType,
   model_format: ModelFormat | undefined,

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

@@ -10,9 +10,11 @@ import {
   ImportModelConfig,
   LoRAModelConfig,
   MainModelConfig,
+  OnnxModelConfig,
   MergeModelConfig,
   TextualInversionModelConfig,
   VaeModelConfig,
+  ModelType,
 } from 'services/api/types';
 
 import queryString from 'query-string';
@@ -27,6 +29,8 @@ export type MainModelConfigEntity =
   | DiffusersModelConfigEntity
   | CheckpointModelConfigEntity;
 
+export type OnnxModelConfigEntity = OnnxModelConfig & { id: string };
+
 export type LoRAModelConfigEntity = LoRAModelConfig & { id: string };
 
 export type ControlNetModelConfigEntity = ControlNetModelConfig & {
@@ -41,6 +45,7 @@ export type VaeModelConfigEntity = VaeModelConfig & { id: string };
 
 type AnyModelConfigEntity =
   | MainModelConfigEntity
+  | OnnxModelConfigEntity
   | LoRAModelConfigEntity
   | ControlNetModelConfigEntity
   | TextualInversionModelConfigEntity
@@ -66,6 +71,7 @@ type UpdateLoRAModelResponse = UpdateMainModelResponse;
 type DeleteMainModelArg = {
   base_model: BaseModelType;
   model_name: string;
+  model_type: ModelType;
 };
 
 type DeleteMainModelResponse = void;
@@ -119,6 +125,10 @@ type SearchFolderArg = operations['search_for_models']['parameters']['query'];
 const mainModelsAdapter = createEntityAdapter<MainModelConfigEntity>({
   sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
 });
+
+const onnxModelsAdapter = createEntityAdapter<OnnxModelConfigEntity>({
+  sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
+});
 const loraModelsAdapter = createEntityAdapter<LoRAModelConfigEntity>({
   sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
 });
@@ -156,6 +166,49 @@ const createModelEntities = <T extends AnyModelConfigEntity>(
 
 export const modelsApi = api.injectEndpoints({
   endpoints: (build) => ({
+    getOnnxModels: build.query<
+      EntityState<OnnxModelConfigEntity>,
+      BaseModelType[]
+    >({
+      query: (base_models) => {
+        const params = {
+          model_type: 'onnx',
+          base_models,
+        };
+
+        const query = queryString.stringify(params, { arrayFormat: 'none' });
+        return `models/?${query}`;
+      },
+      providesTags: (result, error, arg) => {
+        const tags: ApiFullTagDescription[] = [
+          { id: 'OnnxModel', type: LIST_TAG },
+        ];
+
+        if (result) {
+          tags.push(
+            ...result.ids.map((id) => ({
+              type: 'OnnxModel' as const,
+              id,
+            }))
+          );
+        }
+
+        return tags;
+      },
+      transformResponse: (
+        response: { models: OnnxModelConfig[] },
+        meta,
+        arg
+      ) => {
+        const entities = createModelEntities<OnnxModelConfigEntity>(
+          response.models
+        );
+        return onnxModelsAdapter.setAll(
+          onnxModelsAdapter.getInitialState(),
+          entities
+        );
+      },
+    }),
     getMainModels: build.query<
       EntityState<MainModelConfigEntity>,
       BaseModelType[]
@@ -248,9 +301,9 @@ export const modelsApi = api.injectEndpoints({
       DeleteMainModelResponse,
       DeleteMainModelArg
     >({
-      query: ({ base_model, model_name }) => {
+      query: ({ base_model, model_name, model_type }) => {
         return {
-          url: `models/${base_model}/main/${model_name}`,
+          url: `models/${base_model}/${model_type}/${model_name}`,
           method: 'DELETE',
         };
       },
@@ -494,6 +547,7 @@ export const modelsApi = api.injectEndpoints({
 
 export const {
   useGetMainModelsQuery,
+  useGetOnnxModelsQuery,
   useGetControlNetModelsQuery,
   useGetLoRAModelsQuery,
   useGetTextualInversionModelsQuery,

invokeai/frontend/web/src/services/api/schema.d.ts

@@ -1381,7 +1381,7 @@ export type components = {
        * @description The nodes in this graph
        */
       nodes?: {
-        [key: string]: (components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
+        [key: string]: (components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
       };
       /**
        * Edges 
@@ -1424,7 +1424,7 @@ export type components = {
        * @description The results of node executions
        */
       results: {
-        [key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
+        [key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["ONNXModelLoaderOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
       };
       /**
        * Errors 
@@ -2562,10 +2562,10 @@ export type components = {
       /**
        * Infill Method 
        * @description The method used to infill empty regions (px) 
-       * @default patchmatch 
+       * @default tile 
        * @enum {string}
        */
-      infill_method?: "patchmatch" | "tile" | "solid";
+      infill_method?: "tile" | "solid";
       /**
        * Inpaint Width 
        * @description The width of the inpaint region (px)
@@ -3173,6 +3173,8 @@ export type components = {
       model_name: string;
       /** @description Base model */
       base_model: components["schemas"]["BaseModelType"];
+      /** @description Model Type */
+      model_type: components["schemas"]["ModelType"];
     };
     /**
      * MainModelLoaderInvocation 
@@ -3618,7 +3620,7 @@ export type components = {
      * @description An enumeration. 
      * @enum {string}
      */
-    ModelType: "main" | "vae" | "lora" | "controlnet" | "embedding";
+    ModelType: "onnx" | "main" | "vae" | "lora" | "controlnet" | "embedding";
     /**
      * ModelVariantType 
      * @description An enumeration. 
@@ -3628,7 +3630,7 @@ export type components = {
     /** ModelsList */
     ModelsList: {
       /** Models */
-      models: (components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"])[];
+      models: (components["schemas"]["ONNXStableDiffusion1ModelConfig"] | components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["ONNXStableDiffusion2ModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"])[];
     };
     /**
      * MultiplyInvocation 
@@ -3778,6 +3780,261 @@ export type components = {
        */
       image_resolution?: number;
     };
+    /**
+     * ONNXLatentsToImageInvocation 
+     * @description Generates an image from latents.
+     */
+    ONNXLatentsToImageInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default l2i_onnx 
+       * @enum {string}
+       */
+      type?: "l2i_onnx";
+      /**
+       * Latents 
+       * @description The latents to generate an image from
+       */
+      latents?: components["schemas"]["LatentsField"];
+      /**
+       * Vae 
+       * @description Vae submodel
+       */
+      vae?: components["schemas"]["VaeField"];
+      /**
+       * Metadata 
+       * @description Optional core metadata to be written to the image
+       */
+      metadata?: components["schemas"]["CoreMetadata"];
+    };
+    /**
+     * ONNXModelLoaderOutput 
+     * @description Model loader output
+     */
+    ONNXModelLoaderOutput: {
+      /**
+       * Type 
+       * @default model_loader_output_onnx 
+       * @enum {string}
+       */
+      type?: "model_loader_output_onnx";
+      /**
+       * Unet 
+       * @description UNet submodel
+       */
+      unet?: components["schemas"]["UNetField"];
+      /**
+       * Clip 
+       * @description Tokenizer and text_encoder submodels
+       */
+      clip?: components["schemas"]["ClipField"];
+      /**
+       * Vae Decoder 
+       * @description Vae submodel
+       */
+      vae_decoder?: components["schemas"]["VaeField"];
+      /**
+       * Vae Encoder 
+       * @description Vae submodel
+       */
+      vae_encoder?: components["schemas"]["VaeField"];
+    };
+    /**
+     * ONNXPromptInvocation 
+     * @description A node to process inputs and produce outputs.
+     * May use dependency injection in __init__ to receive providers.
+     */
+    ONNXPromptInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default prompt_onnx 
+       * @enum {string}
+       */
+      type?: "prompt_onnx";
+      /**
+       * Prompt 
+       * @description Prompt 
+       * @default
+       */
+      prompt?: string;
+      /**
+       * Clip 
+       * @description Clip to use
+       */
+      clip?: components["schemas"]["ClipField"];
+    };
+    /**
+     * ONNXSD1ModelLoaderInvocation 
+     * @description Loading submodels of selected model.
+     */
+    ONNXSD1ModelLoaderInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default sd1_model_loader_onnx 
+       * @enum {string}
+       */
+      type?: "sd1_model_loader_onnx";
+      /**
+       * Model Name 
+       * @description Model to load 
+       * @default
+       */
+      model_name?: string;
+    };
+    /** ONNXStableDiffusion1ModelConfig */
+    ONNXStableDiffusion1ModelConfig: {
+      /** Model Name */
+      model_name: string;
+      base_model: components["schemas"]["BaseModelType"];
+      /**
+       * Model Type 
+       * @enum {string}
+       */
+      model_type: "onnx";
+      /** Path */
+      path: string;
+      /** Description */
+      description?: string;
+      /**
+       * Model Format 
+       * @enum {string}
+       */
+      model_format: "onnx";
+      error?: components["schemas"]["ModelError"];
+      variant: components["schemas"]["ModelVariantType"];
+    };
+    /** ONNXStableDiffusion2ModelConfig */
+    ONNXStableDiffusion2ModelConfig: {
+      /** Model Name */
+      model_name: string;
+      base_model: components["schemas"]["BaseModelType"];
+      /**
+       * Model Type 
+       * @enum {string}
+       */
+      model_type: "onnx";
+      /** Path */
+      path: string;
+      /** Description */
+      description?: string;
+      /**
+       * Model Format 
+       * @enum {string}
+       */
+      model_format: "onnx";
+      error?: components["schemas"]["ModelError"];
+      variant: components["schemas"]["ModelVariantType"];
+      prediction_type: components["schemas"]["SchedulerPredictionType"];
+      /** Upcast Attention */
+      upcast_attention: boolean;
+    };
+    /**
+     * ONNXTextToLatentsInvocation 
+     * @description Generates latents from conditionings.
+     */
+    ONNXTextToLatentsInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default t2l_onnx 
+       * @enum {string}
+       */
+      type?: "t2l_onnx";
+      /**
+       * Positive Conditioning 
+       * @description Positive conditioning for generation
+       */
+      positive_conditioning?: components["schemas"]["ConditioningField"];
+      /**
+       * Negative Conditioning 
+       * @description Negative conditioning for generation
+       */
+      negative_conditioning?: components["schemas"]["ConditioningField"];
+      /**
+       * Noise 
+       * @description The noise to use
+       */
+      noise?: components["schemas"]["LatentsField"];
+      /**
+       * Steps 
+       * @description The number of steps to use to generate the image 
+       * @default 10
+       */
+      steps?: number;
+      /**
+       * Cfg Scale 
+       * @description The Classifier-Free Guidance, higher values may result in a result closer to the prompt 
+       * @default 7.5
+       */
+      cfg_scale?: number | (number)[];
+      /**
+       * Scheduler 
+       * @description The scheduler to use 
+       * @default euler 
+       * @enum {string}
+       */
+      scheduler?: "ddim" | "ddpm" | "deis" | "lms" | "lms_k" | "pndm" | "heun" | "heun_k" | "euler" | "euler_k" | "euler_a" | "kdpm_2" | "kdpm_2_a" | "dpmpp_2s" | "dpmpp_2s_k" | "dpmpp_2m" | "dpmpp_2m_k" | "dpmpp_2m_sde" | "dpmpp_2m_sde_k" | "dpmpp_sde" | "dpmpp_sde_k" | "unipc";
+      /**
+       * Precision 
+       * @description The precision to use when generating latents 
+       * @default tensor(float16) 
+       * @enum {string}
+       */
+      precision?: "tensor(bool)" | "tensor(int8)" | "tensor(uint8)" | "tensor(int16)" | "tensor(uint16)" | "tensor(int32)" | "tensor(uint32)" | "tensor(int64)" | "tensor(uint64)" | "tensor(float16)" | "tensor(float)" | "tensor(double)";
+      /**
+       * Unet 
+       * @description UNet submodel
+       */
+      unet?: components["schemas"]["UNetField"];
+      /**
+       * Control 
+       * @description The control to use
+       */
+      control?: components["schemas"]["ControlField"] | (components["schemas"]["ControlField"])[];
+    };
     /**
      * OffsetPaginatedResults[BoardDTO] 
      * @description Offset-paginated results
@@ -3830,6 +4087,49 @@ export type components = {
        */
       total: number;
     };
+    /**
+     * OnnxModelField 
+     * @description Onnx model field
+     */
+    OnnxModelField: {
+      /**
+       * Model Name 
+       * @description Name of the model
+       */
+      model_name: string;
+      /** @description Base model */
+      base_model: components["schemas"]["BaseModelType"];
+      /** @description Model Type */
+      model_type: components["schemas"]["ModelType"];
+    };
+    /**
+     * OnnxModelLoaderInvocation 
+     * @description Loads a main model, outputting its submodels.
+     */
+    OnnxModelLoaderInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default onnx_model_loader 
+       * @enum {string}
+       */
+      type?: "onnx_model_loader";
+      /**
+       * Model 
+       * @description The model to load
+       */
+      model: components["schemas"]["OnnxModelField"];
+    };
     /**
      * OpenposeImageProcessorInvocation 
      * @description Applies Openpose processing to image
@@ -4963,6 +5263,12 @@ export type components = {
        */
       antialias?: boolean;
     };
+    /**
+     * SchedulerPredictionType 
+     * @description An enumeration. 
+     * @enum {string}
+     */
+    SchedulerPredictionType: "epsilon" | "v_prediction" | "sample";
     /**
      * SegmentAnythingProcessorInvocation 
      * @description Applies segment anything processing to image
@@ -5273,7 +5579,7 @@ export type components = {
      * @description An enumeration. 
      * @enum {string}
      */
-    SubModelType: "unet" | "text_encoder" | "text_encoder_2" | "tokenizer" | "tokenizer_2" | "vae" | "scheduler" | "safety_checker";
+    SubModelType: "unet" | "text_encoder" | "text_encoder_2" | "tokenizer" | "tokenizer_2" | "vae" | "vae_decoder" | "vae_encoder" | "scheduler" | "safety_checker";
     /**
      * SubtractInvocation 
      * @description Subtracts two numbers
@@ -5586,29 +5892,35 @@ export type components = {
       image?: components["schemas"]["ImageField"];
     };
     /**
-     * StableDiffusion2ModelFormat 
+     * StableDiffusionOnnxModelFormat 
      * @description An enumeration. 
      * @enum {string}
      */
-    StableDiffusion2ModelFormat: "checkpoint" | "diffusers";
+    StableDiffusionOnnxModelFormat: "olive" | "onnx";
     /**
      * StableDiffusionXLModelFormat 
      * @description An enumeration. 
      * @enum {string}
      */
     StableDiffusionXLModelFormat: "checkpoint" | "diffusers";
-    /**
-     * StableDiffusion1ModelFormat 
-     * @description An enumeration. 
-     * @enum {string}
-     */
-    StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
     /**
      * ControlNetModelFormat 
      * @description An enumeration. 
      * @enum {string}
      */
     ControlNetModelFormat: "checkpoint" | "diffusers";
+    /**
+     * StableDiffusion2ModelFormat 
+     * @description An enumeration. 
+     * @enum {string}
+     */
+    StableDiffusion2ModelFormat: "checkpoint" | "diffusers";
+    /**
+     * StableDiffusion1ModelFormat 
+     * @description An enumeration. 
+     * @enum {string}
+     */
+    StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
   };
   responses: never;
   parameters: never;
@@ -5719,7 +6031,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
+        "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
       };
     };
     responses: {
@@ -5756,7 +6068,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
+        "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
       };
     };
     responses: {
@@ -6020,14 +6332,14 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
+        "application/json": components["schemas"]["ONNXStableDiffusion1ModelConfig"] | components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["ONNXStableDiffusion2ModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
       };
     };
     responses: {
       /** @description The model was updated successfully */
       200: {
         content: {
-          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
+          "application/json": components["schemas"]["ONNXStableDiffusion1ModelConfig"] | components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["ONNXStableDiffusion2ModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
         };
       };
       /** @description Bad request */
@@ -6058,7 +6370,7 @@ export type operations = {
       /** @description The model imported successfully */
       201: {
         content: {
-          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
+          "application/json": components["schemas"]["ONNXStableDiffusion1ModelConfig"] | components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["ONNXStableDiffusion2ModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
         };
       };
       /** @description The model could not be found */
@@ -6084,14 +6396,14 @@ export type operations = {
   add_model: {
     requestBody: {
       content: {
-        "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
+        "application/json": components["schemas"]["ONNXStableDiffusion1ModelConfig"] | components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["ONNXStableDiffusion2ModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
       };
     };
     responses: {
       /** @description The model added successfully */
       201: {
         content: {
-          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
+          "application/json": components["schemas"]["ONNXStableDiffusion1ModelConfig"] | components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["ONNXStableDiffusion2ModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
         };
       };
       /** @description The model could not be found */
@@ -6131,7 +6443,7 @@ export type operations = {
       /** @description Model converted successfully */
       200: {
         content: {
-          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
+          "application/json": components["schemas"]["ONNXStableDiffusion1ModelConfig"] | components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["ONNXStableDiffusion2ModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
         };
       };
       /** @description Bad request */
@@ -6220,7 +6532,7 @@ export type operations = {
       /** @description Model converted successfully */
       200: {
         content: {
-          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
+          "application/json": components["schemas"]["ONNXStableDiffusion1ModelConfig"] | components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelCheckpointConfig"] | components["schemas"]["ControlNetModelDiffusersConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["ONNXStableDiffusion2ModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
         };
       };
       /** @description Incompatible models */

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

@@ -32,6 +32,7 @@ export type ModelType = components['schemas']['ModelType'];
 export type SubModelType = components['schemas']['SubModelType'];
 export type BaseModelType = components['schemas']['BaseModelType'];
 export type MainModelField = components['schemas']['MainModelField'];
+export type OnnxModelField = components['schemas']['OnnxModelField'];
 export type VAEModelField = components['schemas']['VAEModelField'];
 export type LoRAModelField = components['schemas']['LoRAModelField'];
 export type ControlNetModelField =
@@ -58,6 +59,8 @@ export type DiffusersModelConfig =
 export type CheckpointModelConfig =
   | components['schemas']['StableDiffusion1ModelCheckpointConfig']
   | components['schemas']['StableDiffusion2ModelCheckpointConfig']
+  | components['schemas']['StableDiffusion2ModelDiffusersConfig'];
+export type OnnxModelConfig = components['schemas']['ONNXStableDiffusion1ModelConfig']
   | components['schemas']['StableDiffusionXLModelCheckpointConfig'];
 export type MainModelConfig = DiffusersModelConfig | CheckpointModelConfig;
 export type AnyModelConfig =
@@ -65,7 +68,8 @@ export type AnyModelConfig =
   | VaeModelConfig
   | ControlNetModelConfig
   | TextualInversionModelConfig
-  | MainModelConfig;
+  | MainModelConfig
+  | OnnxModelConfig;
 
 export type MergeModelConfig = components['schemas']['Body_merge_models'];
 export type ConvertModelConfig = components['schemas']['Body_convert_model'];
@@ -127,6 +131,9 @@ export type ImageCollectionInvocation = TypeReq<
 export type MainModelLoaderInvocation = TypeReq<
   components['schemas']['MainModelLoaderInvocation']
 >;
+export type OnnxModelLoaderInvocation = TypeReq<
+  components['schemas']['OnnxModelLoaderInvocation']
+>;
 export type LoraLoaderInvocation = TypeReq<
   components['schemas']['LoraLoaderInvocation']
 >;

pyproject.toml

@@ -61,6 +61,7 @@ dependencies = [
   "numpy",
   "npyscreen",
   "omegaconf",
+  "onnx",
   "opencv-python",
   "pydantic==1.*",
   "picklescan",
@@ -103,6 +104,15 @@ dependencies = [
 	   "xformers~=0.0.19; sys_platform!='darwin'",
 	   "triton; sys_platform=='linux'",
 ]
+"onnx" = [
+  "onnxruntime",
+]
+"onnx-cuda" = [
+  "onnxruntime-gpu",
+]
+"onnx-directml" = [
+  "onnxruntime-directml",
+]
 
 [project.scripts]
 
@@ -180,4 +190,4 @@ output = "coverage/index.xml"
 max-line-length = 120
 
 [tool.black]
-line-length = 120
+line-length = 120