diff --git a/invokeai/app/invocations/llava_onevision_vllm.py b/invokeai/app/invocations/llava_onevision_vllm.py
new file mode 100644
index 00000000000..fa103545030
--- /dev/null
+++ b/invokeai/app/invocations/llava_onevision_vllm.py
@@ -0,0 +1,52 @@
+import torch
+from PIL.Image import Image
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.fields import ImageField, InputField, UIComponent
+from invokeai.app.invocations.primitives import StringOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.llava_onevision_model import LlavaOnevisionModel
+from invokeai.backend.model_manager.config import BaseModelType, ModelType
+from invokeai.backend.util.devices import TorchDevice
+
+
+@invocation("llava_onevision_vllm", title="LLaVA OneVision VLLM", tags=["vllm"], category="vllm", version="1.0.0")
+class LlavaOnevisionVllmInvocation(BaseInvocation):
+    """Run a LLaVA OneVision VLLM model."""
+
+    images: list[ImageField] | ImageField | None = InputField(default=None, description="Input image.")
+    prompt: str = InputField(
+        default="",
+        description="Input text prompt.",
+        ui_component=UIComponent.Textarea,
+    )
+    # vllm_model: ModelIdentifierField = InputField(
+    #     title="Image-to-Image Model",
+    #     description=FieldDescriptions.vllm_model,
+    #     ui_type=UIType.LlavaOnevisionModel,
+    # )
+
+    def _get_images(self, context: InvocationContext) -> list[Image]:
+        if self.images is None:
+            return []
+
+        image_fields = self.images if isinstance(self.images, list) else [self.images]
+        return [context.images.get_pil(image_field.image_name, "RGB") for image_field in image_fields]
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> StringOutput:
+        images = self._get_images(context)
+
+        # with context.models.load(self.vllm_model) as vllm_model:
+        with context.models.load_by_attrs(
+            name="LLaVA Onevision Qwen2 0.5B", base=BaseModelType.Any, type=ModelType.LlavaOnevision
+        ) as vllm_model:
+            assert isinstance(vllm_model, LlavaOnevisionModel)
+            output = vllm_model.run(
+                prompt=self.prompt,
+                images=images,
+                device=TorchDevice.choose_torch_device(),
+                dtype=TorchDevice.choose_torch_dtype(),
+            )
+
+        return StringOutput(value=output)
diff --git a/invokeai/backend/llava_onevision_model.py b/invokeai/backend/llava_onevision_model.py
new file mode 100644
index 00000000000..af6aee6467d
--- /dev/null
+++ b/invokeai/backend/llava_onevision_model.py
@@ -0,0 +1,49 @@
+from pathlib import Path
+from typing import Optional
+
+import torch
+from PIL.Image import Image
+from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration, LlavaOnevisionProcessor
+
+from invokeai.backend.raw_model import RawModel
+
+
+class LlavaOnevisionModel(RawModel):
+    def __init__(self, vllm_model: LlavaOnevisionForConditionalGeneration, processor: LlavaOnevisionProcessor):
+        self._vllm_model = vllm_model
+        self._processor = processor
+
+    @classmethod
+    def load_from_path(cls, path: str | Path):
+        vllm_model = LlavaOnevisionForConditionalGeneration.from_pretrained(path, local_files_only=True)
+        assert isinstance(vllm_model, LlavaOnevisionForConditionalGeneration)
+        processor = AutoProcessor.from_pretrained(path, local_files_only=True)
+        assert isinstance(processor, LlavaOnevisionProcessor)
+        return cls(vllm_model, processor)
+
+    def run(self, prompt: str, images: list[Image], device: torch.device, dtype: torch.dtype) -> str:
+        # TODO(ryand): Tune the max number of images that are useful for the model.
+        if len(images) > 3:
+            raise ValueError(
+                f"{len(images)} images were provided as input to the LLaVA OneVision model. "
+                "Pass <=3 images for good performance."
+            )
+
+        # Define a chat history and use `apply_chat_template` to get correctly formatted prompt.
+        # "content" is a list of dicts with types "text" or "image".
+        content = [{"type": "text", "text": prompt}]
+        # Add the correct number of images.
+        for _ in images:
+            content.append({"type": "image"})
+
+        conversation = [{"role": "user", "content": content}]
+        prompt = self._processor.apply_chat_template(conversation, add_generation_prompt=True)
+        inputs = self._processor(images=images or None, text=prompt, return_tensors="pt").to(device=device, dtype=dtype)
+        output = self._vllm_model.generate(**inputs, max_new_tokens=400, do_sample=False)
+        output_str: str = self._processor.decode(output[0][2:], skip_special_tokens=True)
+        # The output_str will include the prompt, so we extract the response.
+        response = output_str.split("assistant\n", 1)[1].strip()
+        return response
+
+    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
+        self._vllm_model.to(device=device, dtype=dtype)
diff --git a/invokeai/backend/model_manager/config.py b/invokeai/backend/model_manager/config.py
index 956a8468fa2..1625c8c7e6f 100644
--- a/invokeai/backend/model_manager/config.py
+++ b/invokeai/backend/model_manager/config.py
@@ -76,6 +76,7 @@ class ModelType(str, Enum):
     T2IAdapter = "t2i_adapter"
     T5Encoder = "t5_encoder"
     SpandrelImageToImage = "spandrel_image_to_image"
+    LlavaOnevision = "llava_onevision"
 
 
 class SubModelType(str, Enum):
@@ -528,6 +529,17 @@ def get_tag() -> Tag:
         return Tag(f"{ModelType.SpandrelImageToImage.value}.{ModelFormat.Checkpoint.value}")
 
 
+class LlavaOnevisionConfig(DiffusersConfigBase):
+    """Model config for Llava Onevision models."""
+
+    type: Literal[ModelType.LlavaOnevision] = ModelType.LlavaOnevision
+    format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
+
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.LlavaOnevision.value}.{ModelFormat.Diffusers.value}")
+
+
 def get_model_discriminator_value(v: Any) -> str:
     """
     Computes the discriminator value for a model config.
@@ -575,6 +587,7 @@ def get_model_discriminator_value(v: Any) -> str:
         Annotated[CLIPEmbedDiffusersConfig, CLIPEmbedDiffusersConfig.get_tag()],
         Annotated[CLIPLEmbedDiffusersConfig, CLIPLEmbedDiffusersConfig.get_tag()],
         Annotated[CLIPGEmbedDiffusersConfig, CLIPGEmbedDiffusersConfig.get_tag()],
+        Annotated[LlavaOnevisionConfig, LlavaOnevisionConfig.get_tag()],
     ],
     Discriminator(get_model_discriminator_value),
 ]
diff --git a/invokeai/backend/model_manager/load/model_loaders/llava_onevision.py b/invokeai/backend/model_manager/load/model_loaders/llava_onevision.py
new file mode 100644
index 00000000000..32c98c6a351
--- /dev/null
+++ b/invokeai/backend/model_manager/load/model_loaders/llava_onevision.py
@@ -0,0 +1,32 @@
+from pathlib import Path
+from typing import Optional
+
+from invokeai.backend.llava_onevision_model import LlavaOnevisionModel
+from invokeai.backend.model_manager.config import (
+    AnyModel,
+    AnyModelConfig,
+    BaseModelType,
+    ModelFormat,
+    ModelType,
+    SubModelType,
+)
+from invokeai.backend.model_manager.load.load_default import ModelLoader
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+
+
+@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.LlavaOnevision, format=ModelFormat.Diffusers)
+class LlavaOnevisionModelLoader(ModelLoader):
+    """Class for loading LLaVA Onevision VLLM models."""
+
+    def _load_model(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        if submodel_type is not None:
+            raise ValueError("Unexpected submodel requested for LLaVA OneVision model.")
+
+        model_path = Path(config.path)
+        model = LlavaOnevisionModel.load_from_path(model_path)
+        model.to(dtype=self._torch_dtype)
+        return model
diff --git a/invokeai/backend/model_manager/probe.py b/invokeai/backend/model_manager/probe.py
index 82378d08e01..9ce9ad1ed22 100644
--- a/invokeai/backend/model_manager/probe.py
+++ b/invokeai/backend/model_manager/probe.py
@@ -139,6 +139,7 @@ class ModelProbe(object):
         "FluxControlNetModel": ModelType.ControlNet,
         "SD3Transformer2DModel": ModelType.Main,
         "CLIPTextModelWithProjection": ModelType.CLIPEmbed,
+        "LlavaOnevisionForConditionalGeneration": ModelType.LlavaOnevision,
     }
 
     TYPE2VARIANT: Dict[ModelType, Callable[[str], Optional[AnyVariant]]] = {ModelType.CLIPEmbed: get_clip_variant_type}
@@ -752,6 +753,11 @@ def get_base_type(self) -> BaseModelType:
         return BaseModelType.Any
 
 
+class LlavaOnevisionCheckpointProbe(CheckpointProbeBase):
+    def get_base_type(self) -> BaseModelType:
+        raise NotImplementedError()
+
+
 ########################################################
 # classes for probing folders
 #######################################################
@@ -1022,6 +1028,11 @@ def get_base_type(self) -> BaseModelType:
         raise NotImplementedError()
 
 
+class LlaveOnevisionFolderProbe(FolderProbeBase):
+    def get_base_type(self) -> BaseModelType:
+        return BaseModelType.Any
+
+
 class T2IAdapterFolderProbe(FolderProbeBase):
     def get_base_type(self) -> BaseModelType:
         config_file = self.model_path / "config.json"
@@ -1055,6 +1066,7 @@ def get_base_type(self) -> BaseModelType:
 ModelProbe.register_probe("diffusers", ModelType.CLIPVision, CLIPVisionFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.T2IAdapter, T2IAdapterFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.SpandrelImageToImage, SpandrelImageToImageFolderProbe)
+ModelProbe.register_probe("diffusers", ModelType.LlavaOnevision, LlaveOnevisionFolderProbe)
 
 ModelProbe.register_probe("checkpoint", ModelType.Main, PipelineCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.VAE, VaeCheckpointProbe)
@@ -1066,5 +1078,6 @@ def get_base_type(self) -> BaseModelType:
 ModelProbe.register_probe("checkpoint", ModelType.CLIPVision, CLIPVisionCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.T2IAdapter, T2IAdapterCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.SpandrelImageToImage, SpandrelImageToImageCheckpointProbe)
+ModelProbe.register_probe("checkpoint", ModelType.LlavaOnevision, LlavaOnevisionCheckpointProbe)
 
 ModelProbe.register_probe("onnx", ModelType.ONNX, ONNXFolderProbe)
diff --git a/invokeai/backend/model_manager/starter_models.py b/invokeai/backend/model_manager/starter_models.py
index 5674a0a6034..c2727df877f 100644
--- a/invokeai/backend/model_manager/starter_models.py
+++ b/invokeai/backend/model_manager/starter_models.py
@@ -592,7 +592,15 @@ class StarterModelBundles(BaseModel):
 )
 
 # endregion
-
+# region LlavaOnevisionModel
+llava_onevision = StarterModel(
+    name="LLaVA Onevision Qwen2 0.5B",
+    base=BaseModelType.Any,
+    source="llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
+    description="LLaVA Onevision VLLM model",
+    type=ModelType.LlavaOnevision,
+)
+# endregion
 
 # List of starter models, displayed on the frontend.
 # The order/sort of this list is not changed by the frontend - set it how you want it here.
@@ -661,6 +669,7 @@ class StarterModelBundles(BaseModel):
     t5_base_encoder,
     t5_8b_quantized_encoder,
     clip_l_encoder,
+    llava_onevision,
 ]
 
 sd1_bundle: list[StarterModel] = [