feat: vllm omni text to video generation pipeline (#6104)

Signed-off-by: ayushag <ayushag@nvidia.com>

components/src/dynamo/trtllm/protocols/__init__.py → components/src/dynamo/common/protocols/__init__.py

 # SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 
-"""Protocol types for TensorRT-LLM backend.
+"""Shared protocol types used across multiple Dynamo backends.
 
 This module provides protocol types for various modalities:
 - video_protocol: NvCreateVideoRequest, NvVideosResponse for video generation
-- image_protocol: (future) Protocol types for image generation
 """
 
-from dynamo.trtllm.protocols.video_protocol import (
+from dynamo.common.protocols.video_protocol import (
     NvCreateVideoRequest,
     NvVideosResponse,
     VideoData,

components/src/dynamo/common/protocols/image_protocol.py

+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+
+from typing import Optional
+
+from pydantic import BaseModel
+
+# For omni models, we need to support raw_request parsing and json output format. We need to have these protocols defined here for serialization and deserialization.
+# TODO: Replace these Pydantic models with Python bindings to the Rust protocol types once PyO3 bindings are available.
+
+
+class ImageNvExt(BaseModel):
+    """NVIDIA extensions for image generation requests.
+
+    Matches Rust NvExt in lib/llm/src/protocols/openai/images/nvext.rs.
+    """
+
+    annotations: Optional[list[str]] = None
+    """Annotations for SSE stream events."""
+
+    negative_prompt: Optional[str] = None
+    """Optional negative prompt."""
+
+    num_inference_steps: Optional[int] = None
+    """Number of denoising steps."""
+
+    guidance_scale: Optional[float] = None
+    """CFG guidance scale."""
+
+    seed: Optional[int] = None
+    """Random seed for reproducibility."""
+
+
+class NvCreateImageRequest(BaseModel):
+    """Request for image generation (/v1/images/generations endpoint).
+
+    Matches the flattened Rust NvCreateImageRequest in lib/llm/src/protocols/openai/images.rs
+    """
+
+    prompt: str
+    """The text prompt for image generation."""
+
+    model: Optional[str] = None
+    """The model to use for image generation."""
+
+    n: Optional[int] = None
+    """Number of images to generate (1-10)."""
+
+    quality: Optional[str] = None
+    """Image quality: standard, hd, high, medium, low, auto."""
+
+    response_format: Optional[str] = None
+    """Response format: url or b64_json."""
+
+    size: Optional[str] = None
+    """Image size in WxH format (e.g. 1024x1024)."""
+
+    style: Optional[str] = None
+    """Image style: vivid or natural."""
+
+    user: Optional[str] = None
+    """Optional user identifier."""
+
+    moderation: Optional[str] = None
+    """Content moderation level: auto or low."""
+
+    nvext: Optional[ImageNvExt] = None
+    """NVIDIA extensions."""
+
+
+class ImageData(BaseModel):
+    """Individual image data in a response.
+
+    Matches the flattened Rust Image enum in lib/async-openai/src/types/image.rs.
+    """
+
+    url: Optional[str] = None
+    """URL of the generated image (if response_format is url)."""
+
+    b64_json: Optional[str] = None
+    """Base64-encoded image (if response_format is b64_json)."""
+
+    revised_prompt: Optional[str] = None
+    """Revised prompt, when the model rewrites the original prompt."""
+
+
+class NvImagesResponse(BaseModel):
+    """Response structure for image generation.
+
+    Matches the flattened Rust NvImagesResponse in lib/llm/src/protocols/openai/images.rs
+    """
+
+    created: int
+    """Unix timestamp of creation."""
+
+    data: list[ImageData] = []
+    """List of generated images."""

components/src/dynamo/trtllm/protocols/video_protocol.py → components/src/dynamo/common/protocols/video_protocol.py

@@ -6,6 +6,7 @@
 These types match the Rust protocol types in lib/llm/src/protocols/openai/videos.rs
 to ensure compatibility with the Dynamo HTTP frontend.
 """
+# TODO: Replace these Pydantic models with Python bindings to the Rust protocol types once PyO3 bindings are available.
 
 from typing import Optional
 

components/src/dynamo/common/utils/output_modalities.py

@@ -2,8 +2,12 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from enum import Enum
-from typing import List, Optional
+from typing import Any, Dict, List, Optional, Tuple, Union
 
+from pydantic import BaseModel
+
+from dynamo.common.protocols.image_protocol import NvCreateImageRequest
+from dynamo.common.protocols.video_protocol import NvCreateVideoRequest
 from dynamo.llm import ModelType
 
 
@@ -32,6 +36,15 @@ class OutputModality(Enum):
         return {m.name.lower() for m in cls}
 
 
+class RequestType(Enum):
+    """Identifies the parsed request type returned by parse_request_type."""
+
+    CHAT_COMPLETION = "chat_completion"
+    IMAGE_GENERATION = "image_generation"
+    VIDEO_GENERATION = "video_generation"
+    AUDIO_GENERATION = "audio_generation"
+
+
 def get_output_modalities(cli_input: List[str], model_repo: str) -> Optional[ModelType]:
     """
     Get the combined ModelType flags for omni models based on CLI input.
@@ -52,3 +65,34 @@ def get_output_modalities(cli_input: List[str], model_repo: str) -> Optional[Mod
                 flag if output_modalities is None else output_modalities | flag
             )
     return output_modalities
+
+
+def parse_request_type(
+    raw_request: Dict[str, Any],
+    output_modalities: List[str],
+) -> Tuple[Union[BaseModel, Dict[str, Any]], RequestType]:
+    """
+    Classify the endpoint based on the output modality and serialize the request if necessary.
+
+    Assumption: Right now we only consider user passes only one modality at a time.
+    """
+    # Fetch the first output modality from the list.
+    if not output_modalities:
+        raise ValueError("output_modalities must not be empty")
+    output_modality = output_modalities[0]
+    modality = OutputModality.from_name(output_modality)
+
+    if modality is OutputModality.IMAGE:
+        if "messages" in raw_request:
+            return raw_request, RequestType.CHAT_COMPLETION
+        return NvCreateImageRequest(**raw_request), RequestType.IMAGE_GENERATION
+
+    if modality is OutputModality.VIDEO:
+        return NvCreateVideoRequest(**raw_request), RequestType.VIDEO_GENERATION
+
+    if modality is OutputModality.AUDIO:
+        # Audio protocol types are not yet defined; pass through the raw dict.
+        return raw_request, RequestType.AUDIO_GENERATION
+
+    # Text Modality
+    return raw_request, RequestType.CHAT_COMPLETION

components/src/dynamo/trtllm/request_handlers/video_diffusion/video_utils.py → components/src/dynamo/common/utils/video_utils.py

 # SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 
-"""Video encoding utilities for TensorRT-LLM video diffusion.
+"""Video utilities for video diffusion.
 
-This module provides utilities for encoding numpy video frames to MP4 format.
+Provides helpers for parsing video request parameters and encoding numpy
+video frames to MP4 format.
 """
 
 import io
 import logging
 import os
+from typing import Tuple
 
 import numpy as np
 
 logger = logging.getLogger(__name__)
 
 
+DEFAULT_VIDEO_WIDTH = 832
+DEFAULT_VIDEO_HEIGHT = 480
+DEFAULT_VIDEO_FPS = 16
+DEFAULT_VIDEO_NUM_FRAMES = 97
+
+
+def parse_size(
+    size: str | None,
+    default_w: int = DEFAULT_VIDEO_WIDTH,
+    default_h: int = DEFAULT_VIDEO_HEIGHT,
+) -> Tuple[int, int]:
+    """Parse a 'WxH' string into (width, height).
+
+    Falls back to default_w x default_h when size is None or malformed.
+    """
+    if not size:
+        return default_w, default_h
+    try:
+        w, h = size.split("x")
+        return int(w), int(h)
+    except (ValueError, AttributeError):
+        logger.warning("Invalid size format: %s, using defaults", size)
+        return default_w, default_h
+
+
+def compute_num_frames(
+    num_frames: int | None = None,
+    seconds: int | None = None,
+    fps: int | None = None,
+    default_fps: int = DEFAULT_VIDEO_FPS,
+    default_num_frames: int = DEFAULT_VIDEO_NUM_FRAMES,
+) -> int:
+    """Compute the number of video frames.
+
+    Priority: num_frames > seconds × fps > default_num_frames.
+    """
+    if num_frames is not None:
+        return num_frames
+    if seconds is not None or fps is not None:
+        _seconds = seconds if seconds is not None else 4
+        _fps = fps if fps is not None else default_fps
+        return _seconds * _fps
+    return default_num_frames
+
+
+def normalize_video_frames(images) -> list:
+    """Normalize stage_output.images into a frame list for export_to_video.
+
+    Args:
+        images: stage_output.images -- a list that may contain a single
+            torch.Tensor or np.ndarray representing the full video.
+
+    Returns:
+        List of frames suitable for diffusers export_to_video.
+    """
+    frames = images[0] if len(images) == 1 else images
+
+    if isinstance(frames, np.ndarray):
+        if frames.ndim == 5:
+            frames = frames[0]
+        return list(frames)
+
+    return list(frames)
+
+
+def frames_to_numpy(images: list) -> np.ndarray:
+    """Convert a list of PIL Images to a numpy array suitable for video encoding.
+
+    Args:
+        images: List of PIL Image objects (video frames).
+
+    Returns:
+        Numpy array of shape ``(num_frames, height, width, 3)`` with dtype
+        ``uint8`` and values in ``[0, 255]``.
+
+    Raises:
+        ValueError: If no images are provided or images have inconsistent sizes.
+    """
+    if not images:
+        raise ValueError("No images provided for video encoding")
+
+    frames = []
+    for img in images:
+        arr = np.array(img.convert("RGB"))
+        frames.append(arr)
+
+    # Validate consistent sizes
+    shapes = {f.shape for f in frames}
+    if len(shapes) > 1:
+        raise ValueError(
+            f"Inconsistent frame sizes detected: {shapes}. "
+            "All frames must have the same dimensions."
+        )
+
+    return np.stack(frames, axis=0)
+
+
 def encode_to_mp4(
     frames: np.ndarray,
     output_dir: str,

components/src/dynamo/trtllm/request_handlers/video_diffusion/video_handler.py

@@ -14,19 +14,16 @@ import uuid
 from typing import Any, AsyncGenerator, Optional
 
 from dynamo._core import Component, Context
-from dynamo.trtllm.configs.diffusion_config import DiffusionConfig
-from dynamo.trtllm.engines.diffusion_engine import DiffusionEngine
-from dynamo.trtllm.protocols.video_protocol import (
+from dynamo.common.protocols.video_protocol import (
     NvCreateVideoRequest,
     NvVideosResponse,
     VideoData,
     VideoNvExt,
 )
+from dynamo.common.utils.video_utils import encode_to_mp4, encode_to_mp4_bytes
+from dynamo.trtllm.configs.diffusion_config import DiffusionConfig
+from dynamo.trtllm.engines.diffusion_engine import DiffusionEngine
 from dynamo.trtllm.request_handlers.base_generative_handler import BaseGenerativeHandler
-from dynamo.trtllm.request_handlers.video_diffusion.video_utils import (
-    encode_to_mp4,
-    encode_to_mp4_bytes,
-)
 
 logger = logging.getLogger(__name__)
 

components/src/dynamo/trtllm/tests/test_trtllm_video_diffusion.py

@@ -18,14 +18,14 @@ from unittest.mock import MagicMock, patch
 
 import pytest
 
-from dynamo.trtllm.configs.diffusion_config import DiffusionConfig
-from dynamo.trtllm.constants import Modality
-from dynamo.trtllm.protocols.video_protocol import (
+from dynamo.common.protocols.video_protocol import (
     NvCreateVideoRequest,
     NvVideosResponse,
     VideoData,
     VideoNvExt,
 )
+from dynamo.trtllm.configs.diffusion_config import DiffusionConfig
+from dynamo.trtllm.constants import Modality
 
 pytestmark = [
     pytest.mark.unit,

components/src/dynamo/vllm/backend_args.py

@@ -138,6 +138,135 @@ class DynamoVllmArgGroup(ArgGroup):
             help="Path to vLLM-Omni stage configuration YAML file for --omni mode (optional).",
         )
 
+        # Video diffusion output
+        # TODO: Propose an alternate design to switch to AsyncOmniEngine args while using vLLM-Omni
+        add_argument(
+            g,
+            flag_name="--video-output-dir",
+            env_var="DYN_VLLM_VIDEO_OUTPUT_DIR",
+            default="/tmp/dynamo_videos",  # noqa: S108
+            help="Directory to save generated video MP4 files.",
+        )
+        add_argument(
+            g,
+            flag_name="--default-video-fps",
+            env_var="DYN_VLLM_DEFAULT_VIDEO_FPS",
+            default=16,
+            arg_type=int,
+            help="Default frames per second for generated videos.",
+        )
+
+        # Diffusion engine-level args (passed to AsyncOmni constructor)
+        add_negatable_bool_argument(
+            g,
+            flag_name="--enable-layerwise-offload",
+            env_var="DYN_VLLM_ENABLE_LAYERWISE_OFFLOAD",
+            default=False,
+            help="Enable layerwise (blockwise) offloading on DiT modules to reduce GPU memory.",
+        )
+        add_argument(
+            g,
+            flag_name="--layerwise-num-gpu-layers",
+            env_var="DYN_VLLM_LAYERWISE_NUM_GPU_LAYERS",
+            default=1,
+            arg_type=int,
+            help="Number of ready layers (blocks) to keep on GPU during generation.",
+        )
+        add_negatable_bool_argument(
+            g,
+            flag_name="--vae-use-slicing",
+            env_var="DYN_VLLM_VAE_USE_SLICING",
+            default=False,
+            help="Enable VAE slicing for memory optimization in diffusion models.",
+        )
+        add_negatable_bool_argument(
+            g,
+            flag_name="--vae-use-tiling",
+            env_var="DYN_VLLM_VAE_USE_TILING",
+            default=False,
+            help="Enable VAE tiling for memory optimization in diffusion models.",
+        )
+        add_argument(
+            g,
+            flag_name="--boundary-ratio",
+            env_var="DYN_VLLM_BOUNDARY_RATIO",
+            default=0.875,
+            arg_type=float,
+            help=(
+                "Boundary split ratio for low/high DiT transformers. "
+                "Default 0.875 uses both transformers for best quality. "
+                "Set to 1.0 to load only the low-noise transformer (saves memory). "
+                "Only used with --omni."
+            ),
+        )
+        add_argument(
+            g,
+            flag_name="--flow-shift",
+            env_var="DYN_VLLM_FLOW_SHIFT",
+            default=None,
+            arg_type=float,
+            help="Scheduler flow_shift parameter (5.0 for 720p, 12.0 for 480p). Only used with --omni.",
+        )
+        add_argument(
+            g,
+            flag_name="--diffusion-cache-backend",
+            env_var="DYN_VLLM_DIFFUSION_CACHE_BACKEND",
+            default=None,
+            choices=["cache_dit", "tea_cache"],
+            help=(
+                "Cache backend for diffusion acceleration. "
+                "'cache_dit' enables DBCache + SCM + TaylorSeer. "
+                "'tea_cache' enables TeaCache. Only used with --omni."
+            ),
+        )
+        add_argument(
+            g,
+            flag_name="--diffusion-cache-config",
+            env_var="DYN_VLLM_DIFFUSION_CACHE_CONFIG",
+            default=None,
+            help="Cache configuration as JSON string (overrides defaults). Only used with --omni.",
+        )
+        add_negatable_bool_argument(
+            g,
+            flag_name="--enable-cache-dit-summary",
+            env_var="DYN_VLLM_ENABLE_CACHE_DIT_SUMMARY",
+            default=False,
+            help="Enable cache-dit summary logging after diffusion forward passes.",
+        )
+        add_negatable_bool_argument(
+            g,
+            flag_name="--enable-cpu-offload",
+            env_var="DYN_VLLM_ENABLE_CPU_OFFLOAD",
+            default=False,
+            help="Enable CPU offloading for diffusion models to reduce GPU memory usage.",
+        )
+        # Diffusion parallel configuration
+        add_argument(
+            g,
+            flag_name="--ulysses-degree",
+            env_var="DYN_VLLM_ULYSSES_DEGREE",
+            default=1,
+            arg_type=int,
+            help="Number of GPUs used for Ulysses sequence parallelism in diffusion.",
+        )
+        add_argument(
+            g,
+            flag_name="--ring-degree",
+            env_var="DYN_VLLM_RING_DEGREE",
+            default=1,
+            arg_type=int,
+            help="Number of GPUs used for ring sequence parallelism in diffusion.",
+        )
+        add_argument(
+            g,
+            flag_name="--cfg-parallel-size",
+            env_var="DYN_VLLM_CFG_PARALLEL_SIZE",
+            default=1,
+            arg_type=int,
+            choices=[1, 2],
+            help="Number of GPUs used for classifier free guidance parallelism.",
+        )
+
         # ModelExpress P2P
         add_argument(
             g,
@@ -171,6 +300,27 @@ class DynamoVllmConfig(ConfigBase):
     omni: bool
     stage_configs_path: Optional[str] = None
 
+    # Video diffusion output
+    video_output_dir: str = "/tmp/dynamo_videos"  # noqa: S108
+    default_video_fps: int = 16
+
+    # Diffusion engine-level parameters (passed to AsyncOmni constructor)
+    enable_layerwise_offload: bool = False
+    layerwise_num_gpu_layers: int = 1
+    vae_use_slicing: bool = False
+    vae_use_tiling: bool = False
+    boundary_ratio: float = 0.875
+    flow_shift: Optional[float] = None
+    diffusion_cache_backend: Optional[str] = None
+    diffusion_cache_config: Optional[str] = None
+    enable_cache_dit_summary: bool = False
+    enable_cpu_offload: bool = False
+
+    # Diffusion parallel configuration
+    ulysses_degree: int = 1
+    ring_degree: int = 1
+    cfg_parallel_size: int = 1
+
     # ModelExpress P2P
     model_express_url: Optional[str] = None
 

components/src/dynamo/vllm/main.py

@@ -902,11 +902,10 @@ def get_engine_cache_info(engine: AsyncLLM):
 async def init_omni(
     runtime: DistributedRuntime, config: Config, shutdown_event: asyncio.Event
 ):
-    """
-    Initialize Omni worker for text-to-text generation using vLLM-Omni orchestrator.
+    """Initialize Omni worker for multi-stage pipeline generation using vLLM-Omni.
 
-    Uses vLLM-Omni's Omni class for single-stage text generation pipeline.
-    For now, supports text-to-text only (no multimodal).
+    Supports text-to-text, text-to-image, and text-to-video generation
+    through a single unified OmniHandler.
     """
     # Lazy import to avoid loading vllm-omni unless explicitly needed
     from dynamo.vllm.omni import OmniHandler
@@ -916,7 +915,7 @@ async def init_omni(
     )
     component = generate_endpoint.component()
 
-    # Initialize OmniHandler with Omni orchestrator
+    # Initialize unified OmniHandler
     handler = OmniHandler(
         runtime=runtime,
         component=component,

components/src/dynamo/vllm/omni/__init__.py

@@ -3,6 +3,7 @@
 
 """vLLM-Omni integration for Dynamo."""
 
+from .base_handler import BaseOmniHandler
 from .omni_handler import OmniHandler
 
-__all__ = ["OmniHandler"]
+__all__ = ["BaseOmniHandler", "OmniHandler"]

components/src/dynamo/vllm/omni/base_handler.py

+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Base handler for vLLM-Omni multi-stage pipelines."""
+
+import asyncio
+import logging
+import time
+from typing import Any, AsyncGenerator, Dict
+
+from vllm import SamplingParams
+from vllm_omni.entrypoints import AsyncOmni
+
+try:
+    from vllm_omni.diffusion.data import DiffusionParallelConfig
+except ImportError:
+    DiffusionParallelConfig = None  # type: ignore[assignment, misc]
+
+from dynamo.vllm.handlers import BaseWorkerHandler, build_sampling_params
+
+logger = logging.getLogger(__name__)
+
+
+class BaseOmniHandler(BaseWorkerHandler):
+    """Base handler for multi-stage pipelines using vLLM-Omni's AsyncOmni orchestrator."""
+
+    def __init__(
+        self,
+        runtime,
+        component,
+        config,
+        default_sampling_params: Dict[str, Any],
+        shutdown_event: asyncio.Event | None = None,
+    ):
+        """Initialize handler with AsyncOmni orchestrator.
+
+        Args:
+            runtime: Dynamo distributed runtime.
+            component: Dynamo component handle.
+            config: Parsed Config object from args.py.
+            default_sampling_params: Default sampling parameters dict.
+            shutdown_event: Optional asyncio event for graceful shutdown.
+        """
+        logger.info(
+            f"Initializing {self.__class__.__name__} for multi-stage pipelines "
+            f"with model: {config.model}"
+        )
+
+        omni_kwargs = self._build_omni_kwargs(config)
+        self.engine_client = AsyncOmni(**omni_kwargs)
+
+        # Initialize attributes needed from BaseWorkerHandler
+        # We don't call super().__init__() because VllmEngineMonitor expects AsyncLLM,
+        # but AsyncOmni manages its own engines internally
+
+        # TODO: Kv publishers not supported yet
+        # TODO: Adopt to baseworker initialization pattern
+        self.runtime = runtime
+        self.component = component
+        self.default_sampling_params = default_sampling_params
+        self.config = config
+        self.model_max_len = config.engine_args.max_model_len
+        self.shutdown_event = shutdown_event
+        self.use_vllm_tokenizer = config.use_vllm_tokenizer
+
+        logger.info(f"{self.__class__.__name__} initialized successfully")
+
+    def _build_omni_kwargs(self, config) -> Dict[str, Any]:
+        """Build keyword arguments for AsyncOmni constructor.
+
+        Constructs the full kwargs dict including engine-level diffusion
+        parameters and parallel configuration when available.
+
+        Args:
+            config: Parsed Config object.
+
+        Returns:
+            Dictionary of keyword arguments for AsyncOmni.
+        """
+        omni_kwargs: Dict[str, Any] = {
+            "model": config.model,
+            "trust_remote_code": config.engine_args.trust_remote_code,
+        }
+
+        if config.stage_configs_path:
+            omni_kwargs["stage_configs_path"] = config.stage_configs_path
+
+        # Add diffusion engine-level params if present on config
+        diffusion_params = [
+            "enable_layerwise_offload",
+            "layerwise_num_gpu_layers",
+            "vae_use_slicing",
+            "vae_use_tiling",
+            "boundary_ratio",
+            "flow_shift",
+            "diffusion_cache_backend",
+            "diffusion_cache_config",
+            "enable_cache_dit_summary",
+            "enable_cpu_offload",
+        ]
+        for param in diffusion_params:
+            if hasattr(config, param):
+                value = getattr(config, param)
+                if value is not None:
+                    # Map config attribute names to AsyncOmni kwarg names
+                    kwarg_name = param
+                    if param == "diffusion_cache_backend":
+                        kwarg_name = "cache_backend"
+                    elif param == "diffusion_cache_config":
+                        kwarg_name = "cache_config"
+                    omni_kwargs[kwarg_name] = value
+
+        # Build DiffusionParallelConfig if parallel params are present
+        if DiffusionParallelConfig is not None and hasattr(config, "ulysses_degree"):
+            parallel_config = DiffusionParallelConfig(
+                ulysses_degree=getattr(config, "ulysses_degree", 1),
+                ring_degree=getattr(config, "ring_degree", 1),
+                cfg_parallel_size=getattr(config, "cfg_parallel_size", 1),
+            )
+            omni_kwargs["parallel_config"] = parallel_config
+        elif DiffusionParallelConfig is None:
+            logger.warning(
+                "DiffusionParallelConfig not available; "
+                "skipping parallel config for AsyncOmni"
+            )
+
+        return omni_kwargs
+
+    async def generate(
+        self, request: Dict[str, Any], context
+    ) -> AsyncGenerator[Dict, None]:
+        """Generate outputs using AsyncOmni orchestrator with OpenAI-compatible format.
+
+        Routes to OpenAI mode (detokenized text) or token mode based on config.
+        Subclasses should override ``_generate_openai_mode`` for custom output handling.
+        """
+        request_id = context.id()
+        logger.debug(f"Omni Request ID: {request_id}")
+
+        async for chunk in self._generate_openai_mode(request, context, request_id):
+            yield chunk
+
+    async def _generate_openai_mode(
+        self, request, context, request_id
+    ) -> AsyncGenerator[Dict, None]:
+        """Generate OpenAI-compatible streaming chunks.
+
+        Subclasses should override this to handle their specific output types.
+        The base implementation raises NotImplementedError.
+        """
+        raise NotImplementedError(
+            f"{self.__class__.__name__} must implement _generate_openai_mode"
+        )
+
+    def _extract_text_prompt(self, request: Dict[str, Any]) -> str | None:
+        """Extract text prompt from OpenAI messages format.
+
+        Looks for the last user message and returns its text content.
+        """
+        messages = request.get("messages", [])
+        for message in reversed(messages):
+            if message.get("role") == "user":
+                return message.get("content")
+        return None
+
+    def _extract_extra_body(self, request: Dict[str, Any]) -> Dict[str, Any]:
+        """Extract extra_body parameters from the request.
+
+        The extra_body is passed through by the OpenAI client and contains
+        model-specific parameters (e.g. diffusion sampling params).
+        """
+        return request.get("extra_body", {}) or {}
+
+    def _build_sampling_params(self, request: Dict[str, Any]) -> SamplingParams:
+        """Build sampling params using shared handler utility."""
+        return build_sampling_params(
+            request, self.default_sampling_params, self.model_max_len
+        )
+
+    def _error_chunk(self, request_id: str, error_message: str) -> Dict[str, Any]:
+        """Create an error chunk in OpenAI format."""
+        return {
+            "id": request_id,
+            "created": int(time.time()),
+            "object": "chat.completion.chunk",
+            "model": self.config.served_model_name or self.config.model,
+            "choices": [
+                {
+                    "index": 0,
+                    "delta": {
+                        "role": "assistant",
+                        "content": f"Error: {error_message}",
+                    },
+                    "finish_reason": "error",
+                }
+            ],
+        }
+
+    def cleanup(self):
+        """Cleanup AsyncOmni orchestrator resources."""
+        try:
+            if hasattr(self, "engine_client"):
+                self.engine_client.close()
+                logger.info("AsyncOmni orchestrator closed")
+        except Exception as e:
+            logger.error(f"Error closing AsyncOmni orchestrator: {e}")

components/src/dynamo/vllm/omni/omni_handler.py

 # SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 import asyncio
+import base64
 import logging
+import os
 import time
-from typing import Any, AsyncGenerator, Dict
+import uuid
+from dataclasses import dataclass
+from io import BytesIO
+from typing import Any, AsyncGenerator, Dict, Union
+
+from diffusers.utils import export_to_video
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams, OmniTextPrompt
+
+from dynamo.common.protocols.image_protocol import (
+    ImageData,
+    NvCreateImageRequest,
+    NvImagesResponse,
+)
+from dynamo.common.protocols.video_protocol import (
+    NvCreateVideoRequest,
+    NvVideosResponse,
+    VideoData,
+)
+from dynamo.common.utils.output_modalities import RequestType, parse_request_type
+from dynamo.common.utils.video_utils import (
+    compute_num_frames,
+    normalize_video_frames,
+    parse_size,
+)
+from dynamo.vllm.omni.base_handler import BaseOmniHandler
 
-from vllm import SamplingParams
-from vllm_omni.entrypoints import AsyncOmni
-from vllm_omni.inputs.data import OmniTextPrompt, OmniTokensPrompt
+logger = logging.getLogger(__name__)
 
-from dynamo.vllm.handlers import BaseWorkerHandler, build_sampling_params
+# TODO: Migrate to fs_url based approach in another PR
+DEFAULT_VIDEO_FPS = 16
+DEFAULT_VIDEO_OUTPUT_DIR = "/tmp/dynamo_videos"  # noqa: S108
+
+
+@dataclass
+class EngineInputs:
+    """Parsed engine inputs ready for AsyncOmni.generate().
+
+    Attributes:
+        prompt: OmniTextPrompt dict for the engine.
+        sampling_params_list: Per-stage sampling parameters, or None for defaults.
+        request_type: The resolved request type (may differ from the initial parse
+            when a chat completion request carries video params).
+        fps: Frames per second, only meaningful for video requests.
+        response_format: Desired response format (e.g. "url" or "b64_json" for
+            image requests). None means use the default for the request type.
+    """
+
+    prompt: OmniTextPrompt
+    sampling_params_list: list | None = None
+    request_type: RequestType = RequestType.CHAT_COMPLETION
+    fps: int = 0
+    response_format: str | None = None
+
+
+def prepare_image_output(images: list, response_format: str | None = None):
+    """Prepare image output for response.
+
+    Args:
+        images: List of PIL Image objects.
+        response_format: Response format.
+
+    Returns:
+        List of image URLs or base64 strings.
+    """
+    ## This is a temporary function to prepare image output for response.
+    ## Right now, there are different utilities across components that uploads image/video outputs to urls or b64_json.
+    ## (ayushag) TODO: follow up, move all the utilities to common
+    outlist = []
+
+    for img in images:
+        if response_format == "url":
+            output_dir = "/tmp/dynamo_images"  # noqa: S108
+            os.makedirs(output_dir, exist_ok=True)
+            img_path = os.path.join(output_dir, f"{uuid.uuid4()}.png")
+            img.save(img_path)
+            outlist.append(img_path)
+        elif response_format == "b64_json" or response_format is None:
+            # convert image to base64
+            buffer = BytesIO()
+            img.save(buffer, format="PNG")
+            img_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
+            data_url = f"data:image/png;base64,{img_base64}"
+            outlist.append(data_url)
+        else:
+            raise ValueError(f"Invalid response format: {response_format}")
+    return outlist
 
-logger = logging.getLogger(__name__)
 
+class OmniHandler(BaseOmniHandler):
+    """Unified handler for multi-stage pipelines using vLLM-Omni.
 
-class OmniHandler(BaseWorkerHandler):
-    """Handler for multi-stage pipelines using vLLM-Omni's AsyncOmni orchestrator."""
+    Handles text-to-text, text-to-image, and text-to-video generation.
+    """
 
     def __init__(
         self,
@@ -25,145 +107,75 @@ class OmniHandler(BaseWorkerHandler):
         default_sampling_params: Dict[str, Any],
         shutdown_event: asyncio.Event | None = None,
     ):
-        """Initialize handler with AsyncOmni orchestrator."""
-        logger.info(
-            f"Initializing OmniHandler for multi-stage pipelines with model: {config.model}"
+        """Initialize the unified Omni handler.
+
+        Args:
+            runtime: Dynamo distributed runtime.
+            component: Dynamo component handle.
+            config: Parsed Config object from args.py.
+            default_sampling_params: Default sampling parameters dict.
+            shutdown_event: Optional asyncio event for graceful shutdown.
+        """
+        super().__init__(
+            runtime=runtime,
+            component=component,
+            config=config,
+            default_sampling_params=default_sampling_params,
+            shutdown_event=shutdown_event,
         )
 
-        omni_kwargs = {
-            "model": config.model,
-            "trust_remote_code": config.engine_args.trust_remote_code,
-            "stage_configs_path": config.stage_configs_path,
-        }
-
-        self.engine_client = AsyncOmni(**omni_kwargs)
-
-        # Initialize attributes needed from BaseWorkerHandler
-        # We don't call super().__init__() because VllmEngineMonitor expects AsyncLLM,
-        # but AsyncOmni manages its own engines internally
-
-        # TODO: Kv publishers not supported yet
-        # TODO: Adopt to baseworker initialization pattern
-        self.default_sampling_params = default_sampling_params
-        self.config = config
-        self.model_max_len = config.engine_args.max_model_len
-        self.shutdown_event = shutdown_event
-        self.use_vllm_tokenizer = config.use_vllm_tokenizer
-        logger.info("OmniHandler initialized successfully for text-to-text generation")
-
     async def generate(
         self, request: Dict[str, Any], context
     ) -> AsyncGenerator[Dict, None]:
-        """Generate outputs using AsyncOmni orchestrator with OpenAI-compatible format.
+        """Generate outputs via the unified OpenAI mode.
 
-        Supports text-to-text and text-to-image generation based on stage configuration.
-        Returns OpenAI-compatible streaming chunks with detokenized text.
+        Args:
+            request: Raw request dictionary from the Rust frontend.
+            context: Dynamo context for request tracking.
+
+        Yields:
+            Response dictionaries.
         """
         request_id = context.id()
         logger.debug(f"Omni Request ID: {request_id}")
 
-        if self.use_vllm_tokenizer:
-            async for chunk in self._generate_openai_mode(request, context, request_id):
-                yield chunk
-        else:
-            async for chunk in self._generate_token_mode(request, context, request_id):
-                yield chunk
-
-    # Not used right now
-    async def _generate_token_mode(self, request, context, request_id):
-        """
-        This mode returns token-ids as output
-        Text input -> Token-ids output
-        """
-        token_ids = request.get("token_ids")
-        prompt = OmniTokensPrompt(token_ids=token_ids)
-        num_output_tokens_so_far = 0
-        try:
-            async for stage_output in self.engine_client.generate(
-                prompt=prompt,
-                request_id=request_id,
-            ):
-                vllm_output = stage_output.request_output
-
-                if not vllm_output.outputs:
-                    logger.warning(f"Request {request_id} returned no outputs")
-                    yield {
-                        "finish_reason": "error: No outputs from vLLM engine",
-                        "token_ids": [],
-                    }
-                    break
-
-                output = vllm_output.outputs[0]
-                next_total_toks = len(output.token_ids)
-
-                out = {"token_ids": output.token_ids[num_output_tokens_so_far:]}
-
-                if output.finish_reason:
-                    out["finish_reason"] = self._normalize_finish_reason(
-                        output.finish_reason
-                    )
-                    out["completion_usage"] = self._build_completion_usage(vllm_output)
-                    logger.debug(
-                        f"Completed generation for request {request_id}: "
-                        f"{next_total_toks} output tokens, finish_reason={output.finish_reason}"
-                    )
-
-                if output.stop_reason:
-                    out["stop_reason"] = output.stop_reason
-
-                yield out
-                num_output_tokens_so_far = next_total_toks
-
-        except GeneratorExit:
-            # Shutdown was triggered during generation
-            logger.info(f"Request {request_id} aborted due to shutdown")
-            raise
-        except Exception as e:
-            logger.error(f"Error during generation for request {request_id}: {e}")
-            yield {
-                "finish_reason": f"error: {str(e)}",
-                "token_ids": [],
-            }
+        async for chunk in self._generate_openai_mode(request, context, request_id):
+            yield chunk
 
-    async def _generate_openai_mode(self, request, context, request_id):
-        """
-        This mode returns OpenAI-compatible streaming chunks
-        Text input -> Text output / Image output
-        """
+    async def _generate_openai_mode(
+        self, request: Dict[str, Any], context, request_id: str
+    ) -> AsyncGenerator[Dict[str, Any], None]:
+        """Single generation path for all request protocols and output modalities."""
 
-        # (ayushag) TODO: Support all type of OmniPrompt. Right now it works for only text prompts
-        # (ayushag) TODO: Document all I/O formats from vllm omni
-        # OmniText prompt support additional negative prompts as well. need to support that as well.
-        # Support multimodal content as well. That will involve  applying tokenizer to the prompt and loading images. Follow general multimodal support pattern.
-        prompt = self._extract_text_prompt(request)
-        prompt = OmniTextPrompt(prompt=prompt)
+        parsed_request, request_type = parse_request_type(
+            request, self.config.output_modalities
+        )
+        inputs = self.build_engine_inputs(parsed_request, request_type)
 
-        # Build sampling parameters from request
-        # (ayushag) TODO: Need to add proper multi-stage sampling param support
-        # sampling_params = self._build_sampling_params(request)
-        # sampling_params_list = [sampling_params]
+        generate_kwargs: Dict[str, Any] = {
+            "prompt": inputs.prompt,
+            "request_id": request_id,
+        }
+        if inputs.sampling_params_list is not None:
+            generate_kwargs["sampling_params_list"] = inputs.sampling_params_list
 
         previous_text = ""
 
         async with self._abort_monitor(context, request_id):
             try:
                 async for stage_output in self.engine_client.generate(
-                    prompt=prompt,
-                    request_id=request_id,
-                    # sampling_params_list=sampling_params_list,
+                    **generate_kwargs,
                 ):
                     if (
                         stage_output.final_output_type == "text"
                         and stage_output.request_output
                     ):
-                        # Text generation (LLM stage)
                         chunk = self._format_text_chunk(
                             stage_output.request_output,
                             request_id,
                             previous_text,
                         )
                         if chunk:
-                            # Update previous_text for delta calculation
                             output = stage_output.request_output.outputs[0]
                             previous_text = output.text
                             yield chunk
@@ -172,11 +184,22 @@ class OmniHandler(BaseWorkerHandler):
                         stage_output.final_output_type == "image"
                         and stage_output.images
                     ):
-                        # Image generation (diffusion stage)
-                        chunk = self._format_image_chunk(
-                            stage_output.images,
-                            request_id,
-                        )
+                        # vllm-omni uses final_output_type="image" for both
+                        # image and video diffusion outputs. Use the parsed
+                        # request type to route to the correct formatter.
+                        if inputs.request_type == RequestType.VIDEO_GENERATION:
+                            chunk = await self._format_video_chunk(
+                                stage_output.images,
+                                request_id,
+                                fps=inputs.fps,
+                            )
+                        else:
+                            chunk = self._format_image_chunk(
+                                stage_output.images,
+                                request_id,
+                                response_format=inputs.response_format,
+                                request_type=inputs.request_type,
+                            )
                         if chunk:
                             yield chunk
 
@@ -187,6 +210,273 @@ class OmniHandler(BaseWorkerHandler):
                 logger.error(f"Error during generation for request {request_id}: {e}")
                 yield self._error_chunk(request_id, str(e))
 
+    def build_engine_inputs(
+        self,
+        parsed_request: Union[
+            NvCreateImageRequest, NvCreateVideoRequest, Dict[str, Any]
+        ],
+        request_type: RequestType,
+    ) -> EngineInputs:
+        """Convert a parsed request into AsyncOmni engine inputs.
+
+        Args:
+            parsed_request: Output from parse_request_type -- a Pydantic model
+                for image/video requests, or a raw dict for chat completions.
+            request_type: The RequestType determined by parse_request_type.
+
+        Returns:
+            EngineInputs ready for engine_client.generate().
+        """
+        if request_type == RequestType.CHAT_COMPLETION:
+            return self._engine_inputs_from_chat(parsed_request)
+        elif request_type == RequestType.IMAGE_GENERATION:
+            return self._engine_inputs_from_image(parsed_request)
+        elif request_type == RequestType.VIDEO_GENERATION:
+            return self._engine_inputs_from_video(parsed_request)
+
+        elif request_type == RequestType.AUDIO_GENERATION:
+            raise NotImplementedError("Audio generation is not yet supported")
+
+        raise ValueError(f"Unknown request type: {request_type}")
+
+    def _engine_inputs_from_chat(self, request: Dict[str, Any]) -> EngineInputs:
+        """Build engine inputs from a chat completions request dict."""
+
+        # Chat completions request does not support extra_body passthrough
+        # So, we can't extract any diffusion related params from the raw_request
+        # It falls back to default sampling params
+        text_prompt = self._extract_text_prompt(request)
+        if text_prompt is None:
+            raise ValueError("No user message found in chat completion request")
+
+        prompt = OmniTextPrompt(prompt=text_prompt)
+
+        sampling_params_list = None
+
+        return EngineInputs(
+            prompt=prompt,
+            sampling_params_list=sampling_params_list,
+            request_type=RequestType.CHAT_COMPLETION,
+            fps=0,
+        )
+
+    def _engine_inputs_from_image(self, req: NvCreateImageRequest) -> EngineInputs:
+        """Build engine inputs from an NvCreateImageRequest."""
+        width, height = parse_size(req.size, default_w=1024, default_h=1024)
+        nvext = req.nvext
+
+        prompt = OmniTextPrompt(
+            prompt=req.prompt,
+            negative_prompt=nvext.negative_prompt or "" if nvext else "",
+        )
+
+        sp = OmniDiffusionSamplingParams(
+            height=height,
+            width=width,
+        )
+        if req.n is not None:
+            sp.num_outputs_per_prompt = req.n
+        if nvext:
+            if nvext.num_inference_steps is not None:
+                sp.num_inference_steps = nvext.num_inference_steps
+            if nvext.guidance_scale is not None:
+                sp.guidance_scale = nvext.guidance_scale
+            if nvext.seed is not None:
+                sp.seed = nvext.seed
+
+        return EngineInputs(
+            prompt=prompt,
+            sampling_params_list=[sp],
+            request_type=RequestType.IMAGE_GENERATION,
+            response_format=req.response_format,
+        )
+
+    def _engine_inputs_from_video(self, req: NvCreateVideoRequest) -> EngineInputs:
+        """Build engine inputs from an NvCreateVideoRequest."""
+        width, height = parse_size(req.size)
+        nvext = req.nvext
+
+        nvext_fps = nvext.fps if nvext else None
+        nvext_num_frames = nvext.num_frames if nvext else None
+
+        num_frames = compute_num_frames(
+            num_frames=nvext_num_frames,
+            seconds=req.seconds,
+            fps=nvext_fps,
+            default_fps=DEFAULT_VIDEO_FPS,
+        )
+        fps = nvext_fps if nvext_fps is not None else DEFAULT_VIDEO_FPS
+
+        prompt = OmniTextPrompt(
+            prompt=req.prompt,
+            negative_prompt=nvext.negative_prompt or "" if nvext else "",
+        )
+
+        sp = OmniDiffusionSamplingParams(
+            height=height,
+            width=width,
+            num_frames=num_frames,
+        )
+        if nvext:
+            if nvext.num_inference_steps is not None:
+                sp.num_inference_steps = nvext.num_inference_steps
+            if nvext.guidance_scale is not None:
+                sp.guidance_scale = nvext.guidance_scale
+            if nvext.seed is not None:
+                sp.seed = nvext.seed
+        if fps is not None:
+            sp.fps = fps
+
+        logger.info(
+            f"Video diffusion request: prompt='{req.prompt[:50]}...', "
+            f"size={width}x{height}, frames={num_frames}, fps={fps}"
+        )
+
+        return EngineInputs(
+            prompt=prompt,
+            sampling_params_list=[sp],
+            request_type=RequestType.VIDEO_GENERATION,
+            fps=fps,
+        )
+
+    def _format_image_chunk(
+        self,
+        images: list,
+        request_id: str,
+        response_format: str | None = None,
+        request_type: RequestType = RequestType.IMAGE_GENERATION,
+    ) -> Dict[str, Any] | None:
+        """Format image output as OpenAI chat completion chunk with base64 data URLs.
+
+        Args:
+            images: List of PIL Image objects generated by AsyncOmni engine.
+            request_id: Unique request identifier.
+            response_format: Response format (url, b64_json, None).
+            request_type: Request type (chat completion, image generation).
+
+        Returns:
+            Dict[str, Any] | None: Formatted chunk, or None if no images generated.
+        """
+
+        if not images:
+            return self._error_chunk(request_id, "No images generated")
+
+        data_urls = prepare_image_output(images, response_format)
+
+        if request_type == RequestType.CHAT_COMPLETION:
+            # This branch is used when user send request via /v1/chat/completions endpoint.
+            # We need to return chat completion chunk with image_url content part.
+            chunk = {
+                "id": request_id,
+                "created": int(time.time()),
+                "object": "chat.completion.chunk",
+                "model": self.config.served_model_name or self.config.model,
+                "choices": [
+                    {
+                        "index": 0,
+                        "delta": {
+                            "role": "assistant",
+                            "content": [
+                                {"type": "image_url", "image_url": {"url": data_url}}
+                                for data_url in data_urls
+                            ],
+                        },
+                        "finish_reason": "stop",
+                    }
+                ],
+            }
+            return chunk
+        elif request_type == RequestType.IMAGE_GENERATION:
+            # This branch is used when user send request via /v1/images/generations endpoint.
+            # This will return NvImagesResponse with list of ImageData objects.
+            image_data_list = []
+            for data_url in data_urls:
+                if response_format == "url":
+                    image_data_list.append(ImageData(url=data_url))
+                elif response_format == "b64_json" or response_format is None:
+                    # strip explicit prefix if present
+                    if data_url.startswith("data:image"):
+                        _, b64_part = data_url.split(",", 1)
+                        image_data_list.append(ImageData(b64_json=b64_part))
+                    else:
+                        image_data_list.append(ImageData(b64_json=data_url))
+                else:
+                    raise ValueError(f"Invalid response format: {response_format}")
+
+            output = NvImagesResponse(created=int(time.time()), data=image_data_list)
+            return output.model_dump()
+
+    async def _format_video_chunk(
+        self,
+        images: list,
+        request_id: str,
+        fps: int,
+    ) -> Dict[str, Any] | None:
+        """Convert diffusion output frames to MP4 and return as NvVideosResponse.
+
+        Args:
+            images: List of PIL Image frames from the diffusion stage.
+            request_id: Unique request identifier.
+            fps: Frames per second for the output video.
+
+        Returns:
+            ``NvVideosResponse.model_dump()`` dict, or ``None`` if no frames.
+        """
+        if not images:
+            return None
+
+        try:
+            start_time = time.time()
+
+            frame_list = normalize_video_frames(images)
+
+            logger.info(
+                f"Encoding {len(frame_list)} frames to MP4 for request {request_id} "
+                f"(fps={fps})"
+            )
+
+            os.makedirs(DEFAULT_VIDEO_OUTPUT_DIR, exist_ok=True)
+            video_path = os.path.join(DEFAULT_VIDEO_OUTPUT_DIR, f"{request_id}.mp4")
+
+            loop = asyncio.get_running_loop()
+            await loop.run_in_executor(
+                None,
+                export_to_video,
+                frame_list,
+                video_path,
+                fps,
+            )
+
+            logger.info(f"Video saved to {video_path} for request {request_id}")
+
+            inference_time = time.time() - start_time
+
+            response = NvVideosResponse(
+                id=request_id,
+                object="video",
+                model=self.config.served_model_name or self.config.model,
+                status="completed",
+                progress=100,
+                created=int(time.time()),
+                data=[VideoData(url=video_path)],
+                inference_time_s=inference_time,
+            )
+            return response.model_dump()
+
+        except Exception as e:
+            logger.error(f"Failed to encode video for request {request_id}: {e}")
+            error_response = NvVideosResponse(
+                id=request_id,
+                object="video",
+                model=self.config.served_model_name or self.config.model,
+                status="failed",
+                progress=0,
+                created=int(time.time()),
+                data=[],
+                error=str(e),
+            )
+            return error_response.model_dump()
+
     def _format_text_chunk(
         self,
         request_output,
@@ -226,95 +516,3 @@ class OmniHandler(BaseWorkerHandler):
             chunk["usage"] = self._build_completion_usage(request_output)
 
         return chunk
-
-    def _format_image_chunk(
-        self,
-        images: list,
-        request_id: str,
-    ) -> Dict[str, Any] | None:
-        """Format image output as OpenAI chat completion chunk with base64 data URLs."""
-        import base64
-        from io import BytesIO
-
-        if not images:
-            return self._error_chunk(request_id, "No images generated")
-
-        # Convert images to base64 data URLs
-        data_urls = []
-        for idx, img in enumerate(images):
-            # Convert PIL image to base64
-            buffer = BytesIO()
-            img.save(buffer, format="PNG")
-            img_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
-
-            # Create data URL (can be opened directly in browser)
-            data_url = f"data:image/png;base64,{img_base64}"
-            data_urls.append(data_url)
-            logger.info(f"Generated image {idx} for request {request_id}")
-
-        chunk = {
-            "id": request_id,
-            "created": int(time.time()),
-            "object": "chat.completion.chunk",
-            "model": self.config.served_model_name or self.config.model,
-            "choices": [
-                {
-                    "index": 0,
-                    "delta": {
-                        "role": "assistant",
-                        "content": [
-                            {"type": "image_url", "image_url": {"url": data_url}}
-                            for data_url in data_urls
-                        ],
-                    },
-                    "finish_reason": "stop",
-                }
-            ],
-        }
-
-        return chunk
-
-    def _extract_text_prompt(self, request: Dict[str, Any]) -> str | None:
-        """Extract text prompt from request."""
-
-        # OpenAI messages format - extract text content only
-        messages = request.get("messages", [])
-        # Assumes single user message
-        for message in messages:
-            if message.get("role") == "user":
-                return message.get("content")
-        return ""
-
-    def _build_sampling_params(self, request: Dict[str, Any]) -> SamplingParams:
-        """Build sampling params using shared handler utility."""
-        return build_sampling_params(
-            request, self.default_sampling_params, self.model_max_len
-        )
-
-    def _error_chunk(self, request_id: str, error_message: str) -> Dict[str, Any]:
-        """Create an error chunk in OpenAI format."""
-        return {
-            "id": request_id,
-            "created": int(time.time()),
-            "object": "chat.completion.chunk",
-            "model": self.config.served_model_name or self.config.model,
-            "choices": [
-                {
-                    "index": 0,
-                    "delta": {
-                        "role": "assistant",
-                        "content": f"Error: {error_message}",
-                    },
-                    "finish_reason": "error",
-                }
-            ],
-        }
-
-    def cleanup(self):
-        """Cleanup AsyncOmni orchestrator resources."""
-        try:
-            if hasattr(self, "engine_client"):
-                self.engine_client.close()
-                logger.info("AsyncOmni orchestrator closed")
-        except Exception as e:
-            logger.error(f"Error closing AsyncOmni orchestrator: {e}")

components/src/dynamo/vllm/tests/test_vllm_omni_handler.py

+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from dynamo.common.protocols.image_protocol import NvCreateImageRequest
+from dynamo.common.protocols.video_protocol import NvCreateVideoRequest
+from dynamo.common.utils.output_modalities import RequestType
+
+# TODO: Install vLLM omni dependencies in CI container so this skip is no longer needed.
+try:
+    from dynamo.vllm.omni.omni_handler import (
+        EngineInputs,
+        OmniHandler,
+        prepare_image_output,
+    )
+except ImportError:
+    pytest.skip("vLLM omni dependencies not available", allow_module_level=True)
+
+pytestmark = [
+    pytest.mark.unit,
+    pytest.mark.vllm,
+    pytest.mark.gpu_0,
+    pytest.mark.pre_merge,
+]
+
+
+def _make_handler():
+    with patch(
+        "dynamo.vllm.omni.omni_handler.BaseOmniHandler.__init__", return_value=None
+    ):
+        handler = OmniHandler.__new__(OmniHandler)
+
+    config = MagicMock()
+    config.model = "test-model"
+    config.served_model_name = None
+    config.output_modalities = ["text"]
+    handler.config = config
+    return handler
+
+
+class TestEngineInputs:
+    def test_defaults(self):
+        """EngineInputs uses CHAT_COMPLETION, fps=0, and None optionals by default."""
+        ei = EngineInputs(prompt={"prompt": "hello"})
+        assert ei.request_type == RequestType.CHAT_COMPLETION
+        assert ei.fps == 0
+        assert ei.sampling_params_list is None
+        assert ei.response_format is None
+
+
+class TestPrepareImageOutput:
+    def test_b64_json(self):
+        """b64_json format returns data URI with base64 prefix."""
+        img = MagicMock()
+        img.save = lambda b, format: b.write(b"fake_png_data")
+        results = prepare_image_output([img], "b64_json")
+        assert len(results) == 1
+        assert results[0].startswith("data:image/png;base64,")
+
+    def test_b64_default_when_none(self):
+        """None response_format defaults to base64 encoding."""
+        img = MagicMock()
+        img.save = lambda b, format: b.write(b"data")
+        results = prepare_image_output([img], None)
+        assert results[0].startswith("data:image/png;base64,")
+
+    def test_invalid_format(self):
+        """Unsupported response_format raises ValueError."""
+        with pytest.raises(ValueError, match="Invalid response format"):
+            prepare_image_output([MagicMock()], "invalid")
+
+    def test_multiple_images(self):
+        """Multiple input images produce one output entry each."""
+        imgs = [MagicMock() for _ in range(3)]
+        for img in imgs:
+            img.save = lambda b, format: b.write(b"px")
+        results = prepare_image_output(imgs, "b64_json")
+        assert len(results) == 3
+
+
+class TestBuildEngineInputs:
+    def test_chat_completion(self):
+        """Chat request extracts text prompt with no sampling params."""
+        handler = _make_handler()
+        raw = {"messages": [{"role": "user", "content": "hello"}]}
+        inputs = handler.build_engine_inputs(raw, RequestType.CHAT_COMPLETION)
+        assert inputs.request_type == RequestType.CHAT_COMPLETION
+        assert inputs.prompt["prompt"] == "hello"
+        assert inputs.sampling_params_list is None
+
+    def test_image_generation(self):
+        """Image request parses prompt, size, and creates diffusion sampling params."""
+        handler = _make_handler()
+        req = NvCreateImageRequest(prompt="a cat", size="512x512")
+        inputs = handler.build_engine_inputs(req, RequestType.IMAGE_GENERATION)
+        assert inputs.request_type == RequestType.IMAGE_GENERATION
+        assert inputs.prompt["prompt"] == "a cat"
+        assert len(inputs.sampling_params_list) == 1
+        sp = inputs.sampling_params_list[0]
+        assert sp.height == 512
+        assert sp.width == 512
+
+    def test_video_generation(self):
+        """Video request parses prompt, size, seconds, and sets fps."""
+        handler = _make_handler()
+        req = NvCreateVideoRequest(
+            prompt="a drone", model="test", size="832x480", seconds=2
+        )
+        inputs = handler.build_engine_inputs(req, RequestType.VIDEO_GENERATION)
+        assert inputs.request_type == RequestType.VIDEO_GENERATION
+        assert inputs.prompt["prompt"] == "a drone"
+        assert inputs.fps > 0
+
+    def test_audio_not_implemented(self):
+        """Audio generation raises NotImplementedError."""
+        handler = _make_handler()
+        with pytest.raises(NotImplementedError):
+            handler.build_engine_inputs({}, RequestType.AUDIO_GENERATION)
+
+
+class TestFormatTextChunk:
+    def _make_output(self, text="hello world", finish_reason=None):
+        output = MagicMock()
+        output.text = text
+        output.finish_reason = finish_reason
+        request_output = MagicMock()
+        request_output.outputs = [output]
+        request_output.prompt_token_ids = [1, 2, 3]
+        return request_output
+
+    def test_delta_text(self):
+        """Delta content is the diff between current and previous text."""
+        handler = _make_handler()
+        ro = self._make_output("hello world")
+        chunk = handler._format_text_chunk(ro, "req-1", "hello ")
+        assert chunk["choices"][0]["delta"]["content"] == "world"
+
+    def test_no_outputs_returns_error(self):
+        """Empty engine outputs produce an error chunk."""
+        handler = _make_handler()
+        ro = MagicMock()
+        ro.outputs = []
+        chunk = handler._format_text_chunk(ro, "req-1", "")
+        assert "Error" in chunk["choices"][0]["delta"]["content"]
+
+    def test_finish_reason_included(self):
+        """Final chunk includes finish_reason and usage stats."""
+        handler = _make_handler()
+        handler._normalize_finish_reason = lambda r: r
+        handler._build_completion_usage = lambda ro: {
+            "prompt_tokens": 3,
+            "completion_tokens": 1,
+        }
+        ro = self._make_output("done", finish_reason="stop")
+        chunk = handler._format_text_chunk(ro, "req-1", "")
+        assert chunk["choices"][0]["finish_reason"] == "stop"
+        assert "usage" in chunk
+
+
+class TestFormatImageChunk:
+    def test_chat_completion_format(self):
+        """Chat completion route returns image_url content parts."""
+        handler = _make_handler()
+        img = MagicMock()
+        img.save = lambda b, format: b.write(b"px")
+        chunk = handler._format_image_chunk(
+            [img], "req-1", request_type=RequestType.CHAT_COMPLETION
+        )
+        assert chunk["object"] == "chat.completion.chunk"
+        assert chunk["choices"][0]["delta"]["content"][0]["type"] == "image_url"
+
+    def test_image_generation_b64_format(self):
+        """Image generation with b64_json format returns base64 data."""
+        handler = _make_handler()
+        img = MagicMock()
+        img.save = lambda b, format: b.write(b"px")
+        chunk = handler._format_image_chunk(
+            [img],
+            "req-1",
+            response_format="b64_json",
+            request_type=RequestType.IMAGE_GENERATION,
+        )
+        assert chunk["data"][0]["b64_json"] is not None
+
+    def test_image_generation_default_format_returns_b64(self):
+        """Image generation with response_format=None defaults to b64_json."""
+        handler = _make_handler()
+        img = MagicMock()
+        img.save = lambda b, format: b.write(b"px")
+        chunk = handler._format_image_chunk(
+            [img],
+            "req-1",
+            response_format=None,
+            request_type=RequestType.IMAGE_GENERATION,
+        )
+        assert chunk["data"][0]["b64_json"] is not None
+
+    def test_empty_images_returns_error(self):
+        """Empty image list produces an error chunk."""
+        handler = _make_handler()
+        chunk = handler._format_image_chunk([], "req-1")
+        assert "Error" in chunk["choices"][0]["delta"]["content"]
+
+
+class TestFormatVideoChunk:
+    @pytest.mark.asyncio
+    async def test_empty_frames_returns_none(self):
+        """Empty frame list returns None."""
+        handler = _make_handler()
+        result = await handler._format_video_chunk([], "req-1", fps=16)
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_error_returns_failed_status(self):
+        """Encoding failure returns NvVideosResponse with failed status and error."""
+        handler = _make_handler()
+        with patch(
+            "dynamo.vllm.omni.omni_handler.normalize_video_frames",
+            side_effect=RuntimeError("boom"),
+        ):
+            chunk = await handler._format_video_chunk([MagicMock()], "req-1", fps=16)
+        assert chunk["status"] == "failed"
+        assert "boom" in chunk["error"]

docs/pages/backends/vllm/vllm-omni.md

@@ -4,34 +4,53 @@
 title: vLLM-Omni
 ---
 
-# [Experimental] Running Omni Models with vLLM
+# [Experimental] Omni Models with vLLM
 
-Dynamo supports omni (multimodal generation) models via the [vLLM-Omni](https://github.com/vllm-project/vllm-omni) backend. This enables multi-stage pipelines for tasks like text-to-text and text-to-image generation through an OpenAI-compatible API.
+Dynamo supports multimodal generation through the [vLLM-Omni](https://github.com/vllm-project/vllm-omni) backend. This integration exposes text-to-text, text-to-image, and text-to-video capabilities via OpenAI-compatible API endpoints.
 
 ## Prerequisites
 
-This guide assumes familiarity with deploying Dynamo with vLLM as described in [README.md](/docs/pages/backends/vllm/README.md).
+This guide assumes familiarity with deploying Dynamo with vLLM as described in the [vLLM backend guide](/docs/pages/backends/vllm/README.md).
 
-## Quick Start
+## Supported Modalities
 
-### Text-to-Text
+| Modality | Endpoint(s) | `--output-modalities` |
+|---|---|---|
+| Text-to-Text | `/v1/chat/completions` | `text` (default) |
+| Text-to-Image | `/v1/chat/completions`, `/v1/images/generations` | `image` |
+| Text-to-Video | `/v1/videos` | `video` |
 
-Launch an aggregated deployment (frontend + omni worker) using the provided script:
+The `--output-modalities` flag determines which endpoint(s) the worker registers. When set to `image`, both `/v1/chat/completions` (returns inline base64 images) and `/v1/images/generations` are available. When set to `video`, the worker serves `/v1/videos`.
+
+## Tested Models
+
+| Modality | Models |
+|---|---|
+| Text-to-Text | `Qwen/Qwen2.5-Omni-7B` |
+| Text-to-Image | `Qwen/Qwen-Image`, `AIDC-AI/Ovis-Image-7B` |
+| Text-to-Video | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers`, `Wan-AI/Wan2.2-T2V-A14B-Diffusers` |
+
+To run a non-default model, pass `--model` to any launch script:
 
 ```bash
-bash examples/backends/vllm/launch/agg_omni.sh
+bash examples/backends/vllm/launch/agg_omni_image.sh --model AIDC-AI/Ovis-Image-7B
+bash examples/backends/vllm/launch/agg_omni_video.sh --model Wan-AI/Wan2.2-T2V-A14B-Diffusers
 ```
 
-This starts `Qwen/Qwen2.5-Omni-7B` with a single-stage thinker config on one GPU. Override the model with:
+## Text-to-Text
+
+Launch an aggregated deployment (frontend + omni worker):
 
 ```bash
-bash examples/backends/vllm/launch/agg_omni.sh --model <your-model>
+bash examples/backends/vllm/launch/agg_omni.sh
 ```
 
-Test the deployment:
+This starts `Qwen/Qwen2.5-Omni-7B` with a single-stage thinker config on one GPU.
+
+Verify the deployment:
 
 ```bash
-curl -X POST http://localhost:8000/v1/chat/completions \
+curl -s http://localhost:8000/v1/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
     "model": "Qwen/Qwen2.5-Omni-7B",
@@ -41,34 +60,29 @@ curl -X POST http://localhost:8000/v1/chat/completions \
   }'
 ```
 
-### Text-to-Image
+This script uses a custom stage config (`stage_configs/single_stage_llm.yaml`) that configures the thinker stage for text generation. See [Stage Configuration](#stage-configuration) for details.
+
+## Text-to-Image
 
-Text-to-image uses vLLM-Omni's built-in default stage configs (no custom YAML needed). Launch without a stage config path so vLLM-Omni loads the model's default multi-stage pipeline:
+Launch using the provided script with `Qwen/Qwen-Image`:
 
 ```bash
-# Start frontend
-python -m dynamo.frontend &
-
-# Start omni worker (vLLM-Omni loads default stage configs for the model)
-DYN_SYSTEM_PORT=8081 python -m dynamo.vllm \
-  --model <your-text-to-image-model> \
-  --omni \
-  --connector none
+bash examples/backends/vllm/launch/agg_omni_image.sh
 ```
 
-Images are returned as base64-encoded PNGs in the response:
+### Via `/v1/chat/completions`
 
 ```bash
-curl -X POST http://localhost:8000/v1/chat/completions \
+curl -s http://localhost:8000/v1/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
-    "model": "<your-text-to-image-model>",
+    "model": "Qwen/Qwen-Image",
     "messages": [{"role": "user", "content": "A cat sitting on a windowsill"}],
     "stream": false
   }'
 ```
 
-The response contains image data URLs in the content field:
+The response includes base64-encoded images inline:
 
 ```json
 {
@@ -82,25 +96,79 @@ The response contains image data URLs in the content field:
 }
 ```
 
-## Key Flags
+### Via `/v1/images/generations`
+
+```bash
+curl -s http://localhost:8000/v1/images/generations \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "Qwen/Qwen-Image",
+    "prompt": "A cat sitting on a windowsill",
+    "size": "1024x1024",
+    "response_format": "url"
+  }'
+```
+
+## Text-to-Video
+
+Launch using the provided script with `Wan-AI/Wan2.1-T2V-1.3B-Diffusers`:
+
+```bash
+bash examples/backends/vllm/launch/agg_omni_video.sh
+```
+
+Generate a video via `/v1/videos`:
+
+```bash
+curl -s http://localhost:8000/v1/videos \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+    "prompt": "A drone flyover of a mountain landscape",
+    "seconds": 2,
+    "size": "832x480",
+    "response_format": "url"
+  }'
+```
+
+The response returns a video URL or base64 data depending on `response_format`:
+
+```json
+{
+  "id": "...",
+  "object": "video",
+  "model": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+  "status": "completed",
+  "data": [{"url": "/tmp/generated_video.mp4"}]
+}
+```
+
+The `/v1/videos` endpoint also accepts NVIDIA extensions via the `nvext` field for fine-grained control:
+
+| Field | Description | Default |
+|---|---|---|
+| `nvext.fps` | Frames per second | 24 |
+| `nvext.num_frames` | Number of frames (overrides `fps * seconds`) | -- |
+| `nvext.negative_prompt` | Negative prompt for guidance | -- |
+| `nvext.num_inference_steps` | Number of denoising steps | 50 |
+| `nvext.guidance_scale` | CFG guidance scale | 5.0 |
+| `nvext.seed` | Random seed for reproducibility | -- |
+
+## CLI Reference
 
 | Flag | Description |
-|------|-------------|
-| `--omni` | Enable vLLM-Omni orchestrator (required) |
+|---|---|
+| `--omni` | Enable the vLLM-Omni orchestrator (required for all omni workloads) |
+| `--output-modalities <modality>` | Output modality: `text`, `image`, or `video` |
 | `--stage-configs-path <path>` | Path to stage config YAML (optional; vLLM-Omni uses model defaults if omitted) |
-| `--connector none` | Disable KV connector (recommended for omni) |
+| `--connector none` | Disable KV connector (recommended for omni workers) |
 
 ## Stage Configuration
 
-Omni pipelines are configured via YAML stage configs. See [`examples/backends/vllm/launch/stage_configs/single_stage_llm.yaml`](/examples/backends/vllm/launch/stage_configs/single_stage_llm.yaml) for an example. Key fields:
-
-- **`model_stage`**: Pipeline stage name (e.g., `thinker`, `talker`, `code2wav`)
-- **`final_output_type`**: Output format — `text` or `image`
-- **`is_comprehension`**: Whether this stage processes input text/multimodal content
-
-For full documentation on stage config format, supported fields, and multi-stage pipeline examples, see the [vLLM-Omni Stage Configs documentation](https://docs.vllm.ai/projects/vllm-omni/en/latest/configuration/stage_configs/).
+Omni pipelines are configured via YAML stage configs. See [`examples/backends/vllm/launch/stage_configs/single_stage_llm.yaml`](/examples/backends/vllm/launch/stage_configs/single_stage_llm.yaml) for an example. For full documentation on stage config format and multi-stage pipelines, refer to the [vLLM-Omni Stage Configs documentation](https://docs.vllm.ai/projects/vllm-omni/en/latest/configuration/stage_configs/).
 
 ## Current Limitations
 
-- Only text prompts are supported (no multimodal input yet)
-- KV cache events are not published for omni workers
+- Only text prompts are supported as input (no multimodal input yet).
+- KV cache events are not published for omni workers.
+- Each worker supports a single output modality at a time.

examples/backends/vllm/launch/agg_omni_image.sh

+#!/bin/bash
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+set -e
+trap 'echo Cleaning up...; kill 0' EXIT
+
+MODEL="Qwen/Qwen-Image"
+
+# Parse command line arguments
+EXTRA_ARGS=()
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --model)
+            MODEL="$2"
+            shift 2
+            ;;
+        *)
+            EXTRA_ARGS+=("$1")
+            shift
+            ;;
+    esac
+done
+
+echo "=========================================="
+echo "Starting vLLM-Omni Worker"
+echo "Model: $MODEL"
+echo "=========================================="
+
+echo "Starting frontend on port ${DYN_HTTP_PORT:-8000}..."
+python -m dynamo.frontend &
+FRONTEND_PID=$!
+
+sleep 2
+
+echo "Starting Omni worker..."
+DYN_SYSTEM_PORT=${DYN_SYSTEM_PORT:-8081} \
+    python -m dynamo.vllm \
+    --model "$MODEL" \
+    --omni \
+    --connector none \
+    --output-modalities image \
+    "${EXTRA_ARGS[@]}"

examples/backends/vllm/launch/agg_omni_video.sh

+#!/bin/bash
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+set -e
+trap 'echo Cleaning up...; kill 0' EXIT
+
+
+MODEL="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+
+# Parse command line arguments
+EXTRA_ARGS=()
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --model)
+            MODEL="$2"
+            shift 2
+            ;;
+        *)
+            EXTRA_ARGS+=("$1")
+            shift
+            ;;
+    esac
+done
+
+echo "=========================================="
+echo "Starting vLLM-Omni Worker"
+echo "Model: $MODEL"
+echo "=========================================="
+
+
+echo "Starting frontend on port ${DYN_HTTP_PORT:-8000}..."
+python -m dynamo.frontend &
+FRONTEND_PID=$!
+
+sleep 2
+
+echo "Starting Omni worker..."
+DYN_SYSTEM_PORT=${DYN_SYSTEM_PORT:-8081} \
+    python -m dynamo.vllm \
+    --model "$MODEL" \
+    --omni \
+    --connector none \
+    --output-modalities video \
+    "${EXTRA_ARGS[@]}"
\ No newline at end of file

lib/llm/src/discovery/watcher.rs

@@ -589,8 +589,65 @@ impl ModelWatcher {
             let engine = Arc::new(push_router);
             self.manager
                 .add_embeddings_model(card.name(), checksum, engine)?;
+        }
+        // Case: Text + (Images, Audio, Videos)
+        // Must come before the plain Text+Chat / Text+Completions branches because
+        // diffusion models often set both Images and Chat flags. The branch below
+        // handles the chat registration internally when supports_chat() is true.
+        else if card.model_input == ModelInput::Text
+            && (card.model_type.supports_images()
+                || card.model_type.supports_audios()
+                || card.model_type.supports_videos())
+        {
+            // Image Models can support chat completions (vllm omni way)
+            // So register chat_completions model as well
+            if card.model_type.supports_chat() {
+                let chat_router = PushRouter::<
+                    NvCreateChatCompletionRequest,
+                    Annotated<NvCreateChatCompletionStreamResponse>,
+                >::from_client_with_threshold(
+                    client.clone(),
+                    self.router_config.router_mode,
+                    None,
+                    None,
+                )
+                .await?;
+                self.manager.add_chat_completions_model(
+                    card.name(),
+                    checksum,
+                    Arc::new(chat_router),
+                )?;
+            }
+
+            // This is ModelType::Images : registers /v1/images/* endpoints
+            if card.model_type.supports_images() {
+                let images_router = PushRouter::<
+                    NvCreateImageRequest,
+                    Annotated<NvImagesResponse>,
+                >::from_client_with_threshold(
+                    client.clone(), self.router_config.router_mode, None, None
+                )
+                .await?;
+                self.manager
+                    .add_images_model(card.name(), checksum, Arc::new(images_router))?;
+            }
+
+            // This is ModelType::Videos : registers /v1/videos/* endpoints
+            if card.model_type.supports_videos() {
+                let videos_router = PushRouter::<
+                    NvCreateVideoRequest,
+                    Annotated<NvVideosResponse>,
+                >::from_client_with_threshold(
+                    client.clone(), self.router_config.router_mode, None, None
+                )
+                .await?;
+                self.manager
+                    .add_videos_model(card.name(), checksum, Arc::new(videos_router))?;
+            }
+
+            // TODO: add audio models support
         } else if card.model_input == ModelInput::Text && card.model_type.supports_chat() {
-            // Case 3: Text + Chat
+            // Case: Text + Chat (pure text-to-text, no diffusion)
             let push_router = PushRouter::<
                 NvCreateChatCompletionRequest,
                 Annotated<NvCreateChatCompletionStreamResponse>,
@@ -602,7 +659,7 @@ impl ModelWatcher {
             self.manager
                 .add_chat_completions_model(card.name(), checksum, engine)?;
         } else if card.model_input == ModelInput::Text && card.model_type.supports_completions() {
-            // Case 2: Text + Completions
+            // Case: Text + Completions
             let push_router = PushRouter::<
                 NvCreateCompletionRequest,
                 Annotated<NvCreateCompletionResponse>,
@@ -660,60 +717,6 @@ impl ModelWatcher {
             let engine = Arc::new(push_router);
             self.manager
                 .add_tensor_model(card.name(), checksum, engine)?;
-        }
-        // Case: Text + (Images, Audio, Videos)
-        else if card.model_input == ModelInput::Text
-            && (card.model_type.supports_images()
-                || card.model_type.supports_audios()
-                || card.model_type.supports_videos())
-        {
-            // Image Models can support chat completions (vllm omni way)
-            // So register chat_completions model as well
-            if card.model_type.supports_chat() {
-                let chat_router = PushRouter::<
-                    NvCreateChatCompletionRequest,
-                    Annotated<NvCreateChatCompletionStreamResponse>,
-                >::from_client_with_threshold(
-                    client.clone(),
-                    self.router_config.router_mode,
-                    None,
-                    None,
-                )
-                .await?;
-                self.manager.add_chat_completions_model(
-                    card.name(),
-                    checksum,
-                    Arc::new(chat_router),
-                )?;
-            }
-
-            // This is ModelType::Images : registers /v1/images/* endpoints
-            if card.model_type.supports_images() {
-                let images_router = PushRouter::<
-                    NvCreateImageRequest,
-                    Annotated<NvImagesResponse>,
-                >::from_client_with_threshold(
-                    client.clone(), self.router_config.router_mode, None, None
-                )
-                .await?;
-                self.manager
-                    .add_images_model(card.name(), checksum, Arc::new(images_router))?;
-            }
-
-            // This is ModelType::Videos : registers /v1/videos/* endpoints
-            if card.model_type.supports_videos() {
-                let videos_router = PushRouter::<
-                    NvCreateVideoRequest,
-                    Annotated<NvVideosResponse>,
-                >::from_client_with_threshold(
-                    client.clone(), self.router_config.router_mode, None, None
-                )
-                .await?;
-                self.manager
-                    .add_videos_model(card.name(), checksum, Arc::new(videos_router))?;
-            }
-
-            // TODO: add audio models support
         } else if card.model_type.supports_prefill() {
             // Case 6: Prefill
             // Guardrail: Verify model_input is Tokens

lib/llm/src/model_type.rs

@@ -154,7 +154,6 @@ impl ModelType {
         if self.contains(Self::Embedding) {
             endpoint_types.push(crate::endpoint_type::EndpointType::Embedding);
         }
-        // Images models support both chat and completions endpoints
         if self.contains(Self::Images) {
             endpoint_types.push(crate::endpoint_type::EndpointType::Images);
         }