[Misc] Address LingBot runner review feedback

Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>

benchmarks/lingbot_video/dense_pipeline_parity.py

@@ -167,9 +167,11 @@ def main() -> None:
     ]
     native_cmd = [
         sys.executable,
-        "examples/offline_inference/text_to_video/text_to_video_lingbot.py",
+        "examples/offline_inference/text_to_video/text_to_video.py",
         "--model",
         args.model,
+        "--model-class-name",
+        "LingBotVideoPipeline",
         "--prompt",
         args.prompt,
         "--output",

docs/user_guide/examples/offline_inference/text_to_video.md

@@ -12,6 +12,7 @@ For backend selection and SageAttention usage, see the [Diffusion Attention Back
 | Model | Default Resolution | Default Frames | Default Steps | Guidance | VRAM (BF16) |
 |---|---|---|---|---|---|
 | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | 720x1280 | 81 | 40 | 4.0 | ~60 GiB |
+| `robbyant/lingbot-video-dense-1.3b` / `robbyant/lingbot-video-moe-30b-a3b` | 192x320 | 9 | 2 | 3.0 | ~68 GiB (MoE smoke) |
 | `Lightricks/LTX-2` | 512x768 | 121 | 40 | video 3.0 / audio 7.0 | Model-dependent |
 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v` | 480x832 | 121 | 50 | 6.0 | 1×A100 80GB |
 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v` | 720x1280 | 121 | 50 | 6.0 | FP8 + VAE tiling required |
@@ -35,6 +36,25 @@ python text_to_video.py \
   --output t2v_out.mp4
 ```
 
+### LingBot-Video
+
+The shared runner recognizes both official LingBot checkpoint IDs and selects
+the native `LingBotVideoPipeline` automatically:
+
+```bash
+python text_to_video.py \
+  --model robbyant/lingbot-video-dense-1.3b \
+  --prompt "a robotic arm picks up a red block" \
+  --height 192 --width 320 --num-frames 9 --num-inference-steps 2 \
+  --guidance-scale 3.0 --flow-shift 3.0 --fps 24 \
+  --output lingbot_t2v.mp4
+```
+
+Use `robbyant/lingbot-video-moe-30b-a3b` for the MoE checkpoint. For local
+paths without `lingbot` in the directory name, add
+`--model-class-name LingBotVideoPipeline`. Model-only options use
+`--extra-body`, for example `'{"batch_cfg": true, "output_type": "np"}'`.
+
 ### LTX-2
 
 ```bash

docs/user_guide/examples/online_serving/diffusers_pipeline_adapter.md

@@ -28,7 +28,6 @@ However, as we strive to ensure output similarity between vLLM-Omni's diffuser b
 - Qwen/Qwen-Image
 - Tongyi-MAI/Z-Image-Turbo
 - Wan2.2-I2V-A14B-Diffusers
-- Lightricks/LTX-2.5-Diffusers (source-preview dependencies)
 
 If you find that a model not listed above also produces different outputs from running diffusers model directly.
 Please consider file an issue.
@@ -117,36 +116,6 @@ The model loading and inference strictly follows the diffusers library, and they
 Users are encouraged to double-check the model pipeline's interface in [diffusers' official documentation](https://huggingface.co/docs/diffusers/api/pipelines/overview).
 Some particular examples are below.
 
-#### LTX-2.5 (source preview)
-
-Use the gated Diffusers checkpoint
-[`Lightricks/LTX-2.5-Diffusers`](https://huggingface.co/Lightricks/LTX-2.5-Diffusers);
-[`Lightricks/LTX-2.5`](https://huggingface.co/Lightricks/LTX-2.5) contains
-component weights and is not directly loadable by this backend. Accept the
-model terms, authenticate, and install the source-preview dependencies:
-
-```bash
-hf auth login
-python -m pip install --upgrade 'transformers>=5.10.1' \
-  'diffusers @ git+https://github.com/huggingface/diffusers.git@7564fb016dabda0c943416190fc92398c50b1b20'
-```
-
-```bash
-vllm serve Lightricks/LTX-2.5-Diffusers \
-  --omni \
-  --diffusion-load-format diffusers \
-  --dtype bfloat16 \
-  --enable-cpu-offload \
-  --vae-use-tiling
-```
-
-The converted repository loads the distilled transformer. When no `sigmas`,
-`timesteps`, or `num_inference_steps` is supplied, the adapter applies the
-official eight-sigma unguided schedule and returns synchronized 48 kHz audio.
-Support is limited to one-stage distilled T2V; `transformer_full`, two-stage
-upsampling, image/video conditioning, and the diffusion video decoder are not
-supported. The general Diffusers backend [limitations](#limitations) apply.
-
 #### Wan Series
 
 The Wan series video generation models takes `boundary_ratio` and `flow_shift` during model initialization ([ref](https://huggingface.co/docs/diffusers/api/pipelines/wan)), not during inference.

examples/offline_inference/text_to_video/text_to_video.md

@@ -8,6 +8,7 @@ A unified script for text-to-video generation. Supports multiple models with mod
 |---|---|---|---|---|---|
 | `Wan-AI/Wan2.1-VACE-1.3B-diffusers` | 480x832 | 81 | 30 | 5.0 | ~20 GiB (RTX 5090, VAE tiling) |
 | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | 720x1280 | 81 | 40 | 4.0 | ~60 GiB |
+| `robbyant/lingbot-video-dense-1.3b` / `robbyant/lingbot-video-moe-30b-a3b` | 192x320 | 9 | 2 | 3.0 | ~68 GiB (MoE smoke) |
 | `Lightricks/LTX-2` | 512x768 | 121 | 40 | video 3.0 / audio 7.0 | Model-dependent |
 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v` | 480x832 | 121 | 50 | 6.0 | 1×A100 80GB |
 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v` | 720x1280 | 121 | 50 | 6.0 | FP8 + VAE tiling required |
@@ -57,18 +58,31 @@ python text_to_video.py \
 
 ### LingBot-Video
 
-The dedicated LingBot example explicitly selects the video output modality:
+The shared runner recognizes the official dense and MoE checkpoint IDs, selects
+`LingBotVideoPipeline`, and builds the canonical video request envelope:
 
 ```bash
-python examples/offline_inference/text_to_video/text_to_video_lingbot.py \
+python text_to_video.py \
   --model robbyant/lingbot-video-dense-1.3b \
+  --prompt "a robotic arm picks up a red block" \
   --height 192 --width 320 --num-frames 9 --num-inference-steps 2 \
+  --guidance-scale 3.0 --flow-shift 3.0 --fps 24 \
   --output lingbot_t2v.mp4
 ```
 
 Use the same command with `robbyant/lingbot-video-moe-30b-a3b` for the MoE
 checkpoint. Requested frame counts are rounded upward to the causal
-VAE's `4n+1` grid.
+VAE's `4n+1` grid. For a local checkpoint path whose name does not contain
+`lingbot`, pass `--model-class-name LingBotVideoPipeline`.
+
+LingBot-only options such as `batch_cfg` and `output_type` use the shared
+model-extra channel, for example:
+
+```bash
+python text_to_video.py \
+  --model robbyant/lingbot-video-dense-1.3b \
+  --extra-body '{"batch_cfg": true, "output_type": "np"}'
+```
 
 ### LTX-2
 

examples/offline_inference/text_to_video/text_to_video.py

@@ -16,7 +16,7 @@ from vllm_omni.entrypoints.omni import Omni
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.lora.request import LoRARequest
 from vllm_omni.lora.utils import stable_lora_int_id
-from vllm_omni.model_extras import get_extra_body_params, get_model_class_name
+from vllm_omni.model_extras import get_extra_body_params, get_model_class_name, get_output_tensor_range
 from vllm_omni.outputs import OmniRequestOutput
 from vllm_omni.platforms import current_omni_platform
 
@@ -80,6 +80,17 @@ _MODEL_PRESETS = {
         "fps": 16,
         "output": "helios_output.mp4",
     },
+    "lingbot": {
+        "model_class_name": "LingBotVideoPipeline",
+        "height": 192,
+        "width": 320,
+        "num_frames": 9,
+        "num_inference_steps": 2,
+        "guidance_scale": 3.0,
+        "fps": 24,
+        "flow_shift": 3.0,
+        "output": "lingbot_video_output.mp4",
+    },
     "ltx2": {
         "height": 512,
         "width": 768,
@@ -110,27 +121,51 @@ _MODEL_PRESETS = {
 def _detect_preset(model: str, model_class_name: str | None = None) -> dict:
     model_lower = model.lower()
     class_lower = (model_class_name or "").lower()
-    if "distilled" in class_lower or "distilled" in model_lower:
-        return _MODEL_PRESETS["ltx2_distilled"]
-    if "ltx23" in class_lower or "ltx-2.3" in model_lower or "ltx_2.3" in model_lower:
-        return _MODEL_PRESETS["ltx23"]
-    if "ltx2" in class_lower or "ltx-2" in model_lower or "ltx_2" in model_lower:
+    if "lingbot" in model_lower or "lingbotvideo" in class_lower:
+        return _MODEL_PRESETS["lingbot"]
+    if "ltx" in class_lower or "ltx" in model_lower:
+        if "distilled" in class_lower or "distilled" in model_lower:
+            return _MODEL_PRESETS["ltx2_distilled"]
+        if "ltx23" in class_lower or "ltx-2.3" in model_lower or "ltx_2.3" in model_lower:
+            return _MODEL_PRESETS["ltx23"]
         return _MODEL_PRESETS["ltx2"]
-    if "vace" in model_lower:
+    if "vace" in model_lower or "vace" in class_lower:
         return _MODEL_PRESETS["vace"]
     # Edge must be matched before the generic cosmos branch (its "cosmos" substring would
     # otherwise pick up the Nano/Super 720p / gs 6.0 / flow_shift 10.0 preset).
     if ("cosmos" in model_lower or "cosmos" in class_lower) and ("edge" in model_lower or "edge" in class_lower):
         return _MODEL_PRESETS["cosmos3_edge"]
-    if "cosmos" in model_lower:
+    if "cosmos" in model_lower or "cosmos" in class_lower:
         return _MODEL_PRESETS["cosmos"]
-    if "hunyuan" in model_lower:
+    if "hunyuan" in model_lower or "hunyuan" in class_lower:
         return _MODEL_PRESETS["hunyuan"]
-    if "helios" in model_lower:
+    if "helios" in model_lower or "helios" in class_lower:
         return _MODEL_PRESETS["helios"]
     return _MODEL_PRESETS["wan"]
 
 
+def build_text_to_video_prompt(prompt: str, negative_prompt: str | None) -> dict[str, Any]:
+    """Build the canonical request envelope for the shared T2V example."""
+    result: dict[str, Any] = {
+        "prompt": prompt,
+        "modalities": ["video"],
+    }
+    if negative_prompt is not None:
+        result["negative_prompt"] = negative_prompt
+    return result
+
+
+def _normalize_float_tensor(tensor: torch.Tensor, source_range: str) -> torch.Tensor:
+    """Normalize decoded frames according to the pipeline's output contract."""
+    if not tensor.is_floating_point():
+        return tensor
+    if source_range == "negative_one_to_one":
+        return tensor.clamp(-1, 1) * 0.5 + 0.5
+    if source_range == "zero_to_one":
+        return tensor.clamp(0, 1)
+    raise ValueError(f"Unsupported floating-point tensor range: {source_range!r}")
+
+
 def parse_profiler_config(value: str) -> dict[str, Any]:
     try:
         config = json.loads(value)
@@ -160,7 +195,7 @@ def parse_extra_body(value: str) -> dict[str, Any]:
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(
         description="Generate a video from a text prompt. "
-        "Supports Wan2.2, HunyuanVideo-1.5, Helios, and other text-to-video models."
+        "Supports Wan2.2, HunyuanVideo-1.5, Helios, LingBot-Video, and other text-to-video models."
     )
     parser.add_argument(
         "--model",
@@ -408,6 +443,7 @@ def main():
     for key, default_val in preset.items():
         if getattr(args, key.replace("-", "_"), None) is None:
             setattr(args, key.replace("-", "_"), default_val)
+    model_class_name = args.model_class_name
 
     generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(args.seed)
     # Cache-dit config (Wan2.2 only)
@@ -480,6 +516,7 @@ def main():
     omni = Omni(**omni_kwargs)
     model_class_name = get_model_class_name(omni) or model_class_name
     declared_extra_body_params = get_extra_body_params(model_class_name)
+    output_tensor_range = get_output_tensor_range(model_class_name)
 
     if profiler_enabled:
         print("[Profiler] Starting profiling...")
@@ -513,12 +550,11 @@ def main():
             lora_path=lora_path,
         )
 
-    prompt_dict = {"prompt": args.prompt}
-    if args.negative_prompt is not None:
-        prompt_dict["negative_prompt"] = args.negative_prompt
-    elif preset not in (_MODEL_PRESETS["ltx2"], _MODEL_PRESETS["ltx23"]):
+    negative_prompt = args.negative_prompt
+    if negative_prompt is None and all(preset is not _MODEL_PRESETS[name] for name in ("lingbot", "ltx2", "ltx23")):
         # Preserve the historical empty-prompt behavior for non-LTX examples.
-        prompt_dict["negative_prompt"] = ""
+        negative_prompt = ""
+    prompt_dict = build_text_to_video_prompt(args.prompt, negative_prompt)
 
     extra_args = {}
     if lora_request:
@@ -630,8 +666,7 @@ def main():
                 frame_tensor = frame_tensor[0]
             if frame_tensor.dim() == 3 and frame_tensor.shape[0] in (3, 4):
                 frame_tensor = frame_tensor.permute(1, 2, 0)
-            if frame_tensor.is_floating_point():
-                frame_tensor = frame_tensor.clamp(-1, 1) * 0.5 + 0.5
+            frame_tensor = _normalize_float_tensor(frame_tensor, output_tensor_range)
             return frame_tensor.float().numpy()
         if isinstance(frame, np.ndarray):
             frame_array = frame
@@ -683,8 +718,7 @@ def main():
                 video_tensor = video_tensor[0]
         elif video_tensor.dim() == 4 and video_tensor.shape[0] in (3, 4):
             video_tensor = video_tensor.permute(1, 2, 3, 0)
-        if video_tensor.is_floating_point():
-            video_tensor = video_tensor.clamp(-1, 1) * 0.5 + 0.5
+        video_tensor = _normalize_float_tensor(video_tensor, output_tensor_range)
         video_array = video_tensor.float().numpy()
     elif isinstance(frames, np.ndarray):
         video_array = frames

examples/offline_inference/text_to_video/text_to_video_lingbot.py

@@ -1,131 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from __future__ import annotations
-
-import argparse
-import time
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-import torch
-
-from vllm_omni.diffusion.data import DiffusionParallelConfig
-from vllm_omni.entrypoints.omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-from vllm_omni.outputs import OmniRequestOutput
-from vllm_omni.platforms import current_omni_platform
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description="Generate text-to-video with a LingBot-Video checkpoint.")
-    parser.add_argument("--model", default="robbyant/lingbot-video-dense-1.3b")
-    parser.add_argument("--prompt", default="a robotic arm picks up a red block")
-    parser.add_argument("--negative-prompt", default=None)
-    parser.add_argument("--output", default="lingbot_video_output.mp4")
-    parser.add_argument("--height", type=int, default=192)
-    parser.add_argument("--width", type=int, default=320)
-    parser.add_argument("--num-frames", type=int, default=9)
-    parser.add_argument("--num-inference-steps", type=int, default=2)
-    parser.add_argument("--guidance-scale", type=float, default=3.0)
-    parser.add_argument("--flow-shift", type=float, default=3.0)
-    parser.add_argument("--seed", type=int, default=42)
-    parser.add_argument("--fps", type=int, default=24)
-    parser.add_argument("--output-type", choices=["pt", "np"], default="pt")
-    parser.add_argument("--batch-cfg", action="store_true")
-    return parser.parse_args()
-
-
-def _peak_memory_mb(output: Any) -> float:
-    result = output[0] if isinstance(output, list) and output else output
-    return float(getattr(result, "peak_memory_mb", 0.0) or 0.0)
-
-
-def _extract_video(output: Any) -> Any:
-    if isinstance(output, list):
-        output = output[0] if output else None
-    if isinstance(output, OmniRequestOutput):
-        if output.final_output_type != "image":
-            raise ValueError(f"Unexpected output type {output.final_output_type!r}.")
-        if not output.images:
-            raise ValueError("No video frames found in OmniRequestOutput.")
-        return output.images[0] if len(output.images) == 1 else output.images
-    return output
-
-
-def _to_frame_array(video: Any) -> np.ndarray:
-    if isinstance(video, torch.Tensor):
-        tensor = video.detach().cpu()
-        if tensor.dim() == 5:
-            tensor = tensor[0]
-        if tensor.dim() == 4 and tensor.shape[-1] not in (3, 4):
-            if tensor.shape[0] in (3, 4):
-                tensor = tensor.permute(1, 2, 3, 0)
-            elif tensor.shape[1] in (3, 4):
-                tensor = tensor.permute(0, 2, 3, 1)
-        if tensor.is_floating_point():
-            if float(tensor.min()) < 0.0:
-                tensor = tensor.clamp(-1, 1) * 0.5 + 0.5
-            else:
-                tensor = tensor.clamp(0, 1)
-        return tensor.float().numpy()
-    if isinstance(video, np.ndarray):
-        array = video[0] if video.ndim == 5 else video
-        if np.issubdtype(array.dtype, np.integer):
-            return array.astype(np.float32) / 255.0
-        return array.astype(np.float32, copy=False)
-    if isinstance(video, list):
-        return np.stack([_to_frame_array(frame) for frame in video], axis=0)
-    return np.asarray(video, dtype=np.float32)
-
-
-def main() -> None:
-    args = parse_args()
-    generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(args.seed)
-    omni = Omni(
-        model=args.model,
-        model_class_name="LingBotVideoPipeline",
-        flow_shift=args.flow_shift,
-        parallel_config=DiffusionParallelConfig(),
-    )
-
-    prompt: dict[str, Any] = {
-        "prompt": args.prompt,
-        "modalities": ["video"],
-        "num_frames": args.num_frames,
-    }
-    if args.negative_prompt is not None:
-        prompt["negative_prompt"] = args.negative_prompt
-
-    sampling_params = OmniDiffusionSamplingParams(
-        height=args.height,
-        width=args.width,
-        num_frames=args.num_frames,
-        num_inference_steps=args.num_inference_steps,
-        guidance_scale=args.guidance_scale,
-        generator=generator,
-        output_type=args.output_type,
-        extra_args={"batch_cfg": args.batch_cfg, "shift": args.flow_shift},
-    )
-
-    start = time.perf_counter()
-    output = omni.generate(prompt, sampling_params)
-    elapsed = time.perf_counter() - start
-    print(f"Total generation time: {elapsed:.4f} seconds ({elapsed * 1000:.2f} ms)")
-    peak_mb = _peak_memory_mb(output)
-    if peak_mb:
-        print(f"Worker peak GPU memory (reserved): {peak_mb:.2f} MiB ({peak_mb / 1024:.2f} GiB)")
-
-    video = _to_frame_array(_extract_video(output))
-    output_path = Path(args.output)
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    from diffusers.utils import export_to_video
-
-    export_to_video(list(video), str(output_path), fps=args.fps)
-    print(f"Saved generated video to {output_path}")
-    omni.close()
-
-
-if __name__ == "__main__":
-    main()

examples/online_serving/diffusers_pipeline_adapter/README.md

@@ -25,7 +25,6 @@ However, as we strive to ensure output similarity between vLLM-Omni's diffuser b
 - Qwen/Qwen-Image
 - Tongyi-MAI/Z-Image-Turbo
 - Wan2.2-I2V-A14B-Diffusers
-- Lightricks/LTX-2.5-Diffusers (source-preview dependencies)
 
 If you find that a model not listed above also produces different outputs from running diffusers model directly.
 Please consider file an issue or submit a PR to fix.
@@ -119,36 +118,6 @@ The model loading and inference strictly follows the diffusers library, and they
 Users are encouraged to double-check the model pipeline's interface in [diffusers' official documentation](https://huggingface.co/docs/diffusers/api/pipelines/overview).
 Some particular examples are below.
 
-#### LTX-2.5 (source preview)
-
-Use the gated Diffusers checkpoint
-[`Lightricks/LTX-2.5-Diffusers`](https://huggingface.co/Lightricks/LTX-2.5-Diffusers);
-[`Lightricks/LTX-2.5`](https://huggingface.co/Lightricks/LTX-2.5) contains
-component weights and is not directly loadable by this backend. Accept the
-model terms, authenticate, and install the source-preview dependencies:
-
-```bash
-hf auth login
-python -m pip install --upgrade 'transformers>=5.10.1' \
-  'diffusers @ git+https://github.com/huggingface/diffusers.git@7564fb016dabda0c943416190fc92398c50b1b20'
-```
-
-```bash
-vllm serve Lightricks/LTX-2.5-Diffusers \
-  --omni \
-  --diffusion-load-format diffusers \
-  --dtype bfloat16 \
-  --enable-cpu-offload \
-  --vae-use-tiling
-```
-
-The converted repository loads the distilled transformer. When no `sigmas`,
-`timesteps`, or `num_inference_steps` is supplied, the adapter applies the
-official eight-sigma unguided schedule and returns synchronized 48 kHz audio.
-Support is limited to one-stage distilled T2V; `transformer_full`, two-stage
-upsampling, image/video conditioning, and the diffusion video decoder are not
-supported. The general Diffusers backend [limitations](#limitations) apply.
-
 #### Wan Series
 
 The Wan series video generation models takes `boundary_ratio` and `flow_shift` during model initialization ([ref](https://huggingface.co/docs/diffusers/api/pipelines/wan)), not during inference.

recipes/Robbyant/LingBot-Video.md

@@ -27,7 +27,7 @@ parallel, cache, quantized, or expert-kernel backends.
 - Dense checkpoint: <https://huggingface.co/robbyant/lingbot-video-dense-1.3b>
 - MoE checkpoint: <https://huggingface.co/robbyant/lingbot-video-moe-30b-a3b>
 - Upstream project: <https://github.com/Robbyant/lingbot-video>
-- Related offline example: [`examples/offline_inference/text_to_video/text_to_video_lingbot.py`](../../examples/offline_inference/text_to_video/text_to_video_lingbot.py)
+- Related offline example: [`examples/offline_inference/text_to_video/text_to_video.py`](../../examples/offline_inference/text_to_video/text_to_video.py)
 - Related online video API docs: [`docs/serving/videos_api.md`](../../docs/serving/videos_api.md)
 
 ## Hardware Support
@@ -52,7 +52,7 @@ The MoE path is validated with BF16 expert weights.
 
 ```bash
 CUDA_VISIBLE_DEVICES=0 \
-python examples/offline_inference/text_to_video/text_to_video_lingbot.py \
+python examples/offline_inference/text_to_video/text_to_video.py \
   --model robbyant/lingbot-video-dense-1.3b \
   --prompt "a robotic arm picks up a red block" \
   --output lingbot_t2v.mp4 \
@@ -70,7 +70,7 @@ python examples/offline_inference/text_to_video/text_to_video_lingbot.py \
 
 ```bash
 CUDA_VISIBLE_DEVICES=0 \
-python examples/offline_inference/text_to_video/text_to_video_lingbot.py \
+python examples/offline_inference/text_to_video/text_to_video.py \
   --model robbyant/lingbot-video-moe-30b-a3b \
   --prompt "a robotic arm picks up a red block" \
   --output lingbot_moe_t2v.mp4 \

tests/diffusion/diffusion_backend/test_diffusers_backend.py

@@ -17,10 +17,6 @@ from vllm_omni.diffusion.data import (
     OmniDiffusionConfig,
 )
 from vllm_omni.diffusion.models.diffusers_adapter import DiffusersAdapterPipeline
-from vllm_omni.diffusion.models.diffusers_adapter.pipeline_utils import (
-    LTX25_DISTILLED_SIGMAS,
-    get_pipeline_utils,
-)
 from vllm_omni.diffusion.request import OmniDiffusionRequest
 from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
@@ -184,32 +180,6 @@ class TestPipelineArgumentsHandling:
         with pytest.raises(NotImplementedError):
             DiffusersAdapterPipeline(od_config=od_config)
 
-    def test_ltx25_original_repo_rejected_before_loading(self, mocker):
-        mock_from_pretrained = mocker.patch(
-            "vllm_omni.diffusion.models.diffusers_adapter.pipeline_diffusers_adapter.DiffusionPipeline.from_pretrained"
-        )
-        adapter = DiffusersAdapterPipeline(od_config=_make_od_config(model="Lightricks/LTX-2.5"))
-
-        with pytest.raises(ValueError, match="Lightricks/LTX-2.5-Diffusers"):
-            adapter.load_weights()
-
-        mock_from_pretrained.assert_not_called()
-
-    def test_ltx25_requires_upstream_diffusers_feature(self, mocker):
-        mocker.patch(
-            "vllm_omni.diffusion.models.diffusers_adapter.pipeline_utils._supports_ltx25_diffusers",
-            return_value=False,
-        )
-        mock_from_pretrained = mocker.patch(
-            "vllm_omni.diffusion.models.diffusers_adapter.pipeline_diffusers_adapter.DiffusionPipeline.from_pretrained"
-        )
-        adapter = DiffusersAdapterPipeline(od_config=_make_od_config(model="Lightricks/LTX-2.5-Diffusers"))
-
-        with pytest.raises(ImportError, match="7564fb016dabda0c943416190fc92398c50b1b20"):
-            adapter.load_weights()
-
-        mock_from_pretrained.assert_not_called()
-
     def test_adapter_load_weights_injects_supported_quantization_config(self, mocker):
         (
             FakePipelineQuantizationConfig,
@@ -404,28 +374,6 @@ class TestPipelineArgumentsHandling:
         assert isinstance(output, DiffusionOutput)
         assert output.output == raw_output
 
-    def test_ltx25_preserves_video_and_singular_audio_output(self, tmp_path):
-        frames = torch.zeros(1, 2, 3, 4, 3)
-        audio = torch.zeros(1, 48000)
-        (tmp_path / "model_index.json").write_text(
-            '{"_class_name":"LTX2Pipeline","duration_head":["diffusers","LTX2DurationHead"]}',
-            encoding="utf-8",
-        )
-        od_config = _make_od_config(model=str(tmp_path))
-        adapter = DiffusersAdapterPipeline(od_config=od_config)
-        adapter._pipeline = SimpleNamespace(vocoder=SimpleNamespace(config=SimpleNamespace(output_sampling_rate=48000)))
-        adapter._pipeline_utils = get_pipeline_utils("LTX2Pipeline")
-
-        output = adapter._wrap_output(SimpleNamespace(frames=frames, audio=audio))
-
-        assert isinstance(output.output, dict)
-        assert output.output["payload"]["video"] is frames
-        assert output.output["payload"]["audio"] is audio
-        assert output.output["metadata"] == {"audio": {"sample_rate": 48000}}
-
-        frames_only = adapter._wrap_output(SimpleNamespace(frames=frames, audio=None))
-        assert frames_only.output is frames
-
     def test_adapter_build_call_kwargs(self, mocker):
         class MockPipeline:
             def __call__(
@@ -681,97 +629,6 @@ class TestPipelineArgumentsHandling:
         with pytest.raises(ValueError):
             pipeline.forward(DiffusionRequestBatch(requests=[problematic_request]))
 
-    def test_ltx25_uses_distilled_sampling_defaults_from_renamed_local_directory(self, mocker, tmp_path):
-        class LTX2Pipeline:
-            def __call__(
-                self,
-                prompt=None,
-                num_inference_steps=None,
-                sigmas=None,
-                timesteps=None,
-                guidance_scale=None,
-                audio_guidance_scale=None,
-                stg_scale=None,
-                audio_stg_scale=None,
-                modality_scale=None,
-                audio_modality_scale=None,
-                guidance_rescale=None,
-                audio_guidance_rescale=None,
-                num_frames=None,
-                frame_rate=None,
-                output_type=None,
-                generator=None,
-            ):
-                return None
-
-            def to(self, device):
-                return self
-
-        # Local model directories do not retain their Hub repository name.
-        # Detect 2.5 from its pipeline component metadata instead.
-        (tmp_path / "model_index.json").write_text(
-            '{"_class_name":"LTX2Pipeline","duration_head":["diffusers","LTX2DurationHead"]}',
-            encoding="utf-8",
-        )
-        enable_tiling = mocker.Mock()
-        loaded_pipeline = LTX2Pipeline()
-        loaded_pipeline.vae = SimpleNamespace(enable_tiling=enable_tiling)
-
-        mocker.patch(
-            "vllm_omni.diffusion.models.diffusers_adapter.pipeline_utils._supports_ltx25_diffusers",
-            return_value=True,
-        )
-        mocker.patch(
-            "vllm_omni.diffusion.models.diffusers_adapter.pipeline_diffusers_adapter.DiffusionPipeline.from_pretrained",
-            return_value=loaded_pipeline,
-        )
-        adapter = DiffusersAdapterPipeline(
-            od_config=_make_od_config(
-                model=str(tmp_path),
-                vae_use_tiling=True,
-            )
-        )
-        adapter.load_weights()
-        enable_tiling.assert_called_once_with()
-
-        sampling = OmniDiffusionSamplingParams(
-            num_frames=121,
-            frame_rate=24.0,
-            seed=42,
-            generator_device="cpu",
-        )
-        kwargs = adapter._build_call_kwargs(DiffusionRequestBatch(requests=[_make_request(sampling_params=sampling)]))
-
-        assert kwargs["sigmas"] == LTX25_DISTILLED_SIGMAS
-        assert kwargs["num_inference_steps"] == 8
-        assert kwargs["guidance_scale"] == 1.0
-        assert kwargs["audio_guidance_scale"] == 1.0
-        assert kwargs["stg_scale"] == 0.0
-        assert kwargs["audio_stg_scale"] == 0.0
-        assert kwargs["modality_scale"] == 1.0
-        assert kwargs["audio_modality_scale"] == 1.0
-        assert kwargs["guidance_rescale"] == 0.0
-        assert kwargs["audio_guidance_rescale"] == 0.0
-
-        explicit_steps = OmniDiffusionSamplingParams(num_inference_steps=12, generator_device="cpu")
-        step_kwargs = adapter._build_call_kwargs(
-            DiffusionRequestBatch(requests=[_make_request(sampling_params=explicit_steps)])
-        )
-        assert step_kwargs["num_inference_steps"] == 12
-        assert "sigmas" not in step_kwargs
-
-        custom_sigmas = [1.0, 0.5]
-        explicit_schedule = OmniDiffusionSamplingParams(
-            num_inference_steps=2,
-            sigmas=custom_sigmas,
-            generator_device="cpu",
-        )
-        schedule_kwargs = adapter._build_call_kwargs(
-            DiffusionRequestBatch(requests=[_make_request(sampling_params=explicit_schedule)])
-        )
-        assert schedule_kwargs["num_inference_steps"] == 2
-        assert schedule_kwargs["sigmas"] is custom_sigmas
-
 
 @pytest.mark.advanced_model
 @hardware_test(res={"cuda": "L4"}, num_cards=1)

tests/examples/offline_inference/test_text_to_video_prompts.py

@@ -0,0 +1,91 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+import pytest
+import torch
+
+from examples.offline_inference.text_to_video.text_to_video import (
+    _MODEL_PRESETS,
+    _detect_preset,
+    _normalize_float_tensor,
+    build_text_to_video_prompt,
+)
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+@pytest.mark.parametrize(
+    ("negative_prompt", "expected_negative_prompt"),
+    [(None, None), ("", ""), ("blurry", "blurry")],
+)
+def test_text_to_video_builds_canonical_prompt(
+    negative_prompt: str | None,
+    expected_negative_prompt: str | None,
+) -> None:
+    result = build_text_to_video_prompt("a robot waves", negative_prompt)
+
+    assert result["prompt"] == "a robot waves"
+    assert result["modalities"] == ["video"]
+    if expected_negative_prompt is None:
+        assert "negative_prompt" not in result
+    else:
+        assert result["negative_prompt"] == expected_negative_prompt
+
+
+@pytest.mark.parametrize(
+    ("model", "model_class_name", "preset_name"),
+    [
+        ("robbyant/lingbot-video-dense-1.3b", None, "lingbot"),
+        ("/models/custom-checkpoint", "LingBotVideoPipeline", "lingbot"),
+        ("Lightricks/LTX-2", "LTX2DistilledPipeline", "ltx2_distilled"),
+        ("BestWishYsh/Helios-Distilled", None, "helios"),
+        ("/models/cosmos-edge", "Cosmos3EdgeVFMTransformer", "cosmos3_edge"),
+        ("/models/vace", "WanVACEPipeline", "vace"),
+    ],
+)
+def test_detect_preset_is_scoped_to_model_family(
+    model: str,
+    model_class_name: str | None,
+    preset_name: str,
+) -> None:
+    assert _detect_preset(model, model_class_name) is _MODEL_PRESETS[preset_name]
+
+
+def test_lingbot_preset_matches_lingbot_defaults() -> None:
+    assert _MODEL_PRESETS["lingbot"] == {
+        "model_class_name": "LingBotVideoPipeline",
+        "height": 192,
+        "width": 320,
+        "num_frames": 9,
+        "num_inference_steps": 2,
+        "guidance_scale": 3.0,
+        "fps": 24,
+        "flow_shift": 3.0,
+        "output": "lingbot_video_output.mp4",
+    }
+
+
+def test_normalize_float_tensor_preserves_zero_to_one_frames() -> None:
+    frames = torch.tensor([0.0, 0.25, 1.0, 1.5])
+
+    assert torch.equal(_normalize_float_tensor(frames, "zero_to_one"), torch.tensor([0.0, 0.25, 1.0, 1.0]))
+
+
+def test_normalize_float_tensor_maps_negative_one_to_one_frames() -> None:
+    frames = torch.tensor([-1.5, -1.0, 0.0, 1.0, 1.5])
+
+    assert torch.equal(
+        _normalize_float_tensor(frames, "negative_one_to_one"),
+        torch.tensor([0.0, 0.0, 0.5, 1.0, 1.0]),
+    )
+
+
+def test_normalize_float_tensor_uses_one_contract_for_ambiguous_frames() -> None:
+    frames = [torch.tensor([0.0, 0.5, 1.0]), torch.tensor([-1.0, 0.0, 1.0])]
+
+    normalized = [_normalize_float_tensor(frame, "negative_one_to_one") for frame in frames]
+
+    assert torch.equal(normalized[0], torch.tensor([0.5, 0.75, 1.0]))
+    assert torch.equal(normalized[1], torch.tensor([0.0, 0.5, 1.0]))

tests/model_extras/test_model_extras.py

@@ -17,6 +17,7 @@ from vllm_omni.model_extras import (
     build_x_to_text_prompt,
     get_extra_body_params,
     get_extra_output_params,
+    get_output_tensor_range,
     get_x_to_text_model_family,
     should_init_extra_args_for_non_diffusion_stages,
 )
@@ -134,6 +135,8 @@ def test_lingbot_extra_registry_declares_request_params() -> None:
         }
     )
     assert get_extra_output_params("LingBotVideoPipeline") == frozenset()
+    assert get_output_tensor_range("LingBotVideoPipeline") == "zero_to_one"
+    assert get_output_tensor_range("WanPipeline") == "negative_one_to_one"
     assert should_init_extra_args_for_non_diffusion_stages("LingBotVideoPipeline") is False
 
 

vllm_omni/diffusion/models/diffusers_adapter/pipeline_diffusers_adapter.py

@@ -23,11 +23,7 @@ from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from torch import nn
 
 from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
-from vllm_omni.diffusion.models.diffusers_adapter.pipeline_utils import (
-    BasePipelineUtils,
-    get_pipeline_utils,
-    validate_model_compatibility,
-)
+from vllm_omni.diffusion.models.diffusers_adapter.pipeline_utils import BasePipelineUtils, get_pipeline_utils
 from vllm_omni.diffusion.models.diffusers_adapter.quantization_utils import (
     apply_diffusers_quantization_config,
     convert_diffusers_quantization_config,
@@ -114,7 +110,6 @@ class DiffusersAdapterPipeline(nn.Module, DiffusionPipelineProfilerMixin):
 
         model_id = self.od_config.model
         dtype = self.od_config.dtype
-        validate_model_compatibility(self.od_config)
 
         load_kwargs = {
             "torch_dtype": dtype,
@@ -135,8 +130,6 @@ class DiffusersAdapterPipeline(nn.Module, DiffusionPipelineProfilerMixin):
         logger.debug(f"Loading diffusers pipeline with kwargs: {load_kwargs}")
 
         self._pipeline = DiffusionPipeline.from_pretrained(model_id, **load_kwargs)
-        if pipeline_class_name is None:
-            self._pipeline_utils = get_pipeline_utils(self._pipeline.__class__.__name__)
         self._pipeline_utils.apply_post_load_updates(self._pipeline, self.od_config)
 
         self._pipeline.to(self.device)
@@ -153,14 +146,14 @@ class DiffusersAdapterPipeline(nn.Module, DiffusionPipelineProfilerMixin):
         # VAE slicing and tiling: try-catch because not all models have VAE
         if self.od_config.vae_use_slicing:
             try:
-                self._pipeline_utils.enable_vae_optimization(self._pipeline, "slicing")
+                self._pipeline.enable_vae_slicing()
             except Exception as e:
                 logger.warning(
                     f"Failed to enable VAE slicing for diffusers pipeline {self._pipeline.__class__.__name__}: {e}"
                 )
         if self.od_config.vae_use_tiling:
             try:
-                self._pipeline_utils.enable_vae_optimization(self._pipeline, "tiling")
+                self._pipeline.enable_vae_tiling()
             except Exception as e:
                 logger.warning(
                     f"Failed to enable VAE tiling for diffusers pipeline {self._pipeline.__class__.__name__}: {e}"
@@ -400,8 +393,6 @@ class DiffusersAdapterPipeline(nn.Module, DiffusionPipelineProfilerMixin):
         else:
             kwargs["generator"] = torch.Generator(device=sampling.generator_device)
 
-        self._pipeline_utils.update_call_kwargs(self.od_config, kwargs)
-
         logger.info(
             "Calling diffusers pipeline with kwargs: %s", DiffusersAdapterPipeline._summarize_call_kwargs_value(kwargs)
         )
@@ -506,8 +497,6 @@ class DiffusersAdapterPipeline(nn.Module, DiffusionPipelineProfilerMixin):
         """
         from vllm_omni.diffusion.data import DiffusionOutput
 
-        output = self._pipeline_utils.normalize_output(self._pipeline, self.od_config, output)
-
         if hasattr(output, "images"):
             # Preserve diffusers image format (`output_type`)
             return DiffusionOutput(output=output.images)

vllm_omni/diffusion/models/diffusers_adapter/pipeline_utils.py

@@ -1,10 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import inspect
-import json
-import logging
-from pathlib import Path
 from typing import Any
 
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline
@@ -12,74 +8,6 @@ from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from vllm_omni.diffusion.data import OmniDiffusionConfig
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 
-logger = logging.getLogger(__name__)
-
-LTX25_ORIGINAL_MODEL_ID = "Lightricks/LTX-2.5"
-LTX25_DIFFUSERS_MODEL_ID = "Lightricks/LTX-2.5-Diffusers"
-LTX25_DIFFUSERS_COMMIT = "7564fb016dabda0c943416190fc92398c50b1b20"
-
-# Diffusers' public LTX-2.5 schedule intentionally excludes the scheduler's
-# terminal zero. FlowMatchEulerDiscreteScheduler appends that internally.
-LTX25_DISTILLED_SIGMAS = [1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875]
-
-_LTX25_DISTILLED_GUIDANCE_DEFAULTS: dict[str, float] = {
-    "guidance_scale": 1.0,
-    "audio_guidance_scale": 1.0,
-    "stg_scale": 0.0,
-    "audio_stg_scale": 0.0,
-    "modality_scale": 1.0,
-    "audio_modality_scale": 1.0,
-    "guidance_rescale": 0.0,
-    "audio_guidance_rescale": 0.0,
-}
-
-
-def _supports_ltx25_diffusers() -> bool:
-    """Check the standard-pipeline capabilities added with LTX-2.5."""
-    try:
-        import diffusers
-        import transformers
-
-        pipeline_class = diffusers.LTX2Pipeline
-        init_parameters = inspect.signature(pipeline_class.__init__).parameters
-        return (
-            "duration_head" in init_parameters
-            and "prompt_enhancer" in init_parameters
-            and hasattr(transformers, "Gemma4UnifiedForConditionalGeneration")
-        )
-    except (AttributeError, ImportError, RuntimeError):
-        return False
-
-
-def is_ltx25_diffusers_model(od_config: OmniDiffusionConfig) -> bool:
-    """Return whether config targets the official converted LTX-2.5 repo."""
-    normalized_model = str(od_config.model).rstrip("/\\").replace("\\", "/").lower()
-    if normalized_model == LTX25_DIFFUSERS_MODEL_ID.lower():
-        return True
-
-    # A downloaded checkpoint may be served from any directory name. The
-    # duration-head component is the pipeline-level marker introduced for
-    # LTX-2.5; older LTX-2.0/2.3 indexes do not contain it.
-    model_path = Path(str(od_config.model)).expanduser()
-    subfolder = od_config.diffusers_load_kwargs.get("subfolder")
-    if subfolder is not None:
-        model_path /= str(subfolder)
-    try:
-        with (model_path / "model_index.json").open(encoding="utf-8") as model_index_file:
-            model_index = json.load(model_index_file)
-    except (OSError, TypeError, ValueError):
-        return False
-    if not isinstance(model_index, dict):
-        return False
-
-    duration_head = model_index.get("duration_head")
-    return (
-        model_index.get("_class_name") == "LTX2Pipeline"
-        and isinstance(duration_head, list)
-        and bool(duration_head)
-        and duration_head[0] is not None
-    )
-
 
 class BasePipelineUtils:
     """No-op hooks for pipeline-specific diffusers adapter behavior."""
@@ -90,112 +18,9 @@ class BasePipelineUtils:
     def apply_post_load_updates(self, pipeline: DiffusionPipeline, od_config: OmniDiffusionConfig) -> None:
         pass
 
-    def enable_vae_optimization(self, pipeline: DiffusionPipeline, optimization: str) -> None:
-        getattr(pipeline, f"enable_vae_{optimization}")()
-
     def validate_runtime_sampling_params(self, sampling: OmniDiffusionSamplingParams) -> None:
         pass
 
-    def update_call_kwargs(
-        self,
-        od_config: OmniDiffusionConfig,
-        call_kwargs: dict[str, Any],
-    ) -> None:
-        pass
-
-    def normalize_output(
-        self,
-        pipeline: DiffusionPipeline,
-        od_config: OmniDiffusionConfig,
-        output: Any,
-    ) -> Any:
-        return output
-
-
-def validate_model_compatibility(od_config: OmniDiffusionConfig) -> None:
-    """Reject known model/dependency combinations before loading weights."""
-    normalized_model = str(od_config.model).rstrip("/\\").replace("\\", "/").lower()
-    if normalized_model == LTX25_ORIGINAL_MODEL_ID.lower():
-        raise ValueError(
-            f"{LTX25_ORIGINAL_MODEL_ID} is a component-weight repository without a Diffusers "
-            f"model_index.json. Use the official converted repository {LTX25_DIFFUSERS_MODEL_ID} "
-            "with --diffusion-load-format diffusers."
-        )
-
-    if is_ltx25_diffusers_model(od_config) and not _supports_ltx25_diffusers():
-        raise ImportError(
-            f"{LTX25_DIFFUSERS_MODEL_ID} requires unreleased LTX-2.5 support from Diffusers and "
-            "Gemma 4 Unified support from Transformers. Install the source-preview dependencies with "
-            "`python -m pip install --upgrade 'transformers>=5.10.1' "
-            f"'diffusers @ git+https://github.com/huggingface/diffusers.git@{LTX25_DIFFUSERS_COMMIT}'`."
-        )
-
-
-class LTX2PipelineUtils(BasePipelineUtils):
-    """Diffusers call policy for the official default LTX-2.5 checkpoint."""
-
-    def enable_vae_optimization(self, pipeline: DiffusionPipeline, optimization: str) -> None:
-        pipeline_method = getattr(pipeline, f"enable_vae_{optimization}", None)
-        if callable(pipeline_method):
-            pipeline_method()
-        else:
-            getattr(pipeline.vae, f"enable_{optimization}")()
-
-    def update_call_kwargs(
-        self,
-        od_config: OmniDiffusionConfig,
-        call_kwargs: dict[str, Any],
-    ) -> None:
-        if not is_ltx25_diffusers_model(od_config):
-            return
-
-        has_sigmas = call_kwargs.get("sigmas") is not None
-        has_timesteps = call_kwargs.get("timesteps") is not None
-        has_step_count = call_kwargs.get("num_inference_steps") is not None
-        if not has_sigmas and not has_timesteps:
-            if has_step_count:
-                logger.warning(
-                    "LTX-2.5 distilled inference received num_inference_steps without sigmas. "
-                    "Diffusers will use a generic schedule; omit num_inference_steps to use the "
-                    "official distilled schedule."
-                )
-            else:
-                call_kwargs["sigmas"] = list(LTX25_DISTILLED_SIGMAS)
-                call_kwargs["num_inference_steps"] = len(LTX25_DISTILLED_SIGMAS)
-
-        for key, value in _LTX25_DISTILLED_GUIDANCE_DEFAULTS.items():
-            if call_kwargs.get(key) is None:
-                call_kwargs[key] = value
-
-    def normalize_output(
-        self,
-        pipeline: DiffusionPipeline,
-        od_config: OmniDiffusionConfig,
-        output: Any,
-    ) -> Any:
-        if not is_ltx25_diffusers_model(od_config):
-            return output
-
-        frames = getattr(output, "frames", None)
-        audio = getattr(output, "audio", None)
-        if frames is None or audio is None:
-            return output
-
-        envelope: dict[str, Any] = {"payload": {"video": frames, "audio": audio}}
-        config = getattr(getattr(pipeline, "vocoder", None), "config", None)
-        value = (
-            config.get("output_sampling_rate")
-            if hasattr(config, "get")
-            else getattr(config, "output_sampling_rate", None)
-        )
-        try:
-            sample_rate = None if isinstance(value, bool) else int(value)
-        except (TypeError, ValueError):
-            sample_rate = None
-        if sample_rate is not None and sample_rate > 0:
-            envelope["metadata"] = {"audio": {"sample_rate": sample_rate}}
-        return envelope
-
 
 class WanPipelineUtils(BasePipelineUtils):
     def update_load_kwargs(self, od_config: OmniDiffusionConfig, load_kwargs: dict[str, Any]) -> None:
@@ -224,7 +49,6 @@ class WanPipelineUtils(BasePipelineUtils):
 
 
 PIPELINE_UTILS_REGISTRY: dict[str, type[BasePipelineUtils]] = {
-    "LTX2Pipeline": LTX2PipelineUtils,
     "WanPipeline": WanPipelineUtils,
     "WanImageToVideoPipeline": WanPipelineUtils,
     "WanVACEPipeline": WanPipelineUtils,

vllm_omni/model_extras/__init__.py

@@ -9,6 +9,7 @@ from vllm_omni.model_extras.registry import (
     get_extra_body_params,
     get_extra_output_params,
     get_model_class_name,
+    get_output_tensor_range,
     get_x_to_text_model_family,
     should_init_extra_args_for_non_diffusion_stages,
 )
@@ -21,6 +22,7 @@ __all__ = [
     "get_extra_body_params",
     "get_extra_output_params",
     "get_model_class_name",
+    "get_output_tensor_range",
     "get_x_to_text_model_family",
     "should_init_extra_args_for_non_diffusion_stages",
 ]

vllm_omni/model_extras/registry.py

@@ -4,7 +4,7 @@
 from __future__ import annotations
 
 from collections.abc import Callable, Mapping
-from typing import Any
+from typing import Any, Literal
 
 from PIL import Image
 
@@ -93,6 +93,7 @@ ImageToVideoPromptBuilder = Callable[
     dict[str, Any],
 ]
 XToTextPromptBuilder = Callable[[str, str, bool], tuple[dict[str, Any], list[int] | None]]
+OutputTensorRange = Literal["negative_one_to_one", "zero_to_one"]
 
 
 def default_x_to_text_prompt(
@@ -193,6 +194,7 @@ _EXTRA_SPECS: dict[str, dict[str, Any]] = {
     },
     "LingBotVideoPipeline": {
         "extra_body_params": LINGBOT_VIDEO_EXTRA_BODY_PARAMS,
+        "output_tensor_range": "zero_to_one",
         # Shared T2I/I2V envelopes select the output modality. LingBot's
         # pipeline owns model-specific validation and normalization.
     },
@@ -262,6 +264,18 @@ def get_extra_output_params(model_class_name: str | None) -> frozenset[str]:
     return spec.get("extra_output_params", frozenset()) if spec is not None else frozenset()
 
 
+def get_output_tensor_range(model_class_name: str | None) -> OutputTensorRange:
+    """Return the declared range for floating-point tensor outputs.
+
+    The default preserves the shared examples' historical handling. Pipelines
+    that already return normalized tensors declare ``zero_to_one`` explicitly.
+    """
+    spec = _get_spec(model_class_name)
+    if spec is None:
+        return "negative_one_to_one"
+    return spec.get("output_tensor_range", "negative_one_to_one")
+
+
 def should_init_extra_args_for_non_diffusion_stages(model_class_name: str | None) -> bool:
     spec = _get_spec(model_class_name)
     return bool(spec and spec.get("init_extra_args_for_non_diffusion_stages", False))