[Model] Add LTX-2.5 Diffusers backend support

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

docs/user_guide/examples/online_serving/diffusers_pipeline_adapter.md

@@ -29,6 +29,10 @@ However, as we strive to ensure output similarity between vLLM-Omni's diffuser b
 - Tongyi-MAI/Z-Image-Turbo
 - Wan2.2-I2V-A14B-Diffusers
 
+LTX-2.5 is also supported through a source-preview dependency path described
+below. The adapter behavior is unit tested, but the gated full checkpoint is
+not part of the regular CI matrix.
+
 If you find that a model not listed above also produces different outputs from running diffusers model directly.
 Please consider file an issue.
 
@@ -116,6 +120,69 @@ 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)
+
+The repository linked from the LTX-2.5 release,
+[`Lightricks/LTX-2.5`](https://huggingface.co/Lightricks/LTX-2.5), contains
+component weights and does not have the `model_index.json` required by
+`DiffusionPipeline.from_pretrained()`. Use Lightricks' official converted
+repository, [`Lightricks/LTX-2.5-Diffusers`](https://huggingface.co/Lightricks/LTX-2.5-Diffusers),
+with this backend. Both repositories are gated; accept the model terms on
+Hugging Face and authenticate with a read token before starting the server.
+
+LTX-2.5 support landed after Diffusers 0.39.0 and is not in a stable Diffusers
+release yet. After installing vLLM-Omni, install the reviewed upstream commit
+and Gemma 4-capable Transformers version:
+
+```bash
+python -m pip install --upgrade 'transformers>=5.10.1' \
+  'diffusers @ git+https://github.com/huggingface/diffusers.git@7564fb016dabda0c943416190fc92398c50b1b20'
+hf auth login
+```
+
+Serve the default distilled text-to-video pipeline as follows. The model is
+large; CPU offload and VAE tiling reduce GPU memory pressure at the cost of
+latency and host memory.
+
+```bash
+vllm serve "Lightricks/LTX-2.5-Diffusers" \
+  --omni \
+  --port 8091 \
+  --diffusion-load-format diffusers \
+  --dtype bfloat16 \
+  --enable-cpu-offload \
+  --vae-use-tiling \
+  --default-sampling-params \
+    '{"0":{"height":512,"width":768,"num_frames":121,"fps":24,"frame_rate":24.0,"output_type":"np"}}'
+```
+
+Then request an MP4 with synchronized audio:
+
+```bash
+curl -X POST http://localhost:8091/v1/videos/sync \
+  -F 'prompt=A red fox walks through a snowy forest at dawn while birds sing in the distance.' \
+  -F 'size=768x512' \
+  -F 'num_frames=121' \
+  -F 'fps=24' \
+  -F 'seed=42' \
+  -o ltx-2.5.mp4
+```
+
+The converted repository selects the distilled transformer by default. When a
+request supplies neither `sigmas`, `timesteps`, nor `num_inference_steps`, the
+adapter uses the official eight-sigma distilled schedule and disables CFG,
+spatio-temporal guidance, and modality guidance. An explicit schedule or step
+count takes precedence; a step count without `sigmas` uses Diffusers' generic
+schedule and may reduce quality. The adapter preserves both `frames` and the
+pipeline's singular `audio` output and reads the audio sample rate from the
+loaded vocoder (48 kHz for this checkpoint).
+
+This path currently covers the default one-stage distilled T2V pipeline.
+Loading `transformer_full`, two-stage upsampling, image/video conditioning, and
+the diffusion video decoder require additional pipeline assembly and are not
+enabled by this example. Native vLLM-Omni acceleration features listed under
+[Limitations](#limitations) remain unavailable with the Diffusers backend.
+
 #### 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.