Merge branch 'main' into codex-contributor-agent-skills-docs

[Perf][TTS] step_audio2: keep streaming tokens on-device and batch audio-feature length syncs (#5067 )
Signed-off-by: JuanPZuluaga <juanz9312@gmail.com> Co-authored-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>
2026-08-11 12:59:11 +08:00 · 2026-08-10 21:24:25 -07:00 · 2026-08-10 21:03:32 -07:00 · 2026-08-11 12:00:27 +08:00 · 2026-08-10 23:00:18 -04:00 · 2026-08-11 05:10:50 +08:00
132 changed files with 8240 additions and 2267 deletions
@@ -404,7 +404,7 @@ steps:
      - label: ":full_moon: Diffusion X2V · LingBot Video MoE Function Test"
        timeout_in_minutes: 90
        commands:
-          - pytest -s -v tests/e2e/online_serving/test_lingbot_video_moe.py -m "full_model and cuda" --run-level "full_model"
+          - pytest -s -v tests/e2e/online_serving/test_lingbot_video_moe.py -k "video_generation" -m "full_model and cuda" --run-level "full_model"
        mirror_hardwares: h100_1

      - label: ":full_moon: Diffusion X2V · Doc Test"
@@ -1,9 +1,9 @@
-# Claude Skills for vLLM-Omni
+# Repository Skills for vLLM-Omni

-This directory contains Claude Code skills maintained for the `vllm-omni`
-repository. These skills capture repeatable workflows for common contributor
-tasks such as model integration, pull request review, and release note
-generation.
+This directory contains repository-scale skills maintained for `vllm-omni`.
+They capture repeatable workflows for common contributor and maintainer tasks
+such as model integration, CI-aligned testing, performance optimization, and
+pull request review.

 ## Directory Structure

@@ -14,31 +14,41 @@ include:
 - `references/`: focused reference material used by the skill
 - `scripts/`: small helper scripts used by the skill

+## Using the Skills
+
+Coding agents that discover repository skills can invoke the relevant skill by
+name. For other agents, point them to the skill's `SKILL.md` and ask them to
+read it before changing code. Combine a domain skill with `vllm-omni-test` when
+tests or CI coverage change, then use `precheck-pr` for the contributor's final
+self-check.
+
 ## Available Skills

- `add-diffusion-model`: guides integration of a new diffusion model into
-  `vllm-omni`
- `diffusion-perf-opt`: guides diffusion model performance optimization,
-  including profiling traces, parallel strategies, stage timing analysis, and
-  benchmark-driven tuning
- `quantization`: guides quantization method selection, model integration,
-  checkpoint loading, and quality/performance validation for vLLM-Omni
- `add-omni-model`: covers addition of new omni-modality model support
- `add-tts-model`: covers integration of new TTS models and related serving
-  workflows
- `generate-release-note`: helps prepare release notes for repository changes
- `precheck-pr`: self-check a branch before creating a PR — validates PR title
-  format, catches dead code, verifies accuracy/perf claims, and confirms merge
-  readiness
- `vllm-omni-test`: guides generation and execution of CI-aligned tests (L1–L4),
-  pytest marker selection (`core_model` / `advanced_model` / `full_model`,
-  `omni` / `tts` / `diffusion`), Buildkite wiring (`test-ready.yml`,
-  `test-merge.yml`, `test-nightly.yml`, `test-weekly.yml`), and copy-paste
-  local plus CI-like `pytest` commands; see `references/test-routing.md` for
-  level-to-command mapping
- `review-pr`: provides a structured workflow for reviewing pull requests
- `vllm-omni-npu-model-runner-upgrade`: upgrades NPU model runners to align with the
-  latest vllm-ascend NPUModelRunner
+- [`add-diffusion-model`](add-diffusion-model/SKILL.md): guides integration of
+  a new diffusion model into `vllm-omni`
+- [`add-tts-model`](add-tts-model/SKILL.md): covers integration of new TTS
+  models and related serving workflows
+- [`diffusion-perf-opt`](diffusion-perf-opt/SKILL.md): guides diffusion model
+  performance optimization, including profiling traces, parallel strategies,
+  stage timing analysis, and benchmark-driven tuning
+- [`precheck-pr`](precheck-pr/SKILL.md): self-checks a branch before creating a
+  PR by validating title format, dead code, accuracy and performance claims,
+  and merge readiness
+- [`quantization`](quantization/SKILL.md): guides quantization method selection,
+  model integration, checkpoint loading, and quality/performance validation
+  for vLLM-Omni
+- [`review-pr`](review-pr/SKILL.md): provides a frozen-snapshot, contract-aware
+  workflow for maintainers and reviewers; contributors should use
+  `precheck-pr` for self-review
+- [`vllm-omni-npu-model-runner-upgrade`](vllm-omni-npu-upgrade/SKILL.md):
+  upgrades NPU model runners to align with the latest vllm-ascend
+  `NPUModelRunner`
+- [`vllm-omni-test`](vllm-omni-test/SKILL.md): guides generation and execution
+  of CI-aligned tests (L1–L4), pytest marker selection (`core_model` /
+  `advanced_model` / `full_model`, `omni` / `tts` / `diffusion`), Buildkite
+  wiring (`test-ready.yml`, `test-merge.yml`, `test-nightly.yml`,
+  `test-weekly.yml`), and copy-paste local plus CI-like `pytest` commands; see
+  `references/test-routing.md` for level-to-command mapping

 ## Maintenance Guidelines

@@ -1,6 +1,11 @@
 # CODEOWNERS is used to automatically assign PR reviewers
 # Format: path pattern @username or @team
 # More info: https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-code-owners
+#
+# The vllm_omni/** source-tree rules below are derived from the `owners` and
+# `primary_code_paths` front matter in docs/design/module/*.md. When updating
+# ownership, update the design doc first, then regenerate the matching rule here.
+# Last-match-wins: more specific rules must come AFTER broader ones.

 # Default owners (fallback reviewers for all files)
 * @Gaohan123 @hsliuustc0106 @ywang96
@@ -21,25 +26,10 @@
 # CI
 /.buildkite/ @yenuo26 @congw729 @NickCao

-# Engine and model_executor
-/vllm_omni/core/ @tzhouam @yinpeiqi @fake0fan @Sy0307
-/vllm_omni/engine/ @tzhouam @yinpeiqi @fake0fan @Sy0307
-/vllm_omni/entrypoints/ @tzhouam @yinpeiqi @fake0fan @alex-jw-brooks @NickCao
-/vllm_omni/model_executor/ @tzhouam @gcanlin
-/vllm_omni/worker/ @tzhouam @gcanlin
+# ComfyUI / external apps
+/apps/ @fhfuih @alex-jw-brooks

-# Diffusion
-/vllm_omni/diffusion/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @david6666666 @xuechendi
-/vllm_omni/lora/ @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @yuanheng-zhao
-
-# Diffusion CPU offloading
-/vllm_omni/diffusion/offloader/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @yuanheng-zhao @xuechendi
-
-# Omni and TTS support
-/vllm_omni/transformers_utils/ @tzhouam @yuanheng-zhao
-/vllm_omni/model_executor/models/ @linyueqian @ZeldaHuang @princepride @yuanheng-zhao @amy-why-3459 @Bounty-hunter @Sy0307 @gcanlin @NickCao
-
-# Benchmarks
+# Benchmarks (out-of-tree)
 /benchmarks/accuracy/ @david6666666
 /benchmarks/build_dataset/ @linyueqian @yuanheng-zhao
 /benchmarks/diffusion/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @xuechendi
@@ -47,20 +37,112 @@
 /benchmarks/glm_image/ @princepride @JaredforReal
 /benchmarks/tts/ @linyueqian @ZeldaHuang @princepride @Sy0307 @gcanlin

-# Usability and Observability
-/vllm_omni/deploy/ @tzhouam @lishunyang12
-/vllm_omni/metrics/ @lishunyang12
-/vllm_omni/profiler/ @gcanlin
+# =====================================================================
+# Module design ownership (docs/design/module/*.md)
+# Each section header cites the source design doc.
+# =====================================================================

-# ComfyUI
-/apps/ @fhfuih @alex-jw-brooks
+# Autoregressive runtime — docs/design/module/ar_runtime.md
+/vllm_omni/core/ @tzhouam @yinpeiqi @fake0fan @Sy0307 @Gaohan123
+/vllm_omni/worker/ @tzhouam @yinpeiqi @fake0fan @Sy0307 @Gaohan123

-# Large Scale Serving
+# Engine orchestration & stage runtime — docs/design/module/engine_orchestration.md, stage_runtime.md
+/vllm_omni/engine/ @tzhouam @fake0fan
+# File-level overrides: input/output modality contracts own these engine files
+# — docs/design/module/input_output_modality_contracts.md
+/vllm_omni/engine/messages.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
+/vllm_omni/engine/serialization.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
+/vllm_omni/engine/mm_outputs.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
+/vllm_omni/engine/output_modality.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
+/vllm_omni/engine/output_processor.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
+
+# Input/output modality contracts — docs/design/module/input_output_modality_contracts.md
+/vllm_omni/inputs/ @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
+/vllm_omni/outputs/ @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
+/vllm_omni/request.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
+/vllm_omni/data_entry_keys.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
+
+# Entrypoints and serving boundaries — docs/design/module/entrypoints.md
+/vllm_omni/entrypoints/ @alex-jw-brooks @linyueqian @NickCao
+# File-level overrides within entrypoints/
+/vllm_omni/entrypoints/openai/errors.py @alex-jw-brooks @NickCao
+/vllm_omni/entrypoints/cli/benchmark/ @alex-jw-brooks @Bounty-hunter
+
+# Error contracts — docs/design/module/error_contracts.md
+/vllm_omni/errors.py @alex-jw-brooks @NickCao
+
+# OmniConnector (inter-stage transport) — docs/design/module/omni_connector.md
 /vllm_omni/distributed/ @princepride @yuanheng-zhao @xuechendi @natureofnature @fake0fan

-# Quantization and configuration
+# Model integration — docs/design/module/model_integration.md
+/vllm_omni/model_executor/ @tzhouam @gcanlin
+/vllm_omni/model_executor/models/ @tzhouam @gcanlin @Sy0307 @amy-why-3459 @linyueqian
+/vllm_omni/model_extras/ @tzhouam @gcanlin @princepride
+/vllm_omni/plugins/ @gcanlin @tjtanaa @xuechendi
+# Config override within model_executor — docs/design/module/vllm_omni_config.md
+/vllm_omni/model_executor/stage_configs/ @lishunyang12 @alex-jw-brooks
+
+# Execution platforms (hardware backends) — docs/design/module/execution_platforms.md
+/vllm_omni/platforms/ @gcanlin @tjtanaa @xuechendi
+/vllm_omni/attention/ @gcanlin @tjtanaa @xuechendi @david6666666 @lishunyang12
+
+# Configuration & deploy — docs/design/module/vllm_omni_config.md
 /vllm_omni/config/ @lishunyang12 @alex-jw-brooks
+/vllm_omni/deploy/ @lishunyang12 @alex-jw-brooks
+
+# Quantization — docs/design/module/quantization.md
 /vllm_omni/quantization/ @david6666666 @Isotr0py @lishunyang12

-# Hardware plugins and accelerators
-/vllm_omni/platforms/ @gcanlin @tjtanaa @xuechendi
+# Observability — docs/design/module/observability.md
+/vllm_omni/metrics/ @lishunyang12 @vraiti
+
+# Profiling — docs/design/module/profiling.md
+/vllm_omni/profiler/ @gcanlin
+
+# Benchmarking (in-tree) — docs/design/module/benchmarking.md
+/vllm_omni/benchmarks/ @alex-jw-brooks @Bounty-hunter
+
+# LoRA (no dedicated design doc; ownership intentionally narrowed)
+/vllm_omni/lora/ @SamitHuang @wtomin
+
+# Transformers utils (related path only in design docs; preserves prior ownership)
+/vllm_omni/transformers_utils/ @tzhouam @yuanheng-zhao
+
+# =====================================================================
+# Diffusion module family — docs/design/module/diffusion/
+# Umbrella rule first; sub-module rules after it win via last-match.
+# =====================================================================
+
+# Diffusion umbrella (top-level files: data.py, forward_context.py, etc.)
+# — docs/design/module/diffusion/index.md
+/vllm_omni/diffusion/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @david6666666 @xuechendi
+
+# Cache management — docs/design/module/cache_management.md
+/vllm_omni/diffusion/cache/ @Isotr0py @princepride @SamitHuang
+
+# Diffusion quantization — docs/design/module/quantization.md
+/vllm_omni/diffusion/quantization/ @david6666666 @Isotr0py @lishunyang12
+
+# Diffusion profiling — docs/design/module/profiling.md
+/vllm_omni/diffusion/profiler/ @gcanlin
+
+# Diffusion runtime (scheduler, executor, worker)
+# — docs/design/module/diffusion/diffusion_runtime.md, continuous_batching.md
+/vllm_omni/diffusion/sched/ @Isotr0py @princepride @SamitHuang @fhfuih
+/vllm_omni/diffusion/executor/ @Isotr0py @princepride @SamitHuang @fhfuih
+/vllm_omni/diffusion/worker/ @Isotr0py @princepride @SamitHuang @fhfuih
+
+# Diffusion model integration (pipelines, registration, checkpoint loading)
+# — docs/design/module/diffusion/diffusion_model_integration.md
+/vllm_omni/diffusion/models/ @fhfuih @Bounty-hunter @wtomin
+/vllm_omni/diffusion/model_loader/ @fhfuih @Bounty-hunter @wtomin @RuixiangMa
+/vllm_omni/diffusion/lora/ @fhfuih @Bounty-hunter @wtomin @RuixiangMa
+
+# Diffusion parallelism (rank topology, TP/SP/PP/VAE patch)
+# — docs/design/module/diffusion/parallelism.md
+/vllm_omni/diffusion/distributed/ @princepride @yuanheng-zhao @xuechendi @Bounty-hunter
+/vllm_omni/diffusion/attention/parallel/ @princepride @yuanheng-zhao @xuechendi @Bounty-hunter
+
+# Diffusion offloader (CPU offloading, DLO)
+# — docs/design/module/diffusion/offloader.md
+/vllm_omni/diffusion/offloader/ @yuanheng-zhao @david6666666 @lishunyang12
@@ -66,7 +66,10 @@ repos:
      entry: python tools/pre_commit/check_tts_adapter.py
      language: python
      pass_filenames: false
-      files: ^vllm_omni/entrypoints/openai/(serving_speech\.py|tts_adapters/__init__\.py)$
+      files: ^(vllm_omni/entrypoints/openai/(serving_speech\.py|tts_adapters/__init__\.py)|tools/pre_commit/check_tts_adapter\.py)$
+      # The budget-is-now-slack reminder is printed on a PASSING run, and
+      # pre-commit hides output from passing hooks unless the hook is verbose.
+      verbose: true

    # Keep `suggestion` last
    - id: suggestion
@@ -16,31 +16,21 @@ nav:
  - Examples:
    - examples/README.md
    - Offline Inference:
-      - GLM-Image Multistage End-to-End Inference: user_guide/examples/offline_inference/glm_image.md
-      - HunyuanImage-3.0 Image-to-Text Inference: user_guide/examples/offline_inference/hunyuan_image3.md
      - Image-To-Image: user_guide/examples/offline_inference/image_to_image.md
      - Image-To-Video: user_guide/examples/offline_inference/image_to_video.md
-      - InternVLA-A1: user_guide/examples/offline_inference/internvla_a1.md
-      - MiMo-Audio: user_guide/examples/offline_inference/mimo_audio.md
-      - Qwen2.5-Omni: user_guide/examples/offline_inference/qwen2_5_omni.md
-      - Qwen3-Omni: user_guide/examples/offline_inference/qwen3_omni.md
+      - Speech-To-Video: user_guide/examples/offline_inference/speech_to_video.md
      - Text-To-Audio: user_guide/examples/offline_inference/text_to_audio.md
      - Text-To-Image: user_guide/examples/offline_inference/text_to_image.md
      - Text-To-Speech: user_guide/examples/offline_inference/text_to_speech.md
      - Text-To-Video: user_guide/examples/offline_inference/text_to_video.md
+      - X-To-Text: user_guide/examples/offline_inference/x_to_text.md
      - X-To-Video-Audio: user_guide/examples/offline_inference/x_to_video_audio.md
    - Online Serving:
-      - BAGEL-7B-MoT: user_guide/examples/online_serving/bagel.md
-      - vLLM-Omni Helm Chart: user_guide/examples/online_serving/chart-helm.md
-      - Diffusers Backend Adapter: user_guide/examples/online_serving/diffusers_pipeline_adapter.md
-      - GLM-Image Online Serving: user_guide/examples/online_serving/glm_image.md
      - Image-To-Image: user_guide/examples/online_serving/image_to_image.md
      - Image-To-Video: user_guide/examples/online_serving/image_to_video.md
-      - MiMo-Audio: user_guide/examples/online_serving/mimo_audio.md
-      - Qwen2.5-Omni: user_guide/examples/online_serving/qwen2_5_omni.md
-      - Qwen3-Omni: user_guide/examples/online_serving/qwen3_omni.md
-      - Text-To-Speech: user_guide/examples/online_serving/text_to_speech.md
+      - Speech-To-Video: user_guide/examples/online_serving/speech_to_video.md
      - Text-To-Image: user_guide/examples/online_serving/text_to_image.md
+      - Text-To-Speech: user_guide/examples/online_serving/text_to_speech.md
      - Text-To-Video: user_guide/examples/online_serving/text_to_video.md
  - General:
    - usage/*
@@ -76,6 +76,47 @@ Please refer to the [test instructions](./ci/test_execution_guide.md) for detail
 !!! warning
    Currently, not all unit tests pass when run on CPU platforms. If you don't have access to a GPU platform to run unit tests locally, rely on the continuous integration system to run the tests for now.

+### Using repository skills with coding agents
+
+vLLM-Omni maintains [repository-scale skills](https://github.com/vllm-project/vllm-omni/tree/main/.claude/skills) for common coding, testing, performance, and review workflows. These skills capture repository-specific structure, validation requirements, and evidence standards that a general coding agent may not know.
+
+If your coding agent discovers repository skills automatically, ask it to use the relevant skill by name. Otherwise, point it to the linked `SKILL.md` and ask it to read the file before changing code. Skills can be combined: use a domain skill for the implementation, `vllm-omni-test` for test and CI decisions, and `precheck-pr` before opening the pull request.
+
+| Task | Repository skill |
+| --- | --- |
+| Add or extend a diffusion model | [`add-diffusion-model`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/add-diffusion-model/SKILL.md) |
+| Add or extend a text-to-speech model | [`add-tts-model`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/add-tts-model/SKILL.md) |
+| Add, select, or debug quantization | [`quantization`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/quantization/SKILL.md) |
+| Diagnose or optimize diffusion performance | [`diffusion-perf-opt`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/diffusion-perf-opt/SKILL.md) |
+| Upgrade an NPU model runner | [`vllm-omni-npu-model-runner-upgrade`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/vllm-omni-npu-upgrade/SKILL.md) |
+| Add a regression test, choose L1-L4 coverage, or wire Buildkite | [`vllm-omni-test`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/vllm-omni-test/SKILL.md) |
+| Self-check a branch before opening a pull request | [`precheck-pr`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/precheck-pr/SKILL.md) |
+| Perform a maintainer or reviewer review of an existing pull request or local branch | [`review-pr`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/review-pr/SKILL.md) |
+
+Use the following workflow when collaborating with a coding agent:
+
+1. **Ground the task.** Provide the issue, RFC, or design document that defines the expected behavior. Ask the agent to state its assumptions and identify the affected module or feature contracts before editing code.
+2. **Select the narrowest applicable skills.** Start with the domain skill that owns the change. Add `vllm-omni-test` whenever production behavior or tests change. For a bug fix, ask for the smallest reproducer and a regression test tied to the original symptom.
+3. **Require executable evidence.** Ask the agent to run the narrowest relevant checks and report the exact commands and results. It must identify tests it could not run because of unavailable hardware, model weights, credentials, or dependencies; an unrun check is not a pass.
+4. **Inspect the resulting diff.** Confirm that the implementation stays within the issue or RFC, does not overwrite unrelated work, and includes the required tests, documentation, and benchmark or accuracy evidence.
+5. **Run the pre-submit workflow.** Use `precheck-pr`, then run the applicable local tests and pre-commit hooks before opening the pull request.
+
+For example:
+
+```text
+Use add-diffusion-model and vllm-omni-test to implement the linked issue.
+Read the relevant design documents first, keep the change within the issue,
+and report every test command, result, and hardware validation gap.
+```
+
+```text
+Use vllm-omni-test to turn this bug reproducer into the smallest deterministic
+regression test. Prefer L1 CPU coverage; if the failure requires real weights
+or serving, explain the required L2/L3 environment and provide the exact command.
+```
+
+Repository skills guide the agent, but they do not replace contributor judgment, the accepted issue or RFC, required test evidence, or maintainer review. Review all generated changes before submitting them.
+
 ## Issues

 If you encounter a bug or have a feature request, please search existing issues first to see if it has already been reported. If not, please file a new issue, providing as much relevant information as possible.
@@ -7,6 +7,7 @@ owners:
  - "@yinpeiqi"
  - "@fake0fan"
  - "@Sy0307"
+  - "@Gaohan123"
 primary_code_paths:
  - vllm_omni/core/**
  - vllm_omni/worker/**
@@ -4,6 +4,7 @@ kind: module
 status: draft
 owners:
  - "@alex-jw-brooks"
+  - "@Bounty-hunter"
 primary_code_paths:
  - vllm_omni/benchmarks/**
  - vllm_omni/entrypoints/cli/benchmark/**
@@ -5,6 +5,7 @@ status: draft
 owners:
  - "@Isotr0py"
  - "@princepride"
+  - "@SamitHuang"
 primary_code_paths:
  - vllm_omni/diffusion/cache/**
 related_code_paths:
@@ -3,9 +3,10 @@ title: Diffusion Model Integration
 kind: module
 status: draft
 owners:
-  - "@Isotr0py"
+  - "@fhfuih"
+  - "@Bounty-hunter"
  - "@wtomin"
-  - "@ZJY0516"
+  - "@RuixiangMa"
 primary_code_paths:
  - vllm_omni/diffusion/models/**
  - vllm_omni/diffusion/model_loader/**
@@ -6,6 +6,7 @@ owners:
  - "@Isotr0py"
  - "@princepride"
  - "@SamitHuang"
+  - "@fhfuih"
 primary_code_paths:
  - vllm_omni/diffusion/sched/**
  - vllm_omni/diffusion/executor/**
@@ -4,7 +4,8 @@ kind: module
 status: draft
 owners:
  - "@yuanheng-zhao"
-  - "@Isotr0py"
+  - "@david6666666"
+  - "@lishunyang12"
 primary_code_paths:
  - vllm_omni/diffusion/offloader/**
 related_code_paths:
@@ -6,6 +6,7 @@ owners:
  - "@princepride"
  - "@yuanheng-zhao"
  - "@xuechendi"
+  - "@Bounty-hunter"
 primary_code_paths:
  - vllm_omni/diffusion/distributed/**
  - vllm_omni/diffusion/attention/parallel/**
@@ -6,6 +6,7 @@ architecture_state: current-plus-in-flight
 owners:
  - "@alex-jw-brooks"
  - "@linyueqian"
+  - "@NickCao"
 document_stewards:
  - "@hsliuustc0106"
  - "@Gaohan123"
@@ -8,8 +8,15 @@ owners:
  - "@xuechendi"
 primary_code_paths:
  - vllm_omni/platforms/**
-related_code_paths:
  - vllm_omni/attention/**
+primary_code_path_owners:
+  # Additional owners scoped to attention kernels (quantized/custom attention)
+  - paths:
+      - vllm_omni/attention/**
+    owners:
+      - "@david6666666"
+      - "@lishunyang12"
+related_code_paths: []
 depends_on:
  - ar_runtime.md
  - diffusion/index.md
@@ -6,6 +6,8 @@ architecture_state: current
 owners:
  - "@Sy0307"
  - "@amy-why-3459"
+  - "@Gaohan123"
+  - "@alex-jw-brooks"
 document_stewards:
  - "@hsliuustc0106"
  - "@Gaohan123"
@@ -9,6 +9,14 @@ primary_code_paths:
  - vllm_omni/model_executor/**
  - vllm_omni/model_extras/**
  - vllm_omni/plugins/**
+primary_code_path_owners:
+  # Additional owners scoped to a sub-path of model_executor/**
+  - paths:
+      - vllm_omni/model_executor/models/**
+    owners:
+      - "@Sy0307"
+      - "@amy-why-3459"
+      - "@linyueqian"
 related_code_paths:
  - vllm_omni/transformers_utils/**
  - vllm_omni/tokenizers/**
@@ -4,6 +4,7 @@ kind: module
 status: draft
 owners:
  - "@lishunyang12"
+  - "@vraiti"
 primary_code_paths:
  - vllm_omni/metrics/**
 related_code_paths:
@@ -123,6 +123,7 @@ def extract_omni_serve_subparser_init():
                        "TrackingNamespace": None,  # type annotation only
                        "make_arg_parser": lambda parser: parser,  # no-op for doc
                        "_ensure_vllm_platform": lambda: None,  # no-op for doc
+                        "_nonneg_finite_float": float,
                        "VLLM_SUBCMD_PARSER_EPILOG": "",
                        "logger": logger,
                        "DummySubparsers": DummySubparsers,
@@ -24,6 +24,23 @@ SERVING_MODE_TITLES = {
    "online_serving": "Online serving",
 }

+# Keep the Examples navigation focused on reusable modality/task entry points.
+# Model-specific scripts remain available under ``examples/`` and are surfaced
+# from these shared pages instead of getting one page per model.
+GENERAL_EXAMPLE_SLUGS = frozenset(
+    {
+        "image_to_image",
+        "image_to_video",
+        "speech_to_video",
+        "text_to_audio",
+        "text_to_image",
+        "text_to_speech",
+        "text_to_video",
+        "x_to_text",
+        "x_to_video_audio",
+    }
+)
+

 def load_model_display_names() -> dict[str, str]:
    try:
@@ -46,6 +63,13 @@ def load_model_display_names() -> dict[str, str]:
 MODEL_DISPLAY_NAMES = load_model_display_names()


+def is_general_example(example: "Example") -> bool:
+    """Return whether an example should get a generated serving page."""
+    if example.category not in SERVING_MODE_TITLES:
+        return True
+    return example.path.stem in GENERAL_EXAMPLE_SLUGS
+
+
 def fix_case(text: str) -> str:
    subs = {
        "api": "API",
@@ -340,22 +364,21 @@ def update_nav_file(examples: list[Example]):
            examples_by_category[category] = []
        examples_by_category[category].append(example)

-    # Build the new Examples section - start with preserved items
-    examples_section = preserved_items.copy()
-
-    # Add examples grouped by category, sorted by category name
-    for category in sorted(examples_by_category.keys()):
-        category_examples = sorted(examples_by_category[category], key=lambda e: e.path.stem)
-        category_items = []
-        for example in category_examples:
+    def category_items(category: str) -> list[dict[str, str]]:
+        items = []
+        for example in sorted(examples_by_category[category], key=lambda e: e.path.stem):
            doc_path = EXAMPLE_DOC_DIR / example.category / f"{example.path.stem}.md"
            rel_path = doc_path.relative_to(ROOT_DIR / "docs")
-            category_items.append({example.nav_title: rel_path.as_posix()})
+            items.append({example.nav_title: rel_path.as_posix()})
+        return items

-        if category_items:
-            # Format category name (e.g., "offline_inference" -> "Offline Inference")
+    # Build the new Examples section - start with preserved items.
+    examples_section = preserved_items.copy()
+    for category in sorted(examples_by_category.keys()):
+        items = category_items(category)
+        if items:
            category_title = fix_case(category.replace("_", " ").title())
-            examples_section.append({category_title: category_items})
+            examples_section.append({category_title: items})

    # Update the nav structure
    nav_list[user_guide_idx]["User Guide"][examples_idx]["Examples"] = examples_section
@@ -390,6 +413,8 @@ def on_startup(command: Literal["build", "gh-deploy", "serve"], dirty: bool):
        for path in category.glob("*/*.md"):
            examples.append(Example(path.parent, category.stem))

+    examples = [example for example in examples if is_general_example(example)]
+
    # Generate the example documentation
    for example in sorted(examples, key=lambda e: e.path.stem):
        example_name = f"{example.path.stem}.md"
@@ -17,84 +17,93 @@ th {
 }
 </style>

-| Architecture | Models | Example HF Models | NVIDIA GPU | AMD GPU | Ascend NPU | Intel GPU |
-|--------------|--------|-------------------|------------|---------|-----|-----------|
-| `Qwen3OmniMoeForConditionalGeneration` | Qwen3-Omni | `Qwen/Qwen3-Omni-30B-A3B-Instruct` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `Qwen2_5OmniForConditionalGeneration` | Qwen2.5-Omni | `Qwen/Qwen2.5-Omni-7B`, `Qwen/Qwen2.5-Omni-3B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `MingFlashOmniForConditionalGeneration` + `MingImagePipeline` | Ming-flash-omni-2.0 (omni-speech + imagegen<sup>1</sup>) | `Jonathan1909/Ming-flash-omni-2.0` | ✅︎ |   |   |   |
-| `BagelForConditionalGeneration` | BAGEL (DiT-only) | `ByteDance-Seed/BAGEL-7B-MoT` | ✅︎ | ✅︎ | | ✅︎ |
-| `InternVLAA1Pipeline` | InternVLA-A1 | `InternRobotics/InternVLA-A1-3B` | ✅︎ | ✅︎ | | |
-| `Gr00tN1d7Pipeline` | GR00T N1.7 | `nvidia/GR00T-N1.7-3B` | ✅︎ | | | |
-| `HunyuanImage3ForCausalMM` | HunyuanImage3.0 (DiT-only) | `tencent/HunyuanImage-3.0`, `tencent/HunyuanImage-3.0-Instruct` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `QwenImagePipeline` | Qwen-Image | `Qwen/Qwen-Image` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `QwenImagePipeline` | Qwen-Image-2512 | `Qwen/Qwen-Image-2512` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `QwenImageEditPipeline` | Qwen-Image-Edit | `Qwen/Qwen-Image-Edit` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2509 | `Qwen/Qwen-Image-Edit-2509` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `QwenImageLayeredPipeline` | Qwen-Image-Layered | `Qwen/Qwen-Image-Layered` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2511 | `Qwen/Qwen-Image-Edit-2511` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `GlmImagePipeline` | GLM-Image | `zai-org/GLM-Image` | ✅︎ | ✅︎ | | |
-| `ZImagePipeline` | Z-Image | `Tongyi-MAI/Z-Image-Turbo` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `Krea2Pipeline` | Krea 2 (Raw + Turbo) | `krea/Krea-2-Raw`, `krea/Krea-2-Turbo` | ✅︎ | | | |
-| `WanPipeline` | Wan2.1-T2V, Wan2.2-T2V, Wan2.2-TI2V | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers`, `Wan-AI/Wan2.1-T2V-14B-Diffusers`, `Wan-AI/Wan2.2-T2V-A14B-Diffusers`, `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `WanImageToVideoPipeline` | Wan2.2-I2V | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `Cosmos3OmniDiffusersPipeline` | Cosmos3 T2I, T2V, I2V, V2V, T2V with sound, action policy | `nvidia/Cosmos3-Nano`, `nvidia/Cosmos3-Super` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `Wan22S2VPipeline` | Wan2.2-S2V | `Wan-AI/Wan2.2-S2V-14B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `Wan22VACEPipeline` | Wan2.1-VACE | `Wan-AI/Wan2.1-VACE-1.3B-diffusers`, `Wan-AI/Wan2.1-VACE-14B-diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `Wan22VACEPipeline` | Wan2.2-VACE | `Pyros13/Wan2.2-VACE-Fun-A14B-Diffusers` | ✅︎ |   |   |   |
-| `LTX2Pipeline` | LTX-2 / LTX-2.3 one-stage T2V and I2V | `Lightricks/LTX-2`, `diffusers/LTX-2.3-Diffusers` | ✅︎ | ✅︎ | | |
-| `LTX2DistilledPipeline` | LTX-2 distilled two-stage T2V and I2V | `rootonchair/LTX-2-19b-distilled` | ✅︎ | ✅︎ | | |
-| `LingBotVideoPipeline` | LingBot-Video dense and MoE T2V | `robbyant/lingbot-video-dense-1.3b`, `robbyant/lingbot-video-moe-30b-a3b` | ✅︎ | | | |
-| `MiniMaxH3Pipeline` | MiniMax H3 T2VA, first/last-keyframe FL2VA, and mixed-reference Ref2VA | `MiniMaxAI/MiniMax-H3` | ✅︎ | | | |
-| `DreamZeroPipeline` | DreamZero-DROID | `GEAR-Dreams/DreamZero-DROID` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `HeliosPipeline`, `HeliosPyramidPipeline` | Helios | `BestWishYsh/Helios-Base`, `BestWishYsh/Helios-Mid`, `BestWishYsh/Helios-Distilled` | ✅︎ | ✅︎ | ✅︎ | |
-| `MagiHumanPipeline` | MagiHuman | `SII-GAIR/daVinci-MagiHuman-Base-1080p` | ✅︎ | ✅︎ | | |
-| `OvisImagePipeline` | Ovis-Image | `OvisAI/Ovis-Image` | ✅︎ | ✅︎ | | ✅︎ |
-| `LongcatImagePipeline` | LongCat-Image | `meituan-longcat/LongCat-Image` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `LongCatImageEditPipeline` | LongCat-Image-Edit | `meituan-longcat/LongCat-Image-Edit` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `BooguImagePipeline` | Boogu-Image | `Boogu/Boogu-Image-0.1-Base`, `Boogu/Boogu-Image-0.1-Edit` | ✅︎ | | | |
-| `StableDiffusionXLPipeline` | Stable-Diffusion-XL | `stabilityai/stable-diffusion-xl-base-1.0` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `StableDiffusion3Pipeline` | Stable-Diffusion-3 | `stabilityai/stable-diffusion-3.5-medium` | ✅︎ | ✅︎ | | ✅︎ |
-| `CosyVoice3Model` | CosyVoice3 | `FunAudioLLM/Fun-CosyVoice3-0.5B-2512` | ✅︎ | ✅︎ | | ✅︎ |
-| `OmniVoiceModel` | OmniVoice | `k2-fsa/OmniVoice` | ✅︎ | | | |
-| `VoxCPM2TalkerForConditionalGeneration` | VoxCPM2 | `openbmb/VoxCPM2` | ✅︎ | | | |
-| `MammothModa2ForConditionalGeneration` | MammothModa2-Preview | `bytedance-research/MammothModa2-Preview` | ✅︎ | ✅︎ | | |
-| `MammothModa2ForConditionalGeneration` | MammothModa2-Dev (AR-only image understanding) | `bytedance-research/MammothModa2-Dev` | ✅︎ | | | |
-| `Flux2KleinPipeline` | FLUX.2-klein | `black-forest-labs/FLUX.2-klein-4B`, `black-forest-labs/FLUX.2-klein-9B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `FluxKontextPipeline` | FLUX.1-Kontext-dev | `black-forest-labs/FLUX.1-Kontext-dev` | ✅︎ | ✅︎ | | |
-| `FluxPipeline` | FLUX.1-dev | `black-forest-labs/FLUX.1-dev` | ✅︎ | ✅︎ | | ✅︎ |
-| `FluxPipeline` | FLUX.1-schnell | `black-forest-labs/FLUX.1-schnell` | ✅︎ | ✅︎ | | ✅︎ |
-| `OmniGen2Pipeline` | OmniGen2 | `OmniGen2/OmniGen2` | ✅︎ | ✅︎ | | ✅︎ |
-| `StableAudioPipeline` | Stable-Audio-Open | `stabilityai/stable-audio-open-1.0` | ✅︎ | ✅︎ | | ✅︎ |
-| `SoulXSingerPipeline` | SoulX-Singer (SVS) | `Soul-AILab/SoulX-Singer` | ✅︎ | | | |
-| `SoulXSingerSVCPipeline` | SoulX-Singer-SVC | `Soul-AILab/SoulX-Singer` (`model-svc.pt`) | ✅︎ | | | |
-| `AudioXPipeline` | AudioX | `zhangj1an/AudioX` | ✅︎ | ✅︎ | | |
-| `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-CustomVoice | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-VoiceDesign | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-Base | `Qwen/Qwen3-TTS-12Hz-1.7B-Base` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `MingTTSForConditionalGeneration` | Ming-omni-tts dense 0.5B | `inclusionAI/Ming-omni-tts-0.5B` | ✅︎ | | | |
-| `GLMTTSForConditionalGeneration` | GLM-TTS | `zai-org/GLM-TTS` | ✅︎ | | | |
-| `MossTTSNanoForCausalLM` | MOSS-TTS-Nano | `OpenMOSS-Team/MOSS-TTS-Nano` | ✅︎ | | | |
-| `MossTTSDelayModel` | MOSS-TTS, MOSS-TTSD, MOSS-SoundEffect, MOSS-VoiceGenerator | `OpenMOSS-Team/MOSS-VoiceGenerator` | ✅︎ | | | |
-| `MossTTSRealtime` | MOSS-TTS-Realtime | `OpenMOSS-Team/MOSS-TTS-Realtime` | ✅︎ | | | |
-| `HiggsAudioV2ForConditionalGeneration` | Higgs-Audio v2 | `bosonai/higgs-audio-v2-generation-3B-base` | ✅︎ | | | |
-| `HiggsMultimodalQwen3ForConditionalGeneration` | Higgs-Audio v3 (TTS) | `bosonai/higgs-audio-v3-tts-4b` | ✅︎ | | | |
-| `IndexTTS2TalkerForConditionalGeneration` | IndexTTS-2 | `IndexTeam/IndexTTS-2` | ✅︎ | | | |
-| `NextStep11Pipeline` | NextStep-1.1 | `stepfun-ai/NextStep-1.1` | ✅︎ | ✅︎ | | ✅︎ |
-| `MiMoAudioModel` | MiMo-Audio-7B-Instruct | `XiaomiMiMo/MiMo-Audio-7B-Instruct` | ✅︎ | ✅︎ | | |
-| `MiMoV2ASRForCausalLM` | MiMo-V2.5-ASR | `XiaomiMiMo/MiMo-V2.5-ASR` | ✅︎ | ✅︎ | | |
-| `Flux2Pipeline` | FLUX.2-dev | `black-forest-labs/FLUX.2-dev` | ✅︎ | ✅︎ | | |
-| `FishSpeechSlowARForConditionalGeneration` | Fish Speech S2 Pro | `fishaudio/s2-pro` | ✅︎ | ✅︎ | | ✅︎ |
-| `DreamIDOmniPipeline` | DreamID-Omni | `XuGuo699/DreamID-Omni` | ✅︎ | ✅︎ | | |
-| `SenseNovaU1Pipeline` | SenseNova-U1 (DiT-only) | `SenseNova/SenseNova-U1-8B-MoT` | ✅︎ | | | |
-| `LancePipeline` | Lance | `bytedance-research/Lance` | ✅︎ | | | |
-| `HunyuanVideo15Pipeline` | HunyuanVideo-1.5-T2V | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v`, `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v` | ✅︎ | ✅︎ | | |
-| `HunyuanVideo15ImageToVideoPipeline` | HunyuanVideo-1.5-I2V | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v`, `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v` | ✅︎ | ✅︎ | | |
-| `VoxtralTTSForConditionalGeneration` | Voxtral TTS | `mistralai/Voxtral-4B-TTS-2603` | ✅︎ | ✅︎ | | |
-| `CovoAudioForConditionalGeneration` | Covo-Audio-Chat | `tencent/Covo-Audio-Chat` | ✅︎ | | | |
-|`DyninOmniForConditionalGeneration` | Dynin-Omni | `snu-aidas/Dynin-Omni` | ✅︎ | | | |
-| `MiniCPMO45OmniForConditionalGeneration` | MiniCPM-o 4.5 | `openbmb/MiniCPM-o-4_5` | ✅︎ | | ✅︎ | |
-| `ErnieImagePipeline` | ERNIE-Image | `baidu/ERNIE-Image`, `baidu/ERNIE-Image-Turbo` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-|`HiDreamImagePipeline` | HiDream-I1-Full | `HiDream-ai/HiDream-I1-Full` | ✅︎ | ✅︎ | | |
+| Architecture | Models | Example HF Models | NVIDIA GPU | AMD GPU | Ascend NPU | Intel GPU | Recipe |
+|--------------|--------|-------------------|------------|---------|-----|-----------|--------|
+| `Qwen3OmniMoeForConditionalGeneration` | Qwen3-Omni | `Qwen/Qwen3-Omni-30B-A3B-Instruct` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Qwen/Qwen3-Omni.md) |
+| `Qwen2_5OmniForConditionalGeneration` | Qwen2.5-Omni | `Qwen/Qwen2.5-Omni-7B`, `Qwen/Qwen2.5-Omni-3B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `MingFlashOmniForConditionalGeneration` + `MingImagePipeline` | Ming-flash-omni-2.0 (omni-speech + imagegen<sup>1</sup>) | `Jonathan1909/Ming-flash-omni-2.0` | ✅︎ |   |   |   | — |
+| `BagelForConditionalGeneration` | BAGEL (DiT-only) | `ByteDance-Seed/BAGEL-7B-MoT` | ✅︎ | ✅︎ | | ✅︎ | — |
+| `InternVLAA1Pipeline` | InternVLA-A1 | `InternRobotics/InternVLA-A1-3B` | ✅︎ | ✅︎ | | | — |
+| `Gr00tN1d7Pipeline` | GR00T N1.7 | `nvidia/GR00T-N1.7-3B` | ✅︎ | | | | — |
+| `HunyuanImage3ForCausalMM` | HunyuanImage3.0 (DiT-only) | `tencent/HunyuanImage-3.0`, `tencent/HunyuanImage-3.0-Instruct` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `QwenImagePipeline` | Qwen-Image | `Qwen/Qwen-Image` | ✅︎ | | | | [Published](https://recipes.vllm.ai/Qwen/Qwen-Image) |
+| `QwenImagePipeline` | Qwen-Image-2512 | `Qwen/Qwen-Image-2512` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Qwen/Qwen-Image-2512.md) |
+| `QwenImageEditPipeline` | Qwen-Image-Edit | `Qwen/Qwen-Image-Edit` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2509 | `Qwen/Qwen-Image-Edit-2509` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `QwenImageLayeredPipeline` | Qwen-Image-Layered | `Qwen/Qwen-Image-Layered` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2511 | `Qwen/Qwen-Image-Edit-2511` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `GlmImagePipeline` | GLM-Image | `zai-org/GLM-Image` | ✅︎ | ✅︎ | | | — |
+| `ZImagePipeline` | Z-Image | `Tongyi-MAI/Z-Image-Turbo` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `Krea2Pipeline` | Krea 2 (Raw + Turbo) | `krea/Krea-2-Raw`, `krea/Krea-2-Turbo` | ✅︎ | | | | — |
+| `WanPipeline` | Wan2.1-T2V, Wan2.2-T2V, Wan2.2-TI2V | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers`, `Wan-AI/Wan2.1-T2V-14B-Diffusers`, `Wan-AI/Wan2.2-T2V-A14B-Diffusers`, `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `WanImageToVideoPipeline` | Wan2.2-I2V | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `Cosmos3OmniDiffusersPipeline` | Cosmos3 T2I, T2V, I2V, V2V, T2V with sound, action policy | `nvidia/Cosmos3-Nano`, `nvidia/Cosmos3-Super` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `Wan22S2VPipeline` | Wan2.2-S2V | `Wan-AI/Wan2.2-S2V-14B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `Wan22VACEPipeline` | Wan2.1-VACE | `Wan-AI/Wan2.1-VACE-1.3B-diffusers`, `Wan-AI/Wan2.1-VACE-14B-diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `Wan22VACEPipeline` | Wan2.2-VACE | `Pyros13/Wan2.2-VACE-Fun-A14B-Diffusers` | ✅︎ |   |   |   | — |
+| `LTX2Pipeline` | LTX-2 / LTX-2.3 one-stage T2V and I2V | `Lightricks/LTX-2`, `diffusers/LTX-2.3-Diffusers` | ✅︎ | ✅︎ | | | — |
+| `LTX2DistilledPipeline` | LTX-2 distilled two-stage T2V and I2V | `rootonchair/LTX-2-19b-distilled` | ✅︎ | ✅︎ | | | — |
+| `LingBotVideoPipeline` | LingBot-Video dense and MoE T2I, T2V, TI2V | `robbyant/lingbot-video-dense-1.3b`, `robbyant/lingbot-video-moe-30b-a3b` | ✅︎ | | | | — |
+| `MiniMaxH3Pipeline` | MiniMax H3 T2VA, first/last-keyframe FL2VA, and mixed-reference Ref2VA | `MiniMaxAI/MiniMax-H3` | ✅︎ | ✅︎ | | | [Published](https://recipes.vllm.ai/MiniMaxAI/MiniMax-H3) |
+| `DreamZeroPipeline` | DreamZero-DROID | `GEAR-Dreams/DreamZero-DROID` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `HeliosPipeline`, `HeliosPyramidPipeline` | Helios | `BestWishYsh/Helios-Base`, `BestWishYsh/Helios-Mid`, `BestWishYsh/Helios-Distilled` | ✅︎ | ✅︎ | ✅︎ | | — |
+| `MagiHumanPipeline` | MagiHuman | `SII-GAIR/daVinci-MagiHuman-Base-1080p` | ✅︎ | ✅︎ | | | — |
+| `OvisImagePipeline` | Ovis-Image | `OvisAI/Ovis-Image` | ✅︎ | ✅︎ | | ✅︎ | — |
+| `LongcatImagePipeline` | LongCat-Image | `meituan-longcat/LongCat-Image` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `LongCatImageEditPipeline` | LongCat-Image-Edit | `meituan-longcat/LongCat-Image-Edit` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `BooguImagePipeline` | Boogu-Image | `Boogu/Boogu-Image-0.1-Base`, `Boogu/Boogu-Image-0.1-Edit` | ✅︎ | | | | — |
+| `StableDiffusionXLPipeline` | Stable-Diffusion-XL | `stabilityai/stable-diffusion-xl-base-1.0` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `StableDiffusion3Pipeline` | Stable-Diffusion-3 | `stabilityai/stable-diffusion-3.5-medium` | ✅︎ | ✅︎ | | ✅︎ | — |
+| `CosyVoice3Model` | CosyVoice3 | `FunAudioLLM/Fun-CosyVoice3-0.5B-2512` | ✅︎ | ✅︎ | | ✅︎ | — |
+| `OmniVoiceModel` | OmniVoice | `k2-fsa/OmniVoice` | ✅︎ | | | | — |
+| `VoxCPM2TalkerForConditionalGeneration` | VoxCPM2 | `openbmb/VoxCPM2` | ✅︎ | | | | — |
+| `MammothModa2ForConditionalGeneration` | MammothModa2-Preview | `bytedance-research/MammothModa2-Preview` | ✅︎ | ✅︎ | | | — |
+| `MammothModa2ForConditionalGeneration` | MammothModa2-Dev (AR-only image understanding) | `bytedance-research/MammothModa2-Dev` | ✅︎ | | | | — |
+| `Flux2KleinPipeline` | FLUX.2-klein | `black-forest-labs/FLUX.2-klein-4B`, `black-forest-labs/FLUX.2-klein-9B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `FluxKontextPipeline` | FLUX.1-Kontext-dev | `black-forest-labs/FLUX.1-Kontext-dev` | ✅︎ | ✅︎ | | | — |
+| `FluxPipeline` | FLUX.1-dev | `black-forest-labs/FLUX.1-dev` | ✅︎ | ✅︎ | | ✅︎ | — |
+| `FluxPipeline` | FLUX.1-schnell | `black-forest-labs/FLUX.1-schnell` | ✅︎ | ✅︎ | | ✅︎ | — |
+| `OmniGen2Pipeline` | OmniGen2 | `OmniGen2/OmniGen2` | ✅︎ | ✅︎ | | ✅︎ | — |
+| `StableAudioPipeline` | Stable-Audio-Open | `stabilityai/stable-audio-open-1.0` | ✅︎ | ✅︎ | | ✅︎ | — |
+| `SoulXSingerPipeline` | SoulX-Singer (SVS) | `Soul-AILab/SoulX-Singer` | ✅︎ | | | | — |
+| `SoulXSingerSVCPipeline` | SoulX-Singer-SVC | `Soul-AILab/SoulX-Singer` (`model-svc.pt`) | ✅︎ | | | | — |
+| `AudioXPipeline` | AudioX | `zhangj1an/AudioX` | ✅︎ | ✅︎ | | | — |
+| `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-CustomVoice | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice` | ✅︎ | | | | [Published](https://recipes.vllm.ai/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice) |
+| `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-VoiceDesign | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-Base | `Qwen/Qwen3-TTS-12Hz-1.7B-Base` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `MingTTSForConditionalGeneration` | Ming-omni-tts dense 0.5B | `inclusionAI/Ming-omni-tts-0.5B` | ✅︎ | | | | — |
+| `GLMTTSForConditionalGeneration` | GLM-TTS | `zai-org/GLM-TTS` | ✅︎ | | | | — |
+| `MossTTSNanoForCausalLM` | MOSS-TTS-Nano | `OpenMOSS-Team/MOSS-TTS-Nano` | ✅︎ | | | | — |
+| `MossTTSDelayModel` | MOSS-TTS, MOSS-TTSD, MOSS-SoundEffect, MOSS-VoiceGenerator | `OpenMOSS-Team/MOSS-VoiceGenerator` | ✅︎ | | | | — |
+| `MossTTSRealtime` | MOSS-TTS-Realtime | `OpenMOSS-Team/MOSS-TTS-Realtime` | ✅︎ | | | | — |
+| `HiggsAudioV2ForConditionalGeneration` | Higgs-Audio v2 | `bosonai/higgs-audio-v2-generation-3B-base` | ✅︎ | | | | — |
+| `HiggsMultimodalQwen3ForConditionalGeneration` | Higgs-Audio v3 (TTS) | `bosonai/higgs-audio-v3-tts-4b` | ✅︎ | | | | [Published](https://recipes.vllm.ai/bosonai/higgs-audio-v3-tts-4b) |
+| `IndexTTS2TalkerForConditionalGeneration` | IndexTTS-2 | `IndexTeam/IndexTTS-2` | ✅︎ | | | | — |
+| `NextStep11Pipeline` | NextStep-1.1 | `stepfun-ai/NextStep-1.1` | ✅︎ | ✅︎ | | ✅︎ | — |
+| `MiMoAudioModel` | MiMo-Audio-7B-Instruct | `XiaomiMiMo/MiMo-Audio-7B-Instruct` | ✅︎ | ✅︎ | | | — |
+| `MiMoV2ASRForCausalLM` | MiMo-V2.5-ASR | `XiaomiMiMo/MiMo-V2.5-ASR` | ✅︎ | ✅︎ | | | — |
+| `Flux2Pipeline` | FLUX.2-dev | `black-forest-labs/FLUX.2-dev` | ✅︎ | ✅︎ | | | — |
+| `FishSpeechSlowARForConditionalGeneration` | Fish Speech S2 Pro | `fishaudio/s2-pro` | ✅︎ | ✅︎ | | ✅︎ | — |
+| `DreamIDOmniPipeline` | DreamID-Omni | `XuGuo699/DreamID-Omni` | ✅︎ | ✅︎ | | | — |
+| `SenseNovaU1Pipeline` | SenseNova-U1 (DiT-only) | `SenseNova/SenseNova-U1-8B-MoT` | ✅︎ | | | | — |
+| `LancePipeline` | Lance | `bytedance-research/Lance` | ✅︎ | | | | — |
+| `HunyuanVideo15Pipeline` | HunyuanVideo-1.5-T2V | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v`, `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v` | ✅︎ | ✅︎ | | | — |
+| `HunyuanVideo15ImageToVideoPipeline` | HunyuanVideo-1.5-I2V | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v`, `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v` | ✅︎ | ✅︎ | | | — |
+| `VoxtralTTSForConditionalGeneration` | Voxtral TTS | `mistralai/Voxtral-4B-TTS-2603` | ✅︎ | ✅︎ | | | — |
+| `CovoAudioForConditionalGeneration` | Covo-Audio-Chat | `tencent/Covo-Audio-Chat` | ✅︎ | | | | — |
+|`DyninOmniForConditionalGeneration` | Dynin-Omni | `snu-aidas/Dynin-Omni` | ✅︎ | | | | — |
+| `MiniCPMO45OmniForConditionalGeneration` | MiniCPM-o 4.5 | `openbmb/MiniCPM-o-4_5` | ✅︎ | | ✅︎ | | — |
+| `ErnieImagePipeline` | ERNIE-Image | `baidu/ERNIE-Image`, `baidu/ERNIE-Image-Turbo` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+|`HiDreamImagePipeline` | HiDream-I1-Full | `HiDream-ai/HiDream-I1-Full` | ✅︎ | ✅︎ | | | — |
+
+The `Recipe` column links the corresponding deployment recipe. `Published` is
+preferred when a corresponding `recipes.vllm.ai` page exists; otherwise the
+link points to the repository recipe. `—` means that no recipe link has been
+added for that model row yet.
+
+For rows with an audited recipe link, the hardware checkmarks reflect hardware
+explicitly documented in that recipe. Other rows retain the implementation
+support metadata until their recipe is audited.

 ✅︎ indicates the model is supported on that backend. Empty cells mean not listed as supported on that backend.

@@ -283,23 +283,20 @@ blocks — use Ulysses SP (`ring_degree=1`).

 ### Which geometries run sparse

-Sparsity is only applied when the video segment is a **multiple of 128 rows**, where the row count
-is `latent_t × (height / 32) × (width / 32)`. Otherwise `rf_v2`'s block mask and the kernel's own
-tiling disagree on the block count, and the block straddling the seam mixes video and prefix rows
-that selection may then drop. Any resolution still runs — an unaligned geometry falls back to dense
-attention and logs `RAINFUSION_ATTN staying dense` with the row count it computed — but it gets no
-speedup, so pick an aligned geometry when you want one.
+MindIE-SD rf_v2 handles arbitrary video grids by spatially rearranging video first and, when
+necessary, promoting a real-video suffix to its always-kept prefix segment. It never pads: rf_v2
+does not consume an attention mask for padded keys. The remaining video rows stay sparse, while the
+promoted rows remain visible to every query and key.

-Alignment is necessary, not sufficient, for good quality. `rf_v2` groups video positions into 8x8
-spatial tiles (two tiles fill one 128-row block), which is what makes a selected block a compact
-patch of the frame. When the latent `h` or `w` is not a multiple of 8, the leftover rows or columns
-are peeled off and appended as a flat run instead of being tiled, so they get pooled with spatially
-distant positions and selection can no longer rank them meaningfully. This is silent, and invisible
-to a `sparsity=0` check, because a fully populated mask does not care how blocks are grouped.
+For example, MiniMax-H3 at 1344x768 has grid `(62, 24, 42)`: its 62496 video rows leave a 32-row
+128-block residue, and its two non-8-wide latent columns form a 2928-row rearranged suffix.
+MindIE-SD promotes 2976 rows, leaving 59520 sparse rows (`465 × 128`). This keeps the block mask
+and kernel tiling aligned, at the cost of retaining more blocks.

-"Multiple of 8" on the latent grid means **width and height that are multiples of 256**. Aligned
-resolutions off that grid still run sparse; they just lose more fidelity at the same `sparsity`,
-which you can buy back with `start_step`.
+8x8-aligned spatial grids remain preferable for performance. A latent `h` or `w` not divisible by 8
+creates an always-kept residual suffix and lowers realized sparsity. "Multiple of 8" on the latent
+grid means **width and height that are multiples of 256**. This path requires a MindIE-SD release
+that implements protected video tails for rf_v2.

 ## End-to-End Benchmark (BF16, sm_120 RTX Pro 6000 Blackwell)

@@ -159,7 +159,7 @@ The following tables show which models support each feature:
 | **HunyuanVideo-1.5 T2V I2V** |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |  ✅ (encode/decode)   |       ✅        |        ❌         |
 | **DreamID-Omni**             |     ❌     |     ❌      |           ❌           |       ✅        |         ❌         |         ❌         |   ✅    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **Cosmos3**                  |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |  ✅ (encode/decode)   |       ✅        |        ❌         |
-| **MiniMax-H3**               |     ❌     |     ✅      |           ✅           |       ❌        |       ✅ (DiT/TE)  |         ❌         |   ✅    |             ✅             |       ✅ (tile)       |      ✅ (DiT)      |        ❌         |
+| **MiniMax-H3**               | ✅ (FL2VA) |     ✅      |           ✅           |       ❌        |       ✅ (DiT/TE)  |         ❌         |   ✅    |             ✅             |       ✅ (tile)       |      ✅ (DiT)      |        ❌         |

 **Frame Interpolation Support**

@@ -99,7 +99,7 @@ a native vLLM-Omni pipeline for those cases.
 ### Attention Backends

 The diffusers backend converts
-[vLLM-Omni standard of attention backend setting](../../../docs/user_guide/diffusion/attention_backends.md)
+[vLLM-Omni standard of attention backend setting](../../diffusion/attention_backends.md)
 to [diffusers standard](https://huggingface.co/docs/diffusers/optimization/attention_backends#available-backends).

 Specifically for `FLASH_ATTN`, it will first attempt to use FlashAttention-3 and then FlashAttention-2.
@@ -33,6 +33,25 @@ This folder provides a unified CLI script for image-to-video generation using vL

 Default model: `Wan-AI/Wan2.2-I2V-A14B-Diffusers`.

+### LingBot-Video TI2V
+
+LingBot-Video accepts exactly one first-frame image and uses the same dense or
+MoE checkpoint as its T2I and T2V modes:
+
+```bash
+python examples/offline_inference/image_to_video/image_to_video.py \
+  --model robbyant/lingbot-video-dense-1.3b \
+  --model-class-name LingBotVideoPipeline \
+  --image /path/to/input.png \
+  --prompt "the fox looks toward the camera" \
+  --height 192 --width 320 --num-frames 9 --num-inference-steps 2 \
+  --guidance-scale 3.0 --flow-shift 3.0 --fps 24 \
+  --output lingbot_ti2v.mp4
+```
+
+The input image keeps its original geometry until the LingBot pipeline applies
+its model-specific resize and center crop.
+
 ## Prerequisites

 Download the example image used in the snippets below:
@@ -43,6 +43,23 @@ This folder provides several entrypoints for experimenting with text-to-image di

 Default model: `Qwen/Qwen-Image`

+### LingBot-Video
+
+LingBot-Video uses the same checkpoint for image and video generation. Run the
+shared T2I entry point with:
+
+```bash
+python examples/offline_inference/text_to_image/text_to_image.py \
+  --model robbyant/lingbot-video-dense-1.3b \
+  --prompt "a red fox standing in fresh snow" \
+  --height 192 --width 320 --num-inference-steps 2 \
+  --guidance-scale 3.0 --extra-body '{"flow_shift":3.0}' \
+  --output lingbot_t2i.png
+```
+
+The LingBot prompt builder selects the `image` output modality and requests one
+frame. Passing an input image is not supported in LingBot T2I mode.
+
 ## Quick Start

 ### Python API
@@ -55,6 +55,21 @@ python text_to_video.py \
  --output vace_t2v_output.mp4
 ```

+### LingBot-Video
+
+The dedicated LingBot example explicitly selects the video output modality:
+
+```bash
+python examples/offline_inference/text_to_video/text_to_video_lingbot.py \
+  --model robbyant/lingbot-video-dense-1.3b \
+  --height 192 --width 320 --num-frames 9 --num-inference-steps 2 \
+  --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.
+
 ### LTX-2

 ```bash
@@ -20,9 +20,9 @@ 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="/home/models/lingbot-video-dense-1.3b")
+    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="")
+    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)
@@ -58,9 +58,12 @@ 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.shape[1] not in (3, 4) else tensor[0].permute(1, 2, 3, 0)
-        elif tensor.dim() == 4 and tensor.shape[0] in (3, 4):
-            tensor = tensor.permute(1, 2, 3, 0)
+            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
@@ -87,8 +90,12 @@ def main() -> None:
        parallel_config=DiffusionParallelConfig(),
    )

-    prompt: dict[str, str] = {"prompt": args.prompt}
-    if args.negative_prompt:
+    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(
@@ -0,0 +1,74 @@
+# LingBot-Video
+
+LingBot-Video uses one `LingBotVideoPipeline` for text-to-image (T2I),
+text-to-video (T2V), and text-image-to-video (TI2V) generation. Both the dense
+and MoE checkpoints use the same request format.
+
+## Start the server
+
+```bash
+MODEL=robbyant/lingbot-video-dense-1.3b bash run_server.sh
+```
+
+The MoE checkpoint uses the same server and request scripts, but requires
+substantially more GPU memory:
+
+```bash
+MODEL=robbyant/lingbot-video-moe-30b-a3b bash run_server.sh
+```
+
+## Text to image
+
+The image endpoint selects T2I mode and always generates one frame:
+
+```bash
+bash run_curl_text_to_image.sh
+```
+
+The script sends a `320x192`, two-step smoke request and writes
+`lingbot_t2i.png`.
+
+## Text or text-image to video
+
+Run the video script without an image to select T2V mode:
+
+```bash
+bash run_curl_text_image_to_video.sh
+```
+
+Pass a first-frame image to the same script to select TI2V mode:
+
+```bash
+INPUT_IMAGE=/path/to/input.png bash run_curl_text_image_to_video.sh
+```
+
+The client scripts omit the optional `model` request field, so they target
+whichever dense or MoE checkpoint the server loaded. The video example uses the
+lightweight `320x192`, 9-frame, two-step configuration.
+
+Until the shared `/v1/videos` reference-image resizing is removed, TI2V target
+dimensions must be sent through `extra_params`, for example
+`{"size":"320x192"}`. Do not use the top-level `size`, `width`, or `height`
+fields for TI2V because the serving layer currently applies those dimensions
+to the reference image before the model receives it. T2V requests can continue
+to use the top-level dimension fields.
+
+LingBot video frame counts use the causal VAE `4n+1` grid. The pipeline rounds
+any requested frame count upward to the next valid value. An explicit
+`num_frames` takes precedence over `seconds`; otherwise, the server first
+resolves `seconds * fps` and the pipeline applies the same alignment.
+
+Official `resolution`/`ratio` presets can be sent through `extra_params`, for
+example `{"resolution":"720p","ratio":"16:9"}`. The `2k` and `4k` entries
+only define output dimensions; whether they run successfully depends on the
+checkpoint, GPU memory, and memory optimizations available in the deployment.
+
+For `/v1/images/generations`, the server resolves these aliases to their final
+pixel dimensions before applying `--max-generated-image-size`. Requests above
+the configured limit return HTTP 400 before engine dispatch. LingBot produces
+one output per prompt; image requests with `n>1` are also rejected with HTTP
+400.
+
+LingBot TI2V accepts exactly one image reference. Image editing, video
+references, audio references, batching, and Refiner execution are not supported
+by this pipeline mode.
@@ -0,0 +1,67 @@
+#!/bin/bash
+
+set -euo pipefail
+
+if [ -n "${INPUT_IMAGE:-}" ] && [ ! -f "${INPUT_IMAGE}" ]; then
+    echo "INPUT_IMAGE does not exist: ${INPUT_IMAGE}"
+    exit 1
+fi
+
+BASE_URL="${BASE_URL:-http://localhost:8099}"
+POLL_INTERVAL="${POLL_INTERVAL:-2}"
+
+if [ -n "${INPUT_IMAGE:-}" ]; then
+    default_output="lingbot_ti2v.mp4"
+else
+    default_output="lingbot_t2v.mp4"
+fi
+OUTPUT_PATH="${OUTPUT_PATH:-${default_output}}"
+
+curl_args=(
+    -sS -X POST "${BASE_URL}/v1/videos"
+    -H "Accept: application/json"
+    -F "prompt=the subject turns toward the camera with smooth natural motion"
+    -F "num_frames=9"
+    -F "fps=24"
+    -F "num_inference_steps=2"
+    -F "guidance_scale=3.0"
+    -F "flow_shift=3.0"
+    -F "seed=42"
+)
+if [ -n "${INPUT_IMAGE:-}" ]; then
+    curl_args+=(
+        -F "input_reference=@${INPUT_IMAGE}"
+        -F 'extra_params={"size":"320x192"}'
+    )
+else
+    curl_args+=(-F "width=320" -F "height=192")
+fi
+
+create_response="$(curl "${curl_args[@]}")"
+
+video_id="$(echo "${create_response}" | jq -er '.id')"
+echo "Created video job ${video_id}"
+
+while true; do
+    status_response="$(curl -sS "${BASE_URL}/v1/videos/${video_id}")"
+    status="$(echo "${status_response}" | jq -er '.status')"
+    case "${status}" in
+        queued|in_progress)
+            sleep "${POLL_INTERVAL}"
+            ;;
+        completed)
+            break
+            ;;
+        failed)
+            echo "${status_response}" | jq .
+            exit 1
+            ;;
+        *)
+            echo "Unexpected video job status: ${status}"
+            exit 1
+            ;;
+    esac
+done
+
+curl -sS -L "${BASE_URL}/v1/videos/${video_id}/content" -o "${OUTPUT_PATH}"
+echo "Saved video to ${OUTPUT_PATH}"
@@ -0,0 +1,20 @@
+#!/bin/bash
+
+set -euo pipefail
+
+BASE_URL="${BASE_URL:-http://localhost:8099}"
+OUTPUT_PATH="${OUTPUT_PATH:-lingbot_t2i.png}"
+
+curl -sS -X POST "${BASE_URL}/v1/images/generations" \
+    -H "Content-Type: application/json" \
+    -d "{
+        \"prompt\": \"a red fox standing in fresh snow\",
+        \"size\": \"320x192\",
+        \"num_inference_steps\": 2,
+        \"guidance_scale\": 3.0,
+        \"flow_shift\": 3.0,
+        \"seed\": 42,
+        \"response_format\": \"b64_json\"
+    }" | jq -er '.data[0].b64_json' | base64 -d > "${OUTPUT_PATH}"
+
+echo "Saved image to ${OUTPUT_PATH}"
@@ -0,0 +1,10 @@
+#!/bin/bash
+
+set -euo pipefail
+
+MODEL="${MODEL:-robbyant/lingbot-video-dense-1.3b}"
+PORT="${PORT:-8099}"
+
+vllm serve "${MODEL}" --omni \
+    --model-class-name LingBotVideoPipeline \
+    --port "${PORT}"
@@ -1,247 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Verify a ModelOpt FP8 diffusers checkpoint exported by
-quantize_wan2_2_modelopt_fp8.py / quantize_hunyuanvideo_15_modelopt_fp8.py.
-
-Three checks:
-  A. transformer/config.json has a sane quantization_config block.
-  B. transformer/*.safetensors contains FP8 (float8_e4m3fn) quantized tensors.
-  C. transformer disk size is materially smaller than a BF16 baseline.
-
-Example:
-    python examples/quantization/check_modelopt_fp8_export.py \\
-        --output ./hv15-480p-modelopt-fp8
-
-    # Optional: compare disk size against a local or HF BF16 baseline.
-    python examples/quantization/check_modelopt_fp8_export.py \\
-        --output ./hv15-480p-modelopt-fp8 \\
-        --baseline hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import sys
-from collections import Counter
-from pathlib import Path
-
-SUPPORTED_ALGOS = ("FP8",)
-
-
-def _check_config(transformer_dir: Path) -> tuple[int, str | None]:
-    """Returns (status, quant_algo). status: 0 pass, 1 fail, 2 warn."""
-    cfg_path = transformer_dir / "config.json"
-    if not cfg_path.exists():
-        print(f"[FAIL] {cfg_path} missing.")
-        return 1, None
-
-    with cfg_path.open(encoding="utf-8") as f:
-        cfg = json.load(f)
-
-    qc = cfg.get("quantization_config")
-    if not isinstance(qc, dict):
-        print(f"[FAIL] No `quantization_config` block in {cfg_path}.")
-        return 1, None
-
-    print(f"[A] quantization_config from {cfg_path}:")
-    print(json.dumps(qc, indent=2))
-
-    quant_algo = qc.get("quant_algo")
-    issues = []
-    if qc.get("quant_method") != "modelopt":
-        issues.append(f"quant_method={qc.get('quant_method')!r} (expected 'modelopt')")
-    if quant_algo not in SUPPORTED_ALGOS:
-        issues.append(
-            f"quant_algo={quant_algo!r} (expected one of {SUPPORTED_ALGOS} — vllm-omni adapter may not auto-detect)"
-        )
-
-    if issues:
-        print("[A] WARN — config looks incomplete:")
-        for issue in issues:
-            print(f"    - {issue}")
-        return 2, quant_algo
-    print(f"[A] PASS — config looks correct (quant_algo={quant_algo}).")
-    return 0, quant_algo
-
-
-def _read_safetensors_header(path: Path) -> dict:
-    """Read the JSON header of a safetensors file. Bypass-safe — doesn't materialize tensors.
-
-    Returns {tensor_name: {'dtype': 'F8_E4M3', 'shape': [...], 'data_offsets': [...]}}.
-    Header dtype strings: F8_E4M3, F8_E5M2, BF16, F16, F32, F64, I8, I16, I32, I64, BOOL, U8, ...
-    """
-    import struct
-
-    with open(path, "rb") as f:
-        header_len = struct.unpack("<Q", f.read(8))[0]
-        header = json.loads(f.read(header_len))
-    header.pop("__metadata__", None)
-    return header
-
-
-def _classify_weight_scale_granularity(weight_scale_shapes: list[list[int]]) -> str:
-    """Infer per-tensor vs per-channel vs per-block from sample weight_scale shapes.
-
-    ModelOpt block-wise produces shapes like `[16, 1, 16, 1]` (broadcasting dims of 1
-    interleaved with block-count dims). We count "meaningful" dims — ones with size > 1 —
-    and classify: 0 meaningful dims = per-tensor (scalar), 1 = per-channel, 2+ = per-block.
-    """
-    if not weight_scale_shapes:
-        return "no weight_scale tensors found"
-
-    def meaningful_dims(shape: list[int]) -> int:
-        return sum(1 for d in shape if d > 1)
-
-    per_tensor = sum(1 for s in weight_scale_shapes if meaningful_dims(s) == 0)
-    per_channel = sum(1 for s in weight_scale_shapes if meaningful_dims(s) == 1)
-    per_block = sum(1 for s in weight_scale_shapes if meaningful_dims(s) >= 2)
-    total = len(weight_scale_shapes)
-    if per_tensor == total:
-        return "per-tensor (all scalar scales)"
-    if per_channel == total:
-        return "per-channel (1 meaningful dim)"
-    if per_block == total:
-        return "per-block (2+ meaningful dims — e.g. [M//bm, 1, N//bn, 1] for tiles)"
-    return f"mixed: per-tensor={per_tensor}, per-channel={per_channel}, per-block={per_block} of {total}"
-
-
-def _check_safetensors(transformer_dir: Path) -> int:
-    """Returns 0 on pass, 1 on fail. Reads on-disk dtype from the safetensors header."""
-    files = sorted(transformer_dir.glob("*.safetensors"))
-    if not files:
-        print(f"[FAIL] No *.safetensors in {transformer_dir}.")
-        return 1
-
-    header_dtype_counts: Counter[str] = Counter()
-    sample_quant_weight_keys: list[str] = []
-    sample_scale_keys: list[str] = []
-    weight_scale_shapes: list[list[int]] = []
-    sample_weight_scale_entries: list[tuple[str, list[int]]] = []
-
-    for f in files:
-        try:
-            header = _read_safetensors_header(f)
-        except Exception as exc:
-            print(f"[B] WARN — could not parse header of {f}: {exc}")
-            continue
-        for k, info in header.items():
-            dtype = info.get("dtype", "?")
-            header_dtype_counts[dtype] += 1
-            # FP8 stores weights as F8_E4M3 directly.
-            if dtype.startswith("F8") and k.endswith(".weight") and len(sample_quant_weight_keys) < 5:
-                sample_quant_weight_keys.append(k)
-            if k.endswith(("_scale", ".weight_scale", ".input_scale", "_scale_inv")) and len(sample_scale_keys) < 5:
-                sample_scale_keys.append(k)
-            if k.endswith(".weight_scale"):
-                weight_scale_shapes.append(info.get("shape", []))
-                if len(sample_weight_scale_entries) < 5:
-                    sample_weight_scale_entries.append((k, info.get("shape", [])))
-
-    print(f"\n[B] On-disk dtype counts across {len(files)} safetensors file(s) (from header, not get_tensor):")
-    for dtype, count in sorted(header_dtype_counts.items(), key=lambda kv: -kv[1]):
-        marker = "  <-- FP8" if dtype.startswith("F8") else ""
-        print(f"    {dtype:10s} {count:>6d}{marker}")
-
-    quant_count = sum(c for d, c in header_dtype_counts.items() if d.startswith("F8"))
-    if quant_count == 0:
-        print("[B] FAIL — no FP8 tensors on disk. Calibration likely did not actually quantize the weights.")
-        return 1
-
-    print(f"[B] PASS — {quant_count} FP8 tensors stored on disk.")
-    if sample_quant_weight_keys:
-        print(f"    sample quantized weight tensors: {sample_quant_weight_keys[:3]}")
-    if sample_scale_keys:
-        print(f"    sample scale tensors:            {sample_scale_keys[:3]}")
-    print("    (Note: torch's get_tensor() may return these as bf16 views on some versions —")
-    print("     irrelevant; vLLM's loader uses native FP8 ops.)")
-
-    # Weight-scale granularity — per-tensor (scalar) vs per-channel (1-D) vs per-block (N-D).
-    print(f"\n    weight_scale granularity: {_classify_weight_scale_granularity(weight_scale_shapes)}")
-    for key, shape in sample_weight_scale_entries[:3]:
-        print(f"      {key}: shape {shape}")
-    return 0
-
-
-def _disk_size_gib(p: Path) -> float:
-    return sum(f.stat().st_size for f in p.rglob("*") if f.is_file()) / (1024**3)
-
-
-def _check_size_vs_baseline(transformer_dir: Path, baseline: str | None) -> int:
-    """Returns 0 always (informational only)."""
-    quant_size = _disk_size_gib(transformer_dir)
-    print(f"\n[C] FP8 transformer disk size: {quant_size:.2f} GiB")
-
-    if baseline is None:
-        print("[C] SKIP — pass --baseline <path or HF id> to compare against BF16.")
-        return 0
-
-    baseline_path = Path(baseline)
-    if not baseline_path.exists():
-        # Try HF download.
-        try:
-            from huggingface_hub import snapshot_download
-        except ImportError:
-            print("[C] SKIP — huggingface_hub not installed and baseline not a local path.")
-            return 0
-        print(f"    Downloading baseline transformer from HF: {baseline}")
-        baseline_path = Path(snapshot_download(baseline, allow_patterns=["transformer/*"]))
-
-    bf16_dir = baseline_path / "transformer" if (baseline_path / "transformer").exists() else baseline_path
-    bf16_size = _disk_size_gib(bf16_dir)
-    if bf16_size == 0:
-        print(f"[C] WARN — baseline transformer dir empty: {bf16_dir}")
-        return 0
-
-    # Expected reduction for FP8: ~50%.
-    min_reduction = 30
-    reduction = (1 - quant_size / bf16_size) * 100
-    print(f"[C] BF16 baseline transformer disk size: {bf16_size:.2f} GiB ({bf16_dir})")
-    print(f"[C] Disk reduction: {reduction:.1f}%  (FP8 transformer is {quant_size / bf16_size:.0%} of BF16)")
-    if reduction < min_reduction:
-        print(
-            f"[C] WARN — FP8 should typically reduce disk by ~40-50%; <{min_reduction}% suggests partial quantization."
-        )
-    return 0
-
-
-def main() -> None:
-    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
-    p.add_argument("--output", required=True, help="Path to the exported ModelOpt FP8 checkpoint root.")
-    p.add_argument(
-        "--baseline",
-        default=None,
-        help="Optional BF16 baseline (local diffusers dir or HF id) for disk-size comparison.",
-    )
-    args = p.parse_args()
-
-    out_root = Path(args.output).expanduser().resolve()
-    transformer_dir = out_root / "transformer"
-    if not transformer_dir.exists():
-        print(f"[FAIL] {transformer_dir} does not exist.")
-        sys.exit(1)
-
-    print(f"Checking: {out_root}\n")
-
-    fail = 0
-    config_status, _quant_algo = _check_config(transformer_dir)
-    fail |= config_status
-    fail |= _check_safetensors(transformer_dir)
-    _check_size_vs_baseline(transformer_dir, args.baseline)
-
-    print()
-    if fail == 0:
-        print("=" * 60)
-        print("ALL CHECKS PASSED — checkpoint looks ready for vllm-omni serving.")
-    elif fail == 1:
-        print("=" * 60)
-        print("FAILURES detected — calibration may need to be re-run.")
-        sys.exit(1)
-    else:
-        print("=" * 60)
-        print("WARNINGS only — checkpoint may serve but with caveats. See [A] above.")
-
-
-if __name__ == "__main__":
-    main()
@@ -1,448 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Quantize HunyuanVideo-1.5 (480p T2V) to a ModelOpt FP8 Hugging Face checkpoint.
-
-Calibrates the DiT transformer using a small video prompt set and exports a
-diffusers-style directory whose transformer carries ModelOpt FP8 metadata.
-The exported checkpoint is consumable by vllm-omni's ModelOpt FP8 adapter
-(see vllm_omni/diffusion/model_loader/checkpoint_adapters/modelopt.py).
-
-Layers kept full precision match the #2728 / #2795 pattern: modulation,
-AdaLayerNorm, entry/exit projections, embeddings, the token refiner path,
-and final proj_out. MHA quantizers are off by default; HV-1.5 self-attention
-empirically degrades under FP8 (see #2920 ablation).
-
-Example:
-    python examples/quantization/quantize_hunyuanvideo_15_modelopt_fp8.py \\
-        --model hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v \\
-        --output ./hv15-480p-modelopt-fp8 \\
-        --overwrite
-"""
-
-from __future__ import annotations
-
-import argparse
-import copy
-import json
-import re
-import shutil
-import sys
-from pathlib import Path
-from typing import Any
-
-import torch
-from diffusers import DiffusionPipeline
-
-DEFAULT_PROMPTS = [
-    "A dog running across a field of golden wheat.",
-    "An astronaut riding a horse across the surface of Mars, red dust swirling, cinematic wide shot.",
-    "A hummingbird hovering in front of a vibrant red flower, slow motion, macro shot.",
-    "A crackling campfire at night under a starry sky, sparks rising into the dark.",
-    "An underwater shot of a coral reef with tropical fish swimming by, sun rays piercing the water.",
-    "A close-up of a blooming rose covered in morning dew, soft natural light.",
-    "A peaceful mountain village at dawn, mist rolling over the rooftops, cinematic establishing shot.",
-    "A skateboarder doing a kickflip in an urban plaza, slow motion, golden hour lighting.",
-]
-
-
-def _build_parser() -> argparse.ArgumentParser:
-    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
-    p.add_argument("--model", required=True, help="Input HV-1.5 diffusers directory or HF id.")
-    p.add_argument("--output", required=True, help="Output directory for the ModelOpt FP8 checkpoint.")
-    p.add_argument("--dtype", choices=("bfloat16", "float16"), default="bfloat16")
-    p.add_argument("--height", type=int, default=480)
-    p.add_argument("--width", type=int, default=832)
-    p.add_argument(
-        "--num-frames",
-        type=int,
-        default=33,
-        help="Frames per calibration sample. 33 matches the typical short benchmark.",
-    )
-    p.add_argument("--guidance-scale", type=float, default=6.0)
-    p.add_argument(
-        "--calib-steps",
-        type=int,
-        default=10,
-        help="Denoising steps per calibration prompt (10 is enough for amax statistics).",
-    )
-    p.add_argument("--calib-size", type=int, default=8, help="How many prompts to use for calibration.")
-    p.add_argument("--seed", type=int, default=42)
-    p.add_argument(
-        "--prompt",
-        action="append",
-        default=[],
-        help="Custom calibration prompt. Repeat to provide multiple.",
-    )
-    p.add_argument(
-        "--quantize-mha",
-        action="store_true",
-        help="Enable FP8 attention K/V/softmax quantizers. Off by default — empirically degrades HV-1.5 video output.",
-    )
-    p.add_argument(
-        "--weight-block-size",
-        type=str,
-        default=None,
-        help="Per-block weight quantization as 'M,N' (e.g. '128,128' for 128x128 tiles). "
-        "Default: per-tensor (one scale per linear). Block-wise typically gives tighter quality at "
-        "negligible memory cost. Static FP8 is exempt from upstream vLLM's online block-wise gate.",
-    )
-    p.add_argument("--overwrite", action="store_true", help="Replace an existing output directory.")
-    return p
-
-
-def _parse_block_size(spec: str | None) -> list[int] | None:
-    if spec is None:
-        return None
-    parts = [int(x) for x in spec.split(",") if x.strip()]
-    if len(parts) != 2:
-        raise SystemExit(f"--weight-block-size must be 'M,N' (2 ints), got {spec!r}")
-    return parts
-
-
-def _require_modelopt() -> Any:
-    try:
-        import modelopt.torch.quantization as mtq
-    except ModuleNotFoundError as exc:
-        raise SystemExit(
-            "NVIDIA ModelOpt is not installed. Install with:\n"
-            "  pip install 'nvidia-modelopt[all]'\n"
-            f"Original error: {exc}"
-        ) from exc
-    return mtq
-
-
-def _ensure_paths(args: argparse.Namespace) -> tuple[str, Path]:
-    model_path = args.model
-    output_dir = Path(args.output).expanduser().resolve()
-    if output_dir.exists():
-        if not args.overwrite:
-            raise SystemExit(f"Output directory already exists: {output_dir}\nPass --overwrite to replace it.")
-        shutil.rmtree(output_dir)
-    return model_path, output_dir
-
-
-def _select_dtype(name: str) -> torch.dtype:
-    return {"bfloat16": torch.bfloat16, "float16": torch.float16}[name]
-
-
-def _build_prompts(args: argparse.Namespace) -> list[str]:
-    prompts = args.prompt or DEFAULT_PROMPTS
-    if args.calib_size <= 0:
-        raise SystemExit("--calib-size must be positive.")
-    if len(prompts) < args.calib_size:
-        repeats = (args.calib_size + len(prompts) - 1) // len(prompts)
-        prompts = (prompts * repeats)[: args.calib_size]
-    return prompts[: args.calib_size]
-
-
-# Layers to KEEP at full precision (mirror of the #2920 wiring + #2728/#2795 skip pattern).
-# - x_embedder, image_embedder, context_embedder*, time_embed*, cond_type_embed: entry/embedding
-# - norm_out, norm1*.linear, norm1_context*.linear, norm2*, norm2_context*: AdaLayerNorm modulation
-# - proj_out: final output projection
-# - token_refiner*: text-encoder refinement uses diffusers raw nn.Linear
-def _filter_func_hv15(name: str) -> bool:
-    pattern = re.compile(
-        r"(proj_out.*|"
-        r".*(x_embedder|image_embedder|context_embedder|context_embedder_2|"
-        r"time_embed|cond_type_embed|"
-        r"norm_out|norm1\.linear|norm1_context\.linear|norm2|norm2_context|"
-        r"token_refiner).*)"
-    )
-    return pattern.match(name) is not None
-
-
-def _mha_filter_func(name: str) -> bool:
-    pattern = re.compile(
-        r".*(q_bmm_quantizer|k_bmm_quantizer|v_bmm_quantizer|softmax_quantizer|bmm2_output_quantizer).*"
-    )
-    return pattern.match(name) is not None
-
-
-def _disable_known_problematic_quantizers(mtq: Any, backbone: torch.nn.Module, *, quantize_mha: bool) -> None:
-    if not hasattr(mtq, "disable_quantizer"):
-        return
-    mtq.disable_quantizer(backbone, _filter_func_hv15)
-    if not quantize_mha:
-        mtq.disable_quantizer(backbone, _mha_filter_func)
-
-
-def _load_pipeline(model_path: str, dtype: torch.dtype) -> DiffusionPipeline:
-    pipe = DiffusionPipeline.from_pretrained(model_path, torch_dtype=dtype)
-    if hasattr(pipe, "set_progress_bar_config"):
-        pipe.set_progress_bar_config(disable=True)
-    pipe.to("cuda")
-    return pipe
-
-
-def _build_forward_loop(pipe: DiffusionPipeline, args: argparse.Namespace, prompts: list[str]):
-    generator = torch.Generator(device="cuda")
-
-    # Try to set guidance on the pipeline's guider object up front (modern
-    # diffusers HV-1.5 uses a Guider abstraction, not a per-call kwarg). Falls
-    # back silently — calibration uses whatever default the pipeline ships with.
-    guider = getattr(pipe, "guider", None)
-    if guider is not None and hasattr(guider, "guidance_scale"):
-        try:
-            guider.guidance_scale = args.guidance_scale
-        except Exception:
-            pass
-
-    base_kwargs = dict(
-        height=args.height,
-        width=args.width,
-        num_frames=args.num_frames,
-        num_inference_steps=args.calib_steps,
-        output_type="latent",
-    )
-
-    def forward_loop(*_unused_args, **_unused_kwargs) -> None:
-        with torch.inference_mode():
-            for idx, prompt in enumerate(prompts):
-                generator.manual_seed(args.seed + idx)
-                # Try with guidance_scale first; fall back without on TypeError
-                # for pipelines (like HV-1.5) that take CFG via guider config.
-                try:
-                    pipe(prompt=prompt, generator=generator, guidance_scale=args.guidance_scale, **base_kwargs)
-                except TypeError as exc:
-                    if "guidance_scale" not in str(exc):
-                        raise
-                    pipe(prompt=prompt, generator=generator, **base_kwargs)
-
-    return forward_loop
-
-
-def _summarize_export(output_dir: Path) -> None:
-    cfg_path = output_dir / "transformer" / "config.json"
-    if not cfg_path.exists():
-        print(f"[warn] {cfg_path} missing.", file=sys.stderr)
-        return
-    with cfg_path.open(encoding="utf-8") as f:
-        cfg = json.load(f)
-    qc = cfg.get("quantization_config")
-    if not isinstance(qc, dict):
-        print("[warn] No quantization_config in transformer/config.json.", file=sys.stderr)
-        return
-    print("Export summary:")
-    print(f"  quant_method: {qc.get('quant_method')}")
-    print(f"  quant_algo:   {qc.get('quant_algo')}")
-    producer = qc.get("producer")
-    if isinstance(producer, dict):
-        print(f"  producer:     {producer.get('name')} {producer.get('version')}")
-    print(f"  config path:  {cfg_path}")
-
-
-def _force_export_quantized_weights(backbone: torch.nn.Module, dtype: torch.dtype) -> int:
-    """Convert in-memory weights of quantized modules to actual FP8 storage.
-
-    `export_hf_checkpoint` skips this step for unknown model types (HV-1.5 isn't
-    in ModelOpt's recognized-model registry), so we must call the per-weight
-    export helper ourselves. Same workaround as the HunyuanImage-3 calibration
-    helper.
-    """
-    from modelopt.torch.export.quant_utils import (
-        QUANTIZATION_NONE,
-        get_quantization_format,
-        quantizer_attr_names,
-        weight_attr_names,
-    )
-    from modelopt.torch.export.unified_export_hf import _export_quantized_weight
-
-    exported = 0
-    for name, module in backbone.named_modules():
-        try:
-            quantization_format = get_quantization_format(module)
-        except Exception as exc:
-            print(f"[warn] Could not inspect quantization format for {name}: {exc}", file=sys.stderr)
-            continue
-        if quantization_format == QUANTIZATION_NONE:
-            continue
-        for weight_name in weight_attr_names(module):
-            quantizer_attrs = quantizer_attr_names(weight_name)
-            weight_quantizer = getattr(module, quantizer_attrs.weight_quantizer, None)
-            if weight_quantizer is None or not getattr(weight_quantizer, "is_enabled", False):
-                continue
-            _export_quantized_weight(module, dtype, weight_name)
-            exported += 1
-    return exported
-
-
-def _hv15_quant_config_block(weight_block_size: list[int] | None = None) -> dict:
-    """Mirror ModelOpt FP8 metadata expected by vllm-omni's adapter (#2913).
-
-    Same shape as the HunyuanImage-3 author's _hunyuan_quant_config(). When
-    `weight_block_size` is given, advertise block-wise weight quantization in
-    the saved metadata (so consumers know to expect multi-element scale tensors).
-    """
-    weights_cfg: dict = {"dynamic": False, "num_bits": 8, "type": "float"}
-    if weight_block_size is not None:
-        weights_cfg["strategy"] = "block"
-        weights_cfg["block_structure"] = f"{weight_block_size[0]}x{weight_block_size[1]}"
-    return {
-        "config_groups": {
-            "group_0": {
-                "input_activations": {"dynamic": False, "num_bits": 8, "type": "float"},
-                "weights": weights_cfg,
-                "targets": ["Linear"],
-            }
-        },
-        "ignore": [
-            "context_embedder*",
-            "context_embedder_2*",
-            "cond_type_embed*",
-            "image_embedder*",
-            "norm1.linear*",
-            "norm1_context.linear*",
-            "norm2*",
-            "norm2_context*",
-            "norm_out*",
-            "proj_out*",
-            "time_embed*",
-            "token_refiner*",
-            "x_embedder*",
-        ],
-        "producer": {"name": "modelopt"},
-        "quant_algo": "FP8",
-        "quant_method": "modelopt",
-    }
-
-
-def _patch_quant_config(output_dir: Path, weight_block_size: list[int] | None = None) -> None:
-    """Inject quant_algo: FP8 + config_groups into transformer/config.json so
-    vllm-omni's adapter (#2913) recognises the checkpoint as ModelOpt FP8."""
-    cfg_path = output_dir / "transformer" / "config.json"
-    with cfg_path.open(encoding="utf-8") as f:
-        cfg = json.load(f)
-
-    new_qc = _hv15_quant_config_block(weight_block_size=weight_block_size)
-    existing = cfg.get("quantization_config")
-    if isinstance(existing, dict):
-        producer = existing.get("producer")
-        if isinstance(producer, dict):
-            new_qc["producer"] = producer
-
-    cfg["quantization_config"] = new_qc
-    with cfg_path.open("w", encoding="utf-8") as f:
-        json.dump(cfg, f, indent=2)
-
-
-def _save_pipeline_with_fp8_transformer(
-    pipe: DiffusionPipeline,
-    model_path: str,
-    output_dir: Path,
-    max_shard_size: str = "5GB",
-) -> None:
-    """Save the pipeline with the (now FP8) transformer.
-
-    Copies the source directory verbatim except for `transformer/`, then
-    saves the transformer with quantizers hidden so the state dict contains
-    only the FP8 weights + scale tensors.
-    """
-    from modelopt.torch.export.diffusers_utils import hide_quantizers_from_state_dict
-
-    src = Path(model_path)
-    if not src.exists():
-        from huggingface_hub import snapshot_download
-
-        src = Path(snapshot_download(model_path))
-
-    if output_dir.exists():
-        shutil.rmtree(output_dir)
-    shutil.copytree(src, output_dir, ignore=shutil.ignore_patterns("transformer"))
-
-    transformer_out = output_dir / "transformer"
-    # `hide_quantizers_from_state_dict` walks named_modules(); pass the actual
-    # nn.Module (transformer), not the diffusers Pipeline wrapper.
-    with hide_quantizers_from_state_dict(pipe.transformer):
-        pipe.transformer.save_pretrained(
-            str(transformer_out),
-            safe_serialization=True,
-            max_shard_size=max_shard_size,
-        )
-
-
-def main() -> None:
-    args = _build_parser().parse_args()
-    if not torch.cuda.is_available():
-        raise SystemExit("CUDA is required for ModelOpt FP8 quantization.")
-
-    mtq = _require_modelopt()
-    model_path, output_dir = _ensure_paths(args)
-    dtype = _select_dtype(args.dtype)
-    prompts = _build_prompts(args)
-
-    print("Quantization plan:")
-    weight_block_size = _parse_block_size(args.weight_block_size)
-
-    print(f"  input:           {args.model}")
-    print(f"  output:          {output_dir}")
-    print(f"  dtype:           {dtype}")
-    print(f"  height/width:    {args.height}x{args.width}")
-    print(f"  num_frames:      {args.num_frames}")
-    print(f"  calib_size:      {len(prompts)}")
-    print(f"  calib_steps:     {args.calib_steps}")
-    print(f"  quantize_mha:    {args.quantize_mha}")
-    print(
-        f"  weight strategy: {'block-wise ' + str(weight_block_size) if weight_block_size else 'per-tensor (default)'}"
-    )
-
-    pipe = _load_pipeline(model_path, dtype)
-    backbone = pipe.transformer
-
-    quant_config = copy.deepcopy(mtq.FP8_DEFAULT_CFG)
-    if weight_block_size is not None:
-        # Switch from per-tensor (default) to block-wise weight quantization.
-        # ModelOpt's wildcard "*weight_quantizer" matches every linear's weight quantizer.
-        quant_config["quant_cfg"]["*weight_quantizer"] = {
-            "num_bits": (4, 3),  # E4M3 (FP8 weights, same as default)
-            "block_sizes": {-1: weight_block_size[1], -2: weight_block_size[0]},
-        }
-        print(
-            f"  -> overriding weight quantizer with block_sizes={weight_block_size} "
-            f"({weight_block_size[0]}x{weight_block_size[1]} tiles)"
-        )
-
-    forward_loop = _build_forward_loop(pipe, args, prompts)
-    quantized = mtq.quantize(backbone, quant_config, forward_loop)
-    if quantized is not None:
-        pipe.transformer = quantized
-        backbone = quantized
-
-    _disable_known_problematic_quantizers(mtq, backbone, quantize_mha=args.quantize_mha)
-
-    print("\nForcing FP8 weight serialization (HV-1.5 isn't in ModelOpt's recognized-model registry,")
-    print("so we have to call the per-weight export helper ourselves)...")
-    exported = _force_export_quantized_weights(backbone, dtype)
-    print(f"  -> {exported} weights converted to FP8 in memory")
-    if exported == 0:
-        raise SystemExit(
-            "No quantized weights were exported. Calibration may have skipped every layer "
-            "(check the disable_quantizer regex) or `mtq.quantize` did not actually wrap any "
-            "weight quantizers."
-        )
-
-    print("\nSaving pipeline with FP8 transformer...")
-    _save_pipeline_with_fp8_transformer(pipe, model_path, output_dir)
-    _patch_quant_config(output_dir, weight_block_size=weight_block_size)
-    print(f"Saved to: {output_dir}")
-    _summarize_export(output_dir)
-
-    print("\nNext: validate the checkpoint with vllm-omni:")
-    print(
-        "  python examples/offline_inference/text_to_video/text_to_video.py \\\n"
-        f"    --model {output_dir} \\\n"
-        "    --quantization fp8 \\\n"
-        "    --prompt 'A dog running across a field of golden wheat.' \\\n"
-        f"    --height {args.height} --width {args.width} --num-frames {args.num_frames} \\\n"
-        "    --num-inference-steps 30 --guidance-scale 6.0 --seed 42 \\\n"
-        "    --output outputs/hv15_modelopt_fp8.mp4 \\\n"
-        "    --enforce-eager"
-    )
-    print(
-        "\n  (--quantization fp8 is auto-upgraded to ModelOpt FP8 at runtime because the "
-        "checkpoint's config.json has modelopt metadata.)"
-    )
-
-
-if __name__ == "__main__":
-    main()
@@ -1,471 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Quantize Wan2.2 (TI2V-5B, 704x1280 T2V) to a ModelOpt FP8 Hugging Face checkpoint.
-
-Calibrates the DiT transformer using a small video prompt set and exports a
-diffusers-style directory whose transformer carries ModelOpt FP8 metadata.
-The exported checkpoint is consumable by vllm-omni's ModelOpt FP8 adapter
-(see vllm_omni/diffusion/model_loader/checkpoint_adapters/modelopt.py).
-
-Layers kept full precision match the #2728 / #2795 pattern: condition embedder
-(time/text/image), patch embedding, modulation (scale_shift_table), final
-norm + proj_out, and sequence-parallel helpers. All attention + FFN linears
-are quantized — static calibration handles the numerics that online FP8
-couldn't (see #2920 ablation).
-
-Default target is `Wan-AI/Wan2.2-TI2V-5B-Diffusers`, the dense 5B variant that
-fits 80GB BF16. The A14B MoE variants need 2+ GPUs and are out of scope here.
-
-Example:
-    python examples/quantization/quantize_wan2_2_modelopt_fp8.py \\
-        --model Wan-AI/Wan2.2-TI2V-5B-Diffusers \\
-        --output ./wan22-ti2v-modelopt-fp8 \\
-        --overwrite
-"""
-
-from __future__ import annotations
-
-import argparse
-import copy
-import json
-import re
-import shutil
-import sys
-from pathlib import Path
-from typing import Any
-
-import torch
-from diffusers import DiffusionPipeline
-
-DEFAULT_PROMPTS = [
-    "A dog running across a field of golden wheat.",
-    "An astronaut riding a horse across the surface of Mars, red dust swirling, cinematic wide shot.",
-    "A hummingbird hovering in front of a vibrant red flower, slow motion, macro shot.",
-    "A crackling campfire at night under a starry sky, sparks rising into the dark.",
-    "An underwater shot of a coral reef with tropical fish swimming by, sun rays piercing the water.",
-    "A close-up of a blooming rose covered in morning dew, soft natural light.",
-    "A peaceful mountain village at dawn, mist rolling over the rooftops, cinematic establishing shot.",
-    "A skateboarder doing a kickflip in an urban plaza, slow motion, golden hour lighting.",
-]
-
-
-def _build_parser() -> argparse.ArgumentParser:
-    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
-    p.add_argument("--model", required=True, help="Input Wan2.2 diffusers directory or HF id.")
-    p.add_argument("--output", required=True, help="Output directory for the ModelOpt FP8 checkpoint.")
-    p.add_argument("--dtype", choices=("bfloat16", "float16"), default="bfloat16")
-    p.add_argument("--height", type=int, default=704, help="Calibration video height (Wan2.2 TI2V-5B native: 704).")
-    p.add_argument("--width", type=int, default=1280, help="Calibration video width (Wan2.2 TI2V-5B native: 1280).")
-    p.add_argument(
-        "--num-frames",
-        type=int,
-        default=49,
-        help="Frames per calibration sample. 49 matches the typical short benchmark; "
-        "use 17 to reduce memory pressure during calibration.",
-    )
-    p.add_argument("--guidance-scale", type=float, default=5.0)
-    p.add_argument(
-        "--calib-steps",
-        type=int,
-        default=10,
-        help="Denoising steps per calibration prompt (10 is enough for amax statistics).",
-    )
-    p.add_argument("--calib-size", type=int, default=8, help="How many prompts to use for calibration.")
-    p.add_argument("--seed", type=int, default=42)
-    p.add_argument(
-        "--prompt",
-        action="append",
-        default=[],
-        help="Custom calibration prompt. Repeat to provide multiple.",
-    )
-    p.add_argument(
-        "--quantize-mha",
-        action="store_true",
-        help="Enable FP8 attention K/V/softmax quantizers. Off by default — Wan2.2's long attention "
-        "sequences amplified FP8 drift in the online ablation (see #2920).",
-    )
-    p.add_argument(
-        "--weight-block-size",
-        type=str,
-        default=None,
-        help="Per-block weight quantization as 'M,N' (e.g. '128,128'). Default per-tensor. "
-        "Note: vllm-omni's ModelOpt adapter may not yet dispatch block-wise scales — check #2924 "
-        "for the HV-1.5 investigation status before relying on this.",
-    )
-    p.add_argument("--overwrite", action="store_true", help="Replace an existing output directory.")
-    return p
-
-
-def _parse_block_size(spec: str | None) -> list[int] | None:
-    if spec is None:
-        return None
-    parts = [int(x) for x in spec.split(",") if x.strip()]
-    if len(parts) != 2:
-        raise SystemExit(f"--weight-block-size must be 'M,N' (2 ints), got {spec!r}")
-    return parts
-
-
-def _require_modelopt() -> Any:
-    try:
-        import modelopt.torch.quantization as mtq
-    except ModuleNotFoundError as exc:
-        raise SystemExit(
-            "NVIDIA ModelOpt is not installed. Install with:\n"
-            "  pip install 'nvidia-modelopt[all]'\n"
-            f"Original error: {exc}"
-        ) from exc
-    return mtq
-
-
-def _ensure_paths(args: argparse.Namespace) -> tuple[str, Path]:
-    model_path = args.model
-    output_dir = Path(args.output).expanduser().resolve()
-    if output_dir.exists():
-        if not args.overwrite:
-            raise SystemExit(f"Output directory already exists: {output_dir}\nPass --overwrite to replace it.")
-        shutil.rmtree(output_dir)
-    return model_path, output_dir
-
-
-def _select_dtype(name: str) -> torch.dtype:
-    return {"bfloat16": torch.bfloat16, "float16": torch.float16}[name]
-
-
-def _build_prompts(args: argparse.Namespace) -> list[str]:
-    prompts = args.prompt or DEFAULT_PROMPTS
-    if args.calib_size <= 0:
-        raise SystemExit("--calib-size must be positive.")
-    if len(prompts) < args.calib_size:
-        repeats = (args.calib_size + len(prompts) - 1) // len(prompts)
-        prompts = (prompts * repeats)[: args.calib_size]
-    return prompts[: args.calib_size]
-
-
-# Layers to KEEP at full precision. Wan2.2's module naming:
-# - condition_embedder: time_embedder, time_proj, text_embedder, image_embedder (I2V)
-# - patch_embedding: Conv3dLayer (already not Linear, belt-and-suspenders skip)
-# - scale_shift_table: nn.Parameter modulation (not Linear, but pattern guard)
-# - norm_out: AdaLayerNorm final
-# - proj_out: final nn.Linear
-# - timestep_proj_prepare / output_scale_shift_prepare: SP helpers
-def _filter_func_wan22(name: str) -> bool:
-    pattern = re.compile(
-        r"(proj_out.*|"
-        r".*(condition_embedder|patch_embedding|"
-        r"norm_out|scale_shift_table|"
-        r"timestep_proj_prepare|output_scale_shift_prepare).*)"
-    )
-    return pattern.match(name) is not None
-
-
-def _mha_filter_func(name: str) -> bool:
-    pattern = re.compile(
-        r".*(q_bmm_quantizer|k_bmm_quantizer|v_bmm_quantizer|softmax_quantizer|bmm2_output_quantizer).*"
-    )
-    return pattern.match(name) is not None
-
-
-def _disable_known_problematic_quantizers(mtq: Any, backbone: torch.nn.Module, *, quantize_mha: bool) -> None:
-    if not hasattr(mtq, "disable_quantizer"):
-        return
-    mtq.disable_quantizer(backbone, _filter_func_wan22)
-    if not quantize_mha:
-        mtq.disable_quantizer(backbone, _mha_filter_func)
-
-
-def _load_pipeline(model_path: str, dtype: torch.dtype) -> DiffusionPipeline:
-    pipe = DiffusionPipeline.from_pretrained(model_path, torch_dtype=dtype)
-    if hasattr(pipe, "set_progress_bar_config"):
-        pipe.set_progress_bar_config(disable=True)
-    pipe.to("cuda")
-    return pipe
-
-
-def _build_forward_loop(pipe: DiffusionPipeline, args: argparse.Namespace, prompts: list[str]):
-    generator = torch.Generator(device="cuda")
-
-    # Try setting guidance on the pipeline's guider if present (newer diffusers APIs).
-    guider = getattr(pipe, "guider", None)
-    if guider is not None and hasattr(guider, "guidance_scale"):
-        try:
-            guider.guidance_scale = args.guidance_scale
-        except Exception:
-            pass
-
-    base_kwargs = dict(
-        height=args.height,
-        width=args.width,
-        num_frames=args.num_frames,
-        num_inference_steps=args.calib_steps,
-        output_type="latent",
-    )
-
-    def forward_loop(*_unused_args, **_unused_kwargs) -> None:
-        with torch.inference_mode():
-            for idx, prompt in enumerate(prompts):
-                generator.manual_seed(args.seed + idx)
-                # Try with guidance_scale first; fall back without on TypeError
-                # for pipelines that take CFG via guider config only.
-                try:
-                    pipe(prompt=prompt, generator=generator, guidance_scale=args.guidance_scale, **base_kwargs)
-                except TypeError as exc:
-                    if "guidance_scale" not in str(exc):
-                        raise
-                    pipe(prompt=prompt, generator=generator, **base_kwargs)
-
-    return forward_loop
-
-
-def _summarize_export(output_dir: Path) -> None:
-    cfg_path = output_dir / "transformer" / "config.json"
-    if not cfg_path.exists():
-        print(f"[warn] {cfg_path} missing.", file=sys.stderr)
-        return
-    with cfg_path.open(encoding="utf-8") as f:
-        cfg = json.load(f)
-    qc = cfg.get("quantization_config")
-    if not isinstance(qc, dict):
-        print("[warn] No quantization_config in transformer/config.json.", file=sys.stderr)
-        return
-    print("Export summary:")
-    print(f"  quant_method: {qc.get('quant_method')}")
-    print(f"  quant_algo:   {qc.get('quant_algo')}")
-    producer = qc.get("producer")
-    if isinstance(producer, dict):
-        print(f"  producer:     {producer.get('name')} {producer.get('version')}")
-    print(f"  config path:  {cfg_path}")
-
-
-def _force_export_quantized_weights(backbone: torch.nn.Module, dtype: torch.dtype) -> int:
-    """Convert in-memory weights of quantized modules to actual FP8 storage.
-
-    `export_hf_checkpoint` skips this step for unknown model types (Wan2.2 isn't
-    in ModelOpt's recognized-model registry), so we must call the per-weight
-    export helper ourselves. Same workaround as the HunyuanVideo-1.5 / HunyuanImage-3
-    calibration helpers.
-    """
-    from modelopt.torch.export.quant_utils import (
-        QUANTIZATION_NONE,
-        get_quantization_format,
-        quantizer_attr_names,
-        weight_attr_names,
-    )
-    from modelopt.torch.export.unified_export_hf import _export_quantized_weight
-
-    exported = 0
-    for name, module in backbone.named_modules():
-        try:
-            quantization_format = get_quantization_format(module)
-        except Exception as exc:
-            print(f"[warn] Could not inspect quantization format for {name}: {exc}", file=sys.stderr)
-            continue
-        if quantization_format == QUANTIZATION_NONE:
-            continue
-        for weight_name in weight_attr_names(module):
-            quantizer_attrs = quantizer_attr_names(weight_name)
-            weight_quantizer = getattr(module, quantizer_attrs.weight_quantizer, None)
-            if weight_quantizer is None or not getattr(weight_quantizer, "is_enabled", False):
-                continue
-            _export_quantized_weight(module, dtype, weight_name)
-            exported += 1
-    return exported
-
-
-def _wan22_quant_config_block(weight_block_size: list[int] | None = None) -> dict:
-    """Mirror ModelOpt FP8 metadata expected by vllm-omni's adapter (#2913)."""
-    weights_cfg: dict = {"dynamic": False, "num_bits": 8, "type": "float"}
-    if weight_block_size is not None:
-        weights_cfg["strategy"] = "block"
-        weights_cfg["block_structure"] = f"{weight_block_size[0]}x{weight_block_size[1]}"
-    return {
-        "config_groups": {
-            "group_0": {
-                "input_activations": {"dynamic": False, "num_bits": 8, "type": "float"},
-                "weights": weights_cfg,
-                "targets": ["Linear"],
-            }
-        },
-        "ignore": [
-            "condition_embedder*",
-            "norm_out*",
-            "output_scale_shift_prepare*",
-            "patch_embedding*",
-            "proj_out*",
-            "scale_shift_table*",
-            "timestep_proj_prepare*",
-        ],
-        "producer": {"name": "modelopt"},
-        "quant_algo": "FP8",
-        "quant_method": "modelopt",
-    }
-
-
-def _patch_quant_config(output_dir: Path, weight_block_size: list[int] | None = None) -> None:
-    """Inject quant_algo: FP8 + config_groups into each transformer's config.json
-    so vllm-omni's adapter (#2913) recognises the checkpoint as ModelOpt FP8.
-
-    Patches both ``transformer`` and ``transformer_2`` (A14B) when present."""
-    for sub in ("transformer", "transformer_2"):
-        cfg_path = output_dir / sub / "config.json"
-        if not cfg_path.exists():
-            continue
-        with cfg_path.open(encoding="utf-8") as f:
-            cfg = json.load(f)
-
-        new_qc = _wan22_quant_config_block(weight_block_size=weight_block_size)
-        existing = cfg.get("quantization_config")
-        if isinstance(existing, dict):
-            producer = existing.get("producer")
-            if isinstance(producer, dict):
-                new_qc["producer"] = producer
-
-        cfg["quantization_config"] = new_qc
-        with cfg_path.open("w", encoding="utf-8") as f:
-            json.dump(cfg, f, indent=2)
-
-
-def _list_transformers(pipe: DiffusionPipeline) -> list[tuple[str, torch.nn.Module]]:
-    """Return [(attr_name, module)] for every DiT on the pipeline.
-
-    Wan2.2 A14B has both ``transformer`` (low-noise) and ``transformer_2``
-    (high-noise). TI2V-5B has only ``transformer``.
-    """
-    out = []
-    for attr in ("transformer", "transformer_2"):
-        mod = getattr(pipe, attr, None)
-        if mod is not None:
-            out.append((attr, mod))
-    return out
-
-
-def _save_pipeline_with_fp8_transformer(
-    pipe: DiffusionPipeline,
-    model_path: str,
-    output_dir: Path,
-    max_shard_size: str = "5GB",
-) -> None:
-    """Copy source dir verbatim minus transformer dirs, then save each quantized DiT.
-
-    Handles A14B's dual transformer (``transformer`` + ``transformer_2``) as well
-    as the single-DiT TI2V-5B.
-    """
-    from modelopt.torch.export.diffusers_utils import hide_quantizers_from_state_dict
-
-    src = Path(model_path)
-    if not src.exists():
-        from huggingface_hub import snapshot_download
-
-        src = Path(snapshot_download(model_path))
-
-    if output_dir.exists():
-        shutil.rmtree(output_dir)
-    shutil.copytree(src, output_dir, ignore=shutil.ignore_patterns("transformer", "transformer_2"))
-
-    for attr, backbone in _list_transformers(pipe):
-        transformer_out = output_dir / attr
-        # Pass the nn.Module (transformer), not the Pipeline wrapper.
-        with hide_quantizers_from_state_dict(backbone):
-            backbone.save_pretrained(
-                str(transformer_out),
-                safe_serialization=True,
-                max_shard_size=max_shard_size,
-            )
-
-
-def main() -> None:
-    args = _build_parser().parse_args()
-    if not torch.cuda.is_available():
-        raise SystemExit("CUDA is required for ModelOpt FP8 quantization.")
-
-    mtq = _require_modelopt()
-    model_path, output_dir = _ensure_paths(args)
-    dtype = _select_dtype(args.dtype)
-    prompts = _build_prompts(args)
-    weight_block_size = _parse_block_size(args.weight_block_size)
-
-    print("Quantization plan:")
-    print(f"  input:           {args.model}")
-    print(f"  output:          {output_dir}")
-    print(f"  dtype:           {dtype}")
-    print(f"  height/width:    {args.height}x{args.width}")
-    print(f"  num_frames:      {args.num_frames}")
-    print(f"  calib_size:      {len(prompts)}")
-    print(f"  calib_steps:     {args.calib_steps}")
-    print(f"  quantize_mha:    {args.quantize_mha}")
-    print(
-        f"  weight strategy: {'block-wise ' + str(weight_block_size) if weight_block_size else 'per-tensor (default)'}"
-    )
-
-    pipe = _load_pipeline(model_path, dtype)
-    transformers = _list_transformers(pipe)
-    if not transformers:
-        raise SystemExit("Pipeline has no transformer or transformer_2 attribute.")
-    print(f"  found {len(transformers)} transformer(s): {', '.join(a for a, _ in transformers)}")
-
-    quant_config = copy.deepcopy(mtq.FP8_DEFAULT_CFG)
-    if weight_block_size is not None:
-        quant_config["quant_cfg"]["*weight_quantizer"] = {
-            "num_bits": (4, 3),
-            "block_sizes": {-1: weight_block_size[1], -2: weight_block_size[0]},
-        }
-        print(
-            f"  -> overriding weight quantizer with block_sizes={weight_block_size} "
-            f"({weight_block_size[0]}x{weight_block_size[1]} tiles)"
-        )
-
-    forward_loop = _build_forward_loop(pipe, args, prompts)
-
-    # Quantize each DiT. The forward_loop runs the full pipeline, so quantizers
-    # on every attached transformer (A14B: transformer + transformer_2) get
-    # calibrated during the same passes.
-    for attr, backbone in transformers:
-        print(f"\nQuantizing {attr}...")
-        quantized = mtq.quantize(backbone, quant_config, forward_loop)
-        if quantized is not None:
-            setattr(pipe, attr, quantized)
-        _disable_known_problematic_quantizers(mtq, getattr(pipe, attr), quantize_mha=args.quantize_mha)
-
-    # Export weights to FP8 only AFTER all transformers are calibrated. Exporting
-    # inside the loop would convert transformer's weights to real FP8, and the
-    # next transformer's forward_loop (full pipeline) would then fake-quantize an
-    # already-FP8 weight -> "fake_e4m3fy not implemented for Float8_e4m3fn".
-    print("\nForcing FP8 weight serialization (Wan2.2 isn't in ModelOpt's recognized-model registry,")
-    print("so we have to call the per-weight export helper ourselves)...")
-    total_exported = 0
-    for attr, backbone in _list_transformers(pipe):
-        exported = _force_export_quantized_weights(backbone, dtype)
-        print(f"  -> {exported} weights converted to FP8 in {attr}")
-        total_exported += exported
-
-    if total_exported == 0:
-        raise SystemExit(
-            "No quantized weights were exported. Calibration may have skipped every layer "
-            "(check the disable_quantizer regex) or `mtq.quantize` did not actually wrap any "
-            "weight quantizers."
-        )
-
-    print("\nSaving pipeline with FP8 transformer(s)...")
-    _save_pipeline_with_fp8_transformer(pipe, model_path, output_dir)
-    _patch_quant_config(output_dir, weight_block_size=weight_block_size)
-    print(f"Saved to: {output_dir}")
-    _summarize_export(output_dir)
-
-    print("\nNext: validate the checkpoint with vllm-omni:")
-    print(
-        "  python examples/offline_inference/text_to_video/text_to_video.py \\\n"
-        f"    --model {output_dir} \\\n"
-        "    --quantization fp8 \\\n"
-        "    --prompt 'A dog running across a field of golden wheat.' \\\n"
-        f"    --height {args.height} --width {args.width} --num-frames {args.num_frames} \\\n"
-        "    --num-inference-steps 30 --guidance-scale 5.0 --seed 42 \\\n"
-        "    --output outputs/wan22_modelopt_fp8.mp4"
-    )
-    print(
-        "\n  (--quantization fp8 is auto-upgraded to ModelOpt FP8 at runtime because the "
-        "checkpoint's config.json has modelopt metadata.)"
-    )
-
-
-if __name__ == "__main__":
-    main()
@@ -79,7 +79,8 @@ sharing the tokenizer, processor, text encoder, and VAEs from `FL2VA`.
 ### Multi-NPU: 768P combined-service configuration

 Use Ulysses sequence parallelism degree 8, text-encoder tensor parallelism
-degree 8, native tiled VAE patch parallelism degree 8, and layerwise offload:
+degree 8, native tiled VAE patch parallelism degree 8, and distributed
+layerwise offload:

 ```bash
 export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
@@ -98,7 +99,7 @@ vllm serve "${MODEL}" \
  --usp 8 \
  --ring 1 \
  --text-encoder-tp-size 8 \
-  --enable-layerwise-offload \
+  --enable-distributed-layerwise-offload \
  --vae-parallel-mode tile \
  --vae-use-tiling \
  --vae-patch-parallel-size 8 \
@@ -112,6 +113,11 @@ H3 is CFG-distilled, so `--cfg-parallel-size` must remain 1.
 The same endpoint accepts `task=t2va`, `task=fl2va`, and `task=ref2va`; no
 partition restart is required. Layerwise offload applies to both DiTs.

+On Atlas 800I A3 (64 GB HBM per device) the combined service does not fit at
+768P without offloading or sharding: use distributed layerwise offload (as
+above) or HSDP — see
+[§ Memory and attention optimizations](#memory-and-attention-optimizations-a3).
+
 ### Optional optimizations

 Two independent optimizations may be enabled on top of the configuration
@@ -134,6 +140,63 @@ Keep `--ring 1` when using RainFusion: the `rf_v2` kernel ranks key blocks
 over the whole sequence, so ring parallelism would split away the keys it
 needs. Scale with `--usp` instead.

+## Memory and attention optimizations (A3)
+
+### Fitting 768P into 64 GB HBM: distributed layerwise offload or HSDP
+
+Each Atlas 800I A3 NPU has 64 GB of HBM, which is not enough for the
+combined MiniMax-H3 service at 768P. Enable **one** of the following at
+server startup:
+
+**Distributed layerwise offload** — DiT layers are offloaded with parameters
+gathered across the parallel group instead of replicated per rank:
+
+```bash
+  --enable-distributed-layerwise-offload
+```
+
+Measured peak NPU memory is about 45 GB per device for Ref2VA with a 13.88 s
+input video generating a 15 s 768P (1344x768) output video. Host (CPU) memory
+usage is high with this option.
+
+**HSDP** — hybrid sharded data parallelism for the DiT parameters. The
+multi-stream memory-reuse knob is mandatory with this flag:
+
+```bash
+export MULTI_STREAM_MEMORY_REUSE=2
+```
+
+```bash
+  --use-hsdp
+```
+
+Host memory usage is small, but large shapes may still OOM; HBM usage
+optimizations for this path are ongoing.
+
+### FLASH_ATTN backend with MindIE-SD
+
+Keep `--diffusion-attention-backend FLASH_ATTN` and install MindIE-SD (see
+[§ Environment](#environment)). On NPU this backend carries most of the
+memory-reduction and performance work: a mask-free packed varlen path that
+never materializes the quadratic `full_qk` padding mask, and K/V prefix
+slicing, both driven by the packed `cu_seqlens` metadata emitted by the H3
+transformer.
+
+### Optional: LaserAttention fused kernel
+
+For an additional attention speedup, select the Ascend LaserAttention fused
+kernel before starting the server:
+
+```bash
+export MINDIE_SD_FA_TYPE="ascend_laser_attention"
+```
+
+This requires the FLASH_ATTN backend and MindIE-SD. H3 automatically applies
+exact power-of-two input pre-scaling (`laser_input_scale=256`) so the
+kernel's fp16 workspace cannot overflow on outlier activations. Measured
+speedup numbers will be added here.
+
+
 ## HTTP API examples

 The request format is identical to the GPU recipe; see
@@ -154,8 +217,8 @@ configuration above:

 | Workload | Configuration |
 |----------|---------------|
-| T2VA, 209 frames, 1344x768 | TE TP8, layerwise offload, Ulysses 8, VPP8 tile, regional compile |
-| Ref2VA (prompt + video), 124 frames, 1344x768 | TE TP8, layerwise offload, Ulysses 8, VPP8 tile, regional compile |
+| T2VA, 209 frames, 1344x768 | TE TP8, distributed layerwise offload, Ulysses 8, VPP8 tile, regional compile |
+| Ref2VA (prompt + video), 124 frames, 1344x768 | TE TP8, distributed layerwise offload, Ulysses 8, VPP8 tile, regional compile |

 These measurements describe the validated shapes rather than a general
 throughput guarantee.
@@ -0,0 +1,346 @@
+# MiniMax-H3 on RTX PRO 6000 Blackwell GPUs
+
+This recipe runs MiniMax-H3 in BF16 on 96 GiB RTX PRO 6000 Blackwell GPUs.
+The additional HBM over the RTX PRO 5000 profile removes the four-GPU
+minimum: two GPUs are enough to keep the model resident with TP2. Four and
+eight GPUs raise the Ulysses degree to shard the attention sequence further.
+CPU offload and distributed layerwise offload are not required in any of
+these configurations.
+
+Validated on:
+- Host: YLX Y762
+- GPUs: 8 × RTX PRO 6000 Blackwell (96 GiB)
+- Device order: default (`CUDA_VISIBLE_DEVICES` not set)
+- Driver / CUDA: 580.105.08 / 13.0
+- Container image: `vllm/vllm-omni:minimax-h3`
+- Workload: T2VA, 1344×768, `duration=5.0`, 50 steps, `flow_shift=12`, `seed=1101`
+
+## Capacity requirements
+
+| Resource | Two GPUs | Four GPUs | Eight GPUs |
+| --- | ---: | ---: | ---: |
+| GPU HBM | 96 GiB per GPU | 96 GiB per GPU | 96 GiB per GPU |
+| Checkpoint storage | 135 GiB per partition | 135 GiB per partition | 135 GiB per partition |
+| Available system RAM | 200 GiB minimum | 200 GiB minimum | 200 GiB minimum |
+| Recommended system RAM | 384 GiB | 384 GiB | 384 GiB |
+| Measured peak HBM per GPU | 77.49 GiB | 66.44 GiB | 61.07 GiB |
+
+`FL2VA` and `Ref2VA` are separate checkpoint partitions. Start one server at
+a time on a host sized for the minimum system-memory requirement, or pass
+`--task-type fl2va` or `--task-type ref2va` to download and load a single
+partition. The two-server layout described in the eight-GPU section doubles
+both the storage and the host-memory requirement.
+
+## Parallelism summary
+
+| Configuration | TP | Ulysses | Text-encoder TP | VAE patch parallel |
+| --- | ---: | ---: | ---: | ---: |
+| Two GPUs | 2 | 1 | 2 | 2 |
+| Four GPUs | 2 | 2 | 4 | 4 |
+| Eight GPUs | 2 | 4 | 8 | 8 |
+| Eight GPUs, headroom variant | 4 | 2 | 8 | 8 |
+
+Per-GPU weight residency is set by the tensor-parallel degree alone. Raising
+the Ulysses degree lowers activation memory and per-step latency but leaves
+weight residency unchanged. Ring parallelism stays at 1, and H3 is
+CFG-distilled, so `--cfg-parallel-size` must also remain 1.
+
+## PCIe topology and GPU order
+
+RTX PRO 6000 Blackwell does not provide NVLink. Before starting the server,
+inspect the host topology:
+
+```bash
+nvidia-smi topo -m
+nvidia-smi nvlink -s
+```
+
+Tensor-parallel groups are consecutive ranks. Ulysses groups are strided by
+the tensor-parallel degree. Confirm this grouping against the server log
+before tuning the device order on a new host.
+
+For the two-GPU configuration, pick a single `PXB` pair on one NUMA node.
+For the four-GPU configuration, pick two `PXB` pairs on the same node and
+order them so that each Ulysses group lands inside one pair. If physical
+GPUs `(0,1)` and `(2,3)` are the two local pairs, the order is
+`CUDA_VISIBLE_DEVICES=0,2,1,3`. Do not copy these IDs blindly: reproduce the
+same relationship on the target host.
+
+Device-order pinning and NUMA binding are tuning knobs, not prerequisites.
+The measurements in this recipe were taken without either, so any gain from
+them is additional to the numbers reported below and is host-specific.
+
+## Two-GPU serving configuration
+
+Two 96 GiB GPUs hold the BF16 model with TP2 alone. There is no Ulysses
+group, so the DiT attention sequence is not sharded and activation memory is
+higher than in the four-GPU profile at the same output shape. Re-measure
+peak memory before increasing the output shape, the reference-input length,
+or concurrency.
+
+```bash
+export MODEL_ROOT=/path/to/MiniMax-H3
+export MODEL="${MODEL_ROOT}/FL2VA"
+export PORT=8000
+
+VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
+vllm serve "${MODEL}" \
+  --omni \
+  --host 0.0.0.0 \
+  --port "${PORT}" \
+  --trust-remote-code \
+  --num-gpus 2 \
+  --tensor-parallel-size 2 \
+  --usp 1 \
+  --ring 1 \
+  --text-encoder-tp-size 2 \
+  --vae-patch-parallel-size 2 \
+  --vae-parallel-mode tile \
+  --vae-use-tiling \
+  --diffusion-attention-backend CUDNN_ATTN
+```
+
+## Four-GPU serving configuration
+
+Four GPUs keep TP2 for the weight shard and add Ulysses2, which shards the
+attention sequence and lowers per-GPU activation memory. Text-encoder TP4
+and VAE patch parallelism 4 spread the Qwen3-VL encoder and tiled decode
+across all four GPUs. TP1 is not an option on this card: an unsharded BF16
+partition does not fit in 96 GiB.
+
+```bash
+export MODEL_ROOT=/path/to/MiniMax-H3
+export MODEL="${MODEL_ROOT}/FL2VA"
+export PORT=8000
+
+VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
+vllm serve "${MODEL}" \
+  --omni \
+  --host 0.0.0.0 \
+  --port "${PORT}" \
+  --trust-remote-code \
+  --num-gpus 4 \
+  --tensor-parallel-size 2 \
+  --usp 2 \
+  --ring 1 \
+  --text-encoder-tp-size 4 \
+  --vae-patch-parallel-size 4 \
+  --vae-parallel-mode tile \
+  --vae-use-tiling \
+  --diffusion-attention-backend CUDNN_ATTN
+```
+
+## Eight-GPU serving configuration
+
+Eight GPUs extend the Ulysses degree to 4 while keeping TP2. This does not
+lower per-GPU weight residency relative to the four-GPU profile; it lowers
+activation memory and per-step latency by sharding the attention sequence
+four ways.
+
+```bash
+export MODEL_ROOT=/path/to/MiniMax-H3
+export MODEL="${MODEL_ROOT}/FL2VA"
+export PORT=8000
+
+VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
+vllm serve "${MODEL}" \
+  --omni \
+  --host 0.0.0.0 \
+  --port "${PORT}" \
+  --trust-remote-code \
+  --num-gpus 8 \
+  --tensor-parallel-size 2 \
+  --usp 4 \
+  --ring 1 \
+  --text-encoder-tp-size 8 \
+  --vae-patch-parallel-size 8 \
+  --vae-parallel-mode tile \
+  --vae-use-tiling \
+  --diffusion-attention-backend CUDNN_ATTN
+```
+
+### Rank ordering across two sockets
+
+With TP2 and Ulysses4 the two Ulysses groups are ranks `(0,2,4,6)` and
+`(1,3,5,7)`, and the four tensor-parallel pairs are `(0,1)`, `(2,3)`,
+`(4,5)`, and `(6,7)`. On a dual-socket host these two collectives cannot
+both be socket-local. If you choose to pin the device order, the trade-off
+is:
+
+| Device order | Socket-local collective | Crosses sockets |
+| --- | --- | --- |
+| `0,4,1,5,2,6,3,7` | Ulysses all-to-all | TP all-reduce |
+| `0,1,2,3,4,5,6,7` | TP all-reduce | Ulysses all-to-all |
+
+The first order assumes physical GPUs `0-3` are on NUMA node 0 and `4-7` on
+node 1. Reproduce that relationship on the target host rather than copying
+the IDs. The reported measurements use the default order and no NUMA
+binding; the two orders were not compared on this host.
+
+### Headroom variant: TP4 with Ulysses2
+
+Raising the tensor-parallel degree to 4 halves per-GPU weight residency and
+leaves room for long Ref2VA references, larger output shapes, or
+concurrency greater than one. It also adds a four-way all-reduce per DiT
+block over a link without NVLink. Use it when the TP2 profile runs out of
+headroom, not as the default. Replace the parallel flags above with:
+
+```bash
+  --tensor-parallel-size 4 \
+  --usp 2 \
+```
+
+### Two independent four-GPU servers
+
+An eight-GPU host can instead run one FL2VA server and one Ref2VA server
+side by side, each pinned to its own NUMA node with the four-GPU
+configuration. This removes every cross-socket collective and lifts the
+one-server-at-a-time restriction, at the cost of doubling the requirements
+to 400 GiB available system RAM and 270 GiB of model storage.
+
+Use `CUDA_VISIBLE_DEVICES=0,2,1,3`, `--cpunodebind=0 --membind=0`, and
+`--port 8091` for the FL2VA server. Use `CUDA_VISIBLE_DEVICES=4,6,5,7`,
+`--cpunodebind=1 --membind=1`, and `--port 8092` for the Ref2VA server.
+This layout is described for completeness and was not part of the measured
+runs.
+
+## Shared serving notes
+
+Do not add `--enforce-eager` for a performance run. Warm the server before
+measuring so regional compilation is outside the measured request. On this
+host the first request after startup ran 19% slower than the steady state
+(107.58 s versus 90.48 s on eight GPUs); two warmup requests were enough to
+converge.
+
+For Ref2VA on a single-server layout, stop the FL2VA server and restart the
+same command with `MODEL="${MODEL_ROOT}/Ref2VA"`.
+
+## Attention backend
+
+RTX PRO 6000 Blackwell is an SM120 part, so the datacenter-Blackwell
+`TRTLLM_ATTN` default does not apply here. Every configuration above selects
+cuDNN BF16 attention explicitly. This keeps the recipe independent of
+platform-default backend changes and does not depend on the experimental
+SM120 kernel.
+
+## Optional: online FP8
+
+`--quantization fp8` quantizes eligible DiT linears at load time and is
+compatible with tensor parallelism and VAE tiling. Use it when the resident
+BF16 peak leaves too little headroom for long Ref2VA references, a larger
+output shape, or concurrency greater than one. It cannot be combined with
+layerwise offload.
+
+## Target-hardware validation
+
+T2VA, 1344×768, `duration=5.0`, `fps=24`, 50 steps, `flow_shift=12`,
+`seed=1101`, BF16, `CUDNN_ATTN`, tiled VAE, one request at a time. Servers
+were started with the commands above — default device order, no NUMA
+binding — plus `--enable-diffusion-pipeline-profiler`. Two warmup requests
+preceded each measured request.
+
+| Measurement | Two GPUs | Four GPUs | Eight GPUs |
+| --- | ---: | ---: | ---: |
+| Topology | TP2 × USP1 | TP2 × USP2 | TP2 × USP4 |
+| Text encode | 0.040 s | 0.044 s | 0.035 s |
+| Denoise, 50 steps | 278.55 s | 168.73 s | 87.90 s |
+| Per step | 5.571 s | 3.375 s | 1.758 s |
+| VAE decode | 5.396 s | 2.791 s | 1.798 s |
+| Client E2E | 284.76 s | 172.32 s | 90.48 s |
+| Peak HBM per GPU | 77.49 GiB | 66.44 GiB | 61.07 GiB |
+| Headroom below 96 GiB | 18.5 GiB | 29.6 GiB | 34.9 GiB |
+
+Stage times are read from the `X-Stage-Durations` response header of
+`/v1/videos/sync`. Peak memory is the maximum of `nvidia-smi
+--query-gpu=memory.used` sampled at 1 Hz across every device for the
+duration of the measured request. Per step is denoise wall time divided by
+the 50 requested steps. Stage times sum to roughly 0.75 s less than
+end-to-end in all three configurations; queueing, result transfer, and MP4
+muxing sit outside the profiled stages.
+
+### Scaling behaviour
+
+Denoise speedup is not uniform across the sweep: 1.65× from two to four
+GPUs (83% efficiency) but 1.92× from four to eight (96%). Two GPUs run
+Ulysses1, so there is no sequence parallelism at all; going to four GPUs
+pays the one-time cost of introducing the all-to-all, while going from four
+to eight only widens an all-to-all that already exists. Expect the second
+doubling to pay off better than the first on this topology.
+
+VAE decode behaves in the opposite direction — 1.93× from two to four GPUs
+but only 1.55× from four to eight — as patch parallelism produces smaller
+tiles and tile-boundary overhead grows.
+
+Text encoding is 0.04 s in every configuration and is not worth optimising;
+denoise is 96-98% of end-to-end throughout.
+
+### Per-GPU memory model
+
+Peak memory falls by 11.05 GiB from two to four GPUs and by a further
+5.37 GiB from four to eight — halving each time the Ulysses degree doubles,
+because all three configurations shard the DiT with TP2 and hold identical
+weights per GPU. Fitting the first two points gives:
+
+```
+peak HBM per GPU ≈ 55.4 GiB + 22.1 GiB / ulysses_degree
+```
+
+That fit predicts 60.9 GiB at Ulysses4 against 61.07 GiB measured, a 0.25%
+error. The practical consequence is a floor near 55 GiB per GPU: adding
+GPUs past eight will not reduce the per-GPU requirement further. To serve
+on smaller cards, raise the tensor-parallel degree instead.
+
+Re-measure memory for longer reference inputs, concurrency greater than
+one, or a different output shape.
+
+### Not yet measured
+
+Ref2VA latency and memory have not been measured on this host. Ref2VA
+drives the Qwen3-VL presentation from tens of tokens for text-only prompts
+to several thousand with image or video references, so T2VA numbers do not
+transfer to it. The eight-GPU device-order comparison in the section above
+was also not run.
+
+## T2VA request example
+
+`t2va` requires an explicit `aspect_ratio` even when `width` and `height`
+are supplied.
+
+```bash
+export API_URL="http://127.0.0.1:${PORT}/v1/videos/sync"
+
+curl -sS --max-time 1800 -X POST "${API_URL}" \
+  -F 'prompt=At night, three cats march into a bedroom playing tiny brass instruments, then abruptly file out, with synchronized room ambience.' \
+  -F 'width=1344' \
+  -F 'height=768' \
+  -F 'aspect_ratio=16:9' \
+  -F 'fps=24' \
+  -F 'num_inference_steps=50' \
+  -F 'flow_shift=12' \
+  -F 'seed=1101' \
+  -F 'extra_params={"task":"t2va","duration":5.0,"audio_flow_shift":3.0}' \
+  -o t2va.mp4
+```
+
+To collect the stage breakdown and peak memory for a request, add
+`--enable-diffusion-pipeline-profiler` to the server command and `-D
+headers.txt` to the curl invocation, then read `X-Stage-Durations` and
+`X-Peak-Memory-MB` from the saved headers.
+
+## Ref2VA request example
+
+`ref2va` defaults to a 16:9 aspect ratio, so `aspect_ratio` is optional
+here.
+
+```bash
+curl -X POST "http://127.0.0.1:${PORT}/v1/videos/sync" \
+  --fail-with-body -w '\nHTTP %{http_code}\n' \
+  --max-time 1200 \
+  -F "input_reference=@/root/hand.jpg;type=image/jpeg" \
+  -F "audio_reference=</root/audio_ref.json" \
+  -F "prompt=2D动画融合在一起的影像。夕阳余晖残留在窗边，生活感十足的小厨房里有旧木桌、洗到一半的马克杯、起雾的玻璃瓶、悬挂的抹布。画面带有智能手机单手拍摄的手抖、近距离对焦的犹豫、逆光曝光波动。要像在家中慌忙拍下某个不可思议事件的自然质感，不要广告影像的精心整理。声音只用厨房环境声与手绘生物柔和的电子音、小小的叫声。" \
+  -F 'width=1344' -F 'height=768' -F 'fps=24' \
+  -F 'num_inference_steps=60' -F 'flow_shift=12' -F 'seed=1101' \
+  -F 'extra_params={"task":"ref2va","duration":8,"audio_flow_shift":3.0}' \
+  -o /root/out_ref2va.mp4
+```
@@ -0,0 +1,262 @@
+# MiniMax-H3 on DGX Spark (GB10)
+
+This recipe uses online FP8 weight quantization, tiled VAE decode, and a single
+resident partition. GB10 is a unified-memory platform, so unlike the discrete-GPU
+recipes it uses **no offload of any kind** — see the capacity note below.
+
+Validated on:
+
+- Host: DGX Spark (GB10), aarch64
+- GPUs: 1 (unified memory)
+- Driver: <FILL_DRIVER_VERSION>
+- vLLM: 0.26.0
+- vLLM-Omni: `main` at `e1aa6eae75c460cd1893bc320546e81e66973831`
+- Workloads: T2VA and Ref2VA, 960x576, `duration=8.0`, 50 steps, `flow_shift=12`,
+  `seed=1101`
+
+
+## Capacity requirements
+
+| Resource | DGX Spark (GB10) |
+| --- | ---: |
+| Unified memory | 128 GB LPDDR5X (~121 GiB usable) |
+| GPUs | 1 |
+| Checkpoint storage | 135 GiB per partition |
+| Measured peak allocator high-water, T2VA (FP8) | 97.7 GiB |
+| Measured peak allocator high-water, Ref2VA (FP8) | 102.8 GiB |
+
+`FL2VA` and `Ref2VA` are separate 135 GiB checkpoint partitions. Start one server
+at a time.
+
+On GB10 the CPU and GPU share one physical memory pool, which changes the capacity
+math relative to the RTX and datacenter recipes:
+
+- **Do not use `--enable-distributed-layerwise-offload`.** DLO stages rank-local
+  weights into pinned host memory, which on GB10 is the same pool the weights are
+  already in. Peak usage roughly doubles and the process is killed by the OOM
+  killer (`Exit code: -9`) shortly after `Enabling offloader backend`.
+- **Do not use `--enable-cpu-offload`.** Moving weights from "VRAM" to "host RAM"
+  frees nothing here.
+- **`--quantization fp8` is mandatory.** A BF16 partition is 135 GiB and does not
+  fit in 121 GiB. Online FP8 quantizes the DiT only (62 GiB to ~31 GiB); the
+  Qwen3-VL text encoder and both VAEs stay BF16.
+
+With ~97.7 GiB of the pool held by the allocator at peak, T2VA leaves roughly
+23 GiB of headroom for the OS, page cache, and any other process on the box.
+Ref2VA is tighter on both counts. The allocator high-water rises to 102.8 GiB,
+and the allocator figure understates true pool usage.
+
+Sampling `/proc/meminfo` at 1 Hz across a 50-step Ref2VA request on a host with
+`MemTotal: 127600524 kB` (121.7 GiB), `MemAvailable` started at 8.1 GiB, fell to
+a trough of **7.4 GiB**, and recovered to 8.9 GiB. At the trough the pool held
+roughly **114.3 GiB** — 11.4 GiB more than the 102.8 GiB the allocator reports,
+which is the CUDA context, non-PyTorch allocations, and page cache the header
+does not count.
+
+Treat this platform as strictly single-tenant for Ref2VA. There is no room for a
+second process, and adding reference images, reference videos, or a larger output
+shape will run into the OOM killer.
+
+## One GB10: 960x576, 8 seconds
+
+```bash
+CUDA_VISIBLE_DEVICES=0 \
+FLASHINFER_DISABLE_VERSION_CHECK=1 \
+VLLM_WORKER_MULTIPROC_METHOD=spawn \
+VLLM_OMNI_VIDEO_SYNC_TIMEOUT=7200 \
+vllm serve /path/to/MiniMax-H3/FL2VA \
+  --omni --trust-remote-code --host 0.0.0.0 --port 8000 \
+  --init-timeout 3600 \
+  --num-gpus 1 --tensor-parallel-size 1 --text-encoder-tp-size 1 \
+  --usp 1 --ring 1 --vae-patch-parallel-size 1 \
+  --vae-parallel-mode tile --vae-use-tiling \
+  --quantization fp8 --enforce-eager \
+  --diffusion-attention-backend CUDNN_ATTN
+```
+
+Wait for `Application startup complete`, then run a short smoke test before
+committing to a full 50-step request.
+
+`t2va` requires an explicit `aspect_ratio` even when `width` and `height` are
+supplied; omitting it fails the request with
+`OmniClientError: t2va requires an explicit aspect_ratio`.
+
+```bash
+curl --fail-with-body -sS -X POST http://127.0.0.1:8000/v1/videos/sync \
+  -F 'prompt=At night, three cats march into a bedroom playing tiny brass instruments, then abruptly file out, with synchronized room ambience.' \
+  -F 'width=960' \
+  -F 'height=576' \
+  -F 'aspect_ratio=16:9' \
+  -F 'fps=24' \
+  -F 'num_inference_steps=10' \
+  -F 'flow_shift=12' \
+  -F 'seed=1101' \
+  -F 'extra_params={"task":"t2va","duration":4.0,"audio_flow_shift":3.0}' \
+  -o h3-gb10-smoke.mp4
+```
+
+The full-quality request uses 50 steps:
+
+```bash
+curl --fail-with-body -sS -X POST http://127.0.0.1:8000/v1/videos/sync \
+  -F 'prompt=At night, three cats march into a bedroom playing tiny brass instruments, then abruptly file out, with synchronized room ambience.' \
+  -F 'width=960' \
+  -F 'height=576' \
+  -F 'aspect_ratio=16:9' \
+  -F 'fps=24' \
+  -F 'num_inference_steps=50' \
+  -F 'flow_shift=12' \
+  -F 'seed=1101' \
+  -F 'extra_params={"task":"t2va","duration":8.0,"audio_flow_shift":3.0}' \
+  -o h3-gb10-t2va.mp4
+
+ffprobe -v error -show_entries \
+  stream=index,codec_name,width,height,r_frame_rate,sample_rate,channels \
+  -of json h3-gb10-t2va.mp4
+```
+
+Both requests return `200 OK` with an MP4 body: H.264 video at the requested
+geometry plus a 32 kHz stereo AAC track.
+
+## One GB10: Ref2VA, 960x576, 8 seconds
+
+`FL2VA` and `Ref2VA` are separate 135 GiB partitions and only one fits at a time.
+Stop the FL2VA server before starting this one.
+
+```bash
+CUDA_VISIBLE_DEVICES=0 \
+FLASHINFER_DISABLE_VERSION_CHECK=1 \
+VLLM_WORKER_MULTIPROC_METHOD=spawn \
+VLLM_OMNI_VIDEO_SYNC_TIMEOUT=14400 \
+vllm serve /path/to/MiniMax-H3/Ref2VA \
+  --omni --trust-remote-code --host 0.0.0.0 --port 8000 \
+  --init-timeout 3600 \
+  --num-gpus 1 --tensor-parallel-size 1 --text-encoder-tp-size 1 \
+  --usp 1 --ring 1 --vae-patch-parallel-size 1 \
+  --vae-parallel-mode tile --vae-use-tiling \
+  --quantization fp8 --enforce-eager \
+  --diffusion-attention-backend CUDNN_ATTN
+```
+
+The 7200 s timeout used for T2VA is too short here: a 50-step Ref2VA request
+takes over an hour on this platform. Use 14400 s.
+
+Unlike `t2va`, `ref2va` defaults to a 16:9 output ratio, so `aspect_ratio` can be
+omitted when `width` and `height` are supplied.
+
+```bash
+curl --fail-with-body -sS -X POST http://127.0.0.1:8000/v1/videos/sync \
+  -D ref2va-headers.txt \
+  -w 'client_e2e_s=%{time_total}\n' \
+  --max-time 14400 \
+  -F 'input_reference=@/path/to/reference.jpg;type=image/jpeg' \
+  -F 'prompt=A hand rests on a wooden table in warm evening light, then slowly opens and turns toward the camera, with quiet room ambience.' \
+  -F 'width=960' \
+  -F 'height=576' \
+  -F 'fps=24' \
+  -F 'num_inference_steps=50' \
+  -F 'flow_shift=12' \
+  -F 'seed=1101' \
+  -F 'extra_params={"task":"ref2va","duration":8.0,"audio_flow_shift":3.0}' \
+  -o h3-gb10-ref2va.mp4
+```
+
+The response is `200 OK` with an MP4 body: H.264 at the requested geometry plus a
+32 kHz stereo AAC track. `ffprobe` reports 8.032 s for `duration=8.0`.
+
+## Measured latency
+
+Single GB10, FP8, eager, cuDNN attention, tiled VAE, one request at a time:
+
+| Workload | Steps | Text encode | Denoise | Per step | VAE decode | MP4 mux | Client E2E | Peak memory |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| T2VA, 4 s, smoke shape | 10 | — | — | 8.68 s | — | 0.49 s | 95.9 s | — |
+| T2VA, 8 s, 960x576 | 50 | 0.25 s | 2088 s | 42.61 s | 70 s | 4.84 s | 2169.4 s (36 min 9 s) | 97.7 GiB |
+| Ref2VA, 8 s, 960x576, one image (3 runs) | 10 | 8.43-10.94 s | 665.5-748.8 s | 66.55-74.88 s | 67.9-69.8 s | 3.63 s | 770.3-861.1 s | 102.8 GiB |
+| Ref2VA, 8 s, 960x576, one image | 50 | 9.66 s | 4039.6 s | 80.79 s | 69.4 s | 3.63 s | 4157.0 s (69 min 17 s) | 102.8 GiB |
+
+How these were measured:
+
+- Stage times come from `--enable-diffusion-pipeline-profiler`, read from the
+  `X-Stage-Durations` response header of `/v1/videos/sync`. The keys are
+  `MiniMaxH3Pipeline.encode_prompt` (text encode),
+  `MiniMaxH3Pipeline.diffuse` (denoise), and `MiniMaxH3Pipeline.decode`
+  (VAE decode).
+- MP4 mux is not a header field. It is the
+  `Video response encoding (MP4 bytes)` line in the server log.
+- Peak memory is the `X-Peak-Memory-MB` header, which reports the CUDA allocator
+  high-water mark (`torch.cuda.max_memory_reserved`) for the request. It excludes
+  the CUDA context and non-PyTorch allocations, so true pool usage is somewhat
+  higher. `nvidia-smi --query-gpu=memory.used` is not usable on GB10 — unified
+  memory has no discrete VRAM to report and the field returns `[N/A]`. See the
+  capacity section for a `/proc/meminfo` cross-check.
+- For T2VA, text encode and VAE decode were measured on a separate 10-step run at
+  the same 960x576 / 8 s geometry. Both are independent of step count, so they
+  transfer to the 50-step row unchanged. The Ref2VA MP4 mux figure transfers the
+  same way, from the 10-step run at identical output geometry and duration.
+- Ref2VA peak memory is byte-identical between the 10-step and 50-step runs
+  (`X-Peak-Memory-MB: 105318.000` in both), confirming that peak is set by output
+  geometry and reference input rather than step count.
+- Stage times do not sum exactly to end-to-end. Queueing, result transfer, and
+  engine bookkeeping sit outside the profiled stages.
+
+Denoise dominates at 96% of end-to-end, so step count and geometry are the only
+knobs that materially change the wall clock on this platform.
+
+Ref2VA costs 1.92x the wall clock of T2VA at the same geometry and step count:
+4157.0 s versus 2169.4 s. Text encoding shows the most dramatic ratio — 9.66 s
+versus 0.25 s, 39x — but at 0.2% of end-to-end it is not what makes Ref2VA slow.
+The cost is in denoise. A single reference image expands the Qwen3-VL
+presentation to 6883 tokens (logged as `MiniMax H3 ref2va Qwen presentation:
+6883 tokens, 1 reference images`), and those tokens sit in the attention context
+of every denoise step. Per-step time rises from 42.61 s to 80.79 s, and that
+1.90x multiplies across all 50 steps. VAE decode is unchanged at ~69 s, as
+expected for an identical output shape.
+
+Repeated identical 10-step T2VA requests varied by up to 23% in denoise time
+(397 s to 490 s) on a machine that had already been under sustained load. Treat
+these as single-run measurements on one machine, not a throughput guarantee, and
+expect the slower end of that range for back-to-back jobs.
+
+Ref2VA per-step time is load-dependent in the same way. On an idle machine the
+first 10-step request ran at 66.55 s per step. The 50-step request that followed
+occupied the box for 69 consecutive minutes and averaged 80.79 s per step. Two
+further 10-step requests taken immediately afterwards settled at 74.88 s and
+74.31 s — 12.5% slower than the idle run, and within 0.8% of each other, so this
+is a stable thermal state rather than measurement noise.
+
+Plan for 81 s per step when sizing a full 50-step Ref2VA job; 66 s per step is
+only reachable on a cold machine and does not hold for the length of one request.
+
+Ref2VA text encode converged downward across the four runs (10.94 s, 9.66 s,
+8.52 s, 8.43 s) as first-touch costs fell away; treat ~8.5 s as steady state.
+VAE decode was stable at 67.9-69.8 s throughout.
+
+Set `VLLM_OMNI_VIDEO_SYNC_TIMEOUT` well above the expected request time — the
+default 1800 s is shorter than a 50-step run on this platform, and 7200 s is
+shorter than a 50-step Ref2VA run.
+
+## Known limitations
+
+- One GPU means no Ulysses, ring, TP, or VAE patch parallelism. Pass
+  `--usp 1 --ring 1 --vae-patch-parallel-size 1` explicitly if a profile or
+  script would otherwise supply higher degrees.
+- `--enforce-eager` is used because regional compile adds startup time without a
+  parallelism win on a single rank.
+- `t2va` rejects requests without an explicit `aspect_ratio`. When `width` and
+  `height` are both supplied they take precedence and `aspect_ratio` only has to
+  name one of the supported ratios.
+- Sustained load degrades throughput. See the variance notes above.
+- Only one partition is resident. For Ref2VA, stop the FL2VA server and restart
+  with `/path/to/MiniMax-H3/Ref2VA`. Ref2VA with one reference image is measured
+  above; reference videos and additional images push the token count higher and
+  were not measured. With `MemAvailable` bottoming out at 7.4 GiB during a
+  single-image request, there is very little room to grow — re-measure before
+  adding references or raising the output shape.
+- Ref2VA requires a vLLM-Omni build newer than `v0.26.0`. See the version note
+  under **Validated on**: FP8 weight loading and image-only Ref2VA are both
+  broken on `release/v0.26.0`.suggestion is  `v0.26.1`.
+- Online FP8 is incompatible with layerwise offload — the offload path produces a
+  weight stride the Cutlass FP8 kernel rejects. This is not a practical
+  restriction here since offload is unusable on GB10 anyway.
+```
@@ -750,8 +750,73 @@ audio spectral cosine 0.9589 (minimum 0.80), and audio RMS ratio 0.9342. The
 resident per-GPU peak was 68.52 GiB for BF16 and 53.51 GiB for FP8, a 22%
 reduction.

+## TeaCache acceleration
+
+TeaCache reuses DiT block residuals across denoising steps when consecutive
+timestep embeddings are similar. MiniMax-H3 TeaCache is currently calibrated
+only for the FL2VA partition. In combined serving, FL2VA requests use TeaCache
+while Ref2VA requests run uncached; Ref2VA-only serving rejects TeaCache.
+
+TeaCache and Cache-DiT are mutually exclusive; pick one cache backend per server.
+
+The model-specific default and examples use `rel_l1_thresh=0.17`, which
+provided the best conservative speed/quality balance in the validated 107-frame
+T2VA workload. Lower values
+may produce few or no cache hits, while higher values can improve performance
+at the cost of output quality. Validate the threshold on representative
+prompts and generation settings before changing it.
+
+### Offline (Python API)
+
+Export the directory containing the `FL2VA` and `Ref2VA` subdirectories before
+running the Python example, for example `export MODEL_ROOT=/models/MiniMax-H3`.
+
+```python
+import os
+
+from vllm_omni import Omni
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+
+omni = Omni(
+    model=os.path.join(os.environ["MODEL_ROOT"], "FL2VA"),
+    cache_backend="tea_cache",
+    cache_config={"rel_l1_thresh": 0.17},
+    trust_remote_code=True,
+    enable_cpu_offload=True,
+)
+outputs = omni.generate(
+    "A quiet cinematic night scene with matching ambient sound.",
+    OmniDiffusionSamplingParams(
+        height=256,
+        width=448,
+        num_frames=29,
+        fps=24,
+        num_inference_steps=50,
+        seed=42,
+        extra_args={
+            "task": "t2va",
+            "duration": 4.0,
+            "aspect_ratio": "16:9",
+            "flow_shift": 12.0,
+            "audio_flow_shift": 3.0,
+        },
+    ),
+)
+```
+
+### Online serving
+
+```bash
+vllm serve "${MODEL_ROOT}/FL2VA" \
+  --omni \
+  --trust-remote-code \
+  --cache-backend tea_cache \
+  --cache-config '{"rel_l1_thresh":0.17}'
+```
+
 ## Known limitations

+- TeaCache is calibrated for FL2VA only; Ref2VA requests run uncached.
 - Combined serving requires sibling `FL2VA` and `Ref2VA` directories, loads
  both task-specific DiTs, and loads shared components once from `FL2VA`.
 - H3 currently executes one generation request per diffusion batch.
@@ -770,6 +835,10 @@ reduction.
 - A U2 x Ring2 hybrid currently fails with an attention-mask length mismatch; use
  pure Ulysses.
 - Online FP8 is not compatible with layerwise offload.
+- TeaCache and Cache-DiT cannot be enabled on the same server.
+- Image+audio Ref2VA accepts exactly one image and one audio reference.
+- Video Ref2VA accepts one or more video files, but not an additional standalone
+  audio reference.
 - Pure Ulysses still replicates the full DiT on every rank, so smaller-memory GPUs
  cannot use `--usp N --tp 1` as a resident capacity path. Use DiT tensor parallelism
  or model-level CPU offload; text-encoder TP alone is not sufficient.
@@ -19,6 +19,11 @@ add files for this repo. Use one Markdown file per model family by default.
 > Individual pages resolve at `https://recipes.vllm.ai/<provider>/<model-id>`,
 > e.g. [`bosonai/higgs-audio-v3-tts-4b`](https://recipes.vllm.ai/bosonai/higgs-audio-v3-tts-4b).

+The `Recipe` column in the Supported Models documentation links to the
+published recipe when available and otherwise to the corresponding recipe in
+this directory. The recipe files remain the source of truth for commands and
+validation details.
+
 Example layout:

 ```text
@@ -39,6 +39,7 @@ from vllm_omni.config.stage_config import (
    load_deploy_config,
    merge_pipeline_deploy,
 )
+from vllm_omni.diffusion.diffusion_kv.config import DiffusionKVCacheMode
 from vllm_omni.engine.stage_init_utils import build_legacy_engine_args_dict

 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
@@ -966,20 +967,24 @@ def test_diffusion_config_from_kwargs_reuses_legacy_normalization(monkeypatch):

    cfg = omni_config_module._DiffusionConfigProjection.from_kwargs(
        diffusion_attention_backend="flash_attn",
+        fa_deterministic=True,
        kv_cache_dtype="fp8",
        kv_cache_skip_steps="0-1",
        kv_cache_skip_layers=[2],
        static_lora_scale=0.25,
+        diffusion_kv_mode="paged_scheduler",
        diffusers_load_kwargs=None,
        diffusers_call_kwargs=None,
    )

    assert cfg.diffusion_attention_config.default.backend == "flash_attn"
+    assert cfg.fa_deterministic is True
    assert cfg.diffusion_kv_cache_dtype == "fp8"
    assert cfg.diffusion_kv_cache_skip_step_indices == {0, 1}
    assert cfg.diffusion_kv_cache_skip_layer_indices == {2}
    assert cfg.lora_scale == 0.25
    assert cfg.cache_backend == "tea_cache"
+    assert cfg.diffusion_kv_mode is DiffusionKVCacheMode.PAGED_SCHEDULER
    assert cfg.diffusers_load_kwargs == {}
    assert cfg.diffusers_call_kwargs == {}

@@ -994,6 +999,8 @@ def test_from_pipeline_config_normalizes_diffusion_config_aliases_from_engine_ar
                "stages:",
                "  - stage_id: 0",
                "    diffusion_attention_backend: flash_attn",
+                "    fa_deterministic: true",
+                "    diffusion_kv_mode: paged_scheduler",
            ]
        )
    )
@@ -1005,6 +1012,29 @@ def test_from_pipeline_config_normalizes_diffusion_config_aliases_from_engine_ar

    assert isinstance(stage, VllmOmniDiffusionStageConfig)
    assert stage.diffusion_config.diffusion_attention_config.default.backend == "flash_attn"
+    assert stage.diffusion_config.fa_deterministic is True
+    assert stage.diffusion_config.diffusion_kv_mode is DiffusionKVCacheMode.PAGED_SCHEDULER
+
+
+def test_from_pipeline_config_rejects_reserved_diffusion_kv_mode(tmp_path):
+    deploy_path = tmp_path / "dreamzero_reserved_diffusion_kv.yaml"
+    deploy_path.write_text(
+        "\n".join(
+            [
+                "pipeline: dreamzero",
+                "async_chunk: false",
+                "stages:",
+                "  - stage_id: 0",
+                "    diffusion_kv_mode: paged_worker_local",
+            ]
+        )
+    )
+
+    with pytest.raises(ValueError, match="reserved but not implemented"):
+        _from_pipeline_key(
+            "dreamzero",
+            deploy_config_path=str(deploy_path),
+        )


 def test_diffusion_config_field_classification_covers_current_fields():
@@ -1025,6 +1055,7 @@ def test_diffusion_config_field_classification_covers_current_fields():
        "enable_session_state_manager",
        "prompt_embed_cache_size",
        "diffusion_kv_cache_dtype",
+        "diffusion_kv_mode",
    } <= omni_config_module._DIFFUSION_ONLY_CONFIG_FIELDS
    assert {
        "revision",
@@ -43,9 +43,9 @@
        ],
        "baseline": {
          "H100": {
-            "throughput_qps": 0.0434,
-            "latency_mean": 23.0654,
-            "peak_memory_mb_mean": 76360.0
+            "throughput_qps": 0.0361,
+            "latency_mean": 27.6206,
+            "peak_memory_mb_mean": 80548.0
          }
        }
      }
@@ -100,9 +100,9 @@
        ],
        "baseline": {
          "H100": {
-            "throughput_qps": 0.0606,
-            "latency_mean": 16.4928,
-            "peak_memory_mb_mean": 47164.8
+            "throughput_qps": 0.05263,
+            "latency_mean": 18.9415,
+            "peak_memory_mb_mean": 50053.7
          }
        }
      },
@@ -127,9 +127,9 @@
        ],
        "baseline": {
          "H100": {
-            "throughput_qps": 0.0124,
-            "latency_mean": 80.7784,
-            "peak_memory_mb_mean": 55374.3429
+            "throughput_qps": 0.009595,
+            "latency_mean": 104.1058,
+            "peak_memory_mb_mean": 59718.0
          }
        }
      }
@@ -315,11 +315,32 @@ def _assert_post_fault_health_terminal(host: str, port: int, *, scenario: str) -
    pytest.fail(f"[{scenario} health] no terminal post-fault health observed: {last_observation}")


+def _assert_server_alive_after_fault(host: str, port: int, *, scenario: str) -> None:
+    """#4285: a single-stage engine death must NOT take down the API server.
+
+    The process must stay up and keep answering /health (503 is acceptable —
+    errored but listening). A connection-refused means the server process
+    exited, which is the bug this fix closes.
+    """
+    deadline = time.monotonic() + 15.0
+    responded = False
+    while time.monotonic() < deadline:
+        try:
+            status, _ = get_health_raw(host, port, timeout_sec=5)
+            responded = True
+            assert status in (200, 503), f"[{scenario} alive] unexpected /health status={status}"
+        except Exception as exc:  # noqa: BLE001
+            if _looks_like_server_unreachable(exc):
+                pytest.fail(f"[{scenario} alive] server exited after single-stage OOM (#4285): {exc!r}")
+            raise
+        time.sleep(0.5)
+    assert responded, f"[{scenario} alive] /health never responded after fault"
+
+
 QWEN_PARAMS = create_reliability_omni_server_params(RELIABILITY_SCENARIOS, DEPLOY_CONFIGS_DIR)


@pytest.mark.slow
-@pytest.mark.skip(reason="issue#4285")
@hardware_test(res={"cuda": "H100"}, num_cards=2)
@pytest.mark.parametrize("omni_server_function", QWEN_PARAMS, indirect=True)
 def test_reliability_fault_gpu_oom_error_contract_consistent_chat_speech(
@@ -386,9 +407,11 @@ def test_reliability_fault_gpu_oom_error_contract_consistent_chat_speech(
    assert "code" in chat_error, f"chat error lacks code field: {chat_error!r}"
    assert "code" in speech_error, f"speech error lacks code field: {speech_error!r}"

+    # #4285: a single dead stage must not take the whole API server down.
+    _assert_server_alive_after_fault(host, port, scenario="oom_contract")
+

@pytest.mark.slow
-@pytest.mark.skip(reason="issue#4285")
@hardware_test(res={"cuda": "H100"}, num_cards=2)
@pytest.mark.parametrize("omni_server_function", QWEN_PARAMS, indirect=True)
 def test_reliability_fault_gpu_oom_chat_large_payload_failure(omni_server_function, openai_client_function) -> None:
@@ -431,7 +454,6 @@ def test_reliability_fault_gpu_oom_chat_large_payload_failure(omni_server_functi


@pytest.mark.slow
-@pytest.mark.skip(reason="issue#4285")
@hardware_test(res={"cuda": "H100"}, num_cards=2)
@pytest.mark.parametrize("omni_server_function", QWEN_PARAMS, indirect=True)
 def test_reliability_fault_gpu_oom_concurrent_pressure_failure(omni_server_function, openai_client_function) -> None:
@@ -774,7 +796,11 @@ def test_reliability_fault_process_kill_tree_with_load_fast_fail_and_cleanup(


@pytest.mark.slow
-@pytest.mark.skip(reason="issue#4285")
+@pytest.mark.skip(
+    reason="needs request-scoped OOM recovery: under transient pressure the stage "
+    "EngineCore still dies and is evicted (not respawned), so the engine does not "
+    "recover after the hog stops. Out of scope for the #4285 fault-isolation fix."
+)
@hardware_test(res={"cuda": "H100"}, num_cards=2)
@pytest.mark.parametrize("omni_server_function", QWEN_PARAMS, indirect=True)
 def test_reliability_fault_gpu_oom_state_converges_after_fault_removed(
@@ -9,6 +9,10 @@ This script tests two main scenarios:
 2. Case 2: Comparing FlashAttention and SDPA backends for batch_size=2 with padding
 """

+import sys
+from types import SimpleNamespace
+from unittest.mock import Mock
+
 import pytest
 import torch

@@ -371,6 +375,264 @@ def test_packed_varlen_metadata_must_be_complete(monkeypatch):
        impl.forward_cuda(query, query, query, metadata)


+# ---------------------------------------------------------------------------
+# NPU mask-free packed attention (PR #5891).
+#
+# These tests cover the backend-side NPU branches that ``forward_fa_npu`` adds:
+#   - boundary resolution in ``_resolve_packed_seq_npu`` (pure Python, no device
+#     sync, no MindIE-SD import);
+#   - env dispatch in ``forward_fa_npu`` (which op a packed forward takes);
+#   - the laser input pre-scaling math in ``_forward_prefix_kv_slice_npu``.
+#
+# They run on CPU without a real NPU by injecting a fake ``mindiesd`` module
+# into ``sys.modules`` (same pattern as ``test_paged_attention`` /
+# ``benchmarks/conftest``). No production code is exercised for real kernels.
+
+
+def _npu_impl(causal: bool = False) -> FlashAttentionImpl:
+    return FlashAttentionImpl(num_heads=2, head_size=4, softmax_scale=0.5, causal=causal)
+
+
+def _fake_mindiesd(monkeypatch, *, attention_forward=None, attention_forward_varlen=None):
+    """Install a stub ``mindiesd`` so local ``from mindiesd import ...`` works.
+
+    Restored automatically by monkeypatch. Defaults are no-op callables so the
+    top-level import in ``forward_fa_npu`` succeeds even when the test mocks the
+    instance methods that would otherwise call them.
+    """
+    fake = SimpleNamespace(
+        attention_forward=attention_forward or Mock(return_value=torch.zeros(1)),
+        attention_forward_varlen=attention_forward_varlen or Mock(return_value=torch.zeros(1)),
+    )
+    monkeypatch.setitem(sys.modules, "mindiesd", fake)
+    return fake
+
+
+# --- Test group A: boundary resolution (_resolve_packed_seq_npu) -------------
+
+
+def test_resolve_packed_seq_accepts_real_plus_pad_contract():
+    impl = _npu_impl()
+    q = torch.randn(1, 8, 2, 4)
+    cu = torch.tensor([0, 5, 8], dtype=torch.int32)
+    extra = {"cu_seqlens_q": cu, "cu_seqlens_k": cu, "max_seqlen_q": 5, "max_seqlen_k": 5}
+
+    assert impl._resolve_packed_seq_npu(q, q, extra) == ([5, 8], [5, 8])
+
+
+def test_resolve_packed_seq_single_document_no_padding():
+    impl = _npu_impl()
+    q = torch.randn(1, 8, 2, 4)
+    cu = torch.tensor([0, 8], dtype=torch.int32)
+    extra = {"cu_seqlens_q": cu, "cu_seqlens_k": cu, "max_seqlen_q": 8, "max_seqlen_k": 8}
+
+    assert impl._resolve_packed_seq_npu(q, q, extra) == ([8], [8])
+
+
+@pytest.mark.parametrize(
+    "case",
+    [
+        "causal",
+        "missing_key",
+        "batch_ne_1",
+        "cu_too_long",
+        "cu_qk_mismatch",
+        "max_seqlen_not_int",
+        "used_gt_total",
+        "used_zero",
+        "pad_longer_than_real",
+    ],
+)
+def test_resolve_packed_seq_rejects_malformed_metadata(case):
+    """Any metadata outside the exact [real, pad] contract returns None so the
+    caller falls back to the masked path."""
+    cu3 = torch.tensor([0, 5, 8], dtype=torch.int32)
+    cu2 = torch.tensor([0, 8], dtype=torch.int32)
+    cu4 = torch.tensor([0, 3, 5, 8], dtype=torch.int32)
+    cu_short = torch.tensor([0, 3, 8], dtype=torch.int32)
+    base = {"cu_seqlens_q": cu3, "cu_seqlens_k": cu3, "max_seqlen_q": 5, "max_seqlen_k": 5}
+    q = torch.randn(1, 8, 2, 4)
+
+    impl = _npu_impl(causal=(case == "causal"))
+    if case == "missing_key":
+        extra = {"cu_seqlens_q": cu3, "max_seqlen_q": 5}  # 3 of 4 keys absent
+    elif case == "batch_ne_1":
+        q = torch.randn(2, 8, 2, 4)
+        extra = base
+    elif case == "cu_too_long":
+        extra = {**base, "cu_seqlens_q": cu4, "cu_seqlens_k": cu4}
+    elif case == "cu_qk_mismatch":
+        extra = {**base, "cu_seqlens_q": cu3, "cu_seqlens_k": cu2}
+    elif case == "max_seqlen_not_int":
+        extra = {**base, "max_seqlen_q": 5.0}
+    elif case == "used_gt_total":
+        extra = {**base, "max_seqlen_q": 9}  # 9 > total length 8
+    elif case == "used_zero":
+        extra = {**base, "max_seqlen_q": 0, "max_seqlen_k": 0}
+    elif case == "pad_longer_than_real":
+        # real=3, pad=5: the real document must be the longer one.
+        extra = {
+            "cu_seqlens_q": cu_short,
+            "cu_seqlens_k": cu_short,
+            "max_seqlen_q": 3,
+            "max_seqlen_k": 3,
+        }
+    else:
+        extra = base
+
+    assert impl._resolve_packed_seq_npu(q, q, extra) is None
+
+
+# --- Test group B: env dispatch in forward_fa_npu (current behavior) --------
+
+
+def _npu_impl_with_mocked_paths() -> tuple[FlashAttentionImpl, torch.Tensor]:
+    """FlashAttentionImpl whose two packed paths are Mocks returning a sentinel.
+
+    Lets a test assert which branch ``forward_fa_npu`` dispatched to without
+    running any real kernel.
+    """
+    impl = _npu_impl()
+    sentinel = torch.tensor([1.0])
+    impl._forward_varlen_packed_npu = Mock(return_value=sentinel)
+    impl._forward_prefix_kv_slice_npu = Mock(return_value=sentinel)
+    return impl, sentinel
+
+
+def test_npu_env_unset_uses_varlen(monkeypatch):
+    monkeypatch.delenv("MINDIE_SD_FA_TYPE", raising=False)
+    _fake_mindiesd(monkeypatch)
+    impl, _ = _npu_impl_with_mocked_paths()
+    metadata = AttentionMetadata(extra={"npu_attn_varlen": True})
+
+    impl.forward_fa_npu(torch.randn(1, 8, 2, 4), *[torch.randn(1, 8, 2, 4)] * 2, metadata)
+
+    impl._forward_varlen_packed_npu.assert_called_once()
+    impl._forward_prefix_kv_slice_npu.assert_not_called()
+
+
+def test_npu_env_laser_uses_slice(monkeypatch):
+    monkeypatch.setenv("MINDIE_SD_FA_TYPE", "ascend_laser_attention")
+    _fake_mindiesd(monkeypatch)
+    impl, _ = _npu_impl_with_mocked_paths()
+    metadata = AttentionMetadata(extra={"npu_attn_varlen": True})
+
+    impl.forward_fa_npu(torch.randn(1, 8, 2, 4), *[torch.randn(1, 8, 2, 4)] * 2, metadata)
+
+    impl._forward_prefix_kv_slice_npu.assert_called_once()
+    impl._forward_varlen_packed_npu.assert_not_called()
+
+
+@pytest.mark.parametrize("fa_type", ["prompt_flash_attn", "fused_attn_score", "ascend_laser_attenton", "garbage"])
+def test_npu_env_non_laser_value_falls_back_to_varlen(monkeypatch, fa_type):
+    """Locks in the CURRENT dispatch behavior (PR #5891).
+
+    Any value other than ``ascend_laser_attention`` — including the valid
+    ``prompt_flash_attn``/``fused_attn_score`` and typos — is silently routed to
+    the TND varlen op, which cannot honor ``MINDIE_SD_FA_TYPE``. This is review
+    comment #1 on the PR and is intentionally captured here as a regression
+    net: update this test when the dispatch starts honoring/rejecting these.
+    """
+    monkeypatch.setenv("MINDIE_SD_FA_TYPE", fa_type)
+    _fake_mindiesd(monkeypatch)
+    impl, _ = _npu_impl_with_mocked_paths()
+    metadata = AttentionMetadata(extra={"npu_attn_varlen": True})
+
+    impl.forward_fa_npu(torch.randn(1, 8, 2, 4), *[torch.randn(1, 8, 2, 4)] * 2, metadata)
+
+    impl._forward_varlen_packed_npu.assert_called_once()
+    impl._forward_prefix_kv_slice_npu.assert_not_called()
+
+
+def test_npu_varlen_opt_in_unset_takes_mask_path(monkeypatch):
+    """Models that do not set ``npu_attn_varlen`` (e.g. Wan/Cosmos) keep using
+    the existing masked attention_forward path; the new branches are skipped."""
+    fake_forward = Mock(return_value=torch.zeros(1, 8, 2, 4))
+    _fake_mindiesd(monkeypatch, attention_forward=fake_forward)
+    impl, _ = _npu_impl_with_mocked_paths()
+    metadata = AttentionMetadata(
+        attn_mask=torch.tensor([[True, True, True, True, True, False, False, False]]),
+        extra={},  # no npu_attn_varlen opt-in
+    )
+    q = torch.randn(1, 8, 2, 4)
+
+    out = impl.forward_fa_npu(q, q, q, metadata)
+
+    impl._forward_varlen_packed_npu.assert_not_called()
+    impl._forward_prefix_kv_slice_npu.assert_not_called()
+    fake_forward.assert_called_once()
+    assert out is fake_forward.return_value
+
+
+# --- Test group C: laser input pre-scaling in _forward_prefix_kv_slice_npu --
+
+
+def test_prefix_kv_slice_applies_laser_input_scaling(monkeypatch):
+    monkeypatch.setenv("MINDIE_SD_FA_TYPE", "ascend_laser_attention")
+    captured: dict = {}
+
+    def fake_attention_forward(query, key, value, **kwargs):
+        captured["query"] = query
+        captured["key"] = key
+        captured["value"] = value
+        captured.update(kwargs)
+        return torch.ones_like(query)  # deterministic stand-in kernel output
+
+    _fake_mindiesd(monkeypatch, attention_forward=fake_attention_forward)
+    impl = _npu_impl()  # softmax_scale == 0.5
+    q = torch.randn(1, 8, 2, 4)
+    cu = torch.tensor([0, 5, 8], dtype=torch.int32)
+    extra = {
+        "cu_seqlens_q": cu,
+        "cu_seqlens_k": cu,
+        "max_seqlen_q": 5,
+        "max_seqlen_k": 5,
+        "laser_input_scale": 256.0,
+    }
+
+    out = impl._forward_prefix_kv_slice_npu(q, q, q, extra)
+
+    # K/V sliced to the valid prefix (used_k == 5); q keeps full length.
+    assert captured["key"].shape == (1, 5, 2, 4)
+    assert captured["value"].shape == (1, 5, 2, 4)
+    assert captured["query"].shape == (1, 8, 2, 4)
+    assert captured["attn_mask"] is None
+    # Pre-divide q/k/v by the factor and compensate the kernel scale by its
+    # square (exact for power-of-two); output scaled back by the factor.
+    torch.testing.assert_close(captured["query"], q / 256.0)
+    torch.testing.assert_close(captured["key"], q[:, :5] / 256.0)
+    assert captured["scale"] == pytest.approx(0.5 * 256.0**2)
+    torch.testing.assert_close(out, torch.ones(1, 8, 2, 4) * 256.0)
+
+
+def test_prefix_kv_slice_no_scaling_without_factor(monkeypatch):
+    monkeypatch.setenv("MINDIE_SD_FA_TYPE", "ascend_laser_attention")
+    captured: dict = {}
+
+    def fake_attention_forward(query, key, value, **kwargs):
+        captured["query"] = query
+        captured.update(kwargs)
+        return torch.ones_like(query)
+
+    _fake_mindiesd(monkeypatch, attention_forward=fake_attention_forward)
+    impl = _npu_impl()
+    q = torch.randn(1, 8, 2, 4)
+    cu = torch.tensor([0, 5, 8], dtype=torch.int32)
+    extra = {
+        "cu_seqlens_q": cu,
+        "cu_seqlens_k": cu,
+        "max_seqlen_q": 5,
+        "max_seqlen_k": 5,
+        # no laser_input_scale → no pre-scaling
+    }
+
+    out = impl._forward_prefix_kv_slice_npu(q, q, q, extra)
+
+    torch.testing.assert_close(captured["query"], q)
+    assert captured["scale"] == pytest.approx(0.5)
+    torch.testing.assert_close(out, torch.ones(1, 8, 2, 4))
+
+
 if __name__ == "__main__":
    print("Running FlashAttention Padding Tests...")
    print("=" * 60)
@@ -5,10 +5,9 @@

 The grids here are the ones MiniMax-H3 FL2VA actually produces for an 8.7s clip:
 1280x768 gives a 62x24x40 latent grid whose 59520 video rows land on the 128-row
-kernel block, and 1344x768 gives 62x24x42 whose 62496 rows straddle it. Only the
-first can run sparse: rf_v2 tiles the sequence as a whole but pools its block
-mask from the video and prefix segments separately, so the two only agree on the
-block count when the video segment ends on a boundary.
+kernel block, and 1344x768 gives 62x24x42 whose 62496 rows do not. Both are
+handed to MindIE-SD: its rf_v2 preprocessing promotes an irregular video tail
+to the always-kept prefix segment before generating the block mask.
 """

 import dataclasses
@@ -57,10 +56,13 @@ def test_block_aligned_video_segment_runs_sparse():
    assert plan.latent_shape == list(ALIGNED_GRID)


-def test_misaligned_video_segment_stays_dense():
-    # Realigning the sequence would mean padding it, and rf_v2 ignores attn_mask:
-    # pad keys would take a share of every real query's softmax denominator.
-    assert make_impl()._resolve_plan(make_metadata(MISALIGNED_GRID)) is None
+def test_misaligned_video_segment_runs_sparse_with_updated_mindiesd():
+    plan = make_impl()._resolve_plan(make_metadata(MISALIGNED_GRID))
+
+    assert plan is not None
+    assert plan.prefix_len == PREFIX_ROWS
+    assert plan.used_len == PREFIX_ROWS + 62496
+    assert plan.latent_shape == list(MISALIGNED_GRID)


@pytest.mark.parametrize("prefix_len", [1, 127, _BLOCK_SIZE, 710, 900])
@@ -155,3 +157,28 @@ def test_fully_populated_mask_reproduces_dense_attention():
    ).transpose(1, 2)
    error = (out.float() - reference.float()).abs().mean() / reference.float().abs().mean()
    assert error < 2e-3, f"mean relative error {error:.4%} against dense attention"
+
+
+@pytest.mark.skipif(not current_omni_platform.is_npu(), reason="rf_v2 runs on Ascend NPU only.")
+def test_irregular_video_tail_reproduces_dense_attention_with_updated_mindiesd():
+    """vLLM forwards the full irregular grid; MindIE-SD owns its tail handling."""
+    torch.manual_seed(1)
+    prefix_len, grid = 384, (4, 16, 10)
+    video_len = grid[0] * grid[1] * grid[2]
+    used = prefix_len + video_len
+    heads, head_dim = 4, 128
+    q, k, v = (torch.randn(1, used, heads, head_dim, dtype=torch.bfloat16, device="npu") for _ in range(3))
+
+    impl = make_impl()
+    impl.rainfusion = dataclasses.replace(impl.rainfusion, sparsity=0.0)
+    plan = RainFusionPlan(prefix_len=prefix_len, used_len=used, latent_shape=list(grid))
+    out = impl._forward_sparse_npu(q, k, v, plan)
+
+    reference = torch.nn.functional.scaled_dot_product_attention(
+        q.transpose(1, 2),
+        k.transpose(1, 2),
+        v.transpose(1, 2),
+        scale=impl.softmax_scale,
+    ).transpose(1, 2)
+    error = (out.float() - reference.float()).abs().mean() / reference.float().abs().mean()
+    assert error < 2e-3, f"mean relative error {error:.4%} against dense attention"
@@ -471,6 +471,66 @@ class TestTeaCacheBackend:
        assert backend.enabled is True
        mock_apply_hook.assert_called_once()

+    @patch("vllm_omni.diffusion.cache.teacache.backend.apply_teacache_hook")
+    def test_enable_uses_generic_default_threshold(self, mock_apply_hook):
+        pipeline = Mock()
+        pipeline.__class__.__name__ = "QwenImagePipeline"
+        pipeline.transformer = Mock()
+        pipeline.transformer.__class__.__name__ = "QwenImageTransformer2DModel"
+
+        TeaCacheBackend(DiffusionCacheConfig()).enable(pipeline)
+        assert mock_apply_hook.call_args.args[1].rel_l1_thresh == 0.2
+
+    @pytest.mark.parametrize("partition", ["fl2va", "combined"])
+    @pytest.mark.parametrize(
+        ("configured_threshold", "expected_threshold"),
+        [(None, 0.17), (0.2, 0.2)],
+    )
+    @patch("vllm_omni.diffusion.cache.teacache.backend.apply_teacache_hook")
+    def test_minimax_h3_only_enables_fl2va_teacache(
+        self,
+        mock_apply_hook,
+        partition,
+        configured_threshold,
+        expected_threshold,
+    ):
+        pipeline = Mock()
+        pipeline.__class__.__name__ = "MiniMaxH3Pipeline"
+        pipeline.partition = partition
+        pipeline.transformer = Mock()
+        pipeline.transformer.__class__.__name__ = "MiniMaxH3DiTModel"
+        pipeline.transformers_ref = Mock()
+
+        config = (
+            DiffusionCacheConfig()
+            if configured_threshold is None
+            else DiffusionCacheConfig(rel_l1_thresh=configured_threshold)
+        )
+        backend = TeaCacheBackend(config)
+        backend.enable(pipeline)
+
+        mock_apply_hook.assert_called_once()
+        transformer, teacache_config = mock_apply_hook.call_args.args
+        assert transformer is pipeline.transformer
+        assert transformer is not pipeline.transformers_ref
+        assert teacache_config.rel_l1_thresh == expected_threshold
+
+    @patch("vllm_omni.diffusion.cache.teacache.backend.apply_teacache_hook")
+    def test_minimax_h3_rejects_ref2va_teacache(self, mock_apply_hook):
+        pipeline = Mock()
+        pipeline.__class__.__name__ = "MiniMaxH3Pipeline"
+        pipeline.partition = "ref2va"
+        pipeline.transformer = Mock()
+        pipeline.transformer.__class__.__name__ = "MiniMaxH3DiTModel"
+
+        backend = TeaCacheBackend(DiffusionCacheConfig())
+
+        with pytest.raises(ValueError, match="only supports the MiniMax-H3 FL2VA partition"):
+            backend.enable(pipeline)
+
+        assert backend.enabled is False
+        mock_apply_hook.assert_not_called()
+
    @patch("vllm_omni.diffusion.cache.teacache.backend.apply_teacache_hook")
    def test_enable_with_coefficients(self, mock_apply_hook):
        """Test enabling TeaCache with custom coefficients."""
@@ -15,19 +15,23 @@ Currently implemented:
 - TestFlux2KleinExtractor: Flux2Klein model extractor
 - TestFlux2Extractor: Flux2 model extractor
 - TestFluxExtractor: Flux model extractor
+- TestMiniMaxH3Extractor: MiniMaxH3DiTModel extractor
 """

 from abc import ABC, abstractmethod
+from types import SimpleNamespace
 from unittest.mock import MagicMock, Mock, patch

 import pytest
 import torch
+import torch.nn as nn

 from tests.helpers.mark import hardware_test
 from vllm_omni.diffusion.cache.teacache.extractors import (
    extract_flux2_context,
    extract_flux2_klein_context,
    extract_flux_context,
+    extract_minimax_h3_context,
 )
 from vllm_omni.diffusion.models.flux.flux_transformer import FluxTransformer2DModel
 from vllm_omni.diffusion.models.flux2_klein.flux2_klein_transformer import (
@@ -423,3 +427,288 @@ class TestFluxExtractor(BaseExtractorTest):
                img_ids=torch.randint(0, 64, (1, 16, 3)),
                txt_ids=torch.randint(0, 64, (1, 8, 3)),
            )
+
+
+class _MiniMaxH3FakeLinear(nn.Module):
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        *,
+        bias=False,
+        params_dtype=None,
+        quant_config=None,
+        prefix="",
+        total_num_heads=None,
+        total_num_kv_heads=None,
+        **kwargs,
+    ):
+        del kwargs
+        super().__init__()
+        self.prefix = prefix
+        self.quant_config = quant_config
+        self.weight = nn.Parameter(torch.randn(out_features, in_features, dtype=params_dtype or torch.float32))
+        if bias:
+            self.bias = nn.Parameter(torch.zeros(out_features, dtype=params_dtype or torch.float32))
+        else:
+            self.register_parameter("bias", None)
+        self.num_heads = total_num_heads
+        self.num_kv_heads = total_num_kv_heads
+
+    def forward(self, x):
+        return torch.nn.functional.linear(x, self.weight, self.bias), None
+
+
+class _MiniMaxH3FakeMergedLinear(_MiniMaxH3FakeLinear):
+    def __init__(self, in_features, out_features, **kwargs):
+        super().__init__(in_features, sum(out_features), **kwargs)
+
+
+class _MiniMaxH3FakeQKVLinear(_MiniMaxH3FakeLinear):
+    def __init__(
+        self,
+        *,
+        hidden_size,
+        head_size,
+        total_num_heads,
+        total_num_kv_heads,
+        **kwargs,
+    ):
+        qkv_features = (total_num_heads + 2 * total_num_kv_heads) * head_size
+        super().__init__(
+            hidden_size,
+            qkv_features,
+            total_num_heads=total_num_heads,
+            total_num_kv_heads=total_num_kv_heads,
+            **kwargs,
+        )
+
+
+class _MiniMaxH3FakeAttention(nn.Module):
+    class FakeBackend:
+        supports_prefix_kv_slicing = False
+
+        # Mirror AttentionBackend.supports_packed_mask_free: H3's packed
+        # attention path calls attn_backend.supports_packed_mask_free() to decide
+        # whether to build the padding mask. Return False so the fake exercises
+        # the masked path (the abstract backend default).
+        @classmethod
+        def supports_packed_mask_free(cls) -> bool:
+            return False
+
+    def __init__(self, **kwargs):
+        del kwargs
+        super().__init__()
+        self.attn_backend = self.FakeBackend
+        self.use_ring = False
+
+    def forward(self, q, k, v, metadata=None):
+        del k, v, metadata
+        return q
+
+
+def _minimax_h3_small_od_config():
+    arch = {
+        "num_layers": 1,
+        "token_refiner_num_layers": 1,
+        "hidden_size": 8,
+        "num_attention_heads": 1,
+        "attention_head_dim": 8,
+        "ffn_hidden_size": 16,
+        "latents_dim": 2,
+        "audio_latents_dim": 2,
+        "patch_size": (1, 2, 2),
+        "text_dim": 6,
+        "timestep_input_dim": 4,
+        "time_embed_hidden_size": 8,
+        "time_embed_dim": 4,
+        "adaln_out_features": 18 * 8,
+        "final_adaln_out_features": 2 * 8,
+        "rope_inv_freq_len": 1,
+    }
+    return SimpleNamespace(
+        tf_model_config=arch,
+        parallel_config=SimpleNamespace(ulysses_degree=1),
+    )
+
+
+def _minimax_h3_sample_kwargs(*, seq_len: int = 8, device: torch.device | str = "cpu") -> dict:
+    video_row_dim = 2 * 1 * 2 * 2
+    audio_row_dim = 2
+    text_len = 2
+    img_rows = 2
+
+    img_pos = torch.tensor([2, 3], dtype=torch.long, device=device)
+    audio_pos = torch.tensor([4, 5], dtype=torch.long, device=device)
+    text_pos = torch.tensor([0, 1], dtype=torch.long, device=device)
+    infer_out_pos = img_pos.clone()
+
+    token_tags = torch.full((seq_len,), -1, dtype=torch.long, device=device)
+    token_tags[text_pos] = 1
+    token_tags[img_pos] = 0
+    token_tags[audio_pos] = 2
+
+    inverse_indices = torch.zeros(seq_len, dtype=torch.long, device=device)
+
+    return {
+        "x": torch.randn(1, seq_len, video_row_dim, device=device),
+        "audio_x": torch.randn(1, seq_len, audio_row_dim, device=device),
+        "img_position_ids": torch.zeros(1, seq_len, 3, dtype=torch.float64, device=device),
+        "unique_timesteps": torch.tensor([0.5], dtype=torch.float32, device=device),
+        "inverse_indices": inverse_indices,
+        "update_mask": torch.ones(img_rows, dtype=torch.bool, device=device),
+        "token_tags": token_tags,
+        "prompt_embeds": torch.randn(text_len, 6, device=device),
+        "img_pos_info": {"position_ids": img_pos},
+        "audio_pos_info": {"position_ids": audio_pos},
+        "text_pos_info": {"position_ids": text_pos},
+        "img_pos_for_infer_output_info": {"position_ids": infer_out_pos},
+        "packed_seq_params": {
+            "cu_seqlens_q": torch.tensor([0, seq_len], dtype=torch.int32, device=device),
+            "max_seqlen_q": seq_len,
+        },
+        "refiner_packed_seq_params": {
+            "cu_seqlens_q": torch.tensor([0, text_len], dtype=torch.int32, device=device),
+            "max_seqlen_q": text_len,
+        },
+    }
+
+
+@pytest.mark.cpu
+class TestMiniMaxH3Extractor(BaseExtractorTest):
+    """Test extract_minimax_h3_context function."""
+
+    def get_extractor(self):
+        return extract_minimax_h3_context
+
+    @pytest.fixture
+    def minimax_h3_module(self, monkeypatch):
+        """Create a minimal MiniMaxH3DiTModel with fake parallel linears for CPU tests."""
+        from vllm_omni.diffusion.models.minimax_h3 import minimax_h3_transformer as h3
+
+        monkeypatch.setattr(h3, "ColumnParallelLinear", _MiniMaxH3FakeLinear)
+        monkeypatch.setattr(h3, "MergedColumnParallelLinear", _MiniMaxH3FakeMergedLinear)
+        monkeypatch.setattr(h3, "QKVParallelLinear", _MiniMaxH3FakeQKVLinear)
+        monkeypatch.setattr(h3, "RowParallelLinear", _MiniMaxH3FakeLinear)
+        monkeypatch.setattr(h3, "Attention", _MiniMaxH3FakeAttention)
+        monkeypatch.setattr(h3, "get_tensor_model_parallel_world_size", lambda: 1)
+
+        model = h3.MiniMaxH3DiTModel(_minimax_h3_small_od_config(), quant_config=None)
+        for submodule in model.modules():
+            if isinstance(submodule, h3.MiniMaxH3Attention):
+                submodule.rope._forward_method = submodule.rope.forward_native
+        model.eval()
+        return model
+
+    def get_module(self, minimax_h3_module):
+        return minimax_h3_module
+
+    @pytest.fixture
+    def sample_inputs(self):
+        """Create sample packed forward kwargs for MiniMaxH3DiTModel."""
+        return _minimax_h3_sample_kwargs(seq_len=8)
+
+    def get_sample_inputs(self, sample_inputs):
+        return sample_inputs
+
+    def test_modulated_input_shape(self, minimax_h3_module, sample_inputs):
+        """Test that modulated_input matches packed sequence shape [T, H]."""
+        context = extract_minimax_h3_context(minimax_h3_module, **sample_inputs)
+
+        seq_len = sample_inputs["x"].shape[1]
+        assert context.modulated_input.shape == (seq_len, minimax_h3_module.hidden_size)
+
+    def test_run_transformer_blocks_callable(self, minimax_h3_module, sample_inputs):
+        """Test that run_transformer_blocks is callable."""
+        context = extract_minimax_h3_context(minimax_h3_module, **sample_inputs)
+        assert callable(context.run_transformer_blocks)
+
+    def test_postprocess_callable(self, minimax_h3_module, sample_inputs):
+        """Test that postprocess is callable."""
+        context = extract_minimax_h3_context(minimax_h3_module, **sample_inputs)
+        assert callable(context.postprocess)
+
+    def test_postprocess_output_shape(self, minimax_h3_module, sample_inputs):
+        """Test that postprocess returns video/audio logits for infer rows."""
+        context = extract_minimax_h3_context(minimax_h3_module, **sample_inputs)
+        (hidden,) = context.run_transformer_blocks()
+        video_logits, audio_logits = context.postprocess(hidden)
+
+        num_video_rows = sample_inputs["img_pos_info"]["position_ids"].shape[0]
+        num_audio_rows = sample_inputs["audio_pos_info"]["position_ids"].shape[0]
+        assert video_logits.shape[0] == num_video_rows
+        assert audio_logits.shape[0] == num_audio_rows
+
+    def test_extractor_matches_native_forward(self, minimax_h3_module, sample_inputs):
+        """The uncached extractor path must preserve the model's forward result."""
+        expected_video, expected_audio = minimax_h3_module(**sample_inputs)
+
+        context = extract_minimax_h3_context(minimax_h3_module, **sample_inputs)
+        (hidden,) = context.run_transformer_blocks()
+        actual_video, actual_audio = context.postprocess(hidden)
+
+        torch.testing.assert_close(actual_video, expected_video)
+        torch.testing.assert_close(actual_audio, expected_audio)
+
+    def test_run_transformer_blocks_forwards_packed_layout(
+        self,
+        minimax_h3_module,
+        sample_inputs,
+        monkeypatch,
+    ):
+        """The extractor block call must mirror the native packed contract."""
+        from vllm_omni.diffusion.attention.backends.abstract import VideoTokenLayout
+
+        video_layout = VideoTokenLayout(prefix_len=2, latent_grid=(1, 1, 2))
+        inputs = {**sample_inputs, "video_token_layout": video_layout}
+        block = minimax_h3_module.blocks[0]
+        original_forward = block.forward
+        received: list[tuple[int, VideoTokenLayout | None]] = []
+
+        def capture_forward(*args, **kwargs):
+            received.append((kwargs["packed_total"], kwargs["video_layout"]))
+            return original_forward(*args, **kwargs)
+
+        monkeypatch.setattr(block, "forward", capture_forward)
+        context = extract_minimax_h3_context(minimax_h3_module, **inputs)
+        context.run_transformer_blocks()
+
+        assert received == [(sample_inputs["x"].shape[1], video_layout)]
+
+    def test_teacache_reuses_h3_block_residual(self, minimax_h3_module, sample_inputs, monkeypatch):
+        """A guaranteed cache hit must skip the H3 transformer block."""
+        from vllm_omni.diffusion.cache.teacache.config import TeaCacheConfig
+        from vllm_omni.diffusion.cache.teacache.hook import TeaCacheHook
+
+        block = minimax_h3_module.blocks[0]
+        original_forward = block.forward
+        block_calls = 0
+
+        def counted_forward(*args, **kwargs):
+            nonlocal block_calls
+            block_calls += 1
+            return original_forward(*args, **kwargs)
+
+        monkeypatch.setattr(block, "forward", counted_forward)
+        hook = TeaCacheHook(
+            TeaCacheConfig(
+                transformer_type="MiniMaxH3DiTModel",
+                rel_l1_thresh=0.3,
+            )
+        )
+        hook.initialize_hook(minimax_h3_module)
+
+        hook.new_forward(minimax_h3_module, **sample_inputs)
+        video_logits, audio_logits = hook.new_forward(minimax_h3_module, **sample_inputs)
+
+        assert block_calls == 1
+        assert video_logits.shape[0] == sample_inputs["img_pos_info"]["position_ids"].shape[0]
+        assert audio_logits.shape[0] == sample_inputs["audio_pos_info"]["position_ids"].shape[0]
+
+    def test_invalid_module_raises_error(self):
+        """Test that invalid module without blocks raises ValueError."""
+        invalid_module = Mock()
+        invalid_module.blocks = []
+
+        with pytest.raises(ValueError, match="Module must have blocks"):
+            extract_minimax_h3_context(invalid_module, **_minimax_h3_sample_kwargs())
@@ -0,0 +1,52 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+
+from vllm_omni.diffusion.data import OmniDiffusionConfig
+from vllm_omni.diffusion.diffusion_kv.config import DiffusionKVCacheMode
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
+
+
+@pytest.fixture(autouse=True)
+def _fixed_master_port(monkeypatch) -> None:
+    monkeypatch.setattr(OmniDiffusionConfig, "_resolve_master_port", lambda _self: 29500)
+
+
+def test_dense_legacy_is_default() -> None:
+    config = OmniDiffusionConfig.from_kwargs()
+
+    assert config.diffusion_kv_mode is DiffusionKVCacheMode.DENSE_LEGACY
+
+
+def test_paged_worker_local_is_rejected_until_implemented() -> None:
+    with pytest.raises(ValueError, match="reserved but not implemented"):
+        OmniDiffusionConfig.from_kwargs(diffusion_kv_mode="paged_worker_local")
+
+
+def test_unknown_cache_mode_is_rejected() -> None:
+    with pytest.raises(ValueError, match="Unsupported Diffusion KV diffusion_kv_mode"):
+        OmniDiffusionConfig.from_kwargs(diffusion_kv_mode="unknown")
+
+
+def test_non_mapping_omni_kv_config_is_rejected() -> None:
+    with pytest.raises(TypeError, match="omni_kv_config must be a mapping"):
+        OmniDiffusionConfig.from_kwargs(omni_kv_config="paged_scheduler")
+
+
+def test_paged_scheduler_rejects_dense_legacy_kv_receive() -> None:
+    with pytest.raises(ValueError, match="does not support imported AR KV"):
+        OmniDiffusionConfig.from_kwargs(
+            diffusion_kv_mode="paged_scheduler",
+            omni_kv_config={"need_recv_cache": True},
+        )
+
+
+def test_paged_scheduler_does_not_depend_on_model_registry() -> None:
+    config = OmniDiffusionConfig.from_kwargs(
+        model_class_name="FutureDiffusionModel",
+        diffusion_kv_mode="paged_scheduler",
+    )
+
+    assert config.diffusion_kv_mode is DiffusionKVCacheMode.PAGED_SCHEDULER
@@ -0,0 +1,200 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+import vllm.v1.core.single_type_kv_cache_manager as native_kv_managers
+from vllm.v1.core.kv_cache_manager import KVCacheManager
+from vllm.v1.core.kv_cache_utils import BlockHash
+from vllm.v1.kv_cache_interface import (
+    FullAttentionSpec,
+    KVCacheConfig,
+    KVCacheGroupSpec,
+    KVCacheTensor,
+)
+from vllm.v1.request import RequestStatus
+
+from vllm_omni.diffusion.diffusion_kv.request import DiffusionKVContext, DiffusionKVRequest
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
+
+BLOCK_SIZE = 4
+
+
+def _request(**overrides) -> DiffusionKVRequest:
+    values = dict(
+        request_id="req/diffusion-kv/0",
+        sequence_id=0,
+        prefix_len=4,
+        target_len=4,
+        seq_len=8,
+    )
+    values.update(overrides)
+    return DiffusionKVRequest(**values)
+
+
+def test_request_exposes_native_request_and_diffusion_semantics() -> None:
+    request = _request()
+
+    assert request.sequence_id == 0
+    assert request.prefix_len == 4
+    assert request.target_len == 4
+    assert request.num_tokens == request.seq_len == 8
+    assert request.num_prompt_tokens == 4
+    assert request.num_computed_tokens == 0
+    assert request.block_hashes == []
+    assert request.kv_contexts == ()
+    assert request.skip_reading_prefix_cache is True
+    assert request.shared_prefix_boundary == 0
+    assert request.status is RequestStatus.WAITING
+
+
+@pytest.mark.parametrize(
+    ("overrides", "message"),
+    [
+        ({"sequence_id": -1}, "sequence_id"),
+        ({"prefix_len": -1}, "prefix_len"),
+        ({"target_len": 0}, "target_len"),
+        ({"seq_len": 0}, "seq_len"),
+        ({"prefix_len": 5, "target_len": 4, "seq_len": 8}, "must not exceed"),
+    ],
+)
+def test_request_rejects_invalid_lengths(overrides, message: str) -> None:
+    with pytest.raises(ValueError, match=message):
+        _request(**overrides)
+
+
+@pytest.mark.parametrize(
+    ("overrides", "message"),
+    [
+        ({"context_id": ""}, "context_id"),
+        ({"cache_role": ""}, "cache_role"),
+        ({"num_tokens": 0}, "num_tokens"),
+    ],
+)
+def test_context_rejects_invalid_identity_or_length(overrides, message: str) -> None:
+    values = dict(context_id="text", cache_role="cross.text", num_tokens=8)
+    values.update(overrides)
+
+    with pytest.raises(ValueError, match=message):
+        DiffusionKVContext(**values)
+
+
+def test_request_owns_independent_kv_contexts() -> None:
+    text = DiffusionKVContext(context_id="text", cache_role="cross.text", num_tokens=8)
+    image = DiffusionKVContext(context_id="image", cache_role="cross.image", num_tokens=4)
+
+    request = _request(kv_contexts=(text, image))
+
+    assert request.kv_contexts == (text, image)
+
+
+def test_request_rejects_duplicate_context_ids() -> None:
+    contexts = (
+        DiffusionKVContext(context_id="condition", cache_role="cross.text", num_tokens=8),
+        DiffusionKVContext(context_id="condition", cache_role="cross.image", num_tokens=4),
+    )
+
+    with pytest.raises(ValueError, match="unique context_id"):
+        _request(kv_contexts=contexts)
+
+
+def test_request_rejects_non_context_values() -> None:
+    with pytest.raises(TypeError, match="DiffusionKVContext"):
+        _request(kv_contexts=(object(),))
+
+
+def _manager(*, enable_caching: bool) -> KVCacheManager:
+    # vLLM 0.26 lazily registers its built-in KV specs. Another test may have
+    # already registered an out-of-tree spec, making the registry non-empty
+    # before FullAttentionSpec is registered. Initialize the native mappings
+    # explicitly; vLLM 0.25 has no registration hook and needs no setup.
+    register_all = getattr(native_kv_managers, "register_all_kvcache_specs", None)
+    if register_all is not None:
+        register_all(None)
+
+    spec = FullAttentionSpec(
+        block_size=BLOCK_SIZE,
+        num_kv_heads=2,
+        head_size=8,
+        dtype=torch.bfloat16,
+    )
+    group = KVCacheGroupSpec(layer_names=["layer0"], kv_cache_spec=spec)
+    config = KVCacheConfig(
+        num_blocks=8,
+        kv_cache_tensors=[KVCacheTensor(size=spec.page_size_bytes * 8, shared_by=["layer0"])],
+        kv_cache_groups=[group],
+    )
+    return KVCacheManager(
+        config,
+        max_model_len=64,
+        scheduler_block_size=BLOCK_SIZE,
+        hash_block_size=BLOCK_SIZE,
+        enable_caching=enable_caching,
+    )
+
+
+def test_empty_hash_request_conforms_to_native_request_local_allocation() -> None:
+    manager = _manager(enable_caching=True)
+    free_before = manager.block_pool.get_num_free_blocks()
+    request = _request()
+
+    # vLLM 0.26 adds a shared-prefix-boundary return value.
+    computed_blocks, num_computed_tokens, *_ = manager.get_computed_blocks(request)
+    assert num_computed_tokens == 0
+    blocks = manager.allocate_slots(
+        request,
+        num_new_tokens=request.num_tokens,
+        new_computed_blocks=computed_blocks,
+        delay_cache_blocks=True,
+        full_sequence_must_fit=True,
+    )
+
+    assert blocks is not None
+    assert len(manager.get_block_ids(request.request_id)[0]) == 2
+    # Empty hashes intentionally mean request-local residency only. Calling
+    # native cache_blocks() would require a canonical hash for the full prefix
+    # block and is therefore outside this request's contract.
+    manager.free(request)
+    assert manager.block_pool.get_num_free_blocks() == free_before
+
+
+def test_hashed_request_conforms_to_native_delayed_prefix_commit() -> None:
+    manager = _manager(enable_caching=True)
+    prefix_hash = BlockHash(b"prefix".ljust(32, b"\0"))
+    target_hash = BlockHash(b"target".ljust(32, b"\0"))
+    request = _request(block_hashes=[prefix_hash, target_hash])
+
+    computed_blocks, num_computed_tokens, *_ = manager.get_computed_blocks(request)
+    assert num_computed_tokens == 0
+    blocks = manager.allocate_slots(
+        request,
+        num_new_tokens=request.num_tokens,
+        new_computed_blocks=computed_blocks,
+        delay_cache_blocks=True,
+        full_sequence_must_fit=True,
+    )
+
+    assert blocks is not None
+    manager.cache_blocks(request, request.prefix_len)
+    manager.free(request)
+
+    cached_request = _request(
+        request_id="req/diffusion-kv/cached",
+        block_hashes=[prefix_hash, target_hash],
+    )
+    cached_blocks, num_computed_tokens, *_ = manager.get_computed_blocks(cached_request)
+
+    # Only the stable prefix was published. The dynamic target remains a miss.
+    assert num_computed_tokens == cached_request.prefix_len
+    assert len(cached_blocks.blocks[0]) == 1
+    cached_allocation = manager.allocate_slots(
+        cached_request,
+        num_new_tokens=cached_request.num_tokens - num_computed_tokens,
+        num_new_computed_tokens=num_computed_tokens,
+        new_computed_blocks=cached_blocks,
+        delay_cache_blocks=True,
+        full_sequence_must_fit=True,
+    )
+    assert cached_allocation is not None
+    manager.free(cached_request)
@@ -0,0 +1,447 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from types import SimpleNamespace
+
+import pytest
+import torch
+from transformers.utils.generic import ModelOutput
+
+import vllm_omni.diffusion.models.hunyuan_image3.pipeline_hunyuan_image3 as pipeline_module
+from vllm_omni.diffusion.diffusion_kv.config import DiffusionKVCacheMode
+from vllm_omni.diffusion.diffusion_kv.request import DiffusionKVRequest
+from vllm_omni.diffusion.models.hunyuan_image3.hunyuan_image3_tokenizer import TokenizerEncodeOutput
+from vllm_omni.diffusion.models.hunyuan_image3.hunyuan_image3_transformer import (
+    ImageInfo,
+    JointImageInfo,
+)
+from vllm_omni.diffusion.models.hunyuan_image3.pipeline_hunyuan_image3 import (
+    HunyuanImage3Pipeline,
+    get_hunyuan_image_3_pre_process_func,
+)
+from vllm_omni.diffusion.models.hunyuan_image3.request_layout import (
+    HunyuanPreparedLayout,
+    build_hunyuan_diffusion_kv_requests,
+    extract_hunyuan_prompt_inputs,
+    hunyuan_num_image_tokens,
+    normalize_hunyuan_cot_text,
+    prepare_hunyuan_layout,
+)
+from vllm_omni.diffusion.request import OmniDiffusionRequest
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
+
+
+def test_dense_legacy_preprocess_does_not_initialize_layout_tokenizer(monkeypatch) -> None:
+    hf_config = SimpleNamespace(vae_downsample_factor=(8, 8), patch_size=2)
+    image_processor = SimpleNamespace(vision_encoder_processor=SimpleNamespace(patch_size=1))
+    monkeypatch.setattr(pipeline_module, "get_config", lambda *_args, **_kwargs: hf_config)
+    monkeypatch.setattr(pipeline_module, "HunyuanImage3ImageProcessor", lambda _config: image_processor)
+    monkeypatch.setattr(
+        pipeline_module,
+        "TokenizerWrapper",
+        lambda _model: pytest.fail("dense_legacy must not initialize the Diffusion KV tokenizer"),
+    )
+
+    get_hunyuan_image_3_pre_process_func(
+        SimpleNamespace(model="model", diffusion_kv_mode=DiffusionKVCacheMode.DENSE_LEGACY)
+    )
+
+
+class _FakeTokenizerWrapper:
+    def __init__(self, *, prefix_lens: list[int]) -> None:
+        self.prefix_lens = prefix_lens
+        self.calls: list[dict] = []
+        self.pad_token_id = 0
+        self.eos_token_id = 2
+        self.boi_token_id = 3
+        self.end_recaption_token_id = 4
+        self.end_answer_token_id = 5
+        self.special_token_map = {f"<img_ratio_{index}>": 100 + index for index in range(33)}
+
+    def apply_chat_template(self, **kwargs):
+        self.calls.append(kwargs)
+        rows = len(self.prefix_lens)
+        has_cond_image = kwargs.get("batch_cond_image_info") is not None
+        seq_lens = [prefix_len + 20 for prefix_len in self.prefix_lens]
+        padded_seq_len = max(seq_lens)
+        positions = torch.arange(padded_seq_len)
+        joint_image_slices = [[slice(1, 3), slice(3, 5)] if has_cond_image else [] for _ in range(rows)]
+        gen_image_slices = [[slice(length + 1, length + 17)] for length in self.prefix_lens]
+        all_image_slices = [joint + generated for joint, generated in zip(joint_image_slices, gen_image_slices)]
+        cond_vae_image_mask = (
+            torch.stack([(positions >= 1) & (positions < 3) for _ in range(rows)]) if has_cond_image else None
+        )
+        cond_vit_image_mask = (
+            torch.stack([(positions >= 3) & (positions < 5) for _ in range(rows)]) if has_cond_image else None
+        )
+        sections = []
+        for _ in range(rows):
+            row_sections = []
+            if has_cond_image:
+                row_sections.append(
+                    {
+                        "type": "joint_image",
+                        "token_height": [1, 1],
+                        "token_width": [2, 2],
+                    }
+                )
+            row_sections.append({"type": "gen_image", "token_height": 4, "token_width": 4})
+            sections.append(row_sections)
+        return {
+            "output": TokenizerEncodeOutput(
+                tokens=torch.arange(rows * padded_seq_len, dtype=torch.long).reshape(rows, padded_seq_len),
+                gen_timestep_scatter_index=torch.tensor(self.prefix_lens, dtype=torch.long).reshape(rows, 1),
+                cond_timestep_scatter_index=(torch.zeros(rows, 1, dtype=torch.long) if has_cond_image else None),
+                all_image_slices=all_image_slices,
+                gen_image_mask=torch.stack(
+                    [(positions >= length + 1) & (positions < length + 17) for length in self.prefix_lens]
+                ),
+                cond_vae_image_mask=cond_vae_image_mask,
+                cond_vit_image_mask=cond_vit_image_mask,
+                joint_image_slices=joint_image_slices,
+                gen_image_slices=gen_image_slices,
+                real_pos=torch.tensor(seq_lens, dtype=torch.long).reshape(rows, 1),
+            ),
+            "sections": sections,
+        }
+
+
+class _FakeImageProcessor:
+    def __init__(self, image_token_length: int = 16) -> None:
+        self.image_token_length = image_token_length
+        self.image_sizes: list[tuple[int, int]] = []
+        self.vision_encoder_processor = SimpleNamespace(patch_size=1)
+
+    def build_image_info(self, image_size):
+        self.image_sizes.append(image_size)
+        return ImageInfo(
+            image_type="gen_image",
+            image_width=image_size[1],
+            image_height=image_size[0],
+            token_width=4,
+            token_height=4,
+            image_token_length=self.image_token_length,
+            base_size=1024,
+            ratio_index=0,
+        )
+
+
+def _components(prefix_lens: list[int]):
+    return _FakeTokenizerWrapper(prefix_lens=prefix_lens), _FakeImageProcessor()
+
+
+def _prepare(
+    request: OmniDiffusionRequest,
+    tokenizer: _FakeTokenizerWrapper,
+    image_processor: _FakeImageProcessor,
+) -> HunyuanPreparedLayout:
+    prepared_layout = prepare_hunyuan_layout(
+        request,
+        tokenizer_wrapper=tokenizer,
+        image_processor=image_processor,
+        generation_config=SimpleNamespace(sequence_template="instruct", drop_think=False),
+        image_base_size=1024,
+    )
+    request.prepared_layout = prepared_layout
+    return prepared_layout
+
+
+def _request(
+    *,
+    guidance_scale: float,
+    prompt="draw a cat",
+    request_id: str = "req",
+) -> OmniDiffusionRequest:
+    return OmniDiffusionRequest(
+        prompt=prompt,
+        sampling_params=OmniDiffusionSamplingParams(
+            height=768,
+            width=1024,
+            guidance_scale=guidance_scale,
+            num_inference_steps=4,
+        ),
+        request_id=request_id,
+    )
+
+
+def test_builds_kv_request_lengths_without_model_execution() -> None:
+    tokenizer, image_processor = _components([12])
+    request = _request(guidance_scale=1.0)
+    prepared_layout = _prepare(request, tokenizer, image_processor)
+
+    kv_requests = build_hunyuan_diffusion_kv_requests(request, prepared_layout)
+
+    assert isinstance(request.prepared_layout, HunyuanPreparedLayout)
+    assert all(isinstance(item, DiffusionKVRequest) for item in kv_requests)
+    assert [(item.prefix_len, item.target_len, item.seq_len) for item in kv_requests] == [(12, 17, 32)]
+    assert image_processor.image_sizes == [(768, 1024)]
+    assert tokenizer.calls[0]["sequence_template"] == "instruct"
+    assert tokenizer.calls[0]["cfg_factor"] == 1
+    assert kv_requests[0].block_hashes == []
+    assert kv_requests[0].kv_contexts == ()
+    assert kv_requests[0].skip_reading_prefix_cache is True
+
+
+def test_builds_one_kv_request_per_cfg_row() -> None:
+    tokenizer, image_processor = _components([12, 14])
+    request = _request(guidance_scale=5.0)
+    prepared_layout = _prepare(request, tokenizer, image_processor)
+
+    kv_requests = build_hunyuan_diffusion_kv_requests(request, prepared_layout)
+
+    assert [item.sequence_id for item in kv_requests] == [0, 1]
+    assert [item.prefix_len for item in kv_requests] == [12, 14]
+    assert [item.seq_len for item in kv_requests] == [32, 34]
+    assert tokenizer.calls[0]["cfg_factor"] == 2
+
+
+def _reference_image() -> JointImageInfo:
+    return JointImageInfo(
+        vae_image_info=ImageInfo(
+            image_type="vae",
+            token_width=8,
+            token_height=8,
+            image_token_length=64,
+        ),
+        vision_image_info=ImageInfo(
+            image_type="siglip2",
+            token_width=4,
+            token_height=4,
+            image_token_length=16,
+        ),
+        vision_encoder_kwargs={
+            "spatial_shapes": torch.tensor([4, 4]),
+            "pixel_attention_mask": torch.ones(4, dtype=torch.bool),
+        },
+    )
+
+
+def test_passes_preprocessed_reference_image_geometry_to_tokenizer() -> None:
+    tokenizer, image_processor = _components([20])
+    joint_image = _reference_image()
+    prompt = {
+        "prompt": "edit this image",
+        "additional_information": {"batch_cond_image_info": [joint_image]},
+    }
+    request = _request(guidance_scale=1.0, prompt=prompt)
+    prepared_layout = _prepare(request, tokenizer, image_processor)
+
+    kv_requests = build_hunyuan_diffusion_kv_requests(request, prepared_layout)
+
+    assert tokenizer.calls[0]["batch_cond_image_info"] == [[joint_image]]
+    assert kv_requests[0].prefix_len == 20
+    assert kv_requests[0].target_len == 17
+    assert kv_requests[0].seq_len == 40
+    assert kv_requests[0].kv_contexts == ()
+
+
+def _assert_nested_equal(actual, expected) -> None:
+    if isinstance(expected, torch.Tensor):
+        torch.testing.assert_close(actual, expected)
+    elif isinstance(expected, dict):
+        assert actual.keys() == expected.keys()
+        for key in expected:
+            _assert_nested_equal(actual[key], expected[key])
+    elif isinstance(expected, list | tuple):
+        assert isinstance(actual, type(expected))
+        assert len(actual) == len(expected)
+        for actual_item, expected_item in zip(actual, expected):
+            _assert_nested_equal(actual_item, expected_item)
+    else:
+        assert actual == expected
+
+
+@pytest.mark.parametrize(
+    ("prefix_lens", "guidance_scale", "with_reference_image"),
+    [
+        pytest.param([12], 1.0, False, id="cfg-1"),
+        pytest.param([12, 14], 5.0, False, id="cfg-2"),
+        pytest.param([20], 1.0, True, id="cfg-1-reference-image"),
+        pytest.param([20, 22], 5.0, True, id="cfg-2-reference-image"),
+    ],
+)
+def test_prepared_model_inputs_match_local_tokenization(
+    monkeypatch,
+    prefix_lens: list[int],
+    guidance_scale: float,
+    with_reference_image: bool,
+) -> None:
+    tokenizer, image_processor = _components(prefix_lens)
+    prompt: str | dict = "draw a cat"
+    if with_reference_image:
+        prompt = {
+            "prompt": "edit this image",
+            "additional_information": {"batch_cond_image_info": [_reference_image()]},
+        }
+    request = _request(guidance_scale=guidance_scale, prompt=prompt)
+    prepared_layout = _prepare(request, tokenizer, image_processor)
+
+    prompts, cot_texts, system_prompt, batch_cond_image_info, bot_task = extract_hunyuan_prompt_inputs(
+        [request.prompt],
+        request.sampling_params.extra_args or {},
+        request_id=request.request_id,
+        allow_cond_image=True,
+    )
+    cot_text = (
+        [normalize_hunyuan_cot_text(text) for text in cot_texts]
+        if any(text is not None for text in cot_texts)
+        else None
+    )
+
+    pipeline = object.__new__(HunyuanImage3Pipeline)
+    pipeline._tkwrapper = tokenizer
+    pipeline.image_processor = image_processor
+    pipeline.generation_config = SimpleNamespace(sequence_template="instruct", drop_think=False)
+    pipeline.config = SimpleNamespace(
+        image_base_size=1024,
+        attention_head_dim=2,
+        rope_theta=10000.0,
+    )
+    monkeypatch.setattr(HunyuanImage3Pipeline, "device", property(lambda _self: torch.device("cpu")))
+
+    def fake_encode_cond_image(batch_cond_image_info_list, cfg_factor=1, generator=None):
+        del generator
+        rows = len(batch_cond_image_info_list) * cfg_factor
+        return (
+            torch.arange(rows * 4, dtype=torch.float32).reshape(rows, 1, 2, 2),
+            torch.arange(rows, dtype=torch.float32),
+            torch.arange(rows * 3, dtype=torch.float32).reshape(rows, 3),
+        )
+
+    pipeline._encode_cond_image = fake_encode_cond_image
+
+    def fake_build_batch_2d_rope(*, image_infos, seq_len, **_kwargs):
+        geometry_sum = sum(
+            int(image_slice.start or 0) + int(image_slice.stop or 0) + height + width
+            for row in image_infos
+            for image_slice, (height, width) in row
+        )
+        values = torch.tensor([len(image_infos), seq_len, geometry_sum], dtype=torch.float32)
+        return values, values + 1
+
+    monkeypatch.setattr(pipeline_module, "build_batch_2d_rope", fake_build_batch_2d_rope)
+    common_kwargs = dict(
+        prompt=prompts,
+        cot_text=cot_text,
+        system_prompt=system_prompt,
+        mode="gen_image",
+        guidance_scale=guidance_scale,
+        image_size=(request.sampling_params.height, request.sampling_params.width),
+        generator=[torch.Generator().manual_seed(0)],
+        batch_cond_image_info=batch_cond_image_info,
+        bot_task=bot_task,
+    )
+
+    local_inputs = pipeline.prepare_model_inputs(**common_kwargs)
+    prepared_inputs = pipeline.prepare_model_inputs(**common_kwargs, prepared_layout=prepared_layout)
+
+    comparable_fields = (
+        "input_ids",
+        "position_ids",
+        "custom_pos_emb",
+        "image_mask",
+        "gen_timestep_scatter_index",
+        "cond_vae_images",
+        "cond_timestep",
+        "cond_vae_image_mask",
+        "cond_vit_images",
+        "cond_vit_image_mask",
+        "vit_kwargs",
+        "cond_timestep_scatter_index",
+    )
+    for field in comparable_fields:
+        _assert_nested_equal(prepared_inputs[field], local_inputs[field])
+
+    tokenizer_fields = (
+        "tokens",
+        "gen_image_mask",
+        "cond_vae_image_mask",
+        "cond_vit_image_mask",
+        "gen_timestep_scatter_index",
+        "cond_timestep_scatter_index",
+        "real_pos",
+        "all_image_slices",
+        "joint_image_slices",
+        "gen_image_slices",
+    )
+    for field in tokenizer_fields:
+        _assert_nested_equal(
+            getattr(prepared_inputs["tokenizer_output"], field),
+            getattr(local_inputs["tokenizer_output"], field),
+        )
+
+    local_mask = pipeline._prepare_attention_mask_for_generation(
+        local_inputs["input_ids"],
+        pipeline.generation_config,
+        local_inputs,
+    )
+    prepared_mask = pipeline._prepare_attention_mask_for_generation(
+        prepared_inputs["input_ids"],
+        pipeline.generation_config,
+        prepared_inputs,
+    )
+    torch.testing.assert_close(prepared_mask, local_mask)
+    assert prepared_inputs["full_attn_spans"] == local_inputs["full_attn_spans"]
+
+    num_image_tokens = hunyuan_num_image_tokens(prepared_layout.generated_image_info)
+    local_inputs.update(attention_mask=local_mask, num_image_tokens=num_image_tokens)
+    prepared_inputs.update(attention_mask=prepared_mask, num_image_tokens=num_image_tokens)
+    local_step_inputs = pipeline._update_model_kwargs_for_generation(ModelOutput(), local_inputs)
+    prepared_step_inputs = pipeline._update_model_kwargs_for_generation(ModelOutput(), prepared_inputs)
+
+    for field in ("position_ids", "attention_mask", "gen_timestep_scatter_index", "full_attn_spans"):
+        _assert_nested_equal(prepared_step_inputs[field], local_step_inputs[field])
+
+
+def test_rejects_tokenizer_cfg_row_mismatch() -> None:
+    tokenizer, image_processor = _components([12])
+    request = _request(guidance_scale=5.0)
+    prepared_layout = _prepare(request, tokenizer, image_processor)
+
+    with pytest.raises(ValueError, match="sequence count does not match"):
+        build_hunyuan_diffusion_kv_requests(request, prepared_layout)
+
+
+def test_sender_endpoint_does_not_change_local_kv_request() -> None:
+    tokenizer, image_processor = _components([12])
+    request = _request(guidance_scale=1.0)
+    request.kv_sender_info = {"host": "127.0.0.1", "zmq_port": 5000}
+    prepared_layout = _prepare(request, tokenizer, image_processor)
+
+    kv_requests = build_hunyuan_diffusion_kv_requests(request, prepared_layout)
+
+    assert kv_requests[0].seq_len == 32
+    assert kv_requests[0].num_computed_tokens == 0
+
+
+def test_paged_preprocess_attaches_layout_and_scheduler_kv_requests(monkeypatch) -> None:
+    tokenizer, image_processor = _components([12])
+    hf_config = SimpleNamespace(
+        vae_downsample_factor=(8, 8),
+        patch_size=2,
+        image_base_size=1024,
+    )
+    monkeypatch.setattr(pipeline_module, "get_config", lambda *_args, **_kwargs: hf_config)
+    monkeypatch.setattr(pipeline_module, "HunyuanImage3ImageProcessor", lambda _config: image_processor)
+    monkeypatch.setattr(pipeline_module, "TokenizerWrapper", lambda _model: tokenizer)
+    monkeypatch.setattr(
+        pipeline_module.GenerationConfig,
+        "from_pretrained",
+        lambda _model: SimpleNamespace(sequence_template="instruct", drop_think=False),
+    )
+    preprocess = get_hunyuan_image_3_pre_process_func(
+        SimpleNamespace(model="model", diffusion_kv_mode=DiffusionKVCacheMode.PAGED_SCHEDULER)
+    )
+    request = _request(guidance_scale=1.0)
+
+    prepared_request = preprocess(request)
+
+    assert prepared_request is request
+    assert isinstance(request.prepared_layout, HunyuanPreparedLayout)
+    assert request.diffusion_kv_requests is not None
+    assert [(item.prefix_len, item.target_len, item.seq_len) for item in request.diffusion_kv_requests] == [
+        (12, 17, 32)
+    ]
+    assert len(tokenizer.calls) == 1
@@ -7,7 +7,10 @@ import pytest
 import torch

 import vllm_omni.diffusion.models.hunyuan_image3.pipeline_hunyuan_image3 as hy3_module
+import vllm_omni.diffusion.models.hunyuan_image3.request_layout as hy3_layout_module
 from vllm_omni.diffusion.data import AttentionConfig, AttentionSpec
+from vllm_omni.diffusion.models.hunyuan_image3.hunyuan_image3_tokenizer import TokenizerEncodeOutput
+from vllm_omni.diffusion.models.hunyuan_image3.hunyuan_image3_transformer import ImageInfo
 from vllm_omni.diffusion.models.hunyuan_image3.pipeline_hunyuan_image3 import (
    _STEP_AR_KV,
    _STEP_CFG_FACTOR,
@@ -18,6 +21,7 @@ from vllm_omni.diffusion.models.hunyuan_image3.pipeline_hunyuan_image3 import (
    _STEP_PROMPT_KV,
    HunyuanImage3Pipeline,
 )
+from vllm_omni.diffusion.models.hunyuan_image3.request_layout import HunyuanPreparedLayout
 from vllm_omni.diffusion.worker.input_batch import InputBatch
 from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
 from vllm_omni.diffusion.worker.utils import StepRequestState
@@ -76,6 +80,72 @@ def _sampling_params(**extra_args):
    )


+def _prepared_layout() -> HunyuanPreparedLayout:
+    return HunyuanPreparedLayout(
+        tokenizer_output=TokenizerEncodeOutput(
+            tokens=torch.arange(21).reshape(1, 21),
+            gen_image_mask=torch.ones(1, 21, dtype=torch.bool),
+            gen_timestep_scatter_index=torch.tensor([[4]]),
+            all_image_slices=[[slice(5, 21)]],
+            joint_image_slices=[[]],
+            gen_image_slices=[[slice(5, 21)]],
+            real_pos=torch.tensor([[21]]),
+        ),
+        rope_image_info=[[(slice(5, 21), (4, 4))]],
+        generated_image_info=ImageInfo(
+            image_type="gen_image",
+            image_width=512,
+            image_height=512,
+            token_width=4,
+            token_height=4,
+            image_token_length=16,
+        ),
+    )
+
+
+def test_prepare_model_inputs_reuses_prepared_layout(monkeypatch):
+    pipeline = _pipeline()
+    monkeypatch.setattr(HunyuanImage3Pipeline, "device", property(lambda self: torch.device("cpu")))
+    rope_kwargs = {}
+
+    def fake_build_batch_2d_rope(**kwargs):
+        rope_kwargs.update(kwargs)
+        return torch.zeros(1), torch.zeros(1)
+
+    monkeypatch.setattr(hy3_module, "build_batch_2d_rope", fake_build_batch_2d_rope)
+
+    def fail_apply_chat_template(**_kwargs):
+        pytest.fail("prepared Hunyuan execution must not apply the chat template again")
+
+    pipeline._tkwrapper = SimpleNamespace(
+        apply_chat_template=fail_apply_chat_template,
+        eos_token_id=2,
+        boi_token_id=3,
+        end_recaption_token_id=4,
+        end_answer_token_id=5,
+    )
+    pipeline.config = SimpleNamespace(
+        attention_head_dim=2,
+        rope_theta=10000.0,
+    )
+    prepared = _prepared_layout()
+
+    model_inputs = pipeline.prepare_model_inputs(
+        prompt="prompt",
+        mode="gen_image",
+        guidance_scale=1.0,
+        image_size=(512, 512),
+        device=torch.device("cpu"),
+        generator=[torch.Generator().manual_seed(0)],
+        prepared_layout=prepared,
+    )
+
+    assert model_inputs["tokenizer_output"] is prepared.tokenizer_output
+    assert model_inputs["batch_gen_image_info"] == [prepared.generated_image_info]
+    assert rope_kwargs["image_infos"] is prepared.rope_image_info
+    torch.testing.assert_close(model_inputs["input_ids"], prepared.tokenizer_output.tokens)
+
+
 def test_hunyuan_step_group_key_ignores_step_index_for_later_steps():
    pipeline = _pipeline()
    states = [_state("req-0", 1), _state("req-1", 3)]
@@ -131,12 +201,13 @@ def test_prepare_encode_preserves_normal_hunyuan_bot_task_semantics(
        captured.update(kwargs)
        raise RuntimeError("stop after prepare_model_inputs")

-    monkeypatch.setattr(hy3_module, "get_system_prompt", fake_get_system_prompt)
+    monkeypatch.setattr(hy3_layout_module, "get_system_prompt", fake_get_system_prompt)
    pipeline.prepare_model_inputs = fake_prepare_model_inputs
    state = StepRequestState(
        request_id="req-bot-task",
        sampling=sampling,
        prompt=prompt_item,
+        prepared_layout=_prepared_layout(),
    )

    with pytest.raises(RuntimeError, match="stop after prepare_model_inputs"):
@@ -144,6 +215,7 @@ def test_prepare_encode_preserves_normal_hunyuan_bot_task_semantics(

    assert captured["bot_task"] == expected_model_bot_task
    assert captured["system_prompt_bot_task"] == expected_system_bot_task
+    assert captured["prepared_layout"] is state.prepared_layout


 def test_forward_uses_same_hunyuan_bot_task_semantics(monkeypatch):
@@ -159,23 +231,22 @@ def test_forward_uses_same_hunyuan_bot_task_semantics(monkeypatch):
        captured.update(kwargs)
        raise RuntimeError("stop after prepare_model_inputs")

-    monkeypatch.setattr(hy3_module, "get_system_prompt", fake_get_system_prompt)
+    monkeypatch.setattr(hy3_layout_module, "get_system_prompt", fake_get_system_prompt)
    pipeline.prepare_model_inputs = fake_prepare_model_inputs
-    req = DiffusionRequestBatch(
-        requests=[
-            SimpleNamespace(
-                request_id="req-forward-bot-task",
-                sampling_params=_sampling_params(bot_task="think_recaption", use_system_prompt="dynamic"),
-                prompt={"prompt": "prompt", "bot_task": "vanilla"},
-            )
-        ]
+    request = SimpleNamespace(
+        request_id="req-forward-bot-task",
+        sampling_params=_sampling_params(bot_task="think_recaption", use_system_prompt="dynamic"),
+        prompt={"prompt": "prompt", "bot_task": "vanilla"},
+        prepared_layout=_prepared_layout(),
    )
+    req = DiffusionRequestBatch(requests=[request])

    with pytest.raises(RuntimeError, match="stop after prepare_model_inputs"):
        pipeline.forward(req)

    assert captured["bot_task"] == "think"
    assert captured["system_prompt_bot_task"] == "think"
+    assert captured["prepared_layout"] is request.prepared_layout


 def test_grouped_denoise_rejects_non_sdpa_attention_backend():
@@ -0,0 +1,133 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+import torch
+from PIL import Image
+from torch import nn
+
+from vllm_omni.diffusion.models.lingbot_video.image_condition import (
+    apply_clean_prefix,
+    geometry_align_image,
+    prepare_ti2v_image_condition,
+)
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
+
+
+def _gradient_image(width: int, height: int) -> Image.Image:
+    x = np.linspace(0, 255, width, dtype=np.uint8)[None, :]
+    y = np.linspace(0, 255, height, dtype=np.uint8)[:, None]
+    rgb = np.stack(
+        [
+            np.broadcast_to(x, (height, width)),
+            np.broadcast_to(y, (height, width)),
+            np.full((height, width), 127, dtype=np.uint8),
+        ],
+        axis=-1,
+    )
+    return Image.fromarray(rgb, mode="RGB")
+
+
+@pytest.mark.parametrize("source_size", [(160, 64), (64, 160), (96, 96)])
+def test_geometry_align_image_supports_source_aspect_ratios(source_size):
+    pixel = geometry_align_image(_gradient_image(*source_size), 64, 96)
+
+    assert pixel.shape == (1, 3, 1, 64, 96)
+    assert pixel.dtype == torch.float32
+    assert 0 <= pixel.min() <= pixel.max() <= 1
+
+
+@pytest.mark.parametrize(
+    ("source_size", "target_size"),
+    [((160, 64), (64, 96)), ((64, 160), (96, 64))],
+)
+def test_geometry_align_image_center_crops_without_stretching(source_size, target_size):
+    width, height = source_size
+    target_height, target_width = target_size
+    rgb = np.full((height, width, 3), (255, 0, 0), dtype=np.uint8)
+    if width > target_width:
+        left = (width - target_width) // 2
+        rgb[:, left : left + target_width] = (0, 255, 0)
+    else:
+        top = (height - target_height) // 2
+        rgb[top : top + target_height] = (0, 255, 0)
+
+    pixel = geometry_align_image(
+        Image.fromarray(rgb, mode="RGB"),
+        target_height,
+        target_width,
+    )
+
+    assert pixel.shape == (1, 3, 1, target_height, target_width)
+    expected_green = torch.tensor((0.0, 1.0, 0.0)).view(1, 3, 1, 1, 1)
+    assert torch.equal(pixel, expected_green.expand_as(pixel))
+
+
+class _LatentDistribution:
+    def __init__(self, latent: torch.Tensor):
+        self.latent = latent
+
+    def sample(self, generator=None):
+        del generator
+        return self.latent
+
+
+class _CapturingVAE(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.anchor = nn.Parameter(torch.zeros(()))
+        self.config = SimpleNamespace(
+            latents_mean=[0.0, 0.0, 0.0],
+            latents_std=[1.0, 1.0, 1.0],
+        )
+        self.encoded_pixel = None
+
+    def encode(self, pixel):
+        self.encoded_pixel = pixel.detach().clone()
+        return SimpleNamespace(latent_dist=_LatentDistribution(pixel))
+
+
+def test_ti2v_vlm_and_vae_paths_share_geometry_aligned_image():
+    vae = _CapturingVAE()
+    condition = prepare_ti2v_image_condition(
+        _gradient_image(160, 64),
+        height=64,
+        width=96,
+        vae=vae,
+        vision_patch_size=16,
+        device=torch.device("cpu"),
+    )
+
+    vae_pixel = (vae.encoded_pixel + 1.0) / 2.0
+    vae_image = vae_pixel[0, :, 0].permute(1, 2, 0).mul(255).byte().numpy()
+    assert condition.vlm_image.size == (96, 64)
+    np.testing.assert_allclose(
+        np.asarray(condition.vlm_image),
+        vae_image,
+        rtol=0,
+        atol=1,
+    )
+
+
+def test_apply_clean_prefix_only_overwrites_condition_frames():
+    latents = torch.arange(20, dtype=torch.float32).reshape(1, 1, 5, 2, 2)
+    original_tail = latents[:, :, 2:].clone()
+    clean = torch.full((1, 1, 2, 2, 2), -3.0)
+
+    returned = apply_clean_prefix(latents, clean)
+
+    assert returned is latents
+    assert torch.equal(latents[:, :, :2], clean)
+    assert torch.equal(latents[:, :, 2:], original_tail)
+
+
+def test_apply_clean_prefix_rejects_incompatible_shapes():
+    with pytest.raises(ValueError, match="shape must match"):
+        apply_clean_prefix(
+            torch.zeros(1, 2, 3, 4, 4),
+            torch.zeros(1, 3, 1, 4, 4),
+        )
@@ -6,9 +6,10 @@ from types import SimpleNamespace
 import numpy as np
 import pytest
 import torch
+from PIL import Image
 from torch import nn

-from vllm_omni.diffusion.request import OmniDiffusionRequest
+from vllm_omni.diffusion.request import DUMMY_DIFFUSION_REQUEST_ID, OmniDiffusionRequest
 from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams

@@ -22,6 +23,8 @@ def _make_pipeline():
    nn.Module.__init__(pipeline)
    pipeline.device = torch.device("cpu")
    pipeline.default_negative_prompt = "default negative"
+    pipeline.default_image_negative_prompt = "default image negative"
+    pipeline.img_prompt_template = "<image>"
    pipeline.od_config = SimpleNamespace(flow_shift=None)
    return pipeline

@@ -37,17 +40,24 @@ def test_lingbot_video_pipeline_import_and_registry():
        LingBotVideoPipeline,
        LingBotVideoTransformer3DModel,
        get_lingbot_video_post_process_func,
+        get_lingbot_video_pre_process_func,
+    )
+    from vllm_omni.diffusion.registry import (
+        _DIFFUSION_MODELS,
+        _DIFFUSION_POST_PROCESS_FUNCS,
+        _DIFFUSION_PRE_PROCESS_FUNCS,
    )
-    from vllm_omni.diffusion.registry import _DIFFUSION_MODELS, _DIFFUSION_POST_PROCESS_FUNCS

    assert LingBotVideoPipeline is not None
    assert LingBotVideoTransformer3DModel is not None
+    assert get_lingbot_video_pre_process_func is not None
    assert get_lingbot_video_post_process_func is not None
    assert _DIFFUSION_MODELS["LingBotVideoPipeline"] == (
        "lingbot_video",
        "pipeline_lingbot_video",
        "LingBotVideoPipeline",
    )
+    assert _DIFFUSION_PRE_PROCESS_FUNCS["LingBotVideoPipeline"] == "get_lingbot_video_pre_process_func"
    assert _DIFFUSION_POST_PROCESS_FUNCS["LingBotVideoPipeline"] == "get_lingbot_video_post_process_func"


@@ -58,6 +68,7 @@ def test_component_discovery_declarations():
    assert LingBotVideoPipeline._encoder_modules == ["text_encoder"]
    assert LingBotVideoPipeline._vae_modules == ["vae"]
    assert LingBotVideoPipeline.supports_step_execution is False
+    assert LingBotVideoPipeline.max_outputs_per_prompt == 1


 def test_extra_body_params_include_video_flow_shift_alias():
@@ -69,17 +80,47 @@ def test_extra_body_params_include_video_flow_shift_alias():
    assert "batch_cfg" in params


-def test_extract_prompt_string_and_mapping():
-    from vllm_omni.diffusion.models.lingbot_video.pipeline_lingbot_video import _extract_prompt
+def test_preprocess_preserves_geometry_and_converts_rgb(tmp_path):
+    from vllm_omni.diffusion.models.lingbot_video import (
+        get_lingbot_video_pre_process_func,
+    )

-    string_req = SimpleNamespace(prompt="a robot waves")
-    assert _extract_prompt(string_req) == ("a robot waves", None)
+    image_path = tmp_path / "reference.png"
+    Image.new("L", (48, 32), color=127).save(image_path)
+    request = OmniDiffusionRequest(
+        prompt={
+            "prompt": "a robot walks",
+            "modalities": ["video"],
+            "multi_modal_data": {"image": str(image_path)},
+        },
+        sampling_params=OmniDiffusionSamplingParams(),
+        request_id="lingbot-preprocess-test",
+    )

-    mapping_req = SimpleNamespace(prompt={"prompt": "a robot walks", "negative_prompt": "low quality"})
-    assert _extract_prompt(mapping_req) == ("a robot walks", "low quality")
+    result = get_lingbot_video_pre_process_func(SimpleNamespace())(request)

-    with pytest.raises(TypeError, match="Unsupported LingBot prompt type"):
-        _extract_prompt(SimpleNamespace(prompt=["not", "supported"]))
+    image = result.prompt["multi_modal_data"]["image"]
+    assert image.mode == "RGB"
+    assert image.size == (48, 32)
+
+
+def test_preprocess_marks_internal_image_warmup_as_video():
+    from vllm_omni.diffusion.models.lingbot_video import (
+        get_lingbot_video_pre_process_func,
+    )
+
+    request = OmniDiffusionRequest(
+        prompt={
+            "prompt": "dummy run",
+            "multi_modal_data": {"image": Image.new("RGB", (32, 32))},
+        },
+        sampling_params=OmniDiffusionSamplingParams(),
+        request_id=DUMMY_DIFFUSION_REQUEST_ID,
+    )
+
+    result = get_lingbot_video_pre_process_func(SimpleNamespace())(request)
+
+    assert result.prompt["modalities"] == ["video"]


 def test_check_inputs_accepts_t2v_shapes_and_rejects_invalid_values():
@@ -104,6 +145,59 @@ def test_postprocess_keeps_torch_by_default_and_converts_np_when_requested():
    array = post(frames, SimpleNamespace(output_type="np"))
    assert isinstance(array, np.ndarray)
    assert array.shape == (2, 4, 4, 3)
+    video = post({"video": frames}, SimpleNamespace(output_type="pt"))
+    assert set(video) == {"video"}
+    assert video["video"] is frames
+    image = post(
+        {"image": torch.ones(4, 4, 3)},
+        SimpleNamespace(output_type="pt"),
+    )
+    assert set(image) == {"image"}
+    assert isinstance(image["image"], np.ndarray)
+    assert image["image"].shape == (4, 4, 3)
+    latent = torch.ones(1, 4, 1, 2, 2)
+    latent_output = post({"image": latent}, SimpleNamespace(output_type="latent"))
+    assert set(latent_output) == {"image"}
+    assert latent_output["image"] is latent
+
+
+def test_postprocess_preserves_image_contract_for_serving():
+    from vllm_omni.diffusion.data import DiffusionOutput
+    from vllm_omni.diffusion.models.lingbot_video import get_lingbot_video_post_process_func
+    from vllm_omni.diffusion.output_formatter import (
+        format_diffusion_outputs,
+        normalize_diffusion_postprocess_output,
+    )
+    from vllm_omni.entrypoints.openai.api_server import _extract_images_from_result
+
+    sampling = OmniDiffusionSamplingParams(
+        num_inference_steps=1,
+        output_type="pt",
+        resolution=64,
+    )
+    request = OmniDiffusionRequest(
+        prompt={"prompt": "a robot", "modalities": ["image"]},
+        sampling_params=sampling,
+        request_id="lingbot-image-contract-test",
+    )
+    raw_output = {"image": torch.ones(8, 12, 3)}
+    processed = get_lingbot_video_post_process_func(SimpleNamespace())(raw_output, sampling)
+    normalized = normalize_diffusion_postprocess_output(processed)
+
+    assert normalized.primary_key == "image"
+    [result] = format_diffusion_outputs(
+        request=request,
+        od_config=SimpleNamespace(model_class_name="LingBotVideoPipeline"),
+        diffusion_output=DiffusionOutput(output=raw_output),
+        output_data=raw_output,
+        postprocess_output=normalized,
+    )
+
+    assert len(result.images) == 1
+    assert isinstance(result.images[0], np.ndarray)
+    endpoint_images = _extract_images_from_result(result)
+    assert len(endpoint_images) == 1
+    assert endpoint_images[0].size == (12, 8)


 def test_forward_resolves_t2v_sampling_and_flow_shift_alias():
@@ -116,7 +210,11 @@ def test_forward_resolves_t2v_sampling_and_flow_shift_alias():

    pipeline._generate = fake_generate
    req = _make_request_batch(
-        {"prompt": "a robot arm", "negative_prompt": "bad hands"},
+        {
+            "prompt": "a robot arm",
+            "negative_prompt": "bad hands",
+            "modalities": ["video"],
+        },
        height=192,
        width=320,
        num_frames=9,
@@ -129,10 +227,12 @@ def test_forward_resolves_t2v_sampling_and_flow_shift_alias():

    out = pipeline.forward(req)

-    assert torch.equal(out.output, torch.zeros(9, 192, 320, 3))
+    assert torch.equal(out.output["video"], torch.zeros(9, 192, 320, 3))
    assert len(calls) == 1
    call = calls[0]
    assert call["prompt"] == "a robot arm"
+    assert call["mode"].value == "t2v"
+    assert call["input_image"] is None
    assert call["negative_prompt"] == "bad hands"
    assert call["height"] == 192
    assert call["width"] == 320
@@ -194,6 +294,222 @@ def test_forward_prefers_shift_over_flow_shift_and_defaults_negative_prompt():
    assert calls[0]["negative_prompt"] == "default negative"


+@pytest.mark.parametrize(
+    ("prompt", "num_frames", "expected_mode", "output_key", "default_negative"),
+    [
+        (
+            {"prompt": "a still robot", "modalities": ["image"]},
+            1,
+            "t2i",
+            "image",
+            "default image negative",
+        ),
+        (
+            {
+                "prompt": "a moving robot",
+                "modalities": ["video"],
+                "multi_modal_data": {"image": Image.new("RGB", (48, 32), color="blue")},
+            },
+            9,
+            "ti2v",
+            "video",
+            "default negative",
+        ),
+    ],
+)
+def test_forward_dispatches_generation_mode_and_output(
+    prompt,
+    num_frames,
+    expected_mode,
+    output_key,
+    default_negative,
+):
+    pipeline = _make_pipeline()
+    calls = []
+
+    def fake_generate(**kwargs):
+        calls.append(kwargs)
+        return torch.zeros(192, 320, 3) if output_key == "image" else torch.zeros(num_frames, 192, 320, 3)
+
+    pipeline._generate = fake_generate
+    output = pipeline.forward(
+        _make_request_batch(
+            prompt,
+            height=192,
+            width=320,
+            num_frames=num_frames,
+            num_inference_steps=2,
+            guidance_scale=3.0,
+            seed=42,
+            output_type="pt",
+        )
+    )
+
+    assert set(output.output) == {output_key}
+    assert calls[0]["mode"].value == expected_mode
+    assert calls[0]["negative_prompt"] == default_negative
+    if expected_mode == "ti2v":
+        assert isinstance(calls[0]["input_image"], Image.Image)
+
+
+def test_batched_cfg_pads_image_conditioned_embeddings_and_masks():
+    from vllm_omni.diffusion.models.lingbot_video.pipeline_lingbot_video import (
+        _batch_cfg_prompt_inputs,
+    )
+
+    positive = torch.ones(1, 7, 4)
+    positive_mask = torch.ones(1, 7, dtype=torch.long)
+    negative = torch.full((1, 9, 4), 2.0)
+    negative_mask = torch.ones(1, 9, dtype=torch.long)
+
+    embeds, mask = _batch_cfg_prompt_inputs(
+        positive,
+        positive_mask,
+        negative,
+        negative_mask,
+    )
+
+    assert embeds.shape == (2, 9, 4)
+    assert mask.shape == (2, 9)
+    assert torch.equal(mask[0], torch.tensor([1, 1, 1, 1, 1, 1, 1, 0, 0]))
+    assert torch.equal(mask[1], torch.ones(9, dtype=torch.long))
+
+
+class _RecordingTransformer(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.anchor = nn.Parameter(torch.zeros(()))
+        self.config = SimpleNamespace(in_channels=1)
+        self.latent_inputs = []
+
+    def forward(
+        self,
+        latents,
+        timestep,
+        prompt_embeds,
+        *,
+        encoder_attention_mask,
+        return_dict,
+    ):
+        del timestep, prompt_embeds, encoder_attention_mask, return_dict
+        self.latent_inputs.append(latents.detach().clone())
+        return (torch.zeros_like(latents),)
+
+
+class _CorruptingScheduler:
+    sigma_max = 1.0
+    sigma_min = 0.0
+
+    def set_timesteps(self, num_inference_steps, *, device, shift, **kwargs):
+        del num_inference_steps, shift, kwargs
+        self.timesteps = torch.tensor([1000.0, 500.0], device=device)
+
+    def step(
+        self,
+        noise_pred,
+        timestep,
+        latents,
+        *,
+        return_dict,
+        generator,
+    ):
+        del noise_pred, timestep, return_dict, generator
+        return (latents + 7.0,)
+
+
+@pytest.mark.parametrize(
+    ("guidance_scale", "batch_cfg", "expected_prompts", "expected_transformer_calls"),
+    [
+        pytest.param(2.0, False, ["positive", "negative"], 4, id="sequential-cfg"),
+        pytest.param(2.0, True, ["positive", "negative"], 2, id="batched-cfg"),
+        pytest.param(1.0, False, ["positive"], 2, id="cfg-disabled"),
+    ],
+)
+def test_ti2v_reinjects_clean_prefix_across_cfg_modes(
+    guidance_scale,
+    batch_cfg,
+    expected_prompts,
+    expected_transformer_calls,
+):
+    from vllm_omni.diffusion.models.lingbot_video import (
+        LingBotGenerationMode,
+        LingBotImageCondition,
+    )
+
+    pipeline = _make_pipeline()
+    pipeline.transformer = _RecordingTransformer()
+    pipeline.scheduler = _CorruptingScheduler()
+    pipeline.progress_bar = lambda timesteps: timesteps
+    pipeline.prepare_latents = lambda *args, **kwargs: torch.zeros(1, 1, 3, 1, 1)
+    vlm_image = Image.new("RGB", (32, 32), color="red")
+    condition = LingBotImageCondition(
+        vlm_image=vlm_image,
+        clean_latent=torch.full((1, 1, 1, 1, 1), 5.0),
+    )
+    pipeline.prepare_ti2v_image_condition = lambda *args, **kwargs: condition
+    encode_calls = []
+
+    def fake_encode(prompt, *, images, device):
+        del device
+        encode_calls.append((prompt, images))
+        return torch.ones(1, 2, 4), torch.ones(1, 2, dtype=torch.long)
+
+    pipeline.encode_prompt = fake_encode
+
+    latents = pipeline._generate(
+        prompt="positive",
+        mode=LingBotGenerationMode.TI2V,
+        input_image=vlm_image,
+        negative_prompt="negative",
+        height=16,
+        width=16,
+        num_frames=9,
+        num_inference_steps=2,
+        guidance_scale=guidance_scale,
+        shift=3.0,
+        output_type="latent",
+        batch_cfg=batch_cfg,
+    )
+
+    assert [call[0] for call in encode_calls] == expected_prompts
+    assert all(call[1] == [vlm_image] for call in encode_calls)
+    assert len(pipeline.transformer.latent_inputs) == expected_transformer_calls
+    for item in pipeline.transformer.latent_inputs:
+        expected_prefix = condition.clean_latent.expand(item.shape[0], -1, -1, -1, -1)
+        assert torch.equal(item[:, :, :1], expected_prefix)
+    assert torch.equal(latents[:, :, :1], condition.clean_latent)
+    assert torch.equal(latents[:, :, 1:], torch.full((1, 1, 2, 1, 1), 14.0))
+
+
+def test_t2i_decodes_and_returns_the_unique_frame():
+    from vllm_omni.diffusion.models.lingbot_video import LingBotGenerationMode
+
+    pipeline = _make_pipeline()
+    pipeline.transformer = _RecordingTransformer()
+    pipeline.scheduler = _CorruptingScheduler()
+    pipeline.progress_bar = lambda timesteps: timesteps
+    pipeline.prepare_latents = lambda *args, **kwargs: torch.zeros(1, 1, 1, 2, 2)
+    pipeline.encode_prompt = lambda *args, **kwargs: (
+        torch.ones(1, 2, 4),
+        torch.ones(1, 2, dtype=torch.long),
+    )
+    pipeline._decode_latents = lambda latents: torch.ones(1, 16, 16, 3)
+
+    image = pipeline._generate(
+        prompt="a still robot",
+        mode=LingBotGenerationMode.T2I,
+        height=16,
+        width=16,
+        num_frames=1,
+        num_inference_steps=2,
+        guidance_scale=1.0,
+        shift=3.0,
+        output_type="pt",
+    )
+
+    assert image.shape == (16, 16, 3)
+
+
 def test_forward_rejects_multi_request_batches():
    pipeline = _make_pipeline()
    first = OmniDiffusionRequest(
@@ -211,6 +527,45 @@ def test_forward_rejects_multi_request_batches():
        pipeline.forward(DiffusionRequestBatch([first, second]))


+def test_forward_marks_invalid_request_contract_as_client_error():
+    from vllm_omni.errors import OmniClientError
+
+    pipeline = _make_pipeline()
+    request = _make_request_batch(
+        {
+            "prompt": "unsupported image edit",
+            "modalities": ["image"],
+            "multi_modal_data": {"image": Image.new("RGB", (32, 32), color="blue")},
+        },
+        height=192,
+        width=320,
+        num_frames=1,
+    )
+
+    with pytest.raises(OmniClientError, match="does not accept") as exc_info:
+        pipeline.forward(request)
+
+    assert exc_info.value.status_code == 400
+
+
+def test_forward_marks_unsupported_output_count_as_client_error():
+    from vllm_omni.errors import OmniClientError
+
+    pipeline = _make_pipeline()
+    request = _make_request_batch(
+        {"prompt": "motion", "modalities": ["video"]},
+        height=192,
+        width=320,
+        num_frames=9,
+        num_outputs_per_prompt=2,
+    )
+
+    with pytest.raises(OmniClientError, match="one output per prompt") as exc_info:
+        pipeline.forward(request)
+
+    assert exc_info.value.status_code == 400
+
+
 def test_load_weights_rejects_external_weight_stream():
    pipeline = _make_pipeline()

@@ -0,0 +1,350 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from types import SimpleNamespace
+
+import pytest
+from PIL import Image
+
+from vllm_omni.diffusion.models.lingbot_video.request_utils import (
+    LingBotGenerationMode,
+    caption_from_lingbot_prompt,
+    normalize_lingbot_num_frames,
+    normalize_lingbot_request,
+    resolve_lingbot_mode,
+    resolve_lingbot_num_frames,
+    resolve_lingbot_output_dimensions,
+    resolve_lingbot_size,
+)
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
+
+
+def _request(prompt, **sampling_overrides):
+    return SimpleNamespace(
+        prompt=prompt,
+        sampling_params=OmniDiffusionSamplingParams(**sampling_overrides),
+    )
+
+
+def _normalize(prompt, **sampling_overrides):
+    return normalize_lingbot_request(
+        _request(prompt, **sampling_overrides),
+        default_negative_prompt="video default",
+        default_image_negative_prompt="image default",
+    )
+
+
+@pytest.mark.parametrize(
+    ("prompt", "expected"),
+    [
+        ({"prompt": "still", "modalities": ["image"]}, LingBotGenerationMode.T2I),
+        ({"prompt": "motion", "modalities": ["video"]}, LingBotGenerationMode.T2V),
+        (
+            {
+                "prompt": "motion",
+                "modalities": ["video"],
+                "multi_modal_data": {"image": Image.new("RGB", (8, 8))},
+            },
+            LingBotGenerationMode.TI2V,
+        ),
+    ],
+)
+def test_resolve_lingbot_mode(prompt, expected):
+    assert resolve_lingbot_mode(prompt) is expected
+
+
+@pytest.mark.parametrize(
+    ("prompt", "message"),
+    [
+        (
+            {"prompt": "x", "modalities": ["image"], "multi_modal_data": {"image": Image.new("RGB", (8, 8))}},
+            "does not accept",
+        ),
+        (
+            {
+                "prompt": "x",
+                "modalities": ["video"],
+                "multi_modal_data": {"image": [Image.new("RGB", (8, 8)), Image.new("RGB", (8, 8))]},
+            },
+            "exactly one",
+        ),
+        ({"prompt": "x", "modalities": ["video"], "multi_modal_data": {"video": [1]}}, "reference modalities"),
+        ({"prompt": "x", "multi_modal_data": {"image": Image.new("RGB", (8, 8))}}, "explicitly set"),
+        ({"prompt": "x", "modalities": ["audio"]}, "output modality"),
+    ],
+)
+def test_resolve_lingbot_mode_rejects_invalid_media_combinations(prompt, message):
+    with pytest.raises(ValueError, match=message):
+        resolve_lingbot_mode(prompt)
+
+
+def test_caption_from_lingbot_prompt_preserves_text_and_compact_json():
+    assert caption_from_lingbot_prompt("plain text") == "plain text"
+    assert caption_from_lingbot_prompt({"caption": "caption only", "duration": 4}) == "caption only"
+    assert caption_from_lingbot_prompt({"caption": {"中文": True, "nested": {"value": 2}}}) == (
+        '{"中文":true,"nested":{"value":2}}'
+    )
+    assert (
+        caption_from_lingbot_prompt(
+            {"scene": "海边", "camera": {"move": "pan"}, "duration": 5, "fps": 24, "size": "320x192"}
+        )
+        == '{"scene":"海边","camera":{"move":"pan"}}'
+    )
+
+
+@pytest.mark.parametrize("prompt", [{"duration": 4, "fps": 24}, {"caption": ""}, ""])
+def test_caption_from_lingbot_prompt_rejects_empty_caption(prompt):
+    with pytest.raises(ValueError, match="empty|must not be empty"):
+        caption_from_lingbot_prompt(prompt)
+
+
+@pytest.mark.parametrize(
+    ("num_frames", "expected"),
+    [(1, 1), (2, 5), (4, 5), (5, 5), (96, 97), (98, 101), (121, 121)],
+)
+def test_normalize_lingbot_num_frames_rounds_up_to_4n_plus_1(num_frames, expected):
+    assert normalize_lingbot_num_frames(num_frames) == expected
+
+
+@pytest.mark.parametrize("num_frames", [0, -1, 1.5, "invalid"])
+def test_normalize_lingbot_num_frames_rejects_invalid_values(num_frames):
+    with pytest.raises(ValueError, match="num_frames"):
+        normalize_lingbot_num_frames(num_frames)
+
+
+@pytest.mark.parametrize(
+    ("duration", "fps", "expected"),
+    [(4, 24, 97), (5, 24, 121), (8, 24, 193), (4, 12, 49), (4, 30, 121)],
+)
+def test_resolve_lingbot_num_frames_rounds_up(duration, fps, expected):
+    assert resolve_lingbot_num_frames(duration, fps) == expected
+
+
+@pytest.mark.parametrize(("duration", "fps"), [(0, 24), (-1, 24), (0.01, 24), (1, 0), (1, -1)])
+def test_resolve_lingbot_num_frames_rejects_non_positive_or_empty_results(duration, fps):
+    with pytest.raises(ValueError, match="duration|fps|at least one"):
+        resolve_lingbot_num_frames(duration, fps)
+
+
+@pytest.mark.parametrize(
+    ("resolution", "ratio", "expected"),
+    [
+        ("192p", "1:1", (192, 192)),
+        ("192p", "9:16", (192, 320)),
+        ("192p", "16:9", (320, 192)),
+        ("192p", "3:4", (192, 256)),
+        ("192p", "4:3", (256, 192)),
+        ("480p", "1:1", (480, 480)),
+        ("480p", "9:16", (480, 832)),
+        ("480p", "16:9", (832, 480)),
+        ("480p", "3:4", (480, 640)),
+        ("480p", "4:3", (640, 480)),
+        ("720p", "1:1", (736, 736)),
+        ("720p", "9:16", (736, 1280)),
+        ("720p", "16:9", (1280, 736)),
+        ("720p", "3:4", (736, 960)),
+        ("720p", "4:3", (960, 736)),
+        ("1080p", "1:1", (1088, 1088)),
+        ("1080p", "9:16", (1088, 1920)),
+        ("1080p", "16:9", (1920, 1088)),
+        ("1080p", "3:4", (1088, 1440)),
+        ("1080p", "4:3", (1440, 1088)),
+        ("2k", "1:1", (1440, 1440)),
+        ("2k", "9:16", (1440, 2560)),
+        ("2k", "16:9", (2560, 1440)),
+        ("2k", "3:4", (1440, 1920)),
+        ("2k", "4:3", (1920, 1440)),
+        ("4k", "1:1", (2176, 2176)),
+        ("4k", "9:16", (2176, 3840)),
+        ("4k", "16:9", (3840, 2176)),
+        ("4k", "3:4", (2176, 2880)),
+        ("4k", "4:3", (2880, 2176)),
+    ],
+)
+def test_resolve_lingbot_size_supports_all_official_presets(resolution, ratio, expected):
+    assert resolve_lingbot_size(resolution=resolution, ratio=ratio) == expected
+
+
+def test_resolve_lingbot_size_validates_sources_and_alignment():
+    assert resolve_lingbot_size(size="320x192") == (192, 320)
+    assert resolve_lingbot_size(width=320, height=192) == (192, 320)
+    with pytest.raises(ValueError, match="provided together"):
+        resolve_lingbot_size(width=320)
+    with pytest.raises(ValueError, match="provided together"):
+        resolve_lingbot_size(resolution="192p")
+    with pytest.raises(ValueError, match="mutually exclusive"):
+        resolve_lingbot_size(width=320, height=192, resolution="192p", ratio="9:16")
+    with pytest.raises(ValueError, match="multiples of 16"):
+        resolve_lingbot_size(width=321, height=192)
+
+
+def test_resolve_lingbot_output_dimensions_matches_request_priority():
+    assert resolve_lingbot_output_dimensions(
+        sampling_width=480,
+        sampling_height=480,
+        prompt_fields={"resolution": "720p", "ratio": "16:9"},
+    ) == (736, 1280)
+
+    assert resolve_lingbot_output_dimensions(
+        sampling_width=480,
+        sampling_height=480,
+        prompt_fields={"width": 320, "height": 192},
+    ) == (320, 192)
+
+    assert resolve_lingbot_output_dimensions(
+        sampling_width=320,
+        sampling_height=192,
+    ) == (320, 192)
+
+    assert resolve_lingbot_output_dimensions() == (480, 480)
+
+
+def test_normalize_lingbot_request_uses_mode_defaults_and_preserves_empty_negative():
+    t2i = _normalize(
+        {"prompt": "still", "modalities": ["image"], "negative_prompt": ""},
+        width=320,
+        height=192,
+        num_frames=1,
+    )
+    assert t2i.mode is LingBotGenerationMode.T2I
+    assert t2i.num_frames == 1
+    assert t2i.negative_prompt == ""
+
+    t2v = _normalize(
+        {"prompt": "motion", "modalities": ["video"]},
+        width=320,
+        height=192,
+        num_frames=9,
+    )
+    assert t2v.mode is LingBotGenerationMode.T2V
+    assert t2v.negative_prompt == "video default"
+
+
+def test_normalize_lingbot_request_rejects_explicit_zero_flow_shift():
+    with pytest.raises(ValueError, match="shift.*positive"):
+        _normalize(
+            {"prompt": "still", "modalities": ["image"]},
+            width=320,
+            height=192,
+            num_frames=1,
+            extra_args={"flow_shift": 0},
+        )
+
+
+def test_normalize_lingbot_request_rejects_explicit_zero_width():
+    with pytest.raises(ValueError, match="width"):
+        _normalize(
+            {"prompt": "still", "modalities": ["image"]},
+            width=0,
+            height=192,
+            num_frames=1,
+        )
+
+
+def test_normalize_lingbot_request_rounds_all_video_frame_counts():
+    config = _normalize(
+        {"prompt": "motion", "modalities": ["video"]},
+        width=320,
+        height=192,
+        num_frames=96,
+        fps=24,
+        frame_rate=24,
+    )
+    assert config.num_frames == 97
+
+    config = _normalize(
+        {"prompt": "motion", "modalities": ["video"]},
+        width=320,
+        height=192,
+        num_frames=121,
+        fps=24,
+        frame_rate=24,
+    )
+    assert config.num_frames == 121
+
+
+def test_normalize_lingbot_request_prefers_explicit_frames_over_duration():
+    online = _normalize(
+        {"prompt": "motion", "modalities": ["video"]},
+        width=320,
+        height=192,
+        num_frames=9,
+        fps=24,
+        extra_args={"duration": 5},
+    )
+    assert online.num_frames == 9
+
+    offline = _normalize(
+        {
+            "prompt": {"caption": "motion", "duration": 5, "num_frames": 9},
+            "modalities": ["video"],
+        },
+        width=320,
+        height=192,
+        fps=24,
+    )
+    assert offline.num_frames == 9
+
+
+def test_normalize_lingbot_request_uses_duration_then_model_default():
+    duration = _normalize(
+        {"prompt": {"caption": "motion", "duration": 5}, "modalities": ["video"]},
+        width=320,
+        height=192,
+        fps=24,
+    )
+    assert duration.num_frames == 121
+
+    default = _normalize(
+        {"prompt": "motion", "modalities": ["video"]},
+        width=320,
+        height=192,
+    )
+    assert default.num_frames == 81
+
+
+def test_normalize_lingbot_request_rejects_contract_conflicts():
+    with pytest.raises(ValueError, match="text-to-image.*duration"):
+        _normalize(
+            {"prompt": {"caption": "still", "duration": 4}, "modalities": ["image"]},
+            width=320,
+            height=192,
+            num_frames=1,
+        )
+    with pytest.raises(ValueError, match="text-to-image requires"):
+        _normalize(
+            {"prompt": "still", "modalities": ["image"]},
+            width=320,
+            height=192,
+            num_frames=5,
+        )
+    with pytest.raises(ValueError, match="seconds.*duration"):
+        _normalize(
+            {"prompt": "motion", "modalities": ["video"]},
+            width=320,
+            height=192,
+            num_frames=96,
+            extra_args={"seconds": 4, "duration": 4},
+        )
+
+
+def test_normalize_lingbot_request_model_size_overrides_sampling_dimensions():
+    # A prompt-provided size overrides the sampling width/height.
+    config = _normalize(
+        {"prompt": {"caption": "motion", "size": "320x192"}, "modalities": ["video"]},
+        width=480,
+        height=480,
+        num_frames=81,
+    )
+    assert (config.height, config.width) == (192, 320)
+
+    # Without a prompt size, the sampling dimensions are used.
+    config = _normalize(
+        {"prompt": "motion", "modalities": ["video"]},
+        width=480,
+        height=480,
+        num_frames=81,
+    )
+    assert (config.height, config.width) == (480, 480)
@@ -492,6 +492,10 @@ def test_cudnn_packed_attention_uses_python_length_without_padding_mask():
    class FakeBackend:
        supports_prefix_kv_slicing = True

+        @classmethod
+        def supports_packed_mask_free(cls) -> bool:
+            return False
+
    class FakeAttention(torch.nn.Module):
        attn_backend = FakeBackend

@@ -526,6 +530,48 @@ def test_cudnn_packed_attention_uses_python_length_without_padding_mask():
    assert attention.attention.metadata.extra["valid_kv_length"] == 5


+def test_packed_attention_skips_mask_for_packed_mask_free_backend():
+    from vllm_omni.diffusion.models.minimax_h3.minimax_h3_transformer import (
+        MiniMaxH3Attention,
+    )
+
+    class FakeBackend:
+        supports_prefix_kv_slicing = False
+
+        @classmethod
+        def supports_packed_mask_free(cls) -> bool:
+            return True
+
+    class FakeAttention(torch.nn.Module):
+        attn_backend = FakeBackend
+
+        def __init__(self):
+            super().__init__()
+            self.metadata = None
+
+        def forward(self, query, key, value, metadata):
+            self.metadata = metadata
+            return query
+
+    attention = object.__new__(MiniMaxH3Attention)
+    torch.nn.Module.__init__(attention)
+    attention.attention = FakeAttention()
+    q = torch.randn(8, 2, 4)
+
+    attention._run_packed_attention(
+        q,
+        q,
+        q,
+        cu_seqlens=torch.tensor([0, 5, 8], dtype=torch.int32),
+        max_seqlen=5,
+        packed_total=8,
+    )
+
+    assert attention.attention.metadata.attn_mask is None
+    assert attention.attention.metadata.extra["valid_kv_length"] == 5
+    assert attention.attention.metadata.extra["npu_attn_varlen"] is True
+
+
 def test_packed_attention_keeps_padding_mask_for_other_backends():
    from vllm_omni.diffusion.models.minimax_h3.minimax_h3_transformer import (
        MiniMaxH3Attention,
@@ -534,6 +580,10 @@ def test_packed_attention_keeps_padding_mask_for_other_backends():
    class FakeBackend:
        supports_prefix_kv_slicing = False

+        @classmethod
+        def supports_packed_mask_free(cls) -> bool:
+            return False
+
    class FakeAttention(torch.nn.Module):
        attn_backend = FakeBackend

@@ -19,7 +19,7 @@ MODEL_ENV = "VLLM_TEST_MINIMAX_H3_FL2VA_MODEL"
 def _assert_joint_output(outputs):
    assert len(outputs) == 1
    frames = np.asarray(outputs[0].images[0])
-    assert frames.shape == (39, 256, 448, 3)
+    assert frames.shape == (107, 256, 448, 3)
    multimodal = outputs[0].multimodal_output
    assert multimodal is not None
    assert np.asarray(multimodal["audio"]).shape[1] == 2
@@ -122,6 +122,58 @@ def test_minimax_h3_t2va_fp8_single_gpu_smoke():
                output_type="np",
                extra_args={
                    "task": "t2va",
+                    "duration": 4.0,
+                    "aspect_ratio": "16:9",
+                    "flow_shift": 12.0,
+                    "audio_flow_shift": 3.0,
+                },
+            ),
+            use_tqdm=False,
+        )
+    finally:
+        engine.close()
+
+    _assert_joint_output(outputs)
+
+
+@pytest.mark.skipif(
+    not os.environ.get(MODEL_ENV),
+    reason=f"set {MODEL_ENV} to an authorized FL2VA checkpoint path",
+)
+def test_minimax_h3_t2va_teacache_single_gpu_smoke():
+    import torch
+
+    from vllm_omni.diffusion.data import DiffusionParallelConfig
+    from vllm_omni.entrypoints.omni import Omni
+    from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+
+    if torch.accelerator.device_count() < 1:
+        pytest.skip("MiniMax H3 TeaCache smoke requires a CUDA device")
+
+    engine = Omni(
+        model=os.environ[MODEL_ENV],
+        cache_backend="tea_cache",
+        cache_config={"rel_l1_thresh": 0.17},
+        parallel_config=DiffusionParallelConfig(ulysses_degree=1),
+        trust_remote_code=True,
+        enable_cpu_offload=True,
+        enforce_eager=True,
+    )
+    try:
+        outputs = engine.generate(
+            "A quiet cinematic night scene with matching ambient sound.",
+            OmniDiffusionSamplingParams(
+                height=256,
+                width=448,
+                num_frames=29,
+                fps=24,
+                num_inference_steps=2,
+                seed=42,
+                output_type="np",
+                extra_args={
+                    "task": "t2va",
+                    "duration": 4.0,
+                    "aspect_ratio": "16:9",
                    "flow_shift": 12.0,
                    "audio_flow_shift": 3.0,
                },
@@ -69,6 +69,7 @@ def test_h3_fused_rope_matches_reference_and_preserves_unrotated_dims():

    attention = object.__new__(MiniMaxH3Attention)
    nn.Module.__init__(attention)
+    attention.rot_dim = 96
    attention.rope = RotaryEmbedding(is_neox_style=True, half_head_dim=False)
    attention.rope._forward_method = attention.rope.forward_native

@@ -15,6 +15,7 @@ from vllm_omni.diffusion.data import (
    DiffusionParallelConfig,
    OmniDiffusionConfig,
 )
+from vllm_omni.diffusion.diffusion_kv.config import DiffusionKVCacheMode
 from vllm_omni.diffusion.model_metadata import (
    HUNYUAN_IMAGE3_MAX_INPUT_IMAGES,
    QWEN_IMAGE_EDIT_PLUS_MAX_INPUT_IMAGES,
@@ -124,6 +125,11 @@ class TestCreateDefaultDiffusion:
        assert ea["enforce_eager"] is True
        assert ea["lora_path"] == "/tmp/lora"

+    def test_diffusion_kv_mode_roundtrip(self):
+        od = _roundtrip_diffusion_config(model="x", diffusion_kv_mode="paged_scheduler")
+
+        assert od.diffusion_kv_mode is DiffusionKVCacheMode.PAGED_SCHEDULER
+

 def test_qwen_image_edit_plus_sets_generic_multimodal_limit():
    od_config = OmniDiffusionConfig(model="Qwen/Qwen-Image-Edit-2511", model_class_name="QwenImageEditPlusPipeline")
@@ -25,6 +25,7 @@ from vllm_omni.diffusion.request import OmniDiffusionRequest
 from vllm_omni.diffusion.sched.interface import (
    CachedRequestData,
    NewRequestData,
+    _AdmissionWaitDecision,
 )
 from vllm_omni.diffusion.sched.interface import (
    DiffusionSchedulerOutput as RealDiffusionSchedulerOutput,
@@ -75,6 +76,34 @@ class MockScheduler:
    def num_running_requests(self) -> int:
        return 0

+    def get_admission_wait_decision(
+        self,
+        *,
+        now: float,
+        dp_concurrent: bool = False,
+    ) -> _AdmissionWaitDecision:
+        del dp_concurrent
+        return _AdmissionWaitDecision(
+            should_wait=True,
+            deadline=now + 0.5,
+            stable_window_s=0.05,
+            max_batch=self.max_num_running_reqs,
+        )
+
+    def should_end_admission_wait(
+        self,
+        decision: _AdmissionWaitDecision,
+        *,
+        now: float,
+        stable_since: float,
+    ) -> bool:
+        waiting = self.num_waiting_requests()
+        return (
+            waiting >= decision.max_batch
+            or (waiting > 0 and now - stable_since >= decision.stable_window_s)
+            or (decision.deadline is not None and now >= decision.deadline)
+        )
+
    def schedule(self) -> DiffusionSchedulerOutput:
        if not self._waiting_queue:
            return DiffusionSchedulerOutput(step_id=self._step_id)
@@ -108,6 +137,17 @@ class MockScheduler:
        pass


+def _make_admission_engine(pre_process_func) -> DiffusionEngine:
+    engine = object.__new__(DiffusionEngine)
+    engine.scheduler = MockScheduler()
+    engine.pre_process_func = pre_process_func
+    engine._out_streams = {}
+    engine._rpc_lock = threading.RLock()
+    engine._cv = threading.Condition(engine._rpc_lock)
+    engine._closed = False
+    return engine
+
+
 class _BatchCapablePipeline:
    supports_request_batch = True

@@ -525,6 +565,11 @@ class TestRequestBatchAdmission:
        with pytest.raises(ValueError, match="request_batch_max_wait_ms"):
            OmniDiffusionConfig(model="test", request_batch_max_wait_ms=-1.0)

+    @pytest.mark.parametrize("bad_wait", [float("nan"), float("inf"), float("-inf")])
+    def test_config_rejects_nonfinite_request_batch_max_wait_ms(self, bad_wait: float) -> None:
+        with pytest.raises(ValueError, match="request_batch_max_wait_ms"):
+            OmniDiffusionConfig(model="test", request_batch_max_wait_ms=bad_wait)
+
    def test_config_normalizes_request_batch_max_wait_ms_to_float(self) -> None:
        config = OmniDiffusionConfig(model="test", request_batch_max_wait_ms=5)

@@ -592,7 +637,7 @@ class TestRequestBatchAdmission:

        start = time.monotonic()
        with engine._cv:
-            engine._wait_for_request_batch_admission_locked()
+            engine._wait_for_admission_if_needed_locked()
        waited_s = time.monotonic() - start

        # Stable-window exit (~50ms), not the full 1000ms deadline.
@@ -683,6 +728,34 @@ async def _consume_final_output(generator):
    return final_output


+@pytest.mark.cpu
+@pytest.mark.parametrize("entrypoint", ["add_request", "async_add_req_and_stream_response"])
+def test_engine_admission_preprocesses_request_once(entrypoint: str) -> None:
+    raw_request = OmniDiffusionRequest(
+        prompt="raw",
+        sampling_params=OmniDiffusionSamplingParams(num_inference_steps=1),
+        request_id="admission",
+    )
+    prepared_request = OmniDiffusionRequest(
+        prompt="prepared",
+        sampling_params=OmniDiffusionSamplingParams(num_inference_steps=1),
+        request_id="admission",
+        prepared_layout=object(),
+    )
+    preprocess_calls = []
+
+    def preprocess(request):
+        preprocess_calls.append(request)
+        return prepared_request
+
+    engine = _make_admission_engine(preprocess)
+
+    getattr(engine, entrypoint)(raw_request)
+
+    assert preprocess_calls == [raw_request]
+    assert engine.scheduler._waiting_queue == [prepared_request]
+
+
@pytest.mark.cpu
@pytest.mark.asyncio
 async def test_async_add_req_and_stream_response():
@@ -127,6 +127,7 @@ def _make_request(tag: str):
        request_id=tag,
        prompt=f"prompt_{tag}",
        sampling_params=SimpleNamespace(**sampling_params),
+        diffusion_kv_requests=None,
    )


@@ -145,7 +146,7 @@ def _make_engine_with_loop(
    engine.executor = _ConcurrencyTrackingExecutor(rpc_delay=rpc_delay)

    sched = RequestScheduler()
-    sched.initialize(SimpleNamespace(max_num_seqs=1))
+    sched.initialize(SimpleNamespace(max_num_seqs=1, request_batch_max_wait_ms=0.0))
    engine.scheduler = sched
    engine.step_execution = False
    engine.supports_request_batch = False
@@ -218,6 +218,25 @@ def _make_compile_runner(
    return runner


+@pytest.mark.core_model
+@pytest.mark.cpu
+def test_update_states_carries_prepared_layout() -> None:
+    runner = _make_runner(cache_backend=None, cache_backend_name=None)
+    request = _make_request()
+    prepared_layout = object()
+    request.prepared_layout = prepared_layout
+    scheduler_output = SimpleNamespace(
+        finished_req_ids=set(),
+        scheduled_new_reqs=[SimpleNamespace(request_id=request.request_id, req=request)],
+        scheduled_cached_reqs=SimpleNamespace(request_ids=[]),
+    )
+
+    states, new_request_ids = DiffusionModelRunner._update_states(runner, scheduler_output)
+
+    assert new_request_ids == [request.request_id]
+    assert states[0].prepared_layout is prepared_layout
+
+
@pytest.mark.core_model
@pytest.mark.cpu
@pytest.mark.parametrize("use_hsdp", [False, True])
@@ -12,6 +12,8 @@ from pytest_mock import MockerFixture

 from vllm_omni.diffusion.data import DiffusionOutput, DiffusionRequestAbortedError
 from vllm_omni.diffusion.diffusion_engine import DiffusionEngine, DiffusionExecutionMode
+from vllm_omni.diffusion.diffusion_kv.config import DiffusionKVCacheMode
+from vllm_omni.diffusion.diffusion_kv.request import DiffusionKVContext, DiffusionKVRequest
 from vllm_omni.diffusion.request import OmniDiffusionRequest
 from vllm_omni.diffusion.sched import (
    BaseScheduler,
@@ -242,7 +244,86 @@ class TestGetRequestBatchSamplingParamsKey:
 class TestRequestScheduler:
    def setup_method(self) -> None:
        self.scheduler: RequestScheduler = RequestScheduler()
-        self.scheduler.initialize(SimpleNamespace())
+        self.scheduler.initialize(SimpleNamespace(request_batch_max_wait_ms=0.0))
+
+    def test_admission_wait_disabled_with_zero_max_wait(self) -> None:
+        self.scheduler.initialize(SimpleNamespace(request_batch_max_wait_ms=0.0))
+        decision = self.scheduler.get_admission_wait_decision(now=10.0)
+
+        assert decision.should_wait is False
+
+    @pytest.mark.parametrize(
+        ("dp_concurrent", "expected_stable_window_s"),
+        [(False, 0.05), (True, 0.3)],
+    )
+    def test_admission_wait_decision_encodes_coalescing_policy(
+        self,
+        dp_concurrent: bool,
+        expected_stable_window_s: float,
+    ) -> None:
+        self.scheduler.initialize(
+            SimpleNamespace(
+                max_num_seqs=4,
+                request_batch_max_wait_ms=1000.0,
+            )
+        )
+
+        decision = self.scheduler.get_admission_wait_decision(
+            now=10.0,
+            dp_concurrent=dp_concurrent,
+        )
+
+        assert decision.should_wait is True
+        assert decision.deadline == 11.0
+        assert decision.stable_window_s == expected_stable_window_s
+        assert decision.max_batch == 4
+
+    def test_admission_wait_disabled_while_wave_is_running(self) -> None:
+        self.scheduler.initialize(
+            SimpleNamespace(
+                max_num_seqs=1,
+                request_batch_max_wait_ms=1000.0,
+            )
+        )
+        self.scheduler.add_request(_make_request("running"))
+        self.scheduler.schedule()
+
+        decision = self.scheduler.get_admission_wait_decision(now=10.0)
+
+        assert decision.should_wait is False
+
+    def test_admission_wait_end_conditions(self) -> None:
+        self.scheduler.initialize(
+            SimpleNamespace(
+                max_num_seqs=2,
+                request_batch_max_wait_ms=1000.0,
+            )
+        )
+        decision = self.scheduler.get_admission_wait_decision(now=10.0)
+        self.scheduler.add_request(_make_request("a"))
+
+        assert not self.scheduler.should_end_admission_wait(
+            decision,
+            now=10.01,
+            stable_since=10.0,
+        )
+        assert self.scheduler.should_end_admission_wait(
+            decision,
+            now=10.05,
+            stable_since=10.0,
+        )
+        assert self.scheduler.should_end_admission_wait(
+            decision,
+            now=11.0,
+            stable_since=11.0,
+        )
+
+        self.scheduler.add_request(_make_request("b"))
+        assert self.scheduler.should_end_admission_wait(
+            decision,
+            now=10.01,
+            stable_since=10.01,
+        )

    def test_single_request_success_lifecycle(self) -> None:
        req_id = self.scheduler.add_request(_make_request("a"))
@@ -259,6 +340,90 @@ class TestRequestScheduler:
        assert self.scheduler.get_request_state(req_id).status == DiffusionRequestStatus.FINISHED_COMPLETED
        assert self.scheduler.has_requests() is False

+    def test_diffusion_kv_request_moves_to_scheduler_state(self) -> None:
+        self.scheduler.initialize(SimpleNamespace(diffusion_kv_mode=DiffusionKVCacheMode.PAGED_SCHEDULER))
+        request = _make_request("diffusion-kv")
+        prepared_layout = object()
+        request.prepared_layout = prepared_layout
+        text_context = DiffusionKVContext(context_id="text", cache_role="cross.text", num_tokens=8)
+        kv_request = DiffusionKVRequest(
+            "diffusion-kv/diffusion-kv/0",
+            sequence_id=0,
+            prefix_len=4,
+            target_len=8,
+            seq_len=16,
+            kv_contexts=(text_context,),
+        )
+        request.diffusion_kv_requests = (kv_request,)
+
+        self.scheduler.add_request(request)
+        state = self.scheduler.get_request_state(request.request_id)
+        scheduler_output = self.scheduler.schedule()
+
+        assert state is not None
+        assert state.diffusion_kv_requests == (kv_request,)
+        assert state.diffusion_kv_requests[0].kv_contexts == (text_context,)
+        assert request.diffusion_kv_requests is None
+        assert scheduler_output.scheduled_new_reqs[0].req.prepared_layout is prepared_layout
+
+    def test_paged_scheduler_rejects_missing_kv_request(self) -> None:
+        self.scheduler.initialize(SimpleNamespace(diffusion_kv_mode=DiffusionKVCacheMode.PAGED_SCHEDULER))
+        request = _make_request("diffusion-kv")
+
+        with pytest.raises(ValueError, match="did not produce DiffusionKVRequest"):
+            self.scheduler.add_request(request)
+
+    @pytest.mark.parametrize(
+        ("owner_name", "field_name"),
+        [
+            ("request", "past_key_values"),
+            ("sampling_params", "past_key_values"),
+            ("sampling_params", "cfg_text_past_key_values"),
+            ("sampling_params", "cfg_img_past_key_values"),
+            ("sampling_params", "cfg_branch_past_key_values"),
+        ],
+    )
+    def test_diffusion_kv_request_rejects_legacy_dense_kv(self, owner_name: str, field_name: str) -> None:
+        self.scheduler.initialize(SimpleNamespace(diffusion_kv_mode=DiffusionKVCacheMode.PAGED_SCHEDULER))
+        request = _make_request("diffusion-kv")
+        owner = request if owner_name == "request" else request.sampling_params
+        setattr(owner, field_name, object())
+        request.diffusion_kv_requests = (
+            DiffusionKVRequest(
+                "diffusion-kv/diffusion-kv/0",
+                sequence_id=0,
+                prefix_len=4,
+                target_len=8,
+                seq_len=16,
+            ),
+        )
+
+        with pytest.raises(ValueError, match=rf"{owner_name}\.{field_name}"):
+            self.scheduler.add_request(request)
+
+    def test_dense_scheduler_rejects_scheduler_only_kv_request(self) -> None:
+        request = _make_request("dense-kv-request")
+        request.diffusion_kv_requests = (
+            DiffusionKVRequest(
+                "dense-kv-request/diffusion-kv/0",
+                sequence_id=0,
+                prefix_len=4,
+                target_len=8,
+                seq_len=16,
+            ),
+        )
+
+        with pytest.raises(ValueError, match="dense_legacy request unexpectedly contains"):
+            self.scheduler.add_request(request)
+
+    def test_dense_scheduler_accepts_legacy_dense_kv(self) -> None:
+        request = _make_request("dense-kv")
+        request.sampling_params.past_key_values = object()
+
+        request_id = self.scheduler.add_request(request)
+
+        assert request_id == request.request_id
+
    def test_error_output_marks_finished_error(self) -> None:
        req_id = self.scheduler.add_request(_make_request("err"))

@@ -562,12 +727,20 @@ class TestDiffusionEngine:
        engine.abort_queue = queue.Queue()

        request = _make_request("engine")
+        prepared_layout = object()
+
+        def preprocess(req):
+            req.prepared_layout = prepared_layout
+            return req
+
+        engine.pre_process_func = mocker.Mock(side_effect=preprocess)
        runner_output = _make_request_output("engine")
        engine.execute_fn = mocker.Mock(return_value=runner_output)

        output = engine.add_req_and_wait_for_response(request)

        assert output is runner_output.result
+        engine.pre_process_func.assert_called_once_with(request)
        engine.execute_fn.assert_called_once()

    def test_supports_scheduler_interface_injection(self, mocker: MockerFixture) -> None:
@@ -631,17 +804,29 @@ class TestDiffusionEngine:
    async def test_step_streaming_raises_aborted_error(self, mocker: MockerFixture) -> None:
        engine = DiffusionEngine.__new__(DiffusionEngine)
        engine._check_and_start_background_loop = mocker.AsyncMock()
-        engine.pre_process_func = None
+        request = _make_request("req-abort")
+        prepared_layout = object()
+
+        def preprocess(req):
+            req.prepared_layout = prepared_layout
+            return req
+
+        engine.pre_process_func = mocker.Mock(side_effect=preprocess)

        async def _stream(_request):
            yield DiffusionOutput(aborted=True, abort_message="Request req-abort aborted.")

-        engine.async_add_req_and_stream_response = mocker.Mock(return_value=_stream(None))
+        engine._add_prepared_request = mocker.Mock(return_value=request.request_id)
+        engine.get_output_stream = mocker.Mock(return_value=_stream(None))

        with pytest.raises(DiffusionRequestAbortedError, match="Request req-abort aborted"):
-            async for _ in engine.step_streaming(_make_request("req-abort")):
+            async for _ in engine.step_streaming(request):
                pass

+        engine.pre_process_func.assert_called_once_with(request)
+        engine._add_prepared_request.assert_called_once_with(request)
+        engine.get_output_stream.assert_called_once_with(request.request_id)
+
    def test_abort_queue_marks_request_finished_aborted(self) -> None:
        engine = DiffusionEngine.__new__(DiffusionEngine)
        engine._rpc_lock = threading.RLock()
@@ -774,12 +959,32 @@ class TestDiffusionEngine:
        with pytest.raises(RuntimeError, match="Dummy run failed: boom"):
            engine._dummy_run()

+    def test_dummy_run_delegates_preprocessing_to_sync_admission(self, mocker: MockerFixture) -> None:
+        engine = DiffusionEngine.__new__(DiffusionEngine)
+        engine.od_config = SimpleNamespace(model_class_name="mock_model", diffusion_load_format="default")
+        engine.pre_process_func = mocker.Mock()
+        engine.add_req_and_wait_for_response = mocker.Mock(return_value=DiffusionOutput(output=None))
+
+        engine._dummy_run()
+
+        engine.pre_process_func.assert_not_called()
+        admitted_request = engine.add_req_and_wait_for_response.call_args.args[0]
+        assert admitted_request.request_id == "dummy_req_id"
+

 class TestStepScheduler:
    def setup_method(self) -> None:
        self.scheduler: StepScheduler = StepScheduler()
        self.scheduler.initialize(SimpleNamespace())

+    def test_admission_wait_is_not_supported(self) -> None:
+        decision = self.scheduler.get_admission_wait_decision(
+            now=10.0,
+            dp_concurrent=True,
+        )
+
+        assert decision.should_wait is False
+
    def test_single_request_step_lifecycle(self) -> None:
        request = _make_step_request("step", num_inference_steps=3)
        req_id = self.scheduler.add_request(request)
@@ -48,6 +48,7 @@ def _mock_request(tag: str):
        request_id=tag,
        prompt=f"prompt_{tag}",
        sampling_params=OmniDiffusionSamplingParams(num_inference_steps=1),
+        diffusion_kv_requests=None,
    )


@@ -228,7 +228,6 @@ def _resolve_deploy_config(config_path: str, run_level: str) -> str:


@pytest.mark.advanced_model
-@pytest.mark.skip(reason="#issue 5475")
@pytest.mark.diffusion
@hardware_test(res={"cuda": "H100", "rocm": "MI325"})
 def test_bagel_img2img_shared_memory_connector(run_level):
@@ -3,6 +3,7 @@

 import importlib.util
 from pathlib import Path
+from types import SimpleNamespace

 import pytest

@@ -43,3 +44,11 @@ def test_model_display_names_require_string_mapping(tmp_path, monkeypatch):

    with pytest.raises(ValueError, match="MODEL_DISPLAY_NAMES_FILE"):
        generate_examples.load_model_display_names()
+
+
+def test_only_general_serving_examples_get_generated_pages():
+    general_example = SimpleNamespace(category="offline_inference", path=Path("text_to_image"))
+    model_example = SimpleNamespace(category="offline_inference", path=Path("qwen3_omni"))
+
+    assert generate_examples.is_general_example(general_example)
+    assert not generate_examples.is_general_example(model_example)
@@ -1,18 +1,16 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project

-"""
-Online serving smoke for ``robbyant/lingbot-video-dense-1.3b``.
-
-The LingBot dense PR supports text-to-video only, so this module exercises the
-native ``/v1/videos`` path with a small 9-frame request.
-"""
+"""Online serving smoke coverage for the dense LingBot-Video checkpoint."""

+import json
 import os

 import pytest

+from tests.helpers.assertions import assert_video_first_frame_matches
 from tests.helpers.mark import hardware_marks
+from tests.helpers.media import generate_synthetic_image
 from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHandler

 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
@@ -43,11 +41,32 @@ def _get_diffusion_feature_cases(model: str):
    ]


-@pytest.mark.core_model
-@pytest.mark.advanced_model
+@pytest.mark.slow
@pytest.mark.diffusion
@pytest.mark.parametrize("omni_server", _get_diffusion_feature_cases(MODEL), indirect=True)
-def test_text_to_video_001(omni_server: OmniServer, openai_client: OpenAIClientHandler) -> None:
+def test_text_to_image_001(omni_server: OmniServer, openai_client: OpenAIClientHandler) -> None:
+    openai_client.send_images_generations_http_request(
+        {
+            "json": {
+                "model": omni_server.model,
+                "prompt": PROMPT,
+                "negative_prompt": NEGATIVE_PROMPT,
+                "size": "320x192",
+                "n": 1,
+                "response_format": "b64_json",
+                "num_inference_steps": 2,
+                "guidance_scale": 3.0,
+                "flow_shift": 3.0,
+                "seed": 42,
+            }
+        }
+    )
+
+
+@pytest.mark.slow
+@pytest.mark.diffusion
+@pytest.mark.parametrize("omni_server", _get_diffusion_feature_cases(MODEL), indirect=True)
+def test_video_generation_modes_001(omni_server: OmniServer, openai_client: OpenAIClientHandler) -> None:
    request_config = {
        "model": omni_server.model,
        "form_data": {
@@ -65,3 +84,18 @@ def test_text_to_video_001(omni_server: OmniServer, openai_client: OpenAIClientH
        },
    }
    openai_client.send_video_diffusion_request(request_config)
+
+    synthetic_image = generate_synthetic_image(320, 192, force_regenerate=True, seed=42)
+    request_config["form_data"]["prompt"] = "the red block moves slowly while the camera remains fixed"
+    # Keep TI2V dimensions model-scoped until /v1/videos forwards raw reference images.
+    request_config["form_data"].pop("height")
+    request_config["form_data"].pop("width")
+    request_config["form_data"]["extra_params"] = json.dumps({"size": "320x192"}, separators=(",", ":"))
+    request_config["image_reference"] = f"data:image/jpeg;base64,{synthetic_image['base64']}"
+    responses = openai_client.send_video_diffusion_request(request_config)
+    assert responses[0].videos
+    assert_video_first_frame_matches(
+        responses[0].videos[0],
+        synthetic_image["np_array"],
+        max_mean_absolute_error=0.25,
+    )
@@ -4,9 +4,10 @@
 """
 L4 expansion coverage for ``robbyant/lingbot-video-dense-1.3b``.

-This file remains dense-only. Basic single-GPU MoE serving is covered by
-``test_lingbot_video_moe.py``; T2I, I2V, TI2V, and multi-GPU feature rows
-belong in follow-up PRs.
+This file remains dense-only. Basic T2I/T2V/TI2V serving for both dense and MoE
+checkpoints is covered by ``test_lingbot_video.py`` and
+``test_lingbot_video_moe.py``. Distributed feature rows belong in follow-up
+PRs.
 """

 import json
@@ -3,11 +3,14 @@

 """Single-GPU serving smoke for ``robbyant/lingbot-video-moe-30b-a3b``."""

+import json
 import os

 import pytest

+from tests.helpers.assertions import assert_video_first_frame_matches
 from tests.helpers.mark import hardware_marks
+from tests.helpers.media import generate_synthetic_image
 from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHandler

 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
@@ -38,10 +41,32 @@ def _get_server_cases(model: str):
    ]


+@pytest.mark.slow
+@pytest.mark.diffusion
+@pytest.mark.parametrize("omni_server", _get_server_cases(MODEL), indirect=True)
+def test_text_to_image_moe(omni_server: OmniServer, openai_client: OpenAIClientHandler) -> None:
+    openai_client.send_images_generations_http_request(
+        {
+            "json": {
+                "model": omni_server.model,
+                "prompt": PROMPT,
+                "negative_prompt": NEGATIVE_PROMPT,
+                "size": "320x192",
+                "n": 1,
+                "response_format": "b64_json",
+                "num_inference_steps": 2,
+                "guidance_scale": 3.0,
+                "flow_shift": 3.0,
+                "seed": 42,
+            }
+        }
+    )
+
+
@pytest.mark.full_model
@pytest.mark.diffusion
@pytest.mark.parametrize("omni_server", _get_server_cases(MODEL), indirect=True)
-def test_text_to_video_moe(omni_server: OmniServer, openai_client: OpenAIClientHandler) -> None:
+def test_video_generation_modes_moe(omni_server: OmniServer, openai_client: OpenAIClientHandler) -> None:
    request_config = {
        "model": omni_server.model,
        "form_data": {
@@ -57,3 +82,18 @@ def test_text_to_video_moe(omni_server: OmniServer, openai_client: OpenAIClientH
        },
    }
    openai_client.send_video_diffusion_request(request_config)
+
+    synthetic_image = generate_synthetic_image(320, 192, force_regenerate=True, seed=42)
+    request_config["form_data"]["prompt"] = "the red block moves slowly while the camera remains fixed"
+    # Keep TI2V dimensions model-scoped until /v1/videos forwards raw reference images.
+    request_config["form_data"].pop("height")
+    request_config["form_data"].pop("width")
+    request_config["form_data"]["extra_params"] = json.dumps({"size": "320x192"}, separators=(",", ":"))
+    request_config["image_reference"] = f"data:image/jpeg;base64,{synthetic_image['base64']}"
+    responses = openai_client.send_video_diffusion_request(request_config)
+    assert responses[0].videos
+    assert_video_first_frame_matches(
+        responses[0].videos[0],
+        synthetic_image["np_array"],
+        max_mean_absolute_error=0.25,
+    )
@@ -83,6 +83,11 @@ class _FakePool:
        self.stage_client = SimpleNamespace(requires_multimodal_data=False, custom_process_input_func=None)
        self.aborted: list[list[str]] = []

+    def live_replica_ids(self) -> list[int]:
+        # Single always-live replica: these tests cover CFG bundling, not
+        # replica death, and the forward path fast-fails on an empty pool.
+        return [0]
+
    async def abort_requests(self, request_ids):
        self.aborted.append(list(request_ids))

@@ -74,8 +74,6 @@ async def test_engine_dead_broadcasts_fatal_to_rpc_waiters(monkeypatch: pytest.M
        rpc_async_queue=rpc_queue,
        stage_pools=[],
    )
-    orchestrator._fatal_error = "stage engine died"
-    orchestrator._fatal_error_stage_id = 2

    async def wait_for_requests() -> None:
        await asyncio.Event().wait()
@@ -91,8 +89,7 @@ async def test_engine_dead_broadcasts_fatal_to_rpc_waiters(monkeypatch: pytest.M
    fatal = rpc_queue.get_nowait()
    assert isinstance(fatal, ErrorMessage)
    assert fatal.fatal is True
-    assert fatal.error == "stage engine died"
-    assert fatal.stage_id == 2
+    assert "stage engine died" in fatal.error


@dataclass
@@ -3,7 +3,7 @@
 """Tests for error propagation paths within the Orchestrator.

 Covers:
- EngineDeadError from an LLM stage poll → fatal error broadcast + shutdown
+- EngineDeadError from an LLM stage poll → fatal error broadcast + replica eviction
 - Diffusion stage error output (OmniRequestOutput.from_error) → routed correctly
 """

@@ -14,17 +14,30 @@ import queue
 import time
 from types import SimpleNamespace

+import janus
 import pytest
 from vllm.v1.engine.exceptions import EngineDeadError

-from vllm_omni.engine.messages import EngineQueueMessage, ErrorMessage, ShutdownRequestMessage
+from vllm_omni.engine.messages import (
+    AddCompanionRequestMessage,
+    EngineQueueMessage,
+    ErrorMessage,
+    ShutdownRequestMessage,
+    StageSubmissionMessage,
+)
+from vllm_omni.engine.orchestrator import Orchestrator, OrchestratorRequestState
+from vllm_omni.engine.stage_pool import StageUnavailableError
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.outputs import OmniRequestOutput

 from .test_orchestrator import (
+    FakeOutputProcessor,
    FakeStageClient,
    OrchestratorFixture,
    _build_harness,
+    _build_request_output,
+    _build_stage_pools,
+    _engine_core_outputs,
    _enqueue_add_request,
    _wait_for,
 )
@@ -50,6 +63,22 @@ async def _get_any_output_message(fixture: OrchestratorFixture, *, timeout: floa
            await asyncio.sleep(0.01)


+async def _wait_for_error_message(
+    fixture: OrchestratorFixture,
+    *,
+    request_id: str | None = None,
+    max_messages: int = 50,
+) -> ErrorMessage:
+    """Drain output messages until the ErrorMessage arrives, skipping the
+    non-error outputs (e.g. stage metrics, forwarded stage results) that a
+    driven pipeline can interleave ahead of the failure."""
+    for _ in range(max_messages):
+        msg = await _get_any_output_message(fixture)
+        if isinstance(msg, ErrorMessage) and (request_id is None or msg.request_id == request_id):
+            return msg
+    raise AssertionError(f"No ErrorMessage for req={request_id} within {max_messages} messages")
+
+
@pytest.fixture
 def orchestrator_factory():
    fixtures: list[OrchestratorFixture] = []
@@ -72,32 +101,14 @@ def orchestrator_factory():
 # ───────── EngineDeadError from LLM stage poll ─────────


-class FakeDeadLLMStageClient(FakeStageClient):
-    """LLM stage client that raises EngineDeadError once a request is in-flight.
-
-    Raising on the first poll races with ``add_request``: under suite load the
-    orchestrator can die and drain before the test enqueues anything, so no
-    fatal ErrorMessage is ever published. Keep returning empty outputs until
-    ``add_request_async`` has been called, then fail on the next poll.
-    """
-
-    async def get_output_async(self):
-        if not self.add_request_calls:
-            return SimpleNamespace(
-                outputs=[],
-                scheduler_stats=None,
-                finished_requests=None,
-            )
-        raise EngineDeadError("Stage-0 engine core is dead")
-
-
@pytest.mark.asyncio
-async def test_engine_dead_error_broadcasts_fatal_and_shuts_down(orchestrator_factory) -> None:
-    """When a stage raises EngineDeadError during poll, the orchestrator must:
+async def test_engine_dead_error_evicts_replica_and_keeps_running(orchestrator_factory) -> None:
+    """When a stage replica raises EngineDeadError during poll, the orchestrator must:
    1. Enqueue a fatal error message for each affected request
-    2. Shut itself down (thread exits)
+    2. Evict the dead replica and keep running so the API server stays up
+       (per-replica fault isolation, #4285)
    """
-    stage0 = FakeDeadLLMStageClient(stage_type="llm", final_output=True)
+    stage0 = _DieOnDemandLLMStageClient(stage_type="llm", final_output=True)
    orchestrator_fixture = orchestrator_factory([stage0])
    request = SimpleNamespace(request_id="req-dead", prompt_token_ids=[1, 2])

@@ -110,6 +121,12 @@ async def test_engine_dead_error_broadcasts_fatal_and_shuts_down(orchestrator_fa
            sampling_params_list=[_sampling_params()],
            final_stage_id=0,
        )
+        # Let the request dispatch before the replica dies; a replica dead on
+        # its very first poll can be evicted before dispatch, which routes the
+        # failure through the add-request guard instead (that ordering is
+        # covered by test_add_request_to_dead_stage_fails_request_not_server).
+        await _wait_for(lambda: len(stage0.add_request_calls) == 1)
+        stage0.die = True

        # Collect the fatal error message.
        msg = await _get_any_output_message(orchestrator_fixture)
@@ -118,13 +135,17 @@ async def test_engine_dead_error_broadcasts_fatal_and_shuts_down(orchestrator_fa
        assert msg.type == "error"
        assert msg.fatal is True
        assert msg.request_id == "req-dead"
-        assert "Stage-0 engine core is dead" in msg.error
+        assert "Stage-0 replica-0 is dead" in msg.error

-        # The orchestrator thread should exit after the fatal error.
-        orchestrator_fixture.thread.join(timeout=5)
-        assert not orchestrator_fixture.thread.is_alive()
+        # The dead replica is evicted; the orchestrator keeps running.
+        pool = orchestrator_fixture.orchestrator.stage_pools[0]
+        deadline = time.monotonic() + 5.0
+        while pool.live_replica_ids() and time.monotonic() < deadline:
+            await asyncio.sleep(0.02)
+        assert pool.live_replica_ids() == []
+        assert orchestrator_fixture.thread.is_alive()

-        # Request state should be cleaned up.
+        # Affected request state should be cleaned up.
        assert "req-dead" not in orchestrator_fixture.orchestrator.request_states
    finally:
        if orchestrator_fixture.thread.is_alive():
@@ -132,6 +153,596 @@ async def test_engine_dead_error_broadcasts_fatal_and_shuts_down(orchestrator_fa
            orchestrator_fixture.thread.join(timeout=5)


+class _DieOnDemandLLMStageClient(FakeStageClient):
+    """LLM stage replica that raises EngineDeadError on poll once ``die`` is set."""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.die = False
+
+    async def get_output_async(self):
+        if self.die:
+            raise EngineDeadError(f"Stage-{self.stage_id} replica-{self.replica_id} is dead")
+        return await super().get_output_async()
+
+
+class _DieOnDemandDiffusionStageClient(FakeStageClient):
+    """Diffusion stage replica that raises EngineDeadError on poll once ``die`` is set."""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.die = False
+
+    def get_diffusion_output_nowait(self):
+        if self.die:
+            raise EngineDeadError(f"Stage-{self.stage_id} diffusion replica-{self.replica_id} is dead")
+        return super().get_diffusion_output_nowait()
+
+
+class _UnavailableDiffusionStageClient(FakeStageClient):
+    """Diffusion replica that stays live (so the pool keeps stage_type) but whose
+    dispatch fails with StageUnavailableError, as a downstream prewarm would when
+    its stage has no capacity."""
+
+    async def add_request_async(self, *args, **kwargs) -> None:
+        raise StageUnavailableError(f"stage {self.stage_id} has no live replica")
+
+
+@pytest.mark.asyncio
+async def test_engine_dead_error_fails_only_dead_replica_requests(orchestrator_factory) -> None:
+    """Multi-replica stage: one replica dying must fail only the requests bound
+    to it. Requests bound to a surviving replica keep running, and the stage
+    (still having a live replica) is not torn down (per-replica fault isolation,
+    #4285).
+    """
+    r0 = _DieOnDemandLLMStageClient(stage_type="llm", final_output=True)
+    r1 = FakeStageClient(stage_type="llm", final_output=True)
+    stage_pools = _build_stage_pools([[r0, r1]])
+    orchestrator_fixture = orchestrator_factory([], stage_pools=stage_pools)
+    pool = orchestrator_fixture.orchestrator.stage_pools[0]
+
+    try:
+        # req-0 → replica 0 (round-robin starts at 0)
+        await _enqueue_add_request(
+            orchestrator_fixture,
+            request_id="req-0",
+            prompt=SimpleNamespace(request_id="req-0", prompt_token_ids=[1, 2]),
+            original_prompt={"prompt": "a"},
+            sampling_params_list=[_sampling_params()],
+            final_stage_id=0,
+        )
+        await _wait_for(lambda: len(r0.add_request_calls) == 1)
+
+        # req-1 → replica 1
+        await _enqueue_add_request(
+            orchestrator_fixture,
+            request_id="req-1",
+            prompt=SimpleNamespace(request_id="req-1", prompt_token_ids=[3, 4]),
+            original_prompt={"prompt": "b"},
+            sampling_params_list=[_sampling_params()],
+            final_stage_id=0,
+        )
+        await _wait_for(lambda: len(r1.add_request_calls) == 1)
+        assert pool.get_bound_replica_id("req-0") == 0
+        assert pool.get_bound_replica_id("req-1") == 1
+
+        # Kill replica 0 on its next poll.
+        r0.die = True
+
+        msg = await _get_any_output_message(orchestrator_fixture)
+        assert isinstance(msg, ErrorMessage)
+        assert msg.fatal is True
+        assert msg.request_id == "req-0"
+
+        # Replica 0 evicted, replica 1 still serving; req-1 untouched.
+        await _wait_for(lambda: pool.live_replica_ids() == [1])
+        assert orchestrator_fixture.thread.is_alive()
+        assert "req-0" not in orchestrator_fixture.orchestrator.request_states
+        assert "req-1" in orchestrator_fixture.orchestrator.request_states
+    finally:
+        if orchestrator_fixture.thread.is_alive():
+            orchestrator_fixture.request_sync_q.put_nowait(ShutdownRequestMessage())
+            orchestrator_fixture.thread.join(timeout=5)
+
+
+@pytest.mark.asyncio
+async def test_forward_to_dead_downstream_stage_fails_request_not_server(orchestrator_factory) -> None:
+    """A request mid-pipeline whose downstream stage has lost all replicas must
+    fail that request, not tear down the orchestrator. Forwarding to a stage with
+    no live replica is handled gracefully (per-replica fault isolation, #4285).
+    """
+    stage0 = FakeStageClient(stage_type="llm", final_output=False, next_inputs=[{"prompt_token_ids": [7, 8]}])
+    stage1 = _DieOnDemandLLMStageClient(stage_type="llm", final_output=True)
+    proc0 = FakeOutputProcessor(request_outputs=[_build_request_output("req-x", token_ids=[3], finished=True)])
+    proc1 = FakeOutputProcessor()
+    stage_pools = _build_stage_pools([[stage0], [stage1]], output_processors=[proc0, proc1])
+    orchestrator_fixture = orchestrator_factory([], stage_pools=stage_pools)
+    pool1 = orchestrator_fixture.orchestrator.stage_pools[1]
+
+    try:
+        await _enqueue_add_request(
+            orchestrator_fixture,
+            request_id="req-x",
+            prompt=SimpleNamespace(request_id="req-x", prompt_token_ids=[1, 2]),
+            original_prompt={"prompt": "hello"},
+            sampling_params_list=[_sampling_params(), _sampling_params()],
+            final_stage_id=1,
+        )
+        await _wait_for(lambda: len(stage0.add_request_calls) == 1)
+
+        # Kill stage-1's only replica before stage-0 output is forwarded.
+        stage1.die = True
+        await _wait_for(lambda: pool1.live_replica_ids() == [])
+
+        # Completing stage 0 triggers a forward to the now-empty stage 1.
+        stage0.push_engine_core_outputs(_engine_core_outputs("s0-raw", 1.0))
+
+        # Completing stage 0 also emits its normal outputs; skip past them to
+        # the downstream-forward failure.
+        error_msg = await _wait_for_error_message(orchestrator_fixture, request_id="req-x")
+        assert error_msg.fatal is True
+        assert error_msg.stage_id == 1
+
+        assert orchestrator_fixture.thread.is_alive()
+        assert "req-x" not in orchestrator_fixture.orchestrator.request_states
+    finally:
+        if orchestrator_fixture.thread.is_alive():
+            orchestrator_fixture.request_sync_q.put_nowait(ShutdownRequestMessage())
+            orchestrator_fixture.thread.join(timeout=5)
+
+
+@pytest.mark.asyncio
+async def test_add_request_to_dead_stage_fails_request_not_server(orchestrator_factory) -> None:
+    """A new request entering a stage that already lost all replicas fails that
+    request instead of raising out of the request handler and tearing the server
+    down. The guard runs before request state is registered, so nothing leaks.
+    """
+    r0 = _DieOnDemandLLMStageClient(stage_type="llm", final_output=True)
+    stage_pools = _build_stage_pools([[r0]])
+    orchestrator_fixture = orchestrator_factory([], stage_pools=stage_pools)
+    pool = orchestrator_fixture.orchestrator.stage_pools[0]
+
+    try:
+        # Send a first request so the replica is polled and dies → evicted.
+        await _enqueue_add_request(
+            orchestrator_fixture,
+            request_id="req-0",
+            prompt=SimpleNamespace(request_id="req-0", prompt_token_ids=[1, 2]),
+            original_prompt={"prompt": "a"},
+            sampling_params_list=[_sampling_params()],
+            final_stage_id=0,
+        )
+        r0.die = True
+        await _wait_for(lambda: pool.live_replica_ids() == [])
+
+        # A new request now enters a stage with no live replica.
+        await _enqueue_add_request(
+            orchestrator_fixture,
+            request_id="req-after",
+            prompt=SimpleNamespace(request_id="req-after", prompt_token_ids=[3, 4]),
+            original_prompt={"prompt": "b"},
+            sampling_params_list=[_sampling_params()],
+            final_stage_id=0,
+        )
+
+        error_msg = await _wait_for_error_message(orchestrator_fixture, request_id="req-after")
+        assert error_msg.fatal is True
+        assert error_msg.stage_id == 0
+
+        assert orchestrator_fixture.thread.is_alive()
+        # Guard runs before registration, so the failed request never enters state.
+        assert "req-after" not in orchestrator_fixture.orchestrator.request_states
+    finally:
+        if orchestrator_fixture.thread.is_alive():
+            orchestrator_fixture.request_sync_q.put_nowait(ShutdownRequestMessage())
+            orchestrator_fixture.thread.join(timeout=5)
+
+
+@pytest.mark.asyncio
+async def test_diffusion_replica_death_on_poll_keeps_server(orchestrator_factory) -> None:
+    """A diffusion stage replica raising EngineDeadError during poll must be
+    evicted while the orchestrator keeps serving. The per-replica catch has to
+    cover the diffusion polling path, not just the LLM one (#4285).
+    """
+    stage0 = _DieOnDemandDiffusionStageClient(stage_type="diffusion", final_output=True, final_output_type="image")
+    stage_pools = _build_stage_pools([[stage0]])
+    orchestrator_fixture = orchestrator_factory([], stage_pools=stage_pools)
+    pool = orchestrator_fixture.orchestrator.stage_pools[0]
+
+    try:
+        await _enqueue_add_request(
+            orchestrator_fixture,
+            request_id="req-dit",
+            prompt={"prompt": "a cat"},
+            original_prompt={"prompt": "a cat"},
+            sampling_params_list=[OmniDiffusionSamplingParams()],
+            final_stage_id=0,
+        )
+        await _wait_for(lambda: len(stage0.add_request_calls) == 1)
+
+        stage0.die = True
+
+        error_msg = await _wait_for_error_message(orchestrator_fixture, request_id="req-dit")
+        assert error_msg.fatal is True
+        assert error_msg.stage_id == 0
+
+        await _wait_for(lambda: pool.live_replica_ids() == [])
+        assert orchestrator_fixture.thread.is_alive()
+        assert "req-dit" not in orchestrator_fixture.orchestrator.request_states
+    finally:
+        if orchestrator_fixture.thread.is_alive():
+            orchestrator_fixture.request_sync_q.put_nowait(ShutdownRequestMessage())
+            orchestrator_fixture.thread.join(timeout=5)
+
+
+@pytest.mark.asyncio
+async def test_async_chunk_prewarm_failure_reports_failing_downstream_stage(orchestrator_factory) -> None:
+    """When an async-chunk prewarm dispatch to a downstream stage fails, the
+    failure must be attributed to that stage, not stage 0 (the prewarm's own
+    dispatch stage). Regression for the guard using stage_id=0.
+    """
+    stage0 = FakeStageClient(stage_type="llm", final_output=False)
+    stage1 = FakeStageClient(stage_type="diffusion", final_output=False, final_output_type="image")
+    stage2 = _UnavailableDiffusionStageClient(stage_type="diffusion", final_output=True, final_output_type="image")
+    stage_pools = _build_stage_pools([[stage0], [stage1], [stage2]])
+    orchestrator_fixture = orchestrator_factory([], stage_pools=stage_pools, async_chunk=True)
+
+    try:
+        await _enqueue_add_request(
+            orchestrator_fixture,
+            request_id="req-x",
+            prompt=SimpleNamespace(request_id="req-x", prompt_token_ids=[1, 2, 3]),
+            original_prompt={"prompt": "a cat"},
+            sampling_params_list=[_sampling_params(), OmniDiffusionSamplingParams(), OmniDiffusionSamplingParams()],
+            final_stage_id=2,
+        )
+
+        error_msg = await _wait_for_error_message(orchestrator_fixture, request_id="req-x")
+        assert error_msg.fatal is True
+        assert error_msg.stage_id == 2
+        assert "Stage-2" in error_msg.error
+
+        # Stage 1 prewarm ran before the stage-2 failure; server stays up.
+        assert len(stage1.add_request_calls) == 1
+        assert orchestrator_fixture.thread.is_alive()
+    finally:
+        if orchestrator_fixture.thread.is_alive():
+            orchestrator_fixture.request_sync_q.put_nowait(ShutdownRequestMessage())
+            orchestrator_fixture.thread.join(timeout=5)
+
+
+# ───────── Direct unit tests for the fault-isolation helpers ─────────
+
+
+def _build_bare_orchestrator(stage_pools) -> tuple[Orchestrator, tuple[janus.Queue, ...]]:
+    """Construct an Orchestrator on the current event loop without running it.
+
+    Lets tests await the fault-isolation helpers directly instead of driving
+    them through the orchestration loop.
+    """
+    queues = (janus.Queue(), janus.Queue(), janus.Queue())
+    orchestrator = Orchestrator(
+        request_async_queue=queues[0].async_q,
+        output_async_queue=queues[1].async_q,
+        rpc_async_queue=queues[2].async_q,
+        stage_pools=stage_pools,
+    )
+    return orchestrator, queues
+
+
+def _register_request(orchestrator: Orchestrator, request_id: str, *, submitted_stage: int | None = 0) -> None:
+    state = OrchestratorRequestState(request_id=request_id)
+    if submitted_stage is not None:
+        state.stage_submit_ts[submitted_stage] = time.time()
+    orchestrator.request_states[request_id] = state
+
+
+@pytest.mark.asyncio
+async def test_handle_dead_replica_fails_only_bound_requests() -> None:
+    """Direct helper test: with a surviving replica, only requests bound to the
+    dead replica fail; the survivor's requests and bindings are untouched.
+    """
+    r0 = FakeStageClient(stage_type="llm", final_output=True)
+    r1 = FakeStageClient(stage_type="llm", final_output=True)
+    orchestrator, queues = _build_bare_orchestrator(_build_stage_pools([[r0, r1]]))
+    try:
+        pool = orchestrator.stage_pools[0]
+        _register_request(orchestrator, "req-0")
+        _register_request(orchestrator, "req-1")
+        assert pool.select_replica_id("req-0") == 0
+        assert pool.select_replica_id("req-1") == 1
+
+        await orchestrator._handle_dead_replica(0, 0, EngineDeadError("replica-0 dead"))
+
+        assert pool.live_replica_ids() == [1]
+        assert r0.shutdown_calls == 1
+        msg = queues[1].async_q.get_nowait()
+        assert isinstance(msg, ErrorMessage)
+        assert msg.fatal is True
+        assert msg.request_id == "req-0"
+        assert msg.stage_id == 0
+        assert "replica-0 dead" in msg.error
+        assert queues[1].async_q.empty()
+        assert "req-0" not in orchestrator.request_states
+        assert "req-1" in orchestrator.request_states
+        assert pool.get_bound_replica_id("req-0") is None
+        assert pool.get_bound_replica_id("req-1") == 1
+    finally:
+        for q in queues:
+            q.close()
+
+
+@pytest.mark.asyncio
+async def test_handle_dead_replica_last_replica_fails_all_stage_requests() -> None:
+    """Direct helper test: when the dead replica was the stage's last one, every
+    request routed through the stage fails — bound or not — while requests that
+    never entered the stage survive.
+    """
+    orchestrator, queues = _build_bare_orchestrator(_build_stage_pools([[FakeStageClient(stage_type="llm")]]))
+    try:
+        pool = orchestrator.stage_pools[0]
+        _register_request(orchestrator, "req-bound")
+        _register_request(orchestrator, "req-unbound")
+        _register_request(orchestrator, "req-elsewhere", submitted_stage=None)
+        assert pool.select_replica_id("req-bound") == 0
+
+        await orchestrator._handle_dead_replica(0, 0, EngineDeadError("last replica dead"))
+
+        assert pool.live_replica_ids() == []
+        failed = {queues[1].async_q.get_nowait().request_id for _ in range(2)}
+        assert failed == {"req-bound", "req-unbound"}
+        assert queues[1].async_q.empty()
+        assert set(orchestrator.request_states) == {"req-elsewhere"}
+    finally:
+        for q in queues:
+            q.close()
+
+
+@pytest.mark.asyncio
+async def test_fail_request_dead_stage_cleans_registered_request() -> None:
+    """Direct helper test: a registered request failed against a dead stage gets
+    a fatal ErrorMessage, its state and bindings are released, and its
+    in-flight work on live replicas is aborted.
+    """
+    r0 = FakeStageClient(stage_type="llm")
+    orchestrator, queues = _build_bare_orchestrator(_build_stage_pools([[r0]]))
+    try:
+        pool = orchestrator.stage_pools[0]
+        _register_request(orchestrator, "req-a")
+        assert pool.select_replica_id("req-a") == 0
+
+        await orchestrator._fail_request_dead_stage("req-a", 0)
+
+        msg = queues[1].async_q.get_nowait()
+        assert isinstance(msg, ErrorMessage)
+        assert msg.error == "Stage-0 has no live replica"
+        assert msg.fatal is True
+        assert msg.request_id == "req-a"
+        assert msg.stage_id == 0
+        assert "req-a" not in orchestrator.request_states
+        assert pool.get_bound_replica_id("req-a") is None
+        # abort=True: the failed request's work on the still-live replica is
+        # stopped, not left to run to completion.
+        assert r0.abort_calls == [["req-a"]]
+    finally:
+        for q in queues:
+            q.close()
+
+
+@pytest.mark.asyncio
+async def test_streaming_update_racing_eviction_fails_request_not_server() -> None:
+    """A streaming_update can find its request state still present while the
+    stage has already lost all replicas (_handle_dead_replica awaits between
+    eviction and cleanup). Submitting then raises from replica selection; the
+    handler must fail that request instead of letting the raise escape
+    _request_handler and shut the server down (#4285).
+    """
+    orchestrator, queues = _build_bare_orchestrator(_build_stage_pools([[FakeStageClient(stage_type="llm")]]))
+    try:
+        pool = orchestrator.stage_pools[0]
+        _register_request(orchestrator, "req-s")
+        orchestrator.request_states["req-s"].sampling_params_list = [_sampling_params()]
+        assert pool.select_replica_id("req-s") == 0
+        pool.evict_replica(0)
+
+        msg = StageSubmissionMessage(
+            type="streaming_update",
+            request_id="req-s",
+            prompt=SimpleNamespace(request_id="req-s", prompt_token_ids=[1]),
+            original_prompt={"prompt": "hi"},
+            output_prompt_text=None,
+            sampling_params_list=[],
+            final_stage_id=0,
+            preprocess_ms=0.0,
+            request_timestamp=time.time(),
+            enqueue_ts=time.time(),
+        )
+        await orchestrator._handle_streaming_update(msg)
+
+        err = queues[1].async_q.get_nowait()
+        assert isinstance(err, ErrorMessage)
+        assert err.fatal is True
+        assert err.request_id == "req-s"
+        assert "req-s" not in orchestrator.request_states
+    finally:
+        for q in queues:
+            q.close()
+
+
+@pytest.mark.asyncio
+async def test_streaming_update_unrelated_error_is_not_masked() -> None:
+    """The dispatch guard converts only StageUnavailableError/EngineDeadError;
+    a genuine bug surfacing as a plain RuntimeError must propagate (fail
+    fast), not be misreported as a dead-stage request failure.
+    """
+
+    class _BuggyClient(FakeStageClient):
+        async def add_request_async(self, *args, **kwargs):
+            raise RuntimeError("boom: unrelated bug")
+
+    orchestrator, queues = _build_bare_orchestrator(
+        _build_stage_pools([[_BuggyClient(stage_type="llm", final_output=True)]])
+    )
+    try:
+        _register_request(orchestrator, "req-b")
+        orchestrator.request_states["req-b"].sampling_params_list = [_sampling_params()]
+
+        msg = StageSubmissionMessage(
+            type="streaming_update",
+            request_id="req-b",
+            prompt=SimpleNamespace(request_id="req-b", prompt_token_ids=[1]),
+            original_prompt={"prompt": "hi"},
+            output_prompt_text=None,
+            sampling_params_list=[],
+            final_stage_id=0,
+            preprocess_ms=0.0,
+            request_timestamp=time.time(),
+            enqueue_ts=time.time(),
+        )
+        with pytest.raises(RuntimeError, match="unrelated bug"):
+            await orchestrator._handle_streaming_update(msg)
+    finally:
+        for q in queues:
+            q.close()
+
+
+@pytest.mark.asyncio
+async def test_add_companion_racing_eviction_fails_parent_not_server() -> None:
+    """A CFG companion submission hitting a fully dead stage must fail the
+    parent request (whose cleanup also releases the companion), not raise out
+    of _request_handler (#4285).
+    """
+    orchestrator, queues = _build_bare_orchestrator(_build_stage_pools([[FakeStageClient(stage_type="llm")]]))
+    try:
+        pool = orchestrator.stage_pools[0]
+        _register_request(orchestrator, "req-p")
+        orchestrator.request_states["req-p"].sampling_params_list = [_sampling_params()]
+        pool.evict_replica(0)
+
+        msg = AddCompanionRequestMessage(
+            companion_id="req-p-cfg",
+            parent_id="req-p",
+            role="negative",
+            prompt=SimpleNamespace(request_id="req-p-cfg", prompt_token_ids=[2]),
+            companion_prompt_text=None,
+            sampling_params_list=[_sampling_params()],
+        )
+        await orchestrator._handle_add_companion(msg)
+
+        err = queues[1].async_q.get_nowait()
+        assert isinstance(err, ErrorMessage)
+        assert err.fatal is True
+        assert err.request_id == "req-p"
+        assert "req-p" not in orchestrator.request_states
+        assert "req-p-cfg" not in orchestrator.request_states
+    finally:
+        for q in queues:
+            q.close()
+
+
+@pytest.mark.asyncio
+async def test_fail_request_dead_stage_is_noop_safe_for_unregistered_request() -> None:
+    """Direct helper test: the stage-0 dispatch guard calls the helper before the
+    request is registered; the cleanup must be a safe no-op and still emit the
+    fatal ErrorMessage.
+    """
+    orchestrator, queues = _build_bare_orchestrator(_build_stage_pools([[FakeStageClient(stage_type="llm")]]))
+    try:
+        await orchestrator._fail_request_dead_stage("req-unknown", 0)
+
+        msg = queues[1].async_q.get_nowait()
+        assert isinstance(msg, ErrorMessage)
+        assert msg.error == "Stage-0 has no live replica"
+        assert msg.fatal is True
+        assert msg.request_id == "req-unknown"
+        assert orchestrator.request_states == {}
+    finally:
+        for q in queues:
+            q.close()
+
+
+@pytest.mark.asyncio
+async def test_dispatch_death_evicts_so_subsequent_requests_reroute_to_survivor() -> None:
+    """A replica dying mid-submit (EngineDeadError from dispatch, before the poll
+    loop evicts it) is evicted by the guard so subsequent requests reroute to the
+    survivor instead of round-robining back onto the dead slot until the poll
+    catches up. The survivor and its request are untouched (#4285).
+    """
+    r0 = FakeStageClient(stage_type="llm")
+    r1 = FakeStageClient(stage_type="llm")
+    orchestrator, queues = _build_bare_orchestrator(_build_stage_pools([[r0, r1]]))
+    try:
+        pool = orchestrator.stage_pools[0]
+        _register_request(orchestrator, "req-dead")
+        _register_request(orchestrator, "req-live")
+        assert pool.select_replica_id("req-dead") == 0
+        assert pool.select_replica_id("req-live") == 1
+
+        async def _die() -> None:
+            raise EngineDeadError("stage-0 replica-0 died on submit")
+
+        failed = await orchestrator._dispatch_or_fail_request(
+            _die, req_id="req-dead", stage_id=0, operation="add_request"
+        )
+        assert failed is False
+
+        # The dead replica is evicted; only the request on it fails, the survivor
+        # and its request are untouched.
+        assert pool.live_replica_ids() == [1]
+        assert r0.shutdown_calls == 1
+        assert r1.shutdown_calls == 0
+        msg = queues[1].async_q.get_nowait()
+        assert isinstance(msg, ErrorMessage) and msg.fatal is True and msg.request_id == "req-dead"
+        assert queues[1].async_q.empty()
+        assert "req-dead" not in orchestrator.request_states
+        assert "req-live" in orchestrator.request_states and pool.get_bound_replica_id("req-live") == 1
+
+        # The point of eviction: a burst of subsequent requests all route to the
+        # survivor and never land back on the evicted replica.
+        for rid in ("next-1", "next-2", "next-3"):
+            assert pool.select_replica_id(rid) == 1
+    finally:
+        for q in queues:
+            q.close()
+
+
+@pytest.mark.asyncio
+async def test_dispatch_death_evicts_so_subsequent_requests_fast_fail() -> None:
+    """When the replica dying on submit was the stage's only one, the guard evicts
+    it (leaving the stage empty). A subsequent request then fast-fails at routing —
+    the ``_handle_add_request`` entry guard turns ``StageUnavailableError`` into a
+    fatal 5xx immediately, rather than dispatching onto the dead replica (#4285).
+    """
+    r0 = FakeStageClient(stage_type="llm")
+    orchestrator, queues = _build_bare_orchestrator(_build_stage_pools([[r0]]))
+    try:
+        pool = orchestrator.stage_pools[0]
+        _register_request(orchestrator, "req-a")
+        assert pool.select_replica_id("req-a") == 0
+
+        async def _die() -> None:
+            raise EngineDeadError("stage-0 replica-0 died on submit")
+
+        failed = await orchestrator._dispatch_or_fail_request(_die, req_id="req-a", stage_id=0, operation="add_request")
+        assert failed is False
+
+        assert pool.live_replica_ids() == []
+        assert r0.shutdown_calls == 1
+        msg = queues[1].async_q.get_nowait()
+        assert isinstance(msg, ErrorMessage) and msg.request_id == "req-a"
+        assert queues[1].async_q.empty()
+        assert "req-a" not in orchestrator.request_states
+
+        # A subsequent request cannot be routed at all: the stage has no live
+        # replica, so selection raises fast instead of dispatching onto the dead
+        # slot. _handle_add_request converts this to a fatal 5xx at its entry guard.
+        with pytest.raises(StageUnavailableError):
+            pool.select_replica_id("req-next")
+    finally:
+        for q in queues:
+            q.close()
+
+
 # ───────── Diffusion stage error output routing ─────────


@@ -223,6 +223,17 @@ def test_client(mock_async_diffusion):
    return TestClient(app)


+@pytest.fixture
+def lingbot_test_client(test_client):
+    test_client.app.state.stage_configs = [
+        SimpleNamespace(
+            stage_type="diffusion",
+            engine_args={"model_class_name": "LingBotVideoPipeline"},
+        )
+    ]
+    return test_client
+
+
@pytest.fixture
 def async_omni_test_client():
    """Create test client with mocked AsyncOmni engine."""
@@ -3602,6 +3602,63 @@ class TestGLMTTSServing:
        load_tokenizer.assert_called_once()


+@pytest.fixture
+def ming_flash_omni_tts_server(mocker: MockerFixture):
+    mocker.patch.object(OmniOpenAIServingSpeech, "_load_supported_speakers", return_value=set())
+    mocker.patch.object(OmniOpenAIServingSpeech, "_load_codec_frame_rate", return_value=None)
+
+    mock_engine_client = mocker.MagicMock()
+    mock_engine_client.errored = False
+    mock_engine_client.model_config = mocker.MagicMock(
+        model="inclusionAI/Ming-omni-tts-0.5B",
+    )
+    mock_engine_client.default_sampling_params_list = [
+        SimpleNamespace(max_tokens=2048, min_tokens=None, extra_args=None)
+    ]
+    mock_engine_client.tts_batch_max_items = 32
+    mock_engine_client.generate = mocker.MagicMock(return_value="generator")
+    mock_engine_client.stage_configs = [
+        SimpleNamespace(
+            engine_args=SimpleNamespace(model_stage="ming_tts"),
+            tts_args={},
+        )
+    ]
+
+    mock_models = mocker.MagicMock()
+    mock_models.is_base_model.return_value = True
+
+    return OmniOpenAIServingSpeech(
+        engine_client=mock_engine_client,
+        models=mock_models,
+        request_logger=mocker.MagicMock(),
+    )
+
+
+class TestMingFlashOmniTTSServing:
+    def test_validate_ming_flash_omni_tts_rejects_ref_text(self, ming_flash_omni_tts_server):
+        request = OpenAICreateSpeechRequest(input="Hello", ref_text="Reference transcript")
+        error = ming_flash_omni_tts_server._validate_tts_request(request)
+        assert error is not None
+        assert "ref_text" in error
+
+    def test_validate_ming_flash_omni_tts_rejects_ref_audio(self, ming_flash_omni_tts_server):
+        request = OpenAICreateSpeechRequest(input="Hello", ref_audio="data:audio/wav;base64,abc")
+        error = ming_flash_omni_tts_server._validate_tts_request(request)
+        assert error is not None
+        assert "ref_audio" in error
+
+    def test_ming_flash_omni_tts_adapter_builds_prompt(self, ming_flash_omni_tts_server, mocker: MockerFixture):
+        ming_flash_omni_tts_server._build_ming_flash_omni_prompt = mocker.MagicMock(
+            return_value={
+                "prompt_token_ids": [1, 2, 3],
+                "additional_information": {"voice": ["test"]},
+            }
+        )
+        request = OpenAICreateSpeechRequest(input="Hello", voice="test")
+        asyncio.run(ming_flash_omni_tts_server._prepare_speech_generation(request))
+        ming_flash_omni_tts_server._build_ming_flash_omni_prompt.assert_called_once()
+
+
 class TestTTSAsyncOffloading:
    """Tests for event-loop-safe offloading of blocking TTS operations."""

@@ -63,12 +63,6 @@ def test_resolve_unknown_returns_none():
    assert resolve_adapter(None) is None


-def test_ming_flash_omni_not_migrated():
-    """ming_flash_omni is intentionally excluded from the adapter migration in
-    this PR; it stays on the legacy inline dispatch in serving_speech.py."""
-    assert resolve_adapter("ming_flash_omni_tts") is None
-
-
 def test_voxcpm2_resolves():
    """VoxCPM2 (the served ``latent_generator`` model) resolves cleanly.

@@ -28,6 +28,7 @@ from vllm_omni.entrypoints.openai.api_server import router
 from vllm_omni.entrypoints.openai.protocol.videos import (
    VideoGenerationRequest,
    VideoGenerationStatus,
+    VideoParams,
    VideoResponse,
 )
 from vllm_omni.entrypoints.openai.serving_video import OmniOpenAIServingVideo
@@ -440,6 +441,75 @@ def test_i2v_video_generation_resizes_input_to_requested_dimensions(test_client,
    assert input_image.size == (96, 64)


+def test_i2v_extra_params_dimensions_preserve_input_image_geometry(test_client, mocker: MockerFixture):
+    image_bytes = _make_test_image_bytes((48, 48))
+    mocker.patch(
+        "vllm_omni.entrypoints.openai.serving_video._encode_video_bytes",
+        return_value=b"fake-video",
+    )
+    response = test_client.post(
+        "/v1/videos",
+        data={
+            "prompt": "A bear playing with yarn.",
+            "extra_params": json.dumps({"width": 96, "height": 64}),
+        },
+        files={"input_reference": ("input.png", image_bytes, "image/png")},
+    )
+
+    assert response.status_code == 200
+    video_id = response.json()["id"]
+    _wait_for_status(test_client, video_id, VideoGenerationStatus.COMPLETED.value)
+
+    engine = test_client.app.state.openai_serving_video._engine_client
+    input_image = engine.captured_prompt["multi_modal_data"]["image"]
+    assert isinstance(input_image, Image.Image)
+    assert input_image.size == (48, 48)
+    sampling_params = engine.captured_sampling_params_list[0]
+    assert sampling_params.extra_args["width"] == 96
+    assert sampling_params.extra_args["height"] == 64
+
+
+@pytest.mark.parametrize(
+    ("generation_request", "expected_num_frames", "expected_duration"),
+    [
+        (
+            VideoGenerationRequest(prompt="top-level frames", seconds="5", num_frames=9),
+            9,
+            5.0,
+        ),
+        (
+            VideoGenerationRequest(prompt="nested frames", video_params=VideoParams(num_frames=9)),
+            9,
+            None,
+        ),
+        (
+            VideoGenerationRequest(prompt="seconds only", seconds="5"),
+            120,
+            5.0,
+        ),
+    ],
+)
+def test_video_generation_bridges_request_fields(generation_request, expected_num_frames, expected_duration):
+    engine = FakeAsyncOmni()
+    handler = OmniOpenAIServingVideo.for_diffusion(
+        diffusion_engine=engine,
+        model_name="Wan-AI/Wan2.2-T2V-A14B-Diffusers",
+    )
+
+    asyncio.run(handler._run_and_extract(generation_request, "field-bridge"))
+
+    sampling = engine.captured_sampling_params_list[0]
+    # Top-level ``seconds`` bridges into extra_args["duration"]; num_frames is
+    # passed through (or derived as seconds x fps when omitted). No private
+    # provenance channel is injected.
+    assert "_vllm_request_context" not in sampling.extra_args
+    assert sampling.num_frames == expected_num_frames
+    if expected_duration is None:
+        assert "duration" not in sampling.extra_args
+    else:
+        assert sampling.extra_args["duration"] == expected_duration
+
+
 def test_i2v_video_generation_with_image_reference_form(test_client, mocker: MockerFixture):
    mocker.patch(
        "vllm_omni.entrypoints.openai.serving_video._encode_video_bytes",
@@ -3,6 +3,7 @@ import re
 from types import SimpleNamespace

 import pytest
+from vllm.lora.request import LoRARequest
 from vllm.sampling_params import RequestOutputKind, SamplingParams

 from tests.helpers.mark import hardware_test
@@ -21,10 +22,13 @@ async def _noop(*args, **kw):
    pass


-def get_fake_add_request(submitted_request_ids):
+def get_fake_add_request(submitted_request_ids, submitted_lora_requests=None):
    async def fake_add_request_async(*, request_id, prompt, sampling_params_list, final_stage_id, **kwargs):
+        lora_request = kwargs.get("lora_request")
        del prompt, sampling_params_list, final_stage_id, kwargs
        submitted_request_ids.append(request_id)
+        if submitted_lora_requests is not None:
+            submitted_lora_requests.append(lora_request)

    return fake_add_request_async

@@ -92,6 +96,37 @@ def test_generate_submits_randomized_id_to_engine():
    asyncio.run(run())


+@pytest.mark.cpu
+def test_generate_forwards_lora_request_to_engine():
+    """Ensure the lora_request passed to generate() reaches add_request_async.
+
+    Regression test for https://github.com/vllm-project/vllm-omni/issues/5369:
+    AsyncOmni.generate() accepted lora_request but silently dropped it when
+    submitting the request, so generation always used the base model.
+    """
+
+    async def run():
+        submitted_ids = []
+        submitted_loras = []
+        omni = get_async_omni_instance(fake_add_request=get_fake_add_request(submitted_ids, submitted_loras))
+
+        lora = LoRARequest(lora_name="test", lora_int_id=1, lora_path="/tmp/fake")
+        async for _ in omni.generate(
+            prompt={"prompt": "test"},
+            request_id="lora-req",
+            sampling_params_list=[SimpleNamespace()],
+            output_modalities=["text"],
+            lora_request=lora,
+        ):
+            pass
+
+        assert len(submitted_ids) == 1
+        assert len(submitted_loras) == 1
+        assert submitted_loras[0] is lora
+
+    asyncio.run(run())
+
+
@pytest.mark.cpu
@pytest.mark.parametrize(
    "req_ids,cancel_prefix,expected_cancel_count",
@@ -389,6 +389,24 @@ def test_serve_cli_accepts_request_batch_max_wait_ms():
    assert stage_cfg["engine_args"]["request_batch_max_wait_ms"] == 250.0


+@pytest.mark.parametrize("bad_wait", ["nan", "inf", "-inf", "-1"])
+def test_serve_cli_rejects_invalid_request_batch_max_wait_ms(bad_wait: str):
+    parser = TrackingArgumentParser()
+    subparsers = parser.add_subparsers(dest="command")
+    OmniServeCommand().subparser_init(subparsers)
+
+    with pytest.raises(SystemExit):
+        parser.parse_args(
+            [
+                "serve",
+                "Qwen/Qwen-Image",
+                "--omni",
+                "--request-batch-max-wait-ms",
+                bad_wait,
+            ]
+        )
+
+
 def test_serve_cli_accepts_additional_config():
    """Ensure diffusion serve CLI exposes additional_config and forwards it to stage config."""
    parser = TrackingArgumentParser()
@@ -104,6 +104,9 @@ class FakeAsyncOmniEngine:
        ]
        self.supported_tasks = ("generate",)
        self.stage_clients = [SimpleNamespace(is_comprehension=False) for _ in range(self.num_stages)]
+        # One replica per stage, sharing the stage_clients objects so a test that
+        # marks stage_clients[i] dead is reflected in the pool's liveness.
+        self.stage_pools = [_FakeStagePool([client], stage_id=i) for i, client in enumerate(self.stage_clients)]
        self.stage_vllm_configs = [None for _ in range(self.num_stages)]
        self.output_processors = [SimpleNamespace(tokenizer=None) for _ in range(self.num_stages)]
        self.input_processor = None
@@ -969,11 +972,17 @@ async def test_async_omni_propagates_non_400_client_error_status(
    assert str(exc_info.value) == error_text


+class _FakeStagePool:
+    def __init__(self, clients: list, stage_id: int = 0):
+        self.clients = clients
+        self.stage_id = stage_id
+
+
 def test_async_omni_errored_property_alive():
    omni = object.__new__(AsyncOmni)
    omni.engine = SimpleNamespace(
        is_alive=lambda: True,
-        stage_clients=[SimpleNamespace(is_comprehension=False)],
+        stage_pools=[_FakeStagePool(clients=[SimpleNamespace()])],
    )

    assert omni.errored is False
@@ -983,21 +992,47 @@ def test_async_omni_errored_property_dead_engine():
    omni = object.__new__(AsyncOmni)
    omni.engine = SimpleNamespace(
        is_alive=lambda: False,
-        stage_clients=[SimpleNamespace(is_comprehension=False)],
+        stage_pools=[_FakeStagePool(clients=[SimpleNamespace()])],
    )

    assert omni.errored is True


-def test_async_omni_errored_property_dead_stage():
+def test_async_omni_errored_false_when_stage_dead():
+    # errored is process-fatal only: a dead stage must not trip the serving
+    # precheck, or requests that never touch that stage would be rejected too.
+    # Stage liveness surfaces via check_health / per-request dispatch failures.
    omni = object.__new__(AsyncOmni)
-    dead_stage = SimpleNamespace(is_comprehension=False, _engine_dead=True)
    omni.engine = SimpleNamespace(
        is_alive=lambda: True,
-        stage_clients=[dead_stage],
+        stage_pools=[_FakeStagePool(clients=[SimpleNamespace(_engine_dead=True)])],
    )

-    assert omni.errored is True
+    assert omni.errored is False
+
+
+def test_async_omni_errored_false_when_a_replica_survives():
+    # Per-replica fault isolation (#4285): one replica dies, another lives →
+    # the stage is still serving, so the engine is not errored.
+    omni = object.__new__(AsyncOmni)
+    omni.engine = SimpleNamespace(
+        is_alive=lambda: True,
+        stage_pools=[_FakeStagePool(clients=[SimpleNamespace(_engine_dead=True), SimpleNamespace()])],
+    )
+
+    assert omni.errored is False
+
+
+def test_async_omni_errored_false_when_evicted_replica_leaves_stage_empty():
+    # Even a fully evicted stage (all slots None/dead) is not process-fatal;
+    # only orchestrator death makes errored True.
+    omni = object.__new__(AsyncOmni)
+    omni.engine = SimpleNamespace(
+        is_alive=lambda: True,
+        stage_pools=[_FakeStagePool(clients=[None, SimpleNamespace(_engine_dead=True)])],
+    )
+
+    assert omni.errored is False


 def _enqueue_stage_error(
@@ -1067,20 +1102,22 @@ async def test_async_omni_propagates_engine_generate_error(monkeypatch: pytest.M

 def test_check_health_passes_when_all_healthy():
    base = _make_base()
-    healthy_stage = MagicMock()
-    healthy_stage.check_health = MagicMock()
    base.engine.is_alive.return_value = True
-    base.engine.stage_clients = [healthy_stage]
+    healthy_stage = SimpleNamespace(check_health=lambda: None)
+    base.engine.stage_pools = [_FakeStagePool([healthy_stage])]
    base.check_health()  # should not raise


 def test_check_health_raises_when_stage_dead():
    base = _make_base()
-    dead_stage = MagicMock()
-    dead_stage.check_health = MagicMock(side_effect=EngineDeadError("Stage-1 dead"))
    base.engine.is_alive.return_value = True
-    base.engine.stage_clients = [dead_stage]
-    with pytest.raises(EngineDeadError, match="Stage-1 dead"):
+
+    def _raise_dead() -> None:
+        raise EngineDeadError("Stage-1 engine core is dead")
+
+    dead_stage = SimpleNamespace(check_health=_raise_dead)
+    base.engine.stage_pools = [_FakeStagePool([dead_stage], stage_id=1)]
+    with pytest.raises(EngineDeadError, match="Stage-1"):
        base.check_health()


@@ -1098,22 +1135,25 @@ def test_check_health_raises_when_orchestrator_dead():
 def test_omni_base_errored_false_when_alive():
    base = _make_base()
    base.engine.is_alive.return_value = True
-    base.engine.stage_clients = [SimpleNamespace()]
+    base.engine.stage_pools = [_FakeStagePool([SimpleNamespace()])]
    assert base.errored is False


-def test_omni_base_is_running_false_when_stage_engine_dead():
+def test_omni_base_is_running_true_when_stage_engine_dead():
+    # is_running tracks the orchestrator only; a dead stage must not make
+    # `errored and not is_running` true, which would let terminate_if_errored
+    # kill the server on a partial failure.
    base = _make_base()
    base.engine.is_alive.return_value = True
-    base.engine.stage_clients = [SimpleNamespace(_engine_dead=True)]
-    assert base.is_running is False
+    base.engine.stage_pools = [_FakeStagePool([SimpleNamespace(_engine_dead=True)])]
+    assert base.is_running is True


-def test_omni_base_is_running_false_when_stage_resources_engine_dead():
+def test_omni_base_is_running_true_when_stage_resources_engine_dead():
    base = _make_base()
    base.engine.is_alive.return_value = True
-    base.engine.stage_clients = [SimpleNamespace(resources=SimpleNamespace(engine_dead=True))]
-    assert base.is_running is False
+    base.engine.stage_pools = [_FakeStagePool([SimpleNamespace(resources=SimpleNamespace(engine_dead=True))])]
+    assert base.is_running is True


 def test_omni_base_errored_true_when_orchestrator_dead():
@@ -1123,20 +1163,22 @@ def test_omni_base_errored_true_when_orchestrator_dead():
    assert base.errored is True


-def test_omni_base_errored_true_when_stage_engine_dead():
+def test_omni_base_errored_false_when_stage_engine_dead():
+    # Process-fatal only: the serving precheck reads errored before routing,
+    # so a dead stage here would reject requests that never touch it.
    base = _make_base()
    base.engine.is_alive.return_value = True
    dead_stage = SimpleNamespace(_engine_dead=True)
-    base.engine.stage_clients = [dead_stage]
-    assert base.errored is True
+    base.engine.stage_pools = [_FakeStagePool([dead_stage])]
+    assert base.errored is False


-def test_omni_base_errored_true_when_stage_resources_engine_dead():
+def test_omni_base_errored_false_when_stage_resources_engine_dead():
    base = _make_base()
    base.engine.is_alive.return_value = True
    dead_stage = SimpleNamespace(resources=SimpleNamespace(engine_dead=True))
-    base.engine.stage_clients = [dead_stage]
-    assert base.errored is True
+    base.engine.stage_pools = [_FakeStagePool([dead_stage])]
+    assert base.errored is False


 # ───────── Omni (sync) EngineDeadError / EngineGenerateError ─────────
@@ -1202,13 +1244,14 @@ def test_omni_errored_property_dead_engine(monkeypatch: pytest.MonkeyPatch):


 def test_omni_errored_property_dead_stage(monkeypatch: pytest.MonkeyPatch):
-    """Omni.errored returns True when a stage client is marked dead."""
+    """Omni.errored stays False when only a stage client dies (process-fatal
+    semantics): the request-level failure and check_health carry the signal."""
    engine = FakeAsyncOmniEngine(stage_metadata=THREE_STAGE_META)
    _patch_engine(monkeypatch, engine)

    app = Omni("dummy-model")
    try:
        engine.stage_clients[0]._engine_dead = True
-        assert app.errored is True
+        assert app.errored is False
    finally:
        app.shutdown()
@@ -12,6 +12,7 @@ from typing import TYPE_CHECKING, Any, Literal
 if TYPE_CHECKING:
    from tests.helpers.runtime import DiffusionResponse

+import av
 import numpy as np
 import soundfile as sf
 from PIL import Image
@@ -19,6 +20,7 @@ from PIL import Image
 from tests.helpers.media import (
    convert_audio_bytes_to_text,
    cosine_similarity_text,
+    decode_b64_image,
    preprocess_text,
 )

@@ -112,6 +114,34 @@ def assert_image_diffusion_response(
                    )


+def assert_images_generations_response(
+    response: dict[str, Any],
+    request_config: dict[str, Any],
+    run_level: str | None = None,
+) -> None:
+    """Validate a successful ``/v1/images/generations`` JSON response."""
+    del run_level
+    request_body = request_config.get("json") or {}
+    data = response.get("data")
+    assert isinstance(data, list), "Image generation response is missing data[]"
+
+    expected_count = int(request_body.get("n", 1))
+    assert len(data) == expected_count, f"Expected {expected_count} images, got {len(data)}"
+
+    width = height = None
+    size = request_body.get("size")
+    if isinstance(size, str) and "x" in size:
+        width_value, height_value = size.lower().split("x", 1)
+        width, height = int(width_value), int(height_value)
+
+    for item in data:
+        assert isinstance(item, dict), "Image generation data entries must be objects"
+        b64_json = item.get("b64_json")
+        assert isinstance(b64_json, str) and b64_json, "Image generation response is missing b64_json"
+        image = decode_b64_image(b64_json)
+        assert_image_valid(image, width=width, height=height)
+
+
 def assert_video_diffusion_response(
    response: "DiffusionResponse",
    request_config: dict[str, Any],
@@ -158,6 +188,29 @@ def assert_video_diffusion_response(
        )


+def assert_video_first_frame_matches(
+    video: Path | bytes | BytesIO,
+    expected: np.ndarray,
+    *,
+    max_mean_absolute_error: float,
+) -> None:
+    """Assert that an encoded video's first frame matches a reference image."""
+    if isinstance(video, Path):
+        source: str | BytesIO = str(video)
+    else:
+        video_bytes = video if isinstance(video, bytes) else video.getvalue()
+        source = BytesIO(video_bytes)
+
+    with av.open(source) as container:
+        first_frame = next(container.decode(video=0)).to_ndarray(format="rgb24")
+
+    assert first_frame.shape == expected.shape
+    mean_absolute_error = float(np.abs(first_frame.astype(np.float32) - expected).mean() / 255.0)
+    assert mean_absolute_error < max_mean_absolute_error, (
+        f"Expected first-frame MAE < {max_mean_absolute_error}, got {mean_absolute_error:.6f}."
+    )
+
+
 def assert_audio_diffusion_response(
    response: "DiffusionResponse",
    request_config: dict[str, Any],
@@ -41,6 +41,7 @@ from tests.helpers.assertions import (
    assert_audio_speech_response,
    assert_diffusion_response,
    assert_http_error,
+    assert_images_generations_response,
    assert_omni_response,
 )
 from tests.helpers.env import run_post_test_cleanup, run_pre_test_cleanup
@@ -1504,6 +1505,11 @@ class OpenAIClientHandler:
            err_code=cfg.get("err_code"),
            err_message=cfg.get("err_message"),
        )
+        if cfg.get("err_code") is None:
+            assert resp.success, resp.error_message
+            payload = resp.json_body
+            assert isinstance(payload, dict)
+            assert_images_generations_response(payload, cfg, run_level=self.run_level)
        return [resp]

    def send_images_edits_http_request(
@@ -83,11 +83,16 @@ class SyntheticAcousticTransformer(nn.Module):
        # time_embedding: (B, 1) -> (B, dim)
        self.time_embedding = nn.Linear(1, HIDDEN_DIM, bias=False)

-        # _predict_velocity: takes x_t (B, C), llm_output (B, D), t_emb (B, D) -> (B, C)
+        # Identities: callers pass pre-projected inputs; the synthetic transformer folds
+        # everything into _velocity_proj.
+        self.llm_projection = nn.Identity()
+        self.time_projection = nn.Identity()
+
+        # _predict_velocity: takes x_t (B, C), llm_proj (B, D), t_proj (B, D) -> (B, C)
        self._velocity_proj = nn.Linear(N_ACOUSTIC_CODEBOOK + HIDDEN_DIM + HIDDEN_DIM, N_ACOUSTIC_CODEBOOK, bias=False)

-    def _predict_velocity(self, x_t: torch.Tensor, llm_output: torch.Tensor, t_emb: torch.Tensor) -> torch.Tensor:
-        combined = torch.cat([x_t, llm_output, t_emb], dim=-1)
+    def _predict_velocity(self, x_t: torch.Tensor, llm_proj: torch.Tensor, t_proj: torch.Tensor) -> torch.Tensor:
+        combined = torch.cat([x_t, llm_proj, t_proj], dim=-1)
        return self._velocity_proj(combined)


@@ -129,14 +134,18 @@ class SyntheticModel(nn.Module):
        hidden_zero = torch.zeros_like(hidden_states)
        timesteps = torch.linspace(0, 1, 16, device=hidden_states.device, dtype=hidden_states.dtype)

+        # llm_projection is constant across steps so hoist it (mirrors decode_one_frame).
+        llm_batched = torch.cat([hidden_states, hidden_zero], dim=0)
+        llm_proj_batched = at.llm_projection(llm_batched)
+
        for i in range(len(timesteps) - 1):
            t = timesteps[i]
            dt = timesteps[i + 1] - timesteps[i]
            t_emb = at.time_embedding(t.view(-1, 1).repeat(B, 1)).to(hidden_states.dtype)
+            t_proj = at.time_projection(t_emb)
            x_batched = torch.cat([x, x], dim=0)
-            llm_batched = torch.cat([hidden_states, hidden_zero], dim=0)
-            t_emb_batched = t_emb.repeat(2, 1)
-            v_all = at._predict_velocity(x_t=x_batched, llm_output=llm_batched, t_emb=t_emb_batched)
+            t_proj_batched = t_proj.repeat(2, 1)
+            v_all = at._predict_velocity(x_t=x_batched, llm_proj=llm_proj_batched, t_proj=t_proj_batched)
            v_t, uncond_v_t = v_all[:B], v_all[B:]
            cfg_alpha = 1.2
            v_t = cfg_alpha * v_t + (1 - cfg_alpha) * uncond_v_t
@@ -112,6 +112,90 @@ def test_sensenova_extra_registry_declares_request_and_response_params() -> None
    assert should_init_extra_args_for_non_diffusion_stages("SenseNovaU1Pipeline") is False


+@pytest.mark.core_model
+@pytest.mark.cpu
+def test_lingbot_extra_registry_declares_request_params() -> None:
+    assert get_extra_body_params("LingBotVideoPipeline") == frozenset(
+        {
+            "batch_cfg",
+            "duration",
+            "flow_shift",
+            "negative_prompt",
+            "null_cond_clone_zero",
+            "offload_vae_during_denoise",
+            "output_type",
+            "refiner_sigma_tail_steps",
+            "resolution",
+            "ratio",
+            "shift",
+            "t_thresh",
+        }
+    )
+    assert get_extra_output_params("LingBotVideoPipeline") == frozenset()
+    assert should_init_extra_args_for_non_diffusion_stages("LingBotVideoPipeline") is False
+
+
+@pytest.mark.core_model
+@pytest.mark.cpu
+def test_lingbot_text_to_image_prompt_builder_preserves_empty_negative_prompt() -> None:
+    assert build_text_to_image_prompt(
+        "LingBotVideoPipeline",
+        prompt="a red fox in fresh snow",
+        negative_prompt="",
+        height=192,
+        width=320,
+    ) == {
+        "prompt": "a red fox in fresh snow",
+        "modalities": ["image"],
+        "negative_prompt": "",
+    }
+
+
+@pytest.mark.core_model
+@pytest.mark.cpu
+def test_lingbot_image_to_video_prompt_builder() -> None:
+    image = Image.new("RGB", (320, 192), "red")
+    result = build_image_to_video_prompt(
+        "LingBotVideoPipeline",
+        prompt="the fox looks toward the camera",
+        negative_prompt=None,
+        media_inputs={"image": image},
+        height=192,
+        width=320,
+        num_frames=9,
+    )
+    assert result == {
+        "prompt": "the fox looks toward the camera",
+        "modalities": ["video"],
+        "multi_modal_data": {"image": image},
+        "num_frames": 9,
+    }
+
+
+@pytest.mark.core_model
+@pytest.mark.cpu
+@pytest.mark.parametrize(
+    "media_inputs",
+    [
+        {},
+        {"image": "input.png"},
+        {"image": [Image.new("RGB", (16, 16))]},
+        {"image": Image.new("RGB", (16, 16)), "video": b"video"},
+    ],
+    ids=["missing", "path", "multiple", "extra-modality"],
+)
+def test_lingbot_image_to_video_prompt_builder_rejects_invalid_media(
+    media_inputs: dict[str, object],
+) -> None:
+    with pytest.raises(ValueError, match="exactly one PIL image"):
+        build_image_to_video_prompt(
+            "LingBotVideoPipeline",
+            prompt="move",
+            negative_prompt=None,
+            media_inputs=media_inputs,
+        )
+
+
@pytest.mark.core_model
@pytest.mark.cpu
@pytest.mark.parametrize("pipeline_name", ["Cosmos3OmniDiffusersPipeline", "Cosmos3OmniPipeline"])
@@ -31,8 +31,8 @@ SERVING_SPEECH = Path("vllm_omni/entrypoints/openai/serving_speech.py")
 ADAPTERS_INIT = Path("vllm_omni/entrypoints/openai/tts_adapters/__init__.py")

 # Budgets. These may only go DOWN. See the module docstring.
-MAX_MODEL_TYPE_BRANCHES = 29
-MAX_LEGACY_DETECTORS = 1
+MAX_MODEL_TYPE_BRANCHES = 27
+MAX_LEGACY_DETECTORS = 0

 _GUIDANCE = """
 Move the per-model behaviour into its adapter under
@@ -123,18 +123,29 @@ def _legacy_detectors(tree: ast.AST) -> list[tuple[int, str]]:
    return found


-def _check(label: str, path: Path, actual: int, budget: int, sample: list[str], budget_name: str) -> list[str]:
+def _check(
+    label: str,
+    path: Path,
+    actual: int,
+    budget: int,
+    sample: list[str],
+    budget_name: str,
+    notices: list[str],
+) -> list[str]:
    if actual > budget:
        listing = "\n".join(f"      {line}" for line in sample)
        return [
            f"{path}: {actual} {label} exceeds the budget of {budget}.\n{listing}\n\n{_GUIDANCE}\n",
        ]
    if actual < budget:
-        return [
-            f"{path}: {actual} {label}, down from a budget of {budget}. "
-            f"Lower {budget_name} to {actual} in {Path(__file__).relative_to(REPO_ROOT)} "
-            f"so the ratchet holds the new ground.",
-        ]
+        # Progress is never a build failure. Removing branches is the goal, so a
+        # PR that does it must not hand-edit this file to stay green -- nor break
+        # `main` for everyone else when it merges from a stale base.
+        notices.append(
+            f"{path}: {actual} {label}, below the budget of {budget}. "
+            f"Lower {budget_name} to {actual} in "
+            f"{Path(__file__).relative_to(REPO_ROOT)} to hold the new ground."
+        )
    return []


@@ -152,6 +163,7 @@ def main(argv: list[str] | None = None) -> int:
            print(f"check_tts_adapter: expected file not found: {path}", file=sys.stderr)
            return 1

+    notices: list[str] = []
    branches = _model_type_branches(ast.parse(serving_path.read_text()))
    detectors = _legacy_detectors(ast.parse(adapters_path.read_text()))

@@ -162,6 +174,7 @@ def main(argv: list[str] | None = None) -> int:
        MAX_MODEL_TYPE_BRANCHES,
        [f"line {lineno}: {src}" for lineno, src in branches],
        "MAX_MODEL_TYPE_BRANCHES",
+        notices,
    )
    errors += _check(
        "unmigrated model types in LEGACY_TTS_DETECTORS",
@@ -170,8 +183,11 @@ def main(argv: list[str] | None = None) -> int:
        MAX_LEGACY_DETECTORS,
        [f"line {lineno}: {name}" for lineno, name in detectors],
        "MAX_LEGACY_DETECTORS",
+        notices,
    )

+    for notice in notices:
+        print(f"check_tts_adapter: {notice}", file=sys.stderr)
    for error in errors:
        print(f"check_tts_adapter: {error}", file=sys.stderr)
    return 1 if errors else 0
@@ -37,6 +37,7 @@ from vllm_omni.config.stage_config import (
    build_stage_runtime_overrides,
    load_deploy_config,
 )
+from vllm_omni.diffusion.diffusion_kv.config import DiffusionKVCacheMode

 _EXECUTION_TYPE_TO_STAGE_WORKER: dict[StageExecutionType, tuple[StageType, str | None]] = {
    StageExecutionType.LLM_AR: (StageType.LLM, "ar"),
@@ -549,6 +550,7 @@ class _DiffusionConfigProjection:
    cache_backend: str = "none"
    cache_config: Any = field(default_factory=dict)
    enable_cache_dit_summary: bool = False
+    diffusion_kv_mode: DiffusionKVCacheMode = DiffusionKVCacheMode.DENSE_LEGACY
    enable_prompt_embed_cache: bool = False
    prompt_embed_cache_size: int = Field(default=32, ge=1)
    enable_session_state_manager: bool = False
@@ -623,6 +625,7 @@ class _DiffusionConfigProjection:
            build_attention_config,
            parse_kv_cache_skip_selector,
        )
+        from vllm_omni.diffusion.diffusion_kv.config import parse_diffusion_kv_cache_mode
        from vllm_omni.quantization import build_quant_config

        if self.tf_model_config is None:
@@ -684,6 +687,7 @@ class _DiffusionConfigProjection:
                f"got {type(self.diffusion_attention_config)!r}"
            )

+        self.diffusion_kv_mode = parse_diffusion_kv_cache_mode(self.diffusion_kv_mode)
        self.diffusion_kv_cache_skip_step_indices = parse_kv_cache_skip_selector(self.diffusion_kv_cache_skip_steps)
        self.diffusion_kv_cache_skip_layer_indices = parse_kv_cache_skip_selector(self.diffusion_kv_cache_skip_layers)

@@ -20,6 +20,17 @@ class AttentionBackend(ABC):
    # avoid a slower masked-attention plan when tail padding is not semantic.
    supports_prefix_kv_slicing: bool = False

+    @classmethod
+    def supports_packed_mask_free(cls) -> bool:
+        """Whether packed attention never reads attn_mask on this platform.
+
+        When True, models that pack a [real, pad] two-document layout and
+        carry cu_seqlens/max_seqlen in ``AttentionMetadata.extra`` may skip
+        constructing the padding mask entirely. Backends whose mask-free
+        behavior is platform-dependent must check current_omni_platform.
+        """
+        return False
+
    # ``OmniPlatformEnum`` values this backend runs on; None means unrestricted.
    # Platform resolution rejects an explicit selection outside this set, so a
    # hardware-specific backend fails before the model is built.
@@ -121,6 +132,17 @@ class AttentionMetadata:
    #     the packed cu_seqlens tensors.
    #   "valid_kv_length": int — contiguous valid K/V prefix length for a
    #     backend that advertises supports_prefix_kv_slicing.
+    #   "npu_attn_varlen": bool — model opt-in for the NPU packed varlen path
+    #     (TND npu_fusion_attention driven by cu_seqlens, mask never read).
+    #     Requires the [real, pad] two-document packing contract; see
+    #     FlashAttentionImpl._forward_varlen_packed_npu.
+    #   "laser_input_scale": float — model opt-in input pre-scale for the NPU
+    #     ascend_laser_attention path. The kernel stores unscaled QK^T in an
+    #     fp16 workspace, so outlier activations overflow 65504 into NaN rows;
+    #     with this set (>1), q/k/v are divided by the factor before the op,
+    #     the kernel scale_value is multiplied by its square, and the output
+    #     is scaled back (exact for power-of-two factors). Absent means no
+    #     pre-scaling. See FlashAttentionImpl._forward_prefix_kv_slice_npu.

    # Piecewise attention metadata (mixed causal/full masks).
    # full_attn_spans: per-sample [start, end) spans in global coordinates using full attention.
@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project

+import os
 from functools import partial

 import torch
@@ -16,6 +17,7 @@ from vllm_omni.diffusion.attention.backends.utils.piecewise_attn import (
    piecewise_attn,
 )
 from vllm_omni.diffusion.config import get_current_diffusion_config_or_none
+from vllm_omni.platforms import current_omni_platform

 logger = init_logger(__name__)

@@ -24,6 +26,15 @@ class FlashAttentionBackend(AttentionBackend):
    accept_output_buffer: bool = True
    supports_piecewise_spans: bool = True

+    @classmethod
+    def supports_packed_mask_free(cls) -> bool:
+        # CUDA: forward_cuda dispatches the packed cu_seqlens varlen path
+        # before ever reading attn_mask. NPU: forward_fa_npu honors the
+        # npu_attn_varlen opt-in and rebuilds the padding mask itself if the
+        # packed contract fails. XPU reads attn_mask, so models must keep
+        # constructing it there.
+        return current_omni_platform.is_cuda() or current_omni_platform.is_npu()
+
    @classmethod
    def supports_attention_mask(cls) -> bool:
        return True
@@ -415,7 +426,36 @@ class FlashAttentionImpl(AttentionImpl):
                "For installation details, see https://gitcode.com/Ascend/MindIE-SD"
                "Otherwise, use SDPA backend by setting DIFFUSION_ATTENTION_BACKEND=TORCH_SDPA"
            )
+        # Opt-in mask-free paths (mirror the CUDA cu_seqlens behavior): the
+        # model marks extra["npu_attn_varlen"] and carries packed metadata, so
+        # the padding document is excluded without reading or materializing
+        # the quadratic full_qk mask.
+        #   - default: packed varlen via mindiesd attention_forward_varlen;
+        #   - when MINDIE_SD_FA_TYPE=ascend_laser_attention: prefix K/V
+        #     slicing via mindiesd attention_forward (so the env-selected op
+        #     type is honored; the TND varlen op cannot be).
+        extra = attn_metadata.extra if attn_metadata else {}
+        if extra.get("npu_attn_varlen", False):
+            if os.environ.get("MINDIE_SD_FA_TYPE") == "ascend_laser_attention":
+                out = self._forward_prefix_kv_slice_npu(query, key, value, extra)
+            else:
+                out = self._forward_varlen_packed_npu(query, key, value, extra)
+            if out is not None:
+                return out
        attention_mask = attn_metadata.attn_mask if attn_metadata else None
+        if attention_mask is None and extra.get("npu_attn_varlen", False):
+            # Models skip mask construction when this opt-in is set
+            # (FlashAttentionBackend.supports_packed_mask_free). The packed
+            # paths above declined (contract mismatch), so rebuild the padding
+            # mask here to keep the masked fallback correct.
+            used = extra.get("valid_kv_length")
+            if not isinstance(used, int) or not 0 < used <= query.shape[1]:
+                raise ValueError(
+                    "npu_attn_varlen packed metadata is unusable and no attn_mask "
+                    f"was constructed (valid_kv_length={used!r}, seq_len={query.shape[1]}); "
+                    "refusing to run unmasked attention over padding rows."
+                )
+            attention_mask = torch.arange(query.shape[1], device=query.device)[None] < used

        # NPU aclnnFlashAttentionScore requires mask shape to be one of:
        # [B, N, Sq, Skv], [B, 1, Sq, Skv], [1, 1, Sq, Skv], or [Sq, Skv]
@@ -433,3 +473,161 @@ class FlashAttentionImpl(AttentionImpl):
            op_type="fused_attn_score",
            layout=layout,
        )
+
+    def _resolve_packed_seq_npu(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        extra: dict,
+    ) -> tuple[list[int], list[int]] | None:
+        """Resolve packed document boundaries (cumulative end offsets per doc).
+
+        Returns (seq_q, seq_k), e.g. ([used_q, total_q], [used_k, total_k]),
+        or None unless the metadata matches the exact contract MiniMax-H3
+        produces:
+          - q/k/v are [1, T, N, D] (BSND, single packed batch);
+          - cu_seqlens describe a [real, pad] two-document packing, with the
+            padding document as a strict suffix;
+          - max_seqlen_q/k are Python ints equal to the real document length
+            (so boundaries are derived without any device sync).
+        """
+        if self.causal:
+            return None
+        packed_keys = ("cu_seqlens_q", "cu_seqlens_k", "max_seqlen_q", "max_seqlen_k")
+        if any(k not in extra for k in packed_keys):
+            return None
+        cu_q, cu_k = extra["cu_seqlens_q"], extra["cu_seqlens_k"]
+        used_q, used_k = extra["max_seqlen_q"], extra["max_seqlen_k"]
+        if query.shape[0] != 1 or key.shape[0] != 1:
+            return None
+        if not isinstance(used_q, int) or not isinstance(used_k, int):
+            return None
+        total_q, total_k = query.shape[1], key.shape[1]
+        # .shape is host-side metadata: counting documents never syncs.
+        if cu_q.shape[0] != cu_k.shape[0] or cu_q.shape[0] > 3:
+            return None
+        if not (0 < used_q <= total_q) or not (0 < used_k <= total_k):
+            return None
+        if used_q == total_q and used_k == total_k:
+            # No padding document: one full-length document.
+            return [total_q], [total_k]
+        if cu_q.shape[0] == 3 and used_q >= total_q - used_q and used_k >= total_k - used_k:
+            # [real, pad] packing: the real document must be the longer one
+            # (consistent with the max_seqlen naming).
+            return [used_q, total_q], [used_k, total_k]
+        return None
+
+    def _forward_varlen_packed_npu(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        extra: dict,
+    ) -> torch.Tensor | None:
+        """Packed varlen attention on NPU via mindiesd attention_forward_varlen.
+
+        Returns None (caller falls back to the mask path) when the packed
+        contract does not hold; see _resolve_packed_seq_npu.
+        """
+        resolved = self._resolve_packed_seq_npu(query, key, extra)
+        if resolved is None:
+            return None
+        seq_q, seq_k = resolved
+
+        try:
+            from mindiesd import attention_forward_varlen
+        except ImportError:
+            raise ImportError(
+                "FlashAttentionBackend NPU implementation requires MindIE-SD. "
+                "Please install MindIE-SD to enable NPU attention support. "
+                "For installation details, see https://gitcode.com/Ascend/MindIE-SD"
+                "Otherwise, use SDPA backend by setting DIFFUSION_ATTENTION_BACKEND=TORCH_SDPA"
+            )
+        q = query.squeeze(0)  # [T, N, D] == TND
+        k = key.squeeze(0)
+        v = value.squeeze(0)
+        # attention_forward_varlen wants cu_seqlens as host lists of cumulative
+        # offsets and forwards cu[1:] as actual_seq_qlen/actual_seq_kvlen.
+        out = attention_forward_varlen(
+            q,
+            k,
+            v,
+            [0, *seq_q],
+            [0, *seq_k],
+            softmax_scale=self.softmax_scale,
+        )
+        return out.unsqueeze(0)
+
+    def _forward_prefix_kv_slice_npu(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        extra: dict,
+    ) -> torch.Tensor | None:
+        """Mask-free attention by slicing K/V to the valid prefix (zero-copy
+        views), then mindiesd attention_forward without a mask.
+
+        Same numerical contract as the varlen path: the padding document is a
+        strict suffix, so dropping it from K/V is identical to masking it out.
+        Query keeps full length; outputs on padding rows are never consumed
+        downstream. Returns None when the packed contract does not hold.
+
+        Used when MINDIE_SD_FA_TYPE=ascend_laser_attention: the TND varlen op
+        cannot honor the env-selected op type, while attention_forward can.
+
+        The laser kernel stores unscaled S=QK^T in an fp16 GM workspace, so
+        bf16 activations with large outliers overflow 65504 into ±inf and the
+        affected rows turn NaN. Models may opt into exact power-of-two input
+        pre-scaling via extra["laser_input_scale"] (see abstract.py): q/k/v
+        are divided by the factor, the kernel scale_value is multiplied by its
+        square, and the output is scaled back. Absent or invalid factor means
+        no pre-scaling.
+        """
+        resolved = self._resolve_packed_seq_npu(query, key, extra)
+        if resolved is None:
+            return None
+        _, seq_k = resolved
+        used_k = seq_k[0]  # real document length (first cumulative end)
+
+        try:
+            from mindiesd import attention_forward
+        except ImportError:
+            raise ImportError(
+                "FlashAttentionBackend NPU implementation requires MindIE-SD. "
+                "Please install MindIE-SD to enable NPU attention support. "
+                "For installation details, see https://gitcode.com/Ascend/MindIE-SD"
+                "Otherwise, use SDPA backend by setting DIFFUSION_ATTENTION_BACKEND=TORCH_SDPA"
+            )
+        # mindiesd always takes BSND input regardless of `layout`; the arg
+        # selects the op-internal layout and laser only supports BNSD, so do
+        # NOT forward the model's qkv_layout ("BSND" for MiniMax-H3) here.
+        layout = "BNSD"
+        key = key[:, :used_k]
+        value = value[:, :used_k]
+
+        input_scale = extra.get("laser_input_scale")
+        preserve_input_range = isinstance(input_scale, (int, float)) and input_scale > 1
+        if preserve_input_range:
+            query = query / input_scale
+            key = key / input_scale
+            value = value / input_scale
+        scale = self.softmax_scale
+        if preserve_input_range:
+            scale *= input_scale**2
+
+        def _postprocess(out: torch.Tensor) -> torch.Tensor:
+            return out * input_scale if preserve_input_range else out
+
+        return _postprocess(
+            attention_forward(
+                query,
+                key,
+                value,
+                attn_mask=None,
+                opt_mode="manual",
+                op_type="fused_attn_score",  # MINDIE_SD_FA_TYPE env overrides this
+                layout=layout,
+                scale=scale,
+            )
+        )
@@ -128,9 +128,9 @@ class RainFusionAttentionImpl(AttentionImpl):
    whose extent the model publishes as ``AttentionMetadata.video_layout``. Every
    other case — warmup denoise steps, exempt layers, a layer that does not declare
    ``qkv_layout="BSND"``, sequences without a published video segment, video
-    segments too short to pay for block selection or not aligned to the kernel's
-    block size — delegates to FlashAttention, so a model can select this backend
-    unconditionally.
+    segments too short to pay for block selection — delegates to FlashAttention,
+    so a model can select this backend unconditionally. MindIE-SD handles an
+    irregular video tail internally, retaining it outside the sparse blocks.
    """

    def __init__(
@@ -292,25 +292,6 @@ class RainFusionAttentionImpl(AttentionImpl):
                _MIN_VIDEO_BLOCKS,
            )
            return None
-        if video_len % _BLOCK_SIZE != 0:
-            # rf_v2 tiles the sequence into ceil((video + prefix) / 128) blocks but
-            # builds its block mask from the two segments pooled apart. The counts
-            # only agree when the video segment ends on a block boundary; otherwise
-            # a prefix block is forced on out of range, and the block straddling the
-            # seam holds real prefix rows that selection may drop. Padding the
-            # sequence to realign it is not an option because rf_v2 ignores
-            # attn_mask, so pad keys would take a share of every softmax denominator.
-            logger.warning_once(
-                "RAINFUSION_ATTN staying dense: latent grid %s gives %d video rows, which is not a "
-                "multiple of the %d-row kernel block. Choose a geometry whose latent_t * h * w is a "
-                "multiple of %d to enable sparsity.",
-                tuple(latent_shape),
-                video_len,
-                _BLOCK_SIZE,
-                _BLOCK_SIZE,
-            )
-            return None
-
        logger.info_once(
            "RAINFUSION_ATTN active: sparsity=%.2f, start_step=%d, exempt_layers=%d, "
            "latent_grid=%s, prefix_rows=%d, video_rows=%d. Realized sparsity is lower than nominal "
@@ -73,6 +73,34 @@ def enable_sensenova_u1_teacache(pipeline: Any, config: DiffusionCacheConfig) ->
    )


+def enable_minimax_h3_teacache(pipeline: Any, config: DiffusionCacheConfig) -> None:
+    """Enable TeaCache for the calibrated MiniMax-H3 FL2VA partition."""
+    partition = getattr(pipeline, "partition", None)
+    if partition == "ref2va":
+        raise ValueError(
+            "TeaCache only supports the MiniMax-H3 FL2VA partition; Ref2VA TeaCache has not been calibrated"
+        )
+    if partition not in {"fl2va", "combined"}:
+        raise ValueError(f"Unsupported MiniMax-H3 partition for TeaCache: {partition!r}")
+
+    teacache_config = TeaCacheConfig(
+        transformer_type="MiniMaxH3DiTModel",
+        rel_l1_thresh=config.rel_l1_thresh,
+        coefficients=config.coefficients,
+    )
+    apply_teacache_hook(pipeline.transformer, teacache_config)
+
+    if partition == "combined":
+        logger.warning(
+            "TeaCache is enabled only for MiniMax-H3 FL2VA; "
+            "Ref2VA requests on this combined pipeline will run without TeaCache"
+        )
+    logger.info(
+        f"TeaCache applied with rel_l1_thresh={teacache_config.rel_l1_thresh}, "
+        "transformer_class=MiniMaxH3DiTModel, partition=FL2VA"
+    )
+
+
 def enable_flux2_klein_teacache(pipeline: Any, config: DiffusionCacheConfig) -> None:
    """
    Enable TeaCache for Flux2 Klein model.
@@ -96,6 +124,7 @@ CUSTOM_TEACACHE_ENABLERS = {
    "BagelPipeline": enable_bagel_teacache,
    "Flux2KleinPipeline": enable_flux2_klein_teacache,
    "HunyuanImage3Pipeline": enable_hunyuan_image3_teacache,
+    "MiniMaxH3Pipeline": enable_minimax_h3_teacache,
    "SenseNovaU1Pipeline": enable_sensenova_u1_teacache,
 }

@@ -145,7 +174,6 @@ class TeaCacheBackend(CacheBackend):

            # Create TeaCacheConfig from DiffusionCacheConfig with transformer_type
            # Access parameters via attribute access: config.rel_l1_thresh
-            # rel_l1_thresh already has a default value of 0.2 in DiffusionCacheConfig
            try:
                teacache_config = TeaCacheConfig(
                    transformer_type=transformer_type,
@@ -83,6 +83,19 @@ _MODEL_COEFFICIENTS = {
    ],
    # LongCat Image transformer coefficients
    "LongCatImageTransformer2DModel": [652.5980, -424.1615, 84.5526, -4.5923, 0.1694],
+    # MiniMax-H3 FL2VA coefficients.
+    "MiniMaxH3DiTModel": [
+        2.283704065852778e03,
+        -7.775977277886368e02,
+        9.408414741359490e01,
+        -4.232669906169421e00,
+        2.173782527946167e-01,
+    ],
+}
+
+_DEFAULT_REL_L1_THRESH = 0.2
+_MODEL_DEFAULT_REL_L1_THRESH = {
+    "MiniMaxH3DiTModel": 0.17,
 }


@@ -97,10 +110,8 @@ class TeaCacheConfig:

    Args:
        rel_l1_thresh: Threshold for accumulated relative L1 distance. When below threshold,
-            cached residual is reused. Values in [0.1, 0.3] work best:
-            - 0.2: ~1.5x speedup with minimal quality loss
-            - 0.4: ~1.8x speedup with slight quality loss
-            - 0.6: ~2.0x speedup with noticeable quality loss
+            cached residual is reused. If None, uses the model-specific default or 0.2
+            when no model-specific default is registered.
        coefficients: Polynomial coefficients for rescaling L1 distance. If None, uses
            model-specific defaults based on transformer_type.
        transformer_type: Transformer class name (e.g., "QwenImageTransformer2DModel").
@@ -108,14 +119,18 @@ class TeaCacheConfig:
            Defaults to "QwenImageTransformer2DModel".
    """

-    rel_l1_thresh: float = 0.2
+    rel_l1_thresh: float | None = None
    coefficients: list[float] | None = None
    transformer_type: str = "QwenImageTransformer2DModel"

    def __post_init__(self) -> None:
        """Validate and set default coefficients."""
-        if self.rel_l1_thresh <= 0:
-            raise ValueError(f"rel_l1_thresh must be positive, got {self.rel_l1_thresh}")
+        threshold = self.rel_l1_thresh
+        if threshold is None:
+            threshold = _MODEL_DEFAULT_REL_L1_THRESH.get(self.transformer_type, _DEFAULT_REL_L1_THRESH)
+        if threshold <= 0:
+            raise ValueError(f"rel_l1_thresh must be positive, got {threshold}")
+        self.rel_l1_thresh = threshold

        if self.coefficients is None:
            # Use model-specific coefficients, explicitly check if the type exists or not
@@ -1249,6 +1249,152 @@ def extract_sensenova_u1_context(
    )


+def extract_minimax_h3_context(
+    module: nn.Module,
+    **kwargs: Any,
+) -> CacheContext:
+    """Extract cache context for MiniMaxH3DiTModel.
+
+    Mirrors the packed multimodal forward in ``MiniMaxH3DiTModel.forward``:
+    preprocessing via ``_embed``, cacheable ``blocks`` loop, and
+    ``final_layer`` postprocessing with row selection and update masks.
+    """
+    from vllm_omni.diffusion.models.minimax_h3.minimax_h3_transformer import (
+        _BF16_DTYPE,
+        _FORWARD_SUPPORTED_KWARGS,
+        MINIMAX_H3_ADALN_MODALITY_NUM,
+        _modulate_scale_shift,
+        _required_kwarg,
+    )
+
+    if not hasattr(module, "blocks") or len(module.blocks) == 0:
+        raise ValueError("Module must have blocks")
+
+    unexpected = sorted(set(kwargs) - _FORWARD_SUPPORTED_KWARGS)
+    if unexpected:
+        raise TypeError(
+            "MiniMaxH3DiTModel.forward received unexpected kwargs: "
+            f"{unexpected}; supported kwargs: "
+            f"{sorted(_FORWARD_SUPPORTED_KWARGS)}"
+        )
+
+    x = _required_kwarg(kwargs, "x")
+    audio_x = _required_kwarg(kwargs, "audio_x")
+    img_position_ids = _required_kwarg(kwargs, "img_position_ids")
+    unique_timesteps = _required_kwarg(kwargs, "unique_timesteps")
+    inverse_indices = _required_kwarg(kwargs, "inverse_indices").view(-1).to(torch.long)
+    update_mask = _required_kwarg(kwargs, "update_mask")
+    token_tags = _required_kwarg(kwargs, "token_tags").view(-1).to(torch.long)
+    skip_mask_out_condition = bool(kwargs.get("skip_mask_out_condition", False))
+    update_audio_mask = kwargs.get("update_audio_mask")
+
+    text_selected = _required_kwarg(kwargs, "prompt_embeds")
+
+    img_pos = module._pos_ids(_required_kwarg(kwargs, "img_pos_info"), "img_pos_info")
+    audio_pos = module._pos_ids(_required_kwarg(kwargs, "audio_pos_info"), "audio_pos_info")
+    text_pos = module._pos_ids(
+        _required_kwarg(kwargs, "text_pos_info"),
+        "text_pos_info",
+    )
+    infer_out_pos = module._pos_ids(
+        _required_kwarg(kwargs, "img_pos_for_infer_output_info"),
+        "img_pos_for_infer_output_info",
+    )
+
+    psp = _required_kwarg(kwargs, "packed_seq_params")
+    cu_seqlens = module._psp_field(psp, "packed_seq_params", "cu_seqlens_q").to(torch.int32)
+    max_seqlen = int(module._psp_field(psp, "packed_seq_params", "max_seqlen_q"))
+    refiner_psp = _required_kwarg(kwargs, "refiner_packed_seq_params")
+    refiner_cu = module._psp_field(refiner_psp, "refiner_packed_seq_params", "cu_seqlens_q").to(torch.int32)
+    refiner_max = int(module._psp_field(refiner_psp, "refiner_packed_seq_params", "max_seqlen_q"))
+    video_layout = kwargs.get("video_token_layout")
+
+    if x.dim() != 3 or x.shape[0] != 1:
+        raise ValueError(f"x must be [1, S, C], got {list(x.shape)}")
+    seq_len = int(x.shape[1])
+    if token_tags.shape[0] != seq_len:
+        raise ValueError(f"token_tags must cover the full packed sequence ({seq_len}), got {token_tags.shape[0]}.")
+    if inverse_indices.shape[0] != seq_len:
+        raise ValueError(f"inverse_indices must be [{seq_len}], got {list(inverse_indices.shape)}")
+    device = x.device
+
+    rope_freqs = module.rope(img_position_ids).to(device)
+
+    decoder_input, t_emb = module._embed(
+        x=x,
+        audio_x=audio_x,
+        text_embeddings_selected=text_selected,
+        unique_timesteps=unique_timesteps.view(-1).to(device),
+        img_pos=img_pos.to(device),
+        audio_pos=audio_pos.to(device),
+        text_pos=text_pos.to(device),
+        refiner_cu_seqlens=refiner_cu.to(device),
+        refiner_max_seqlen=refiner_max,
+        seq_len=seq_len,
+        device=device,
+    )
+
+    combined_indices = (inverse_indices * MINIMAX_H3_ADALN_MODALITY_NUM + token_tags.clamp(min=0)).to(device)
+    inverse_indices = inverse_indices.to(device)
+    cu_seqlens = cu_seqlens.to(device)
+
+    shift_msa, scale_msa, *_ = module.blocks[0].adaln_proj(t_emb)
+    modulated_hidden = module.blocks[0].norm1(decoder_input)
+    modulated_input = _modulate_scale_shift(
+        modulated_hidden,
+        shift_msa,
+        scale_msa,
+        combined_indices,
+        dtype=_BF16_DTYPE,
+    )
+
+    def run_transformer_blocks() -> tuple[torch.Tensor, ...]:
+        hidden, block_rope, block_combined = module.sp_prepare(
+            decoder_input,
+            rope_freqs,
+            combined_indices,
+        )
+        for block in module.blocks:
+            hidden = block(
+                hidden,
+                t_emb=t_emb,
+                combined_indices=block_combined,
+                rope_freqs=block_rope,
+                cu_seqlens=cu_seqlens,
+                max_seqlen=max_seqlen,
+                packed_total=seq_len,
+                video_layout=video_layout,
+            )
+        hidden = module.sp_gather(hidden)
+        return (hidden,)
+
+    def postprocess(hidden: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        video_logits, audio_logits = module.final_layer(
+            hidden,
+            t_emb=t_emb,
+            inverse_indices=inverse_indices,
+        )
+        video_logits = video_logits.index_select(0, infer_out_pos.to(device))
+        audio_logits = audio_logits.index_select(0, audio_pos.to(device))
+        if not skip_mask_out_condition:
+            mask = update_mask.view(-1).to(device)
+            if mask.shape[0] != video_logits.shape[0]:
+                raise ValueError(f"update_mask length mismatch: {mask.shape[0]} != {video_logits.shape[0]}")
+            video_logits = video_logits * mask.unsqueeze(-1)
+            if update_audio_mask is not None:
+                audio_logits = audio_logits * update_audio_mask.view(-1).unsqueeze(-1)
+        return video_logits, audio_logits
+
+    return CacheContext(
+        modulated_input=modulated_input,
+        hidden_states=decoder_input,
+        encoder_hidden_states=None,
+        temb=t_emb,
+        run_transformer_blocks=run_transformer_blocks,
+        postprocess=postprocess,
+    )
+
+
 # Registry for model-specific extractors
 # Key: Transformer class name
 # Value: extractor function with signature (module, *args, **kwargs) -> CacheContext
@@ -1265,6 +1411,7 @@ EXTRACTOR_REGISTRY: dict[str, Callable] = {
    "LongCatImageTransformer2DModel": extract_longcat_context,
    "FluxTransformer2DModel": extract_flux_context,
    "SenseNovaU1ForCausalLM": extract_sensenova_u1_context,
+    "MiniMaxH3DiTModel": extract_minimax_h3_context,
    # Future models:
    # "CogVideoXTransformer3DModel": extract_cogvideox_context,
 }
@@ -231,7 +231,9 @@ class TeaCacheHook(ModelHook):
        state.accumulated_rel_l1_distance += abs(rescaled_distance)

        # Decision: below threshold = cache, above = compute
-        if state.accumulated_rel_l1_distance < self.config.rel_l1_thresh:
+        rel_l1_thresh = self.config.rel_l1_thresh
+        assert rel_l1_thresh is not None
+        if state.accumulated_rel_l1_distance < rel_l1_thresh:
            return False  # Use cache
        else:
            state.accumulated_rel_l1_distance = 0.0  # Reset accumulator
@@ -23,6 +23,10 @@ from vllm.model_executor.layers.quantization.base_config import (
 )
 from vllm.transformers_utils.repo_utils import get_model_path

+from vllm_omni.diffusion.diffusion_kv.config import (
+    DiffusionKVCacheMode,
+    parse_diffusion_kv_cache_mode,
+)
 from vllm_omni.diffusion.model_metadata import get_diffusion_model_metadata
 from vllm_omni.diffusion.utils.network_utils import is_port_available
 from vllm_omni.errors import client_error_metadata
@@ -412,14 +416,14 @@ class DiffusionCacheConfig:
        >>> # Access via attribute
        >>> print(config.rel_l1_thresh)  # 0.3 (from dict)
        >>> print(config.Fn_compute_blocks)  # 8 (default)
-        >>> # Empty dict uses all defaults
+        >>> # Empty dict defers model-specific defaults to the TeaCache backend
        >>> default_config = DiffusionCacheConfig.from_dict({})
-        >>> print(config.rel_l1_thresh)  # 0.2 (default)
+        >>> print(default_config.rel_l1_thresh)  # None
    """

    # TeaCache parameters [tea_cache only]
-    # Default: 0.2 provides ~1.5x speedup with minimal quality loss (optimal balance)
-    rel_l1_thresh: float = 0.2
+    # None defers to the model-specific TeaCache default (0.2 fallback).
+    rel_l1_thresh: float | None = None
    coefficients: list[float] | None = None  # Uses model-specific defaults if None

    # MagCache parameters [mag_cache only]
@@ -658,6 +662,9 @@ class OmniDiffusionConfig:
    # the string form only).
    engine_backend: str | type = "default"

+    # Local Diffusion KV ownership and cache-layout mode.
+    diffusion_kv_mode: DiffusionKVCacheMode = DiffusionKVCacheMode.DENSE_LEGACY
+
    # Optional override for the diffusion model runner class (import path).
    # Precedence in the worker: this override > the runner declared by the
    # selected engine class (``default_diffusion_model_runner_cls``) > the
@@ -923,10 +930,28 @@ class OmniDiffusionConfig:
            )
        if not isinstance(self.diffusion_compile_dynamic, bool):
            raise TypeError(f"diffusion_compile_dynamic must be a bool, got {type(self.diffusion_compile_dynamic)!r}")
+        self.diffusion_kv_mode = parse_diffusion_kv_cache_mode(self.diffusion_kv_mode)
+
+        if self.omni_kv_config is None:
+            self.omni_kv_config = {}
+        elif isinstance(self.omni_kv_config, Mapping):
+            self.omni_kv_config = dict(self.omni_kv_config)
+        else:
+            raise TypeError("omni_kv_config must be a mapping")
+        if self.diffusion_kv_mode is DiffusionKVCacheMode.PAGED_SCHEDULER and self.omni_kv_config.get(
+            "need_recv_cache", False
+        ):
+            raise ValueError(
+                "paged_scheduler Diffusion KV does not support imported AR KV in Phase 1; "
+                "disable need_recv_cache until connector-aware admission is implemented"
+            )
+
        self.master_port = self._resolve_master_port()
        self.request_batch_max_wait_ms = float(self.request_batch_max_wait_ms or 0.0)
-        if self.request_batch_max_wait_ms < 0:
-            raise ValueError(f"request_batch_max_wait_ms must be non-negative, got {self.request_batch_max_wait_ms}.")
+        if not math.isfinite(self.request_batch_max_wait_ms) or self.request_batch_max_wait_ms < 0:
+            raise ValueError(
+                f"request_batch_max_wait_ms must be a finite non-negative number, got {self.request_batch_max_wait_ms}."
+            )

        if isinstance(self.profiler_config, dict):
            from vllm.config import ProfilerConfig
@@ -310,14 +310,16 @@ class DiffusionEngine:
        diffusion_engine_start_time = time.perf_counter()

        preprocess_time = 0.0
-        if self.pre_process_func is not None:
-            preprocess_start_time = time.perf_counter()
-            request = self.pre_process_func(request)
+        has_preprocessor = getattr(self, "pre_process_func", None) is not None
+        preprocess_start_time = time.perf_counter() if has_preprocessor else None
+        request = self._prepare_request_for_admission(request)
+        if preprocess_start_time is not None:
            preprocess_time = time.perf_counter() - preprocess_start_time
            logger.debug("Pre-processing completed in %.4f seconds", preprocess_time)

        exec_start_time = time.perf_counter()
-        generator = self.async_add_req_and_stream_response(request)
+        request_id = self._add_prepared_request(request)
+        generator = self.get_output_stream(request_id)
        async for output in generator:
            exec_total_time = time.perf_counter() - exec_start_time
            # Async mode: wait for background D2H/SHM to complete.
@@ -432,8 +434,7 @@ class DiffusionEngine:
                    # Only RPC / abort work pending; loop back to drain it.
                    continue

-                if self.supports_request_batch or self.dp_concurrent:
-                    self._wait_for_request_batch_admission_locked()
+                self._wait_for_admission_if_needed_locked()

                sched_output = self.scheduler.schedule()

@@ -467,53 +468,39 @@ class DiffusionEngine:
        # Engine is stopping: fail any RPCs still queued so callers don't hang.
        self._fail_pending_rpcs(RuntimeError("DiffusionEngine is shutting down."))

-    def _wait_for_request_batch_admission_locked(self) -> None:
-        """Wait for compatible requests to accumulate before scheduling a wave.
+    def _wait_for_admission_if_needed_locked(self) -> None:
+        """Apply scheduler admission policy while holding the engine condition.

        Caller must hold ``self._cv``.
        """
-        if self.step_execution or (not self.supports_request_batch and not self.dp_concurrent):
-            return
-
-        max_wait_s = self.od_config.request_batch_max_wait_ms / 1000.0
-        if max_wait_s == 0:
-            return
-
-        max_batch = self.scheduler.max_num_running_reqs
-        waiting = self.scheduler.num_waiting_requests()
-        running = self.scheduler.num_running_requests()
-
-        if running > 0:
-            return
-
        start = time.monotonic()
-        deadline = start + max_wait_s
+        decision = self.scheduler.get_admission_wait_decision(
+            now=start,
+            dp_concurrent=self.dp_concurrent,
+        )
+        if not decision.should_wait:
+            return
+
        last_waiting = -1
        stable_since = start
-        # For dp_concurrent, use a longer stable window so all dp_size
-        # HTTP requests have time to land before scheduling.
-        if self.dp_concurrent:
-            stable_window_s = min(0.3, max_wait_s / 2.0)
-        else:
-            stable_window_s = min(0.05, max_wait_s / 5.0)

        while not self.stop_event.is_set():
            waiting = self.scheduler.num_waiting_requests()
            now = time.monotonic()

-            if waiting >= max_batch:
-                break
-            if waiting > 0 and (now - stable_since) >= stable_window_s:
-                break
-            if now >= deadline:
-                break
-
            if waiting > last_waiting:
                stable_since = now
                last_waiting = waiting

-            remaining = deadline - now
-            self._cv.wait(timeout=min(remaining, 0.002))
+            if self.scheduler.should_end_admission_wait(
+                decision,
+                now=now,
+                stable_since=stable_since,
+            ):
+                break
+
+            remaining = decision.deadline - now if decision.deadline is not None else 0.002
+            self._cv.wait(timeout=min(max(remaining, 0.0), 0.002))

        waited_ms = (time.monotonic() - start) * 1000.0
        final_waiting = self.scheduler.num_waiting_requests()
@@ -521,7 +508,7 @@ class DiffusionEngine:
            logger.info(
                "[RequestBatch] admission wait done waiting=%d max_batch=%d waited_ms=%.1f",
                final_waiting,
-                max_batch,
+                decision.max_batch,
                waited_ms,
            )

@@ -689,7 +676,17 @@ class DiffusionEngine:
        engine.run_startup_warmup()
        return engine

-    def add_request(self, request: OmniDiffusionRequest) -> str:
+    def _prepare_request_for_admission(self, request: OmniDiffusionRequest) -> OmniDiffusionRequest:
+        """Run model-owned preprocessing once, before entering Engine locks."""
+
+        pre_process_func = getattr(self, "pre_process_func", None)
+        if pre_process_func is None:
+            return request
+        return pre_process_func(request)
+
+    def _add_prepared_request(self, request: OmniDiffusionRequest) -> str:
+        """Admit a request whose model-owned preprocessing is complete."""
+
        with self._cv:
            if self._closed:
                raise RuntimeError("DiffusionEngine is closed.")
@@ -700,6 +697,10 @@ class DiffusionEngine:

        return request_id

+    def add_request(self, request: OmniDiffusionRequest) -> str:
+        request = self._prepare_request_for_admission(request)
+        return self._add_prepared_request(request)
+
    async def get_output_stream(self, request_id: str) -> AsyncGenerator[DiffusionOutput, None]:
        with self._cv:
            queue = self._out_streams.get(request_id)
@@ -742,6 +743,7 @@ class DiffusionEngine:
        return final_output

    def add_req_and_wait_for_response(self, request: OmniDiffusionRequest) -> DiffusionOutput:
+        request = self._prepare_request_for_admission(request)
        with self._rpc_lock:
            if self._closed:
                raise RuntimeError("DiffusionEngine is closed.")
@@ -886,8 +888,7 @@ class DiffusionEngine:
            ),
        )
        logger.info("dummy run to warm up the model")
-        request = self.pre_process_func(req) if self.pre_process_func is not None else req
-        output = self.add_req_and_wait_for_response(request)
+        output = self.add_req_and_wait_for_response(req)
        if output.error:
            raise RuntimeError(f"Dummy run failed: {output.error}")

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
@@ -0,0 +1,39 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+from enum import Enum
+
+
+class DiffusionKVCacheMode(str, Enum):
+    """Migration mode for diffusion KV ownership."""
+
+    DENSE_LEGACY = "dense_legacy"
+    # Reserved for the experimental Worker-owned paging design. Production
+    # configuration rejects it until that engine is migrated to the common
+    # Scheduler-owned path.
+    PAGED_WORKER_LOCAL = "paged_worker_local"
+    PAGED_SCHEDULER = "paged_scheduler"
+
+
+def parse_diffusion_kv_cache_mode(value: object) -> DiffusionKVCacheMode:
+    """Parse a selectable Diffusion KV ownership mode.
+
+    ``PAGED_WORKER_LOCAL`` remains in the enum to document the migration
+    topology, but it is not a valid production selection until that runtime is
+    wired. Keep this validation shared by structured and runtime configs so an
+    unsupported mode fails before managed-process startup.
+    """
+
+    try:
+        mode = DiffusionKVCacheMode(value)
+    except (TypeError, ValueError) as exc:
+        supported = ", ".join(mode.value for mode in DiffusionKVCacheMode)
+        raise ValueError(f"Unsupported Diffusion KV diffusion_kv_mode {value!r}; expected one of: {supported}") from exc
+    if mode is DiffusionKVCacheMode.PAGED_WORKER_LOCAL:
+        raise ValueError(
+            "Diffusion KV diffusion_kv_mode 'paged_worker_local' is reserved but not implemented; "
+            "use 'dense_legacy' or 'paged_scheduler'"
+        )
+    return mode
@@ -0,0 +1,116 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+from dataclasses import dataclass
+
+from vllm.v1.core.kv_cache_utils import BlockHash
+from vllm.v1.request import RequestStatus
+
+
+@dataclass(frozen=True)
+class DiffusionKVContext:
+    """An independently managed K/V context outside the primary sequence.
+
+    ``context_id`` identifies the logical context within one execution
+    sequence. ``cache_role`` binds it to a logical attention cache role exposed
+    by the Worker. Physical cache geometry remains native ``KVCacheSpec`` /
+    ``KVCacheConfig`` state and is deliberately absent here.
+    """
+
+    context_id: str
+    cache_role: str
+    num_tokens: int
+    block_hashes: tuple[BlockHash, ...] = ()
+
+    def __post_init__(self) -> None:
+        if not self.context_id:
+            raise ValueError("context_id must be non-empty")
+        if not self.cache_role:
+            raise ValueError("cache_role must be non-empty")
+        if self.num_tokens <= 0:
+            raise ValueError(f"num_tokens must be positive, got {self.num_tokens}")
+        object.__setattr__(self, "block_hashes", tuple(self.block_hashes))
+
+
+class DiffusionKVRequest:
+    """Scheduler-owned KV state for one diffusion execution sequence.
+
+    The primary sequence follows an ordered ``[prefix | target]`` policy, such
+    as one Hunyuan CFG row. ``prefix_len`` is the contiguous reusable prefix;
+    ``target_len`` is overwritten by every denoise step; ``num_tokens`` is the
+    complete first-step allocation boundary.
+
+    Independent cross/joint-attention K/V does not belong to that token axis
+    and is described by ``kv_contexts``. This object also exposes the minimal
+    mutable Request surface consumed by native ``KVCacheManager``.
+
+    An empty ``block_hashes`` sequence means the prefix has no canonical cache
+    identity yet. Such a request may use native request-local page allocation,
+    but consumers must not publish it through ``KVCacheManager.cache_blocks``.
+    Prefix publication becomes valid only after model preprocessing supplies
+    one canonical hash for every cacheable full block.
+    """
+
+    def __init__(
+        self,
+        request_id: str,
+        *,
+        sequence_id: int,
+        prefix_len: int,
+        target_len: int,
+        seq_len: int,
+        block_hashes: Sequence[BlockHash] = (),
+        kv_contexts: Sequence[DiffusionKVContext] = (),
+    ) -> None:
+        if not request_id:
+            raise ValueError("request_id must be non-empty")
+        if sequence_id < 0:
+            raise ValueError(f"sequence_id must be non-negative, got {sequence_id}")
+        if prefix_len < 0:
+            raise ValueError(f"prefix_len must be non-negative, got {prefix_len}")
+        if target_len <= 0:
+            raise ValueError(f"target_len must be positive, got {target_len}")
+        if seq_len <= 0:
+            raise ValueError(f"seq_len must be positive, got {seq_len}")
+        if prefix_len + target_len > seq_len:
+            raise ValueError(
+                "prefix_len + target_len must not exceed seq_len: "
+                f"prefix_len={prefix_len}, target_len={target_len}, seq_len={seq_len}"
+            )
+
+        contexts = tuple(kv_contexts)
+        if any(not isinstance(context, DiffusionKVContext) for context in contexts):
+            raise TypeError("kv_contexts must contain only DiffusionKVContext values")
+        context_ids = [context.context_id for context in contexts]
+        if len(context_ids) != len(set(context_ids)):
+            raise ValueError(f"kv_contexts must have unique context_id values, got {context_ids}")
+
+        # Diffusion semantics consumed by the Scheduler-side facade.
+        self.sequence_id = sequence_id
+        self.prefix_len = prefix_len
+        self.target_len = target_len
+        self.kv_contexts = contexts
+
+        # Native vLLM Request surface. Keep this list intentionally small and
+        # cover it with real-KVCacheManager conformance tests.
+        self.request_id = request_id
+        self.num_tokens = seq_len
+        self.num_prompt_tokens = prefix_len
+        self.num_computed_tokens = 0
+        self.block_hashes = list(block_hashes)
+        self.skip_reading_prefix_cache = not self.block_hashes
+        self.status = RequestStatus.WAITING
+        self.num_preemptions = 0
+        self.num_in_flight_tokens = 0
+        # vLLM 0.26 uses this optional boundary when publishing cached blocks;
+        # zero means that no sparse-retention boundary is active.
+        self.shared_prefix_boundary = 0
+
+    @property
+    def seq_len(self) -> int:
+        """Complete first-step sequence length and allocation boundary."""
+
+        return self.num_tokens
@@ -68,6 +68,7 @@ class RMSNorm(CustomOp):
    def __init__(self, hidden_size: int, eps: float = 1e-6, dtype: torch.dtype = torch.float32) -> None:
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size, dtype=dtype))
+        self.hidden_size = hidden_size
        self.variance_epsilon = eps

    def _forward_fused(self, x: torch.Tensor) -> torch.Tensor:
@@ -107,6 +108,19 @@ class RMSNorm(CustomOp):
        except Exception:
            return self.forward_native(x)

+    def forward_musa(
+        self,
+        x: torch.Tensor,
+    ) -> torch.Tensor:
+        # Preserve the aten::rms_norm graph so dynamic Inductor can fuse the
+        # H3 Q/K norm with its inline RoPE path on MUSA.
+        return F.rms_norm(
+            x,
+            (self.hidden_size,),
+            self.weight,
+            self.variance_epsilon,
+        )
+
    def forward_npu(
        self,
        x: torch.Tensor,
@@ -193,7 +193,16 @@ class RotaryEmbedding(CustomOp):
        cos: torch.Tensor,
        sin: torch.Tensor,
    ) -> torch.Tensor:
-        return self.forward_native(x, cos, sin)
+        # Keep the full-dimension NeoX form inline for MUSA compilation.
+        # Other layouts use the shared native implementation.
+        if self.half_head_dim or self.interleaved:
+            return self.forward_native(x, cos, sin)
+        if cos.dim() == 3:
+            cos, sin = cos[0], sin[0]
+        cos = cos.unsqueeze(-2)
+        sin = sin.unsqueeze(-2)
+        x1, x2 = x.chunk(2, dim=-1)
+        return x * cos + torch.cat((-x2, x1), dim=-1) * sin

    def forward_native(
        self,
@@ -21,6 +21,7 @@ from vllm.model_executor.models.utils import AutoWeightsLoader, WeightsMapper
 from vllm.transformers_utils.config import get_config

 from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
+from vllm_omni.diffusion.diffusion_kv.config import DiffusionKVCacheMode
 from vllm_omni.diffusion.distributed.utils import get_local_device
 from vllm_omni.diffusion.forward_context import set_forward_context_denoise_step_idx
 from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
@@ -33,6 +34,7 @@ from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
 from vllm_omni.inputs.data import OmniTextPrompt
 from vllm_omni.model_executor.models.hunyuan_image3.siglip2 import Siglip2VisionTransformer

+from . import request_layout as request_layout_utils
 from .hunyuan_image3_tokenizer import TokenizerEncodeOutput, TokenizerWrapper
 from .hunyuan_image3_transformer import (
    CausalMMOutputWithPast,
@@ -50,7 +52,7 @@ from .hunyuan_image3_transformer import (
    real_batched_index_select,
    retrieve_timesteps,
 )
-from .system_prompt import get_system_prompt
+from .request_layout import HunyuanPreparedLayout

 if TYPE_CHECKING:
    from vllm_omni.diffusion.worker.input_batch import InputBatch
@@ -146,12 +148,6 @@ def _resize_and_crop_center(image: PILImage.Image, target_width: int, target_hei
    return resized.crop((crop_left, crop_top, crop_left + tw, crop_top + th))


-def _to_python_scalar(value: Any) -> Any:
-    if isinstance(value, np.generic):
-        return value.item()
-    return value
-
-
 def _shift_full_attn_spans(
    spans: list[tuple[int, int]],
    prefix_len: int | None,
@@ -165,80 +161,12 @@ def _shift_full_attn_spans(
    return [(start + shift, end + shift) if start >= prefix_len else (start, end) for start, end in spans]


-def _image_info_to_payload(image_info: ImageInfo) -> dict[str, Any]:
-    return {
-        "image_type": image_info.image_type,
-        "image_tensor": image_info.image_tensor,
-        "image_width": _to_python_scalar(image_info.image_width),
-        "image_height": _to_python_scalar(image_info.image_height),
-        "token_width": _to_python_scalar(image_info.token_width),
-        "token_height": _to_python_scalar(image_info.token_height),
-        "image_token_length": _to_python_scalar(image_info.image_token_length),
-        "base_size": _to_python_scalar(image_info.base_size),
-        "ratio_index": _to_python_scalar(image_info.ratio_index),
-        "add_timestep_token": image_info.add_timestep_token,
-        "add_guidance_token": image_info.add_guidance_token,
-        "use_front_boi_token": image_info.use_front_boi_token,
-        "add_image_shape_token": image_info.add_image_shape_token,
-    }
-
-
-def _to_tensor_if_needed(value: Any) -> Any:
-    if isinstance(value, np.generic):
-        return value.item()
-    if isinstance(value, list):
-        return torch.tensor(value)
-    return value
-
-
-def _image_info_from_payload(payload: dict[str, Any]) -> ImageInfo:
-    return ImageInfo(
-        image_type=payload.get("image_type"),
-        image_tensor=_to_tensor_if_needed(payload.get("image_tensor")),
-        image_width=payload.get("image_width"),
-        image_height=payload.get("image_height"),
-        token_width=payload.get("token_width"),
-        token_height=payload.get("token_height"),
-        image_token_length=payload.get("image_token_length"),
-        base_size=payload.get("base_size"),
-        ratio_index=payload.get("ratio_index"),
-        add_timestep_token=payload.get("add_timestep_token", True),
-        add_guidance_token=payload.get("add_guidance_token", False),
-        use_front_boi_token=payload.get("use_front_boi_token", True),
-        add_image_shape_token=payload.get("add_image_shape_token", True),
-    )
-
-
-def _joint_image_info_to_payload(joint_image_info: JointImageInfo) -> dict[str, Any]:
-    return {
-        "type": "joint_image_info",
-        "vae_image_info": _image_info_to_payload(joint_image_info.vae_image_info),
-        "vision_image_info": _image_info_to_payload(joint_image_info.vision_image_info),
-        "vision_encoder_kwargs": joint_image_info.vision_encoder_kwargs,
-    }
-
-
-def _joint_image_info_from_payload(payload: Any) -> JointImageInfo:
-    if isinstance(payload, JointImageInfo):
-        return payload
-    if not isinstance(payload, dict):
-        raise TypeError(f"Expected dict or JointImageInfo for conditional image payload, got {type(payload)}.")
-
-    vae_image_info = _image_info_from_payload(payload["vae_image_info"])
-    vision_image_info = _image_info_from_payload(payload["vision_image_info"])
-    vision_encoder_kwargs = payload.get("vision_encoder_kwargs") or {}
-    if isinstance(vision_encoder_kwargs, dict):
-        vision_encoder_kwargs = {key: _to_tensor_if_needed(value) for key, value in vision_encoder_kwargs.items()}
-    return JointImageInfo(
-        vae_image_info=vae_image_info,
-        vision_image_info=vision_image_info,
-        vision_encoder_kwargs=vision_encoder_kwargs,
-    )
-
-
 def get_hunyuan_image_3_pre_process_func(od_config: OmniDiffusionConfig):
    hf_config = get_config(od_config.model, trust_remote_code=True)
    image_processor = HunyuanImage3ImageProcessor(hf_config)
+    prepare_diffusion_kv_layout = od_config.diffusion_kv_mode is DiffusionKVCacheMode.PAGED_SCHEDULER
+    tokenizer_wrapper = TokenizerWrapper(od_config.model) if prepare_diffusion_kv_layout else None
+    generation_config = GenerationConfig.from_pretrained(od_config.model) if prepare_diffusion_kv_layout else None
    vae_h_factor = hf_config.vae_downsample_factor[0] * hf_config.patch_size
    vae_w_factor = hf_config.vae_downsample_factor[1] * hf_config.patch_size
    vit_patch_size = getattr(image_processor.vision_encoder_processor, "patch_size", 1)
@@ -291,7 +219,7 @@ def get_hunyuan_image_3_pre_process_func(od_config: OmniDiffusionConfig):
            image_token_length=int(vit_tensor.shape[1]),
        )

-        return _joint_image_info_to_payload(
+        return request_layout_utils.joint_image_info_to_payload(
            JointImageInfo(
                vae_image_info=vae_info,
                vision_image_info=vit_info,
@@ -329,6 +257,20 @@ def get_hunyuan_image_3_pre_process_func(od_config: OmniDiffusionConfig):
                request.sampling_params.height = int(bridge_h or first_image_h)

        request.prompt = prompt
+        if prepare_diffusion_kv_layout:
+            assert tokenizer_wrapper is not None and generation_config is not None
+            prepared_layout = request_layout_utils.prepare_hunyuan_layout(
+                request,
+                tokenizer_wrapper=tokenizer_wrapper,
+                image_processor=image_processor,
+                generation_config=generation_config,
+                image_base_size=hf_config.image_base_size,
+            )
+            request.prepared_layout = prepared_layout
+            request.diffusion_kv_requests = request_layout_utils.build_hunyuan_diffusion_kv_requests(
+                request,
+                prepared_layout,
+            )

        return request

@@ -543,94 +485,6 @@ class HunyuanImage3Pipeline(
        if getattr(self.od_config, "diffusion_kv_cache_skip_step_indices", None):
            raise ValueError("HunyuanImage3 step execution does not support diffusion KV cache step skips yet.")

-    @staticmethod
-    def _normalize_single_stage_bot_task(bot_task: Any) -> str:
-        if isinstance(bot_task, str) and bot_task.lower() == "none":
-            bot_task = None
-        tokenizer_bot_task = bot_task
-        if tokenizer_bot_task == "think_recaption":
-            tokenizer_bot_task = "think"
-        elif tokenizer_bot_task == "vanilla":
-            tokenizer_bot_task = "image"
-        supported_bot_tasks = {"auto", "image", "think", "recaption", "img_ratio"}
-        if tokenizer_bot_task is not None and tokenizer_bot_task not in supported_bot_tasks:
-            raise ValueError(
-                f"Unsupported HunyuanImage3 single-stage bot_task: {tokenizer_bot_task!r}. "
-                f"Supported values are: {sorted(supported_bot_tasks)}."
-            )
-        return tokenizer_bot_task or "auto"
-
-    def _extract_prompt_inputs(
-        self,
-        prompts: list[Any],
-        extra_args: dict[str, Any],
-        *,
-        request_id: str,
-        allow_cond_image: bool,
-    ) -> tuple[list[str], list[str | None], str | None, list[list[JointImageInfo]] | None, str]:
-        is_dummy_warmup = OmniDiffusionRequest.is_dummy_run_request_id(request_id)
-        bot_task = extra_args.get("bot_task")
-        use_system_prompt = extra_args.get("use_system_prompt")
-        system_prompt = extra_args.get("system_prompt")
-
-        first_prompt = prompts[0] if prompts else None
-        if isinstance(first_prompt, dict):
-            if bot_task is None:
-                bot_task = first_prompt.get("bot_task")
-            if use_system_prompt is None:
-                use_system_prompt = first_prompt.get("use_system_prompt")
-            if system_prompt is None:
-                system_prompt = first_prompt.get("system_prompt")
-        tokenizer_bot_task = self._normalize_single_stage_bot_task(bot_task)
-        if use_system_prompt is not None:
-            system_prompt_bot_task = "image" if tokenizer_bot_task == "auto" else tokenizer_bot_task
-            system_prompt = get_system_prompt(use_system_prompt, system_prompt_bot_task, system_prompt)
-            system_prompt = system_prompt.strip() if system_prompt is not None else ""
-
-        prompt = [p if isinstance(p, str) else (p.get("prompt") or "") for p in prompts]
-        cot_text_list = [
-            (p.get("extra", {}).get("ar_generated_text") if isinstance(p, dict) else None) or None for p in prompts
-        ]
-
-        batch_cond_image_info: list[list[JointImageInfo]] | None = None
-        if any(not isinstance(p, str) for p in prompts):
-            batch_cond_image_info = []
-            for prompt_item in prompts:
-                if isinstance(prompt_item, str):
-                    batch_cond_image_info.append([])
-                    continue
-                additional_info = prompt_item.get("additional_information") or {}
-                cond_infos = additional_info.get("batch_cond_image_info", [])
-                if isinstance(cond_infos, JointImageInfo | dict):
-                    cond_infos = [cond_infos]
-                if cond_infos is None:
-                    cond_infos = []
-                batch_cond_image_info.append([_joint_image_info_from_payload(item) for item in cond_infos])
-
-            has_cond_image = [len(cond_infos) > 0 for cond_infos in batch_cond_image_info]
-            if any(has_cond_image) and (not allow_cond_image) and not is_dummy_warmup:
-                raise ValueError("HunyuanImage3 step execution does not support image editing requests yet.")
-            if allow_cond_image and any(has_cond_image) and not all(has_cond_image):
-                raise ValueError(
-                    "When batching Hunyuan image editing requests, every prompt must include input image(s)."
-                )
-            if not allow_cond_image or not any(has_cond_image):
-                batch_cond_image_info = None
-
-        return prompt, cot_text_list, system_prompt, batch_cond_image_info, tokenizer_bot_task
-
-    def _extract_step_prompt_inputs(
-        self,
-        state: "StepRequestState",
-    ) -> tuple[list[str], list[str | None], str | None, list[list[JointImageInfo]] | None, str]:
-        sampling = state.sampling
-        return self._extract_prompt_inputs(
-            [state.prompt] if state.prompt is not None else [],
-            getattr(sampling, "extra_args", {}) or {},
-            request_id=state.request_id,
-            allow_cond_image=False,
-        )
-
    def _snapshot_injected_ar_kv(self) -> list[list[tuple[torch.Tensor, torch.Tensor]] | None] | None:
        snapshot: list[list[tuple[torch.Tensor, torch.Tensor]] | None] = []
        found = False
@@ -1081,27 +935,6 @@ class HunyuanImage3Pipeline(
        pred = self.final_layer(image_output, timestep_emb, token_h, token_w)
        return pred

-    @staticmethod
-    def build_batch_rope_image_info(output, sections):
-        rope_image_info = []
-        for image_slices, sections_i in zip(output.all_image_slices, sections):
-            image_shapes = []
-            for section in sections_i:
-                if "image" in section["type"]:
-                    if isinstance(section["token_height"], list):
-                        assert len(section["token_height"]) == len(section["token_width"]), (
-                            f"token_height and token_width should have the same length, "
-                            f"but got {len(section['token_height'])} and {len(section['token_width'])}"
-                        )
-                        image_shapes.extend(list(zip(section["token_height"], section["token_width"])))
-                    else:
-                        image_shapes.append((section["token_height"], section["token_width"]))
-            assert len(image_slices) == len(image_shapes), (
-                f"Size miss matching: Image slices({len(image_slices)}) != image shapes({len(image_shapes)})"
-            )
-            rope_image_info.append(list(zip(image_slices, image_shapes)))
-        return rope_image_info
-
    def vae_encode(self, image, cfg_factor=1, generator=None):
        config = self.vae.config

@@ -1210,26 +1043,6 @@ class HunyuanImage3Pipeline(
                    f"Each message should be a list of dicts or a dict, but got {type(message)}."
                )

-    @staticmethod
-    def _normalize_cot_text(cot: str | None) -> str | None:
-        """
-        Ensure cot_text starts with the appropriate opening tag.
-
-        AR-generated text may omit the leading <think> or <recaption> tag
-        (since it was used as a generation trigger). This normalizes the text
-        so downstream parsing in get_cot_sections works correctly.
-        """
-        if not cot:
-            return cot
-
-        if "</think>" in cot and not cot.startswith("<think>"):
-            cot = "<think>" + cot
-            return cot
-        if "</recaption>" in cot and not cot.startswith("<recaption>"):
-            cot = "<recaption>" + cot
-            return cot
-        return cot
-
    def prepare_model_inputs(
        self,
        prompt=None,
@@ -1242,6 +1055,7 @@ class HunyuanImage3Pipeline(
        message_list=None,
        device=None,
        max_new_tokens=None,
+        prepared_layout: HunyuanPreparedLayout | None = None,
        **kwargs,
    ):
        # 1. Sanity check
@@ -1297,7 +1111,16 @@ class HunyuanImage3Pipeline(
                    )

            if mode == "gen_image":
-                batch_gen_image_info = [self.image_processor.build_image_info(image_size) for _ in range(batch_size)]
+                if prepared_layout is not None:
+                    # TODO: Support batch_size > 1 once prepared layouts can
+                    # preserve the public-request-to-CFG-branch mapping.
+                    if batch_size != 1:
+                        raise ValueError("Hunyuan prepared layout currently supports one public request at a time.")
+                    batch_gen_image_info = [prepared_layout.generated_image_info]
+                else:
+                    batch_gen_image_info = [
+                        self.image_processor.build_image_info(image_size) for _ in range(batch_size)
+                    ]

            if batch_cond_image_info is not None:
                assert isinstance(batch_cond_image_info, list) and len(batch_cond_image_info) == batch_size, (
@@ -1314,30 +1137,41 @@ class HunyuanImage3Pipeline(
        # 3. apply chat template
        cfg_factor = {"gen_text": 1, "gen_image": 1 + int(guidance_scale > 1.0)}
        bot_task = kwargs.pop("bot_task", "auto")
-        # If `drop_think` enabled, always drop <think> parts in the context.
-        drop_think = kwargs.get("drop_think", self.generation_config.drop_think)
-        # Pull sequence_template from the model's generation_config so the DiT
-        # text prefix matches how the model was trained (Instruct for the
-        # HunyuanImage-3.0-Instruct checkpoint).  Falling back to "pretrain"
-        # only if the config does not specify it.
-        sequence_template = getattr(self.generation_config, "sequence_template", "pretrain")
-        # Apply batched prompt or batched message_list to build input sequence with associated info.
-        out = self._tkwrapper.apply_chat_template(
-            batch_prompt=batch_prompt,
-            batch_message_list=batch_message_list,
-            mode=mode,
-            batch_gen_image_info=batch_gen_image_info,
-            batch_cond_image_info=batch_cond_image_info,
-            batch_system_prompt=batch_system_prompt,
-            batch_cot_text=batch_cot_text,
-            max_length=kwargs.get("max_length"),
-            bot_task=bot_task,
-            image_base_size=self.config.image_base_size,
-            sequence_template=sequence_template,
-            cfg_factor=cfg_factor[mode],
-            drop_think=drop_think,
-        )
-        output, sections = out["output"], out["sections"]
+        if prepared_layout is None:
+            # Pull template options from generation_config only when the
+            # Worker must tokenize locally.
+            drop_think = kwargs.get("drop_think", self.generation_config.drop_think)
+            sequence_template = getattr(self.generation_config, "sequence_template", "pretrain")
+            # Apply batched prompt or batched message_list to build input
+            # sequence with associated info.
+            out = self._tkwrapper.apply_chat_template(
+                batch_prompt=batch_prompt,
+                batch_message_list=batch_message_list,
+                mode=mode,
+                batch_gen_image_info=batch_gen_image_info,
+                batch_cond_image_info=batch_cond_image_info,
+                batch_system_prompt=batch_system_prompt,
+                batch_cot_text=batch_cot_text,
+                max_length=kwargs.get("max_length"),
+                bot_task=bot_task,
+                image_base_size=self.config.image_base_size,
+                sequence_template=sequence_template,
+                cfg_factor=cfg_factor[mode],
+                drop_think=drop_think,
+            )
+            output = out["output"]
+            rope_image_info = request_layout_utils.build_hunyuan_batch_rope_image_info(output, out["sections"])
+        else:
+            if mode != "gen_image" or batch_message_list is not None:
+                raise ValueError("Hunyuan prepared layout only supports prompt-based gen_image execution.")
+            output = prepared_layout.tokenizer_output
+            rope_image_info = prepared_layout.rope_image_info
+            expected_rows = batch_size * cfg_factor[mode]
+            if prepared_layout.num_branches != expected_rows:
+                raise ValueError(
+                    "Hunyuan prepared branches do not match execution CFG layout: "
+                    f"expected_rows={expected_rows}, prepared_branches={prepared_layout.num_branches}"
+                )

        # 4. Encode conditional images
        # Skip encoding if AR KV reuse is enabled
@@ -1362,7 +1196,6 @@ class HunyuanImage3Pipeline(
            vit_kwargs = None

        # 5. Build position embeddings
-        rope_image_info = self.build_batch_rope_image_info(output, sections)
        if mode == "gen_text":
            seq_len = self.generation_config.max_length
        else:
@@ -1616,12 +1449,7 @@ class HunyuanImage3Pipeline(
                raise ValueError("`batch_gen_image_info` should be provided when `mode` is `gen_image`.")

            image_info: ImageInfo = batch_gen_image_info[0]
-            num_image_tokens = (
-                image_info.image_token_length
-                + (1 if image_info.add_timestep_token else 0)
-                + (1 if image_info.add_guidance_token else 0)
-            )
-            kwargs["num_image_tokens"] = num_image_tokens
+            kwargs["num_image_tokens"] = request_layout_utils.hunyuan_num_image_tokens(image_info)
            # 50 and 5.0 hard code
            results = self.pipeline(
                batch_size=len(batch_gen_image_info),
@@ -1888,9 +1716,14 @@ class HunyuanImage3Pipeline(
            system_prompt,
            batch_cond_image_info,
            tokenizer_bot_task,
-        ) = self._extract_step_prompt_inputs(state)
+        ) = request_layout_utils.extract_hunyuan_prompt_inputs(
+            [state.prompt] if state.prompt is not None else [],
+            getattr(sampling, "extra_args", {}) or {},
+            request_id=state.request_id,
+            allow_cond_image=False,
+        )
        cot_text = (
-            [self._normalize_cot_text(text) for text in cot_text_list]
+            [request_layout_utils.normalize_hunyuan_cot_text(text) for text in cot_text_list]
            if any(text is not None for text in cot_text_list)
            else None
        )
@@ -1899,7 +1732,7 @@ class HunyuanImage3Pipeline(
        width = sampling.width or 1024
        image_size = (height, width)
        num_inference_steps = sampling.num_inference_steps or 50
-        guidance_scale = sampling.guidance_scale if sampling.guidance_scale_provided else 5.0
+        guidance_scale = request_layout_utils.resolve_hunyuan_guidance_scale(sampling)
        if guidance_scale <= 1.0:
            logger.info("HunyuanImage3.0 step execution runs without classifier-free guidance.")
        pipe._guidance_scale = guidance_scale
@@ -1916,6 +1749,7 @@ class HunyuanImage3Pipeline(
            guidance_scale=guidance_scale,
            batch_cond_image_info=batch_cond_image_info,
            bot_task=tokenizer_bot_task,
+            prepared_layout=request_layout_utils.get_hunyuan_prepared_layout(state),
        )
        model_kwargs.update(self._extract_ar_kv_from_sampling(sampling))
        model_kwargs["use_cache"] = False
@@ -1923,14 +1757,9 @@ class HunyuanImage3Pipeline(
        input_ids = model_kwargs.pop("input_ids")
        batch_gen_image_info: list[ImageInfo] = model_kwargs["batch_gen_image_info"]
        image_info = batch_gen_image_info[0]
-        target_height = int(_to_python_scalar(image_info.image_height))
-        target_width = int(_to_python_scalar(image_info.image_width))
-        num_image_tokens = (
-            image_info.image_token_length
-            + (1 if image_info.add_timestep_token else 0)
-            + (1 if image_info.add_guidance_token else 0)
-        )
-        model_kwargs["num_image_tokens"] = num_image_tokens
+        target_height = int(image_info.image_height)
+        target_width = int(image_info.image_width)
+        model_kwargs["num_image_tokens"] = request_layout_utils.hunyuan_num_image_tokens(image_info)

        timesteps, _ = retrieve_timesteps(
            self.scheduler,
@@ -2294,7 +2123,7 @@ class HunyuanImage3Pipeline(
            system_prompt,
            batch_cond_image_info,
            tokenizer_bot_task,
-        ) = self._extract_prompt_inputs(
+        ) = request_layout_utils.extract_hunyuan_prompt_inputs(
            req.prompts,
            extra_args,
            request_id=req.request_id,
@@ -2302,15 +2131,16 @@ class HunyuanImage3Pipeline(
        )
        prompt = prompt_from_req or prompt
        cot_text = (
-            [self._normalize_cot_text(t) for t in cot_text_list] if any(t is not None for t in cot_text_list) else None
+            [request_layout_utils.normalize_hunyuan_cot_text(t) for t in cot_text_list]
+            if any(t is not None for t in cot_text_list)
+            else None
        )

        generator = req.sampling_params.generator or generator
        height = req.sampling_params.height or height
        width = req.sampling_params.width or width
        num_inference_steps = req.sampling_params.num_inference_steps or num_inference_steps
-        if req.sampling_params.guidance_scale_provided:
-            guidance_scale = req.sampling_params.guidance_scale
+        guidance_scale = request_layout_utils.resolve_hunyuan_guidance_scale(req.sampling_params, guidance_scale)
        if guidance_scale <= 1.0:
            logger.info("HunyuanImage3.0 runs without classifier-free guidance when guidance_scale <= 1.0.")
        image_size = (height, width)
@@ -2329,6 +2159,7 @@ class HunyuanImage3Pipeline(
            guidance_scale=guidance_scale,
            batch_cond_image_info=batch_cond_image_info,
            bot_task=tokenizer_bot_task,
+            prepared_layout=request_layout_utils.get_hunyuan_prepared_layout(req.requests[0]),
            **ar_kv_kwargs,
        )
Author	SHA1	Message	Date
Hongsheng Liu	c03cd912b4	Merge branch 'main' into codex-contributor-agent-skills-docs	2026-08-11 12:59:11 +08:00
Juan Pablo ZuluagaandYueqian Lin	12a5f6fbf3	[Perf][TTS] step_audio2: keep streaming tokens on-device and batch audio-feature length syncs (#5067 ) Signed-off-by: JuanPZuluaga <juanz9312@gmail.com> Co-authored-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>	2026-08-10 21:24:25 -07:00
Juan Pablo ZuluagaandYueqian Lin	53afbf3a0c	[Perf][TTS] voxtral_tts hot path: cudagraph opt-out, host-sync removal, kernel caches (#5175 ) Signed-off-by: JuanPZuluaga <juanz9312@gmail.com> Co-authored-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>	2026-08-10 21:03:32 -07:00
Hongsheng Liu	3f1357c26b	[Doc] Explain repository skills for agentic contributions Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-11 12:00:27 +08:00
Shenglei Fu Claude Opus 4.8 Yueqian Lin	ee118ab492	[Bugfix] Per-replica fault isolation: keep API server alive on single-stage death (#4285 ) (#4583 ) Signed-off-by: Shenglei Fu <sfu@confluent.io> Signed-off-by: Shenglei Fu <117230642+ShengleiFu@users.noreply.github.com> Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Co-authored-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>	2026-08-10 23:00:18 -04:00
fan2956andFan	c27623c29c	[Bugfix] rainfusion support all shape video (#6000 ) Signed-off-by: Fan <fan@FandeMacBook-Air.local> Co-authored-by: Fan <fan@FandeMacBook-Air.local>	2026-08-11 05:10:50 +08:00
Yueqian Lin	f8fa86fb1f	[BugFix] Don't fail the TTS ratchet when the branch count goes down (#6008 ) Signed-off-by: Yueqian Lin <linyueqian@outlook.com>	2026-08-11 05:08:31 +08:00
ooooooyeandHongsheng Liu	422d8fd8f9	[Bugfix][Diffusion] Restore supports_packed_mask_free on teacache FakeBackend (#5997 ) Signed-off-by: brandneway <gyuan4892@gmail.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-10 22:20:37 +08:00
Hongsheng Liu	62e239bf1e	[Docs] Fix broken attention backend link (#5998 ) Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-10 21:55:01 +08:00
ooooooye	740f45abcd	[Perf][Diffusion] MiniMax-H3: opt into packed varlen attention on NPU to eliminate quadratic mask materialization (#5891 ) Signed-off-by: brandneway <gyuan4892@gmail.com>	2026-08-10 19:47:03 +08:00
Hongsheng Liu hsliu_ustc Copilot Autofix powered by AI	5e4b9f8c6e	[Misc] Align CODEOWNERS with module/feature design docs (#5958 ) Signed-off-by: hsliu_ustc <hsliu_ustc@noreply.gitcode.com> Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com> Co-authored-by: hsliu_ustc <hsliu_ustc@noreply.gitcode.com> Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>	2026-08-10 19:25:03 +08:00
Hongsheng Liu	ae811043e9	docs: keep shared task examples in navigation (#5987 ) Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-10 19:13:52 +08:00
R0CKSTAR	636d44e272	[Perf][MUSA] Restore MiniMax-H3 dynamic RoPE fusion (#5881 ) Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>	2026-08-10 17:24:46 +08:00
yiminghub2024	8e44e78038	Add Ref2VA measurements to the MiniMax-H3 DGX Spark (GB10) recipe (#5972 ) Signed-off-by: yiminghub2024 <482890@qq.com>	2026-08-10 15:51:15 +08:00
Hongsheng Liu	b98b7c8529	[Docs] Preserve generated quantization URLs and link recipes in supported models (#5969 ) Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-10 15:45:50 +08:00
bjf-frzandAlicia	1ee2fff532	Update Wan2.2 I2V performance baselines (#5977 ) Signed-off-by: bjf-frz <frz123db@gmail.com> Co-authored-by: Alicia <115451386+congw729@users.noreply.github.com>	2026-08-10 15:24:59 +08:00
Zeyu Huang \| 黃澤宇andHongsheng Liu	33dd9a5a3e	[Core][Refactor][Diffusion] refactor request scheduler's admission wait policy (#5843 ) Signed-off-by: Huang, Zeyu <11222265+fhfuih@users.noreply.github.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-10 15:06:27 +08:00
Nguyen Kha Nhat Long Samit Gao Han Hongsheng Liu	c588208cc6	[bugfix] Support lora request for non diffusion model (#5374 ) Signed-off-by: knlnguyen1802 <knlnguyen1802@gmail.com> Co-authored-by: Samit <285365963@qq.com> Co-authored-by: Gao Han <hgaoaf@connect.ust.hk> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-10 11:27:51 +08:00
wangyuandamy-why-3459	e28bf8ff8b	[CI] Re-enable bagel shared-memory connector test (#5475 ) (#5898 ) Signed-off-by: wangyu <410167048@qq.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-10 11:26:10 +08:00
WeiQing Chen	e1aa6eae75	docs: update vLLM-Omni WeChat QR code (#5959 ) Signed-off-by: David Chen <530634352@qq.com>	2026-08-10 10:04:24 +08:00
wtz2333andClaude	36135ef09a	[diffusion][feature] Add LingBot-Video T2I and TI2V generation modes (#5311 ) Signed-off-by: wtz2333 <2955110911@qq.com> Co-authored-by: Claude <noreply@anthropic.com>	2026-08-10 10:02:31 +08:00
boatman	11f633aa83	[Refactor] Add Ming Flash Omni TTS adapter (#5746 ) Signed-off-by: boatman <109857087+sphinxkkkbc@users.noreply.github.com>	2026-08-10 01:47:09 +00:00
Yukim1 zwhzzz0821 Hongsheng Liu	6ae7ff7846	[Diffusion] Prepare HunyuanImage3 for Scheduler-managed paged KV cache (#5541 ) Signed-off-by: zwhzzz0821 <zwhzzz0821@users.noreply.github.com> Co-authored-by: zwhzzz0821 <zwhzzz0821@users.noreply.github.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-10 08:48:56 +08:00
yiminghub2024	615de4e041	[doc]Add recipe for MiniMax-H3 on RTX PRO 6000 (#5863 ) Signed-off-by: yiminghub2024 <482890@qq.com>	2026-08-09 23:52:40 +08:00
yiminghub2024	be48b0797e	Add MiniMax-H3 recipe for DGX Spark (GB10) (#5946 ) Signed-off-by: yiminghub2024 <482890@qq.com>	2026-08-09 23:15:05 +08:00
Diya Pengandlishunyang12	45629a8e26	[MiniMax-H3] TeaCache support and Cache-DiT validation (#5840 ) Signed-off-by: dpeng123 <diyapeng123@gmail.com> Signed-off-by: lishunyang12 <lishunyang12@163.com> Co-authored-by: lishunyang12 <lishunyang12@163.com>	2026-08-09 15:07:21 +00:00