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docs: align User Guide feature taxonomy

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Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>
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Hongsheng Liu committed 2026-08-11 16:16:46 +08:00
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docs/.nav.yml
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@@ -40,16 +40,31 @@ nav:
- Models:
- models/supported_models.md
- Features:
- Sleep Mode: features/sleep_mode.md
- Diffusion Features:
- Overview: user_guide/diffusion_features.md
- Feature Compatibility: user_guide/feature_compatibility.md
- Regional Compilation: user_guide/diffusion/regional_compilation.md
- Overview: features/README.md
- Runtime and Stage Execution:
- Execution Modes and Streaming: user_guide/diffusion/execution_modes.md
- Sleep Mode: features/sleep_mode.md
- Diffusion Acceleration:
- Overview and Compatibility:
- Overview: user_guide/diffusion_features.md
- Feature Compatibility: user_guide/feature_compatibility.md
- Quantization:
- Overview: user_guide/quantization/overview.md
- Online Quantization: user_guide/quantization/online.md
- Quantized KV Cache: user_guide/quantization/quantized_kvcache.md
- FP8 W8A8: user_guide/quantization/fp8.md
- Int8 W8A8: user_guide/quantization/int8.md
- BitsAndBytes W4: user_guide/quantization/bitsandbytes.md
- MXFP8 W8A8: user_guide/quantization/mxfp8.md
- MXFP4 W4A4: user_guide/quantization/mxfp4.md
- ModelOpt: user_guide/quantization/modelopt.md
- GGUF: user_guide/quantization/gguf.md
- AutoRound: user_guide/quantization/autoround.md
- msModelSlim: user_guide/quantization/msmodelslim.md
- CPU Offloading: user_guide/diffusion/cpu_offload.md
- Cache Acceleration:
- TeaCache: user_guide/diffusion/cache_acceleration/teacache.md
- Cache-DiT: user_guide/diffusion/cache_acceleration/cache_dit.md
- Attention Backends: user_guide/diffusion/attention_backends.md
- Frame Interpolation: user_guide/diffusion/frame_interpolation.md
- Parallelism:
- Overview: user_guide/diffusion/parallelism/overview.md
- CFG Parallel: user_guide/diffusion/parallelism/cfg_parallel.md
@@ -58,23 +73,15 @@ nav:
- Sequence Parallel: user_guide/diffusion/parallelism/sequence_parallel.md
- Tensor Parallel: user_guide/diffusion/parallelism/tensor_parallel.md
- VAE Parallelism: user_guide/diffusion/parallelism/vae_parallelism.md
- CPU Offloading: user_guide/diffusion/cpu_offload.md
- Attention Backends: user_guide/diffusion/attention_backends.md
- Regional Compilation: user_guide/diffusion/regional_compilation.md
- Frame Interpolation: user_guide/diffusion/frame_interpolation.md
- Startup and Loading: user_guide/diffusion/startup_and_loading.md
- LoRA: user_guide/diffusion/lora.md
- Custom Pipeline: features/custom_pipeline.md
- Execution Modes: user_guide/diffusion/execution_modes.md
- Quantization:
- Quantization: user_guide/quantization/overview.md
- Online Quantization: user_guide/quantization/online.md
- Quantized KV Cache: user_guide/quantization/quantized_kvcache.md
- FP8 W8A8: user_guide/quantization/fp8.md
- Int8 W8A8: user_guide/quantization/int8.md
- BitsAndBytes W4: user_guide/quantization/bitsandbytes.md
- MXFP8 W8A8: user_guide/quantization/mxfp8.md
- MXFP4 W4A4: user_guide/quantization/mxfp4.md
- ModelOpt: user_guide/quantization/modelopt.md
- GGUF: user_guide/quantization/gguf.md
- AutoRound: user_guide/quantization/autoround.md
- msModelSlim: user_guide/quantization/msmodelslim.md
- Experimental:
- Session State Manager: features/session_state_manager.md
- Integrations:
- Custom Diffusion Pipeline: features/custom_pipeline.md
- ComfyUI: features/comfyui.md
- Fault Tolerance:
- Failure Modes: user_guide/fault_injection_reliability_matrix.md
docs/design/index.md
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## Feature Design Documents
For user-facing configuration and current compatibility, see the
[Features overview](../features/README.md). A design document defines an
implementation contract; it is not, by itself, a general support claim.
### Runtime and stage execution
- [Disaggregated Inference](feature/disaggregated_inference.md)
docs/features/README.md
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# Features
Use this section to choose and configure vLLM-Omni runtime and optimization
features. The navigation follows the active
[Feature Design](../design/index.md#feature-design-documents) taxonomy whenever
an implementation contract has a user-facing workflow.
!!! note "User guides, design documents, and recipes"
User guides explain how to enable a feature and where it is supported.
Design documents define internal contracts and do not, by themselves,
imply general or production support. For model-specific launch flags and
hardware requirements, use the linked examples and validated recipes from
[Supported Models](../models/supported_models.md).
## Runtime and Stage Execution
| Goal | User guide | Related design contract |
| --- | --- | --- |
| Choose serial, batched, step-wise, or streaming diffusion execution | [Execution Modes and Streaming](../user_guide/diffusion/execution_modes.md) | [Diffusion Continuous Batching](../design/feature/diffusion_continuous_batching.md), [Async Diffusion Output](../design/feature/async_diffusion_output.md) |
| Reclaim stage memory without restarting the server | [Sleep Mode](sleep_mode.md) | Runtime lifecycle behavior is documented in the user guide |
Some runtime designs are deliberately not promoted as standalone User Guide
features yet:
- [Disaggregated Inference](../design/feature/disaggregated_inference.md) and
[OmniConnector implementations](../design/index.md#communication) describe
topology and transport contracts. Practical deployment configuration remains
under [Pipeline and deploy configurations](../configuration/stage_configs.md).
- [Async Chunk](../design/feature/async_chunk.md) and
[Async Omni Output Materialization](../design/feature/omni_async_output_materialization.md)
are model- and pipeline-dependent. Use the selected model's deploy
configuration and recipe for the supported settings.
- [Automatic Prefix Caching](../design/feature/prefix_caching.md) remains a
design-level contract until its user-facing configuration and compatibility
surface is consolidated.
## Diffusion Acceleration
| Goal | User guide | Related design contract |
| --- | --- | --- |
| Compare acceleration methods and supported combinations | [Overview](../user_guide/diffusion_features.md), [Feature Compatibility](../user_guide/feature_compatibility.md) | [Diffusion acceleration designs](../design/index.md#diffusion-acceleration) |
| Reduce model memory with lower-precision weights | [Quantization](../user_guide/quantization/overview.md) | [Quantization](../design/feature/quantization.md) |
| Move weights between host and device memory | [CPU Offloading](../user_guide/diffusion/cpu_offload.md) | [Distributed Layerwise Offload](../design/feature/distributed_layerwise_offload.md) |
| Reuse denoising computation | [Cache Acceleration](../user_guide/diffusion/cache_acceleration/cache_dit.md) | [Cache-DiT](../design/feature/cache_dit.md), [TeaCache](../design/feature/teacache.md) |
| Distribute diffusion work across devices | [Parallelism](../user_guide/diffusion/parallelism/overview.md) | [Parallelism designs](../design/index.md#parallelism) |
| Select dense, sparse, or quantized attention paths | [Attention Backends](../user_guide/diffusion/attention_backends.md) | [Skip-Softmax](../design/feature/skip_softmax.md) |
| Compile repeated diffusion regions | [Regional Compilation](../user_guide/diffusion/regional_compilation.md) | User-facing optimization guide |
| Add generated video frames | [Frame Interpolation](../user_guide/diffusion/frame_interpolation.md) | User-facing extension guide |
| Reduce diffusion model startup time | [Startup and Loading](../user_guide/diffusion/startup_and_loading.md) | User-facing loading guide |
| Apply diffusion adapters | [LoRA](../user_guide/diffusion/lora.md) | User-facing extension guide |
The [Pipeline Parallelism guide](../user_guide/diffusion/parallelism/pipeline_parallel.md)
remains available by direct link, but it is not promoted in the primary User
Guide navigation until its user-facing support and placement are settled.
## Experimental
[Session State Manager](session_state_manager.md) is opt-in and experimental.
Its APIs and compatibility may change without notice.
docs/user_guide/diffusion/startup_and_loading.md
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# Diffusion Startup and Loading
Large diffusion models can take several minutes to load at startup. vLLM-Omni
loads safetensors shards in parallel to reduce this initialization time.
Multi-thread weight loading is enabled by default with four threads. No
configuration is needed for the default behavior.
## Configuration
| Parameter | CLI flag | Default | Description |
| --- | --- | --- | --- |
| `enable_multithread_weight_load` | `--disable-multithread-weight-load` | `True` | Pass the flag to disable multi-thread loading |
| `num_weight_load_threads` | `--num-weight-load-threads` | `4` | Number of parallel weight-loading threads |
!!! tip
The default balances startup speed and disk I/O contention. Fast NVMe
storage may benefit from more threads, while network storage or hard disks
may not.
## Online Serving
```bash
# Default: multi-thread loading with four threads
vllm serve Qwen/Qwen-Image --omni --port 8091
# Increase the thread count
vllm serve Wan-AI/Wan2.2-I2V-A14B-Diffusers --omni \
--num-weight-load-threads 8
# Disable multi-thread loading
vllm serve Qwen/Qwen-Image --omni --disable-multithread-weight-load
```
## Offline Inference
```python
from vllm_omni import Omni
# Default: multi-thread loading with four threads
omni = Omni(model="Qwen/Qwen-Image")
# Increase the thread count
omni = Omni(
model="Wan-AI/Wan2.2-I2V-A14B-Diffusers",
num_weight_load_threads=8,
)
```
## Reference Benchmarks
The following measurements were collected on NVIDIA H800 hardware. Treat them
as reference results rather than a guarantee for other storage or hardware
configurations.
| Model | Sequential loading | Multi-thread loading | Speedup |
| --- | ---: | ---: | ---: |
| **Qwen/Qwen-Image** (53.7 GiB) | 168 s | 27 s | **6.2x** |
| **Wan-AI/Wan2.2-I2V-A14B-Diffusers** (64.5 GiB) | 283 s | 56 s | **5.1x** |
docs/user_guide/diffusion_features.md
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@@ -49,7 +49,7 @@ Parallelism methods distribute computation across GPUs without quality loss (mat
| Method | Description | Best For |
|--------|-------------|----------|
| **[Multi-Thread Weight Loading](#multi-thread-weight-loading)** | Loads safetensors shards in parallel using a thread pool | All diffusion models; reduces startup from minutes to seconds |
| **[Multi-Thread Weight Loading](diffusion/startup_and_loading.md)** | Loads safetensors shards in parallel using a thread pool | All diffusion models; reduces startup from minutes to seconds |
**Note:** Some acceleration methods can be combined together for optimized performance. See [Feature Compatibility Table](#feature-compatibility) and [Feature Compatibility Tutorial](feature_compatibility.md) for detailed configuration examples.
@@ -100,7 +100,9 @@ The following tables show which models support each feature:
- **🔀SP (Ulysses & Ring)**: Includes both Ulysses-SP and Ring-Attention methods
- ✅ = Fully supported
- ✅* = Supported with the constraint listed below the table
- ❌ = Not supported
- ❓ = Not verified; not recommended
> Notes:
@@ -119,7 +121,7 @@ The following tables show which models support each feature:
| **FLUX.2-dev** | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
| **GLM-Image** | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
| **Hidream-I1-Full** | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
| **HunyuanImage3** | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ |
| **HunyuanImage3** | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅* |
| **Krea 2** | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ (decode) | ❌ | ❌ |
| **LongCat-Image** | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| **LongCat-Image-Edit** | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
@@ -145,6 +147,7 @@ The following tables show which models support each feature:
> 2. `Tongyi-MAI/Z-Image-Turbo` and `SII-GAIR/daVinci-MagiHuman-Base-1080p` are distilled models with minimal NFEs; CFG-Parallel is not necessary.
> 3. Cosmos3 T2I uses `Cosmos3OmniDiffusersPipeline` with `modalities=["image"]`. Model-level CPU offload swaps the nested UND reasoner and GEN generator pathways; layerwise offload remains available for blockwise GEN/UND offload.
> 4. Krea 2 currently supports single-GPU inference plus LoRA, Cache-DiT, HSDP, CPU/layerwise offload, and VAE-patch-parallel (decode). TP/SP/CFG-Parallel are not yet wired. The few-step distilled (Turbo) checkpoint uses `is_distilled=true` (fixed timestep shift `mu=1.15`); generate at 2048x2048 by default with `num_inference_steps≈8` and `guidance_scale=0`. The Raw checkpoint uses 1024x1024, `num_inference_steps=28`, and `guidance_scale=4.5`.
> 5. HunyuanImage3 supports step execution. Multi-request step execution requires `TORCH_SDPA`; see [Diffusion Execution Modes](diffusion/execution_modes.md#step-execution).
### VideoGen
@@ -155,13 +158,16 @@ The following tables show which models support each feature:
| **Wan2.1-VACE** | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ (decode) | ❌ | ❌ |
| **LTX-2** | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ (decode) | ❌ | ❌ |
| **LTX-2.3** | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ (decode) | ❌ | ❌ |
| **Helios** | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| **Helios** | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅* |
| **HunyuanVideo-1.5 T2V I2V** | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ (encode/decode) | ✅ | ❌ |
| **DreamID-Omni** | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| **Cosmos3** | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ (encode/decode) | ✅ | ❌ |
| **LongCat-Video-Avatar-1.5** | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
| **MiniMax-H3** | ✅ (FL2VA) | ✅ | ✅ | ❌ | ✅ (DiT/TE) | ❌ | ✅ | ✅ | ✅ (tile) | ✅ (DiT) | ❌ |
> **Step execution note:** Helios supports single-request step execution only;
> use `max_num_seqs=1`.
**Frame Interpolation Support**
- **Supported**: Wan2.2 text-to-video, image-to-video, and TI2V pipelines
@@ -190,7 +196,7 @@ The following tables show which models support each feature:
| **🔀Ring-Attn** | ✅ | ✅ | ✅ | | | | | | | | | | | |
| **🔀CFG-Parallel** | ✅ | ✅ | ✅ | ✅ | | | | | | | | | | |
| **🔀Tensor Parallel** | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | | | | |
| **🔀HSDP** | ❓ | ❓ | ❓ | ❓ | ❓ | ❌ | | | | | | | | |
| **🔀HSDP** | ❓ | ❓ | ✅ | ❓ | ✅ | ❌ | | | | | | | | |
| **🔀Expert Parallel** | ❓ | ❓ | ❓ | ❓ | ❓ | ❓ | ❓ | | | | | | | |
| **💾CPU Offloading (Layerwise)** | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | | | | | |
| **💾CPU Offloading (Module-wise)** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❓ | ❓ | ❌ | | | | | |
@@ -201,66 +207,23 @@ The following tables show which models support each feature:
!!! info
1. Tensor Parallel and HSDP are not compatible.
1. HSDP can be combined with Ulysses-SP or CFG-Parallel. Tensor Parallel
and HSDP are not compatible; other HSDP combinations in the table
remain unverified.
2. TeaCache and Cache-DiT are not compatible.
3. CPU Offloading (Layerwise) and CPU Offloading (Module-wise) are not compatible.
4. CPU Offloading (Layerwise) supports single-card for now.
5. Using FP8-Quant as an example of qunatization methods.
4. The CPU Offloading (Layerwise) row describes local layerwise offload.
Multi-device Distributed Layerwise Offload has a separate topology and
compatibility matrix in the [CPU Offloading guide](diffusion/cpu_offload.md#distributed-layerwise-offloading).
5. The compatibility matrix uses FP8 as the representative quantization method.
6. Step Execution is not compatible with any diffusion cache backend. LoRA is supported, but each scheduled batch must use a single adapter (requests with different `lora_request` or `lora_scale` are kept in separate batches).
## Multi-Thread Weight Loading
Large diffusion models can take several minutes to load weights at startup (e.g., ~3 min for Qwen-Image, ~5 min for Wan2.2 I2V 14B). Multi-thread weight loading speeds up this process by loading safetensors shards in parallel using a thread pool instead of sequentially.
This optimization is **enabled by default** with 4 threads. No configuration is needed for the default behavior.
### Configuration
| Parameter | CLI Flag | Default | Description |
|-----------|----------|---------|-------------|
| `enable_multithread_weight_load` | `--disable-multithread-weight-load` | `True` (enabled) | Pass the flag to disable multi-thread loading |
| `num_weight_load_threads` | `--num-weight-load-threads` | `4` | Number of threads for parallel weight loading |
!!! tip
The default of 4 threads balances speed and disk I/O contention. On fast NVMe storage you may benefit from more threads (e.g., 8). On HDD or network storage, the default of 4 avoids saturating I/O bandwidth.
### Online Serving
```bash
# Default (multi-thread enabled, 4 threads)
vllm serve Qwen/Qwen-Image --omni --port 8091
# Custom thread count
vllm serve Wan-AI/Wan2.2-I2V-A14B-Diffusers --omni --num-weight-load-threads 8
# Disable multi-thread loading
vllm serve Qwen/Qwen-Image --omni --disable-multithread-weight-load
```
### Offline Inference
```python
from vllm_omni import Omni
# Default (multi-thread enabled, 4 threads)
omni = Omni(model="Qwen/Qwen-Image")
# Custom thread count
omni = Omni(
model="Wan-AI/Wan2.2-I2V-A14B-Diffusers",
num_weight_load_threads=8,
)
```
### Benchmarks
Measured on NVIDIA H800:
| Model | Before | After | Speedup |
|-------|--------|-------|---------|
| **Qwen/Qwen-Image** (53.7 GiB) | 168s | 27s | **6.2x** |
| **Wan-AI/Wan2.2-I2V-A14B-Diffusers** (64.5 GiB) | 283s | 56s | **5.1x** |
The loading guide now lives at [Diffusion Startup and
Loading](diffusion/startup_and_loading.md). This heading remains so existing
links to this section continue to work.
## Learn More