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# Diffusion Advanced Features
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<!-- markdownlint-disable MD028 MD060 -->
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## Table of Contents
- [Overview ](#overview )
- [Supported Features ](#supported-features )
- [Supported Models ](#supported-models )
- [Feature Compatibility ](#feature-compatibility )
- [Learn More ](#learn-more )
## Overview
vLLM-Omni supports various advanced features for diffusion models:
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- Acceleration: **cache methods** , **parallelism methods** , **startup optimizations**
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- Memory optimization: **cpu offloading** , **quantization**
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- Extensions: **LoRA inference** , **frame interpolation**
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- Execution modes: **step execution**
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## Supported Features
### Acceleration
#### Lossy Acceleration
Cache methods trade minimal quality for significant speedup. Quality loss is typically imperceptible with proper tuning.
| Method | Description | Best For |
|--------|-------------|----------|
| ** [TeaCache ](diffusion/cache_acceleration/teacache.md )** | Adaptive caching using modulated inputs | Quick setup, balanced quality/speed on single GPU |
| ** [Cache-DiT ](diffusion/cache_acceleration/cache_dit.md )** | Multiple caching techniques: DBCache, TaylorSeer, SCM | Fine-grained control, tunable quality-speed tradeoff |
#### Lossless Acceleration
Parallelism methods distribute computation across GPUs without quality loss (mathematically equivalent to single-GPU).
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| Method | Description | Best For |
|------------------------------------------------------------------------|--------------------------------------------------------------------------|-------------------------------------------------------------------|
| ** [Ulysses-SP ](diffusion/parallelism/sequence_parallel.md )** | Sequence parallelism via all-to-all communication | High-resolution images (>1536px) or long videos with 2-8 GPUs |
| ** [Ring-Attention ](diffusion/parallelism/sequence_parallel.md )** | Sequence parallelism via ring-based communication | Videos, very long sequences, memory-constrained, with 2-8 GPUs |
| ** [CFG-Parallel ](diffusion/parallelism/cfg_parallel.md )** | Splits CFG positive/negative branches across devices | Image editing with CFG guidance (true_cfg_scale > 1) on 2 GPUs |
| ** [Tensor Parallelism ](diffusion/parallelism/tensor_parallel.md )** | Shards model weights across devices | Large models that don't fit in single GPU, with 2+ GPUs |
| ** [Pipeline Parallelism ](diffusion/parallelism/pipeline_parallel.md )** | Splits the denoising transformer block-wise across sequential GPU stages | Large diffusion transformers that need lower per-GPU model memory |
| ** [HSDP ](diffusion/parallelism/hsdp.md )** | Weight sharding via FSDP2, redistributed on-demand at runtime | Very large models (14B+) on limited VRAM, combinable with SP |
| ** [Expert Parallelism ](diffusion/parallelism/expert_parallel.md )** | Shards MoE expert MLP blocks across devices | MoE diffusion models (e.g., HunyuanImage3.0) |
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#### Startup Optimization
| Method | Description | Best For |
|--------|-------------|----------|
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| ** [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 |
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**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.
### Memory Optimization
Memory optimization methods help reduce GPU memory usage, enabling inference on resource-constrained hardware or larger models.
| Method | Description | Best For |
|--------|-------------|----------|
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| ** [CPU Offload ](diffusion/cpu_offload.md )** | Offloads model components to CPU memory | Limited VRAM, large models on consumer GPUs |
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| ** [Quantization ](quantization/overview.md )** | Reduces transformer stages from BF16 to FP8/INT8/etc. | Limited VRAM, minimal accuracy loss |
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| ** [VAE Parallelism ](diffusion/parallelism/vae_parallelism.md )** | Distributes VAE decode work across GPUs | High-resolution generation with reduced VAE memory peak |
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### Extensions
Extension methods add specialized capabilities to diffusion models beyond standard inference.
| Method | Description | Best For |
|--------|-------------|----------|
| ** [LoRA Inference ](diffusion/lora.md )** | Enables inference with Low-Rank Adaptation (LoRA) adapters weights | Reinforcement learning extensions |
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| ** [Frame Interpolation ](diffusion/frame_interpolation.md )** | Inserts intermediate video frames after generation for smoother motion | Video generation pipelines that need higher temporal smoothness |
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### Execution Modes
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Execution modes control how the diffusion pipeline processes requests and
denoise steps.
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| Method | Description | Best For |
|--------|-------------|----------|
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| ** [Diffusion Execution Modes ](diffusion/execution_modes.md )** | Configures serial requests, request batching, step execution, continuous batching, and streaming output | Matching latency, throughput, cancellation, and output-delivery requirements |
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**Note:** Request-level batching and step execution are capability-based.
Consult the execution guide and the selected pipeline's documentation for
current support.
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### Quantization Methods
| Method | Configuration | Description | Best For |
|--------|--------------|-------------|----------|
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| ** [FP8 ](quantization/fp8.md )** | `quantization="fp8"` | FP8 W8A8 on validated transformer stages | Memory reduction, inference speedup |
| ** [INT8 ](quantization/int8.md )** | `quantization="int8"` | INT8 W8A8 on validated transformer stages | Memory reduction, broad GPU compatibility |
| ** [GGUF ](quantization/gguf.md )** | `quantization="gguf"` | Native GGUF transformer-only weights (Q4, Q8, etc.) | Memory reduction on consumer GPUs |
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## Supported Models
The following tables show which models support each feature:
- **🔀SP (Ulysses & Ring)**: Includes both Ulysses-SP and Ring-Attention methods
- ✅ = Fully supported
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- ✅* = Supported with the constraint listed below the table
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- ❌ = Not supported
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- ❓ = Not verified; not recommended
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> Notes:
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> 1. CPU Offload has three strategies: model-level, layerwise, and distributed
> layerwise. The tables below show **layerwise support** only. Split models
> like Cosmos3 swap their reasoner/generator components for model-level
> offload; see the [CPU Offload Guide](diffusion/cpu_offload.md).
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> 2. The **💾Quantization** column is collapsed for readability. See [Quantization](quantization/overview.md) for per-method and per-model support details.
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### ImageGen
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| Model | ⚡TeaCache | ⚡Cache-DiT | 🔀SP (Ulysses & Ring) | 🔀CFG-Parallel | 🔀Tensor-Parallel | 🔀Pipeline-Parallel | 🔀HSDP | 💾CPU Offload (Layerwise) | 💾VAE-Patch-Parallel | 💾Quantization | 🔄Step Execution |
|--------------------------|:---------:|:----------:|:---------------------:|:--------------:|:-----------------:|:-------------------:|:------:|:-------------------------:|:--------------------:|:--------------:|:----------------:|
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| **Bagel** | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ |
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| **Boogu-Image** | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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| **FLUX.1-dev** | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ |
| **FLUX.1-schnell** | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ |
| **FLUX.2-klein** | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ |
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| **FLUX.1-Kontext-dev** | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
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| **FLUX.2-dev** | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
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| **GLM-Image** | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
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| **Hidream-I1-Full** | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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| **HiDream-O1-Image** | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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| **HunyuanImage3** | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅* |
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| **Krea 2** | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ (decode) | ❌ | ❌ |
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| **LongCat-Image** | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| **LongCat-Image-Edit** | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
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| **MammothModa2(T2I)** | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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| **Nextstep_1(T2I)** | ❓ | ❓ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
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| **OmniGen2** | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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| **Ovis-Image** | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| **Qwen-Image** | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ (decode) | ✅ | ✅ |
| **Qwen-Image-2512** | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ (decode) | ✅ | ✅ |
| **Qwen-Image-Edit** | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ (decode) | ❌ | ❌ |
| **Qwen-Image-Edit-2509** | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ (decode) | ✅ | ❌ | ❌ |
| **Qwen-Image-Layered** | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ (decode) | ❌ | ❌ |
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| **Qwen-Image-2.1** | ❌ | ❌ | ✅ (Ulysses only) | ✅ | ❓ | ❌ | ❓ | ❓ | ✅ (decode) | ✅ | ✅ |
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| **SenseNova-U1 / U1.5** | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
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| **Stable-Diffusion-XL** | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ (decode) | ❌ | ❌ |
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| **Stable-Diffusion3.5** | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ (decode) | ❌ | ❌ |
| **Z-Image** | ✅ | ✅ | ✅ | ❓ | ✅ (TP=2 only) | ❌ | ✅ | ❌ | ✅ (decode) | ✅ | ❌ |
| **ERNIE-Image** | ❌ | ✅ | ✅ | ❓ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
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| **Cosmos3** | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ (decode) | ✅ | ❌ |
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> Notes:
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>
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> 1. Nextstep_1(T2I) does not support cache acceleration methods such as TeaCache or Cache-DiT.
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> 2. `Tongyi-MAI/Z-Image-Turbo` is a distilled model with minimal NFEs; CFG-Parallel is not necessary.
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> 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.
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> 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`.
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> 5. HunyuanImage3 supports step execution. Multi-request step execution requires `TORCH_SDPA`; see [Diffusion Execution Modes](diffusion/execution_modes.md#step-execution).
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> 6. BAGEL step execution supports image generation with `bagel.yaml`, `bagel_think.yaml`, and `bagel_single_stage.yaml`; two-stage Thinker execution and explicit single-stage text output remain on their existing complete-request paths. Image requests require `num_inference_steps >= 2`. BAGEL step execution cannot currently be combined with sequence parallelism or a diffusion cache backend; see [Diffusion Execution Modes](diffusion/execution_modes.md#step-execution).
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> 7. Qwen-Image-2.1 (`QwenImage21Pipeline`) handles both text-to-image and image-conditioned generation (up to 4 condition images) and supports LoRA through `QwenImageLoraLoaderMixin`. Sequence parallelism is Ulysses-only (ring attention is not supported), and cache acceleration (TeaCache / Cache-DiT) is unavailable; its built-in KV cache for the text/condition prefix is a separate feature from those cache backends.
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### VideoGen
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| Model | ⚡TeaCache | ⚡Cache-DiT | 🔀SP (Ulysses & Ring) | 🔀CFG-Parallel | 🔀Tensor-Parallel | Pipeline-Parallel | 🔀HSDP | 💾CPU Offload (Layerwise) | 💾VAE-Patch-Parallel | 💾Quantization | 🔄Step Execution |
|------------------------------|:---------:|:----------:|:---------------------:|:--------------:|:-----------------:|:-----------------:|:------:|:-------------------------:|:--------------------:|:--------------:|:----------------:|
| **Wan2.2** | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ (encode/decode) | ❌ | ❌ |
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| **Wan2.2-S2V** | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ (encode/decode) | ❌ | ❌ |
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| **Wan2.1-VACE** | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ (decode) | ❌ | ❌ |
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| **LTX-2** | ❌ | ✅ | ✅ (Ulysses only) | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ (decode) | ❌ | ❌ |
| **LTX-2.3** | ❌ | ✅ | ✅ (Ulysses only) | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ (decode) | ❌ | ❌ |
| **LTX-2.5** | ❌ | ❓ (one-stage) | ✅* (Ulysses only) | ✅ (Full only) | ❓ | ❌ | ✅ | ✅ | ✅ (decode) | ❓ (FP8) | ❌ |
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| **SANA-Video-2B T2V I2V** | ❌ | ❌ | ✅ (`--usp` , frame-sharded) | ✅ | ✅ (TP=2 only) | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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| **Helios** | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅* |
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| **HunyuanVideo-1.5 T2V I2V** | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ (encode/decode) | ✅ | ❌ |
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| **Cosmos3** | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ (encode/decode) | ✅ | ❌ |
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| **LongCat-Video-Avatar-1.5** | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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| **MiniMax-H3** | ✅ (FL2VA) | ✅ | ✅ | ❌ | ✅ (DiT/TE) | ❌ | ✅ | ✅ | ✅ (tile) | ✅ (DiT) | ❌ |
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| **MAGI-2 Preview** | ❌ | ✅ | ✅ (Ulysses) | ✅ (2-way) | ✅ | ❌ | ✅ | ✅ (1-GPU/LW; DLO DP-AG/SP no-AG) | ✅ (tile) | ❌ | ❌ |
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| **SANA-WM** | ❌ | ❌ | ❌<sup>5</sup> | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
> Notes:
> 5. SANA-WM cannot support sequence parallelism: its bidirectional gated delta
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> recurrence carries state across frames, so a rank cannot denoise a slice of
> the token sequence in isolation. Doing so would need a distributed scan or
> an all-gather before every GDN block. The remaining ❌ columns are simply
> unvalidated on this model, not known-broken.
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> **Step execution note:** Helios supports single-request step execution only;
> use `max_num_seqs=1`.
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**Frame Interpolation Support**
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- **Supported**: Wan2.2 text-to-video, image-to-video, and TI2V pipelines; SANA-Video-2B native text-to-video and image-to-video pipelines
- **Not supported**: Wan2.1-VACE, LTX-2, LTX-2.3, LTX-2.5, Helios, HunyuanVideo-1.5, DreamID-Omni, SANA-WM; SANA-Video-2B Diffusers-adapter text-to-video and image-to-video pipelines
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### AudioGen
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| Model | ⚡TeaCache | ⚡Cache-DiT | 🔀SP (Ulysses & Ring) | 🔀CFG-Parallel | 🔀Tensor-Parallel | 🔀Pipeline-Parallel | 🔀HSDP | 💾CPU Offload (Layerwise) | 💾VAE-Patch-Parallel | 💾Quantization | 🔄Step Execution |
|-----------------------|:---------:|:----------:|:---------------------:|:--------------:|:-----------------:|:-------------------:|:------:|:-------------------------:|:--------------------:|:--------------:|:----------------:|
| **Stable-Audio-Open** | ✅ | ❌ | ❓ | ❓ | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ |
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## Feature Compatibility
**Legend:**
- ✅: Functionality is supported
- ❌: No support plan
- ❓: Not verified yet and Not Recommended
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| | ⚡TeaCache | ⚡Cache-DiT | 🔀Ulysses-SP | 🔀Ring-Attn | 🔀CFG-Parallel | 🔀Tensor Parallel | 🔀HSDP | 🔀Expert Parallel | 💾CPU Offloading (Layerwise) | 💾CPU Offloading (Module-wise) | 💾VAE Patch Parallel | 💾FP8 Quant | 🔧LoRA Inference | 🔄Step Execution |
|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| ** ⚡TeaCache** | | | | | | | | | | | | | | |
| ** ⚡Cache-DiT** | ❌ | | | | | | | | | | | | | |
| ** 🔀Ulysses-SP** | ✅ | ✅ | | | | | | | | | | | | |
| ** 🔀Ring-Attn** | ✅ | ✅ | ✅ | | | | | | | | | | | |
| ** 🔀CFG-Parallel** | ✅ | ✅ | ✅ | ✅ | | | | | | | | | | |
| ** 🔀Tensor Parallel** | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | | | | |
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| ** 🔀HSDP** | ❓ | ❓ | ✅ | ❓ | ✅ | ❌ | | | | | | | | |
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| ** 🔀Expert Parallel** | ❓ | ❓ | ❓ | ❓ | ❓ | ❓ | ❓ | | | | | | | |
| ** 💾CPU Offloading (Layerwise)** | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | | | | | |
| ** 💾CPU Offloading (Module-wise)** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❓ | ❓ | ❌ | | | | | |
| ** 💾VAE Patch Parallel** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | | | |
| ** 💾FP8 Quant** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❓ | ❓ | ✅ | ✅ | ✅ | | | |
| ** 🔧LoRA Inference** | ❓ | ❓ | ❓ | ❓ | ❓ | ❓ | ❓ | ❓ | ❓ | ❓ | ❓ | ❓ | | |
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| ** 🔄Step Execution** | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❓ | ❓ | ✅ | ❓ | ✅ | ✅ | ✅ | |
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!!! info
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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.
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2. TeaCache and Cache-DiT are not compatible.
3. CPU Offloading (Layerwise) and CPU Offloading (Module-wise) are not compatible.
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4. The CPU Offloading (Layerwise) row describes local layerwise offload.
Multi-device Distributed Layerwise Offload has a separate topology and
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compatibility matrix in the [Distributed Layerwise Offloading guide ](diffusion/offloader/distributed_layerwise_offload.md ).
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5. The compatibility matrix uses FP8 as the representative quantization method.
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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).
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## Multi-Thread Weight Loading
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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.
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## Learn More
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The Diffusion Acceleration navigation groups the remaining guides as follows:
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| Area | Guide |
| --- | --- |
| Compatibility | [Feature Compatibility ](feature_compatibility.md ) |
| CPU offloading | [CPU Offloading ](diffusion/cpu_offload.md ) |
| Cache acceleration | [TeaCache ](diffusion/cache_acceleration/teacache.md ), [Cache-DiT ](diffusion/cache_acceleration/cache_dit.md ) |
| Parallelism | [Parallelism Overview ](diffusion/parallelism/overview.md ) |
| Attention | [Attention Backends ](diffusion/attention_backends.md ) |
| Compilation | [Regional Compilation ](diffusion/regional_compilation.md ) |
| Video extension | [Frame Interpolation ](diffusion/frame_interpolation.md ) |
| Startup | [Startup and Loading ](diffusion/startup_and_loading.md ) |
| Adapters | [LoRA ](diffusion/lora.md ) |
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Related cross-model and runtime features are documented separately:
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- [Quantization ](quantization/overview.md ) covers diffusion-only models,
multi-stage omni/TTS models, and multi-stage diffusion models.
- [Execution Modes and Streaming ](diffusion/execution_modes.md ) covers the
diffusion runtime, including batching, step execution, and streaming output.
