# Parallelism Acceleration Guide

This guide includes how to use parallelism methods in vLLM-Omni to speed up diffusion model inference as well as reduce the memory requirement on each device.

## Overview

The following parallelism methods are currently supported in vLLM-Omni:

1. DeepSpeed Ulysses Sequence Parallel (DeepSpeed Ulysses-SP) ([arxiv paper](https://arxiv.org/pdf/2309.14509)): Ulysses-SP splits the input along the sequence dimension and uses all-to-all communication to allow each device to compute only a subset of attention heads.

2. [Ring-Attention](#ring-attention) - splits the input along the sequence dimension and uses ring-based P2P communication to accumulate attention results, keeping the sequence dimension sharded

3. Classifier-Free-Guidance Parallel (CFG-Parallel): CFG-Parallel runs the positive/negative prompts of classifier-free guidance (CFG) on different devices, then merges on a single device to perform the scheduler step.

4. [Tensor Parallelism](#tensor-parallelism): Tensor parallelism shards model weights across devices. This can reduce per-GPU memory usage. Note that for diffusion models we currently shard the majority of layers within the DiT.

The following table shows which models are currently supported by parallelism method:

### ImageGen

| Model                    | Model Identifier                     | Ulysses-SP | Ring-SP | CFG-Parallel | Tensor-Parallel |
|--------------------------|--------------------------------------|:----------:|:-------:|:------------:|:---------------:|
| **LongCat-Image**        | `meituan-longcat/LongCat-Image`      |     ✅      |    ✅    |      ❌       |        ✅        |
| **LongCat-Image-Edit**   | `meituan-longcat/LongCat-Image-Edit` |     ✅      |    ✅    |      ❌       |        ✅        |
| **Ovis-Image**           | `OvisAI/Ovis-Image`                  |     ❌      |    ❌    |      ❌       |        ❌        |
| **Qwen-Image**           | `Qwen/Qwen-Image`                    |     ✅      |    ✅    |      ✅       |        ✅        |
| **Qwen-Image-Edit**      | `Qwen/Qwen-Image-Edit`               |     ✅      |    ✅    |      ✅       |        ✅        |
| **Qwen-Image-Edit-2509** | `Qwen/Qwen-Image-Edit-2509`          |     ✅      |    ✅    |      ✅       |        ✅        |
| **Qwen-Image-Layered**   | `Qwen/Qwen-Image-Layered`            |     ✅      |    ✅    |      ✅       |        ✅        |
| **Z-Image**              | `Tongyi-MAI/Z-Image-Turbo`           |     ✅      |    ✅    |      ❌       |  ✅ (TP=2 only)  |
| **Stable-Diffusion3.5**  | `stabilityai/stable-diffusion-3.5`   |     ❌      |    ❌    |      ❌       |        ❌        |
| **FLUX.2-klein**         | `black-forest-labs/FLUX.2-klein-4B`  |     ❌      |    ❌    |      ❌       |        ✅        |
| **FLUX.1-dev**           | `black-forest-labs/FLUX.1-dev`       |     ❌      |    ❌    |      ✅       |        ✅        |

!!! note "TP Limitations for Diffusion Models"
    We currently implement Tensor Parallelism (TP) only for the DiT (Diffusion Transformer) blocks. This is because the `text_encoder` component in vLLM-Omni uses the original Transformers implementation, which does not yet support TP.

    - Good news: The text_encoder typically has minimal impact on overall inference performance.
    - Bad news: When TP is enabled, every TP process retains a full copy of the text_encoder weights, leading to significant GPU memory waste.

    We are actively refactoring this design to address this. For details and progress, please refer to [Issue #771](https://github.com/vllm-project/vllm-omni/issues/771).


!!! note "Why Z-Image is TP=2 only"
    Z-Image Turbo is currently limited to `tensor_parallel_size` of **1 or 2** due to model shape divisibility constraints.
    For example, the model has `n_heads=30` and a final projection out dimension of `64`, so valid TP sizes must divide both 30 and 64; the only common divisors are **1 and 2**.

### VideoGen

| Model | Model Identifier | Ulysses-SP | Ring-SP | Tensor-Parallel |
|-------|------------------|------------|---------|--------------------------|
| **Wan2.2** | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | ✅ | ✅ | ✅ |

### Tensor Parallelism

Tensor parallelism splits model parameters across GPUs. In vLLM-Omni, tensor parallelism is configured via `DiffusionParallelConfig.tensor_parallel_size`.

#### Offline Inference

```python
from vllm_omni import Omni
from vllm_omni.diffusion.data import DiffusionParallelConfig

omni = Omni(
    model="Tongyi-MAI/Z-Image-Turbo",
    parallel_config=DiffusionParallelConfig(tensor_parallel_size=2),
)

outputs = omni.generate(
    "a cat reading a book",
    OmniDiffusionSamplingParams(
        num_inference_steps=9,
        width=512,
        height=512,
    ),
)
```

### Sequence Parallelism

#### Ulysses-SP

##### Offline Inference

An example of offline inference script using [Ulysses-SP](https://arxiv.org/pdf/2309.14509) is shown below:
```python
from vllm_omni import Omni
from vllm_omni.inputs.data import OmniDiffusionSamplingParams
from vllm_omni.diffusion.data import DiffusionParallelConfig
ulysses_degree = 2

omni = Omni(
    model="Qwen/Qwen-Image",
    parallel_config=DiffusionParallelConfig(ulysses_degree=2)
)

outputs = omni.generate(
    "A cat sitting on a windowsill",
    OmniDiffusionSamplingParams(num_inference_steps=50, width=2048, height=2048),
)
```

See `examples/offline_inference/text_to_image/text_to_image.py` for a complete working example.

##### Online Serving

You can enable Ulysses-SP in online serving for diffusion models via `--usp`:

```bash
# Text-to-image (requires >= 2 GPUs)
vllm serve Qwen/Qwen-Image --omni --port 8091 --usp 2
```

##### Benchmarks
!!! note "Benchmark Disclaimer"
    These benchmarks are provided for **general reference only**. The configurations shown use default or common parameter settings and have not been exhaustively optimized for maximum performance. Actual performance may vary based on:

    - Specific model and use case
    - Hardware configuration
    - Careful parameter tuning
    - Different inference settings (e.g., number of steps, image resolution)


To measure the parallelism methods, we run benchmarks with **Qwen/Qwen-Image** model generating images (**2048x2048** as long sequence input) with 50 inference steps. The hardware devices are NVIDIA H800 GPUs. `sdpa` is the attention backends.

| Configuration | Ulysses degree |Generation Time | Speedup |
|---------------|----------------|---------|---------|
| **Baseline (diffusers)** | - | 112.5s | 1.0x |
| Ulysses-SP  |  2  |  65.2s | 1.73x |
| Ulysses-SP  |  4  | 39.6s | 2.84x |
| Ulysses-SP  |  8  | 30.8s | 3.65x |

#### Ring-Attention

Ring-Attention ([arxiv paper](https://arxiv.org/abs/2310.01889)) splits the input along the sequence dimension and uses ring-based P2P communication to accumulate attention results. Unlike Ulysses-SP which uses all-to-all communication, Ring-Attention keeps the sequence dimension sharded throughout the computation and circulates Key/Value blocks through a ring topology.

##### Offline Inference

An example of offline inference script using Ring-Attention is shown below:
```python
from vllm_omni import Omni
from vllm_omni.inputs.data import OmniDiffusionSamplingParams
from vllm_omni.diffusion.data import DiffusionParallelConfig
ring_degree = 2

omni = Omni(
    model="Qwen/Qwen-Image",
    parallel_config=DiffusionParallelConfig(ring_degree=2)
)

outputs = omni.generate(
    "A cat sitting on a windowsill",
    OmniDiffusionSamplingParams(num_inference_steps=50, width=2048, height=2048),
)
```

See `examples/offline_inference/text_to_image/text_to_image.py` for a complete working example.


##### Online Serving

You can enable Ring-Attention in online serving for diffusion models via `--ring`:

```bash
# Text-to-image (requires >= 2 GPUs)
vllm serve Qwen/Qwen-Image --omni --port 8091 --ring 2
```

##### Benchmarks
!!! note "Benchmark Disclaimer"
    These benchmarks are provided for **general reference only**. The configurations shown use default or common parameter settings and have not been exhaustively optimized for maximum performance. Actual performance may vary based on:

    - Specific model and use case
    - Hardware configuration
    - Careful parameter tuning
    - Different inference settings (e.g., number of steps, image resolution)


To measure the parallelism methods, we run benchmarks with **Qwen/Qwen-Image** model generating images (**1024x1024** as long sequence input) with 50 inference steps. The hardware devices are NVIDIA A100 GPUs. `flash_attn` is the attention backends.

| Configuration | Ring degree |Generation Time | Speedup |
|---------------|----------------|---------|---------|
| **Baseline (diffusers)** | - | 45.2s | 1.0x |
| Ring-Attention  |  2  |  29.9s | 1.51x |
| Ring-Attention  |  4  | 23.3s | 1.94x |


#### Hybrid Ulysses + Ring

You can combine both Ulysses-SP and Ring-Attention for larger scale parallelism. The total sequence parallel size equals `ulysses_degree × ring_degree`.

##### Offline Inference

```python
from vllm_omni import Omni
from vllm_omni.inputs.data import OmniDiffusionSamplingParams
from vllm_omni.diffusion.data import DiffusionParallelConfig

# Hybrid: 2 Ulysses × 2 Ring = 4 GPUs total
omni = Omni(
    model="Qwen/Qwen-Image",
    parallel_config=DiffusionParallelConfig(ulysses_degree=2, ring_degree=2)
)

outputs = omni.generate(
    "A cat sitting on a windowsill",
    OmniDiffusionSamplingParams(num_inference_steps=50, width=2048, height=2048),
)
```

##### Online Serving

```bash
# Text-to-image (requires >= 4 GPUs)
vllm serve Qwen/Qwen-Image --omni --port 8091 --usp 2 --ring 2
```

##### Benchmarks
!!! note "Benchmark Disclaimer"
    These benchmarks are provided for **general reference only**. The configurations shown use default or common parameter settings and have not been exhaustively optimized for maximum performance. Actual performance may vary based on:

    - Specific model and use case
    - Hardware configuration
    - Careful parameter tuning
    - Different inference settings (e.g., number of steps, image resolution)


To measure the parallelism methods, we run benchmarks with **Qwen/Qwen-Image** model generating images (**1024x1024** as long sequence input) with 50 inference steps. The hardware devices are NVIDIA A100 GPUs. `flash_attn` is the attention backends.

| Configuration | Ulysses degree | Ring degree | Generation Time | Speedup |
|---------------|----------------|-------------|-----------------|---------|
| **Baseline (diffusers)** | - | - | 45.2s | 1.0x |
| Hybrid Ulysses + Ring  |  2  |  2  |  24.3s | 1.87x |


### CFG-Parallel

#### Offline Inference

CFG-Parallel is enabled through `DiffusionParallelConfig(cfg_parallel_size=2)`, which runs one rank for the positive branch and one rank for the negative branch.

An example of offline inference using CFG-Parallel (image-to-image) is shown below:

```python
from vllm_omni import Omni
from vllm_omni.diffusion.data import DiffusionParallelConfig

image_path = "path_to_image.png"
omni = Omni(
    model="Qwen/Qwen-Image-Edit",
    parallel_config=DiffusionParallelConfig(cfg_parallel_size=2),
)
input_image = Image.open(image_path).convert("RGB")

outputs = omni.generate(
    {
        "prompt": "turn this cat to a dog",
        "negative_prompt": "low quality, blurry",
        "multi_modal_data": {"image": input_image},
    },
    OmniDiffusionSamplingParams(
        true_cfg_scale=4.0,
        num_inference_steps=50,
    ),
)
```

Notes:

- CFG-Parallel is only effective when a `negative_prompt` is provided AND a guidance scale (or `cfg_scale`) is greater than 1.

See `examples/offline_inference/image_to_image/image_edit.py` for a complete working example.
```bash
cd examples/offline_inference/image_to_image/
python image_edit.py \
  --model "Qwen/Qwen-Image-Edit" \
  --image "qwen_image_output.png" \
  --prompt "turn this cat to a dog" \
  --negative_prompt "low quality, blurry" \
  --cfg_scale 4.0 \
  --output "edited_image.png" \
  --cfg_parallel_size 2
```

#### Online Serving

You can enable CFG-Parallel in online serving for diffusion models via `--cfg-parallel-size`:

```bash
vllm serve Qwen/Qwen-Image-Edit --omni --port 8091 --cfg-parallel-size 2
```