vllm-omni/docs/user_guide/diffusion/lora.md

# LoRA (Low-Rank Adaptation) Guide

LoRA (Low-Rank Adaptation) enables fine-tuning diffusion models by adding trainable low-rank matrices to existing model weights. vLLM-Omni supports two LoRA backends: **PEFT** for PEFT-style adapters, and **Distill** usually for few-steps inference. **PEFT** backend allows you to customize model behavior without modifying the base model weights. **Distill** backend fuses LoRA weights into the base model at initialization.

## Overview

LoRA adapters are lightweight, model-specific fine-tuning weights that can be applied to diffusion models in two ways:

- **PEFT backend** (`--lora-backend peft`, default): Loads a PEFT-format adapter folder via `DiffusionLoRAManager`. Adapters are cached (LRU) and activated per request via `LoRARequest`. It uses a unified LoRA handling mechanisms similar to vLLM with LRU cache management.
- **Distill backend** (`--lora-backend distill`): Calls `pipeline.load_lora_weights` once at initialization to fuse one or more concrete checkpoint files directly into the base weights. Typically used for distilled few-step LoRAs (e.g. Lightning, LightX2V).

## LoRA Adapter Format

### PEFT (Parameter-Efficient Fine-Tuning) format (default)
A typical PEFT-format LoRA adapter directory structure:

```
lora_adapter/
├── adapter_config.json
└── adapter_model.safetensors
```

The `adapter_config.json` file contains metadata about the LoRA adapter, including:
- `r`: LoRA rank
- `lora_alpha`: LoRA alpha scaling factor
- `target_modules`: List of module names to apply LoRA to

## Quick Start

### Offline Inference

#### PEFT backend: pre-loaded LoRA

Load a PEFT-format LoRA adapter at initialization. The adapter is pre-loaded into the cache and can be activated per request:

```python
from vllm_omni import Omni
from vllm_omni.lora.request import LoRARequest

lora_path = "/path/to/lora_adapter"

omni = Omni(
    model="stabilityai/stable-diffusion-3.5-medium",
    lora_path=lora_path,
    lora_backend="peft",  # default, can be omitted
)

lora_request = LoRARequest(
    lora_name="preloaded",
    lora_int_id=1,
    lora_path=lora_path
)

outputs = omni.generate(
    prompt="A piece of cheesecake",
    lora_request=lora_request,
    lora_scale=2.0, # optional arg, default 1.0
)
```

#### Distill backend: fuse distilled LoRA at init

For distilled few-step LoRAs, pass `lora_backend="distill"` together with one or more concrete `.safetensors` files. The weights are fused into the base model once at init; subsequent `generate()` calls do not need a `LoRARequest`.

##### Supported pipelines

For Qwen-Image and Wan pipelines, the distill backend calls `pipeline.load_lora_weights(...)` during worker initialization.

| Pipeline | Supported distilled LoRA repo | Notes |
|----------|--------------------------------|-------|
| `QwenImagePipeline` | `lightx2v/Qwen-Image-2512-Lightning` | Used with Qwen-Image-2512 Lightning-style few-step inference. |
| `Wan22Pipeline` | `lightx2v/Wan2.1-Distill-Loras`, `lightx2v/Wan2.2-Distill-Loras` | Wan2.1 uses one LoRA file. For dual-transformer Wan2.2 MoE, pass high-noise then low-noise LoRA files. |
| `Wan22I2VPipeline` | `lightx2v/Wan2.2-Distill-Loras` | For dual-transformer Wan2.2 MoE, pass high-noise then low-noise LoRA files. |

Other diffusion pipelines are not currently listed as supporting distilled LoRA. Use the PEFT backend for request-time adapters, or bake converted weights into a local Diffusers directory before serving.

Single-file example (Qwen-Image-Lightning):

```python
from vllm_omni import Omni

omni = Omni(
    model="Qwen/Qwen-Image-2512",
    lora_path="/path/to/Qwen-Image-2512-Lightning.safetensors",
    lora_backend="distill",
)

outputs = omni.generate(prompt="A piece of cheesecake")
```

Multi-file example (Wan2.2 MoE, high + low noise):

```python
from vllm_omni import Omni

omni = Omni(
    model="Wan-AI/Wan2.2-T2V-A14B-Diffusers",
    lora_path=[
        "/path/to/wan2.2_high_noise_lora.safetensors",   # -> transformer
        "/path/to/wan2.2_low_noise_lora.safetensors",    # -> transformer_2
    ],
    lora_backend="distill",
)
```

The CLI examples under `examples/offline_inference/` accept the same flags, e.g.:

```bash
python examples/offline_inference/text_to_video/text_to_video.py \
  --model Wan-AI/Wan2.2-T2V-A14B-Diffusers \
  --lora-backend distill \
  --lora-path /path/to/high.safetensors /path/to/low.safetensors \
  --prompt "A cat playing with yarn"
```

##### Online serving

Distilled LoRAs can also be fused when an online diffusion server starts. The
adapter remains active for the lifetime of that server, so requests do not pass
a per-request `lora` field.

```bash
vllm serve Qwen/Qwen-Image-2512 \
  --omni \
  --port 8091 \
  --lora-backend distill \
  --lora-path /path/to/Qwen-Image-2512-Lightning-4steps.safetensors
```

Then send a normal generation request with the sampling settings expected by
the distilled checkpoint:

```bash
curl -s http://localhost:8091/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": "A Chinese female college student, around 20 years old, with a very short haircut that conveys a gentle, artistic vibe. Her hair naturally falls to partially cover her cheeks, projecting a tomboyish yet charming demeanor. She has cool-toned fair skin and delicate features, with a slightly shy yet subtly confident expression—her mouth crooked in a playful, youthful smirk. She wears an off-shoulder top, revealing one shoulder, with a well-proportioned figure. The image is framed as a close-up selfie: she dominates the foreground, while the background clearly shows her dormitory—a neatly made bed with white linens on the top bunk, a tidy study desk with organized stationery, and wooden cabinets and drawers. The photo is captured on a smartphone under soft, even ambient lighting, with natural tones, high clarity, and a bright, lively atmosphere full of youthful, everyday energy."}
    ],
    "extra_body": {
      "height": 1024,
      "width": 1024,
      "num_inference_steps": 4,
      "true_cfg_scale": 1.0,
      "seed": 42
    }
  }' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2 | base64 -d > qwen_image_distill_lora_output.png
```

For Wan2.2 MoE serving, pass the high-noise checkpoint first and the low-noise
checkpoint second to `--lora-path`.

!!! note "Server-side Path Requirement"
    The LoRA adapter path (`local_path`) must be readable on the **server** machine. If your client and server are on different machines, ensure the LoRA adapter is accessible via a shared mount or copied to the server.

## Wan2.2 LightX2V Offline Assembly

This workflow is LoRA-adjacent: it uses external LightX2V conversion plus
`Wan2.2-Distill-Loras` to bake converted Wan2.2 I2V checkpoints into a local
Diffusers directory, instead of loading LoRA adapters at runtime.

### Required assets

- Base model: `Wan-AI/Wan2.2-I2V-A14B`
- Diffusers skeleton: `Wan-AI/Wan2.2-I2V-A14B-Diffusers`
- Optional external converter from the LightX2V project (not shipped in this repository)
- Optional LoRA weights: `lightx2v/Wan2.2-Distill-Loras`

### Step 1: Optional - convert high/low-noise DiT weights with LightX2V

Install or clone LightX2V from the upstream repository
(`https://github.com/ModelTC/LightX2V`). After cloning, the converter used
below is available at `<lightx2v_root>/tools/convert/converter.py`.

```bash
python /path/to/lightx2v/tools/convert/converter.py \
  --source /path/to/Wan2.2-I2V-A14B/high_noise_model \
  --output /tmp/wan22_lightx2v/high_noise_out \
  --output_ext .safetensors \
  --output_name diffusion_pytorch_model \
  --model_type wan_dit \
  --direction forward \
  --lora_path /path/to/wan2.2_i2v_A14b_high_noise_lora_rank64_lightx2v_4step_1022.safetensors \
  --lora_key_convert auto \
  --single_file

python /path/to/lightx2v/tools/convert/converter.py \
  --source /path/to/Wan2.2-I2V-A14B/low_noise_model \
  --output /tmp/wan22_lightx2v/low_noise_out \
  --output_ext .safetensors \
  --output_name diffusion_pytorch_model \
  --model_type wan_dit \
  --direction forward \
  --lora_path /path/to/wan2.2_i2v_A14b_low_noise_lora_rank64_lightx2v_4step_1022.safetensors \
  --lora_key_convert auto \
  --single_file
```

If you are not using LightX2V, skip this step and either keep the original
Diffusers weights from the skeleton or point Step 2 at any other converted
`transformer/` and `transformer_2/` checkpoints.

### Step 2: Assemble a final Diffusers-style directory

```bash
python tools/wan22/assemble_wan22_i2v_diffusers.py \
  --diffusers-skeleton /path/to/Wan2.2-I2V-A14B-Diffusers \
  --transformer-weight /tmp/wan22_lightx2v/high_noise_out \
  --transformer-2-weight /tmp/wan22_lightx2v/low_noise_out \
  --output-dir /path/to/Wan2.2-I2V-A14B-Custom-Diffusers \
  --asset-mode symlink \
  --overwrite
```

`--transformer-weight` and `--transformer-2-weight` are optional. If you omit
them, the tool keeps the original weights from the Diffusers skeleton.

### Step 3: Run offline inference

```bash
python examples/offline_inference/image_to_video/image_to_video.py \
  --model /path/to/Wan2.2-I2V-A14B-Custom-Diffusers \
  --image /path/to/input.jpg \
  --prompt "A cat playing with yarn" \
  --num-frames 81 \
  --num-inference-steps 4 \
  --tensor-parallel-size 4 \
  --height 480 \
  --width 832 \
  --flow-shift 12 \
  --sample-solver euler \
  --guidance-scale 1.0 \
  --guidance-scale-high 1.0 \
  --boundary-ratio 0.875
```

Notes:

- This route avoids runtime LoRA loading changes in vLLM-Omni when you choose to bake converted weights into a local Diffusers directory.
- Output quality and speed depend on the replacement checkpoints and sampling params you choose.
- If you only need to fuse distilled LoRAs into a Wan2.2 checkpoint at load time (without the full LightX2V convert + assemble pipeline), you can instead pass them directly via `--lora-backend distill --lora-path <high>.safetensors <low>.safetensors`. See the [Distill backend](#distill-backend-fuse-distilled-lora-at-init) section above.


## See Also

- [Text-to-Image Offline Example](../examples/offline_inference/text_to_image.md#lora) - Complete offline LoRA example
- [Text-to-Image Online Example](../examples/online_serving/text_to_image.md#lora) - Complete online LoRA example