add-diffusion-model

分类: 内容与多媒体 | 上传者: hsliuustc0106 | 下载: 0 | 版本: v1.0（最新）
向 vLLM-Omni 添加新的扩散模型（文本到图像、文本到视频、图像到视频、文本到音频、图像编辑）。在集成新的扩散模型、将扩散器管道或自定义模型存储库移植到 vllm-omni、创建新的 DiT 变压器适配器或添加扩散模型支持时使用。
更新日志: Source: GitHub https://github.com/hsliuustc0106/vllm-omni-skills
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📁 references/
- 📄 custom-model-patterns.md 9.6 KB
- 📄 parallelism-patterns.md 4.0 KB
- 📄 transformer-adaptation.md 6.8 KB
- 📄 troubleshooting.md 4.3 KB
📄 SKILL.md 12.9 KB
SKILL.md

---
name: add-diffusion-model
description: Add a new diffusion model (text-to-image, text-to-video, image-to-video, text-to-audio, image editing) to vLLM-Omni. Use when integrating a new diffusion model, porting a diffusers pipeline or a custom model repo to vllm-omni, creating a new DiT transformer adapter, or adding diffusion model support.
---

# Adding a Diffusion Model to vLLM-Omni

## Overview

This skill guides you through adding a new diffusion model to vLLM-Omni. The model may come from HuggingFace Diffusers (structured pipeline) or from a private/custom repo. The workflow differs significantly depending on the source.

## Prerequisites

Before starting, determine:

1. **Model category**: Text-to-Image, Text-to-Video, Image-to-Video, Image Editing, Text-to-Audio, or Omni
2. **Reference source**: Diffusers pipeline, custom repo, or a combination
3. **Model HuggingFace ID** or local checkpoint path
4. **Architecture**: Scheduler, text encoder, VAE, transformer/backbone

## Step 0: Classify the Migration Path

Check the model's HF repo for `model_index.json`. This determines your path:

| Scenario | How to identify | Migration path |
|----------|----------------|----------------|
| **Already supported** | `_class_name` in `model_index.json` matches a key in `_DIFFUSION_MODELS` in `registry.py` | Skip to Step 5 (test) and Step 7 (docs) |
| **Diffusers-based** | Has standard `model_index.json` with `_diffusers_version`, subfolders for `transformer/`, `vae/`, etc. | Follow **Path A** below |
| **Custom/private repo** | No diffusers `model_index.json`, weights in non-standard format, custom model code in a separate git repo | Follow **Path B** below |
| **Hybrid** | Has some diffusers components (VAE) but custom transformer/fusion | Mix of Path A and Path B |

## Path A: Diffusers-Based Model

For models with a standard diffusers layout. See [references/transformer-adaptation.md](references/transformer-adaptation.md) for detailed code patterns.

### A1. Analyze `model_index.json`

Identify components: `transformer`, `scheduler`, `vae`, `text_encoder`, `tokenizer`.

### A2. Create model directory

```
vllm_omni/diffusion/models/your_model_name/
├── __init__.py
├── pipeline_your_model.py
└── your_model_transformer.py
```

### A3. Adapt transformer

1. Copy from diffusers source. Remove mixins (`ModelMixin`, `ConfigMixin`, `AttentionModuleMixin`).
2. Replace attention with `vllm_omni.diffusion.attention.layer.Attention` (QKV shape: `[B, seq, heads, head_dim]`).
3. Add `od_config: OmniDiffusionConfig | None = None` to `__init__`.
4. Add `load_weights()` method mapping diffusers weight names to vllm-omni names.
5. Add class attributes: `_repeated_blocks`, `_layerwise_offload_blocks_attr`.

### A4. Adapt pipeline

Inherit from `nn.Module`. The key contract:

```python
class YourPipeline(nn.Module):
    def __init__(self, *, od_config: OmniDiffusionConfig, prefix: str = ""):
        # Load VAE, text encoder, tokenizer via from_pretrained()
        # Instantiate transformer (weights loaded later via weights_sources)
        self.weights_sources = [
            DiffusersPipelineLoader.ComponentSource(
                model_or_path=od_config.model, subfolder="transformer",
                prefix="transformer.", fall_back_to_pt=True)]

    def forward(self, req: OmniDiffusionRequest) -> DiffusionOutput:
        # Encode prompt → prepare latents → denoise loop → VAE decode
        return DiffusionOutput(output=output)

    def load_weights(self, weights):
        return AutoWeightsLoader(self).load_weights(weights)
```

Add post/pre-process functions in the same pipeline file. Register them in `registry.py`.

### A5. Register, test, docs → continue at Step 4 below.

---

## Path B: Custom/Private Repo Model

For models without a diffusers pipeline — weights in custom format, model code in a private repo. Real examples: DreamID-Omni, BAGEL, HunyuanImage3.

### B1. Understand the reference repo

Study the original model's code to identify:
- **Model architecture files** (transformers, fusion modules, embeddings)
- **Weight format** (safetensors, `.pth`, custom checkpoint structure)
- **Weight loading helpers** (custom init functions, checkpoint loaders)
- **Pre/post-processing** (image/audio transforms, tokenization, VAE encode/decode)
- **External dependencies** (packages not on PyPI)
- **Config format** (JSON config files, hardcoded dicts)

### B2. Decide what lives WHERE

This is the key design decision for custom models. Follow these placement rules:

| Code type | Where to place | Example |
|-----------|---------------|---------|
| **Pipeline orchestration** (init, forward, denoise loop) | `vllm_omni/diffusion/models/<name>/pipeline_<name>.py` | Always required |
| **Custom transformer/backbone** (ported and adapted to vllm-omni) | `vllm_omni/diffusion/models/<name>/<name>_transformer.py` or similar | `wan2_2.py`, `fusion.py`, `bagel_transformer.py` |
| **Custom sub-models** (VAE, fusion, autoencoder) | `vllm_omni/diffusion/models/<name>/` as separate files | `autoencoder.py`, `fusion.py` |
| **External dependency code** (original repo utilities) | **External repo**, installed via download script or pip | `dreamid_omni` package via git clone |
| **Hardcoded model configs** | Module-level dicts in pipeline file | `VIDEO_CONFIG`, `AUDIO_CONFIG` dicts |
| **Download/setup script** | `examples/offline_inference/<name>/download_<name>.py` | `download_dreamid_omni.py` |
| **Custom `model_index.json`** | Generated by download script, placed at model root | Minimal: `{"_class_name": "YourPipeline", ...}` |

### B3. Handle external dependencies

If the model's code lives in a separate git repo:

**Option 1: Import with graceful fallback** (recommended for models with external utils)

```python
try:
    from external_model.utils import init_vae, load_checkpoint
except ImportError:
    raise ImportError(
        "Failed to import from dependency 'external_model'. "
        "Please run the download script first."
    )
```

**Option 2: Port the code directly** (preferred when feasible)

Copy the essential model files into `vllm_omni/diffusion/models/<name>/` and adapt them. This avoids external dependencies. BAGEL does this — `autoencoder.py` and `bagel_transformer.py` are ported directly.

**Decision criteria**: Port if the code is self-contained and won't diverge. Use external deps if the model repo is actively maintained and the code is complex.

### B4. Handle custom weight loading

Custom models have two patterns for weight loading:

**Pattern 1: Bypass standard loader** (DreamID-Omni style)

When the original model has complex custom init functions that load weights in `__init__`:

```python
class CustomPipeline(nn.Module):
    def __init__(self, *, od_config, prefix=""):
        super().__init__()
        model = od_config.model
        # Load everything eagerly in __init__ using custom helpers
        self.vae = custom_init_vae(model, device=self.device)
        self.text_encoder = custom_init_text_encoder(model, device=self.device)
        self.transformer = CustomFusionModel(CONFIG)
        load_custom_checkpoint(self.transformer,
            checkpoint_path=os.path.join(model, "model.safetensors"))
        # NO weights_sources defined — bypasses standard loader

    def load_weights(self, weights):
        pass  # No-op — all weights loaded in __init__
```

**Pattern 2: Use standard loader with custom `load_weights`** (BAGEL style)

When weights are in safetensors format but need name remapping:

```python
class CustomPipeline(nn.Module):
    def __init__(self, *, od_config, prefix=""):
        super().__init__()
        # Instantiate model architecture without weights
        self.bagel = BagelModel(config)
        self.vae = AutoEncoder(ae_params)

        # Point loader at the safetensors in the model root
        self.weights_sources = [
            DiffusersPipelineLoader.ComponentSource(
                model_or_path=od_config.model,
                subfolder=None,  # weights at root, not in subfolder
                prefix="",
                fall_back_to_pt=False,
            )
        ]

    def load_weights(self, weights):
        # Custom name remapping for non-diffusers weight names
        params = dict(self.named_parameters())
        loaded = set()
        for name, tensor in weights:
            # Remap original weight names to vllm-omni module names
            name = self._remap_weight_name(name)
            if name in params:
                default_weight_loader(params[name], tensor)
                loaded.add(name)
        return loaded
```

### B5. Create the `model_index.json`

Custom models need a `model_index.json` at the model root for vllm-omni to discover them. For custom models, this is minimal:

```json
{
    "_class_name": "YourModelPipeline",
    "custom_key": "path/to/custom_weights.safetensors"
}
```

The `_class_name` must match a key in `_DIFFUSION_MODELS` in `registry.py`. Additional keys are model-specific (accessed via `od_config.model_config`).

If the model's weights come from multiple HF repos, write a **download script** that:
1. Downloads from each repo
2. Assembles into a single directory
3. Generates `model_index.json`
4. Installs any external dependencies (git clone + `.pth` file)

Place at: `examples/offline_inference/<name>/download_<name>.py`

### B6. Handle multi-modal inputs

If the model accepts images, audio, or other multi-modal inputs, implement the protocol classes from `vllm_omni/diffusion/models/interface.py`:

```python
from vllm_omni.diffusion.models.interface import SupportImageInput, SupportAudioInput

class MyPipeline(nn.Module, SupportImageInput, SupportAudioInput):
    # Protocol markers — the engine uses these to enable proper input routing
    pass
```

Preprocessing for custom models is typically done **inside `forward()`** rather than via registered pre-process functions, since the logic is often tightly coupled to the model.

### B7. Continue at Step 4 below.

---

## Common Steps (Both Paths)

### Step 4: Register Model in registry.py

Edit `vllm_omni/diffusion/registry.py`:

```python
_DIFFUSION_MODELS = {
    "YourModelPipeline": ("your_model_name", "pipeline_your_model", "YourModelPipeline"),
}
_DIFFUSION_POST_PROCESS_FUNCS = {
    "YourModelPipeline": "get_your_model_post_process_func",  # if applicable
}
_DIFFUSION_PRE_PROCESS_FUNCS = {
    "YourModelPipeline": "get_your_model_pre_process_func",  # if applicable
}
```

The registry key is the `_class_name` from `model_index.json`. The tuple is `(folder_name, module_file, class_name)`.

Create `__init__.py` exporting the pipeline class and any factory functions.

### Step 5: Run, Test, Debug

Use the appropriate existing example script:

| Category | Script |
|----------|--------|
| Text-to-Image | `examples/offline_inference/text_to_image/text_to_image.py` |
| Text-to-Video | `examples/offline_inference/text_to_video/text_to_video.py` |
| Image-to-Video | `examples/offline_inference/image_to_video/image_to_video.py` |
| Image-to-Image | `examples/offline_inference/image_to_image/image_edit.py` |
| Text-to-Audio | `examples/offline_inference/text_to_audio/text_to_audio.py` |

For custom/Omni models that don't fit these categories, create a dedicated example script.

**Validation**: No errors, output is meaningful, quality matches reference implementation.

See [references/troubleshooting.md](references/troubleshooting.md) for common errors.

### Step 6: Add Example Scripts

For Omni or custom models, create:
- `examples/offline_inference/your_model_name/` — offline script + README
- `examples/online_serving/your_model_name/` — server script + client
- Download script if weights require assembly from multiple sources

### Step 7: Update Documentation

Required updates:
1. `docs/user_guide/diffusion/parallelism_acceleration.md` — parallelism support table
2. `docs/user_guide/diffusion/teacache.md` — if TeaCache supported
3. `docs/user_guide/diffusion/cache_dit_acceleration.md` — if Cache-DiT supported
4. `examples/offline_inference/xxx/README.md` — offline example docs
5. `examples/online_serve/xxx/README.md` — online serve docs

### Step 8: Add E2E Tests (Recommended)

Create `tests/e2e/online_serving/test_your_model_expansion.py`.

## Iterative Development Tips

1. **Start minimal**: Basic generation first, no parallelism/caching
2. **Use `--enforce-eager`**: Disable torch.compile during debugging
3. **Use small models**: Test with smaller variants first
4. **Check tensor shapes**: Most errors are reshape mismatches in attention
5. **Add parallelism incrementally**: TP → SP → CFG parallel
6. **For custom models**: Get the model running with the original code first, then progressively replace components with vllm-omni equivalents

## Reference Files

- [Transformer Adaptation](references/transformer-adaptation.md) — porting transformers from diffusers
- [Custom Model Patterns](references/custom-model-patterns.md) — patterns for non-diffusers models
- [Parallelism Patterns](references/parallelism-patterns.md) — TP, SP, CFG parallel
- [Troubleshooting](references/troubleshooting.md) — common errors and fixes
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