add-diffusion-model

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, including Cache-DiT acceleration and parallelism support (TP, SP/USP, CFG-Parallel, HSDP). 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, adding diffusion model support, or enabling multi-GPU parallelism and cache acceleration for an existing model.

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 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:

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)

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__:

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:

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:

{
    "_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:

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:

_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 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/cpu_offload_diffusion.md — if CPU offload supported (add to supported models table)
3. docs/user_guide/diffusion/teacache.md — if TeaCache supported
4. docs/user_guide/diffusion/cache_dit_acceleration.md — if Cache-DiT supported
5. examples/offline_inference/xxx/README.md — offline example docs
6. 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.

Step 9: Add Cache-DiT Acceleration

Cache-DiT accelerates inference by caching intermediate computation results across denoising steps. After your model is working correctly on a single GPU, add cache-dit support.

See references/cache-dit-patterns.md for detailed code patterns.

9a. Determine your model type

Model Type	Description	Action
Standard single-transformer	One transformer with one ModuleList of blocks	No code needed — CacheDiTBackend auto-detects via enable_cache_for_dit()
Multi-block-list	One transformer with multiple block lists (e.g., transformer_blocks + single_transformer_blocks)	Write custom enabler with BlockAdapter
Dual-transformer	Two transformers (e.g., high-noise + low-noise)	Write custom enabler with BlockAdapter wrapping both

9b. Standard models — verify automatic support

For standard single-transformer models, test directly:

omni = Omni(
    model="your-model-name",
    cache_backend="cache_dit",
    cache_config={
        "Fn_compute_blocks": 1,
        "Bn_compute_blocks": 0,
        "max_warmup_steps": 4,
    }
)

Check logs for "Cache-dit enabled successfully on xxx". If it works, skip to Step 9e.

9c. Custom architectures — write a custom enabler

For multi-block-list or dual-transformer models, write a custom enabler function:

from cache_dit import BlockAdapter, ForwardPattern, ParamsModifier, DBCacheConfig

def enable_cache_for_your_model(pipeline, cache_config):
    db_cache_config = DBCacheConfig(
        num_inference_steps=None,
        Fn_compute_blocks=cache_config.Fn_compute_blocks,
        Bn_compute_blocks=cache_config.Bn_compute_blocks,
        max_warmup_steps=cache_config.max_warmup_steps,
        max_cached_steps=cache_config.max_cached_steps,
        max_continuous_cached_steps=cache_config.max_continuous_cached_steps,
        residual_diff_threshold=cache_config.residual_diff_threshold,
    )

    cache_dit.enable_cache(
        BlockAdapter(
            transformer=pipeline.transformer,
            blocks=[
                pipeline.transformer.transformer_blocks,
                pipeline.transformer.single_transformer_blocks,
            ],
            forward_pattern=[ForwardPattern.Pattern_1, ForwardPattern.Pattern_1],
            params_modifiers=[ParamsModifier(...)],
        ),
        cache_config=db_cache_config,
    )

    def refresh_cache_context(pipeline, num_inference_steps, verbose=True):
        cache_dit.refresh_context(
            pipeline.transformer, num_inference_steps=num_inference_steps, verbose=verbose
        )
    return refresh_cache_context

9d. Register the custom enabler

Add your enabler to CUSTOM_DIT_ENABLERS in vllm_omni/diffusion/cache/cache_dit_backend.py:

CUSTOM_DIT_ENABLERS = {
    "Wan22Pipeline": enable_cache_for_wan22,
    "LongCatImagePipeline": enable_cache_for_longcat_image,
    "YourModelPipeline": enable_cache_for_your_model,  # Add here
}

9e. Test Cache-DiT

omni = Omni(
    model="your-model-name",
    cache_backend="cache_dit",
    cache_config={
        "Fn_compute_blocks": 1, "Bn_compute_blocks": 0,
        "max_warmup_steps": 4, "residual_diff_threshold": 0.24,
    }
)
images = omni.generate("a beautiful landscape",
    OmniDiffusionSamplingParams(num_inference_steps=50))

Verify: 1) logs show cache enabled, 2) 1.5-2x speedup, 3) output quality acceptable vs baseline.

If quality degrades, lower residual_diff_threshold (try 0.12-0.18) or increase max_warmup_steps (try 6-8).

---

Step 10: Add Parallelism Support

After the model works on a single GPU, add multi-GPU parallelism. Add each type incrementally, testing after each addition.

See references/parallelism-patterns.md for detailed code patterns and API reference.

Recommended order: TP → SP/USP → CFG Parallel → HSDP

10a. Tensor Parallelism (TP)

Shards DiT linear layers across GPUs. Requires code changes in the transformer.

What to change in the transformer:

1. Replace nn.Linear with ColumnParallelLinear / RowParallelLinear / QKVParallelLinear
2. Update load_weights() to handle QKV fusion with stacked_params_mapping
3. Use self.to_qkv.num_heads (local heads) instead of total heads for split sizes

from vllm.model_executor.layers.linear import (
    QKVParallelLinear, RowParallelLinear, ColumnParallelLinear,
)

# Attention: QKV → RowParallel output
self.to_qkv = QKVParallelLinear(dim, head_dim, num_heads, num_kv_heads)
self.to_out = RowParallelLinear(dim, dim, input_is_parallel=True)

# FFN: ColumnParallel → RowParallel
self.w1 = ColumnParallelLinear(dim, ffn_dim)
self.w2 = RowParallelLinear(ffn_dim, dim, input_is_parallel=True)

Constraints: num_heads % tp_size == 0 and num_kv_heads % tp_size == 0.

Test: --tensor-parallel-size 2

10b. Sequence Parallelism (SP / USP)

Splits sequence tokens across GPUs. Non-intrusive via _sp_plan on the transformer class — no changes to forward().

What to change in the transformer:

Add _sp_plan class attribute:

from vllm_omni.diffusion.distributed.sp_plan import (
    SequenceParallelInput, SequenceParallelOutput,
)

class YourTransformer(nn.Module):
    _sp_plan = {
        "blocks.0": {
            "hidden_states": SequenceParallelInput(split_dim=1, expected_dims=3),
        },
        "proj_out": SequenceParallelOutput(gather_dim=1, expected_dims=3),
    }

If inline tensor ops (e.g., torch.cat) exist between shard/gather points, extract them into nn.Module submodules so hooks can intercept them.

For RoPE that needs splitting, add an entry for the RoPE module with split_output=True.

Test: --ulysses-degree 2 (offline) or --usp 2 (online serving)

10c. CFG Parallel

Distributes positive/negative CFG branches across 2 GPUs. Requires the pipeline to inherit CFGParallelMixin.

What to change in the pipeline:

from vllm_omni.diffusion.distributed.cfg_parallel import CFGParallelMixin

class YourPipeline(nn.Module, CFGParallelMixin):
    def diffuse(self, ...) -> torch.Tensor:
        for i, t in enumerate(timesteps):
            positive_kwargs = {...}
            negative_kwargs = {...} if do_true_cfg else None
            noise_pred = self.predict_noise_maybe_with_cfg(
                do_true_cfg=do_true_cfg, true_cfg_scale=cfg_scale,
                positive_kwargs=positive_kwargs, negative_kwargs=negative_kwargs,
            )
            latents = self.scheduler_step_maybe_with_cfg(
                noise_pred, t, latents, do_true_cfg
            )
        return latents

Override predict_noise() if your transformer call is non-standard. Override combine_cfg_noise() for multi-output models (e.g., video + audio).

Constraint: Exactly 2 GPUs. Only for models using classifier-free guidance.

Test: --cfg-parallel-size 2

10d. HSDP (Hybrid Sharded Data Parallel)

Shards transformer weights via PyTorch FSDP2 to reduce per-GPU VRAM. No code changes to the forward pass — just add a class attribute.

What to change in the transformer:

class YourTransformer(nn.Module):
    @staticmethod
    def _is_transformer_block(name: str, module) -> bool:
        return "blocks" in name and name.split(".")[-1].isdigit()

    _hsdp_shard_conditions = [_is_transformer_block]

Constraint: Cannot combine with TP. For standalone HSDP, set hsdp_shard_size explicitly.

Test: --use-hsdp or DiffusionParallelConfig(use_hsdp=True)

10e. Update parallelism documentation

After adding parallelism support, update:

1. docs/user_guide/diffusion/parallelism_acceleration.md — add your model to the support table
2. Record which parallelism methods are supported (USP, Ring, CFG, TP, HSDP, VAE-Patch)

Step 11: Add CPU Offload Support

Implement SupportsComponentDiscovery on your pipeline class to enable --enable-cpu-offload and --enable-layerwise-offload. The protocol declares which submodules the offloader should manage:

from typing import ClassVar
from vllm_omni.diffusion.models.interface import SupportsComponentDiscovery

class YourPipeline(nn.Module, SupportsComponentDiscovery):
    _dit_modules: ClassVar[list[str]] = ["transformer"]
    _encoder_modules: ClassVar[list[str]] = ["text_encoder"]
    _vae_modules: ClassVar[list[str]] = ["vae"]
    _resident_modules: ClassVar[list[str]] = []  # optional

• _dit_modules: denoising submodules (kept on GPU during diffusion loop)
• _encoder_modules: encoder/vision submodules (offloaded to CPU during diffusion loop)
• _vae_modules: VAE(s) (handled by both sequential and layerwise backends)
• _resident_modules: additional modules to pin on GPU during layerwise offloading (e.g. embedders, connectors). Only used by the layerwise backend. Optional — defaults to [].

All attribute names support dotted paths for nested submodules (e.g. "pipe.transformer", "bagel.time_embedder").

Pipelines without SupportsComponentDiscovery fall back to scanning well-known attribute names (transformer, text_encoder, vae, etc.), which fails for non-standard names.

Step 12: Performance Profiling

After verifying correctness and implementing parallelism/caching, profile the model's performance to identify bottlenecks and ensure optimal execution.

See the Profiling Single-Stage Diffusion guide for detailed instructions on:

1. Using the PyTorch profiler (profiler: "torch") to capture detailed CPU/CUDA traces.
2. Using Nsight Systems (nsys) with profiler: "cuda" for low-overhead CUDA traces.
3. Controlling profiling via omni.start_profile() and omni.stop_profile().

---

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 features incrementally: Single GPU → TP → SP → CFG → HSDP → Cache-DiT
6. For custom models: Get the model running with the original code first, then progressively replace components with vllm-omni equivalents
7. Cache-DiT before parallelism tuning: Cache-DiT is lossy — verify quality at baseline before combining with parallelism
8. Combine lossless + lossy: e.g., TP + SP + Cache-DiT for maximum throughput

Reference Files

• Transformer Adaptation — porting transformers from diffusers
• Custom Model Patterns — patterns for non-diffusers models
• Parallelism Patterns — TP, SP/USP, CFG parallel, HSDP implementation details
• Cache-DiT Patterns — cache-dit acceleration for standard and custom architectures
• Troubleshooting — common errors and fixes