sglang-diffusion-add-model

name	sglang-diffusion-add-model
description	Use when adding a new diffusion model or Diffusers pipeline to SGLang.

Add a Diffusion Model to SGLang

Use this skill when adding a new diffusion model or pipeline variant to sglang.multimodal_gen.

Two Pipeline Styles

Style A: Hybrid Monolithic Pipeline (Recommended)

The recommended default for most new models. Uses a three-stage structure:

BeforeDenoisingStage (model-specific)  -->  DenoisingStage (standard)  -->  DecodingStage (standard)

• BeforeDenoisingStage: A single, model-specific stage that consolidates all pre-processing logic: input validation, text encoding, image encoding, latent preparation, timestep setup. This stage is unique per model.
• DenoisingStage: Framework-standard stage for the denoising loop (DiT/UNet forward passes). Shared across models.
• DecodingStage: Framework-standard stage for VAE decoding. Shared across models.

Why recommended? Modern diffusion models have highly heterogeneous pre-processing requirements (different text encoders, different latent formats, different conditioning mechanisms). The Hybrid approach keeps pre-processing isolated per model, avoids fragile shared stages with excessive conditional logic, and lets developers port Diffusers reference code quickly.

Style B: Modular Composition Style

Uses the framework's fine-grained standard stages (TextEncodingStage, LatentPreparationStage, TimestepPreparationStage, etc.) to build the pipeline by composition.

This style is appropriate when:

• The new model's pre-processing can largely reuse existing stages — e.g., a model that uses standard CLIP/T5 text encoding + standard latent preparation with minimal customization. In this case, add_standard_t2i_stages() or add_standard_ti2i_stages() may be all you need.
• A model-specific optimization needs to be extracted as a standalone stage — e.g., a specialized encoding or conditioning step that benefits from being a separate stage for profiling, parallelism control, or reuse across multiple pipeline variants.

See existing Modular examples: QwenImagePipeline (uses add_standard_t2i_stages), FluxPipeline, WanPipeline.

How to Choose

Situation	Recommended Style
Model has unique/complex pre-processing (VLM captioning, AR token generation, custom latent packing, etc.)	Hybrid — consolidate into a BeforeDenoisingStage
Model fits neatly into standard text-to-image or text+image-to-image pattern	Modular — use add_standard_t2i_stages() / add_standard_ti2i_stages()
Porting a Diffusers pipeline with many custom steps	Hybrid — copy the __call__ logic into a single stage
Adding a variant of an existing model that shares most logic	Modular — reuse existing stages, customize via PipelineConfig callbacks
A specific pre-processing step needs special parallelism or profiling isolation	Modular — extract that step as a dedicated stage

Key principle (both styles): The stage(s) before DenoisingStage must produce a Req batch object with all the standard tensor fields that DenoisingStage expects (latents, timesteps, prompt_embeds, etc.). As long as this contract is met, the pipeline remains composable regardless of which style you use.

---

Key Files and Directories

Purpose	Path
Pipeline classes	python/sglang/multimodal_gen/runtime/pipelines/
Model-specific stages	python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/
PipelineStage base class	python/sglang/multimodal_gen/runtime/pipelines_core/stages/base.py
Pipeline base class	python/sglang/multimodal_gen/runtime/pipelines_core/composed_pipeline_base.py
Standard stages (Denoising, Decoding)	python/sglang/multimodal_gen/runtime/pipelines_core/stages/
Pipeline configs	python/sglang/multimodal_gen/configs/pipeline_configs/
Sampling params	python/sglang/multimodal_gen/configs/sample/
DiT model implementations	python/sglang/multimodal_gen/runtime/models/dits/
VAE implementations	python/sglang/multimodal_gen/runtime/models/vaes/
Encoder implementations	python/sglang/multimodal_gen/runtime/models/encoders/
Scheduler implementations	python/sglang/multimodal_gen/runtime/models/schedulers/
Model/VAE/DiT configs	python/sglang/multimodal_gen/configs/models/dits/, vaes/, encoders/
Central registry	python/sglang/multimodal_gen/registry.py

---

Step-by-Step Implementation

Step 1: Obtain and Study the Reference Implementation

Before writing any code, obtain the model's reference implementation or Diffusers pipeline code. You need the actual source code to work from — do not guess or assume the model's architecture. If the user already gave a HuggingFace model ID or repo, inspect that yourself first. Ask the user only when the reference implementation is private, ambiguous, or otherwise unavailable. Typical sources are:

• The model's Diffusers pipeline source (e.g., the pipeline_*.py file from the diffusers library or HuggingFace repo)
• Or the model's official reference implementation (e.g., from the model author's GitHub repo)
• Or the HuggingFace model ID so you can look up model_index.json and the associated pipeline class

Once you have the reference code, study it thoroughly:

1. Find the model's model_index.json to identify required modules (text_encoder, vae, transformer, scheduler, etc.)
2. Read the Diffusers pipeline's __call__ method end-to-end. Identify:
   • How text prompts are encoded
   • How latents are prepared (shape, dtype, scaling)
   • How timesteps/sigmas are computed
   • What conditioning kwargs the DiT/UNet expects
   • How the denoising loop works (classifier-free guidance, etc.)
   • How VAE decoding is done (scaling factors, tiling, etc.)

Step 2: Evaluate Reuse of Existing Pipelines and Stages

Before creating any new files, check whether an existing pipeline or stage can be reused or extended. Only create new pipelines/stages when the existing ones would require extensive modifications or when no similar implementation exists.

Specifically:

1. Compare the new model's architecture against existing pipelines (Flux, Wan, Qwen-Image, GLM-Image, HunyuanVideo, LTX, etc.). If the new model shares most of its structure with an existing one (e.g., same text encoders, similar latent format, compatible denoising loop), prefer:
   • Adding a new config variant to the existing pipeline rather than creating a new pipeline class
   • Reusing the existing BeforeDenoisingStage with minor parameter differences
   • Using add_standard_t2i_stages() / add_standard_ti2i_stages() / add_standard_ti2v_stages() if the model fits standard patterns
2. Check existing stages in runtime/pipelines_core/stages/ and stages/model_specific_stages/. If an existing stage handles 80%+ of what the new model needs, extend it rather than duplicating it.
3. Check existing model components — many models share VAEs (e.g., AutoencoderKL), text encoders (CLIP, T5), and schedulers. Reuse these directly instead of re-implementing.

Rule of thumb: Only create a new file when the existing implementation would need substantial structural changes to accommodate the new model, or when no architecturally similar implementation exists.

Step 3: Implement Model Components

Adapt or implement the model's core components in the appropriate directories.

DiT/Transformer (runtime/models/dits/{model_name}.py):

# python/sglang/multimodal_gen/runtime/models/dits/my_model.py

import torch
import torch.nn as nn

from sglang.multimodal_gen.runtime.layers.layernorm import (
    LayerNormScaleShift,
    RMSNormScaleShift,
)
from sglang.multimodal_gen.runtime.layers.attention.selector import (
    get_attn_backend,
)


class MyModelTransformer2DModel(nn.Module):
    """DiT model for MyModel.

    Adapt from the Diffusers/reference implementation. Key points:
    - Use SGLang's fused LayerNorm/RMSNorm ops (see `existing-fast-paths.md` under the benchmark/profile skill)
    - Use SGLang's attention backend selector
    - Keep the same parameter naming as Diffusers for weight loading compatibility
    """

    def __init__(self, config):
        super().__init__()
        # ... model layers ...

    def forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: torch.Tensor,
        timestep: torch.Tensor,
        # ... model-specific kwargs ...
    ) -> torch.Tensor:
        # ... forward pass ...
        return output

Tensor Parallel (TP) and Sequence Parallel (SP): For multi-GPU deployment, it is recommended to add TP/SP support to the DiT model. This can be done incrementally after the single-GPU implementation is verified. Reference existing implementations and adapt to your model's architecture:

• Wan model (runtime/models/dits/wanvideo.py) — Full TP + SP reference:
  • TP: Uses ColumnParallelLinear for Q/K/V projections, RowParallelLinear for output projections, attention heads divided by tp_size
  • SP: Sequence dimension sharding via get_sp_world_size(), padding for alignment, sequence_model_parallel_all_gather for aggregation
  • Cross-attention skips SP (skip_sequence_parallel=is_cross_attention)
• Qwen-Image model (runtime/models/dits/qwen_image.py) — SP + USPAttention reference:
  • SP: Uses USPAttention (Ulysses + Ring Attention), configured via --ulysses-degree / --ring-degree
  • TP: Uses MergedColumnParallelLinear for QKV (with Nunchaku quantization), ReplicatedLinear otherwise

Important: These are references only — each model has its own architecture and parallelism requirements. Consider:

• How attention heads can be divided across TP ranks
• Whether the model's sequence dimension is naturally shardable for SP
• Which linear layers benefit from column/row parallel sharding vs. replication
• Whether cross-attention or other special modules need SP exclusion

Key imports for distributed support:

from sglang.multimodal_gen.runtime.distributed import (
    divide,
    get_sp_group,
    get_sp_world_size,
    get_tp_world_size,
    sequence_model_parallel_all_gather,
)
from sglang.multimodal_gen.runtime.layers.linear import (
    ColumnParallelLinear,
    RowParallelLinear,
    ReplicatedLinear,
)

VAE (runtime/models/vaes/{model_name}.py): Implement if the model uses a non-standard VAE. Many models reuse existing VAEs.

Encoders (runtime/models/encoders/{model_name}.py): Implement if the model uses custom text/image encoders.

Schedulers (runtime/models/schedulers/{scheduler_name}.py): Implement if the model requires a custom scheduler not available in Diffusers.

Step 4: Create Model Configs

DiT Config (configs/models/dits/{model_name}.py):

# python/sglang/multimodal_gen/configs/models/dits/mymodel.py

from dataclasses import dataclass, field

from sglang.multimodal_gen.configs.models.dits.base import DiTConfig


@dataclass
class MyModelDitConfig(DiTConfig):
    arch_config: dict = field(default_factory=lambda: {
        "in_channels": 16,
        "num_layers": 24,
        "patch_size": 2,
        # ... model-specific architecture params ...
    })

VAE Config (configs/models/vaes/{model_name}.py):

from dataclasses import dataclass, field

from sglang.multimodal_gen.configs.models.vaes.base import VAEConfig


@dataclass
class MyModelVAEConfig(VAEConfig):
    vae_scale_factor: int = 8
    # ... VAE-specific params ...

Sampling Params (configs/sample/{model_name}.py):

from dataclasses import dataclass

from sglang.multimodal_gen.configs.sample.base import SamplingParams


@dataclass
class MyModelSamplingParams(SamplingParams):
    num_inference_steps: int = 50
    guidance_scale: float = 7.5
    height: int = 1024
    width: int = 1024
    # ... model-specific defaults ...

Step 5: Create PipelineConfig

The PipelineConfig holds static model configuration and defines callback methods used by the standard DenoisingStage and DecodingStage.

# python/sglang/multimodal_gen/configs/pipeline_configs/my_model.py

from dataclasses import dataclass, field

from sglang.multimodal_gen.configs.pipeline_configs.base import (
    ImagePipelineConfig,      # for image generation
    # SpatialImagePipelineConfig,  # alternative base
    # VideoPipelineConfig,         # for video generation
)
from sglang.multimodal_gen.configs.models.dits.mymodel import MyModelDitConfig
from sglang.multimodal_gen.configs.models.vaes.mymodel import MyModelVAEConfig


@dataclass
class MyModelPipelineConfig(ImagePipelineConfig):
    """Pipeline config for MyModel.

    This config provides callbacks that the standard DenoisingStage and
    DecodingStage use during execution. The BeforeDenoisingStage handles
    all model-specific pre-processing independently.
    """

    task_type: ModelTaskType = ModelTaskType.T2I
    vae_precision: str = "bf16"
    should_use_guidance: bool = True
    vae_tiling: bool = False
    enable_autocast: bool = False

    dit_config: DiTConfig = field(default_factory=MyModelDitConfig)
    vae_config: VAEConfig = field(default_factory=MyModelVAEConfig)

    # --- Callbacks used by DenoisingStage ---

    def get_freqs_cis(self, batch, device, rotary_emb, dtype):
        """Prepare rotary position embeddings for the DiT."""
        # Model-specific RoPE computation
        ...
        return freqs_cis

    def prepare_pos_cond_kwargs(self, batch, latent_model_input, t, **kwargs):
        
         {
            : latent_model_input,
            : batch.prompt_embeds[],
            : t,
            
        }

     ():
        
         {
            : latent_model_input,
            : batch.negative_prompt_embeds[],
            : t,
            
        }

    

     ():
        
         .vae_config.latents_std, .vae_config.latents_mean

     ():
        
         latents.to(torch.bfloat16)

     ():
        
         frames

Important: The prepare_pos_cond_kwargs / prepare_neg_cond_kwargs methods define what the DiT receives at each denoising step. These must match the DiT's forward() signature.

Step 6: Implement the BeforeDenoisingStage (Core Step)

This is the heart of the Hybrid pattern. Create a single stage that handles ALL pre-processing.

# python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/my_model.py

import torch
from typing import List, Optional, Union

from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import Req
from sglang.multimodal_gen.runtime.pipelines_core.stages.base import PipelineStage
from sglang.multimodal_gen.runtime.server_args import ServerArgs
from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger

logger = init_logger(__name__)


class MyModelBeforeDenoisingStage(PipelineStage):
    """Monolithic pre-processing stage for MyModel.

    Consolidates all logic before the denoising loop:
    - Input validation
    - Text/image encoding
    - Latent preparation
    - Timestep/sigma computation

    This stage produces a Req batch with all fields required by
    the standard DenoisingStage.
    """

    def __init__(self, vae, text_encoder, tokenizer, transformer, scheduler):
        super().__init__()
        self.vae = vae
        self.text_encoder = text_encoder
        self.tokenizer = tokenizer
        self.transformer = transformer
        self.scheduler = scheduler
        # ... other initialization (image processors, scale factors, etc.) ...

    # --- Internal helper methods ---
    # Copy/adapt directly from the Diffusers reference pipeline.
    # These are private to this stage; no need to make them reusable.

     ():
        
        
         prompt_embeds, negative_prompt_embeds

     ():
        
        
         latents

     ():
        
        
         timesteps, sigmas

    


     () -> Req:
        
        device = get_local_torch_device()
        dtype = torch.bfloat16
        generator = torch.Generator(device=device).manual_seed(batch.seed)

        
        prompt_embeds, negative_prompt_embeds = ._encode_prompt(
            batch.prompt, device, dtype
        )

        
        latents = ._prepare_latents(
            batch_size=,
            height=batch.height,
            width=batch.width,
            dtype=dtype,
            device=device,
            generator=generator,
        )

        
        timesteps, sigmas = ._prepare_timesteps(
            batch.num_inference_steps, device
        )

        
        batch.prompt_embeds = [prompt_embeds]
        batch.negative_prompt_embeds = [negative_prompt_embeds]
        batch.latents = latents
        batch.timesteps = timesteps
        batch.num_inference_steps = (timesteps)
        batch.sigmas = sigmas
        batch.generator = generator
        batch.raw_latent_shape = latents.shape
        batch.height = batch.height
        batch.width = batch.width

         batch

Key fields that DenoisingStage expects on the batch (set these in your forward):

Field	Type	Description
batch.latents	torch.Tensor	Initial noisy latent tensor
batch.timesteps	torch.Tensor	Timestep schedule
batch.num_inference_steps	int	Number of denoising steps
batch.sigmas	list[float]	Sigma schedule (as a list, not numpy)
batch.prompt_embeds	list[torch.Tensor]	Positive prompt embeddings (wrapped in list)
batch.negative_prompt_embeds	list[torch.Tensor]	Negative prompt embeddings (wrapped in list)
batch.generator	torch.Generator	RNG generator for reproducibility
batch.raw_latent_shape	tuple	Original latent shape before any packing
batch.height / batch.width	int	Output dimensions

Step 7: Define the Pipeline Class

The pipeline class is minimal -- it just wires the stages together.

# python/sglang/multimodal_gen/runtime/pipelines/my_model.py

from sglang.multimodal_gen.runtime.pipelines_core import LoRAPipeline
from sglang.multimodal_gen.runtime.pipelines_core.composed_pipeline_base import (
    ComposedPipelineBase,
)
from sglang.multimodal_gen.runtime.pipelines_core.stages import DenoisingStage
from sglang.multimodal_gen.runtime.pipelines_core.stages.model_specific_stages.my_model import (
    MyModelBeforeDenoisingStage,
)
from sglang.multimodal_gen.runtime.server_args import ServerArgs


class MyModelPipeline(LoRAPipeline, ComposedPipelineBase):
    pipeline_name = "MyModelPipeline"  # Must match model_index.json _class_name

    _required_config_modules = [
        "text_encoder",
        "tokenizer",
        "vae",
        "transformer",
        "scheduler",
        # ... list all modules from model_index.json ...
    ]

    def create_pipeline_stages(self, server_args: ServerArgs):
        # 1. Monolithic pre-processing (model-specific)
        self.add_stage(
            MyModelBeforeDenoisingStage(
                vae=self.get_module("vae"),
                text_encoder=self.get_module("text_encoder"),
                tokenizer=self.get_module("tokenizer"),
                transformer=self.get_module("transformer"),
                scheduler=self.get_module("scheduler"),
            ),
        )

        
        .add_stage(
            DenoisingStage(
                transformer=.get_module(),
                scheduler=.get_module(),
            ),
        )

        
        .add_standard_decoding_stage()



EntryClass = [MyModelPipeline]

Step 8: Register the Model

In python/sglang/multimodal_gen/registry.py, register your configs:

register_configs(
    model_family="my_model",
    sampling_param_cls=MyModelSamplingParams,
    pipeline_config_cls=MyModelPipelineConfig,
    hf_model_paths=[
        "org/my-model-name",  # HuggingFace model ID(s)
    ],
)

The EntryClass in your pipeline file is automatically discovered by the registry's _discover_and_register_pipelines() function -- no additional registration needed for the pipeline class itself.

Step 9: Verify Output Quality

After implementation, you must verify that the generated output is not noise. A noisy or garbled output image/video is the most common sign of an incorrect implementation. Common causes include:

• Incorrect latent scale/shift factors (get_decode_scale_and_shift returning wrong values)
• Wrong timestep/sigma schedule (order, dtype, or value range)
• Mismatched conditioning kwargs (fields not matching the DiT's forward() signature)
• Incorrect VAE decoder configuration (wrong vae_scale_factor, missing denormalization)
• Rotary embedding style mismatch (is_neox_style set incorrectly)
• Wrong prompt embedding format (missing list wrapping, wrong encoder output selection)

If the output is noise, the implementation is incorrect — do not ship it. Debug by:

1. Comparing intermediate tensor values (latents, prompt_embeds, timesteps) against the Diffusers reference pipeline
2. Running the Diffusers pipeline and SGLang pipeline side-by-side with the same seed
3. Checking each stage's output shape and value range independently

Reference Implementations

Hybrid Style (recommended for most new models)

Model	Pipeline	BeforeDenoisingStage	PipelineConfig
GLM-Image	runtime/pipelines/glm_image.py	stages/model_specific_stages/glm_image.py	configs/pipeline_configs/glm_image.py
Qwen-Image-Layered	runtime/pipelines/qwen_image.py (QwenImageLayeredPipeline)	stages/model_specific_stages/qwen_image_layered.py	configs/pipeline_configs/qwen_image.py (QwenImageLayeredPipelineConfig)

Modular Style (when standard stages fit well)

Model	Pipeline	Notes
Qwen-Image (T2I)	runtime/pipelines/qwen_image.py	Uses add_standard_t2i_stages() — standard text encoding + latent prep fits this model
Qwen-Image-Edit	runtime/pipelines/qwen_image.py	Uses add_standard_ti2i_stages() — standard image-to-image flow
Flux	runtime/pipelines/flux.py	Uses add_standard_t2i_stages() with custom prepare_mu
Wan	runtime/pipelines/wan_pipeline.py	Uses add_standard_ti2v_stages()

---

Checklist

Before submitting, verify:

Common (both styles):

• Pipeline file exists at runtime/pipelines/{model_name}.py with EntryClass
• PipelineConfig at configs/pipeline_configs/{model_name}.py
• SamplingParams at configs/sample/{model_name}.py
• DiT model at runtime/models/dits/{model_name}.py
• DiT config at configs/models/dits/{model_name}.py
• VAE — reuse existing (e.g., AutoencoderKL) or create new at runtime/models/vaes/
• VAE config — reuse existing or create new at configs/models/vaes/{model_name}.py
• Registry entry in registry.py via register_configs()
• pipeline_name matches Diffusers model_index.json _class_name
• _required_config_modules lists all modules from model_index.json
• PipelineConfig callbacks (prepare_pos_cond_kwargs, get_freqs_cis, etc.) match DiT's forward() signature
• Latent scale/shift factors are correctly configured
• Use fused kernels where possible (see existing-fast-paths.md under the benchmark/profile skill)
• Weight names match Diffusers for automatic loading
• TP/SP support considered for DiT model (recommended; reference for TP+SP, for USPAttention)

Hybrid style only:

• BeforeDenoisingStage at stages/model_specific_stages/{model_name}.py
• BeforeDenoisingStage.forward() populates all fields needed by DenoisingStage

Common Pitfalls

1. batch.sigmas must be a Python list, not a numpy array. Use .tolist() to convert.
2. batch.prompt_embeds is a list of tensors (one per encoder), not a single tensor. Wrap with [tensor].
3. Don't forget batch.raw_latent_shape -- DecodingStage uses it to unpack latents.
4. Rotary embedding style matters: is_neox_style=True = split-half rotation, is_neox_style=False = interleaved. Check the reference model carefully.
5. VAE precision: Many VAEs need fp32 or bf16 for numerical stability. Set vae_precision in the PipelineConfig accordingly.
6. Avoid forcing model-specific logic into shared stages: If your model's pre-processing doesn't naturally fit the existing standard stages, prefer the Hybrid pattern with a dedicated BeforeDenoisingStage rather than adding conditional branches to shared stages.

After Implementation: Tests and Performance Data

Once the model is working and output quality is verified, ask the user whether they would like to:

1. Add tests — Create unit tests and/or integration tests for the new model. Tests should cover:

   • Pipeline construction and stage wiring
   • Single-GPU inference producing non-noise output
   • Multi-GPU inference (TP/SP) if supported
   • See the write-sglang-test skill for test conventions and placement guidelines

2. Generate performance data — Run benchmarks and collect perf metrics:

   • Single-GPU latency and throughput (look for Pixel data generated successfully in xxxx seconds in console output; use the warmup excluded line for accurate timing)
   • Multi-GPU scaling (TP/SP) throughput comparison
   • Use python/sglang/multimodal_gen/benchmarks/bench_serving.py for serving benchmarks

Do not skip this step — always ask the user before proceeding, as test and benchmark requirements vary per model.