> ## Documentation Index > Fetch the complete documentation index at: https://docs.sglang.io/llms.txt > Use this file to discover all available pages before exploring further. # Support New Diffusion Models > A concise implementation guide for adding diffusion model families to SGLang-Diffusion. Use this guide as a triage flow for finding the smallest change that can support a model. Most new model work should touch a small set of files, even though the runtime is split into separate folders. ## Read the Code in This Order The files are split by runtime responsibility. For a new model, read the request path first: 1. `registry.py` chooses the model family, sampling params, and pipeline config. 2. `configs/pipeline_configs/{model}.py` defines model-specific denoising and decoding behavior. 3. `runtime/pipelines/{model}.py` wires modules into stages. 4. `runtime/pipelines_core/stages/` runs the shared stage logic. 5. `runtime/models/` contains native model components only when the architecture cannot be reused. That is the dependency direction. Avoid making a model PR that requires readers to jump between folders in a different order. `runtime/models/` owns modeling code: checkpoint-defined neural modules, architecture wrappers, and weight-loading or forward-path details that are intrinsic to one model family. Reusable serving infrastructure belongs in SGLang-Diffusion runtime folders such as `runtime/cache/`, `runtime/distributed/`, `runtime/utils/`, or shared pipeline stages. This includes cache managers, graph runners, process-group transport, request utilities, and common action-policy helpers. Model packages may call these helpers. Keep ownership in shared runtime folders unless the code is truly architecture-specific. ## Start With the Smallest Change Before adding files, decide which path fits the model. | Situation | What to do | | ----------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | A new checkpoint uses an existing native family | Add the Hugging Face path and, if needed, a small `SamplingParams` or `PipelineConfig` variant. Reuse the existing pipeline and modules. | | The model has a new native DiT/UNet architecture | Add a native SGLang pipeline and the missing model components. Keep denoising and decoding on the shared stages unless measured behavior requires model-specific logic. | | The model is long-tail or you only need compatibility first | Prefer the Diffusers backend for compatibility-first support. Add native support later if performance or deployment needs justify it. | Do not add a folder just to mirror the Diffusers repository layout. Add a new file only when an existing pipeline, stage, module, config, or sampler cannot express the behavior clearly. ## Minimal File Map The source tree is split by runtime responsibility. That split is useful for optimization. Keep new model PRs focused on the files required by model behavior. | Area | Add or edit when | Typical file | | ----------------------------- | ------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- | | Registry | Always, unless extending an already registered family | `python/sglang/multimodal_gen/registry.py` | | Runtime parameters | The request schema differs from existing models | `configs/sample/{model}.py` | | Pipeline config | Denoising, decoding, precision, position encoding, or CFG hooks differ | `configs/pipeline_configs/{model}.py` | | Pipeline wiring | The model needs a new stage layout or module list | `runtime/pipelines/{model}.py` | | DiT/UNet module | The denoising network is new | `runtime/models/dits/{model}.py` | | dVLA or policy module | The checkpoint defines a new action-policy architecture | `runtime/models/{family}/modeling_*.py` or a task-specific model subfolder | | Shared runtime infrastructure | Cache, CUDA graph, distributed transfer, request utilities, or action-policy helpers can be reused by future models | `runtime/cache/`, `runtime/distributed/`, `runtime/utils/`, `runtime/pipelines_core/stages/` | | Model component config | A model component has static architecture config | `configs/models/dits/{model}.py`, `configs/models/vaes/{model}.py` | | Model-specific stage | Pre-processing is too custom for the standard stages | `runtime/pipelines_core/stages/model_specific_stages/{model}.py` | | Encoder, VAE, scheduler | No existing implementation can be reused | `runtime/models/encoders/`, `runtime/models/vaes/`, `runtime/models/schedulers/` | For a new native architecture, the common minimum is: 1. `registry.py` 2. `configs/sample/{model}.py` 3. `configs/pipeline_configs/{model}.py` 4. `runtime/pipelines/{model}.py` 5. `runtime/models/dits/{model}.py` Every extra file should map to model behavior that existing code cannot express clearly. For dVLA or other non-image diffusion policies, keep the same ownership rule. The policy network, VLM/action expert modules, checkpoint mapping, and model-specific forward code belong under `runtime/models/`. Prefix caches, request-local contexts, denoising graph runners, OpenPI-compatible transport, and prefix/action process-group utilities should be shared SGLang-Diffusion runtime infrastructure when they are useful beyond the first model. ## Read the Reference First Use the model's Diffusers pipeline, official implementation, or `model_index.json` as the source of truth. Write down: * Which modules must be loaded: tokenizer, text encoder, image encoder, transformer, scheduler, VAE, processor, and any extra adapters. * The prompt and image encoding flow. * Latent shape, packing, scale, shift, dtype, and device rules. * Timestep and sigma schedule. * The exact `forward()` kwargs expected by the denoising network. * VAE decode rules and output post-processing. If the new model is close to Flux, Qwen-Image, GLM-Image, Wan, HunyuanVideo, or LTX, extend that implementation before starting from an empty file. ## Choose a Pipeline Shape SGLang-Diffusion uses `ComposedPipelineBase` to wire stages together. Most native pipelines should use one of these two shapes. | Shape | Use when | Layout | | ----------------------------- | --------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------ | | Standard stages | Text/image encoding, latent prep, denoising, and decoding match existing helpers | `add_standard_t2i_stages()`, `add_standard_ti2i_stages()`, or a similar helper | | Model-specific pre-processing | The reference pipeline has custom captioning, image conditioning, latent packing, or timestep preparation | `{Model}BeforeDenoisingStage -> DenoisingStage -> DecodingStage` | Prefer standard stages when possible. Use a model-specific `BeforeDenoisingStage` when trying to force the model into shared stages would create many conditionals. ## Implement the Pieces ### 1. Sampling Params Create request parameters only for values users can set at runtime. ```python theme={null} # python/sglang/multimodal_gen/configs/sample/my_model.py from dataclasses import dataclass from sglang.multimodal_gen.configs.sample.sampling_params import ImageSamplingParams @dataclass class MyModelSamplingParams(ImageSamplingParams): guidance_scale: float = 4.0 num_inference_steps: int = 28 ``` ### 2. Pipeline Config `PipelineConfig` is where shared denoising and decoding stages get model-specific callbacks. ```python theme={null} # python/sglang/multimodal_gen/configs/pipeline_configs/my_model.py from dataclasses import dataclass, field @dataclass class MyModelPipelineConfig(ImagePipelineConfig): task_type: ModelTaskType = ModelTaskType.T2I should_use_guidance: bool = True dit_config: DiTConfig = field(default_factory=MyModelDiTConfig) vae_config: VAEConfig = field(default_factory=MyModelVAEConfig) def prepare_pos_cond_kwargs(self, batch, latent_model_input, t, **kwargs): return { "hidden_states": latent_model_input, "encoder_hidden_states": batch.prompt_embeds[0], "timestep": t, } def prepare_neg_cond_kwargs(self, batch, latent_model_input, t, **kwargs): return { "hidden_states": latent_model_input, "encoder_hidden_states": batch.negative_prompt_embeds[0], "timestep": t, } ``` Make these kwargs match the denoising module's `forward()` signature exactly. ### 3. Pipeline Wiring Use the standard helper when the model fits it. ```python theme={null} # python/sglang/multimodal_gen/runtime/pipelines/my_model.py class MyModelPipeline(LoRAPipeline, ComposedPipelineBase): pipeline_name = "MyModelPipeline" _required_config_modules = [ "text_encoder", "tokenizer", "transformer", "scheduler", "vae", ] def create_pipeline_stages(self, server_args: ServerArgs): self.add_standard_t2i_stages() EntryClass = [MyModelPipeline] ``` Use a model-specific pre-processing stage when the reference pipeline cannot be cleanly expressed by standard helpers. ```python theme={null} class MyModelPipeline(LoRAPipeline, ComposedPipelineBase): pipeline_name = "MyModelPipeline" _required_config_modules = [ "text_encoder", "tokenizer", "transformer", "scheduler", "vae", ] def create_pipeline_stages(self, server_args: ServerArgs): self.add_stage( MyModelBeforeDenoisingStage( text_encoder=self.get_module("text_encoder"), tokenizer=self.get_module("tokenizer"), transformer=self.get_module("transformer"), scheduler=self.get_module("scheduler"), vae=self.get_module("vae"), ) ) self.add_stage( DenoisingStage( transformer=self.get_module("transformer"), scheduler=self.get_module("scheduler"), ) ) self.add_standard_decoding_stage() EntryClass = [MyModelPipeline] ``` ### 4. Optional Before-Denoising Stage A `BeforeDenoisingStage` should populate the batch fields consumed by `DenoisingStage`. ```python theme={null} class MyModelBeforeDenoisingStage(PipelineStage): @torch.no_grad() def forward(self, batch: Req, server_args: ServerArgs) -> Req: prompt_embeds, negative_prompt_embeds = self._encode_prompt(batch) latents = self._prepare_latents(batch) timesteps, sigmas = self._prepare_timesteps(batch) batch.prompt_embeds = [prompt_embeds] batch.negative_prompt_embeds = [negative_prompt_embeds] batch.latents = latents batch.timesteps = timesteps batch.num_inference_steps = len(timesteps) batch.sigmas = sigmas.tolist() batch.raw_latent_shape = latents.shape return batch ``` Required fields for `DenoisingStage`: | Field | Notes | | ------------------------------ | ------------------------------------------------------------------- | | `batch.latents` | Initial latent tensor, including any packing required by the model. | | `batch.timesteps` | Timestep tensor in the exact order used by the reference pipeline. | | `batch.sigmas` | Python list when the scheduler expects sigma values. | | `batch.prompt_embeds` | Positive embeddings, wrapped in a list. | | `batch.negative_prompt_embeds` | Negative embeddings, wrapped in a list when CFG is used. | | `batch.num_inference_steps` | Number of denoising iterations. | | `batch.raw_latent_shape` | Original latent shape before packing, if decode needs it. | ### 5. Denoising Module Add a file under `runtime/models/dits/` only when the architecture is new. Reuse existing encoders, VAEs, schedulers, normalization layers, and fused kernels whenever possible. For multi-GPU serving, add TP/SP support after the single-GPU path is correct. Useful references: * `runtime/models/dits/wanvideo.py` for TP plus SP. * `runtime/models/dits/qwen_image.py` for USP attention. ### 6. Registry Register the family once the sampling params and pipeline config exist. ```python theme={null} register_configs( model_family="my_model", sampling_param_cls=MyModelSamplingParams, pipeline_config_cls=MyModelPipelineConfig, hf_model_paths=["org/my-model"], ) ``` The pipeline file is discovered through its `EntryClass`; do not add a second pipeline registry unless the existing registry requires it. ## Verify the Port Use one deterministic prompt and seed while comparing with the reference implementation. 1. Run a single-GPU smoke test and check that the output contains coherent content. 2. Compare latent scale and shift, timestep order, sigma values, and conditioning kwargs against Diffusers or the official implementation. 3. Verify VAE decode and post-processing separately from denoising. 4. If the model supports LoRA, CFG parallelism, TP, SP, or disaggregation, test each feature explicitly. 5. Add or update docs, examples, or the compatibility matrix when users need a new launch command. Common failure points: * Wrong latent scale or shift. * Reversed or dtype-mismatched timesteps. * Missing negative embeddings when CFG is enabled. * Conditioning kwarg names mismatched with the DiT `forward()`. * Rotary embedding shape or style mismatch. * Decoding packed latents without restoring `raw_latent_shape`. ## PR Checklist * [ ] Reused an existing family, stage, module, scheduler, or VAE wherever possible. * [ ] Kept the new-model touch surface small and justified any extra files. * [ ] Added `SamplingParams`, `PipelineConfig`, pipeline wiring, DiT module, and registry entry when native support is needed. * [ ] Confirmed `pipeline_name` matches the Diffusers `model_index.json` `_class_name` when applicable. * [ ] Confirmed `_required_config_modules` matches the model repo. * [ ] Verified image or video quality against a reference output. * [ ] Tested multi-GPU paths if the PR claims TP, SP, CFG parallelism, or distributed serving support.