> ## 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. # Supported Models and Optimization Compatibility > Check supported SGLang Diffusion models and their optimization compatibility. This page tracks supported SGLang Diffusion model families and their optimization compatibility. It also covers long-tail models that do not yet have dedicated cookbook recipes. For model-specific usage recipes, start from the [Diffusion Cookbook](/cookbook/diffusion/intro). Cookbook pages cover the primary models with examples; this page keeps the compact support and compatibility inventory. ## Supported model inventory Pass the `Hugging Face Model ID` to `--model-path` for `sglang generate` or `sglang serve`. Python API users can pass the same ID to SGLang Diffusion model-loading helpers. Missing checkpoint aliases do not imply that a model family is unsupported. The runtime registry may also accept detector-based aliases or local model directories that match the same family. Rows are grouped when a family shares the same runtime path or optimization support. Use the detailed matrix below when you need per-optimization compatibility.
Model family Model IDs
FLUX
black-forest-labs/FLUX.1-devblack-forest-labs/FLUX.2-devblack-forest-labs/FLUX.2-dev-NVFP4black-forest-labs/FLUX.2-klein-4Bblack-forest-labs/FLUX.2-klein-9Bblack-forest-labs/FLUX.2-klein-base-4Bblack-forest-labs/FLUX.2-klein-base-9B
Z-Image
Tongyi-MAI/Z-ImageTongyi-MAI/Z-Image-Turbo
Qwen-Image
Qwen/Qwen-ImageQwen/Qwen-Image-2512Qwen/Qwen-Image-EditQwen/Qwen-Image-Edit-2509Qwen/Qwen-Image-Edit-2511Qwen/Qwen-Image-Layered
SD3 / SD3.5
stabilityai/stable-diffusion-3-mediumstabilityai/stable-diffusion-3-medium-diffusersstabilityai/stable-diffusion-3.5-mediumstabilityai/stable-diffusion-3.5-medium-diffusersstabilityai/stable-diffusion-3.5-largestabilityai/stable-diffusion-3.5-large-diffusers
SANA
Efficient-Large-Model/SANA1.5\_1.6B\_1024px\_diffusersEfficient-Large-Model/SANA1.5\_4.8B\_1024px\_diffusersEfficient-Large-Model/Sana\_1600M\_1024px\_diffusersEfficient-Large-Model/Sana\_600M\_1024px\_diffusersEfficient-Large-Model/Sana\_1600M\_512px\_diffusersEfficient-Large-Model/Sana\_600M\_512px\_diffusers
FireRed-Image
FireRedTeam/FireRed-Image-Edit-1.0FireRedTeam/FireRed-Image-Edit-1.1
JoyAI-Image
jdopensource/JoyAI-Image-Edit-Diffusers
Other image pipelines
zai-org/GLM-Imagetencent/Hunyuan3D-2baidu/ERNIE-Imagebaidu/ERNIE-Image-Turboideogram-ai/ideogram-4-fp8ideogram-ai/ideogram-4-nf4Comfy-Org/Ideogram-4
Model family Model IDs Resolution / mode Optimization support
FastWan
FastVideo/FastWan2.1-T2V-1.3B-DiffusersFastVideo/FastWan2.2-TI2V-5B-FullAttn-DiffusersFastVideo/FastWan2.2-TI2V-5B-Diffusers
 480p / 720p VSA
Wan2.2
Wan-AI/Wan2.2-TI2V-5B-DiffusersWan-AI/Wan2.2-T2V-A14B-Diffusersnvidia/Wan2.2-T2V-A14B-Diffusers-NVFP4Wan-AI/Wan2.2-I2V-A14B-Diffusers
 TI2V / T2V / I2V, 480p / 720p SageLaserBSARain Fusion
HunyuanVideo
hunyuanvideo-community/HunyuanVideoFastVideo/FastHunyuan-diffusers
 720×1280 / 544×960 TileSageSVG2
Wan2.1
Wan-AI/Wan2.1-T2V-1.3B-DiffusersWan-AI/Wan2.1-T2V-14B-DiffusersWan-AI/Wan2.1-I2V-14B-480P-DiffusersWan-AI/Wan2.1-I2V-14B-720P-Diffusers
 T2V / I2V, 480p / 720p TeaCacheTileSageSVG2LaserBSARain Fusion
TurboWan
IPostYellow/TurboWan2.1-T2V-1.3B-DiffusersIPostYellow/TurboWan2.1-T2V-14B-DiffusersIPostYellow/TurboWan2.1-T2V-14B-720P-DiffusersIPostYellow/TurboWan2.2-I2V-A14B-Diffusers
 480p / 720p TeaCacheSLASageSLA
MOVA
OpenMOSS-Team/MOVA-360pOpenMOSS-Team/MOVA-720p
 Video-audio, 360p / 720p; local MOVA detector aliases are also supported. No dedicated optimization listed
Wan2.1 Fun
weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers
 480p inpainting TeaCacheTileSageSVG2
Helios
BestWishYsh/Helios-BaseBestWishYsh/Helios-MidBestWishYsh/Helios-Distilled
 720p No dedicated optimization listed
LTX-2
Lightricks/LTX-2Lightricks/LTX-2.3
 One-stage, two-stage, TI2V, HQ No dedicated optimization listed
Cosmos3
nvidia/Cosmos3-Nanonvidia/Cosmos3-Supernvidia/Cosmos3-Super-Text2Imagenvidia/Cosmos3-Super-Image2Video
 T2V / I2V / T2I No dedicated optimization listed
Model family Model IDs / detector Notes
LingBotWorld
robbyant/lingbot-world-fast-diffusers
 Realtime world model with causal state and control tokens.
SANA-WM
Efficient-Large-Model/SANA-WM\_bidirectionalEfficient-Large-Model/SANA-WM\_streaming
 World-model pipeline with bidirectional and streaming checkpoints.
Wan2.2 TI2V 5B currently has known quality issues when used for I2V generation. ## Optimization compatibility The detailed video matrix uses these symbols: * ✅ = Full compatibility * ❌ = No compatibility * ⭕ = Does not apply to this model ### Video Generation Models Optimization columns are abbreviated to keep the matrix readable: * `Tea` = TeaCache * `Tile` = Sliding Tile Attention * `Sage` = Sage Attention * `VSA` = Video Sparse Attention * `SLA` = Sparse Linear Attention * `SageSLA` = Sage Sparse Linear Attention * `SVG2` = Sparse Video Gen 2 * `LA` = Laser Attention * `BSA` = Block Sparse Attention * `RF` = Rain Fusion Attention
Model Name Hugging Face Model ID Resolution Tea Tile Sage VSA SLA SageSLA SVG2 LA BSA RF
FastWan2.1 T2V 1.3B `FastVideo/FastWan2.1-T2V-1.3B-Diffusers` 480p ⭕ ⭕ ⭕ ✅ ❌ ❌ ❌ ❌ ❌ ❌
FastWan2.2 TI2V 5B FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers
FastVideo/FastWan2.2-TI2V-5B-Diffusers		 720p ⭕ ⭕ ⭕ ✅ ❌ ❌ ❌ ❌ ❌ ❌
Wan2.2 TI2V 5B `Wan-AI/Wan2.2-TI2V-5B-Diffusers` 720p ⭕ ⭕ ✅ ⭕ ❌ ❌ ❌ ✅ ✅ ✅
Wan2.2 T2V A14B Wan-AI/Wan2.2-T2V-A14B-Diffusers
nvidia/Wan2.2-T2V-A14B-Diffusers-NVFP4		 480p
720p		 ❌ ❌ ✅ ⭕ ❌ ❌ ❌ ✅ ✅ ✅
Wan2.2 I2V A14B `Wan-AI/Wan2.2-I2V-A14B-Diffusers` 480p
720p		 ❌ ❌ ✅ ⭕ ❌ ❌ ❌ ✅ ✅ ✅
HunyuanVideo `hunyuanvideo-community/HunyuanVideo` 720×1280
544×960		 ❌ ✅ ✅ ⭕ ❌ ❌ ✅ ❌ ❌ ❌
FastHunyuan `FastVideo/FastHunyuan-diffusers` 720×1280
544×960		 ❌ ✅ ✅ ⭕ ❌ ❌ ✅ ❌ ❌ ❌
Wan2.1 T2V 1.3B `Wan-AI/Wan2.1-T2V-1.3B-Diffusers` 480p ✅ ✅ ✅ ⭕ ❌ ❌ ✅ ✅ ✅ ✅
Wan2.1 T2V 14B `Wan-AI/Wan2.1-T2V-14B-Diffusers` 480p, 720p ✅ ✅ ✅ ⭕ ❌ ❌ ✅ ✅ ✅ ✅
Wan2.1 I2V 480P `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers` 480p ✅ ✅ ✅ ⭕ ❌ ❌ ✅ ✅ ✅ ✅
Wan2.1 I2V 720P `Wan-AI/Wan2.1-I2V-14B-720P-Diffusers` 720p ✅ ✅ ✅ ⭕ ❌ ❌ ✅ ✅ ✅ ✅
TurboWan2.1 T2V 1.3B `IPostYellow/TurboWan2.1-T2V-1.3B-Diffusers` 480p ✅ ❌ ❌ ❌ ✅ ✅ ⭕ ❌ ❌ ❌
TurboWan2.1 T2V 14B `IPostYellow/TurboWan2.1-T2V-14B-Diffusers` 480p ✅ ❌ ❌ ❌ ✅ ✅ ⭕ ❌ ❌ ❌
TurboWan2.1 T2V 14B 720P `IPostYellow/TurboWan2.1-T2V-14B-720P-Diffusers` 720p ✅ ❌ ❌ ❌ ✅ ✅ ⭕ ❌ ❌ ❌
TurboWan2.2 I2V A14B `IPostYellow/TurboWan2.2-I2V-A14B-Diffusers` 720p ✅ ❌ ❌ ❌ ✅ ✅ ⭕ ❌ ❌ ❌
Wan2.1 Fun 1.3B InP weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers 480p ✅ ✅ ✅ ⭕ ❌ ❌ ✅ ❌ ❌ ❌
Helios Base BestWishYsh/Helios-Base 720p ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌
Helios Mid BestWishYsh/Helios-Mid 720p ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌
Helios Distilled BestWishYsh/Helios-Distilled 720p ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌
LTX-2 (one/two-stage/TI2V) Lightricks/LTX-2 768×512
1536×1024		 ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌
LTX-2.3 (one/two-stage/TI2V/HQ) Lightricks/LTX-2.3 768×512
1536×1024
1920×1088 (HQ default)		 ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌
Cosmos3-Nano (T2V / I2V / T2I) nvidia/Cosmos3-Nano 720p · 480p
1024×1024 (T2I)		 ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌
Cosmos3-Super (T2V / I2V / T2I) nvidia/Cosmos3-Super
nvidia/Cosmos3-Super-Text2Image
nvidia/Cosmos3-Super-Image2Video		 720p · 480p
1024×1024 (T2I)		 ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌
**Note**: 1. Wan2.2 TI2V 5B has some quality issues when performing I2V generation. We are working on fixing this issue. 2. SageSLA is based on SpargeAttn. Install it first with `pip install git+https://github.com/thu-ml/SpargeAttn.git --no-build-isolation` 3. LTX pipeline selection: * One-stage: `--pipeline-class-name LTX2Pipeline` * Two-stage: `--pipeline-class-name LTX2TwoStagePipeline` * Two-stage HQ: `--pipeline-class-name LTX2TwoStageHQPipeline` (HQ defaults to 1920×1088; you can still override `--width/--height`) * LTX-2 and LTX-2.3 support both T2V and TI2V (`--image-path`) on one-stage and two-stage pipelines (including HQ). * The spatial upsampler and distilled LoRA are auto-resolved from the model snapshot by default, and can still be overridden with `--spatial-upsampler-path` and `--distilled-lora-path`. * For LTX models, the `Resolutions` column uses output video `width×height` semantics, matching `sglang generate --width ... --height ...`. 4. LTX-2 / LTX-2.3 two-stage also supports `--ltx2-two-stage-device-mode {original,resident}`: * `original` keeps official two-stage semantics without the premerged stage-2 transformer path. * `resident` usually provides the best latency/throughput but uses much more VRAM. * Default is auto: `resident` on H200/high-memory CUDA GPUs, otherwise `original`. * Deprecated compatibility: `snapshot` is accepted as an alias for `original` and may be removed after two release cycles. 5. Cosmos3 ships in two sizes — `nvidia/Cosmos3-Nano` (8B) and `nvidia/Cosmos3-Super` (32B). Both share the same pipeline; the only difference is transformer depth and width, picked up from `transformer/config.json` at load time. A single checkpoint serves T2V, I2V (`--image-path`), and T2I (`--num-frames 1`). ## Supported Components SGLang Diffusion supports overriding individual pipeline components with `---path`. The value can be either a Hugging Face repo ID or a local component directory. The same overrides can also be provided in config files through `component_paths.`. ### Common Syntax CLI: ```bash Command theme={null} sglang generate \ --model-path black-forest-labs/FLUX.2-dev \ --vae-path black-forest-labs/FLUX.2-small-decoder \ --transformer-path /models/flux2/transformer ``` Config file: ```yaml Config theme={null} model_path: black-forest-labs/FLUX.2-dev component_paths: vae: black-forest-labs/FLUX.2-small-decoder transformer: /models/flux2/transformer ``` Use the component name from the pipeline's `model_index.json` or the native pipeline's registered module name:
Component Type Supported Keys Notes
VAE vae, video\_vae, audio\_vae vae is the common image-generation override
Transformer / DiT transformer, video\_dit, audio\_dit transformer is the standard override for the main denoiser
Text / Preprocess text\_encoder, text\_encoder\_2, tokenizer, processor, image\_processor Replacement encoders often need matching preprocessing assets
Auxiliary scheduler, spatial\_upsampler, vocoder, connectors, dual\_tower\_bridge, image\_encoder, vision\_language\_encoder Only valid for pipelines that expose these components
### Known Component Repos The table below lists concrete Hugging Face component repos that are already used in SGLang Diffusion docs or tests. It is not an exhaustive catalog of all compatible component repos.
Base Model Override Key Example Repo Notes
black-forest-labs/FLUX.2-dev vae black-forest-labs/FLUX.2-small-decoder Decoder-only FLUX.2 VAE override
black-forest-labs/FLUX.2-dev vae fal/FLUX.2-Tiny-AutoEncoder Existing tested custom VAE path
### VAE * `--vae-path` is the common image-generation override. * `--video-vae-path` and `--audio-vae-path` are only relevant for pipelines with separate video or audio VAEs. ### Transformer / DiT * `--transformer-path` is the standard override for the main denoising transformer. * For quantized transformers, prefer `--transformer-path` or `--transformer-weights-path`; see `quantization.md`. * `--video-dit-path` and `--audio-dit-path` are only for pipelines that split denoisers by modality. ### Text Encoders and Preprocessors * `--text-encoder-path` and `--text-encoder-2-path` override primary and secondary text encoders. * `--tokenizer-path`, `--processor-path`, and `--image-processor-path` are useful when the replacement encoder requires matching preprocessing assets. ### Auxiliary Components * `--scheduler-path` is only relevant when the pipeline exposes a scheduler component. * `--spatial-upsampler-path` is mainly for two-stage pipelines such as `LTX2TwoStagePipeline`. * `--vocoder-path`, `--connectors-path`, `--dual-tower-bridge-path`, `--image-encoder-path`, and `--vision-language-encoder-path` are only valid for pipelines that expose those components. ### Notes 1. Component overrides are only valid when the target pipeline actually uses that component. 2. The override key should match the component name in the pipeline's `model_index.json` or the native pipeline's registered module name. ## Verified LoRA Examples This section lists example LoRAs that have been explicitly tested and verified with each base model in the **SGLang Diffusion** pipeline. LoRAs that are not listed here are not necessarily incompatible. In practice, most standard LoRAs are expected to work, especially those following common Diffusers or SD-style conventions. The entries below simply reflect configurations that have been manually validated by the SGLang team. ### Verified LoRAs by Base Model
Base Model Supported LoRAs
Wan2.2 `lightx2v/Wan2.2-Distill-Loras`
`Cseti/wan2.2-14B-Arcane_Jinx-lora-v1`
Wan2.1 `lightx2v/Wan2.1-Distill-Loras`
Z-Image-Turbo `tarn59/pixel_art_style_lora_z_image_turbo`
`wcde/Z-Image-Turbo-DeJPEG-Lora`
Qwen-Image `lightx2v/Qwen-Image-Lightning`
`flymy-ai/qwen-image-realism-lora`
`prithivMLmods/Qwen-Image-HeadshotX`
`starsfriday/Qwen-Image-EVA-LoRA`
Qwen-Image-Edit `ostris/qwen_image_edit_inpainting`
`lightx2v/Qwen-Image-Edit-2511-Lightning`
Flux `dvyio/flux-lora-simple-illustration`
`XLabs-AI/flux-furry-lora`
`XLabs-AI/flux-RealismLora`
## Special requirements ### Sliding Tile Attention * Currently, only Hopper GPUs (H100s) are supported.