sglang/docs/diffusion/compatibility_matrix.md

Compatibility Matrix

The table below shows every supported model and the optimizations supported for them.

The symbols used have the following meanings:

• ✅ = Full compatibility
• ❌ = No compatibility
• ⭕ = Does not apply to this model

Models x Optimization

The HuggingFace Model ID can be passed directly to from_pretrained() methods, and sglang-diffusion will use the optimal default parameters when initializing and generating videos.

Video Generation Models

Model Name	Hugging Face Model ID	Resolutions	TeaCache	Sliding Tile Attn	Sage Attn	Video Sparse Attention (VSA)	Sparse Linear Attention (SLA)	Sage Sparse Linear Attention (SageSLA)	Sparse Video Gen 2 (SVG2)
FastWan2.1 T2V 1.3B	FastVideo/FastWan2.1-T2V-1.3B-Diffusers	480p	⭕	⭕	⭕	✅	❌	❌	❌
FastWan2.2 TI2V 5B Full Attn	FastVideo/FastWan2.2-TI2V-5B-FullAttn-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	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)	❌	❌	❌	❌	❌	❌	❌

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,snapshot,resident}:
   • snapshot is the default and recommended mode.
   • resident usually provides the best latency/throughput but uses much more VRAM.
   • original keeps official two-stage semantics without the premerged stage-2 transformer path.
   • Example (one prior run): original 154.67s, snapshot 114.05s, resident 75.71s; peak VRAM trend is original < snapshot < resident.

Image Generation Models

Model Name	HuggingFace Model ID
FLUX.1-dev	black-forest-labs/FLUX.1-dev
FLUX.2-dev	black-forest-labs/FLUX.2-dev
FLUX.2-dev-NVFP4	black-forest-labs/FLUX.2-dev-NVFP4
FLUX.2-Klein-4B	black-forest-labs/FLUX.2-klein-4B
FLUX.2-Klein-9B	black-forest-labs/FLUX.2-klein-9B
Z-Image	Tongyi-MAI/Z-Image
Z-Image-Turbo	Tongyi-MAI/Z-Image-Turbo
GLM-Image	zai-org/GLM-Image
Qwen Image	Qwen/Qwen-Image
Qwen Image 2512	Qwen/Qwen-Image-2512
Qwen Image Edit	Qwen/Qwen-Image-Edit
Qwen Image Edit 2509	Qwen/Qwen-Image-Edit-2509
Qwen Image Edit 2511	Qwen/Qwen-Image-Edit-2511
Qwen Image Layered	Qwen/Qwen-Image-Layered
SD3 Medium	stabilityai/stable-diffusion-3-medium-diffusers
SD3.5 Medium	stabilityai/stable-diffusion-3.5-medium-diffusers
SD3.5 Large	stabilityai/stable-diffusion-3.5-large-diffusers
Hunyuan3D-2	tencent/Hunyuan3D-2
SANA 1.5 1.6B	Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers
SANA 1.5 4.8B	Efficient-Large-Model/SANA1.5_4.8B_1024px_diffusers
SANA 1600M 1024px	Efficient-Large-Model/Sana_1600M_1024px_diffusers
SANA 600M 1024px	Efficient-Large-Model/Sana_600M_1024px_diffusers
SANA 1600M 512px	Efficient-Large-Model/Sana_1600M_512px_diffusers
SANA 600M 512px	Efficient-Large-Model/Sana_600M_512px_diffusers
FireRed-Image-Edit 1.0	FireRedTeam/FireRed-Image-Edit-1.0
FireRed-Image-Edit 1.1	FireRedTeam/FireRed-Image-Edit-1.1
ERNIE-Image	baidu/ERNIE-Image
ERNIE-Image-Turbo	baidu/ERNIE-Image-Turbo

Supported Components

SGLang Diffusion supports overriding individual pipeline components with --<component>-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.<component>.

Common Syntax

CLI:

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:

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.

Important: 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.