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Compatibility Matrix

This page summarizes common model + optimization combinations.

For the canonical, code-level list of model IDs recognized by VideoGenerator.from_pretrained(...), see the registrations in fastvideo/registry.py (register_configs(...) entries).

Note

The full STA integration in fastvideo/ is archived from main and kept in sta_do_not_delete: https://github.com/hao-ai-lab/FastVideo/tree/sta_do_not_delete We do this because we believe VSA is strictly better than STA for the actively maintained main inference path.

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(). FastVideo then uses model-specific default settings for pipeline initialization and sampling.

Model Name HuggingFace Model ID Resolutions TeaCache Sliding Tile Attn (Legacy Branch) Sage Attn VSA BSA
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 720px1280p
544px960p		 ❌ ✅ ✅ ⭕ ⭕
FastHunyuan FastVideo/FastHunyuan-diffusers 720px1280p
544px960p		 ❌ ✅ ✅ ⭕ ⭕
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 loayrashid/TurboWan2.1-T2V-1.3B-Diffusers 480P ⭕ ⭕ ⭕ ⭕ ⭕
TurboWan2.1 T2V 14B loayrashid/TurboWan2.1-T2V-14B-Diffusers 480P, 720P ⭕ ⭕ ⭕ ⭕ ⭕
TurboWan2.2 I2V A14B loayrashid/TurboWan2.2-I2V-A14B-Diffusers 480P
720P		 ⭕ ⭕ ⭕ ⭕ ⭕
LongCat T2V 13.6B See note** 480P
720P		 ❌ ❌ ❌ ⭕ ✅
Matrix Game 2.0 Base FastVideo/Matrix-Game-2.0-Base-Diffusers 352x640 ⭕ ⭕ ⭕ ⭕ ⭕
Matrix Game 2.0 GTA FastVideo/Matrix-Game-2.0-GTA-Diffusers 352x640 ⭕ ⭕ ⭕ ⭕ ⭕
Matrix Game 2.0 TempleRun FastVideo/Matrix-Game-2.0-TempleRun-Diffusers 352x640 ⭕ ⭕ ⭕ ⭕ ⭕

Note: Wan2.2 TI2V 5B has some quality issues when performing I2V generation. We are working on fixing this issue.

Sliding Tile Attn (Legacy Branch) entries refer to the archived sta_do_not_delete branch workflow, not active main inference wiring.

Canonical Supported IDs

The authoritative source for model-ID recognition is fastvideo/registry.py. If a model ID is registered there, FastVideo can resolve default pipeline and sampling configuration for it.

Special requirements

Sliding Tile Attention

  • Full STA pipeline usage is on the archived branch: https://github.com/hao-ai-lab/FastVideo/tree/sta_do_not_delete
  • STA currently requires Hopper GPUs (H100s).

TurboWan2.1 (TurboDiffusion)

  • Uses TurboDiffusionPipeline with RCM scheduler for 1-4 step generation
  • Requires SLA attention backend: export FASTVIDEO_ATTENTION_BACKEND=SLA_ATTN
  • Uses guidance_scale=1.0 (no classifier-free guidance)

Matrix Game 2.0

  • Image-to-video game world models with keyboard/mouse control input
  • Three variants available: Base (universal), GTA, and TempleRun
  • Each variant has different keyboard dimensions for control inputs