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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.
## Registered Model IDs
Every Hugging Face model ID registered in `fastvideo/registry.py` on `main`
(commit `8d89f30d`), grouped by family. Any ID below can
be passed to `VideoGenerator.from_pretrained(...)`; FastVideo resolves the
matching pipeline and sampling defaults. The **Family** column is a
documentation grouping: it follows each registration's declared `model_family`,
except `black-forest-labs/FLUX.1-dev`, which declares none and is listed under
`flux` for readability. The **Workloads** column shows each
registration's declared `workload_types`; `—` means the entry is registered
without a UI workload option but is still loadable by ID. The **Example**
column links a runnable script in `examples/inference/basic/` where one exists.
| Family | HuggingFace Model ID | Workloads | Example |
|--------|----------------------|-----------|---------|
| cosmos | `nvidia/Cosmos-Predict2-2B-Video2World` | T2V | — |
| cosmos25 | `KyleShao/Cosmos-Predict2.5-2B-Diffusers` | T2V | [basic_cosmos2_5_t2w.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_cosmos2_5_t2w.py) |
| cosmos25 | `nvidia/Cosmos-Predict2.5-14B` | T2V | [basic_cosmos2_5_t2w.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_cosmos2_5_t2w.py) |
| dreamx_world | `FastVideo/DreamX-World-5B-Cam-Diffusers` | I2V | [basic_dreamx_world.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_dreamx_world.py) |
| dreamx_world | `FastVideo/DreamX-World-5B-Diffusers` | I2V | [basic_dreamx_world.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_dreamx_world.py) |
| flux | `black-forest-labs/FLUX.1-dev` | T2I | [basic_flux_dev.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_flux_dev.py) |
| flux2 | `black-forest-labs/FLUX.2-klein-4B`<br>`black-forest-labs/FLUX.2-klein-9B` | T2I | [basic_flux2_klein.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_flux2_klein.py) |
| flux2 | `black-forest-labs/FLUX.2-dev` | T2I | [basic_flux2.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_flux2.py) |
| gamecraft | `FastVideo/HunyuanGameCraft-Diffusers` | I2V | [basic_gamecraft.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_gamecraft.py) |
| gen3c | `FastVideo/GEN3C-Cosmos-7B-Diffusers` | T2V | [basic_gen3c.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_gen3c.py) |
| glm_image | `zai-org/GLM-Image` | T2I | [basic_glm_image.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_glm_image.py) |
| hunyuan | `hunyuanvideo-community/HunyuanVideo` | T2V | — |
| hunyuan | `FastVideo/FastHunyuan-diffusers` | T2V | — |
| hunyuan15 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v` | T2V | [basic_hy15.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_hy15.py) |
| hunyuan15 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled` | I2V | [basic_hy15.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_hy15.py) |
| hunyuan15 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v` | T2V | [basic_hy15.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_hy15.py) |
| hunyuan15 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v_distilled` | I2V | [basic_hy15.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_hy15.py) |
| hunyuan15 | `weizhou03/HunyuanVideo-1.5-Diffusers-1080p`<br>`weizhou03/HunyuanVideo-1.5-Diffusers-1080p-2SR` | — | [basic_hy15_1080p.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_hy15_1080p.py) |
| hyworld | `FastVideo/HY-WorldPlay-Bidirectional-Diffusers` | — | [basic_hyworld.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_hyworld.py) |
| kandinsky5 | `kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers` | T2V | [basic_kandinsky5_t2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_kandinsky5_t2v.py) |
| kandinsky5 | `kandinskylab/Kandinsky-5.0-T2V-Pro-sft-5s-Diffusers` | T2V | [basic_kandinsky5_t2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_kandinsky5_t2v.py) |
| kandinsky5 | `kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers` | T2V | [basic_kandinsky5_t2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_kandinsky5_t2v.py) |
| kandinsky5 | `kandinskylab/Kandinsky-5.0-T2V-Pro-distilled-5s-Diffusers` | T2V | [basic_kandinsky5_t2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_kandinsky5_t2v.py) |
| kandinsky5 | `kandinskylab/Kandinsky-5.0-I2V-Lite-5s-Diffusers` | I2V | [basic_kandinsky5_i2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_kandinsky5_i2v.py) |
| kandinsky5 | `kandinskylab/Kandinsky-5.0-I2V-Pro-sft-5s-Diffusers` | I2V | [basic_kandinsky5_i2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_kandinsky5_i2v.py) |
| kandinsky5 | `kandinskylab/Kandinsky-5.0-I2V-Pro-distilled-5s-Diffusers` | I2V | [basic_kandinsky5_i2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_kandinsky5_i2v.py) |
| kandinsky6 | `kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers`<br>`kandinskylab/Kandinsky-6.0-Pro-sft-5s-Diffusers`<br>`kandinskylab/Kandinsky-6.0-Lite-5s-Diffusers` | T2V, I2V | [basic_kandinsky6_ti2va.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_kandinsky6_ti2va.py) |
| kandinsky6 | `kandinskylab/Kandinsky-6.0-Pro-distill-5s-Diffusers`<br>`kandinskylab/Kandinsky-6.0-Lite-distill-5s-Diffusers` | T2V, I2V | [basic_kandinsky6_ti2va.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_kandinsky6_ti2va.py) |
| kandinsky6_sr | `kandinskylab/Kandinsky-6.0-VSR-5s-Diffusers`<br>`kandinskylab/Kandinsky-6.0-VSR-distilled2steps-5s-Diffusers` | — | [basic_kandinsky6_sr.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_kandinsky6_sr.py) |
| lingbot_video | `FastVideo/LingBot-Video-MoE-30B-A3B-Diffusers` | T2V | [basic_lingbot_video.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_lingbot_video.py) |
| lingbot_video | `FastVideo/LingBot-Video-Dense-1.3B-Diffusers` | T2V | [basic_lingbot_video.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_lingbot_video.py) |
| lingbotworld | `FastVideo/LingBot-World-Base-Cam-Diffusers` | I2V | [basic_lingbotworld_base_cam.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_lingbotworld_base_cam.py) |
| lingbotworld2 | `robbyant/lingbot-world-v2-14b-causal-fast` | I2V | [basic_lingbotworld2_causal_fast.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_lingbotworld2_causal_fast.py) |
| longcat | `FastVideo/LongCat-Video-T2V-Diffusers` | T2V | [basic_longcat_t2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_longcat_t2v.py) |
| longcat | `FastVideo/LongCat-Video-I2V-Diffusers` | I2V | [basic_longcat_i2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_longcat_i2v.py) |
| longcat | `FastVideo/LongCat-Video-VC-Diffusers` | — | [basic_longcat_vc.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_longcat_vc.py) |
| ltx2 | `FastVideo/LTX2-Distilled-Diffusers`<br>`FastVideo/LTX2.3-Distilled-Diffusers`<br>`FastVideo/LTX-2.3-Distilled-Diffusers` | T2V | [basic_ltx2_distilled.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2_distilled.py) |
| ltx2 | `Lightricks/LTX-2.3`<br>`FastVideo/LTX2.3-base`<br>`FastVideo/LTX2.3-Diffusers` | T2V | [basic_ltx2.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2.py) |
| ltx2 | `Lightricks/LTX-2`<br>`FastVideo/LTX2-base`<br>`FastVideo/LTX2-Diffusers` | T2V | [basic_ltx2.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2.py) |
| mmaudio | `FastVideo/MMAudio-large-44k-v2-Diffusers` | V2A, T2A | [basic_mmaudio.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_mmaudio.py) |
| matrixgame | `FastVideo/Matrix-Game-2.0-Base-Distilled-Diffusers`<br>`FastVideo/Matrix-Game-2.0-GTA-Distilled-Diffusers`<br>`FastVideo/Matrix-Game-2.0-TempleRun-Distilled-Diffusers`<br>`FastVideo/Matrix-Game-2.0-Base-Diffusers`<br>`FastVideo/Matrix-Game-2.0-GTA-Diffusers`<br>`FastVideo/Matrix-Game-2.0-TempleRun-Diffusers`<br>`mignonjia/mg_longtuning_distilled_zelda`<br>`mignonjia/mg_sf_distilled_zelda_1k_steps`<br>`mignonjia/mg_sf_distilled_zelda`<br>`mignonjia/mg_causal_zelda`<br>`mignonjia/mg_bidirectional_zelda` | I2V | [basic_matrixgame2.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_matrixgame2.py) |
| matrixgame | `FastVideo/Matrix-Game-3.0-Base-Distilled-Diffusers` | I2V | [basic_matrixgame3.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_matrixgame3.py) |
| minimax_h3 | `MiniMaxAI/MiniMax-H3` | T2V, I2V | [T2VA](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_minimax_h3_t2v.py)<br>[FL2VA](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_minimax_h3_fl2va.py)<br>[Ref2VA](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_minimax_h3_ref2va.py) |
| sd35 | `stabilityai/stable-diffusion-3.5-medium` | T2I | [basic_sd35_t2i.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_sd35_t2i.py) |
| stable_audio | `FastVideo/stable-audio-open-1.0-Diffusers` | T2V | [basic_stable_audio.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_stable_audio.py) |
| stable_audio | `FastVideo/stable-audio-open-small-Diffusers` | T2V | [basic_stable_audio_small.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_stable_audio_small.py) |
| turbodiffusion | `loayrashid/TurboWan2.1-T2V-1.3B-Diffusers` | T2V | [basic_turbodiffusion.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_turbodiffusion.py) |
| turbodiffusion | `loayrashid/TurboWan2.1-T2V-14B-Diffusers` | T2V | [basic_turbodiffusion_14b.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_turbodiffusion_14b.py) |
| turbodiffusion | `loayrashid/TurboWan2.2-I2V-A14B-Diffusers` | I2V | [basic_turbodiffusion_i2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_turbodiffusion_i2v.py) |
| wan | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers` | T2V | [basic.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic.py) |
| wan | `Wan-AI/Wan2.1-T2V-14B-Diffusers`<br>`FastVideo/Wan2.1-VSA-T2V-14B-720P-Diffusers` | T2V | — |
| wan | `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers` | I2V | — |
| wan | `Wan-AI/Wan2.1-I2V-14B-720P-Diffusers` | I2V | — |
| wan | `weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers` | I2V | — |
| wan | `IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers` | — | [basic_wan2_2_Fun.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_wan2_2_Fun.py) |
| wan | `FastVideo/FastWan2.1-T2V-1.3B-Diffusers`<br>`FastVideo/FastWan2.1-T2V-14B-480P-Diffusers` | T2V | [basic_dmd.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_dmd.py) |
| wan | `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | T2V, I2V | [basic_wan2_2_ti2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_wan2_2_ti2v.py) |
| wan | `FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers`<br>`FastVideo/FastWan2.2-TI2V-5B-Diffusers` | T2V, I2V | [basic_dmd.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_dmd.py) |
| wan | `decart-ai/Lucy-Edit-Dev`<br>`decart-ai/Lucy-Edit-1.1-Dev` | — | [basic_lucy_edit.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_lucy_edit.py) |
| wan | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | T2V | [basic_wan2_2.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_wan2_2.py) |
| wan | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | I2V | [basic_wan2_2_i2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_wan2_2_i2v.py) |
| wan | `wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers` | T2V | [basic_self_forcing_causal.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal.py) |
| wan | `rand0nmr/SFWan2.2-T2V-A14B-Diffusers` | T2V | [basic_self_forcing_causal_wan2_2_t2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_t2v.py) |
| wan | `FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers` | I2V | [basic_self_forcing_causal_wan2_2_i2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py) |
| zimage | `Tongyi-MAI/Z-Image-Turbo` | T2I | [basic_zimage.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_zimage.py) |
**Note (stable_audio)**: the Stable Audio Open pipelines generate audio
(`StableAudioT2AConfig` / `StableAudioOpenSmallConfig`); they are registered
under the generic T2V workload option in the registry.
**Note (MMAudio)**: the registered Hugging Face model ID is reserved but not
yet public. Follow the [MMAudio inference guide](https://github.com/hao-ai-lab/FastVideo/blob/main/fastvideo/pipelines/basic/mmaudio/README.md)
to convert the official weights locally and set `MMAUDIO_MODEL_PATH`.
**Note (MiniMax H3)**: T2VA, FL2VA, and Ref2VA all generate video with stereo
audio. Use the Ref2VA example when passing ordered image, video, or audio
references. Distilled Ref2VA PDD students (eight transformer forwards, with
reference videos as sparse VSA regions) run through
[basic_fasth3_omniref_pdd.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_fasth3_omniref_pdd.py);
see [FastH3 distilled checkpoint schedules](fasth3-distilled.md#ref2va-pdd-students).
**Note (Kandinsky 6)**: the `kandinsky6` IDs (Pro 30.1B and Lite 3.2B, each base
and distilled) generate video with audio from text, optionally plus an image (see the [T2IVA guide](kandinsky6.md)); the
`kandinsky6_sr` IDs upscale an existing video (see the
[Video SR guide](kandinsky6_sr.md)).
**Note (Wan-VACE)**: not currently supported — no VACE pipeline or registered
model ID exists on `main`
([#1435](https://github.com/hao-ai-lab/FastVideo/issues/1435)). The closest
supported path is the Wan2.1-Fun control pipeline
(`IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers`).
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.
Registered models absent from this table have not been validated against these
optimizations: absence means **untested**, not incompatible.
<style>
/* Target tables in this section */
#models-x-optimization + p + table {
display: block;
overflow-x: auto;
width: 100%;
font-size: 0.85rem;
}
#models-x-optimization + p + table td,
#models-x-optimization + p + table th {
text-align: center;
white-space: nowrap;
padding: 0.5em;
}
/* First two columns can wrap */
#models-x-optimization + p + table td:nth-child(1),
#models-x-optimization + p + table td:nth-child(2) {
white-space: normal;
min-width: 120px;
}
#models-x-optimization + p + table td:nth-child(2) code {
font-size: 0.75rem;
}
</style>
| 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 | ⭕ | ⭕ | ✅ | ⭕ | ⭕ |
| DreamX-World 5B Cam | `FastVideo/DreamX-World-5B-Cam-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
| DreamX-World 5B AR | `FastVideo/DreamX-World-5B-Diffusers` | 704px1280p | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
| Lucy Edit Dev 5B*** | `decart-ai/Lucy-Edit-Dev` | 480P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
| Wan2.2 T2V A14B | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ | ⭕ |
| Wan2.2 I2V A14B | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ | ⭕ |
| HunyuanVideo | `hunyuanvideo-community/HunyuanVideo` | 720px1280p<br>544px960p | ❌ | ✅ | ✅ | ⭕ | ⭕ |
| FastHunyuan | `FastVideo/FastHunyuan-diffusers` | 720px1280p<br>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<br>720P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
| LongCat T2V 13.6B | `FastVideo/LongCat-Video-T2V-Diffusers` | 480P<br>720P | ❌ | ❌ | ❌ | ⭕ | ✅ |
| Matrix Game 2.0 Base Distilled | `FastVideo/Matrix-Game-2.0-Base-Distilled-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
| Matrix Game 2.0 GTA Distilled | `FastVideo/Matrix-Game-2.0-GTA-Distilled-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
| Matrix Game 2.0 TempleRun Distilled | `FastVideo/Matrix-Game-2.0-TempleRun-Distilled-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
| Matrix Game 3.0 Base Distilled | `FastVideo/Matrix-Game-3.0-Base-Distilled-Diffusers` | 720x1280 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
| GEN3C Cosmos 7B | `FastVideo/GEN3C-Cosmos-7B-Diffusers` | 704px1280p | ❌ | ❌ | ❌ | ⭕ | ⭕ |
## Apple Silicon native runtime
| Release path | Model | Mode | Validated hardware | Status |
| --- | --- | --- | --- | --- |
| MLX FastMetal T2V 1.3B | [`FastVideo/FastMetal-1.3B-QAD`](https://huggingface.co/FastVideo/FastMetal-1.3B-QAD) | 480x832, 81 frames, 3-step DMD, INT8 DiT + TAEHV decode | Apple M4 Max, 16 GB+ unified memory | Released |
| MLX FastMetal T2V 5B | [`FastVideo/FastMetal-5B-QAD`](https://huggingface.co/FastVideo/FastMetal-5B-QAD) | 480p / 720p, 81 frames, 3-step DMD, INT8 DiT + TAEHV decode; optional `--fast`, `--fast-spatial`, `--refine`. The checked-in example is T2V. CUDA Wan2.2 TI2V 5B is the image-capable path. | Apple M4 Max, 16 GB+ unified memory | Released |
| MLX FastMetal T2V 14B | [`FastVideo/FastMetal-14B-QAD`](https://huggingface.co/FastVideo/FastMetal-14B-QAD) | 480p / 720p, 81 frames, 3-step DMD, INT8 DiT + TAEHV decode | Apple M4 Max, 36 GB+ unified memory | Released |
| MLX FastH3 V1 T2VA | [`FastVideo/FastVideo-Minimax-FastH3-Preview-v0.2`](https://huggingface.co/FastVideo/FastVideo-Minimax-FastH3-Preview-v0.2) + locally converted DiT | 480p / 720p, 124 frames, 4-step DMD2, INT8/INT6/INT4 **weight-only** DiT, native video + audio VAE; optional temporal RIFE fast mode; optional spatial fast mode; optional VSA (tile 64/256, exempt/compete) on `--include-vsa` checkpoints | Apple M4 Max, 36 GB unified memory | Source runtime; T2VA only |
| MLX FastH3 V2 T2VA | [`FastVideo/FastVideo-FastH3-8-Step-V2`](https://huggingface.co/FastVideo/FastVideo-FastH3-8-Step-V2) + locally converted DiT | 480p / 720p, 124 frames, 8-step DMD2, INT8 **weight-only** DiT with `--include-vsa`, trained VSA 0.8 / tile 64, native video + audio VAE; AdaLN cache from `fastvideo_inference.json` | Apple M4 Max, 36 GB unified memory | Source runtime; T2VA only |
Apple Silicon uses the native MLX runtime. FastMetal-QAD is the packaged Wan
release. FastH3 V1 and FastH3 V2 use a source checkout and local DiT conversion.
CUDA FastWan-QAD (`FastVideo/FastWan-QAD-1.3B`,
`FastVideo/FastWan-QAD-FP8-1.3B`) is the NVIDIA release. See the
[MLX install guide](../getting_started/installation/mlx.md) and the
[FastMetal-QAD blog](https://haoailab.com/blogs/fastmetal/).
**Note**: Wan2.2 TI2V 5B has some quality issues when performing I2V generation. We are working on fixing this issue.
***Lucy Edit Dev uses a non-commercial model license. FastVideo support is
focused on inference integration for video editing workflows.
`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.
**Note (GEN3C)**: The official `nvidia/GEN3C-Cosmos-7B` repo provides a raw
`model.pt` checkpoint. Use a Diffusers-format repo (for example,
`FastVideo/GEN3C-Cosmos-7B-Diffusers`) or convert locally with
`scripts/checkpoint_conversion/convert_gen3c_to_fastvideo.py`.
## Hardware and OS
Per the installation guides:
- **NVIDIA GPU (x86_64)** — CUDA 12.6 or 13.0; see the
[GPU install guide](../getting_started/installation/gpu.md).
- **NVIDIA DGX Spark (GB10, aarch64)** — CUDA 13, from-source kernel build; see
the [DGX Spark install guide](../getting_started/installation/spark.md).
Two Sparks over QSFP use Ray sequence parallel; see
[Pair two NVIDIA DGX Sparks](../getting_started/installation/spark_pair.md).
- **Apple silicon** — macOS 14 or newer; MLX runtime for FastMetal-QAD, FastH3
V1, and FastH3 V2. See the
[MLX install guide](../getting_started/installation/mlx.md). The older
[`basic_mps.py`](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_mps.py)
demo is PyTorch MPS only.
Optimization-specific hardware constraints (e.g. STA requiring Hopper) are
listed under [Special requirements](#special-requirements).
## 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
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