This GGUF file is a direct conversion of Wan-AI/Wan2.2-T2V-A14B Since this is a quantized model, all original licensing terms and usage restrictions remain in effect. Usage The model can be used with the ComfyUI custom node ComfyUI-GGUF by city96 Place model files in ComfyUI/models/unet see the GitHub readme for further installation instructions.
Every GGUF quantisation of Wan2.2-TI2V-5B that QuantStack/Wan2.2-TI2V-5B-GGUF publishes, plus the companion VAE, mirrored here.
Model Card
By Unsloth AI, published under apache-2.0, revision b5c2a3816e70.
Every GGUF quantisation of Wan2.2-TI2V-5B that QuantStack/Wan2.2-TI2V-5B-GGUF publishes, plus the companion VAE, mirrored here. Unsloth Studio offers this repo as the curated one-click GGUF pick for Wan2.2 TI2V 5B, so its availability is Studio's problem rather than the repacker's: a rename or a takedown turns the pick into a 404 no client can work around. All 13 quants are mirrored, not a chosen few, because the picker lets you choose the precision. The weights are unmodified: byte for byte the files of the same name in the source repo. TI2V-5B is a 720P-only checkpoint: the supported sizes are 1280x704 and 704x1280, and its VAE has temporal compression 4, so valid frame counts are 4k+1.…
Read Unsloth AI's full model card
Wan2.2 TI2V 5B, GGUF (mirror)
Every GGUF quantisation of Wan2.2-TI2V-5B that QuantStack/Wan2.2-TI2V-5B-GGUF publishes, plus the companion VAE, mirrored here.
Unsloth Studio offers this repo as the curated one-click GGUF pick for Wan2.2 TI2V 5B, so its availability is Studio's problem rather than the repacker's: a rename or a takedown turns the pick into a 404 no client can work around. All 13 quants are mirrored, not a chosen few, because the picker lets you choose the precision. The weights are unmodified: byte for byte the files of the same name in the source repo.
| File | Size |
|---|---|
Wan2.2-TI2V-5B-Q2_K.gguf |
1.73 GiB |
Wan2.2-TI2V-5B-Q3_K_S.gguf |
2.14 GiB |
Wan2.2-TI2V-5B-Q3_K_M.gguf |
2.37 GiB |
Wan2.2-TI2V-5B-Q4_0.gguf |
2.82 GiB |
Wan2.2-TI2V-5B-Q4_K_S.gguf |
2.90 GiB |
Wan2.2-TI2V-5B-Q4_1.gguf |
3.03 GiB |
Wan2.2-TI2V-5B-Q4_K_M.gguf |
3.20 GiB |
Wan2.2-TI2V-5B-Q5_K_S.gguf |
3.32 GiB |
Wan2.2-TI2V-5B-Q5_0.gguf |
3.39 GiB |
Wan2.2-TI2V-5B-Q5_K_M.gguf |
3.55 GiB |
Wan2.2-TI2V-5B-Q5_1.gguf |
3.60 GiB |
Wan2.2-TI2V-5B-Q6_K.gguf |
3.92 GiB |
Wan2.2-TI2V-5B-Q8_0.gguf |
5.03 GiB |
VAE/Wan2.2_VAE.safetensors |
1.31 GiB |
TI2V-5B is a 720P-only checkpoint: the supported sizes are 1280x704 and 704x1280, and its VAE has temporal compression 4, so valid frame counts are 4k+1.
Licence
Apache-2.0, from Wan2.2-TI2V-5B. Full text in
LICENSE.
These files are Derivative Works, not a plain copy: the transformer is quantised and the VAE was
converted from the original .pth to .safetensors. Apache-2.0 section 4(b) wants that stated,
so NOTICE lists every
change along with the attribution. Both sets of changes are QuantStack's work. Not an official
Alibaba Wan Team or QuantStack product, and not endorsed by either.
Identity and Version
- Repository
- unsloth/Wan2.2-TI2V-5B-GGUF
- Publisher
- Unsloth AI
- Task
- Text to video
- Modality
- Video
- Library
- gguf
- Parameters
- Not stated by the source
- Languages
- en, zh
- Revision
- b5c2a3816e7056e57e200f6be726fb36ce523b49
- First published
- 2026-08-06
- Last updated
- 2026-08-06
Files and Weights
18 files, 45.4 GB in total. The weights are 14 files totalling 45.4 GB in gguf, safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| VAE/Wan2.2_VAE.safetensors | Weights | 1.4 GB | e40321bd36b9 |
| Wan2.2-TI2V-5B-Q2_K.gguf | Weights | 1.9 GB | bebb995b18ad |
| Wan2.2-TI2V-5B-Q3_K_M.gguf | Weights | 2.5 GB | 93cab80a36db |
| Wan2.2-TI2V-5B-Q3_K_S.gguf | Weights | 2.3 GB | 2cc11c77652b |
| Wan2.2-TI2V-5B-Q4_0.gguf | Weights | 3.0 GB | fcf40dd62cb5 |
| Wan2.2-TI2V-5B-Q4_1.gguf | Weights | 3.3 GB | dac5c36bf2ae |
| Wan2.2-TI2V-5B-Q4_K_M.gguf | Weights | 3.4 GB | 95b19697b7f9 |
| Wan2.2-TI2V-5B-Q4_K_S.gguf | Weights | 3.1 GB | ab4195ecd022 |
| Wan2.2-TI2V-5B-Q5_0.gguf | Weights | 3.6 GB | ce3fe1bcc8e2 |
| Wan2.2-TI2V-5B-Q5_1.gguf | Weights | 3.9 GB | 5c92f4f14c53 |
| Wan2.2-TI2V-5B-Q5_K_M.gguf | Weights | 3.8 GB | 4424633a8765 |
| Wan2.2-TI2V-5B-Q5_K_S.gguf | Weights | 3.6 GB | 47a4960ac7ed |
| Wan2.2-TI2V-5B-Q6_K.gguf | Weights | 4.2 GB | 355f6bee35c4 |
| Wan2.2-TI2V-5B-Q8_0.gguf | Weights | 5.4 GB | 57bece983817 |
| LICENSE | Documentation | 11.4 KB | — |
| NOTICE | Documentation | 1.2 KB | — |
| README.md | Documentation | 2.2 KB | — |
| .gitattributes | Repository | 2.3 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 45.4 GB
Released by Unsloth AI through its official repository on Hugging Face. Read the license.
Built From
- Derived from Wan-AI/Wan2.2-TI2V-5B
- Quantized from Wan-AI/Wan2.2-TI2V-5B
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 45.4 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About Wan2.2-TI2V-5B-GGUF
Can I use Wan2.2-TI2V-5B-GGUF commercially?
Yes. Wan2.2-TI2V-5B-GGUF is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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