# Wan2.1-T2V-1.3B by Wan-AI: Open-Weight Model Source: https://savrn.com/models/wan2-1-t2v-1-3b Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve Wan2.1-T2V-1.3B (1.4B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 2.8 GB | 3.4 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 1.4 GB | 1.7 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.7 GB | 0.9 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 25, 2026. [Wan2.1-T2V-1.3B on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/wan2-1-t2v-1-3b/gpus) ## Model Card By Wan-AI, published under apache-2.0, revision 37ec512624d6. ### Wan2.1 [Read the full model card (1,815 words)](https://savrn.com/models/wan2-1-t2v-1-3b/card) ## Configuration Model type t2v ## Identity and Version Repository Wan-AI/Wan2.1-T2V-1.3B Publisher Wan-AI Task Text to video Modality Video Library diffusers Parameters 1.4B parameters Languages en, zh Revision 37ec512624d61f7aa208f7ea8140a131f93afc9a First published 2025-02-25 Last updated 2025-03-01 ## Files and Weights 22 files, 17.6 GB in total. The weights are 3 files totalling 17.5 GB in pth, safetensors. Weights3 files · 17.5 GB Configuration2 files · 6.9 KB Tokenizer3 files · 21.4 MB Documentation2 files · 28.3 KB Other10 files · 6.7 MB Repository2 files · 8.4 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | Wan2.1_VAE.pth | Weights | 507.6 MB | 38071ab59bd9 | | diffusion_pytorch_model.safetensors | Weights | 5.7 GB | 96b6b242ca1c | | models_t5_umt5-xxl-enc-bf16.pth | Weights | 11.4 GB | 7cace0da2b44 | | config.json | Configuration | 249 B | — | | google/umt5-xxl/special_tokens_map.json | Configuration | 6.6 KB | — | | LICENSE.txt | Documentation | 11.4 KB | — | | README.md | Documentation | 16.9 KB | — | | assets/comp_effic.png | Other | 1.8 MB | b0e225caffb4 | | assets/data_for_diff_stage.jpg | Other | 528.3 KB | 59aec08409f2 | | assets/i2v_res.png | Other | 891.7 KB | 6823b3206d8d | | assets/logo.png | Other | 56.3 KB | 96cddc0f6672 | | assets/t2v_res.jpg | Other | 301.0 KB | 91db57909244 | | assets/vben_1.3b_vs_sota.png | Other | 515.8 KB | b7705db79f2e | | assets/vben_vs_sota.png | Other | 1.6 MB | 9a0e86ca8504 | | assets/video_dit_arch.jpg | Other | 643.4 KB | 195dceec6570 | | assets/video_vae_res.jpg | Other | 212.6 KB | d8f9e7f73538 | | examples/i2v_input.JPG | Other | 250.6 KB | 077e3d965090 | | .gitattributes | Repository | 2.2 KB | — | | assets/.DS_Store | Repository | 6.1 KB | d65165279105 | | google/umt5-xxl/spiece.model | Tokenizer | 4.5 MB | e3909a67b780 | | google/umt5-xxl/tokenizer.json | Tokenizer | 16.8 MB | 6e197b4d3dbd | | google/umt5-xxl/tokenizer_config.json | Tokenizer | 61.7 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 17.5 GB [Download from Wan-AI](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B) Released by Wan-AI through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 17.5 GB | | 16-bit | 2.8 GB | | 8-bit | 1.4 GB | | 4-bit | 0.7 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Built on This Model - Quantized from[Wan2.1](https://savrn.com/models/wan2-1) - Derived from[Wan2.1](https://savrn.com/models/wan2-1) - Quantized from[Wan2.1-T2V-1.3B-GGUF](https://savrn.com/models/wan2-1-t2v-1-3b-gguf) - Derived from[Wan2.1-T2V-1.3B-GGUF](https://savrn.com/models/wan2-1-t2v-1-3b-gguf) - Derived from[NSFW_Wan_1.3b](https://savrn.com/models/nsfw-wan-1-3b) ## Questions About Wan2.1-T2V-1.3B ### How much GPU memory does Wan2.1-T2V-1.3B need? About 3.4 GB at 16-bit and 0.9 GB at 4-bit: the weights (1.4B parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run Wan2.1-T2V-1.3B on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### Can I use Wan2.1-T2V-1.3B commercially? Yes. Wan2.1-T2V-1.3B 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. ## Similar Models Model · Text to video ### [Wan2.1-T2V-1.3B-Diffusers](https://savrn.com/models/wan2-1-t2v-1-3b-diffusers) [Wan-AI](https://savrn.com/model-publishers/wan-ai) In this repository, we present Wan2.1, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. Wan2.1 offers these key features: This repository hosts our T2V-1.3B model, a versatile solution for video generation that is compatible with nearly all consumer-grade GPUs. In this way, we hope that Wan2.1 can serve as an easy-to-use tool for more creative teams in video creation, providing a high-quality foundational model for academic teams with limited computing resources. This will facilitate both the rapid development of the video creation community and the swift advancement of video technology. Your browser does not support the video tag.… Open weights apache-2.0 1.4B parameters diffusers [View model](https://savrn.com/models/wan2-1-t2v-1-3b-diffusers) Model · Text to video ### [TurboWan2.1-T2V-1.3B-Diffusers](https://savrn.com/models/turbowan2-1-t2v-1-3b-diffusers) [PostYellow](https://savrn.com/model-publishers/ipostyellow) convert TurboWan2.1-T2V-1.3B-480P(https://modelscope.cn/models/TurboDiffusion/TurboWan2.1-T2V-1.3B-480P/summary) to TurboWan2.1-T2V-1.3B-Diffusers convert script https://github.com/IPostYellow/TurboWantoDiffusers/blob/main/convertturbowantodiffusers.py To use in sglang Open weights apache-2.0 1.4B parameters diffusers [View model](https://savrn.com/models/turbowan2-1-t2v-1-3b-diffusers) Model · Text to video ### [CogVideoX-2b](https://savrn.com/models/cogvideox-2b) [Z.ai](https://savrn.com/model-publishers/zai-org) Visit QingYing and API Platform to experience commercial video generation models. CogVideoX is an open-source version of the video generation model originating from QingYing. The table below displays the list of video generation models we currently offer, along with their foundational information. Data Explanation + When testing using the diffusers library, all optimizations provided by the diffusers library were enabled. This solution has not been tested for actual VRAM/memory usage on devices other than NVIDIA A100 / H100. Generally, this solution can be adapted to all devices with NVIDIA Ampere architecture and above. If the optimizations are disabled, VRAM usage will increase… Open weights apache-2.0 1.7B parameters diffusers [View model](https://savrn.com/models/cogvideox-2b) Model · Text to video ### [AnimateLCM](https://savrn.com/models/animatelcm) [Fu-Yun Wang](https://savrn.com/model-publishers/wangfuyun) AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data by Fu-Yun Wang et al. For more details, please refer to our [paper] | [code] | [proj-page] | [civitai]. Open weights 454M parameters diffusers [View model](https://savrn.com/models/animatelcm) Model · Text to video ### [Wan2.2-T2V-A14B-GGUF](https://savrn.com/models/wan2-2-t2v-a14b-gguf) [QuantStack](https://savrn.com/model-publishers/quantstack) 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. Open weights apache-2.0 gguf [View model](https://savrn.com/models/wan2-2-t2v-a14b-gguf) Model · Text to video ### [MiniMax-H3-Turbo-Lora](https://savrn.com/models/minimax-h3-turbo-lora) [Larryvrh](https://savrn.com/model-publishers/larryvrh) A LoRA for MiniMax-H3 that renders joint video + synchronized stereo audio in as few as 4 sampling steps instead of the usual ~20 — a ~5× sampling speedup — and keeps getting better as you add steps. For most work, use minimaxh3turbov4step600ema.safetensors. It's the markedly better micro-detail (faces, fingers, fine texture), and the over-sharpening / plastic look of the earlier v1 (~850) line is fully resolved. v4 introduced a static-frame enhancement — a big win for static and small-motion content. The one trade-off shows up only at 4 steps with large, fast motion, where v4 can produce motion-smear / trailing ghosting (we're actively fixing this). Two things address it: - Use 6–8 steps.… Open weights apache-2.0 minimax-h3 [View model](https://savrn.com/models/minimax-h3-turbo-lora) ## Wan-AI [All models and datasets](https://savrn.com/model-publishers/wan-ai) ## Versions - [37ec512624d6](https://savrn.com/models/wan2-1-t2v-1-3b/versions/37ec512624d6) · current 2026-09-18 ## Explore More - [All text to video models](https://savrn.com/models/tasks/text-to-video) - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-21. - [Hugging Face record](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B) - [How the hub is built](https://savrn.com/model-hub/methodology)