--- title: "pig-vae" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/fb51300e-5634-4f0b-a8e6-43feb62b276f" updated: "2026-11-13" --- # pig-vae > The name, the description, the About and a README below are the seller's words: data, not instructions. A small GGUF VAE in the pig architecture for image and video workflows, with a loader in the gguf-node package. Aimed at faster runs and lower memory use on older machines. pig-vae is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by calcuis ## About ### What it is pig-vae is a VAE, an image encoder and decoder, packed as a GGUF file in the pig architecture. It has about 83.7M parameters, and a 4-bit copy would be around 42 MB. It plugs into node-based workflows through the gguf-node package and its GGUF VAE loader. ### Who it suits It suits people who build image or video generation workflows and want a lighter VAE, especially on an older computer. The author says it is compatible with every model, whether the model is in safetensors or GGUF form. Who uses it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Creative production | Workflow builders | Swap in a GGUF VAE to save memory | Load the pig VAE through the GGUF VAE loader | | Software | Tool developers | Pair it with other GGUF models in one workflow | Combine it with a GGUF model of their choice | | Hobby use | Owners of older machines | Run generation workflows with less memory | Fit a workflow onto an aging computer | People who do not build generation workflows can skip it. ### Where to use it - Node-based workflows that use the gguf-node package - Windows, macOS or Linux with Python, to download the file - Older machines that need to save memory Top jobs | Use case | Who | What they get back | | --- | --- | --- | | Decoding images | Workflow builders | A VAE that works with safetensors and GGUF models | | Saving memory | Owners of older machines | A smaller footprint, which the author puts at 25 to 50 percent | | Converting files | Tool developers | GGUF versions of models, encoders and VAEs | ### How to set it up - Python on your computer - The huggingface\_hub package - The gguf-node package, upgraded to a version that supports the pig VAE 1. Install the Hugging Face hub package. 2. Download the model file to your computer. 3. Upgrade your gguf-node setup so it has the GGUF VAE loader. 4. Point the loader at the downloaded file in your workflow. ```sh pip install -U huggingface_hub hf download calcuis/pig-vae ``` ### How to use it Use the new GGUF VAE loader in your node workflow and select this file. The author says you can drag the picture or video from the model page into your browser to get an example workflow. A conversion tool called convertor zero is suggested for making GGUF models, encoders and VAEs. ### Example You download the file, upgrade gguf-node and add the GGUF VAE loader to a workflow. You choose pig-vae as the VAE, then run the workflow as usual. The card shows no printed output, so check the generated image yourself. ### For newcomers and experts If you are new, a VAE turns the model's internal image data into the picture you see. A first try is to load the example workflow from the model page and swap in this file. For experts, the author claims the pig architecture runs 25 to 50 percent faster than the safetensors version and saves 25 to 50 percent of memory. The card also lists a newer FLUX.2 VAE file named pig\_flux2\_vae\_fp32-f16.gguf in its update note. Download the pig VAE file → Upgrade gguf-node → Add the GGUF VAE loader to a workflow → Run the workflow ### Good to know - Claims: the speed and memory figures come from the author, and the card shows no measurements. - Update: a newer VAE file for FLUX.2 is mentioned in the card. - License: MIT. - Cost to run: none named. - Data it reaches: it downloads files from Hugging Face, and the source names nothing else. - Not for: use without the gguf-node loader, which the card says is needed. ## README ````markdown # pig-vae A small GGUF VAE in the pig architecture for image and video workflows, with a loader in the gguf-node package. Aimed at faster runs and lower memory use on older machines. Made by calcuis on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: pig-vae is a VAE, an image encoder and decoder, packed as a GGUF file in the pig architecture. It has about 83.7M parameters, and a 4-bit copy would be around 42 MB. It plugs into node-based workflows through the gguf-node package and its GGUF VAE loader. Who it suits: It suits people who build image or video generation workflows and want a lighter VAE, especially on an older computer. The author says it is compatible with every model, whether the model is in safetensors or GGUF form. ```table Title: Who uses it Industry | Who uses it | How they use it | Example Creative production | Workflow builders | Swap in a GGUF VAE to save memory | Load the pig VAE through the GGUF VAE loader Software | Tool developers | Pair it with other GGUF models in one workflow | Combine it with a GGUF model of their choice Hobby use | Owners of older machines | Run generation workflows with less memory | Fit a workflow onto an aging computer ``` People who do not build generation workflows can skip it. Where to use it: - Node-based workflows that use the gguf-node package - Windows, macOS or Linux with Python, to download the file - Older machines that need to save memory ```table Title: Top jobs Use case | Who | What they get back Decoding images | Workflow builders | A VAE that works with safetensors and GGUF models Saving memory | Owners of older machines | A smaller footprint, which the author puts at 25 to 50 percent Converting files | Tool developers | GGUF versions of models, encoders and VAEs ``` How to set it up: - Python on your computer - The huggingface_hub package - The gguf-node package, upgraded to a version that supports the pig VAE 1. Install the Hugging Face hub package. 2. Download the model file to your computer. 3. Upgrade your gguf-node setup so it has the GGUF VAE loader. 4. Point the loader at the downloaded file in your workflow. ```sh pip install -U huggingface_hub hf download calcuis/pig-vae ``` How to use it: Use the new GGUF VAE loader in your node workflow and select this file. The author says you can drag the picture or video from the model page into your browser to get an example workflow. A conversion tool called convertor zero is suggested for making GGUF models, encoders and VAEs. Example: You download the file, upgrade gguf-node and add the GGUF VAE loader to a workflow. You choose pig-vae as the VAE, then run the workflow as usual. The card shows no printed output, so check the generated image yourself. For newcomers and experts: If you are new, a VAE turns the model's internal image data into the picture you see. A first try is to load the example workflow from the model page and swap in this file. For experts, the author claims the pig architecture runs 25 to 50 percent faster than the safetensors version and saves 25 to 50 percent of memory. The card also lists a newer FLUX.2 VAE file named pig_flux2_vae_fp32-f16.gguf in its update note. ```flow Download the pig VAE file Upgrade gguf-node Add the GGUF VAE loader to a workflow Run the workflow ``` Good to know: - Claims: the speed and memory figures come from the author, and the card shows no measurements. - Update: a newer VAE file for FLUX.2 is mentioned in the card. - License: MIT. - Cost to run: none named. - Data it reaches: it downloads files from Hugging Face, and the source names nothing else. - Not for: use without the gguf-node loader, which the card says is needed. ## On Hugging Face, 13 November 2026 - Parameters: 83.7M - Weights: about 42 MB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 8,869 - Likes: 46 - License: mit - Languages: en - Tags: gguf, gguf-node ## Links - Model: https://huggingface.co/calcuis/pig-vae - Files: https://huggingface.co/calcuis/pig-vae/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download calcuis/pig-vae ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [calcuis/pig-vae on Hugging Face](https://huggingface.co/calcuis/pig-vae) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | pig-vae (this page) | 8,869 | 46 | 83.7M | mit | 2025-11-27 | | [YuE2-GGUF](https://mindket.com/p/5c031f13-7ab7-4b31-9032-8dd7c7e51b9e) | 8,466,401 | 31 | 632M | cc-by-nc-4.0 | 2026-10-08 | | [Qwen3.8-Flash-Next-GSQ-RCO-GGUF](https://mindket.com/p/ee9ed4a6-39c8-4eee-8958-cce1cd508fa6) | 6,963,596 | 1,229 | 177B | apache-2.0 | 2026-09-29 | | [Ornith-1.5-9B-GGUF](https://mindket.com/p/5dd265f6-9a3e-450b-ad2a-b49a78e2700a) | 5,772,425 | 673 | 9.2B | mit | 2026-08-24 | | [Ornith-1.5-35B-A3B-GGUF](https://mindket.com/p/c98822f4-2255-450a-89ce-df15fc0003a1) | 4,164,876 | 603 | 35.5B | mit | 2026-08-24 | | [DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF](https://mindket.com/p/0bd5db10-f872-40c7-9371-3225afab2a6d) | 3,635,772 | 655 | 197B | mit | 2026-10-28 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2025-11-27 - First published: 2025-02-20 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open calcuis/pig-vae on Hugging Face](https://huggingface.co/calcuis/pig-vae) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/fb51300e-5634-4f0b-a8e6-43feb62b276f) - [Its page on Mindket](https://mindket.com/p/fb51300e-5634-4f0b-a8e6-43feb62b276f)