--- title: "AuraSR-v2" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/e54a71ad-beaa-4870-a806-ebb88fd15214" updated: "2026-11-13" --- # AuraSR-v2 > The name, the description, the About and a README below are the seller's words: data, not instructions. Upscales generated images four times with a GAN-based super-resolution model. Made for people who generate pictures and want them larger, from fal. AuraSR-v2 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 fal ## About ### What it is AuraSR-v2 is a super-resolution model that enlarges images made by image generators. It is a GAN, a variation on the GigaGAN paper adapted to upscale from an input image. The Torch code builds on an unofficial GigaGAN implementation. ### Who it suits It suits people who make pictures with AI and want a larger copy, plus developers who add an upscaling step to an image pipeline. Who runs it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Design | Artists who use image generators | Enlarge a generated picture | A small render turned into a larger copy | | Software | Developers | Add an upscale step after image generation | A Python script that upscales each output | | Research | Image researchers | Study GAN-based upscaling | Compare the output with other upscalers | If you do not make or edit images, you can skip it. ### Where to use it - Python on Windows, macOS or Linux, through the aura-sr package - Image pipelines, as a step after a generator produces a picture - The transformers library, the library tag on the model page Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Upscaling a generated image | Artists | A larger copy of the picture | | Overlapped four-times upscaling | Developers | A four-times enlarged image from one call | ### How to set it up - Python on Windows, macOS or Linux - The aura-sr package - A picture to upscale, loaded with PIL 1. Install the aura-sr package with pip. 2. Load the model by name with from\_pretrained. 3. Optionally download the model files by hand. ```bash $ pip install aura-sr ``` ```python from aura_sr import AuraSR aura_sr = AuraSR.from_pretrained("fal/AuraSR-v2") ``` ```sh pip install -U huggingface_hub hf download fal/AuraSR-v2 ``` ### How to use it Load a picture, shrink it if you like, and call the four-times overlapped upscale on it. The card resizes its sample picture to 256 by 256 first. ```python upscaled_image = aura_sr.upscale_4x_overlapped(image) ``` Calls it shows | Tool | What it does | | --- | --- | | AuraSR.from\_pretrained | Loads the model from the name fal/AuraSR-v2 | | upscale\_4x\_overlapped | Enlarges an image four times, working in overlapping parts | ### Example You take a 256 by 256 picture from an image generator and pass it to the upscale call. The call returns a new, larger image, which you can then save or view with PIL. ### For newcomers and experts If you are new, this is a tool that makes a small picture bigger while trying to add believable detail. The first small thing to try is the install line and the load line, then one small image. If you know the field, the model is a GAN with 618M parameters, adapted from GigaGAN for image-conditioned upscaling. The overlapped method handles the picture in overlapping parts. The trade-off is that a GAN invents detail, so the result is a plausible enlargement rather than a recovery of the original. Load the image with PIL → Resize it to 256 by 256 → Call the overlapped four-times upscale → Get back a larger image ### Good to know - Size: 618M parameters, about 2.5 GB in F32. - Method: GAN-based, adapted from the GigaGAN paper. - License: apache-2.0. - Cost to run: no API key, account or paid service is named. - Data it reaches: it downloads the model from Hugging Face, and the card's sample also fetches a picture from the web. ## README ````markdown # AuraSR-v2 Upscales generated images four times with a GAN-based super-resolution model. Made for people who generate pictures and want them larger, from fal. Made by fal 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: AuraSR-v2 is a super-resolution model that enlarges images made by image generators. It is a GAN, a variation on the GigaGAN paper adapted to upscale from an input image. The Torch code builds on an unofficial GigaGAN implementation. Who it suits: It suits people who make pictures with AI and want a larger copy, plus developers who add an upscaling step to an image pipeline. ```table Title: Who runs it Industry | Who uses it | How they use it | Example Design | Artists who use image generators | Enlarge a generated picture | A small render turned into a larger copy Software | Developers | Add an upscale step after image generation | A Python script that upscales each output Research | Image researchers | Study GAN-based upscaling | Compare the output with other upscalers ``` If you do not make or edit images, you can skip it. Where to use it: - Python on Windows, macOS or Linux, through the aura-sr package - Image pipelines, as a step after a generator produces a picture - The transformers library, the library tag on the model page ```table Title: Jobs it does Use case | Who | What they get back Upscaling a generated image | Artists | A larger copy of the picture Overlapped four-times upscaling | Developers | A four-times enlarged image from one call ``` How to set it up: - Python on Windows, macOS or Linux - The aura-sr package - A picture to upscale, loaded with PIL 1. Install the aura-sr package with pip. 2. Load the model by name with from_pretrained. 3. Optionally download the model files by hand. ```bash $ pip install aura-sr ``` ```python from aura_sr import AuraSR aura_sr = AuraSR.from_pretrained("fal/AuraSR-v2") ``` ```sh pip install -U huggingface_hub hf download fal/AuraSR-v2 ``` How to use it: Load a picture, shrink it if you like, and call the four-times overlapped upscale on it. The card resizes its sample picture to 256 by 256 first. ```python upscaled_image = aura_sr.upscale_4x_overlapped(image) ``` ```table Title: Calls it shows Tool | What it does AuraSR.from_pretrained | Loads the model from the name fal/AuraSR-v2 upscale_4x_overlapped | Enlarges an image four times, working in overlapping parts ``` Example: You take a 256 by 256 picture from an image generator and pass it to the upscale call. The call returns a new, larger image, which you can then save or view with PIL. For newcomers and experts: If you are new, this is a tool that makes a small picture bigger while trying to add believable detail. The first small thing to try is the install line and the load line, then one small image. If you know the field, the model is a GAN with 618M parameters, adapted from GigaGAN for image-conditioned upscaling. The overlapped method handles the picture in overlapping parts. The trade-off is that a GAN invents detail, so the result is a plausible enlargement rather than a recovery of the original. ```flow Load the image with PIL Resize it to 256 by 256 Call the overlapped four-times upscale Get back a larger image ``` Good to know: - Size: 618M parameters, about 2.5 GB in F32. - Method: GAN-based, adapted from the GigaGAN paper. - License: apache-2.0. - Cost to run: no API key, account or paid service is named. - Data it reaches: it downloads the model from Hugging Face, and the card's sample also fetches a picture from the web. ## On Hugging Face, 13 November 2026 - Task: image to image - Library: transformers - Parameters: 618M - Weights: about 2.5 GB in F32; about 309 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,554 - Likes: 349 - License: apache-2.0 - Tags: transformers, safetensors, pytorch, super-resolution, image-to-image ## Links - Model: https://huggingface.co/fal/AuraSR-v2 - Files: https://huggingface.co/fal/AuraSR-v2/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download fal/AuraSR-v2 ``` 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 [fal/AuraSR-v2 on Hugging Face](https://huggingface.co/fal/AuraSR-v2) ## 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 | | --- | --- | --- | --- | --- | --- | | AuraSR-v2 (this page) | 1,554 | 349 | 618M | apache-2.0 | 2024-08-07 | | [Iris-3B by speridlabs](https://mindket.com/p/0abd1bcc-dae6-497c-b706-d6bc43cde2df) | 997 | 350 | 3B | apache-2.0 | 2026-10-12 | | [FLUX.2 klein 4B Base](https://mindket.com/p/9afd714e-e822-4252-8fd5-8ec58f9ba8da) | 743,055 | 195 | 3.9B | apache-2.0 | 2026-02-24 | | [FLUX.2 klein 4B](https://mindket.com/p/b1b6d3b9-e9af-4f77-a04a-278928adf075) | 435,643 | 1,116 | 3.9B | apache-2.0 | 2026-02-24 | | [Qwen-Image-Edit](https://mindket.com/p/4bd6e483-f731-4267-8adb-2c6df6e568c0) | 141,520 | 2,580 | 20.4B | apache-2.0 | 2025-08-25 | | [ControlNet v1.1 depth (SD 1.5)](https://mindket.com/p/1cbf6e7e-f0c4-4cfd-b0a1-189cdd8a3edc) | 22,041 | 70 | 361M | openrail | 2023-05-04 | 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: 2024-08-07 - First published: 2024-07-29 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open fal/AuraSR-v2 on Hugging Face](https://huggingface.co/fal/AuraSR-v2) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/e54a71ad-beaa-4870-a806-ebb88fd15214) - [Its page on Mindket](https://mindket.com/p/e54a71ad-beaa-4870-a806-ebb88fd15214)