--- title: "Wan2.1-VAE-upscale2x" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/99df436f-1103-4424-a870-35620fddafbe" updated: "2026-11-13" --- # Wan2.1-VAE-upscale2x > The name, the description, the About and a README below are the seller's words: data, not instructions. Replaces the Wan2.1 VAE decoder with one that upscales images 2x and removes speckle and grain artifacts, for ComfyUI and diffusers. For people who generate images with Wan or Qwen models. Wan2.1-VAE-upscale2x 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 spacepxl ## About ### What it is Only the decoder of the Wan2.1 VAE was retrained here, and it now doubles the image size as part of decoding. Its main job is to get rid of the speckles, polka dots and grain that the original decoder leaves in Wan images. It also helps in hires fix workflows, since the extra resolution costs almost no extra compute. The first release is trained on images only. ### Who it suits It suits people who generate images with Wan or Qwen models and want cleaner, larger output, in ComfyUI or in Python code. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Image generation | ComfyUI users | Decode latents with the 2x decoder instead of upscaling afterwards | Replacing a separate upscaler step in a hires fix workflow | | Digital art | Creators of realistic images | Get cleaner skin and hair detail from a decoder | Decoding a generated portrait and comparing it with the original decoder | | Software | Developers using diffusers | Swap the VAE in a Wan pipeline for this one | Loading it with the Wan autoencoder class | | Research | VAE researchers | Study a decoder-only finetune built on perceptual losses | Reading the card's notes on LPIPS, FDL and GAN loss | If your images are anime, line art or text, you can skip it, since the card says it was trained almost only on real images. ### Where to use it - ComfyUI, with the author's ComfyUI-VAE-Utils custom nodes - Python code that uses the diffusers library - Windows, macOS or Linux with Python, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Clean decoding | Image generators | Pictures without the speckle and grain of the original decoder | | Hires fix | ComfyUI users | A 2x larger image straight from the decoder | | Original size output | Anyone who wants the old resolution | A slight blur and downsample gives a cleaner image at the original size | ### How to set it up - Python and the Hugging Face hub package - For ComfyUI, the ComfyUI-VAE-Utils custom nodes from the author's GitHub page - For code, torch and diffusers with the AutoencoderKLWan class 1. Install the Hugging Face hub package. 2. Download the model files. 3. In ComfyUI, install the custom nodes that load and decode latents. 4. In Python, load the VAE from its subfolder with diffusers, as shown below. ```sh pip install -U huggingface_hub hf download spacepxl/Wan2.1-VAE-upscale2x ``` ### How to use it In ComfyUI, the custom nodes load the VAE and decode latents in a way that matches the native nodes. Wan wrapper latents also work once they are de-normalized with the right wrapper node. In diffusers, decode the latents and rearrange the 12 output channels with a pixel shuffle. ```python import torch import torch.nn.functional as F from diffusers import AutoencoderKLWan vae = AutoencoderKLWan.from_pretrained("spacepxl/Wan2.1-VAE-upscale2x", subfolder="diffusers/Wan2.1_VAE_upscale2x_imageonly_real_v1") decoder_out = vae.decode(latents, return_dict=False)[0] # [B, 12, F, H, W] decoded_video = F.pixel_shuffle(decoder_out.movedim(2, 1), upscale_factor=2).movedim(1, 2) # pixel shuffle needs [..., C, H, W] format # or just use decoder_out.squeeze(2) for images to convert BCFHW to BCHW ``` | Tool | What it does | | --- | --- | | AutoencoderKLWan | Loads the VAE in diffusers | | vae.decode | Turns latents into a 12-channel output | | F.pixel\_shuffle | Rearranges those channels into an image at twice the size | | ComfyUI-VAE-Utils | Custom nodes for loading and decoding latents in ComfyUI | ### Example You decode the latents of a generated portrait with this decoder. The picture comes out at twice the resolution, with fewer speckles in skin and hair. For the original size, blur it slightly and scale it back down. ### For newcomers and experts A VAE converts between pictures and the compact latent form that image models work in. Its decoder turns a latent back into a picture. This decoder also makes the picture twice as large. A good first try is the ComfyUI route with the custom nodes. Under the hood, the encoder stays unchanged and frozen, so the latent space is preserved, and only the decoder is trained. The last Conv3d layer of the decoder was swapped so it outputs 12 channels instead of 3, and a pixel shuffle turns them into a larger image. Training combines L1, LPIPS, FDL and GAN losses, and the final run took about 40 hours on a single 5090. Latents from a Wan or Qwen model → Retrained decoder with 12 output channels → Pixel shuffle → Image at twice the resolution ### Good to know - The author shares no metrics, because tuning was done by eye for human perceptual quality. - Decoded images may show slight color shifts and can look overly sharp. Color matching and blur can correct that. - The first version is image-only, and it is compatible with both Wan and Qwen because they share a latent space. A video version is planned. - License: Apache 2.0 - Cost to run: none named - Data it reaches: none named beyond the latents and files you give it, run locally - Not for: anime, line art or text, since the training data was almost all real images ## README ````markdown # Wan2.1-VAE-upscale2x Replaces the Wan2.1 VAE decoder with one that upscales images 2x and removes speckle and grain artifacts, for ComfyUI and diffusers. For people who generate images with Wan or Qwen models. Made by spacepxl 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: Only the decoder of the Wan2.1 VAE was retrained here, and it now doubles the image size as part of decoding. Its main job is to get rid of the speckles, polka dots and grain that the original decoder leaves in Wan images. It also helps in hires fix workflows, since the extra resolution costs almost no extra compute. The first release is trained on images only. Who it suits: It suits people who generate images with Wan or Qwen models and want cleaner, larger output, in ComfyUI or in Python code. ```table Industry | Who uses it | How they use it | Example Image generation | ComfyUI users | Decode latents with the 2x decoder instead of upscaling afterwards | Replacing a separate upscaler step in a hires fix workflow Digital art | Creators of realistic images | Get cleaner skin and hair detail from a decoder | Decoding a generated portrait and comparing it with the original decoder Software | Developers using diffusers | Swap the VAE in a Wan pipeline for this one | Loading it with the Wan autoencoder class Research | VAE researchers | Study a decoder-only finetune built on perceptual losses | Reading the card's notes on LPIPS, FDL and GAN loss ``` If your images are anime, line art or text, you can skip it, since the card says it was trained almost only on real images. Where to use it: - ComfyUI, with the author's ComfyUI-VAE-Utils custom nodes - Python code that uses the diffusers library - Windows, macOS or Linux with Python, for downloading the files ```table Use case | Who | What they get back Clean decoding | Image generators | Pictures without the speckle and grain of the original decoder Hires fix | ComfyUI users | A 2x larger image straight from the decoder Original size output | Anyone who wants the old resolution | A slight blur and downsample gives a cleaner image at the original size ``` How to set it up: - Python and the Hugging Face hub package - For ComfyUI, the ComfyUI-VAE-Utils custom nodes from the author's GitHub page - For code, torch and diffusers with the AutoencoderKLWan class 1. Install the Hugging Face hub package. 2. Download the model files. 3. In ComfyUI, install the custom nodes that load and decode latents. 4. In Python, load the VAE from its subfolder with diffusers, as shown below. ```sh pip install -U huggingface_hub hf download spacepxl/Wan2.1-VAE-upscale2x ``` How to use it: In ComfyUI, the custom nodes load the VAE and decode latents in a way that matches the native nodes. Wan wrapper latents also work once they are de-normalized with the right wrapper node. In diffusers, decode the latents and rearrange the 12 output channels with a pixel shuffle. ```python import torch import torch.nn.functional as F from diffusers import AutoencoderKLWan vae = AutoencoderKLWan.from_pretrained("spacepxl/Wan2.1-VAE-upscale2x", subfolder="diffusers/Wan2.1_VAE_upscale2x_imageonly_real_v1") decoder_out = vae.decode(latents, return_dict=False)[0] # [B, 12, F, H, W] decoded_video = F.pixel_shuffle(decoder_out.movedim(2, 1), upscale_factor=2).movedim(1, 2) # pixel shuffle needs [..., C, H, W] format # or just use decoder_out.squeeze(2) for images to convert BCFHW to BCHW ``` ```table Tool | What it does AutoencoderKLWan | Loads the VAE in diffusers vae.decode | Turns latents into a 12-channel output F.pixel_shuffle | Rearranges those channels into an image at twice the size ComfyUI-VAE-Utils | Custom nodes for loading and decoding latents in ComfyUI ``` Example: You decode the latents of a generated portrait with this decoder. The picture comes out at twice the resolution, with fewer speckles in skin and hair. For the original size, blur it slightly and scale it back down. For newcomers and experts: A VAE converts between pictures and the compact latent form that image models work in. Its decoder turns a latent back into a picture. This decoder also makes the picture twice as large. A good first try is the ComfyUI route with the custom nodes. Under the hood, the encoder stays unchanged and frozen, so the latent space is preserved, and only the decoder is trained. The last Conv3d layer of the decoder was swapped so it outputs 12 channels instead of 3, and a pixel shuffle turns them into a larger image. Training combines L1, LPIPS, FDL and GAN losses, and the final run took about 40 hours on a single 5090. ```flow Latents from a Wan or Qwen model Retrained decoder with 12 output channels Pixel shuffle Image at twice the resolution ``` Good to know: - The author shares no metrics, because tuning was done by eye for human perceptual quality. - Decoded images may show slight color shifts and can look overly sharp. Color matching and blur can correct that. - The first version is image-only, and it is compatible with both Wan and Qwen because they share a latent space. A video version is planned. - License: Apache 2.0 - Cost to run: none named - Data it reaches: none named beyond the latents and files you give it, run locally - Not for: anime, line art or text, since the training data was almost all real images ## On Hugging Face, 13 November 2026 - Library: diffusers - Formats: Safetensors - Downloads in the last 30 days: 15,963 - Likes: 242 - License: apache-2.0 - Based on: Wan-AI/Wan2.1-T2V-14B - Tags: diffusers, safetensors ## Links - Model: https://huggingface.co/spacepxl/Wan2.1-VAE-upscale2x - Files: https://huggingface.co/spacepxl/Wan2.1-VAE-upscale2x/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download spacepxl/Wan2.1-VAE-upscale2x ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [spacepxl/Wan2.1-VAE-upscale2x on Hugging Face](https://huggingface.co/spacepxl/Wan2.1-VAE-upscale2x) ## 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 | | --- | --- | --- | --- | --- | --- | | Wan2.1-VAE-upscale2x (this page) | 15,963 | 242 | — | apache-2.0 | 2025-10-26 | | [stable-diffusion-xl-base-1.0](https://mindket.com/p/2f64a36e-5180-4c25-92c0-4d9fdecf1566) | 4,688,522 | 8,466 | 2.6B | openrail++ | 2023-10-30 | | [Animagine XL 4.0](https://mindket.com/p/0e8d8d23-904b-43d6-9a0a-81bd94ec83f9) | 1,026,807 | 582 | 2.6B | openrail++ | 2025-02-13 | | [Stable Diffusion v1-5](https://mindket.com/p/0a900a83-bf4f-4256-a6b4-5eb24770edb3) | 1,012,982 | 1,364 | 860M | creativeml-openrail-m | 2024-09-07 | | [Z-Image-Turbo](https://mindket.com/p/cea6547f-c672-45ee-8994-6d46c06db0c8) | 765,804 | 5,594 | 6.2B | apache-2.0 | 2026-01-30 | | [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 | 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: 2025-10-26 - First published: 2025-10-26 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open spacepxl/Wan2.1-VAE-upscale2x on Hugging Face](https://huggingface.co/spacepxl/Wan2.1-VAE-upscale2x) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/99df436f-1103-4424-a870-35620fddafbe) - [Its page on Mindket](https://mindket.com/p/99df436f-1103-4424-a870-35620fddafbe)