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    8bitdiffuser 64x | a perfect pixel art model - v2.2
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    This is 8bitdiffuser! A model trained on ~2k images

    This page is for the 64x model!

    PLEASE when using this model, use one of the AnyLoRA forks, Qteamix, Angelbreed or Animekawa. it works best with stylized anime / cell shaded models, while it can work on many other models, it might not produce the "pixel perfect" results

    When generating images scaling them down 8x will produce pixel perfect results! you can downscale them via billinear or nearest neighbor, but i have had the BEST results when using astropulse's Pixelize plugin for A1111, it is completely free and can run even on lower end devices.

    make sure to use an 8X downscale factor in post processing!

    • the 64x model, while a bit worse at generating even pixels, still operates greatly when downsampling images to native size, it also works much better at full body character portraits. with all v3 versions and above, high resolutions (768x and 1024x) are highly reccomended

    Note for versions v4 and above! i used some filtering on the dataset, so if it gives you problems with NSFW content, use the v3 or earlier models.

    Note that even doe this page specifies a 64x model, u can generate different resolutions of pixel art by just scaling the picture!

    256x256 will generate a 32x32 pixel art

    512x512 will generate a 64x64 pixel art

    1024x1024 will generate a 128x128 pixel art

    etc etc...

    best settings for this LoRA is:

    • Steps: 10-30 (On lower step counts, pixels will look very noisy, however if you downsample this to native pixel size this issue is resolved, increase step count if you want a great generation without downsampling)

    • CFG scale: 4-10

    • LoRA scale: 0.85-1.25 (the lower you will set the scale, the more it will look like traditional art and the pixels will become uneven, but the higher the scale is it will appear much noisy)

    • Sampling method: DPM++ 2M SDE karras

    • Recommended prompt: "pixel_art" (Add these to the start every generation, this isn't "required" but I found it gets the best results)

    • Resolution: 512-1024 (on lower resolutions, the pixels will be less even, on higher ones the artifacts will be less noticeable but will take much more time)

    Description

    Trained on much less steps, solved some issues with over cooking.

    FAQ

    Comments (3)

    SasanarxNov 26, 2023· 2 reactions
    CivitAI

    Thank you for sharing your works!

    I have a question that I'm struggling to figure out. You mentioned using the pixelate plugin to downscale the original image. Could you please provide details on how to use this script? When I attempt to download and install the script, it doesn't appear(I don know where to find it in my webui). You also mentioned bilinear, which I also don't fully comprehend.

    Your assistance in clarifying these points would be greatly appreciated.

    extraordicord_byrtexploit
    Author
    Nov 28, 2023· 1 reaction

    For the script, you will need to reload the webUI (if you havnt already) and the scripts menu should be at the bottom of ur generation menu, and if it still dosnt work contact astropulse! (the developer of the plugin)

    Billinear is a method of downsampling images, there are many ways to use it but my personal favorite is using aseprite (a tool for making pixel art) simply open the ai generated image in the aseprite program and click Ctrl+Alt+I (or navigate to the sprite scaling menu) then in the percentage menu enter 12.5 on both width and height. and select billinear for downscaling!

    hope this helps!

    SasanarxDec 1, 2023

    @extraordicord Thank you so much !

    Details

    Downloads
    4,779
    Platform
    CivitAI
    Platform Status
    Available
    Created
    11/19/2023
    Updated
    7/26/2026
    Deleted
    -
    Trigger Words:
    Pixel art

    Files

    64x64v3-02.safetensors

    Mirrors

    HuggingFace (1 mirrors)
    CivitAI (1 mirrors)

    Available On (1 platform)

    Same model published on other platforms. May have additional downloads or version variants.