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    Myne Factory - Base - v1.0
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    http://logo.mynefactory.ai/

    Myne Factory Base Model

    The foundation of our models

    Technical Details

    Model Training

    MyneFactoryBase was trained using ~18000 high scored samples from Yande.re and ~5000 high scored samples from Konachan. File captions were generated using 3 iterations of WD1.4 tagger to ensure maximum identification of objects within the training data. A second captioning run was done using one tagger with a reduced threshold to produce shorter captions for later use. Adam optimizer was used with a manually set maximum learning rate and cosine decay. Training was done on an RTX 4090 with a batch size of 4, utilizing DDIM sample scheduler and DDPM noise scheduler with mix precision.

    Text Encoder Training

    Text Encoder was trained for 50% of the training durations, freezing and unfreezing every 10ep. During the final 20ep of finetuning, the TE was frozen.

    Block Merge

    At the ep20 milestone, a block merge was done with BasilMix. However, it was evident that the merged weights were being trained out quickly, and the weights had entirely shifted back to the training data by the end of the training. Ultimately, the decision was made to not use a block merge for the final release.

    For more detailed technical information on the training process and model architecture, please refer to this document.

    Authors: 金Goldkoron, tsmkirby, Juusoz

    Visit our Discord community if you have any questions.

    Prompt Format

    It is recommended to use booru styled tags to for the prompts.

    Example: woman, decorated horns, long robes, fog, long curly hair, freckles, solo, masterpiece, reflective, depth of field, caustics, detailed night, forest, leaves, moonlight, eyes, orange hair, green eyes, vines

    Example: 1girl, solo, skirt, book, glasses, long hair, looking at viewer, bookshelf, jacket, plaid skirt, school uniform, long sleeves, parted lips, semi-rimless eyewear, bangs, blush, holding, blazer, indoors, sweater, under-rim eyewear, red-framed eyewear, holding book, brown eyes, library, sitting

    The tags were generated with WD14 tagger for the dataset.

    The model has also been fine tuned to be better at handling shorter prompts.

    Recommended Settings

    This model performs best with the following settings:

    • Image Size

      1024x576 for wide 16:9, 768x768 for square, and 640x1024 for portrait

      Feel free to experiment with higher resolutions, Juusoz made all the examples at higher than recommended resolutions

    • Vae

      vae-ft-mse-840000-ema-pruned.ckpt

    • Sampler

      DPM++ SDE Karras (preferred)

      2S Karras

      Karras samplers tend to create more dynamic and interesting generations

      Euler A

      Results tends to look smoother and more Airbrushed

    • Steps

      30 minimum and +70 can give nice results

    • Skip Clip:

      Clip 1

      Clip 2 and 4 are valid for experimentation and we recommend trying it for more variation.

    • CFG

      9-12

    • Not required, but these tags improve the quality of the image:

      Prompt: best quality, masterpiece

      Negative Prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry

    Socials

    Website | Discord | Patreon

    Description

    FAQ

    Comments (1)

    JustMaierFeb 15, 2023· 10 reactions
    CivitAI

    Excited to see this new base develop more. With so many merge models out there, a new trained model is a boon to the community!

    Checkpoint
    Other

    Details

    Downloads
    562
    Platform
    CivitAI
    Platform Status
    Available
    Created
    2/15/2023
    Updated
    4/24/2026
    Deleted
    -

    Files

    myneFactoryBase_v10.safetensors

    Mirrors

    Huggingface (1 mirrors)

    myneFactoryBase_v10.ckpt

    Mirrors

    Available On (1 platform)

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