Belle Delphine

    DI_belle_delphine_v1.safetensors LORA / SD 1.5

    Model Information
    Model Name
    Belle Delphine
    Version
    v1.0
    Size
    108.11 MB
    Downloads
    25,103
    Torrent Details
    BTIH
    3E928679888C9991B0CA45A2229A1FFCF62EC717
    BTMH
    25F7D0D62CDB274A60036E3CD961CA54EEB3588A6829886393D44FC3D1A1003E
    SHA256
    A1DB19E5E30068C934F3A73ABD2F3901CBE35F1F4F236447B9362A1E0D7C3BBE
    Upload Date
    10 months ago
    Uploader
    CivitasBay.org
    Status
    1 Seeders
    0 Peers
    Info

    This is a Lora of the internet celebrity Belle Delphine.

    There already exist multiple LoRAs and checkpoints of her, like here, here or here.

    But I wasn’t happy with their results, as they either do not look how I would want it to look, or they are very prone to cat ears or pink hair. But everyone has their own taste, so check them out if you are unhappy with this one.

     

    This LoRA does not use any trigger word, and I suggest a default weight of 1.

    The preview images mainly use ChilloutMix, additional images are from Deliberate and Realistic Vision. The Overwatch images are using NeverEnding Dream.

     

    The results are good enough for me to upload it here, but it is not perfect, and you might encounter the following problems:

    • It really likes generating the same lip shape, despite it not even being that frequent in the training images.

    • This also generates cat ears, especially if you have something with ear in your prompt or something on the head which could become ears, but it is less frequent than with other models in my experiments and opinion.

    • Depending on the prompt certain noise / “paint” on the face might appear.

     

    Additionally, this LoRA is mostly doing its job only when the face is sufficiently big (also dependent on model). If it becomes too small, the quality decreases and I advise using the high resolution fix then. In the example images multiple images also make use of that.

     

    This Lora is trained on the 1.5 base model with ~140 images, 50 repeats per image, 1e-5 learning rate, 5e-05 for the text encoder, 0.00015 for the unet. All training images were cropped to 512x512 and no bucketing was used.

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