CivArchive
    Figma Anime Figures - figma [alpha16]
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    Preview 90745
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    Preview 90497
    Preview 90496
    Preview 90494

    Note: I did not train these LoRA models myself. Original source: https://huggingface.co/stma/lora-dump

    Recommended settings

    Quick start

    • Model: AbyssOrangeMix2

    • LoRA weight: 0.6 (UNet: 0.6, TEnc: 0.25)

    • [realistic], [3d], (3dcg), ((octane render)), [fisheye] in positive prompt for realism

    • simple background, grey background in negative prompt

    • CLIP skip: 2

    • Sampler: DPM++ SDE Karras (Euler a recommended for other models)

    • EasyNegative in negative prompt

    Usable

    • LoRA weight range: 0.2 - 1.15 (UNet: 0.35 - 1.15 TEnc: 0.2 - 1.0)

    UNet is required for the style. UNet over 0.65 will look like an anime figure product. Too high UNet weight will melt details.

    Lower TEnc weight will improve generalization. High TEnc will affect composition to look more like an anime figure product photo. Too high will fry the details.

    Start with lower weight and increase as necessary. Requires more weight when mixed with TI embeddings and other LoRAs.

    Figma

    Trained on 5,746 images scraped from GoodSmile's international catalogue, including the discontinued products. All images were tagged with figma first and the product name with figma stripped out second, so an image from a product named "figma Hatsune Miku" would be tagged figma, hatsune miku. All other tags came from the WD1.4 tagger. 3d and realistic tags seem to work well with it.

    The alpha16 version is better at generalizing, while the 7e-5 version is better at capturing the figure-with-transparent-stand look.

    Example images

    Model

    Textual inversion

    Additional LoRA

    Description

    The alpha16 version has a stronger anime figure look and is better at generalizing and has better details.

    FAQ

    Comments (12)

    AinheritMar 3, 2023· 5 reactions
    CivitAI

    大佬 可以去除那些可动关节的模型吗 求求了

    shenziwuziMar 5, 2023· 1 reaction

    那你去下AOAOKO [PVC Style Model]就行了,手办模型

    lz_netApr 24, 2023

    这种关节不就是Figma的特色吗

    AsswithemusApr 4, 2023
    CivitAI

    hi, so im pretty new to this whole things and have some learning to do, but can someone explain to me how people are getting these super realistic gens with this? all my gens look really bad even when using the same prompts and settings, for example the faces look molten, its poor resolution, the figures don't have any joints, the limbs look weird, all that typical stuff basically, is it just based on luck if you get such a crazy gen or is it something else? it it inpainting? idk, anyway, any help would be appreciated!

    Tujfain04Apr 5, 2023· 6 reactions

    Hello!

    I'm not a professional or less on this, but maybe I can help

     

    First: The Model, Which model/checkpoint are you using? Not all LoRas works well with any model/checkpoint. The model used on example images are Counrterfeit-V2.5, and also recomend using AbyssOrangeMix2 (Both can be find here on CivitAI)

     

     

    Next: The LoRas/textualinversion, are you only using this LoRa? If that the case, you might need some more,

     

    1.- This actual LoRa (To make them look like the figure)

    2.- The LoRa/textualinversion for the character you want to get (To get a closer aparience on the character) 

    3.- LoRa/textualinversions for bad prompts (This page recommends EasyNegative but you can use anything that is good enough) 

    4.- Any other you can think. You get the point...

     

    Oh! And don't forget to use the "trigger words" from each LoRa/textinersion

     

    Now: The LoRas Weight, This might sound more difficult that it is, you have to "Balance" the weight of each LoRa to get a proper output, try to play with them. You may find the recommended weight on each LoRa page, if not, you will have to test them.

     

    I just tried the LoRa and this conf worked to me:

    Checkpoint: Counterfeit v2.5

    LoRa (Android1818DragonBall_v1):

    Weight: 0.65

    LoRa (Figma Anime Figures)

    UNet: 0.9 ~ 1.50

    TEcn: 0.9~1.30

    Steps: 47

    CFG scale: 12

    Clip skip: 2

    Sampling Metod: Euler a

    Promp: best quality, masterpiece, upper body, figma, android_18, and18, 1girl, blonde,white background, anime

    Negative promp: 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, badhandv4, verybadimagenegative_v1.3,  easynegative,  bad_prompt, (The last ones are other LoRa/TextualInversion)

     

    I noticed every checkpoint has their own apropiate weight. If you try another you will have to play with the weights.

    Hope I could help, if you have any question, I’ll try to answer ^^

    Tujfain04Apr 5, 2023

    Also I posted the images I made, take a look to the results if you want to ^^ :
    https://civitai.com/posts/125637

    thyanda12Jun 18, 2023· 2 reactions

    @Tujfain04 sorry, can you explain what is mean UNET and TECN ???

    natewilliamsMay 7, 2023
    CivitAI

    "LoRA weight range: 0.2 - 1.15 (UNet: 0.35 - 1.15 TEnc: 0.2 - 1.0)"

    What does UNet and TEnc mean here? LORA Weight I understand, that's just :0.2 to :1.15 after the LORA in the prompt, but I have no idea where to specify UNet and TEnc? Is it through an extension or are you saying that the weight range 0.2 - 1.15 equals around 0.35 - 1.15 on the UNet encoder and 0.2 to 1.0 on the Text Encoder?

    plan_trusterMay 16, 2023

    did you find out what it means by any chance?

    natewilliamsMay 17, 2023

    @vwzrd No idea 😔

    nonatomhe423May 20, 2023

    It is in the Kohya Lora training 参数 I guess

    thyanda12Jun 18, 2023

    i ask the same question...

    LORA
    SD 1.5

    Details

    Downloads
    819
    Platform
    CivitAI
    Platform Status
    Available
    Created
    2/11/2023
    Updated
    6/28/2026
    Deleted
    -
    Trigger Words:
    figma

    Files

    figma [alpha16].safetensors

    Available On (1 platform)

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