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    UnNamedIXL - Realistic model - IllustriousXL V2.0 based - V3
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    UnNamedIXL V5!

    This will probably be the last release of UnNamedIXL. This version improve the details at the cost of slightly less following of prompts. It was made by making a merge of Illustrious Realism and CatPony-Real and using this merge as a refiner on top of V4 to generate a dataset. Generated images were used to train a "booster" LoRA which i merged with V4 (by pruning and reweighting some layers). Check the showcase for some sample/scheduler pairs that works nicely 🤩

    Origin

    Per request, i have uploaded the "Arn0-realistic" model found here as a "V0" of UnNamedIXL. This checkpoint merge was used during the initial build of V2.

    UnNamedIXL V4!

    Merry Xmas! I'll update the recipe and more info once i figure out a consistent negative prompt and parameters 😅

    V4 is based on a careful merge of some WaiRealism and Perfection Realistic v5.0 to avoid loosing what makes UnNamedIXL different while fixing some funkiness that can happen in the background.

    Involved block merge (in order):

    1. WaiRealism (at 0.3): IN01, IN02, OUT03, OUT04, OUT07

    2. Perfection Realistic (at 0.5): IN03, OUT03

    3. UnNamedIXL 3 (after adding UnNamed Spice LoRA) (at 0.5): BASE, M00

    For the record, here is a sd-mecha scripts for MBW + LoRA which also fix NaN in layers using IXL V2.0 (a plague in my previous merge):

    import sd_mecha as sdm
    from sd_mecha import merge_method, Parameter, Return
    from torch import Tensor
    
    sdm.set_log_level()
    
    @merge_method
    def fix_nan(
      a: Parameter(Tensor),
      b: Parameter(Tensor),
    ) -> Return(Tensor):
      if a.isfinite().all():
        return a
      else:
        return b
    
    base = sdm.model("UnNamedIXL_V3.9.safetensors")
    src = sdm.model("mine\\UnNamedIXL\\UnNamedIXL_V3.fp16.safetensors")
    ill = sdm.model("base\\illustriousXLV20_v20Stable.safetensors")
    lora = sdm.model("..\\loras\\UnNamedSpice.safetensors")
    
    l = sdm.convert(lora, base)
    a = sdm.add_difference(base, l, alpha=0.1)
    
    b = fix_nan(src,ill)
    
    blocks = {
        "BASE": 0.5,
        "IN00": 0.0,
        "IN01": 0.0,
        "IN02": 0.0,
        "IN03": 0.0,
        "IN04": 0.0,
        "IN05": 0.0,
        "IN06": 0.0,
        "IN07": 0.0,
        "IN08": 0.0,
        "M00": 0.5,
        "OUT00": 0.0,
        "OUT01": 0.0,
        "OUT02": 0.0,
        "OUT03": 0.0,
        "OUT04": 0.0,
        "OUT05": 0.0,
        "OUT06": 0.0,
        "OUT07": 0.0,
        "OUT08": 0.0,
        "VAE": 0.0,
    }
    blocks = sdm.convert(blocks,b)
    blocks = blocks | 0.0
    
    c = sdm.slerp(a,b, alpha=blocks)
    
    sdm.merge(c,output="test.safetensors")

    V3

    I didn't want to stray too far from what made V2 a different realistic checkpoint, but i still wanted to give it a small lifting. I'll use this as a base for a future V4 ;-)

    V3 + DMD2

    Since i am going to play a bit with the DMD2 LoRA, i have integrated it in UnNamedIXL V3. This version is really experimental, prefer V3 if you want to do a classic realistic image as usual. Otherwise, this version is better used as a "refiner" or "Hires.Fix" model switch leveraging an other model to give a realistic boost on other composition. Here is an example of Hires.Fix using this version and both DMD2 version of AnBan and HoJ:

    (click on show more for the rest of the story 😁)

    Simply use Euler A, CFG 1, 8 steps in both standard and Hires.Fix run but switch to UnNamedIXL V3_DMD2 during Hires. You can also try other models for the base pictures as usual and do img2img only.

    Here is a base picture using WAI14 (30 steps, CFG 5):

    And the img2img, CFG1, 8steps, 0.4 denoise, x1.5 resize:


    Previously

    Brand new V2 is a full realistic model!

    Build from scratch using:

    It then received the "ILX v2 upgrade" before some precise MBW with UnNamedIXL V1 to get its composition more to my liking :D

    Usage:

    • DPM++ 2M SDE Heun + SGM Uniform

    • 40 steps

    • CFG > 6 is prefered, but feel free to experiment

    • Positive prompt: Can handle NLP 🎉

    • Negative prompt: whatever suit your fancy, just be aware that Illustrious generate "young" characters

    If you prefere "semi-realistic" bordering on realistic, V1 is still your best bet :D


    V1: An "almost" realistic checkpoint, because pure realistic models feels too uncanny for me yet.

    Long Story

    First, special thanks to @rambo943 who gave me the idea and the base for this model.

    The release of RealismHarem and my tests (and the associated message received) gave me the hint that merging a bit of my other checkpoints into it could be a way to "cure" a real realistic model to better fit my current mood.

    The merge started as a CosineB merge of RealismHarem into CuteIXL.

    Then, i added MM Gothic NJ + Asian Beauty XL + Eyes High Definition + Snake Oil to get a specific style to the picture.

    But since i threaded too much outside of the realistic feel i wanted, I redid a merge with the previous checkpoint before integrating a bit of HoJ.

    Afterward, Web Realistic Illustrious got in the mix at small dose and step by step, on the course of 9 Release Candidates, i smoothed the result but adding and substracting RealismHarem and some of my checkpoints at block level until i got what i wanted.

    Thanks for reading! 💖

    Update for the more curious, some intermediary steps:

    V0

    RC1

    RC4

    RC4 vs RC5 vs RC6

    CuteIXL vs HoJ vs RC6 vs V1

    Description

    FAQ

    Comments (12)

    JoyDopamineJul 27, 2025· 7 reactions
    CivitAI

    I have always had a question: theoretically, since Illu's base model doesn't have real images and lacks the effects of Flux, if we keep adding material sets for training, will Illu get infinitely close to Flux?

    n_Arno
    Author
    Jul 27, 2025· 1 reaction

    The main issue is the understanding of the prompt from my point of view: Flux, HiDream and so on use a CLIP plus a Sentence Embedding model (T5) which allow for a far better understanding of prompt and thus a superior conditionning of the UNET.

    With pure Illustrious, we can get something close, but only by losing something else (most realistic models based on SDXL and derivatives are pretty rigid)

    JoyDopamineJul 27, 2025· 5 reactions

    n_Arno I will try to create a few SDXL 100% untrained style Loras to see what kind of magic reactions they produce.

    SouthtownBluzJul 31, 2025· 3 reactions

    Honestly, at this point we need to be adding Wan 2.2 i2i...it looks better than Flux. More like SDXL 2.0.

    n_Arno
    Author
    Jul 31, 2025· 1 reaction

    SouthtownBluz I'll need to test it, i hope i'll be able to run it locally ^^;

    SouthtownBluzJul 31, 2025· 2 reactions

    n_Arno It's amazing what people are posting on reddit. Images so crisp and clear, it's like a brand new image generation model. I'm surprised people haven't been creating datasets with it to merge with illustrious or another model type to produce something original and new.

    n_Arno
    Author
    Jul 31, 2025· 1 reaction

    SouthtownBluz I'll definitely have to check that

    satoshi264Aug 24, 2025· 1 reaction

    Absolutely. While I do not care much about videos, the static images wan22 is generating are just exceptional. And inference is available for as low as 4 GB vram with low gguf. While kohya-ss musubi-tuner github page mentions training with images available with 12 GB (video with 24 GB), though here I am not talking from experience.

    n_Arno
    Author
    Aug 24, 2025

    @satoshi264 image generation with wan22? with 4GB?!? i definitely have to try. I recently acquired a good old Mac Mini M1 for dirt cheap, i am trying to set everything using ComfyUI on it and except poor fp8 support for MPS devices, i am hoping low gguf quantization will be possible (as long as inference is done in fp16, i should be good to go)

    satoshi264Aug 24, 2025· 1 reaction

    @n_Arno I have not tried it personally, just saw that comment: "Try Rapid AiO gguf q3_k_m it works even at 4Gb vram and 16Gb ram. And results are good and very fast." The context was with the idea that big wan with low quantization could give better results then wan 5b without quantization while taking similar amount of vram.

    discandyAug 10, 2025· 4 reactions
    CivitAI

    Excellent checkpoint - does what is asked of it - love it!

    n_Arno
    Author
    Aug 11, 2025

    Thank you for your kind words! 💕

    Checkpoint
    Illustrious

    Details

    Downloads
    5,210
    Platform
    CivitAI
    Platform Status
    Available
    Created
    7/27/2025
    Updated
    8/20/2026
    Deleted
    -

    Available On (2 platforms)

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