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SDXL

YiffyMix (Noob AI), Furry Checkpoint Model by chilon249

#Furry#Animal

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YiffyMix (Noob AI), Furry Checkpoint Model by chilon249 - AI Model cover image
Caricato su
7 giu 2025, 08:57
Utilizzi
95.4k
Reviews
Excellent  (7)
Passaggi di campionamento

28

Metodo di campionamento

Euler a

Scala CFG

4

Negativo

malformed, worst quality, bad quality, censor bar, signature, text, url

Permessi
  • Allow image generation & sharing
  • Permettete agli utenti di scaricare il vostro modello
  • Usi commerciali

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Descrizione

https://civitai.com/models/3671?modelVersionId=1876492

About this version:

Recipes v62-Noob:

partA = Chroma XL Mix v331 Mango + Plant Milk Model Suite HempII * 0.55

partB = ZoinksNoob + partA * 0.45

partC = (partB + Shift v10 - ZoinksNoob) * trainDiff:0.45

YiffyMix v62-Noob = partC + LoRA:[SPO-SDXL_4k-p_10ep_LoRA_webui] * 0.55

Usage notes:

Use e621 tags (no underscore), Artist tag very effective in YiffyMix.

YiffyMix doesn't need Pony score tags, Don't use SD1.5 vae in SDXL, you will get fried image.

Example Setting

Setting SDXL (SDXL-lightning & NoobXL)

Steps = 12~24

Sampler = "DDPM", "Euler A SGMUniform", "Euler SGMUniform"

CFG scale = 4

Negative embeddings SDXL = ac-neg1, ac-neg2 (You don't really need this)

Postive LoRA SDXL = SeaArt Quality Tags LoRA (You don't really need this)

Stop at CLIP layers = 2

Hires. fix

Hires steps = Steps * Denoising strength

Denoising strength = 0.25

Hires upscaler = 4x-UltraMix_Smooth [SD-WebUI\models\ESRGAN]

Img2img First Pass ( Upscale enhance )

Scale to = 1.5

Denoising strength = 0.4~0.45

Extra ( seed ) = true

Variation seed = -1 ( use same seed )

Variation strength = 0.9

Img2img Second Pass ( Detail enhance )

Scale to = 1

Denoising strength = 0.3~0.35

Extra ( seed ) = true

Variation seed = -1 ( use same seed )

Variation strength = 0.95

ControlNet

ControlNet = softedge_hed, control_v11p_sd15_softedge

ControlNet SDXL = softedge_hed, sdxlsoftedge-dexined, noobai-xl-controlnet

ControlNet Weight = 0.35~0.5

ControlNet Pixel Perfect = true

LoRA Training

imgs count = 15~50

total steps = epoch * imgs count * folder loop = 3000~4500

network_dim = 64

network_alpha = 128 (SDXL) \ 16 (Noob)

learning_rate = 0.0002

unet_lr = 0.0001

text_encoder_lr = 0.00005

lr_scheduler = "cosine_with_restarts"

mixed_precision = "bf16"

optimizer_type = "Adafactor"

optimizer_args = [ "scale_parameter=False", "relative_step=False", "warmup_init=False", ]

Not for commercial use.

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