The

basic model of SDXL's LCM2 has been adjusted and optimized, so it is not suitable for LCM samplers and is very friendly to SDE samplers.

(Contact me on WeChat: QQ1007872291)

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If you don't read tedious documentation, then the recommended configuration here is as follows: Sampler: DPM++ SDE Karras

CFG: 2 Steps:

5

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Sampler Recommended

for SDE

The sampler is relatively friendly, the recommended sampler is as follows

1. DPM++ SDE Karras - slow speed, more perfect detail (5 steps +) DPM++ 2M SDE Karras - fast (5 steps +)
2. DPM++
SDE - suitable for high steps (8 steps+), slow speed, more perfect details

CFG recommended

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CFG is usually set to 1, and can be adjusted to 1-3. The higher the value, the faster the folding speed, that is, the image can be generated using fewer steps

.

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Test case

Prompt: (Well-Bred's works: 1.13), a beautiful girl, 24yo, skinny, pretty, [k-pop: detailed eyes, detailed beautiful face: 0.5],

Negative Prompt:

ng_deepnegative_v1_75t, negative_hand, disfigured, ugly, deformed, worst quality, low quality, extra legs, extra arms, deformed hands, deformed anatomy, umbrella, sampling: DPM++

2M SDE Karras or DPM++ SDE Karras

Step: 5+ Size: 768 x 1024 or above Sampler tes* ***ng 5 steps

as an example, the effects of the different samplers are as follows

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in 10 steps For example,

the effects of different samplers are as follows

in 20 steps For example,

the effects of different samplers are as follows

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DPM++ 2M SDE Karras step count test DPM++ 2M SDE Karras is more suitable for fast card drawing. The results of steps 1 to 8 are as follows

Step 4 is A usable image was formed, and the details gradually improved as the number of steps increased

.

More test reports:

https://www.yuque.com/mengxiangyuan-7fhri/eo7oa2/gtifyimxamueeixg?singleDoc # SDXL-LCM Sampler Test Report My Github:

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https://github.com/SoftMeng/ComfyUI_Mexx_Styler

https://github.com/SoftMeng/ComfyUI_Mexx_Poster

https://github.com/SoftMeng/ComfyUI_Mexx_Image_Mask

https://github.com/SoftMeng/stable-diffusion-prompt-pai

https://github.com/SoftMeng/Fooocus-zh

Or My libLib

:

https://www.liblib.art/userpage/0f37258e861044879b8728309c0ac3b0/publish