VisualNote Extra Detailer XL (Style, Art Texture)

LORA
Original


Updated:

DPM++ samplers are recommended.

🔹 Karras vs Normal (Scheduler)

Normal (linear/exponential) = evenly distributes timesteps. Good enough, but less efficient.

Karras = clusters more steps near the end (low-noise region) → spends more effort refining tiny details → better for grain, freckles, pores, brush textures.

👉 Most people pair DPM++ 2M/3M SDE + Karras for realism + detail retention.

👉 If you want more “looseness” or organic look, use DPM++ 2S a (Karras) or even Euler a.

LoRA

Extra Detailer XL

strength:

0.1 (recommended)

0.2-0.6 (ok)

0.7 (experimental)

0.8-1.0 (Experimental Warning: over saturation, over highlighting, over taking styles, reducing/removing other LoRA styles and texture effects)

Recommended CFG:

Euler/Euler a:

3-7

DPM/DPM++ samplers:

3

2.5 - 3.5 (experimental)

2 - 4 (more experimental)

5 - ... (Experimental Warning: causes over saturation, over exaggerated weird details, over highlighting)

Euler/Euler a - good for Simple, Flat anime/illust styles but

Flattens/Removes Fine-Details

like art textures, textures for film effects, visual effects, traditional art medium, dust, fabric, hair, etc.

1.: Strengths

2.: Weaknesses

Euler a

Simple + ancestral noise

1. Fast, stylized, organic variation

2. Eats fine texture, less sharp, inconsistent

DPM2 a

2nd-order + noise

1. Sharper than Euler a, some variation

2. Outdated vs DPM++

DPM++ 2S a

2nd-order single-step + noise

1. Punchy, organic, slightly sharper than Euler a

2. Less stable at low steps

DPM++ 2M SDE

2nd-order multi-step SDE

1. Stable, detailed, great texture

2. Slightly slower, less random

DPM++ 3M SDE

3rd-order multi-step SDE

1. Maximum sharpness, photorealism, ultra-detail

2. Rigid, slower, less stylistic flexibility

Version Detail

Trained by Tensor
Illustrious
2
Use this Pro LoRA and unlock exclusive knowledge I've learned from countless experiments. Using this supports me to support you with better practical knowledge you can use right away. Please feel free to ask (e.g. why I use certain kinds of prompts, generation data, training methods). Sampler (recommended combination): dpmpp_sde_gpu (CFG 3-4) (LoRA strength: 0.1-0.7) (recommended) dpmpp_2m_sde_gpu (CFG 2-3) (LoRA strength: 0.1-0.4) (2nd recommended) dpmpp_3m_sde_gpu (CFG 2-2.7) (LoRA strength: 0.1-0.3) dpmpp_2s_ancestral (CFG 3-7) (LoRA strength: 0.1-1.0) Scheduler karras training configuration: dataset length 720 Network Module LoRA Use Base Model WAI-illustrious - v14 Trigger words visualnote style Image Processing Parameters Repeat 10 Epoch 2 Save Every N Epochs 1 Training Parameters Seed - Clip Skip 2 Text Encoder learning rate 0.00001 Unet learning rate 0.0001 LR Scheduler cosine_with_restarts Optimizer AdamW8bit Network Dim 56 Network Alpha 56 Gradient Accumulation Steps - Label Parameters Shuffle caption - Keep n tokens - Advanced Parameters Noise offset 0.05 Multires noise discount 0.1 Multires noise iterations 10 conv_dim - conv_alpha - Batch Size 2

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