DORO EPIC COVERS - Sepultura 93 - Chaos AD

Versions

• v1 | DEC_CHAOSAD_1 - Initial release. Full biomechanical world reskin distilled from a single highest resolution available master file - not a compressed web image.

Compatibility

• Illustrious XL 🟢 full

• Pony XL 🟢 full

Quick Start

🏷️ Trigger: DEC_CHAOSAD_1

⚠️ High-offset LoRA - effective range starts at 1.5+, not the usual 0.5-1.0.

This is intentional: UNet-only training preserves composition integrity, prevents bleed,

and maximizes cross-model compatibility. Not broken. Just needs more pressure.

🏆 Sweet spot:

• 1.5 - passive threshold, minimal effect without booster tags

• 2.0-2.5 - GOLDEN ZONE, full world stylization ⭐

• 3.0 - extreme surreal overload, structure holds but reality bends

Booster tags (for weight < 1.5):

bandages, cables, wires, machinery, pipes, brick wall, many faces

Description

A style LoRA that acts as a visual virus - transforming environments and objects into dense biomechanical and technogenic aesthetics.

📸 Dataset: 16 images, one master painting - Cacophony (1993) by Michael R. Whelan, used as the cover for Sepultura's Chaos A.D. Sourced from the highest resolution available master file - not a compressed web image. Mixed media: airbrush over grey ground. Dataset structured into 4 -scenes: shroud textures, wall souls, techno-organic cables, and compositional anchors.

✨ Emergent effects:

• Material replacement - stones, rubble and plain surfaces become dense tangles of bronze/golden pipes, tubes and cables

• Background transformation - generates complex technogenic panels and organic structures with relief faces embedded in walls

• Airbrush texture overlay - imposes soft gradient quality and near-photographic material depth

• Color palette lock - deep indigo and violet shadows, warm ochre and rust metallic highlights, sharp temperature contrast

⚠️ Side effect: Selective activation - works only on elements structurally similar to the training (pipes, wires, mechanisms, hard backgrounds). Faces and human are largely protected and stay clean. Only environment and equipment are transformed. Workaround: none needed - this is by design.

💡 Bonus use: Stack with airbrush-style LoRAs for maximum material depth and painterly quality.

What happened under the hood

This LoRA was trained on a single painting distilled into 16 carefully composed images across 4 -scenes. The dataset isolation - one visual source, one atmosphere, one color logic - is what gives it such a decisive stylistic grip. There's no averaging, no drift, no compromise.

UNet-only training (no Text Encoder) means the LoRA operates purely on the visual signal, not the language side. This is why it needs higher weights to activate - the text encoder isn't pushing it. What you gain is protection: the model has no semantic handle on "person", so it doesn't touch faces or skin. The biomechanical transformation happens around the character, not on them.

Alpha/Dim ratio is 1.0 (dim=32, alpha=32), which gives full signal without scaling compression. The high effective weight range is a direct consequence of UNet-only architecture plus the strong visual coherence of the training source - not a flaw in calibration.

Michael R. Whelan - official site:

https://www.michaelwhelan.com

Kohya LoRA training - UNet-only:

https://github.com/kohya-ss/sd-scripts

❤️ Artificial Inspiration by DORO