DORO EPIC AIRBRUSH - Soft Gradients

Versions

• v1 | DEA_SOFT_GRAD_1 - Initial release

• v2 | DEA_SOFT_GRAD_2 - more concentrated and powerful than v1

Compatibility

• Illustrious XL 🟢 v1 full | 🟢 v2 full

• Pony XL 🟡 v1 partially | 🟢 v2 full

Quick Start

🏷️Trigger v1: DEA_SOFT_GRAD_1

🏷️Trigger v2: DEA_SOFT_GRAD_2

⚠️ High-offset LoRA - effective range starts at 2.0+, not the usual 0.5–1.0

🏆 Sweet spot v1:

• 0.7–1.9 - subtle, barely visible

• 2.0–3.0 - effect kicks in, full style ⭐

• 3.0+ - overpowered, style compression

🏆 Sweet spot v2:

• 0.7-1.0 - subtle atmosphere, clean polishing range

• 1.5-2.5 - full effect: dramatic lighting, deep shadows, vortex glow ⭐

• 3.0+ - overpowered, latent saturation, loss of fine detail

Description

📸 Dataset: 15 abstract airbrush gradient crops - no objects, pure tonal transitions and color blending. Trained at 768px, 600 steps, AdamW8bit, cosine_with_restarts.

✨ Emergent effects:

• Gradient surfaces - clouds, smoke, fog, fire, atmosphere: deep analog painterly quality, as if airbrushed on paper

• Smooth surfaces - skin, plastic, metal: surface-blur-like effect, removes micro-noise and texture artifacts, evens gradients

• Object edges - sharpened and stylized, more "painted" feel

• Background-first - at moderate weights affects mainly background; at high weights touches subjects too

⚠️ Side effect: Smoothing suppresses fine texture (pores, grain, fabric). Not ideal when texture detail is the goal. Workaround: generate smooth, then add noise + slight blur in Photoshop.

💡 Bonus use: Pre-upscale prep - smooths surfaces and reduces artifacts for a cleaner upscale input.

What happened under the hood

This LoRA was trained on abstract gradient crops with no recognizable objects - and that turned out to be the key.

The model couldn't learn any specific object, so it learned pure rendering principles: how to blend tones, transitions, and light. When applied, it rewrites the model's rendering language across all volumetric, gradient-by-nature subjects - clouds, smoke, fire - because those subjects are gradients at their core.

This is a case of spontaneous feature disentanglement: an abstract dataset forced style and content to separate. The result is a universal style modifier, not a content LoRA - similar in principle to implicit style-content separation described in B-LoRA (ECCV 2024).

B-LoRA paper:

https://arxiv.org/abs/2403.14572

❤️ Artificial Inspiration by DORO