









Monsoon is the latest entry in my "storm" ecosystem (joining Typhoon and Tornado), but unlike its turbulent siblings, Monsoon isn’t here to smash through prompts. It drifts in — slow, stylized, and quietly overwhelming.
This is version 0.1, an early but promising stage. While checkpoint training and a few LoRA merges were block-weighted into the base, the model remains almost raw. It hasn’t been overfit, distilled, or polished — and it’s nowhere near full capability yet, which is exactly why it’s not v1.0. There’s still plenty it can’t do, but what it does do? Stunning.
My personal target? Stylized ethereal portraits and scenery, and it’s already exceeding expectations there.
The result? A model that often feels like Lumina, while keeping the freedom, openness, and tooling compatibility of SDXL.
Stylized landscape shots
Dreamlike seasonal art (spring blossoms, winter haze, soft sunsets)
Painterly portraits with glow and ambience
Emotional, cinematic lighting
Strong silhouette and shape language
Hyperrealism — not the goal here
Clean photographic detail
Fast results on the wrong scheduler
Hands, like every SDXL base ever 😅
⚙️ Sampler Suggestions
These are the samplers Monsoon responds best to, based on extensive testing:
DPM++ 2M — Clean and detailed, works great right out the gate
DPM++ 2M SDE — Needs more steps, but rewards with a soft, painterly glow
Euler A — Smooth, creamy, expressive — especially at lower step counts
Euler — Similar to Euler A, but slightly sharper and more defined
DPM Adaptive — Slow, but ultra-refined and moody — worth the extra time
DDIM — Surprisingly balanced; great for stylized and consistent output
DDPM — Dreamy and soft, like Euler A but with a floatier finish
This model was tested head-to-head against Lumina Image 2.0, using matched prompts and seeds where possible. While Lumina relies on flow-based schedulers (like res_multistep) due to its transformer-flow architecture, Monsoon (being SDXL-based) uses diffusion. So, the outputs won't be identical — but vibes? Shockingly close.
Every bit helps cover compute costs, caffeine, and the occasional “why is this broken?” meltdown.
Thanks for keeping the storm alive.
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