mirage-landscape 0.1.0

Generate AI landscape images from the command line

mirage-landscape

Generate AI landscape images from the command line. Runs fully offline after the first use.

mirage -n 3 -o ~/Desktop
  saved → /Users/mike/Desktop/landscape_20240506_142301_001.png
  saved → /Users/mike/Desktop/landscape_20240506_142341_002.png
  saved → /Users/mike/Desktop/landscape_20240506_142421_003.png

How it works

1. DDPM (crab27/ddpm-landscape) generates a 256×256 landscape using DPM-Solver++ in 20 steps
2. Real-ESRGAN 4× upscales it to 1024×1024
3. Lanczos resize to your target resolution (default 3840×2160)

Model weights download automatically on first run and are cached in ~/.cache/mirage/.

Install

CPU / Apple Silicon (MPS)

pip install mirage-landscape

NVIDIA GPU (CUDA) — install PyTorch with CUDA support first, then install the package so pip doesn't overwrite it with the CPU build:

pip install torch --index-url https://download.pytorch.org/whl/cu124
pip install mirage-landscape

Replace cu124 with your CUDA version (cu118, cu121, etc.). Check pytorch.org if unsure.

Usage

# One 4K image in the current directory
mirage

# Multiple images to a specific folder
mirage -n 5 -o ~/Pictures/landscapes

# Skip upscaling for a quick 256×256 preview
mirage --no-upscale

# Custom resolution
mirage --resolution 2560x1440

# Reproducible output
mirage --seed 42

# Fewer steps = faster, slightly lower quality
mirage --steps 10

All options

Option	Default	Description
-o, --output	.	Output directory
-n, --count	1	Number of images
--steps	20	Denoising steps
--no-upscale	off	Skip Real-ESRGAN (outputs 256×256)
--resolution	3840x2160	Output resolution as WxH
--seed	random	Fix seed for reproducibility

Python API

from mirage import generate

paths = generate(output_dir="~/Pictures", count=3, seed=42)

Requirements

• Python 3.10+
• ~3 GB disk for model weights (cached after first download)
• CUDA GPU recommended; Apple Silicon (MPS) and CPU also supported