Scenic is a Python package designed for generating visually appealing, high-quality landscape images using procedural generation techniques and Generative Adversarial Networks (GANs). It aims to bridge the gap between traditional procedural content generation and the realism achievable with modern deep learning methods, specifically focusing on creating diverse and believable natural environments. The core idea is to combine hand-crafted procedural rules with the learning capabilities of GANs to produce images that are both controllable and aesthetically pleasing.

At its heart, Scenic utilizes a modular pipeline. This pipeline begins with a "scene description," a relatively low-resolution representation of the landscape defined by parameters like elevation, water bodies, and vegetation types. This scene description isn't an image itself, but rather a set of instructions for how the landscape *should* be. Crucially, Scenic allows users to directly manipulate these parameters, offering a degree of artistic control not typically found in purely GAN-based image generation. This scene description is then progressively refined through a series of stages.

The refinement process involves several key components. First, a procedural generator takes the scene description and creates a higher-resolution, but still relatively simple, representation. This generator uses techniques like Perlin noise, fractal terrain generation, and rule-based placement of objects (trees, rocks, etc.). Next, a series of GAN-based "refinement networks" are applied. These networks are trained to take the output of the previous stage and enhance it, adding detail, realism, and visual coherence. Multiple refinement networks can be chained together, each focusing on a different aspect of the image – for example, one network might specialize in adding realistic textures to rocks, while another focuses on improving the appearance of foliage.

A significant aspect of Scenic is its emphasis on disentanglement. The GANs are trained in a way that encourages them to learn independent representations of different landscape features. This means that changing one parameter in the scene description (e.g., the amount of forest cover) should primarily affect the corresponding feature in the generated image, without causing unintended changes elsewhere. This disentanglement is achieved through careful network architecture design and training strategies, including the use of style-based generators and adversarial losses that promote feature separation.

The repository provides pre-trained models for various landscape types (mountains, forests, coasts, etc.), allowing users to quickly generate images without needing to train their own GANs. However, it also includes tools and documentation for training custom refinement networks, enabling users to tailor the system to specific artistic styles or geographic regions. Furthermore, Scenic supports features like semantic segmentation maps, allowing for more precise control over the placement and appearance of different landscape elements. The project is actively developed, with ongoing research focused on improving image quality, increasing control, and expanding the range of supported landscape types. It's a powerful tool for artists, game developers, and researchers interested in procedural landscape generation and the intersection of AI and art.