SuperAPI AI Directory

All AI tools

35,738 results · Page 395 of 993

SrefHunter

Other AI tools

SrefHunter is an online resource library that collects Midjourney --sref style reference codes, offering 5,676 sref codes for users to copy and use to improve the aesthetic style of images generated by Midjourney. The website also provides a Midjourney cheat sheet, creative prompt inspiration, popular community content, a contributor leaderboard, and monthly updates. Some sref codes on the page can be copied, while other codes require an upgrade to view. The website also mentions Melies as an AI creative studio that can be used to generate images, shots, and videos based on visual styles.

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sssaicode

AI coding

Historical third-party summary: Two development and API-information indexes associate sssaicode.com with a relay API for connecting development tools to model services; one records a compatible-provider configuration example. Only that development-integration purpose is summarized. Authors’ experience, vendor channels, actual model provenance, prices, domestic payments and compatibility are unverified and do not establish quality or authorization. No client was configured, key entered, credit purchased or service called. Retained materials were checked on 2026-10-18; indexed references have source full text not verified, with complete resources identified separately.

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Stable diffusion XL

AI images

Stable Diffusion XL (SDXL) is a new version of the Stable Diffusion image generation system released by Stability AI in July 2023. According to the information, it uses a dual-model system with 6 billion parameters, supports 1024x1024 resolution, can generate highly realistic images and clear text, and simplifies prompt input with multiple built-in preset styles. Stable Diffusion itself is an open-source text-to-image tool based on diffusion models, developed by the CompVis team at the University of Munich and Runway ML, with computing support from Stability AI. It can perform image inpainting, outpainting, and text-guided image-to-image translation, and has open-sourced its code, pre-trained models, and license, allowing users to run it locally on a single GPU. Users can generate images through text prompts and customize them with Lora, embeddings, and negative prompts; it can be used online via web platforms or installed and run locally, and when run locally it can be fully offline. The information also introduces downloading, installation, training, and features such as ControlNet and face replacement, while noting limitations such as training data biased toward Western culture and poor generation of human limbs and faces.

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stable-diffusion-videos

AI video

stable-diffusion-videos is an open-source project based on Stable Diffusion that generates videos by exploring latent space and morphing between text prompts. Users can try it directly in Colab or use the provided example scripts. The project supports adding music; audio affects the interpolation rate, making the video move with the beat. Note: on Apple M1 architecture, torch.float32 must be used because MPS does not support torch.float16. This project is modified from a script shared by @karpathy.

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stable-worldmodel

AI developer platforms

stable-worldmodel is a platform for reproducible world model research and evaluation, developed by galilai-group. It provides a unified interface covering the three stages of world model research: data collection, training, and evaluation based on model predictive control (MPC), and supports a large number of standardized environments. The platform includes reference implementations of common baseline models and planning solvers, allowing research code to focus on the models and objectives themselves. On the data side, the default dataset format is based on the Lance stack (lancedb, pylance, pyarrow), provided separately as an optional dependency; it also supports LeRobot datasets (requires Python 3.12+, installed via pip install 'stable-worldmodel[lerobot]'). Datasets and checkpoints are stored by default in $STABLEWM_HOME (default ~/.stable_worldmodel/) and can be customized. Environments come from the DeepMind Control Suite, Gymnasium classic control, OGBench, Craftax, the Arcade Learning Environment (100+ Atari games), and classic world model benchmarks (Two-Room, PushT). Most environments include independently controllable visual and physical variation factors, making it easy to evaluate zero-shot generalization. After installation, the swm command-line tool is provided for inspecting/converting datasets, environments, and checkpoints. The library is under active development, and the API may change between minor versions.

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StableCascade

AI images

Stable Cascade is the official codebase released by Stability AI, based on the Würstchen architecture, providing training and inference scripts as well as a variety of available models. Its core feature is that it operates in a highly compressed latent space: with a compression factor of 42, it can encode a 1024×1024 image into 24×24 while maintaining clear reconstruction, thereby bringing faster inference speed and lower training cost, making it suitable for image generation scenarios that value efficiency. The model consists of three parts: Stage A, Stage B, and Stage C. Stage A is a VAE, while Stage B and C are diffusion models. It is officially stated to support extensions such as fine-tuning, LoRA, ControlNet, IP-Adapter, and LCM, and performs well in prompt alignment and aesthetic quality evaluations. The code uses the MIT license, and the model weights follow the STABILITY AI NON-COMMERCIAL RESEARCH COMMUNITY LICENSE.

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StackGen

AI agents

StackGen is an Autonomous Operations platform for DevOps and platform engineering, with its core being the Aiden agent. It can perform infrastructure provisioning, deployment, incident remediation, and observability management within the user's existing toolchain. Aiden can generate governed Terraform based on approved modules, match and execute Runbooks, manage OpenTelemetry-based Grafana and Prometheus observability, and investigate alerts and automatically remediate after approval. The platform supports aggregating operational requests from sources such as ServiceNow, Jira, and Linear, triaging by autonomy level, matching workflows, and displaying decision trails. StackGen emphasizes integration with existing developer and platform toolchains (such as Kubernetes, observability, and DevOps tools), and can be used through MCP in environments such as Cursor, Claude Code, and Kiro.

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