AI image generation and moderation tools cover the full lifecycle of visual content: creation, editing, attribution, detection, and compliance. This topic examines generative-image engines and accessible editors alongside the content-safety, marketing-attribution, and governance solutions organizations use to manage risk and legal obligations. Key generator and editor options include Adobe Firefly (Creative Cloud–integrated text-to-image, vectors and effects), Stability AI (enterprise multimodal platform and Dream Studio for image/video/3D), Google Gemini (multimodal models and developer APIs), Clipdrop (background removal, upscaling, relighting and APIs), Pixlr (browser-first editor and generator), PFPMaker (single-photo-to-many profile-picture variants), Dzine AI Photo Filter (style-transfer filters) and IC-Edit AI (open-source in‑context text-driven editing). As adoption of image-generative AI grows across marketing, entertainment, and product design, pressures on content safety and compliance have intensified. Organizations now pair generators with AI content detectors to flag synthetic or manipulated imagery, marketing-attribution tools to track usage and licensing, and security/governance platforms to enforce policies and provenance requirements. Regulatory scrutiny around copyright, deepfakes, and consumer protection has made traceability and documented model usage a practical necessity rather than optional best practice. Open-source frameworks and APIs have expanded developer control and customization, while integrated suites aim to simplify workflows for creative teams. This landscape is practical and operational: selecting a tool means balancing image quality, integration (APIs or Creative Cloud), editability, and the downstream capabilities—detectors, attribution, and compliance—that reduce legal, reputational, and safety risks. Understanding how generators and moderation systems interoperate is essential for deploying visual AI responsibly and at scale.