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Generate and edit high-quality images using FLUX and Gemini AI models via OpenRouter. \n \n Supports both image generation from text prompts and editing of existing images with instruction-based modifications \n Two primary models available: Gemini 3 Pro (high quality, supports both generation and editing) and FLUX.2 Pro (fast, high quality, supports both modes); FLUX.2 Flex available for generation-only use cases \n Requires OpenRouter API key configured via .env file or environment variable; s

Build Azure managed images and Azure Compute Gallery images using Packer's azure-arm builder.

Text-to-image generation and image transformation with Stable Diffusion models via HuggingFace Diffusers. \n \n Supports multiple generation modes: text-to-image, image-to-image translation, inpainting, outpainting, and ControlNet spatial conditioning for precise control \n Compatible with SD 1.5, SDXL, SD 3.0, and Flux models; includes scheduler swapping (Euler, DPM-Solver, LCM) for quality and speed trade-offs \n LoRA adapter support for efficient style fine-tuning and multi-adapter compositio

Convert still images to animated videos via inference.sh CLI.

Generate a handful of “random but structured” prompts and render them via the OpenAI Images API.

Image resizing, format conversion, optimization, and OG card generation using Pillow. \n \n Handles resize, crop, whitespace trimming, format conversion (PNG/WebP/JPG), compression, thumbnail generation, and Open Graph card creation \n Generates Python scripts adapted to your environment; falls back to sips (macOS), sharp (Node.js), or ffmpeg if Pillow unavailable \n Includes RGBA-to-JPG compositing, cross-platform font discovery, and format-specific quality settings (WebP 85, JPG 90, PNG optimi

Text extraction from images using six OCR engines with preprocessing, cloud APIs, and structured output. \n \n Supports six OCR tools with decision tree: Tesseract and EasyOCR for local processing, PaddleOCR for CJK and tables, Google Vision and AWS Textract for cloud accuracy, Claude Vision for semantic understanding \n Includes full preprocessing pipeline (grayscale, deskew, denoise, binarization, morphological cleanup) to maximize accuracy on real-world images \n Provides Python and Node.js i

Automate GraalVM native image configuration, build, and error resolution for Java applications. \n \n Detects project structure (Maven/Gradle) and framework (Spring Boot, Quarkus, Micronaut) to apply framework-specific native image setup \n Adds GraalVM Native Build Tools plugins with appropriate configuration profiles and iteratively resolves build errors \n Handles common native image issues including reflection, resource access, JNI, and dynamic proxy configuration through generated metadata

Multi-provider AI image generation with text-to-image, reference images, batch processing, and quality presets. \n \n Supports seven providers: OpenAI, Google, OpenRouter, DashScope, Jimeng, Seedream, and Replicate with automatic provider selection or explicit override \n Handles single images via CLI flags or batch parallel generation from JSON files with configurable worker counts and per-provider throttling \n Supports reference images (Google, OpenAI, OpenRouter, Replicate, Seedream), custom

Image processing and manipulation using ImageMagick across Windows, Linux, and macOS. \n \n Retrieve image metadata, dimensions, and format information with the identify command \n Resize single or batch images with aspect ratio control and thumbnail generation \n Filter and process images based on dimensions or file type criteria \n Requires ImageMagick installed and available as magick on PATH; includes platform-specific setup for PowerShell (Windows) and Bash (Linux/macOS) \n