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Generate contextual images for web projects using the Gemini API. Produces hero backgrounds, OG cards, placeholder photos, textures, and style-matched variants.

使用 MiniMax MCP 服务器进行图像理解和分析。

Generate high-quality AI images using Volcengine's Jimeng 4.0 API with text prompts or image inputs.

Generate images with 50+ AI models including FLUX, Gemini, Grok, and Seedream via inference.sh CLI. \n \n Supports text-to-image, image-to-image, inpainting, LoRA customization, image editing, upscaling, and text rendering across multiple model families \n Models range from ultra-fast budget options (FLUX Klein at $0.0001/image) to high-fidelity 4K outputs (Seedream 4.5, ImagineArt 1.5 Pro) \n Includes Google Gemini, xAI Grok, ByteDance Seedream, and Pruna P-Image variants with configurable aspe

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

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

Upscale and enhance images using Topaz, Real-ESRGAN, and other inference.sh models. \n \n Supports multiple upscaling models including Topaz Image Upscaler, Real-ESRGAN, and FLUX Dev Upscaler via the inference.sh CLI \n Handles common image enhancement tasks: restoring old photos, preparing AI-generated art for print, creating high-resolution versions for displays, and enlarging thumbnails \n Integrates with image generation workflows, allowing you to generate and upscale images in sequence \n R

AI-powered image editing with style transfer, object removal, background changes, and inpainting. \n \n Supports five core operations: style transfer, object removal, background replacement, inpainting with masks, and general instruction-based edits \n Configurable strength parameter (0.0–1.0) to control edit intensity from subtle to dramatic \n Includes model discovery via search API to find the best current model for each operation type \n Requires image URL and editing prompt; optional mask U

Use this skill when you need to analyze, describe, or extract information from images.

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