> For the complete documentation index, see [llms.txt](https://docs.pletor.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.pletor.ai/model-library/image-models/reve.md). # Reve ## Overview Reve excels at creating editorial-quality visuals with Midjourney-like aesthetics. Use it when you need polished, magazine-worthy images that can serve as creative inspiration or high-end reference material for your campaigns. The model has three core capabilites: generating images from scratch, editing existing images with precision, and creating new compositions from multiple image references. ### **Strengths for marketers** * Premium editorial aesthetic that elevates brand perception * Versatile editing capabilities: add, remove, or modify image elements seamlessly * Multi-image synthesis for creating cohesive visual concepts * Strong artistic direction that produces inspiration-worthy outputs * Consistent high-quality results suitable for client presentations and mood boards ### **Ideal use cases** * **Editorial (fashion) shots**: Generate editorial-style visuals to inspire campaign directions * **Mood board creation**: Produce cohesive visual references for brand aesthetics * **Reference material**: Create polished visuals that guide other model outputs * **Image refinement**: Remove unwanted elements, adjust details, or enhance existing photos * **Concept development**: Combine multiple images to prototype new creative directions ### **Weaknesses** * Higher aesthetic quality may not suit all brand styles (especially casual or gritty brands) * Editorial approach can feel too polished for authentic UGC-style content * Generation time may be longer than other generation models ### Pro tips * Combine with more product-focused models for complete campaign workflows * Experiment with multi-image inputs to develop unique visual concepts that blend brand elements *** --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.pletor.ai/model-library/image-models/reve.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.