DREAMMMER

DREAMMMER is a web AI image editor that works as an agent: describe the result, and it plans the steps, chooses a model and refines the image through chat. It offers templates, a showcase of prompts, a library and 100 free credits on signup.

DREAMMMER is a web-based AI image editor built around an agent workflow. Instead of asking users to write technical prompts, choose a model and tune settings, it takes a plain description of the desired result and handles those decisions itself. The agent plans the steps, selects a suitable model, generates the image and then refines it through conversation, so edits happen by talking to the tool rather than by adjusting sliders. The product is aimed at people who want finished visuals without learning the quirks of individual image models: marketers, designers, social creators and hobbyists. The home screen offers quick starting points such as cover design, style transfer, virtual makeup and logo design, along with a template library featuring looks like Pixar 3D, Disney style, Ghibli style and chibi avatars. Users can also upload a reference image and ask for changes, which the showcase demonstrates with character sheets that keep a subject's identity across multiple angles, and with split-panel posters that pair an original photo with a restyled version. In practice, a session starts in the chat view, where the model setting can stay on Auto or be chosen manually from a models page that lists options such as GPT Image 2.5 Sunburst, Nano Banana Pro, Seedream 5.0 Pro and Qwen Image 3.0 Pro. A showcase gallery lets visitors open the creation process behind each result and see the exact prompt used, which doubles as a learning resource. Created work is kept in a library, and signing in syncs it across sessions. Video generation, including MiniMax H3 clips, appears in the showcase, but the homepage states that video starts with the Pro plan, while images are available first. Among alternatives, DREAMMMER sits between single-model generators and manual photo editors. Its distinguishing idea is model-agnostic routing: the user describes the outcome and the agent decides which engine fits. That suits people who want results quickly, though those who want precise control over one specific model may prefer a dedicated interface.