Reve 2.1

Reve 2.1 is an image remixing model that transforms and combines existing images using plain-language text instructions. Instead of starting from a blank canvas, you feed it one or more reference images and describe the changes you want — swapping outfits, adding objects, changing backgrounds, restyling scenes — and it produces a polished new image that honors your instructions closely. Its standout strengths are strong prompt adherence, an understanding of layout and composition, and accurate text rendering, making it a reliable choice for creators who need edits that actually land the way they were described.

At the heart of Reve 2.1 is a flexible reference system. You can supply between one and eight reference images per generation, and then point to them directly in your instructions. Because each reference is numbered in the order you add it, you can tell the model exactly which source to pull from — for example, taking clothing from one image, a hat from another, and dropping both onto a subject from a third. This makes it especially useful for compositing tasks where elements from separate photos need to come together into a single believable scene. A prompt like "Dress the model in the clothes and hat. Add a cat to the scene and change the background to a Victorian era building" is exactly the kind of multi-step, multi-reference edit the model is built to handle.

The layout intelligence built into Reve 2.1 means it doesn't just paste elements together — it arranges them with an awareness of how a scene should be composed. Clothing sits naturally on a subject, added objects find sensible placement, and backgrounds are swapped without breaking the overall balance of the image. Combined with its accurate text rendering, this makes the model a strong fit for design work where readable words matter, such as posters, product mockups, typographic layouts, and stylized graphics that need legible copy rather than garbled lettering.

Reve 2.1 gives you meaningful control over the shape of your output. You can choose from a wide range of aspect ratios — everything from ultra-wide panoramic formats and cinematic widescreen to square and tall vertical framing suited to social feeds and stories. If you're not sure which framing fits best, an automatic option lets the model choose an appropriate aspect ratio based on your request and reference material. You can also generate up to four variations at once, giving you several interpretations of the same instruction to compare and pick from. Output is available in common image formats, so you can select the one that best matches your workflow, whether you need a lossless file for further editing or a lighter format for quick sharing.

The model accepts a broad set of input image formats, including PNG, JPEG, WebP, AVIF, and HEIF, so you can bring in source material from a wide variety of devices and libraries without converting files first. Reference images can be sizeable, and the model works comfortably with high-resolution sources, so detail from your originals carries through into the edit. Your text instructions can be quite detailed as well, giving you room to describe complex, multi-part changes in a single prompt rather than chaining together many small edits.

Who benefits most from Reve 2.1? Photographers and retouchers can use it to restyle shoots, swap wardrobes, or reimagine backdrops without a full reshoot. E-commerce and product teams can dress models, place products into new environments, and generate lifestyle variations from existing catalog shots. Graphic designers and marketers gain a fast way to produce stylized graphics and typographic pieces where the text needs to remain crisp and correct. Concept artists and art directors can quickly explore "what if" versions of a scene, combining moodboard references into a unified image. Content creators and social media managers can adapt a single hero image into multiple aspect ratios and stylistic treatments for different platforms.

Because Reve 2.1 is built specifically for remixing rather than pure generation, its best results come from starting with clear reference material and describing your intended changes precisely. The more specific your instructions — naming which reference to draw from, what to add or remove, and how the final scene should look — the more faithfully the model delivers. Its strong prompt adherence rewards detailed direction, so treating your prompt like a brief to a skilled assistant tends to produce the most reliable outcomes. When you want to explore stylistic transformation, the model handles restyling and creative reinterpretation well, letting you push an image toward a new era, mood, or aesthetic while keeping the core subject recognizable.

A few practical considerations are worth keeping in mind. Reve 2.1 always requires at least one reference image alongside your prompt — it's designed to transform existing visuals rather than invent an image from text alone. You can combine up to eight references in a single request, which is generous for most compositing needs, and each source should stay within a reasonable file size. Generating several variations at once is a good habit when you want options, since even a well-written prompt can be interpreted in more than one valid way. Overall, Reve 2.1 is a dependable, direction-friendly tool for anyone whose work involves reshaping, combining, and restyling images with precision and readable results.