Correction, 2026-10-01: an earlier version said Reve "consistently produces" a given look, that Seedream executes traditional visual styles accurately, that you can add a style reference image to Flux Kontext, and that 8frame offers classical two-image style transfer. We have not measured those results, the Flux Kontext tool takes a single image, and 8frame has no classical neural style-transfer tool. The 8frame section is rewritten below.
Style transfer is an AI technique that separates the visual style of one image from its content and applies that style to a different image or prompt output.
The simplest way to think about it: you have a product photo and a reference image with a specific look (muted film grain, bold graphic illustration, oil painting texture). Style transfer takes the aesthetic from the reference and renders your content through it. The subject stays yours. The visual language comes from the reference.
There are two distinct problems the phrase covers. One is classical neural style transfer, where you feed a content image and a style image and the algorithm blends them. The other, more practically useful version, is what modern generative models do: take a text prompt or an existing image and produce output that matches a described or referenced style, without the classical two-image setup. On 8frame you work with the second kind.
How style transfer works
Classical neural style transfer, introduced by Gatys, Ecker and Bethge in 2015, works by separating a convolutional neural network's representation of content (the shapes, objects, spatial layout) from its representation of style (textures, brushstrokes, color relationships). The algorithm then iteratively adjusts a generated image to minimize both content loss against your source photo and style loss against your reference.
Modern diffusion-based style transfer skips the two-image math. Models are trained on enough stylistically diverse image-text pairs that you can describe a style in a prompt ("rendered in 35mm Kodachrome, high contrast, slightly desaturated") and the model samples from that learned distribution. Some models go further: you supply a reference image and the model picks up its style without you having to put it into words.
The practical difference is control. Classical methods produce strong stylization but can distort subjects. Diffusion-based methods tend to keep subjects more recognizable and handle photorealistic styles, though they can struggle with very abstract or painterly references.
When you use style transfer
You'll reach for style transfer when:
- You need brand-consistent output across many assets. A reference image defines the look and every generation follows it.
- You're adapting existing content for a different market or format. Same subject, different visual treatment.
- You're prototyping creative directions fast. Instead of briefing a visual direction in text, you hand the model an example.
- You need to make a client's product photography look like it belongs in a campaign aesthetic you can't reshoot in.
It's not the right tool when you need the model to create entirely new content with no reference anchor. For that, prompt engineering and model selection matter more than style inputs.
Style transfer on 8frame
Style from a description: Reve, Seedream, Nano Banana. Describe the look precisely: "editorial fashion photography, shot on medium format, muted earth tones, shallow depth of field, grain." Reve is 34 credits an image, Seedream 5 Lite 6, Nano Banana 2 11 at 1K. Run the same description on two models and keep the one closer to what you had in mind; for a specific visual tradition (ink wash, brutalist graphic design, constructivist poster), name it and check the details against real examples.
Restyle your own image: Flux Kontext. Upload the photo and describe the treatment: "turn this into a two-color risograph print with visible grain, keep the composition." Kontext edits the existing image instead of generating from scratch, so composition is meant to carry over; check faces, text and product details. It takes one image, so the style goes in as words. FLUX.2 [pro] edit is about 7 credits for a 1-megapixel input; Kontext Max is 13.
Content image plus style image: Nano Banana or Reve Remix. These take several images at once (Nano Banana up to 10, Reve Remix up to 6), so you can attach your photo and a style reference and say in the prompt which is which. That is the closest thing on 8frame to the classical two-image setup.
Video: ID-V2V Restyle. For a clip, restyle one frame (for example with Flux Kontext), then feed that frame and the source clip to ID-V2V Restyle, which is built to carry the look through the footage while keeping the people recognizable; check faces in the result. It costs 27 credits per second of source footage, up to 15 seconds (135 for 5 seconds).
FAQ
Can I give 8frame a style image and a content image? Yes, on models that take several images, such as Nano Banana (up to 10) or Reve Remix (up to 6). Flux Kontext takes one image, so describe the style in words there.
Can I restyle a video? Yes, with ID-V2V Restyle: an edited first frame plus the source clip, 27 credits per second of source, up to 15 seconds.
Related concepts
- Nano Banana vs Seedream vs Flux: Which Model Wins? compares the image models available on 8frame.
- Flux Kontext Prompts for Editing Existing Images covers prompting patterns for applying style changes to photos you already have.
Sources
- Gatys, Ecker and Bethge, "A Neural Algorithm of Artistic Style" (submitted 2015-08-26): https://arxiv.org/abs/1508.06576, checked 2026-10-01
- Image inputs, reference limits and prices for Reve, Seedream, Nano Banana and Flux Kontext; ID-V2V Restyle price and limit (calculateIdV2vCost): 8frame tool registry, read 2026-10-01
Want to run style transfer on your own images? Open the 8frame canvas and start with Flux Kontext or Nano Banana.
Try it without signing up: the 8frame canvas opens on a real board. The canvas is free and unlimited; generation is paid from $19/month.