Image Style Transfer

MambaPainter can turn images into an oil painting style by predicting over 100 brush strokes in one step.

CSGO can perform image-driven style transfer and text-driven stylized synthesis. It uses a large dataset with 210k image triplets to improve style control in image generation.

IPAdapter-Instruct can efficiently combine natural-image conditioning with “Instruct” prompts! It enables users to switch between various interpretations of the same image, such as style transfer and object extraction.

Artist stylizes images based on text prompts, preserving the original content while producing high aesthetic quality results. No finetuning, no ControlNets, it just works with your pretrained StableDiffusion model.

StyleShot can mimic and style transfer various styles from an image, such as 3D, flat, abstract or even fine-grained styles, without tuning.

Pair Customization can customize text-to-image models by learning style differences from a single image pair. It separates style and content into different weight spaces, allowing for effective style application without overfitting to specific images.

StyleBooth is a unified style editing method supporting text-based, exemplar-based and compositional style editing. So basically, you can take an image and change its style by either giving it a text prompt or an example image.

DEADiff can synthesize images that combine the style of a reference image with text prompts. It uses a Q-Former mechanism to separate style and meaning.

Visual Style Prompting can generate images with a specific style from a reference image. Compared to other methods like IP-Adapter and LoRAs, Visual Style Prompting is better at retainining the style of the referenced image while avoiding style leakage from text prompts.

InstantID is a ID embedding-based method that can be used to personalize images in various styles using just a single facial image, while ensuring high fidelity.