Text-to-Image

Regional-Prompting-FLUX adds regional prompting capabilities to diffusion transformers like FLUX. It effectively manages complex prompts and works well with tools like LoRA and ControlNet.

FreCaS can generate high-resolution images quickly using a method that breaks the process into stages with increasing detail. It is about 2.86× to 6.07× faster than other tools for creating 2048×2048 images and improves image quality significantly.

HART is an autoregressive transformer model that can generate high-quality 1024x1024 images from text 3x times faster than SD3-Medium.

RFNet is a training-free approach that bring better prompt understanding to image generation. Adding support for prompt reasoning, conceptual and metaphorical thinking, imaginative scenarios and more.

OmniBooth can generate images with precise control over their layout and style. It allows users to customize images using masks and text or image guidance, making the process flexible and personal.

Love this one! SVGCustomization is a novel pipeline that is able to edit existing vector images with text prompts while preserving the properties and layer information vector images are made of.

One-DM can generate handwritten text from a single reference sample, mimicking the style of the input. It captures unique writing patterns and works well across multiple languages.

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.

Iterative Object Count Optimization can improve object counting accuracy in text-to-image diffusion models.

[Matryoshka Diffusion Models] can generate high-quality images and videos using a NestedUNet architecture that denoises inputs at different resolutions. This method allows for strong performance at resolutions up to 1024x1024 pixels and supports effective training without needing specific examples.

Lumina-mGPT can create photorealistic images from text and handle different visual and language tasks! It uses a special transformer model, making it possible to control image generation, do segmentation, estimate depth, and answer visual questions in multiple steps.

VAR-CLIP creates detailed fantasy images that match text descriptions closely by combining Visual Auto-Regressive techniques with CLIP! It uses text embeddings to guide image creation, ensuring strong results by training on a large image-text dataset.

Magic Clothing can generate customized characters wearing specific garments from diverse text prompts while preserving the details of the target garments and maintain faithfulness to the text prompts.

MasterWeaver can generate photo-realistic images from a single reference image while keeping the person’s identity and allowing for easy edits. It uses an encoder to capture identity features and a unique editing direction loss to improve text control, enabling changes to clothing, accessories, and facial features.

ColorPeel can generate objects in images with specific colors and shapes.

AnyControl is a new text-to-image guidance method that can generate images from diverse control signals, such as color, shape, texture, and layout.

iCD can be used for zero-shot text-guided image editing with diffusion models. The method is able to encode real images into their latent space in only 3-4 inference steps and can then be used to edit the image with a text prompt.

Make It Count can generate images with the exact number of objects specified in the prompt while keeping a natural layout. It uses the diffusion model to accurately count and separate objects during the image creation process.

Similar to ConsistentID, PuLID is a tuning-free ID customization method for text-to-image generation. This one can also be used to edit images generated by diffusion models by adding or changing the text prompt.

CustomDiffusion360 brings camera viewpoint control to text-to-image models. Only caveat: it requires a 360 degree multi-view dataset of around 50 images per object to work.