Text-to-Image

MV-Adapter can generate images from multiple views while keeping them consistent across views. It enhances text-to-image models like Stable Diffusion XL, supporting both text and image inputs, and achieves high-resolution outputs at 768x768.

Anagram-MTL can generate visual anagrams that change appearance with transformations like flipping or rotating.

Negative Token Merging can improve image diversity by pushing apart similar features during the reverse diffusion process. It reduces visual similarity with copyrighted content by 34.57% and works well with Stable Diffusion as well as Flux.

FlowEdit can edit images using only text prompts with Flux and Stable Diffusion 3.

MegaFusion can extend existing diffusion models for high-resolution image generation. It achieves images up to 2048x2048 with only 40% of the original computational cost by enhancing denoising processes across different resolutions.

Omegance can control detail levels in diffusion-based synthesis using a single parameter, ω. It allows for precise granularity control in generated outputs and enables specific adjustments through spatial masks and denoising schedules.

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.

From Text to Pose to Image can generate high-quality images from text prompts by first creating poses and then using them to guide image generation. This method improves control over human poses and enhances image fidelity in diffusion models.

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.