Text-to-Image

Text2Cinemagraph can create cinemagraphs from text descriptions, animating elements like flowing rivers and drifting clouds. It combines artistic images with realistic ones to accurately show motion, outperforming other methods in generating cinemagraphs for natural and artistic scenes.

DiffSketcher is a tool that can turn words into vectorized free-hand sketches. The method also supports the ability to define the level of abstraction, allowing for more abstract or concrete generations.

Cocktail is a pipeline for guiding image generating. Compared to ControlNet, it only requires one generalized model for multiple modalities like Edge, Pose and Mask guidance.

There is a new text-to-image player called RAPHAEL in town. The model aims to generate highly artistic images, which accurately portray the text prompts, encompassing multiple nouns, adjectives, and verbs. This is all great, but only if someone actually releases the model for open-source consumption as the community is craving a model that can achieve Midjourney quality.

FastComposer can generate personalized images of multiple unseen individuals in various styles and actions without fine-tuning. It is 300x-2500x faster than traditional methods and requires no extra storage for new subjects, using subject embeddings and localized attention to keep identities clear.

What if you could generate images from an untrained concept by providing a few images and without having to fine-tune a model first? InstantBooth from Adobe might be the answer. The novel approach is built upon pre-trained text-to-image models that enables instant text-guided image personalization without finetuning. Compared to methods like DreamBooth and Textual-Inversion, InstantBooth model can generate competitive results on unseen concepts concerning language-image alignment, image fidelity, and identity preservation while being 100 times faster. Wen open-source?

Expressive Text-to-Image Generation with Rich Text can create detailed images from text by using rich text formatting like font style, size, and color. This method allows for better control over styles and colors, making it easier to generate complex scenes compared to regular text.

Inst-Inpaint can remove objects from images using natural language instructions, which saves time by not needing binary masks. It uses a new dataset called GQA-Inpaint, improving the quality and accuracy of image inpainting significantly.

eDiff-I can generate high-resolution images from text prompts using different diffusion models for each stage. It also allows users to control image creation by selecting and moving words on a canvas.

Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models can quickly personalize text-to-image models using just one image and only 5 training steps. This method reduces training time from minutes to seconds while maintaining quality through regularized weight-offsets.

Reduce, Reuse, Recycle can enable compositional generation using energy-based diffusion models and MCMC samplers. It improves tasks like classifier-guided ImageNet modeling and text-to-image generation by introducing new samplers that enhance performance.

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ControlNet can add control to text-to-image diffusion models. It lets users manipulate image generation using methods like edge detection and depth maps, while working well with both small and large datasets.

StyleGAN-T can generate high-quality images at 512x512 resolution in just 2 seconds using a single NVIDIA A100 GPU. It solves problems in text-to-image synthesis, like stable training on diverse datasets and strong text alignment.

VectorFusion can generate SVG-exportable vector graphics from text prompts. It uses a text-conditioned diffusion model to create high-quality outputs in various styles, like pixel art and sketches, without needing large datasets of captioned SVGs.