# Generation Model

LongRAG is a robust dual-perspective, retrieval-augmented generation system paradigm based on large language models (LLM), designed to enhance the understanding and retrieval capabilities of complex long-text knowledge. This model is particularly suited for Long-Context Question Answering (LCQA), as it effectively handles global information and factual details. Background information indicates that LongRAG improves performance on long-text question-answering tasks by integrating retrieval and generation techniques, especially in scenarios requiring multi-hop reasoning. The model is open-source and freely available, primarily targeting researchers and developers.

FluxMusic is a text-to-music generation model implemented in PyTorch that explores a straightforward method for generating music from text prompts using a diffusion-based flow transformer. This model can create music segments based on textual cues, showcasing both innovation and technical complexity. It represents cutting-edge technology in the field of music generation, offering new possibilities for musical creativity.

ViPer is a personalized generation model that creates images based on the user's visual preferences. Utilizing stable diffusion XL technology, it achieves personalization while maintaining image quality. The primary advantage of ViPer is its ability to provide tailored image generation services, fulfilling users' unique customization needs.

Cos Stable Diffusion XL 1.0 Base adjusted to use cosine continuous EDM VPred scheduling. Its most important feature is the generation of images across the full color range, from pure black to pure white, with more subtle improvements in the rate of change at each step of the image. Edit Stable Diffusion XL 1.0 Base adjusted to use cosine continuous EDM VPred scheduling and upgraded for image editing. This model takes the source image and prompt as input, interpreting the prompt as instructions for how to change the image. Pricing: Free to use. Positioning: Used in creative processes such as art generation and design, applied in educational or creative tools, research generation models, deployment of models with the potential to generate harmful content, exploring the limitations and biases of understanding generation models.

Ideogram 1.0 is a text-to-image generation model capable of creating high-fidelity images based on textual descriptions. It boasts advanced text rendering capabilities, unparalleled photo realism, and tip accuracy, along with a new feature called 'Magic Prompt' to help users craft detailed descriptions for beautiful creative images. Ideogram 1.0 is now open to all ideogram.ai users! Sign up for free on ideogram.ai to join our global creator community, meet new friends, share your images and prompts, and draw inspiration from fellow creativity. In addition to our daily free generation quota, we offer a paid subscription plan with features such as priority generation, private generation, image upload, and access to the Ideogram editor.

SCEdit is an efficient generation model fine-tuning framework proposed by Alibaba. It enhances the fine-tuning capability for downstream text-to-image generation tasks and enables fast adaptation to specific generation scenarios. Compared to LoRA, it can save 30%-50% of training memory costs. Moreover, it can be directly extended to controllable image generation tasks, requiring only 7.9% of the parameter amount of ControlNet conditional generation and saving 30% of memory usage. It supports various conditional generation tasks, including edge maps, depth maps, segmentation maps, poses, color maps, and image completion.

HAAR is a text-input based generation model capable of generating realistic 3D hairstyles. It takes text prompts as input and generates 3D hairstyle assets ready for various computer graphics animation applications. Unlike current AI-based generation models, HAAR utilizes 3D hair strands as the foundation representation. It automatically annotates the generated synthetic hairstyle model using a 2D visual question answering system. We propose a text-guided generation method that uses a conditional diffusion model to generate guided hair strands in the latent hairstyle UV space and reconstructs a dense hairstyle containing hundreds of thousands of hair strands using a latent upsampling process given a textual description. The generated hairstyles can be rendered using existing computer graphics techniques.

StemGen is an end-to-end music generation model trained to listen to musical contexts and generate appropriate responses. It is built upon a non-autoregressive language modeling architecture, similar to SoundStorm and VampNet. More details can be found in the paper. This page showcases various example outputs of this architecture model.

Stability AI Generation Models is an open-source generation model library providing functions for training, inference, and application of various generation models. The library supports training diverse generation models, including PyTorch Lightning-based training, offering rich configuration options and a modular design. Users can leverage this library to train generation models and perform inference and application using the provided models. The library also includes example training configurations and data processing functionalities, facilitating quick start and customization for users.