AI image generation

MouSi is a multi-modal visual language model designed to address current challenges faced by large-scale visual language models (VLMs). It uses integrated expert technology to collaborate the capabilities of individual visual encoders, including image-text matching, OCR, image segmentation, etc. This model introduces a fusion network to uniformly process outputs from different vision experts and bridge the gap between image encoders and pre-trained LLMs. In addition, MouSi also explored different position encoding schemes to effectively solve the problems of position encoding waste and length limitation. Experimental results show that VLMs with multiple experts exhibit superior performance than isolated visual encoders, and obtain significant performance improvements as more experts are integrated.

Zhipu AI released GLM-4 and CogView3 at the first Technology Open Day. The overall performance of GLM-4 has been improved by nearly 60%, supporting longer context, stronger multi-modal support and faster reasoning. CogView3 approaches the multi-modal generation capabilities of DALL·E 3. The product is positioned as the next generation of base model and image generation AI.

Diffusion Bee is the easiest way to run stable Diffusion models natively on Intel/M1 Macs, offering a one-click installer with no dependencies or technical knowledge required. Diffusion Bee runs locally on your computer and does not send any data to the cloud (unless you choose to upload images). Main functions: - Image conversion - Image repair - Image generation history - Image zoom - Multiple image sizes - Optimized for M1/M2 chips - Supports negative prompts and advanced prompt options - Control network Diffusion Bee is a GUI wrapper based on Stable Diffusion, so all Stable Diffusion terms apply to the output results. For more information, please visit the documentation. System requirements: - Macs with Intel or M1/M2 chips - For Intel chips: MacOS 12.3.1 or later - For M1/M2 chips: MacOS 11.0.0 or higher License: Stable Diffusion is released under the CreativeML OpenRAIL M license.

FABRIC is a tool for personalizing diffusion models through iterative feedback. It provides an easy way to improve model performance based on user feedback. Users can interact with the model in an iterative manner and adjust the model's predictions through feedback. FABRIC also provides rich functionality, including model training, parameter tuning, and performance evaluation. Its pricing is based on user usage and can meet the needs of different users.

The pseudo-flexible base model (ptx0/pseudo-flex-base) is a text-to-image generation model based on Diffusion technology. It provides flexible image generation capabilities by converting text descriptions into realistic images. This model can generate images consistent with text descriptions based on given text prompts, with a high degree of flexibility and generation effects. The model also has stable performance and a reliable training basis, and can be widely used in image generation tasks in the field of artificial intelligence.