Batch insert and replace images in WordPress posts
WordPress autoblogging. How to add and replace images in posts in bulk with images generated by Midjourney, DALL-E and Stable Diffusion.
Learn about artificial intelligence, GPT usage, prompt engineering and other technology news and updates from Land of GPT. The site aggregates articles from official RSS feeds under their original authorship. Each article has a do-follow link to the original source.
WordPress autoblogging. How to add and replace images in posts in bulk with images generated by Midjourney, DALL-E and Stable Diffusion.
Novel method makes tools like Stable Diffusion and DALL-E-3 faster by simplifying the image-generating process to a single step while maintaining or enhancing image quality.
As computer vision researchers, we believe that every pixel can tell a story. However, there seems to be a writer’s block settling into the field when it comes to dealing…
Are you tired of feeling overwhelmed by life's challenges? Do you want to discover the secrets of living a resilient and unshakable life? Look no further than "Secrets of the…
In this post, multi-shot prompts are retrieved from an embedding containing successful Python code run on a similar data type (for example, high-resolution time series data from Internet of Things…
With the batch inference API, you can use Amazon Bedrock to run inference with foundation models in batches and get responses more efficiently. This post shows how to implement self-consistency…
NVIDIA NIM microservices now integrate with Amazon SageMaker, allowing you to deploy industry-leading large language models (LLMs) and optimize model performance and cost. You can deploy state-of-the-art LLMs in minutes…
FeatUp, developed by MIT CSAIL researchers, boosts the resolution of any deep network or visual foundation for computer vision systems.
Today, we are excited to announce the capability to fine-tune Code Llama models by Meta using Amazon SageMaker JumpStart. The Code Llama family of large language models (LLMs) is a…
Joining three teams backed by a total of $75 million, MIT researchers will tackle some of cancer’s toughest challenges.