custom-diffusion VS aws-node-termination-handler

I think https://github.com/adobe-research/custom-diffusion

I attempted Custom Diffusion which, too, did not yield anywhere near as photorealistic face outputs as Dreambooth.

Curious if anyone has better experiences? I've also been trying Adobe's Custom Diffusion code, which probably works the best out of Textual Inversion and Dreambooth, but the code they provide is super buggy and very hard to use with Automatic1111.

Well, actually it is https://github.com/adobe-research/custom-diffusion. It's Adobe, so this is probably only once in the lifetime :P

A Few Comments - The "caveat" I mentioned above is that there are a few apps that are running Stable Diffusion locally on the user's device. Apple recently released a tool to convert Stable Diffusion models to CoreML. CoreML is their proprietary format for machine learning models, and runs insanely well on Apple devices with a Neural Engine (like newer Macs, iPhones, and iPads). However, this technology is in its infancy, almost certainly isn't capable of "training" a Stable Diffusion model, and isn't anywhere near as fast as running Dreambooth or Stable Diffusion on powerful servers. In the long-run, it might be possible to do all of this processing on a user's device, but it's likely that we're a long ways away from that. - Dreambooth itself isn't that hard to run and play around with yourself, nor is it that hard to integrate into an automated server pipeline, though what it does under the hood is pretty amazing. Dreambooth isn't the only way to "train" a Stable Diffusion model with custom photos, and other people/companies (like Adobe), have found other ways to create amazing AI-generated images with user-provided photos (see their Custom Diffusion GitHub). - Given how long Lensa has been around, and that they're pretty decently funded (they raised $6m in 2019, I believe), it's very likely they they've developed their own in-house way of training Stable Diffusion models, just like the Adobe reference above. But if any of us were to build an app that works like Lensa, a starting point would probably be to use Dreambooth since it's well-built out and easy to integrate, and get similar results. - A very popular way to run Dreambooth is to use a Google Colab notebook, like this one from TheLastBen's GitHub. Since the vast majority of us don't have super powerful GPU cards in our computer, and are just playing around with Dreambooth/Stable Diffusion, the Google Colab notebook lets you go step-by-step to "setup" an environment for doing Dreambooth, but runs it on Google's super-powerful servers. The cool thing about Google Colab, besides not having to have a super-powerful computer yourself to still do Dreambooth with great performance, is that you can look through the code of how the Google Colab notebook works, and that could be a foundation for an engineer to learn how to implement Dreambooth in your own scripts that run on the "Backend Server" for your app.

If it is Stable Diffusion-based, I'd guess a few things. Given the small size that would be needed to handle this for every possible user, I wonder if it's using something that isn't Dreambooth based, like Adobe's [Custom Diffusion(https://github.com/adobe-research/custom-diffusion), or some in-house variant of that, that's able to basically generate a small file that could be processed for each user.

Adobe Research also has "Custom Diffusion" out: https://github.com/adobe-research/custom-diffusion. It's got a similar goal of ~megabytes sized outputs. Warning that it's got a proprietary license.

Disaster recovery is a critical component of any IT infrastructure. It ensures that your applications and data are protected in the event of an unexpected outage or disaster. In this blog post, we will explore different disaster recovery strategies for Amazon Elastic Compute Cloud (EC2) deployments.

When you are using compute you have a lot of options. One of these options is Amazon EC2. In a world where more and more workloads become serverless. You might still have this use-case that is better off on EC2. But, how do you combine EC2 with compliance and security? In this blog post we will explore how we can build a compliant and secure EC2 stack.

In this article, a WEB application using the latest version of Angular in a built Docker image will be hosted on Amazon EC2 (Elastic Compute Cloud) and deployed by Amazon ECS (Elastic Container Service) using an Amazon ECR (Elastic Container Registry) containers repository.

The single most important development in hosting since the invention of EC2 is defined by its own 3-letter acronym: k8s. Kubernetes has won the “container orchestrator” space, becoming the default way that teams across industries are managing their compute nodes and scheduling their workloads, from data pipelines to web services.

EC2

Then in 2020, I started working with AWS. My first two years with AWS were mostly interacting with the Node.js apps I've deployed in EC2 and reviewing logs since we had a DevOps engineer who managed the cloud infrastructure.

If you want to learn more about EC2 and get detailed information, I suggest you start your journey by visiting https://aws.amazon.com/ec2/. This is the best place to begin learning about EC2.

AWS - Cloud Computing AWS - EC2 Wikipedia - Cloud Computing Guru99 - Cloud Computing Cloudflare - Cloud Computing Cloudzero - Statistics Zippia - Statistics

Say that we have an application running behind a public-facing Application Load Balancer (ALB). The load balancer's target can be any supported target, including ECS containers, EC2 instances or even Lambda functions. Because the application is only available to authenticated users, we want to find a solution to identify them.

Although I'll use Lambda functions in the examples, we can transfer the concepts to other compute resources, like EC2 instances, and ECS or EKS containers.