custom-diffusion VS aws-lambda-java-libs

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.

In today's world of cloud computing, AWS Lambda is a serverless, event-driven compute service that lets you run code for virtually any type of application or backend service without provisioning or managing servers. You can trigger Lambda from over 200 AWS services and software as a service (SaaS) applications, and only pay for what you use.

The first reason is that serverless architectures are inherently scalable and elastic. They automatically scale up or down based on the incoming workload without requiring manual intervention through serverless compute services like AWS Lambda, Azure Functions, or Google Cloud Functions.

On this day, we both first learned about Lambda. This was the world's first public Functions-as-a-Service platform, better known as FaaS. They told us that this was the next evolution in Cloud Computing. With Lambda, you could now host snippets of code on AWS. There were no more idle workers, and you could auto-scale with minimal additional configuration required. Also, these snippets were event-driven by nature. This was a fully serverless platform.

AWS Lambda simplifies composable applications by offering serverless execution, seamless integration with AWS services, automatic scaling, and cost efficiency without the need to manage servers.

Deploying Dart functions to AWS Lambda enables you to utilize them not only within AWS Lambda but also integrate them with services like Amazon API Gateway, allowing you to leverage them in Flutter applications as well. This unified codebase in Dart offers great convenience.

Event Producers: Generate streams of events, which can be implemented using straightforward microservices with AWS Lambda (for serverless computing), Amazon DynamoDB Streams (to captures changes to DynamoDB tables in real-time), Amazon S3 Event Notifications (Notify when certain events occur in S3 buckets) or AWS Fargate (a serverless compute engine for containers).

Amazon Web Services (AWS) Lambda is a serverless function-as-a-service (FaaS) platform that lets you deploy, run, and scale code in the cloud as self-contained functions without having to manually configure any infrastructure. Lambda runs your functions on demand in response to specific events, such as an HTTP request from the internet or activity in another AWS service.

FaaS is specifically focused on building and running applications as a set of independent functions or microservices. Major cloud providers like AWS (Lambda), Microsoft Azure (Functions), and Google Cloud (Cloud Functions) offer FaaS platforms that allow developers to write and deploy individual functions without managing the underlying infrastructure.

So AWS Lambda is basically a serverless computing service that is offered by AWS. It enables developers to run the code in response to various events. It protects the developers from the pain of managing the servers. Using a serverless execution model helps the developers to handle provision, manage and scale the servers automatically. Through this approach the developers can fully focus on writing the code instead of dealing with other aspects.

The first product that popularized the term “serverless” was AWS Lambda, which is both the prototypical and archetypical function as a service provider. It also has a great name, which pings back to its envisioned place in the cloud of the future. In computer programming, a lambda, often referred to as a lambda function or lambda expression, is a concise way to represent an anonymous function, which is a function without a name. The concept originates from lambda calculus in mathematical logic and has been adopted by many programming languages, each with its own syntax and characteristics.