roop VS qdrant

Even when faces (asian skin color) in both source and targe images are saturated and colorful, roop generate quite pale faces. I just ran it with the suggested params, and wonder if I miss anything?

I tend to somewhat agree. This [0] repository was posted here last week and really made me think about much of what you are discussing here.

[0] https://github.com/s0md3v/roop

I don't quite understand this post:"https://github.com/s0md3v/roop/issues/122"

You have a separate program for this https://github.com/s0md3v/roop

no idea. I reinstalled it https://github.com/s0md3v/roop/wiki/1.-Installation and still no live button

Roop, respectively its uncensored fork.

9) in Windows Explorer, create folder, right click folder > Git Bash Here, type: git clone https://github.com/s0md3v/roop.git, enter

Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker.

I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours.

I'm currently looking to implement locally, using QDrant [1] for instance.

I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2].

[1]. https://qdrant.tech/

Also compare with qdrant's Rust implementation; they tout their performance. https://github.com/qdrant/qdrant/tree/master/lib/segment/src...

There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb

For more information, see our release notes. Qdrant is an open source project. We welcome your contributions; raise issues, or contribute via pull requests!

Initialize the Qdrant Client with in-memory storage. The collection name will be “imagebind_data” and we will be using cosine distance.

Qdrant is an open-source vector search engine optimized for performance and flexibility. It supports both exact and approximate nearest neighbor search, providing a balance between accuracy and speed for various AI and ML applications.