ConvNeXt VS latent-diffusion

Excited to share a python package that I've released (working on it for a while now): git: https://github.com/sashank-tirumala/yaml_config_override pypi: https://pypi.org/project/yaml-config-override/ The idea is simple, often you need to write hundred lines of 'argparse' code for deep learning and machine learning projects (example). To avoid that we create config files (yaml) but then there are times when you just want to overwrite the config values with 'argparse'. This package automates that process. It automatically adds command line arguments from config file definitions and then also overwrites config file arguments with your command line arguments. So for example:

Found relevant code at https://github.com/facebookresearch/ConvNeXt + all code implementations here

Have you considered using monitor services such as Wandb? https://github.com/facebookresearch/ConvNeXt provides the code for distributed training, which you can refer to. It is a big tricky to make it work when using distributed training, but obviously it is better than regularly checking progress.

The ConvNeXt code is available on the project’s GitHub. The paper A ConvNet for the 2020s is on arXiv.

The ConvNeXt code is available on the project’s GitHub. The paper A ConvNet for the 2020s is on arXiv.

Code: https://github.com/facebookresearch/ConvNeXt

Stable Diffusion XL (SDXL) is the latest text-to-image generation model developed by Stability AI, based on the latent diffusion techniques. SDXL has the potential to create highly realistic images for media, entertainment, education, and industry domains, opening new ways in practical uses of AI imagery.

Here's a link to the early latent-diffusion git, that might be able to create a blank model (I haven't tested it): https://github.com/CompVis/latent-diffusion

Latent diffusion could work for you: https://github.com/CompVis/latent-diffusion (https://arxiv.org/abs/2112.10752)

There are a lot but the one implemented as LDSR in most stable guis is this one. https://github.com/CompVis/latent-diffusion

CompVis/latent-diffusion: High-Resolution Image Synthesis with Latent Diffusion Models (github.com)

The "retrieval based model" refers to https://github.com/CompVis/latent-diffusion#retrieval-augmen..., which uses ScaNN to train a knn embedding searcher.

Stability is basically https://github.com/CompVis/latent-diffusion + training data.

Found relevant code at https://github.com/CompVis/latent-diffusion + all code implementations here

DDIM and PLMS were the original samplers. They were part of Latent Diffusion's repository. They stand for the papers that introduced them, Denoising Diffusion Implicit Models and Pseudo Numerical Methods for Diffusion Models on Manifolds.

yes, https://github.com/CompVis/latent-diffusion