glid-3-xl VS dalle-playground

If you’re anything like us, though, you’ll feel compelled to poke around the code and run the model yourself. We’ll do that in this article using Meadowrun, an open-source library that makes it easy to run Python code in the cloud. For ML models in particular, we just added a feature for requesting GPU machines in a recent release. We’ll also feed the images generated by DALL·E Mini into additional image processing models (GLID-3-xl and SwinIR) to improve the quality of our generated images. Along the way we’ll deal with the speedbumps that come up when running open-source ML models on EC2.

Personally, I've found GLID3 and GLID3-XL to be nice and straightforward. Worked right out of the box.

GLID-3-XL?

I've been using the Glid-3 XL model. Here are some examples of what I've been able to do with it:

This was made with Glid-3 XL

The above notebooks use GitHub repo GLID-3-XL from Jack000. Regarding CLIP guidance, Jack000 states, "better adherence to prompt, much slower" (compared to classifier-free guidance).

Next, we want the code in the https://github.com/hrichardlee/dalle-playground repo, and we want to construct a pip environment from the backend/requirements.txt file in that repo. We were almost able to use the saharmor/dalle-playground repo as-is, but we had to make one change to add the jax[cuda] package to the requirements.txt file. In case you haven’t seen jax before, jax is a machine-learning library from Google, roughly equivalent to Tensorflow or PyTorch. It combines Autograd for automatic differentiation and XLA (accelerated linear algebra) for JIT-compiling numpy-like code for Google’s TPUs or Nvidia’s CUDA API for GPUs. The CUDA support requires explicitly selecting the [cuda] option when we install the package.