dalle-playground VS min-dalle

Built on dalle-playground because it is simple and I like it.

[1] https://github.com/saharmor/dalle-playground

If you decide to abandon the idea of running locally and want to run in the cloud instead, https://github.com/saharmor/dalle-playground has a Google Colab notebook that's relatively easy to run (although Google Colab's free tier is relatively limited).

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.

Is their any way to run DALLE-2 inside of a docker container similarly to this DALLE-PLAYGROUND repo on github?

I know there are open source projects like dalle-playground. Would it literally be enough to host this app on an EC2 instance with the mini model?

min(DALL·E).

Additionally, it's also open-sourced on GitHub and can be self-hosted, with easy instructions to do so: https://github.com/kuprel/min-dalle

For CPU, even highly-optimized models like mindalle are prohibitively slow.

Maybe you would be interested in this GitHub repo.

These were generated with DALL-E Mega via min-dalle, which is a more advanced version of DALL-E Mini with better visual fidelity (less blurry) but otherwise similar results.

GitHub repo (contains links to Colab notebook and web app at site Replicate[dot]com). The times mentioned in the title don't included setup time.