deep-daze VS feed_forward_vqgan_clip

it's not nessecary to specify the model with the tool he is using also known as deep daze

If you want do the same thing for free yourself: https://github.com/lucidrains/deep-daze

(Added Mar. 15, 2021) deep-daze Fourier Feature Map - Colaboratory by afiaka87. Uses SIREN to generate images. Reference. Reddit post.

(Added Feb. 5, 2021) Deep Daze - Colaboratory by lucidrains. Uses SIREN to generate images. The GitHub repo has a local machine version. GitHub. Notebook copy by levindabhi.

Thanks! I'll be posting more over the week. I run these on Python. Here's the link for the program https://github.com/lucidrains/deep-daze

In case you have some damn good gamer-level GPU, you can try this FOSS alternative locally instead: https://github.com/lucidrains/deep-daze

I'm not sure exactly what they used, but deep-daze can be used to generate similar things that turn out quite cool

WOMBO I suspect uses the feed forward inferential approach to VQGAN + CLIP (instead of finetuning, predict the final z latent vector for a given text input) which is why their outputs are less sophisticated: as a result there are many deployment optimizations you can do to speed that up, which may be complicated.

The system used to produce these images is unlike most other VQGAN+CLIP systems because it uses a neural network trained by the developer(s) instead of an iterative process. This system is known to have a "formula" for image layout.

Hello, some news. For those who are interested, I released new models (release 0.2) that you could try and you might find them better (depending on the prompt) than the current one(s), also the problem that was mentioned by /u/Wiskkey is less visible (object parts appearing systematically on top-left), but still not 100% solved, there is still a common global structure that can be identified, but it's more centered on the image. The Colab notebook was updated to use the new models.