Each of these links has about 9 images and the prompts that made them. Sometimes the image does not look like an animal right off the bat, then it seems like you asked for something the network had to say.
https://postimg.cc/vDvYdBhC https://postimg.cc/XpHnpzT8 https://postimg.cc/HVspWgPn
That Syd Mead style ... mwa
Were you able to complete the 1050 iterations, and how long did it take?
- the model usually locks in within 200-300 iterations, so if you don’t like the result by then, retry
- in fact, you can tell if the model is off to a good start within 25-50 iterations and I encourage you to cherry-pick runs early and often; don’t be afraid to restart
- time to render depends on which GPU you get from colab, but I usually run the renders for 10 minutes a pop. About 1-2 minutes if I run them on a 3090 locally
- the prompt plays a big role in the quality of the result; “A painting of a dog playing fetch” will usually turn out better than “dog playing fetch”
- lucidrains/bigsleep produces better results generally than lucidrains/deepdaze (this is my subjective preference)
- the colabs linked to from the big-sleep GitHub repo produce poorer results than running them as a python package locally (this one might honestly be placebo)
However, it can get taken very literally, in that you might get a picture that features a frame around the painting.
Another thing that comes to mind as a corollary is that the AI seems to like being constrained in its outputs. So adding something like “in the style of Monet” to the end of the prompt will return much more coherent results.
[1]The Deep-Daze author's most popular T2I mashup: https://github.com/lucidrains/big-sleep
https://colab.research.google.com/drive/1NCceX2mbiKOSlAd_o7I...