4 ms·
Not the OP, but some tips: - 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 t
by indiv0 6y ago
Not the OP, but some tips:
- 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)
- indigodaddy 6y agoVery helpful feedback, thanks very much!
- Hard_Space 6y ago> - 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” However, it can get taken very literally, in that you might get a picture that features a frame around the painting.
- indiv0 6y agoIndeed! 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.
- Hard_Space 6y agoTrue. For the article, I experimented with a number of 'in the style of' prompts. Where there's a distinctive visual iconography with strong key features, BigSleep [1]does an amazing job of abstracting and reproducing that style. Besides artists, it also does very well with iconic movies like Blade Runner and The Matrix. [1]The Deep-Daze author's most popular T2I mashup: https://github.com/lucidrains/big-sleep https://github.com/lucidrains/big-sleep https://colab.research.google.com/drive/1NCceX2mbiKOSlAd_o7IU7nA9UskKN5WR?usp=sharing#scrollTo=WztSrRF23Rqg https://colab.research.google.com/drive/1NCceX2mbiKOSlAd_o7I...