When they released the first Dall-E, didn’t OpenAI mention that prompts which repeated the same description several times with slight re-phrasing produced improved results?
I wonder how a prompt like:
“A post-singularity tribesman with a pet steampunk panther robot. Illustration by James Gurney.”
-would compare with something like:
“A post-singularity tribesman with a pet steampunk panther robot. Illustration by James Gurney. A painting of an ornate robotic feline made of brass and a man wearing futuristic tribal clothing. A steampunk scene by James Gurney featuring a robot shaped like a panther and a high-tech shaman.”
“A post-singularity tribesman with a pet steampunk panther robot. Illustration by James Gurney.”
Vs “A post-singularity tribesman with a pet steampunk panther robot. Illustration by James Gurney. A painting of an ornate robotic feline made of brass and a man wearing futuristic tribal clothing. A steampunk scene by James Gurney featuring a robot shaped like a panther and a high-tech shaman.” Huh! Yeah, the second one definitely does seem to incorporate more detail.
I’m not sure how much the repetitions helped much with accuracy for this prompt- it’s still sort of randomizing traits between the two subjects. Though with a prompt this complex, the token limit may be an issue- it might be interesting to test at some point whether very simple prompts get more accurate with repetitions.
That said, the second set are pretty awesome- asking for a scene may have helped encourage some more interesting compositions. One benefit of repetition may just be that you’re more likely to include phrases that more accurately describe what you’re looking for.
Good point. I’ve also noticed good results for adding multiple details by mentioning each individually.
E.g. instead of “tribesman with you blue robe, holding a club, looking angry, with a pet robot tiger” try “A tribesman with a pet tiger. The tribesman wears a blue robe. The tribesman is angry. The tribesman is holding a club. The tiger is a cyberpunk robot robot.”
When they released the first Dall-E, didn’t OpenAI mention that prompts which repeated the same description several times with slight re-phrasing produced improved results?
I wonder how a prompt like:
“A post-singularity tribesman with a pet steampunk panther robot. Illustration by James Gurney.”
-would compare with something like:
“A post-singularity tribesman with a pet steampunk panther robot. Illustration by James Gurney. A painting of an ornate robotic feline made of brass and a man wearing futuristic tribal clothing. A steampunk scene by James Gurney featuring a robot shaped like a panther and a high-tech shaman.”
“A post-singularity tribesman with a pet steampunk panther robot. Illustration by James Gurney.”
Vs “A post-singularity tribesman with a pet steampunk panther robot. Illustration by James Gurney. A painting of an ornate robotic feline made of brass and a man wearing futuristic tribal clothing. A steampunk scene by James Gurney featuring a robot shaped like a panther and a high-tech shaman.” Huh! Yeah, the second one definitely does seem to incorporate more detail.
Thanks!
I’m not sure how much the repetitions helped much with accuracy for this prompt- it’s still sort of randomizing traits between the two subjects. Though with a prompt this complex, the token limit may be an issue- it might be interesting to test at some point whether very simple prompts get more accurate with repetitions.
That said, the second set are pretty awesome- asking for a scene may have helped encourage some more interesting compositions. One benefit of repetition may just be that you’re more likely to include phrases that more accurately describe what you’re looking for.
Good point. I’ve also noticed good results for adding multiple details by mentioning each individually. E.g. instead of “tribesman with you blue robe, holding a club, looking angry, with a pet robot tiger” try “A tribesman with a pet tiger. The tribesman wears a blue robe. The tribesman is angry. The tribesman is holding a club. The tiger is a cyberpunk robot robot.”