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I feel like the next killer feature of these models will be when they can retain contextual visuals between different scenes being generated As in, the same mo
by temp_account_32 4y ago
I feel like the next killer feature of these models will be when they can retain contextual visuals between different scenes being generated
As in, the same mouse in four different pictures in different situations, not four different mice styled in the same way over four pictures
- karencarits 4y agoTextual inversion for stable diffusion, which is related, was discussed a few days ago: https://news.ycombinator.com/item?id=32643564 https://news.ycombinator.com/item?id=32643564
- temp_account_32 4y agoThis is nice but still has some huge limitations, e.g. you need 3-5 images of something to train it. It becomes a chicken and egg problem if I start by generating something, since I cannot create 5 different images of it to train the textual inversion. It is also intensive and slow to train on each object. Also I think we're running into limitations of using a single prompt to describe things, as it's often hard to separate the 'what' is in the scene and the 'how' to draw the scene, with many prompts looking like a giant word salad trying to tap in the internal secret language of the model
- deleted 4y ago[deleted]
- dr_dshiv 4y agoYeah, this seems to be the key challenge right now. Some how an image and its elements might be used as context to condition the subsequent generations.
- dr_dshiv 4y agoOh, looks like Google solved this last week with dream booth: https://arxiv.org/pdf/2208.12242.pdf https://arxiv.org/pdf/2208.12242.pdf
- hirundo 4y agoThe same tool can also be used to evolve an image by generating variations and selecting a few to reproduce. You could breed and cross the results at will. You could use multiple preference models for the fitness test and evolve them afk. An "Evolve" button could become a standard feature on content editors across media.