How different models work | AI Art | Know Your Meme

Dreambooth Fine-tunes the diffusion model itself until it understands the new concept text embedding "A text embedding photo of 0.43 0.43 0.43 | 0.43 0.23 0.23 0.23 0.23 0.48 0.4૪ 0.48 0.48 0.01 0.01 0.01 0.01 0.05 0.05 0,05 0.05 0.78 0.78 0.78 0.78 "A SKS" apply noise n photo 10.43 0.43 0.43 0.43 0.23 0.23 0.23 0.23 0.4s 0.48 0.48 0.48 0.01 0,05 0.01 0.01 0.01 0.05 0.05 0.05 0.78 0.78 0,78 0.78 of SKS" LORA Adds a tiny number of weights to the diffusion model and trains therse until the modified model understands the concept Diffusion Model apply noise n training sample apply noise * (n-1) Additional Weights + probably the most effective - storage inefficient (whole new model to deal with) Diffusion Model training sample Gradient Update apply noise + (n-1) compare loss + quick to train Gradient Update compare loss Textual Inversion Creates a special word embedding which captures the new concept text embedding "A text embedding photo 'А of 0.43 0.43 0.43 0.431 0.23 0.23 0.23 0.231 0.48 0.48 0.48 0.48! 0.01 0.01 0.01 1 0.01 0.05 0.05 0.05 10.05₁ 0.78 0.78 0.78 10.781 I SKS" photo apply noise n 1 1 of Hypernetworks Use a secondary network to predict new weights for the original network. The new weights are swapped in at inference SKS" 0.43 0.43 0.43 0.43 0.23 0.23 0.23 0,23 0.48 0.48 0.48 0.48 0.01 0.01 0.01 0.01 0.05 0.05 0.05 0.05 0.78 0.78 0.78 0.78 Diffusion Model apply noise n training sample apply noise * Hypernetwork i Diffusion Model training sample Gradient Update (n-1) apply noise + output is a (n-1) compare tiny embedding Gradient Update compare loss loss

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comparison dreambooth textual inversion model training stable diffusion

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Reid Southen @Rahll Apparently using the term "AI" in marketing makes people LESS likely to make a purchase. Just like crypto and NFTs before it, Al is now a dirty word that the average consumer wants to steer clear of. Congrats big tech, you did it again. news.wsu.edu/press-release/... Traduire le post Using the term 'artificial intelligence' in product descriptions reduces purchase intentions By Eric Hollenbeck, Carson College of Business fin

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