Consistency Models are faster but, based on complaints on Reddit and my own experience, reduce quality and diversity. People who need fast image generation seem to have moved on to “Turbo” models, based on Adversarial Diffusion Distillation. How does coalescer models compare to that?
That approach just adds a loss from a GAN-type discriminator to the distillation loss when training the distilled sampler. Coalescer models should be able to do generation in at least as few steps as consistency models, so, if you want, you could do the same thing with coalescer models: initial training as normal, then more training that adds in a loss from a discriminator.
Consistency Models are faster but, based on complaints on Reddit and my own experience, reduce quality and diversity. People who need fast image generation seem to have moved on to “Turbo” models, based on Adversarial Diffusion Distillation. How does coalescer models compare to that?
That approach just adds a loss from a GAN-type discriminator to the distillation loss when training the distilled sampler. Coalescer models should be able to do generation in at least as few steps as consistency models, so, if you want, you could do the same thing with coalescer models: initial training as normal, then more training that adds in a loss from a discriminator.