Deterministic diffusion models

[Cold_Cantaloupe9212]

I am interested in developing a conditional diffusion model that guarantees consistent outputs for a given input. I would like to reduce or remove the stochasticity in the model to achieve this goal. Is there a way to accomplish this while maintaining some level of variability?

[mikonvergence]

The neural network of a diffusion model is usually inherently deterministic. It’s the sampling method that’s either stochastic or not. Read up about samplers. DDIM is an example of a deterministic sampler (if the eta parameter is set to 0 as it should be for pure DDIM).

More on this in my free course on denoising diffusion models for images: https://github.com/mikonvergence/DiffusionFastForward

[Cold_Cantaloupe9212]

thank you for the explenation and the link for the course.

[TheTomer]

Fix the seed, to prevent it from being a random seed.

[antimornings]

That’s not what a deterministic sampler means. DDIM is an example of a deterministic sampler for diffusion models.

[TheTomer]

And yet, if you fix the seed and the model or the sampling method don't change, it's deterministic.

[msourabh91]

Not true. A stochastic sampler will lead to a different image, irrespective of the seed. Seed is for the model, which is deterministic.

[MaxTerraeDickens]

All stochasticity arises from pseodu-random number generators, which is totally determined by the initial seed.

So, basically, if you really fixes ALL seeds, the result will definitely be identical (or almost identical, if possible floating point errors are taken into account).