In theory you can do this, but your image will not be very good. Removing all the noise in one step would be the equivalent of asking your model to recover an image that has been completely corrupted by noise.

All the model will be able to do during this prediction is guess some vague fuzzy features shared by your whole dataset, as that's as far as it could get by optimizing the objective at $t=T$. You would then re-corrupt your model's prediction up until we are just a bit under the level of pure noise we were at to start with. Your model now is taking something other than pure noise as input, so it can be a bit more confident about what image may have been corrupted, so it guesses a few more (still fairly vague and fuzzy) characteristics of the image. Then again we renoise up to just a little bit less, and repeat.

When we are half way through sampling, we have something resembling, for example, a human face corrupted with noise. Hopefully your model has seen a lot of human faces corrupted with noise, and the corresponding real human face, so the model will be able to more accurately guess the original uncorrupted image. You can imagine as we continue to denoise, the model becomes more and more sure of what the true image is, and is able to add more and more fine grained details that it couldn't when it was just given pure noise.

[These slides](https://hal.cse.msu.edu/teaching/2024-spring-deep-learning/21-diffusion-models/#/12/0/0) contain some good visuals of the recovered images you get at different time steps.