PC sampler mismatched?

[mh-nguyen712]

Hello, thanks for your interesting work!

I have a question about your implementation of PC sampler:

  def pc_sampler(model):
        with torch.no_grad():
            # Initial sample
            x = sde.prior_sampling(shape).to(device)
            timesteps = torch.linspace(sde.T, eps, sde.N, device=device)
      
            for i in range(sde.N):
                t = timesteps[i]
                vec_t = torch.ones(shape[0], device=t.device) * t
                x, x_mean = corrector_update_fn(x, vec_t, model=model)
                x, x_mean = predictor_update_fn(x, vec_t, model=model)
      
            return inverse_scaler(x_mean if denoise else x), sde.N * (n_steps + 1)

Why you start by correcter instead of predictor as in Alg 1. of your original paper? Is there any reason?
Thank you very much!

[LiChenda]

I have the same question after reviewing these lines of code. @NguyenHai7120 Have you figured it out?

[Long-louis]

Is it because after the prior sampling, you need to run a corrector first?

[PhilippHoellmer]

Same question here. I guess it makes somewhat sense to use the corrector on the prior sampling. But even then the corrector is never applied to the final sample?

[PhilippHoellmer]

For anybody interested, there is an answer to this question in the DiffCSP repository: jiaor17/DiffCSP#7 (comment)