DDM2
DDM2 copied to clipboard
Question about the diffusion training process.
I'm having difficulty understanding the code you provided. Could you please clarify the following points for me?
- Does x_noisy represent noisy images at different steps t?
- Is x_recon supervised by another noisy observation of the clean image?
- Typically, in diffusion models, isn't noise estimated step by step? But according to this code, we directly estimate the image.
Thank you for your patience and assistance in clarifying these points.
“”“
x_noisy = self.q_sample(
x_start=x_start, continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod.view(-1, 1, 1, 1), noise=noise.detach())
x_recon = self.denoisor(x_noisy, continuous_sqrt_alpha_cumprod)
J-Invariance optimization
total_loss = self.mseloss(x_recon, x_in['X']) ”“”