jmtomczak / intro_dgm Goto Github PK

Hi, it seems there is a copy-paste typo in fm_example (not sure if eval/train are needed there at all):
self.vnet.eval() # set the vector field net to train again

Hi,
As far as I understand, the option A in arm_example.ipynb refers to causality. Therefore A = True should signal that the model should not be taking the current time step t as an input. In the code implementation we discard the latest outpout of the convolution layer. I am confused by this since I thought we are supposed to discard the latest input. Are we not supposed to discard the latest (current time step) input to the model. In other words, for example, for the first convolution operation for t = 0, are we not supposed to pad left (for 1D) with F-1 many elements, and feed into the first convolution the {(F-1), (F-2), ..., -1} time step elements to produce the output for t = 0 (initial) ?

if self.A:
# Remember , we cannot be dependent on the current
component; therefore , the last element is removed .
return conv1d_out [:, :, : −1]

Sorry if I'm off. Just started to learn about these from the book. I will appreciate any help.

I'm checking your most recent update on conditional flow matching and I see here you passed a tanh function after the Euler forward step. I wonder if there is any reason behind it?

https://github.com/jmtomczak/intro_dgm/blob/main/sbgms/fm_example.ipynb

    def sample(self,  batch_size=64):
        # Euler method
        # sample x_0 first
        x_t = self.sample_base(torch.empty(batch_size, self.D))
        
        # then go step-by-step to x_1 (data)        
        ts = torch.linspace(0., 1., self.T)
        delta_t = ts[1] - ts[0]
        
        for t in ts[1:]:
            t_embedding = self.time_embedding(torch.Tensor([t]))
            x_t = x_t + self.vnet(x_t + t_embedding) * delta_t
            # Stochastic Euler method
            if self.stochastic_euler:
                x_t = x_t + torch.randn_like(x_t) * delta_t
        
        x_final = torch.tanh(x_t) # **here's my question** 
        return x_final

Hi,

I might be missing a step here but in your explanation for the reparameterization trick you state the following formula

And I can see $i$ as an argument, but I fail to see how you are using it in this function - could you explain what I missed?

def reparameterization_gaussian_diffusion(self, x, i):
    return torch.sqrt(1. - self.beta) * x + torch.sqrt(self.beta) * torch.randn_like(x)

In the ddgm example shouldn't the line:

self.p_dnns = p_dnns

be:

self.p_dnns = nn.ModuleList( p_dnns)

My understanding is that by not placing them in a ModuleList the p_dnns networks are not added in the learned parameters and are left unoptimized.

Thanks for an awesome resource by the way!