We read every piece of feedback, and take your input very seriously.
To see all available qualifiers, see our documentation.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
What are the design principles? Is it enough to just ensure the boundary conditions?
def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0): c_skip = sigma_data2 / ((timestep / 0.1) ** 2 + sigma_data2) c_out = (timestep / 0.1) / ((timestep / 0.1) ** 2 + sigma_data**2) ** 0.5 return c_skip, c_out
The text was updated successfully, but these errors were encountered:
I think a 'model_pred = pred_x_0' is enough rather than 'model_pred = c_skip_start * noisy_model_input + c_out_start * pred_x_0'
Sorry, something went wrong.
No branches or pull requests
What are the design principles? Is it enough to just ensure the boundary conditions?
def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0):
c_skip = sigma_data2 / ((timestep / 0.1) ** 2 + sigma_data2)
c_out = (timestep / 0.1) / ((timestep / 0.1) ** 2 + sigma_data**2) ** 0.5
return c_skip, c_out
The text was updated successfully, but these errors were encountered: