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hub / github.com/NVlabs/DiffPure / training_losses

Method training_losses

guided_diffusion/gaussian_diffusion.py:752–825  ·  view source on GitHub ↗

Compute training losses for a single timestep. :param model: the model to evaluate loss on. :param x_start: the [N x C x ...] tensor of inputs. :param t: a batch of timestep indices. :param model_kwargs: if not None, a dict of extra keyword arguments to

(self, model, x_start, t, model_kwargs=None, noise=None)

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Callers

nothing calls this directly

Calls 5

q_sampleMethod · 0.95
_vb_terms_bpdMethod · 0.95
_scale_timestepsMethod · 0.95
mean_flatFunction · 0.85

Tested by

no test coverage detected