| import torch | |
| from musubi_tuner.ltx_2.components.protocols import DiffusionStepProtocol | |
| from musubi_tuner.ltx_2.utils import to_velocity | |
| class EulerDiffusionStep(DiffusionStepProtocol): | |
| """ | |
| First-order Euler method for diffusion sampling. | |
| Takes a single step from the current noise level (sigma) to the next by | |
| computing velocity from the denoised prediction and applying: sample + velocity * dt. | |
| """ | |
| def step( | |
| self, sample: torch.Tensor, denoised_sample: torch.Tensor, sigmas: torch.Tensor, step_index: int | |
| ) -> torch.Tensor: | |
| sigma = sigmas[step_index] | |
| sigma_next = sigmas[step_index + 1] | |
| dt = sigma_next - sigma | |
| velocity = to_velocity(sample, sigma, denoised_sample) | |
| return (sample.to(torch.float32) + velocity.to(torch.float32) * dt).to(sample.dtype) | |