toolkit/samplers/custom_flowmatch_sampler.py

import math
from typing import Union

from diffusers import FlowMatchEulerDiscreteScheduler
import torch


class CustomFlowMatchEulerDiscreteScheduler(FlowMatchEulerDiscreteScheduler):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.init_noise_sigma = 1.0

        with torch.no_grad():
            # create weights for timesteps
            num_timesteps = 1000
            # Bell-Shaped Mean-Normalized Timestep Weighting
            # bsmntw? need a better name

            x = torch.arange(num_timesteps, dtype=torch.float32)
            y = torch.exp(-2 * ((x - num_timesteps / 2) / num_timesteps) ** 2)

            # Shift minimum to 0
            y_shifted = y - y.min()

            # Scale to make mean 1
            bsmntw_weighing = y_shifted * (num_timesteps / y_shifted.sum())

            # only do half bell
            hbsmntw_weighing = y_shifted * (num_timesteps / y_shifted.sum())

            # flatten second half to max
            hbsmntw_weighing[num_timesteps // 2:] = hbsmntw_weighing[num_timesteps // 2:].max()

            # Create linear timesteps from 1000 to 0
            timesteps = torch.linspace(1000, 0, num_timesteps, device='cpu')

            self.linear_timesteps = timesteps
            self.linear_timesteps_weights = bsmntw_weighing
            self.linear_timesteps_weights2 = hbsmntw_weighing
            pass

    def get_weights_for_timesteps(self, timesteps: torch.Tensor, v2=False) -> torch.Tensor:
        # Get the indices of the timesteps
        step_indices = [(self.timesteps == t).nonzero().item() for t in timesteps]

        # Get the weights for the timesteps
        if v2:
            weights = self.linear_timesteps_weights2[step_indices].flatten()
        else:
            weights = self.linear_timesteps_weights[step_indices].flatten()

        return weights

    def get_sigmas(self, timesteps: torch.Tensor, n_dim, dtype, device) -> torch.Tensor:
        sigmas = self.sigmas.to(device=device, dtype=dtype)
        schedule_timesteps = self.timesteps.to(device)
        timesteps = timesteps.to(device)
        step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]

        sigma = sigmas[step_indices].flatten()
        while len(sigma.shape) < n_dim:
            sigma = sigma.unsqueeze(-1)

        return sigma

    def add_noise(
            self,
            original_samples: torch.Tensor,
            noise: torch.Tensor,
            timesteps: torch.Tensor,
    ) -> torch.Tensor:
        ## ref https://github.com/huggingface/diffusers/blob/fbe29c62984c33c6cf9cf7ad120a992fe6d20854/examples/dreambooth/train_dreambooth_sd3.py#L1578
        ## Add noise according to flow matching.
        ## zt = (1 - texp) * x + texp * z1

        # sigmas = get_sigmas(timesteps, n_dim=model_input.ndim, dtype=model_input.dtype)
        # noisy_model_input = (1.0 - sigmas) * model_input + sigmas * noise

        # timestep needs to be in [0, 1], we store them in [0, 1000]
        # noisy_sample = (1 - timestep) * latent + timestep * noise
        t_01 = (timesteps / 1000).to(original_samples.device)
        noisy_model_input = (1 - t_01) * original_samples + t_01 * noise

        # n_dim = original_samples.ndim
        # sigmas = self.get_sigmas(timesteps, n_dim, original_samples.dtype, original_samples.device)
        # noisy_model_input = (1.0 - sigmas) * original_samples + sigmas * noise
        return noisy_model_input

    def scale_model_input(self, sample: torch.Tensor, timestep: Union[float, torch.Tensor]) -> torch.Tensor:
        return sample

    def set_train_timesteps(self, num_timesteps, device, linear=False):
        if linear:
            timesteps = torch.linspace(1000, 0, num_timesteps, device=device)
            self.timesteps = timesteps
            return timesteps
        else:
            # distribute them closer to center. Inference distributes them as a bias toward first
            # Generate values from 0 to 1
            t = torch.sigmoid(torch.randn((num_timesteps,), device=device))

            # Scale and reverse the values to go from 1000 to 0
            timesteps = ((1 - t) * 1000)

            # Sort the timesteps in descending order
            timesteps, _ = torch.sort(timesteps, descending=True)

            self.timesteps = timesteps.to(device=device)

            return timesteps