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106 lines
4.3 KiB
Python
106 lines
4.3 KiB
Python
import math
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from typing import Union
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from diffusers import FlowMatchEulerDiscreteScheduler
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import torch
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class CustomFlowMatchEulerDiscreteScheduler(FlowMatchEulerDiscreteScheduler):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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with torch.no_grad():
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# create weights for timesteps
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num_timesteps = 1000
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# generate the multiplier based on cosmap loss weighing
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# this is only used on linear timesteps for now
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# cosine map weighing is higher in the middle and lower at the ends
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# bot = 1 - 2 * self.sigmas + 2 * self.sigmas ** 2
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# cosmap_weighing = 2 / (math.pi * bot)
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# sigma sqrt weighing is significantly higher at the end and lower at the beginning
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sigma_sqrt_weighing = (self.sigmas ** -2.0).float()
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# clip at 1e4 (1e6 is too high)
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sigma_sqrt_weighing = torch.clamp(sigma_sqrt_weighing, max=1e4)
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# bring to a mean of 1
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sigma_sqrt_weighing = sigma_sqrt_weighing / sigma_sqrt_weighing.mean()
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# Create linear timesteps from 1000 to 0
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timesteps = torch.linspace(1000, 0, num_timesteps, device='cpu')
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self.linear_timesteps = timesteps
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# self.linear_timesteps_weights = cosmap_weighing
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self.linear_timesteps_weights = sigma_sqrt_weighing
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# self.sigmas = self.get_sigmas(timesteps, n_dim=1, dtype=torch.float32, device='cpu')
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pass
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def get_weights_for_timesteps(self, timesteps: torch.Tensor) -> torch.Tensor:
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# Get the indices of the timesteps
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step_indices = [(self.timesteps == t).nonzero().item() for t in timesteps]
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# Get the weights for the timesteps
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weights = self.linear_timesteps_weights[step_indices].flatten()
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return weights
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def get_sigmas(self, timesteps: torch.Tensor, n_dim, dtype, device) -> torch.Tensor:
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sigmas = self.sigmas.to(device=device, dtype=dtype)
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schedule_timesteps = self.timesteps.to(device)
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timesteps = timesteps.to(device)
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step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
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sigma = sigmas[step_indices].flatten()
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while len(sigma.shape) < n_dim:
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sigma = sigma.unsqueeze(-1)
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return sigma
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def add_noise(
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self,
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original_samples: torch.Tensor,
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noise: torch.Tensor,
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timesteps: torch.Tensor,
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) -> torch.Tensor:
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## ref https://github.com/huggingface/diffusers/blob/fbe29c62984c33c6cf9cf7ad120a992fe6d20854/examples/dreambooth/train_dreambooth_sd3.py#L1578
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## Add noise according to flow matching.
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## zt = (1 - texp) * x + texp * z1
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# sigmas = get_sigmas(timesteps, n_dim=model_input.ndim, dtype=model_input.dtype)
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# noisy_model_input = (1.0 - sigmas) * model_input + sigmas * noise
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# timestep needs to be in [0, 1], we store them in [0, 1000]
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# noisy_sample = (1 - timestep) * latent + timestep * noise
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t_01 = (timesteps / 1000).to(original_samples.device)
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noisy_model_input = (1 - t_01) * original_samples + t_01 * noise
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# n_dim = original_samples.ndim
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# sigmas = self.get_sigmas(timesteps, n_dim, original_samples.dtype, original_samples.device)
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# noisy_model_input = (1.0 - sigmas) * original_samples + sigmas * noise
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return noisy_model_input
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def scale_model_input(self, sample: torch.Tensor, timestep: Union[float, torch.Tensor]) -> torch.Tensor:
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return sample
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def set_train_timesteps(self, num_timesteps, device, linear=False):
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if linear:
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timesteps = torch.linspace(1000, 0, num_timesteps, device=device)
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self.timesteps = timesteps
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return timesteps
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else:
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# distribute them closer to center. Inference distributes them as a bias toward first
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# Generate values from 0 to 1
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t = torch.sigmoid(torch.randn((num_timesteps,), device=device))
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# Scale and reverse the values to go from 1000 to 0
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timesteps = ((1 - t) * 1000)
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# Sort the timesteps in descending order
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timesteps, _ = torch.sort(timesteps, descending=True)
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self.timesteps = timesteps.to(device=device)
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return timesteps
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