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220 lines
8.2 KiB
Python
220 lines
8.2 KiB
Python
import math
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from typing import Union
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from torch.distributions import LogNormal
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from diffusers import FlowMatchEulerDiscreteScheduler
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import torch
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import numpy as np
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from toolkit.timestep_weighing.default_weighing_scheme import default_weighing_scheme
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def calculate_shift(
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image_seq_len,
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base_seq_len: int = 256,
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max_seq_len: int = 4096,
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base_shift: float = 0.5,
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max_shift: float = 1.16,
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):
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m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
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b = base_shift - m * base_seq_len
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mu = image_seq_len * m + b
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return mu
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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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self.init_noise_sigma = 1.0
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self.timestep_type = "linear"
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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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# Bell-Shaped Mean-Normalized Timestep Weighting
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# bsmntw? need a better name
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x = torch.arange(num_timesteps, dtype=torch.float32)
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y = torch.exp(-2 * ((x - num_timesteps / 2) / num_timesteps) ** 2)
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# Shift minimum to 0
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y_shifted = y - y.min()
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# Scale to make mean 1
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bsmntw_weighing = y_shifted * (num_timesteps / y_shifted.sum())
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# only do half bell
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hbsmntw_weighing = y_shifted * (num_timesteps / y_shifted.sum())
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# flatten second half to max
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hbsmntw_weighing[num_timesteps //
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2:] = hbsmntw_weighing[num_timesteps // 2:].max()
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# Create linear timesteps from 1000 to 1
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timesteps = torch.linspace(1000, 1, num_timesteps, device='cpu')
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self.linear_timesteps = timesteps
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self.linear_timesteps_weights = bsmntw_weighing
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self.linear_timesteps_weights2 = hbsmntw_weighing
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pass
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def get_weights_for_timesteps(self, timesteps: torch.Tensor, v2=False, timestep_type="linear") -> torch.Tensor:
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# Get the indices of the timesteps
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step_indices = [(self.timesteps == t).nonzero().item()
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for t in timesteps]
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# Get the weights for the timesteps
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if timestep_type == "weighted":
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weights = torch.tensor(
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[default_weighing_scheme[i] for i in step_indices],
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device=timesteps.device,
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dtype=timesteps.dtype
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)
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if v2:
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weights = self.linear_timesteps_weights2[step_indices].flatten()
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else:
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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()
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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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t_01 = (timesteps / 1000).to(original_samples.device)
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# forward ODE
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noisy_model_input = (1.0 - t_01) * original_samples + t_01 * noise
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# reverse ODE
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# noisy_model_input = (1 - t_01) * noise + t_01 * original_samples
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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(
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self,
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num_timesteps,
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device,
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timestep_type='linear',
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latents=None,
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patch_size=1
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):
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self.timestep_type = timestep_type
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if timestep_type == 'linear' or timestep_type == 'weighted':
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timesteps = torch.linspace(1000, 1, num_timesteps, device=device)
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self.timesteps = timesteps
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return timesteps
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elif timestep_type == 'sigmoid':
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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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elif timestep_type in ['flux_shift', 'lumina2_shift', 'shift']:
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# matches inference dynamic shifting
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timesteps = np.linspace(
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self._sigma_to_t(self.sigma_max), self._sigma_to_t(
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self.sigma_min), num_timesteps
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)
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sigmas = timesteps / self.config.num_train_timesteps
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if self.config.use_dynamic_shifting:
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if latents is None:
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raise ValueError('latents is None')
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# for flux we double up the patch size before sending her to simulate the latent reduction
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h = latents.shape[2]
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w = latents.shape[3]
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image_seq_len = h * w // (patch_size**2)
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mu = calculate_shift(
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image_seq_len,
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self.config.get("base_image_seq_len", 256),
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self.config.get("max_image_seq_len", 4096),
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self.config.get("base_shift", 0.5),
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self.config.get("max_shift", 1.16),
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)
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sigmas = self.time_shift(mu, 1.0, sigmas)
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else:
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sigmas = self.shift * sigmas / (1 + (self.shift - 1) * sigmas)
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if self.config.shift_terminal:
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sigmas = self.stretch_shift_to_terminal(sigmas)
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if self.config.use_karras_sigmas:
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sigmas = self._convert_to_karras(
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in_sigmas=sigmas, num_inference_steps=self.config.num_train_timesteps)
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elif self.config.use_exponential_sigmas:
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sigmas = self._convert_to_exponential(
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in_sigmas=sigmas, num_inference_steps=self.config.num_train_timesteps)
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elif self.config.use_beta_sigmas:
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sigmas = self._convert_to_beta(
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in_sigmas=sigmas, num_inference_steps=self.config.num_train_timesteps)
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sigmas = torch.from_numpy(sigmas).to(
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dtype=torch.float32, device=device)
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timesteps = sigmas * self.config.num_train_timesteps
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if self.config.invert_sigmas:
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sigmas = 1.0 - sigmas
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timesteps = sigmas * self.config.num_train_timesteps
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sigmas = torch.cat(
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[sigmas, torch.ones(1, device=sigmas.device)])
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else:
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sigmas = torch.cat(
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[sigmas, torch.zeros(1, device=sigmas.device)])
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self.timesteps = timesteps.to(device=device)
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self.sigmas = sigmas
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self.timesteps = timesteps.to(device=device)
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return timesteps
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elif timestep_type == 'lognorm_blend':
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# disgtribute timestepd to the center/early and blend in linear
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alpha = 0.75
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lognormal = LogNormal(loc=0, scale=0.333)
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# Sample from the distribution
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t1 = lognormal.sample((int(num_timesteps * alpha),)).to(device)
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# Scale and reverse the values to go from 1000 to 0
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t1 = ((1 - t1/t1.max()) * 1000)
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# add half of linear
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t2 = torch.linspace(1000, 1, int(
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num_timesteps * (1 - alpha)), device=device)
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timesteps = torch.cat((t1, t2))
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# Sort the timesteps in descending order
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timesteps, _ = torch.sort(timesteps, descending=True)
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timesteps = timesteps.to(torch.int)
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self.timesteps = timesteps.to(device=device)
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return timesteps
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else:
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raise ValueError(f"Invalid timestep type: {timestep_type}")
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