Preserve pinned official Ideogram 4 FP8 release, components, original terms and reference code
8ebf922 verified | """Logit-normal schedule and Euler flow-matching sampler.""" | |
| from __future__ import annotations | |
| import math | |
| from dataclasses import dataclass | |
| import torch | |
| class LogitNormalSchedule: | |
| mean: float | |
| std: float = 1.0 | |
| logsnr_min: float = -15.0 | |
| logsnr_max: float = 18.0 | |
| def __call__(self, t: torch.Tensor) -> torch.Tensor: | |
| t = t.to(torch.float64) | |
| z = torch.special.ndtri(t) | |
| y = self.mean + self.std * z | |
| t_ = torch.special.expit(y) | |
| t_ = 1 - t_ | |
| t_min = 1.0 / (1 + math.exp(0.5 * self.logsnr_max)) | |
| t_max = 1.0 / (1 + math.exp(0.5 * self.logsnr_min)) | |
| return t_.clamp(t_min, t_max).to(torch.float32) | |
| def get_schedule_for_resolution( | |
| image_resolution: tuple[int, int], | |
| known_resolution: tuple[int, int] = (512, 512), | |
| known_mean: float = 1.0, | |
| std: float = 1.0, | |
| ) -> LogitNormalSchedule: | |
| """Resolution-aware schedule used at eval time.""" | |
| num_pixels = image_resolution[0] * image_resolution[1] | |
| known_pixels = known_resolution[0] * known_resolution[1] | |
| mean = known_mean + 0.5 * math.log(num_pixels / known_pixels) | |
| return LogitNormalSchedule(mean=mean, std=std) | |
| def make_step_intervals(num_steps: int) -> torch.Tensor: | |
| """Default linear step schedule used by the v4 eval config.""" | |
| return torch.linspace(0.0, 1.0, num_steps + 1, dtype=torch.float32) | |
| class SamplerParameters: | |
| """Bundle of sampling hyperparameters for a named preset. | |
| ``guidance_schedule`` is in LOOP-INDEX order: index 0 is the LAST sampling | |
| step (final polish), index ``num_steps - 1`` is the FIRST sampling step. | |
| ``mu`` and ``std`` are the mean and stddev of the logit-normal noise | |
| schedule passed to ``get_schedule_for_resolution`` (as ``known_mean`` and | |
| ``std`` respectively). | |
| See ``ideogram4.sampler_configs.PRESETS`` for the named preset registry. | |
| """ | |
| num_steps: int | |
| guidance_schedule: tuple[float, ...] | |
| mu: float | |
| std: float = 1.0 | |
| def __post_init__(self) -> None: | |
| if len(self.guidance_schedule) != self.num_steps: | |
| raise ValueError( | |
| f"guidance_schedule has length {len(self.guidance_schedule)}, " | |
| f"expected num_steps={self.num_steps}" | |
| ) | |