"""Ideogram 4 sampling helper """ import math import torch from typing_extensions import override from comfy_api.latest import ComfyExtension, io _LOGSNR_MIN = -15.0 _LOGSNR_MAX = 18.0 def _logit_normal_schedule(u, mean, std): # Reference time (0=noise..1=clean) via the probit/ndtri quantile. u = torch.as_tensor(u, dtype=torch.float64) t = 1.0 - torch.special.expit(mean + std * torch.special.ndtri(u)) t_min = 1.0 / (1.0 + math.exp(0.5 * _LOGSNR_MAX)) t_max = 1.0 / (1.0 + math.exp(0.5 * _LOGSNR_MIN)) return t.clamp(t_min, t_max) def ideogram4_sigmas(num_steps, width, height, mu, std): """Descending sigmas (len num_steps+1) for the reference schedule. mu + the resolution term form the logSNR shift; std is the spread. """ mean = mu + 0.5 * math.log((width * height) / (512 * 512)) u = torch.linspace(0.0, 1.0, num_steps + 1, dtype=torch.float64) sigmas = (1.0 - _logit_normal_schedule(u, mean, std)).flip(0) sigmas[-1] = 0.0 # clamp leaves ~6e-4; force full denoise return sigmas.to(torch.float32) class Ideogram4Scheduler(io.ComfyNode): @classmethod def define_schema(cls) -> io.Schema: return io.Schema( node_id="Ideogram4Scheduler", display_name="Ideogram 4 Scheduler", category="sampling/custom_sampling/schedulers", inputs=[ io.Int.Input("steps", default=20, min=1, max=200), io.Int.Input("width", default=1024, min=256, max=8192, step=16), io.Int.Input("height", default=1024, min=256, max=8192, step=16), io.Float.Input("mu", default=0.0, min=-10.0, max=10.0, step=0.05), io.Float.Input("std", default=1.75, min=0.1, max=5.0, step=0.05), ], outputs=[io.Sigmas.Output()], ) @classmethod def execute(cls, steps, width, height, mu, std) -> io.NodeOutput: return io.NodeOutput(ideogram4_sigmas(steps, width, height, mu, std)) class Ideogram4Extension(ComfyExtension): @override async def get_node_list(self) -> list[type[io.ComfyNode]]: return [Ideogram4Scheduler] async def comfy_entrypoint() -> Ideogram4Extension: return Ideogram4Extension()