Track bundled vendor runtime sources

vendor/ComfyUI/comfy_extras/nodes_ideogram4.py

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+"""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="model/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()