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chore: import upstream snapshot with attribution
2026-07-13 12:38:16 +08:00

79 lines
2.6 KiB
Python

# SPDX-License-Identifier: Apache-2.0
from dataclasses import dataclass
from sglang.multimodal_gen.configs.sample.sampling_params import (
DataType,
SamplingParams,
)
IDEOGRAM4_PRESETS: dict[str, dict[str, object]] = {
"V4_QUALITY_48": {
"num_steps": 48,
"guidance_schedule": (3.0,) * 3 + (7.0,) * 45,
"mu": 0.0,
"std": 1.5,
},
"V4_DEFAULT_20": {
"num_steps": 20,
"guidance_schedule": (3.0,) * 2 + (7.0,) * 18,
"mu": 0.0,
"std": 1.75,
},
"V4_TURBO_12": {
"num_steps": 12,
"guidance_schedule": (3.0,) * 1 + (7.0,) * 11,
"mu": 0.5,
"std": 1.75,
},
}
@dataclass
class Ideogram4SamplingParams(SamplingParams):
data_type: DataType = DataType.IMAGE
prompt: str = " "
negative_prompt: str = " "
height: int = 1024
width: int = 1024
num_frames: int = 1
num_inference_steps: int | None = None
guidance_scale: float | None = None
preset: str = "V4_DEFAULT_20"
def __post_init__(self) -> None:
if self.preset not in IDEOGRAM4_PRESETS:
raise ValueError(
f"Unknown Ideogram 4 preset {self.preset!r}; "
f"expected one of {sorted(IDEOGRAM4_PRESETS)}"
)
preset_cfg = IDEOGRAM4_PRESETS[self.preset]
preset_steps = int(preset_cfg["num_steps"])
explicit_fields = getattr(self, "_explicit_fields", None)
num_steps_is_explicit = (
explicit_fields is None or "num_inference_steps" in explicit_fields
)
guidance_is_explicit = (
explicit_fields is None or "guidance_scale" in explicit_fields
)
if (
self.num_inference_steps is not None
and self.num_inference_steps != preset_steps
and num_steps_is_explicit
):
raise ValueError(
"Ideogram 4 derives num_inference_steps from preset "
f"{self.preset!r}; got {self.num_inference_steps}, expected "
f"{preset_steps}."
)
if self.guidance_scale is not None and guidance_is_explicit:
preset_guidance = float(preset_cfg["guidance_schedule"][-1])
if self.guidance_scale != preset_guidance:
raise ValueError(
"Ideogram 4 derives guidance from the preset guidance_schedule; "
"guidance_scale cannot be set directly."
)
self.num_inference_steps = preset_steps
self.guidance_scale = float(preset_cfg["guidance_schedule"][-1])
super().__post_init__()