Artificial Sweetener
f89b436a09
Capture the nearest existing source model evaluation without inserting sampling steps, retain its actual noise coordinate, and recover residuals with stable native scaling. Observe source and receiver states at ComfyUI's shared boundaries, discover compatible named/custom source nodes, and report graph node identities and selected samplers in failures. Admit the complete sampler catalog through CD and tiled routes with regression coverage for future additions. Verify the isolated CD-only tree: 4322 Python tests passed, 48 skipped; 115 frontend tests passed; format, lint, strict typing, architecture, and test governance passed. Contextual Attention remains outside this commit.
49 lines
1.8 KiB
Python
49 lines
1.8 KiB
Python
# SimpleSyrup - workflow-focused ComfyUI extensions for image generation
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# Copyright (C) 2026 Artificial Sweetener and contributors
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# SPDX-License-Identifier: AGPL-3.0-or-later
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"""Validate shared model scaling and partial-denoise receiver coordinates."""
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from __future__ import annotations
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import math
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from importlib import import_module
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from typing import Any
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import torch
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MINIMUM_PARTIAL_SIGMA = 0.0001
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def validate_partial_sampling_target(
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model: Any, sigmas: torch.Tensor, *, operation: str
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) -> float:
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"""Reject unsupported scaling and singular targets before model execution."""
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sampling_types = import_module("comfy.model_sampling")
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sampling = model.get_model_object("model_sampling")
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if not isinstance(
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sampling, (sampling_types.CONST, sampling_types.EPS)
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) or isinstance(
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sampling, (sampling_types.IMG_TO_IMG, sampling_types.IMG_TO_IMG_FLOW)
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):
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raise ValueError(f"{operation} requires a flow or EPS-compatible image model.")
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if sigmas.ndim != 1 or len(sigmas) < 2 or not bool(torch.isfinite(sigmas).all()):
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raise ValueError(f"{operation} requires a finite forward sampling schedule.")
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target = float(sigmas[0])
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if target <= MINIMUM_PARTIAL_SIGMA:
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raise ValueError(
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f"{operation} requires a positive partial-denoise start sigma."
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)
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if isinstance(sampling, sampling_types.CONST) and target >= 0.9999:
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raise ValueError(
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f"Flow {operation.lower()} requires denoise below the full-noise endpoint."
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)
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if isinstance(sampling, sampling_types.EPS):
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maximum = float(sampling.sigma_max)
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if target > maximum or math.isclose(target, maximum, rel_tol=1e-5):
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raise ValueError(
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f"{operation} requires partial denoise, below the model's "
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"maximum sigma."
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)
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return target
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