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.
141 lines
4.2 KiB
Python
141 lines
4.2 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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"""Select ComfyUI's CFG or positive-only guider for latent sampling."""
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from __future__ import annotations
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from importlib import import_module
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from typing import Any, cast
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import torch
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from ..domain.noise_inversion import NoiseInversionOptions
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from ..shared.logging import get_logger
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from .noise_inversion import InversionModelFactory, invert_sampling_noise
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from .noise_reuse_sampling import prepare_reused_noise
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from .noise_reuse_scope import current_noise_reuse
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from .noise_reuse_start import NoiseReuseStartVerification
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LOGGER = get_logger(__name__)
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def sample_with_optional_negative(
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*,
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comfy_sample: Any,
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model: Any,
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noise: torch.Tensor,
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cfg: float,
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sampler: Any,
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sampler_name: str,
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sigmas: torch.Tensor,
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positive: Any,
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negative: Any | None,
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latent_image: torch.Tensor,
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noise_mask: Any = None,
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callback: Any = None,
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disable_pbar: bool = False,
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seed: int | None = None,
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noise_inversion: NoiseInversionOptions | None = None,
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inversion_model_factory: InversionModelFactory | None = None,
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) -> torch.Tensor:
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"""Prepare optional source-derived noise and select the actual Comfy guider."""
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reuse = current_noise_reuse()
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start_verification: NoiseReuseStartVerification | None = None
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if reuse is not None:
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if noise_inversion is not None:
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raise ValueError(
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"Noise reuse and full noise inversion cannot own the same "
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"sampling pass."
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)
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noise, expected = prepare_reused_noise(
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execution=reuse,
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model=model,
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fresh=noise,
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latent=latent_image,
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sigmas=sigmas,
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positive=positive,
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negative=negative,
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cfg=cfg,
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seed=seed,
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noise_mask=noise_mask,
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model_factory=inversion_model_factory,
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)
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start_verification = NoiseReuseStartVerification(
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model.get_model_object("model_sampling.noise_scaling"),
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expected,
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graph_context=reuse.provenance.graph_context,
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sampler_name=sampler_name,
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)
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model = start_verification.derive_model(model)
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if noise_inversion is not None:
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inversion = invert_sampling_noise(
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model=model,
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latent=latent_image,
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forward_sigmas=sigmas,
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positive=positive,
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negative=negative,
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cfg=cfg,
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seed=seed,
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options=noise_inversion,
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model_factory=inversion_model_factory,
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noise_mask=noise_mask,
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)
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noise = inversion.noise.to(noise)
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if negative is not None:
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samples = cast(
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torch.Tensor,
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comfy_sample.sample_custom(
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model,
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noise,
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cfg,
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sampler,
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sigmas,
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positive,
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negative,
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latent_image,
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noise_mask=noise_mask,
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callback=callback,
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disable_pbar=disable_pbar,
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seed=seed,
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),
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)
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if start_verification is not None:
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start_verification.require_verified()
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return samples
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comfy_samplers = import_module("comfy.samplers")
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model_management = import_module("comfy.model_management")
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guider = comfy_samplers.CFGGuider(model)
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guider.inner_set_conds({"positive": positive})
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samples = guider.sample(
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noise,
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latent_image,
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sampler,
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sigmas,
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denoise_mask=noise_mask,
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callback=callback,
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disable_pbar=disable_pbar,
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seed=seed,
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)
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if start_verification is not None:
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start_verification.require_verified()
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LOGGER.debug(
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"Positive-only ComfyUI guider selected",
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extra={
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"operation": "sample_with_optional_negative",
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"guidance_mode": "positive_only",
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},
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)
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return cast(
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torch.Tensor,
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samples.to(
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device=model_management.intermediate_device(),
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dtype=model_management.intermediate_dtype(),
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),
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)
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