Joseph IbrahimandClaude Opus 4.5
3bdb98b138
LICENSING: - License: Apache 2.0 → AGPL-3.0 + Commercial dual-license - Added COMMERCIAL_LICENSE.md with pricing tiers - Added COPYRIGHT with IP documentation - Added LICENSE_HEADER.txt for source files - SPDX identifiers in all Python files ARTIST UX: - Category: deterministic → JI/Reproducible - Display names: Technical jargon → Artist-friendly terms - Deterministic Sampler → Locked Sampler ⟳ - Checksum Validator → Output Matcher - MoE Router → Expert Selector - Cascade Refiner → Multi-Pass Refiner - ECHO Context → Memory Recall TECHNICAL: - pyproject.toml: Updated license, classifiers - README: Updated with new naming and workflow diagrams - Consistent with ThinkingMachines batch-invariant research - batch_size=1 enforcement documented Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
# ComfyUI Deterministic Nodes
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# Copyright (C) 2025 Joseph Ibrahim <joseph@josephibrahim.com>
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#
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# SPDX-License-Identifier: AGPL-3.0-only
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published
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# by the Free Software Foundation, version 3 of the License.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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#
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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#
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# Commercial licensing available: See COMMERCIAL_LICENSE.md
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"""
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ComfyUI Deterministic Nodes
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===========================
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Batch-invariant inference nodes for guaranteed reproducibility.
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Based on:
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- ThinkingMachines batch-invariant-ops research
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- NVIDIA CES 2026 multi-model agent patterns
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- Ralph v3 file-centric state management
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- ECHO 2.0 4-tier context memory
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Nodes:
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- DeterministicSampler: Batch-invariant sampling (batch_size=1 per item)
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- ChecksumValidator: Reproducibility proof with tolerance modes
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- MoERouterNode: Deterministic expert routing (hash-based)
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- CascadeRefiner: Sequential 3-stage refinement (Nemotron pattern)
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- ECHOContextNode: 4-tier context memory retrieval
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"""
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from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
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__version__ = "1.0.0"
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