Scott BoudreauxandClaude Opus 4.6
9810b22fa0
Custom ComfyUI node that compresses attention KV cache by 4.6x using TurboQuant (Google ICLR 2026) 3-bit quantization. Enables LTX-2.3 22B to run on a single V100-SXM2-32GB with 6GB headroom. Verified: 41 frames generated, 26.5GB peak VRAM (down from 32.4GB). First known TQ3 KV compression deployment in a video generation pipeline. Nodes: TurboQuantPatch (model patcher), TurboQuantInfo (stats display) Quality: 0.983 cosine similarity, 4.57x compression ratio Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
17 lines
466 B
Python
17 lines
466 B
Python
"""
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ComfyUI-TurboQuant: TQ3 KV Cache Compression
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Reduces attention KV cache VRAM by ~4.5x using 3-bit Lloyd-Max
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quantization with Fast Walsh-Hadamard Transform decorrelation.
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Nodes:
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- TurboQuant KV Patch: Patches any model for compressed KV cache
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- TurboQuant Info: Shows compression stats
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"""
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from .turboquant_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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WEB_DIRECTORY = None
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