digital-garbageandClaude Opus 5.5
9d028ccc92
Prompt Markup strips it before the encoder; Apply Prompt Markup puts weights and timed windows on attention and blends on the conditioning. Where the words sit is the H3 module's (text_tokenizer, prompt_rows, video_time_rows). Wired into both H3 presets. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
30 lines
1.2 KiB
Python
30 lines
1.2 KiB
Python
"""Prompt markup -- (word:1.5), [phrase@2-4], [a|b]. A node-only module.
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Two nodes, because the markup has to be split around the encoder: it must come
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OUT before the encoder sees the text (Qwen reads "(word:1.5)" as punctuation in
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your sentence), and it can only be applied AFTER, once there is a conditioning
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to find the words in and a model to bias.
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text -> Prompt Markup -> clean -> (any H3 encode node) -> positive
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-> markup ---------------------> Apply Prompt Markup
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(+ model, clip, latent)
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Where the words sit, and how seconds map to rows, is the MODEL's business: model
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modules announce `text_tokenizer`, `prompt_rows` and `video_time_rows`. With
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none that recognise the encoder, the markup is stripped and said to be unused --
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never silently ignored.
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The weight is an attention BIAS, and SLA runs any biased call dense, so a run
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with weights or timed phrases runs dense attention.
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"""
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from .nodes import FunPackApplyPromptMarkup, FunPackPromptMarkup
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ID = "conditioning_prompt_markup"
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TITLE = "Prompt markup"
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STAGE = "conditioning"
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CATEGORY = "conditioning"
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STATUS = "proven"
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NODES = [FunPackPromptMarkup, FunPackApplyPromptMarkup]
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