42 lines
2.2 KiB
Markdown
42 lines
2.2 KiB
Markdown
# FunPack Gemma Embedding Refiner
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This node analyzes the positive conditioning produced by your text encoder, tracks your feedback over time, and nudges the conditioning toward directions that should score higher in future generations.
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## Parameters
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**positive_conditioning**: Conditioning to refine. Place this node after your text encoder and before the downstream video conditioning node.
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**mode**: Tokenizer mode used for prompt analysis. Use `ltx2` for LTX-style workflows and `wan` for Wan workflows.
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**rating**: Rating for the previously generated result, from 1 to 10, where 1 means "awful" and 10 means "masterpiece".
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**refinement_key**: Name of the refinement session. The node stores its learned refinement data under this key so you can continue training the same style later.
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**scheduler_mode**: Learning behavior preset. `original` keeps the legacy behavior, while `accurate` and `aggressive` adjust how strongly the node updates the refinement.
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**positive_prompt**: Optional prompt text used for token analysis and concept-level feedback.
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**reset_session**: If enabled, clears the saved training history for the selected refinement key and starts a fresh session.
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**unlimited_history**: If enabled, disables history pruning.
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**seed**: Seed used for exploration behavior during refinement.
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**feedback_enabled**: If enabled, the node can return a concept-focused feedback question alongside the refined conditioning.
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**feedback_rating**: Answer to the feedback question on its own 1 to 6 scale. This is only useful when concept feedback is enabled.
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## Outputs
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**modified_positive**: Refined conditioning. Connect this output to the next conditioning stage in your workflow.
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**status**: Human-readable training status, including iteration and refinement diagnostics.
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**feedback_question**: Optional concept feedback prompt generated by the node when feedback mode is active.
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**training_info**: Additional training details for the current refinement step.
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## Purpose
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Use this node when you want to iteratively tune prompt conditioning based on your own ratings instead of manually rewriting prompts every time. Over multiple runs, it builds a refinement profile for the selected `refinement_key`.
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