2.1 KiB
FunPack LoRA Weight Workflow
This workflow lets FunPack Gemma Embedding Refiner suggest prompt-specific LoRA weights while keeping LoRA loading deterministic.
Node Order
Use this pattern:
FunPack Apply LoRA Weights -> FunPack LoRA Loader -> FunPack Gemma Embedding Refiner
The loader does not learn. The refiner owns prompt parsing, concept matching, rating interpretation, and saving suggested weights.
FunPack Apply LoRA Weights
This node defines the selected LoRAs and their base weights.
Inputs:
- positive_prompt: Exact prompt text for lookup.
- refinement_key: Same key used by
FunPack Gemma Embedding Refiner. - mode: Same tokenizer mode as the refiner.
- lora_N: LoRA file.
- lora_N_type:
general,concept,style,quality, orcharacter. - lora_N_concept: Human-readable concept hint for the refiner.
- lora_N_base_weight: Trainer-recommended model base weight.
- lora_N_clip_base_weight: Trainer-recommended CLIP base weight.
On the first run for an exact prompt, no saved suggestion exists, so base weights are used. On later runs, the node reads the refiner JSON and applies saved suggestions for that exact prompt only.
FunPack LoRA Loader
This node only loads the LoRA stack. It applies the model and CLIP weights prepared by FunPack Apply LoRA Weights, then passes the same stack forward so the refiner can learn from it.
Gemma Refiner Integration
Connect the lora_stack output from FunPack LoRA Loader into the optional lora_stack input on FunPack Gemma Embedding Refiner.
The refiner compares each LoRA's declared type/concept with the concepts it already extracted from the prompt. After processing the rating, it saves lora_weight_suggestions into the same prompt entry in the refiner JSON.
Examples:
- A
qualityLoRA matching a valuable quality concept can be boosted after good ratings. - A
conceptLoRA matching a bad-rated prompt can be reduced if it may be over-weighting or distorting that concept. - Consistently good ratings can stabilize around a saved offset instead of continually drifting.