trace_model_path dead-ended on KJNodes GetNode virtual links and fell back
to the first entry in MODELS, which could be a stale checkpoint name
resurrected from the process-lifetime node_cache — images ended up with a
wrong or missing Hashes.model.
- SetNodeExtractor now records MODEL passthrough as a model_variable entry
(variable name -> source node id), cached per node like other metadata
- trace_model_path resolves GetNode variable references through those
entries instead of giving up (last SetNode wins, per KJNodes semantics)
- _fill_missing_metadata validates cached checkpoint names against the
node's current widget values and rebuilds stale entries from the current
inputs instead of resurrecting the previous model
Users can now right-click nodes and assign meta hints
(primary_model, primary_sampler, positive_prompt,
negative_prompt) to override the metadata processor's
heuristic inference.
- Store extra_data from the API request so workflow node
properties (including lm_marker_role) are accessible
during metadata processing.
- _get_user_marks scans extra_data.extra_pnginfo.workflow
for meta_* marks, falling back to prompt.original_prompt.
- extract_generation_params checks user marks before
heuristic inference for sampler, model, and prompts.
- Warn on duplicate marks or invalid marked nodes.
- Add pre-processing step to populate missing parameters for candidate samplers, especially for SamplerCustomAdvanced requiring tracing
- Change sampler selection from most recent (closest to downstream) to first in execution order to prioritize base samplers over refine samplers
- Improve parameter handling by updating sampler parameters with traced values before ranking
- Maintain backward compatibility with fallback to first sampler if no criteria match
- Add support for `basic_pipe` nodes in metadata processor to handle pipeline nodes like FromBasicPipe
- Optimize `find_primary_checkpoint` by accepting optional `primary_sampler_id` to avoid redundant calculations
- Update `get_workflow_trace` to pass known primary sampler ID for improved efficiency
Will Miao