For a workflow without fingerprints, a missing ordinary model took the local same-name file's hash as its evidence, so it showed as "the same file under another name" and counted among those put in at once. Only the workflow's own record is evidence now; the one same-name file the node can load is a candidate, and a size candidate with the same name is shown as likely, first. Foundation components still go by fingerprint only. Model Check also reads GGUF values and resolves upscale models. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Architecture Topic Guide
Start with the repository-root ARCHITECTURE.md, then
open only the topic needed for the current task.
backend.md: Python routes, filesystem safety, persistence, scan state, model sidecars, and backend performance.frontend.md: extension bootstrap, UI ownership, localization, media lifecycle, panel state, and graph mutations.recipes.md: Workflow Recipes, packages, galleries, Parameter Notebooks, prompt roles, and recipe-to-model transitions.material-library.md: generated-image snapshots, material assets, node-block reuse, and Model Check handoff.recipe-model-lifecycle.md: authoritative model fields, Hash synchronization, local matching, partial append, and note privacy.model-resolution.md: model provenance, Model Check, hash injection, model scanning, and recovery policy.../decisions/README.md: concise records of product and architectural decisions that still explain current behavior.
These documents describe current contracts rather than implementation history.
For exact changes, use Git history. For user-visible releases, use
CHANGELOG.md. For recurring implementation traps, use
.agents/logs/ai_lessons.md.