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Training window: data, tags, train and results steps; docs and changelog

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Built only from kit elements. UNTESTED on real models or GPUs; backend proven on tiny stand-ins on CPU.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MpnF7GwNGMbngU11wjym7P
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Claude
2026-10-17 15:18:38 +00:00
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@@ -148,6 +148,7 @@ A capability is a name in `PROVIDES`. Core defines none: the contract is between
| `anchor_pin` | `(kwargs, transform) -> kwargs` or `None` | |
| `rescale_conditioning` | `(conditioning, height, width) -> (conditioning, count)` or `None` | |
| `latent_upscaler` | `(state_dict) -> object with funpack_latent_upscale(x, scale)` or `None` | |
| `training_adapt` | `(model) -> {name, step, video, batch_limit, fps, frames, pack, shims, silence}` or `None` | what LoRA training must know about a model whose latent is not a plain tensor (H3 packs picture and sound). `shims()` is a context manager that applies the repairs only for the length of a run |
### Learning
@@ -158,6 +159,25 @@ A capability is a name in `PROVIDES`. Core defines none: the contract is between
| `taste_latest_key` | `taste` | `() -> the key the most recent capture went to, or None` |
| `on_rating` | `taste` calls every module that provides it | `(prompt_id, rating, axis) -> None` |
## An add-on module: LoRA training
`modules/training/lora/` is announced as `training_lora` and switched on in Settings > Modules (the `lora_training` add-on, which asks for `onnxruntime`, needed only by the automatic tagger). It is a node-and-routes module with no settings of its own: the window passes every value to the node.
| File | What it holds |
|---|---|
| `store.py` | Training sets as JSON under `training/sets/`. An item points at a Bin file or an output file; the path is resolved at run time, inside its root. |
| `rated.py` | Finds rated results in the saved projects and maps a rating to `liked`, `disliked`, `bad_composition` or `bad_image`. |
| `captions.py` | Tag text helpers, shared in spirit with `app/ui/features/training/captions.js`. |
| `taggers.py` | WD tagger models (ONNX) downloaded from Hugging Face on request, or any repo with `model.onnx` and `selected_tags.csv`; one tagging job at a time. |
| `media_io.py`, `frames.py` | Pictures and clips as tensors, fitted to a size bucket. |
| `plan.py` | Builds the graph a run is queued as: the pipeline's own loaders (ids `model`, `clip`, `vae`, `audio_vae`, or the first of each kind) feed `FunPackTrainLora`. |
| `train.py`, `nodes.py` | The run, on ComfyUI's own Train LoRA node. Latents and conditioning are built from the set; each step's loss goes to `jobs.py`; Stop and ComfyUI's interrupt end it at the next step. |
| `jobs.py` | The run in progress and finished run records under `training/runs/`. |
"Learn what I like" trains on liked results and your own media; "learn what to avoid" trains on the disliked ones, for a LoRA applied at negative strength. This is not VADER (reward gradients), which stays parked.
The diffusion model loader sets `cached_patcher_init` for a plain file, because the trainer clones the model from it. An H3 run uses the repairs in `modules/models/minimax_h3/training.py`. **UNTESTED on real weights or hardware**: proven only on a tiny H3 and a stand-in text encoder and VAEs, on CPU.
## Helpers a module may import from `_core`
`from ..._core import ...` offers exactly these: `addons`, `chain`, `config`, `contract`, `control`, `dit_hooks`, `graph`, `input_steer`, `log`, `media`, `patching`, `projects`, `rated_dial`, `registry`, `relations`, `run`, `schema`, `shortcuts`, `streams`, `temp_files`, `traits`, `vae_tiles`. Adding one means editing `modules/_core.py` in both of its branches.