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name: training-and-postprocessing description: "Use textual inversion and hypernetwork create/train endpoints, plan preprocessing for training datasets, and operate Stable Diffusion WebUI extras/postprocessing workflows." disable-model-invocation: true metadata: disco-role: operating license: AGPL 3.0

Training and Postprocessing

Use this sub-skill when the task mentions textual inversion, hypernetwork training, creating embeddings/hypernetworks, preprocessing training images, captioning/splitting/cropping datasets, extras upscaling, face restoration, or postprocessing scripts.

Route First

• Use ../assets-and-models/SKILL.md for embedding/hypernetwork/checkpoint placement, model discovery, refresh lists, and missing asset folders.
• Use ../api-automation/SKILL.md for API server startup, authentication, request transport, base64 image encoding/decoding, and polling mechanics.
• Use ../launch-and-config/SKILL.md for launch flags, low VRAM setup, CUDA/device flags, and server process lifecycle.
• Use ../extension-scripting/SKILL.md for authoring custom scripts or extensions beyond built-in postprocessing controls.

What This Covers

• Training endpoints: /sdapi/v1/create/embedding, /sdapi/v1/train/embedding, /sdapi/v1/create/hypernetwork, /sdapi/v1/train/hypernetwork.
• Training inputs: templates, dataset directory layout, captions, width/height, learning-rate strings, save/preview intervals, latent sampling, tag shuffle/dropout, and interrupt behavior.
• Preprocessing-for-training: split oversized images, auto focal crop, auto-sized crop, flipped copies, captioning, and safe validation of preprocessing plans.
• Extras/postprocessing: /sdapi/v1/extra-single-image, /sdapi/v1/extra-batch-images, built-in Upscale, GFPGAN, CodeFormer, PNG info/caption side effects, and image utility workflows.

Core Workflow

1. Classify the request as training, preprocessing, extras/postprocessing, or troubleshooting.
2. Read training-reference.md for endpoint names, payload fields, template expectations, validation order, and long-run safeguards.
3. For dataset preprocessing, draft a JSON plan and run validate_preprocess_plan.py before any image/model work.
4. Read postprocessing-reference.md for extras payload fields, postprocessing script names, ordering, upscaler/face-restorer checks, and output behavior.
5. Use troubleshooting.md when an endpoint returns an info string with error:, skips images, fails to find templates/assets, or appears stuck.

Safe Plan Validator

Validate a preprocessing plan without importing image, model, or WebUI modules:

python scripts/validate_preprocess_plan.py plan.json

The script accepts JSON with source_dir, output_dir, optional target_width/target_height, and an operations list containing objects such as split_oversized, autosized_crop, focal_crop, create_flipped_copies, caption, upscale, and face_restoration. It reports blocking errors, long-run/model prerequisites, and warnings about risky overwrites or expensive operations.

Success Signals

• Training create endpoints return info containing the created file path; train endpoints return info containing complete, filename, and error: None.
• Training validates the selected model name, non-empty learning rate, positive integer batch/gradient/steps, existing non-empty dataset directory, selected template file, and log directory when saving previews/checkpoints.
• Extras endpoints return HTTP 200 with generated image(s) or HTML info when enabled; upscaler and face restoration names must be available from the running WebUI.
• Preprocessing plans validate cleanly before long-running operations and make output/overwrite behavior explicit.