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name: model-configuration description: "Route config parsing, SR3/DDPM selection, conditional inputs, beta schedules, checkpoints, GPU ids, and model API facts." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Model Configuration

Use this sub-skill when a request is about the repo's comment-bearing JSON configs, model family choice, noise schedules, checkpoint resume, GPU selection, or the diffusion model object's public API.

What this sub-skill owns

• JSON-with-comments config files under config/.
• The which_model_G switch between ddpm and sr3.
• Conditional super-resolution versus unconditional generation.
• Beta schedules, resume_state, gpu_ids, and W&B project metadata.
• Verified method signatures and save/load behavior in model/.
• A local config inspector for quick summaries.

Start here

1. Read references/configuration.md for field meaning and config-family mapping.
2. Read references/model-api.md for the verified class and method contracts.
3. Read references/troubleshooting.md when a config looks valid but parse, load, or runtime setup fails.
4. Run scripts/inspect_config.py to summarize one or more comment-bearing config files.

Route elsewhere

• Dataset layout, LR/HR preparation, and LMDB/image-store checks are outside this sub-skill.
• Long training, validation, inference, and sampling workflows are outside this sub-skill except for the config facts they consume.
• Evaluation metrics and result-folder inspection are outside this sub-skill.

Operating notes

• Treat config/*.json as JSON-with-comments, not strict JSON.
• The CLI -p/--phase and -gpu/--gpu_ids flags override config values at runtime.
• The stored phase is only the default input; core.logger.parse rewrites it from the CLI.
• path.resume_state names the checkpoint stem only; the loader appends _gen.pth and _opt.pth.
• conditional=true implies a 6-channel UNet input in this repo; conditional=false uses 3 channels.
• gpu_ids controls CUDA_VISIBLE_DEVICES and distributed wrapping; verify the rendered GPU string before assuming multi-GPU behavior.
• norm_groups must divide every UNet stage width.
• W&B logging is split between the JSON wandb.project field and CLI flags such as -enable_wandb, -log_wandb_ckpt, -log_eval, and -log_infer.

Evidence basis

This router is grounded in the project README, the config/*.json families, core/logger.py, model/networks.py, model/model.py, model/ddpm_modules/*, and model/sr3_modules/*.