---

name: inference description: "Use pix2pixHD checkpointed inference, HTML result generation, and optional ONNX/TensorRT export or runtime paths." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Inference

Use this sub-skill for pix2pixHD test-time synthesis, result browsing, checkpoint preflight, and the optional accelerator/export paths.

Route here for

• test.py runs that synthesize images from a trained checkpoint.
• --how_many, --results_dir, --which_epoch, --phase, and checkpoint-name selection.
• 512p and 1024p inference recipes adapted from scripts/test_512p.sh, scripts/test_1024p.sh, scripts/test_512p_feat.sh, and scripts/test_1024p_feat.sh.
• Feature-conditioned inference with --instance_feat, --label_feat, --load_features, --cluster_path, and --use_encoded_image.
• --export_onnx, --engine, and --onnx only as optional, reference-only paths.

Start with

1. Workflows for the recipe table and result-location map.
2. check_checkpoint.py to preflight the expected checkpoint files and feature-cache paths.
3. CLI reference for flag defaults, hard-coded test-time overrides, and output locations.
4. Accelerated inference for ONNX/TensorRT caveats and legacy helper limits.
5. Troubleshooting for missing checkpoints, HTML/dependency issues, feature-ordering mistakes, and optional-backend failures.

Expected outputs

• HTML results at results_dir/name/phase_which_epoch/index.html.
• Rendered images under results_dir/name/phase_which_epoch/images/.
• Generator checkpoint at checkpoints_dir/name/which_epoch_net_G.pth.
• Optional encoder checkpoint which_epoch_net_E.pth when feature guidance uses netE.
• Optional sampled-feature cache at checkpoints_dir/name/cluster_path.

Boundaries

• For dataset layout, paired label/instance/image folders, and dataroot conventions, use setup-and-data.
• For feature cache creation, clustering, and load_features workflow details, use instance-features.
• For checkpoint naming and save/load behavior, use training.
• Do not cover training-loop, loss, optimizer, or dataset-authoring details here.
• Do not present TensorRT/ONNX as the default supported path; keep it clearly optional.

Helper scripts

• build_inference_command.py prints safe commands from an explicit --repo-root.
• check_checkpoint.py checks checkpoint and feature-cache expectations before a run.

Fast recipes

• 512p labels only: python test.py --name label2city_512p
• 1024p labels only: python test.py --name label2city_1024p --netG local --ngf 32 --resize_or_crop none
• 512p feature-conditioned: python encode_features.py --name label2city_512p_feat; then python test.py --name label2city_512p_feat --instance_feat
• 1024p feature-conditioned: python encode_features.py --name label2city_1024p_feat --netG local --ngf 32 --resize_or_crop none; then python test.py --name label2city_1024p_feat --netG local --ngf 32 --resize_or_crop none --instance_feat
• Encoded-image feature inference: add --use_encoded_image to a feature-enabled command when the real image should be encoded instead of using clustered features.
• ONNX export: python test.py --name <exp> --export_onnx <file.onnx>
• TensorRT/ONNX runtime: see accelerated inference