---

name: inference-and-sampling description: "Routes pretrained super-resolution inference and unconditional sampling workflows for this SR3/DDPM iterative-refinement repo skill." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Inference and Sampling

Use this sub-skill when the task is to assemble or review commands for:

• pretrained super-resolution inference with infer.py on prepared low/high-resolution validation images;
• unconditional image generation or unconditional training/evaluation with sample.py;
• W&B flag selection for those two scripts.

Do not use this sub-skill for sr.py super-resolution training, standalone PSNR/SSIM evaluation, dataset conversion, or checkpoint download instructions beyond prerequisite notes.

Read first

• references/workflows.md for workflow prerequisites, config fields, outputs, and safe command construction.
• references/troubleshooting.md for checkpoint, data, CUDA, JSON-comment, W&B, and output-location failures.
• scripts/build_inference_command.py to parse a comment-bearing SR config and print an infer.py command without running inference.
• scripts/build_sample_command.py to parse a comment-bearing sample config and print a sample.py command without running training or generation.

Routing checklist

1. Classify the user request:
   • infer.py: conditional super-resolution from a validation dataset using a pretrained checkpoint stem in path.resume_state.
   • sample.py -p val: unconditional generation from a pretrained checkpoint; datasets.val.data_len controls how many samples are produced.
   • sample.py -p train: unconditional training or resume training; this is not SR training and still needs the repo's training data layout.
2. Confirm the config matches the workflow before suggesting a run:
   • super-resolution inference should use a conditional config such as config/sr_sr3_64_512.json;
   • unconditional sampling should use a non-conditional config such as config/sample_sr3_128.json or config/sample_ddpm_128.json;
   • pretrained inference/generation should have path.resume_state set to the checkpoint prefix, not the _gen.pth file name.
3. Prefer the bundled builders for command assembly. They validate JSON-with-// comments and can enforce checkpoint-file checks when --require-resume-state is passed.
4. Surface external prerequisites explicitly before a future Researcher runs anything: CUDA-capable runtime expected by the stock configs, matching data layout for infer.py, pretrained checkpoint files, and the long reverse-diffusion cost of 2000-step configs.