---

name: torch-examples description: "Plan and troubleshoot PyTorch DPM-Solver example workflows for DDPM, guided-diffusion, and ScoreSDE without running heavyweight checkpoints by default." disable-model-invocation: true metadata: disco-role: operating license: MIT

PyTorch Examples

Use this sub-skill when a user asks how to adapt the repository's PyTorch example workflows: DDPM, improved-DDPM, classifier-guided diffusion, or ScoreSDE PyTorch sampling/evaluation with DPM-Solver.

When To Read

Read this for tasks that mention:

• examples/ddpm_and_guided-diffusion, sample_type=dpmsolver++, --dpm_solver_order, --skip_type, --thresholding, --scale, or FID sampling commands.
• ScoreSDE PyTorch configs, sampling.method="dpm_solver", get_dpm_solver_sampler, CIFAR-10 ddpmpp_deep_continuous, or configs/vp/cifar10_ddpmpp_deep_continuous.py.
• PyTorch checkpoint/data/FID stats requirements for DPM-Solver examples.

Workflow

1. Decide which PyTorch family owns the task:
   • DDPM / guided-diffusion commands: read references/ddpm-guided-workflows.md.
   • ScoreSDE PyTorch sampling: read references/score-sde-pytorch-workflows.md.
2. Use the root ../../references/solver-choice-guide.md to choose steps, order, method, and skip_type.
3. Build a safe command plan with scripts/build_torch_example_command.py instead of copying a shell script with fixed GPU IDs or paths.
4. Validate DPM-Solver option combinations with scripts/validate_torch_options.py before launching a full run.
5. Only run real examples after the user confirms checkpoint/stat files, datasets, GPU availability, output directory, and expected duration.

Common Command Families

DDPM / guided-diffusion CIFAR-10 style:

python main.py --config cifar10.yml --exp <workdir> --sample --fid \
  --timesteps 10 --eta 0 --ni --skip_type logSNR \
  --sample_type dpmsolver++ --dpm_solver_order 3 \
  --dpm_solver_method multistep --dpm_solver_type dpmsolver

Guided ImageNet-style large-guidance family:

python main.py --config imagenet256_guided.yml --exp <workdir> --sample --fid \
  --timesteps 20 --eta 0 --ni --skip_type time_uniform \
  --sample_type dpmsolver++ --dpm_solver_order 2 \
  --dpm_solver_method multistep --dpm_solver_type dpmsolver \
  --scale 8.0 --thresholding

ScoreSDE PyTorch CIFAR-10 DPM-Solver sampler family:

python main.py --config configs/vp/cifar10_ddpmpp_deep_continuous.py \
  --mode eval --workdir <workdir> \
  --config.sampling.eps=1e-3 \
  --config.sampling.method=dpm_solver \
  --config.sampling.steps=10 \
  --config.sampling.skip_type=logSNR \
  --config.sampling.dpm_solver_order=3 \
  --config.sampling.dpm_solver_method=singlestep \
  --config.eval.batch_size=1000

Safety Notes

• Original sample commands assume external checkpoints and FID stats. Some checkpoint paths auto-download; others must be placed manually.
• The DDPM/guided example uses distributed PyTorch with NCCL and a port. Avoid launching it on CPU-only hosts or without explicit GPU selection.
• ScoreSDE PyTorch requirements include TensorFlow-related evaluation packages and can conflict with modern Python stacks. Prefer a separate environment for full example execution.
• FID and full sample generation are long-running and should not be used as a routine skill verification check.

Troubleshooting

Use references/troubleshooting.md for PyTorch example-specific checkpoint, config, distributed launch, option, and FID errors. Use the root ../../references/troubleshooting.md for shared import/backend/schedule issues.