DPM-Solver

Use this skill when a task asks how to integrate, configure, debug, or adapt the DPM-Solver / DPM-Solver++ samplers from Cheng Lu et al.'s dpm-solver project. The repository is a source-code distribution rather than a normal PyPI package: the durable user-facing artifacts are the single-file PyTorch and JAX solver modules plus example integrations for DDPM/guided-diffusion, ScoreSDE, and Stable Diffusion.

Start Here

1. Identify whether the user wants direct solver API integration, a PyTorch example workflow, a JAX ScoreSDE workflow, or Stable Diffusion sampling acceleration.
2. If they need code, prefer the bundled implementation copies in scripts/dpm_solver_pytorch.py and scripts/dpm_solver_jax.py instead of relying on an original checkout.
3. Use references/solver-choice-guide.md before recommending algorithm_type, order, method, steps, skip_type, thresholding, or denoising settings.
4. Run scripts/check_dpm_solver_environment.py when imports, PyTorch/JAX availability, CUDA visibility, or a tiny numerical smoke test are uncertain.
5. Treat full image generation, pretrained checkpoints, FID evaluation, Stable Diffusion weights, dataset downloads, and multi-GPU training as opt-in: they are network-, credential-, GPU-, memory-, and time-sensitive.
6. Read references/repo-provenance.md before refreshing this skill against a newer source snapshot.

Route By Task

User task	Read
Copy DPM-Solver into custom code, wrap a diffusion model, choose noise schedules, inspect API signatures, run tiny smoke tests	sub-skills/core-api/SKILL.md
Adapt DDPM, guided-diffusion, ScoreSDE PyTorch sampling commands, configs, checkpoints, or DPM-Solver flags	sub-skills/torch-examples/SKILL.md
Use the JAX ScoreSDE integration, understand JAX-specific solver differences, pmap/device behavior, or JAX caveats	sub-skills/jax-examples/SKILL.md
Add DPM-Solver to latent Stable Diffusion, plan txt2img --dpm_solver, use the sampler adapter, or troubleshoot model-weight/runtime constraints	sub-skills/stable-diffusion/SKILL.md

Key Operating Facts

• Direct use imports NoiseScheduleVP, model_wrapper, and DPM_Solver from the PyTorch or JAX module.
• NoiseScheduleVP(schedule="discrete", betas=...) or NoiseScheduleVP(schedule="discrete", alphas_cumprod=...) converts a discrete diffusion schedule to continuous time labels in (0, 1].
• NoiseScheduleVP(schedule="linear", continuous_beta_0=0.1, continuous_beta_1=20.) covers continuous VP SDEs used by ScoreSDE-style examples. The bundled JAX module also exposes schedule="cosine"; the root PyTorch file does not, while the Stable Diffusion nested copy does.
• PyTorch DPM_Solver(..., algorithm_type="dpmsolver++") selects data prediction internally and is the common default for guided sampling. The JAX API uses predict_x0=True for the DPM-Solver++-style path.
• method="multistep", order=2, skip_type="time_uniform", and roughly 20-25 steps are the Stable Diffusion / large-guidance default family.
• method="singlestep" or method="multistep" with order 3 is the common exploration path for unconditional or lightly guided sampling.
• correcting_x0_fn="dynamic_thresholding" is for pixel-space guided sampling; do not use dynamic thresholding for latent-space Stable Diffusion.

Installation And Import Baseline

There is no root package metadata. For custom code, copy one bundled solver file into the target project or keep it on PYTHONPATH and install only the backend needed by that file:

python -m pip install torch        # for dpm_solver_pytorch.py
python -m pip install jax jaxlib   # for dpm_solver_jax.py CPU use

Minimal PyTorch import and smoke check:

python scripts/check_dpm_solver_environment.py --backend torch --smoke

Minimal JAX import and smoke check:

python scripts/check_dpm_solver_environment.py --backend jax --smoke

Root References And Tools

• references/solver-choice-guide.md: practical solver setting choices for unconditional, guided, low-resolution, high-resolution, and latent-space workflows.
• references/api-summary.md: compact signatures, naming differences, return behavior, and caveats for the PyTorch and JAX solver files.
• references/troubleshooting.md: install, import, backend, numerical, guidance, checkpoint, and optional dependency troubleshooting shared across sub-skills.
• scripts/check_dpm_solver_environment.py: self-contained import/backend/smoke checker for the bundled solver copies.
• scripts/dpm_solver_pytorch.py and scripts/dpm_solver_jax.py: source-derived bundled implementations future agents can copy into projects.

Safety And Scope Boundaries

• Do not start model downloads, dataset downloads, FID computation, Stable Diffusion generation, notebook execution, or multi-GPU sampling unless the user explicitly approves the cost and runtime.
• Do not assume a CUDA, ROCm, MPS, TPU, or JAX accelerator backend is available; run a backend probe and use CPU only for tiny API smoke tests unless real generation is requested.
• Do not route Hugging Face diffusers scheduler API tasks here when the user is using the diffusers package directly; prefer a Diffusers-specific skill for DPMSolverMultistepScheduler pipelines.
• Do not tell future agents to run original repository examples. If a workflow is useful, use this skill's distilled references and bundled command builders.
• Keep Stable Diffusion safety, license, checkpoint, and gated-weight requirements explicit when constructing commands.

Verification Expectations

Start with the smallest safe checks: import the backend, construct a linear noise schedule, run a zero-model sample on a tiny tensor, and validate planned command arguments without loading checkpoints. Full native example parity is blocked by external checkpoints/datasets and should be recorded as skipped or opt-in unless the user provides the assets and hardware.