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name: stable-diffusion description: "Use DPM-Solver with latent Stable Diffusion workflows, including DPMSolverSampler adaptation, txt2img command planning, inversion/editing concepts, and checkpoint/backend troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

Stable Diffusion

Use this sub-skill when a user asks how this repository accelerates latent Stable Diffusion sampling with DPM-Solver or how to adapt the repository's DPMSolverSampler pattern into another latent-diffusion codebase.

When To Read

Read this for tasks that mention:

• txt2img.py --dpm_solver, Stable Diffusion with DPM-Solver, 20-25 step Stable Diffusion sampling, or the original latent-diffusion scripts.
• DPMSolverSampler, stochastic_encode, encode, inversion for DiffEdit, inpainting/editing, or correcting_xt_fn in a latent sampler.
• Stable Diffusion checkpoint/config placement, safety checker/watermarking, GPU memory, or model-license constraints in this example family.

Workflow

1. Read references/stable-diffusion-workflows.md for command families, model asset requirements, and DPM-Solver settings.
2. Read references/dpmsolver-sampler-adapter.md when adapting the sampler class to a latent-diffusion model object.
3. Use scripts/build_sd_dpm_command.py to construct a command plan without launching image generation.
4. Use scripts/stable_diffusion_dpmsolver_sampler.py as a bundled, self-contained adapter template when a project already has a compatible latent diffusion model object.
5. Route direct Hugging Face Diffusers scheduler questions to a Diffusers skill instead of translating everything through this legacy latent-diffusion adapter.

Default Stable Diffusion Sampling Family

python scripts/txt2img.py \
  --prompt "a photograph of an astronaut riding a horse" \
  --dpm_solver \
  --ddim_steps 25 \
  --scale 7.5

The original example uses the same --ddim_steps argument name for the number of DPM-Solver steps when --dpm_solver is selected.

Sampler Defaults

The adapter builds a discrete schedule from the model's alphas_cumprod, wraps model.apply_model with classifier-free guidance, and calls the PyTorch DPM-Solver++ path:

DPM_Solver(model_fn, noise_schedule, algorithm_type="dpmsolver++")

Recommended defaults:

• skip_type="time_uniform"
• method="multistep"
• order=2
• lower_order_final=True
• 20-25 steps for common text-to-image usage

Do not enable dynamic thresholding for Stable Diffusion latents.

Safety Notes

• Stable Diffusion checkpoints may be gated, licensed, unsafe for some uses, or absent. Confirm asset availability and intended use before running.
• Real generation requires substantial GPU memory for classic latent-diffusion code. CPU execution is not a practical verification check.
• Safety checker and watermark dependencies can download models or require optional packages in the original script.
• DiffEdit/inpainting notebooks are evidence for workflows, not safe default commands.

Troubleshooting

Read references/troubleshooting.md for Stable Diffusion-specific checkpoint, config, sampler, safety checker, memory, DiffEdit, and command issues. Shared solver settings live in ../../references/solver-choice-guide.md.