---

name: inference description: "Generate images from Custom Diffusion delta checkpoints for SD 1.x-style and SDXL models, including prompt files, compressed deltas, and pipeline load behavior." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Inference

Use this sub-skill when you already have a Custom Diffusion delta checkpoint and need image generation.

It covers:

• single-prompt and prompt-file sampling
• compressed and uncompressed delta checkpoints
• SD 1.x-style and SDXL sampling paths
• output naming and sample-layout expectations

It does not cover training or delta math. Route those tasks to ../training/SKILL.md and ../checkpoint-tools/SKILL.md.

Start here

1. Read references/api-reference.md.
2. Read references/workflows.md.
3. Run ../checkpoint-tools/scripts/check_delta_layout.py before a long sampling run.
4. Check references/troubleshooting.md when the delta layout, model cache, or prompt file is wrong.

Runtime notes

• The source sampler uses CUDA, a fixed seed, 200 inference steps, and guidance scale 6.
• A compressed delta needs the matching compression flag or a layout checker will flag the u / v payload.
• The SDXL path uses the XL pipeline and the dual-text-encoder load behavior described in the API reference.