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name: training description: "Guide diffusers Custom Diffusion fine-tuning for single concept, multi-concept, prior preservation, modifier tokens, and the SDXL variant." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Training

Use this sub-skill when you need to fine-tune Custom Diffusion with the diffusers training entry points.

It covers:

• single-concept and multi-concept training
• generated or real prior preservation
• modifier tokens and initializer tokens
• cross-attention freeze choices
• SDXL training and checkpoint output planning

It does not cover the legacy Stable Diffusion checkout path. Route those tasks to the diffusers-side route and treat the legacy checkout files as reference-only or excluded.

Start here

1. Read references/workflows.md.
2. Read references/cli-reference.md.
3. Read references/data-formats.md.
4. Run scripts/validate_training_inputs.py before an expensive launch.
5. Check references/troubleshooting.md when the run fails or stalls.

Route onward

• Use ../data-preparation/SKILL.md first when the concept data or prior bundle is not ready.
• Use ../inference/SKILL.md after a successful training run when you want to sample the result.
• Use ../checkpoint-tools/SKILL.md when you need to extract, compress, or compose the delta outputs.

Runtime notes

• The default path updates K/V cross-attention only.
• crossattn updates all cross-attention weights.
• SDXL uses a dual-text-encoder path and a larger default resolution.
• CUDA is required for truthful training guidance.