---

name: adapters-and-loaders description: "Use when a Diffusers task involves loading, composing, validating, fusing, unloading, or diagnosing LoRA/PEFT adapters, textual inversion embeddings, IP-Adapters, T2I-Adapters, ControlNet components, or single-file checkpoints." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Adapters And Loaders

Use this sub-skill for adapter and checkpoint-loading work after the base Diffusers environment and pipeline family are known.

Route Here For

• LoRA pipeline loading with load_lora_weights, adapter_name, set_adapters, fuse_lora, unfuse_lora, unload_lora_weights, delete_adapters, get_list_adapters, and enable_lora_hotswap.
• PEFT component adapters on UNet/transformer classes with load_lora_adapter, add_adapter, set_adapter, set_adapters, enable_lora, disable_lora, fuse_lora, unfuse_lora, unload_lora, and delete_adapters.
• Textual inversion with load_textual_inversion, token, weight_name, negative embeddings, and multi-vector prompt conversion.
• IP-Adapter loading with load_ip_adapter, set_ip_adapter_scale, unload_ip_adapter, image encoder choices, FaceID/Plus variants, and ip_adapter_image_embeds.
• T2I-Adapter and ControlNet component loading plans, including T2IAdapter.from_pretrained, MultiAdapter, ControlNetModel.from_pretrained, and passing components into compatible pipelines.
• Single-file checkpoint loading with from_single_file, local config, original_config, safetensors/ckpt/DDUF/GGUF decisions, and offline path checks.

Route Elsewhere

• End-to-end generation, prompt tuning, scheduler choice during inference, memory/offload execution, or image postprocessing: use ../pipelines-and-inference/SKILL.md.
• Training LoRA/textual inversion/adapter weights: use ../training-recipes/SKILL.md.
• Conversion scripts, repository maintenance, copied-code propagation, or docs build work: use ../conversion-and-maintenance/SKILL.md.

Fast Workflow

1. Classify the request: LoRA/PEFT, textual inversion, IP-Adapter, T2I-Adapter, ControlNet component, or single-file checkpoint.
2. Run a no-download preflight for local files and imports when useful: python scripts/adapter_state_check.py --path ./weights.safetensors --class StableDiffusionXLPipeline.
3. Choose explicit names and paths before loading: adapter_name, weight_name, subfolder, token, config, local_files_only, device, and dtype.
4. Use references/workflows.md for copy-ready loading plans and references/api-map.md for the method surface.
5. If an error mentions PEFT, adapter names, missing keys, shape mismatch, offline configs, image encoders, scale length, or device/dtype, use references/troubleshooting.md before changing dependencies or model code.

Key Rules

• Use explicit adapter_name values for every LoRA/PEFT load; inspect get_list_adapters() and get_active_adapters() before activating, fusing, or deleting adapters.
• Treat fuse_lora as a final inference optimization. Call unfuse_lora() before changing adapter combinations and keep unfused state if reversibility matters.
• For local/offline work, pass exact weight_name, subfolder, config, and local_files_only=True; do not rely on Hub sibling lookup or config inference.
• Match components to pipeline family: SD/SDXL usually use unet; SD3/Flux/newer transformer pipelines usually use transformer; SDXL can include text_encoder_2.
• ControlNet and T2I-Adapter are loaded as separate components and passed into compatible pipelines; they are not LoRA weights.
• Prefer .safetensors over .ckpt for untrusted files; .ckpt can execute pickle payloads during loading.

References

• Adapter workflows: references/workflows.md
• API map: references/api-map.md
• Troubleshooting: references/troubleshooting.md
• Safe checker: scripts/adapter_state_check.py
• Single-file template: scripts/single_file_loader_template.py