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Blog
July 22, 2026·7 min read·app.nz

ComfyUI lesson 6: Use LoRAs without losing the base model

Balance model and CLIP strength, compare with a fixed seed, and avoid adapter pileups.

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A LoRA is a small adapter that nudges selected layers of a base model. It can add a style, character, object, or behavior without replacing the checkpoint. Small file does not mean “safe at any strength.”

Wire the adapter correctly

Put Load LoRA between the checkpoint and both downstream branches. Feed its MODEL to KSampler and its CLIP to the prompt encoders. If only the model path is adapted, trigger words and style semantics may behave differently from the LoRA author's examples.

Start with model strength 0.6 and CLIP strength 0.4–0.6. Fix the seed and render:

  • no LoRA (strength zero);
  • 0.4;
  • 0.7;
  • 1.0.

Look for the first point where the desired feature appears, then stop. Overshooting often causes crunchy edges, repeated motifs, distorted anatomy, or the same face in every composition.

Compatibility is not optional

An SD 1.5 LoRA does not belong on SDXL. A FLUX adapter does not belong on Proteus. Match architecture, read the source license, preserve the original filename, and record the source URL. The app.nz model linker accepts public Hugging Face files and Civitai versions and can warm a shared content-addressed cache before the worker starts.

The runnable lesson uses Proteus V0.4 plus one detail adapter and exposes LoRA strength as an API input. After the single-adapter experiment, try two LoRAs only if each one works alone. Blend at modest strengths; adapter stacking is a composition problem, not a contest to reach 1.0.

Run the lesson, do not just read it

  • Open the tested workflow in Comfy Studio
  • Download the exact API-format JSON
  • CLI: app comfy download proteus-v04-lora

The gallery records model sources, minimum VRAM, custom-node requirements, and verification state beside the graph. That is the portable unit for this series.


Series progress: lesson 6 of 10. Previous: Build image-to-image and master denoise Next: Control composition with ControlNet

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ComfyUI lesson 5: Build image-to-image and master denoise

Encode a source image and use denoise to choose between a retouch and a rebuild.

ComfyUI lesson 3: Prompt with intent, not incantations

Layer subject, composition, light, and finish; use negatives only to correct observed failures.

ComfyUI lesson 9: Automate batches, prompt lists, REST, CLI, and MCP

Treat workflow inputs as an API contract and keep a manifest for every batch.