In this week’s episode of Denoised, Addy breaks down what a LoRA (Low-Rank Adaptation) is and why creative teams should care. He uses a simple metaphor to make the mechanics intuitive, then moves through where LoRAs fit across image, audio, and video models, what they are not, practical uses for production workflows, and how LoRAs compare to reference-image workflows.
What a LoRA actually is — the Mexican restaurant metaphor
Addy compares an image model to a busy Mexican restaurant. The restaurant represents the base model: it has trained staff, a kitchen, equipment, and a stocked fridge of raw ingredients. Its job is simple — take inputs and produce consistent outputs, like tacos, burritos, or chips and salsa.
A LoRA is like bringing a guest chef into that kitchen — someone with a distinct culinary background who doesn’t redo the restaurant but shifts how the ingredients are used. In the episode, Addy uses chef Roy Choi as the example. Introduced into the restaurant, Roy can take the same tortillas, meats, and produce and create bulgogi kimchi tacos — a new flavor profile that borrows from the base kitchen but carries a clear, repeatable signature.
Translated back to model terms: a LoRA is a small, attachable module that encodes specific stylistic or content-related knowledge and biases the base model’s outputs. It does so without retraining the entire model or replacing the underlying weights. The result is consistent, repeatable outputs that retain the underlying model’s capabilities while adopting the LoRA’s unique influence.
LoRAs across image, audio, and video models
Addy points out that the LoRA concept is not limited to images. The same idea maps cleanly to audio and video systems — it’s about adding a targeted layer of expertise to an existing pipeline.
Image models: The classic use case. Train a LoRA on a set of images and it nudges the base model toward that look or subject.
Audio models: Think of a food truck — smaller, mobile, less capacity. A LoRA can still inject a vocal timbre or performance style into an audio model that has less bandwidth than a full studio system.
Video models: Visualize a Michelin-starred kitchen — more budget, more ingredients, larger scope. A LoRA can add a director-like signature to a video generation pipeline without rebuilding the whole stack.
What a LoRA is not
Clearing up common misinterpretations helps teams choose the right approach. Addy emphasizes three negative definitions to avoid:
A LoRA is not a new model. It does not replace the base architecture or its training data.
A LoRA is not a full weight retrain. It’s not a complete remodel of the restaurant — no new stoves, furniture, or HVAC systems.
A LoRA is not a superficial filter. It adds learned behavior rather than simply applying a post-process look.
Practically, that means LoRAs are lightweight, attachable, and focused. They work alongside the existing model, using the same “ingredients” and equipment to produce outputs with a distinct bias or signature.
How filmmakers and creators can use LoRAs
Addy runs through a shortlist of production-friendly applications where LoRAs make tangible workflow improvements.
Consistent characters — Train a LoRA on reference photos of a character or actor and the model will reproduce that likeness consistently across many generations. This is useful for concept art, previs, and automated asset creation.
Repeatable wardrobe or props — When costumes or key props need to be identical across shots, a LoRA can stabilize appearance across angles and lighting.
Specific look and feel — Want a film noir palette or a particular color grading baked into generated frames? A LoRA trained on those samples will bias future outputs toward that aesthetic.
Detail enhancement — Train a LoRA to add micro details such as skin texture, wrinkles, or hair rendering. Small, focused tasks can be handled exceptionally well by a targeted LoRA.
For production teams, the major advantages are predictability and scale. When the goal is to automate hundreds or thousands of assets with a single visual signature, a LoRA attached to an open model lets studios push consistent results through batch generation.
LoRA versus reference-image workflows
Addy draws a practical distinction between two common customization routes: attaching a LoRA to an open model or feeding reference images into a closed model.
Reference-image workflows — Common in closed models. You supply several reference images per generation and the model tries to match them on the fly. This is straightforward for one-offs or small runs where quality and hands-on control matter more than scale.
LoRA workflows — Common with open-source models that allow weight attachments. A LoRA is trained once and then scales across large batches, enabling automation and consistent outputs without repeatedly supplying references.
Addy’s rule-of-thumb: use reference-image methods for high-quality, low-volume needs; use LoRAs when you need volume, automation, and repeatability. The choice also hinges on model openness. Closed systems like some commercial tools typically rely on reference inputs, while open-source models support LoRA attachments and more flexible retraining.
Closing thoughts
LoRAs are a pragmatic middle path: they let creative teams add targeted knowledge to existing models without the time and cost of full retraining. For filmmakers, that means a reliable way to automate consistent characters, styles, and small but critical details across large batches of generative assets. When combined with an appropriate model choice and a clear production brief, LoRAs become a practical lever for scaling visual workflows.
