Edit Videos with AI for Free, Locally — Lucy Edit Dev Setup & Demo

Introduction

Editing video with natural language is finally practical on a local machine. Lucy Edit Dev is an instruction‑guided video editing model that runs on your own hardware, accepts free‑form text prompts, and applies precise edits to existing footage. It requires no masks, no keyframes, and no fine‑tuning. In this guide, I set it up locally, outline the model’s internals at a high level, and explain how to use it for prompt‑based editing.

The walkthrough follows the same flow as the original script: a quick model snapshot, local installation, first run, usage with prompts, performance notes, and closing thoughts.

What Is Lucy Edit Dev?

Lucy Edit Dev is an open‑weight, text‑driven video editing model with about 5 billion parameters. It’s designed to modify existing videos based on natural language instructions. You describe the change you want—such as recoloring an object, adjusting appearance, or adding/removing an element—and the model applies those edits across the clip while maintaining temporal consistency.

Edit Videos with AI for Free, Locally — Lucy Edit Dev Setup & Demo

Instruction‑guided: Accepts rich text prompts that describe the desired edit.
No extra scaffolding: Does not require manual masks, keyframes, or fine‑tuning.
Local‑first: Runs on your machine, with weights downloaded on first launch.

Quick Model Snapshot

Internally, Lucy Edit Dev builds on the VAN 2.2 foundation and combines:

A high‑compression variational autoencoder (VAE)
A diffusion transformer backbone

This composition helps the model maintain temporal coherence across frames—a core challenge in video editing—while also accounting for camera trajectory and preserving original motion dynamics. At around 5B parameters, it aims to balance edit fidelity with practical deployment on a single GPU.

Table Overview

Model at a Glance

Attribute	Detail
Model name	Lucy Edit Dev
Parameter size	~5B
Base	VAN 2.2
Input	Existing video + text instruction
Output	Edited video
Masks/keyframes required	No
Fine‑tuning required	No
Key capability	Text‑driven edits with temporal coherence
Typical edits	Recoloring, appearance changes, adding/removing objects

Local Setup and Performance (Observed)

Item	Detail
OS tested	Ubuntu
GPU tested	NVIDIA H100 80 GB VRAM
Initial GPU memory load	~28 GB (model fully loaded)
Peak usage (editing)	Up to ~39 GB (varies by edit and settings)
First run	Downloads the model and VAN 2.2 weights automatically
Interface used	Simple Gradio UI wrapper over the app script

Notes:

The VRAM numbers above reflect a high‑end GPU and can vary with edit complexity, video resolution, and settings.
The model’s first run triggers the weight downloads, which can take time depending on bandwidth.

Key Features

Free‑text editing: Modify videos using natural language instructions.
Object‑level control: Change colors, textures, and attributes for subjects and objects.
Add or remove elements: Insert new objects or accessories, or remove distractions.
Appearance edits: Adjust hair, skin, clothing color and material characteristics.
Temporal coherence: Edits remain consistent across frames, aligning with camera movement and motion.
Local operation: Run privately on your hardware with no third‑party editing services.

Installation and Setup

I set up Lucy Edit Dev on Ubuntu with an NVIDIA GPU. The process below mirrors the steps from the original walkthrough while keeping it general so you can adapt it to your environment.

Step 1: Prepare a Python Environment

Create a clean virtual environment (for example, with conda).
Use a recent Python version compatible with the model’s requirements.
Activate the environment.

Tip: A clean environment helps avoid dependency conflicts when installing the prerequisites listed in the model card.

Step 2: Install Prerequisites

From your project directory, install all required packages as specified by the model’s documentation.
Ensure your CUDA, drivers, and relevant libraries are correctly set up for GPU acceleration.

Step 3: Obtain the App Script

The original setup uses a Python script (for example, app.py) inspired by the model card’s code.
You can keep it simple: load the model and expose a function that takes a video + text prompt and returns the edited result.

Step 4: Add a Minimal UI (Optional)

For easier testing, place a lightweight Gradio interface on top of the script.
The UI should accept:
- A video file input
- A text field for the prompt
- Optional advanced settings (if supported)
The UI then displays or lets you download the edited output.

Step 5: Run the App

Start the app with your Python command.
On first run, the model and VAN 2.2 weights will download.
After initialization, open the local URL in your browser to access the interface.

Running Locally with a Simple UI

Once the app is running in your browser:

Upload a source video.
Enter a detailed text instruction describing the edit.
Adjust advanced settings if needed (some builds expose parameters for quality and consistency).
Start the edit and monitor GPU memory usage if you want a sense of the workload.
When the output is ready, preview and download it.

During my setup, the model fully loaded at around 28 GB of VRAM and peaked near 39 GB for more complex edits. Numbers vary with video length, resolution, and prompt complexity.

Editing Workflow

The editing process is straightforward:

Pick a short to medium‑length clip for testing.
Write a clear prompt that describes:
- The subject(s) or region(s) to change
- The change itself (color, texture, accessory, object)
- Desired attributes (material qualities, highlights, finish, shading)
Start the edit.
Review the output and refine the prompt if needed.

The more specific the instruction, the better the model tends to align with your intent. Rich, grounded prompts consistently produce higher‑quality results.

Prompt Examples (Structure, Not Content)

Avoid concrete examples or storytelling. Instead, structure your prompt like this:

Target specification: Identify the subject and location in the frame (e.g., “the person’s jacket,” “the left‑side building,” “the object on the table”).
Desired change: Specify the transformation (e.g., “change to [color/material],” “add [object/accessory],” “remove [element]”).
Visual qualities: Add descriptors for finish, highlights, shading, or material behavior under light.
Consistency cues: If the camera moves, emphasize maintaining consistent appearance across motion.

VRAM and Performance Notes

Model load: Expect about ~28 GB VRAM usage with a large setup when the model is fully loaded.
Editing peak: Usage can climb to the high 30s (GB) during more complex operations.
Video length and resolution: Higher resolution and longer clips increase memory pressure and processing time.
First run: Model weight downloads add to start‑up time; subsequent runs are faster.
Practical scale: With a ~5B parameter model, local deployment is viable on a single high‑memory GPU.

Temporal coherence is a strong point here. The combination of a VAE for compact latent representations and a diffusion transformer backbone helps the system maintain consistent edits frame‑to‑frame and respect underlying motion and camera trajectory.

Prompting Tips for Better Results

I’ve found these principles helpful when writing instructions:

Be specific about targets: Identify who/what to change and where they are in the frame.
Describe the change in visual terms: Color, material, texture, reflectivity, highlights, shadowing, and finish can all guide the outcome.
Provide context: If multiple similar objects exist, disambiguate the one you want edited.
Avoid ambiguity: Replace vague adjectives with clear visual descriptors.
Iterate: If the result is close but not exact, refine the prompt with concrete adjustments.
Keep edits scoped: Large, complex global changes can be harder to control. Targeted instructions are more predictable.

Capabilities and Typical Edits

Lucy Edit Dev supports a range of text‑guided modifications to existing footage. These were emphasized in the original walkthrough and align well with local production needs:

Recoloring and appearance edits
- Hair, skin, clothing
- Material properties (e.g., glossy vs. matte, subtle highlights, root shadowing)
Object addition
- Add a clearly described object in a specified location (e.g., on a shoulder, on the head, in the background)
- Include attributes such as colors, materials, and distinctive features
Object removal
- Remove a distraction or unwanted element
Accessories and apparel
- Add items such as sunglasses, hats, or other wearable elements
Motion‑aware edits
- Edits track with motion and camera changes, preserving underlying dynamics

These tasks are all instruction‑driven and do not require you to hand‑draw masks or set keyframes. The system reads your prompt and applies the change consistently across the clip.

How to Use Lucy Edit Dev (Step‑by‑Step)

Here is a practical, ordered guide following the original flow:

Prepare your system
- Use a Linux machine (Ubuntu tested) with an NVIDIA GPU and sufficient VRAM.
- Ensure CUDA drivers and dependencies are installed.
- Create and activate a fresh Python environment.
Install dependencies
- Follow the model card’s instructions for required packages and versions.
- Test GPU access from Python to confirm everything is configured.
Create the app script
- Base your script on the code provided in the model card.
- Expose a function that:
  - Loads the model
  - Accepts a source video and a text prompt
  - Returns the edited video
Add a simple Gradio interface (optional but convenient)
- Video input component
- Text input for the instruction
- Optional advanced settings section
- Output preview/download
Start the local app
- Run the Python script.
- Allow the model weights to download on first launch.
- Open the provided local URL in a browser.
Perform an edit
- Upload a video.
- Provide a detailed prompt.
- Start the edit, monitor progress, and review the output.
Refine as needed
- If the result is close but not exact, adjust the prompt with clearer descriptors.
- For persistent mismatches, try narrowing the scope or clarifying the target area and attributes.

Frequently Asked Questions

Do I need masks, keyframes, or fine‑tuning?

No. Lucy Edit Dev is instruction‑guided and applies edits based solely on your text prompt and the video input.

Will it respect motion and camera movement?

Yes. The architecture is designed to preserve motion dynamics and align with camera trajectory while maintaining temporal coherence.

How large is the model?

Approximately 5 billion parameters.

What is the base architecture?

It builds on VAN 2.2, pairing a high‑compression VAE with a diffusion transformer backbone.

Can it run locally?

Yes. You can run it on your own machine. The original walkthrough used Ubuntu with an NVIDIA GPU, downloading the weights on first run.

How much VRAM do I need?

The observed usage on a high‑end GPU was:

~28 GB when the model is fully loaded
Up to ~39 GB during editing, depending on complexity

Your mileage will vary based on clip length, resolution, and settings.

What kinds of edits does it support?

Recoloring and appearance changes for subjects and objects
Adding or removing elements
Accessories and apparel edits
Generally, targeted, text‑driven modifications to existing footage

Do prompts matter?

Yes. Detailed, grounded prompts tend to produce better results. Specify the subject, change, and visual qualities clearly.

Can I build a simple interface for testing?

Yes. A minimal Gradio interface makes it easy to upload a video, enter a prompt, and preview the result.

Conclusion

Lucy Edit Dev brings natural‑language video editing to local workflows in a practical way. With a ~5B parameter model, a VAE + diffusion transformer backbone, and a focus on temporal coherence, it can apply targeted, text‑driven changes without masks, keyframes, or fine‑tuning. The setup is straightforward: prepare a Python environment, install dependencies from the model card, run the app script, and (optionally) add a simple UI for rapid testing.

On a capable NVIDIA GPU, the model loads and runs locally with observed VRAM usage around 28 GB at rest and higher during complex edits. The key to reliable outcomes is the prompt: precise, well‑structured instructions make a clear difference in edit fidelity. For recoloring, appearance adjustments, and adding or removing objects, Lucy Edit Dev is a strong local option for instruction‑guided video editing.