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decart-ai/Lucy-Edit-Dev
Lucy-Edit-Dev is a video-to-video model from decart-ai. Use it for the video-to-video task on the model card, and read the license before you ship it in a product. It is set up for diffusers. The card lists the license as other.
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From the Hugging Face model README
Lucy Edit Dev is an open-weight video editing model that performs instruction-guided edits on videos using free-text prompts — it supports a variety of edits, such as clothing & accessory changes, character changes, object insertions, and scene replacements while preserving the motion and composition perfectly.
ℹ️ Model size: ~5B params. Build on top of Wan2.2 5B.
Note: The prompts above are not enriched, the model will react better to enriched prompts - as described in the prompt guideline section below.
pip install git+https://github.com/huggingface/diffusers
Please refer to the "Prompting Guidelines & Supported Edits" section for the best experience.
from typing import List
import torch
from PIL import Image
from diffusers import AutoencoderKLWan, LucyEditPipeline
from diffusers.utils import export_to_video, load_video
# Arguments
url = "https://d2drjpuinn46lb.cloudfront.net/painter_original_edit.mp4"
prompt = "Change the apron and blouse to a classic clown costume: satin polka-dot jumpsuit in bright primary colors, ruffled white collar, oversized pom-pom buttons, white gloves, oversized red shoes, red foam nose; soft window light from left, eye-level medium shot, natural folds and fabric highlights."
negative_prompt = ""
num_frames = 81
height = 480
width = 832
# Load video
def convert_video(video: List[Image.Image]) -> List[Image.Image]:
video = load_video(url)[:num_frames]
video = [video[i].resize((width, height)) for i in range(num_frames)]
return video
video = load_video(url, convert_method=convert_video)
# Load model
model_id = "decart-ai/Lucy-Edit-Dev"
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = LucyEditPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
pipe.to("cuda")
# Generate video
output = pipe(
prompt=prompt,
video=video,
negative_prompt=negative_prompt,
height=480,
width=832,
num_frames=81,
guidance_scale=5.0
).frames[0]
# Export video
export_to_video(output, "output.mp4", fps=24)
Lucy Edit is built for precise, realistic, and identity-preserving video edits.
Prompts with ~20–30 descriptive words work best. Using the right trigger words helps the model understand your intent.
✅ Best performance. Lucy Edit excels at swapping outfits while preserving motion, pose, and identity.
Example: “Change the shirt to a kimono with wide sleeves and patterned fabric.”
✅ Strong results. Works well for transforming people into new characters or creatures. Detailed prompts are key.
Example: “Replace the person with a tiger, striped orange fur, muscular build, and glowing green eyes.”
Example: “Replace the person with an 2D anime character, big eyes, blue gown and battle scars.”
✅ Reliable for structure-preserving swaps. Ideal when replacing one object with another of similar scale.
Example: “Replace the apple with a glowing crystal ball emitting blue light.”
⚠️ Mixed reliability. Sometimes subtle, sometimes exaggerated. Works best with precise descriptions.
Example: “Change the jacket color to deep red leather with a glossy finish.”
⚠️ Often attaches to the subject. Works best for wearable or handheld props.
Example: “Add a golden crown on the person’s head, decorated with ornate jewels.”
⚠️ Effective for backgrounds or scene-wide changes, might alter the subject Alter environment or style, might, Often changes the identity of the subject. Example: “Transform the sunny beach into a snowy tundra with falling snowflakes.”
LucyEditPipeline)@article{decart2025lucyedit,
title = {Lucy Edit: Open-Weight Text-Guided Video Editing},
author = {DecartAI Team},
year = {2025}
url = { https://d2drjpuinn46lb.cloudfront.net/Lucy_Edit__High_Fidelity_Text_Guided_Video_Editing.pdf}
}
Lucy Edit Dev builds on the excellent foundations of Wan2.2 (5B), and thanks the broader open-source community including diffusers and Hugging Face.