Model reference · open weights

LongCat-Image-Ed

Available as managed deployment Image rootlocalghost · community Image edit 1 variants 1k dl/mo

LongCat-Image-Ed is an open-weight image model from rootlocalghost. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

Released byrootlocalghost
TypeImage models
TaskImage edit
Runs withtransformers
Released2026-05-04
Popularity1k downloads / month
LicenceOpen weights

About

What LongCat-Image-Ed is

Introduction

We introduce LongCat-Image-Edit-Turbo, the distilled version of LongCat-Image-Edit. It achieves high-quality image editing with only 8 NFEs (Number of Function Evaluations) , offering extremely low inference latency.

Installation

pip install git+https://github.com/huggingface/diffusers

Run Image Editing

[!CAUTION] 📝 Special Handling for Text Rendering

Read the full model card

For both Text-to-Image and Image Editing tasks involving text generation, you must enclose the target text within single or double quotation marks (both English '...' / "..." and Chinese ‘...’ / “...” styles are supported).

Reasoning: The model utilizes a specialized character-level encoding strategy specifically for quoted content. Failure to use explicit quotation marks prevents this mechanism from triggering, which will severely compromise the text rendering capability.

import torch
from PIL import Image
from diffusers import LongCatImageEditPipeline

if __name__ == '__main__':
    device = torch.device('cuda')
    pipe = LongCatImageEditPipeline.from_pretrained("meituan-longcat/LongCat-Image-Edit-Turbo", torch_dtype= torch.bfloat16 )
    # pipe.to(device, torch.bfloat16)  # Uncomment for high VRAM devices (Faster inference)
    pipe.enable_model_cpu_offload()  # Offload to CPU to save VRAM (Required ~18 GB); slower but prevents OOM
    img = Image.open('assets/test.png').convert('RGB')
    prompt = '将猫变成狗'
    image = pipe(
        img,
        prompt,
        negative_prompt='',
        guidance_scale=1,
        num_inference_steps=8,
        num_images_per_prompt=1,
        generator=torch.Generator("cpu").manual_seed(43)
    ).images[0]
    image.save('./edit_example.png')

From the published model card. Full card on the HuggingFace links in the sidebar.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys rootlocalghost-longcat-image-ed for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (rootlocalghost-longcat-image-ed below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/images/generations \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"rootlocalghost-longcat-image-ed","prompt":"a red bicycle","size":"1024x1024"}'

Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.

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