ModelsLab/RedCraft | 红潮 CADS | UPdated-May11 | Commercial & Advertising Design System - RED-Edit (ICEdit)1.1🔥多任务

redcraftcadsupdatedmay11commercialadvertisingdesignsystem-redediticedit11

Text to Image Community ModelFree for Premium Users LLMs.txt

5.11 更新RED-Edit v1.1(基于ICEdit normal LoRA) 多指令优化

5.11 右侧下载列表中的 Trainning data 工作流更新

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ICEdit 多模态控制器(IC引导下的图像编辑)

In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer

基于 Flux.tools-Fill 最低仅需 6G 显存

近期出现的多模态控制器对C端用户越来越不友好

动不动就要20-30+显存，望而却步

ICEdit 让我们重新相信光

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RED-Edit 是在 RED-Fill (NSFW) 基础上合并了 ICEdit 训练权重

最低仅需 8步推理

工作流及模型文件在下载列表中

工作流打包为 "Trainning data" 压缩文件

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感谢： @river-zhang 及团队成员 浙江大学 & Harvard University

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Compared with commercial models such as Gemini and GPT-4O, our methods are comparable to and even superior to these commercial models in terms of character ID preservation and instruction following. We are more open-source than them, with lower costs, faster speed (it takes about 9 seconds to process one image), and powerful performance.

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进阶玩法1 Advanced 1：叠加 Flux.fill LoRas

可以获得更稳定的修改结果：

[Flux Fill] Virtual Try Off 虚拟脱衣

@xiaozhijason / [Flux Fill] Virtual Try Off - alpha

Object Removal Flux Fill v2 物体移除

@xiaozhijason / Object Removal Flux Fill v2 - v2.0

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进阶玩法2 Advanced 2：叠加 Flux IC-LoRA

虚拟换装 In-Context 模型地址：

xiaozaa/catvton-flux-lora-alpha at main

感谢 T8x 提到的这个方案，十字鱼提供整合包下载

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@article{zhang2025ICEdit,
  title={In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer},
  author={Zhang, Zechuan and Xie, Ji and Lu, Yu and Yang, Zongxin and Yang, Yi},
  journal={arXiv},
  year={2025},
  url={https://arxiv.org/abs/2504.20690},
}

API Playground API Documentation

API Endpoint URL

Base URL for all API requests to this endpoint.

https://modelslab.com/api/v6/images/text2img

API Authentication

Authentication requires a valid API key included in the request. Generate and manage your API keys from your developer dashboard. Include the key in the key parameter for all API requests.

Integration Examples

Production-ready code samples for API integration

{
  "prompt": "R3alisticF, hauntingly beautiful oriental necromancer, long flowing brown hair, bangs, darkly tanned skin, earrings, bone necklaces, dark eyeshadow, red lips, vibrant, front-laced transparent, filmy silk blouse, cleavage, holding skull, in a sandstone room lit by candles, High Detail, Perfect Composition, high contrast, silhouetted, chiascuro",
  "model_id": "redcraftcadsupdatedmay11commercialadvertisingdesignsystem-redediticedit11",
  "lora_model": [],
  "width": "1024",
  "height": "1024",
  "negative_prompt": "(worst quality:2), (low quality:2), (normal quality:2), (jpeg artifacts), (blurry), (duplicate), (morbid), (mutilated), (out of frame), (extra limbs), (bad anatomy), (disfigured), (deformed), (cross-eye), (glitch), (oversaturated), (overexposed), (underexposed), (bad proportions), (bad hands), (bad feet), (cloned face), (long neck), (missing arms), (missing legs), (extra fingers), (fused fingers), (poorly drawn hands), (poorly drawn face), (mutation), (deformed eyes), watermark, text, logo, signature, grainy, tiling, censored, nsfw, ugly, blurry eyes, noisy image, bad lighting, unnatural skin, asymmetry",
  "num_inference_steps": "31",
  "scheduler": "DPMSolverMultistepScheduler",
  "guidance_scale": "7.5",
  "enhance_prompt": false,
  "key": "YOUR_API_KEY"
}

SDKs

Official SDKs

Production-ready SDKs and client libraries for all major programming languages

JavaScript

Python

PHP

API Parameters

Technical specifications for API request parameters.

Field Name	Parameter	Technical Description
prompt	`prompt`	prompt help in image generation
Model	`model_id`	Enter model_id that can help in image generation
lora Model	`lora_model`	No description available
Width	`width`	width of the image
Height	`height`	height of the image
Negative Prompt	`negative_prompt`	Negative prompt help in avoid things that you do not want in image
Steps	`num_inference_steps`	Number of inference steps
Scheduler	`scheduler`	Sampling scheduler
Guidance Scale	`guidance_scale`	How closely to follow the prompt (1-10)
enhance_prompt	`enhance_prompt`	Automatically enhance the prompt