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Preview Note: This document provides a quick overview of deploying and using CKNOOB; it is suitable for any audience.
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When publishing AI-generated images online, you must comply with local laws and regulations.
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Machine Translation Warning: This document was translated using machine translation and may contain errors or omissions. The original text before translation is [here].
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NOOBXL was an exceptional model, boasting knowledge and ease of use that far surpassed any open-source model in the anime niche at the time, while also holding its own against closed-source models. One year after NOOBXL's open-sourcing, the community welcomed its official successor: CKNOOB.Operationally, CKNOOB maintains NOOBXL's simplicity while preserving extensive character concepts and incorporating newly updated concepts and imagery from the past year. Simply put, this represents a comprehensive upgrade to NOOBXL. For first-time users of anime models, CKNOOB is currently the optimal choice.
CKNOOB is trained on the NOOBXLEPS model, incorporating new datasets while preserving NOOBXL's original data. This allows the model to acquire vast new concepts without forgetting its existing knowledge, expanding the total dataset to nine million entries. Architecturally, it remains based on SDXL with no additional modifications.For those unfamiliar with NOOBXL or SDXL architecture, here's a brief overview: SDXL is a text-to-image model open-sourced by Stability AI in early 2024. It employs a UNET backbone with a dual-text encoder architecture and utilizes a 4-channel VAE, making it relatively lightweight in resource consumption.
Regarding usage, CKNOOB supports prompts written in danbooruTAG format as well as simple natural language. The dataset is current as of December 2025; characters with over 100 danbooru images typically yield good results. Since CKNOOB currently lacks aesthetic fine-tuning or any post-training, complete prompts are required to achieve optimal outcomes.
Currently, CKNOOB offers the following models, each with distinct parameters and specific usage requirements. Click a model name to jump directly to its download page.
| Model Name | Features |
|---|---|
| Chenkin Noob XL V0.1 | CKNOOB's first version, an early test release. |
| Chenkin Noob XL V0.2 | The second version of CKNOOB, further trained based on V0.1, currently the latest model in the CKNOOB series. |
| Chenkin Noob XL RF | Experimental version of CKNOOB, adapted for RF prediction based on V0.2. |
RF prediction will be covered separately due to its use of distinct techniques. For detailed instructions, refer to [the Chenkin Noob XL RF User Guide ].
If you're new to charting AI and unsure which version to download, start with Chenkin Noob XL V0.2.
The following explanation will use the main model branch of Chenkin Noob XL.
To use CKNOOB, you typically have two options: online cloud generation or offline local deployment. The key difference is that online cloud services are usually paid, while local deployment has certain hardware requirements for your device. If you're already using either generation method, you can proceed directly to the subsequent sections.
Online generation generally offers faster processing speeds, a more comfortable generation experience, and the ability to generate images from any device. However, these services are typically paid.
We won't recommend specific online services here, but will use Civitai as an example for demonstration purposes.