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MODEL Listed

animagine-xl-3.1

animagine-xl-3.1 is a text-to-image diffusion model optimized for generating high-quality anime and manga-style artwork. It's designed for straightforward integration with Hugging Face's diffusers library, making it accessible for developers building creative applications or prototyping generative art features. The model works well with standard Stable Diffusion pipelines, though you'll want to review the model card and OpenRail++ license before deploying in production. It's particularly strong at rendering detailed character designs, expressive faces, and vibrant color palettes typical of Japanese animation aesthetics. While parameter count isn't specified, the model balances quality and performance reasonably well for local inference. Compared to other anime-focused models, it offers good prompt adherence and supports various customization techniques like LoRA fine-tuning. Ideal use cases include game asset generation, character design assistance, and fan art creation tools. Keep in mind it may struggle with non-anime styles or complex scene compositions involving multiple characters.

cagliostrolabtext to image
01 / MODEL CARD

Model card

animagine-xl-3.1 is a text-to-image diffusion model optimized for generating high-quality anime and manga-style artwork. It's designed for straightforward integration with Hugging Face's diffusers library, making it accessible for developers building creative applications or prototyping generative art features. The model works well with standard Stable Diffusion pipelines, though you'll want to review the model card and OpenRail++ license before deploying in production. It's particularly strong at rendering detailed character designs, expressive faces, and vibrant color palettes typical of Japanese animation aesthetics. While parameter count isn't specified, the model balances quality and performance reasonably well for local inference. Compared to other anime-focused models, it offers good prompt adherence and supports various customization techniques like LoRA fine-tuning. Ideal use cases include game asset generation, character design assistance, and fan art creation tools. Keep in mind it may struggle with non-anime styles or complex scene compositions involving multiple characters.

Model typetext to image
Providercagliostrolab
Licenseopenrail++
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/cagliostrolab/animagine-xl-3.1
View model source
Version informationUse the source repository for the latest version
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03 / DOWNLOAD

Download this model

We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.

This entry points to Hugging Face. The commands use the matching ModelScope repository format; confirm that the repository exists on ModelScope before running them. Model repository: cagliostrolab/animagine-xl-3.1
Install ModelScope

Install the CLI and SDK dependency before downloading.

pip install modelscope
Download the full model repository

Download the complete weights, configuration and model card.

modelscope download --model cagliostrolab/animagine-xl-3.1
Download one file to a local directory

README.md is used as an example; replace it with another repository file when needed.

modelscope download --model cagliostrolab/animagine-xl-3.1 README.md --local_dir ./dir
Download with the SDK

Useful in Python projects and automation scripts.

from modelscope import snapshot_download
model_dir = snapshot_download('cagliostrolab/animagine-xl-3.1')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/cagliostrolab/animagine-xl-3.1.git
Clone without downloading LFS blobs

Fetch the repository structure first, then pull large files when needed.

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/cagliostrolab/animagine-xl-3.1.git
04 / WORKFLOW

How to use

  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

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