Image models · Cagliostro Lab

Animagine XL 3.1

Name: Animagine XL 3.1
Author: Cagliostro Lab

Backbone: 2.6B
Native output: 1024×1024
Default steps: 28
License: openrail++
Released: 2024-03

How to run it

Original weights · Hugging Face

Full-precision files from Cagliostro Lab, for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/cagliostrolab/animagine-xl-3.1 (opens in a new tab)

No sign-in needed.

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

Apps that run Animagine XL 3.1

Our pick for each kind of computer, assuming the model fits in its memory. Pick your own GPU or device in the Model Finder for an exact answer.

Mac (Apple silicon)

Draw Things

Native Mac app with its own fast Metal engine and quantized downloads.

Also good:

Windows PC, NVIDIA GPU

ComfyUI

Runs this model with built-in or popular custom nodes; GGUF files load with ComfyUI-GGUF.

Also good:

Linux, NVIDIA or AMD GPU

ComfyUI

Runs this model with built-in or popular custom nodes; GGUF files load with ComfyUI-GGUF.

Also good:

App	Type	Opens it as	Systems	Engine	Start
ComfyUI	Desktop app	Built-in support	macOSWindowsLinux	PyTorch, GGUF via ComfyUI-GGUF	Get ComfyUI
Draw Things	Desktop app	Built-in support	macOSiOS	Draw Things	Open in Draw Things
DiffusionBee	Desktop app	Built-in support	macOS	DiffusionBee	Open in DiffusionBee
Invoke	Desktop app	Built-in support	macOSWindowsLinux	PyTorch	Get Invoke
SwarmUI	Desktop app	Built-in support	macOSWindowsLinux	ComfyUI	Get SwarmUI
Forge Neo	Desktop app	Built-in support	WindowsLinux	PyTorch	Get Forge Neo
Fooocus	Desktop app	Built-in support	macOSWindowsLinux	PyTorch	Get Fooocus
SD.Next	Desktop app	Built-in support	macOSWindowsLinux	PyTorch	Get SD.Next
Stability Matrix	Desktop app	Built-in support	macOSWindowsLinux	installs ComfyUI, Forge, InvokeAI, SwarmUI, SD.Next or Fooocus	Get Stability Matrix
Krita AI Diffusion	Desktop app	Built-in support	macOSWindowsLinux	ComfyUI	Get Krita AI Diffusion

Which apps can open a model depends on the files published for it: GGUF for llama.cpp-based apps, MLX for Apple’s engine, the original weights for vLLM and SGLang. Compare every app.

Memory needed

Peak GPU memory at the native output size. Offloading moves the text encoder, or the whole pipeline, to system RAM between steps.

Precision	Everything on GPU	Text encoder offloaded	Sequential offload	Download
`FP16`	8.9 GB	7.3 GB	3.8 GB	Original weights (opens in a new tab)
`FP8`	5.6 GB	4.7 GB	2.9 GB	Original weights (opens in a new tab)

Components

Backbone: 2.57B
VAE: 0.084B
CLIP-L: 0.123B
OpenCLIP bigG: 0.695B

Best hardware for Animagine XL 3.1

The least expensive GPU in our catalog that runs it without offloading is the Intel Arc A380 (launch price $139) at FP8.

Quality	Hardware	Quant	Memory	Speed	Runs
56Good quality (B) · 56 of 100: how capable the model is at FP16, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 4070 SUPER	`FP16`	8.9 GB / 12.0 GB	7.9 s	Runs great
56Good quality (B) · 56 of 100: how capable the model is at FP16, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking.	AMD Radeon RX 9070 XT	`FP16`	8.9 GB / 16.0 GB	5.6 s	Runs great
56Good quality (B) · 56 of 100: how capable the model is at FP16, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 4070 Ti SUPER	`FP16`	8.9 GB / 16.0 GB	6.7 s	Runs great
56Good quality (B) · 56 of 100: how capable the model is at FP16, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 4080 SUPER	`FP16`	8.9 GB / 16.0 GB	5.9 s	Runs great
56Good quality (B) · 56 of 100: how capable the model is at FP16, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 5070 Ti	`FP16`	8.9 GB / 16.0 GB	6.7 s	Runs great
56Good quality (B) · 56 of 100: how capable the model is at FP16, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 5080	`FP16`	8.9 GB / 16.0 GB	5.6 s	Runs great
54Fair quality (C) · 54 of 100: how capable the model is at FP8, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 4060	`FP8`	5.6 GB / 8.0 GB	14 s	Runs well
56Good quality (B) · 56 of 100: how capable the model is at FP16, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking.	AMD Radeon RX 7900 XTX	`FP16`	8.9 GB / 24.0 GB	4.7 s	Runs great
56Good quality (B) · 56 of 100: how capable the model is at FP16, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 3090	`FP16`	8.9 GB / 24.0 GB	7.9 s	Runs great
56Good quality (B) · 56 of 100: how capable the model is at FP16, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 4090	`FP16`	8.9 GB / 24.0 GB	4.2 s	Runs great

Check it on your own hardware

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.