mirror of https://github.com/vladmandic/automatic
Page:
IPAdapter
Pages
AMD MIOpen
AMD ROCm
API
Advanced Install
Backend
Benchmark
CHANGELOG
CLI Arguments
CLI Tools
CLiP Skip
Caption
Control HowTo
Control Settings
Control Technical
Debug
Detailer
DirectML
Docker
Docs
Enso
Extensions
FAQ
FLUX
Features
FramePack
Gated
Getting Started
Google GenAI
HiDream
Hints
Home
Hotkeys
HuggingFace
IPAdapter
Installation
Intel ARC
Kanvas
LTX
Launcher
LoRA
Loader
Locale
MacOS Python
Malloc
Model Support
Models Tab
Models
Networks Search
Networks
Notes
NudeNet
Nunchaku
ONNX Runtime
Offload
OpenVINO
Outpaint
Parameters
Performance Tuning
Process
Profiling
Prompt Enhance
Prompting
Python
Quantization
Reprocess
SD Pipeline How it Works
SD Training Methods
SD XL
SD3
SDNQ Quantization
Schedulers
Scripts
Stability Matrix
Stable Cascade
Styles
Theme User
Themes
Troubleshooting
Update
Using LCM
VAE
Video
WSL
Wildcards
XYZ Grid
ZLUDA
_ToDo
index
nVidia
2
IPAdapter
Vladimir Mandic edited this page 2024-11-08 10:49:52 -05:00
Table of Contents
IP-Adapter
The IP-Adapter is a tool designed for style transfer with minimal resource usage. It provides an efficient way to clone faces or apply image transformations. It supports both SD 1.5 and SD-XL models, allowing for quick and cost-effective style transfer processes.
Key Features
- Low Resource Usage: The IP-Adapter is lightweight, with memory requirements under 100MB for SD 1.5 and 700MB for SD-XL, making it an efficient choice for style transfer tasks.
- Style Transfer: It offers powerful style transfer capabilities, allowing you to clone faces or apply various image styles.
- Integration with ControlNet: IP-Adapter can be combined with ControlNet for more stable results, especially useful for batch processing or video tasks.
Available Models
The TenecentAILab IP-Adapter includes 10 models for various image or face cloning needs, enabling flexibility and versatility in different scenarios.