# IPAdapter Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/ipadapter
> Markdown URL: https://aitinkerers.org/technologies/ipadapter.md
> Technology record last updated: 2026-02-26T21:35:41Z
> Generated: 2026-09-23T00:36:07Z

IPAdapter is a lightweight, 22M-parameter module that enables image prompting for pre-trained text-to-image diffusion models (e.g., Stable Diffusion) without extensive fine-tuning.

This is the Image Prompt Adapter (IPAdapter), a highly efficient solution from Tencent AI Lab for multimodal image generation. It integrates image conditioning into models like Stable Diffusion, using a decoupled cross-attention mechanism to process both image and text features simultaneously. With only 22M parameters, IPAdapter delivers performance comparable to fully fine-tuned models, making it resource-friendly (under 100MB for SD 1.5). The technology excels at specific tasks: style transfer, composition cloning, and specialized face ID applications, all while maintaining compatibility with existing control tools like ControlNet.

- Official technology site: https://github.com/tencent-ailab/IP-Adapter
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [StableGen - diffusion powered texturing within Blender](https://prague.aitinkerers.org/talks/rsvp_5HqwtbOGNaU)

This will be a 5-minute live demonstration of StableGen, a Blender plugin designed for AI-driven 3D texturing. I will begin with a standard untextured 3D model in Blender and walk through a typical texturing workflow. First, I'll set up the camera viewpoints and assign a text prompt for the desired texture. I will then initiate the generation process, which runs asynchronously in the background. While the plugin is working, I will show the key artifacts and workflows it manages, including the exported ControlNet images (like depth maps) used for geometric guidance and the dynamically generated ComfyUI workflow graph sent to the backend. I will then show the final output images generated for each viewpoint. To conclude, I will show the final, fully-textured 3D model in the Blender viewport and point to the complex material node tree that StableGen automatically built to seamlessly blend all the individual textures together. I could also make it interactive by having the audience choose what to generate. Note: I could also go through the code, but as the project is pretty large, I don't think 5 minutes is nearly enough to even start diving into that.

- Event context: 🍂 AI Tinkerers Prague – October 2025 Meetup — 2025-10-23 — Prague
- Public talk page: https://prague.aitinkerers.org/talks/rsvp_5HqwtbOGNaU

## Related Technologies

- [Blender](https://aitinkerers.org/technologies/blender) ([Markdown](https://aitinkerers.org/technologies/blender.md)) — 4 public demos
- [ComfyUI](https://aitinkerers.org/technologies/comfyui) ([Markdown](https://aitinkerers.org/technologies/comfyui.md)) — 13 public demos
- [ControlNet](https://aitinkerers.org/technologies/controlnet) ([Markdown](https://aitinkerers.org/technologies/controlnet.md)) — 6 public demos
- [Diffusion models](https://aitinkerers.org/technologies/diffusion-models) ([Markdown](https://aitinkerers.org/technologies/diffusion-models.md)) — 5 public demos