T2I-Adapter
Maintainer: TencentARC
770
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The T2I-Adapter is a text-to-image generation model developed by TencentARC that provides additional conditioning to the Stable Diffusion model. The T2I-Adapter is designed to work with the StableDiffusionXL (SDXL) base model, and there are several variants of the T2I-Adapter that accept different types of conditioning inputs, such as sketch, canny edge detection, and depth maps.
The T2I-Adapter model is built on top of the Stable Diffusion model and aims to provide more controllable and expressive text-to-image generation capabilities. The model was trained on 3 million high-resolution image-text pairs from the LAION-Aesthetics V2 dataset.
Model inputs and outputs
Inputs
- Text prompt: A natural language description of the desired image.
- Control image: A conditioning image, such as a sketch or depth map, that provides additional guidance to the model during the generation process.
Outputs
- Generated image: The resulting image generated by the model based on the provided text prompt and control image.
Capabilities
The T2I-Adapter model can generate high-quality and detailed images based on text prompts, with the added control provided by the conditioning input. The model's ability to generate images from sketches or depth maps can be particularly useful for applications such as digital art, concept design, and product visualization.
What can I use it for?
The T2I-Adapter model can be used for a variety of applications, such as:
- Digital art and illustration: Generate custom artwork and illustrations based on text prompts and sketches.
- Product design and visualization: Create product renderings and visualizations by providing depth maps or sketches as input.
- Concept design: Quickly generate visual concepts and ideas based on textual descriptions.
- Education and research: Explore the capabilities of text-to-image generation models and experiment with different conditioning inputs.
Things to try
One interesting aspect of the T2I-Adapter model is its ability to generate images from different types of conditioning inputs, such as sketches, depth maps, and edge maps. Try experimenting with these different conditioning inputs and see how they affect the generated images. You can also try combining the T2I-Adapter with other AI models, such as GFPGAN, to further enhance the quality and realism of the generated images.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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