glid-3-xl
Maintainer: jack000
45
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
glid-3-xl is a 1.4B parameter text-to-image model developed by CompVis and fine-tuned by jack000. It is a back-ported version of CompVis' latent diffusion model to the guided diffusion codebase. Unlike the original stable-diffusion model, glid-3-xl has been split into three checkpoints, allowing for fine-tuning on new datasets and additional tasks like inpainting and super-resolution.
Model inputs and outputs
The glid-3-xl model takes in a text prompt, an optional init image, and various parameters to control the image generation process. It outputs one or more generated images that match the given text prompt.
Inputs
- Prompt: Your text prompt describing the image you want to generate.
- Negative Prompt: (Optional) Text to negatively influence the model's prediction.
- Init Image: (Optional) An initial image to use as a starting point for the generation.
- Seed: (Optional) A seed value for the random number generator.
- Steps: The number of diffusion steps to run, controlling the quality and detail of the output.
- Guidance Scale: A value controlling the trade-off between faithfulness to the prompt and sample diversity.
- Width/Height: The target size of the generated image.
- Batch Size: The number of images to generate at once.
Outputs
- Image(s): One or more generated images that match the given text prompt.
Capabilities
glid-3-xl is capable of generating high-quality, photorealistic images from text prompts. It can handle a wide range of subjects and styles, from realistic scenes to abstract and surreal compositions. The model has also been fine-tuned for inpainting, allowing you to edit and modify existing images.
What can I use it for?
You can use glid-3-xl to generate custom images for a variety of applications, such as:
- Illustration and concept art
- Product visualizations
- Social media content
- Advertising and marketing materials
- Educational resources
- Personal creative projects
The ability to fine-tune the model on new datasets also opens up possibilities for domain-specific applications, such as generating medical illustrations or architectural visualizations.
Things to try
One interesting aspect of glid-3-xl is the ability to use an init image and apply human-guided diffusion to iteratively refine the generation. This allows you to start with a basic image and progressively edit it to better match your desired prompt. You can also experiment with the various sampling techniques, such as PLMS and classifier-free guidance, to find the approach that works best for your use case.
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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