This is the official codebase for running the small, filtered-data GLIDE model from [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://arxiv.org/abs/2112.10741).
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š ARTIFICIAL INTELLIGENCE 2.0 ā¢ : FOR MAKING IMAGINATION PROXIA F
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REQUIREMENTS:
[*] Software Requirements: Google Colab/Jupyter Notebook, Python, Tensor Flow
[*] HARDWARE REQUIREMENTS: fast GPU Graphics Processing Unit)
[*] DEPENDENCIES: INCLUDED
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e.g On an ASTRAL MINDCLOUD this PROXIA can be used to process INFORMATION sent to it from an INSTINCTIVE MIND PROXIA/MINDCLOUD (OBJECT DETECTION). It can use inpainting from text prompts to reimagine IMAGES (INCLUDING IMAGES FROM OTHER IMAGINATION PROXIA) with different elements in it to better understand what those things are.
CLICK ON THE FOLLOWING LINKS FOR JUPYTER NOTEBOOKS ON MAKING IMAGINATION PROXIA:
https://github.com/GCABC123/magnetron.artificial-intelligence-2.0.mincloud.proxia--IMAGINATION-A1
https://github.com/GCABC123/magnetron.artificial-intelligence-2.0.mincloud.proxia--IMAGINATION-B
https://github.com/GCABC123/magnetron.artificial-intelligence-2.0.mincloud.proxia--IMAGINATION-C
https://github.com/GCABC123/magnetron.artificial-intelligence-2.0.mincloud.proxia--IMAGINATION-D
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This is the official codebase for running the small, filtered-data GLIDE model from GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.
For details on the pre-trained models in this repository, see the Model Card.
To install this package, clone this repository and then run:
pip install -e .
For detailed usage examples, see the notebooks directory.
notebook shows how to use GLIDE (filtered) with classifier-free guidance to produce images conditioned on text prompts. notebook shows how to use GLIDE (filtered) to fill in a masked region of an image, conditioned on a text prompt. notebook shows how to use GLIDE (filtered) + a filtered noise-aware CLIP model to produce images conditioned on text prompts.