Raphaël SemeteysIn the first two parts of this series, we explored Stable Diffusion, ComfyUI, and how to build Text-to-Image and Image-to-Image workflows to generate images of Yoga poses. With the help of ControlNets, we learned how to transfer a pose from an abstract reference image to our final generated image.
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Raphaël SemeteysIn the first part of this series on image generation, we explored how to set up a simple Text-to-Image workflow using Stable Diffusion and ComfyUI, running it locally. We also introduced embeddings to enhance prompts and adjust image styles. However, we found that embeddings alone were not sufficient for our specific use case: generating accurate yoga poses.
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Raphaël SemeteysIn this series of articles, I will introduce image generation using Stable Diffusion and ComfyUI. This first article discusses the theory and provides basic examples. My goal is to generate precise images of yoga poses using only your local machine. Due to legal restrictions, I cannot use images from the Internet. Given these limitations, how can generative AI assist?
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Raphaël Semeteys
Luxin ZhangEmbarking on the second and last part of our ‘Generative AI Openness’ series, we earlier established a straightforward framework to gauge the openness of Large Language Models (LLMs) and utilized it to explore LLM development and the positioning of key players. We noticed a trend towards increasingly restricted LLM artifacts for OpenAI and Google, contrasted with Meta’s more open approach.
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Raphaël Semeteys
Luxin ZhangWelcome to this two-part series on Generative AI Openness, where we explore the history, current landscape, and potential future of open collaboration and proprietary control in the development of Language Language Models (LLMs). In this first part, we will delve into the importance of inspecting the openness of each component in the LLM training process, and how this can impact the potential limitations on the model use or reuse imposed by one or more of its components. In the second part of this series, we will explore the potential benefits and drawbacks of sharing Generative AIs openly for the collective advancement of society.
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Luxin Zhang
Liyun He-Guelton
Olivier Caelen
Li GuoIn 2021, the International Energy Agency (IEA) published a Net-Zero Emissions (NZE) Roadmap [1] by 2050 which outlines a scenario that the IEA has built to comply with the Paris climate agreement and, from this report, we know that we have an imperative to reduce urgently our energy consumption. For instance, according to the NZE, the total energy supply must fall by 7% between 2020 and 2030 and remain at around this level to 2050. The energy resource regenerates much more slowly than the needs and at the same time the world’s population will still grow. Using energy efficiently and reducing energy consumption becomes a key concern.
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