Summary of Latent Pollution Model: the Hidden Carbon Footprint in 3d Image Synthesis, by Marvin Seyfarth et al.
Latent Pollution Model: The Hidden Carbon Footprint in 3D Image Synthesis
by Marvin Seyfarth, Salman Ul Hassan Dar, Sandy Engelhardt
First submitted to arxiv on: 20 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study assesses the environmental impact of generative AI models, particularly latent diffusion models (LDMs), during their training and data generation phases. The authors reveal that large image synthesis contributes significantly to carbon emissions from these models. They investigate various scenarios, including model sizes, image dimensions, distributed training, and data generation steps, and find substantial carbon emissions from 2D and 3D model training and data generation. For instance, training a 2D LDM is equivalent to driving a car for 10 km, while 3D model training is comparable to driving 90 km. Data generation processes are even more significant, with CO2 emissions equivalent to driving up to 3345 km for 3D synthesis. The authors also highlight the impact of location and time on carbon emissions, emphasizing the need for sustainable strategies in generative AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how much energy and pollution are created by big computer models that make fake images and videos. These models use a lot of power and can create huge amounts of greenhouse gases, like CO2. The researchers found that making really large pictures is the biggest cause of these emissions. They also looked at different ways to train these models and how they are affected by where they are located and what time of year it is. They say that we need to find ways to make these models more environmentally friendly because they could have a big impact on our planet. |
Keywords
* Artificial intelligence * Diffusion * Image synthesis
