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@@ -92,6 +92,7 @@ The model was not trained to be factual or true representations of people or eve
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  - The model struggles with more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
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  - Faces and people in general may not be generated properly.
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  - The autoencoding part of the model is lossy.
 
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  ### Bias
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  While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
 
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  - The model struggles with more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
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  - Faces and people in general may not be generated properly.
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  - The autoencoding part of the model is lossy.
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+ - When the strength parameter is set to 1 (i.e. starting in-painting from a fully masked image), the quality of the image is degraded. The model retains the non-masked contents of the image, but images look less sharp. We're investing this and working on the next version.
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  ### Bias
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  While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.