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π’ Exciting developments from @GoogleAI ! Introducing Imagen Editor & EditBench, revolutionary tools that advance and evaluate text-guided image inpainting. π¨ποΈ #AI #MachineLearning 
Imagen Editor is a diffusion-based model, fine-tuned on Imagen, with improved representations of linguistic inputs, fine-grained control, and high-fidelity outputs. πΈ
It uses three user inputs: the image, a binary mask, and a text prompt. #AI
It uses three user inputs: the image, a binary mask, and a text prompt. #AI
The system relies on 3 core techniques: an object detector masking policy, high resolution editing through conditioning on full resolution, and classifier-free guidance (CFG) to bias samples to text prompts.
#AI #DeepLearning
#AI #DeepLearning
Meet EditBench, a new dataset for evaluating text-guided image inpainting. π It contains 240 images (120 generated, 120 natural) and captures a wide variety of language, image types, and text prompt specificity.
EditBench goes beyond traditional evaluation metrics. It prioritizes human evaluation and is designed to test fine-grained details along three categories: attributes, object types, and scenes.
Imagen Editor outperforms comparable models in all EditBench evaluation categories, demonstrating its significant advantages in the field of text-guided image inpainting. π
More specific prompts seem to boost performance. Results from the EditBench evaluation show that the performance of the models improved with more specific prompts.
#AI #MachineLearning
#AI #MachineLearning
Looking ahead, these advancements in image editing and evaluation herald a future where users can guide image edits precisely and understand exactly what was edited and how. π
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