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Incrementally adds noise over a series of steps via a noise scheduler, and feeds images through a CNN to learn features/patterns to then "regenerate" the image.
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Incrementally adds noise over a series of steps via a noise scheduler, and feeds images through a CNN to learn features/patterns to then "regenerate" the image.
The .ipynb holds the tests and download of the dataset if needed.
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1 fonteVerificado 26 de set.
Artefatos de modelo
1 artefatoTrechos de fonte
2 trechos--- license: mit language: - en metrics: - accuracy pipeline_tag: unconditional-image-generation tags: - art --- # Photo Regenerator w/Diffusion Denoiser Model Incrementally adds noise over a series of steps via a noise scheduler, and feeds images through a CNN to learn features/patterns to then "regenerate" the image. The `.ipynb` holds the tests and download of the dataset if needed. ## Goals: - **Denoise a 25-step noisy image (Blurry)** - Denoise a 100-step noisy image - Denoise a 500-step noisy image - Denoise a 1000-step noisy image **Dataset Used: COCO 2017**
Dataset Used: COCO 2017
Source context: 0 downloads · 0 likes · Pipeline unconditional-image-generation · Repo reeeemo/photo-regenerator
