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Implementation of aspect ratio bucketing for training generative image models as described in: https://blog.novelai.net/novelai-improvements-on-stable-diffusion-e10d38db82ac
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# NovelAI Aspect Ratio Bucketing
Training with aspect ratio bucketing can greatly improve the quality of outputs (and I personally don't want another base model trained with center crops), so we have decided to release the bucketing code under a permissive MIT license.
This repository provides an implementation of aspect ratio bucketing for training generative image models as described in our [blogpost](https://blog.novelai.net/novelai-improvements-on-stable-diffusion-e10d38db82ac). The relevant section of the post is reproduced below.
## Description
One common issue of existing image generation models is that they are very prone to producing images with unnatural crops. This is due to the fact that these models are trained to produce square images. However, most photos and artworks are not square. However, the model can only work on images of the same size at the same time, and during training, it is common practice to operate on multiple training samples at once to optimize the efficiency of the GPUs used. As a compromise, square images are chosen, and during training, only the center of each image is cropped out and then shown to the image generation model as a training example.
For example, humans are often generated without feet or heads, and swords consist of only a blade with a hilt and point outside the frame.
As we are creating an image generation model to accompany our storytelling experience, it is important that our model is able to produce proper, uncropped characters, and generated knights should not be holding a metallic-looking straight line extending to infinity.
Another issue with training on cropped images is that it can lead to a mismatch between the text and the image.
For example, an image with a `crown` tag will often no longer contain a crown after a center crop is applied and the monarch has been, thereby, decapitated.
We found that using random crops instead of center crops only slightly improves these issues.
Using Stable Diffusion with variable image sizes is possible, although it can be noticed that going too far beyond the native resolution of 512x512 tends to introduce repeated image elements, and very low resolutions produce indiscernible images.
Still, this indicated to us that training the model on variable sized images should be possible. Training on single, variable sized samples would be trivial, but also extremely slow and more liable to training instability due to the lack of regularization provided by the use of mini batches.
### Custom Batch Generation
As no existing solution for this problem seems to exist, we have implemented custom batch generation code for our dataset that allows the creation of batches where every item in the batch has the same size, but the image size of batches may differ.
We do this through a method we call aspect ratio bucketing. An alternative approach would be to use a fixed image size, scale each image to fit within this fixed size and apply padding that is masked out during training. Since this leads to unnecessary computation during training, we have not chosen to follow this alternative approach.
In the following, we describe the original idea behind our custom batch generation scheme for aspect ratio bucketing.
First, we have to define which buckets we want to sort the images of our dataset into. For this purpose, we define a maximum image size of 512x768 with a maximum dimension size of 1024. Since the maximum image size is 512x768, which is larger than 512x512 and requires more VRAM, per-GPU batch size has to be lowered, which can be compensated through gradient accumulation.
We generate buckets by applying the following algorithm:
* Set the width to 256.
* While the width is less than or equal to 1024:
* Find the largest height such that height is less than or equal to 1024 and that width multiplied by height is less than or equal to 512 * 76Excerpt of 9,852 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a1b2a42a94de4213, desc:stable diffusion