Improve image quality with deterministic generation
Improve image quality with deterministic generation
A common way to improve the quality of generated images is with deterministic batch generation, generate a batch of images and select one image to improve with a more detailed prompt in a second round of inference. The key is to pass a list of torch.Generator
’s to the pipeline for batched image generation, and tie each Generator
to a seed so you can reuse it for an image.
Let’s use runwayml/stable-diffusion-v1-5
for example, and generate several versions of the following prompt:
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Instantiate a pipeline with DiffusionPipeline.from_pretrained() and place it on a GPU (if available):
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Now, define four different Generator
’s and assign each Generator
a seed (0
to 3
) so you can reuse a Generator
later for a specific image:
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Generate the images and have a look:
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In this example, you’ll improve upon the first image - but in reality, you can use any image you want (even the image with double sets of eyes!). The first image used the Generator
with seed 0
, so you’ll reuse that Generator
for the second round of inference. To improve the quality of the image, add some additional text to the prompt:
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Create four generators with seed 0
, and generate another batch of images, all of which should look like the first image from the previous round!
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