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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:

    prompt = "Labrador in the style of Vermeer"

Instantiate a pipeline with DiffusionPipeline.from_pretrained() and place it on a GPU (if available):

    >>> from diffusers import DiffusionPipeline

    >>> pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
    >>> pipe = pipe.to("cuda")

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:

    >>> import torch

    >>> generator = [torch.Generator(device="cuda").manual_seed(i) for i in range(4)]

Generate the images and have a look:

    >>> images = pipe(prompt, generator=generator, num_images_per_prompt=4).images
    >>> images

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:

    prompt = [prompt + t for t in [", highly realistic", ", artsy", ", trending", ", colorful"]]
    generator = [torch.Generator(device="cuda").manual_seed(0) for i in range(4)]

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!

    >>> images = pipe(prompt, generator=generator).images
    >>> images