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Improve image quality with deterministic generation

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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):

import torch
from diffusers import DiffusionPipeline
from diffusers.utils import make_image_grid

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

Now, define four different Generators and assign each Generator a seed (0 to 3) so you can reuse a Generator later for a specific image:

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

To create a batched seed, you should use a list comprehension that iterates over the length specified in range(). This creates a unique Generator object for each image in the batch. If you only multiply the Generator by the batch size, this only creates one Generator object that is used sequentially for each image in the batch.

For example, if you want to use the same seed to create 4 identical images:

❌ [torch.Generator().manual_seed(seed)] * 4

✅ [torch.Generator().manual_seed(seed) for _ in range(4)]

Generate the images and have a look:

images = pipe(prompt, generator=generator, num_images_per_prompt=4).images
make_image_grid(images, rows=2, cols=2)


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
make_image_grid(images, rows=2, cols=2)