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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`](https://pytorch.org/docs/stable/generated/torch.Generator.html#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`](runwayml/stable-diffusion-v1-5) for example, and generate several versions of the following prompt: | |
```py | |
prompt = "Labrador in the style of Vermeer" | |
``` | |
Instantiate a pipeline with [`DiffusionPipeline.from_pretrained`] and place it on a GPU (if available): | |
```python | |
>>> 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: | |
```python | |
>>> import torch | |
>>> generator = [torch.Generator(device="cuda").manual_seed(i) for i in range(4)] | |
``` | |
Generate the images and have a look: | |
```python | |
>>> images = pipe(prompt, generator=generator, num_images_per_prompt=4).images | |
>>> images | |
``` | |
![img](https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/reusabe_seeds.jpg) | |
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: | |
```python | |
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! | |
```python | |
>>> images = pipe(prompt, generator=generator).images | |
>>> images | |
``` | |
![img](https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/reusabe_seeds_2.jpg) | |