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# Re-using seeds for fast prompt engineering

A common use case when generating images is to generate a batch of images, select one image and improve it with a better, more detailed prompt in a second run.
To do this, one needs to make each generated image of the batch deterministic.
Images are generated by denoising gaussian random noise which can be instantiated by passing a [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html#generator).

Now, for batched generation, we need to make sure that every single generated image in the batch is tied exactly to one seed. In 🧨 Diffusers, this can be achieved by not passing one `generator`, but a list 
of `generators` to the pipeline.

Let's go through an example using [`runwayml/stable-diffusion-v1-5`](runwayml/stable-diffusion-v1-5).
We want to generate several versions of the prompt:

```py
prompt = "Labrador in the style of Vermeer"
```

Let's load the pipeline

```python
>>> from diffusers import DiffusionPipeline

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

Now, let's define 4 different generators, since we would like to reproduce a certain image. We'll use seeds `0` to `3` to create our generators.

```python
>>> import torch

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

Let's generate 4 images:

```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)

Ok, the last images has some double eyes, but the first image looks good! 
Let's try to make the prompt a bit better **while keeping the first seed** 
so that the images are similar to the first image.

```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)]
```

We create 4 generators with seed `0`, which is the first seed we used before.

Let's run the pipeline again.

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

![img](https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/reusabe_seeds_2.jpg)
