diffusers/docs/source/en/using-diffusers/reusing_seeds.mdx

<!--Copyright 2022 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->

# 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)