---
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
base_model: runwayml/stable-diffusion-v1-5
inference: true
---

<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->


# Text-to-image finetuning - Peachman/sd-hubble-model

This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **Supermaxman/esa-hubble** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['Hubble image of a colorful ringed nebula: A new vibrant ring-shaped nebula was imaged by the NASA/ESA Hubble Space Telescope', 'Pink-tinted plumes in the Large Magellanic Cloud: The aggressively pink plumes seen in this image are extremely uncommon, with purple-tinted currents and nebulous strands reaching out into the surrounding space']: 

![val_imgs_grid](./val_imgs_grid.png)


## Pipeline usage

You can use the pipeline like so:

```python
from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("Peachman/sd-hubble-model", torch_dtype=torch.float16)
prompt = "Hubble image of a colorful ringed nebula: A new vibrant ring-shaped nebula was imaged by the NASA/ESA Hubble Space Telescope"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```

## Training info

These are the key hyperparameters used during training:

* Epochs: 1
* Learning rate: 1e-05
* Batch size: 1
* Gradient accumulation steps: 4
* Image resolution: 512
* Mixed-precision: bf16



## Intended uses & limitations

#### How to use

```python
# TODO: add an example code snippet for running this diffusion pipeline
```

#### Limitations and bias

[TODO: provide examples of latent issues and potential remediations]

## Training details

[TODO: describe the data used to train the model]