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---
license: creativeml-openrail-m
language:
- en
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
widget:
- text: "Hubble snaps images of the birthplace of stars within a cluster: The dust and gas expand within the cluster due to the powerful influence of baby stars. With these new images comes improved detail and a clearer view for astronomers to study how early stars are born and change over time."
example_title: Baby Stars
- text: "Hubble captures the death of a star: Old stars, nearing the end of their life, collapse under the weight of their own gravity and the outer layers explode as a 'supernova'. In this image Hubble captures the moments after collapse, where the star has exploded and left an empty void in its place, where a new black hole has emerged."
example_title: Old Stars
- text: "Hubble image of galaxies colliding: The distorted spirals of two distant galaxies colliding are captured here in a new image from the NASA/ESA Hubble Space Telescope. The typically symmetric spirals common in spiral galaxies appear significantly warped, as the shape of both galaxies is torn apart by their gravitational pulls."
example_title: Galaxies Collide
- text: "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."
example_title: Pink Plumes
- text: "The stellar plasma of Wolf 359: The red dwarf star Wolf 359 from the constellation Leo is captured in extreme detail in a new image from the NASA/ESA Hubble Space Telescope. Wolf 359, classified as a M6 red dwarf, has certain peculiar qualities indicated by an unusual ejection of plasma. The Hubble telescope was able to capture one such event"
example_title: Wolf 359
extra_gated_prompt: |-
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
extra_gated_heading: Please read the LICENSE to access this model
thumbnail: ""
inference: true
datasets:
- Supermaxman/esa-hubble
---
# Hubble Diffusion v1: Stable Diffusion v1.4 fine tuned on ESA Hubble Deep Space Images & Captions
Put in a detailed text prompt and generate Hubble Deep Space Images!
> Hubble captures the death of a star: Old stars, nearing the end of their life, collapse under the
> weight of their own gravity and the outer layers explode as a 'supernova'. In this image Hubble
> captures the moments after collapse, where the star has exploded and left an empty void in its
> place, where a new black hole has emerged.
> Hubble snaps images of the birthplace of stars within a cluster:
> The dust and gas expand within the cluster due to the powerful influence of baby stars.
> With these new images comes improved detail and a clearer view for astronomers to
> study how early stars are born and change over time.
> Hubble image of galaxies colliding: The distorted spirals of two distant galaxies colliding are
> captured here in a new image from the NASA/ESA Hubble Space Telescope. The typically symmetric
> spirals common in spiral galaxies appear significantly warped, as the shape of both galaxies is torn
> apart by their gravitational pulls.
## Model Details
- **Developed by:** Maxwell Weinzierl
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487), with initial weights from [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4). It was fine-tuned on [Supermaxman/esa-hubble](https://huggingface.co/datasets/Supermaxman/esa-hubble).
- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
- **Cite as:**
```bibtex
@misc{weinzierl2023sdhubble1,
author = {Weinzierl, Maxwell A.},
title = {Hubble Diffusion v1: Stable Diffusion v1.4 fine tuned on ESA Hubble Deep Space Images & Captions},
year={2023},
howpublished= {\url{https://huggingface.co/Supermaxman/hubble-diffusion-1}}
}
```
Also, be sure to check out the new and improved [Hubble Diffusion v2](https://huggingface.co/Supermaxman/hubble-diffusion-2)!
## Examples
We recommend using [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Hubble Diffusion.
### Usage
```bash
pip install transformers diffusers accelerate
```
```python
import torch
from diffusers import StableDiffusionPipeline
model_id = "Supermaxman/hubble-diffusion-1"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
# saves significant GPU memory for small inference cost
pipe.enable_attention_slicing()
prompt = "Hubble snaps images of the birthplace of stars within a cluster: The dust and gas expand within the cluster due to the powerful influence of baby stars. With these new images comes improved detail and a clearer view for astronomers to study how early stars are born and change over time."
image = pipe(prompt).images[0]
image
```
## Model description
Trained on [ESA Hubble Deep Space Images & Captions](https://huggingface.co/datasets/Supermaxman/esa-hubble) using [Google Colab Pro](https://colab.research.google.com/signup) with a single A100 GPU for around 33,000 steps (about 12 hours, at a cost of about $20).
## Links
- [Captioned Hubble Deep Space Scans dataset](https://huggingface.co/datasets/Supermaxman/esa-hubble)
- [Model weights in Diffusers format](https://huggingface.co/Supermaxman/hubble-diffusion-1)
- [Training code](https://github.com/Supermaxman/hubble-diffusion)
- [Hubble Diffusion v2](https://huggingface.co/Supermaxman/hubble-diffusion-2)
Trained by [Maxwell Weinzierl](https://personal.utdallas.edu/~maxwell.weinzierl/) ([@Supermaxman1](https://twitter.com/Supermaxman1)).
## Citation
```bibtex
@misc{weinzierl2023sdhubble1,
author = {Weinzierl, Maxwell A.},
title = {Hubble Diffusion v1: Stable Diffusion v1.4 fine tuned on ESA Hubble Deep Space Images & Captions},
year={2023},
howpublished= {\url{https://huggingface.co/Supermaxman/hubble-diffusion-1}}
}
```
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