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---
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
---
<!-- 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 - eazzy/sd-hubble-model
This pipeline was finetuned from **stable-diffusion-v1-5/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.']:
## Pipeline usage
You can use the pipeline like so:
```python
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("eazzy/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: fp16
#
## 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] |