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']:

Pipeline usage

You can use the pipeline like so:

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

# 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]