import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("rcannizzaro/vae-dsprites-counterfactual", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Text-to-image finetuning - rcannizzaro/vae-dsprites-counterfactual
This pipeline was finetuned from None on the osazuwa/dsprite-counterfactual dataset. Below are some example images generated with the finetuned pipeline using the following prompts:
Training info
These are the key hyperparameters used during training:
- Epochs: 1
- Learning rate: 1e-05
- Batch size: 250
- Gradient accumulation steps: 1
- Image resolution: 64
- Mixed-precision: fp16
More information on all the CLI arguments and the environment are available on your wandb run page.
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]
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