Instructions to use rcannizzaro/vae-dsprites-counterfactual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rcannizzaro/vae-dsprites-counterfactual with Diffusers:
pip install -U diffusers transformers accelerate
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] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
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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