Text-to-image finetuning - michaelyli/sd-dsprites-counterfactual-exp-decouple_factors-iter-0
This pipeline was finetuned from michaelyli/sd-dsprites-incorrect_counterfactual_coupled_factors on the michaelyli/dsprites-counterfactual-exp-decouple_factors-filtered-iter-0 dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A square.', 'A ellipse.', 'A heart.']:
Pipeline usage
You can use the pipeline like so:
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("michaelyli/sd-dsprites-counterfactual-exp-decouple_factors-iter-0", torch_dtype=torch.float16)
prompt = "A square."
image = pipeline(prompt).images[0]
image.save("my_image.png")
Training info
These are the key hyperparameters used during training:
- Epochs: 400
- Learning rate: 1e-05
- Batch size: 100
- 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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Model tree for michaelyli/sd-dsprites-counterfactual-exp-decouple_factors-iter-0
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michaelyli/sd-dsprites-vanilla-baseline
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