michaelyli/sd-dsprites-incorrect_counterfactual_coupled_factors

<!-- 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 - michaelyli/sd-dsprites-incorrectcounterfactualcoupled_factors

This pipeline was finetuned from michaelyli/sd-dsprites-vanilla-baseline on the michaelyli/dsprites-counterfactual-chain-under-specified dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A square.', 'A ellipse.', 'A heart.']:

[image]

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("michaelyli/sd-dsprites-incorrect_counterfactual_coupled_factors", 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: 25
• —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]