README.md

metadata

base_model: runwayml/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
  - diffusers-training
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
  - diffusers-training
inference: true

Text-to-image finetuning - michaelyli/sd-dsprites-incorrect_counterfactual_coupled_factors_init_stable-diffusion-v1-5

This pipeline was finetuned from runwayml/stable-diffusion-v1-5 on the michaelyli/dsprites-coupled-under-specified 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-incorrect_counterfactual_coupled_factors_init_stable-diffusion-v1-5", 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: 1
• 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]