This pipeline was finetuned from stable-diffusion-v1-5/stable-diffusion-v1-5 on the rohith2812/atoi-finetuning-v2 dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A picture explaining the visualization of backpropagation in neural network architecture']:

Pipeline usage

You can use the pipeline like so:

python

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("rohith2812/atoi-finetuned-model-v2", torch_dtype=torch.float16)
prompt = "A picture explaining the visualization of backpropagation in neural network architecture"
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

Epochs: 3
Learning rate: 1e-05
Batch size: 1
Gradient accumulation steps: 4
Image resolution: 512
Mixed-precision: fp16

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]