This pipeline was finetuned from stable-diffusion-v1-5/stable-diffusion-v1-5 on the abarget/my-waterbottle-dataset dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['a teal colored water bottle A stainless steel water bottle a water bottle with a flip-up spout', 'ombre teal to pink colored water bottle black water bottle with stainless steel accents, dark green water bottle with simple lid with a carrying handle']:

Pipeline usage

You can use the pipeline like so:

python

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("abarget/sd-waterbottle-model", torch_dtype=torch.float16)
prompt = "a teal colored water bottle  A stainless steel water bottle  a water bottle with a flip-up spout"
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

Epochs: 20
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]