sd-waterbottle-model

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Text-to-image finetuning - abarget/sd-waterbottle-model

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:

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]