This pipeline was finetuned from stable-diffusion-v1-5/stable-diffusion-v1-5 on the None dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A ground-level photo of Chinee apple weed in rangeland soil.', 'A close-up field photo of Lantana weed leaves.', 'A top-down photo of Parkinsonia weed foliage.', 'A natural field photo of Parthenium weed.']:

Pipeline usage

You can use the pipeline like so:

python

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("ButterChicken98/sd15_deepweeds_clip_corpus_256_bs48_snr5_noise01_10k", torch_dtype=torch.float16)
prompt = "A ground-level photo of Chinee apple weed in rangeland soil."
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

Epochs: 31
Learning rate: 1e-05
Batch size: 48
Gradient accumulation steps: 1
Image resolution: 256
Mixed-precision: bf16

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]