Text-to-image finetuning - ButterChicken98/weeds_all_sd_2_1

This pipeline was finetuned from Manojb/stable-diffusion-2-1-base on the ButterChicken98/CottonWeedD10_v1 dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A Carpetweed (Mollugo verticillata) plant growing in a dirt field. It has small, whorled green leaves and a low-spreading growth pattern. The plant is centered, in a natural agricultural field.']:

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("ButterChicken98/weeds_all_sd_2_1", torch_dtype=torch.float16)
prompt = "A Carpetweed (Mollugo verticillata) plant growing in a dirt field. It has small, whorled green leaves and a low-spreading growth pattern. The plant is centered, in a natural agricultural field."
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

• Epochs: 26
• Learning rate: 1e-05
• Batch size: 8
• Gradient accumulation steps: 1
• Image resolution: 512
• Mixed-precision: None

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]