This pipeline was finetuned from stable-diffusion-v1-5/stable-diffusion-v1-5 on the ButterChicken98/controlnet_canny_segmented_tomato_Tomato_mosaic_virus dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A tomato leaf with mosaic-like patterns of light and dark green areas, indicative of Tomato mosaic virus.']:

Pipeline usage

You can use the pipeline like so:

python

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("ButterChicken98/plantVillage-stableDiffusion-2-test", torch_dtype=torch.float16)
prompt = "A tomato leaf with mosaic-like patterns of light and dark green areas, indicative of Tomato mosaic virus."
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

Epochs: 64
Learning rate: 1e-06
Batch size: 4
Gradient accumulation steps: 1
Image resolution: 256
Mixed-precision: fp16

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]