Text-to-image finetuning - ButterChicken98/CottonWeedID15_SD21_Finetune

This pipeline was finetuned from Manojb/stable-diffusion-2-1-base on the ButterChicken98/CottonWeedID15_SD21_Captions dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A photo of Carpetweeds, single seedling, small, oval-shaped green leaves, smooth texture, growing through cracked concrete, natural light.']:

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("ButterChicken98/CottonWeedID15_SD21_Finetune", torch_dtype=torch.float16)
prompt = "A photo of Carpetweeds, single seedling, small, oval-shaped green leaves, smooth texture, growing through cracked concrete, natural light."
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

• Epochs: 92
• Learning rate: 1e-05
• Batch size: 48
• Gradient accumulation steps: 1
• Image resolution: 512
• 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]