sd21_cottonweed15_rag_k2_prompt_train

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Text-to-image finetuning - ButterChicken98/sd21_cottonweed15_rag_k2_prompt_train

This pipeline was finetuned from sd2-community/stable-diffusion-2-1 on the ButterChicken98/CottonWeedID15_RAG_Captions dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A close-up field photo of Morningglory weed, heart-shaped green leaves, natural soil background, daylight.', 'A close-up field photo of Carpetweed, small oval leaves in a low spreading cluster, natural soil background.', 'A close-up field photo of Palmer Amaranth weed seedling, pointed oval leaves, green upright stem, daylight.', 'A close-up field photo of Waterhemp weed plant, narrow lance-shaped leaves, green upright seedling, soil background.']:

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("ButterChicken98/sd21_cottonweed15_rag_k2_prompt_train", torch_dtype=torch.float16)
prompt = "A close-up field photo of Morningglory weed, heart-shaped green leaves, natural soil background, daylight."
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

• Epochs: 25
• Learning rate: 1e-05
• Batch size: 8
• 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]