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Soybean ControlNet Sd15 v1

This pipeline was finetuned from stable-diffusion-v1-5/stable-diffusion-v1-5 on the ButterChicken98/soybean-disease-controlnet dataset. Below are some example images generated with the finetuned pipeline using the...

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Model source

Kind
Other
File size
1.13 GB
Base model
stable-diffusion-v1-5/stable-diffusion-v1-5
Version
vmodel
Updated
Oct 8, 2026
Downloads
3
Source
Hugging Face

Source description

Text-to-image finetuning - ButterChicken98/soybean_controlnet_sd15_v1

This pipeline was finetuned from stable-diffusion-v1-5/stable-diffusion-v1-5 on the ButterChicken98/soybean-disease-controlnet dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A soybean leaf with early symptoms of Aerial Blight, showing small water-soaked spots at the leaf tip.']:

val_imgs_grid

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("ButterChicken98/soybean_controlnet_sd15_v1", torch_dtype=torch.float16)
prompt = "A soybean leaf with early symptoms of Aerial Blight, showing small water-soaked spots at the leaf tip."
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

  • Epochs: 115
  • Learning rate: 5e-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]

Sources

1 source
Hugging FacePrimaryVersion af0811f50b59bac07b21f7ee5a7201ca5d86c447
huggingface.co/ButterChicken98/soybean_controlnet_sd15_v1/tree/af0811f50b59bac07b21f7ee5a7201ca5d86c447

Verified Oct 8

Model artifacts

1 artifact

safety_checker/model.safetensors

safetensors · 1.13 GB · SHA-256 fb351a5ded81…fde8 · Hugging Face

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Source excerpts

3 excerpts

--- base_model: stable-diffusion-v1-5/stable-diffusion-v1-5 library_name: diffusers license: creativeml-openrail-m inference: true tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Text-to-image finetuning - ButterChicken98/soybean_controlnet_sd15_v1 This pipeline was finetuned from **stable-diffusion-v1-5/stable-diffusion-v1-5** on the **ButterChicken98/soybean-disease-controlnet** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A soybean leaf with early symptoms of Aerial Blight, showing small water-soaked spots at the leaf tip.']: ![val_imgs_grid](./val_imgs_grid.png) ## Pipeline usage You can use the pipeline like so: ```python from diffusers import DiffusionPipeline import torch pipeline = DiffusionPipeline.from_pretrained("ButterChicken98/soybean_controlnet_sd15_v1", torch_dtype=torch.float16) prompt = "A soybean leaf with early symptoms of Aerial Blight, showing small water-soaked spots at the leaf tip." image = pipeline(prompt).images[0] image.save("my_image.png") ``` ## Training info These are the key hyperparameters used during training: * Epochs: 115 * Learning rate: 5e-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](https://wandb.ai/butterchicken-iit-indore/text2image-fine-tune/runs/dd5fnkki). ## Intended uses & limitations #### How to use ```python # 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]

Source context: 3 downloads · 0 likes · Pipeline text-to-image · Library diffusers · Repo ButterChicken98/soybean_controlnet_sd15_v1

Source context: 1 downloads · 0 likes · Pipeline text-to-image · Library diffusers · Repo ButterChicken98/soybean_controlnet_sd15_v1