ButterChicken98/soy_qwen_aerial_v1

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Text-to-image finetuning - ButterChicken98/soyqwenaerial_v1

This pipeline was finetuned from Manojb/stable-diffusion-2-1-base on the ButterChicken98/soyabean_aerial_plus_healthy_Qwen_Detailed dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A photo of a soybean leaf with Early stage Aerial Blight, showing small yellow spots, round lesions, and slight leaf deformation.']:

[image]

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("ButterChicken98/soy_qwen_aerial_v1", torch_dtype=torch.float16)
prompt = "A photo of a soybean leaf with Early stage Aerial Blight, showing small yellow spots, round lesions, and slight leaf deformation."
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

• —Epochs: 29
• —Learning rate: 1e-05
• —Batch size: 8
• —Gradient accumulation steps: 1
• —Image resolution: 512
• —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]