--- base_model: Manojb/stable-diffusion-2-1-base library_name: diffusers license: creativeml-openrail-m inference: true tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training --- # Text-to-image finetuning - ButterChicken98/soy_qwen_bact_v2 This pipeline was finetuned from **Manojb/stable-diffusion-2-1-base** on the **ButterChicken98/soyabean_bact_puls_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 Bacterial Pustule, showing small, yellowish-brown lesions with a water-soaked appearance.']: ![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/soy_qwen_bact_v2", torch_dtype=torch.float16) prompt = "A photo of a soybean leaf with Early stage Bacterial Pustule, showing small, yellowish-brown lesions with a water-soaked appearance." 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](https://wandb.ai/butterchicken-iit-indore/text2image-fine-tune/runs/hvt7i83s). ## 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]