| --- |
| base_model: CompVis/stable-diffusion-v1-4 |
| library_name: diffusers |
| license: creativeml-openrail-m |
| inference: true |
| tags: |
| - stable-diffusion |
| - stable-diffusion-diffusers |
| - text-to-image |
| - diffusers |
| - diffusers-training |
| - lora |
| --- |
| |
| <!-- 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. --> |
|
|
|
|
| # LoRA text2image fine-tuning - RiddleHe/SD14_pathology_lora |
| These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the None dataset. You can find some example images in the following. |
|
|
| <table> |
| <tr> |
| <td><img src="./image_1.png"></td> |
| <td><img src="./image_2.png"></td> |
| <td><img src="./image_3.png"></td> |
| </tr> |
| </table> |
|
|
|
|
| ## Intended uses & limitations |
|
|
| #### How to use |
|
|
| ```python |
| pipe = DiffusionPipeline.from_pretrained( |
| "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16 |
| ) |
| |
| pipe.load_lora_weights("RiddleHe/SD14_pathology_lora") |
| pipe.to('cuda') |
| |
| prompt = "A histopathology image of breast cancer tissue" |
| ``` |
|
|
| #### Limitations and bias |
|
|
| [TODO: provide examples of latent issues and potential remediations] |
|
|
| ## Training details |
|
|
| This model is trained on 28216 breast cancer tissue images from the BRCA dataset. |
