|
|
--- |
|
|
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. |
