File size: 1,293 Bytes
983b2e4 e3db5b9 983b2e4 e3db5b9 bc1e1cb e3db5b9 bc1e1cb e3db5b9 983b2e4 e3db5b9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
---
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. |