---
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
base_model: runwayml/stable-diffusion-v1-5
inference: true
---

<!-- 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 - iamkaikai/MATISSEE-LORA
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the iamkaikai/MATISSEE-ART dataset. You can find some example images in the following. 

![img_0](./image_0.jpg)
![img_2](./image_2.jpg)
![img_3](./image_3.jpg)
![img_4](./image_0.png)
![img_5](./image_13.png)
![img_6](./image_17.png)



## Intended uses & limitations

#### How to use

```python
from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, safety_checker=None).to("cuda")
pipeline.load_lora_weights("iamkaikai/MATISSEE-LORA", weight_name="pytorch_lora_weights.safetensors")
prompt = "MATISSEE-ART, brown, beige, coral, gray, orange red, violet, black, teal"
for i in range(20):
    image = pipeline(prompt, num_inference_steps=20).images[0]
    image.save(f"./image_{str(i)}.png")
```

#### Limitations and bias
For some reason, this LORA model will often trigger the NSFW filter. Make sure you turn it off in the pipeline.

## Training details

[TODO: describe the data used to train the model]