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


# Text-to-image finetuning - omkar1799/script-sd-annalaura-model

This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **omkar1799/annalaura-diffusion-dataset** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['blue and yellow cats shopping at plant store in an annalaura watercolor drawing style', 'a turtle character dressed as teacher standing next to a chalkboard with equations on it in an annalaura watercolor drawing style', 'blue and yellow cats riding bikes together through tropical forest path in an annalaura watercolor drawing style', 'raccoon character wearing gold chain driving red sports car down highway in an annalaura watercolor drawing style']: 

![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("omkar1799/script-sd-annalaura-model", torch_dtype=torch.float16)
prompt = "blue and yellow cats shopping at plant store in an annalaura watercolor drawing style"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```

## Training info

These are the key hyperparameters used during training:

* Epochs: 5
* Learning rate: 1e-05
* Batch size: 1
* Gradient accumulation steps: 4
* Image resolution: 512
* Mixed-precision: fp16



## 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]