README.md

---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
datasets:
- ktennyson6/augmented-vsr-v2
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
    
# Text-to-image finetuning - ktennyson6/sd-vsr-5k

This pipeline was finetuned from **CompVis/stable-diffusion-v1-4** on the **ktennyson6/augmented-vsr-v2** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['prompts']: 

![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("ktennyson6/sd-vsr-5k", torch_dtype=torch.float16)
prompt = "prompts"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```

## Training info

These are the key hyperparameters used during training:

* Epochs: 4
* Learning rate: 1e-05
* Batch size: 1
* Gradient accumulation steps: 4
* Image resolution: 256
* Mixed-precision: fp16


More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/kayla-m-tennyson/text2image-fine-tune/runs/avqlqpfd).