README.md

---
license: creativeml-openrail-m
base_model: OFA-Sys/small-stable-diffusion-v0
datasets:
- jwl25b/final_project_dataset
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
    
# Text-to-image finetuning - arpachat/stable-diffusion_unclip-small-v21-th-800-e4

This pipeline was finetuned from **OFA-Sys/small-stable-diffusion-v0** on the **jwl25b/final_project_dataset** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ["Tommy Hilfiger men's Regular Fit Round Logo Grey Polo"]: 

![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("arpachat/stable-diffusion_unclip-small-v21-th-800-e4", torch_dtype=torch.float16)
prompt = "Tommy Hilfiger men's Regular Fit Round Logo Grey Polo"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```

## Training info

These are the key hyperparameters used during training:

* Epochs: 400
* Learning rate: 0.0001
* Batch size: 8
* Gradient accumulation steps: 4
* Image resolution: 128
* Mixed-precision: fp16


More information on all the CLI arguments and the environment are available on your [`wandb` run page](wandb_run_url).