    
# Text-to-image finetuning - arpachat/output-fashion-3

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: ['Blue Tommy Hilfiger jacket']: 

![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/output-fashion-3", torch_dtype=torch.float16)
prompt = "Blue Tommy Hilfiger jacket"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```

## Training info

These are the key hyperparameters used during training:

* Epochs: 1
* Learning rate: 1e-05
* Batch size: 1
* Gradient accumulation steps: 2
* Image resolution: 512
* Mixed-precision: fp16


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

{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers"], "datasets": ["jwl25b/final_project_dataset"], "base_model": "OFA-Sys/small-stable-diffusion-v0", "inference": true}
diffusers tensorboard safetensors stable-diffusion stable-diffusion-diffusers text-to-image dataset:jwl25b/final_project_dataset base_model:OFA-Sys/small-stable-diffusion-v0 license:creativeml-openrail-m endpoints_compatible diffusers:StableDiffusionPipeline region:us