---
base_model: stabilityai/stable-diffusion-2-base
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 - jffacevedo/pxla_trained_model

This pipeline was finetuned from **stabilityai/stable-diffusion-2-base** on the **lambdalabs/naruto-blip-captions** dataset. 


## Pipeline usage

You can use the pipeline like so:

```python
import torch
import os
import sys
import  numpy as np

import torch_xla.core.xla_model as xm
from time import time
from typing import Tuple
from diffusers import StableDiffusionPipeline

def main(args):
    device = xm.xla_device()
    model_path = <output_dir>
    pipe = StableDiffusionPipeline.from_pretrained(
        model_path, 
        torch_dtype=torch.bfloat16
    )
    pipe.to(device)
    prompt = ["A naruto with green eyes and red legs."]
    image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5).images[0]
    image.save("naruto.png")

if __name__ == '__main__':
    main()
```

## Training info

These are the key hyperparameters used during training:

* Steps: 50
* Learning rate: 1e-06
* Batch size: 32
* Image resolution: 512
* Mixed-precision: bf16



## 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]