diffusers stable-diffusion stable-diffusion-diffusers text-to-image base_model:runwayml/stable-diffusion-v1-5 license:creativeml-openrail-m region:us
---
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
base_model: runwayml/stable-diffusion-v1-5
---

<!-- 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 - cosmo3769/test

This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **your_dataset_name** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['prompt1', 'prompt2', 'prompt3']: 

![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("cosmo3769/test", torch_dtype=torch.float16)
prompt = "prompt1"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```

## Training info

These are the key hyperparameters used during training:

* Epochs: num_train_epochs
* Learning rate: lr
* Batch size: batch_size
* Gradient accumulation steps: ga_steps
* Image resolution: img_resolution
* Mixed-precision: boolean



## 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]