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---
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']:
## 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] |