README.md

---
base_model: CompVis/stable-diffusion-v1-4
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 - haorandai/png_Random_Noise_banana_Gaussian_Noise_epsilon0.1_1samples_with1constraints

This pipeline was finetuned from **CompVis/stable-diffusion-v1-4** on the **haorandai/png_Random_Noise_banana_Gaussian_Noise_epsilon0.1_1samples_with1constraints** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: None: 



## Pipeline usage

You can use the pipeline like so:

```python
from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("haorandai/png_Random_Noise_banana_Gaussian_Noise_epsilon0.1_1samples_with1constraints", torch_dtype=torch.float16)
prompt = "None"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```

## Training info

These are the key hyperparameters used during training:

* Epochs: 200
* Learning rate: 1e-05
* Batch size: 1
* Gradient accumulation steps: 4
* Image resolution: 224
* Mixed-precision: fp16



## 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]