diffusers tensorboard safetensors stable-diffusion stable-diffusion-diffusers text-to-image dataset:jytjyt05/t_to_m6 base_model:kerianheyi/CS245-fine-tunedSD0_100_14122 license:creativeml-openrail-m autotrain_compatible endpoints_compatible diffusers:StableDiffusionPipeline region:us

---
license: creativeml-openrail-m
base_model: kerianheyi/CS245-fine-tunedSD0_100_14122
datasets:
- jytjyt05/t_to_m6
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
    
# Text-to-image finetuning - kerianheYi/CS245-fine-tunedSD100_400_14122

This pipeline was finetuned from **kerianheyi/CS245-fine-tunedSD0_100_14122** on the **jytjyt05/t_to_m6** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A melSpectrogram for a C-Major piano solo']: 

![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("kerianheYi/CS245-fine-tunedSD100_400_14122", torch_dtype=torch.float16)
prompt = "A melSpectrogram for a C-Major piano solo"
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: 4
* Image resolution: 512
* Mixed-precision: fp16

