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---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
datasets:
- MaxReynolds/Lee_Souder_Combined
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# Text-to-image finetuning - MaxReynolds/SouderRocketLauncherNetCombined-v1-4-400
This pipeline was finetuned from **CompVis/stable-diffusion-v1-4** on the **MaxReynolds/Lee_Souder_Combined** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['Rocket Launcher by Lee Souder']:
![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("MaxReynolds/SouderRocketLauncherNetCombined-v1-4-400", torch_dtype=torch.float16)
prompt = "Rocket Launcher by Lee Souder"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```
## Training info
These are the key hyperparameters used during training:
* Epochs: 40
* Learning rate: 1e-05
* Batch size: 1
* Gradient accumulation steps: 4
* Image resolution: 512
* Mixed-precision: fp16
More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/max-f-reynolds/text2image-fine-tune/runs/zydn7drl).