---
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
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 - jangmin/foodai-pipeline-ko

This pipeline was finetuned from **CompVis/stable-diffusion-v1-4** with replacement of text encoder **Bingsu/my-korean-stable-diffusion-v1-5** on the **AI-HUB: 건강관리를 위한 음식 이미지** dataset.

## Pipeline usage

You can use the pipeline like so:

```python
from diffusers import StableDiffusionPipeline
import torch

# Set device
device = (
    "mps"
    if torch.backends.mps.is_available()
    else "cuda"
    if torch.cuda.is_available()
    else "cpu"
)
torch_dtype = torch.float16 if device == "cuda" else torch.float32
pipeline = StableDiffusionPipeline.from_pretrained("jangmin/foodai-pipeline-ko", torch_dtype=torch_dtype)
pipeline.to(device)

prompt = "짜장면, 정면에서 본 사진, 그릇에 담긴"
image = pipeline(prompt, guidance_scale=8, num_inference_steps=35).images[0]
image
```

## Training info

These are the key hyperparameters used during training:

* Epochs: 1
* Learning rate: 1e-05
* Batch size: 8
* Gradient accumulation steps: 4
* Image resolution:512
* Mixed-precision: bf16