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KorAI
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sdxl-base-1.0-onepiece-lora

Text-to-Image
Diffusers
stable-diffusion-xl
stable-diffusion-xl-diffusers
diffusers-training
lora
Model card Files Community
sdxl-base-1.0-onepiece-lora
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---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: creativeml-openrail-m
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
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. -->


# LoRA text2image fine-tuning - KorAI/sdxl-base-1.0-onepiece-lora

These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the KorAI/onepiece-captioned dataset. You can find some example images in the following. 

![img_0](./image_0.png)
![img_1](./image_1.png)
![img_2](./image_2.png)
![img_3](./image_3.png)


LoRA for the text encoder was enabled: True.

Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.


## Intended uses & limitations

#### How to use

```python
# TODO: add an example code snippet for running this diffusion pipeline
from diffusers import DiffusionPipeline
import torch

# Load Stable Diffusion XL Base1.0
pipe = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16,
    variant="fp16",
    use_safetensors=True
).to("cuda")

# Optional CPU offloading to save some GPU Memory
pipe.enable_model_cpu_offload()

# Loading Trained LoRA Weights
pipe.load_lora_weights("KorAI/sdxl-base-1.0-onepiece-lora")

prompt = "Acilia Anime, anime character in a bikini with a sword and shield"

# Invoke pipeline to generate image
image = pipe(
    prompt = prompt,
    num_inference_steps=50,
    height=1024,
    width=1024,
    guidance_scale=7.0,
).images[0]

# Display image
image

# Save Image
image.save(f"sdxl_onepiece.png")
```

#### Limitations and bias

[TODO: provide examples of latent issues and potential remediations]

## Training details

[TODO: describe the data used to train the model]