
# LoRA text2image fine-tuning - 0x7o/RussianVibe-XL-v1.0

These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the 0x7o/RussianVibe-data 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: False.

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


## Intended uses & limitations

#### How to use

```python
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
import torch

pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
pipe.load_lora_weights("0x7o/RussianVibe-XL-v1.0")
pipe.to("cuda")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)

prompt = "The sun is setting through a window, casting a warm glow on the cityscape beyond. The sun casts a warm orange glow on the buildings in the distance, creating a beautiful and serene atmosphere."
image = pipe(prompt, num_inference_steps=30, guidance_scale=5.0, negative_prompt="bad quality, painting, art").images[0]

image.save("output.png")
```
{"license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora"], "datasets": ["0x7o/RussianVibe-data"], "inference": true, "base_model": "stabilityai/stable-diffusion-xl-base-1.0"}
diffusers stable-diffusion-xl stable-diffusion-xl-diffusers text-to-image lora dataset:0x7o/RussianVibe-data base_model:stabilityai/stable-diffusion-xl-base-1.0 license:creativeml-openrail-m has_space region:us