metadata

license: creativeml-openrail-m
library_name: diffusers
tags:
  - stable-diffusion-xl
  - stable-diffusion-xl-diffusers
  - text-to-image
  - diffusers
  - lora
inference: true
base_model: stabilityai/stable-diffusion-xl-base-1.0
datasets:
  - 0x7o/RussianVibe-data

RussianVibe XL v2.0

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.

LoRA for the text encoder was enabled: False.

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

Intended uses & limitations

How to use

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-v2.0")
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")