How to use from the
Use from the
Diffusers library
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline

# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("jugg1024/sdxl-lora-conan")

prompt = "surprised, dark-haired male character in suit; background with dim purple hue and visible door or entrance."
image = pipe(prompt).images[0]

SDXL-Conan

Prompt
surprised, dark-haired male character in suit; background with dim purple hue and visible door or entrance.
Prompt
surprised, dark-haired male character in suit; background with dim purple hue and visible door or entrance.
Prompt
surprised, dark-haired male character in suit; background with dim purple hue and visible door or entrance.
Prompt
surprised, dark-haired male character in suit; background with dim purple hue and visible door or entrance.
Prompt
young woman with dark hair and red top in foreground with man in glasses; background crowd with outdoor setting; vibrant color palette
Prompt
young woman with dark hair and red top in foreground with man in glasses; background crowd with outdoor setting; vibrant color palette
Prompt
young woman with dark hair and red top in foreground with man in glasses; background crowd with outdoor setting; vibrant color palette
Prompt
young woman with dark hair and red top in foreground with man in glasses; background crowd with outdoor setting; vibrant color palette

These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the jugg1024/conan-gpt4-captions dataset.

LoRA for the text encoder was enabled.

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

Intended uses & limitations

How to use

# TODO: add an example code snippet for running this diffusion pipeline

Limitations and bias

  • currently the text decoder is also add a lora-adapter. So the results may be a little bit overfitting, will be recover soon

Training details

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

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