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metadata
tags:
  - stable-diffusion-xl
  - stable-diffusion-xl-diffusers
  - diffusers-training
  - text-to-image
  - diffusers
  - lora
  - template:sd-lora
widget:
  - text: a TOK word of mychar, white background, in the style of TOK
    output:
      url: image_0.png
  - text: a TOK word of mychar, white background, in the style of TOK
    output:
      url: image_1.png
  - text: a TOK word of mychar, white background, in the style of TOK
    output:
      url: image_2.png
  - text: a TOK word of mychar, white background, in the style of TOK
    output:
      url: image_3.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: character in the style of TOK
license: openrail++

SDXL LoRA DreamBooth - nicolaus-huang/3d-icon-SDXL-LoRA-eng

Prompt
a TOK word of mychar, white background, in the style of TOK
Prompt
a TOK word of mychar, white background, in the style of TOK
Prompt
a TOK word of mychar, white background, in the style of TOK
Prompt
a TOK word of mychar, white background, in the style of TOK

Model description

These are nicolaus-huang/3d-icon-SDXL-LoRA-eng LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.

Download model

Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke

Use it with the 🧨 diffusers library

from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('nicolaus-huang/3d-icon-SDXL-LoRA-eng', weight_name='pytorch_lora_weights.safetensors')

image = pipeline('a TOK word of mychar, white background, in the style of TOK').images[0]

For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers

Trigger words

You should use character in the style of TOK to trigger the image generation.

Details

All Files & versions.

The weights were trained using 🧨 diffusers Advanced Dreambooth Training Script.

LoRA for the text encoder was enabled. True.

Pivotal tuning was enabled: False.

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