metadata

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
datasets:
  - Aedancodes/monet_dataset
widget:
  - text: monet,  a landscape of a snowy mountain region big clouds
    output:
      url: images/example_r809dxj86.png
  - text: >-
      monet, majestic cliffs overlooking a serene ocean, with dramatic rock
      formations bathed in soft light. The cliffs are painted in shades of
      green, ochre, and brown, contrasting with the smooth, flowing waves below,
      capturing the raw, natural beauty of the landscape
    output:
      url: images/example_xm434dzxp.png
  - text: >-
      monet,  a landscape of a snowy mountain region big clouds, street lamps
      with warm yellow colrs, night
    output:
      url: images/example_c6adjuo36.png
  - text: >-
      monet,  mountains far back, street lamps shines with warm yellow colors,
      black night
    output:
      url: images/example_dzoc9trs1.png
  - text: Monet lakeside at sunset
    output:
      url: images/example_4hq20p721.png

Prompt
monet, a landscape of a snowy mountain region big clouds

Prompt
monet, majestic cliffs overlooking a serene ocean, with dramatic rock formations bathed in soft light. The cliffs are painted in shades of green, ochre, and brown, contrasting with the smooth, flowing waves below, capturing the raw, natural beauty of the landscape

Prompt
monet, a landscape of a snowy mountain region big clouds, street lamps with warm yellow colrs, night

Prompt
monet, mountains far back, street lamps shines with warm yellow colors, black night

LoRA text2image fine-tuning

These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the Aedancodes/monet_dataset dataset.

Trigger words

Trigger words: You should use Monet to trigger the image generation.

Training details

resolution=1024*1024
train batch_size = 1
max train steps = 200
learning rate = 1e-4
lr scheduler = constant
mixed precision = fp16
8bit_adam