davidrd123's picture
Model card auto-generated by SimpleTuner
f54c3c5 verified
metadata
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-xl-base-1.0
tags:
  - sdxl
  - sdxl-diffusers
  - text-to-image
  - diffusers
  - simpletuner
  - not-for-all-audiences
  - lora
  - template:sd-lora
  - lycoris
inference: true
widget:
  - text: unconditional (blank prompt)
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_0_0.png
  - text: >-
      ggn_style, Five women are in a field. Two women on the right stand, one
      with arms outstretched, the other in a pink headscarf. A nude woman in the
      center stretches upward. Two nude women sit on the left, facing forward.
      Trees and hills are in the backgrou
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_1_0.png

paul-gaugin-sdxl-lora-2

This is a LyCORIS adapter derived from stabilityai/stable-diffusion-xl-base-1.0.

The main validation prompt used during training was:

ggn_style, Five women are in a field. Two women on the right stand, one with arms outstretched, the other in a pink headscarf. A nude woman in the center stretches upward. Two nude women sit on the left, facing forward. Trees and hills are in the backgrou

Validation settings

  • CFG: 4.2
  • CFG Rescale: 0.0
  • Steps: 20
  • Sampler: None
  • Seed: 42
  • Resolution: 1024x1024

Note: The validation settings are not necessarily the same as the training settings.

You can find some example images in the following gallery:

Prompt
unconditional (blank prompt)
Negative Prompt
blurry, cropped, ugly
Prompt
ggn_style, Five women are in a field. Two women on the right stand, one with arms outstretched, the other in a pink headscarf. A nude woman in the center stretches upward. Two nude women sit on the left, facing forward. Trees and hills are in the backgrou
Negative Prompt
blurry, cropped, ugly

The text encoder was not trained. You may reuse the base model text encoder for inference.

Training settings

  • Training epochs: 9
  • Training steps: 7000
  • Learning rate: 0.0001
  • Effective batch size: 4
    • Micro-batch size: 4
    • Gradient accumulation steps: 1
    • Number of GPUs: 1
  • Prediction type: epsilon
  • Rescaled betas zero SNR: False
  • Optimizer: optimi-stableadamw
  • Precision: Pure BF16
  • Quantised: Yes: int8-quanto
  • Xformers: Not used
  • LyCORIS Config:
{
    "algo": "lokr",
    "multiplier": 1.0,
    "linear_dim": 10000,
    "linear_alpha": 1,
    "factor": 16,
    "apply_preset": {
        "target_module": [
            "Attention",
            "FeedForward"
        ],
        "module_algo_map": {
            "Attention": {
                "factor": 16
            },
            "FeedForward": {
                "factor": 8
            }
        }
    }
}

Datasets

paul-gaugin-sdxl-512

  • Repeats: 6
  • Total number of images: 87
  • Total number of aspect buckets: 1
  • Resolution: 0.262144 megapixels
  • Cropped: False
  • Crop style: None
  • Crop aspect: None

paul-gaugin-sdxl-1024

  • Repeats: 6
  • Total number of images: 87
  • Total number of aspect buckets: 17
  • Resolution: 1.048576 megapixels
  • Cropped: False
  • Crop style: None
  • Crop aspect: None

paul-gaugin-sdxl-512-crop

  • Repeats: 6
  • Total number of images: 87
  • Total number of aspect buckets: 1
  • Resolution: 0.262144 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: square

paul-gaugin-sdxl-1024-crop

  • Repeats: 6
  • Total number of images: 87
  • Total number of aspect buckets: 1
  • Resolution: 1.048576 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: square

Inference

import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights

model_id = 'stabilityai/stable-diffusion-xl-base-1.0'
adapter_id = 'pytorch_lora_weights.safetensors' # you will have to download this manually
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer)
wrapper.merge_to()

prompt = "ggn_style, Five women are in a field. Two women on the right stand, one with arms outstretched, the other in a pink headscarf. A nude woman in the center stretches upward. Two nude women sit on the left, facing forward. Trees and hills are in the backgrou"
negative_prompt = 'blurry, cropped, ugly'
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=20,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
    width=1024,
    height=1024,
    guidance_scale=4.2,
    guidance_rescale=0.0,
).images[0]
image.save("output.png", format="PNG")