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paul-gaugin-sdxl-lora-03

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

The main validation prompt used during training was:

ggn_style, A nude woman sits on a patterned cloth with crossed legs. She holds a small object in her hand. A low table with fruit is in front of her. Pink flowers are in the background, with mountains and palm trees beyond. Text is in the bottom left corner.

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
your prompt to validate on
Negative Prompt
blurry, cropped, ugly
Prompt
another prompt to validate on
Negative Prompt
blurry, cropped, ugly
Prompt
ggn_style, A nude woman sits on a patterned cloth with crossed legs. She holds a small object in her hand. A low table with fruit is in front of her. Pink flowers are in the background, with mountains and palm trees beyond. Text is in the bottom left corner.
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: 0
  • Training steps: 400
  • Learning rate: 5e-05
  • 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-lionweight_decay=1e-3
  • 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: 10
  • Total number of images: 85
  • Total number of aspect buckets: 8
  • Resolution: 0.262144 megapixels
  • Cropped: False
  • Crop style: None
  • Crop aspect: None

paul-gaugin-sdxl-1024

  • Repeats: 10
  • Total number of images: 85
  • Total number of aspect buckets: 6
  • Resolution: 1.048576 megapixels
  • Cropped: False
  • Crop style: None
  • Crop aspect: None

paul-gaugin-sdxl-512-crop

  • Repeats: 10
  • Total number of images: 85
  • Total number of aspect buckets: 1
  • Resolution: 0.262144 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: square

paul-gaugin-sdxl-1024-crop

  • Repeats: 10
  • Total number of images: 85
  • 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, A nude woman sits on a patterned cloth with crossed legs. She holds a small object in her hand. A low table with fruit is in front of her. Pink flowers are in the background, with mountains and palm trees beyond. Text is in the bottom left corner."
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")
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