paul-gaugin-sdxl-lora

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

The main validation prompt used during training was:

ggn_style, Three women are seated or standing in a grassy area with chickens. A fourth woman is seated in front of a thatched-roof hut. Palm trees stand nearby. A fifth person appears in the background, engaging with the environment. The scene is outdoors and tropical. Text is present in the bottom right 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

ggn_style, Three women are seated or standing in a grassy area with chickens. A fourth woman is seated in front of a thatched-roof hut. Palm trees stand nearby. A fifth person appears in the background, engaging with the environment. The scene is outdoors and tropical. Text is present in the bottom right 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: 6
• Training steps: 4500
• Learning rate: 0.0001
• Effective batch size: 8
  • Micro-batch size: 8
  • Gradient accumulation steps: 1
  • Number of GPUs: 1
• Prediction type: epsilon
• Rescaled betas zero SNR: False
• Optimizer: adamw_bf16
• 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: 87
• Total number of aspect buckets: 3
• Resolution: 0.262144 megapixels
• Cropped: False
• Crop style: None
• Crop aspect: None

paul-gaugin-sdxl-1024

• Repeats: 10
• 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: 10
• 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: 10
• 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, Three women are seated or standing in a grassy area with chickens. A fourth woman is seated in front of a thatched-roof hut. Palm trees stand nearby. A fifth person appears in the background, engaging with the environment. The scene is outdoors and tropical. Text is present in the bottom right 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")