---
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](https://huggingface.co/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](#training-settings).

You can find some example images in the following gallery:


<Gallery />

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:
```json
{
    "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


```python
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")
```