---
license: creativeml-openrail-m
base_model: "stabilityai/stable-diffusion-xl-base-1.0"
tags:
  - sdxl
  - sdxl-diffusers
  - text-to-image
  - diffusers
  - simpletuner
  - safe-for-work
  - lora
  - template:sd-lora
  - standard
inference: true
widget:
- text: 'unconditional (blank prompt)'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_0_0.png
- text: 'jss_style, A woman sits slouched on a sofa beneath a mantelpiece, wrapped in blankets. Her eyes are closed, and her head rests on the back.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_1_0.png
- text: 'jss_style, A man wearing a lavish robe holds a large, decorated sword. A young boy stands behind him, holding a cushion with a crown. They are in an ornate hall.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_2_0.png
- text: 'jss_style, Three young girls in white aprons. One stands alone, two stand together by a large vase. A girl sits on the floor holding a doll. Large vases flank the scene.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_3_0.png
- text: 'jss_style, hamster'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_4_0.png
- text: 'jss_style, hipster making a chair'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_5_0.png
- text: 'jss_style, A elegant woman in a long, black evening gown stands in a grand, dimly lit room. She has a confident pose, with one hand on her hip. A ornate gold frame mirror is visible in the background.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_6_0.png
- text: 'jss_style, An opulent interior with a grand piano in the foreground. A woman in a white dress sits at the piano, her back to the viewer. Sunlight streams through tall windows.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_7_0.png
- text: 'jss_style, A portrait of a modern tech CEO in a casual outfit, standing in front of a wall of computer screens'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_8_0.png
- text: 'jss_style, A group portrait of a modern diverse family in a living room. Capture individual personalities while maintaining group cohesion.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_9_0.png
- text: 'jss_style, A lively scene in a 1920s dance hall. Couples dancing, musicians playing, ambient lighting. Capture the movement and atmosphere in Sargent''s distinctive style.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_10_0.png
- text: 'jss_style, People dining at tables outside a Parisian café.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_11_0.png
- text: 'jss_style, A young boy holds a small black dog in his arms. He wears a red bow tie and stands in front of a textured backdrop. His red socks and shoes are notable.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_12_0.png
---

# john-singer-sargent-sdxl-lora-01

This is a standard PEFT LoRA 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:



```
jss_style, A young boy holds a small black dog in his arms. He wears a red bow tie and stands in front of a textured backdrop. His red socks and shoes are notable.
```

## Validation settings
- CFG: `4.2`
- CFG Rescale: `0.0`
- Steps: `30`
- 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: 2
- Training steps: 750
- Learning rate: 8e-05
- Effective batch size: 16
  - Micro-batch size: 16
  - Gradient accumulation steps: 1
  - Number of GPUs: 1
- Prediction type: epsilon
- Rescaled betas zero SNR: False
- Optimizer: optimi-lion
- Precision: Pure BF16
- Quantised: Yes: int8-quanto
- Xformers: Not used
- LoRA Rank: 64
- LoRA Alpha: None
- LoRA Dropout: 0.1
- LoRA initialisation style: default
    

## Datasets

### jss-sdxl-01-512
- Repeats: 10
- Total number of images: 84
- Total number of aspect buckets: 7
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### jss-sdxl-01-1024
- Repeats: 10
- Total number of images: 81
- Total number of aspect buckets: 11
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### jss-sdxl-01-512-crop
- Repeats: 10
- Total number of images: 84
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
### jss-sdxl-01-1024-crop
- Repeats: 10
- Total number of images: 84
- 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

model_id = 'stabilityai/stable-diffusion-xl-base-1.0'
adapter_id = 'davidrd123/john-singer-sargent-sdxl-lora-01'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "jss_style, A young boy holds a small black dog in his arms. He wears a red bow tie and stands in front of a textured backdrop. His red socks and shoes are notable."
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=30,
    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")
```