README.md

---
license: creativeml-openrail-m
base_model: "stabilityai/stable-diffusion-xl-base-1.0"
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
  - simpletuner
  - lora
  - template:sd-lora
inference: true

---

# xhsgirl_v0.2SDXL

This is a 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:



```
a woman side view upper body, hands clasped near mouth, strapless dress, long wavy hair, black car hood, forest background
```

## Validation settings
- CFG: `5.5`
- CFG Rescale: `0.0`
- Steps: `30`
- Sampler: `None`
- Seed: `42`
- Resolution: `1024`

Note: The validation settings are not necessarily the same as the [training settings](#training-settings).




<Gallery />

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


## Training settings

- Training epochs: 2999
- Training steps: 3000
- Learning rate: 8e-06
- Effective batch size: 16
  - Micro-batch size: 4
  - Gradient accumulation steps: 4
  - Number of GPUs: 1
- Prediction type: epsilon
- Rescaled betas zero SNR: False
- Optimizer: AdamW, stochastic bf16
- Precision: Pure BF16
- Xformers: Not used
- LoRA Rank: 16
- LoRA Alpha: None
- LoRA Dropout: 0.1
- LoRA initialisation style: default


## Datasets

### xhsgirl_v0.2sdxl
- Repeats: 0
- Total number of images: 16
- Total number of aspect buckets: 1
- Resolution: 1024 px
- Cropped: True
- Crop style: center
- Crop aspect: square


## Inference


```python
import torch
from diffusers import DiffusionPipeline

model_id = 'stabilityai/stable-diffusion-xl-base-1.0'
adapter_id = 'ChandlerGIS/xhsgirl_v0.2SDXL'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "a woman side view upper body, hands clasped near mouth, strapless dress, long wavy hair, black car hood, forest background"
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=5.5,
    guidance_rescale=0.0,
).images[0]
image.save("output.png", format="PNG")
```