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---
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")
```
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