xhsgirl_v0.2SDXL

This is a LoRA derived from 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.

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

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")