sdxl-training

This is a LoRA derived from stabilityai/stable-diffusion-3-medium-diffusers.

The main validation prompt used during training was:

a photo of a naked woman with large breasts

Validation settings

• CFG: 7.5
• CFG Rescale: 0.0
• Steps: 50
• Sampler: euler
• Seed: 42
• Resolution: 1024

Note: The validation settings are not necessarily the same as the training settings.

You can find some example images in the following gallery:

Prompt

unconditional (blank prompt)

Negative Prompt

blurry, cropped, ugly

Prompt

a photo of a naked woman with large breasts

Negative Prompt

blurry, cropped, ugly

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

Training settings

• Training epochs: 517
• Training steps: 10350
• Learning rate: 0.0002
• Effective batch size: 20
  • Micro-batch size: 5
  • Gradient accumulation steps: 4
  • Number of GPUs: 1
• Prediction type: epsilon
• Rescaled betas zero SNR: False
• Optimizer: AdamW, stochastic bf16
• Precision: Pure BF16
• Xformers: Enabled
• LoRA Rank: 64
• LoRA Alpha: 64.0
• LoRA Dropout: 0.1
• LoRA initialisation style: default

Datasets

curated3

• Repeats: 0
• Total number of images: 400
• Total number of aspect buckets: 1
• Resolution: 0.5 megapixels
• Cropped: False
• Crop style: None
• Crop aspect: None

Inference

import torch
from diffusers import DiffusionPipeline




model_id = 'stabilityai/stable-diffusion-3-medium-diffusers'
adapter_id = 'sdxl-training'
prompt = 'a photo of a naked woman with large breasts'
negative_prompt = 'blurry, cropped, ugly'
pipeline = DiffusionPipeline.from_pretrained(model_id)\pipeline.load_adapter(adapter_id)
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')

prompt = "a photo of a naked woman with large breasts"
negative_prompt = "blurry, cropped, ugly"

pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
    prompt=prompt,
    negative_prompt='blurry, cropped, ugly',
    num_inference_steps=50,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
    width=1152,
    height=768,
    guidance_scale=7.5,
    guidance_rescale=0.0,
).images[0]
image.save("output.png", format="PNG")