sd3-lora-celebrities

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

The main validation prompt used during training was:

a studio portrait photograph of emma watson. she looks relaxed and happy.

Validation settings

CFG: 5.0
CFG Rescale: 0.2
Steps: 50
Sampler: euler
Seed: 2
Resolution: 1280x768

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

You can find some example images in the following gallery:

Prompt
a studio portrait photograph of emma watson. she looks relaxed and happy.

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: 5
Training steps: 9316
Learning rate: 0.0001
Effective batch size: 1
- Micro-batch size: 1
- Gradient accumulation steps: 1
- Number of GPUs: 1
Prediction type: v_prediction
Rescaled betas zero SNR: True
Optimizer: AdamW, stochastic bf16
Precision: Pure BF16
Xformers: Not used
LoRA Rank: 16
LoRA Alpha: 16
LoRA Dropout: 0.1
LoRA initialisation style: default

Datasets

celebrities-sd3

Repeats: 0
Total number of images: 1830
Total number of aspect buckets: 27
Resolution: 0.5 megapixels
Cropped: False
Crop style: None
Crop aspect: None

Inference

import torch
from diffusers import StableDiffusion3Pipeline



model_id = "sd3-lora-celebrities"
prompt = "a studio portrait photograph of emma watson. she looks relaxed and happy."
negative_prompt = "malformed, disgusting, overexposed, washed-out"

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=5.0,
    guidance_rescale=0.2,
).images[0]
image.save("output.png", format="PNG")