p1-y3o-lora

This is a standard PEFT LoRA derived from stabilityai/stable-diffusion-3.5-large.

The main validation prompt used during training was:

This image shows a y3o man. Behind him, crystal-clear turquoise waters gently lap against pristine white sand, while lush palm trees sway softly in the ocean breeze, creating an idyllic Caribbean setting.

Validation settings

• CFG: 7.5
• 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.

You can find some example images in the following gallery:

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

Training settings

• Training epochs: 49
• Training steps: 300
• Learning rate: 0.000505
• Max grad norm: 0.01
• Effective batch size: 12
  • Micro-batch size: 12
  • Gradient accumulation steps: 1
  • Number of GPUs: 1
• Prediction type: flow-matching
• Rescaled betas zero SNR: False
• Optimizer: adamw_bf16
• Precision: Pure BF16
• Quantised: No
• Xformers: Not used
• LoRA Rank: 768
• LoRA Alpha: 768.0
• LoRA Dropout: 0.1
• LoRA initialisation style: default

Datasets

subject

• Repeats: 5
• Total number of images: 10
• Total number of aspect buckets: 1
• Resolution: 1.0 megapixels
• Cropped: False
• Crop style: None
• Crop aspect: None
• Used for regularisation data: No

Inference

import torch
from diffusers import DiffusionPipeline

model_id = 'stabilityai/stable-diffusion-3.5-large'
adapter_id = 'AngelZeur/p1-y3o-lora'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "This image shows a y3o man happily standing on a sunlit Caribbean beach, smiling warmly with a cheerful and relaxed expression. His gentle, dark eyes reflect a joyful and friendly demeanor. Behind him, crystal-clear turquoise waters gently lap against pristine white sand, while lush palm trees sway softly in the ocean breeze, creating an idyllic Caribbean setting that perfectly complements his cheerful presence."
negative_prompt = ''
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=7.5,
).images[0]
image.save("output.png", format="PNG")