An illustration of a serene landscape at night, moonlit mountain scene, tall pine trees on a small island, snow-capped mountains in the background, still lake reflecting trees and full moon, cloud-speckled sky dotted with stars, soft ambient lighting, primary color tones of blue and white, ambient and tranquil atmosphere, high resolution, extremely detailed.

Validation settings

CFG: 3.0
CFG Rescale: 0.0
Steps: 20
Sampler: None
Seed: 42
Resolution: 1344x768

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: 9
Training steps: 10000
Learning rate: 0.0001
Effective batch size: 1
- Micro-batch size: 1
- Gradient accumulation steps: 1
- Number of GPUs: 1
Prediction type: flow-matching
Rescaled betas zero SNR: False
Optimizer: adamw_bf16
Precision: bf16
Quantised: Yes: int8-quanto
Xformers: Not used
LoRA Rank: 16
LoRA Alpha: 16.0
LoRA Dropout: 0.1
LoRA initialisation style: default

Datasets

pseudo-natural-booru-flux

Repeats: 0
Total number of images: 1089
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: True
Crop style: center
Crop aspect: square

Inference

python

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'datnt114/simpletuner-lora'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "An illustration of a serene landscape at night, moonlit mountain scene, tall pine trees on a small island, snow-capped mountains in the background, still lake reflecting trees and full moon, cloud-speckled sky dotted with stars, soft ambient lighting, primary color tones of blue and white, ambient and tranquil atmosphere, high resolution, extremely detailed."

pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
    prompt=prompt,
    num_inference_steps=20,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
    width=1344,
    height=768,
    guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")