A peaceful Japanese-inspired scene unfolds, showcasing a cozy retreat nestled in the heart of nature. Towering mountains rise in the distance, framing a serene environment filled with vibrant plants and lush greenery. A calm pond reflects the bright sunlight, its surface adorned with delicate ripples and blooming lotus flowers. Nearby, a rose garden adds a touch of romance, its soft petals contrasting beautifully with the earthy tones of the surroundings. The setting exudes tranquility, inviting you to pause, breathe, and connect with the harmony of nature. It's a perfect haven where the natural beauty of Japan's landscapes meets cozy serenity.

Validation settings

CFG: 3.0
CFG Rescale: 0.0
Steps: 20
Sampler: FlowMatchEulerDiscreteScheduler
Seed: 42
Resolution: 1920x1080
Skip-layer guidance:

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: 6
Training steps: 15000
Learning rate: 1e-05
- Learning rate schedule: polynomial
- Warmup steps: 1000
Max grad norm: 1.0
Effective batch size: 1
- Micro-batch size: 1
- Gradient accumulation steps: 1
- Number of GPUs: 1
Gradient checkpointing: True
Prediction type: flow-matching (extra parameters=['shift=3', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0', 'flow_matching_loss=compatible', 'flux_lora_target=all'])
Optimizer: adamw_bf16
Trainable parameter precision: Pure BF16
Caption dropout probability: 10.0%
LoRA Rank: 16
LoRA Alpha: 16.0
LoRA Dropout: 0.1
LoRA initialisation style: default

Datasets

lofi512_051

Repeats: 10
Total number of images: 67
Total number of aspect buckets: 1
Resolution: 0.262144 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

lofi768_051

Repeats: 10
Total number of images: 67
Total number of aspect buckets: 1
Resolution: 0.589824 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

lofi1024_051

Repeats: 10
Total number of images: 67
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

Inference

python

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'maver1chh/maver1chh/lofi0501'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "A peaceful Japanese-inspired scene unfolds, showcasing a cozy retreat nestled in the heart of nature. Towering mountains rise in the distance, framing a serene environment filled with vibrant plants and lush greenery. A calm pond reflects the bright sunlight, its surface adorned with delicate ripples and blooming lotus flowers. Nearby, a rose garden adds a touch of romance, its soft petals contrasting beautifully with the earthy tones of the surroundings. The setting exudes tranquility, inviting you to pause, breathe, and connect with the harmony of nature. It's a perfect haven where the natural beauty of Japan's landscapes meets cozy serenity."


## Optional: quantise the model to save on vram.
## Note: The model was quantised during training, and so it is recommended to do the same during inference time.
from optimum.quanto import quantize, freeze, qint8
quantize(pipeline.transformer, weights=qint8)
freeze(pipeline.transformer)
    
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
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(42),
    width=1920,
    height=1080,
    guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")

Exponential Moving Average (EMA)

SimpleTuner generates a safetensors variant of the EMA weights and a pt file.

The safetensors file is intended to be used for inference, and the pt file is for continuing finetuning.

The EMA model may provide a more well-rounded result, but typically will feel undertrained compared to the full model as it is a running decayed average of the model weights.