b8m home bathroom interior design. The bright and clean bathroom area. The walls are finished with white tiles, and the ceiling is also neatly finished with white paint. The floor is finished with white tiles in a herringbone pattern, further emphasizing the bright feel of the entire space. The main color is white, silver is used as a secondary color. White makes the space look wide and clean, silver adds a sleek modern touch.

Validation settings

CFG: 4.0
CFG Rescale: 0.0
Steps: 50
Sampler: FlowMatchEulerDiscreteScheduler
Seed: 42
Resolutions: 1024x1024, 1024x896, 1280x768
Skip-layer guidance: skip_guidance_layers=[7, 8, 9],

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: 20
Training steps: 16000
Learning rate: 3e-05
- Learning rate schedule: polynomial
- Warmup steps: 1000
Max grad norm: 0.01
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=1.0'])
Optimizer: adamw_bf16
Trainable parameter precision: Pure BF16
Caption dropout probability: 30.0%
LoRA Rank: 1024
LoRA Alpha: None
LoRA Dropout: 0.1
LoRA initialisation style: default

Datasets

bathroom_lora_1024

Repeats: 30
Total number of images: 25
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: preserve
Used for regularisation data: No

Inference

python

import torch
from diffusers import DiffusionPipeline

model_id = 'stabilityai/stable-diffusion-3.5-large'
adapter_id = 'daehuncho/bathroom_lora_1024'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "b8m home bathroom interior design. The bright and clean bathroom area. The walls are finished with white tiles, and the ceiling is also neatly finished with white paint. The floor is finished with white tiles in a herringbone pattern, further emphasizing the bright feel of the entire space. The main color is white, silver is used as a secondary color. White makes the space look wide and clean, silver adds a sleek modern touch."
negative_prompt = 'blurry, cropped, ugly'

## 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,
    negative_prompt=negative_prompt,
    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(42),
    width=1024,
    height=1024,
    guidance_scale=4.0,
    skip_guidance_layers=[7, 8, 9],
).images[0]
image.save("output.png", format="PNG")