mixqwerpoi The bright and clean kitchen area. The walls are finished with white paint, and the ceiling is also neatly finished with white paint. The floor is finished with light wood in a herringbone pattern, further emphasizing the bright feel of the entire space.The main color is white, silver (from the appliances and fixtures) is used as a secondary color, and warm wood tones are utilized as an accent color. White makes the space look wide and clean, silver adds a sleek modern touch, and the wood elements add warmth and naturalness. White cabinetry lines the kitchen, offering a clean and modern feel. A built-in microwave and dishwasher keep the space tidy, and a sleek faucet is installed beneath a window fitted with silver blinds. A white coffee machine sits on the counter, accompanied by wooden cutting boards that add a natural accent. Overall, it shows a minimal and sophisticated style. A bright, clean, and comfortable atmosphere is felt throughout the kitchen.

Validation settings

CFG: 3.0
CFG Rescale: 0.0
Steps: 25
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: 148
Training steps: 8000
Learning rate: 1e-05
- Learning rate schedule: polynomial
- Warmup steps: 200
Max grad norm: 0.01
Effective batch size: 2
- Micro-batch size: 2
- 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: 20.0%
LoRA Rank: 64
LoRA Alpha: None
LoRA Dropout: 0.1
LoRA initialisation style: default

Datasets

mini-mix-lora-very-small-2

Repeats: 5
Total number of images: 16
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/mini-mix-lora-very-small-2'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "mixqwerpoi The bright and clean kitchen area. The walls are finished with white paint, and the ceiling is also neatly finished with white paint. The floor is finished with light wood in a herringbone pattern, further emphasizing the bright feel of the entire space.The main color is white, silver (from the appliances and fixtures) is used as a secondary color, and warm wood tones are utilized as an accent color. White makes the space look wide and clean, silver adds a sleek modern touch, and the wood elements add warmth and naturalness. White cabinetry lines the kitchen, offering a clean and modern feel. A built-in microwave and dishwasher keep the space tidy, and a sleek faucet is installed beneath a window fitted with silver blinds. A white coffee machine sits on the counter, accompanied by wooden cutting boards that add a natural accent. Overall, it shows a minimal and sophisticated style. A bright, clean, and comfortable atmosphere is felt throughout the kitchen."
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=25,
    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=3.0,
    skip_guidance_layers=[7, 8, 9],
).images[0]
image.save("output.png", format="PNG")