home-interior. two brown chairs and a coffee table. A framed abstract artwork hangs above the armchairs. The walls are painted in a soft neutral tone, and a large flat-screen TV is mounted on wall. A rug covers part of the light oak flooring. On the left, large black-framed glass sliding doors lead to outdoor.

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: 18
Training steps: 2000
Learning rate: 1e-05
- Learning rate schedule: polynomial
- Warmup steps: 100
Max grad norm: 0.1
Effective batch size: 4
- Micro-batch size: 4
- 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: FP32
Caption dropout probability: 0.0%
LoRA Rank: 512
LoRA Alpha: None
LoRA Dropout: 0.1
LoRA initialisation style: default

Datasets

home-interior-3

Repeats: 1
Total number of images: 209
Total number of aspect buckets: 9
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/home-interior-3'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "home-interior. two brown chairs and a coffee table. A framed abstract artwork hangs above the armchairs. The walls are painted in a soft neutral tone, and a large flat-screen TV is mounted on wall. A rug covers part of the light oak flooring. On the left, large black-framed glass sliding doors lead to outdoor."
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")