Designed by Hyundai, front view, compact sedan, three-box silhouette, long wheelbase, wide track, short overhangs, low stance, taut body surfacing, aggressive front fascia, mesh grille, large air intakes, swept-back DRL signature, LED headlamps, flush door handles, multi-spoke 18-inch wheels, low-profile tires, chrome window trim, low beltline, roof rails, metallic gray paint, gloss black accents

Validation settings

CFG: 7.5
CFG Rescale: 0.0
Steps: 20
Sampler: FlowMatchEulerDiscreteScheduler
Seed: 42
Resolution: 1024x1024
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: 2
Training steps: 3222
Learning rate: 0.0001
- Learning rate schedule: sine
- Warmup steps: 500
Max grad value: 2.0
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'])
Optimizer: adamw_bf16
Trainable parameter precision: Pure BF16
Base model precision: no_change
Caption dropout probability: 0.0%
LoRA Rank: 512
LoRA Alpha: 512.0
LoRA Dropout: 0.1
LoRA initialisation style: default
LoRA mode: Standard

Datasets

test_dataset

Repeats: 5
Total number of images: 179
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 = 'stabilityai/stable-diffusion-3.5-medium'
adapter_id = 'mingyu-oo/stable-diffusion-3.5-medium-HD2'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "Designed by Hyundai, front view, compact sedan, three-box silhouette, long wheelbase, wide track, short overhangs, low stance, taut body surfacing, aggressive front fascia, mesh grille, large air intakes, swept-back DRL signature, LED headlamps, flush door handles, multi-spoke 18-inch wheels, low-profile tires, chrome window trim, low beltline, roof rails, metallic gray paint, gloss black accents"
negative_prompt = 'blurry, cropped, ugly'

## Optional: quantise the model to save on vram.
## Note: The model was not quantised during training, so it is not necessary to quantise it 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
model_output = pipeline(
    prompt=prompt,
    negative_prompt=negative_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=1024,
    height=1024,
    guidance_scale=7.5,
).images[0]

model_output.save("output.png", format="PNG")