stable-diffusion-3.5-medium-HC
This is a PEFT LoRA derived from stabilityai/stable-diffusion-3.5-medium.
The main validation prompt used during training was:
Designed by Hyundai, 3/4 front view, red Palisade SUV parked in a misty forest clearing, early morning fog and golden sun rays filtering through pine trees, dew drops on side mirror, Hyundai emblem shining, lush green plants, dramatic backlight, reflections of the woods on glass, ultra-realistic design, cool color grading, natural sunlight, macro details, premium feel, elegant and powerful stance.
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:

- Prompt
- unconditional (blank prompt)
- Negative Prompt
- blurry, cropped, ugly

- Prompt
- Designed by Hyundai, 3/4 front view, red Palisade SUV parked in a misty forest clearing, early morning fog and golden sun rays filtering through pine trees, dew drops on side mirror, Hyundai emblem shining, lush green plants, dramatic backlight, reflections of the woods on glass, ultra-realistic design, cool color grading, natural sunlight, macro details, premium feel, elegant and powerful stance.
- Negative Prompt
- blurry, cropped, ugly
The text encoder was not trained. You may reuse the base model text encoder for inference.
Training settings
Training epochs: 4
Training steps: 3000
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: 256
LoRA Alpha: 256.0
LoRA Dropout: 0.1
LoRA initialisation style: default
LoRA mode: Standard
Datasets
test1_data_set
- Repeats: 5
- Total number of images: 101
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
Inference
import torch
from diffusers import DiffusionPipeline
model_id = 'stabilityai/stable-diffusion-3.5-medium'
adapter_id = 'mingyu-oo/stable-diffusion-3.5-medium-HC'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)
prompt = "Designed by Hyundai, 3/4 front view, red Palisade SUV parked in a misty forest clearing, early morning fog and golden sun rays filtering through pine trees, dew drops on side mirror, Hyundai emblem shining, lush green plants, dramatic backlight, reflections of the woods on glass, ultra-realistic design, cool color grading, natural sunlight, macro details, premium feel, elegant and powerful stance."
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")
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stabilityai/stable-diffusion-3.5-medium