README.md

---
license: other
base_model: "stabilityai/stable-diffusion-3.5-large"
tags:
  - sd3
  - sd3-diffusers
  - text-to-image
  - diffusers
  - simpletuner
  - lora
  - template:sd-lora
  - lycoris
widget:
- text: Using a strawberry and a stainless steel whisk, create a composition that expresses a sense of rhythm and vibrancy, basic design.
  output:
    url: assets/1.png
- text: A dynamic interplay of tumbling dice and flowing red ribbons wrapping around metallic pipes, basic design style.
  output:
    url: assets/2.png
- text: A dynamic composition of transparent light bulbs intertwined with flowing golden and blue ribbons, basic design style.
  output:
    url: assets/3.png
- text: Using a wine glass and wooden tongs, express a moment of tension and destruction, basic design.
  output:
    url: assets/4.png
- text: A dynamic explosion of metallic whistles and shattering biscuit sticks, all entangled with vibrant, colorful ribbons, basic design style.
  output:
    url: assets/5.png
- text: A glass chess piece being tightly wrapped and constricted by a heavy, metallic chain, creating a sense of tension and imminent fracture, basic design.
  output:
    url: assets/6.png
---

# SD3.5-LoRA-Korean-Basic-Design

This is a LyCORIS adapter derived from [stabilityai/stable-diffusion-3.5-large](https://huggingface.co/stabilityai/stable-diffusion-3.5-large).


The main validation prompt used during training was:

```
Using a strawberry and a stainless steel whisk, create a composition that expresses a sense of rhythm and vibrancy, basic design.
```

## Validation settings
- CFG: `5.0`
- CFG Rescale: `0.0`
- Steps: `20`
- Sampler: `None`
- Seed: `42`
- Resolution: `1024x1024`

Note: The validation settings are not necessarily the same as the [training settings](#training-settings).

You can find some example images in the following gallery:


<Gallery />

The text encoder **was not** trained.
You may reuse the base model text encoder for inference.


## Training settings

- Training epochs: 2
- Training steps: 2000
- Learning rate: 0.0001
- Max grad norm: 0.01
- Effective batch size: 1
  - Micro-batch size: 1
  - Gradient accumulation steps: 1
  - Number of GPUs: 1
- Prediction type: flow-matching
- Rescaled betas zero SNR: False
- Optimizer: adamw_bf16
- Precision: Pure BF16
- Quantised: Yes: int8-quanto
- Xformers: Not used
- LyCORIS Config:
```json
{
    "algo": "lokr",
    "multiplier": 1.0,
    "linear_dim": 10000,
    "linear_alpha": 1,
    "factor": 16,
    "apply_preset": {
        "target_module": [
            "Attention",
            "FeedForward"
        ],
        "module_algo_map": {
            "Attention": {
                "factor": 16
            },
            "FeedForward": {
                "factor": 8
            }
        }
    }
}
```

## Datasets

### my-dataset-512
- Repeats: 10
- Total number of images: 17
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### my-dataset-1024
- Repeats: 10
- Total number of images: 17
- Total number of aspect buckets: 4
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### my-dataset-512-crop
- Repeats: 10
- Total number of images: 17
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
- Used for regularisation data: No
### my-dataset-1024-crop
- Repeats: 10
- Total number of images: 17
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
- Used for regularisation data: No


## Inference


```python
import torch
from diffusers import StableDiffusion3Pipeline
from lycoris import create_lycoris_from_weights

adapter_id = 'taewan2002/SD3.5-LoRA-Korean-Basic-Design'
pipeline = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3.5-large", torch_dtype=torch.bfloat16)
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer)
wrapper.merge_to()

prompt = "Using a wine glass and wooden tongs, express a moment of tension and destruction, basic design."

pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
    prompt=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(1641421826),
    width=1072,
    height=720,
    guidance_scale=5.0,
).images[0]
image.save("output.png", format="PNG")
```