---
license: other
base_model: stabilityai/stable-diffusion-3-medium-diffusers
tags:
- sd3
- sd3-diffusers
- text-to-image
- diffusers
- simpletuner
- lora
- template:sd-lora
- lycoris
inference: true
widget:
- text: A swift and agile elven archer perched in a tree, nocking an arrow.
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_1_0.png
- text: A cyberpunk hunter in neon-lit city alleys, armed with a high-tech rifle.
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_2_0.png
- text: A mighty fantasy knight in gleaming armor, wielding a sword and shield.
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_3_0.png
- text: >-
    A space pirate captain standing on the bridge of a starship, ready for
    adventure.
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_4_0.png
- text: A powerful demonic sorcerer casting a spell in a dark, mysterious chamber.
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_5_0.png
- text: >-
    A friendly robotic assistant with a sleek design, helping a player navigate
    a game.
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_6_0.png
- text: A stealthy ninja warrior crouching in the shadows, ready to strike.
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_7_0.png
- text: >-
    A group of survivors in a post-apocalyptic world, fending off a zombie
    horde.
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_8_0.png
- text: >-
    A brave dragon tamer soaring through the sky on the back of a majestic
    dragon.
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_9_0.png
- text: >-
    A wise medieval wizard in a towering castle, studying ancient tomes of
    magic.
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_10_0.png
pipeline_tag: text-to-image
---

# SD3M/simpletuner-lora (Text2Img)

This is a LyCORIS adapter minicing the art style of John Singer Sargent,
derived from [stabilityai/stable-diffusion-3-medium-diffusers](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers).


The main validation prompt used during training:
```
A swift and agile elven archer perched in a tree, nocking an arrow.
```


## Validation settings
- CFG: `3.0`
- CFG Rescale: `0.0`
- Steps: `20`
- Sampler: `FlowMatchEulerDiscreteScheduler`
- Seed: `42`
- Resolution: `1024x1024`
- Skip-layer guidance: `None`

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:

<div style="display: flex; flex-wrap: wrap; gap: 10px;">
  <div style="flex: 1 1 30%;">
    <img src="./assets/image_1_0.png" alt="A swift and agile elven archer perched in a tree, nocking an arrow" style="width: 100%;">
    <p>prompt: A swift and agile elven archer perched in a tree, nocking an arrow.</p>
  </div>
  <div style="flex: 1 1 30%;">
    <img src="./assets/image_5_0.png" alt="A powerful demonic sorcerer casting a spell in a dark, mysterious chamber" style="width: 100%;">
    <p>prompt: A powerful demonic sorcerer casting a spell in a dark, mysterious chamber.</p>
  </div>
  <div style="flex: 1 1 30%;">
    <img src="./assets/image_10_0.png" alt="A wise medieval wizard in a towering castle, studying ancient tomes of magic" style="width: 100%;">
    <p>prompt: A wise medieval wizard in a towering castle, studying ancient tomes of magic.</p>
  </div>
</div>

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


## Training settings

- Training epochs: 10
- Training steps: 10000
- Learning rate: 0.0001
  - Learning rate schedule: polynomial
  - Warmup steps: 100
- Max grad norm: 0.01
- 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
- Caption dropout probability: 10.0%


### 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

### wikiart_sargent
- Repeats: 0
- Total number of images: 920
- Total number of aspect buckets: 4
- 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
from lycoris import create_lycoris_from_weights


def download_adapter(repo_id: str):
    import os
    from huggingface_hub import hf_hub_download
    adapter_filename = "pytorch_lora_weights.safetensors"
    cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models'))
    cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_")
    path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path)
    path_to_adapter_file = os.path.join(path_to_adapter, adapter_filename)
    os.makedirs(path_to_adapter, exist_ok=True)
    hf_hub_download(
        repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter
    )

    return path_to_adapter_file
    
model_id = 'stabilityai/stable-diffusion-3-medium-diffusers'
adapter_repo_id = 'jimchoi/simpletuner-lora'
adapter_filename = 'pytorch_lora_weights.safetensors'
adapter_file_path = download_adapter(repo_id=adapter_repo_id)
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer)
wrapper.merge_to()

prompt = "A swift and agile elven archer perched in a tree, nocking an arrow."
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=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=3.0,
).images[0]
image.save("output.png", format="PNG")
```