Full Code of maszhongming/Multi-LoRA-Composition for AI

Repository: maszhongming/Multi-LoRA-Composition
Branch: main
Commit: 6a5486cd98b6
Files: 34
Total size: 594.1 KB

Directory structure:
gitextract_umsbyxvg/

├── .gitignore
├── README.md
├── analyze.py
├── anime_lora_info.json
├── callbacks.py
├── compose_anime.sh
├── compose_lora.py
├── compose_reality.sh
├── docs/
│   ├── index.html
│   └── static/
│       ├── css/
│       │   ├── bulma.css.map.txt
│       │   └── index.css
│       ├── js/
│       │   ├── bulma-carousel.js
│       │   ├── bulma-slider.js
│       │   └── index.js
│       └── videos/
│           └── ._Icon
├── eval.sh
├── evaluate.py
├── example.py
├── human_eval/
│   ├── README.md
│   ├── calculate_correlations.py
│   ├── clipscore.py
│   ├── gpt4v.py
│   ├── image_info.json
│   └── results.json
├── lcm_example.py
├── lcm_lora_example.py
├── models/
│   └── README.md
├── pipelines/
│   ├── sd1.5_0.26.3/
│   │   └── pipeline.py
│   └── sdxl_0.26.3/
│       ├── pipeline.py
│       └── watermark.py
├── reality_lora_info.json
├── requirements.txt
├── sdxl_example.py
└── utils.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
models/lora/*
models/Realistic-LCM-LoRA/*


================================================
FILE: README.md
================================================
# <img src="images/tangram.png" alt="title" width="4%"> Multi-LoRA Composition for Image Generation
<p align="center">
  <a href="https://maszhongming.github.io/Multi-LoRA-Composition/"><img src="https://img.shields.io/badge/🌐-Website-red" height="25"></a>
  <a href="https://arxiv.org/abs/2402.16843"><img src="https://img.shields.io/badge/📝-Paper-blue" height="25"></a>
  <a href="https://drive.google.com/file/d/1SuwRgV1LtEud8dfjftnw-zxBMgzSCwIT/view?usp=sharing" ><img src="https://img.shields.io/badge/🎨-ComposLoRA-green" height="25"></a>
  <a href="https://colab.research.google.com/drive/1eSTj6qGOtSY5NaazwwN3meXOzEZxgaZq?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="25"></a>
</p>

🖋 **Authors:** [Ming Zhong](https://maszhongming.github.io/), [Yelong Shen](https://scholar.google.com/citations?user=S6OFEFEAAAAJ&hl=en), [Shuohang Wang](https://www.microsoft.com/en-us/research/people/shuowa/), [Yadong Lu](https://adamlu123.github.io/), [Yizhu Jiao](https://yzjiao.github.io/), [Siru Ouyang](https://ozyyshr.github.io/), [Donghan Yu](https://plusross.github.io/), [Jiawei Han](https://hanj.cs.illinois.edu/), [Weizhu Chen](https://www.microsoft.com/en-us/research/people/wzchen/)

## 📜 Overview

Low-Rank Adaptation (LoRA) is extensively utilized in text-to-image models for the accurate rendition of specific elements like distinct characters or unique styles in generated images.

Our project presents two training-free methods: **LoRA Switch** and **LoRA Composite** for integrating any number of elements in an image through multi-LoRA composition.

The figure below illustrates differences between the traditional LoRA Merge approach and our newly proposed techniques:

<p align="center">
    <img src="images/intro_fig.png" width="100%" alt="intro_case">
</p>

## 🚀 Getting Started

### Setting Up the Environment
To begin, set up your environment with the necessary packages:
```bash
conda create --name multi-lora python=3.10
conda activate multi-lora
pip install -r requirements.txt
```

### Downloading Pre-trained LoRAs
Our **ComposLoRA** testbed collects 22 pre-trained LoRAs, spanning characters, clothing, styles, backgrounds, and objects. Download `ComposLoRA.zip` from [this link](https://drive.google.com/file/d/1SuwRgV1LtEud8dfjftnw-zxBMgzSCwIT/view?usp=sharing), put it in the [models](./models) folder, and unzip it.

## 🖼️ Image Generation with Multi-LoRA Composition

To compose multiple LoRAs using different methods during image generation, follow these steps:

First, load the base model:

```python
from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained(
    'SG161222/Realistic_Vision_V5.1_noVAE',
    custom_pipeline="MingZhong/StableDiffusionPipeline-with-LoRA-C",
    use_safetensors=True
).to("cuda")
```

This model from Hugging Face is selected for realistic-style image generation. Additionally, our custom pipeline integrates the LoRA composite method into the standard Stable Diffusion pipeline.

Next, choose a character LoRA and a clothing LoRA from ComposLoRA for composition:

```python
# Load LoRAs
lora_path = 'models/lora/reality'
pipeline.load_lora_weights(lora_path, weight_name="character_2.safetensors", adapter_name="character")
pipeline.load_lora_weights(lora_path, weight_name="clothing_2.safetensors", adapter_name="clothing")

# List of LoRAs to be composed
cur_loras = ["character", "clothing"]
```
Select a composition method. "switch" and "composite" are our new proposals, offering alternatives to the traditional "merge" method:

```python
from callbacks import make_callback

method = 'switch'

# Initialize based on the selected composition method
if method == "merge":
    pipeline.set_adapters(cur_loras)
    switch_callback = None
elif method == "switch":
    pipeline.set_adapters([cur_loras[0]])
    switch_callback = make_callback(switch_step=args.switch_step, loras=cur_loras)
else:
    pipeline.set_adapters(cur_loras)
    switch_callback = None
```

Finally, set your prompt and generate the image:.

```python
# Set the prompts for image generation
prompt = "RAW photo, subject, 8k uhd, dslr, high quality, Fujifilm XT3, half-length portrait from knees up, scarlett, short red hair, blue eyes, school uniform, white shirt, red tie, blue pleated microskirt"
negative_prompt = "extra heads, nsfw, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck"

# Generate and save the image
generator = torch.manual_seed(11)
image = pipeline(
    prompt=prompt, 
    negative_prompt=negative_prompt,
    height=1024,
    width=768,
    num_inference_steps=100,
    guidance_scale=7,
    generator=generator,
    cross_attention_kwargs={"scale": 0.8},
    callback_on_step_end=switch_callback,
    lora_composite=True if method == "composite" else False
).images[0]
image.save('example.png')
```

Refer to `example.py` for the full code, and adjust the following command to see results from different composition methods:

```bash
python example.py --method switch
```

Images generated by each of the three methods are showcased below:

| Merge | Switch | Composite |
|-------|--------|-----------|
|  <p align="center"><img src="images/merge_example.png" alt="merge_example"></p> | <p align="center"><img src="images/switch_example.png" alt="switch_example"></p> | <p align="center"><img src="images/composite_example.png" alt="composite_example"></p> |

### Examples for LCM and LCM-LoRA

Our methods can seamlessly integrate with both [LCM and LCM-LoRA](https://github.com/luosiallen/latent-consistency-model), significantly accelerating the image generation process by reducing the need for denoising steps to just 2-8. Below are example code and generated images:

```bash
# LCM
python lcm_example.py --method switch
```

| Merge | Switch | Composite |
|-------|--------|-----------|
|  <p align="center"><img src="images/merge_lcm_example.png" alt="merge_lcm_example"></p> | <p align="center"><img src="images/switch_lcm_example.png" alt="switch_lcm_example"></p> | <p align="center"><img src="images/composite_lcm_example.png" alt="composite_lcm_example"></p> |

```bash
# LCM-LoRA
python lcm_lora_example.py --method composite
```

| Merge | Switch | Composite |
|-------|--------|-----------|
|  <p align="center"><img src="images/merge_lcm_lora_example.png" alt="merge_lcm_lora_example"></p> | <p align="center"><img src="images/switch_lcm_lora_example.png" alt="switch_lcm_lora_example"></p> | <p align="center"><img src="images/composite_lcm_lora_example.png" alt="composite_lcm_lora_example"></p> |

### Example for SDXL

In addition to SD1.5-based backbones, our method can also be applied to SDXL. The following example demonstrates how to combine [Pikachu](https://huggingface.co/TheLastBen/Pikachu_SDXL) and [Vision Pro](https://huggingface.co/fofr/sdxl-vision-pro) in an image:

```bash
python sdxl_example.py --method switch
```

| Merge | Switch | Composite |
|-------|--------|-----------|
|  <p align="center"><img src="images/merge_sdxl_example.png" alt="merge_sdxl_example"></p> | <p align="center"><img src="images/switch_sdxl_example.png" alt="switch_sdxl_example"></p> | <p align="center"><img src="images/composite_sdxl_example.png" alt="composite_sdxl_example"></p> |

## 🎨 Experiments on ComposLoRA

**ComposLoRA** features 22 LoRAs and 480 different composition sets, allowing for the generation of images with any composition of 2-5 LoRAs, including at least one character LoRA.

### Image Generation
To generate anime-style images incorporating 2 LoRAs using LoRA Composite method, use the following command:

```bash
export CUDA_VISIBLE_DEVICES=0

python compose_lora.py \
    --method composite \
    --compos_num 2 \
    --save_path output \
    --lora_scale 0.8 \
    --image_style anime \
    --denoise_steps 200 \
    --cfg_scale 10 \
```

Adjust the parameters in `compos_reality.sh` and `compose_anime.sh` for different compositions.

### Comparative Evaluation with GPT-4V

For comparative evaluation on composition efficacy and image quality, we use GPT-4V. Set your OpenAI API key first:

```bash
export OPENAI_API_KEY='your_openai_api_key_here'
```

Then, compare the composite and merge methods with this command:

```bash
python evaluate.py \
    --base_method merge \
    --comp_method composite \
    --compos_num 2 \
    --image_style anime \
    --image_path output \
    --save_path eval_result \
```

Modify `eval.sh` for comparative evaluation under different conditions. Note the position bias of GPT-4V as mentioned in our paper, making it essential to input images in both orders and average the scores for a fair final assessment.

## Human Evaluation
We also conduct human evaluations on 120 generated images to assess composition and image quality from a human perspective. These evaluations offer additional insights into the performance of our Multi-LoRA Composition methods and metrics. For detailed information on the evaluation process and results, please visit the [human_eval](./human_eval) folder.

## 📚 Citation
If you find this work useful, please kindly cite our paper:
```
@article{zhong2024multi,
    title={Multi-LoRA Composition for Image Generation},
    author={Zhong, Ming and Shen, Yelong and Wang, Shuohang and Lu, Yadong and Jiao, Yizhu and Ouyang, Siru and Yu, Donghan and Han, Jiawei and Chen, Weizhu},
    journal={arXiv preprint arXiv:2402.16843},
    year={2024}
}
```



================================================
FILE: analyze.py
================================================
import json
from os.path import join

image_style = "anime"
compos_num = 2
method_1 = 'merge'
method_2 = 'composite'

def calculate_averages(evals):
    # Initialize sum and count dictionaries
    sum_scores = {'image 1': {'composition quality': 0, 'image quality': 0},
                  'image 2': {'composition quality': 0, 'image quality': 0}}
    count = {'image 1': 0, 'image 2': 0}

    # Sum up scores and count entries
    for entry in evals:
        for image, image_scores in entry['scores'].items():
            for dimension, value in image_scores.items():
                sum_scores[image][dimension] += value
                count[image] += 1

    # Calculate averages
    avg_scores = {}
    for image, image_scores in sum_scores.items():
        avg_scores[image] = {dimension: value / (count[image] // 2) for dimension, value in image_scores.items()}

    return avg_scores

def compare_methods(evals):
    comparison = {'composition quality': {'wins': 0, 'ties': 0, 'losses': 0},
                  'image quality': {'wins': 0, 'ties': 0, 'losses': 0}}
    total_comparisons = 0

    for entry in evals:
        total_comparisons += 1
        for dimension in ['composition quality', 'image quality']:
            if entry['scores']['image 2'][dimension] > entry['scores']['image 1'][dimension]:
                comparison[dimension]['wins'] += 1
            elif entry['scores']['image 2'][dimension] < entry['scores']['image 1'][dimension]:
                comparison[dimension]['losses'] += 1
            else:
                comparison[dimension]['ties'] += 1

    # Convert wins/losses/ties counts to win rates
    win_rates = {dim: {outcome: count / total_comparisons for outcome, count in outcomes.items()}
                 for dim, outcomes in comparison.items()}

    return win_rates

with open(join(f'eval_result/{image_style}_{compos_num}_elements_{method_1}_vs_{method_2}.json')) as f:
    eval_results = json.loads(f.read())

# Total numbers
print(f'Total {len(eval_results)} evaluation pairs!')

# Calculate averages
average_scores = calculate_averages(eval_results)
print("Average Scores:")
for method, dimensions in average_scores.items():
    if method == "image 1":
        print(f"  {method_1}:")
    else:
        print(f"  {method_2}:")
    for dimension, score in dimensions.items():
        print(f"    {dimension}: {score:.2f}")

# Compare methods (Method 2 vs Method 1)
method_comparison = compare_methods(eval_results)
print(f"\nMethod Comparison ({method_2} vs {method_1}):")
for dimension, outcomes in method_comparison.items():
    print(f"  {dimension.capitalize()}:")
    for outcome, rate in outcomes.items():
        print(f"    {outcome.capitalize()} Rate: {rate:.2f}")




================================================
FILE: anime_lora_info.json
================================================
{
    "character": [
        {
            "id": "character_1",
            "name": "Kamado Nezuko",
            "trigger": [
                "kamado nezuko",
                "black hair",
                "pink eyes",
                "forehead"
            ],
            "url": "https://civitai.com/models/20346/nezuko-demon-slayer-kimetsu-no-yaiba-lora?modelVersionId=24191"
        },
        {
            "id": "character_2",
            "name": "Texas the Omertosa in Arknights",
            "trigger": [
                "omertosa",
                "1girl",
                "wolf ears",
                "long hair"
            ],
            "url": "https://civitai.com/models/6779/arknights-texas-the-omertosa?modelVersionId=7974"
        },
        {
            "id": "character_3",
            "name": "Son Goku",
            "trigger": [
                "son goku",
                "spiked hair",
                "muscular male",
                "wristband"
            ],
            "url": "https://civitai.com/models/18279/son-goku-dragon-ball-all-series-lora?modelVersionId=21654"
        }
    ],
    "clothing": [
        {
            "id": "clothing_1",
            "name": "Garreg Mach Monastery Uniform",
            "trigger": [
                "gmuniform",
                "blue thighhighs",
                "long sleeves"
            ],
            "url": "https://civitai.com/models/151330/garreg-mach-monastery-uniform-fire-emblem-three-houses-lora-or-2-variants?modelVersionId=169217"
        },
        {
            "id": "clothing_2",
            "name": "Zero Suit (Metroid)",
            "trigger": [
                "zero suit",
                "blue gloves",
                "high heels"
            ],
            "url": "https://civitai.com/models/82304/zero-suit-metroid-outfit-lora?modelVersionId=87396"
        }
    ],
    "style": [
        {
            "id": "style_1",
            "name": "Hand-drawn Style",
            "trigger": [
                "lineart",
                "hand-drawn style"
            ],
            "url": "https://civitai.com/models/143532/handdrawn"
        },
        {
            "id": "style_2",
            "name": "Shuimobysim (Chinese Ink Wash Style)",
            "trigger": [
                "shuimobysim",
                "traditional chinese ink painting"
            ],
            "url": "https://civitai.com/models/12597?modelVersionId=14856"
        }
    ],
    "background": [
        {
            "id": "background_1",
            "name": "Bamboolight Background",
            "trigger": [
                "bamboolight",
                "outdoors",
                "bamboo"
            ],
            "url": "https://civitai.com/models/113381/lora-bamboolight-concept-with-dropout-and-noise-version?modelVersionId=122477"
        },
        {
            "id": "background_2",
            "name": "Auroral Background",
            "trigger": [
                "auroral",
                "starry sky",
                "outdoors"
            ],
            "url": "https://civitai.com/models/85026?modelVersionId=156828"
        }
    ],
    "object": [
        {
            "id": "object_1",
            "name": "Huge Two-Handed Burger",
            "trigger": [
                "two-handed burger",
                "holding a huge burger with both hands"
            ],
            "url": "https://civitai.com/models/27858/huge-two-handed-burger-lora?modelVersionId=34071"
        },
        {
            "id": "object_2",
            "name": "Toast",
            "trigger": [
                "toast",
                "toast in mouth"
            ],
            "url": "https://civitai.com/models/26945/toast-in-mouth-lora?modelVersionId=32252"
        }
    ]
}

================================================
FILE: callbacks.py
================================================
import json

def make_callback(switch_step, loras):
    def switch_callback(pipeline, step_index, timestep, callback_kwargs):
        callback_outputs = {}
        if step_index > 0 and step_index % switch_step == 0:
            for cur_lora_index, lora in enumerate(loras):
                if lora in pipeline.get_active_adapters():
                    next_lora_index = (cur_lora_index + 1) % len(loras)
                    pipeline.set_adapters(loras[next_lora_index])
                    break
        return callback_outputs
    return switch_callback

================================================
FILE: compose_anime.sh
================================================
export CUDA_VISIBLE_DEVICES=0

python compose_lora.py \
    --method composite \
    --compos_num 2 \
    --save_path output \
    --lora_scale 0.8 \
    --image_style anime \
    --denoise_steps 200 \
    --cfg_scale 10 \


================================================
FILE: compose_lora.py
================================================
import os
import torch
import argparse
from tqdm import tqdm
from os.path import join, exists
from diffusers import DiffusionPipeline, AutoencoderKL
from diffusers import DPMSolverMultistepScheduler

from utils import load_lora_info, generate_combinations
from utils import get_prompt
from callbacks import make_callback

def main(args):

    # set path based on the image style
    args.save_path = args.save_path + "_" + args.image_style
    args.lora_path = join(args.lora_path, args.image_style)

    # load all the information of LoRAs
    lora_info = load_lora_info(args.image_style, args.lora_info_path)

    # set base model based on the image style
    if args.image_style == 'anime':
        model_name = 'gsdf/Counterfeit-V2.5'
    else:
        model_name = 'SG161222/Realistic_Vision_V5.1_noVAE'

    pipeline = DiffusionPipeline.from_pretrained(
        model_name,
        custom_pipeline="MingZhong/StableDiffusionPipeline-with-LoRA-C",
        # torch_dtype=torch.float16,
        use_safetensors=True
    ).to("cuda")

    # set vae
    if args.image_style == "reality":
        vae = AutoencoderKL.from_pretrained(
            "stabilityai/sd-vae-ft-mse",
            # torch_dtype=torch.float16
        ).to("cuda")
        pipeline.vae = vae

    # set scheduler
    schedule_config = dict(pipeline.scheduler.config)
    schedule_config["algorithm_type"] = "dpmsolver++"
    pipeline.scheduler = DPMSolverMultistepScheduler.from_config(schedule_config)

    # initialize LoRAs
    for element in list(lora_info.keys()):
        for lora in lora_info[element]:
            pipeline.load_lora_weights(
                args.lora_path,
                weight_name=lora['id'] + '.safetensors',
                adapter_name=lora['id']
            )

    # generate all combinations that can be composed
    combinations = generate_combinations(lora_info, args.compos_num)
    
    # prompt initialization
    init_prompt, negative_prompt = get_prompt(args.image_style)

    # generate images for each combination based on LoRAs
    for combo in tqdm(combinations):
        cur_loras = [lora['id'] for lora in combo]

        # set prompt
        triggers = [trigger for lora in combo for trigger in lora['trigger']]
        prompt = init_prompt + ', ' + ', '.join(triggers)
        
        # set LoRAs
        if args.method == "switch":
            pipeline.set_adapters([cur_loras[0]])
            switch_callback = make_callback(args.switch_step,
                                        cur_loras)
        elif args.method == "merge":
            pipeline.set_adapters(cur_loras)
            switch_callback = None
        else:
            pipeline.set_adapters(cur_loras)
            switch_callback = None

        # generate images
        image = pipeline(
            prompt=prompt, 
            negative_prompt=negative_prompt,
            height=args.height,
            width=args.width,
            num_inference_steps=args.denoise_steps,
            guidance_scale=args.cfg_scale,
            generator=args.generator,
            cross_attention_kwargs={"scale": args.lora_scale},
            callback_on_step_end=switch_callback,
            lora_composite=True if args.method == "composite" else False
        ).images[0]
        
        # save image
        save_path = join(args.save_path, f'{args.compos_num}_elements')
        if not exists(save_path):
            os.makedirs(save_path)
        file_name = args.method + '_' + '_'.join([lora['id'] for lora in combo]) + '.png'
        image.save(join(save_path, file_name))

if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description='Given LoRAs in the ComposLoRA benchmark, generate images with arbitrary combinations based on LoRAs'
    )

    # Arguments for composing LoRAs
    parser.add_argument('--compos_num', default=2,
                        help='number of elements to be combined in a single image', type=int)
    parser.add_argument('--method', default='switch',
                        choices=['merge', 'switch', 'composite'],
                        help='methods for combining LoRAs', type=str)
    parser.add_argument('--save_path', default='output',
                        help='path to save the generated image', type=str)
    parser.add_argument('--lora_path', default='models/lora',
                        help='path to store all LoRA models', type=str)
    parser.add_argument('--lora_info_path', default='lora_info.json',
                        help='path to stroe all LoRA information', type=str)
    parser.add_argument('--lora_scale', default=0.8,
                        help='scale of each LoRA when generating images', type=float)
    parser.add_argument('--switch_step', default=5,
                        help='number of steps to switch LoRA during denoising, applicable only in the switch method', type=int)

    # Arguments for generating images
    parser.add_argument('--height', default=512,
                        help='height of the generated images', type=int)
    parser.add_argument('--width', default=512,
                        help='width of the generated images', type=int)
    parser.add_argument('--denoise_steps', default=200,
                        help='number of the denoising steps', type=int)
    parser.add_argument('--cfg_scale', default=10,
                        help='scale for classifier-free guidance', type=float)
    parser.add_argument('--seed', default=111,
                        help='seed for generating images', type=int)
    parser.add_argument('--image_style', default='anime',
                        choices=['anime', 'reality'],
                        help='sytles of the generated images', type=str)

    args = parser.parse_args()
    args.generator = torch.manual_seed(args.seed)

    main(args)

================================================
FILE: compose_reality.sh
================================================
export CUDA_VISIBLE_DEVICES=0

python compose_lora.py \
    --method switch \
    --compos_num 2 \
    --save_path output \
    --lora_scale 0.8 \
    --image_style reality \
    --denoise_steps 100 \
    --cfg_scale 7 \
    --height 1024 \
    --width 768 \


================================================
FILE: docs/index.html
================================================
<!DOCTYPE html>
<html>
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          <h1 class="title is-1 publication-title">Multi-LoRA Composition for Image Generation</h1>
          <div class="is-size-5 publication-authors">
            <span class="author-block">
              <a href="https://maszhongming.github.io">Ming Zhong</a><sup>1</sup>,</span>
            <span class="author-block">
              <a href="https://scholar.google.com/citations?user=S6OFEFEAAAAJ&hl=en">Yelong Shen</a><sup>2</sup>,</span>
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              <a href="https://www.microsoft.com/en-us/research/people/shuowa">Shuohang Wang</a><sup>2</sup>,
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              <a href="https://adamlu123.github.io">Yadong Lu</a><sup>2</sup>,
            </span>
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              <a href="https://yzjiao.github.io">Yizhu Jiao</a><sup>1</sup>,
            </span>
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              <a href="https://ozyyshr.github.io">Siru Ouyang</a><sup>1</sup>,
            </span>
            <span class="author-block">
              <a href="https://plusross.github.io">Donghan Yu</a><sup>2</sup>
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            <span class="author-block">
              <a href="https://hanj.cs.illinois.edu">Jiawei Han</a><sup>1</sup>
            </span>
            <span class="author-block">
              <a href="https://www.microsoft.com/en-us/research/people/wzchen">Weizhu Chen</a><sup>2</sup>
            </span>
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          <div class="is-size-5 publication-authors">
            <span class="author-block"><sup>1</sup>University of Illinois Urbana-Champaign,</span>
            <span class="author-block"><sup>2</sup>Microsoft Corporation</span>
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                  <span>ComposLoRA</span>
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          This project explores new methods for text-to-image generation, with a focus on the integration of multiple Low-Rank Adaptations (LoRAs) to create highly customized and detailed images. We present <strong>LoRA Switch</strong> and <strong>LoRA Composite</strong>, approaches that aim to surpass traditional techniques in terms of accuracy and image quality, especially in complex compositions.
          <br><br><strong>Project Features</strong>: 
          <ul>
            <li><strong>🚀 Training-Free Methods</strong>
              <ul>
              <li> LoRA Switch and LoRA Composite enable dynamic and precise integration of multiple LoRAs without fine-tuning.</li> 
              <li> Unlike methods that merge LoRA weights, ours focuses on the decoding process, keeping all LoRA weights intact.</li>
              </ul>
            </li> 
            <li><strong>📊 ComposLoRA Testbed</strong>
              <ul>
              <li> A new comprehensive platform, featuring 480 composition sets and 22 pre-trained LoRAs across six categories.</li> 
              <li> ComposLoRA is designed for the quantitative evaluation of LoRA-based composable image generation tasks.</li>
              </ul>
            </li>
            <li><strong>📝 GPT-4V-based Evaluator</strong>
              <ul>
              <li>We propose using GPT-4V as an evaluator to assess the efficacy of compositions and the quality of images.</li>
              <li>This evaluator has demonstrated a better correlation with human judgments.</li>
              </ul>
            </li>
            <li><strong>🏆 Superior Performance</strong>
              <ul>
              <li>Both automated and human evaluations show that our approaches substantially outperform the prevalent LoRA Merge.</li>
              <li>Our methods exhibit a more significant advantage when generating complex compositions.</li>
              </ul>
            </li>
            <li><strong>🕵️‍♂️ Detailed Analysis</strong>
              <ul>
              <li>We delve deeply into the scenarios where each method excels.</li>
              <li>We explore the potential bias associated with using GPT-4V for evaluation.</li>
              </ul>
            </li>
          </ul>
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      <h2 class="subtitle has-text-centered">
        Multi-LoRA composition techniques effectively blend different elements into a cohesive image. Unlike the conventional LoRA Merge approach, which can lead to detail loss and image distortion as more LoRAs are added, our methods retain the accuracy of each element and the overall image quality.
      </h2>
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        <h2 class="title is-3">Methods of Multi-LoRA Composition</h2>
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          <ul>
            <li><strong>LoRA Merge:</strong>
              <ul>
              <li>Prevalent approach to integrating multiple elements in a unified way in an image.</li> 
              <li>It is realized by linearly combining multiple LoRAs to synthesize a unified LoRA, subsequently plugged into the text-to-image model.</li>
              <li>LoAR Merge completely overlooks the interaction with the diffusion model during the generative process, resulting in the deformation of the hamburger and fingers in the Figure.</li>
              </ul>
            </li>
            <li><strong>LoRA Switch (LoRA-S):</strong>
              <ul>
              <li>To explore activating a single LoRA in each denoising step, we propose LoRA Switch.</li>
              <li>This method introduces a dynamic adaptation mechanism within diffusion models by sequentially activating individual LoRAs at designated intervals throughout the decoding process.</li>
              <li>As illustrated in the Figure, each LoRA is represented by a unique color corresponding to a specific element, with only one LoRA engaged per denoising step.</li>
              </ul>
            </li>
            <li><strong>LoRA Composite (LoRA-C):</strong>
              <ul>
              <li>To explore incorporating all LoRAs at each timestep without merging weight matrices, we propose LoRA Composite.</li>
              <li>It involves calculating both unconditional and conditional score estimates for each LoRA individually at each step.</li>
              <li>By aggregating these scores, the technique ensures balanced guidance throughout the image generation process, facilitating the cohesive integration of all elements represented by different LoRAs.</li>
              </ul>
            </li>
          </ul>
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        <h2 class="title is-3">GPT-4V-based Evaluator</h2>
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        </p>
        <div class="content has-text-justified">
          <ul>
            <li>While existing metrics can calculate the alignment between text and images, they fall short in assessing the intricacies of specific elements within an image and the quality of their composition.</li>
            <li>We employ a comparative evaluation method, utilizing GPT-4V to rate generated images across two dimensions: composition quality and image quality.</li>
          </ul>
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        <h2 class="title is-3">Experimental Results</h2>
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        </p>
        <div class="content has-text-justified">
          <ul>
            <li>Our proposed method consistently outperforms LoRA Merge across all configurations and in both dimensions, with the margin of superiority increasing as the number of LoRAs grows.</li>
            <li>LoRA Switch shows superior performance in composition quality, whereas LoRA Composite excels in image quality.</li>
            <li>The task of compositional image generation remains highly challenging, especially as the number of elements to be composed increases.</li>
          </ul>
        </div>
        <p align="center">
          <img src="./static/images/human_eval.png" class="center" width="50%">
        </p>
        <div class="content has-text-justified">
          <ul>
            <li>Human evaluations aligh with GPT-4V's findings.</li>
            <li>GPT-4V-based evaluator we adopt shows substantially higher correlations with human judgments, affirming the validity of our evaluation framework.</li>
          </ul>
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        <h2 class="title is-3">Analysis</h2>
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        <img src="./static/images/style_analysis.png" class="center" width="70%">
        </p>
        <div class="content has-text-justified">
          <ul>
            <li>LoRA Switch is more adept at composing elements in realistic-style images.</li>
            <li>LoRA Composite shows a stronger performance in anime-style imagery.</li>
          </ul>
        </div>
        <p align="center">
          <img src="./static/images/switch_analysis.png" class="center" width="70%">
        </p>
        <div class="content has-text-justified">
          <ul>
            <li>The efficiency of the LoRA Switch improves progressively with increased step size, reaching peak performance at 5.</li>
            <li>The initial choice of LoRA in the activation sequence clearly influences overall performance, while alterations in the subsequent order have minimal impact.</li>
          </ul>
        </div>
        <p align="center">
          <img src="./static/images/bias_analysis.png" class="center" width="70%">
        </p>
        <div class="content has-text-justified">
          <ul>
            <li>GPT-4V exhibits significant positional bias in comparative evaluation.</li>
            <li>This bias varies depending on the input position of the image and the dimension of the evaluation.</li>
          </ul>
        </div>
      </div>
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<section class="section" id="BibTeX">
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    <h2 class="title">BibTeX</h2>
    <pre><code>@article{zhong2024multi,
      title={Multi-LoRA Composition for Image Generation},
      author={Zhong, Ming and Shen, Yelong and Wang, Shuohang and Lu, Yadong and Jiao, Yizhu and Ouyang, Siru and Yu, Donghan and Han, Jiawei and Chen, Weizhu},
      journal={arXiv preprint arXiv:2402.16843},
      year={2024}
}</code></pre>
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================================================
FILE: docs/static/css/index.css
================================================
body {
  font-family: 'Noto Sans', sans-serif;
}


.footer .icon-link {
    font-size: 25px;
    color: #000;
}

.link-block a {
    margin-top: 5px;
    margin-bottom: 5px;
}

.dnerf {
  font-variant: small-caps;
}


.teaser .hero-body {
  padding-top: 0;
  padding-bottom: 3rem;
}

.teaser {
  font-family: 'Google Sans', sans-serif;
}


.publication-title {
}

.publication-banner {
  max-height: parent;

}

.publication-banner video {
  position: relative;
  left: auto;
  top: auto;
  transform: none;
  object-fit: fit;
}

.publication-header .hero-body {
}

.publication-title {
    font-family: 'Google Sans', sans-serif;
}

.publication-authors {
    font-family: 'Google Sans', sans-serif;
}

.publication-venue {
    color: #555;
    width: fit-content;
    font-weight: bold;
}

.publication-awards {
    color: #ff3860;
    width: fit-content;
    font-weight: bolder;
}

.publication-authors {
}

.publication-authors a {
   color: hsl(204, 86%, 53%) !important;
}

.publication-authors a:hover {
    text-decoration: underline;
}

.author-block {
  display: inline-block;
}

.publication-banner img {
}

.publication-authors {
  /*color: #4286f4;*/
}

.publication-video {
    position: relative;
    width: 100%;
    height: 0;
    padding-bottom: 56.25%;

    overflow: hidden;
    border-radius: 10px !important;
}

.publication-video iframe {
    position: absolute;
    top: 0;
    left: 0;
    width: 100%;
    height: 100%;
}

.publication-body img {
}

.results-carousel {
  overflow: hidden;
}

.results-carousel .item {
  margin: 5px;
  overflow: hidden;
  border: 1px solid #bbb;
  border-radius: 10px;
  padding: 0;
  font-size: 0;
}

.results-carousel video {
  margin: 0;
}


.interpolation-panel {
  background: #f5f5f5;
  border-radius: 10px;
}

.interpolation-panel .interpolation-image {
  width: 100%;
  border-radius: 5px;
}

.interpolation-video-column {
}

.interpolation-panel .slider {
  margin: 0 !important;
}

.interpolation-panel .slider {
  margin: 0 !important;
}

#interpolation-image-wrapper {
  width: 100%;
}
#interpolation-image-wrapper img {
  border-radius: 5px;
}


================================================
FILE: docs/static/js/bulma-carousel.js
================================================
(function webpackUniversalModuleDefinition(root, factory) {
	if(typeof exports === 'object' && typeof module === 'object')
		module.exports = factory();
	else if(typeof define === 'function' && define.amd)
		define([], factory);
	else if(typeof exports === 'object')
		exports["bulmaCarousel"] = factory();
	else
		root["bulmaCarousel"] = factory();
})(typeof self !== 'undefined' ? self : this, function() {
return /******/ (function(modules) { // webpackBootstrap
/******/ 	// The module cache
/******/ 	var installedModules = {};
/******/
/******/ 	// The require function
/******/ 	function __webpack_require__(moduleId) {
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/******/ 			return installedModules[moduleId].exports;
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/******/ 		var module = installedModules[moduleId] = {
/******/ 			i: moduleId,
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/******/ 		modules[moduleId].call(module.exports, module, module.exports, __webpack_require__);
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/******/ 		module.l = true;
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/******/ 	// expose the modules object (__webpack_modules__)
/******/ 	__webpack_require__.m = modules;
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/******/ 	// expose the module cache
/******/ 	__webpack_require__.c = installedModules;
/******/
/******/ 	// define getter function for harmony exports
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/******/ 			Object.defineProperty(exports, name, {
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/******/ 				get: getter
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/******/ 	};
/******/
/******/ 	// getDefaultExport function for compatibility with non-harmony modules
/******/ 	__webpack_require__.n = function(module) {
/******/ 		var getter = module && module.__esModule ?
/******/ 			function getDefault() { return module['default']; } :
/******/ 			function getModuleExports() { return module; };
/******/ 		__webpack_require__.d(getter, 'a', getter);
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/******/ 	// Object.prototype.hasOwnProperty.call
/******/ 	__webpack_require__.o = function(object, property) { return Object.prototype.hasOwnProperty.call(object, property); };
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/******/ 	// __webpack_public_path__
/******/ 	__webpack_require__.p = "";
/******/
/******/ 	// Load entry module and return exports
/******/ 	return __webpack_require__(__webpack_require__.s = 5);
/******/ })
/************************************************************************/
/******/ ([
/* 0 */
/***/ (function(module, __webpack_exports__, __webpack_require__) {

"use strict";
/* unused harmony export addClasses */
/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "d", function() { return removeClasses; });
/* unused harmony export show */
/* unused harmony export hide */
/* unused harmony export offset */
/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "e", function() { return width; });
/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return height; });
/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "c", function() { return outerHeight; });
/* unused harmony export outerWidth */
/* unused harmony export position */
/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return css; });
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__type__ = __webpack_require__(2);


var addClasses = function addClasses(element, classes) {
	classes = Array.isArray(classes) ? classes : classes.split(' ');
	classes.forEach(function (cls) {
		element.classList.add(cls);
	});
};

var removeClasses = function removeClasses(element, classes) {
	classes = Array.isArray(classes) ? classes : classes.split(' ');
	classes.forEach(function (cls) {
		element.classList.remove(cls);
	});
};

var show = function show(elements) {
	elements = Array.isArray(elements) ? elements : [elements];
	elements.forEach(function (element) {
		element.style.display = '';
	});
};

var hide = function hide(elements) {
	elements = Array.isArray(elements) ? elements : [elements];
	elements.forEach(function (element) {
		element.style.display = 'none';
	});
};

var offset = function offset(element) {
	var rect = element.getBoundingClientRect();
	return {
		top: rect.top + document.body.scrollTop,
		left: rect.left + document.body.scrollLeft
	};
};

// returns an element's width
var width = function width(element) {
	return element.getBoundingClientRect().width || element.offsetWidth;
};
// returns an element's height
var height = function height(element) {
	return element.getBoundingClientRect().height || element.offsetHeight;
};

var outerHeight = function outerHeight(element) {
	var withMargin = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false;

	var height = element.offsetHeight;
	if (withMargin) {
		var style = window.getComputedStyle(element);
		height += parseInt(style.marginTop) + parseInt(style.marginBottom);
	}
	return height;
};

var outerWidth = function outerWidth(element) {
	var withMargin = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false;

	var width = element.offsetWidth;
	if (withMargin) {
		var style = window.getComputedStyle(element);
		width += parseInt(style.marginLeft) + parseInt(style.marginRight);
	}
	return width;
};

var position = function position(element) {
	return {
		left: element.offsetLeft,
		top: element.offsetTop
	};
};

var css = function css(element, obj) {
	if (!obj) {
		return window.getComputedStyle(element);
	}
	if (Object(__WEBPACK_IMPORTED_MODULE_0__type__["b" /* isObject */])(obj)) {
		var style = '';
		Object.keys(obj).forEach(function (key) {
			style += key + ': ' + obj[key] + ';';
		});

		element.style.cssText += style;
	}
};

/***/ }),
/* 1 */
/***/ (function(module, __webpack_exports__, __webpack_require__) {

"use strict";
/* harmony export (immutable) */ __webpack_exports__["a"] = detectSupportsPassive;
function detectSupportsPassive() {
	var supportsPassive = false;

	try {
		var opts = Object.defineProperty({}, 'passive', {
			get: function get() {
				supportsPassive = true;
			}
		});

		window.addEventListener('testPassive', null, opts);
		window.removeEventListener('testPassive', null, opts);
	} catch (e) {}

	return supportsPassive;
}

/***/ }),
/* 2 */
/***/ (function(module, __webpack_exports__, __webpack_require__) {

"use strict";
/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return isFunction; });
/* unused harmony export isNumber */
/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "c", function() { return isString; });
/* unused harmony export isDate */
/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return isObject; });
/* unused harmony export isEmptyObject */
/* unused harmony export isNode */
/* unused harmony export isVideo */
/* unused harmony export isHTML5 */
/* unused harmony export isIFrame */
/* unused harmony export isYoutube */
/* unused harmony export isVimeo */
var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) { return typeof obj; } : function (obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; };

var isFunction = function isFunction(unknown) {
	return typeof unknown === 'function';
};
var isNumber = function isNumber(unknown) {
	return typeof unknown === "number";
};
var isString = function isString(unknown) {
	return typeof unknown === 'string' || !!unknown && (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && Object.prototype.toString.call(unknown) === '[object String]';
};
var isDate = function isDate(unknown) {
	return (Object.prototype.toString.call(unknown) === '[object Date]' || unknown instanceof Date) && !isNaN(unknown.valueOf());
};
var isObject = function isObject(unknown) {
	return (typeof unknown === 'function' || (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && !!unknown) && !Array.isArray(unknown);
};
var isEmptyObject = function isEmptyObject(unknown) {
	for (var name in unknown) {
		if (unknown.hasOwnProperty(name)) {
			return false;
		}
	}
	return true;
};

var isNode = function isNode(unknown) {
	return !!(unknown && unknown.nodeType === HTMLElement | SVGElement);
};
var isVideo = function isVideo(unknown) {
	return isYoutube(unknown) || isVimeo(unknown) || isHTML5(unknown);
};
var isHTML5 = function isHTML5(unknown) {
	return isNode(unknown) && unknown.tagName === 'VIDEO';
};
var isIFrame = function isIFrame(unknown) {
	return isNode(unknown) && unknown.tagName === 'IFRAME';
};
var isYoutube = function isYoutube(unknown) {
	return isIFrame(unknown) && !!unknown.src.match(/\/\/.*?youtube(-nocookie)?\.[a-z]+\/(watch\?v=[^&\s]+|embed)|youtu\.be\/.*/);
};
var isVimeo = function isVimeo(unknown) {
	return isIFrame(unknown) && !!unknown.src.match(/vimeo\.com\/video\/.*/);
};

/***/ }),
/* 3 */
/***/ (function(module, __webpack_exports__, __webpack_require__) {

"use strict";
var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();

function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } }

function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }

var EventEmitter = function () {
  function EventEmitter() {
    var events = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : [];

    _classCallCheck(this, EventEmitter);

    this.events = new Map(events);
  }

  _createClass(EventEmitter, [{
    key: "on",
    value: function on(name, cb) {
      var _this = this;

      this.events.set(name, [].concat(_toConsumableArray(this.events.has(name) ? this.events.get(name) : []), [cb]));

      return function () {
        return _this.events.set(name, _this.events.get(name).filter(function (fn) {
          return fn !== cb;
        }));
      };
    }
  }, {
    key: "emit",
    value: function emit(name) {
      for (var _len = arguments.length, args = Array(_len > 1 ? _len - 1 : 0), _key = 1; _key < _len; _key++) {
        args[_key - 1] = arguments[_key];
      }

      return this.events.has(name) && this.events.get(name).map(function (fn) {
        return fn.apply(undefined, args);
      });
    }
  }]);

  return EventEmitter;
}();

/* harmony default export */ __webpack_exports__["a"] = (EventEmitter);

/***/ }),
/* 4 */
/***/ (function(module, __webpack_exports__, __webpack_require__) {

"use strict";
var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();

function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }

var Coordinate = function () {
	function Coordinate() {
		var x = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0;
		var y = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : 0;

		_classCallCheck(this, Coordinate);

		this._x = x;
		this._y = y;
	}

	_createClass(Coordinate, [{
		key: 'add',
		value: function add(coord) {
			return new Coordinate(this._x + coord._x, this._y + coord._y);
		}
	}, {
		key: 'sub',
		value: function sub(coord) {
			return new Coordinate(this._x - coord._x, this._y - coord._y);
		}
	}, {
		key: 'distance',
		value: function distance(coord) {
			var deltaX = this._x - coord._x;
			var deltaY = this._y - coord._y;

			return Math.sqrt(Math.pow(deltaX, 2) + Math.pow(deltaY, 2));
		}
	}, {
		key: 'max',
		value: function max(coord) {
			var x = Math.max(this._x, coord._x);
			var y = Math.max(this._y, coord._y);

			return new Coordinate(x, y);
		}
	}, {
		key: 'equals',
		value: function equals(coord) {
			if (this == coord) {
				return true;
			}
			if (!coord || coord == null) {
				return false;
			}
			return this._x == coord._x && this._y == coord._y;
		}
	}, {
		key: 'inside',
		value: function inside(northwest, southeast) {
			if (this._x >= northwest._x && this._x <= southeast._x && this._y >= northwest._y && this._y <= southeast._y) {

				return true;
			}
			return false;
		}
	}, {
		key: 'constrain',
		value: function constrain(min, max) {
			if (min._x > max._x || min._y > max._y) {
				return this;
			}

			var x = this._x,
			    y = this._y;

			if (min._x !== null) {
				x = Math.max(x, min._x);
			}
			if (max._x !== null) {
				x = Math.min(x, max._x);
			}
			if (min._y !== null) {
				y = Math.max(y, min._y);
			}
			if (max._y !== null) {
				y = Math.min(y, max._y);
			}

			return new Coordinate(x, y);
		}
	}, {
		key: 'reposition',
		value: function reposition(element) {
			element.style['top'] = this._y + 'px';
			element.style['left'] = this._x + 'px';
		}
	}, {
		key: 'toString',
		value: function toString() {
			return '(' + this._x + ',' + this._y + ')';
		}
	}, {
		key: 'x',
		get: function get() {
			return this._x;
		},
		set: function set() {
			var value = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0;

			this._x = value;
			return this;
		}
	}, {
		key: 'y',
		get: function get() {
			return this._y;
		},
		set: function set() {
			var value = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0;

			this._y = value;
			return this;
		}
	}]);

	return Coordinate;
}();

/* harmony default export */ __webpack_exports__["a"] = (Coordinate);

/***/ }),
/* 5 */
/***/ (function(module, __webpack_exports__, __webpack_require__) {

"use strict";
Object.defineProperty(__webpack_exports__, "__esModule", { value: true });
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_index__ = __webpack_require__(6);
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_css__ = __webpack_require__(0);
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_2__utils_type__ = __webpack_require__(2);
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_3__utils_eventEmitter__ = __webpack_require__(3);
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_4__components_autoplay__ = __webpack_require__(7);
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_5__components_breakpoint__ = __webpack_require__(9);
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_6__components_infinite__ = __webpack_require__(10);
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_7__components_loop__ = __webpack_require__(11);
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_8__components_navigation__ = __webpack_require__(13);
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_9__components_pagination__ = __webpack_require__(15);
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_10__components_swipe__ = __webpack_require__(18);
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_11__components_transitioner__ = __webpack_require__(19);
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_12__defaultOptions__ = __webpack_require__(22);
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_13__templates__ = __webpack_require__(23);
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_14__templates_item__ = __webpack_require__(24);
var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; };

var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();

function _defineProperty(obj, key, value) { if (key in obj) { Object.defineProperty(obj, key, { value: value, enumerable: true, configurable: true, writable: true }); } else { obj[key] = value; } return obj; }

function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }

function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; }

function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; }



















var bulmaCarousel = function (_EventEmitter) {
  _inherits(bulmaCarousel, _EventEmitter);

  function bulmaCarousel(selector) {
    var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};

    _classCallCheck(this, bulmaCarousel);

    var _this = _possibleConstructorReturn(this, (bulmaCarousel.__proto__ || Object.getPrototypeOf(bulmaCarousel)).call(this));

    _this.element = Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["c" /* isString */])(selector) ? document.querySelector(selector) : selector;
    // An invalid selector or non-DOM node has been provided.
    if (!_this.element) {
      throw new Error('An invalid selector or non-DOM node has been provided.');
    }
    _this._clickEvents = ['click', 'touch'];

    // Use Element dataset values to override options
    var elementConfig = _this.element.dataset ? Object.keys(_this.element.dataset).filter(function (key) {
      return Object.keys(__WEBPACK_IMPORTED_MODULE_12__defaultOptions__["a" /* default */]).includes(key);
    }).reduce(function (obj, key) {
      return _extends({}, obj, _defineProperty({}, key, _this.element.dataset[key]));
    }, {}) : {};
    // Set default options - dataset attributes are master
    _this.options = _extends({}, __WEBPACK_IMPORTED_MODULE_12__defaultOptions__["a" /* default */], options, elementConfig);

    _this._id = Object(__WEBPACK_IMPORTED_MODULE_0__utils_index__["a" /* uuid */])('slider');

    _this.onShow = _this.onShow.bind(_this);

    // Initiate plugin
    _this._init();
    return _this;
  }

  /**
   * Initiate all DOM element containing datePicker class
   * @method
   * @return {Array} Array of all datePicker instances
   */


  _createClass(bulmaCarousel, [{
    key: '_init',


    /****************************************************
     *                                                  *
     * PRIVATE FUNCTIONS                                *
     *                                                  *
     ****************************************************/
    /**
     * Initiate plugin instance
     * @method _init
     * @return {Slider} Current plugin instance
     */
    value: function _init() {
      this._items = Array.from(this.element.children);

      // Load plugins
      this._breakpoint = new __WEBPACK_IMPORTED_MODULE_5__components_breakpoint__["a" /* default */](this);
      this._autoplay = new __WEBPACK_IMPORTED_MODULE_4__components_autoplay__["a" /* default */](this);
      this._navigation = new __WEBPACK_IMPORTED_MODULE_8__components_navigation__["a" /* default */](this);
      this._pagination = new __WEBPACK_IMPORTED_MODULE_9__components_pagination__["a" /* default */](this);
      this._infinite = new __WEBPACK_IMPORTED_MODULE_6__components_infinite__["a" /* default */](this);
      this._loop = new __WEBPACK_IMPORTED_MODULE_7__components_loop__["a" /* default */](this);
      this._swipe = new __WEBPACK_IMPORTED_MODULE_10__components_swipe__["a" /* default */](this);

      this._build();

      if (Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["a" /* isFunction */])(this.options.onReady)) {
        this.options.onReady(this);
      }

      return this;
    }

    /**
     * Build Slider HTML component and append it to the DOM
     * @method _build
     */

  }, {
    key: '_build',
    value: function _build() {
      var _this2 = this;

      // Generate HTML Fragment of template
      this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_13__templates__["a" /* default */])(this.id));
      // Save pointers to template parts
      this._ui = {
        wrapper: this.node.firstChild,
        container: this.node.querySelector('.slider-container')

        // Add slider to DOM
      };this.element.appendChild(this.node);
      this._ui.wrapper.classList.add('is-loading');
      this._ui.container.style.opacity = 0;

      this._transitioner = new __WEBPACK_IMPORTED_MODULE_11__components_transitioner__["a" /* default */](this);

      // Wrap all items by slide element
      this._slides = this._items.map(function (item, index) {
        return _this2._createSlide(item, index);
      });

      this.reset();

      this._bindEvents();

      this._ui.container.style.opacity = 1;
      this._ui.wrapper.classList.remove('is-loading');
    }

    /**
     * Bind all events
     * @method _bindEvents
     * @return {void}
     */

  }, {
    key: '_bindEvents',
    value: function _bindEvents() {
      this.on('show', this.onShow);
    }
  }, {
    key: '_unbindEvents',
    value: function _unbindEvents() {
      this.off('show', this.onShow);
    }
  }, {
    key: '_createSlide',
    value: function _createSlide(item, index) {
      var slide = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_14__templates_item__["a" /* default */])()).firstChild;
      slide.dataset.sliderIndex = index;
      slide.appendChild(item);
      return slide;
    }

    /**
     * Calculate slider dimensions
     */

  }, {
    key: '_setDimensions',
    value: function _setDimensions() {
      var _this3 = this;

      if (!this.options.vertical) {
        if (this.options.centerMode) {
          this._ui.wrapper.style.padding = '0px ' + this.options.centerPadding;
        }
      } else {
        this._ui.wrapper.style.height = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[0]) * this.slidesToShow;
        if (this.options.centerMode) {
          this._ui.wrapper.style.padding = this.options.centerPadding + ' 0px';
        }
      }

      this._wrapperWidth = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this._ui.wrapper);
      this._wrapperHeight = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._ui.wrapper);

      if (!this.options.vertical) {
        this._slideWidth = Math.ceil(this._wrapperWidth / this.slidesToShow);
        this._containerWidth = Math.ceil(this._slideWidth * this._slides.length);
        this._ui.container.style.width = this._containerWidth + 'px';
      } else {
        this._slideWidth = Math.ceil(this._wrapperWidth);
        this._containerHeight = Math.ceil(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[0]) * this._slides.length);
        this._ui.container.style.height = this._containerHeight + 'px';
      }

      this._slides.forEach(function (slide) {
        slide.style.width = _this3._slideWidth + 'px';
      });
    }
  }, {
    key: '_setHeight',
    value: function _setHeight() {
      if (this.options.effect !== 'translate') {
        this._ui.container.style.height = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[this.state.index]) + 'px';
      }
    }

    // Update slides classes

  }, {
    key: '_setClasses',
    value: function _setClasses() {
      var _this4 = this;

      this._slides.forEach(function (slide) {
        Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["d" /* removeClasses */])(slide, 'is-active is-current is-slide-previous is-slide-next');
        if (Math.abs((_this4.state.index - 1) % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) {
          slide.classList.add('is-slide-previous');
        }
        if (Math.abs(_this4.state.index % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) {
          slide.classList.add('is-current');
        }
        if (Math.abs((_this4.state.index + 1) % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) {
          slide.classList.add('is-slide-next');
        }
      });
    }

    /****************************************************
     *                                                  *
     * GETTERS and SETTERS                              *
     *                                                  *
     ****************************************************/

    /**
     * Get id of current datePicker
     */

  }, {
    key: 'onShow',


    /****************************************************
     *                                                  *
     * EVENTS FUNCTIONS                                 *
     *                                                  *
     ****************************************************/
    value: function onShow(e) {
      this._navigation.refresh();
      this._pagination.refresh();
      this._setClasses();
    }

    /****************************************************
     *                                                  *
     * PUBLIC FUNCTIONS                                 *
     *                                                  *
     ****************************************************/

  }, {
    key: 'next',
    value: function next() {
      if (!this.options.loop && !this.options.infinite && this.state.index + this.slidesToScroll > this.state.length - this.slidesToShow && !this.options.centerMode) {
        this.state.next = this.state.index;
      } else {
        this.state.next = this.state.index + this.slidesToScroll;
      }
      this.show();
    }
  }, {
    key: 'previous',
    value: function previous() {
      if (!this.options.loop && !this.options.infinite && this.state.index === 0) {
        this.state.next = this.state.index;
      } else {
        this.state.next = this.state.index - this.slidesToScroll;
      }
      this.show();
    }
  }, {
    key: 'start',
    value: function start() {
      this._autoplay.start();
    }
  }, {
    key: 'pause',
    value: function pause() {
      this._autoplay.pause();
    }
  }, {
    key: 'stop',
    value: function stop() {
      this._autoplay.stop();
    }
  }, {
    key: 'show',
    value: function show(index) {
      var force = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false;

      // If all slides are already visible then return
      if (!this.state.length || this.state.length <= this.slidesToShow) {
        return;
      }

      if (typeof index === 'Number') {
        this.state.next = index;
      }

      if (this.options.loop) {
        this._loop.apply();
      }
      if (this.options.infinite) {
        this._infinite.apply();
      }

      // If new slide is already the current one then return
      if (this.state.index === this.state.next) {
        return;
      }

      this.emit('before:show', this.state);
      this._transitioner.apply(force, this._setHeight.bind(this));
      this.emit('after:show', this.state);

      this.emit('show', this);
    }
  }, {
    key: 'reset',
    value: function reset() {
      var _this5 = this;

      this.state = {
        length: this._items.length,
        index: Math.abs(this.options.initialSlide),
        next: Math.abs(this.options.initialSlide),
        prev: undefined
      };

      // Fix options
      if (this.options.loop && this.options.infinite) {
        this.options.loop = false;
      }
      if (this.options.slidesToScroll > this.options.slidesToShow) {
        this.options.slidesToScroll = this.slidesToShow;
      }
      this._breakpoint.init();

      if (this.state.index >= this.state.length && this.state.index !== 0) {
        this.state.index = this.state.index - this.slidesToScroll;
      }
      if (this.state.length <= this.slidesToShow) {
        this.state.index = 0;
      }

      this._ui.wrapper.appendChild(this._navigation.init().render());
      this._ui.wrapper.appendChild(this._pagination.init().render());

      if (this.options.navigationSwipe) {
        this._swipe.bindEvents();
      } else {
        this._swipe._bindEvents();
      }

      this._breakpoint.apply();
      // Move all created slides into slider
      this._slides.forEach(function (slide) {
        return _this5._ui.container.appendChild(slide);
      });
      this._transitioner.init().apply(true, this._setHeight.bind(this));

      if (this.options.autoplay) {
        this._autoplay.init().start();
      }
    }

    /**
     * Destroy Slider
     * @method destroy
     */

  }, {
    key: 'destroy',
    value: function destroy() {
      var _this6 = this;

      this._unbindEvents();
      this._items.forEach(function (item) {
        _this6.element.appendChild(item);
      });
      this.node.remove();
    }
  }, {
    key: 'id',
    get: function get() {
      return this._id;
    }
  }, {
    key: 'index',
    set: function set(index) {
      this._index = index;
    },
    get: function get() {
      return this._index;
    }
  }, {
    key: 'length',
    set: function set(length) {
      this._length = length;
    },
    get: function get() {
      return this._length;
    }
  }, {
    key: 'slides',
    get: function get() {
      return this._slides;
    },
    set: function set(slides) {
      this._slides = slides;
    }
  }, {
    key: 'slidesToScroll',
    get: function get() {
      return this.options.effect === 'translate' ? this._breakpoint.getSlidesToScroll() : 1;
    }
  }, {
    key: 'slidesToShow',
    get: function get() {
      return this.options.effect === 'translate' ? this._breakpoint.getSlidesToShow() : 1;
    }
  }, {
    key: 'direction',
    get: function get() {
      return this.element.dir.toLowerCase() === 'rtl' || this.element.style.direction === 'rtl' ? 'rtl' : 'ltr';
    }
  }, {
    key: 'wrapper',
    get: function get() {
      return this._ui.wrapper;
    }
  }, {
    key: 'wrapperWidth',
    get: function get() {
      return this._wrapperWidth || 0;
    }
  }, {
    key: 'container',
    get: function get() {
      return this._ui.container;
    }
  }, {
    key: 'containerWidth',
    get: function get() {
      return this._containerWidth || 0;
    }
  }, {
    key: 'slideWidth',
    get: function get() {
      return this._slideWidth || 0;
    }
  }, {
    key: 'transitioner',
    get: function get() {
      return this._transitioner;
    }
  }], [{
    key: 'attach',
    value: function attach() {
      var _this7 = this;

      var selector = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : '.slider';
      var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};

      var instances = new Array();

      var elements = Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["c" /* isString */])(selector) ? document.querySelectorAll(selector) : Array.isArray(selector) ? selector : [selector];
      [].forEach.call(elements, function (element) {
        if (typeof element[_this7.constructor.name] === 'undefined') {
          var instance = new bulmaCarousel(element, options);
          element[_this7.constructor.name] = instance;
          instances.push(instance);
        } else {
          instances.push(element[_this7.constructor.name]);
        }
      });

      return instances;
    }
  }]);

  return bulmaCarousel;
}(__WEBPACK_IMPORTED_MODULE_3__utils_eventEmitter__["a" /* default */]);

/* harmony default export */ __webpack_exports__["default"] = (bulmaCarousel);

/***/ }),
/* 6 */
/***/ (function(module, __webpack_exports__, __webpack_require__) {

"use strict";
/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return uuid; });
/* unused harmony export isRtl */
/* unused harmony export defer */
/* unused harmony export getNodeIndex */
/* unused harmony export camelize */
function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } }

var uuid = function uuid() {
	var prefix = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : '';
	return prefix + ([1e7] + -1e3 + -4e3 + -8e3 + -1e11).replace(/[018]/g, function (c) {
		return (c ^ crypto.getRandomValues(new Uint8Array(1))[0] & 15 >> c / 4).toString(16);
	});
};
var isRtl = function isRtl() {
	return document.documentElement.getAttribute('dir') === 'rtl';
};

var defer = function defer() {
	this.promise = new Promise(function (resolve, reject) {
		this.resolve = resolve;
		this.reject = reject;
	}.bind(this));

	this.then = this.promise.then.bind(this.promise);
	this.catch = this.promise.catch.bind(this.promise);
};

var getNodeIndex = function getNodeIndex(node) {
	return [].concat(_toConsumableArray(node.parentNode.children)).indexOf(node);
};
var camelize = function camelize(str) {
	return str.replace(/-(\w)/g, toUpper);
};

/***/ }),
/* 7 */
/***/ (function(module, __webpack_exports__, __webpack_require__) {

"use strict";
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_eventEmitter__ = __webpack_require__(3);
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_device__ = __webpack_require__(8);
var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();

function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }

function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; }

function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; }




var onVisibilityChange = Symbol('onVisibilityChange');
var onMouseEnter = Symbol('onMouseEnter');
var onMouseLeave = Symbol('onMouseLeave');

var defaultOptions = {
	autoplay: false,
	autoplaySpeed: 3000
};

var Autoplay = function (_EventEmitter) {
	_inherits(Autoplay, _EventEmitter);

	function Autoplay(slider) {
		_classCallCheck(this, Autoplay);

		var _this = _possibleConstructorReturn(this, (Autoplay.__proto__ || Object.getPrototypeOf(Autoplay)).call(this));

		_this.slider = slider;

		_this.onVisibilityChange = _this.onVisibilityChange.bind(_this);
		_this.onMouseEnter = _this.onMouseEnter.bind(_this);
		_this.onMouseLeave = _this.onMouseLeave.bind(_this);
		return _this;
	}

	_createClass(Autoplay, [{
		key: 'init',
		value: function init() {
			this._bindEvents();
			return this;
		}
	}, {
		key: '_bindEvents',
		value: function _bindEvents() {
			document.addEventListener('visibilitychange', this.onVisibilityChange);
			if (this.slider.options.pauseOnHover) {
				this.slider.container.addEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["a" /* pointerEnter */], this.onMouseEnter);
				this.slider.container.addEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["b" /* pointerLeave */], this.onMouseLeave);
			}
		}
	}, {
		key: '_unbindEvents',
		value: function _unbindEvents() {
			document.removeEventListener('visibilitychange', this.onVisibilityChange);
			this.slider.container.removeEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["a" /* pointerEnter */], this.onMouseEnter);
			this.slider.container.removeEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["b" /* pointerLeave */], this.onMouseLeave);
		}
	}, {
		key: 'start',
		value: function start() {
			var _this2 = this;

			this.stop();
			if (this.slider.options.autoplay) {
				this.emit('start', this);
				this._interval = setInterval(function () {
					if (!(_this2._hovering && _this2.slider.options.pauseOnHover)) {
						if (!_this2.slider.options.centerMode && _this2.slider.state.next >= _this2.slider.state.length - _this2.slider.slidesToShow && !_this2.slider.options.loop && !_this2.slider.options.infinite) {
							_this2.stop();
						} else {
							_this2.slider.next();
						}
					}
				}, this.slider.options.autoplaySpeed);
			}
		}
	}, {
		key: 'stop',
		value: function stop() {
			this._interval = clearInterval(this._interval);
			this.emit('stop', this);
		}
	}, {
		key: 'pause',
		value: function pause() {
			var _this3 = this;

			var speed = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0;

			if (this.paused) {
				return;
			}
			if (this.timer) {
				this.stop();
			}
			this.paused = true;
			if (speed === 0) {
				this.paused = false;
				this.start();
			} else {
				this.slider.on('transition:end', function () {
					if (!_this3) {
						return;
					}
					_this3.paused = false;
					if (!_this3.run) {
						_this3.stop();
					} else {
						_this3.start();
					}
				});
			}
		}
	}, {
		key: 'onVisibilityChange',
		value: function onVisibilityChange(e) {
			if (document.hidden) {
				this.stop();
			} else {
				this.start();
			}
		}
	}, {
		key: 'onMouseEnter',
		value: function onMouseEnter(e) {
			this._hovering = true;
			if (this.slider.options.pauseOnHover) {
				this.pause();
			}
		}
	}, {
		key: 'onMouseLeave',
		value: function onMouseLeave(e) {
			this._hovering = false;
			if (this.slider.options.pauseOnHover) {
				this.pause();
			}
		}
	}]);

	return Autoplay;
}(__WEBPACK_IMPORTED_MODULE_0__utils_eventEmitter__["a" /* default */]);

/* harmony default export */ __webpack_exports__["a"] = (Autoplay);

/***/ }),
/* 8 */
/***/ (function(module, __webpack_exports__, __webpack_require__) {

"use strict";
/* unused harmony export isIE */
/* unused harmony export isIETouch */
/* unused harmony export isAndroid */
/* unused harmony export isiPad */
/* unused harmony export isiPod */
/* unused harmony export isiPhone */
/* unused harmony export isSafari */
/* unused harmony export isUiWebView */
/* unused harmony export supportsTouchEvents */
/* unused harmony export supportsPointerEvents */
/* unused harmony export supportsTouch */
/* unused harmony export pointerDown */
/* unused harmony export pointerMove */
/* unused harmony export pointerUp */
/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return pointerEnter; });
/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return pointerLeave; });
var isIE = window.navigator.pointerEnabled || window.navigator.msPointerEnabled;
var isIETouch = window.navigator.msPointerEnabled && window.navigator.msMaxTouchPoints > 1 || window.navigator.pointerEnabled && window.navigator.maxTouchPoints > 1;
var isAndroid = navigator.userAgent.match(/(Android);?[\s\/]+([\d.]+)?/);
var isiPad = navigator.userAgent.match(/(iPad).*OS\s([\d_]+)/);
var isiPod = navigator.userAgent.match(/(iPod)(.*OS\s([\d_]+))?/);
var isiPhone = !navigator.userAgent.match(/(iPad).*OS\s([\d_]+)/) && navigator.userAgent.match(/(iPhone\sOS)\s([\d_]+)/);
var isSafari = navigator.userAgent.toLowerCase().indexOf('safari') >= 0 && navigator.userAgent.toLowerCase().indexOf('chrome') < 0 && navigator.userAgent.toLowerCase().indexOf('android') < 0;
var isUiWebView = /(iPhone|iPod|iPad).*AppleWebKit(?!.*Safari)/i.test(navigator.userAgent);

var supportsTouchEvents = !!('ontouchstart' in window);
var supportsPointerEvents = !!('PointerEvent' in window);
var supportsTouch = supportsTouchEvents || window.DocumentTouch && document instanceof DocumentTouch || navigator.maxTouchPoints; // IE >=11
var pointerDown = !supportsTouch ? 'mousedown' : 'mousedown ' + (supportsTouchEvents ? 'touchstart' : 'pointerdown');
var pointerMove = !supportsTouch ? 'mousemove' : 'mousemove ' + (supportsTouchEvents ? 'touchmove' : 'pointermove');
var pointerUp = !supportsTouch ? 'mouseup' : 'mouseup ' + (supportsTouchEvents ? 'touchend' : 'pointerup');
var pointerEnter = supportsTouch && supportsPointerEvents ? 'pointerenter' : 'mouseenter';
var pointerLeave = supportsTouch && supportsPointerEvents ? 'pointerleave' : 'mouseleave';

/***/ }),
/* 9 */
/***/ (function(module, __webpack_exports__, __webpack_require__) {

"use strict";
var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();

function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }

var onResize = Symbol('onResize');

var Breakpoints = function () {
	function Breakpoints(slider) {
		_classCallCheck(this, Breakpoints);

		this.slider = slider;
		this.options = slider.options;

		this[onResize] = this[onResize].bind(this);

		this._bindEvents();
	}

	_createClass(Breakpoints, [{
		key: 'init',
		value: function init() {
			this._defaultBreakpoint = {
				slidesToShow: this.options.slidesToShow,
				slidesToScroll: this.options.slidesToScroll
			};
			this.options.breakpoints.sort(function (a, b) {
				return parseInt(a.changePoint, 10) > parseInt(b.changePoint, 10);
			});
			this._currentBreakpoint = this._getActiveBreakpoint();

			return this;
		}
	}, {
		key: 'destroy',
		value: function destroy() {
			this._unbindEvents();
		}
	}, {
		key: '_bindEvents',
		value: function _bindEvents() {
			window.addEventListener('resize', this[onResize]);
			window.addEventListener('orientationchange', this[onResize]);
		}
	}, {
		key: '_unbindEvents',
		value: function _unbindEvents() {
			window.removeEventListener('resize', this[onResize]);
			window.removeEventListener('orientationchange', this[onResize]);
		}
	}, {
		key: '_getActiveBreakpoint',
		value: function _getActiveBreakpoint() {
			//Get breakpoint for window width
			var _iteratorNormalCompletion = true;
			var _didIteratorError = false;
			var _iteratorError = undefined;

			try {
				for (var _iterator = this.options.breakpoints[Symbol.iterator](), _step; !(_iteratorNormalCompletion = (_step = _iterator.next()).done); _iteratorNormalCompletion = true) {
					var point = _step.value;

					if (point.changePoint >= window.innerWidth) {
						return point;
					}
				}
			} catch (err) {
				_didIteratorError = true;
				_iteratorError = err;
			} finally {
				try {
					if (!_iteratorNormalCompletion && _iterator.return) {
						_iterator.return();
					}
				} finally {
					if (_didIteratorError) {
						throw _iteratorError;
					}
				}
			}

			return this._defaultBreakpoint;
		}
	}, {
		key: 'getSlidesToShow',
		value: function getSlidesToShow() {
			return this._currentBreakpoint ? this._currentBreakpoint.slidesToShow : this._defaultBreakpoint.slidesToShow;
		}
	}, {
		key: 'getSlidesToScroll',
		value: function getSlidesToScroll() {
			return this._currentBreakpoint ? this._currentBreakpoint.slidesToScroll : this._defaultBreakpoint.slidesToScroll;
		}
	}, {
		key: 'apply',
		value: function apply() {
			if (this.slider.state.index >= this.slider.state.length && this.slider.state.index !== 0) {
				this.slider.state.index = this.slider.state.index - this._currentBreakpoint.slidesToScroll;
			}
			if (this.slider.state.length <= this._currentBreakpoint.slidesToShow) {
				this.slider.state.index = 0;
			}

			if (this.options.loop) {
				this.slider._loop.init().apply();
			}

			if (this.options.infinite) {
				this.slider._infinite.init().apply();
			}

			this.slider._setDimensions();
			this.slider._transitioner.init().apply(true, this.slider._setHeight.bind(this.slider));
			this.slider._setClasses();

			this.slider._navigation.refresh();
			this.slider._pagination.refresh();
		}
	}, {
		key: onResize,
		value: function value(e) {
			var newBreakPoint = this._getActiveBreakpoint();
			if (newBreakPoint.slidesToShow !== this._currentBreakpoint.slidesToShow) {
				this._currentBreakpoint = newBreakPoint;
				this.apply();
			}
		}
	}]);

	return Breakpoints;
}();

/* harmony default export */ __webpack_exports__["a"] = (Breakpoints);

/***/ }),
/* 10 */
/***/ (function(module, __webpack_exports__, __webpack_require__) {

"use strict";
var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();

function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } }

function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }

var Infinite = function () {
	function Infinite(slider) {
		_classCallCheck(this, Infinite);

		this.slider = slider;
	}

	_createClass(Infinite, [{
		key: 'init',
		value: function init() {
			if (this.slider.options.infinite && this.slider.options.effect === 'translate') {
				if (this.slider.options.centerMode) {
					this._infiniteCount = Math.ceil(this.slider.slidesToShow + this.slider.slidesToShow / 2);
				} else {
					this._infiniteCount = this.slider.slidesToShow;
				}

				var frontClones = [];
				var slideIndex = 0;
				for (var i = this.slider.state.length; i > this.slider.state.length - 1 - this._infiniteCount; i -= 1) {
					slideIndex = i - 1;
					frontClones.unshift(this._cloneSlide(this.slider.slides[slideIndex], slideIndex - this.slider.state.length));
				}

				var backClones = [];
				for (var _i = 0; _i < this._infiniteCount + this.slider.state.length; _i += 1) {
					backClones.push(this._cloneSlide(this.slider.slides[_i % this.slider.state.length], _i + this.slider.state.length));
				}

				this.slider.slides = [].concat(frontClones, _toConsumableArray(this.slider.slides), backClones);
			}
			return this;
		}
	}, {
		key: 'apply',
		value: function apply() {}
	}, {
		key: 'onTransitionEnd',
		value: function onTransitionEnd(e) {
			if (this.slider.options.infinite) {
				if (this.slider.state.next >= this.slider.state.length) {
					this.slider.state.index = this.slider.state.next = this.slider.state.next - this.slider.state.length;
					this.slider.transitioner.apply(true);
				} else if (this.slider.state.next < 0) {
					this.slider.state.index = this.slider.state.next = this.slider.state.length + this.slider.state.next;
					this.slider.transitioner.apply(true);
				}
			}
		}
	}, {
		key: '_cloneSlide',
		value: function _cloneSlide(slide, index) {
			var newSlide = slide.cloneNode(true);
			newSlide.dataset.sliderIndex = index;
			newSlide.dataset.cloned = true;
			var ids = newSlide.querySelectorAll('[id]') || [];
			ids.forEach(function (id) {
				id.setAttribute('id', '');
			});
			return newSlide;
		}
	}]);

	return Infinite;
}();

/* harmony default export */ __webpack_exports__["a"] = (Infinite);

/***/ }),
/* 11 */
/***/ (function(module, __webpack_exports__, __webpack_require__) {

"use strict";
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_dom__ = __webpack_require__(12);
var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();

function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }



var Loop = function () {
	function Loop(slider) {
		_classCallCheck(this, Loop);

		this.slider = slider;
	}

	_createClass(Loop, [{
		key: "init",
		value: function init() {
			return this;
		}
	}, {
		key: "apply",
		value: function apply() {
			if (this.slider.options.loop) {
				if (this.slider.state.next > 0) {
					if (this.slider.state.next < this.slider.state.length) {
						if (this.slider.state.next > this.slider.state.length - this.slider.slidesToShow && Object(__WEBPACK_IMPORTED_MODULE_0__utils_dom__["a" /* isInViewport */])(this.slider._slides[this.slider.state.length - 1], this.slider.wrapper)) {
							this.slider.state.next = 0;
						} else {
							this.slider.state.next = Math.min(Math.max(this.slider.state.next, 0), this.slider.state.length - this.slider.slidesToShow);
						}
					} else {
						this.slider.state.next = 0;
					}
				} else {
					if (this.slider.state.next <= 0 - this.slider.slidesToScroll) {
						this.slider.state.next = this.slider.state.length - this.slider.slidesToShow;
					} else {
						this.slider.state.next = 0;
					}
				}
			}
		}
	}]);

	return Loop;
}();

/* harmony default export */ __webpack_exports__["a"] = (Loop);

/***/ }),
/* 12 */
/***/ (function(module, __webpack_exports__, __webpack_require__) {

"use strict";
/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return isInViewport; });
var isInViewport = function isInViewport(element, html) {
	var rect = element.getBoundingClientRect();
	html = html || document.documentElement;
	return rect.top >= 0 && rect.left >= 0 && rect.bottom <= (window.innerHeight || html.clientHeight) && rect.right <= (window.innerWidth || html.clientWidth);
};

/***/ }),
/* 13 */
/***/ (function(module, __webpack_exports__, __webpack_require__) {

"use strict";
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__templates_navigation__ = __webpack_require__(14);
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__ = __webpack_require__(1);
var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();

function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }




var Navigation = function () {
	function Navigation(slider) {
		_classCallCheck(this, Navigation);

		this.slider = slider;

		this._clickEvents = ['click', 'touch'];
		this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__["a" /* default */])();

		this.onPreviousClick = this.onPreviousClick.bind(this);
		this.onNextClick = this.onNextClick.bind(this);
		this.onKeyUp = this.onKeyUp.bind(this);
	}

	_createClass(Navigation, [{
		key: 'init',
		value: function init() {
			this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_0__templates_navigation__["a" /* default */])(this.slider.options.icons));
			this._ui = {
				previous: this.node.querySelector('.slider-navigation-previous'),
				next: this.node.querySelector('.slider-navigation-next')
			};

			this._unbindEvents();
			this._bindEvents();

			this.refresh();

			return this;
		}
	}, {
		key: 'destroy',
		value: function destroy() {
			this._unbindEvents();
		}
	}, {
		key: '_bindEvents',
		value: function _bindEvents() {
			var _this = this;

			this.slider.wrapper.addEventListener('keyup', this.onKeyUp);
			this._clickEvents.forEach(function (clickEvent) {
				_this._ui.previous.addEventListener(clickEvent, _this.onPreviousClick);
				_this._ui.next.addEventListener(clickEvent, _this.onNextClick);
			});
		}
	}, {
		key: '_unbindEvents',
		value: function _unbindEvents() {
			var _this2 = this;

			this.slider.wrapper.removeEventListener('keyup', this.onKeyUp);
			this._clickEvents.forEach(function (clickEvent) {
				_this2._ui.previous.removeEventListener(clickEvent, _this2.onPreviousClick);
				_this2._ui.next.removeEventListener(clickEvent, _this2.onNextClick);
			});
		}
	}, {
		key: 'onNextClick',
		value: function onNextClick(e) {
			if (!this._supportsPassive) {
				e.preventDefault();
			}

			if (this.slider.options.navigation) {
				this.slider.next();
			}
		}
	}, {
		key: 'onPreviousClick',
		value: function onPreviousClick(e) {
			if (!this._supportsPassive) {
				e.preventDefault();
			}

			if (this.slider.options.navigation) {
				this.slider.previous();
			}
		}
	}, {
		key: 'onKeyUp',
		value: function onKeyUp(e) {
			if (this.slider.options.keyNavigation) {
				if (e.key === 'ArrowRight' || e.key === 'Right') {
					this.slider.next();
				} else if (e.key === 'ArrowLeft' || e.key === 'Left') {
					this.slider.previous();
				}
			}
		}
	}, {
		key: 'refresh',
		value: function refresh() {
			// let centerOffset = Math.floor(this.options.slidesToShow / 2);
			if (!this.slider.options.loop && !this.slider.options.infinite) {
				if (this.slider.options.navigation && this.slider.state.length > this.slider.slidesToShow) {
					this._ui.previous.classList.remove('is-hidden');
					this._ui.next.classList.remove('is-hidden');
					if (this.slider.state.next === 0) {
						this._ui.previous.classList.add('is-hidden');
						this._ui.next.classList.remove('is-hidden');
					} else if (this.slider.state.next >= this.slider.state.length - this.slider.slidesToShow && !this.slider.options.centerMode) {
						this._ui.previous.classList.remove('is-hidden');
						this._ui.next.classList.add('is-hidden');
					} else if (this.slider.state.next >= this.slider.state.length - 1 && this.slider.options.centerMode) {
						this._ui.previous.classList.remove('is-hidden');
						this._ui.next.classList.add('is-hidden');
					}
				} else {
					this._ui.previous.classList.add('is-hidden');
					this._ui.next.classList.add('is-hidden');
				}
			}
		}
	}, {
		key: 'render',
		value: function render() {
			return this.node;
		}
	}]);

	return Navigation;
}();

/* harmony default export */ __webpack_exports__["a"] = (Navigation);

/***/ }),
/* 14 */
/***/ (function(module, __webpack_exports__, __webpack_require__) {

"use strict";
/* harmony default export */ __webpack_exports__["a"] = (function (icons) {
	return "<div class=\"slider-navigation-previous\">" + icons.previous + "</div>\n<div class=\"slider-navigation-next\">" + icons.next + "</div>";
});

/***/ }),
/* 15 */
/***/ (function(module, __webpack_exports__, __webpack_require__) {

"use strict";
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__templates_pagination__ = __webpack_require__(16);
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__templates_pagination_page__ = __webpack_require__(17);
/* harmony import */ var __WEBPACK_IMPORTED_MODULE_2__utils_detect_supportsPassive__ = __webpack_require__(1);
var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Objec