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README copy.md

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+# Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Tasks
+<div style="display: flex; justify-content: center; align-items: center;">
+  <a href="https://arxiv.org/abs/2501.15891" style="margin: 0 2px;">
+    <img src='https://img.shields.io/badge/arXiv-2501.15891-red?style=flat&logo=arXiv&logoColor=red' alt='arxiv'>
+  </a>
+  <a href='https://logn-2024.github.io/Any2anyTryon/' style="margin: 0 2px;">
+    <img src='https://img.shields.io/badge/Webpage-Project-silver?style=flat&logo=&logoColor=orange' alt='webpage'>
+  </a>
+  <a href="https://huggingface.co/spaces/jamesliu1217/Any2anyTryon_exp" style="margin: 0 2px;">
+    <img src='https://img.shields.io/badge/Demo-Gradio-gold?style=flat&logo=Gradio&logoColor=red' alt='Demo'>
+  </a>
+  <a href='https://huggingface.co/loooooong/Any2anyTryon' style="margin: 0 2px;">
+    <img src='https://img.shields.io/badge/Hugging Face-ckpts-orange?style=flat&logo=HuggingFace&logoColor=orange' alt='huggingface'>
+  </a>
+  <a href='https://huggingface.co/datasets/loooooong/LAION-Garment' style="margin: 0 2px;">
+    <img src='https://img.shields.io/badge/Hugging Face-datasets-orange?style=flat&logo=HuggingFace&logoColor=orange' alt='huggingface'>
+  </a>
+</div>
+
+![teaser](asset/images/teaser.png)
+
+## Environment
+```bash
+conda create -n Any2AnyTryon python=3.11
+conda activate Any2AnyTryon
+pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124
+pip install --upgrade -r requirements.txt
+```
+
+## Demo
+Using `--group_offloading` saves memory but slows processing.
+```bash
+python app.py
+```
+
+## Inference
+Choose a LoRA from [here](https://huggingface.co/loooooong/Any2anyTryon) or use `--lora_name` with the script.
+```bash
+python infer.py --model_image ./asset/images/model/model1.png --garment_image ./asset/images/garment/garment1.jpg \
+--prompt="<MODEL> a man <GARMENT> t-shirt with pockets <TARGET> man wearing the t-shirt"
+```
+
+## Test
+
+Download the dataset from the [VITON-HD](https://github.com/shadow2496/VITON-HD). Generate segmentation masks using [AutoMasker](https://github.com/Zheng-Chong/CatVTON/blob/edited/preprocess_agnostic_mask.py) for repainting. For unpaired data evaluation, execute the following command:
+```bash
+CUDA_VISIBLE_DEVICES=0 python test_vitonhd.py --model_path black-forest-labs/FLUX.1-dev \
+--model_dir data/zalando-hd-resized/test/image --garment_dir data/zalando-hd-resized/test/cloth \
+--output_dir ./results/vitonhd_test_unpaired_repaint --meta_file data/zalando-hd-resized/test_pairs.txt \
+--mask_dir data/zalando-hd-resized/test/mask --source_dir data/zalando-hd-resized/test/image \
+--train_double_block_only --repaint 
+```
+For paired test, download [test set](https://huggingface.co/datasets/loooooong/Any2anyTryon_vitonhd_test) and extract images to local directory.
+```bash
+python src/download_data.py
+```
+```bash
+CUDA_VISIBLE_DEVICES=0 python test_vitonhd.py --model_path black-forest-labs/FLUX.1-dev \
+--model_dir data/zalando-hd-resized/test/image_synthesis --garment_dir data/zalando-hd-resized/test/cloth \
+--output_dir ./results/vitonhd_test_paired_repaint --meta_file data/zalando-hd-resized/test_pairs.txt \
+--mask_dir data/zalando-hd-resized/test/mask --source_dir data/zalando-hd-resized/test/image \
+--train_double_block_only --repaint --paired
+```
+
+## Datasets
+Some data can be downloaded from [here](https://huggingface.co/datasets/loooooong/LAION-Garment). Use the following code to ensure pixel correspondence between the original image and the synthesized image:
+```python
+from src.utils import crop_to_multiple_of_16
+img_src = Image.open(<img_path>)
+img_inpaint = Image.open(io.BytesIO(<inpaint_bytes>))
+
+img_src = crop_to_multiple_of_16(img_src)
+assert img_src.size==img_inpaint.size
+```
+
+## To-Do List
+- \[x\] Demo code and gradio interface
+- \[x\] Inference code
+- \[x\] Tryon checkpoint
+- \[x\] Model generation checkpoint
+- \[x\] Garment reconstruction checkpoint
+- \[x\] Base all tasks checkpoint
+- \[ \] Dataset preparation
+- \[ \] Training code
+
+## Citation
+
+```bibtex
+@misc{guo2025any2anytryonleveragingadaptiveposition,
+    title={Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Tasks}, 
+    author={Hailong Guo and Bohan Zeng and Yiren Song and Wentao Zhang and Chuang Zhang and Jiaming Liu},
+    year={2025},
+    eprint={2501.15891},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV},
+    url={https://arxiv.org/abs/2501.15891}, 
+}
+```

app.py

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+import torch
+import numpy as np
+from PIL import Image
+import gradio as gr
+
+import os
+import json
+import argparse
+
+from diffusers import FluxTransformer2DModel, AutoencoderKL
+from diffusers.hooks import apply_group_offloading
+from transformers import T5EncoderModel, CLIPTextModel
+from src.pipeline_tryon import FluxTryonPipeline
+from optimum.quanto import freeze, qfloat8, quantize
+
+device = torch.device("cuda")
+torch_dtype = torch.bfloat16 # torch.float16
+
+def load_models(device=device, torch_dtype=torch_dtype,group_offloading=False):
+    bfl_repo = "black-forest-labs/FLUX.1-dev"
+    # Enable memory efficient attention
+    text_encoder = CLIPTextModel.from_pretrained(bfl_repo, subfolder="text_encoder", torch_dtype=torch_dtype,)
+    text_encoder_2 = T5EncoderModel.from_pretrained(bfl_repo, subfolder="text_encoder_2", torch_dtype=torch_dtype,)
+    transformer = FluxTransformer2DModel.from_pretrained(bfl_repo, subfolder="transformer", torch_dtype=torch_dtype,)
+    vae = AutoencoderKL.from_pretrained(bfl_repo, subfolder="vae", torch_dtype=torch_dtype)
+    # transformer = FluxTransformer2DModel.from_single_file("Kijai/flux-fp8/flux1-dev-fp8.safetensors", torch_dtype=torch_dtype)
+    pipe = FluxTryonPipeline.from_pretrained(
+        bfl_repo,
+        transformer=transformer,
+        text_encoder=text_encoder,
+        text_encoder_2=text_encoder_2,
+        vae=vae,
+        torch_dtype=torch_dtype,
+    )#.to(device="cpu", dtype=torch_dtype)
+    # pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead", fullgraph=True) # Do not use this if resolution can change
+    # # quantize transformer cause severe degration
+    # quantize(pipe.transformer, weights=qfloat8)
+    # freeze(pipe.transformer)
+    quantize(pipe.text_encoder_2, weights=qfloat8)
+    freeze(pipe.text_encoder_2)    
+    # pipe.to(device=device)
+
+    # Enable memory efficient attention and VAE optimization
+    pipe.enable_attention_slicing()
+    pipe.vae.enable_slicing()
+    pipe.vae.enable_tiling()
+
+    pipe.enable_model_cpu_offload()
+    # pipe.enable_sequential_cpu_offload()
+    pipe.load_lora_weights(
+        "loooooong/Any2anyTryon",
+        weight_name="dev_lora_any2any_alltasks.safetensors",
+        adapter_name="tryon",
+    )
+    pipe.remove_all_hooks()
+
+    if group_offloading:
+        # https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux#group-offloading
+        apply_group_offloading(
+            pipe.transformer,
+            offload_type="leaf_level",
+            offload_device=torch.device("cpu"),
+            onload_device=torch.device(device),
+            use_stream=True,
+        )
+        apply_group_offloading(
+            pipe.text_encoder, 
+            offload_device=torch.device("cpu"),
+            onload_device=torch.device(device),
+            offload_type="leaf_level",
+            use_stream=True,
+        )
+        # apply_group_offloading(
+        #     pipe.text_encoder_2, 
+        #     offload_device=torch.device("cpu"),
+        #     onload_device=torch.device(device),
+        #     offload_type="leaf_level",
+        #     use_stream=True,
+        # )
+        apply_group_offloading(
+            pipe.vae, 
+            offload_device=torch.device("cpu"),
+            onload_device=torch.device(device),
+            offload_type="leaf_level",
+            use_stream=True,
+        )
+
+    pipe.to(device=device)
+    return pipe
+
+def crop_to_multiple_of_16(img):
+    width, height = img.size
+    
+    # Calculate new dimensions that are multiples of 8
+    new_width = width - (width % 16)  
+    new_height = height - (height % 16)
+    
+    # Calculate crop box coordinates
+    left = (width - new_width) // 2
+    top = (height - new_height) // 2
+    right = left + new_width
+    bottom = top + new_height
+    
+    # Crop the image
+    cropped_img = img.crop((left, top, right, bottom))
+    
+    return cropped_img
+
+def resize_and_pad_to_size(image, target_width, target_height):
+    # Convert numpy array to PIL Image if needed
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+        
+    # Get original dimensions
+    orig_width, orig_height = image.size
+    
+    # Calculate aspect ratios
+    target_ratio = target_width / target_height
+    orig_ratio = orig_width / orig_height
+    
+    # Calculate new dimensions while maintaining aspect ratio
+    if orig_ratio > target_ratio:
+        # Image is wider than target ratio - scale by width
+        new_width = target_width
+        new_height = int(new_width / orig_ratio)
+    else:
+        # Image is taller than target ratio - scale by height
+        new_height = target_height
+        new_width = int(new_height * orig_ratio)
+        
+    # Resize image
+    resized_image = image.resize((new_width, new_height))
+    
+    # Create white background image of target size
+    padded_image = Image.new('RGB', (target_width, target_height), 'white')
+    
+    # Calculate padding to center the image
+    left_padding = (target_width - new_width) // 2
+    top_padding = (target_height - new_height) // 2
+    
+    # Paste resized image onto padded background
+    padded_image.paste(resized_image, (left_padding, top_padding))
+    
+    return padded_image, left_padding, top_padding, target_width - new_width - left_padding, target_height - new_height - top_padding
+
+def resize_by_height(image, height):
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+    # image is a PIL image
+    image = image.resize((int(image.width * height / image.height), height))
+    return crop_to_multiple_of_16(image)
+
+# @spaces.GPU()
+@torch.no_grad
+def generate_image(prompt, model_image, garment_image, height=512, width=384, seed=0, guidance_scale=3.5, show_type="follow model image", num_inference_steps=30):
+    height, width = int(height), int(width)
+    width = width - (width % 16)  
+    height = height - (height % 16)
+
+    concat_image_list = [np.zeros((height, width, 3), dtype=np.uint8)]
+    has_model_image = model_image is not None
+    has_garment_image = garment_image is not None
+    if has_model_image:
+        if has_garment_image:
+            # if both model and garment image are provided, ensure model image and target image have the same size
+            input_height, input_width = model_image.shape[:2]
+            model_image, lp, tp, rp, bp = resize_and_pad_to_size(Image.fromarray(model_image), width, height)
+        else:
+            model_image = resize_by_height(model_image, height)
+        # model_image = resize_and_pad_to_size(Image.fromarray(model_image), width, height)
+        concat_image_list.append(model_image)
+    if has_garment_image:
+        # if has_model_image:
+        #     garment_image = resize_and_pad_to_size(Image.fromarray(garment_image), width, height)
+        # else:
+        garment_image = resize_by_height(garment_image, height)
+        concat_image_list.append(garment_image)
+
+    image = np.concatenate([np.array(img) for img in concat_image_list], axis=1)
+    image = Image.fromarray(image)
+    
+    mask = np.zeros_like(image)
+    mask[:,:width] = 255
+    mask_image = Image.fromarray(mask)
+    
+    assert height==image.height, "ensure same height"
+    # with torch.cuda.amp.autocast(): # this cause black image
+    # with torch.no_grad():
+    output = pipe(
+        prompt,
+        image=image,
+        mask_image=mask_image,
+        strength=1.,
+        height=height,
+        width=image.width,
+        target_width=width,
+        tryon=has_model_image and has_garment_image,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        max_sequence_length=512,
+        generator=torch.Generator().manual_seed(seed),
+        output_type="latent",
+    ).images
+    
+    latents = pipe._unpack_latents(output, image.height, image.width, pipe.vae_scale_factor)
+    if show_type!="all outputs":
+        latents = latents[:,:,:,:width//pipe.vae_scale_factor]
+    latents = (latents / pipe.vae.config.scaling_factor) + pipe.vae.config.shift_factor
+    image = pipe.vae.decode(latents, return_dict=False)[0]
+    image = pipe.image_processor.postprocess(image, output_type="pil")[0]
+    output = image
+    if show_type=="follow model image" and has_model_image and has_garment_image:
+        output = output.crop((lp, tp, output.width-rp, output.height-bp)).resize((input_width, input_height))
+    
+    return output
+
+def update_dimensions(model_image, garment_image, height, width, auto_ar):
+    if not auto_ar:
+        return height, width
+    if model_image is not None:
+        height = model_image.shape[0]
+        width = model_image.shape[1]
+    elif garment_image is not None:
+        height = garment_image.shape[0]
+        width = garment_image.shape[1]
+    else:
+        height = 512
+        width = 384
+
+    # Set max dimensions and minimum size
+    max_height = 1024
+    max_width = 1024
+    min_size = 384
+
+    # Scale down if exceeds max dimensions while maintaining aspect ratio
+    if height > max_height or width > max_width:
+        aspect_ratio = width / height
+        if height > max_height:
+            height = max_height
+            width = int(height * aspect_ratio)
+        if width > max_width:
+            width = max_width
+            height = int(width / aspect_ratio)
+
+    # Scale up if below minimum size while maintaining aspect ratio
+    if height < min_size and width < min_size:
+        aspect_ratio = width / height
+        if height < width:
+            height = min_size
+            width = int(height * aspect_ratio)
+        else:
+            width = min_size
+            height = int(width / aspect_ratio)
+
+    return height, width
+
+model1 = Image.open("asset/images/model/model1.png") 
+model2 = Image.open("asset/images/model/model2.jpg")
+model3 = Image.open("asset/images/model/model3.png") 
+model4 = Image.open("asset/images/model/model4.png")
+
+garment1 = Image.open("asset/images/garment/garment1.jpg") 
+garment2 = Image.open("asset/images/garment/garment2.jpg")
+garment3 = Image.open("asset/images/garment/garment3.jpg") 
+garment4 = Image.open("asset/images/garment/garment4.jpg")
+
+def launch_demo():
+    with gr.Blocks() as demo:   
+        gr.Markdown("# Any2AnyTryon")
+        gr.Markdown("Demo(experimental) for [Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Tasks](https://arxiv.org/abs/2501.15891) ([Code](https://github.com/logn-2024/Any2anyTryon)).") 
+        with gr.Row():
+            with gr.Column():
+                model_image = gr.Image(label="Model Image", type="numpy", interactive=True,)
+                with gr.Row():
+                    garment_image = gr.Image(label="Garment Image", type="numpy", interactive=True,)
+                    with gr.Column():
+                        prompt = gr.Textbox(
+                            label="Prompt",
+                            info="Try example prompts from right side",
+                            placeholder="Enter your prompt here...",
+                            value="",
+                            # visible=False,
+                        )
+                        with gr.Row():
+                            height = gr.Number(label="Height", value=576, precision=0)
+                            width = gr.Number(label="Width", value=576, precision=0)
+                        seed = gr.Number(label="Seed", value=0, precision=0)
+                        with gr.Accordion("Advanced Settings", open=False):
+                            guidance_scale = gr.Number(label="Guidance Scale", value=3.5)
+                            num_inference_steps = gr.Number(label="Inference Steps", value=15)
+                            show_type = gr.Radio(label="Show Type",choices=["follow model image", "follow height & width", "all outputs"],value="follow model image")
+                            auto_ar = gr.Checkbox(label="Detect Image Size(From Uploaded Images)", value=False, visible=True,)
+                btn = gr.Button("Generate")
+            
+            with gr.Column():
+                output = gr.Image(label="Generated Image")
+                example_prompts = gr.Examples(
+                        [
+                            "<MODEL> a person with fashion garment. <GARMENT> a garment. <TARGET> model with fashion garment",
+                            "<MODEL> a person with fashion garment. <TARGET> the same garment laid flat.",
+                            "<GARMENT> The image shows a fashion garment. <TARGET> a smiling person with the garment in white background",
+                        ],
+                        inputs=prompt,
+                        label="Example Prompts",
+                        # visible=False
+                    )
+                example_model = gr.Examples(
+                    examples=[
+                        model1, model2, model3, model4
+                    ],
+                    inputs=model_image,
+                    label="Example Model Images"
+                )
+                example_garment = gr.Examples(
+                    examples=[
+                        garment1, garment2, garment3, garment4
+                    ],
+                    inputs=garment_image,
+                    label="Example Garment Images"
+                )
+
+        # Update dimensions when images change
+        model_image.change(fn=update_dimensions, 
+                        inputs=[model_image, garment_image, height, width, auto_ar],
+                        outputs=[height, width])
+        garment_image.change(fn=update_dimensions,
+                            inputs=[model_image, garment_image, height, width, auto_ar], 
+                            outputs=[height, width])    
+        btn.click(fn=generate_image,
+                inputs=[prompt, model_image, garment_image, height, width, seed, guidance_scale, show_type, num_inference_steps],
+                outputs=output)
+
+        demo.title = "FLUX Image Generation Demo"
+        demo.description = "Generate images using FLUX model with LoRA"
+        
+        examples = [
+            # tryon
+            [
+                '''<MODEL> a man <GARMENT> a medium-sized, short-sleeved, blue t-shirt with a round neckline and a pocket on the front. <TARGET> model with fashion garment''',
+                model1,
+                garment1,
+                576, 576
+            ],
+            [
+                '''<MODEL> a man with gray hair and a beard wearing a black jacket and sunglasses, standing in front of a body of water with mountains in the background and a cloudy sky above <GARMENT> a black and white striped t-shirt with a red heart embroidered on the chest <TARGET> ''',
+                model2,
+                garment2,
+                576, 576
+            ],
+            [
+                '''<MODEL> a person with fashion garment. <GARMENT> a garment. <TARGET> model with fashion garment''',
+                model3,
+                garment3,
+                576, 576
+            ],
+            [
+                '''<MODEL> a woman lift up her right leg. <GARMENT> a pair of black and white patterned pajama pants. <TARGET> model with fashion garment''',
+                model4,
+                garment4,
+                576, 576
+            ],
+        ]
+        
+        gr.Examples(
+            examples=examples,
+            inputs=[prompt, model_image, garment_image],
+            outputs=output,
+            fn=generate_image,
+            cache_examples=False,
+            examples_per_page=20
+        )
+    demo.queue().launch(share=False, show_error=False,
+        server_name="0.0.0.0"
+    )
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--group_offloading', action="store_true")
+    args=parser.parse_args()
+    pipe = load_models(group_offloading=args.group_offloading)
+    launch_demo()

asset/css/bulma-carousel.min.css

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+@-webkit-keyframes spinAround{from{-webkit-transform:rotate(0);transform:rotate(0)}to{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}@keyframes spinAround{from{-webkit-transform:rotate(0);transform:rotate(0)}to{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}.slider{position:relative;width:100%}.slider-container{display:flex;flex-wrap:nowrap;flex-direction:row;overflow:hidden;-webkit-transform:translate3d(0,0,0);transform:translate3d(0,0,0);min-height:100%}.slider-container.is-vertical{flex-direction:column}.slider-container .slider-item{flex:none}.slider-container .slider-item .image.is-covered img{-o-object-fit:cover;object-fit:cover;-o-object-position:center center;object-position:center center;height:100%;width:100%}.slider-container .slider-item .video-container{height:0;padding-bottom:0;padding-top:56.25%;margin:0;position:relative}.slider-container .slider-item .video-container.is-1by1,.slider-container .slider-item .video-container.is-square{padding-top:100%}.slider-container .slider-item .video-container.is-4by3{padding-top:75%}.slider-container .slider-item .video-container.is-21by9{padding-top:42.857143%}.slider-container .slider-item .video-container embed,.slider-container .slider-item .video-container iframe,.slider-container .slider-item .video-container object{position:absolute;top:0;left:0;width:100%!important;height:100%!important}.slider-navigation-next,.slider-navigation-previous{display:flex;justify-content:center;align-items:center;position:absolute;width:42px;height:42px;background:#fff center center no-repeat;background-size:20px 20px;border:1px solid #fff;border-radius:25091983px;box-shadow:0 2px 5px #3232321a;top:50%;margin-top:-20px;left:0;cursor:pointer;transition:opacity .3s,-webkit-transform .3s;transition:transform .3s,opacity .3s;transition:transform .3s,opacity .3s,-webkit-transform .3s}.slider-navigation-next:hover,.slider-navigation-previous:hover{-webkit-transform:scale(1.2);transform:scale(1.2)}.slider-navigation-next.is-hidden,.slider-navigation-previous.is-hidden{display:none;opacity:0}.slider-navigation-next svg,.slider-navigation-previous svg{width:25%}.slider-navigation-next{left:auto;right:0;background:#fff center center no-repeat;background-size:20px 20px}.slider-pagination{display:none;justify-content:center;align-items:center;position:absolute;bottom:0;left:0;right:0;padding:.5rem 1rem;text-align:center}.slider-pagination .slider-page{background:#fff;width:10px;height:10px;border-radius:25091983px;display:inline-block;margin:0 3px;box-shadow:0 2px 5px #3232321a;transition:-webkit-transform .3s;transition:transform .3s;transition:transform .3s,-webkit-transform .3s;cursor:pointer}.slider-pagination .slider-page.is-active,.slider-pagination .slider-page:hover{-webkit-transform:scale(1.4);transform:scale(1.4)}@media screen and (min-width:800px){.slider-pagination{display:flex}}.hero.has-carousel{position:relative}.hero.has-carousel+.hero-body,.hero.has-carousel+.hero-footer,.hero.has-carousel+.hero-head{z-index:10;overflow:hidden}.hero.has-carousel .hero-carousel{position:absolute;top:0;left:0;bottom:0;right:0;height:auto;border:none;margin:auto;padding:0;z-index:0}.hero.has-carousel .hero-carousel .slider{width:100%;max-width:100%;overflow:hidden;height:100%!important;max-height:100%;z-index:0}.hero.has-carousel .hero-carousel .slider .has-background{max-height:100%}.hero.has-carousel .hero-carousel .slider .has-background .is-background{-o-object-fit:cover;object-fit:cover;-o-object-position:center center;object-position:center center;height:100%;width:100%}.hero.has-carousel .hero-body{margin:0 3rem;z-index:10}

asset/css/bulma-slider.min.css

@@ -0,0 +1 @@
+@-webkit-keyframes spinAround{from{-webkit-transform:rotate(0);transform:rotate(0)}to{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}@keyframes spinAround{from{-webkit-transform:rotate(0);transform:rotate(0)}to{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}input[type=range].slider{-webkit-appearance:none;-moz-appearance:none;appearance:none;margin:1rem 0;background:0 0;touch-action:none}input[type=range].slider.is-fullwidth{display:block;width:100%}input[type=range].slider:focus{outline:0}input[type=range].slider:not([orient=vertical])::-webkit-slider-runnable-track{width:100%}input[type=range].slider:not([orient=vertical])::-moz-range-track{width:100%}input[type=range].slider:not([orient=vertical])::-ms-track{width:100%}input[type=range].slider:not([orient=vertical]).has-output+output,input[type=range].slider:not([orient=vertical]).has-output-tooltip+output{width:3rem;background:#4a4a4a;border-radius:4px;padding:.4rem .8rem;font-size:.75rem;line-height:.75rem;text-align:center;text-overflow:ellipsis;white-space:nowrap;color:#fff;overflow:hidden;pointer-events:none;z-index:200}input[type=range].slider:not([orient=vertical]).has-output-tooltip:disabled+output,input[type=range].slider:not([orient=vertical]).has-output:disabled+output{opacity:.5}input[type=range].slider:not([orient=vertical]).has-output{display:inline-block;vertical-align:middle;width:calc(100% - (4.2rem))}input[type=range].slider:not([orient=vertical]).has-output+output{display:inline-block;margin-left:.75rem;vertical-align:middle}input[type=range].slider:not([orient=vertical]).has-output-tooltip{display:block}input[type=range].slider:not([orient=vertical]).has-output-tooltip+output{position:absolute;left:0;top:-.1rem}input[type=range].slider[orient=vertical]{-webkit-appearance:slider-vertical;-moz-appearance:slider-vertical;appearance:slider-vertical;-webkit-writing-mode:bt-lr;-ms-writing-mode:bt-lr;writing-mode:bt-lr}input[type=range].slider[orient=vertical]::-webkit-slider-runnable-track{height:100%}input[type=range].slider[orient=vertical]::-moz-range-track{height:100%}input[type=range].slider[orient=vertical]::-ms-track{height:100%}input[type=range].slider::-webkit-slider-runnable-track{cursor:pointer;animate:.2s;box-shadow:0 0 0 #7a7a7a;background:#dbdbdb;border-radius:4px;border:0 solid #7a7a7a}input[type=range].slider::-moz-range-track{cursor:pointer;animate:.2s;box-shadow:0 0 0 #7a7a7a;background:#dbdbdb;border-radius:4px;border:0 solid #7a7a7a}input[type=range].slider::-ms-track{cursor:pointer;animate:.2s;box-shadow:0 0 0 #7a7a7a;background:#dbdbdb;border-radius:4px;border:0 solid #7a7a7a}input[type=range].slider::-ms-fill-lower{background:#dbdbdb;border-radius:4px}input[type=range].slider::-ms-fill-upper{background:#dbdbdb;border-radius:4px}input[type=range].slider::-webkit-slider-thumb{box-shadow:none;border:1px solid #b5b5b5;border-radius:4px;background:#fff;cursor:pointer}input[type=range].slider::-moz-range-thumb{box-shadow:none;border:1px solid #b5b5b5;border-radius:4px;background:#fff;cursor:pointer}input[type=range].slider::-ms-thumb{box-shadow:none;border:1px solid #b5b5b5;border-radius:4px;background:#fff;cursor:pointer}input[type=range].slider::-webkit-slider-thumb{-webkit-appearance:none;appearance:none}input[type=range].slider.is-circle::-webkit-slider-thumb{border-radius:290486px}input[type=range].slider.is-circle::-moz-range-thumb{border-radius:290486px}input[type=range].slider.is-circle::-ms-thumb{border-radius:290486px}input[type=range].slider:active::-webkit-slider-thumb{-webkit-transform:scale(1.25);transform:scale(1.25)}input[type=range].slider:active::-moz-range-thumb{transform:scale(1.25)}input[type=range].slider:active::-ms-thumb{transform:scale(1.25)}input[type=range].slider:disabled{opacity:.5;cursor:not-allowed}input[type=range].slider:disabled::-webkit-slider-thumb{cursor:not-allowed;-webkit-transform:scale(1);transform:scale(1)}input[type=range].slider:disabled::-moz-range-thumb{cursor:not-allowed;transform:scale(1)}input[type=range].slider:disabled::-ms-thumb{cursor:not-allowed;transform:scale(1)}input[type=range].slider:not([orient=vertical]){min-height:calc((1rem + 2px) * 1.25)}input[type=range].slider:not([orient=vertical])::-webkit-slider-runnable-track{height:.5rem}input[type=range].slider:not([orient=vertical])::-moz-range-track{height:.5rem}input[type=range].slider:not([orient=vertical])::-ms-track{height:.5rem}input[type=range].slider[orient=vertical]::-webkit-slider-runnable-track{width:.5rem}input[type=range].slider[orient=vertical]::-moz-range-track{width:.5rem}input[type=range].slider[orient=vertical]::-ms-track{width:.5rem}input[type=range].slider::-webkit-slider-thumb{height:1rem;width:1rem}input[type=range].slider::-moz-range-thumb{height:1rem;width:1rem}input[type=range].slider::-ms-thumb{height:1rem;width:1rem}input[type=range].slider::-ms-thumb{margin-top:0}input[type=range].slider::-webkit-slider-thumb{margin-top:-.25rem}input[type=range].slider[orient=vertical]::-webkit-slider-thumb{margin-top:auto;margin-left:-.25rem}input[type=range].slider.is-small:not([orient=vertical]){min-height:calc((.75rem + 2px) * 1.25)}input[type=range].slider.is-small:not([orient=vertical])::-webkit-slider-runnable-track{height:.375rem}input[type=range].slider.is-small:not([orient=vertical])::-moz-range-track{height:.375rem}input[type=range].slider.is-small:not([orient=vertical])::-ms-track{height:.375rem}input[type=range].slider.is-small[orient=vertical]::-webkit-slider-runnable-track{width:.375rem}input[type=range].slider.is-small[orient=vertical]::-moz-range-track{width:.375rem}input[type=range].slider.is-small[orient=vertical]::-ms-track{width:.375rem}input[type=range].slider.is-small::-webkit-slider-thumb{height:.75rem;width:.75rem}input[type=range].slider.is-small::-moz-range-thumb{height:.75rem;width:.75rem}input[type=range].slider.is-small::-ms-thumb{height:.75rem;width:.75rem}input[type=range].slider.is-small::-ms-thumb{margin-top:0}input[type=range].slider.is-small::-webkit-slider-thumb{margin-top:-.1875rem}input[type=range].slider.is-small[orient=vertical]::-webkit-slider-thumb{margin-top:auto;margin-left:-.1875rem}input[type=range].slider.is-medium:not([orient=vertical]){min-height:calc((1.25rem + 2px) * 1.25)}input[type=range].slider.is-medium:not([orient=vertical])::-webkit-slider-runnable-track{height:.625rem}input[type=range].slider.is-medium:not([orient=vertical])::-moz-range-track{height:.625rem}input[type=range].slider.is-medium:not([orient=vertical])::-ms-track{height:.625rem}input[type=range].slider.is-medium[orient=vertical]::-webkit-slider-runnable-track{width:.625rem}input[type=range].slider.is-medium[orient=vertical]::-moz-range-track{width:.625rem}input[type=range].slider.is-medium[orient=vertical]::-ms-track{width:.625rem}input[type=range].slider.is-medium::-webkit-slider-thumb{height:1.25rem;width:1.25rem}input[type=range].slider.is-medium::-moz-range-thumb{height:1.25rem;width:1.25rem}input[type=range].slider.is-medium::-ms-thumb{height:1.25rem;width:1.25rem}input[type=range].slider.is-medium::-ms-thumb{margin-top:0}input[type=range].slider.is-medium::-webkit-slider-thumb{margin-top:-.3125rem}input[type=range].slider.is-medium[orient=vertical]::-webkit-slider-thumb{margin-top:auto;margin-left:-.3125rem}input[type=range].slider.is-large:not([orient=vertical]){min-height:calc((1.5rem + 2px) * 1.25)}input[type=range].slider.is-large:not([orient=vertical])::-webkit-slider-runnable-track{height:.75rem}input[type=range].slider.is-large:not([orient=vertical])::-moz-range-track{height:.75rem}input[type=range].slider.is-large:not([orient=vertical])::-ms-track{height:.75rem}input[type=range].slider.is-large[orient=vertical]::-webkit-slider-runnable-track{width:.75rem}input[type=range].slider.is-large[orient=vertical]::-moz-range-track{width:.75rem}input[type=range].slider.is-large[orient=vertical]::-ms-track{width:.75rem}input[type=range].slider.is-large::-webkit-slider-thumb{height:1.5rem;width:1.5rem}input[type=range].slider.is-large::-moz-range-thumb{height:1.5rem;width:1.5rem}input[type=range].slider.is-large::-ms-thumb{height:1.5rem;width:1.5rem}input[type=range].slider.is-large::-ms-thumb{margin-top:0}input[type=range].slider.is-large::-webkit-slider-thumb{margin-top:-.375rem}input[type=range].slider.is-large[orient=vertical]::-webkit-slider-thumb{margin-top:auto;margin-left:-.375rem}input[type=range].slider.is-white::-moz-range-track{background:#fff!important}input[type=range].slider.is-white::-webkit-slider-runnable-track{background:#fff!important}input[type=range].slider.is-white::-ms-track{background:#fff!important}input[type=range].slider.is-white::-ms-fill-lower{background:#fff}input[type=range].slider.is-white::-ms-fill-upper{background:#fff}input[type=range].slider.is-white .has-output-tooltip+output,input[type=range].slider.is-white.has-output+output{background-color:#fff;color:#0a0a0a}input[type=range].slider.is-black::-moz-range-track{background:#0a0a0a!important}input[type=range].slider.is-black::-webkit-slider-runnable-track{background:#0a0a0a!important}input[type=range].slider.is-black::-ms-track{background:#0a0a0a!important}input[type=range].slider.is-black::-ms-fill-lower{background:#0a0a0a}input[type=range].slider.is-black::-ms-fill-upper{background:#0a0a0a}input[type=range].slider.is-black .has-output-tooltip+output,input[type=range].slider.is-black.has-output+output{background-color:#0a0a0a;color:#fff}input[type=range].slider.is-light::-moz-range-track{background:#f5f5f5!important}input[type=range].slider.is-light::-webkit-slider-runnable-track{background:#f5f5f5!important}input[type=range].slider.is-light::-ms-track{background:#f5f5f5!important}input[type=range].slider.is-light::-ms-fill-lower{background:#f5f5f5}input[type=range].slider.is-light::-ms-fill-upper{background:#f5f5f5}input[type=range].slider.is-light .has-output-tooltip+output,input[type=range].slider.is-light.has-output+output{background-color:#f5f5f5;color:#363636}input[type=range].slider.is-dark::-moz-range-track{background:#363636!important}input[type=range].slider.is-dark::-webkit-slider-runnable-track{background:#363636!important}input[type=range].slider.is-dark::-ms-track{background:#363636!important}input[type=range].slider.is-dark::-ms-fill-lower{background:#363636}input[type=range].slider.is-dark::-ms-fill-upper{background:#363636}input[type=range].slider.is-dark .has-output-tooltip+output,input[type=range].slider.is-dark.has-output+output{background-color:#363636;color:#f5f5f5}input[type=range].slider.is-primary::-moz-range-track{background:#00d1b2!important}input[type=range].slider.is-primary::-webkit-slider-runnable-track{background:#00d1b2!important}input[type=range].slider.is-primary::-ms-track{background:#00d1b2!important}input[type=range].slider.is-primary::-ms-fill-lower{background:#00d1b2}input[type=range].slider.is-primary::-ms-fill-upper{background:#00d1b2}input[type=range].slider.is-primary .has-output-tooltip+output,input[type=range].slider.is-primary.has-output+output{background-color:#00d1b2;color:#fff}input[type=range].slider.is-link::-moz-range-track{background:#3273dc!important}input[type=range].slider.is-link::-webkit-slider-runnable-track{background:#3273dc!important}input[type=range].slider.is-link::-ms-track{background:#3273dc!important}input[type=range].slider.is-link::-ms-fill-lower{background:#3273dc}input[type=range].slider.is-link::-ms-fill-upper{background:#3273dc}input[type=range].slider.is-link .has-output-tooltip+output,input[type=range].slider.is-link.has-output+output{background-color:#3273dc;color:#fff}input[type=range].slider.is-info::-moz-range-track{background:#209cee!important}input[type=range].slider.is-info::-webkit-slider-runnable-track{background:#209cee!important}input[type=range].slider.is-info::-ms-track{background:#209cee!important}input[type=range].slider.is-info::-ms-fill-lower{background:#209cee}input[type=range].slider.is-info::-ms-fill-upper{background:#209cee}input[type=range].slider.is-info .has-output-tooltip+output,input[type=range].slider.is-info.has-output+output{background-color:#209cee;color:#fff}input[type=range].slider.is-success::-moz-range-track{background:#23d160!important}input[type=range].slider.is-success::-webkit-slider-runnable-track{background:#23d160!important}input[type=range].slider.is-success::-ms-track{background:#23d160!important}input[type=range].slider.is-success::-ms-fill-lower{background:#23d160}input[type=range].slider.is-success::-ms-fill-upper{background:#23d160}input[type=range].slider.is-success .has-output-tooltip+output,input[type=range].slider.is-success.has-output+output{background-color:#23d160;color:#fff}input[type=range].slider.is-warning::-moz-range-track{background:#ffdd57!important}input[type=range].slider.is-warning::-webkit-slider-runnable-track{background:#ffdd57!important}input[type=range].slider.is-warning::-ms-track{background:#ffdd57!important}input[type=range].slider.is-warning::-ms-fill-lower{background:#ffdd57}input[type=range].slider.is-warning::-ms-fill-upper{background:#ffdd57}input[type=range].slider.is-warning .has-output-tooltip+output,input[type=range].slider.is-warning.has-output+output{background-color:#ffdd57;color:rgba(0,0,0,.7)}input[type=range].slider.is-danger::-moz-range-track{background:#ff3860!important}input[type=range].slider.is-danger::-webkit-slider-runnable-track{background:#ff3860!important}input[type=range].slider.is-danger::-ms-track{background:#ff3860!important}input[type=range].slider.is-danger::-ms-fill-lower{background:#ff3860}input[type=range].slider.is-danger::-ms-fill-upper{background:#ff3860}input[type=range].slider.is-danger .has-output-tooltip+output,input[type=range].slider.is-danger.has-output+output{background-color:#ff3860;color:#fff}

asset/css/bulma.css.map.txt

@@ -0,0 +1 @@
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asset/css/fontawesome.all.min.css

@@ -0,0 +1,5 @@
+/*!
+ * Font Awesome Free 5.15.1 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ */
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+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;
+}
+
+
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+}
+
+.publication-banner {
+  max-height: parent;
+
+}
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+  position: relative;
+  left: auto;
+  top: auto;
+  transform: none;
+  object-fit: fit;
+}
+
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+}
+
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+    font-family: 'Google Sans', sans-serif;
+}
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+    font-family: 'Google Sans', sans-serif;
+}
+
+.publication-venue {
+    color: #555;
+    width: fit-content;
+    font-weight: bold;
+}
+
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+    color: #ff3860;
+    width: fit-content;
+    font-weight: bolder;
+}
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+.publication-authors {
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+   color: hsl(204, 86%, 53%) !important;
+}
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+    text-decoration: underline;
+}
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+  display: inline-block;
+}
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+}
+
+.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;
+  padding: 20px;
+  font-size: 0;
+}
+
+.results-carousel video {
+  margin: 0;
+}
+
+.slider-pagination .slider-page {
+  background: #000000;
+}
+
+.eql-cntrb { 
+  font-size: smaller;
+}
+
+
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+	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();
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+		root["bulmaCarousel"] = factory();
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+/******/ 		if(!__webpack_require__.o(exports, name)) {
+/******/ 			Object.defineProperty(exports, name, {
+/******/ 				configurable: false,
+/******/ 				enumerable: true,
+/******/ 				get: getter
+/******/ 			});
+/******/ 		}
+/******/ 	};
+/******/
+/******/ 	// 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);
+/******/ 		return getter;
+/******/ 	};
+/******/
+/******/ 	// Object.prototype.hasOwnProperty.call
+/******/ 	__webpack_require__.o = function(object, property) { return Object.prototype.hasOwnProperty.call(object, property); };
+/******/
+/******/ 	// __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; 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 Pagination = function () {
+	function Pagination(slider) {
+		_classCallCheck(this, Pagination);
+
+		this.slider = slider;
+
+		this._clickEvents = ['click', 'touch'];
+		this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_2__utils_detect_supportsPassive__["a" /* default */])();
+
+		this.onPageClick = this.onPageClick.bind(this);
+		this.onResize = this.onResize.bind(this);
+	}
+
+	_createClass(Pagination, [{
+		key: 'init',
+		value: function init() {
+			this._pages = [];
+			this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_0__templates_pagination__["a" /* default */])());
+			this._ui = {
+				container: this.node.firstChild
+			};
+
+			this._count = Math.ceil((this.slider.state.length - this.slider.slidesToShow) / this.slider.slidesToScroll);
+
+			this._draw();
+			this.refresh();
+
+			return this;
+		}
+	}, {
+		key: 'destroy',
+		value: function destroy() {
+			this._unbindEvents();
+		}
+	}, {
+		key: '_bindEvents',
+		value: function _bindEvents() {
+			var _this = this;
+
+			window.addEventListener('resize', this.onResize);
+			window.addEventListener('orientationchange', this.onResize);
+
+			this._clickEvents.forEach(function (clickEvent) {
+				_this._pages.forEach(function (page) {
+					return page.addEventListener(clickEvent, _this.onPageClick);
+				});
+			});
+		}
+	}, {
+		key: '_unbindEvents',
+		value: function _unbindEvents() {
+			var _this2 = this;
+
+			window.removeEventListener('resize', this.onResize);
+			window.removeEventListener('orientationchange', this.onResize);
+
+			this._clickEvents.forEach(function (clickEvent) {
+				_this2._pages.forEach(function (page) {
+					return page.removeEventListener(clickEvent, _this2.onPageClick);
+				});
+			});
+		}
+	}, {
+		key: '_draw',
+		value: function _draw() {
+			this._ui.container.innerHTML = '';
+			if (this.slider.options.pagination && this.slider.state.length > this.slider.slidesToShow) {
+				for (var i = 0; i <= this._count; i++) {
+					var newPageNode = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_1__templates_pagination_page__["a" /* default */])()).firstChild;
+					newPageNode.dataset.index = i * this.slider.slidesToScroll;
+					this._pages.push(newPageNode);
+					this._ui.container.appendChild(newPageNode);
+				}
+				this._bindEvents();
+			}
+		}
+	}, {
+		key: 'onPageClick',
+		value: function onPageClick(e) {
+			if (!this._supportsPassive) {
+				e.preventDefault();
+			}
+
+			this.slider.state.next = e.currentTarget.dataset.index;
+			this.slider.show();
+		}
+	}, {
+		key: 'onResize',
+		value: function onResize() {
+			this._draw();
+		}
+	}, {
+		key: 'refresh',
+		value: function refresh() {
+			var _this3 = this;
+
+			var newCount = void 0;
+
+			if (this.slider.options.infinite) {
+				newCount = Math.ceil(this.slider.state.length - 1 / this.slider.slidesToScroll);
+			} else {
+				newCount = Math.ceil((this.slider.state.length - this.slider.slidesToShow) / this.slider.slidesToScroll);
+			}
+			if (newCount !== this._count) {
+				this._count = newCount;
+				this._draw();
+			}
+
+			this._pages.forEach(function (page) {
+				page.classList.remove('is-active');
+				if (parseInt(page.dataset.index, 10) === _this3.slider.state.next % _this3.slider.state.length) {
+					page.classList.add('is-active');
+				}
+			});
+		}
+	}, {
+		key: 'render',
+		value: function render() {
+			return this.node;
+		}
+	}]);
+
+	return Pagination;
+}();
+
+/* harmony default export */ __webpack_exports__["a"] = (Pagination);
+
+/***/ }),
+/* 16 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony default export */ __webpack_exports__["a"] = (function () {
+	return "<div class=\"slider-pagination\"></div>";
+});
+
+/***/ }),
+/* 17 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony default export */ __webpack_exports__["a"] = (function () {
+  return "<div class=\"slider-page\"></div>";
+});
+
+/***/ }),
+/* 18 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__ = __webpack_require__(4);
+/* 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 Swipe = function () {
+	function Swipe(slider) {
+		_classCallCheck(this, Swipe);
+
+		this.slider = slider;
+
+		this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__["a" /* default */])();
+
+		this.onStartDrag = this.onStartDrag.bind(this);
+		this.onMoveDrag = this.onMoveDrag.bind(this);
+		this.onStopDrag = this.onStopDrag.bind(this);
+
+		this._init();
+	}
+
+	_createClass(Swipe, [{
+		key: '_init',
+		value: function _init() {}
+	}, {
+		key: 'bindEvents',
+		value: function bindEvents() {
+			var _this = this;
+
+			this.slider.container.addEventListener('dragstart', function (e) {
+				if (!_this._supportsPassive) {
+					e.preventDefault();
+				}
+			});
+			this.slider.container.addEventListener('mousedown', this.onStartDrag);
+			this.slider.container.addEventListener('touchstart', this.onStartDrag);
+
+			window.addEventListener('mousemove', this.onMoveDrag);
+			window.addEventListener('touchmove', this.onMoveDrag);
+
+			window.addEventListener('mouseup', this.onStopDrag);
+			window.addEventListener('touchend', this.onStopDrag);
+			window.addEventListener('touchcancel', this.onStopDrag);
+		}
+	}, {
+		key: 'unbindEvents',
+		value: function unbindEvents() {
+			var _this2 = this;
+
+			this.slider.container.removeEventListener('dragstart', function (e) {
+				if (!_this2._supportsPassive) {
+					e.preventDefault();
+				}
+			});
+			this.slider.container.removeEventListener('mousedown', this.onStartDrag);
+			this.slider.container.removeEventListener('touchstart', this.onStartDrag);
+
+			window.removeEventListener('mousemove', this.onMoveDrag);
+			window.removeEventListener('touchmove', this.onMoveDrag);
+
+			window.removeEventListener('mouseup', this.onStopDrag);
+			window.removeEventListener('mouseup', this.onStopDrag);
+			window.removeEventListener('touchcancel', this.onStopDrag);
+		}
+
+		/**
+   * @param {MouseEvent|TouchEvent}
+   */
+
+	}, {
+		key: 'onStartDrag',
+		value: function onStartDrag(e) {
+			if (e.touches) {
+				if (e.touches.length > 1) {
+					return;
+				} else {
+					e = e.touches[0];
+				}
+			}
+
+			this._origin = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](e.screenX, e.screenY);
+			this.width = this.slider.wrapperWidth;
+			this.slider.transitioner.disable();
+		}
+
+		/**
+   * @param {MouseEvent|TouchEvent}
+   */
+
+	}, {
+		key: 'onMoveDrag',
+		value: function onMoveDrag(e) {
+			if (this._origin) {
+				var point = e.touches ? e.touches[0] : e;
+				this._lastTranslate = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](point.screenX - this._origin.x, point.screenY - this._origin.y);
+				if (e.touches) {
+					if (Math.abs(this._lastTranslate.x) > Math.abs(this._lastTranslate.y)) {
+						if (!this._supportsPassive) {
+							e.preventDefault();
+						}
+						e.stopPropagation();
+					}
+				}
+			}
+		}
+
+		/**
+   * @param {MouseEvent|TouchEvent}
+   */
+
+	}, {
+		key: 'onStopDrag',
+		value: function onStopDrag(e) {
+			if (this._origin && this._lastTranslate) {
+				if (Math.abs(this._lastTranslate.x) > 0.2 * this.width) {
+					if (this._lastTranslate.x < 0) {
+						this.slider.next();
+					} else {
+						this.slider.previous();
+					}
+				} else {
+					this.slider.show(true);
+				}
+			}
+			this._origin = null;
+			this._lastTranslate = null;
+		}
+	}]);
+
+	return Swipe;
+}();
+
+/* harmony default export */ __webpack_exports__["a"] = (Swipe);
+
+/***/ }),
+/* 19 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__transitions_fade__ = __webpack_require__(20);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__transitions_translate__ = __webpack_require__(21);
+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 Transitioner = function () {
+	function Transitioner(slider) {
+		_classCallCheck(this, Transitioner);
+
+		this.slider = slider;
+		this.options = slider.options;
+
+		this._animating = false;
+		this._animation = undefined;
+
+		this._translate = new __WEBPACK_IMPORTED_MODULE_1__transitions_translate__["a" /* default */](this, slider, slider.options);
+		this._fade = new __WEBPACK_IMPORTED_MODULE_0__transitions_fade__["a" /* default */](this, slider, slider.options);
+	}
+
+	_createClass(Transitioner, [{
+		key: 'init',
+		value: function init() {
+			this._fade.init();
+			this._translate.init();
+			return this;
+		}
+	}, {
+		key: 'isAnimating',
+		value: function isAnimating() {
+			return this._animating;
+		}
+	}, {
+		key: 'enable',
+		value: function enable() {
+			this._animation && this._animation.enable();
+		}
+	}, {
+		key: 'disable',
+		value: function disable() {
+			this._animation && this._animation.disable();
+		}
+	}, {
+		key: 'apply',
+		value: function apply(force, callback) {
+			// If we don't force refresh and animation in progress then return
+			if (this._animating && !force) {
+				return;
+			}
+
+			switch (this.options.effect) {
+				case 'fade':
+					this._animation = this._fade;
+					break;
+				case 'translate':
+				default:
+					this._animation = this._translate;
+					break;
+			}
+
+			this._animationCallback = callback;
+
+			if (force) {
+				this._animation && this._animation.disable();
+			} else {
+				this._animation && this._animation.enable();
+				this._animating = true;
+			}
+
+			this._animation && this._animation.apply();
+
+			if (force) {
+				this.end();
+			}
+		}
+	}, {
+		key: 'end',
+		value: function end() {
+			this._animating = false;
+			this._animation = undefined;
+			this.slider.state.index = this.slider.state.next;
+			if (this._animationCallback) {
+				this._animationCallback();
+			}
+		}
+	}]);
+
+	return Transitioner;
+}();
+
+/* harmony default export */ __webpack_exports__["a"] = (Transitioner);
+
+/***/ }),
+/* 20 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_css__ = __webpack_require__(0);
+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 _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+
+
+var Fade = function () {
+	function Fade(transitioner, slider) {
+		var options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};
+
+		_classCallCheck(this, Fade);
+
+		this.transitioner = transitioner;
+		this.slider = slider;
+		this.options = _extends({}, options);
+	}
+
+	_createClass(Fade, [{
+		key: 'init',
+		value: function init() {
+			var _this = this;
+
+			if (this.options.effect === 'fade') {
+				this.slider.slides.forEach(function (slide, index) {
+					Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(slide, {
+						position: 'absolute',
+						left: 0,
+						top: 0,
+						bottom: 0,
+						'z-index': slide.dataset.sliderIndex == _this.slider.state.index ? 0 : -2,
+						opacity: slide.dataset.sliderIndex == _this.slider.state.index ? 1 : 0
+					});
+				});
+			}
+			return this;
+		}
+	}, {
+		key: 'enable',
+		value: function enable() {
+			var _this2 = this;
+
+			this._oldSlide = this.slider.slides.filter(function (slide) {
+				return slide.dataset.sliderIndex == _this2.slider.state.index;
+			})[0];
+			this._newSlide = this.slider.slides.filter(function (slide) {
+				return slide.dataset.sliderIndex == _this2.slider.state.next;
+			})[0];
+			if (this._newSlide) {
+				this._newSlide.addEventListener('transitionend', this.onTransitionEnd.bind(this));
+				this._newSlide.style.transition = this.options.duration + 'ms ' + this.options.timing;
+				if (this._oldSlide) {
+					this._oldSlide.addEventListener('transitionend', this.onTransitionEnd.bind(this));
+					this._oldSlide.style.transition = this.options.duration + 'ms ' + this.options.timing;
+				}
+			}
+		}
+	}, {
+		key: 'disable',
+		value: function disable() {
+			var _this3 = this;
+
+			this._oldSlide = this.slider.slides.filter(function (slide) {
+				return slide.dataset.sliderIndex == _this3.slider.state.index;
+			})[0];
+			this._newSlide = this.slider.slides.filter(function (slide) {
+				return slide.dataset.sliderIndex == _this3.slider.state.next;
+			})[0];
+			if (this._newSlide) {
+				this._newSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this));
+				this._newSlide.style.transition = 'none';
+				if (this._oldSlide) {
+					this._oldSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this));
+					this._oldSlide.style.transition = 'none';
+				}
+			}
+		}
+	}, {
+		key: 'apply',
+		value: function apply(force) {
+			var _this4 = this;
+
+			this._oldSlide = this.slider.slides.filter(function (slide) {
+				return slide.dataset.sliderIndex == _this4.slider.state.index;
+			})[0];
+			this._newSlide = this.slider.slides.filter(function (slide) {
+				return slide.dataset.sliderIndex == _this4.slider.state.next;
+			})[0];
+
+			if (this._oldSlide && this._newSlide) {
+				Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._oldSlide, {
+					opacity: 0
+				});
+				Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._newSlide, {
+					opacity: 1,
+					'z-index': force ? 0 : -1
+				});
+			}
+		}
+	}, {
+		key: 'onTransitionEnd',
+		value: function onTransitionEnd(e) {
+			if (this.options.effect === 'fade') {
+				if (this.transitioner.isAnimating() && e.target == this._newSlide) {
+					if (this._newSlide) {
+						Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._newSlide, {
+							'z-index': 0
+						});
+						this._newSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this));
+					}
+					if (this._oldSlide) {
+						Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._oldSlide, {
+							'z-index': -2
+						});
+						this._oldSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this));
+					}
+				}
+				this.transitioner.end();
+			}
+		}
+	}]);
+
+	return Fade;
+}();
+
+/* harmony default export */ __webpack_exports__["a"] = (Fade);
+
+/***/ }),
+/* 21 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__ = __webpack_require__(4);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_css__ = __webpack_require__(0);
+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 _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+
+
+
+var Translate = function () {
+	function Translate(transitioner, slider) {
+		var options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};
+
+		_classCallCheck(this, Translate);
+
+		this.transitioner = transitioner;
+		this.slider = slider;
+		this.options = _extends({}, options);
+
+		this.onTransitionEnd = this.onTransitionEnd.bind(this);
+	}
+
+	_createClass(Translate, [{
+		key: 'init',
+		value: function init() {
+			this._position = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](this.slider.container.offsetLeft, this.slider.container.offsetTop);
+			this._bindEvents();
+			return this;
+		}
+	}, {
+		key: 'destroy',
+		value: function destroy() {
+			this._unbindEvents();
+		}
+	}, {
+		key: '_bindEvents',
+		value: function _bindEvents() {
+			this.slider.container.addEventListener('transitionend', this.onTransitionEnd);
+		}
+	}, {
+		key: '_unbindEvents',
+		value: function _unbindEvents() {
+			this.slider.container.removeEventListener('transitionend', this.onTransitionEnd);
+		}
+	}, {
+		key: 'enable',
+		value: function enable() {
+			this.slider.container.style.transition = this.options.duration + 'ms ' + this.options.timing;
+		}
+	}, {
+		key: 'disable',
+		value: function disable() {
+			this.slider.container.style.transition = 'none';
+		}
+	}, {
+		key: 'apply',
+		value: function apply() {
+			var _this = this;
+
+			var maxOffset = void 0;
+			if (this.options.effect === 'translate') {
+				var slide = this.slider.slides.filter(function (slide) {
+					return slide.dataset.sliderIndex == _this.slider.state.next;
+				})[0];
+				var slideOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](slide.offsetLeft, slide.offsetTop);
+				if (this.options.centerMode) {
+					maxOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.container)), Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.container)));
+				} else {
+					maxOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.container) - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.wrapper)), Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.container) - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.wrapper)));
+				}
+				var nextOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.min(Math.max(slideOffset.x * -1, maxOffset.x * -1), 0), Math.min(Math.max(slideOffset.y * -1, maxOffset.y * -1), 0));
+				if (this.options.loop) {
+					if (!this.options.vertical && Math.abs(this._position.x) > maxOffset.x) {
+						nextOffset.x = 0;
+						this.slider.state.next = 0;
+					} else if (this.options.vertical && Math.abs(this._position.y) > maxOffset.y) {
+						nextOffset.y = 0;
+						this.slider.state.next = 0;
+					}
+				}
+
+				this._position.x = nextOffset.x;
+				this._position.y = nextOffset.y;
+				if (this.options.centerMode) {
+					this._position.x = this._position.x + this.slider.wrapperWidth / 2 - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(slide) / 2;
+				}
+
+				if (this.slider.direction === 'rtl') {
+					this._position.x = -this._position.x;
+					this._position.y = -this._position.y;
+				}
+				this.slider.container.style.transform = 'translate3d(' + this._position.x + 'px, ' + this._position.y + 'px, 0)';
+
+				/**
+     * update the index with the nextIndex only if
+     * the offset of the nextIndex is in the range of the maxOffset
+     */
+				if (slideOffset.x > maxOffset.x) {
+					this.slider.transitioner.end();
+				}
+			}
+		}
+	}, {
+		key: 'onTransitionEnd',
+		value: function onTransitionEnd(e) {
+			if (this.options.effect === 'translate') {
+
+				if (this.transitioner.isAnimating() && e.target == this.slider.container) {
+					if (this.options.infinite) {
+						this.slider._infinite.onTransitionEnd(e);
+					}
+				}
+				this.transitioner.end();
+			}
+		}
+	}]);
+
+	return Translate;
+}();
+
+/* harmony default export */ __webpack_exports__["a"] = (Translate);
+
+/***/ }),
+/* 22 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+var defaultOptions = {
+  initialSlide: 0,
+  slidesToScroll: 1,
+  slidesToShow: 1,
+
+  navigation: true,
+  navigationKeys: true,
+  navigationSwipe: true,
+
+  pagination: true,
+
+  loop: false,
+  infinite: false,
+
+  effect: 'translate',
+  duration: 300,
+  timing: 'ease',
+
+  autoplay: false,
+  autoplaySpeed: 3000,
+  pauseOnHover: true,
+  breakpoints: [{
+    changePoint: 480,
+    slidesToShow: 1,
+    slidesToScroll: 1
+  }, {
+    changePoint: 640,
+    slidesToShow: 2,
+    slidesToScroll: 2
+  }, {
+    changePoint: 768,
+    slidesToShow: 3,
+    slidesToScroll: 3
+  }],
+
+  onReady: null,
+  icons: {
+    'previous': '<svg viewBox="0 0 50 80" xml:space="preserve">\n      <polyline fill="currentColor" stroke-width=".5em" stroke-linecap="round" stroke-linejoin="round" points="45.63,75.8 0.375,38.087 45.63,0.375 "/>\n    </svg>',
+    'next': '<svg viewBox="0 0 50 80" xml:space="preserve">\n      <polyline fill="currentColor" stroke-width=".5em" stroke-linecap="round" stroke-linejoin="round" points="0.375,0.375 45.63,38.087 0.375,75.8 "/>\n    </svg>'
+  }
+};
+
+/* harmony default export */ __webpack_exports__["a"] = (defaultOptions);
+
+/***/ }),
+/* 23 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony default export */ __webpack_exports__["a"] = (function (id) {
+  return "<div id=\"" + id + "\" class=\"slider\" tabindex=\"0\">\n    <div class=\"slider-container\"></div>\n  </div>";
+});
+
+/***/ }),
+/* 24 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony default export */ __webpack_exports__["a"] = (function () {
+  return "<div class=\"slider-item\"></div>";
+});
+
+/***/ })
+/******/ ])["default"];
+});

asset/js/bulma-carousel.min.js

@@ -0,0 +1 @@
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.bulmaCarousel=e():t.bulmaCarousel=e()}("undefined"!=typeof self?self:this,function(){return function(i){var n={};function s(t){if(n[t])return n[t].exports;var e=n[t]={i:t,l:!1,exports:{}};return i[t].call(e.exports,e,e.exports,s),e.l=!0,e.exports}return s.m=i,s.c=n,s.d=function(t,e,i){s.o(t,e)||Object.defineProperty(t,e,{configurable:!1,enumerable:!0,get:i})},s.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return s.d(e,"a",e),e},s.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},s.p="",s(s.s=5)}([function(t,e,i){"use strict";i.d(e,"d",function(){return s}),i.d(e,"e",function(){return r}),i.d(e,"b",function(){return o}),i.d(e,"c",function(){return a}),i.d(e,"a",function(){return l});var n=i(2),s=function(e,t){(t=Array.isArray(t)?t:t.split(" 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t=this;this.stop(),this.slider.options.autoplay&&(this.emit("start",this),this._interval=setInterval(function(){t._hovering&&t.slider.options.pauseOnHover||(!t.slider.options.centerMode&&t.slider.state.next>=t.slider.state.length-t.slider.slidesToShow&&!t.slider.options.loop&&!t.slider.options.infinite?t.stop():t.slider.next())},this.slider.options.autoplaySpeed))}},{key:"stop",value:function(){this._interval=clearInterval(this._interval),this.emit("stop",this)}},{key:"pause",value:function(){var t=this,e=0<arguments.length&&void 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this._currentBreakpoint?this._currentBreakpoint.slidesToScroll:this._defaultBreakpoint.slidesToScroll}},{key:"apply",value:function(){this.slider.state.index>=this.slider.state.length&&0!==this.slider.state.index&&(this.slider.state.index=this.slider.state.index-this._currentBreakpoint.slidesToScroll),this.slider.state.length<=this._currentBreakpoint.slidesToShow&&(this.slider.state.index=0),this.options.loop&&this.slider._loop.init().apply(),this.options.infinite&&this.slider._infinite.init().apply(),this.slider._setDimensions(),this.slider._transitioner.init().apply(!0,this.slider._setHeight.bind(this.slider)),this.slider._setClasses(),this.slider._navigation.refresh(),this.slider._pagination.refresh()}},{key:s,value:function(t){var e=this._getActiveBreakpoint();e.slidesToShow!==this._currentBreakpoint.slidesToShow&&(this._currentBreakpoint=e,this.apply())}}]),e}();e.a=r},function(t,e,i){"use strict";var n=function(){function n(t,e){for(var i=0;i<e.length;i++){var 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n=[],s=0;s<this._infiniteCount+this.slider.state.length;s+=1)n.push(this._cloneSlide(this.slider.slides[s%this.slider.state.length],s+this.slider.state.length));this.slider.slides=[].concat(t,function(t){if(Array.isArray(t)){for(var e=0,i=Array(t.length);e<t.length;e++)i[e]=t[e];return i}return Array.from(t)}(this.slider.slides),n)}return this}},{key:"apply",value:function(){}},{key:"onTransitionEnd",value:function(t){this.slider.options.infinite&&(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(!0)):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(!0)))}},{key:"_cloneSlide",value:function(t,e){var i=t.cloneNode(!0);return i.dataset.sliderIndex=e,i.dataset.cloned=!0,(i.querySelectorAll("[id]")||[]).forEach(function(t){t.setAttribute("id","")}),i}}]),e}();e.a=s},function(t,e,i){"use strict";var n=i(12),s=function(){function n(t,e){for(var i=0;i<e.length;i++){var n=e[i];n.enumerable=n.enumerable||!1,n.configurable=!0,"value"in n&&(n.writable=!0),Object.defineProperty(t,n.key,n)}}return function(t,e,i){return e&&n(t.prototype,e),i&&n(t,i),t}}();var r=function(){function e(t){!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,e),this.slider=t}return s(e,[{key:"init",value:function(){return this}},{key:"apply",value:function(){this.slider.options.loop&&(0<this.slider.state.next?this.slider.state.next<this.slider.state.length?this.slider.state.next>this.slider.state.length-this.slider.slidesToShow&&Object(n.a)(this.slider._slides[this.slider.state.length-1],this.slider.wrapper)?this.slider.state.next=0:this.slider.state.next=Math.min(Math.max(this.slider.state.next,0),this.slider.state.length-this.slider.slidesToShow):this.slider.state.next=0:this.slider.state.next<=0-this.slider.slidesToScroll?this.slider.state.next=this.slider.state.length-this.slider.slidesToShow:this.slider.state.next=0)}}]),e}();e.a=r},function(t,e,i){"use strict";i.d(e,"a",function(){return n});var n=function(t,e){var i=t.getBoundingClientRect();return e=e||document.documentElement,0<=i.top&&0<=i.left&&i.bottom<=(window.innerHeight||e.clientHeight)&&i.right<=(window.innerWidth||e.clientWidth)}},function(t,e,i){"use strict";var n=i(14),s=i(1),r=function(){function n(t,e){for(var i=0;i<e.length;i++){var n=e[i];n.enumerable=n.enumerable||!1,n.configurable=!0,"value"in n&&(n.writable=!0),Object.defineProperty(t,n.key,n)}}return function(t,e,i){return e&&n(t.prototype,e),i&&n(t,i),t}}();var o=function(){function e(t){!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,e),this.slider=t,this._clickEvents=["click","touch"],this._supportsPassive=Object(s.a)(),this.onPreviousClick=this.onPreviousClick.bind(this),this.onNextClick=this.onNextClick.bind(this),this.onKeyUp=this.onKeyUp.bind(this)}return r(e,[{key:"init",value:function(){return this.node=document.createRange().createContextualFragment(Object(n.a)(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(),this}},{key:"destroy",value:function(){this._unbindEvents()}},{key:"_bindEvents",value:function(){var e=this;this.slider.wrapper.addEventListener("keyup",this.onKeyUp),this._clickEvents.forEach(function(t){e._ui.previous.addEventListener(t,e.onPreviousClick),e._ui.next.addEventListener(t,e.onNextClick)})}},{key:"_unbindEvents",value:function(){var e=this;this.slider.wrapper.removeEventListener("keyup",this.onKeyUp),this._clickEvents.forEach(function(t){e._ui.previous.removeEventListener(t,e.onPreviousClick),e._ui.next.removeEventListener(t,e.onNextClick)})}},{key:"onNextClick",value:function(t){this._supportsPassive||t.preventDefault(),this.slider.options.navigation&&this.slider.next()}},{key:"onPreviousClick",value:function(t){this._supportsPassive||t.preventDefault(),this.slider.options.navigation&&this.slider.previous()}},{key:"onKeyUp",value:function(t){this.slider.options.keyNavigation&&("ArrowRight"===t.key||"Right"===t.key?this.slider.next():"ArrowLeft"!==t.key&&"Left"!==t.key||this.slider.previous())}},{key:"refresh",value:function(){this.slider.options.loop||this.slider.options.infinite||(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"),0===this.slider.state.next?(this._ui.previous.classList.add("is-hidden"),this._ui.next.classList.remove("is-hidden")):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")):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"))):(this._ui.previous.classList.add("is-hidden"),this._ui.next.classList.add("is-hidden")))}},{key:"render",value:function(){return this.node}}]),e}();e.a=o},function(t,e,i){"use strict";e.a=function(t){return'<div class="slider-navigation-previous">'+t.previous+'</div>\n<div class="slider-navigation-next">'+t.next+"</div>"}},function(t,e,i){"use strict";var n=i(16),s=i(17),r=i(1),o=function(){function n(t,e){for(var i=0;i<e.length;i++){var n=e[i];n.enumerable=n.enumerable||!1,n.configurable=!0,"value"in n&&(n.writable=!0),Object.defineProperty(t,n.key,n)}}return function(t,e,i){return e&&n(t.prototype,e),i&&n(t,i),t}}();var a=function(){function e(t){!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,e),this.slider=t,this._clickEvents=["click","touch"],this._supportsPassive=Object(r.a)(),this.onPageClick=this.onPageClick.bind(this),this.onResize=this.onResize.bind(this)}return o(e,[{key:"init",value:function(){return this._pages=[],this.node=document.createRange().createContextualFragment(Object(n.a)()),this._ui={container:this.node.firstChild},this._count=Math.ceil((this.slider.state.length-this.slider.slidesToShow)/this.slider.slidesToScroll),this._draw(),this.refresh(),this}},{key:"destroy",value:function(){this._unbindEvents()}},{key:"_bindEvents",value:function(){var i=this;window.addEventListener("resize",this.onResize),window.addEventListener("orientationchange",this.onResize),this._clickEvents.forEach(function(e){i._pages.forEach(function(t){return t.addEventListener(e,i.onPageClick)})})}},{key:"_unbindEvents",value:function(){var i=this;window.removeEventListener("resize",this.onResize),window.removeEventListener("orientationchange",this.onResize),this._clickEvents.forEach(function(e){i._pages.forEach(function(t){return t.removeEventListener(e,i.onPageClick)})})}},{key:"_draw",value:function(){if(this._ui.container.innerHTML="",this.slider.options.pagination&&this.slider.state.length>this.slider.slidesToShow){for(var t=0;t<=this._count;t++){var e=document.createRange().createContextualFragment(Object(s.a)()).firstChild;e.dataset.index=t*this.slider.slidesToScroll,this._pages.push(e),this._ui.container.appendChild(e)}this._bindEvents()}}},{key:"onPageClick",value:function(t){this._supportsPassive||t.preventDefault(),this.slider.state.next=t.currentTarget.dataset.index,this.slider.show()}},{key:"onResize",value:function(){this._draw()}},{key:"refresh",value:function(){var e=this,t=void 0;(t=this.slider.options.infinite?Math.ceil(this.slider.state.length-1/this.slider.slidesToScroll):Math.ceil((this.slider.state.length-this.slider.slidesToShow)/this.slider.slidesToScroll))!==this._count&&(this._count=t,this._draw()),this._pages.forEach(function(t){t.classList.remove("is-active"),parseInt(t.dataset.index,10)===e.slider.state.next%e.slider.state.length&&t.classList.add("is-active")})}},{key:"render",value:function(){return this.node}}]),e}();e.a=a},function(t,e,i){"use strict";e.a=function(){return'<div class="slider-pagination"></div>'}},function(t,e,i){"use strict";e.a=function(){return'<div class="slider-page"></div>'}},function(t,e,i){"use strict";var n=i(4),s=i(1),r=function(){function n(t,e){for(var i=0;i<e.length;i++){var n=e[i];n.enumerable=n.enumerable||!1,n.configurable=!0,"value"in n&&(n.writable=!0),Object.defineProperty(t,n.key,n)}}return function(t,e,i){return e&&n(t.prototype,e),i&&n(t,i),t}}();var o=function(){function e(t){!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,e),this.slider=t,this._supportsPassive=Object(s.a)(),this.onStartDrag=this.onStartDrag.bind(this),this.onMoveDrag=this.onMoveDrag.bind(this),this.onStopDrag=this.onStopDrag.bind(this),this._init()}return r(e,[{key:"_init",value:function(){}},{key:"bindEvents",value:function(){var e=this;this.slider.container.addEventListener("dragstart",function(t){e._supportsPassive||t.preventDefault()}),this.slider.container.addEventListener("mousedown",this.onStartDrag),this.slider.container.addEventListener("touchstart",this.onStartDrag),window.addEventListener("mousemove",this.onMoveDrag),window.addEventListener("touchmove",this.onMoveDrag),window.addEventListener("mouseup",this.onStopDrag),window.addEventListener("touchend",this.onStopDrag),window.addEventListener("touchcancel",this.onStopDrag)}},{key:"unbindEvents",value:function(){var e=this;this.slider.container.removeEventListener("dragstart",function(t){e._supportsPassive||t.preventDefault()}),this.slider.container.removeEventListener("mousedown",this.onStartDrag),this.slider.container.removeEventListener("touchstart",this.onStartDrag),window.removeEventListener("mousemove",this.onMoveDrag),window.removeEventListener("touchmove",this.onMoveDrag),window.removeEventListener("mouseup",this.onStopDrag),window.removeEventListener("mouseup",this.onStopDrag),window.removeEventListener("touchcancel",this.onStopDrag)}},{key:"onStartDrag",value:function(t){if(t.touches){if(1<t.touches.length)return;t=t.touches[0]}this._origin=new n.a(t.screenX,t.screenY),this.width=this.slider.wrapperWidth,this.slider.transitioner.disable()}},{key:"onMoveDrag",value:function(t){if(this._origin){var e=t.touches?t.touches[0]:t;this._lastTranslate=new n.a(e.screenX-this._origin.x,e.screenY-this._origin.y),t.touches&&Math.abs(this._lastTranslate.x)>Math.abs(this._lastTranslate.y)&&(this._supportsPassive||t.preventDefault(),t.stopPropagation())}}},{key:"onStopDrag",value:function(t){this._origin&&this._lastTranslate&&(Math.abs(this._lastTranslate.x)>.2*this.width?this._lastTranslate.x<0?this.slider.next():this.slider.previous():this.slider.show(!0)),this._origin=null,this._lastTranslate=null}}]),e}();e.a=o},function(t,e,i){"use strict";var n=i(20),s=i(21),r=function(){function n(t,e){for(var i=0;i<e.length;i++){var n=e[i];n.enumerable=n.enumerable||!1,n.configurable=!0,"value"in n&&(n.writable=!0),Object.defineProperty(t,n.key,n)}}return function(t,e,i){return e&&n(t.prototype,e),i&&n(t,i),t}}();var o=function(){function e(t){!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,e),this.slider=t,this.options=t.options,this._animating=!1,this._animation=void 0,this._translate=new s.a(this,t,t.options),this._fade=new n.a(this,t,t.options)}return r(e,[{key:"init",value:function(){return this._fade.init(),this._translate.init(),this}},{key:"isAnimating",value:function(){return this._animating}},{key:"enable",value:function(){this._animation&&this._animation.enable()}},{key:"disable",value:function(){this._animation&&this._animation.disable()}},{key:"apply",value:function(t,e){if(!this._animating||t){switch(this.options.effect){case"fade":this._animation=this._fade;break;case"translate":default:this._animation=this._translate}this._animationCallback=e,t?this._animation&&this._animation.disable():(this._animation&&this._animation.enable(),this._animating=!0),this._animation&&this._animation.apply(),t&&this.end()}}},{key:"end",value:function(){this._animating=!1,this._animation=void 0,this.slider.state.index=this.slider.state.next,this._animationCallback&&this._animationCallback()}}]),e}();e.a=o},function(t,e,i){"use strict";var s=i(0),r=Object.assign||function(t){for(var e=1;e<arguments.length;e++){var i=arguments[e];for(var n in i)Object.prototype.hasOwnProperty.call(i,n)&&(t[n]=i[n])}return t},o=function(){function n(t,e){for(var i=0;i<e.length;i++){var n=e[i];n.enumerable=n.enumerable||!1,n.configurable=!0,"value"in n&&(n.writable=!0),Object.defineProperty(t,n.key,n)}}return function(t,e,i){return e&&n(t.prototype,e),i&&n(t,i),t}}();var n=function(){function n(t,e){var i=2<arguments.length&&void 0!==arguments[2]?arguments[2]:{};!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,n),this.transitioner=t,this.slider=e,this.options=r({},i)}return o(n,[{key:"init",value:function(){var i=this;return"fade"===this.options.effect&&this.slider.slides.forEach(function(t,e){Object(s.a)(t,{position:"absolute",left:0,top:0,bottom:0,"z-index":t.dataset.sliderIndex==i.slider.state.index?0:-2,opacity:t.dataset.sliderIndex==i.slider.state.index?1:0})}),this}},{key:"enable",value:function(){var e=this;this._oldSlide=this.slider.slides.filter(function(t){return t.dataset.sliderIndex==e.slider.state.index})[0],this._newSlide=this.slider.slides.filter(function(t){return t.dataset.sliderIndex==e.slider.state.next})[0],this._newSlide&&(this._newSlide.addEventListener("transitionend",this.onTransitionEnd.bind(this)),this._newSlide.style.transition=this.options.duration+"ms "+this.options.timing,this._oldSlide&&(this._oldSlide.addEventListener("transitionend",this.onTransitionEnd.bind(this)),this._oldSlide.style.transition=this.options.duration+"ms "+this.options.timing))}},{key:"disable",value:function(){var e=this;this._oldSlide=this.slider.slides.filter(function(t){return t.dataset.sliderIndex==e.slider.state.index})[0],this._newSlide=this.slider.slides.filter(function(t){return t.dataset.sliderIndex==e.slider.state.next})[0],this._newSlide&&(this._newSlide.removeEventListener("transitionend",this.onTransitionEnd.bind(this)),this._newSlide.style.transition="none",this._oldSlide&&(this._oldSlide.removeEventListener("transitionend",this.onTransitionEnd.bind(this)),this._oldSlide.style.transition="none"))}},{key:"apply",value:function(t){var e=this;this._oldSlide=this.slider.slides.filter(function(t){return t.dataset.sliderIndex==e.slider.state.index})[0],this._newSlide=this.slider.slides.filter(function(t){return t.dataset.sliderIndex==e.slider.state.next})[0],this._oldSlide&&this._newSlide&&(Object(s.a)(this._oldSlide,{opacity:0}),Object(s.a)(this._newSlide,{opacity:1,"z-index":t?0:-1}))}},{key:"onTransitionEnd",value:function(t){"fade"===this.options.effect&&(this.transitioner.isAnimating()&&t.target==this._newSlide&&(this._newSlide&&(Object(s.a)(this._newSlide,{"z-index":0}),this._newSlide.removeEventListener("transitionend",this.onTransitionEnd.bind(this))),this._oldSlide&&(Object(s.a)(this._oldSlide,{"z-index":-2}),this._oldSlide.removeEventListener("transitionend",this.onTransitionEnd.bind(this)))),this.transitioner.end())}}]),n}();e.a=n},function(t,e,i){"use strict";var r=i(4),o=i(0),s=Object.assign||function(t){for(var e=1;e<arguments.length;e++){var i=arguments[e];for(var n in i)Object.prototype.hasOwnProperty.call(i,n)&&(t[n]=i[n])}return t},a=function(){function n(t,e){for(var i=0;i<e.length;i++){var n=e[i];n.enumerable=n.enumerable||!1,n.configurable=!0,"value"in n&&(n.writable=!0),Object.defineProperty(t,n.key,n)}}return function(t,e,i){return e&&n(t.prototype,e),i&&n(t,i),t}}();var n=function(){function n(t,e){var i=2<arguments.length&&void 0!==arguments[2]?arguments[2]:{};!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,n),this.transitioner=t,this.slider=e,this.options=s({},i),this.onTransitionEnd=this.onTransitionEnd.bind(this)}return a(n,[{key:"init",value:function(){return this._position=new r.a(this.slider.container.offsetLeft,this.slider.container.offsetTop),this._bindEvents(),this}},{key:"destroy",value:function(){this._unbindEvents()}},{key:"_bindEvents",value:function(){this.slider.container.addEventListener("transitionend",this.onTransitionEnd)}},{key:"_unbindEvents",value:function(){this.slider.container.removeEventListener("transitionend",this.onTransitionEnd)}},{key:"enable",value:function(){this.slider.container.style.transition=this.options.duration+"ms "+this.options.timing}},{key:"disable",value:function(){this.slider.container.style.transition="none"}},{key:"apply",value:function(){var e=this,t=void 0;if("translate"===this.options.effect){var i=this.slider.slides.filter(function(t){return t.dataset.sliderIndex==e.slider.state.next})[0],n=new r.a(i.offsetLeft,i.offsetTop);t=this.options.centerMode?new r.a(Math.round(Object(o.e)(this.slider.container)),Math.round(Object(o.b)(this.slider.container))):new r.a(Math.round(Object(o.e)(this.slider.container)-Object(o.e)(this.slider.wrapper)),Math.round(Object(o.b)(this.slider.container)-Object(o.b)(this.slider.wrapper)));var s=new r.a(Math.min(Math.max(-1*n.x,-1*t.x),0),Math.min(Math.max(-1*n.y,-1*t.y),0));this.options.loop&&(!this.options.vertical&&Math.abs(this._position.x)>t.x?(s.x=0,this.slider.state.next=0):this.options.vertical&&Math.abs(this._position.y)>t.y&&(s.y=0,this.slider.state.next=0)),this._position.x=s.x,this._position.y=s.y,this.options.centerMode&&(this._position.x=this._position.x+this.slider.wrapperWidth/2-Object(o.e)(i)/2),"rtl"===this.slider.direction&&(this._position.x=-this._position.x,this._position.y=-this._position.y),this.slider.container.style.transform="translate3d("+this._position.x+"px, "+this._position.y+"px, 0)",n.x>t.x&&this.slider.transitioner.end()}}},{key:"onTransitionEnd",value:function(t){"translate"===this.options.effect&&(this.transitioner.isAnimating()&&t.target==this.slider.container&&this.options.infinite&&this.slider._infinite.onTransitionEnd(t),this.transitioner.end())}}]),n}();e.a=n},function(t,e,i){"use strict";e.a={initialSlide:0,slidesToScroll:1,slidesToShow:1,navigation:!0,navigationKeys:!0,navigationSwipe:!0,pagination:!0,loop:!1,infinite:!1,effect:"translate",duration:300,timing:"ease",autoplay:!1,autoplaySpeed:3e3,pauseOnHover:!0,breakpoints:[{changePoint:480,slidesToShow:1,slidesToScroll:1},{changePoint:640,slidesToShow:2,slidesToScroll:2},{changePoint:768,slidesToShow:3,slidesToScroll:3}],onReady:null,icons:{previous:'<svg viewBox="0 0 50 80" xml:space="preserve">\n      <polyline fill="currentColor" stroke-width=".5em" stroke-linecap="round" stroke-linejoin="round" points="45.63,75.8 0.375,38.087 45.63,0.375 "/>\n    </svg>',next:'<svg viewBox="0 0 50 80" xml:space="preserve">\n      <polyline fill="currentColor" stroke-width=".5em" stroke-linecap="round" stroke-linejoin="round" points="0.375,0.375 45.63,38.087 0.375,75.8 "/>\n    </svg>'}}},function(t,e,i){"use strict";e.a=function(t){return'<div id="'+t+'" class="slider" tabindex="0">\n    <div class="slider-container"></div>\n  </div>'}},function(t,e,i){"use strict";e.a=function(){return'<div class="slider-item"></div>'}}]).default});

asset/js/bulma-slider.js

@@ -0,0 +1,461 @@
+(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["bulmaSlider"] = factory();
+	else
+		root["bulmaSlider"] = factory();
+})(typeof self !== 'undefined' ? self : this, function() {
+return /******/ (function(modules) { // webpackBootstrap
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+/******/ })
+/************************************************************************/
+/******/ ([
+/* 0 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+Object.defineProperty(__webpack_exports__, "__esModule", { value: true });
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "isString", function() { return isString; });
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__events__ = __webpack_require__(1);
+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; }; }();
+
+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; };
+
+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 isString = function isString(unknown) {
+  return typeof unknown === 'string' || !!unknown && (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && Object.prototype.toString.call(unknown) === '[object String]';
+};
+
+var bulmaSlider = function (_EventEmitter) {
+  _inherits(bulmaSlider, _EventEmitter);
+
+  function bulmaSlider(selector) {
+    var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+
+    _classCallCheck(this, bulmaSlider);
+
+    var _this = _possibleConstructorReturn(this, (bulmaSlider.__proto__ || Object.getPrototypeOf(bulmaSlider)).call(this));
+
+    _this.element = typeof selector === 'string' ? 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'];
+    /// Set default options and merge with instance defined
+    _this.options = _extends({}, options);
+
+    _this.onSliderInput = _this.onSliderInput.bind(_this);
+
+    _this.init();
+    return _this;
+  }
+
+  /**
+   * Initiate all DOM element containing selector
+   * @method
+   * @return {Array} Array of all slider instances
+   */
+
+
+  _createClass(bulmaSlider, [{
+    key: 'init',
+
+
+    /**
+     * Initiate plugin
+     * @method init
+     * @return {void}
+     */
+    value: function init() {
+      this._id = 'bulmaSlider' + new Date().getTime() + Math.floor(Math.random() * Math.floor(9999));
+      this.output = this._findOutputForSlider();
+
+      this._bindEvents();
+
+      if (this.output) {
+        if (this.element.classList.contains('has-output-tooltip')) {
+          // Get new output position
+          var newPosition = this._getSliderOutputPosition();
+
+          // Set output position
+          this.output.style['left'] = newPosition.position;
+        }
+      }
+
+      this.emit('bulmaslider:ready', this.element.value);
+    }
+  }, {
+    key: '_findOutputForSlider',
+    value: function _findOutputForSlider() {
+      var _this2 = this;
+
+      var result = null;
+      var outputs = document.getElementsByTagName('output') || [];
+
+      Array.from(outputs).forEach(function (output) {
+        if (output.htmlFor == _this2.element.getAttribute('id')) {
+          result = output;
+          return true;
+        }
+      });
+      return result;
+    }
+  }, {
+    key: '_getSliderOutputPosition',
+    value: function _getSliderOutputPosition() {
+      // Update output position
+      var newPlace, minValue;
+
+      var style = window.getComputedStyle(this.element, null);
+      // Measure width of range input
+      var sliderWidth = parseInt(style.getPropertyValue('width'), 10);
+
+      // Figure out placement percentage between left and right of input
+      if (!this.element.getAttribute('min')) {
+        minValue = 0;
+      } else {
+        minValue = this.element.getAttribute('min');
+      }
+      var newPoint = (this.element.value - minValue) / (this.element.getAttribute('max') - minValue);
+
+      // Prevent bubble from going beyond left or right (unsupported browsers)
+      if (newPoint < 0) {
+        newPlace = 0;
+      } else if (newPoint > 1) {
+        newPlace = sliderWidth;
+      } else {
+        newPlace = sliderWidth * newPoint;
+      }
+
+      return {
+        'position': newPlace + 'px'
+      };
+    }
+
+    /**
+     * Bind all events
+     * @method _bindEvents
+     * @return {void}
+     */
+
+  }, {
+    key: '_bindEvents',
+    value: function _bindEvents() {
+      if (this.output) {
+        // Add event listener to update output when slider value change
+        this.element.addEventListener('input', this.onSliderInput, false);
+      }
+    }
+  }, {
+    key: 'onSliderInput',
+    value: function onSliderInput(e) {
+      e.preventDefault();
+
+      if (this.element.classList.contains('has-output-tooltip')) {
+        // Get new output position
+        var newPosition = this._getSliderOutputPosition();
+
+        // Set output position
+        this.output.style['left'] = newPosition.position;
+      }
+
+      // Check for prefix and postfix
+      var prefix = this.output.hasAttribute('data-prefix') ? this.output.getAttribute('data-prefix') : '';
+      var postfix = this.output.hasAttribute('data-postfix') ? this.output.getAttribute('data-postfix') : '';
+
+      // Update output with slider value
+      this.output.value = prefix + this.element.value + postfix;
+
+      this.emit('bulmaslider:ready', this.element.value);
+    }
+  }], [{
+    key: 'attach',
+    value: function attach() {
+      var _this3 = this;
+
+      var selector = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 'input[type="range"].slider';
+      var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+
+      var instances = new Array();
+
+      var elements = isString(selector) ? document.querySelectorAll(selector) : Array.isArray(selector) ? selector : [selector];
+      elements.forEach(function (element) {
+        if (typeof element[_this3.constructor.name] === 'undefined') {
+          var instance = new bulmaSlider(element, options);
+          element[_this3.constructor.name] = instance;
+          instances.push(instance);
+        } else {
+          instances.push(element[_this3.constructor.name]);
+        }
+      });
+
+      return instances;
+    }
+  }]);
+
+  return bulmaSlider;
+}(__WEBPACK_IMPORTED_MODULE_0__events__["a" /* default */]);
+
+/* harmony default export */ __webpack_exports__["default"] = (bulmaSlider);
+
+/***/ }),
+/* 1 */
+/***/ (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 EventEmitter = function () {
+  function EventEmitter() {
+    var listeners = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : [];
+
+    _classCallCheck(this, EventEmitter);
+
+    this._listeners = new Map(listeners);
+    this._middlewares = new Map();
+  }
+
+  _createClass(EventEmitter, [{
+    key: "listenerCount",
+    value: function listenerCount(eventName) {
+      if (!this._listeners.has(eventName)) {
+        return 0;
+      }
+
+      var eventListeners = this._listeners.get(eventName);
+      return eventListeners.length;
+    }
+  }, {
+    key: "removeListeners",
+    value: function removeListeners() {
+      var _this = this;
+
+      var eventName = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null;
+      var middleware = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false;
+
+      if (eventName !== null) {
+        if (Array.isArray(eventName)) {
+          name.forEach(function (e) {
+            return _this.removeListeners(e, middleware);
+          });
+        } else {
+          this._listeners.delete(eventName);
+
+          if (middleware) {
+            this.removeMiddleware(eventName);
+          }
+        }
+      } else {
+        this._listeners = new Map();
+      }
+    }
+  }, {
+    key: "middleware",
+    value: function middleware(eventName, fn) {
+      var _this2 = this;
+
+      if (Array.isArray(eventName)) {
+        name.forEach(function (e) {
+          return _this2.middleware(e, fn);
+        });
+      } else {
+        if (!Array.isArray(this._middlewares.get(eventName))) {
+          this._middlewares.set(eventName, []);
+        }
+
+        this._middlewares.get(eventName).push(fn);
+      }
+    }
+  }, {
+    key: "removeMiddleware",
+    value: function removeMiddleware() {
+      var _this3 = this;
+
+      var eventName = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null;
+
+      if (eventName !== null) {
+        if (Array.isArray(eventName)) {
+          name.forEach(function (e) {
+            return _this3.removeMiddleware(e);
+          });
+        } else {
+          this._middlewares.delete(eventName);
+        }
+      } else {
+        this._middlewares = new Map();
+      }
+    }
+  }, {
+    key: "on",
+    value: function on(name, callback) {
+      var _this4 = this;
+
+      var once = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false;
+
+      if (Array.isArray(name)) {
+        name.forEach(function (e) {
+          return _this4.on(e, callback);
+        });
+      } else {
+        name = name.toString();
+        var split = name.split(/,|, | /);
+
+        if (split.length > 1) {
+          split.forEach(function (e) {
+            return _this4.on(e, callback);
+          });
+        } else {
+          if (!Array.isArray(this._listeners.get(name))) {
+            this._listeners.set(name, []);
+          }
+
+          this._listeners.get(name).push({ once: once, callback: callback });
+        }
+      }
+    }
+  }, {
+    key: "once",
+    value: function once(name, callback) {
+      this.on(name, callback, true);
+    }
+  }, {
+    key: "emit",
+    value: function emit(name, data) {
+      var _this5 = this;
+
+      var silent = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false;
+
+      name = name.toString();
+      var listeners = this._listeners.get(name);
+      var middlewares = null;
+      var doneCount = 0;
+      var execute = silent;
+
+      if (Array.isArray(listeners)) {
+        listeners.forEach(function (listener, index) {
+          // Start Middleware checks unless we're doing a silent emit
+          if (!silent) {
+            middlewares = _this5._middlewares.get(name);
+            // Check and execute Middleware
+            if (Array.isArray(middlewares)) {
+              middlewares.forEach(function (middleware) {
+                middleware(data, function () {
+                  var newData = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null;
+
+                  if (newData !== null) {
+                    data = newData;
+                  }
+                  doneCount++;
+                }, name);
+              });
+
+              if (doneCount >= middlewares.length) {
+                execute = true;
+              }
+            } else {
+              execute = true;
+            }
+          }
+
+          // If Middleware checks have been passed, execute
+          if (execute) {
+            if (listener.once) {
+              listeners[index] = null;
+            }
+            listener.callback(data);
+          }
+        });
+
+        // Dirty way of removing used Events
+        while (listeners.indexOf(null) !== -1) {
+          listeners.splice(listeners.indexOf(null), 1);
+        }
+      }
+    }
+  }]);
+
+  return EventEmitter;
+}();
+
+/* harmony default export */ __webpack_exports__["a"] = (EventEmitter);
+
+/***/ })
+/******/ ])["default"];
+});

asset/js/bulma-slider.min.js

@@ -0,0 +1 @@
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.bulmaSlider=e():t.bulmaSlider=e()}("undefined"!=typeof self?self:this,function(){return function(n){var r={};function i(t){if(r[t])return r[t].exports;var e=r[t]={i:t,l:!1,exports:{}};return n[t].call(e.exports,e,e.exports,i),e.l=!0,e.exports}return i.m=n,i.c=r,i.d=function(t,e,n){i.o(t,e)||Object.defineProperty(t,e,{configurable:!1,enumerable:!0,get:n})},i.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return i.d(e,"a",e),e},i.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},i.p="",i(i.s=0)}([function(t,e,n){"use strict";Object.defineProperty(e,"__esModule",{value:!0}),n.d(e,"isString",function(){return l});var r=n(1),i=Object.assign||function(t){for(var e=1;e<arguments.length;e++){var n=arguments[e];for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&(t[r]=n[r])}return t},u=function(){function r(t,e){for(var n=0;n<e.length;n++){var r=e[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(t,r.key,r)}}return function(t,e,n){return e&&r(t.prototype,e),n&&r(t,n),t}}(),o="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t};var l=function(t){return"string"==typeof t||!!t&&"object"===(void 0===t?"undefined":o(t))&&"[object String]"===Object.prototype.toString.call(t)},a=function(t){function o(t){var e=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{};!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,o);var n=function(t,e){if(!t)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return!e||"object"!=typeof e&&"function"!=typeof e?t:e}(this,(o.__proto__||Object.getPrototypeOf(o)).call(this));if(n.element="string"==typeof t?document.querySelector(t):t,!n.element)throw new Error("An invalid selector or non-DOM node has been provided.");return n._clickEvents=["click"],n.options=i({},e),n.onSliderInput=n.onSliderInput.bind(n),n.init(),n}return function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Super expression must either be null or a function, not "+typeof e);t.prototype=Object.create(e&&e.prototype,{constructor:{value:t,enumerable:!1,writable:!0,configurable:!0}}),e&&(Object.setPrototypeOf?Object.setPrototypeOf(t,e):t.__proto__=e)}(o,r["a"]),u(o,[{key:"init",value:function(){if(this._id="bulmaSlider"+(new Date).getTime()+Math.floor(Math.random()*Math.floor(9999)),this.output=this._findOutputForSlider(),this._bindEvents(),this.output&&this.element.classList.contains("has-output-tooltip")){var t=this._getSliderOutputPosition();this.output.style.left=t.position}this.emit("bulmaslider:ready",this.element.value)}},{key:"_findOutputForSlider",value:function(){var e=this,n=null,t=document.getElementsByTagName("output")||[];return Array.from(t).forEach(function(t){if(t.htmlFor==e.element.getAttribute("id"))return n=t,!0}),n}},{key:"_getSliderOutputPosition",value:function(){var t,e=window.getComputedStyle(this.element,null),n=parseInt(e.getPropertyValue("width"),10);t=this.element.getAttribute("min")?this.element.getAttribute("min"):0;var r=(this.element.value-t)/(this.element.getAttribute("max")-t);return{position:(r<0?0:1<r?n:n*r)+"px"}}},{key:"_bindEvents",value:function(){this.output&&this.element.addEventListener("input",this.onSliderInput,!1)}},{key:"onSliderInput",value:function(t){if(t.preventDefault(),this.element.classList.contains("has-output-tooltip")){var e=this._getSliderOutputPosition();this.output.style.left=e.position}var n=this.output.hasAttribute("data-prefix")?this.output.getAttribute("data-prefix"):"",r=this.output.hasAttribute("data-postfix")?this.output.getAttribute("data-postfix"):"";this.output.value=n+this.element.value+r,this.emit("bulmaslider:ready",this.element.value)}}],[{key:"attach",value:function(){var n=this,t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:'input[type="range"].slider',r=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{},i=new Array;return(l(t)?document.querySelectorAll(t):Array.isArray(t)?t:[t]).forEach(function(t){if(void 0===t[n.constructor.name]){var e=new o(t,r);t[n.constructor.name]=e,i.push(e)}else i.push(t[n.constructor.name])}),i}}]),o}();e.default=a},function(t,e,n){"use strict";var r=function(){function r(t,e){for(var n=0;n<e.length;n++){var r=e[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(t,r.key,r)}}return function(t,e,n){return e&&r(t.prototype,e),n&&r(t,n),t}}();var i=function(){function e(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:[];!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,e),this._listeners=new Map(t),this._middlewares=new Map}return r(e,[{key:"listenerCount",value:function(t){return this._listeners.has(t)?this._listeners.get(t).length:0}},{key:"removeListeners",value:function(){var e=this,t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:null,n=1<arguments.length&&void 0!==arguments[1]&&arguments[1];null!==t?Array.isArray(t)?name.forEach(function(t){return e.removeListeners(t,n)}):(this._listeners.delete(t),n&&this.removeMiddleware(t)):this._listeners=new Map}},{key:"middleware",value:function(t,e){var n=this;Array.isArray(t)?name.forEach(function(t){return n.middleware(t,e)}):(Array.isArray(this._middlewares.get(t))||this._middlewares.set(t,[]),this._middlewares.get(t).push(e))}},{key:"removeMiddleware",value:function(){var e=this,t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:null;null!==t?Array.isArray(t)?name.forEach(function(t){return e.removeMiddleware(t)}):this._middlewares.delete(t):this._middlewares=new Map}},{key:"on",value:function(t,e){var n=this,r=2<arguments.length&&void 0!==arguments[2]&&arguments[2];if(Array.isArray(t))t.forEach(function(t){return n.on(t,e)});else{var i=(t=t.toString()).split(/,|, | /);1<i.length?i.forEach(function(t){return n.on(t,e)}):(Array.isArray(this._listeners.get(t))||this._listeners.set(t,[]),this._listeners.get(t).push({once:r,callback:e}))}}},{key:"once",value:function(t,e){this.on(t,e,!0)}},{key:"emit",value:function(n,r){var i=this,o=2<arguments.length&&void 0!==arguments[2]&&arguments[2];n=n.toString();var u=this._listeners.get(n),l=null,a=0,s=o;if(Array.isArray(u))for(u.forEach(function(t,e){o||(l=i._middlewares.get(n),Array.isArray(l)?(l.forEach(function(t){t(r,function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:null;null!==t&&(r=t),a++},n)}),a>=l.length&&(s=!0)):s=!0),s&&(t.once&&(u[e]=null),t.callback(r))});-1!==u.indexOf(null);)u.splice(u.indexOf(null),1)}}]),e}();e.a=i}]).default});

asset/js/index.js

@@ -0,0 +1,21 @@
+window.HELP_IMPROVE_VIDEOJS = false;
+
+
+$(document).ready(function() {
+    // Check for click events on the navbar burger icon
+
+    var options = {
+			slidesToScroll: 1,
+			slidesToShow: 1,
+			loop: true,
+			infinite: true,
+			autoplay: true,
+			autoplaySpeed: 5000,
+    }
+
+		// Initialize all div with carousel class
+    var carousels = bulmaCarousel.attach('.carousel', options);
+	
+    bulmaSlider.attach();
+
+})

index.html

@@ -0,0 +1,274 @@
+<!DOCTYPE html>
+<html>
+<head>
+  <meta charset="utf-8">
+  <!-- Meta tags for social media banners, these should be filled in appropriatly as they are your "business card" -->
+  <!-- Replace the content tag with appropriate information -->
+  <meta name="description" content="Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Tasks">
+  <meta property="og:title" content="Any2AnyTryon"/>
+  <meta property="og:description" content="A versatile framework for virtual clothing tasks including garment reconstruction, model-free virtual try-on and virtual try-on"/>
+  <meta property="og:url" content="https://logn-2024.github.io/Any2anyTryon/"/>
+  <!-- Path to banner image, should be in the path listed below. Optimal dimenssions are 1200X630-->
+  <meta property="og:image" content="asset/image/teaser.png" />
+  <meta property="og:image:width" content="1200"/>
+  <meta property="og:image:height" content="630"/>
+
+
+  <meta name="twitter:title" content="Any2AnyTryon">
+  <meta name="twitter:description" content="A versatile framework for virtual clothing tasks">
+  <!-- Path to banner image, should be in the path listed below. Optimal dimenssions are 1200X600-->
+  <meta name="twitter:image" content="asset/images/teaser.png">
+  <meta name="twitter:card" content="summary_large_image">
+  <!-- Keywords for your paper to be indexed by-->
+  <meta name="keywords" content="virtual try-on, garment reconstruction, diffusion models">
+  <meta name="viewport" content="width=device-width, initial-scale=1">
+
+
+  <title>Any2AnyTryon</title>
+  <link rel="icon" type="image/x-icon" href="asset/images/favicon.ico">
+  <link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
+  rel="stylesheet">
+
+  <link rel="stylesheet" href="asset/css/bulma.min.css">
+  <link rel="stylesheet" href="asset/css/bulma-carousel.min.css">
+  <link rel="stylesheet" href="asset/css/bulma-slider.min.css">
+  <link rel="stylesheet" href="asset/css/fontawesome.all.min.css">
+  <link rel="stylesheet"
+  href="https://cdn.jsdelivr.net/gh/jpswalsh/academicons@1/css/academicons.min.css">
+  <link rel="stylesheet" href="asset/css/index.css">
+
+  <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
+  <script src="https://documentcloud.adobe.com/view-sdk/main.js"></script>
+  <script defer src="asset/js/fontawesome.all.min.js"></script>
+  <script src="asset/js/bulma-carousel.min.js"></script>
+  <script src="asset/js/bulma-slider.min.js"></script>
+  <script src="asset/js/index.js"></script>
+</head>
+<body>
+
+
+  <section class="hero">
+    <div class="hero-body">
+      <div class="container is-max-desktop">
+        <div class="columns is-centered">
+          <div class="column has-text-centered">
+            <h1 class="title is-1 publication-title">Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Tasks</h1>
+            <div class="is-size-5 publication-authors">
+              <!-- Paper authors -->
+              <span class="author-block">
+                <a href="https://github.com/logn-2024" target="_blank">Hailong Guo</a><sup>1</sup>,</span>
+                <span class="author-block">
+                  <a href="#" target="_blank">Bohan Zeng</a><sup>2</sup>,</span>
+                  <span class="author-block">
+                    <a href="#" target="_blank">Yiren Song</a><sup>3</sup>
+                  </span>
+                  <span class="author-block">
+                    <a href="#" target="_blank">Wentao Zhang</a><sup>2</sup>
+                  </span>
+                  <span class="author-block">
+                    <a href="#" target="_blank">Chuang Zhang</a><sup>1</sup>
+                  </span>
+                  <span class="author-block">
+                    <a href="#" target="_blank">Jiaming Liu</a><sup>4</sup>
+                  </span>
+                  </div>
+
+                  <div class="is-size-5 publication-authors">
+                    <span class="author-block"><br><sup>1</sup>Beijing University of Posts and Telecommunications</span>
+                    <span class="author-block"><br><sup>2</sup>Peking University</span><br>
+                    <span class="author-block"><br><sup>3</sup>National University of Singapore</span>
+                    <span class="author-block"><br><sup>4</sup>TiamatAI</span>
+                  </div>
+
+                  <!-- Github link -->
+                  <span class="link-block">
+                    <a href="https://github.com/logn-2024/Any2anyTryon" target="_blank"
+                    class="external-link button is-normal is-rounded is-dark">
+                    <span class="icon">
+                      <i class="fab fa-github"></i>
+                    </span>
+                    <span>Code</span>
+                  </a>
+                </span>
+
+                <!-- ArXiv abstract Link -->
+                <span class="link-block">
+                  <a href="https://arxiv.org/abs/2501.15891" target="_blank"
+                  class="external-link button is-normal is-rounded is-dark">
+                  <span class="icon">
+                    <i class="ai ai-arxiv"></i>
+                  </span>
+                  <span>arXiv</span>
+                </a>
+              </span>
+
+                <!-- Demo link -->
+                <span class="link-block">
+                  <a href="https://huggingface.co/spaces/jamesliu1217/Any2anyTryon_exp" target="_blank"
+                  class="external-link button is-normal is-rounded is-dark">
+                  <span class="icon">
+                    <i class="fas fa-gamepad"></i>
+                  </span>
+                  <span>Demo</span>
+                </a>
+              </span>
+
+                  <!-- Models link -->
+                  <span class="link-block">
+                    <a href="https://huggingface.co/loooooong/Any2anyTryon" target="_blank"
+                    class="external-link button is-normal is-rounded is-dark">
+                    <span class="icon">
+                      <i class="fas fa-cube"></i>
+                    </span>
+                    <span>Models</span>
+                  </a>
+                </span>
+
+
+            </div>
+          </div>
+        </div>
+      </div>
+    </div>
+  </div>
+</section>
+
+
+<!-- Paper abstract -->
+<section class="section hero is-light">
+  <div class="container is-max-desktop">
+    <div class="columns is-centered has-text-centered">
+      <div class="column is-four-fifths">
+        <h2 class="title is-3">Abstract</h2>
+        <div class="content has-text-justified">
+          <p>
+Image-based virtual try-on (VTON) aims to generate a virtual try-on result by transferring an input garment onto a target person's image. However, the scarcity of paired garment-model data makes it challenging for existing methods to achieve high generalization and quality in VTON. Also, it limits the ability to generate mask-free try-ons. To tackle the data scarcity problem, approaches such as Stable Garment and MMTryon use a synthetic data strategy, effectively increasing the amount of paired data on the model side. However, existing methods are typically limited to performing specific try-on tasks and lack user-friendliness.
+To enhance the generalization and controllability of VTON generation, we propose Any2AnyTryon, which can generate try-on results based on different textual instructions and model garment images to meet various needs, eliminating the reliance on masks, poses, or other conditions. Specifically, we first construct the virtual try-on dataset LAION-Garment, the largest known open-source garment try-on dataset. Then, we introduce adaptive position embedding, which enables the model to generate satisfactory outfitted model images or garment images based on input images of different sizes and categories, significantly enhancing the generalization and controllability of VTON generation. In our experiments, we demonstrate the effectiveness of our Any2AnyTryon and compare it with existing methods. The results show that Any2AnyTryon enables flexible, controllable, and high-quality image-based virtual try-on generation.            
+          </p>
+        </div>
+      </div>
+    </div>
+  </div>
+</section>
+<!-- End paper abstract -->
+
+<!-- Image carousel -->
+<section class="hero is-small" style="display: none;">
+  <div class="hero-body">
+    <div class="container">
+      <h3 class="title is-4">Garment Reconstruction</h3>
+      <div id="results-garment" class="carousel results-carousel">
+       <div class="item">
+        <!-- Your image here -->
+        <img src="asset/images/supp_tryoff_wild.png" alt="Garment Reconstruction Results"/>
+        <h2 class="subtitle has-text-centered">
+          Garment reconstruction results in the wild.
+        </h2>
+      </div>
+  </div>
+</div>
+</div>
+</section>
+<!-- End image carousel -->
+
+<!-- Image carousel -->
+<section class="hero is-small">
+  <div class="hero-body">
+    <div class="container">
+      <h3 class="title is-4">Garment Reconstruction</h3>
+      <div id="results-garment" class="carousel results-carousel">
+       <div class="item">
+        <!-- Your image here style="max-height: 600px; width: auto;" -->
+        <img src="asset/images/supp_tryoff_wild.png" alt="Garment Reconstruction Results" />
+        <h2 class="subtitle has-text-centered">
+          Garment reconstruction results in the wild.
+        </h2>
+      </div>
+  </div>
+</div>
+</div>
+</section>
+<!-- End image carousel -->
+
+<!-- Image carousel -->
+<section class="hero is-small">
+  <div class="hero-body">
+    <div class="container">
+      <h3 class="title is-4">Model-free Virtual Try-on</h3>
+      <div id="results-model" class="carousel results-carousel">
+       <div class="item">
+        <!-- Your image here -->
+        <img src="asset/images/model_generation_supp.png" alt="Model-free VTON Results"/>
+        <h2 class="subtitle has-text-centered">
+          Model-free virtual tryon results
+        </h2>
+      </div>
+  </div>
+</div>
+</div>
+</section>
+<!-- End image carousel -->
+
+<!-- Image carousel -->
+<section class="hero is-small">
+  <div class="hero-body">
+    <div class="container">
+      <h3 class="title is-4">Virtual Try-on</h3>
+      <div id="results-vton" class="carousel results-carousel">
+       <div class="item">
+        <!-- Your image here -->
+        <img src="asset/images/tryon_compare.png" alt="VTON Results"/>
+        <h2 class="subtitle has-text-centered">
+          Virtual tryon results in the shop
+        </h2>
+      </div>
+  </div>
+</div>
+</div>
+</section>
+<!-- End image carousel -->
+
+
+
+<!--BibTex citation -->
+  <section class="section" id="BibTeX">
+    <div class="container is-max-desktop content">
+      <h2 class="title">BibTeX</h2>
+      <pre><code>@misc{guo2025any2anytryonleveragingadaptiveposition,
+        title={Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Tasks}, 
+        author={Hailong Guo and Bohan Zeng and Yiren Song and Wentao Zhang and Chuang Zhang and Jiaming Liu},
+        year={2025},
+        eprint={2501.15891},
+        archivePrefix={arXiv},
+        primaryClass={cs.CV},
+        url={https://arxiv.org/abs/2501.15891}, 
+  }</code></pre>
+    </div>
+</section>
+<!--End BibTex citation -->
+
+
+  <footer class="footer">
+  <div class="container">
+    <div class="columns is-centered">
+      <div class="column is-8">
+        <div class="content">
+
+          <p>
+            Website template from <a href="https://github.com/eliahuhorwitz/Academic-project-page-template">Academic Project Page Template</a>
+          </p>
+
+        </div>
+      </div>
+    </div>
+  </div>
+</footer>
+
+<!-- Statcounter tracking code -->
+  
+<!-- You can add a tracker to track page visits by creating an account at statcounter.com -->
+
+    <!-- End of Statcounter Code -->
+
+  </body>
+  </html>

infer.py

@@ -0,0 +1,161 @@
+import torch
+import numpy as np
+from PIL import Image
+import argparse
+import os
+
+from diffusers import FluxTransformer2DModel, FluxPipeline
+from transformers import T5EncoderModel, CLIPTextModel
+from diffusers import FluxInpaintPipeline, AutoencoderKL
+from diffusers.hooks import apply_group_offloading
+from src.pipeline_tryon import FluxTryonPipeline, crop_to_multiple_of_16, resize_and_pad_to_size, resize_by_height
+
+def load_models(model_path, lora_name=None, device="cuda", torch_dtype=torch.bfloat16, group_offloading=False):
+    text_encoder = CLIPTextModel.from_pretrained(model_path, subfolder="text_encoder", torch_dtype=torch_dtype)
+    text_encoder_2 = T5EncoderModel.from_pretrained(model_path, subfolder="text_encoder_2", torch_dtype=torch_dtype)
+    transformer = FluxTransformer2DModel.from_pretrained(model_path, subfolder="transformer", torch_dtype=torch_dtype)
+    vae = AutoencoderKL.from_pretrained(model_path, subfolder="vae", torch_dtype=torch_dtype)
+
+    pipe = FluxTryonPipeline.from_pretrained(
+        model_path,
+        transformer=transformer,
+        text_encoder=text_encoder,
+        text_encoder_2=text_encoder_2,
+        vae=vae,
+        torch_dtype=torch_dtype,
+    )
+    pipe.enable_attention_slicing()
+    pipe.vae.enable_slicing()
+    pipe.vae.enable_tiling()
+
+    if lora_name is not None:
+        pipe.enable_model_cpu_offload()
+        pipe.load_lora_weights(
+            "loooooong/Any2anyTryon",
+            weight_name=lora_name,
+            adapter_name="tryon",
+        )
+        pipe.remove_all_hooks()
+    
+    if group_offloading:
+        # https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux#group-offloading
+        apply_group_offloading(
+            pipe.transformer,
+            offload_type="leaf_level",
+            offload_device=torch.device("cpu"),
+            onload_device=torch.device(device),
+            use_stream=True,
+        )
+        apply_group_offloading(
+            pipe.text_encoder, 
+            offload_device=torch.device("cpu"),
+            onload_device=torch.device(device),
+            offload_type="leaf_level",
+            use_stream=True,
+        )
+        apply_group_offloading(
+            pipe.text_encoder_2, 
+            offload_device=torch.device("cpu"),
+            onload_device=torch.device(device),
+            offload_type="leaf_level",
+            use_stream=True,
+        )
+        apply_group_offloading(
+            pipe.vae, 
+            offload_device=torch.device("cpu"),
+            onload_device=torch.device(device),
+            offload_type="leaf_level",
+            use_stream=True,
+        )
+    pipe.to(device=device)
+    return pipe
+
+@torch.no_grad()
+def generate_image(pipe, model_image_path, garment_image_path, prompt="", height=512, width=384, 
+                  seed=0, guidance_scale=3.5, num_inference_steps=30):
+    height, width = int(height), int(width)
+    width = width - (width % 16)  
+    height = height - (height % 16)
+
+    concat_image_list = [Image.fromarray(np.zeros((height, width, 3), dtype=np.uint8))]
+    has_model_image = model_image_path is not None
+    has_garment_image = garment_image_path is not None
+
+    if has_model_image:
+        model_image = Image.open(model_image_path)
+        if has_garment_image:
+            input_height, input_width = model_image.size[1], model_image.size[0]
+            model_image, lp, tp, rp, bp = resize_and_pad_to_size(model_image, width, height)
+        else:
+            model_image = resize_by_height(model_image, height)
+        concat_image_list.append(model_image)
+
+    if has_garment_image:
+        garment_image = Image.open(garment_image_path)
+        garment_image = resize_by_height(garment_image, height)
+        concat_image_list.append(garment_image)
+
+    image = Image.fromarray(np.concatenate([np.array(img) for img in concat_image_list], axis=1))
+    
+    mask = np.zeros_like(np.array(image))
+    mask[:,:width] = 255
+    mask_image = Image.fromarray(mask)
+    
+    image = pipe(
+        prompt,
+        image=image,
+        mask_image=mask_image,
+        strength=1.,
+        height=height,
+        width=image.width,
+        target_width=width,
+        tryon=has_model_image and has_garment_image,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        max_sequence_length=512,
+        generator=torch.Generator("cpu").manual_seed(seed),
+        output_type="pil",
+    ).images[0]
+    
+    if has_model_image and has_garment_image:
+        image = image.crop((lp, tp, image.width-rp, image.height-bp)).resize((input_width, input_height))
+    
+    return image
+
+def main():
+    parser = argparse.ArgumentParser(description='Virtual Try-on Image Generation')
+    parser.add_argument('--model_path', type=str, default="black-forest-labs/FLUX.1-dev", help='Path to the model')
+    parser.add_argument('--lora_name', type=str, default="dev_lora_any2any_alltasks.safetensors", help='choose from dev_lora_any2any_alltasks.safetensors, dev_lora_any2any_tryon.safetensors and dev_lora_garment_reconstruction.safetensors')
+    parser.add_argument('--model_image', type=str, help='Path to the model image')
+    parser.add_argument('--garment_image', type=str, help='Path to the garment image')
+    parser.add_argument('--prompt', type=str, default="")
+    parser.add_argument('--height', type=int, default=576)
+    parser.add_argument('--width', type=int, default=576)
+    parser.add_argument('--seed', type=int, default=0)
+    parser.add_argument('--guidance_scale', type=float, default=3.5)
+    parser.add_argument('--num_inference_steps', type=int, default=30)
+    parser.add_argument('--output_path', type=str, default='./results/output.png')
+    parser.add_argument('--device', type=str, default='cuda')
+    parser.add_argument('--group_offloading', action="store_true")
+    
+    args = parser.parse_args()
+    
+    pipe = load_models(args.model_path, lora_name=args.lora_name, device=args.device,group_offloading=args.group_offloading)
+    
+    output_image = generate_image(
+        pipe=pipe,
+        model_image_path=args.model_image,
+        garment_image_path=args.garment_image,
+        prompt=args.prompt,
+        height=args.height,
+        width=args.width,
+        seed=args.seed,
+        guidance_scale=args.guidance_scale,
+        num_inference_steps=args.num_inference_steps
+    )
+    os.makedirs(os.path.dirname(args.output_path), exist_ok=True)
+    output_image.save(args.output_path)
+    print(f"Generated image saved to {args.output_path}")
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,11 @@
+accelerate==1.2.1
+optimum-quanto==0.2.6
+peft==0.14.0
+sentencepiece==0.2.0
+transformers==4.44.2
+diffusers @ git+https://github.com/huggingface/diffusers@dc62e6931e3aac1d78375928693186764dcb8492
+datasets
+pandas==2.2.3
+gradio
+pillow==11.1.0
+protobuf

src/download_data.py

@@ -0,0 +1,40 @@
+import os
+import io
+from PIL import Image
+import pandas as pd
+from datasets import load_dataset
+
+def save_images_from_parquet(parquet_path, target_folder):
+    """
+    Load parquet file using datasets, read each row's BYTES and FILENAME, 
+    and save the image to the target folder with the name FILENAME.
+    
+    Args:
+        parquet_path: Path to the input parquet file
+        target_folder: Path to the target folder to save images
+    """
+    # Load the parquet file
+    dataset = load_dataset('loooooong/Any2anyTryon_vitonhd_test', data_files=parquet_path)['train']
+    
+    # Ensure target folder exists
+    os.makedirs(target_folder, exist_ok=True)
+    
+    # Process each row
+    for row in dataset:
+        try:
+            # Read image bytes and filename
+            image = row['BYTES']
+            filename = row['FILENAME']
+            # Open image using PIL
+            # image = Image.open(io.BytesIO(image_bytes))
+            
+            # Save image to target folder
+            image.save(os.path.join(target_folder, filename))
+        except Exception as e:
+            print(f"Failed to process row with filename {row['FILENAME']}: {e}")
+
+if __name__ == "__main__":
+    parquet_path = "data/vitonhd_test.parquet"
+    target_folder = "data/zalando-hd-resized/test/image_synthesis"
+    
+    save_images_from_parquet(parquet_path, target_folder)

src/pipeline_tryon.py

@@ -0,0 +1,561 @@
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import numpy as np
+import PIL.Image
+from PIL import Image
+import torch
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
+
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.loaders import FluxLoraLoaderMixin, TextualInversionLoaderMixin
+from diffusers.models.autoencoders import AutoencoderKL
+from diffusers.models.transformers import FluxTransformer2DModel
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    is_torch_xla_available,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
+
+from diffusers.pipelines import FluxInpaintPipeline
+from diffusers.pipelines.flux.pipeline_flux_inpaint import calculate_shift, retrieve_latents, retrieve_timesteps
+
+
+class FluxTryonPipeline(FluxInpaintPipeline): 
+    @staticmethod
+    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._prepare_latent_image_ids
+    def _prepare_latent_image_ids(batch_size, height, width, device, dtype, target_width=-1, tryon=False):
+        latent_image_ids = torch.zeros(height, width, 3)
+        if target_width==-1:
+            latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
+            latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :]
+        else:
+            latent_image_ids[:, target_width:, 0] = 1
+            # height keep as before
+            latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
+            if tryon:
+                latent_image_ids[:, target_width*2:, 0] = 2
+                # left
+                latent_image_ids[:, :target_width, 2] = latent_image_ids[:, :target_width, 2] + torch.arange(target_width)[None, :]
+                # right
+                latent_image_ids[:, target_width:, 2] = latent_image_ids[:, target_width:, 2] + torch.arange(width-target_width)[None, :]
+            else:
+                latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :]                
+
+        latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
+
+        latent_image_ids = latent_image_ids.reshape(
+            latent_image_id_height * latent_image_id_width, latent_image_id_channels
+        )
+
+        return latent_image_ids.to(device=device, dtype=dtype)
+
+
+    def prepare_latents(
+        self,
+        image,
+        timestep,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        target_width,
+        tryon,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        # VAE applies 8x compression on images but we must also account for packing which requires
+        # latent height and width to be divisible by 2.
+        height = 2 * (int(height) // (self.vae_scale_factor * 2))
+        width = 2 * (int(width) // (self.vae_scale_factor * 2))
+        shape = (batch_size, num_channels_latents, height, width)
+        sp = 2 * (int(target_width) // (self.vae_scale_factor * 2))//2 # -1 
+        latent_image_ids = self._prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype, sp, tryon)
+
+        image = image.to(device=device, dtype=dtype)
+        # image_latents = self._encode_vae_image(image=image, generator=generator)
+        img_parts = [image[:,:,:,:target_width], image[:,:,:,target_width:]]
+        image_latents = [self._encode_vae_image(image=img, generator=generator) for img in img_parts]
+        image_latents = torch.cat(image_latents, dim=-1)
+
+        if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
+            # expand init_latents for batch_size
+            additional_image_per_prompt = batch_size // image_latents.shape[0]
+            image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)
+        elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:
+            raise ValueError(
+                f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
+            )
+        else:
+            image_latents = torch.cat([image_latents], dim=0)
+
+        if latents is None:
+            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            latents = self.scheduler.scale_noise(image_latents, timestep, noise)
+        else:
+            noise = latents.to(device)
+            latents = noise
+
+        noise = self._pack_latents(noise, batch_size, num_channels_latents, height, width)
+        image_latents = self._pack_latents(image_latents, batch_size, num_channels_latents, height, width)
+        latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)
+        return latents, noise, image_latents, latent_image_ids
+    
+    def prepare_mask_latents(
+        self,
+        mask,
+        masked_image,
+        batch_size,
+        num_channels_latents,
+        num_images_per_prompt,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+    ):
+        # VAE applies 8x compression on images but we must also account for packing which requires
+        # latent height and width to be divisible by 2.
+        height = 2 * (int(height) // (self.vae_scale_factor * 2))
+        width = 2 * (int(width) // (self.vae_scale_factor * 2))
+        # resize the mask to latents shape as we concatenate the mask to the latents
+        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
+        # and half precision
+        mask = torch.nn.functional.interpolate(mask, size=(height, width), mode="nearest")
+        mask = mask.to(device=device, dtype=dtype)
+
+        batch_size = batch_size * num_images_per_prompt
+
+        masked_image = masked_image.to(device=device, dtype=dtype)
+
+        if masked_image.shape[1] == 16:
+            masked_image_latents = masked_image
+        else:
+            masked_image_latents = retrieve_latents(self.vae.encode(masked_image), generator=generator)
+
+        masked_image_latents = (masked_image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+
+        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
+        if mask.shape[0] < batch_size:
+            if not batch_size % mask.shape[0] == 0:
+                raise ValueError(
+                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
+                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
+                    " of masks that you pass is divisible by the total requested batch size."
+                )
+            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
+        if masked_image_latents.shape[0] < batch_size:
+            if not batch_size % masked_image_latents.shape[0] == 0:
+                raise ValueError(
+                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
+                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
+                    " Make sure the number of images that you pass is divisible by the total requested batch size."
+                )
+            masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)
+
+        # aligning device to prevent device errors when concating it with the latent model input
+        masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
+        masked_image_latents = self._pack_latents(
+            masked_image_latents,
+            batch_size,
+            num_channels_latents,
+            height,
+            width,
+        )
+        mask = self._pack_latents(
+            mask.repeat(1, num_channels_latents, 1, 1),
+            batch_size,
+            num_channels_latents,
+            height,
+            width,
+        )
+
+        return mask, masked_image_latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        image: PipelineImageInput = None,
+        mask_image: PipelineImageInput = None,
+        masked_image_latents: PipelineImageInput = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        target_width: Optional[int] = None,
+        tryon: bool = False,
+        padding_mask_crop: Optional[int] = None,
+        strength: float = 0.6,
+        num_inference_steps: int = 28,
+        timesteps: List[int] = None,
+        guidance_scale: float = 7.0,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 512,
+    ):
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            image,
+            mask_image,
+            strength,
+            height,
+            width,
+            output_type=output_type,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            padding_mask_crop=padding_mask_crop,
+            max_sequence_length=max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._interrupt = False
+
+        # 2. Preprocess mask and image
+        if padding_mask_crop is not None:
+            crops_coords = self.mask_processor.get_crop_region(mask_image, width, height, pad=padding_mask_crop)
+            resize_mode = "fill"
+        else:
+            crops_coords = None
+            resize_mode = "default"
+
+        original_image = image
+        init_image = self.image_processor.preprocess(
+            image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode
+        )
+        init_image = init_image.to(dtype=torch.float32)
+
+        # 3. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        lora_scale = (
+            self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
+        )
+        (
+            prompt_embeds,
+            pooled_prompt_embeds,
+            text_ids,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+        )
+
+        # 4.Prepare timesteps
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+        image_seq_len = (int(height) // self.vae_scale_factor // 2) * (int(width) // self.vae_scale_factor // 2)
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.base_image_seq_len,
+            self.scheduler.config.max_image_seq_len,
+            self.scheduler.config.base_shift,
+            self.scheduler.config.max_shift,
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            timesteps,
+            sigmas,
+            mu=mu,
+        )
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
+
+        if num_inference_steps < 1:
+            raise ValueError(
+                f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline"
+                f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline."
+            )
+        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels // 4
+        num_channels_transformer = self.transformer.config.in_channels
+
+        latents, noise, image_latents, latent_image_ids= self.prepare_latents(
+            init_image,
+            latent_timestep,
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            target_width,
+            tryon,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        mask_condition = self.mask_processor.preprocess(
+            mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords
+        )
+
+        if masked_image_latents is None:
+            masked_image = init_image * (mask_condition < 0.5)
+        else:
+            masked_image = masked_image_latents
+
+        mask, masked_image_latents = self.prepare_mask_latents(
+            mask_condition,
+            masked_image,
+            batch_size,
+            num_channels_latents,
+            num_images_per_prompt,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+        )
+
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # handle guidance
+        if self.transformer.config.guidance_embeds:
+            guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
+            guidance = guidance.expand(latents.shape[0])
+        else:
+            guidance = None
+
+        # 6. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+                
+                # for 64 channel transformer only.
+                init_latents_proper = image_latents
+                init_mask = mask
+                latents = (1 - init_mask) * init_latents_proper + init_mask * latents
+                
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latents.shape[0]).to(latents.dtype)
+                noise_pred = self.transformer(
+                    hidden_states=latents,
+                    timestep=timestep / 1000,
+                    guidance=guidance,
+                    pooled_projections=pooled_prompt_embeds,
+                    encoder_hidden_states=prompt_embeds,
+                    txt_ids=text_ids,
+                    img_ids=latent_image_ids,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+                
+                '''
+                # for 64 channel transformer only.
+                init_latents_proper = image_latents
+                init_mask = mask
+
+                # NOTE: we just use clean latents
+                # if i < len(timesteps) - 1:
+                #     noise_timestep = timesteps[i + 1]
+                #     init_latents_proper = self.scheduler.scale_noise(
+                #         init_latents_proper, torch.tensor([noise_timestep]), noise
+                #     )
+
+                latents = (1 - init_mask) * init_latents_proper + init_mask * latents
+                '''
+                
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                # if XLA_AVAILABLE:
+                #     xm.mark_step()
+        # latents = (1 - mask) * image_latents + mask * latents
+        
+        if output_type == "latent":
+            image = latents
+        else:
+            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+            latents = latents[:,:,:,:target_width//self.vae_scale_factor]
+            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+            image = self.vae.decode(latents.to(device=self.vae.device, dtype=self.vae.dtype), return_dict=False)[0]
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return FluxPipelineOutput(images=image)
+
+# Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._pack_latents    
+def flux_pack_latents(latents, batch_size, num_channels_latents, height, width):
+    latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
+    latents = latents.permute(0, 2, 4, 1, 3, 5)
+    latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4)
+
+    return latents
+
+# Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._unpack_latents
+def flux_unpack_latents(latents, height, width, vae_scale_factor):
+    batch_size, num_patches, channels = latents.shape
+
+    # VAE applies 8x compression on images but we must also account for packing which requires
+    # latent height and width to be divisible by 2.
+    height = 2 * (int(height) // (vae_scale_factor * 2))
+    width = 2 * (int(width) // (vae_scale_factor * 2))
+
+    latents = latents.view(batch_size, height // 2, width // 2, channels // 4, 2, 2)
+    latents = latents.permute(0, 3, 1, 4, 2, 5)
+
+    latents = latents.reshape(batch_size, channels // (2 * 2), height, width)
+
+    return latents
+
+# TODO: it is more reasonable to have target pe staring at 0
+def prepare_latent_image_ids(height, width_tgt, height_spa, width_spa, height_sub, width_sub, device, dtype):
+    assert width_spa==0 or width_tgt==width_spa
+    latent_image_ids = torch.zeros(height, width_tgt, 3, device=device, dtype=dtype)
+    latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height, device=device)[:, None]  # y坐标
+    latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width_tgt, device=device)[None, :]   # x坐标
+    
+    cond_mark = 0
+    if width_spa>0:
+        cond_mark += 1
+        condspa_image_ids = torch.zeros(height_spa, width_spa, 3, device=device, dtype=dtype)
+        condspa_image_ids[..., 0] = cond_mark
+        condspa_image_ids[..., 1] = condspa_image_ids[..., 1] + torch.arange(height_spa, device=device)[:, None]
+        condspa_image_ids[..., 2] = condspa_image_ids[..., 2] + torch.arange(width_spa, device=device)[None, :]
+        condspa_image_ids = condspa_image_ids.reshape(-1, condspa_image_ids.shape[-1])
+        
+
+    if width_sub>0:
+        cond_mark += 1
+        condsub_image_ids = torch.zeros(height_sub, width_sub, 3, device=device, dtype=dtype)
+        condsub_image_ids[..., 0] = cond_mark
+        condsub_image_ids[..., 1] = condsub_image_ids[..., 1] + torch.arange(height_sub, device=device)[:, None]
+        condsub_image_ids[..., 2] = condsub_image_ids[..., 2] + torch.arange(width_sub, device=device)[None, :] + width_tgt
+        condsub_image_ids = condsub_image_ids.reshape(-1, condsub_image_ids.shape[-1])
+
+    latent_image_ids = latent_image_ids.reshape(-1, latent_image_ids.shape[-1])
+    latent_image_ids = torch.cat([latent_image_ids, condspa_image_ids],dim=-2) if width_spa>0 else latent_image_ids
+    latent_image_ids = torch.cat([latent_image_ids, condsub_image_ids],dim=-2) if width_sub>0 else latent_image_ids
+    return latent_image_ids
+
+
+def crop_to_multiple_of_16(img):
+    width, height = img.size
+    
+    # Calculate new dimensions that are multiples of 8
+    new_width = width - (width % 16)  
+    new_height = height - (height % 16)
+    
+    # Calculate crop box coordinates
+    left = (width - new_width) // 2
+    top = (height - new_height) // 2
+    right = left + new_width
+    bottom = top + new_height
+    
+    # Crop the image
+    cropped_img = img.crop((left, top, right, bottom))
+    
+    return cropped_img
+
+def resize_and_pad_to_size(image, target_width, target_height):
+    # Convert numpy array to PIL Image if needed
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+        
+    # Get original dimensions
+    orig_width, orig_height = image.size
+    
+    # Calculate aspect ratios
+    target_ratio = target_width / target_height
+    orig_ratio = orig_width / orig_height
+    
+    # Calculate new dimensions while maintaining aspect ratio
+    if orig_ratio > target_ratio:
+        # Image is wider than target ratio - scale by width
+        new_width = target_width
+        new_height = int(new_width / orig_ratio)
+    else:
+        # Image is taller than target ratio - scale by height
+        new_height = target_height
+        new_width = int(new_height * orig_ratio)
+        
+    # Resize image
+    resized_image = image.resize((new_width, new_height))
+    
+    # Create white background image of target size
+    padded_image = Image.new('RGB', (target_width, target_height), 'white')
+    
+    # Calculate padding to center the image
+    left_padding = (target_width - new_width) // 2
+    top_padding = (target_height - new_height) // 2
+    
+    # Paste resized image onto padded background
+    padded_image.paste(resized_image, (left_padding, top_padding))
+    
+    return padded_image, left_padding, top_padding, target_width - new_width - left_padding, target_height - new_height - top_padding
+
+def resize_by_height(image, height):
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+    # image is a PIL image
+    image = image.resize((int(image.width * height / image.height), height))
+    return crop_to_multiple_of_16(image)

src/utils.py

@@ -0,0 +1,17 @@
+def crop_to_multiple_of_16(img):
+    width, height = img.size
+    
+    # Calculate new dimensions that are multiples of 8
+    new_width = width - (width % 16)  
+    new_height = height - (height % 16)
+    
+    # Calculate crop box coordinates
+    left = (width - new_width) // 2
+    top = (height - new_height) // 2
+    right = left + new_width
+    bottom = top + new_height
+    
+    # Crop the image
+    cropped_img = img.crop((left, top, right, bottom))
+    
+    return cropped_img

test_vitonhd.py

@@ -0,0 +1,381 @@
+from diffusers import FluxTransformer2DModel
+from transformers import T5EncoderModel, CLIPTextModel
+from diffusers import AutoencoderKL
+from src.pipeline_tryon import FluxTryonPipeline
+
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+import numpy as np
+from PIL import Image
+import os
+import json
+from datetime import datetime
+from pathlib import Path
+import argparse
+
+
+torch_dtype = torch.bfloat16
+model_caption_data, garment_caption_data, model_caption_inpaint = {},{},{}
+model_caption_json = "data/zalando-hd-resized/model_caption.json"
+if os.path.exists(model_caption_json):
+    with open(model_caption_json, "r") as f:
+        model_caption_data = json.load(f)
+garment_caption_json = "data/zalando-hd-resized/cloth_caption.json"
+if os.path.exists(garment_caption_json):
+    with open(garment_caption_json, "r") as f:
+        garment_caption_data = json.load(f)
+model_inpaint_caption_json = "data/zalando-hd-resized/model_inpaint_caption.json"
+if os.path.exists(model_inpaint_caption_json):
+    with open(model_inpaint_caption_json, "r") as f:
+        model_caption_inpaint = json.load(f)
+
+def setup(rank, world_size):
+    os.environ['MASTER_ADDR'] = 'localhost'
+    os.environ['MASTER_PORT'] = '12345'
+    dist.init_process_group("nccl", rank=rank, world_size=world_size)
+
+def cleanup():
+    dist.destroy_process_group()
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model_dir', type=str, default="data/zalando-hd-resized/test/image")
+    parser.add_argument('--garment_dir', type=str, default="data/zalando-hd-resized/test/cloth")
+    parser.add_argument('--output_dir', type=str, default=None)
+    parser.add_argument('--meta_file', type=str, default="data/zalando-hd-resized/test_pairs.txt")
+    parser.add_argument('--model_path', type=str, default="black-forest-labs/FLUX.1-dev", help='Path to the model')
+    parser.add_argument("--train_double_block_only", action="store_true")
+    parser.add_argument('--paired', action="store_true")
+    parser.add_argument('--repaint', action="store_true")
+    parser.add_argument("--mask_dir", type=str, default=None, help="Directory containing mask images")
+    parser.add_argument("--source_dir", type=str, default=None, help="Directory containing source model images") 
+    return parser.parse_args()
+
+def load_models(model_path, device="cuda"):
+    # Enable memory efficient attention
+    text_encoder = CLIPTextModel.from_pretrained(f"{model_path}/text_encoder", torch_dtype=torch_dtype,)
+    text_encoder_2 = T5EncoderModel.from_pretrained(f"{model_path}/text_encoder_2", torch_dtype=torch_dtype,)
+    transformer = FluxTransformer2DModel.from_pretrained(f"{model_path}/transformer", torch_dtype=torch_dtype,)
+    vae = AutoencoderKL.from_pretrained(f"{model_path}/vae")
+    
+    pipe = FluxTryonPipeline.from_pretrained(
+        model_path,
+        transformer=transformer,
+        text_encoder=text_encoder,
+        text_encoder_2=text_encoder_2,
+        vae=vae,
+        torch_dtype=torch_dtype,
+    ).to(device=device, dtype=torch_dtype)
+    
+    # Enable memory efficient attention and VAE optimization
+    pipe.enable_attention_slicing()
+    # pipe.enable_sequential_cpu_offload()
+    # pipe.enable_model_cpu_offload()
+    vae.enable_slicing()
+    vae.enable_tiling()
+    pipe.load_lora_weights(
+        "loooooong/Any2anyTryon",
+        weight_name="dev_lora_tryon_vitonhd_512.safetensors",
+        adapter_name="tryon",
+    )
+    return pipe
+
+def resize_by_height(image, height):
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+    # image is a PIL image
+    image = image.resize((int(image.width * height / image.height), height))
+    width = image.width - (image.width % 16)
+    height = image.height - (image.height % 16)
+    return image.resize((width, height))
+
+from diffusers.utils import USE_PEFT_BACKEND, scale_lora_layers, unscale_lora_layers
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+
+@torch.no_grad
+def generate_image(pipe, prompt, model_image, garment_image, height=512, width=384, seed=0, guidance_scale=3.5, train_double_block_only=False):
+    height, width = int(height), int(width)
+    width = width - (width % 16)  
+    height = height - (height % 16)
+
+    concat_image_list = [Image.fromarray(np.zeros((height, width, 3), dtype=np.uint8))]
+    model_image = resize_by_height(model_image, height)
+    garment_image = resize_by_height(garment_image, height)
+    concat_image_list.extend([model_image, garment_image])
+
+    image = np.concatenate([np.array(img) for img in concat_image_list], axis=1)
+    image = Image.fromarray(image)
+    
+    mask = np.zeros_like(np.array(image))
+    mask[:,:width] = 255
+    mask_image = Image.fromarray(mask)
+
+    def forward_flux(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor = None,
+        pooled_projections: torch.Tensor = None,
+        timestep: torch.LongTensor = None,
+        img_ids: torch.Tensor = None,
+        img_ids_old = pipe._prepare_latent_image_ids(1, int(image.height) // (pipe.vae_scale_factor * 2), int(image.width) // (pipe.vae_scale_factor * 2), pipe.device, torch_dtype),
+        txt_ids: torch.Tensor = None,
+        guidance: torch.Tensor = None,
+        joint_attention_kwargs = None,
+        controlnet_block_samples=None,
+        controlnet_single_block_samples=None,
+        return_dict: bool = True,
+        controlnet_blocks_repeat: bool = False,
+    ):
+        if joint_attention_kwargs is not None:
+            joint_attention_kwargs = joint_attention_kwargs.copy()
+            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
+        else:
+            lora_scale = 1.0
+
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+        else:
+            if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
+                print(
+                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
+                )
+        hidden_states = self.x_embedder(hidden_states)
+
+        timestep = timestep.to(hidden_states.dtype) * 1000
+        if guidance is not None:
+            guidance = guidance.to(hidden_states.dtype) * 1000
+        else:
+            guidance = None
+
+        temb = (
+            self.time_text_embed(timestep, pooled_projections)
+            if guidance is None
+            else self.time_text_embed(timestep, guidance, pooled_projections)
+        )
+        encoder_hidden_states = self.context_embedder(encoder_hidden_states)
+
+        if txt_ids.ndim == 3:
+            print(
+                "Passing `txt_ids` 3d torch.Tensor is deprecated."
+                "Please remove the batch dimension and pass it as a 2d torch Tensor"
+            )
+            txt_ids = txt_ids[0]
+        if img_ids.ndim == 3:
+            print(
+                "Passing `img_ids` 3d torch.Tensor is deprecated."
+                "Please remove the batch dimension and pass it as a 2d torch Tensor"
+            )
+            img_ids = img_ids[0]
+
+        ids = torch.cat((txt_ids, img_ids), dim=0)
+        image_rotary_emb = self.pos_embed(ids)
+        image_rotary_emb2 = self.pos_embed(torch.cat((txt_ids, img_ids_old), dim=0))
+
+        if joint_attention_kwargs is not None and "ip_adapter_image_embeds" in joint_attention_kwargs:
+            ip_adapter_image_embeds = joint_attention_kwargs.pop("ip_adapter_image_embeds")
+            ip_hidden_states = self.encoder_hid_proj(ip_adapter_image_embeds)
+            joint_attention_kwargs.update({"ip_hidden_states": ip_hidden_states})
+
+        for index_block, block in enumerate(self.transformer_blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs = {"use_reentrant": False}
+                encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+
+            else:
+                encoder_hidden_states, hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    joint_attention_kwargs=joint_attention_kwargs,
+                )
+
+            # controlnet residual
+            if controlnet_block_samples is not None:
+                interval_control = len(self.transformer_blocks) / len(controlnet_block_samples)
+                interval_control = int(np.ceil(interval_control))
+                # For Xlabs ControlNet.
+                if controlnet_blocks_repeat:
+                    hidden_states = (
+                        hidden_states + controlnet_block_samples[index_block % len(controlnet_block_samples)]
+                    )
+                else:
+                    hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]
+        
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+        image_rotary_emb = image_rotary_emb2
+
+        for index_block, block in enumerate(self.single_transformer_blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs = {"use_reentrant": False}
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+
+            else:
+                hidden_states = block(
+                    hidden_states=hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    joint_attention_kwargs=joint_attention_kwargs,
+                )
+
+            # controlnet residual
+            if controlnet_single_block_samples is not None:
+                interval_control = len(self.single_transformer_blocks) / len(controlnet_single_block_samples)
+                interval_control = int(np.ceil(interval_control))
+                hidden_states[:, encoder_hidden_states.shape[1] :, ...] = (
+                    hidden_states[:, encoder_hidden_states.shape[1] :, ...]
+                    + controlnet_single_block_samples[index_block // interval_control]
+                )
+
+        hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :, ...]
+        hidden_states = self.norm_out(hidden_states, temb)
+        output = self.proj_out(hidden_states)
+
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (output,)
+
+        return Transformer2DModelOutput(sample=output)
+    if train_double_block_only:
+        pipe.transformer.forward = forward_flux.__get__(pipe.transformer)
+    output = pipe(
+        prompt,
+        image=image,
+        mask_image=mask_image,
+        strength=1.,
+        height=height,
+        width=image.width,
+        target_width=width,
+        tryon=True,
+        guidance_scale=guidance_scale,
+        num_inference_steps=30,
+        max_sequence_length=512,
+        generator=torch.Generator("cpu").manual_seed(seed),
+        output_type="latent",
+    ).images
+
+    latents = pipe._unpack_latents(output, image.height, image.width, pipe.vae_scale_factor)
+    latents = latents[:,:,:,:width//pipe.vae_scale_factor]
+    latents = (latents / pipe.vae.config.scaling_factor) + pipe.vae.config.shift_factor
+    image = pipe.vae.decode(latents, return_dict=False)[0]
+    image = pipe.image_processor.postprocess(image, output_type="pil")[0]
+    
+    return image
+
+def inference(rank, world_size, args):
+    setup(rank, world_size)
+    device = torch.device(f"cuda:{rank}")
+
+    pairs_dict = {}
+    with open(args.meta_file, "r") as f:
+        pairs = f.read().splitlines()
+    for line in pairs:
+        img_fn, gmt_fn = line.split()
+        pairs_dict[img_fn] = img_fn if args.paired else gmt_fn
+
+    pipe = load_models(args.model_path, device=device)
+    
+    scale = 1.0
+    pipe.set_adapters("tryon",adapter_weights=[scale]) 
+    height, width = 512, 384 # 768, 576 # 1024, 768 # 
+    if args.output_dir is None:
+        args.output_dir = f"./results/vitonhd_test_{('paired' if args.paired else 'unpaired')}-"+f"_{height}_{width}"
+
+    os.makedirs(args.output_dir, exist_ok=True)
+    
+    model_files = sorted(os.listdir(args.model_dir)) # sorted(list(pairs_dict.keys())) #
+    
+    # Split work across GPUs
+    model_files = model_files[rank::world_size]
+    
+    for model_file in model_files:
+        name = Path(model_file).stem
+        garment_file = pairs_dict[model_file]
+        
+        if not os.path.exists(os.path.join(args.garment_dir, garment_file)):
+            print(f"Skipping {model_file} - no matching garment image")
+            continue
+            
+        output_path = os.path.join(args.output_dir, f"{name}.jpg")
+        if os.path.exists(output_path):
+            print(f"Skipping {model_file} - output already exists")
+            continue
+            
+        model_image = Image.open(os.path.join(args.model_dir, model_file))
+        garment_image = Image.open(os.path.join(args.garment_dir, garment_file))
+        
+
+        model_caption = model_caption_inpaint.get(model_file, "a woman wearing fashion garment.") if args.paired else model_caption_data.get(model_file, "a woman wearing fashion garment.")
+        garment_caption = garment_caption_data.get(garment_file, "a fashion garment.")
+        prompt = f"The set of three images display model, garment and the model wearing the garment. <IMAGE1> {model_caption} <IMAGE2> {garment_caption} <IMAGE3> <IMAGE1> model wear <IMAGE2> garment."
+        
+        output = generate_image(pipe, prompt, model_image, garment_image, height=height, width=width, train_double_block_only=args.train_double_block_only)
+
+        if args.repaint:
+            mask_file = os.path.join(args.mask_dir, name+"_mask.png")
+            source_model_file = os.path.join(args.source_dir, name+".jpg")
+            
+            # Check if corresponding mask and source files exist
+            if not os.path.exists(mask_file) or not os.path.exists(source_model_file):
+                print(f"Skipping {model_file} - missing corresponding mask or source image files")
+                continue
+
+            mask = Image.open(mask_file).convert('L').resize(output.size)
+            source_model = Image.open(source_model_file).resize(output.size)
+            output = Image.composite(output, source_model, mask)
+        
+        output.save(output_path)
+        print(f"Generated result for {name}", prompt)
+    
+    cleanup()
+
+def main():
+    args = parse_args()
+    # check args
+    if args.repaint:
+        if args.source_dir is None:
+            args.source_dir = args.model_dir
+        assert args.mask_dir is not None
+    world_size = torch.cuda.device_count()
+    mp.spawn(inference, args=(world_size, args), nprocs=world_size)
+
+if __name__ == "__main__":
+    main()