Delete folder code with huggingface_hub

code/.gitignore

@@ -1,15 +0,0 @@
-Qwen
-tools
-ckpts
-universal_pix_workdirs
-pretrained_weights
-wandb
-merge_weight.py
-*.pyc*.pyc
-__pycache__/
-medical_workdirs/
-*.pdf
-segmentation/checkpoints/
-segmentation/pretrained/
-segmentation/logs/
-*.log

code/JiT_medical_v2.py

@@ -1,490 +0,0 @@
-# JiT Medical - Mask-Conditional JiT for Medical Image Generation
-# Based on JiT.py with mask condition injection
-# V2: Added mask_mode='spatial' for patch-level mask conditioning
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import math
-
-# Import from JiT.py in the same directory
-from src.models.transformer.JiT import VisionRotaryEmbeddingFast, get_2d_sincos_pos_embed, RMSNorm
-
-
-def modulate(x, shift, scale):
-    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
-
-
-class BottleneckPatchEmbed(nn.Module):
-    """Image to Patch Embedding"""
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, pca_dim=768, embed_dim=768, bias=True):
-        super().__init__()
-        img_size = (img_size, img_size)
-        patch_size = (patch_size, patch_size)
-        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.num_patches = num_patches
-
-        self.proj1 = nn.Conv2d(in_chans, pca_dim, kernel_size=patch_size, stride=patch_size, bias=False)
-        self.proj2 = nn.Conv2d(pca_dim, embed_dim, kernel_size=1, stride=1, bias=bias)
-
-    def forward(self, x):
-        B, C, H, W = x.shape
-        assert H == self.img_size[0] and W == self.img_size[1], \
-            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-        x = self.proj2(self.proj1(x)).flatten(2).transpose(1, 2)
-        return x
-
-
-class MaskEncoder(nn.Module):
-    """
-    Encode segmentation mask to a conditioning vector (global mode).
-    Uses a simple CNN to extract spatial features, then global pooling.
-    """
-    def __init__(self, hidden_size, in_channels=1, img_size=256):
-        super().__init__()
-        self.hidden_size = hidden_size
-
-        # Simple CNN encoder for mask
-        self.encoder = nn.Sequential(
-            nn.Conv2d(in_channels, 32, kernel_size=7, stride=2, padding=3),
-            nn.GroupNorm(8, 32),
-            nn.SiLU(),
-            nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1),
-            nn.GroupNorm(8, 64),
-            nn.SiLU(),
-            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
-            nn.GroupNorm(8, 128),
-            nn.SiLU(),
-            nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
-            nn.GroupNorm(8, 256),
-            nn.SiLU(),
-            nn.AdaptiveAvgPool2d((1, 1)),
-        )
-        self.proj = nn.Sequential(
-            nn.Linear(256, hidden_size),
-            nn.SiLU(),
-            nn.Linear(hidden_size, hidden_size),
-        )
-
-    def forward(self, mask):
-        feat = self.encoder(mask)
-        feat = feat.flatten(1)
-        return self.proj(feat)
-
-
-class TimestepEmbedder(nn.Module):
-    """Embeds scalar timesteps into vector representations."""
-    def __init__(self, hidden_size, frequency_embedding_size=256):
-        super().__init__()
-        self.mlp = nn.Sequential(
-            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
-            nn.SiLU(),
-            nn.Linear(hidden_size, hidden_size, bias=True),
-        )
-        self.frequency_embedding_size = frequency_embedding_size
-
-    @staticmethod
-    def timestep_embedding(t, dim, max_period=10000):
-        half = dim // 2
-        freqs = torch.exp(
-            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
-        ).to(device=t.device)
-        args = t[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-        return embedding
-
-    def forward(self, t):
-        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
-        t_emb = self.mlp(t_freq)
-        return t_emb
-
-
-class LabelEmbedder(nn.Module):
-    """Embeds class labels into vector representations."""
-    def __init__(self, num_classes, hidden_size):
-        super().__init__()
-        self.embedding_table = nn.Embedding(num_classes + 1, hidden_size)
-        self.num_classes = num_classes
-
-    def forward(self, labels):
-        embeddings = self.embedding_table(labels)
-        return embeddings
-
-
-def scaled_dot_product_attention(query, key, value, dropout_p=0.0):
-    L, S = query.size(-2), key.size(-2)
-    scale_factor = 1 / math.sqrt(query.size(-1))
-    attn_bias = torch.zeros(query.size(0), 1, L, S, dtype=query.dtype, device=query.device)
-
-    with torch.cuda.amp.autocast(enabled=False):
-        attn_weight = query.float() @ key.float().transpose(-2, -1) * scale_factor
-    attn_weight += attn_bias
-    attn_weight = torch.softmax(attn_weight, dim=-1)
-    attn_weight = torch.dropout(attn_weight, dropout_p, train=True)
-    return attn_weight @ value
-
-
-class Attention(nn.Module):
-    def __init__(self, dim, num_heads=8, qkv_bias=True, qk_norm=True, attn_drop=0., proj_drop=0.):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-
-        self.q_norm = RMSNorm(head_dim) if qk_norm else nn.Identity()
-        self.k_norm = RMSNorm(head_dim) if qk_norm else nn.Identity()
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x, rope):
-        B, N, C = x.shape
-        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]
-
-        q = self.q_norm(q)
-        k = self.k_norm(k)
-
-        q = rope(q)
-        k = rope(k)
-
-        x = scaled_dot_product_attention(q, k, v, dropout_p=self.attn_drop.p if self.training else 0.)
-        x = x.transpose(1, 2).reshape(B, N, C)
-
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class SwiGLUFFN(nn.Module):
-    def __init__(self, dim, hidden_dim, drop=0.0, bias=True):
-        super().__init__()
-        hidden_dim = int(hidden_dim * 2 / 3)
-        self.w12 = nn.Linear(dim, 2 * hidden_dim, bias=bias)
-        self.w3 = nn.Linear(hidden_dim, dim, bias=bias)
-        self.ffn_dropout = nn.Dropout(drop)
-
-    def forward(self, x):
-        x12 = self.w12(x)
-        x1, x2 = x12.chunk(2, dim=-1)
-        hidden = F.silu(x1) * x2
-        return self.w3(self.ffn_dropout(hidden))
-
-
-class FinalLayer(nn.Module):
-    """The final layer of JiT."""
-    def __init__(self, hidden_size, patch_size, out_channels):
-        super().__init__()
-        self.norm_final = RMSNorm(hidden_size)
-        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
-        self.adaLN_modulation = nn.Sequential(
-            nn.SiLU(),
-            nn.Linear(hidden_size, 2 * hidden_size, bias=True)
-        )
-
-    def forward(self, x, c):
-        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
-        x = modulate(self.norm_final(x), shift, scale)
-        x = self.linear(x)
-        return x
-
-
-class JiTBlock(nn.Module):
-    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, attn_drop=0.0, proj_drop=0.0):
-        super().__init__()
-        self.norm1 = RMSNorm(hidden_size, eps=1e-6)
-        self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=True,
-                              attn_drop=attn_drop, proj_drop=proj_drop)
-        self.norm2 = RMSNorm(hidden_size, eps=1e-6)
-        mlp_hidden_dim = int(hidden_size * mlp_ratio)
-        self.mlp = SwiGLUFFN(hidden_size, mlp_hidden_dim, drop=proj_drop)
-        self.adaLN_modulation = nn.Sequential(
-            nn.SiLU(),
-            nn.Linear(hidden_size, 6 * hidden_size, bias=True)
-        )
-
-    def forward(self, x, c, feat_rope=None):
-        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=-1)
-        x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa), rope=feat_rope)
-        x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
-        return x
-
-
-class JiTMedical(nn.Module):
-    """
-    JiT for Medical Image Generation with Mask Conditioning.
-
-    Supports two mask conditioning modes:
-    - 'global': Mask encoded to single vector via CNN+GlobalPool, injected via AdaLN.
-                Good for structured masks (e.g. OCTA500 layer segmentation).
-    - 'spatial': Mask patchified and added to patch embeddings, preserving spatial info.
-                 Good for localized masks (e.g. CVC-ClinicDB polyp segmentation).
-    """
-    def __init__(
-        self,
-        input_size=256,
-        patch_size=16,
-        in_channels=3,
-        hidden_size=1024,
-        depth=24,
-        num_heads=16,
-        mlp_ratio=4.0,
-        attn_drop=0.0,
-        proj_drop=0.0,
-        num_classes=1,
-        bottleneck_dim=128,
-        use_bottleneck=True,
-        in_context_len=32,
-        in_context_start=8,
-        mask_in_channels=1,
-        mask_mode='global',  # 'global' or 'spatial'
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = in_channels
-        self.patch_size = patch_size
-        self.num_heads = num_heads
-        self.hidden_size = hidden_size
-        self.input_size = input_size
-        self.in_context_len = in_context_len
-        self.in_context_start = in_context_start
-        self.num_classes = num_classes
-        self.use_bottleneck = use_bottleneck
-        self.mask_mode = mask_mode
-
-        # Condition embedders
-        self.t_embedder = TimestepEmbedder(hidden_size)
-        self.y_embedder = LabelEmbedder(num_classes, hidden_size)
-
-        # Mask conditioning
-        if mask_mode == 'global':
-            self.mask_embedder = MaskEncoder(hidden_size, mask_in_channels, input_size)
-        elif mask_mode == 'spatial':
-            self.mask_patch_embed = nn.Conv2d(
-                mask_in_channels, hidden_size,
-                kernel_size=patch_size, stride=patch_size, bias=True
-            )
-        else:
-            raise ValueError(f"Unknown mask_mode: {mask_mode}")
-
-        # Patch embedding
-        if use_bottleneck:
-            self.x_embedder = BottleneckPatchEmbed(input_size, patch_size, in_channels, bottleneck_dim, hidden_size, bias=True)
-        else:
-            self.x_embedder = nn.Conv2d(in_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
-
-        # Position embedding
-        num_patches = (input_size // patch_size) ** 2
-        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size), requires_grad=False)
-
-        # In-context tokens
-        if self.in_context_len > 0:
-            self.in_context_posemb = nn.Parameter(torch.zeros(1, self.in_context_len, hidden_size), requires_grad=True)
-
-        # RoPE
-        half_head_dim = hidden_size // num_heads // 2
-        hw_seq_len = input_size // patch_size
-        self.feat_rope = VisionRotaryEmbeddingFast(
-            dim=half_head_dim,
-            pt_seq_len=hw_seq_len,
-            num_cls_token=0
-        )
-        self.feat_rope_incontext = VisionRotaryEmbeddingFast(
-            dim=half_head_dim,
-            pt_seq_len=hw_seq_len,
-            num_cls_token=self.in_context_len
-        )
-
-        # Transformer blocks
-        self.blocks = nn.ModuleList([
-            JiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio,
-                     attn_drop=attn_drop if (depth // 4 * 3 > i >= depth // 4) else 0.0,
-                     proj_drop=proj_drop if (depth // 4 * 3 > i >= depth // 4) else 0.0)
-            for i in range(depth)
-        ])
-        self.depth = depth
-
-        # Output layer
-        self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels)
-
-        self.initialize_weights()
-
-    def initialize_weights(self):
-        def _basic_init(module):
-            if isinstance(module, nn.Linear):
-                torch.nn.init.xavier_uniform_(module.weight)
-                if module.bias is not None:
-                    nn.init.constant_(module.bias, 0)
-        self.apply(_basic_init)
-
-        # Sin-cos position embedding
-        num_patches = (self.input_size // self.patch_size) ** 2
-        pos_embed = get_2d_sincos_pos_embed(self.hidden_size, int(num_patches ** 0.5))
-        self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
-
-        # In-context position embedding
-        if self.in_context_len > 0:
-            nn.init.normal_(self.in_context_posemb, std=0.02)
-
-        # Patch embed init
-        if self.use_bottleneck:
-            w1 = self.x_embedder.proj1.weight.data
-            nn.init.xavier_uniform_(w1.view([w1.shape[0], -1]))
-            w2 = self.x_embedder.proj2.weight.data
-            nn.init.xavier_uniform_(w2.view([w2.shape[0], -1]))
-            nn.init.constant_(self.x_embedder.proj2.bias, 0)
-        else:
-            w = self.x_embedder.weight.data
-            nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
-
-        # Label embedding init
-        nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)
-
-        # Time embedder init
-        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
-        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
-
-        # Mask conditioning init
-        if self.mask_mode == 'global':
-            nn.init.normal_(self.mask_embedder.proj[0].weight, std=0.02)
-            nn.init.normal_(self.mask_embedder.proj[2].weight, std=0.02)
-        elif self.mask_mode == 'spatial':
-            w = self.mask_patch_embed.weight.data
-            nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
-            nn.init.constant_(self.mask_patch_embed.bias, 0)
-
-        # Zero-out adaLN modulation layers
-        for block in self.blocks:
-            nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
-            nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
-
-        # Zero-out output layers
-        nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
-        nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
-        nn.init.constant_(self.final_layer.linear.weight, 0)
-        nn.init.constant_(self.final_layer.linear.bias, 0)
-
-    def unpatchify(self, x, p):
-        c = self.out_channels
-        h = w = int(x.shape[1] ** 0.5)
-        assert h * w == x.shape[1]
-
-        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
-        x = torch.einsum('nhwpqc->nchpwq', x)
-        imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
-        return imgs
-
-    def forward(self, x, t, y, mask=None, return_layer=None):
-        """
-        Forward pass for mask-conditional generation.
-
-        Args:
-            x: Noisy image [N, 3, H, W]
-            t: Timesteps [N,]
-            y: Class labels [N,]
-            mask: Segmentation mask [N, C, H, W]
-            return_layer: If specified, return intermediate features at this layer
-
-        Returns:
-            Predicted clean image [N, 3, H, W]
-            (optionally) intermediate features if return_layer is specified
-        """
-        # Time embedding
-        t_emb = self.t_embedder(t)
-
-        # Class embedding
-        y_emb = self.y_embedder(y)
-
-        # Conditioning via AdaLN
-        if self.mask_mode == 'global':
-            if mask is not None:
-                mask_emb = self.mask_embedder(mask)
-            else:
-                mask_emb = torch.zeros_like(t_emb)
-            c = t_emb + y_emb + mask_emb
-        else:
-            # spatial mode: no global mask embedding in AdaLN
-            c = t_emb + y_emb
-
-        # Patch embedding
-        if self.use_bottleneck:
-            x = self.x_embedder(x)
-        else:
-            x = self.x_embedder(x).flatten(2).transpose(1, 2)
-        x = x + self.pos_embed
-
-        # Spatial mask conditioning: add mask patch embeddings to image patch embeddings
-        if self.mask_mode == 'spatial':
-            if mask is not None:
-                mask_tokens = self.mask_patch_embed(mask).flatten(2).transpose(1, 2)
-                x = x + mask_tokens
-            # else: no mask (null condition for CFG) - x stays as-is
-
-        # Transformer blocks
-        hidden_states = None
-        for i, block in enumerate(self.blocks):
-            # In-context tokens
-            if self.in_context_len > 0 and i == self.in_context_start:
-                in_context_tokens = y_emb.unsqueeze(1).repeat(1, self.in_context_len, 1)
-                in_context_tokens = in_context_tokens + self.in_context_posemb
-                x = torch.cat([in_context_tokens, x], dim=1)
-
-            x = block(x, c, self.feat_rope if i < self.in_context_start else self.feat_rope_incontext)
-
-            # Return intermediate features if requested
-            if return_layer is not None and i == return_layer:
-                hidden_states = x[:, self.in_context_len:] if i >= self.in_context_start else x
-
-        # Remove in-context tokens
-        if self.in_context_len > 0:
-            x = x[:, self.in_context_len:]
-
-        # Final layer
-        x = self.final_layer(x, c)
-        output = self.unpatchify(x, self.patch_size)
-
-        if return_layer is not None:
-            return output, hidden_states
-        return output
-
-
-# Model factory functions
-
-def JiTMedical_S_8(**kwargs):
-    """Small model with patch_size=8"""
-    return JiTMedical(depth=8, hidden_size=512, num_heads=8,
-                      bottleneck_dim=64, in_context_len=64, in_context_start=2, patch_size=8, **kwargs)
-
-def JiTMedical_B_8(**kwargs):
-    """Base model with patch_size=8"""
-    return JiTMedical(depth=12, hidden_size=768, num_heads=12,
-                      bottleneck_dim=128, in_context_len=64, in_context_start=4, patch_size=8, **kwargs)
-
-def JiTMedical_B_16(**kwargs):
-    """Base model with patch_size=16"""
-    return JiTMedical(depth=12, hidden_size=768, num_heads=12,
-                      bottleneck_dim=128, in_context_len=32, in_context_start=4, patch_size=16, **kwargs)
-
-def JiTMedical_L_16(**kwargs):
-    """Large model with patch_size=16"""
-    return JiTMedical(depth=24, hidden_size=1024, num_heads=16,
-                      bottleneck_dim=128, in_context_len=32, in_context_start=8, patch_size=16, **kwargs)
-
-def JiTMedical_XL_16(**kwargs):
-    """XL model with patch_size=16 (similar to PixelGen-XL)"""
-    return JiTMedical(depth=28, hidden_size=1152, num_heads=16,
-                      bottleneck_dim=128, in_context_len=32, in_context_start=8, patch_size=16, **kwargs)
-
-
-JiTMedical_models = {
-    'JiTMedical-S/8': JiTMedical_S_8,
-    'JiTMedical-B/8': JiTMedical_B_8,
-    'JiTMedical-B/16': JiTMedical_B_16,
-    'JiTMedical-L/16': JiTMedical_L_16,
-    'JiTMedical-XL/16': JiTMedical_XL_16,
-}

code/PixelGen_Medical_CVC.yaml

@@ -1,132 +0,0 @@
-# PixelGen Medical - CVC-ClinicDB Polyp Segmentation
-# Binary mask-conditional colonoscopy image generation
-# Dataset: 612 RGB images, binary polyp masks
-# mask_mode=spatial: mask patchified and added to patch embeddings (preserves spatial info)
-# Includes null condition dropout for proper CFG
-seed_everything: 1234
-tags:
-  exp: &exp PixelGen_Medical_CVC
-
-trainer:
-  default_root_dir: ./medical_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-    class_path: lightning.pytorch.loggers.WandbLogger
-    init_args:
-      project: pixelgen_medical_cvc
-      name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 100000
-  val_check_interval: 5000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 5000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-        save_dir: val_samples
-        save_compressed: true
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-      scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT_medical.JiTMedical
-    init_args:
-      input_size: 256
-      patch_size: 16
-      in_channels: 3
-      hidden_size: &hidden_dim 768
-      depth: 12
-      num_heads: 12
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.1
-      num_classes: 1
-      use_bottleneck: true
-      bottleneck_dim: 128
-      in_context_len: 32
-      in_context_start: 4
-      mask_in_channels: 1
-      mask_mode: spatial
-  conditioner:
-    class_path: src.models.conditioner.mask_conditioner.MaskConditioner
-    init_args:
-      hidden_size: *hidden_dim
-      in_channels: 1
-      img_size: 256
-      null_condition_p: 0.1
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_medical.MedicalTrainerSimple
-    init_args:
-      lognorm_t: true
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      percept_t_threshold: 0.3
-      null_condition_p: 0.1
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling_medical.EulerSamplerMedical
-    init_args:
-      num_steps: 50
-      guidance: 2.0
-      timeshift: 1.0
-      guidance_interval_min: 0.1
-      guidance_interval_max: 0.9
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-
-data:
-  train_dataset:
-    class_path: src.data.dataset.cvc_clinicdb.CVCClinicDBDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/CVC-ClinicDB
-      resolution: 256
-      split: train
-      train_ratio: 0.9
-      augment: true
-  eval_dataset:
-    class_path: src.data.dataset.cvc_clinicdb.CVCClinicDBRandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/CVC-ClinicDB
-      resolution: 256
-      max_num_instances: 200
-  pred_dataset:
-    class_path: src.data.dataset.cvc_clinicdb.CVCClinicDBRandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/CVC-ClinicDB
-      resolution: 256
-      max_num_instances: 612
-      noise_scale: 1.0
-  train_batch_size: 16
-  train_num_workers: 4
-  pred_batch_size: 16
-  pred_num_workers: 1

code/PixelGen_Medical_Kvasir.yaml

@@ -1,132 +0,0 @@
-# PixelGen Medical - Kvasir-SEG Polyp Segmentation
-# Binary mask-conditional colonoscopy image generation
-# Dataset: 1000 RGB images, binary polyp masks
-# mask_mode=spatial: mask patchified and added to patch embeddings
-# Includes null condition dropout for proper CFG
-seed_everything: 1234
-tags:
-  exp: &exp PixelGen_Medical_Kvasir
-
-trainer:
-  default_root_dir: ./medical_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-    class_path: lightning.pytorch.loggers.WandbLogger
-    init_args:
-      project: pixelgen_medical_kvasir
-      name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 100000
-  val_check_interval: 10000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 10000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-        save_dir: val_samples
-        save_compressed: true
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-      scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT_medical.JiTMedical
-    init_args:
-      input_size: 256
-      patch_size: 16
-      in_channels: 3
-      hidden_size: &hidden_dim 768
-      depth: 12
-      num_heads: 12
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.1
-      num_classes: 1
-      use_bottleneck: true
-      bottleneck_dim: 128
-      in_context_len: 32
-      in_context_start: 4
-      mask_in_channels: 1
-      mask_mode: spatial
-  conditioner:
-    class_path: src.models.conditioner.mask_conditioner.MaskConditioner
-    init_args:
-      hidden_size: *hidden_dim
-      in_channels: 1
-      img_size: 256
-      null_condition_p: 0.1
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_medical.MedicalTrainerSimple
-    init_args:
-      lognorm_t: true
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      percept_t_threshold: 0.3
-      null_condition_p: 0.1
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling_medical.EulerSamplerMedical
-    init_args:
-      num_steps: 50
-      guidance: 2.0
-      timeshift: 1.0
-      guidance_interval_min: 0.1
-      guidance_interval_max: 0.9
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-
-data:
-  train_dataset:
-    class_path: src.data.dataset.kvasir_seg.KvasirSEGDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/Kvasir-SEG/Kvasir-SEG
-      resolution: 256
-      split: train
-      train_ratio: 0.9
-      augment: true
-  eval_dataset:
-    class_path: src.data.dataset.kvasir_seg.KvasirSEGRandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/Kvasir-SEG/Kvasir-SEG
-      resolution: 256
-      max_num_instances: 200
-  pred_dataset:
-    class_path: src.data.dataset.kvasir_seg.KvasirSEGRandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/Kvasir-SEG/Kvasir-SEG
-      resolution: 256
-      max_num_instances: 1000
-      noise_scale: 1.0
-  train_batch_size: 16
-  train_num_workers: 4
-  pred_batch_size: 16
-  pred_num_workers: 1

code/PixelGen_Medical_REFUGE2.yaml

@@ -1,134 +0,0 @@
-# PixelGen Medical - REFUGE2 Optic Disc/Cup Segmentation
-# 3-class mask-conditional fundus image generation
-# Dataset: 1200 RGB images (train+val+test), 3-class masks (0/128/255)
-# mask_mode=spatial: mask patchified and added to patch embeddings
-# Includes null condition dropout for proper CFG
-seed_everything: 1234
-tags:
-  exp: &exp PixelGen_Medical_REFUGE2
-
-trainer:
-  default_root_dir: ./medical_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-    class_path: lightning.pytorch.loggers.WandbLogger
-    init_args:
-      project: pixelgen_medical_refuge2
-      name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 100000
-  val_check_interval: 10000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 10000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-        save_dir: val_samples
-        save_compressed: true
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-      scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT_medical.JiTMedical
-    init_args:
-      input_size: 256
-      patch_size: 16
-      in_channels: 3
-      hidden_size: &hidden_dim 768
-      depth: 12
-      num_heads: 12
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.1
-      num_classes: 1
-      use_bottleneck: true
-      bottleneck_dim: 128
-      in_context_len: 32
-      in_context_start: 4
-      mask_in_channels: 1
-      mask_mode: spatial
-  conditioner:
-    class_path: src.models.conditioner.mask_conditioner.MaskConditioner
-    init_args:
-      hidden_size: *hidden_dim
-      in_channels: 1
-      img_size: 256
-      null_condition_p: 0.1
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_medical.MedicalTrainerSimple
-    init_args:
-      lognorm_t: true
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      percept_t_threshold: 0.3
-      null_condition_p: 0.1
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling_medical.EulerSamplerMedical
-    init_args:
-      num_steps: 50
-      guidance: 2.0
-      timeshift: 1.0
-      guidance_interval_min: 0.1
-      guidance_interval_max: 0.9
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-
-data:
-  train_dataset:
-    class_path: src.data.dataset.refuge2.REFUGE2Dataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/REFUGE2
-      resolution: 256
-      splits:
-        - train
-        - val
-      augment: true
-      val_ratio: 0.1
-  eval_dataset:
-    class_path: src.data.dataset.refuge2.REFUGE2RandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/REFUGE2
-      resolution: 256
-      max_num_instances: 200
-  pred_dataset:
-    class_path: src.data.dataset.refuge2.REFUGE2RandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/REFUGE2
-      resolution: 256
-      max_num_instances: 1000
-      noise_scale: 1.0
-  train_batch_size: 16
-  train_num_workers: 4
-  pred_batch_size: 16
-  pred_num_workers: 1

code/README.md

@@ -1,152 +0,0 @@
-# PixelGen: Pixel Diffusion Beats Latent Diffusion with Perceptual Loss
-
-<div class="is-size-5 publication-authors", align="center",>
-  <span class="author-block">
-    <a href="https://zehong-ma.github.io/" target="_blank">Zehong Ma</a><sup>1</sup>,&nbsp;
-  </span>
-  <span class="author-block">
-    <a href="https://xuruihan.github.io/" target="_blank">Ruihan Xu</a><sup>1</sup>,&nbsp;
-  </span>
-  <span class="author-block">
-    <a href="https://www.pkuvmc.com/" target="_blank">Shiliang Zhang</a><sup>1</sup><sup>*</sup>,&nbsp;
-  </span>
-</div>
-
-<div class="is-size-5 publication-authors", align="center",>
-  <span class="author-block"><sup>1</sup>State Key Laboratory of Multimedia Information Processing, <br>School of Computer Science, Peking University,&nbsp;</span>
-</div>
-
-<h5 align="center">
-
-[![hf_paper](https://img.shields.io/badge/🤗-Paper%20In%20HF-red.svg)](https://huggingface.co/papers/2602.02493)
-[![arXiv](https://img.shields.io/badge/Arxiv-2602.02493-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2602.02493) 
-[![Home Page](https://img.shields.io/badge/Project-<Website>-blue.svg)](https://zehong-ma.github.io/PixelGen/) 
-[![Huggingface](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Online_Demo-green)](https://dd0d187fc54e4b00ee.gradio.live/) 
-[![github](https://img.shields.io/github/stars/Zehong-Ma/PixelGen.svg?style=social)](https://github.com/Zehong-Ma/PixelGen/)
-</h5>
-
-<div class="content has-text-centered">
-          <img src="./docs/static/images/t2i_vis1.jpg" style="width: 100%;"><br>
-          <span style="font-size: 0.8em; width: 100%; display: inline-block;">Figure 1: Visualization of 512x512 images generated by our PixelGen.</span>
-</div>
-
-
-## 🫖 Introduction 
-We introduce PixelGen, a simple pixel diffusion framework with perceptual loss. Instead of modeling the full image manifold, PixelGen introduces two complementary perceptual losses to guide diffusion model towards learning a more meaningful **perceptual manifold**. An LPIPS loss facilitates learning better local patterns, while a DINO-based perceptual loss strengthens global semantics. With perceptual supervision, PixelGen surpasses strong latent diffusion baselines. It achieves an FID of 5.11 on ImageNet-256 without classifier-free guidance using only 80 training epochs, and demonstrates favorable scaling performance on large-scale text-to-image generation with a GenEval score of 0.79.
-
-<div class="content">
-            <img src="./docs/static/images/intro.jpg" style="width: 100%;"><br>
-</div>
-
-- We achieve **5.11 FID** on ImageNet256x256 without CFG at 80 epochs, surpassing REPA's 5.90 FID at 800 epochs.
-- We achieve **1.83 FID** on ImageNet256x256 with CFG 160 epochs, competetive with latent diffusion models.
-- We achieve **0.79 overall score** on GenEval Benchmark with PixelGen-XXL/16.
-- **If you like our project, please kindly give us a star ⭐ on GitHub.** We hope to collaborate with you on building better pixel diffusion models, specifically looking at better samplers, CFG strategies, architectures, and refined loss design. Please feel free to reach out if you'd like to discuss ideas.
-
-## <img src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyNCAyNCIgZmlsbD0iIzg4OCI+PHBhdGggZD0iTTEyLDJBNy45LDcuOSwwLDAsMCw0LjEsOS45LDcuOCw3LjgsMCwwLDAsMiwxN2gzYTEsMSwwLDAsMCwxLDFoM3YyYTEsMSwwLDAsMCwxLDFoNHYwYTEsMSwwLDAsMCwxLTFWMThoM2ExLDEsMCwwLDAsMS0xaDNjMC0zLjgtMS42LTcuNS01LjgtOS4yQTguMSw4LjEsMCwwLDAsMTIsMlpNMTQsMTMuNWwtLjUuOGExLDEsMCwwLDEtLjkuNWgtMS4yYTEsMSwwLDAsMS0uOS0uNWwtLjUtLjhDOS4zLDEzLDksMTIuNyw5LDEyLjRzLjMtLjYsLjktMWwuNS0uOGExLDEsMCwwLDEsLjktLjVoMS4yYTEsMSwwLDAsMSwuOS41bC41LjhjLjYuNC45LjcuOSwxUzE0LjYsMTMuMSwxNCwxMy41WiIvPjwvc3ZnPg==" width="20" style="vertical-align: middle;"/> Illustration of Perceptual Manifold 
-
-
-<div class="content">
-            <img src="./docs/static/images/manifold.jpg"  style="width: 50%;"><br>
-            <span style="font-size: 0.8em; width: 85%; display: inline-block;">Illustration of different manifolds within the pixel space. The image manifold is a large manifold containing both perceptually significant information and imperceptible signals. The perceptual manifold contains perceptually important signals, providing a better target for pixel space diffusion. P-DINO and LPIPS are the two complementary perceptual supervision utilized in PixelGen.
-            </span>
-          </div>
-
-## 🧩 Visualizations
-+ Effectiveness of the perceptual losses in PixelGen.
-<div class="content">
-            <img src="./docs/static/images/empirical_vis.jpg" style="width: 50%;"><br>
-</div>
-
-+ Visualization of more images generated by our text-to-image PixelGen.
-<div class="content">
-            <img src="./docs/static/images/t2i_vis2.jpg" style="width: 100%;"><br>
-</div>
-
-+ Visualization of 256*256 images generated by our class-to-image PixelGen.
-<div class="content">
-            <img src="./docs/static/images/c2i_vis1.jpg" style="width: 100%;"><br>
-</div>
-
-## 🎉 Checkpoints
-
-| Dataset       | Epoch | Model         | Params | FID   | HuggingFace                           |
-|---------------|-------|---------------|--------|-------|---------------------------------------|
-| ImageNet256   | 80   |   PixelGen-XL/16 | 676M   | 5.11 (w/o CFG)  | [🤗](https://huggingface.co/zehongma/PixelGen/blob/main/PixelGen_XL_80ep.ckpt) |
-| ImageNet256    | 160   |   PixelGen-XL/16 | 676M   | 1.83 (w/ CFG) | [🤗](https://huggingface.co/zehongma/PixelGen/blob/main/PixelGen_XL_160ep.ckpt) |
-
-| Dataset       | Model         | Params | GenEval | HuggingFace                                              |
-|---------------|---------------|--------|------|----------------------------------------------------------|
-| Text-to-Image | PixelGen-XXL/16| 1.1B | 0.79 | [🤗](https://huggingface.co/zehongma/PixelGen/blob/main/PixelGen_XXL_T2I.ckpt) |
-## 🔥 Online Demos
-![](./docs/static/images/demo.jpg)
-We provide online demos for PixelGen-XXL/16(text-to-image) on HuggingFace Spaces.
-
-HF spaces: [https://dd0d187fc54e4b00ee.gradio.live](https://dd0d187fc54e4b00ee.gradio.live)
-
-To host the local gradio demo, run the following command:
-```bash
-# for text-to-image applications
-python app.py --config ./configs_t2i/sft_res512.yaml --ckpt_path=./ckpts/PixelGen_XXL_T2I.ckpt
-```
-
-## 🤖 Usages
-In class-to-image(ImageNet) experiments, We use [ADM evaluation suite](https://github.com/openai/guided-diffusion/tree/main/evaluations) to report FID. 
-In text-to-image experiments, we use BLIP3o dataset as training set and utilize GenEval to collect metrics.
-
-+ Environments
-```bash
-# for installation (recommend python 3.10)
-pip install -r requirements.txt
-```
-
-+ Inference
-```bash
-# for inference without CFG using 80-epoch checkpoint
-python main.py predict -c ./configs_c2i/PixelGen_XL_without_CFG.yaml --ckpt_path=./ckpts/PixelGen_XL_80ep.ckpt
-# for inference with CFG using 160-epoch checkpoint
-python main.py predict -c ./configs_c2i/PixelGen_XL.yaml --ckpt_path=./ckpts/PixelGen_XL_160ep.ckpt
-```
-
-+ Train
-```bash
-# for c2i training
-# Please modify the ImageNet1k path in the config file before training.
-python main.py fit -c ./configs_c2i/PixelGen_XL.yaml
-```
-
-```bash
-# multi-node training in lightning style, e.g., 4 nodes
-export MASTER_ADDR={Your Config}
-export MASTER_PORT={Your Config}
-export NODE_RANK={Your Config}
-export NNODES={Your Config}
-export NGPUS_PER_NODE={Your Config}
-python main.py fit -c ./configs_c2i/PixelGen_XL.yaml --trainer.num_nodes=4
-```
-
-```bash
-# for t2i training
-python main.py fit -c ./configs_t2i/pretraining_res256.yaml
-python main.py fit -c ./configs_t2i/pretraining_res512.yaml --ckpt_path=./ckpts/pretrain256.ckpt
-python main.py fit -c ./configs_t2i/sft_res512.yaml  --ckpt_path=./ckpts/pretrain512.ckpt
-```
-
-## 💐 Acknowledgement 
-
-This repository is built based on [DeCo](https://github.com/Zehong-Ma/DeCo). Thanks for their contributions and [Shuai Wang](https://github.com/WANGSSSSSSS)'s support!
-
-## 📖 Citation 
-If you find PixelGen is useful in your research or applications, please consider giving us a star ⭐ and citing it by the following BibTeX entry.
-
-```
-@article{ma2026pixelgen,
-      title={PixelGen: Pixel Diffusion Beats Latent Diffusion with Perceptual Loss}, 
-      author={Zehong Ma and Ruihan Xu and Shiliang Zhang},
-      year={2026},
-      eprint={2602.02493},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2602.02493}, 
-}
-```

code/V2_idea.md

@@ -1,143 +0,0 @@
-利用一个冻结的分割网络（SegNet）作为“监工”，在PixelGen生成图像的过程中，实时计算梯度并修正生成轨迹，强制生成的病灶形态与输入的Mask完美对齐，并具有高度的解剖学真实感。
-
-一、 实验目的 (Experimental Purpose)
-实现“生成即对齐” (Generation-Alignment Synergy): 解决传统生成模型（如Nazir ICCV'25）“生成完了再筛选”的低效问题。通过梯度引导，确保每一张生成的图像，其病灶位置都精确地落在Mask区域内。
-
-挖掘“困难样本” (Hard Example Mining): 通过引入对抗性或边界不确定性梯度，生成那些位于分割网络“决策边界”上的样本（例如：边缘模糊的积液、纹理复杂的息肉），强迫下游模型学习更鲁棒的特征。
-
-提升数据效率 (Data Efficiency): 证明通过这种“靶向生成”得到的 100 张图，比随机生成的 1000 张图对分割任务更有价值。
-
-二、 实验步骤 (Experimental Steps)
-阶段 1: 准备“导师” (Preparation)
-
-你需要一个已经训练好的分割网络（我们称之为 Guidance SegNet）。
-
-注意: 这个网络不需要非常完美，用现有真实数据训练一个标准的 U-Net 即可。它的作用是提供“什么是息肉/病灶”的梯度方向。
-
-状态: Frozen (参数冻结，requires_grad=False)，Eval 模式。
-
-阶段 2: 改造采样循环 (Modification)
-
-这是你主要修改代码的地方。在 PixelGen 的 sample 或 solve 函数中，插入梯度计算模块。
-
-核心逻辑: x_t (噪声) -> PixelGen -> x_pred (预测原图) -> Guidance SegNet -> Loss -> Gradient -> Correct x_t.
-
-阶段 3: 批量生成与验证 (Generation & Verification)
-
-使用带引导的代码生成一批数据（例如 500 张）。
-
-可视化检查: 画出采样过程（t=1.0 -> 0.0），观察梯度是如何把一团混乱的像素“拉”成一个精确的病灶的。
-
-三、 核心代码实现 (Code Implementation)
-请将以下代码逻辑集成到你的 PixelGen 采样循环中（通常在 sampler.py 或 inference.py）。
-
-假设环境:
-
-model: 你的 PixelGen 模型 (Flow Matching)。
-
-seg_model: 你的冻结分割网络 (U-Net)。
-
-mask: 输入的病灶掩码 (B, 1, H, W)。
-
-Python
-import torch
-import torch.nn.functional as F
-
-def guided_sampling_step(
-    model, 
-    seg_model, 
-    x_t, 
-    t, 
-    dt, 
-    mask, 
-    cond, 
-    guidance_scale=100.0, 
-    decay_start=0.3
-):
-    """
-    执行带梯度引导的单步 Flow Matching 采样
-    Args:
-        x_t: 当前时刻的噪声图像
-        t: 当前时间步 (float)
-        dt: 时间步长 (通常为负，如 -1/N)
-        mask: 目标分割 Mask (Ground Truth)
-        cond: PixelGen 的条件 (如 class label 或 text embedding)
-        guidance_scale: 引导强度
-        decay_start: 时间小于此值时停止引导 (保护高频纹理)
-    """
-    
-    # 1. 开启梯度计算 (关键步骤)
-    # 即使在推理阶段，我们也需要对输入 x_t 求导
-    with torch.enable_grad():
-        x_t = x_t.detach().requires_grad_(True)
-        
-        # 2. PixelGen 预测 x_0 (Clean Image Prediction)
-        # 注意：PixelGen 的优势是直接预测 x_0，而不是噪声 epsilon
-        # 这让我们能直接把预测结果喂给分割网络
-        model_output = model(x_t, t, cond)
-        x_pred = model_output['x_pred'] # 假设输出字典里有预测的原图
-        
-        # --- 梯度引导核心模块 Start ---
-        gradient = torch.zeros_like(x_t)
-        
-        # 策略：只在生成的前期和中期 (t > decay_start) 加引导
-        # 后期 (t < decay_start) 让模型专注于纹理去噪，避免破坏散斑细节
-        if t > decay_start and guidance_scale > 0:
-            
-            # (A) 将预测图送入分割网络
-            # 为了数值稳定性，最好确保 x_pred 在 [0,1] 或标准化范围内
-            # seg_input = normalize(x_pred) 
-            seg_logits = seg_model(x_pred)
-            
-            # (B) 计算对齐 Loss (Alignment Loss)
-            # 我们希望 SegNet 预测的 mask 尽可能接近 GT mask
-            # 使用 BCE Loss 或 Dice Loss
-            loss_align = F.binary_cross_entropy_with_logits(seg_logits, mask)
-            
-            # (C) [可选] 计算不确定性/对抗 Loss (Uncertainty Loss)
-            # 如果你想做 Hard Example Mining：
-            # 我们希望在 Mask 的边界区域 (Boundary)，SegNet 感到困惑 (Entropy 最大化)
-            probs = torch.sigmoid(seg_logits)
-            entropy = -(probs * torch.log(probs + 1e-6) + (1-probs) * torch.log(1-probs + 1e-6))
-            # 只关注边界的不确定性 (需要先提取 Mask 边缘)
-            # loss_adv = - entropy.mean() # 简单的对抗：让网络整体困惑
-            
-            # 总 Loss
-            total_loss = loss_align # + lambda * loss_adv
-            
-            # (D) 计算对 x_t 的梯度
-            # 这一步告诉我们：如何修改当前的噪声 x_t，才能让预测出的 x_pred 更符合 Mask
-            grad = torch.autograd.grad(total_loss, x_t)[0]
-            
-            # (E) 梯度归一化 (防止梯度爆炸毁掉图像)
-            # 这是一个工程 Trick，非常重要
-            grad_norm = torch.norm(grad.reshape(grad.shape[0], -1), dim=-1).mean()
-            grad = grad / (grad_norm + 1e-8)
-            
-            gradient = grad
-
-        # --- 梯度引导核心模块 End ---
-
-    # 3. 修正速度场 (Correct the Flow)
-    # 此时我们需要 detach，因为不再需要反向传播了
-    x_pred = x_pred.detach()
-    gradient = gradient.detach()
-    
-    # 计算原始 Flow (Velocity)
-    # v = (x_1 - x_t) / (1 - t)  -> Flow Matching 公式
-    v_theta = (x_pred - x_t.detach()) / (1 - t + 1e-5)
-    
-    # 加入梯度修正
-    # 我们希望 Loss 变小，所以我们要沿着负梯度方向移动图像
-    # 在 Flow Matching 中，这相当于修改速度场的方向
-    # 注意：这里的正负号取决于你的求解器方向，通常 Loss 下降方向是 -grad
-    v_guided = v_theta - guidance_scale * gradient
-
-    # 4. 执行欧拉步 (Euler Step)
-    x_next = x_t.detach() + dt * v_guided
-    
-    return x_next
-
-四、 代码集成的注意事项引导强度的衰减 (Time-Dependent Scheduling):在采样开始时 ($t=1.0$)，轮廓未定，建议使用较大的 guidance_scale (例如 200-500)。随着 $t$ 减小，建议线性衰减 guidance_scale。当 $t < 0.2$ 时，建议关闭引导 (set scale to 0)。因为此时图像主要在生成高频纹理，强行加分割 Loss 会导致医学图像特有的“散斑”变成奇怪的“伪影”。分割网络的输入域:PixelGen 输出的 x_pred 通常在 [-1, 1] 或 [0, 1] 之间。请确保你的 SegNet 训练时使用的归一化方式与 x_pred 一致。如果不一致，需要在代码里加一个 transform。调试技巧:先设置 guidance_scale = 0，确保代码能跑通且结果与原来一致。然后设置一个较小的 scale，打印 loss_align 的值。你应该能看到随着时间步推进，Loss 迅速下降，这说明引导生效了。
-
-五、 下一步动作 (Next Step)建议你现在的下一步是：将上述代码片段插入你的推理脚本中，选取一张 Mask，设置 guidance_scale 为一个非零值（如 100），跑一次生成，并保存中间步骤的 x_pred 图像。你需要确认：在 $t=0.5$ 左右时，生成的病灶轮廓是否已经“紧紧贴合”在 Mask 的边缘上了？如果是，你的实验就成功了一大半。

code/app.py

@@ -1,208 +0,0 @@
-#   vae:
-#     class_path: src.models.vae.LatentVAE
-#     init_args:
-#       precompute: true
-#       weight_path: /mnt/bn/wangshuai6/models/sd-vae-ft-ema/
-#   denoiser:
-#     class_path: src.models.denoiser.decoupled_improved_dit.DDT
-#     init_args:
-#       in_channels: 4
-#       patch_size: 2
-#       num_groups: 16
-#       hidden_size: &hidden_dim 1152
-#       num_blocks: 28
-#       num_encoder_blocks: 22
-#       num_classes: 1000
-#   conditioner:
-#     class_path: src.models.conditioner.LabelConditioner
-#     init_args:
-#       null_class: 1000
-#   diffusion_sampler:
-#     class_path: src.diffusion.stateful_flow_matching.sampling.EulerSampler
-#     init_args:
-#       num_steps: 250
-#       guidance: 3.0
-#       state_refresh_rate: 1
-#       guidance_interval_min: 0.3
-#       guidance_interval_max: 1.0
-#       timeshift: 1.0
-#       last_step: 0.04
-#       scheduler: *scheduler
-#       w_scheduler: src.diffusion.stateful_flow_matching.scheduling.LinearScheduler
-#       guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-#       step_fn: src.diffusion.stateful_flow_matching.sampling.ode_step_fn
-import random
-import os
-import torch
-import argparse
-from omegaconf import OmegaConf
-from src.models.autoencoder.base import fp2uint8
-from src.diffusion.base.guidance import simple_guidance_fn
-from src.diffusion.flow_matching.adam_sampling import AdamLMSamplerJiT
-from src.diffusion.flow_matching.scheduling import LinearScheduler
-from PIL import Image
-import gradio as gr
-import tempfile
-from huggingface_hub import snapshot_download
-
-
-def instantiate_class(config):
-    kwargs = config.get("init_args", {})
-    class_module, class_name = config["class_path"].rsplit(".", 1)
-    module = __import__(class_module, fromlist=[class_name])
-    args_class = getattr(module, class_name)
-    return args_class(**kwargs)
-
-def load_model(weight_dict, denoiser):
-    prefix = "ema_denoiser."
-    for k, v in denoiser.state_dict().items():
-        try:
-            v.copy_(weight_dict["state_dict"][prefix + k])
-        except:
-            print(f"Failed to copy {prefix + k} to denoiser weight")
-    return denoiser
-
-
-class Pipeline:
-    def __init__(self, vae, denoiser, conditioner, resolution):
-        self.vae = vae.cuda()
-        self.denoiser = denoiser.cuda()
-        self.conditioner = conditioner.cuda()
-        self.conditioner.compile()
-        self.resolution = resolution
-        self.tmp_dir = tempfile.TemporaryDirectory(prefix="traj_gifs_")
-        # self.denoiser.compile()
-
-    def __del__(self):
-        self.tmp_dir.cleanup()
-
-    @torch.no_grad()
-    @torch.autocast(device_type="cuda", dtype=torch.bfloat16)
-    def __call__(self, y, neg_prompt, num_images, seed, image_height, image_width, num_steps, guidance, timeshift, order):
-        diffusion_sampler = AdamLMSamplerJiT(
-            order=order,
-            scheduler=LinearScheduler(),
-            guidance_fn=simple_guidance_fn,
-            num_steps=num_steps,
-            guidance=guidance,
-            timeshift=timeshift
-        )
-        generator = torch.Generator(device="cpu").manual_seed(seed)
-        image_height = image_height // 32 * 32
-        image_width = image_width // 32 * 32
-        self.denoiser.decoder_patch_scaling_h = image_height / 512
-        self.denoiser.decoder_patch_scaling_w = image_width / 512
-        xT = torch.randn((num_images, 3, image_height, image_width), device="cpu", dtype=torch.float32,
-                         generator=generator)
-        xT = xT.to("cuda")
-        with torch.no_grad():
-            condition, uncondition = conditioner([y,]*num_images, {"negative_prompt": neg_prompt})
-
-
-        # Sample images:
-        samples, trajs = diffusion_sampler(denoiser, xT, condition, uncondition, return_x_trajs=True)
-
-        def decode_images(samples):
-            samples = vae.decode(samples)
-            samples = fp2uint8(samples)
-            samples = samples.permute(0, 2, 3, 1).cpu().numpy()
-            images = []
-            for i in range(len(samples)):
-                image = Image.fromarray(samples[i])
-                images.append(image)
-            return images
-
-        def decode_trajs(trajs):
-            cat_trajs = torch.stack(trajs, dim=0).permute(1, 0, 2, 3, 4)
-            animations = []
-            for i in range(cat_trajs.shape[0]):
-                frames = decode_images(
-                    cat_trajs[i]
-                )
-                # 生成唯一文件名（结合seed和样本索引，避免冲突）
-                gif_filename = f"{random.randint(0, 100000)}.gif"
-                gif_path = os.path.join(self.tmp_dir.name, gif_filename)
-                frames[0].save(
-                    gif_path,
-                    format="GIF",
-                    append_images=frames[1:],
-                    save_all=True,
-                    duration=200,
-                    loop=0
-                )
-                animations.append(gif_path)
-            return animations
-
-        images = decode_images(samples)
-        animations = decode_trajs(trajs)
-
-        return images, animations
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--config", type=str, default="configs_t2i/sft_res512.yaml")
-    parser.add_argument("--resolution", type=int, default=512)
-    parser.add_argument("--model_id", type=str, default="MCG-NJU/PixNerd-XXL-P16-T2I")
-    parser.add_argument("--ckpt_path", type=str, default="models")
-
-    args = parser.parse_args()
-    if not os.path.exists(args.ckpt_path):
-        snapshot_download(repo_id=args.model_id, local_dir=args.ckpt_path)
-        ckpt_path = os.path.join(args.ckpt_path, "model.ckpt")
-    else:
-        ckpt_path = args.ckpt_path
-
-    config = OmegaConf.load(args.config)
-    vae_config = config.model.vae
-    denoiser_config = config.model.denoiser
-    conditioner_config = config.model.conditioner
-
-    vae = instantiate_class(vae_config)
-    denoiser = instantiate_class(denoiser_config)
-    conditioner = instantiate_class(conditioner_config)
-
-
-    ckpt = torch.load(ckpt_path, map_location="cpu")
-    denoiser = load_model(ckpt, denoiser)
-    denoiser = denoiser.cuda()
-    vae = vae.cuda()
-    denoiser.eval()
-
-
-    pipeline = Pipeline(vae, denoiser, conditioner, args.resolution)
-
-    with gr.Blocks() as demo:
-        # gr.Markdown(f"config:{args.config}\n\n ckpt_path:{args.ckpt_path}")
-        with gr.Row():
-            with gr.Column(scale=1):
-                num_steps = gr.Slider(minimum=1, maximum=100, step=1, label="num steps", value=25)
-                guidance = gr.Slider(minimum=0.1, maximum=10.0, step=0.1, label="CFG", value=4.0)
-                image_height = gr.Slider(minimum=128, maximum=1024, step=32, label="image height", value=512)
-                image_width = gr.Slider(minimum=128, maximum=1024, step=32, label="image width", value=512)
-                num_images = gr.Slider(minimum=1, maximum=4, step=1, label="num images", value=4)
-                label = gr.Textbox(label="positive prompt", value="A beautiful woman.")
-                neg_label = gr.Textbox(label="negative prompt", value="Unrealistic, JPEG artifacts.")
-                seed = gr.Slider(minimum=0, maximum=1000000, step=1, label="seed", value=0)
-                timeshift = gr.Slider(minimum=0.1, maximum=5.0, step=0.1, label="timeshift", value=3.0)
-                order = gr.Slider(minimum=1, maximum=4, step=1, label="order", value=2)
-            with gr.Column(scale=2):
-                btn = gr.Button("Generate")
-                output_sample = gr.Gallery(label="Images", columns=2, rows=2)
-            with gr.Column(scale=2):
-                output_trajs = gr.Gallery(label="Trajs of Diffusion", columns=2, rows=2)
-
-        btn.click(fn=pipeline,
-                  inputs=[
-                      label,
-                      neg_label,
-                      num_images,
-                      seed,
-                      image_height,
-                      image_width,
-                      num_steps,
-                      guidance,
-                      timeshift,
-                      order
-                  ], outputs=[output_sample, output_trajs])
-    # demo.launch(server_name="0.0.0.0", server_port=23231)
-    demo.launch(share=True, server_name="0.0.0.0", server_port=23231)

code/config.yaml

@@ -1,239 +0,0 @@
-# lightning.pytorch==2.6.1
-seed_everything: 1234
-torch_hub_dir: null
-huggingface_cache_dir: null
-trainer:
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-    class_path: lightning.pytorch.loggers.WandbLogger
-    init_args:
-      name: PixelGen_Medical_REFUGE2
-      save_dir: .
-      version: null
-      offline: false
-      dir: null
-      id: null
-      anonymous: null
-      project: pixelgen_medical_refuge2
-      log_model: false
-      experiment: null
-      prefix: ''
-      checkpoint_name: null
-      add_file_policy: mutable
-      entity: null
-      notes: null
-      tags: null
-      config: null
-      config_exclude_keys: null
-      config_include_keys: null
-      allow_val_change: null
-      group: null
-      job_type: null
-      mode: null
-      force: null
-      reinit: null
-      resume: null
-      resume_from: null
-      fork_from: null
-      save_code: null
-      tensorboard: null
-      sync_tensorboard: null
-      monitor_gym: null
-      settings: null
-  callbacks:
-  - class_path: src.callbacks.model_checkpoint.CheckpointHook
-    init_args:
-      dirpath: null
-      filename: null
-      monitor: null
-      verbose: false
-      save_last: true
-      save_top_k: -1
-      save_on_exception: false
-      save_weights_only: false
-      mode: min
-      auto_insert_metric_name: true
-      every_n_train_steps: 10000
-      train_time_interval: null
-      every_n_epochs: null
-      save_on_train_epoch_end: null
-      enable_version_counter: true
-  - class_path: src.callbacks.save_images.SaveImagesHook
-    init_args:
-      save_dir: val_samples
-      save_compressed: true
-  fast_dev_run: false
-  max_epochs: null
-  min_epochs: null
-  max_steps: 100000
-  min_steps: null
-  max_time: null
-  limit_train_batches: null
-  limit_val_batches: null
-  limit_test_batches: null
-  limit_predict_batches: null
-  overfit_batches: 0.0
-  val_check_interval: 10000
-  check_val_every_n_epoch: null
-  num_sanity_val_steps: 0
-  log_every_n_steps: 50
-  enable_checkpointing: null
-  enable_progress_bar: null
-  enable_model_summary: null
-  accumulate_grad_batches: 1
-  gradient_clip_val: null
-  gradient_clip_algorithm: null
-  deterministic: null
-  benchmark: null
-  inference_mode: true
-  use_distributed_sampler: false
-  profiler: null
-  detect_anomaly: false
-  barebones: false
-  plugins:
-  - class_path: src.plugins.bd_env.BDEnvironment
-  sync_batchnorm: false
-  reload_dataloaders_every_n_epochs: 0
-  default_root_dir: ./medical_workdirs
-  enable_autolog_hparams: true
-  model_registry: null
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-      scale: 1.0
-      shift: 0.0
-  conditioner:
-    class_path: src.models.conditioner.mask_conditioner.MaskConditioner
-    init_args:
-      hidden_size: 768
-      in_channels: 1
-      img_size: 256
-      null_condition_p: 0.1
-  denoiser:
-    class_path: src.models.transformer.JiT_medical.JiTMedical
-    init_args:
-      input_size: 256
-      patch_size: 16
-      in_channels: 3
-      hidden_size: 768
-      depth: 12
-      num_heads: 12
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.1
-      num_classes: 1
-      bottleneck_dim: 128
-      use_bottleneck: true
-      in_context_len: 32
-      in_context_start: 4
-      mask_in_channels: 1
-      mask_mode: spatial
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_medical.MedicalTrainerSimple
-    init_args:
-      scheduler:
-        class_path: src.diffusion.flow_matching.scheduling.LinearScheduler
-      loss_weight_fn: src.diffusion.flow_matching.training_medical.constant
-      lognorm_t: true
-      timeshift: 1.0
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      lpips_weight: 0.1
-      percept_t_threshold: 0.3
-      noise_scale: 1.0
-      patch_size: 16
-      null_condition_p: 0.1
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling_medical.EulerSamplerMedical
-    init_args:
-      w_scheduler:
-        class_path: src.diffusion.flow_matching.scheduling.LinearScheduler
-      timeshift: 1.0
-      guidance_interval_min: 0.1
-      guidance_interval_max: 0.9
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-      last_step: null
-      last_step_fn: src.diffusion.flow_matching.sampling_medical.ode_step_fn
-      t_eps: 0.05
-      scheduler:
-        class_path: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      num_steps: 50
-      guidance: 2.0
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-      every_n_steps: 1
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 0.0001
-      betas:
-      - 0.9
-      - 0.999
-      eps: 1.0e-08
-      weight_decay: 0.0
-      amsgrad: false
-      maximize: false
-      foreach: null
-      capturable: false
-      differentiable: false
-      fused: null
-  lr_scheduler: null
-  eval_original_model: false
-data:
-  train_dataset:
-    class_path: src.data.dataset.refuge2.REFUGE2Dataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/REFUGE2
-      resolution: 256
-      splits:
-      - train
-      - val
-      augment: true
-      seed: 42
-      max_samples: null
-      random_flip: true
-      val_ratio: 0.1
-  eval_dataset:
-    class_path: src.data.dataset.refuge2.REFUGE2RandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/REFUGE2
-      resolution: 256
-      max_num_instances: 200
-      noise_scale: 1.0
-      seed: 42
-      splits:
-      - train
-      - val
-      - test
-  pred_dataset:
-    class_path: src.data.dataset.refuge2.REFUGE2RandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/REFUGE2
-      resolution: 256
-      max_num_instances: 1000
-      noise_scale: 1.0
-      seed: 42
-      splits:
-      - train
-      - val
-      - test
-  train_batch_size: 16
-  train_num_workers: 4
-  train_prefetch_factor: 8
-  eval_batch_size: 32
-  eval_num_workers: 4
-  pred_batch_size: 16
-  pred_num_workers: 1
-tags:
-  exp: PixelGen_Medical_REFUGE2
-ckpt_path: null
-weights_only: null

code/configs_c2i/JiT_Large_Baseline.yaml

@@ -1,134 +0,0 @@
-# lightning.pytorch==2.4.0
-seed_everything: true
-tags:
-  exp: &exp JiT_REPA_256_baseline
-huggingface_cache_dir: null
-trainer:
-  default_root_dir: ./universal_pix_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-      class_path: lightning.pytorch.loggers.WandbLogger
-      init_args:
-        project: universal_flow_JiT_bs256
-        name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 200000
-  val_check_interval: 100000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 100000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-         save_dir: val_ode50_cfg2.0
-         save_compressed: true
-    # - class_path: lightning.pytorch.callbacks.LearningRateMonitor
-    #   init_args:
-    #     logging_interval: 'step'
-    #     log_momentum: False  
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-       scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT.JiT
-    init_args:
-      input_size: 256
-      patch_size: 16
-      in_channels: 3
-      hidden_size: &hidden_dim 1024
-      depth: 24
-      num_heads: 16
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.0
-      num_classes: &num_classes 1000
-      bottleneck_dim: 128
-      in_context_len: 32
-      in_context_start: 8
-  conditioner:
-    class_path: src.models.conditioner.class_label.LabelConditioner
-    init_args:
-      num_classes: *num_classes
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_repa_JiT.REPATrainer
-    init_args:
-      lognorm_t: true
-      encoder:
-        class_path: src.models.encoder.DINOv2
-        init_args:
-          weight_path: ~/.cache/torch/hub/checkpoints/dinov2_vitb14_pretrain.pth
-      align_layer: 8
-      proj_denoiser_dim: *hidden_dim
-      proj_hidden_dim: *hidden_dim
-      proj_encoder_dim: 768
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling.EulerSamplerJiT
-    init_args:
-      num_steps: 50
-      guidance: 1.0
-      guidance_interval_min: 0.1
-      guidance_interval_max: 1.0
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-  # lr_scheduler:
-  #   class_path: src.utils.lr_scheduler.ConstantWithWarmup
-  #   init_args:
-  #     num_warmup_steps: 5005
-data:
-  train_dataset:
-    class_path: src.data.dataset.imagenet.PixImageNet
-    init_args:
-      root: /data/datasets/ImageNet-1K/train
-      resolution: 256
-  eval_dataset:
-    class_path: src.data.dataset.randn.ClassLabelRandomNDataset
-    init_args:
-      num_classes: 1000
-      max_num_instances: 50000
-      latent_shape:
-        - 3
-        - 256
-        - 256
-  pred_dataset:
-    class_path: src.data.dataset.randn.ClassLabelRandomNDataset
-    init_args:
-      num_classes: *num_classes
-      max_num_instances: 50000
-      latent_shape:
-        - 3
-        - 256
-        - 256
-  train_batch_size: 32
-  train_num_workers: 4
-  pred_batch_size: 32
-  pred_num_workers: 1

code/configs_c2i/PixelGen_Large.yaml

@@ -1,137 +0,0 @@
-# lightning.pytorch==2.4.0
-seed_everything: true
-tags:
-  exp: &exp PixelGen_Large
-huggingface_cache_dir: null
-trainer:
-  default_root_dir: ./universal_pix_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-      class_path: lightning.pytorch.loggers.WandbLogger
-      init_args:
-        project: universal_flow_JiT_bs256
-        name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 200000
-  val_check_interval: 100000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 100000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-         save_dir: val_ode50_cfg1.0
-         save_compressed: true
-    # - class_path: lightning.pytorch.callbacks.LearningRateMonitor
-    #   init_args:
-    #     logging_interval: 'step'
-    #     log_momentum: False  
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-       scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT.JiT
-    init_args:
-      input_size: 256
-      patch_size: 16
-      in_channels: 3
-      hidden_size: &hidden_dim 1024
-      depth: 24
-      num_heads: 16
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.0
-      num_classes: &num_classes 1000
-      bottleneck_dim: 128
-      in_context_len: 32
-      in_context_start: 8
-  conditioner:
-    class_path: src.models.conditioner.class_label.LabelConditioner
-    init_args:
-      num_classes: *num_classes
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_repa_JiT_LPIPS_DINO_NoiseGating.REPATrainer # see other trainers for ablation experiments.
-    init_args:
-      lognorm_t: true
-      encoder:
-        class_path: src.models.encoder.DINOv2
-        init_args:
-          weight_path: ~/.cache/torch/hub/checkpoints/dinov2_vitb14_pretrain.pth
-      align_layer: 8
-      proj_denoiser_dim: *hidden_dim
-      proj_hidden_dim: *hidden_dim
-      proj_encoder_dim: 768
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1 # LPIPS loss, 0.0 to disable
-      dino_weight: 0.01 # P-DINO loss, 0.0 to disable
-      percept_t_threshold: 0.3 # Noise-Gating, 0.0 to disable
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling.EulerSamplerJiT
-    init_args:
-      num_steps: 50
-      guidance: 1.0
-      guidance_interval_min: 0.1
-      guidance_interval_max: 1.0
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-  # lr_scheduler:
-  #   class_path: src.utils.lr_scheduler.ConstantWithWarmup
-  #   init_args:
-  #     num_warmup_steps: 5005
-data:
-  train_dataset:
-    class_path: src.data.dataset.imagenet.PixImageNet
-    init_args:
-      root: /data/datasets/ImageNet-1K/train
-      resolution: 256
-  eval_dataset:
-    class_path: src.data.dataset.randn.ClassLabelRandomNDataset
-    init_args:
-      num_classes: 1000
-      max_num_instances: 50000
-      latent_shape:
-        - 3
-        - 256
-        - 256
-  pred_dataset:
-    class_path: src.data.dataset.randn.ClassLabelRandomNDataset
-    init_args:
-      num_classes: *num_classes
-      max_num_instances: 50000
-      latent_shape:
-        - 3
-        - 256
-        - 256
-  train_batch_size: 32
-  train_num_workers: 4
-  pred_batch_size: 32
-  pred_num_workers: 1

code/configs_c2i/PixelGen_XL.yaml

@@ -1,141 +0,0 @@
-# lightning.pytorch==2.4.0
-seed_everything: 1234
-tags:
-  exp: &exp PixelGen_XL
-huggingface_cache_dir: null
-trainer:
-  default_root_dir: ./universal_pix_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-      class_path: lightning.pytorch.loggers.WandbLogger
-      init_args:
-        project: universal_flow_JiT_XL_bs256
-        name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 800000 # 800k for 160 epochs, 4000k for 800 epochs
-  val_check_interval: 100000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 100000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-         save_dir: val_ode50_cfg2.25
-         save_compressed: true
-    # - class_path: lightning.pytorch.callbacks.LearningRateMonitor
-    #   init_args:
-    #     logging_interval: 'step'
-    #     log_momentum: False  
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-       scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT.JiT
-    init_args:
-      input_size: 256
-      patch_size: 16
-      in_channels: 3
-      hidden_size: &hidden_dim 1152
-      depth: 28
-      num_heads: 16
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.1 # please enable proj_drop when training a DiT-XL with lots of parameters
-      num_classes: &num_classes 1000
-      use_bottleneck: false
-      in_context_len: 32
-      in_context_start: 8
-  conditioner:
-    class_path: src.models.conditioner.class_label.LabelConditioner
-    init_args:
-      num_classes: *num_classes
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_repa_JiT_LPIPS_DINO_NoiseGating.REPATrainer
-    init_args:
-      lognorm_t: true
-      encoder:
-        class_path: src.models.encoder.DINOv2
-        init_args:
-          weight_path: ~/.cache/torch/hub/checkpoints/dinov2_vitb14_pretrain.pth
-      align_layer: 8
-      proj_denoiser_dim: *hidden_dim
-      proj_hidden_dim: *hidden_dim
-      proj_encoder_dim: 768
-      null_condition_p: 0.1
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      dino_weight: 0.01
-      percept_t_threshold: 0.3
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling.HeunSamplerJiT
-    init_args:
-      exact_henu: true
-      num_steps: 50
-      guidance: 2.25 # Guidance scale is set to 2.25 for 160 epochs. Please set to 1.0 for the experiments without CFG.
-      timeshift: 2.0 # For XL version, the timesift is set to 2.0 during all inference.
-      guidance_interval_min: 0.1
-      guidance_interval_max: 0.9
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-  # lr_scheduler:
-  #   class_path: src.utils.lr_scheduler.ConstantWithWarmup
-  #   init_args:
-  #     num_warmup_steps: 5005
-data:
-  train_dataset:
-    class_path: src.data.dataset.imagenet.PixImageNet
-    init_args:
-      root: /data/datasets/ImageNet-1K/train # set your data dir
-      resolution: 256
-  eval_dataset:
-    class_path: src.data.dataset.randn.ClassLabelRandomNDataset
-    init_args:
-      num_classes: 1000
-      max_num_instances: 50000
-      latent_shape:
-        - 3
-        - 256
-        - 256
-  pred_dataset:
-    class_path: src.data.dataset.randn.ClassLabelRandomNDataset
-    init_args:
-      num_classes: *num_classes
-      max_num_instances: 50000
-      noise_scale: 1.0
-      latent_shape:
-        - 3
-        - 256
-        - 256
-  train_batch_size: 32
-  train_num_workers: 4
-  pred_batch_size: 32
-  pred_num_workers: 1

code/configs_c2i/PixelGen_XL_without_CFG.yaml

@@ -1,141 +0,0 @@
-# lightning.pytorch==2.4.0
-seed_everything: true
-tags:
-  exp: &exp PixelGen_XL
-huggingface_cache_dir: null
-trainer:
-  default_root_dir: ./universal_pix_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-      class_path: lightning.pytorch.loggers.WandbLogger
-      init_args:
-        project: universal_flow_JiT_XL_bs256
-        name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 800000 # 800k for 160 epochs, 4000k for 800 epochs
-  val_check_interval: 100000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 100000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-         save_dir: val_ode50_cfg1.0
-         save_compressed: true
-    # - class_path: lightning.pytorch.callbacks.LearningRateMonitor
-    #   init_args:
-    #     logging_interval: 'step'
-    #     log_momentum: False  
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-       scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT.JiT
-    init_args:
-      input_size: 256
-      patch_size: 16
-      in_channels: 3
-      hidden_size: &hidden_dim 1152
-      depth: 28
-      num_heads: 16
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.1 # please enable proj_drop when training a DiT-XL with lots of parameters
-      num_classes: &num_classes 1000
-      use_bottleneck: false
-      in_context_len: 32
-      in_context_start: 8
-  conditioner:
-    class_path: src.models.conditioner.class_label.LabelConditioner
-    init_args:
-      num_classes: *num_classes
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_repa_JiT_LPIPS_DINO_NoiseGating.REPATrainer
-    init_args:
-      lognorm_t: true
-      encoder:
-        class_path: src.models.encoder.DINOv2
-        init_args:
-          weight_path: ~/.cache/torch/hub/checkpoints/dinov2_vitb14_pretrain.pth
-      align_layer: 8
-      proj_denoiser_dim: *hidden_dim
-      proj_hidden_dim: *hidden_dim
-      proj_encoder_dim: 768
-      null_condition_p: 0.1
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      dino_weight: 0.01
-      percept_t_threshold: 0.3
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling.HeunSamplerJiT
-    init_args:
-      exact_henu: true
-      num_steps: 50
-      guidance: 1.0 # Guidance scale is set to 1.0 for inferring with 80 epochs ckpt without CFG.
-      timeshift: 2.0 # For XL version, the timesift is set to 2.0 during all inference.
-      guidance_interval_min: 0.1
-      guidance_interval_max: 0.9
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-  # lr_scheduler:
-  #   class_path: src.utils.lr_scheduler.ConstantWithWarmup
-  #   init_args:
-  #     num_warmup_steps: 5005
-data:
-  train_dataset:
-    class_path: src.data.dataset.imagenet.PixImageNet
-    init_args:
-      root: /data/datasets/ImageNet-1K/train # set your data dir
-      resolution: 256
-  eval_dataset:
-    class_path: src.data.dataset.randn.ClassLabelRandomNDataset
-    init_args:
-      num_classes: 1000
-      max_num_instances: 50000
-      latent_shape:
-        - 3
-        - 256
-        - 256
-  pred_dataset:
-    class_path: src.data.dataset.randn.ClassLabelRandomNDataset
-    init_args:
-      num_classes: *num_classes
-      max_num_instances: 50000
-      noise_scale: 1.0
-      latent_shape:
-        - 3
-        - 256
-        - 256
-  train_batch_size: 32
-  train_num_workers: 4
-  pred_batch_size: 32
-  pred_num_workers: 1

code/configs_medical/PixelGen_Medical_B16.yaml

@@ -1,128 +0,0 @@
-# PixelGen Medical - Base model with LPIPS loss
-# For OCTA500 mask-conditional image generation
-seed_everything: 1234
-tags:
-  exp: &exp PixelGen_Medical_B16
-
-trainer:
-  default_root_dir: ./medical_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-    class_path: lightning.pytorch.loggers.WandbLogger
-    init_args:
-      project: pixelgen_medical_octa500
-      name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 100000
-  val_check_interval: 10000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 10000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-        save_dir: val_samples
-        save_compressed: true
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-      scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT_medical.JiTMedical
-    init_args:
-      input_size: 256
-      patch_size: 16
-      in_channels: 3
-      hidden_size: &hidden_dim 768
-      depth: 12
-      num_heads: 12
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.1
-      num_classes: 1
-      use_bottleneck: true
-      bottleneck_dim: 128
-      in_context_len: 32
-      in_context_start: 4
-      mask_in_channels: 1
-  conditioner:
-    class_path: src.models.conditioner.mask_conditioner.MaskConditioner
-    init_args:
-      hidden_size: *hidden_dim
-      in_channels: 1
-      img_size: 256
-      null_condition_p: 0.1
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_medical.MedicalTrainerSimple
-    init_args:
-      lognorm_t: true
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      percept_t_threshold: 0.3
-      null_condition_p: 0.1
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling_medical.EulerSamplerMedical
-    init_args:
-      num_steps: 50
-      guidance: 2.0
-      timeshift: 1.0
-      guidance_interval_min: 0.1
-      guidance_interval_max: 0.9
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-
-data:
-  train_dataset:
-    class_path: src.data.dataset.octa500.OCTA500Dataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/OCTA500  # Set your OCTA500 path
-      resolution: 256
-      split: train
-      train_ratio: 0.9
-      augment: true
-  eval_dataset:
-    class_path: src.data.dataset.octa500.OCTA500RandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/OCTA500
-      resolution: 256
-      max_num_instances: 1000
-  pred_dataset:
-    class_path: src.data.dataset.octa500.OCTA500RandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/OCTA500
-      resolution: 256
-      max_num_instances: 5000
-      noise_scale: 1.0
-  train_batch_size: 32
-  train_num_workers: 4
-  pred_batch_size: 32
-  pred_num_workers: 1

code/configs_medical/PixelGen_Medical_CVC_B16_CrossAttn.yaml

@@ -1,137 +0,0 @@
-# PixelGen Medical - CVC-ClinicDB Polyp Segmentation - Binary mask-conditional colonoscopy image generation
-# Model: JiTMedical-B/16, mask_mode=cross_attention
-# Optimized for 2x H800 80GB
-seed_everything: 1234
-tags:
-  exp: &exp PixelGen_Medical_CVC_B16_CrossAttn
-
-trainer:
-  default_root_dir: ./medical_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-    class_path: lightning.pytorch.loggers.WandbLogger
-    init_args:
-      project: pixelgen_medical_cvc
-      name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 100000
-  val_check_interval: 5000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 5000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-        save_dir: val_samples
-        save_compressed: true
-    - class_path: src.callbacks.medical_visualization.MedicalVisualizationCallback
-      init_args:
-        every_n_steps: 5000
-        num_samples: 8
-        num_sampling_steps: 50
-        cfg_scale: 2.0
-        save_dir: training_vis
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-      scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT_medical.JiTMedical
-    init_args:
-      input_size: 256
-      patch_size: 16
-      in_channels: 3
-      hidden_size: &hidden_dim 768
-      depth: 12
-      num_heads: 12
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.1
-      num_classes: 1
-      use_bottleneck: true
-      bottleneck_dim: 128
-      in_context_len: 32
-      in_context_start: 4
-      mask_in_channels: 1
-      mask_mode: cross_attention
-  conditioner:
-    class_path: src.models.conditioner.mask_conditioner.MaskConditioner
-    init_args:
-      hidden_size: *hidden_dim
-      in_channels: 1
-      img_size: 256
-      null_condition_p: 0.1
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_medical.MedicalTrainerSimple
-    init_args:
-      lognorm_t: true
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      percept_t_threshold: 0.3
-      null_condition_p: 0.1
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling_medical.EulerSamplerMedical
-    init_args:
-      num_steps: 50
-      guidance: 2.0
-      timeshift: 1.0
-      guidance_interval_min: 0.1
-      guidance_interval_max: 0.9
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-
-data:
-  train_dataset:
-    class_path: src.data.dataset.cvc_clinicdb.CVCClinicDBDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/CVC-ClinicDB
-      resolution: 256
-      split: train
-      train_ratio: 0.9
-      augment: true
-  eval_dataset:
-    class_path: src.data.dataset.cvc_clinicdb.CVCClinicDBRandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/CVC-ClinicDB
-      resolution: 256
-      max_num_instances: 200
-  pred_dataset:
-    class_path: src.data.dataset.cvc_clinicdb.CVCClinicDBRandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/CVC-ClinicDB
-      resolution: 256
-      max_num_instances: 612
-      noise_scale: 1.0
-  train_batch_size: 96
-  train_num_workers: 4
-  pred_batch_size: 32
-  pred_num_workers: 1

code/configs_medical/PixelGen_Medical_CVC_B8_Spatial.yaml

@@ -1,137 +0,0 @@
-# PixelGen Medical - CVC-ClinicDB Polyp Segmentation - Binary mask-conditional colonoscopy image generation
-# Model: JiTMedical-B/8, mask_mode=spatial
-# Optimized for 2x H800 80GB
-seed_everything: 1234
-tags:
-  exp: &exp PixelGen_Medical_CVC_B8_Spatial
-
-trainer:
-  default_root_dir: ./medical_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-    class_path: lightning.pytorch.loggers.WandbLogger
-    init_args:
-      project: pixelgen_medical_cvc
-      name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 100000
-  val_check_interval: 5000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 5000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-        save_dir: val_samples
-        save_compressed: true
-    - class_path: src.callbacks.medical_visualization.MedicalVisualizationCallback
-      init_args:
-        every_n_steps: 5000
-        num_samples: 8
-        num_sampling_steps: 50
-        cfg_scale: 2.0
-        save_dir: training_vis
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-      scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT_medical.JiTMedical
-    init_args:
-      input_size: 256
-      patch_size: 8
-      in_channels: 3
-      hidden_size: &hidden_dim 768
-      depth: 12
-      num_heads: 12
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.1
-      num_classes: 1
-      use_bottleneck: true
-      bottleneck_dim: 128
-      in_context_len: 64
-      in_context_start: 4
-      mask_in_channels: 1
-      mask_mode: spatial
-  conditioner:
-    class_path: src.models.conditioner.mask_conditioner.MaskConditioner
-    init_args:
-      hidden_size: *hidden_dim
-      in_channels: 1
-      img_size: 256
-      null_condition_p: 0.1
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_medical.MedicalTrainerSimple
-    init_args:
-      lognorm_t: true
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      percept_t_threshold: 0.3
-      null_condition_p: 0.1
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling_medical.EulerSamplerMedical
-    init_args:
-      num_steps: 50
-      guidance: 2.0
-      timeshift: 1.0
-      guidance_interval_min: 0.1
-      guidance_interval_max: 0.9
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-
-data:
-  train_dataset:
-    class_path: src.data.dataset.cvc_clinicdb.CVCClinicDBDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/CVC-ClinicDB
-      resolution: 256
-      split: train
-      train_ratio: 0.9
-      augment: true
-  eval_dataset:
-    class_path: src.data.dataset.cvc_clinicdb.CVCClinicDBRandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/CVC-ClinicDB
-      resolution: 256
-      max_num_instances: 200
-  pred_dataset:
-    class_path: src.data.dataset.cvc_clinicdb.CVCClinicDBRandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/CVC-ClinicDB
-      resolution: 256
-      max_num_instances: 612
-      noise_scale: 1.0
-  train_batch_size: 32
-  train_num_workers: 4
-  pred_batch_size: 32
-  pred_num_workers: 1

code/configs_medical/PixelGen_Medical_CVC_S8_Spatial.yaml

@@ -1,137 +0,0 @@
-# PixelGen Medical - CVC-ClinicDB Polyp Segmentation - Binary mask-conditional colonoscopy image generation
-# Model: JiTMedical-S/8, mask_mode=spatial
-# Optimized for 2x H800 80GB
-seed_everything: 1234
-tags:
-  exp: &exp PixelGen_Medical_CVC_S8_Spatial
-
-trainer:
-  default_root_dir: ./medical_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-    class_path: lightning.pytorch.loggers.WandbLogger
-    init_args:
-      project: pixelgen_medical_cvc
-      name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 100000
-  val_check_interval: 5000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 5000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-        save_dir: val_samples
-        save_compressed: true
-    - class_path: src.callbacks.medical_visualization.MedicalVisualizationCallback
-      init_args:
-        every_n_steps: 5000
-        num_samples: 8
-        num_sampling_steps: 50
-        cfg_scale: 2.0
-        save_dir: training_vis
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-      scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT_medical.JiTMedical
-    init_args:
-      input_size: 256
-      patch_size: 8
-      in_channels: 3
-      hidden_size: &hidden_dim 512
-      depth: 8
-      num_heads: 8
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.1
-      num_classes: 1
-      use_bottleneck: true
-      bottleneck_dim: 64
-      in_context_len: 64
-      in_context_start: 2
-      mask_in_channels: 1
-      mask_mode: spatial
-  conditioner:
-    class_path: src.models.conditioner.mask_conditioner.MaskConditioner
-    init_args:
-      hidden_size: *hidden_dim
-      in_channels: 1
-      img_size: 256
-      null_condition_p: 0.1
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_medical.MedicalTrainerSimple
-    init_args:
-      lognorm_t: true
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      percept_t_threshold: 0.3
-      null_condition_p: 0.1
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling_medical.EulerSamplerMedical
-    init_args:
-      num_steps: 50
-      guidance: 2.0
-      timeshift: 1.0
-      guidance_interval_min: 0.1
-      guidance_interval_max: 0.9
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-
-data:
-  train_dataset:
-    class_path: src.data.dataset.cvc_clinicdb.CVCClinicDBDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/CVC-ClinicDB
-      resolution: 256
-      split: train
-      train_ratio: 0.9
-      augment: true
-  eval_dataset:
-    class_path: src.data.dataset.cvc_clinicdb.CVCClinicDBRandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/CVC-ClinicDB
-      resolution: 256
-      max_num_instances: 200
-  pred_dataset:
-    class_path: src.data.dataset.cvc_clinicdb.CVCClinicDBRandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/CVC-ClinicDB
-      resolution: 256
-      max_num_instances: 612
-      noise_scale: 1.0
-  train_batch_size: 64
-  train_num_workers: 4
-  pred_batch_size: 32
-  pred_num_workers: 1

code/configs_medical/PixelGen_Medical_Kvasir_B16_CrossAttn.yaml

@@ -1,137 +0,0 @@
-# PixelGen Medical - Kvasir-SEG Polyp Segmentation - Binary mask-conditional colonoscopy image generation
-# Model: JiTMedical-B/16, mask_mode=cross_attention
-# Optimized for 2x H800 80GB
-seed_everything: 1234
-tags:
-  exp: &exp PixelGen_Medical_Kvasir_B16_CrossAttn
-
-trainer:
-  default_root_dir: ./medical_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-    class_path: lightning.pytorch.loggers.WandbLogger
-    init_args:
-      project: pixelgen_medical_kvasir
-      name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 100000
-  val_check_interval: 10000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 10000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-        save_dir: val_samples
-        save_compressed: true
-    - class_path: src.callbacks.medical_visualization.MedicalVisualizationCallback
-      init_args:
-        every_n_steps: 5000
-        num_samples: 8
-        num_sampling_steps: 50
-        cfg_scale: 2.0
-        save_dir: training_vis
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-      scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT_medical.JiTMedical
-    init_args:
-      input_size: 256
-      patch_size: 16
-      in_channels: 3
-      hidden_size: &hidden_dim 768
-      depth: 12
-      num_heads: 12
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.1
-      num_classes: 1
-      use_bottleneck: true
-      bottleneck_dim: 128
-      in_context_len: 32
-      in_context_start: 4
-      mask_in_channels: 1
-      mask_mode: cross_attention
-  conditioner:
-    class_path: src.models.conditioner.mask_conditioner.MaskConditioner
-    init_args:
-      hidden_size: *hidden_dim
-      in_channels: 1
-      img_size: 256
-      null_condition_p: 0.1
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_medical.MedicalTrainerSimple
-    init_args:
-      lognorm_t: true
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      percept_t_threshold: 0.3
-      null_condition_p: 0.1
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling_medical.EulerSamplerMedical
-    init_args:
-      num_steps: 50
-      guidance: 2.0
-      timeshift: 1.0
-      guidance_interval_min: 0.1
-      guidance_interval_max: 0.9
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-
-data:
-  train_dataset:
-    class_path: src.data.dataset.kvasir_seg.KvasirSEGDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/Kvasir-SEG/Kvasir-SEG
-      resolution: 256
-      split: train
-      train_ratio: 0.9
-      augment: true
-  eval_dataset:
-    class_path: src.data.dataset.kvasir_seg.KvasirSEGRandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/Kvasir-SEG/Kvasir-SEG
-      resolution: 256
-      max_num_instances: 200
-  pred_dataset:
-    class_path: src.data.dataset.kvasir_seg.KvasirSEGRandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/Kvasir-SEG/Kvasir-SEG
-      resolution: 256
-      max_num_instances: 1000
-      noise_scale: 1.0
-  train_batch_size: 96
-  train_num_workers: 4
-  pred_batch_size: 32
-  pred_num_workers: 1

code/configs_medical/PixelGen_Medical_Kvasir_B8_Spatial.yaml

@@ -1,137 +0,0 @@
-# PixelGen Medical - Kvasir-SEG Polyp Segmentation - Binary mask-conditional colonoscopy image generation
-# Model: JiTMedical-B/8, mask_mode=spatial
-# Optimized for 2x H800 80GB
-seed_everything: 1234
-tags:
-  exp: &exp PixelGen_Medical_Kvasir_B8_Spatial
-
-trainer:
-  default_root_dir: ./medical_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-    class_path: lightning.pytorch.loggers.WandbLogger
-    init_args:
-      project: pixelgen_medical_kvasir
-      name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 100000
-  val_check_interval: 10000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 10000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-        save_dir: val_samples
-        save_compressed: true
-    - class_path: src.callbacks.medical_visualization.MedicalVisualizationCallback
-      init_args:
-        every_n_steps: 5000
-        num_samples: 8
-        num_sampling_steps: 50
-        cfg_scale: 2.0
-        save_dir: training_vis
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-      scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT_medical.JiTMedical
-    init_args:
-      input_size: 256
-      patch_size: 8
-      in_channels: 3
-      hidden_size: &hidden_dim 768
-      depth: 12
-      num_heads: 12
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.1
-      num_classes: 1
-      use_bottleneck: true
-      bottleneck_dim: 128
-      in_context_len: 64
-      in_context_start: 4
-      mask_in_channels: 1
-      mask_mode: spatial
-  conditioner:
-    class_path: src.models.conditioner.mask_conditioner.MaskConditioner
-    init_args:
-      hidden_size: *hidden_dim
-      in_channels: 1
-      img_size: 256
-      null_condition_p: 0.1
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_medical.MedicalTrainerSimple
-    init_args:
-      lognorm_t: true
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      percept_t_threshold: 0.3
-      null_condition_p: 0.1
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling_medical.EulerSamplerMedical
-    init_args:
-      num_steps: 50
-      guidance: 2.0
-      timeshift: 1.0
-      guidance_interval_min: 0.1
-      guidance_interval_max: 0.9
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-
-data:
-  train_dataset:
-    class_path: src.data.dataset.kvasir_seg.KvasirSEGDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/Kvasir-SEG/Kvasir-SEG
-      resolution: 256
-      split: train
-      train_ratio: 0.9
-      augment: true
-  eval_dataset:
-    class_path: src.data.dataset.kvasir_seg.KvasirSEGRandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/Kvasir-SEG/Kvasir-SEG
-      resolution: 256
-      max_num_instances: 200
-  pred_dataset:
-    class_path: src.data.dataset.kvasir_seg.KvasirSEGRandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/Kvasir-SEG/Kvasir-SEG
-      resolution: 256
-      max_num_instances: 1000
-      noise_scale: 1.0
-  train_batch_size: 32
-  train_num_workers: 4
-  pred_batch_size: 32
-  pred_num_workers: 1

code/configs_medical/PixelGen_Medical_Kvasir_S8_Spatial.yaml

@@ -1,137 +0,0 @@
-# PixelGen Medical - Kvasir-SEG Polyp Segmentation - Binary mask-conditional colonoscopy image generation
-# Model: JiTMedical-S/8, mask_mode=spatial
-# Optimized for 2x H800 80GB
-seed_everything: 1234
-tags:
-  exp: &exp PixelGen_Medical_Kvasir_S8_Spatial
-
-trainer:
-  default_root_dir: ./medical_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-    class_path: lightning.pytorch.loggers.WandbLogger
-    init_args:
-      project: pixelgen_medical_kvasir
-      name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 100000
-  val_check_interval: 10000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 10000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-        save_dir: val_samples
-        save_compressed: true
-    - class_path: src.callbacks.medical_visualization.MedicalVisualizationCallback
-      init_args:
-        every_n_steps: 5000
-        num_samples: 8
-        num_sampling_steps: 50
-        cfg_scale: 2.0
-        save_dir: training_vis
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-      scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT_medical.JiTMedical
-    init_args:
-      input_size: 256
-      patch_size: 8
-      in_channels: 3
-      hidden_size: &hidden_dim 512
-      depth: 8
-      num_heads: 8
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.1
-      num_classes: 1
-      use_bottleneck: true
-      bottleneck_dim: 64
-      in_context_len: 64
-      in_context_start: 2
-      mask_in_channels: 1
-      mask_mode: spatial
-  conditioner:
-    class_path: src.models.conditioner.mask_conditioner.MaskConditioner
-    init_args:
-      hidden_size: *hidden_dim
-      in_channels: 1
-      img_size: 256
-      null_condition_p: 0.1
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_medical.MedicalTrainerSimple
-    init_args:
-      lognorm_t: true
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      percept_t_threshold: 0.3
-      null_condition_p: 0.1
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling_medical.EulerSamplerMedical
-    init_args:
-      num_steps: 50
-      guidance: 2.0
-      timeshift: 1.0
-      guidance_interval_min: 0.1
-      guidance_interval_max: 0.9
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-
-data:
-  train_dataset:
-    class_path: src.data.dataset.kvasir_seg.KvasirSEGDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/Kvasir-SEG/Kvasir-SEG
-      resolution: 256
-      split: train
-      train_ratio: 0.9
-      augment: true
-  eval_dataset:
-    class_path: src.data.dataset.kvasir_seg.KvasirSEGRandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/Kvasir-SEG/Kvasir-SEG
-      resolution: 256
-      max_num_instances: 200
-  pred_dataset:
-    class_path: src.data.dataset.kvasir_seg.KvasirSEGRandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/Kvasir-SEG/Kvasir-SEG
-      resolution: 256
-      max_num_instances: 1000
-      noise_scale: 1.0
-  train_batch_size: 64
-  train_num_workers: 4
-  pred_batch_size: 32
-  pred_num_workers: 1

code/configs_medical/PixelGen_Medical_L16_DINO.yaml

@@ -1,140 +0,0 @@
-# PixelGen Medical Large - With LPIPS + DINO losses
-# For OCTA500 mask-conditional image generation
-seed_everything: 1234
-tags:
-  exp: &exp PixelGen_Medical_L16_DINO
-
-trainer:
-  default_root_dir: ./medical_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-    class_path: lightning.pytorch.loggers.WandbLogger
-    init_args:
-      project: pixelgen_medical_octa500
-      name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 200000
-  val_check_interval: 20000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 20000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-        save_dir: val_samples
-        save_compressed: true
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-      scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT_medical.JiTMedical
-    init_args:
-      input_size: 256
-      patch_size: 16
-      in_channels: 3
-      hidden_size: &hidden_dim 1024
-      depth: 24
-      num_heads: 16
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.1
-      num_classes: 1
-      use_bottleneck: true
-      bottleneck_dim: 128
-      in_context_len: 32
-      in_context_start: 8
-      mask_in_channels: 1
-  conditioner:
-    class_path: src.models.conditioner.mask_conditioner.MaskConditioner
-    init_args:
-      hidden_size: *hidden_dim
-      in_channels: 1
-      img_size: 256
-      null_condition_p: 0.1
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_medical.MedicalREPATrainer
-    init_args:
-      lognorm_t: true
-      encoder:
-        class_path: src.models.encoder.DINOv2
-        init_args:
-          weight_path: ~/.cache/torch/hub/checkpoints/dinov2_vitb14_pretrain.pth
-      use_dino: true
-      align_layer: 8
-      proj_denoiser_dim: *hidden_dim
-      proj_hidden_dim: *hidden_dim
-      proj_encoder_dim: 768
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      dino_weight: 0.01
-      feat_loss_weight: 0.5
-      percept_t_threshold: 0.3
-      null_condition_p: 0.1
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling_medical.HeunSamplerMedical
-    init_args:
-      exact_heun: true
-      num_steps: 50
-      guidance: 2.0
-      timeshift: 1.5
-      guidance_interval_min: 0.1
-      guidance_interval_max: 0.9
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-
-data:
-  train_dataset:
-    class_path: src.data.dataset.octa500.OCTA500Dataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/OCTA500
-      resolution: 256
-      split: train
-      train_ratio: 0.9
-      augment: true
-  eval_dataset:
-    class_path: src.data.dataset.octa500.OCTA500RandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/OCTA500
-      resolution: 256
-      max_num_instances: 1000
-  pred_dataset:
-    class_path: src.data.dataset.octa500.OCTA500RandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/OCTA500
-      resolution: 256
-      max_num_instances: 5000
-      noise_scale: 1.0
-  train_batch_size: 16  # Smaller batch for larger model
-  train_num_workers: 4
-  pred_batch_size: 16
-  pred_num_workers: 1

code/configs_medical/PixelGen_Medical_OCTA500_B16_CrossAttn.yaml

@@ -1,137 +0,0 @@
-# PixelGen Medical - OCTA500 OCT Retinal Layer Segmentation - 6-class mask-conditional image generation
-# Model: JiTMedical-B/16, mask_mode=cross_attention
-# Optimized for 2x H800 80GB
-seed_everything: 1234
-tags:
-  exp: &exp PixelGen_Medical_OCTA500_B16_CrossAttn
-
-trainer:
-  default_root_dir: ./medical_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-    class_path: lightning.pytorch.loggers.WandbLogger
-    init_args:
-      project: pixelgen_medical_octa500
-      name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 100000
-  val_check_interval: 10000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 10000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-        save_dir: val_samples
-        save_compressed: true
-    - class_path: src.callbacks.medical_visualization.MedicalVisualizationCallback
-      init_args:
-        every_n_steps: 5000
-        num_samples: 8
-        num_sampling_steps: 50
-        cfg_scale: 2.0
-        save_dir: training_vis
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-      scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT_medical.JiTMedical
-    init_args:
-      input_size: 256
-      patch_size: 16
-      in_channels: 3
-      hidden_size: &hidden_dim 768
-      depth: 12
-      num_heads: 12
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.1
-      num_classes: 1
-      use_bottleneck: true
-      bottleneck_dim: 128
-      in_context_len: 32
-      in_context_start: 4
-      mask_in_channels: 1
-      mask_mode: cross_attention
-  conditioner:
-    class_path: src.models.conditioner.mask_conditioner.MaskConditioner
-    init_args:
-      hidden_size: *hidden_dim
-      in_channels: 1
-      img_size: 256
-      null_condition_p: 0.1
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_medical.MedicalTrainerSimple
-    init_args:
-      lognorm_t: true
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      percept_t_threshold: 0.3
-      null_condition_p: 0.1
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling_medical.EulerSamplerMedical
-    init_args:
-      num_steps: 50
-      guidance: 2.0
-      timeshift: 1.0
-      guidance_interval_min: 0.1
-      guidance_interval_max: 0.9
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-
-data:
-  train_dataset:
-    class_path: src.data.dataset.octa500.OCTA500Dataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/OCTA500
-      resolution: 256
-      split: train
-      train_ratio: 0.9
-      augment: true
-  eval_dataset:
-    class_path: src.data.dataset.octa500.OCTA500RandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/OCTA500
-      resolution: 256
-      max_num_instances: 1000
-  pred_dataset:
-    class_path: src.data.dataset.octa500.OCTA500RandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/OCTA500
-      resolution: 256
-      max_num_instances: 5000
-      noise_scale: 1.0
-  train_batch_size: 96
-  train_num_workers: 4
-  pred_batch_size: 32
-  pred_num_workers: 1

code/configs_medical/PixelGen_Medical_OCTA500_L16_Global.yaml

@@ -1,137 +0,0 @@
-# PixelGen Medical - OCTA500 OCT Retinal Layer Segmentation - 6-class mask-conditional image generation
-# Model: JiTMedical-L/16, mask_mode=global
-# Optimized for 2x H800 80GB
-seed_everything: 1234
-tags:
-  exp: &exp PixelGen_Medical_OCTA500_L16_Global
-
-trainer:
-  default_root_dir: ./medical_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-    class_path: lightning.pytorch.loggers.WandbLogger
-    init_args:
-      project: pixelgen_medical_octa500
-      name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 100000
-  val_check_interval: 10000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 10000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-        save_dir: val_samples
-        save_compressed: true
-    - class_path: src.callbacks.medical_visualization.MedicalVisualizationCallback
-      init_args:
-        every_n_steps: 5000
-        num_samples: 8
-        num_sampling_steps: 50
-        cfg_scale: 2.0
-        save_dir: training_vis
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-      scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT_medical.JiTMedical
-    init_args:
-      input_size: 256
-      patch_size: 16
-      in_channels: 3
-      hidden_size: &hidden_dim 1024
-      depth: 24
-      num_heads: 16
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.1
-      num_classes: 1
-      use_bottleneck: true
-      bottleneck_dim: 128
-      in_context_len: 32
-      in_context_start: 8
-      mask_in_channels: 1
-      mask_mode: global
-  conditioner:
-    class_path: src.models.conditioner.mask_conditioner.MaskConditioner
-    init_args:
-      hidden_size: *hidden_dim
-      in_channels: 1
-      img_size: 256
-      null_condition_p: 0.1
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_medical.MedicalTrainerSimple
-    init_args:
-      lognorm_t: true
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      percept_t_threshold: 0.3
-      null_condition_p: 0.1
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling_medical.EulerSamplerMedical
-    init_args:
-      num_steps: 50
-      guidance: 2.0
-      timeshift: 1.0
-      guidance_interval_min: 0.1
-      guidance_interval_max: 0.9
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-
-data:
-  train_dataset:
-    class_path: src.data.dataset.octa500.OCTA500Dataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/OCTA500
-      resolution: 256
-      split: train
-      train_ratio: 0.9
-      augment: true
-  eval_dataset:
-    class_path: src.data.dataset.octa500.OCTA500RandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/OCTA500
-      resolution: 256
-      max_num_instances: 1000
-  pred_dataset:
-    class_path: src.data.dataset.octa500.OCTA500RandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/OCTA500
-      resolution: 256
-      max_num_instances: 5000
-      noise_scale: 1.0
-  train_batch_size: 48
-  train_num_workers: 4
-  pred_batch_size: 32
-  pred_num_workers: 1

code/configs_medical/PixelGen_Medical_OCTA500_XL16_Global.yaml

@@ -1,137 +0,0 @@
-# PixelGen Medical - OCTA500 OCT Retinal Layer Segmentation - 6-class mask-conditional image generation
-# Model: JiTMedical-XL/16, mask_mode=global
-# Optimized for 2x H800 80GB
-seed_everything: 1234
-tags:
-  exp: &exp PixelGen_Medical_OCTA500_XL16_Global
-
-trainer:
-  default_root_dir: ./medical_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-    class_path: lightning.pytorch.loggers.WandbLogger
-    init_args:
-      project: pixelgen_medical_octa500
-      name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 100000
-  val_check_interval: 10000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 10000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-        save_dir: val_samples
-        save_compressed: true
-    - class_path: src.callbacks.medical_visualization.MedicalVisualizationCallback
-      init_args:
-        every_n_steps: 5000
-        num_samples: 8
-        num_sampling_steps: 50
-        cfg_scale: 2.0
-        save_dir: training_vis
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-      scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT_medical.JiTMedical
-    init_args:
-      input_size: 256
-      patch_size: 16
-      in_channels: 3
-      hidden_size: &hidden_dim 1152
-      depth: 28
-      num_heads: 16
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.1
-      num_classes: 1
-      use_bottleneck: true
-      bottleneck_dim: 128
-      in_context_len: 32
-      in_context_start: 8
-      mask_in_channels: 1
-      mask_mode: global
-  conditioner:
-    class_path: src.models.conditioner.mask_conditioner.MaskConditioner
-    init_args:
-      hidden_size: *hidden_dim
-      in_channels: 1
-      img_size: 256
-      null_condition_p: 0.1
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_medical.MedicalTrainerSimple
-    init_args:
-      lognorm_t: true
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      percept_t_threshold: 0.3
-      null_condition_p: 0.1
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling_medical.EulerSamplerMedical
-    init_args:
-      num_steps: 50
-      guidance: 2.0
-      timeshift: 1.0
-      guidance_interval_min: 0.1
-      guidance_interval_max: 0.9
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-
-data:
-  train_dataset:
-    class_path: src.data.dataset.octa500.OCTA500Dataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/OCTA500
-      resolution: 256
-      split: train
-      train_ratio: 0.9
-      augment: true
-  eval_dataset:
-    class_path: src.data.dataset.octa500.OCTA500RandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/OCTA500
-      resolution: 256
-      max_num_instances: 1000
-  pred_dataset:
-    class_path: src.data.dataset.octa500.OCTA500RandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/OCTA500
-      resolution: 256
-      max_num_instances: 5000
-      noise_scale: 1.0
-  train_batch_size: 32
-  train_num_workers: 4
-  pred_batch_size: 32
-  pred_num_workers: 1

code/configs_medical/PixelGen_Medical_REFUGE2_B16_CrossAttn.yaml

@@ -1,139 +0,0 @@
-# PixelGen Medical - REFUGE2 Optic Disc/Cup Segmentation - 3-class mask-conditional fundus image generation
-# Model: JiTMedical-B/16, mask_mode=cross_attention
-# Optimized for 2x H800 80GB
-seed_everything: 1234
-tags:
-  exp: &exp PixelGen_Medical_REFUGE2_B16_CrossAttn
-
-trainer:
-  default_root_dir: ./medical_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-    class_path: lightning.pytorch.loggers.WandbLogger
-    init_args:
-      project: pixelgen_medical_refuge2
-      name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 100000
-  val_check_interval: 10000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 10000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-        save_dir: val_samples
-        save_compressed: true
-    - class_path: src.callbacks.medical_visualization.MedicalVisualizationCallback
-      init_args:
-        every_n_steps: 5000
-        num_samples: 8
-        num_sampling_steps: 50
-        cfg_scale: 2.0
-        save_dir: training_vis
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-      scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT_medical.JiTMedical
-    init_args:
-      input_size: 256
-      patch_size: 16
-      in_channels: 3
-      hidden_size: &hidden_dim 768
-      depth: 12
-      num_heads: 12
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.1
-      num_classes: 1
-      use_bottleneck: true
-      bottleneck_dim: 128
-      in_context_len: 32
-      in_context_start: 4
-      mask_in_channels: 1
-      mask_mode: cross_attention
-  conditioner:
-    class_path: src.models.conditioner.mask_conditioner.MaskConditioner
-    init_args:
-      hidden_size: *hidden_dim
-      in_channels: 1
-      img_size: 256
-      null_condition_p: 0.1
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_medical.MedicalTrainerSimple
-    init_args:
-      lognorm_t: true
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      percept_t_threshold: 0.3
-      null_condition_p: 0.1
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling_medical.EulerSamplerMedical
-    init_args:
-      num_steps: 50
-      guidance: 2.0
-      timeshift: 1.0
-      guidance_interval_min: 0.1
-      guidance_interval_max: 0.9
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-
-data:
-  train_dataset:
-    class_path: src.data.dataset.refuge2.REFUGE2Dataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/REFUGE2
-      resolution: 256
-      splits:
-        - train
-        - val
-      augment: true
-      val_ratio: 0.1
-  eval_dataset:
-    class_path: src.data.dataset.refuge2.REFUGE2RandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/REFUGE2
-      resolution: 256
-      max_num_instances: 200
-  pred_dataset:
-    class_path: src.data.dataset.refuge2.REFUGE2RandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/REFUGE2
-      resolution: 256
-      max_num_instances: 1000
-      noise_scale: 1.0
-  train_batch_size: 96
-  train_num_workers: 4
-  pred_batch_size: 32
-  pred_num_workers: 1

code/configs_medical/PixelGen_Medical_REFUGE2_B8_Spatial.yaml

@@ -1,139 +0,0 @@
-# PixelGen Medical - REFUGE2 Optic Disc/Cup Segmentation - 3-class mask-conditional fundus image generation
-# Model: JiTMedical-B/8, mask_mode=spatial
-# Optimized for 2x H800 80GB
-seed_everything: 1234
-tags:
-  exp: &exp PixelGen_Medical_REFUGE2_B8_Spatial
-
-trainer:
-  default_root_dir: ./medical_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-    class_path: lightning.pytorch.loggers.WandbLogger
-    init_args:
-      project: pixelgen_medical_refuge2
-      name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 100000
-  val_check_interval: 10000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 10000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-        save_dir: val_samples
-        save_compressed: true
-    - class_path: src.callbacks.medical_visualization.MedicalVisualizationCallback
-      init_args:
-        every_n_steps: 5000
-        num_samples: 8
-        num_sampling_steps: 50
-        cfg_scale: 2.0
-        save_dir: training_vis
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-      scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT_medical.JiTMedical
-    init_args:
-      input_size: 256
-      patch_size: 8
-      in_channels: 3
-      hidden_size: &hidden_dim 768
-      depth: 12
-      num_heads: 12
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.1
-      num_classes: 1
-      use_bottleneck: true
-      bottleneck_dim: 128
-      in_context_len: 64
-      in_context_start: 4
-      mask_in_channels: 1
-      mask_mode: spatial
-  conditioner:
-    class_path: src.models.conditioner.mask_conditioner.MaskConditioner
-    init_args:
-      hidden_size: *hidden_dim
-      in_channels: 1
-      img_size: 256
-      null_condition_p: 0.1
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_medical.MedicalTrainerSimple
-    init_args:
-      lognorm_t: true
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      percept_t_threshold: 0.3
-      null_condition_p: 0.1
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling_medical.EulerSamplerMedical
-    init_args:
-      num_steps: 50
-      guidance: 2.0
-      timeshift: 1.0
-      guidance_interval_min: 0.1
-      guidance_interval_max: 0.9
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-
-data:
-  train_dataset:
-    class_path: src.data.dataset.refuge2.REFUGE2Dataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/REFUGE2
-      resolution: 256
-      splits:
-        - train
-        - val
-      augment: true
-      val_ratio: 0.1
-  eval_dataset:
-    class_path: src.data.dataset.refuge2.REFUGE2RandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/REFUGE2
-      resolution: 256
-      max_num_instances: 200
-  pred_dataset:
-    class_path: src.data.dataset.refuge2.REFUGE2RandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/REFUGE2
-      resolution: 256
-      max_num_instances: 1000
-      noise_scale: 1.0
-  train_batch_size: 32
-  train_num_workers: 4
-  pred_batch_size: 32
-  pred_num_workers: 1

code/configs_medical/PixelGen_Medical_REFUGE2_S8_Spatial.yaml

@@ -1,139 +0,0 @@
-# PixelGen Medical - REFUGE2 Optic Disc/Cup Segmentation - 3-class mask-conditional fundus image generation
-# Model: JiTMedical-S/8, mask_mode=spatial
-# Optimized for 2x H800 80GB
-seed_everything: 1234
-tags:
-  exp: &exp PixelGen_Medical_REFUGE2_S8_Spatial
-
-trainer:
-  default_root_dir: ./medical_workdirs
-  accelerator: auto
-  strategy: auto
-  devices: auto
-  num_nodes: 1
-  precision: bf16-mixed
-  logger:
-    class_path: lightning.pytorch.loggers.WandbLogger
-    init_args:
-      project: pixelgen_medical_refuge2
-      name: *exp
-  num_sanity_val_steps: 0
-  max_steps: 100000
-  val_check_interval: 10000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 10000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-        save_dir: val_samples
-        save_compressed: true
-    - class_path: src.callbacks.medical_visualization.MedicalVisualizationCallback
-      init_args:
-        every_n_steps: 5000
-        num_samples: 8
-        num_sampling_steps: 50
-        cfg_scale: 2.0
-        save_dir: training_vis
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-    init_args:
-      scale: 1.0
-  denoiser:
-    class_path: src.models.transformer.JiT_medical.JiTMedical
-    init_args:
-      input_size: 256
-      patch_size: 8
-      in_channels: 3
-      hidden_size: &hidden_dim 512
-      depth: 8
-      num_heads: 8
-      mlp_ratio: 4.0
-      attn_drop: 0.0
-      proj_drop: 0.1
-      num_classes: 1
-      use_bottleneck: true
-      bottleneck_dim: 64
-      in_context_len: 64
-      in_context_start: 2
-      mask_in_channels: 1
-      mask_mode: spatial
-  conditioner:
-    class_path: src.models.conditioner.mask_conditioner.MaskConditioner
-    init_args:
-      hidden_size: *hidden_dim
-      in_channels: 1
-      img_size: 256
-      null_condition_p: 0.1
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_medical.MedicalTrainerSimple
-    init_args:
-      lognorm_t: true
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      percept_t_threshold: 0.3
-      null_condition_p: 0.1
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling_medical.EulerSamplerMedical
-    init_args:
-      num_steps: 50
-      guidance: 2.0
-      timeshift: 1.0
-      guidance_interval_min: 0.1
-      guidance_interval_max: 0.9
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      weight_decay: 0.0
-
-data:
-  train_dataset:
-    class_path: src.data.dataset.refuge2.REFUGE2Dataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/REFUGE2
-      resolution: 256
-      splits:
-        - train
-        - val
-      augment: true
-      val_ratio: 0.1
-  eval_dataset:
-    class_path: src.data.dataset.refuge2.REFUGE2RandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/REFUGE2
-      resolution: 256
-      max_num_instances: 200
-  pred_dataset:
-    class_path: src.data.dataset.refuge2.REFUGE2RandnDataset
-    init_args:
-      data_root: /data2/sichengli/Data/test/Segmentation/REFUGE2
-      resolution: 256
-      max_num_instances: 1000
-      noise_scale: 1.0
-  train_batch_size: 64
-  train_num_workers: 4
-  pred_batch_size: 32
-  pred_num_workers: 1

code/configs_t2i/pretraining_res256.yaml

@@ -1,134 +0,0 @@
-# lightning.pytorch==2.4.0
-seed_everything: true
-tags:
-  exp: &exp pretraining_pix256
-torch_hub_dir: ~/.cache/torch/hub
-huggingface_cache_dir: null
-trainer:
-  default_root_dir: ./universal_pix_t2i_workdirs
-  accelerator: auto
-  strategy: ddp
-  devices: auto
-  num_nodes: 1
-  accumulate_grad_batches: 3
-  precision: bf16-mixed
-  logger:
-      class_path: lightning.pytorch.loggers.WandbLogger
-      init_args:
-        project: universal_flow_JiT_t2i
-        name: *exp
-  num_sanity_val_steps: 2
-  max_steps: 200000
-  gradient_clip_val: 1.0
-  gradient_clip_algorithm: norm
-  val_check_interval: 20000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 20000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-         save_dir: val
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-  denoiser:
-    class_path: src.models.transformer.JiT_T2I.JiT_T2I
-    init_args:
-      patch_size: 16
-      in_channels: 3
-      hidden_size: &hidden_dim 1536
-      num_blocks: 16
-      num_groups: 24
-      txt_embed_dim: &txt_embed_dim 2048
-      txt_max_length: 128
-      bottleneck_dim: 256
-      num_text_blocks: 4
-  conditioner:
-    class_path: src.models.conditioner.qwen3_text_encoder.Qwen3TextEncoder
-    init_args:
-      weight_path: Qwen/Qwen3-1.7B
-      embed_dim: *txt_embed_dim
-      max_length: 128
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_repa_JiT_LPIPS_DINO_NoiseGating.REPATrainer
-    init_args:
-      lognorm_t: true
-      encoder:
-        class_path: src.models.encoder.DINOv2
-        init_args:
-          weight_path: ~/.cache/torch/hub/checkpoints/dinov2_vitb14_pretrain.pth
-      align_layer: 8
-      proj_denoiser_dim: *hidden_dim
-      proj_hidden_dim: *hidden_dim
-      proj_encoder_dim: 768
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      dino_weight: 0.01
-      percept_t_threshold: 0.3
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling.EulerSamplerJiT
-    init_args:
-      num_steps: 100
-      guidance: 4.0
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 2e-4
-      betas:
-        - 0.9
-        - 0.95
-      weight_decay: 0.00
-data:
-  train_dataset:
-    class_path: src.data.dataset.blip3o_dataset.WebDatasetPackedDataset
-    init_args:
-      urls: [/data/datasets/BLIP-3o/BLIP3o-Pretrain-Long-Caption, /data/datasets/BLIP-3o/BLIP3o-Pretrain-Short-Caption, /data/datasets/BLIP-3o/BLIP3o-Pretrain-JourneyDB] # [/data/datasets/BLIP-3o/BLIP3o-Pretrain-JourneyDB,]
-      resolution: 256
-      random_crop: false
-      shuffle_buffer: 20000
-      sample_shuffle: true
-      repeat: true
-  eval_dataset:
-    class_path: src.data.dataset.geneval.GenEvalDataset
-    init_args:
-      meta_json_path: ./evaluations/geneval/evaluation_metadata.jsonl
-      num_samples_per_instance: 4
-      latent_shape:
-        - 3
-        - 256
-        - 256
-  pred_dataset:
-    class_path: src.data.dataset.geneval.GenEvalDataset
-    init_args:
-      # meta_json_path: ./evaluations/geneval/evaluation_metadata_rephrased.jsonl
-      meta_json_path: ./evaluations/geneval/evaluation_metadata.jsonl
-      num_samples_per_instance: 4
-      latent_shape:
-        - 3
-        - 256
-        - 256
-  train_batch_size: 64 # 96
-  train_num_workers: 8
-  pred_batch_size: 8
-  pred_num_workers: 1

code/configs_t2i/pretraining_res512.yaml

@@ -1,136 +0,0 @@
-# lightning.pytorch==2.4.0
-seed_everything: true
-tags:
-  exp: &exp pretraining_pix512
-torch_hub_dir: ~/.cache/torch/hub
-ckpt_path: /data/github_project/DeCo/universal_pix_t2i_workdirs/exp_pretraining_pix256/epoch=0-step=200000.ckpt
-huggingface_cache_dir: null
-trainer:
-  default_root_dir: ./universal_pix_t2i_workdirs
-  accelerator: auto
-  strategy: ddp
-  devices: auto
-  num_nodes: 1
-  accumulate_grad_batches: 4
-  precision: bf16-mixed
-  logger:
-      class_path: lightning.pytorch.loggers.WandbLogger
-      init_args:
-        project: universal_flow_JiT_t2i
-        name: *exp
-  num_sanity_val_steps: 2
-  max_steps: 300000
-  gradient_clip_val: 1.0
-  gradient_clip_algorithm: norm
-  val_check_interval: 40000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 10000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-         save_dir: val
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-  denoiser:
-    class_path: src.models.transformer.JiT_T2I.JiT_T2I
-    init_args:
-      patch_size: 16
-      input_size: 512
-      in_channels: 3
-      hidden_size: &hidden_dim 1536
-      num_blocks: 16
-      num_groups: 24
-      txt_embed_dim: &txt_embed_dim 2048
-      txt_max_length: 128
-      bottleneck_dim: 256
-      num_text_blocks: 4
-  conditioner:
-    class_path: src.models.conditioner.qwen3_text_encoder.Qwen3TextEncoder
-    init_args:
-      weight_path: Qwen/Qwen3-1.7B
-      embed_dim: *txt_embed_dim
-      max_length: 128
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_repa_JiT_LPIPS_DINO_NoiseGating.REPATrainer
-    init_args:
-      lognorm_t: true
-      encoder:
-        class_path: src.models.encoder.DINOv2
-        init_args:
-          weight_path: ~/.cache/torch/hub/checkpoints/dinov2_vitb14_pretrain.pth
-      align_layer: 8
-      proj_denoiser_dim: *hidden_dim
-      proj_hidden_dim: *hidden_dim
-      proj_encoder_dim: 768
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      dino_weight: 0.01
-      percept_t_threshold: 0.3
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.sampling.EulerSamplerJiT
-    init_args:
-      num_steps: 100
-      guidance: 4.0
-      scheduler: *scheduler
-      w_scheduler: src.diffusion.flow_matching.scheduling.LinearScheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-      step_fn: src.diffusion.flow_matching.sampling.ode_step_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-4
-      betas:
-        - 0.9
-        - 0.95
-      weight_decay: 0.00
-data:
-  train_dataset:
-    class_path: src.data.dataset.blip3o_dataset.WebDatasetPackedDataset
-    init_args:
-      urls: [/data/datasets/BLIP-3o/BLIP3o-Pretrain-Long-Caption, /data/datasets/BLIP-3o/BLIP3o-Pretrain-Short-Caption, /data/datasets/BLIP-3o/BLIP3o-Pretrain-JourneyDB] # [/data/datasets/BLIP-3o/BLIP3o-Pretrain-JourneyDB,]
-      resolution: 512
-      random_crop: false
-      shuffle_buffer: 20000
-      sample_shuffle: true
-      repeat: true
-  eval_dataset:
-    class_path: src.data.dataset.geneval.GenEvalDataset
-    init_args:
-      meta_json_path: ./evaluations/geneval/evaluation_metadata.jsonl
-      num_samples_per_instance: 4
-      latent_shape:
-        - 3
-        - 512
-        - 512
-  pred_dataset:
-    class_path: src.data.dataset.geneval.GenEvalDataset
-    init_args:
-      # meta_json_path: ./evaluations/geneval/evaluation_metadata_rephrased.jsonl
-      meta_json_path: ./evaluations/geneval/evaluation_metadata.jsonl
-      num_samples_per_instance: 4
-      latent_shape:
-        - 3
-        - 512
-        - 512
-  train_batch_size: 16
-  train_num_workers: 8
-  pred_batch_size: 8
-  pred_num_workers: 1

code/configs_t2i/sft_res512.yaml

@@ -1,148 +0,0 @@
-# lightning.pytorch==2.4.0
-seed_everything: true
-tags:
-  exp: &exp sftpix512ema
-torch_hub_dir: ~/.cache/torch/hub
-ckpt_path: /data/github_project/DeCo/universal_pix_t2i_workdirs/exp_pretraining_pix512/epoch=0-step=300000.ckpt
-huggingface_cache_dir: null
-trainer:
-  default_root_dir: ./universal_pix_t2i_workdirs
-  accelerator: auto
-  strategy: ddp
-  devices: auto
-  num_nodes: 1
-  accumulate_grad_batches: 4
-  precision: bf16-mixed
-  logger:
-      class_path: lightning.pytorch.loggers.WandbLogger
-      init_args:
-        project: universal_flow_JiT_t2i
-        name: *exp
-  num_sanity_val_steps: 2
-  max_steps: 360000
-  gradient_clip_val: 1.0
-  gradient_clip_algorithm: norm
-  val_check_interval: 40000
-  check_val_every_n_epoch: null
-  log_every_n_steps: 50
-  deterministic: null
-  inference_mode: true
-  use_distributed_sampler: false
-  callbacks:
-    - class_path: src.callbacks.model_checkpoint.CheckpointHook
-      init_args:
-        every_n_train_steps: 10000
-        save_top_k: -1
-        save_last: true
-    - class_path: src.callbacks.save_images.SaveImagesHook
-      init_args:
-         save_dir: val
-  plugins:
-    - src.plugins.bd_env.BDEnvironment
-model:
-  vae:
-    class_path: src.models.autoencoder.pixel.PixelAE
-  denoiser:
-    class_path: src.models.transformer.JiT_T2I.JiT_T2I
-    init_args:
-      patch_size: 16
-      input_size: 512
-      in_channels: 3
-      hidden_size: &hidden_dim 1536
-      num_blocks: 16
-      num_groups: 24
-      txt_embed_dim: &txt_embed_dim 2048
-      txt_max_length: 128
-      bottleneck_dim: 256
-      num_text_blocks: 4
-  conditioner:
-    class_path: src.models.conditioner.qwen3_text_encoder.Qwen3TextEncoder
-    init_args:
-      weight_path: Qwen/Qwen3-1.7B
-      embed_dim: *txt_embed_dim
-      max_length: 128
-  diffusion_trainer:
-    class_path: src.diffusion.flow_matching.training_repa_JiT_LPIPS_DINO_NoiseGating.REPATrainer
-    init_args:
-      lognorm_t: true
-      encoder:
-        class_path: src.models.encoder.DINOv2
-        init_args:
-          weight_path: ~/.cache/torch/hub/checkpoints/dinov2_vitb14_pretrain.pth
-      align_layer: 8
-      proj_denoiser_dim: *hidden_dim
-      proj_hidden_dim: *hidden_dim
-      proj_encoder_dim: 768
-      P_mean: -0.8
-      P_std: 0.8
-      t_eps: 0.05
-      scheduler: &scheduler src.diffusion.flow_matching.scheduling.LinearScheduler
-      lpips_weight: 0.1
-      dino_weight: 0.01
-      percept_t_threshold: 0.3
-  diffusion_sampler:
-    class_path: src.diffusion.flow_matching.adam_sampling.AdamLMSamplerJiT
-    init_args:
-      num_steps: 25
-      guidance: 4.0
-      timeshift: 3.0 # 3.0 for geneval
-      order: 2
-      scheduler: *scheduler
-      guidance_fn: src.diffusion.base.guidance.simple_guidance_fn
-  ema_tracker:
-    class_path: src.callbacks.simple_ema.SimpleEMA
-    init_args:
-      decay: 0.9999
-  optimizer:
-    class_path: torch.optim.AdamW
-    init_args:
-      lr: 1e-5
-      betas:
-        - 0.9
-        - 0.999
-      weight_decay: 0.00
-data:
-  train_dataset:
-    class_path: src.data.dataset.image_txt.ImageText
-    init_args:
-      root: /data/datasets/BLIP-3o/BLIP3o-60k
-      resolution: 512
-  # train_dataset:
-  #   class_path: src.data.dataset.blip3o_dataset.WebDatasetPackedDataset
-  #   init_args:
-  #     urls: [/data/datasets/BLIP-3o/BLIP3o-60k] # [/data/datasets/BLIP-3o/BLIP3o-Pretrain-JourneyDB,]
-  #     resolution: 512
-  #     random_crop: false
-  #     shuffle_buffer: 2000
-  #     sample_shuffle: true
-  #     repeat: true
-  eval_dataset:
-    class_path: src.data.dataset.geneval.GenEvalDataset
-    init_args:
-      meta_json_path: ./evaluations/geneval/evaluation_metadata.jsonl
-      num_samples_per_instance: 4
-      latent_shape:
-        - 3
-        - 512
-        - 512
-  pred_dataset:
-    class_path: src.data.dataset.geneval.GenEvalDataset
-    init_args:
-      meta_json_path: ./evaluations/geneval/evaluation_metadata.jsonl
-      num_samples_per_instance: 4
-      latent_shape:
-        - 3
-        - 512
-        - 512
-    # class_path: src.data.dataset.dpg.DPGDataset
-    # init_args:
-    #   prompt_path: ./evaluations/dpg/prompts
-    #   num_samples_per_instance: 4
-    #   latent_shape:
-    #     - 3
-    #     - 512
-    #     - 512
-  train_batch_size: 16
-  train_num_workers: 8
-  pred_batch_size: 8
-  pred_num_workers: 1

code/cvc_clinicdb.py

@@ -1,195 +0,0 @@
-# CVC-ClinicDB Dataset for PixelGen Medical Image Generation
-# Polyp segmentation dataset: 612 RGB colonoscopy images with binary masks
-
-import os
-import torch
-import random
-import numpy as np
-from torch.utils.data import Dataset
-from PIL import Image
-import torchvision.transforms as transforms
-import torchvision.transforms.functional as TF
-from torchvision.transforms import Normalize
-
-
-class CVCClinicDBDataset(Dataset):
-    """
-    CVC-ClinicDB dataset for mask-conditional image generation.
-
-    Data format:
-    - Images: 384x288 RGB colonoscopy images
-    - Masks: Binary polyp segmentation (0/255)
-    - 612 image pairs total
-
-    Returns format compatible with PixelGen:
-    - normalized_image: [3, H, W] in range [-1, 1]
-    - label: class label (0 for all)
-    - metadata: dict with 'raw_image', 'mask', 'class'
-    """
-
-    def __init__(self, data_root, resolution=256, split='train', train_ratio=0.9,
-                 augment=True, seed=42, max_samples=None, random_flip=True):
-        super().__init__()
-        self.data_root = data_root
-        self.resolution = resolution
-        self.split = split
-        self.augment = augment and (split == 'train')
-        self.random_flip = random_flip and (split == 'train')
-
-        self.img_dir = os.path.join(data_root, 'PNG', 'Original')
-        self.mask_dir = os.path.join(data_root, 'PNG', 'Ground Truth')
-
-        # Get all image files
-        all_files = sorted([f for f in os.listdir(self.img_dir) if f.endswith('.png')])
-
-        # Split by index (no case structure in CVC-ClinicDB)
-        random.seed(seed)
-        indices = list(range(len(all_files)))
-        random.shuffle(indices)
-        split_idx = int(len(indices) * train_ratio)
-
-        if split == 'train':
-            selected_indices = indices[:split_idx]
-        else:
-            selected_indices = indices[split_idx:]
-
-        self.images = [all_files[i] for i in sorted(selected_indices)]
-
-        # Limit samples if specified
-        if max_samples is not None and max_samples < len(self.images):
-            random.seed(seed)
-            self.images = random.sample(self.images, max_samples)
-
-        # Normalization for images ([-1, 1] range)
-        self.normalize = Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-        print(f"[CVCClinicDBDataset] {split} set: {len(self.images)} images")
-
-    def __len__(self):
-        return len(self.images)
-
-    def _load_and_process(self, idx):
-        """Load and process a single sample."""
-        img_name = self.images[idx]
-        img_path = os.path.join(self.img_dir, img_name)
-        mask_path = os.path.join(self.mask_dir, img_name)
-
-        # Load images - RGB colonoscopy
-        image = Image.open(img_path).convert('RGB')
-        mask = Image.open(mask_path).convert('L')  # Binary mask -> single channel
-
-        # Resize to target size (square)
-        image = TF.resize(image, (self.resolution, self.resolution),
-                         interpolation=transforms.InterpolationMode.BILINEAR)
-        mask = TF.resize(mask, (self.resolution, self.resolution),
-                        interpolation=transforms.InterpolationMode.NEAREST)
-
-        # Data augmentation
-        if self.augment:
-            # Random horizontal flip
-            if self.random_flip and random.random() > 0.5:
-                image = TF.hflip(image)
-                mask = TF.hflip(mask)
-
-            # Random vertical flip
-            if self.random_flip and random.random() > 0.5:
-                image = TF.vflip(image)
-                mask = TF.vflip(mask)
-
-            # Random color jitter for image only
-            if random.random() > 0.5:
-                brightness_factor = random.uniform(0.85, 1.15)
-                image = TF.adjust_brightness(image, brightness_factor)
-                contrast_factor = random.uniform(0.85, 1.15)
-                image = TF.adjust_contrast(image, contrast_factor)
-                saturation_factor = random.uniform(0.85, 1.15)
-                image = TF.adjust_saturation(image, saturation_factor)
-
-        return image, mask
-
-    def __getitem__(self, idx):
-        # Load with retry logic
-        max_retries = 10
-        for retry in range(max_retries):
-            try:
-                actual_idx = (idx + retry) % len(self.images)
-                image, mask = self._load_and_process(actual_idx)
-                break
-            except Exception as e:
-                if retry == max_retries - 1:
-                    raise RuntimeError(f"Failed to load image after {max_retries} retries: {e}")
-                continue
-
-        # Convert to tensor
-        raw_image = TF.to_tensor(image)  # [3, H, W], range [0, 1]
-
-        # Normalize to [-1, 1] for model input
-        normalized_image = self.normalize(raw_image)
-
-        # Convert mask to tensor [1, H, W], range [0, 1]
-        mask_tensor = TF.to_tensor(mask)  # Already in [0, 1] after to_tensor
-
-        # Label (single class)
-        label = 0
-
-        # Metadata for PixelGen compatibility
-        metadata = {
-            "raw_image": raw_image,  # [3, H, W] in [0, 1] for LPIPS/DINO
-            "mask": mask_tensor,     # [1, H, W] for mask conditioning
-            "class": label,
-        }
-
-        return normalized_image, label, metadata
-
-
-class CVCClinicDBRandnDataset(Dataset):
-    """
-    Random noise dataset for evaluation/prediction.
-    Samples random masks from the dataset.
-    """
-
-    def __init__(self, data_root, resolution=256, max_num_instances=1000,
-                 noise_scale=1.0, seed=42):
-        super().__init__()
-        self.resolution = resolution
-        self.noise_scale = noise_scale
-
-        # Load masks
-        mask_dir = os.path.join(data_root, 'PNG', 'Ground Truth')
-        all_files = sorted([f for f in os.listdir(mask_dir) if f.endswith('.png')])
-
-        # Sample a subset of masks (with repetition if needed)
-        random.seed(seed)
-        if max_num_instances <= len(all_files):
-            self.mask_files = random.sample(all_files, max_num_instances)
-        else:
-            # Repeat masks to reach desired count
-            self.mask_files = all_files * (max_num_instances // len(all_files) + 1)
-            self.mask_files = self.mask_files[:max_num_instances]
-
-        self.mask_dir = mask_dir
-
-        print(f"[CVCClinicDBRandnDataset] {len(self.mask_files)} samples for generation")
-
-    def __len__(self):
-        return len(self.mask_files)
-
-    def __getitem__(self, idx):
-        # Random noise
-        xT = self.noise_scale * torch.randn(3, self.resolution, self.resolution)
-
-        # Load mask
-        mask_path = os.path.join(self.mask_dir, self.mask_files[idx])
-        mask = Image.open(mask_path).convert('L')
-        mask = TF.resize(mask, (self.resolution, self.resolution),
-                        interpolation=transforms.InterpolationMode.NEAREST)
-        mask_tensor = TF.to_tensor(mask)
-
-        label = 0
-
-        metadata = {
-            "mask": mask_tensor,
-            "class": label,
-        }
-
-        return xT, label, metadata

code/docs/index.html

@@ -1,335 +0,0 @@
-<!DOCTYPE html>
-<html>
-<meta property='og:title' content="PixelGen: Pixel Diffusion Beats Latent Diffusion with Perceptual Loss"/>
-<meta property='og:description' content="PixelGen: Pixel Diffusion Beats Latent Diffusion with Perceptual Loss"/>
-<meta property='og:url' content='https://Zehong-Ma.github.io/PixelGen/'/>
-<meta property='og:image:width' content='1200' />
-<meta property='og:image:height' content='663' />
-<!-- TYPE BELOW IS PROBABLY: 'website' or 'article' or look on https://ogp.me/#types -->
-<meta property="og:type" content='website'/>
-<head>
-  <meta charset="utf-8">
-  <meta name="description"
-        content="PixelGen: Pixel Diffusion Beats Latent Diffusion with Perceptual Loss">
-  <meta name="keywords" content="PixelGen: Pixel Diffusion Beats Latent Diffusion with Perceptual Loss">
-  <meta name="viewport" content="width=device-width, initial-scale=1">
-  <title>PixelGen: Pixel Diffusion Beats Latent Diffusion with Perceptual Loss</title>
-
-  <link rel="stylesheet" href="./static/css/bulma.min.css">
-  <link rel="stylesheet" href="./static/css/bulma-carousel.min.css">
-  <link rel="stylesheet" href="./static/css/bulma-slider.min.css">
-  <link rel="stylesheet" href="./static/css/tab_gallery.css">
-  <link rel="stylesheet" href="./static/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="./static/css/index.css">
-  <link rel="icon" href="./static/images/favicon.svg">
-  <link rel="stylesheet" href="juxtapose/css/juxtapose.css">
-
-  <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
-  <script defer src="./static/js/fontawesome.all.min.js"></script>
-  <script src="./static/js/bulma-carousel.min.js"></script>
-  <script src="./static/js/bulma-slider.min.js"></script>
-  <script src="./static/js/index.js"></script>
-  <script src="./static/js/magnifier.js"></script>
-  <link href="https://fonts.cdnfonts.com/css/menlo" rel="stylesheet">
-  <link rel="stylesheet" href="./static/css/image_card_fader.css">
-  <link rel="stylesheet" href="./static/css/image_card_slider.css">
-  <link rel="stylesheet"
-        href="https://fonts.googleapis.com/css?family=Patrick+Hand|Google+Sans|Noto+Sans|Castoro|Lato|Open+Sans&effect=shadow-multiple|emboss|3d"> 
-  <link rel="icon" href="./static/images/clock.png" type="image/x-icon">
-  <link rel="shortcut icon" href="./static/images/clock.png" type="image/x-icon">
-  <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
-  <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-</head>
-
-<style>
-  @import url('https://fonts.cdnfonts.com/css/menlo');
-
-  .video-table td, .video-table th {
-    padding-top: 2px;
-    padding-bottom: 2px;
-    padding-left: 4px;
-    padding-right: 4px;
-    font-weight: normal;
-  }
-  .first-col {
-    width: 7%;
-    vertical-align: middle;
-  }
-  .other-col {
-    width: 31%;
-  }
-  body {
-    font-family: "Lato", sans-serif;
-    font-size: 1.1em;
-  }
-  .title.is-3 {
-    font-weight: 900;
-    font-size: 2.0rem;
-  }
-  .title.is-4 {
-    font-weight: 700;
-    font-size: 1.7rem;
-  }
-  .custom-emoji {
-    width: 1em;
-    height: 1em;
-    display: inline-block;
-    background-image: url('./static/images/clock.png');
-    background-size: cover;
-    vertical-align: middle;
-    line-height: 1;
-}
-
-</style>
-
-
-<body>
-
-  <section class="hero is-small">
-  <div class="hero-body">
-    <div class="container is-max-desktop">
-      <div class="columns is-centered">
-        <div class="column has-text-centered">
-          <br><br>
-          <h1 class="title is-2 publication-title" style="font-size: 2.15rem">
-            PixelGen: Pixel Diffusion Beats Latent Diffusion with Perceptual Loss
-          </h1>
-        
-          <div class="is-size-5 publication-authors">
-            <span class="author-block">
-              <a href="https://zehong-ma.github.io/" target="_blank">Zehong Ma</a><sup>1</sup>,&nbsp;
-            </span>
-            <span class="author-block">
-              <a href="https://xuruihan.github.io/" target="_blank">Ruihan Xu</a><sup>1</sup>,&nbsp;
-            </span>
-            <span class="author-block">
-              <a href="https://www.pkuvmc.com/" target="_blank">Shiliang Zhang</a><sup>1</sup><sup>*</sup>,&nbsp;
-            </span>
-          </div>
-
-          <div class="is-size-5 publication-authors">
-            <span class="author-block"><sup>1</sup>State Key Laboratory of Multimedia Information Processing, School of Computer Science,&nbsp;</span>
-            <span class="author-block">Peking University&nbsp;</span>
-          </div>
-
-          <!-- <div class="is-size-5 publication-venue">
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-  <section class="hero teaser">
-    <div class="hero-body">
-      <div class="container is-max-desktop">
-        <div class="content has-text-centered">
-          <img src="./static/images/t2i_vis1.jpg" style="width: 100%;"><br>
-          <span style="font-size: 0.8em; width: 100%; display: inline-block;">Figure 1: Visualization of images generated by our PixelGen. All images are at a 512x512 resolution. </span>
-        </div>
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-            Pixel diffusion generates images directly in pixel space in an end-to-end manner, avoiding the artifacts and bottlenecks introduced by VAEs in two-stage latent diffusion. However, it is challenging to optimize high-dimensional pixel manifolds that contain many perceptually irrelevant signals, leaving existing pixel diffusion methods lagging behind latent diffusion models. We propose <b><span style="color: blue;">PixelGen</span></b>, a simple pixel diffusion framework with perceptual supervision. Instead of modeling the full image manifold, PixelGen introduces two complementary perceptual losses to guide diffusion model towards learning a more meaningful perceptual manifold. An LPIPS loss facilitates learning better local patterns, while a DINO-based perceptual loss strengthens global semantics. With perceptual supervision, PixelGen surpasses strong latent diffusion baselines. It achieves an FID of 5.11 on ImageNet-256 without classifier-free guidance using only 80 training epochs, and demonstrates favorable scaling performance on large-scale text-to-image generation with a GenEval score of 0.79. PixelGen requires no VAEs, no latent representations, and no auxiliary stages, providing a simpler yet more powerful generative paradigm.
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-        <div class="content has-text-justified">
-          <p>
-            It is challenging to optimize high-dimensional pixel manifolds that contain many perceptually irrelevant signals, leaving existing pixel diffusion methods lagging behind latent diffusion models. We introduce PixelGen, a simple pixel diffusion framework with perceptual loss. <b><span style="color: blue;">Instead of modeling the full image manifold, PixelGen introduces two complementary perceptual losses to guide diffusion model towards learning a more meaningful perceptual manifold.</span></b> An LPIPS loss facilitates learning better local patterns, while a DINO-based perceptual loss strengthens global semantics. 
-          </p>
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-            <img src="./static/images/intro.jpg" style="width: 100%;"><br>
-            <span style="font-size: 0.8em; width: 100%; display: inline-block;">Figure 2: This work shows that pixel diffusion with perceptual loss outperforms latent diffusion. (a) A traditional two-stage latent diffusion denoises in the latent space, which is influenced by the artifacts of the VAE. (b) PixelGen introduces perceptual loss to encourage the diffusion model to focus on the perceptual manifold, enabling the pixel diffusion to learn a meaningful manifold rather than the complex full image manifold. (c) PixelGen outperforms the latent diffusion models using only 80 training epochs on ImageNet without CFG.</span>
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-        <div class="content has-text-centered">
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-            <img src="./static/images/manifold.jpg"  style="width: 50%; display: block; margin: 0 auto;"><br>
-            <span style="font-size: 0.8em; width: 75%; display: inline-block;">Figure 3: Illustration of different manifolds within the pixel space. The image manifold is a large manifold containing both perceptually significant information and imperceptible signals. The perceptual manifold contains perceptually important signals, providing a better target for pixel space diffusion. P-DINO and LPIPS are the two complementary perceptual supervision utilized in PixelGen.</span>
-          </div>
-        </div>
-
-        <h2 class="title is-4">Implementation</h2>
-        <div class="content has-text-justified">
-          <p>
-            We propose PixelGen, a simple yet effective pixel diffusion framework with perceptual supervision. PixelGen directly operates in the pixel domain without relying on latent representations, VAEs, or auxiliary stages. Following the x-prediction paradigm, the diffusion model predicts clean images instead of noise or velocity. To retain the benefits of flow matching, the predicted image is converted into velocity, resulting in a flow-matching objective.
-            PixelGen focuses on the perceptual manifold rather than the full image manifold. To this end, we introduce two complementary perceptual losses. An LPIPS loss emphasizes local textures and fine-grained details, while a Perceptual DINO (P-DINO) loss aligns global semantics using patch-level features from a frozen DINOv2 encoder.
-          </p>
-          <div class="content has-text-centered">
-            <img src="./static/images/method.jpg"  style="width: 55%; display: block; margin: 0 auto;"><br>
-            <span style="font-size: 0.8em; width: 75%; display: inline-block;">Figure 4: Overview of PixelGen. The diffusion model directly predicts the image x instead of velocity or noise to simplify the prediction target. A flow-matching diffusion loss is retained to keep the advantages of flow matching via velocity conversion. Two complementary perceptual losses are introduced to encourage the diffusion model to focus on the perceptual manifold.</span>
-          </div>
-        </div>
-
-        <h2 class="title is-4">Empirically Analysis</h2>
-        <div class="content has-text-justified">
-          <p>
-            Perceptual supervision improves pixel diffusion by enhancing local details and global semantics. Starting from the JiT baseline, we progressively introduce the LPIPS loss and the P-DINO loss. As shown in Figure 5, the baseline model produces blurry images with weak structural consistency. After adding the LPIPS loss, local textures become sharper, and fine details are better preserved. This indicates that LPIPS effectively emphasizes perceptually important local patterns. When the P-DINO loss is further introduced, the generated images exhibit improved global structure and better semantics. These qualitative improvements are supported by quantitative results. The baseline model achieves an FID of 23.67 on ImageNet without classifier-free guidance. With LPIPS loss, the FID decreases to 10.00. After adding the P-DINO loss, it further drops to 7.46. This confirms that LPIPS and P-DINO provide complementary supervision. LPIPS focuses on local perceptual fidelity, while P-DINO enhances global semantics. Together, they guide the diffusion model toward a perceptually meaningful manifold.
-          </p>
-          <div class="content has-text-centered">
-            <img src="./static/images/empirical_vis.jpg"  style="width: 50%; display: block; margin: 0 auto;"><br>
-            <span style="font-size: 0.8em; width: 60%; display: inline-block;">Figure 5: Effectiveness of perceptual supervision in PixelGen. LPIPS and P-DINO losses are progressively added to a baseline pixel diffusion model. The LPIPS loss improves local texture fidelity, while P-DINO further enhances global semantics.
-            </span>
-          </div>
-        </div>
-      </div>
-    </div>
-  </div>
-</section>
-
-<section class="section">
-  <div class="container is-max-desktop">
-    <div class="columns is-centered">
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-        <h2 class="title is-3">Evaluations</h2>
-        <h2 class="title is-4">Quantitative Results</h2>
-        <div class="content has-text-centered">
-          <img src="./static/images/ablation_result.jpg", style="width: 50%;">
-          <br>
-          <br>
-          <img src="./static/images/imagenet_results.jpg", style="width: 100%;">
-          <br>
-          <br>
-          <img src="./static/images/t2i_results.jpg", style="width: 100%;">
-        </div>
-
-        <h2 class="title is-4">Qualitative Results</h2>
-        <div class="content has-text-centered">
-          <img src="./static/images/t2i_vis2.jpg", style="width: 80%;">
-          <span style="font-size: 0.8em; width: 80%; display: inline-block;">Figure 6: More Qualitative results of text-to-image generation at a 512x512 resolution. Our PixelGen supports multiple languages with the Qwen3 text encoder, such as Chinese and English.
-          </span>
-          <br>
-          <br>
-          <img src="./static/images/c2i_vis1.jpg", style="width: 80%;">
-          <span style="font-size: 0.8em; width: 80%; display: inline-block;">Figure 7: Qualitative results of class-to-image generation at a 256x256 resolution.
-          </span>
-          <br>
-        </div>
-
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-    </div>
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-</section>
-
-<section class="section" id="BibTeX">
-  <div class="container is-max-desktop content">
-    <h2 class="title"><a id="bibtex">BibTeX</a></h2>
-    <pre><code>@misc{ma2025PixelGenfrequencyPixelGenupledpixeldiffusion,
-      title={PixelGen: Pixel Diffusion Beats Latent Diffusion with Perceptual Loss}, 
-      author={Zehong Ma and Ruihan Xu and Shiliang Zhang},
-      year={2025},
-      eprint={2602.02493},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2602.02493}, 
-    }</code></pre>
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-                if ((xhr.readyState == 4 && xhr.status == 200 && !ie) || ie) {
-                    var json = JSON.parse(xhr.responseText);
-                    createJuxtapose(json);
-                }
-            };
-            setTimeout(function() {
-                xhr.send();
-            }, 0);
-        }
-
-        var head = document.head || document.getElementsByTagName("head")[0];
-        var oembed_link = document.createElement('link');
-        oembed_link['rel'] = 'alternate';
-        oembed_link['type'] = 'application/json+oembed';
-        oembed_link['href'] = 'https://oembed.knightlab.com/juxtapose/?url=' + encodeURIComponent(window.location.href);
-        head.appendChild(oembed_link);
-    </script>
-
-</body>
-
-</html>

code/docs/juxtapose/embed/test.json

@@ -1,19 +0,0 @@
-{
-    "images": [{
-            "src": "https://placebear.com/g/500/300",
-            "label": "2009",
-            "credit": "Image Credit"
-        },
-        {
-            "src": "https://placebear.com/500/300",
-            "label": "2014",
-            "credit": "Image Credit"
-        }
-    ],
-    "options": {
-        "animate": true,
-        "showLabels": true,
-        "showCredits": false,
-        "startingPosition": "50%"
-    }
-}

code/docs/juxtapose/js/juxtapose.js

@@ -1,757 +0,0 @@
-/* juxtapose - v1.1.2 - 2015-07-16
- * Copyright (c) 2015 Alex Duner and Northwestern University Knight Lab
- */
-
-(function(document, window) {
-
-    var juxtapose = {
-        sliders: [],
-        OPTIMIZATION_ACCEPTED: 1,
-        OPTIMIZATION_WAS_CONSTRAINED: 2
-    };
-
-    var flickr_key = "d90fc2d1f4acc584e08b8eaea5bf4d6c";
-    var FLICKR_SIZE_PREFERENCES = ['Large', 'Medium'];
-
-    function Graphic(properties, slider) {
-        var self = this;
-        this.image = new Image();
-
-        this.loaded = false;
-        this.image.onload = function() {
-            self.loaded = true;
-            slider._onLoaded();
-        };
-
-        this.image.src = properties.src;
-        this.image.alt = properties.alt || '';
-        this.label = properties.label || false;
-        this.credit = properties.credit || false;
-    }
-
-    function FlickrGraphic(properties, slider) {
-        var self = this;
-        this.image = new Image();
-
-        this.loaded = false;
-        this.image.onload = function() {
-            self.loaded = true;
-            slider._onLoaded();
-        };
-
-        this.flickrID = this.getFlickrID(properties.src);
-        this.callFlickrAPI(this.flickrID, self);
-
-        this.label = properties.label || false;
-        this.credit = properties.credit || false;
-    }
-
-    FlickrGraphic.prototype = {
-        getFlickrID: function(url) {
-            if (url.match(/flic.kr\/.+/i)) {
-                var encoded = url.split('/').slice(-1)[0];
-                return base58Decode(encoded);
-            }
-            var idx = url.indexOf("flickr.com/photos/");
-            var pos = idx + "flickr.com/photos/".length;
-            var photo_info = url.substr(pos);
-            if (photo_info.indexOf('/') == -1) return null;
-            if (photo_info.indexOf('/') === 0) photo_info = photo_info.substr(1);
-            id = photo_info.split("/")[1];
-            return id;
-        },
-
-        callFlickrAPI: function(id, self) {
-            var url = 'https://api.flickr.com/services/rest/?method=flickr.photos.getSizes' +
-                '&api_key=' + flickr_key +
-                '&photo_id=' + id + '&format=json&nojsoncallback=1';
-
-            var request = new XMLHttpRequest();
-            request.open('GET', url, true);
-            request.onload = function() {
-                if (request.status >= 200 && request.status < 400) {
-                    data = JSON.parse(request.responseText);
-                    var flickr_url = self.bestFlickrUrl(data.sizes.size);
-                    self.setFlickrImage(flickr_url);
-                } else {
-                    console.error("There was an error getting the picture from Flickr");
-                }
-            };
-            request.onerror = function() {
-                console.error("There was an error getting the picture from Flickr");
-            };
-            request.send();
-        },
-
-        setFlickrImage: function(src) {
-            this.image.src = src;
-        },
-
-        bestFlickrUrl: function(ary) {
-            var dict = {};
-            for (var i = 0; i < ary.length; i++) {
-                dict[ary[i].label] = ary[i].source;
-            }
-            for (var j = 0; j < FLICKR_SIZE_PREFERENCES.length; j++) {
-                if (FLICKR_SIZE_PREFERENCES[j] in dict) {
-                    return dict[FLICKR_SIZE_PREFERENCES[j]];
-                }
-            }
-            return ary[0].source;
-        }
-    };
-
-    function getNaturalDimensions(DOMelement) {
-        if (DOMelement.naturalWidth && DOMelement.naturalHeight) {
-            return { width: DOMelement.naturalWidth, height: DOMelement.naturalHeight };
-        }
-        // https://www.jacklmoore.com/notes/naturalwidth-and-naturalheight-in-ie/
-        var img = new Image();
-        img.src = DOMelement.src;
-        return { width: img.width, height: img.height };
-    }
-
-    function getImageDimensions(img) {
-        var dimensions = {
-            width: getNaturalDimensions(img).width,
-            height: getNaturalDimensions(img).height,
-            aspect: function() { return (this.width / this.height); }
-        };
-        return dimensions;
-    }
-
-    function addClass(element, c) {
-        if (element.classList) {
-            element.classList.add(c);
-        } else {
-            element.className += " " + c;
-        }
-    }
-
-    function removeClass(element, c) {
-        element.className = element.className.replace(/(\S+)\s*/g, function(w, match) {
-            if (match === c) {
-                return '';
-            }
-            return w;
-        }).replace(/^\s+/, '');
-    }
-
-    function setText(element, text) {
-        if (document.body.textContent) {
-            element.textContent = text;
-        } else {
-            element.innerText = text;
-        }
-    }
-
-    function getComputedWidthAndHeight(element) {
-        if (window.getComputedStyle) {
-            return {
-                width: parseInt(getComputedStyle(element).width, 10),
-                height: parseInt(getComputedStyle(element).height, 10)
-            };
-        } else {
-            w = element.getBoundingClientRect().right - element.getBoundingClientRect().left;
-            h = element.getBoundingClientRect().bottom - element.getBoundingClientRect().top;
-            return {
-                width: parseInt(w, 10) || 0,
-                height: parseInt(h, 10) || 0
-            };
-        }
-    }
-
-    function viewport() {
-        var e = window,
-            a = 'inner';
-        if (!('innerWidth' in window)) {
-            a = 'client';
-            e = document.documentElement || document.body;
-        }
-        return { width: e[a + 'Width'], height: e[a + 'Height'] }
-    }
-
-    function getPageX(e) {
-        var pageX;
-        if (e.pageX) {
-            pageX = e.pageX;
-        } else if (e.touches) {
-            pageX = e.touches[0].pageX;
-        } else {
-            pageX = e.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
-        }
-        return pageX;
-    }
-
-    function getPageY(e) {
-        var pageY;
-        if (e.pageY) {
-            pageY = e.pageY;
-        } else if (e.touches) {
-            pageY = e.touches[0].pageY;
-        } else {
-            pageY = e.clientY + document.body.scrollTop + document.documentElement.scrollTop;
-        }
-        return pageY;
-    }
-
-    function checkFlickr(url) {
-        if (url.match(/flic.kr\/.+/i)) {
-            return true;
-        }
-        var idx = url.indexOf("flickr.com/photos/");
-        if (idx == -1) {
-            return false;
-        } else {
-            return true;
-        }
-    }
-
-    function base58Decode(encoded) {
-        var alphabet = '123456789abcdefghijkmnopqrstuvwxyzABCDEFGHJKLMNPQRSTUVWXYZ',
-            base = alphabet.length;
-        if (typeof encoded !== 'string') {
-            throw '"base58Decode" only accepts strings.';
-        }
-        var decoded = 0;
-        while (encoded) {
-            var alphabetPosition = alphabet.indexOf(encoded[0]);
-            if (alphabetPosition < 0) {
-                throw '"base58Decode" can\'t find "' + encoded[0] + '" in the alphabet: "' + alphabet + '"';
-            }
-            var powerOf = encoded.length - 1;
-            decoded += alphabetPosition * (Math.pow(base, powerOf));
-            encoded = encoded.substring(1);
-        }
-        return decoded.toString();
-    }
-
-    function getLeftPercent(slider, input) {
-        if (typeof(input) === "string" || typeof(input) === "number") {
-            leftPercent = parseInt(input, 10);
-        } else {
-            var sliderRect = slider.getBoundingClientRect();
-            var offset = {
-                top: sliderRect.top + document.body.scrollTop + document.documentElement.scrollTop,
-                left: sliderRect.left + document.body.scrollLeft + document.documentElement.scrollLeft
-            };
-            var width = slider.offsetWidth;
-            var pageX = getPageX(input);
-            var relativeX = pageX - offset.left;
-            leftPercent = (relativeX / width) * 100;
-        }
-        return leftPercent;
-    }
-
-    function getTopPercent(slider, input) {
-        if (typeof(input) === "string" || typeof(input) === "number") {
-            topPercent = parseInt(input, 10);
-        } else {
-            var sliderRect = slider.getBoundingClientRect();
-            var offset = {
-                top: sliderRect.top + document.body.scrollTop + document.documentElement.scrollTop,
-                left: sliderRect.left + document.body.scrollLeft + document.documentElement.scrollLeft
-            };
-            var width = slider.offsetHeight;
-            var pageY = getPageY(input);
-            var relativeY = pageY - offset.top;
-            topPercent = (relativeY / width) * 100;
-        }
-        return topPercent;
-    }
-
-    // values of BOOLEAN_OPTIONS are ignored. just used for 'in' test on keys
-    var BOOLEAN_OPTIONS = { 'animate': true, 'showLabels': true, 'showCredits': true, 'makeResponsive': true };
-
-    function interpret_boolean(x) {
-        if (typeof(x) != 'string') {
-            return Boolean(x);
-        }
-        return !(x === 'false' || x === '');
-    }
-
-    function JXSlider(selector, images, options) {
-
-        this.selector = selector;
-
-        var i;
-        this.options = { // new options must have default values set here.
-            animate: true,
-            showLabels: true,
-            showCredits: true,
-            makeResponsive: true,
-            startingPosition: "50%",
-            mode: 'horizontal',
-            callback: null // pass a callback function if you like
-        };
-
-        for (i in this.options) {
-            if (i in options) {
-                if (i in BOOLEAN_OPTIONS) {
-                    this.options[i] = interpret_boolean(options[i]);
-                } else {
-                    this.options[i] = options[i];
-                }
-            }
-        }
-
-        if (images.length == 2) {
-
-            if (checkFlickr(images[0].src)) {
-                this.imgBefore = new FlickrGraphic(images[0], this);
-            } else {
-                this.imgBefore = new Graphic(images[0], this);
-            }
-
-            if (checkFlickr(images[1].src)) {
-                this.imgAfter = new FlickrGraphic(images[1], this);
-            } else {
-                this.imgAfter = new Graphic(images[1], this);
-            }
-
-        } else {
-            console.warn("The images parameter takes two Image objects.");
-        }
-
-        if (this.imgBefore.credit || this.imgAfter.credit) {
-            this.options.showCredits = true;
-        } else {
-            this.options.showCredits = false;
-        }
-    }
-
-    JXSlider.prototype = {
-
-        updateSlider: function(input, animate) {
-            var leftPercent, rightPercent;
-
-            if (this.options.mode === "vertical") {
-                leftPercent = getTopPercent(this.slider, input);
-            } else {
-                leftPercent = getLeftPercent(this.slider, input);
-            }
-
-            leftPercent = leftPercent.toFixed(2) + "%";
-            leftPercentNum = parseFloat(leftPercent);
-            rightPercent = (100 - leftPercentNum) + "%";
-
-            if (leftPercentNum > 0 && leftPercentNum < 100) {
-                removeClass(this.handle, 'transition');
-                removeClass(this.rightImage, 'transition');
-                removeClass(this.leftImage, 'transition');
-
-                if (this.options.animate && animate) {
-                    addClass(this.handle, 'transition');
-                    addClass(this.leftImage, 'transition');
-                    addClass(this.rightImage, 'transition');
-                }
-
-                if (this.options.mode === "vertical") {
-                    this.handle.style.top = leftPercent;
-                    this.leftImage.style.height = leftPercent;
-                    this.rightImage.style.height = rightPercent;
-                } else {
-                    this.handle.style.left = leftPercent;
-                    this.leftImage.style.width = leftPercent;
-                    this.rightImage.style.width = rightPercent;
-                }
-                this.sliderPosition = leftPercent;
-            }
-        },
-
-        getPosition: function() {
-            return this.sliderPosition;
-        },
-
-        displayLabel: function(element, labelText) {
-            label = document.createElement("div");
-            label.className = 'jx-label';
-            label.setAttribute('tabindex', 0); //put the controller in the natural tab order of the document
-
-            setText(label, labelText);
-            element.appendChild(label);
-        },
-
-        displayCredits: function() {
-            credit = document.createElement("div");
-            credit.className = "jx-credit";
-
-            text = "<em>Photo Credits:</em>";
-            if (this.imgBefore.credit) { text += " <em>Before</em> " + this.imgBefore.credit; }
-            if (this.imgAfter.credit) { text += " <em>After</em> " + this.imgAfter.credit; }
-
-            credit.innerHTML = text;
-
-            this.wrapper.appendChild(credit);
-        },
-
-        setStartingPosition: function(s) {
-            this.options.startingPosition = s;
-        },
-
-        checkImages: function() {
-            if (getImageDimensions(this.imgBefore.image).aspect() ==
-                getImageDimensions(this.imgAfter.image).aspect()) {
-                return true;
-            } else {
-                return false;
-            }
-        },
-
-        calculateDims: function(width, height) {
-            var ratio = getImageDimensions(this.imgBefore.image).aspect();
-            if (width) {
-                height = width / ratio;
-            } else if (height) {
-                width = height * ratio;
-            }
-            return {
-                width: width,
-                height: height,
-                ratio: ratio
-            };
-        },
-
-        responsivizeIframe: function(dims) {
-            //Check the slider dimensions against the iframe (window) dimensions
-            if (dims.height < window.innerHeight) {
-                //If the aspect ratio is greater than 1, imgs are landscape, so letterbox top and bottom
-                if (dims.ratio >= 1) {
-                    this.wrapper.style.paddingTop = parseInt((window.innerHeight - dims.height) / 2) + "px";
-                }
-            } else if (dims.height > window.innerHeight) {
-                /* If the image is too tall for the window, which happens at 100% width on large screens,
-                 * force dimension recalculation based on height instead of width */
-                dims = this.calculateDims(0, window.innerHeight);
-                this.wrapper.style.paddingLeft = parseInt((window.innerWidth - dims.width) / 2) + "px";
-            }
-            if (this.options.showCredits) {
-                // accommodate the credits box within the iframe
-                dims.height -= 13;
-            }
-            return dims;
-        },
-
-        setWrapperDimensions: function() {
-            var wrapperWidth = getComputedWidthAndHeight(this.wrapper).width;
-            var wrapperHeight = getComputedWidthAndHeight(this.wrapper).height;
-            var dims = this.calculateDims(wrapperWidth, wrapperHeight);
-            // if window is in iframe, make sure images don't overflow boundaries
-            if (window.location !== window.parent.location && !this.options.makeResponsive) {
-                dims = this.responsivizeIframe(dims);
-            }
-
-            this.wrapper.style.height = parseInt(dims.height) + "px";
-            this.wrapper.style.width = parseInt(dims.width) + "px";
-        },
-
-        optimizeWrapper: function(maxWidth) {
-            var result = juxtapose.OPTIMIZATION_ACCEPTED;
-            if ((this.imgBefore.image.naturalWidth >= maxWidth) && (this.imgAfter.image.naturalWidth >= maxWidth)) {
-                this.wrapper.style.width = maxWidth + "px";
-                result = juxtapose.OPTIMIZATION_WAS_CONSTRAINED;
-            } else if (this.imgAfter.image.naturalWidth < maxWidth) {
-                this.wrapper.style.width = this.imgAfter.image.naturalWidth + "px";
-            } else {
-                this.wrapper.style.width = this.imgBefore.image.naturalWidth + "px";
-            }
-            this.setWrapperDimensions();
-            return result;
-        },
-
-        _onLoaded: function() {
-
-            if (this.imgBefore && this.imgBefore.loaded === true &&
-                this.imgAfter && this.imgAfter.loaded === true) {
-
-                this.wrapper = document.querySelector(this.selector);
-                addClass(this.wrapper, 'juxtapose');
-
-                this.wrapper.style.width = getNaturalDimensions(this.imgBefore.image).width;
-                this.setWrapperDimensions();
-
-                this.slider = document.createElement("div");
-                this.slider.className = 'jx-slider';
-                this.wrapper.appendChild(this.slider);
-
-                if (this.options.mode != "horizontal") {
-                    addClass(this.slider, this.options.mode);
-                }
-
-                this.handle = document.createElement("div");
-                this.handle.className = 'jx-handle';
-
-                this.rightImage = document.createElement("div");
-                this.rightImage.className = 'jx-image jx-right';
-                this.rightImage.appendChild(this.imgAfter.image);
-
-
-                this.leftImage = document.createElement("div");
-                this.leftImage.className = 'jx-image jx-left';
-                this.leftImage.appendChild(this.imgBefore.image);
-
-                this.labCredit = document.createElement("a");
-                this.labCredit.setAttribute('href', 'https://juxtapose.knightlab.com');
-                this.labCredit.setAttribute('target', '_blank');
-                this.labCredit.setAttribute('rel', 'noopener');
-                this.labCredit.className = 'jx-knightlab';
-                this.labLogo = document.createElement("div");
-                this.labLogo.className = 'knightlab-logo';
-                this.labCredit.appendChild(this.labLogo);
-                this.projectName = document.createElement("span");
-                this.projectName.className = 'juxtapose-name';
-                setText(this.projectName, 'JuxtaposeJS');
-                this.labCredit.appendChild(this.projectName);
-
-                this.slider.appendChild(this.handle);
-                this.slider.appendChild(this.leftImage);
-                this.slider.appendChild(this.rightImage);
-                this.slider.appendChild(this.labCredit);
-
-                this.leftArrow = document.createElement("div");
-                this.rightArrow = document.createElement("div");
-                this.control = document.createElement("div");
-                this.controller = document.createElement("div");
-
-                this.leftArrow.className = 'jx-arrow jx-left';
-                this.rightArrow.className = 'jx-arrow jx-right';
-                this.control.className = 'jx-control';
-                this.controller.className = 'jx-controller';
-
-                this.controller.setAttribute('tabindex', 0); //put the controller in the natural tab order of the document
-                this.controller.setAttribute('role', 'slider');
-                this.controller.setAttribute('aria-valuenow', 50);
-                this.controller.setAttribute('aria-valuemin', 0);
-                this.controller.setAttribute('aria-valuemax', 100);
-
-                this.handle.appendChild(this.leftArrow);
-                this.handle.appendChild(this.control);
-                this.handle.appendChild(this.rightArrow);
-                this.control.appendChild(this.controller);
-
-                this._init();
-            }
-        },
-
-        _init: function() {
-
-            if (this.checkImages() === false) {
-                console.warn(this, "Check that the two images have the same aspect ratio for the slider to work correctly.");
-            }
-
-            this.updateSlider(this.options.startingPosition, false);
-
-            if (this.options.showLabels === true) {
-                if (this.imgBefore.label) { this.displayLabel(this.leftImage, this.imgBefore.label); }
-                if (this.imgAfter.label) { this.displayLabel(this.rightImage, this.imgAfter.label); }
-            }
-
-            if (this.options.showCredits === true) {
-                this.displayCredits();
-            }
-
-            var self = this;
-            window.addEventListener("resize", function() {
-                self.setWrapperDimensions();
-            });
-
-
-            // Set up Javascript Events
-            // On mousedown, call updateSlider then set animate to false
-            // (if animate is true, adds css transition when updating).
-
-            this.slider.addEventListener("mousedown", function(e) {
-                e = e || window.event;
-                e.preventDefault();
-                self.updateSlider(e, true);
-                animate = true;
-
-                this.addEventListener("mousemove", function(e) {
-                    e = e || window.event;
-                    e.preventDefault();
-                    if (animate) { self.updateSlider(e, false); }
-                });
-
-                this.addEventListener('mouseup', function(e) {
-                    e = e || window.event;
-                    e.preventDefault();
-                    e.stopPropagation();
-                    this.removeEventListener('mouseup', arguments.callee);
-                    animate = false;
-                });
-            });
-
-            this.slider.addEventListener("touchstart", function(e) {
-                e = e || window.event;
-                e.preventDefault();
-                e.stopPropagation();
-                self.updateSlider(e, true);
-
-                this.addEventListener("touchmove", function(e) {
-                    e = e || window.event;
-                    e.preventDefault();
-                    e.stopPropagation();
-                    self.updateSlider(event, false);
-                });
-
-            });
-
-            /* keyboard accessibility */
-
-            this.handle.addEventListener("keydown", function(e) {
-                e = e || window.event;
-                var key = e.which || e.keyCode;
-                var ariaValue = parseFloat(this.style.left);
-
-                //move jx-controller left
-                if (key == 37) {
-                    ariaValue = ariaValue - 1;
-                    var leftStart = parseFloat(this.style.left) - 1;
-                    self.updateSlider(leftStart, false);
-                    self.controller.setAttribute('aria-valuenow', ariaValue);
-                }
-
-                //move jx-controller right
-                if (key == 39) {
-                    ariaValue = ariaValue + 1;
-                    var rightStart = parseFloat(this.style.left) + 1;
-                    self.updateSlider(rightStart, false);
-                    self.controller.setAttribute('aria-valuenow', ariaValue);
-                }
-            });
-
-            //toggle right-hand image visibility
-            this.leftImage.addEventListener("keydown", function(event) {
-                var key = event.which || event.keyCode;
-                if ((key == 13) || (key == 32)) {
-                    self.updateSlider("90%", true);
-                    self.controller.setAttribute('aria-valuenow', 90);
-                }
-            });
-
-            //toggle left-hand image visibility
-            this.rightImage.addEventListener("keydown", function(event) {
-                var key = event.which || event.keyCode;
-                if ((key == 13) || (key == 32)) {
-                    self.updateSlider("10%", true);
-                    self.controller.setAttribute('aria-valuenow', 10);
-                }
-            });
-
-            juxtapose.sliders.push(this);
-
-            if (this.options.callback && typeof(this.options.callback) == 'function') {
-                this.options.callback(this);
-            }
-        }
-
-    };
-
-    /*
-      Given an element that is configured with the proper data elements, make a slider out of it.
-      Normally this will just be used by scanPage.
-    */
-    juxtapose.makeSlider = function(element, idx) {
-        if (typeof idx == 'undefined') {
-            idx = juxtapose.sliders.length; // not super threadsafe...
-        }
-
-        var w = element;
-
-        var images = w.querySelectorAll('img');
-
-        var options = {};
-        // don't set empty string into options, that's a false false.
-        if (w.getAttribute('data-animate')) {
-            options.animate = w.getAttribute('data-animate');
-        }
-        if (w.getAttribute('data-showlabels')) {
-            options.showLabels = w.getAttribute('data-showlabels');
-        }
-        if (w.getAttribute('data-showcredits')) {
-            options.showCredits = w.getAttribute('data-showcredits');
-        }
-        if (w.getAttribute('data-startingposition')) {
-            options.startingPosition = w.getAttribute('data-startingposition');
-        }
-        if (w.getAttribute('data-mode')) {
-            options.mode = w.getAttribute('data-mode');
-        }
-        if (w.getAttribute('data-makeresponsive')) {
-            options.mode = w.getAttribute('data-makeresponsive');
-        }
-
-        specificClass = 'juxtapose-' + idx;
-        addClass(element, specificClass);
-
-        selector = '.' + specificClass;
-
-        if (w.innerHTML) {
-            w.innerHTML = '';
-        } else {
-            w.innerText = '';
-        }
-
-        slider = new juxtapose.JXSlider(
-            selector, [{
-                    src: images[0].src,
-                    label: images[0].getAttribute('data-label'),
-                    credit: images[0].getAttribute('data-credit'),
-                    alt: images[0].alt
-                },
-                {
-                    src: images[1].src,
-                    label: images[1].getAttribute('data-label'),
-                    credit: images[1].getAttribute('data-credit'),
-                    alt: images[1].alt
-                }
-            ],
-            options
-        );
-    };
-
-    //Enable HTML Implementation
-    juxtapose.scanPage = function() {
-        var elements = document.querySelectorAll('.juxtapose');
-        for (var i = 0; i < elements.length; i++) {
-            juxtapose.makeSlider(elements[i], i);
-        }
-    };
-
-    juxtapose.JXSlider = JXSlider;
-    window.juxtapose = juxtapose;
-
-    juxtapose.scanPage();
-
-}(document, window));
-
-
-// addEventListener polyfill / jonathantneal
-!window.addEventListener && (function(WindowPrototype, DocumentPrototype, ElementPrototype, addEventListener, removeEventListener, dispatchEvent, registry) {
-    WindowPrototype[addEventListener] = DocumentPrototype[addEventListener] = ElementPrototype[addEventListener] = function(type, listener) {
-        var target = this;
-
-        registry.unshift([target, type, listener, function(event) {
-            event.currentTarget = target;
-            event.preventDefault = function() { event.returnValue = false };
-            event.stopPropagation = function() { event.cancelBubble = true };
-            event.target = event.srcElement || target;
-
-            listener.call(target, event);
-        }]);
-
-        this.attachEvent("on" + type, registry[0][3]);
-    };
-
-    WindowPrototype[removeEventListener] = DocumentPrototype[removeEventListener] = ElementPrototype[removeEventListener] = function(type, listener) {
-        for (var index = 0, register; register = registry[index]; ++index) {
-            if (register[0] == this && register[1] == type && register[2] == listener) {
-                return this.detachEvent("on" + type, registry.splice(index, 1)[0][3]);
-            }
-        }
-    };
-
-    WindowPrototype[dispatchEvent] = DocumentPrototype[dispatchEvent] = ElementPrototype[dispatchEvent] = function(eventObject) {
-        return this.fireEvent("on" + eventObject.type, eventObject);
-    };
-})(Window.prototype, HTMLDocument.prototype, Element.prototype, "addEventListener", "removeEventListener", "dispatchEvent", []);

code/docs/static/css/bulma-carousel.min.css

@@ -1 +0,0 @@
-@-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}

code/docs/static/css/bulma-slider.min.css

@@ -1 +0,0 @@
-@-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}

code/docs/static/css/bulma.css.map.txt

@@ -1 +0,0 @@
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code/docs/static/css/fontawesome.all.min.css

@@ -1,5 +0,0 @@
-/*!
- * 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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-			return Math.sqrt(Math.pow(deltaX, 2) + Math.pow(deltaY, 2));
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-
-    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"];
-});