fa041677180ea69638193882a16d82d986bd9d622f496af1a36c5226756a5355

.gitattributes

@@ -39,3 +39,4 @@ Auto-Photoshop-StableDiffusion-Plugin/docs/prompt_shortcut.gif filter=lfs diff=l
 Stable-Diffusion-Webui-Civitai-Helper/img/all_in_one.png filter=lfs diff=lfs merge=lfs -text
 posex/image/sample-webui2.jpg filter=lfs diff=lfs merge=lfs -text
 posex/image/sample-webui2.png filter=lfs diff=lfs merge=lfs -text
+sd_feed/assets/pinterest.png filter=lfs diff=lfs merge=lfs -text

sd-webui-stablesr/srmodule/attn.py

@@ -0,0 +1,111 @@
+'''
+    This file is modified from the TiledVAE attn.py, so that the StableSR can save much VRAM.
+'''
+import math
+import torch
+
+from modules import shared, sd_hijack
+from modules.sd_hijack_optimizations import get_available_vram, get_xformers_flash_attention_op, sub_quad_attention
+
+try:
+    import xformers
+    import xformers.ops
+except ImportError:
+    pass
+
+
+def get_attn_func():
+    method = sd_hijack.model_hijack.optimization_method
+    if method is None:
+        return attn_forward
+    method = method.lower()
+    # The method should be one of the following:
+    # ['none', 'sdp-no-mem', 'sdp', 'xformers', ''sub-quadratic', 'v1', 'invokeai', 'doggettx']
+    if method not in ['none', 'sdp-no-mem', 'sdp', 'xformers', 'sub-quadratic', 'v1', 'invokeai', 'doggettx']:
+        print(f"[StableSR] Warning: Unknown attention optimization method {method}. Please try to update the extension.")
+        return attn_forward
+    
+    if method == 'none':
+        return attn_forward
+    elif method == 'xformers':
+        return xformers_attnblock_forward
+    elif method == 'sdp-no-mem':
+        return sdp_no_mem_attnblock_forward
+    elif method == 'sdp':
+        return sdp_attnblock_forward
+    elif method == 'sub-quadratic':
+        return sub_quad_attnblock_forward
+    elif method == 'doggettx':
+        return cross_attention_attnblock_forward
+    
+    return attn_forward
+
+
+# The following functions are all copied from modules.sd_hijack_optimizations
+# However, the residual & normalization are removed and computed separately.
+
+def attn_forward(q, k, v):
+    # compute attention
+    # q: b,hw,c
+    k = k.permute(0, 2, 1)  # b,c,hw
+    c = k.shape[1]
+    w_ = torch.bmm(q, k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+    w_ = w_ * (int(c)**(-0.5))
+    w_ = torch.nn.functional.softmax(w_, dim=2)
+
+    # attend to values
+    v = v.permute(0, 2, 1)   # b,c,hw
+    w_ = w_.permute(0, 2, 1)   # b,hw,hw (first hw of k, second of q)
+    # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+    h_ = torch.bmm(v, w_)
+
+    return h_.permute(0, 2, 1)
+
+def xformers_attnblock_forward(q, k, v):
+    return xformers.ops.memory_efficient_attention(q, k, v, op=get_xformers_flash_attention_op(q, k, v))
+   
+
+def cross_attention_attnblock_forward(q, k, v):
+    # compute attention
+    k = k.permute(0, 2, 1)# b,c,hw
+    v = v.permute(0, 2, 1)# b,c,hw
+    c = k.shape[1]
+    h_ = torch.zeros_like(k, device=q.device)
+
+    mem_free_total = get_available_vram()
+
+    tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
+    mem_required = tensor_size * 2.5
+    steps = 1
+
+    if mem_required > mem_free_total:
+        steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
+
+    slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+    for i in range(0, q.shape[1], slice_size):
+        end = i + slice_size
+
+        w1 = torch.bmm(q[:, i:end], k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+        w2 = w1 * (int(c)**(-0.5))
+        del w1
+        w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
+        del w2
+
+        # attend to values
+        w4 = w3.permute(0, 2, 1)   # b,hw,hw (first hw of k, second of q)
+        del w3
+
+        h_[:, :, i:end] = torch.bmm(v, w4)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+        del w4
+
+    return h_.permute(0, 2, 1)
+
+def sdp_no_mem_attnblock_forward(q, k, v):
+    with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
+        return sdp_attnblock_forward(q, k, v)
+
+def sdp_attnblock_forward(q, k, v):
+    return torch.nn.functional.scaled_dot_product_attention(q, k, v, dropout_p=0.0, is_causal=False)
+
+def sub_quad_attnblock_forward(q, k, v):
+    return sub_quad_attention(q, k, v, q_chunk_size=shared.cmd_opts.sub_quad_q_chunk_size, kv_chunk_size=shared.cmd_opts.sub_quad_kv_chunk_size, chunk_threshold=shared.cmd_opts.sub_quad_chunk_threshold, use_checkpoint=True)

sd-webui-stablesr/srmodule/colorfix.py

@@ -0,0 +1,114 @@
+import torch
+from PIL import Image
+from torch import Tensor
+from torch.nn import functional as F
+
+from torchvision.transforms import ToTensor, ToPILImage
+
+def adain_color_fix(target: Image, source: Image):
+    # Convert images to tensors
+    to_tensor = ToTensor()
+    target_tensor = to_tensor(target).unsqueeze(0)
+    source_tensor = to_tensor(source).unsqueeze(0)
+
+    # Apply adaptive instance normalization
+    result_tensor = adaptive_instance_normalization(target_tensor, source_tensor)
+
+    # Convert tensor back to image
+    to_image = ToPILImage()
+    result_image = to_image(result_tensor.squeeze(0).clamp_(0.0, 1.0))
+
+    return result_image
+
+def wavelet_color_fix(target: Image, source: Image):
+    # Convert images to tensors
+    to_tensor = ToTensor()
+    target_tensor = to_tensor(target).unsqueeze(0)
+    source_tensor = to_tensor(source).unsqueeze(0)
+
+    # Apply wavelet reconstruction
+    result_tensor = wavelet_reconstruction(target_tensor, source_tensor)
+
+    # Convert tensor back to image
+    to_image = ToPILImage()
+    result_image = to_image(result_tensor.squeeze(0).clamp_(0.0, 1.0))
+
+    return result_image
+
+def calc_mean_std(feat: Tensor, eps=1e-5):
+    """Calculate mean and std for adaptive_instance_normalization.
+    Args:
+        feat (Tensor): 4D tensor.
+        eps (float): A small value added to the variance to avoid
+            divide-by-zero. Default: 1e-5.
+    """
+    size = feat.size()
+    assert len(size) == 4, 'The input feature should be 4D tensor.'
+    b, c = size[:2]
+    feat_var = feat.view(b, c, -1).var(dim=2) + eps
+    feat_std = feat_var.sqrt().view(b, c, 1, 1)
+    feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
+    return feat_mean, feat_std
+
+def adaptive_instance_normalization(content_feat:Tensor, style_feat:Tensor):
+    """Adaptive instance normalization.
+    Adjust the reference features to have the similar color and illuminations
+    as those in the degradate features.
+    Args:
+        content_feat (Tensor): The reference feature.
+        style_feat (Tensor): The degradate features.
+    """
+    size = content_feat.size()
+    style_mean, style_std = calc_mean_std(style_feat)
+    content_mean, content_std = calc_mean_std(content_feat)
+    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
+    return normalized_feat * style_std.expand(size) + style_mean.expand(size)
+
+def wavelet_blur(image: Tensor, radius: int):
+    """
+    Apply wavelet blur to the input tensor.
+    """
+    # input shape: (1, 3, H, W)
+    # convolution kernel
+    kernel_vals = [
+        [0.0625, 0.125, 0.0625],
+        [0.125, 0.25, 0.125],
+        [0.0625, 0.125, 0.0625],
+    ]
+    kernel = torch.tensor(kernel_vals, dtype=image.dtype, device=image.device)
+    # add channel dimensions to the kernel to make it a 4D tensor
+    kernel = kernel[None, None]
+    # repeat the kernel across all input channels
+    kernel = kernel.repeat(3, 1, 1, 1)
+    image = F.pad(image, (radius, radius, radius, radius), mode='replicate')
+    # apply convolution
+    output = F.conv2d(image, kernel, groups=3, dilation=radius)
+    return output
+
+def wavelet_decomposition(image: Tensor, levels=5):
+    """
+    Apply wavelet decomposition to the input tensor.
+    This function only returns the low frequency & the high frequency.
+    """
+    high_freq = torch.zeros_like(image)
+    for i in range(levels):
+        radius = 2 ** i
+        low_freq = wavelet_blur(image, radius)
+        high_freq += (image - low_freq)
+        image = low_freq
+
+    return high_freq, low_freq
+
+def wavelet_reconstruction(content_feat:Tensor, style_feat:Tensor):
+    """
+    Apply wavelet decomposition, so that the content will have the same color as the style.
+    """
+    # calculate the wavelet decomposition of the content feature
+    content_high_freq, content_low_freq = wavelet_decomposition(content_feat)
+    del content_low_freq
+    # calculate the wavelet decomposition of the style feature
+    style_high_freq, style_low_freq = wavelet_decomposition(style_feat)
+    del style_high_freq
+    # reconstruct the content feature with the style's high frequency
+    return content_high_freq + style_low_freq
+

sd-webui-stablesr/srmodule/spade.py

@@ -0,0 +1,206 @@
+"""
+Copyright (C) 2019 NVIDIA Corporation.  All rights reserved.
+Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
+"""
+
+import re
+import torch
+import torch.nn as nn
+
+from ldm.modules.diffusionmodules.util import normalization, checkpoint
+from ldm.modules.diffusionmodules.openaimodel import ResBlock, UNetModel
+
+
+class SPADE(nn.Module):
+    def __init__(self, norm_nc, label_nc=256, config_text='spadeinstance3x3'):
+        super().__init__()
+        assert config_text.startswith('spade')
+        parsed = re.search('spade(\D+)(\d)x\d', config_text)
+        ks = int(parsed.group(2))
+        self.param_free_norm = normalization(norm_nc)
+
+        # The dimension of the intermediate embedding space. Yes, hardcoded.
+        nhidden = 128
+
+        pw = ks // 2
+        self.mlp_shared = nn.Sequential(
+            nn.Conv2d(label_nc, nhidden, kernel_size=ks, padding=pw),
+            nn.ReLU()
+        )
+        self.mlp_gamma = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw)
+        self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw)
+
+    def forward(self, x_dic, segmap_dic):
+        return checkpoint(
+            self._forward, (x_dic, segmap_dic), self.parameters(), True
+        )
+
+    def _forward(self, x_dic, segmap_dic):
+        segmap = segmap_dic[str(x_dic.size(-1))]
+        x = x_dic
+
+        # Part 1. generate parameter-free normalized activations
+        normalized = self.param_free_norm(x)
+
+        # Part 2. produce scaling and bias conditioned on semantic map
+        # segmap = F.interpolate(segmap, size=x.size()[2:], mode='nearest')
+        actv = self.mlp_shared(segmap)
+
+        repeat_factor = normalized.shape[0]//segmap.shape[0]
+        if repeat_factor > 1:
+            out = normalized
+            out *= (1 + self.mlp_gamma(actv).repeat_interleave(repeat_factor, dim=0))
+            out += self.mlp_beta(actv).repeat_interleave(repeat_factor, dim=0)
+        else:
+            out = normalized
+            out *= (1 + self.mlp_gamma(actv))
+            out += self.mlp_beta(actv)
+        return out
+    
+def dual_resblock_forward(self: ResBlock, x, emb, spade: SPADE, get_struct_cond):
+    if self.updown:
+        in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+        h = in_rest(x)
+        h = self.h_upd(h)
+        x = self.x_upd(x)
+        h = in_conv(h)
+    else:
+        h = self.in_layers(x)
+    emb_out = self.emb_layers(emb).type(h.dtype)
+    while len(emb_out.shape) < len(h.shape):
+        emb_out = emb_out[..., None]
+    if self.use_scale_shift_norm:
+        out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+        scale, shift = torch.chunk(emb_out, 2, dim=1)
+        h = out_norm(h) * (1 + scale) + shift
+        h = out_rest(h)
+    else:
+        h = h + emb_out
+        h = self.out_layers(h)
+    h = spade(h, get_struct_cond())
+    return self.skip_connection(x) + h
+
+    
+class SPADELayers(nn.Module):
+    def __init__(self):
+        '''
+        A container class for fast SPADE layer loading.
+        params inferred from the official checkpoint
+        '''
+        super().__init__()
+        self.input_blocks = nn.ModuleList([
+            nn.Identity(),
+            SPADE(320),
+            SPADE(320),
+            nn.Identity(),
+            SPADE(640),
+            SPADE(640),
+            nn.Identity(),
+            SPADE(1280),
+            SPADE(1280),
+            nn.Identity(),
+            SPADE(1280),
+            SPADE(1280),
+        ])
+        self.middle_block = nn.ModuleList([
+            SPADE(1280),
+            nn.Identity(),
+            SPADE(1280),
+        ])
+        self.output_blocks = nn.ModuleList([
+            SPADE(1280),
+            SPADE(1280),
+            SPADE(1280),
+            SPADE(1280),
+            SPADE(1280),
+            SPADE(1280),
+            SPADE(640),
+            SPADE(640),
+            SPADE(640),
+            SPADE(320),
+            SPADE(320),
+            SPADE(320),
+        ])
+        self.input_ids = [1,2,4,5,7,8,10,11]
+        self.output_ids = list(range(12))
+        self.mid_ids = [0,2]
+        self.forward_cache_name = 'org_forward_stablesr'
+        self.unet = None
+
+
+    def hook(self, unet: UNetModel, get_struct_cond):
+        # hook all resblocks
+        self.unet = unet
+        resblock: ResBlock = None
+        for i in self.input_ids:
+            resblock = unet.input_blocks[i][0]
+            # debug
+            # assert isinstance(resblock, ResBlock)
+            if not hasattr(resblock, self.forward_cache_name):
+                setattr(resblock, self.forward_cache_name, resblock._forward)
+            resblock._forward = lambda x, timesteps, resblock=resblock, spade=self.input_blocks[i]: dual_resblock_forward(resblock, x, timesteps, spade, get_struct_cond)
+
+        for i in self.output_ids:
+            resblock = unet.output_blocks[i][0]
+            # debug
+            # assert isinstance(resblock, ResBlock)
+            if not hasattr(resblock, self.forward_cache_name):
+                setattr(resblock, self.forward_cache_name, resblock._forward)
+            resblock._forward = lambda x, timesteps, resblock=resblock, spade=self.output_blocks[i]: dual_resblock_forward(resblock, x, timesteps, spade, get_struct_cond)
+
+        for i in self.mid_ids:
+            resblock = unet.middle_block[i]
+            # debug
+            # assert isinstance(resblock, ResBlock)
+            if not hasattr(resblock, self.forward_cache_name):
+                setattr(resblock, self.forward_cache_name, resblock._forward)
+            resblock._forward = lambda x, timesteps, resblock=resblock, spade=self.middle_block[i]: dual_resblock_forward(resblock, x, timesteps, spade, get_struct_cond)
+
+    def unhook(self):
+        unet = self.unet
+        if unet is None: return
+        resblock: ResBlock = None
+        for i in self.input_ids:
+            resblock = unet.input_blocks[i][0]
+            if hasattr(resblock, self.forward_cache_name):
+                resblock._forward = getattr(resblock, self.forward_cache_name)
+                delattr(resblock, self.forward_cache_name)
+
+        for i in self.output_ids:
+            resblock = unet.output_blocks[i][0]
+            if hasattr(resblock, self.forward_cache_name):
+                resblock._forward = getattr(resblock, self.forward_cache_name)
+                delattr(resblock, self.forward_cache_name)
+
+        for i in self.mid_ids:
+            resblock = unet.middle_block[i]
+            if hasattr(resblock, self.forward_cache_name):
+                resblock._forward = getattr(resblock, self.forward_cache_name)
+                delattr(resblock, self.forward_cache_name)
+        self.unet = None
+
+
+    def load_from_dict(self, state_dict):
+        """
+        Load model weights from a dictionary.
+        :param state_dict: a dict of parameters.
+        """
+        filtered_dict = {}
+        for k, v in state_dict.items():
+            if k.startswith("model.diffusion_model."):
+                key = k[len("model.diffusion_model.") :]
+                # remove the '.0.spade' within the key
+                if 'middle_block' not in key:
+                    key = key.replace('.0.spade', '')
+                else:
+                    key = key.replace('.spade', '')
+                filtered_dict[key] = v
+        self.load_state_dict(filtered_dict)
+
+
+if __name__ == '__main__':
+    path = '../models/stablesr_sd21.ckpt'
+    state_dict = torch.load(path)
+    model = SPADELayers()
+    model.load_from_dict(state_dict)
+    print(model)

sd-webui-stablesr/srmodule/struct_cond.py

@@ -0,0 +1,353 @@
+import math
+import torch
+import torch.nn as nn
+
+from ldm.modules.diffusionmodules.openaimodel import (
+    TimestepEmbedSequential, 
+    ResBlock, 
+    Downsample, 
+)
+
+from ldm.modules.diffusionmodules.util import (
+    conv_nd,
+    linear,
+    timestep_embedding,
+    checkpoint,
+    normalization,
+    zero_module,
+)
+
+from srmodule.attn import get_attn_func
+
+attn_func = None
+
+
+class QKVAttentionLegacy(nn.Module):
+    """
+    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
+    """
+
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv):
+        """
+        Apply QKV attention.
+        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+        # Legacy Attention
+        # scale = 1 / math.sqrt(math.sqrt(ch))
+        # weight = torch.einsum(
+        #     "bct,bcs->bts", q * scale, k * scale
+        # )  # More stable with f16 than dividing afterwards
+        # weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        # a = torch.einsum("bts,bcs->bct", weight, v)
+        # a = a.reshape(bs, -1, length)
+        q, k, v = map(
+            lambda t:t.permute(0,2,1)
+            .contiguous(),
+            (q, k, v),
+        )
+        global attn_func
+        a = attn_func(q, k, v)
+        a = (
+            a.permute(0,2,1)
+            .reshape(bs, -1, length)
+        )
+        return a
+
+class AttentionBlock(nn.Module):
+    """
+    An attention block that allows spatial positions to attend to each other.
+    Originally ported from here, but adapted to the N-d case.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+    """
+
+    def __init__(
+        self,
+        channels,
+        num_heads=1,
+        num_head_channels=-1,
+        use_checkpoint=False,
+        use_new_attention_order=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        if num_head_channels == -1:
+            self.num_heads = num_heads
+        else:
+            assert (
+                channels % num_head_channels == 0
+            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+            self.num_heads = channels // num_head_channels
+        self.norm = normalization(channels)
+        self.qkv = conv_nd(1, channels, channels * 3, 1)
+        self.attention = QKVAttentionLegacy(self.num_heads)
+
+        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
+
+    def forward(self, x):
+        return checkpoint(self._forward, (x,), self.parameters(), True)   # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
+
+    def _forward(self, x):
+        b, c, *spatial = x.shape
+        x = x.reshape(b, c, -1)
+        qkv = self.qkv(self.norm(x))
+        h = self.attention(qkv)
+        h = self.proj_out(h)
+        return (x + h).reshape(b, c, *spatial)
+
+
+class EncoderUNetModelWT(nn.Module):
+    """
+    The half UNet model with attention and timestep embedding.
+    For usage, see UNet.
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        model_channels,
+        out_channels,
+        num_res_blocks,
+        attention_resolutions,
+        dropout=0,
+        channel_mult=(1, 2, 4, 8),
+        conv_resample=True,
+        dims=2,
+        use_checkpoint=False,
+        use_fp16=False,
+        num_heads=4,
+        num_head_channels=-1,
+        num_heads_upsample=-1,
+        use_scale_shift_norm=False,
+        resblock_updown=False,
+        use_new_attention_order=False,
+    ):
+        super().__init__()
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.num_res_blocks = num_res_blocks
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.use_checkpoint = use_checkpoint
+        self.dtype = torch.float16 if use_fp16 else torch.float32
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
+                )
+            ]
+        )
+        self._feature_size = model_channels
+        input_block_chans = []
+        ch = model_channels
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for _ in range(num_res_blocks):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=mult * model_channels,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = mult * model_channels
+                if ds in attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            use_checkpoint=use_checkpoint,
+                            num_heads=num_heads,
+                            num_head_channels=num_head_channels,
+                            use_new_attention_order=use_new_attention_order,
+                        )
+                    )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            AttentionBlock(
+                ch,
+                use_checkpoint=use_checkpoint,
+                num_heads=num_heads,
+                num_head_channels=num_head_channels,
+                use_new_attention_order=use_new_attention_order,
+            ),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        input_block_chans.append(ch)
+        self._feature_size += ch
+        self.input_block_chans = input_block_chans
+
+        self.fea_tran = nn.ModuleList([])
+
+        for i in range(len(input_block_chans)):
+            self.fea_tran.append(
+                ResBlock(
+                    input_block_chans[i],
+                    time_embed_dim,
+                    dropout,
+                    out_channels=out_channels,
+                    dims=dims,
+                    use_checkpoint=use_checkpoint,
+                    use_scale_shift_norm=use_scale_shift_norm,
+                )
+            )
+
+    @torch.no_grad()
+    def forward(self, x, timesteps):
+        """
+        Apply the model to an input batch.
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :return: an [N x K] Tensor of outputs.
+        """
+        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+
+        result_list = []
+        results = {}
+        h = x.type(self.dtype)
+        for module in self.input_blocks:
+            last_h = h
+            h = module(h, emb)
+            if h.size(-1) != last_h.size(-1):
+                result_list.append(last_h)
+        h = self.middle_block(h, emb)
+        result_list.append(h)
+
+        assert len(result_list) == len(self.fea_tran)
+
+        for i in range(len(result_list)):
+            results[str(result_list[i].size(-1))] = self.fea_tran[i](result_list[i], emb)
+
+        return results
+    
+    def load_from_dict(self, state_dict):
+        """
+        Load model weights from a dictionary.
+        :param state_dict: a dict of parameters.
+        """
+        filtered_dict = {}
+        for k, v in state_dict.items():
+            if k.startswith("structcond_stage_model."):
+                filtered_dict[k[len("structcond_stage_model.") :]] = v
+        self.load_state_dict(filtered_dict)
+
+
+def build_unetwt() -> EncoderUNetModelWT:
+    """
+    Build a model from a state dict.
+    :param state_dict: a dict of parameters.
+    :return: a nn.Module.
+    """
+    # The settings is from official setting yaml file.
+    # https://github.com/IceClear/StableSR/blob/main/configs/stableSRNew/v2-finetune_text_T_512.yaml
+
+    model = EncoderUNetModelWT(
+        in_channels=4,
+        model_channels=256,
+        out_channels=256,
+        num_res_blocks=2,
+        attention_resolutions=[ 4, 2, 1 ],
+        dropout=0.0,
+        channel_mult=[1, 1, 2, 2],
+        conv_resample=True,
+        dims=2,
+        use_checkpoint=False,
+        use_fp16=False,
+        num_heads=4,
+        num_head_channels=-1,
+        num_heads_upsample=-1,
+        use_scale_shift_norm=False,
+        resblock_updown=False,
+        use_new_attention_order=False,
+    )
+    global attn_func
+    attn_func = get_attn_func()
+    return model
+
+
+if __name__ == "__main__":
+    '''
+    Test the lr encoder model.
+    '''
+    path = '../models/stablesr_sd21.ckpt'
+    state_dict = torch.load(path)
+    for key in state_dict.keys():
+        print(key)
+    model = build_unetwt()
+    model.load_from_dict(state_dict)
+    model = model.cuda()
+    test_latent = torch.randn(1, 4, 64, 64).half().cuda()
+    test_timesteps = torch.tensor([0]).half().cuda()
+    with torch.no_grad():
+        test_result = model(test_latent, test_timesteps)
+    print(test_result.keys())

sd-webui-stablesr/tools/extract_srmodule.py

@@ -0,0 +1,20 @@
+'''
+    This script extracts the spade and structcond module from the official stablesr_000117.ckpt
+'''
+
+import torch
+
+stablesr_path = 'models/stablesr_000117.ckpt'
+
+
+with open(stablesr_path, 'rb') as f:
+    stablesr_ckpt = torch.load(f, map_location='cpu')
+
+srmodule = {}
+for k, v in stablesr_ckpt['state_dict'].items():
+    if 'spade' in k or 'structcond' in k:
+        srmodule[k] = v
+        # print(k) 
+# save
+
+torch.save(srmodule, 'models/stablesr_sd21.ckpt')

sd-webui-stablesr/tools/extract_vaecfw.py

@@ -0,0 +1,20 @@
+import torch
+
+vae_path = 'models/vqgan_cfw_00011.ckpt'
+
+with open(vae_path, 'rb') as f:
+    vae_ckpt = torch.load(f, map_location='cpu')
+
+prune_keys = []
+for k, v in vae_ckpt['state_dict'].items():
+    if 'decoder.fusion_layer' in k:
+        prune_keys.append(k)
+        print(k)
+
+vae_cfw = {}
+for k in prune_keys:
+    vae_cfw[k] = vae_ckpt['state_dict'][k]
+    del vae_ckpt['state_dict'][k]
+
+torch.save(vae_ckpt, 'models/vqgan_cfw_00011_vae_only.ckpt')
+torch.save(vae_cfw, 'models/vqgan_cfw_00011_cfw_only.ckpt')

sd_feed/.gitignore

@@ -0,0 +1,5 @@
+__pycache__
+pyc
+user.json
+*.pin
+temp.png

sd_feed/Readme.md

@@ -0,0 +1,82 @@
+## Feed
+<p align="center">
+ <img  src="assets/feed.png" alt="Filter Images"/>
+ </p>
+
+- This is a custom extension for AUTOMATIC1111/stable-diffusion-webui.
+- You can enjoy browsing from tab and getting reputation with your pictures
+
+
+
+   [![Watch the video](https://storage.googleapis.com/newtypev3/assets/%E1%84%89%E1%85%B3%E1%84%8F%E1%85%B3%E1%84%85%E1%85%B5%E1%86%AB%E1%84%89%E1%85%A3%E1%86%BA%202023-04-18%20%E1%84%8B%E1%85%A9%E1%84%92%E1%85%AE%2012.13.25.png)](https://www.youtube.com/watch?v=hq1MMXSYjTQ)
+
+### Installation
+- The extension can be installed directly from within the **Extension** tab within the Webui.
+- You can also install it manually by running the following command from within the webui directory:
+
+  git clone https://github.com/todhm/sd_feed.git
+
+  and restart your stable-diffusion-webui, then you can see the new tab "Feed", and other features on txt2img tab and img2img tab.
+<p align="center">
+  <a href="" rel="noopener">
+ <img  src="assets/extensions.png" alt="Project logo"></a>
+</p>
+
+### Introduction
+- You can **upload** your masterpiece on the Feed Tap. 
+- You can press the recommend button on the photos created by others.
+- You can see **Pics Of The Day**(POTD) which is most recommended today. 
+- You can easily check the generation data of your favorite photos.
+- You can easily Send your favorite photos to t2i or i2i.
+
+### Features
+#### Upload Your Masterpiece! 
+ <img  src="assets/feedsetting1.png" alt="Filter Images"/>
+
+Upload your image right after generation!
+
+### Feed
+ <img  src="assets/5ulvww8bblta1.webp" alt="Filter Images"/>
+
+Browse People's images!
+
+there are four feeds
+- newest : pics of all over the world
+- popular : pics what people like
+- favorite : pics that you pushed like button
+- my pics : that you uploaded
+
+### Tweak!
+ <img  src="assets/lhijrt5lblta1.webp" alt="Filter Images"/>
+
+Check the parameters and generate your own!
+
+You can easily send to t2i,i2i or Copy the Generation data and also communicate!
+
+### Pics Of The Day!📷
+ <img  src="assets/potd.png" alt="Filter Images"/>
+you can be the king of the day!
+
+most popular pic of the day will be exhibited on generation tab!
+
+
+#### Getting Started
+To start using our WebUI Extension for Reputation-Based Feed Sharing, simply install the extension on your browser and create an account. You can then start uploading your feed and browsing other people's feed and prompts.
+
+#### Feedback and Support
+We are constantly improving our extension based on user feedback, so please let us know if you have any suggestions or issues. You can contact us at 
+
+---
+### Warning
+- Your uploaded image might be exposed to our promotion contents.
+- It does not mean we will make a profit from your contents. There is possibility that other people might see your content indirectly when we trying to introduce our feed to other people.
+
+
+### Todo & Further Features. 
+
+- We want to support a image producers by adding a sponsor features to pouplar images. 
+- We also planned to extend community functions by adding comments on each functions. 
+- Any suggestions are welcomed. Please leave us your comments.
+
+
+

sd_feed/html/feedtab.html

@@ -0,0 +1,291 @@
+<div class="feed-tab-header" style="display:flex;width:100%;justify-content:space-between;" id="feed-header">
+    <div class="feed-tab-class"> 
+        <button
+                v-for="(item, index) in modeSelectList"
+                :key="index"
+                class="feed-tab-default"
+                :class="{ 'selected' : item.value === mode}"
+                @click="setMode(item.value)"
+            >{{ item.name }}</button>
+        <button
+            class="feed-tab-default"
+            :class="{ 'selected' : mode === 'nicknameSearch'}"
+            id="feed-user-tab"
+            @click="setNicknameSearch">
+        </button>
+    </div>
+    <div class="feed-tab-class-search">
+        <input id="prompt-input" type="text" v-model="prompt"  @keyup.enter="fetchNewImage" style="margin-right:5px;">
+        <button @click="fetchNewImage" id="feed-search-btn">Search</button>
+    </div>
+</div>
+<div>
+    <div class="product-slider" style="display:flex;justify-content:center;">
+        <div style="display:flex;justify-content:flex-end;width:100%;margin-top:20px;">
+            <div style="display:flex;">
+                <button style="margin-right:20px;" @click="selectRealTag" class="tag-button" :class="{ 'tag-button-selected': realTag, 'tag-button-unselected': !realTag }">REAL</button>
+            </div>
+            <div style="display:flex;">
+                <button style="margin-right:100px;" @click="selectAnimeTag" class="tag-button" :class="{ 'tag-button-selected': animeTag, 'tag-button-unselected': !animeTag }">ILLUST</button>
+            </div>
+            <div>
+                <label class="toggle-btn">
+                    <span class="toggle-letter">Filter NSFW</span>
+                    <input type="checkbox"  v-model="filterNsfw" @change="fetchNewImage">
+                    <span class="slider round"></span>
+                </label>
+            </div>
+        </div>
+    </div>
+    <div class="product-slider" style="display:flex;justify-content:center;" v-if="mode=='nicknameSearch'">
+        <div class="page-max-width">
+            <div style="max-width:100%;display:flex;align-items: start;">
+                <div style="margin-left:37px;margin-right:37px;margin-bottom:37px;">
+                    <svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="160" zoomAndPan="magnify" viewBox="0 0 39 38.999999" height="160" preserveAspectRatio="xMidYMid meet" version="1.0"><rect x="-3.9" width="46.8" fill="#ffffff" y="-3.9" height="46.799999" fill-opacity="1"/><rect x="-3.9" width="46.8" fill="#ffffff" y="-3.9" height="46.799999" fill-opacity="1"/><rect x="-3.9" width="46.8" fill="#ffffff" y="-3.9" height="46.799999" fill-opacity="1"/><path fill="#000000" d="M 33.289062 5.710938 C 29.605469 2.027344 24.707031 0 19.5 0 C 14.292969 0 9.394531 2.027344 5.710938 5.710938 C 2.027344 9.394531 0 14.292969 0 19.5 C 0 24.707031 2.027344 29.605469 5.710938 33.289062 C 9.394531 36.972656 14.292969 39 19.5 39 C 24.707031 39 29.605469 36.972656 33.289062 33.289062 C 36.972656 29.605469 39 24.707031 39 19.5 C 39 14.292969 36.972656 9.394531 33.289062 5.710938 Z M 6.597656 6.597656 C 10.042969 3.148438 14.625 1.25 19.5 1.25 C 24.375 1.25 28.957031 3.148438 32.402344 6.597656 C 35.851562 10.042969 37.746094 14.625 37.746094 19.5 C 37.746094 23.550781 36.433594 27.402344 34.011719 30.566406 C 32.085938 27.871094 29.007812 26.277344 25.664062 26.277344 L 13.335938 26.277344 C 9.992188 26.277344 6.914062 27.871094 4.988281 30.566406 C 2.5625 27.402344 1.25 23.550781 1.25 19.5 C 1.25 14.625 3.148438 10.042969 6.597656 6.597656 Z M 19.5 25.25 C 24.507812 25.25 28.585938 21.171875 28.585938 16.164062 C 28.585938 11.152344 24.507812 7.078125 19.5 7.078125 C 14.488281 7.078125 10.414062 11.152344 10.414062 16.164062 C 10.414062 21.171875 14.488281 25.25 19.5 25.25 Z M 19.5 25.25 " fill-opacity="1" fill-rule="nonzero"/></svg>
+                </div>
+                <div>
+                    <div style="display:flex;align-items:center;">
+                        <div style="padding: 10px; border-radius: 5px; display:flex;align-items:center;">
+                            <p style="line-height:normal;line-height:0;font-size: 25px;font-weight: 800;margin-right: 10px;">By.</p>
+                            <p style="line-height: 0;font-size: 20px;margin-right: 10p;">{{searchNickname}}</p>
+                        </div>
+                    </div>
+                    <div v-html="parsedSearchProfile" style="padding: 10px; border-radius: 5px; display:flex;align-items:center;">
+                        
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+    <div class="product-slider" style="display:flex;justify-content:center;" v-if="mode=='private'">
+        <div class="page-max-width">
+            <div style="max-width:100%;display:flex;align-items: start;">
+                <div style="margin-left:37px;margin-right:37px;margin-bottom:37px;">
+                    <svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="160" zoomAndPan="magnify" viewBox="0 0 39 38.999999" height="160" preserveAspectRatio="xMidYMid meet" version="1.0"><rect x="-3.9" width="46.8" fill="#ffffff" y="-3.9" height="46.799999" fill-opacity="1"/><rect x="-3.9" width="46.8" fill="#ffffff" y="-3.9" height="46.799999" fill-opacity="1"/><rect x="-3.9" width="46.8" fill="#ffffff" y="-3.9" height="46.799999" fill-opacity="1"/><path fill="#000000" d="M 33.289062 5.710938 C 29.605469 2.027344 24.707031 0 19.5 0 C 14.292969 0 9.394531 2.027344 5.710938 5.710938 C 2.027344 9.394531 0 14.292969 0 19.5 C 0 24.707031 2.027344 29.605469 5.710938 33.289062 C 9.394531 36.972656 14.292969 39 19.5 39 C 24.707031 39 29.605469 36.972656 33.289062 33.289062 C 36.972656 29.605469 39 24.707031 39 19.5 C 39 14.292969 36.972656 9.394531 33.289062 5.710938 Z M 6.597656 6.597656 C 10.042969 3.148438 14.625 1.25 19.5 1.25 C 24.375 1.25 28.957031 3.148438 32.402344 6.597656 C 35.851562 10.042969 37.746094 14.625 37.746094 19.5 C 37.746094 23.550781 36.433594 27.402344 34.011719 30.566406 C 32.085938 27.871094 29.007812 26.277344 25.664062 26.277344 L 13.335938 26.277344 C 9.992188 26.277344 6.914062 27.871094 4.988281 30.566406 C 2.5625 27.402344 1.25 23.550781 1.25 19.5 C 1.25 14.625 3.148438 10.042969 6.597656 6.597656 Z M 19.5 25.25 C 24.507812 25.25 28.585938 21.171875 28.585938 16.164062 C 28.585938 11.152344 24.507812 7.078125 19.5 7.078125 C 14.488281 7.078125 10.414062 11.152344 10.414062 16.164062 C 10.414062 21.171875 14.488281 25.25 19.5 25.25 Z M 19.5 25.25 " fill-opacity="1" fill-rule="nonzero"/></svg>
+                </div>
+                <template v-if="!editMode">
+                    <div>
+                        <div style="display:flex;align-items:center;">
+                            <div style="padding: 10px; border-radius: 5px; display:flex;align-items:center;">
+                                <p style="line-height:normal;line-height:0;font-size: 25px;font-weight: 800;margin-right: 10px;">By.</p>
+                                <p style="line-height: 0;font-size: 20px;margin-right: 10p;">{{nickname}}</p>
+                            </div>
+                            <button @click="setEditMode" style="width:24px;height:24px;">
+                                <svg xmlns="http://www.w3.org/2000/svg" width="100%" height="100%" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather feather-edit-2"><path d="M17 3a2.828 2.828 0 1 1 4 4L7.5 20.5 2 22l1.5-5.5L17 3z"></path></svg>
+                            </button>
+                        </div>
+                        <div style="padding: 10px; border-radius: 5px; display:flex;align-items:center;" v-html="parsedProfile">
+                        </div>
+                        <div style="padding: 10px; border-radius: 5px; display:flex;align-items:center;">
+                            If you have any question or want to modify your data contact us at Discord: #Zag5956
+                        </div>
+                    </div>
+                </template>
+                <template v-else>
+                    <div style="width:100%;">
+                        <div style="width:100%;">
+                    <input  style="padding: 10px; border-radius: 5px; border: 1px solid #ccc; flex-grow: 1;" type="text" v-model="nickname">
+                    <button  style="width:24px;height:24px;margin-right:15px;margin-left:15px;" @click="saveNickname">💾</button>
+                    <button style="width:24px;height:24px;" @click="setEditMode">
+                        ✖️
+                    </button>
+                    </div>
+                    <div style="width:100%;">
+                        <textarea v-model="profile" @input="handleTextareaInput" id="profile-writer-type" class="profile-writer-type" placeholder="Type your profile"></textarea>
+                    </div>
+                    </div>
+                </template>
+            </div>
+            <div style="display:flex;justify-content:center;" v-if="imageList.length == 0 & mode=='private'" class="private-upload">
+                <div style="width:100%;">
+                    <label for="image-input-file" style="background-color:#AFB6BD;width:100%;aspect-ratio:1/1;">
+                        <div style="background-color:#AFB6BD;width:100%;aspect-ratio:1/1;display:flex; align-items:center;justify-content:center;">
+                            <div style="background-color:white;width:80%;aspect-ratio:1/1;display:flex; align-items:center;justify-content:center;">
+                                <div v-if="!loading">
+                                    <div style="color:#AFB6BD;font-size:18px;display:flex;justify-content:center;">
+                                        Please Upload Image
+                                    </div>
+                                    <div style="color:#AFB6BD;font-size:15px;display:flex;justify-content:center;">-or-</div>
+                                    <div style="color:#AFB6BD;font-size:18px;display:flex;justify-content:center;">Click and upload</div>
+                                </div>
+                                <div v-if="loading">
+                                    <div style="color:#AFB6BD;font-size:18px;display:center;justify-content:center;">
+                                        Loading Image...
+                                    </div>
+                                </div>
+                            </div>
+                        </div>
+                    </label>
+                    <input
+                        id="image-input-file"
+                        type="file"
+                        multiple
+                        accept="image/*"
+                        style="display:none;"
+                        @change="uploadImage"
+                    >
+                </div>
+            </div>
+        </div>
+    </div>
+    <div class="product-slider" v-if="imageList.length > 0">
+        <div class="pinterest-column-gallery" id="columns">
+            <div v-if="mode=='private'" class="pinterest-column-item">
+                <label for="image-input-file" style="background-color:#AFB6BD;width:100%;aspect-ratio:1/1;">
+                    <div style="background-color:#AFB6BD;width:100%;aspect-ratio:1/1;display:flex; align-items:center;justify-content:center;">
+                        <div style="background-color:white;width:80%;aspect-ratio:1/1;display:flex; align-items:center;justify-content:center;">
+                            <div v-if="!uploadLoading">
+                                <div style="color:#AFB6BD;font-size:18px;display:flex;justify-content:center;">
+                                    Please Upload Image
+                                </div>
+                                <div style="color:#AFB6BD;font-size:15px;display:flex;justify-content:center;">-or-</div>
+                                <div style="color:#AFB6BD;font-size:18px;display:flex;justify-content:center;">Click and upload</div>
+                            </div>
+                            <div v-if="uploadLoading">
+                                <div style="color:#AFB6BD;font-size:18px;display:center;justify-content:center;">
+                                    Loading Image...
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                </label>
+                <input
+                    id="image-input-file"
+                    type="file"
+                    multiple
+                    accept="image/*"
+                    style="display:none;"
+                    @change="uploadImage"
+                >
+            </div>
+            <template v-for="(imageData, j) in sortedDataList" :key="j">
+               <div  class="pinterest-column-item">
+                    <img :src="imageData.url" @click="openModal(imageData)">
+                    <div class="item-overlay">
+                        <div style="display:flex;align-items: center;height:100%;">
+                            <template v-if="!imageData.liked">
+                                <div class="liked-button-div not-liked-div" @click="addToWishList(imageData)">
+                                    <button class="like-button">👍 {{formatNumber(imageData.likecount)}}</button>
+                                </div>
+                            </template>
+                            <template v-else>
+                                <div class="liked-button-div already-liked-div" @click="deleteWishList(imageData)">
+                                    <button class="like-button already-liked-button">👍 {{formatNumber(imageData.likecount)}}</button>
+                                </div>
+                            </template>
+                        <div style="
+                                display: flex;
+                                justify-content: center;
+                                align-items: center;
+                                margin-left:5px;
+                                height:100%;
+                            ">
+                            <svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="20" zoomAndPan="magnify" viewBox="0 0 39 38.999999" height="20" preserveAspectRatio="xMidYMid meet" version="1.0"><defs><clipPath id="a06d7ef085"><path d="M 1.484375 7.214844 L 37.484375 7.214844 L 37.484375 31.964844 L 1.484375 31.964844 Z M 1.484375 7.214844 " clip-rule="nonzero"/></clipPath></defs><g clip-path="url(#a06d7ef085)"><path fill="#ffffff" d="M 19.484375 7.214844 C 11.300781 7.214844 4.316406 12.347656 1.484375 19.589844 C 4.316406 26.835938 11.300781 31.964844 19.484375 31.964844 C 27.664062 31.964844 34.652344 26.835938 37.484375 19.589844 C 34.652344 12.347656 27.671875 7.214844 19.484375 7.214844 Z M 19.484375 27.839844 C 14.96875 27.839844 11.300781 24.144531 11.300781 19.589844 C 11.300781 15.039062 14.96875 11.339844 19.484375 11.339844 C 24 11.339844 27.664062 15.039062 27.664062 19.589844 C 27.664062 24.144531 24 27.839844 19.484375 27.839844 Z M 19.484375 14.640625 C 16.777344 14.640625 14.574219 16.859375 14.574219 19.589844 C 14.574219 22.320312 16.777344 24.539062 19.484375 24.539062 C 22.191406 24.539062 24.394531 22.320312 24.394531 19.589844 C 24.394531 16.859375 22.191406 14.640625 19.484375 14.640625 Z M 19.484375 14.640625 " fill-opacity="1" fill-rule="nonzero"/></g></svg>
+                        </div>
+                        <div style="display:flex;justify-content: center;align-items: center;color:white;">
+                            {{formatNumber(imageData.total_clicks)}}
+                        </div>
+                        <div style="display: flex;justify-content: center;align-items: center;margin-left:5px;height:100%;">
+                            <svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="16" zoomAndPan="magnify" viewBox="0 0 39 38.999999" height="16" preserveAspectRatio="xMidYMid meet" version="1.0"><defs><clipPath id="6d2be58645"><path d="M 2.515625 24 L 36.265625 24 L 36.265625 39 L 2.515625 39 Z M 2.515625 24 " clip-rule="nonzero"/></clipPath></defs><path fill="#ffffff" d="M 19.453125 0.21875 C 18.429688 0.21875 17.601562 1.054688 17.601562 2.085938 L 17.601562 22.101562 L 13.253906 17.730469 C 11.507812 16.023438 8.933594 18.601562 10.632812 20.367188 C 10.632812 20.367188 18.144531 27.925781 18.144531 27.925781 C 18.84375 28.65625 20.0625 28.632812 20.765625 27.925781 C 20.765625 27.925781 28.277344 20.367188 28.277344 20.367188 C 29.96875 18.605469 27.410156 16.023438 25.652344 17.730469 C 25.652344 17.730469 21.308594 22.101562 21.308594 22.101562 L 21.308594 2.085938 C 21.308594 1.054688 20.476562 0.21875 19.453125 0.21875 Z M 19.453125 0.21875 " fill-opacity="1" fill-rule="nonzero"/><g clip-path="url(#6d2be58645)"><path fill="#ffffff" d="M 4.433594 24.738281 C 3.410156 24.738281 2.578125 25.574219 2.578125 26.605469 L 2.578125 29.359375 C 2.578125 34.554688 6.78125 38.78125 11.945312 38.78125 L 26.964844 38.78125 C 32.128906 38.78125 36.328125 34.554688 36.328125 29.359375 L 36.328125 26.605469 C 36.328125 25.574219 35.5 24.738281 34.476562 24.738281 C 33.453125 24.738281 32.621094 25.574219 32.621094 26.605469 L 32.621094 29.359375 C 32.621094 32.496094 30.082031 35.050781 26.964844 35.050781 L 11.945312 35.050781 C 8.824219 35.050781 6.289062 32.496094 6.289062 29.359375 L 6.289062 26.605469 C 6.289062 25.574219 5.457031 24.738281 4.433594 24.738281 Z M 4.433594 24.738281 " fill-opacity="1" fill-rule="nonzero"/></g></svg>
+                        </div>
+                        <div style="display:flex;justify-content: center;align-items: center;color:white;">
+                            {{formatNumber(imageData.total_sendto)}}
+                        </div>
+                        <div style="display: flex;justify-content: center;align-items: center;margin-left:5px;height:100%;">
+                            <svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="20" zoomAndPan="magnify" viewBox="0 0 39 38.999999" height="20" preserveAspectRatio="xMidYMid meet" version="1.0"><defs><clipPath id="ae37d71e45"><path d="M 5.074219 5.542969 L 33.574219 5.542969 L 33.574219 33.292969 L 5.074219 33.292969 Z M 5.074219 5.542969 " clip-rule="nonzero"/></clipPath></defs><g clip-path="url(#ae37d71e45)"><path fill="#ffffff" d="M 11.796875 33.273438 C 11.722656 33.273438 11.648438 33.253906 11.582031 33.21875 C 11.4375 33.136719 11.347656 32.988281 11.347656 32.820312 L 11.347656 28.015625 L 9.152344 28.015625 C 6.902344 28.015625 5.074219 26.171875 5.074219 23.90625 L 5.074219 9.671875 C 5.074219 7.410156 6.902344 5.566406 9.152344 5.566406 L 29.492188 5.566406 C 31.742188 5.566406 33.570312 7.410156 33.570312 9.671875 L 33.570312 23.90625 C 33.570312 26.171875 31.742188 28.015625 29.492188 28.015625 L 20.226562 28.015625 C 17.496094 29.746094 14.765625 31.472656 12.035156 33.203125 C 11.964844 33.25 11.878906 33.273438 11.796875 33.273438 Z M 11.796875 33.273438 " fill-opacity="1" fill-rule="nonzero"/></g></svg>
+                        </div>
+                        <div style="display:flex;justify-content: center;align-items: center;color:white;">
+                            {{formatNumber(imageData.total_comments)}}
+                        </div>
+                    </div>
+                    <div style="display:flex;align-items:center;">
+                        <a class="item-author" @click="moveFeedProfileTab(imageData.nickname)" href="javascript:void(0);">by {{imageData.nickname}}</a>
+                    </div>
+                </div>
+            </template>
+        </div>
+    </div>
+</div>
+<template v-if="this.showModal">
+    <div class="promptModal">
+        <div class="prompt-modal-page">
+            <div class="prompt-modal-image" @click="closeOutsideModal">
+                <img :src="src" alt="Pretty Image" class="prompt-modal-image-tag"/>
+            </div>
+            <div class="prompt-modal-sidebar">
+                <div class="modal-sidebar-header">
+                    <div style="display:flex;">
+                        <div class="modal-like-wrapper">
+                            <template v-if="!currentItem.liked">
+                                <div class="big-liked-button-div not-liked-div" @click="addToWishList(currentItem)">
+                                    <button class="like-button">👍 {{formatNumber(currentItem.likecount)}}</button>
+                                </div>
+                            </template>
+                            <template v-else>
+                                <div class="big-liked-button-div already-liked-div" @click="deleteWishList(currentItem)">
+                                    <button class="like-button already-liked-button">👍 {{formatNumber(currentItem.likecount)}}</button>
+                                </div>
+                            </template>
+                        </div>
+                        <div class="modal-writer-wrappers">
+                            <a href="javascript:void(0);" @click="moveFeedProfileTab(currentItem.nickname)"  class="modal-writer">By {{currentItem.nickname}}</a>
+                            <div class="modal-writer-time">{{currentItem.created_at}}</div>
+                        </div>
+                    </div>
+                    <div class="modal-writer-wrapper-exit" @click="close()">
+                        <div>
+                            <svg xmlns="http://www.w3.org/2000/svg" width="35" height="35" viewBox="0 0 35 35" fill="none">
+                            <path d="M19.9037 17.8831L27.752 25.7314V27.7998H25.6837L17.8354 19.9514L9.98703 27.7998H7.9187V25.7314L15.767 17.8831L7.9187 10.0348V7.96643H9.98703L17.8354 15.8148L25.6837 7.96643H27.752V10.0348L19.9037 17.8831Z" fill="white"/>
+                            <rect x="1.32679" y="1.37439" width="33.0173" height="33.0173" rx="4.50866" stroke="#0E0E0E" stroke-width="0.982675"/>
+                            </svg>
+                        </div>
+                    </div>
+                </div>
+                <div class="modal-write-input-wrapper">
+                    <div class="modal-write-input-div">
+                        <textarea v-model="comment" @input="handleTextareaInput" id="modal-writer-type" class="modal-writer-type" @focus="makeSaveButtonVisible" placeholder="Type your comment"></textarea>
+                    </div>
+                    <div style="display:flex;justify-content:center;width:100%;">
+                        <div v-if="showCommentWrite" class="write-row-button">
+                            <button class="btn-write-row btn-cancel" @click="hideSaveButtonVisible">Cancel</button>
+                            <button class="btn-write-row btn-save" @click="writeComment()">Comment</button>
+                                         </div>
+                    </div>
+                </div>
+                <div style="margin-top:10px;border-bottom: 1px solid #AFB6BD;padding-bottom:17px;">
+                    <template v-for="(commentData, j) in comments" :key="j">
+                        <div style="margin-left:10px;margin-right:10px;">
+                            <div style="display:flex;width:100%;margin-bottom:12px;color:white;">
+                                <div style="margin-right:10px;color:white;font-size:17px;font-weight:700;">{{commentData.user.nickname}}</div>
+                                <div style="color:white;font-size:12px;font-weight:300;">{{commentData.created_at}}</div>
+                            </div>
+                            <div style="display:flex;width:100%;color:white;">
+                                <div style="color:white;font-size:13px;">{{commentData.text}}</div>
+                            </div>
+                        </div>
+                    </template>
+                </div>
+                <div class="prompt-send-to">
+                    <button class="btn-send-to btn-send-to-text" @click="this.sendToTextToImage()">Send to t2i</button>
+                    <button class="btn-send-to btn-send-to-img" @click="this.sendToImageToImage()">Send to i2i</button>
+                </div>
+                <div style="margin-top:12px;">
+                    <div style="margin-left: 10px;margin-right: 10px;">
+                        <div style="color:white;margin-bottom:12px;">Generation Data</div>
+                        <div style="box-sizing: border-box;display: flex;flex-direction: column;justify-content: center;align-items: flex-start;padding-left:20px;padding-right:20px;padding-top:10px;padding-bottom:10px;border: 1px solid #AFB6BD;border-radius: 5px;color:white;">
+                            <template v-for="(value, key) in metadata">
+                                <template v-if="/^[A-Z]/.test(key)">
+                                    {{key}}:{{value}},
+                                </template>
+                            </template>
+                        </div>
+                        <div style="display:flex;justify-content:center;margin-top:20px;">
+                            <button @click="copyClipboard" style="text-align:center;box-sizing: border-box;display: flex;flex-direction: column;justify-content: center;align-items: flex-start;padding-top:10px;padding-bottom:10px;border: 1px solid #AFB6BD;border-radius: 5px;color:white;width:100%;">
+                            <div style="color:white;margin:auto;">Copy Generation Data</div></button>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+</template>

sd_feed/html/lora.html

@@ -0,0 +1,24 @@
+<div class="feed-tab-header" style="display:flex;width:100%;justify-content:space-between;" id="lora-search-table">
+    <input value="" id="lora-download-url" style="display:none;"/>
+    <input value="" id="lora-filename" style="display:none;"/>
+    <table v-if="loraList.length>0">
+        <thead>
+            <th>File Name</th>
+            <th></th>
+        </thead>
+        <tbody>
+            <template v-for="(loraData, j) in loraList" :key="j">
+                <tr>
+                    <td>{{loraData.fileName}}</td>
+                    <template v-if="loraData.downloadUrl != ''">
+                        <td><button @click="setUrlData(loraData.downloadUrl, loraData.fileName)">Download</button></td>
+                    </template>
+                    <template v-else>
+                        <td>Not Found OTL</td>
+                    </template>
+                </tr>
+            </template>
+
+        </tbody>
+    </table>
+</div>

sd_feed/html/potd.html

@@ -0,0 +1,135 @@
+<div>
+    <div style="display: flex;justify-content: space-between;">
+        <div class="potd">
+            👑 POTD 👑
+        </div>
+        <div>
+            <label class="toggle-btn">
+                <span class="toggle-letter">Filter NSFW</span>
+                <input type="checkbox"  v-model="filterNsfw" @change="fetchBests">
+                <span class="slider round"></span>
+            </label>
+        </div>
+    </div>
+    <div class="product-slider" v-if="bestFeeds.length > 0">
+        <div class="pinterest-gallery">
+            <template v-for="(imageData, j) in bestFeeds" :key="j">
+                <div class="pinterest-item">
+                    <button @click="openModal(imageData)">
+                        <img :src="imageData.url">
+                    </button>
+                    <div class="item-overlay">
+                        <div style="display:flex;align-items: center;height:100%;">
+                            <template v-if="!imageData.liked">
+                                <div class="liked-button-div not-liked-div" @click="addToWishList(imageData)">
+                                    <button class="like-button">👍 {{formatNumber(imageData.likecount)}}</button>
+                                </div>
+                            </template>
+                            <template v-else>
+                                <div class="liked-button-div already-liked-div" @click="deleteWishList(imageData)">
+                                    <button class="like-button already-liked-button">👍 {{formatNumber(imageData.likecount)}}</button>
+                                </div>
+                            </template>
+                            <div style="display: flex;justify-content: center;align-items: center;margin-left:5px;height:100%;">
+                                <svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="12" zoomAndPan="magnify" viewBox="0 0 39 38.999999" height="12" preserveAspectRatio="xMidYMid meet" version="1.0"><defs><clipPath id="6d2be58645"><path d="M 2.515625 24 L 36.265625 24 L 36.265625 39 L 2.515625 39 Z M 2.515625 24 " clip-rule="nonzero"/></clipPath></defs><path fill="#ffffff" d="M 19.453125 0.21875 C 18.429688 0.21875 17.601562 1.054688 17.601562 2.085938 L 17.601562 22.101562 L 13.253906 17.730469 C 11.507812 16.023438 8.933594 18.601562 10.632812 20.367188 C 10.632812 20.367188 18.144531 27.925781 18.144531 27.925781 C 18.84375 28.65625 20.0625 28.632812 20.765625 27.925781 C 20.765625 27.925781 28.277344 20.367188 28.277344 20.367188 C 29.96875 18.605469 27.410156 16.023438 25.652344 17.730469 C 25.652344 17.730469 21.308594 22.101562 21.308594 22.101562 L 21.308594 2.085938 C 21.308594 1.054688 20.476562 0.21875 19.453125 0.21875 Z M 19.453125 0.21875 " fill-opacity="1" fill-rule="nonzero"/><g clip-path="url(#6d2be58645)"><path fill="#ffffff" d="M 4.433594 24.738281 C 3.410156 24.738281 2.578125 25.574219 2.578125 26.605469 L 2.578125 29.359375 C 2.578125 34.554688 6.78125 38.78125 11.945312 38.78125 L 26.964844 38.78125 C 32.128906 38.78125 36.328125 34.554688 36.328125 29.359375 L 36.328125 26.605469 C 36.328125 25.574219 35.5 24.738281 34.476562 24.738281 C 33.453125 24.738281 32.621094 25.574219 32.621094 26.605469 L 32.621094 29.359375 C 32.621094 32.496094 30.082031 35.050781 26.964844 35.050781 L 11.945312 35.050781 C 8.824219 35.050781 6.289062 32.496094 6.289062 29.359375 L 6.289062 26.605469 C 6.289062 25.574219 5.457031 24.738281 4.433594 24.738281 Z M 4.433594 24.738281 " fill-opacity="1" fill-rule="nonzero"/></g></svg>
+                            </div>
+                            <div style="display:flex;justify-content: center;align-items: center;color:white;">
+                                {{formatNumber(imageData.total_sendto)}}
+                            </div>
+                            <div style="display: flex;justify-content: center;align-items: center;margin-left:5px;height:100%;">
+                                <svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="20" zoomAndPan="magnify" viewBox="0 0 39 38.999999" height="20" preserveAspectRatio="xMidYMid meet" version="1.0"><defs><clipPath id="ae37d71e45"><path d="M 5.074219 5.542969 L 33.574219 5.542969 L 33.574219 33.292969 L 5.074219 33.292969 Z M 5.074219 5.542969 " clip-rule="nonzero"/></clipPath></defs><g clip-path="url(#ae37d71e45)"><path fill="#ffffff" d="M 11.796875 33.273438 C 11.722656 33.273438 11.648438 33.253906 11.582031 33.21875 C 11.4375 33.136719 11.347656 32.988281 11.347656 32.820312 L 11.347656 28.015625 L 9.152344 28.015625 C 6.902344 28.015625 5.074219 26.171875 5.074219 23.90625 L 5.074219 9.671875 C 5.074219 7.410156 6.902344 5.566406 9.152344 5.566406 L 29.492188 5.566406 C 31.742188 5.566406 33.570312 7.410156 33.570312 9.671875 L 33.570312 23.90625 C 33.570312 26.171875 31.742188 28.015625 29.492188 28.015625 L 20.226562 28.015625 C 17.496094 29.746094 14.765625 31.472656 12.035156 33.203125 C 11.964844 33.25 11.878906 33.273438 11.796875 33.273438 Z M 11.796875 33.273438 " fill-opacity="1" fill-rule="nonzero"/></g></svg>
+                            </div>
+                            <div style="display:flex;justify-content: center;align-items: center;color:white;">
+                                {{formatNumber(imageData.total_comments)}}
+                            </div>
+                        </div>
+                        <div style="display:flex;align-items:center;">
+                            <a href="javascript:void(0);" target="_self"  @click="(e)=>moveFeedProfileTab(e,imageData.nickname)" class="item-author">by {{imageData.nickname}}</a>
+                        </div>
+                    </div>
+                </div>
+            </template>
+        </div>
+    </div>
+    <div :class="{promptModal: showModal}" v-if="showModal">
+        <div class="prompt-modal-page">
+            <div class="prompt-modal-image" @click="closeOutsideModal">
+                <img :src="src" alt="Pretty Image" class="prompt-modal-image-tag"/>
+            </div>
+            <div class="prompt-modal-sidebar">
+                <div class="modal-sidebar-header">
+                    <div style="display:flex;">
+                        <div class="modal-like-wrapper">
+                            <template v-if="!currentItem.liked">
+                                <div class="big-liked-button-div not-liked-div" @click="addToWishList(currentItem)">
+                                    <button class="like-button">👍 {{formatNumber(currentItem.likecount)}}</button>
+                                </div>
+                            </template>
+                            <template v-else>
+                                <div class="big-liked-button-div already-liked-div" @click="deleteWishList(currentItem)">
+                                    <button class="like-button already-liked-button">👍 {{formatNumber(currentItem.likecount)}}</button>
+                                </div>
+                            </template>
+                        </div>
+                        <div class="modal-writer-wrappers">
+                            <a class="modal-writer" href="javascript:void(0);" target="_self" @click="(e)=>moveFeedProfileTab(e,currentItem.nickname)">By {{currentItem.nickname}}</a>
+                            <div class="modal-writer-time">{{currentItem.created_at}}</div>
+                        </div>
+                    </div>
+                    <div class="modal-writer-wrapper-exit" @click="close()">
+                        <div>
+                            <svg xmlns="http://www.w3.org/2000/svg" width="35" height="35" viewBox="0 0 35 35" fill="none">
+                            <path d="M19.9037 17.8831L27.752 25.7314V27.7998H25.6837L17.8354 19.9514L9.98703 27.7998H7.9187V25.7314L15.767 17.8831L7.9187 10.0348V7.96643H9.98703L17.8354 15.8148L25.6837 7.96643H27.752V10.0348L19.9037 17.8831Z" fill="white"/>
+                            <rect x="1.32679" y="1.37439" width="33.0173" height="33.0173" rx="4.50866" stroke="#0E0E0E" stroke-width="0.982675"/>
+                            </svg>
+                        </div>
+                    </div>
+                </div>
+                <div class="modal-write-input-wrapper">
+                    <div class="modal-write-input-div">
+                        <textarea v-model="comment" @input="handleTextareaInput" id="modal-writer-type" class="modal-writer-type" @focus="makeSaveButtonVisible" placeholder="Type your comment"></textarea>
+                    </div>
+                    <div style="display:flex;justify-content:center;width:100%;">
+                        <div v-if="showCommentWrite" class="write-row-button">
+                            <button class="btn-write-row btn-cancel" @click="hideSaveButtonVisible">Cancel</button>
+                            <button class="btn-write-row btn-save" @click="writeComment()">Comment</button>
+/                                         </div>
+                    </div>
+                </div>
+                <div style="margin-top:10px;border-bottom: 1px solid #AFB6BD;padding-bottom:17px;">
+                    <template v-for="(commentData, j) in comments" :key="j">
+                        <div style="margin-left:10px;margin-right:10px;">
+                            <div style="display:flex;width:100%;margin-bottom:12px;color:white;">
+                                <div style="margin-right:10px;color:white;font-size:17px;font-weight:700;">{{commentData.user.nickname}}</div>
+                                <div style="color:white;font-size:12px;font-weight:300;">{{commentData.created_at}}</div>
+                            </div>
+                            <div style="display:flex;width:100%;color:white;">
+                                <div style="color:white;font-size:13px;">{{commentData.text}}</div>
+                            </div>
+                        </div>
+                    </template>
+                </div>
+                <div class="prompt-send-to">
+                    <button class="btn-send-to btn-send-to-text" @click="this.sendToTextToImage()">Send to t2i</button>
+                    <button class="btn-send-to btn-send-to-img" @click="this.sendToImageToImage()">Send to i2i</button>
+                </div>
+                <div style="margin-top:12px;">
+                    <div style="margin-left: 10px;margin-right: 10px;">
+                        <div style="color:white;margin-bottom:12px;">Generation Data</div>
+                        <div style="box-sizing: border-box;display: flex;flex-direction: column;justify-content: center;align-items: flex-start;padding-left:20px;padding-right:20px;padding-top:10px;padding-bottom:10px;border: 1px solid #AFB6BD;border-radius: 5px;color:white;">
+                            <template v-for="(value, key) in metadata">
+                                <template v-if="/^[A-Z]/.test(key)">
+                                    {{key}}:{{value}},
+                                </template>
+                            </template>
+                        </div>
+                        <div style="display:flex;justify-content:center;margin-top:20px;">
+                            <button @click="copyClipboard" style="text-align:center;box-sizing: border-box;display: flex;flex-direction: column;justify-content: center;align-items: flex-start;padding-top:10px;padding-bottom:10px;border: 1px solid #AFB6BD;border-radius: 5px;color:white;width:100%;">
+                            <div style="color:white;margin:auto;">Copy Generation Data</div></button>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>

sd_feed/html/profile.html

@@ -0,0 +1,21 @@
+<template v-if="!editMode">
+    <div style="display:flex;align-items:center;">
+        <div style="padding: 10px; border-radius: 5px; display:flex;align-items:center;">
+            <p style="line-height:normal;line-height:0;font-size: 25px;font-weight: 800;margin-right: 10px;">By.</p>
+            <p style="line-height: 0;font-size: 20px;margin-right: 10p;">{{nickname}}</p>
+        </div>
+        <div style="padding: 10px; border-radius: 5px; display:flex;align-items:center;">
+            <p style="line-height: 0;font-size: 20px;margin-right: 10p;">{{profile}}</p>
+        </div>
+        <button @click="setEditMode" style="width:24px;height:24px;">
+            <svg xmlns="http://www.w3.org/2000/svg" width="100%" height="100%" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather feather-edit-2"><path d="M17 3a2.828 2.828 0 1 1 4 4L7.5 20.5 2 22l1.5-5.5L17 3z"></path></svg>
+        </button>
+    </div>
+</template>
+<template v-else>
+    <input  style="padding: 10px; border-radius: 5px; border: 1px solid #ccc; flex-grow: 1;" type="text" v-model="nickname">
+    <button  style="width:24px;height:24px;margin-right:15px;margin-left:15px;" @click="saveNickname">💾</button>
+    <button style="width:24px;height:24px;" @click="setEditMode">
+        ✖️
+    </button>
+</template>

sd_feed/install.py

@@ -0,0 +1,5 @@
+import launch
+
+
+if not launch.is_installed("browsercookie"):
+    launch.run_pip("install browsercookie", "requirements for Browser Cookie")

sd_feed/javascript/axios.min.js

@@ -0,0 +1,2 @@
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+// # sourceMappingURL=axios.min.js.map

sd_feed/javascript/best_feed.js

@@ -0,0 +1,638 @@
+
+function waitForElementToDisplay2(selector, callback, checkFrequencyInMs, timeoutInMs) {
+    (function loopSearch() {
+      if (document.querySelector(selector) != null && window.localStorage.getItem("userId")) {
+        callback();
+        return;
+      }
+      else if(document.getElementsByTagName("gradio-app").length > 0 && 
+      document.getElementsByTagName("gradio-app")[0].shadowRoot){
+        callback();
+        return;
+      }
+      else {
+        setTimeout(function () {
+          loopSearch();
+        }, checkFrequencyInMs);
+      }
+    })();
+  }
+  waitForElementToDisplay2(
+    "#html_id",
+    function(){
+        async function copyToClipboard(textToCopy) {
+          // Navigator clipboard api needs a secure context (https)
+          if (navigator.clipboard && window.isSecureContext) {
+              await navigator.clipboard.writeText(textToCopy);
+          } else {
+              // Use the 'out of viewport hidden text area' trick
+              const textArea = document.createElement("textarea");
+              textArea.value = textToCopy;
+                  
+              // Move textarea out of the viewport so it's not visible
+              textArea.style.position = "absolute";
+              textArea.style.left = "-999999px";
+                  
+              document.body.prepend(textArea);
+              textArea.select();
+      
+              try {
+                  document.execCommand('copy');
+              } catch (error) {
+                  console.error(error);
+              } finally {
+                  textArea.remove();
+              }
+          }
+      };
+      const userModel = {
+            data() {
+                return {
+                    bestFeeds: [],
+                    comments: [],
+                    userId: '',
+                    showCommentWrite: false,
+                    comment: '',
+                    src: '',
+                    imageId: "",
+                    metadata: {},
+                    currentItem: {},
+                    filterNsfw: false,
+                    showModal:false,
+
+                }
+            },
+            watch: {
+              showModal() {   
+                if (this.showModal === false) {
+                  window.removeEventListener("keyup", this.onEscapeKey);
+                } else {
+                  window.addEventListener("keyup", this.onEscapeKey);
+                }
+              }
+            },
+            methods: {
+                close() {
+                    this.$emit('close');
+                    this.showModal=false;
+                    this.imageId = "";
+                    this.src = "";
+                    this.comment = '';
+                    this.comments = [];
+                },
+                moveFeedProfileTab(event, nickname){
+                  event.preventDefault();
+                  if(nickname){
+                    this.close();
+                    const buttonList = gradioApp().querySelector('#tabs').querySelectorAll('button');
+                    for (step = 0; step < buttonList.length; step++) {
+                      // Runs 5 times, with values of step 0 through 4.
+                      if(buttonList[step].textContent.trim() == 'Feed'){
+                        buttonList[step].click();
+                        break;
+                      }
+                    }
+                    try{
+                      const feedUserButton = gradioApp().querySelector('#feed-user-tab');
+                      feedUserButton.setAttribute("data-value", nickname);
+                      feedUserButton.innerHTML = nickname;
+                      feedUserButton.click();
+                      
+                    }catch(e){
+                      console.log(e);
+                    }
+                    
+                  }
+                  
+                },
+                closeOutsideModal(event) {
+                  if(!event.target.closest('.prompt-modal-image-tag')){
+                    this.$emit('close');
+                    this.showModal=false;
+                    this.imageId = "";
+                    this.src = "";
+                    this.comment = '';
+                    this.comments = [];
+                  }
+                },
+                onEscapeKey(event) {
+                  if (event.keyCode === 27 || event.key === 'Escape') {
+                      // Perform action when "Esc" key is pressed
+                      this.close();
+                  }
+                  // Perform action when "Esc" key is pressed
+                },
+                fetchBests(){
+                  const token = localStorage.getItem("token");
+                  const userId = localStorage.getItem("userId");
+                  const headers={"Authorization":"Token " + token};
+                  const params = {
+                    filter_nsfw: this.filterNsfw,
+                  };
+                  axios.get(
+                        `https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/best/${userId}`,
+                        {
+                          headers,
+                          params
+                        }
+                    )
+                    .then(response => {
+                        const dataObject = response.data;
+                        this.bestFeeds = dataObject.slice(0, 4);
+                    }).catch(error => {
+                        this.loading = false;
+                        console.log(error)
+                    });
+                },
+                formatNumber(num) {
+                    if (num >= 1000000) {
+                      return (num / 1000000).toFixed(1) + 'm';
+                    } else if (num >= 1000) {
+                      return (num / 1000).toFixed(1) + 'k';
+                    } else {
+                      return num.toString();
+                    }
+                },
+                openModal(item){
+                  this.showModal = true;
+                  this.$forceUpdate();
+                  this.currentItem = item;
+                  this.metadata = item.metadata;
+                  this.src = item.url;
+                  this.imageId = item.id;
+                  item.total_clicks += 1;
+                  this.addImageClick();
+                  this.fetchComments();
+                },
+                openModalSecond(event){
+                  console.log(event);
+
+
+                },
+                addImageClick(){
+                  const userId = localStorage.getItem("userId");
+                  const imageId = this.imageId;
+                  const token = localStorage.getItem("token");
+                  const headers={"Authorization":"Token " + token};
+                  axios.post(
+                      'https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/click',
+                      {
+                        userId,
+                        imageId,
+                      },
+                      {
+                        headers
+                      }
+                      )
+                  .then(response => {
+                    this.showModal = true;
+                  }).catch(error => {
+                      this.loading = false;
+                      console.log(error)
+                  })
+                },
+                sendToTextToImage(){
+                  const userId = localStorage.getItem("userId");
+                  const token = localStorage.getItem("token");
+                  const headers={"Authorization":"Token " + token};
+
+                  const imageId = this.imageId;
+                  this.currentItem.total_sendto = this.currentItem.total_sendto + 1;
+                  axios.post(
+                      'https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/send_to',
+                      {
+                        userId,
+                        imageId,
+                        sendType: "SEND_TO_TEXT",
+                      },
+                      {
+                        headers
+                      }
+                      )
+                  .then(response => {
+                    this.sendToTextProcess();
+                  }).catch(error => {
+                      this.loading = false;
+                      console.log(error)
+                  })
+                },
+                
+                copyClipboard(){
+                  const userId = localStorage.getItem("userId");
+                  const imageId = this.imageId;
+                  const token = localStorage.getItem("token");
+                  const headers={"Authorization":"Token " + token};
+                  axios.post(
+                      'https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/send_to',
+                      {
+                        userId,
+                        imageId,
+                        sendType: "COPY_TO_CLIPBOARD",
+                      },
+                      {
+                        headers
+                      }
+                      )
+                  .then(response => {
+                    let filteredObject = '';
+                    for (const [k, v] of Object.entries(this.metadata)) {
+                      if (/^[A-Z]/.test(k)) {
+                        filteredObject += `${k}:${v},`;
+                      }
+                    }
+                    this.currentItem.total_sendto = this.currentItem.total_sendto + 1;
+                    copyToClipboard(filteredObject);
+                  }).catch(error => {
+                      this.loading = false;
+                      console.log(error)
+                  });
+
+                },
+                sendToTextProcess(){
+                  const textPrompt = gradioApp().querySelector('#txt2img_prompt').getElementsByTagName( 'textarea' );
+                  if(textPrompt&&textPrompt.length> 0){
+                    window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_prompt')[0].props.value = this.metadata.prompt;
+                    textPrompt[0].value = this.metadata.prompt;
+                  }
+                  const negativePrompt = gradioApp().querySelector('#txt2img_neg_prompt').getElementsByTagName( 'textarea' );
+                  if(negativePrompt&&negativePrompt.length> 0){
+                    window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_neg_prompt')[0].props.value = this.metadata.negative_prompt;
+                    negativePrompt[0].value = this.metadata.negative_prompt;
+                  }
+                  try{
+                    const samplerList = gradioApp().querySelector( '#txt2img_sampling' ).getElementsByTagName('span');
+                    if(samplerList&&samplerList.length> 1){
+                      window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_sampling')[0].props.value = this.metadata.sampler;
+                      samplerList[1].textContent = this.metadata.sampler;
+                    }
+                  }catch(e){
+                  }
+
+                  try{
+                    const textImageStepsList = gradioApp().querySelector( '#txt2img_steps' ).getElementsByTagName('input');
+                    if(textImageStepsList&&textImageStepsList.length> 1){
+                      window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_steps')[0].props.value = parseInt(this.metadata.n_iter);
+                      textImageStepsList[0].value = this.metadata.n_iter;
+                      textImageStepsList[1].value = this.metadata.n_iter;
+                    }
+                  }catch(e){
+                  }
+                  try{
+                    window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_width')[0].props.value = parseInt(this.metadata['Size-1']);
+                    const textWidthList = gradioApp().querySelector( '#txt2img_width' ).getElementsByTagName('input');
+                    if(textWidthList&&textWidthList.length> 1){
+                      textWidthList[0].value = this.metadata['Size-1'];
+                      textWidthList[1].value = this.metadata['Size-1'];
+                    }
+                  }catch(e){
+                  }
+                  try{
+                    window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_height')[0].props.value = parseInt(this.metadata['Size-2']);
+                    const textWidthList = gradioApp().querySelector( '#txt2img_height' ).getElementsByTagName('input');
+                    if(textWidthList&&textWidthList.length> 1){
+                      textWidthList[0].value = this.metadata['Size-2'];
+                      textWidthList[1].value = this.metadata['Size-2'];
+                    }
+                  }catch(e){
+                  }
+                  try{
+                    window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_cfg_scale')[0].props.value = parseFloat(this.metadata['cfg_scale']);
+                    const cfgScaleList = gradioApp().querySelector( '#txt2img_cfg_scale' ).getElementsByTagName('input');
+                    if(cfgScaleList&&cfgScaleList.length> 1){
+                      cfgScaleList[0].value = this.metadata['cfg_scale'];
+                      cfgScaleList[1].value = this.metadata['cfg_scale'];
+                    }
+                  }catch(e){
+                  }
+                  try{
+                    const steps = this.metadata['Hires upscaler'];
+                    if(steps){
+                      window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_enable_hr')[0].props.value = true;
+                      gradioApp().querySelector( '#txt2img_enable_hr' ).getElementsByTagName('input')[0].checked = true;
+                      try{
+                        const originalHires = gradioApp().querySelector( '#txt2img_hires_fix' );
+                        originalHires.classList.remove('hidden');
+                      }catch(e){
+                        
+                      }
+                      const scalerMethod = this.metadata['Hires upscaler'];
+                      const containUpscaler = window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_hr_upscaler')[0].props.choices.includes(scalerMethod);
+                      if(containUpscaler){
+                        window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_hr_upscaler')[0].props.value = this.metadata['Hires upscaler'];
+                        const hr_span = gradioApp().querySelector( '#txt2img_hr_upscaler' ).getElementsByTagName('span');
+                        hr_span[0].textContent = this.metadata['Hires upscaler'];
+
+                        const cfgScaleList = gradioApp().querySelector( '#txt2img_enable_hr' ).getElementsByTagName('input');
+                      if(cfgScaleList&&cfgScaleList.length> 1){
+                        cfgScaleList[0].value = this.metadata['cfg_scale'];
+                        cfgScaleList[1].value = this.metadata['cfg_scale'];
+                      }
+                    }  
+                    try{
+                      try{
+                        window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_hires_steps')[0].props.value = parseInt(this.metadata['Hires steps']);
+                      }catch(e){
+                        window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_hires_steps')[0].props.value = 0;
+                      }
+                      
+                      const cfgScaleList = gradioApp().querySelector( '#txt2img_hires_steps' ).getElementsByTagName('input');
+                      if(cfgScaleList&&cfgScaleList.length> 1){
+                        cfgScaleList[0].value = this.metadata['Hires steps'];
+                        cfgScaleList[1].value = this.metadata['Hires steps'];
+                      }
+                    }catch(e){
+                      console.log(e);
+                      console.log('errorhr0');
+                    }
+
+                    try{
+                      window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_hr_scale')[0].props.value = this.metadata['Hires upscale'];
+                      const cfgScaleList = gradioApp().querySelector( '#txt2img_hr_scale' ).getElementsByTagName('input');
+                      if(cfgScaleList&&cfgScaleList.length> 1){
+                        cfgScaleList[0].value = this.metadata['Hires upscale'];
+                        cfgScaleList[1].value = this.metadata['Hires upscale'];
+                      }
+                    }catch(e){
+                      console.log(e);
+                      console.log('errorhr');
+                    }
+                    try{
+                      let hiresValue = parseInt(this.metadata['Hires resize-1']);
+                      if(hiresValue == 0){
+                        hiresValue = parseInt(this.metadata['Size-1']);
+                      }
+                      window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_hr_resize_x')[0].props.value = hiresValue;
+                      const cfgScaleList = gradioApp().querySelector( '#txt2img_hr_resize_x' ).getElementsByTagName('input');
+                      if(cfgScaleList&&cfgScaleList.length> 1){
+                        cfgScaleList[0].value = this.metadata['Hires resize-1'];
+                        cfgScaleList[1].value = this.metadata['Hires resize-1'];
+                      }
+                    }catch(e){
+                      console.log(e);
+                      console.log('errorhr2');
+                    }
+                    try{
+                      let hiresValueSecond = parseInt(this.metadata['Hires resize-2']);
+                      if(hiresValueSecond == 0){
+                        hiresValueSecond = parseInt(this.metadata['Size-2']);
+                      }
+                      window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_hr_resize_y')[0].props.value = hiresValueSecond;
+                      const cfgScaleList = gradioApp().querySelector( '#txt2img_hr_resize_y' ).getElementsByTagName('input');
+                      if(cfgScaleList&&cfgScaleList.length> 1){
+                        cfgScaleList[0].value = this.metadata['Hires resize-2'];
+                        cfgScaleList[1].value = this.metadata['Hires resize-2'];
+                      }
+                    }catch(e){
+                      console.log(e);
+                      console.log('errorhr3');
+                    }
+                    }
+                    
+
+                  }catch(e){
+                    console.log(e);
+                    console.log('errorhr4');
+                  }
+                  this.close();
+                },
+                sendToImageToImage(){
+                  const userId = localStorage.getItem("userId");
+                  const imageId = this.imageId;
+                  const token = localStorage.getItem("token");
+                  const headers={"Authorization":"Token " + token};
+                  axios.post(
+                      'https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/send_to',
+                      {
+                        userId,
+                        imageId,
+                        sendType: "SEND_TO_IMAGE",
+                      },
+                      {
+                        headers
+                      }
+                      )
+                  .then(response => {
+                    var xhr = new XMLHttpRequest();
+                    var self = this;
+                    xhr.onload = function() {
+                        var reader = new FileReader();
+                        reader.onloadend = function() {
+                          self.sendToImageToImageProcess(reader.result);
+                        }
+                        reader.readAsDataURL(xhr.response);
+                    };
+                    xhr.open('GET', this.src);
+                    xhr.responseType = 'blob';
+                    xhr.send();
+                    this.currentItem.total_sendto = this.currentItem.total_sendto + 1;
+                    
+                  }).catch(error => {
+                      this.loading = false;
+                      console.log(error)
+                  })
+                },
+                sendToImageToImageProcess(date64Image){
+                  try{
+                    window.gradio_config.components.filter(el=>el.props.elem_id=='img2img_image')[0].props.value = date64Image;
+                  }catch(e){
+
+                  }
+                  try{
+                    const textPrompt = gradioApp().querySelector('#img2img_prompt').getElementsByTagName( 'textarea' );
+                    if(textPrompt&&textPrompt.length> 0){
+                      window.gradio_config.components.filter(el=>el.props.elem_id=='img2img_prompt')[0].props.value = this.metadata.prompt;
+                      textPrompt[0].value = this.metadata.prompt;
+                    }
+                  }catch(e){
+                    console.log('Error prompt img2img');
+                    console.log(e);
+
+                  }
+                  try{
+                    const negativePrompt = gradioApp().querySelector('#img2img_neg_prompt').getElementsByTagName( 'textarea' );
+                    if(negativePrompt&&negativePrompt.length> 0){
+                      window.gradio_config.components.filter(el=>el.props.elem_id=='img2img_neg_prompt')[0].props.value = this.metadata.negative_prompt;
+                      negativePrompt[0].value = this.metadata.negative_prompt;
+                    }
+                  }catch(e){
+                    console.log('Error neg img2img');
+                    console.log(e);
+
+                  }
+                  
+                  
+                  try{
+                    const samplerList = gradioApp().querySelector( '#img2img_sampling' ).getElementsByTagName('span');
+                    if(samplerList&&samplerList.length> 1){
+                      window.gradio_config.components.filter(el=>el.props.elem_id=='img2img_sampling')[0].props.value = this.metadata.sampler;
+                      samplerList[1].textContent = this.metadata.sampler;
+                    }
+                  }catch(e){
+                  }
+
+                  try{
+                    const textImageStepsList = gradioApp().querySelector( '#img2img_steps' ).getElementsByTagName('input');
+                    if(textImageStepsList&&textImageStepsList.length> 1){
+                      window.gradio_config.components.filter(el=>el.props.elem_id=='img2img_steps')[0].props.value = parseInt(this.metadata.n_iter);
+                      textImageStepsList[0].value = this.metadata.n_iter;
+                      textImageStepsList[1].value = this.metadata.n_iter;
+                    }
+                  }catch(e){
+                    console.log('Error steps img2img');
+                    console.log(e);
+
+                  }
+                  try{
+                    window.gradio_config.components.filter(el=>el.props.elem_id=='img2img_width')[0].props.value = parseInt(this.metadata['Size-1']);
+                    const textWidthList = gradioApp().querySelector( '#img2img_width' ).getElementsByTagName('input');
+                    if(textWidthList&&textWidthList.length> 1){
+                      textWidthList[0].value = this.metadata['Size-1'];
+                      textWidthList[1].value = this.metadata['Size-1'];
+                    }
+                  }catch(e){
+                    console.log('Error width img2img');
+                    console.log(e);
+                  }
+                  try{
+                    window.gradio_config.components.filter(el=>el.props.elem_id=='img2img_height')[0].props.value = parseInt(this.metadata['Size-2']);
+                    const textWidthList = gradioApp().querySelector( '#img2img_height' ).getElementsByTagName('input');
+                    if(textWidthList&&textWidthList.length> 1){
+                      textWidthList[0].value = this.metadata['Size-2'];
+                      textWidthList[1].value = this.metadata['Size-2'];
+                    }
+                  }catch(e){
+                    console.log('Error width height2img');
+                    console.log(e);
+                  }
+                  this.close();
+                  switch_to_img2img();
+                },
+                addToWishList(item){
+                    const userId = localStorage.getItem("userId");
+                    const token = localStorage.getItem("token");
+                    const headers={"Authorization":"Token " + token};
+                    item.liked = true;
+                    item.likecount = item.likecount + 1;
+                    axios.post(
+                      `https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/like`,
+                      {
+                        userId, 
+                        imageId: item.id,
+                      },
+                      {
+                        headers
+                      }
+                    )
+                },
+                handleTextareaInput(event) {
+                  let textarea = event.target;
+                  textarea.style.height = 'auto';
+                  textarea.style.height = (textarea.scrollHeight) + 'px';
+                  comment = event.target.text;
+                },
+                deleteWishList(item){
+                  console.log('delete');
+                  const userId = localStorage.getItem("userId");
+                  item.liked = false;
+                  item.likecount = item.likecount -1 ;
+                  const token = localStorage.getItem("token");
+                  const headers={"Authorization":"Token " + token};
+                  axios.delete(
+                    `https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/like`,
+                    {
+                      data: {
+                        userId, 
+                        imageId: item.id,
+                      },
+                      headers
+                    }, 
+                  )
+                },
+                makeSaveButtonVisible(){
+                  this.showCommentWrite = true;
+                },
+                hideSaveButtonVisible(){
+                  this.comment = '';
+                  this.showCommentWrite = false;
+                  const textArea = gradioApp().querySelector("#modal-writer-type");
+                  textArea.style.height = 'auto';
+                  textArea.style.height = (textarea.scrollHeight) + 'px';
+                },
+                writeComment(){
+                  if(this.comment.length > 0){
+                    const userId = localStorage.getItem("userId");
+                    const token = localStorage.getItem("token");
+                    const headers={"Authorization":"Token " + token};
+                    axios.post(
+                      `https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/comment`,
+                      {
+                        userId, 
+                        imageId: this.currentItem.id,
+                        text: this.comment,
+                      },
+                      {
+                        headers
+                      } 
+                    ).then((res)=>{
+                      this.fetchComments();
+                      this.showCommentWrite = false;
+                      this.comment = '';
+                      this.currentItem.total_comments = this.currentItem.total_comments + 1;
+                    }).catch((e)=>{
+                      console.log(e);
+                    });
+
+                  }else{
+                    return false;
+                  }
+                },
+                fetchComments(){
+                  var self = this;
+                  const token = localStorage.getItem("token");
+                  const headers={"Authorization":"Token " + token};
+                  axios.get(
+                    `https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/comments/${this.currentItem.id}`,
+                    {
+                      headers
+                    }
+                  ).then((res)=>{
+                    self.comments = res.data;
+                  }).catch((e)=>{
+                    console.log(e);
+                  })
+                },
+            },
+            computed:{
+              isModalOpen(){
+                return this.showModal;
+              },
+            },
+            created() {
+                const userId = localStorage.getItem("userId");
+                const token = localStorage.getItem("token");
+                const headers={"Authorization":"Token " + token};
+                axios.get(
+                    `https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/best/${userId}`,
+                    {
+                      headers
+                    }
+                    )
+                .then(response => {
+                    const dataObject = response.data;
+                    this.userId = userId;
+                    this.bestFeeds = dataObject.slice(0, 4);
+                }).catch(error => {
+                    this.loading = false;
+                    console.log(error)
+                })
+            },
+        };
+        const fetchApp =  Vue.createApp(userModel);
+        if (document.querySelector("#best-product-tab") != null) {
+          fetchApp.mount('#best-product-tab');
+        }else{
+          fetchApp.mount(document.getElementsByTagName("gradio-app")[0].shadowRoot.getElementById("best-product-tab"));
+        }
+        console.log('mount success');
+    },1000,9000);
+

sd_feed/javascript/goosviewmodel.js

@@ -0,0 +1,860 @@
+var installLoaded = {};
+function addScript(url) {
+    if (!installLoaded[url]) {
+        var s = document.createElement('script');
+        s.src = url;
+        document.head.appendChild(s);
+        installLoaded[url] = true;
+    }
+}
+function waitForElementToDisplay(selector, callback, checkFrequencyInMs, timeoutInMs) {
+  (function loopSearch() {
+    if (document.querySelector(selector) != null&& window.localStorage.getItem("userId")) {
+      callback();
+      return;
+    }
+    else if(document.getElementsByTagName("gradio-app").length > 0 && 
+    document.getElementsByTagName("gradio-app")[0].shadowRoot){
+      callback();
+      return;
+    }
+    else {
+      setTimeout(function () {
+        loopSearch();
+      }, checkFrequencyInMs);
+    }
+  })();
+}
+const reorderData=(arr, columns)=>{
+  const cols = columns;
+  const out = [];
+  let col = 0;
+  while(col < cols) {
+      for(let i = 0; i < arr.length; i += cols) {
+          let _val = arr[i + col];
+          if (_val !== undefined)
+              out.push(_val);
+      }
+      col++;
+  }
+  return out;
+}
+
+// addScript('https://cdn.jsdelivr.net/npm/axios/dist/axios.min.js');
+// addScript('https://unpkg.com/vue@3.2.6/dist/vue.global.prod.js');
+
+
+waitForElementToDisplay(
+    "#html_id",
+    function(){
+      async function copyToClipboard(textToCopy) {
+        // Navigator clipboard api needs a secure context (https)
+        if (navigator.clipboard && window.isSecureContext) {
+            await navigator.clipboard.writeText(textToCopy);
+        } else {
+            // Use the 'out of viewport hidden text area' trick
+            const textArea = document.createElement("textarea");
+            textArea.value = textToCopy;
+                
+            // Move textarea out of the viewport so it's not visible
+            textArea.style.position = "absolute";
+            textArea.style.left = "-999999px";
+                
+            document.body.prepend(textArea);
+            textArea.select();
+    
+            try {
+                document.execCommand('copy');
+            } catch (error) {
+                console.error(error);
+            } finally {
+                textArea.remove();
+            }
+        }
+      };
+        const imageModel = {
+            data() {
+                return {
+                    page: 1,
+                    loading: false,
+                    uploadLoading: false,
+                    imageList: [],
+                    sortedDataList: [],
+                    carouselBtnTop: 150,
+                    showCommentWrite: false,
+                    modeSelectList: [
+                      { name: "Newest", value: "latest" },
+                      { name: "Popular", value: "likes" },
+                      { name: "Favorite", value: "favorite" },
+                      { name: "My Feed", value: "private" },
+                    ],
+                    mode: "latest",
+                    prompt: "",
+                    showModal: false,
+                    metadata: {},
+                    filterNsfw: false,
+                    animeTag: false,
+                    realTag: false,
+                    nickname: '',
+                    profile: '',
+                    editMode: false,
+                    userId: '',
+                    searchNickname: "",
+                    searchProfile: "",
+                }
+            },
+            computed: {
+              parsedProfile(){
+                if(!this.profile){
+                  return this.profile;
+                }
+                return this.profile.replaceAll("\n", "<br/>");
+              },
+              parsedSearchProfile(){
+                if(!this.searchProfile){
+                  return '';
+                }
+                return this.searchProfile.replaceAll("\n", "<br/>");
+              }
+            },
+            watch: {
+              showModal() {   
+                if (this.showModal === false) {
+                  window.removeEventListener("keyup", this.onEscapeKey);
+                } else {
+                  window.addEventListener("keyup", this.onEscapeKey);
+                }
+              }
+            },
+            methods: {
+            setEditMode(){
+                this.editMode = !this.editMode;
+            },
+            setNicknameSearch(){
+              this.mode = 'nicknameSearch';
+              const feedUserButton = gradioApp().querySelector('#feed-user-tab');
+              const nickname = feedUserButton.getAttribute("data-value");
+              this.searchNickname = nickname;
+              this.fetchSearchProfile();
+              this.fetchNewImage();
+            },
+            moveFeedProfileTab(nickname){
+              if(nickname){
+                this.close();
+                const buttonList = gradioApp().querySelector('#tabs').querySelectorAll('button');
+                for (step = 0; step < buttonList.length; step++) {
+                  // Runs 5 times, with values of step 0 through 4.
+                  if(buttonList[step].textContent.trim() == 'Feed'){
+                    buttonList[step].click();
+                    break;
+                  }
+                }
+                try{
+                  const feedUserButton = gradioApp().querySelector('#feed-user-tab');
+                  feedUserButton.setAttribute("data-value", nickname);
+                  feedUserButton.innerHTML = nickname;
+                  feedUserButton.click();
+                  
+                }catch(e){
+                  console.log(e);
+                }
+                
+              }
+              
+            },
+            saveNickname(){
+                const userId = localStorage.getItem("userId");
+                const token = localStorage.getItem("token");
+                const headers={"Authorization":"Token " + token};
+                axios.post(
+                    `https://newtypev3-server-vjiloyvjvq-an.a.run.app/user/profile`,
+                    {
+                        userId,
+                        nickname: this.nickname,
+                        profile: this.profile,
+                    }, 
+                    {
+                        headers
+                    }
+                  )
+                  .then(response => {
+                    this.editMode = false;        
+                  })
+                  .catch(error => {
+                    this.loading = false;
+                    alert(error.response.data.detail);
+                  })
+            },
+            selectAnimeTag(){
+                if(this.animeTag == false){
+                    this.animeTag = true;
+                    this.realTag = false;
+                }else{
+                    this.animeTag = false;
+                    this.realTag = false;
+                }
+                this.fetchNewImage();
+            },
+            selectRealTag(){
+                if(this.realTag == false){
+                    this.realTag = true;
+                    this.animeTag = false;
+                }else{
+                    this.realTag = false;
+                    this.animeTag = false;
+                }
+                this.fetchNewImage();
+            },
+              async uploadImage(event){
+                this.uploadLoading = true;
+                const files = event.target.files;
+                const userId = localStorage.getItem("userId");
+                for (let i=0; i<files.length; i++) {
+                  if(!files[i].type.match(/^image\//)){
+                    alert('이미지 파일만 업로드할 수 있습니다.');
+                  } else if(files[i].size > 50000000) {
+                    alert('You cannot upload file larger than 50MB');
+                  } else {
+                    var body = new FormData();
+                    body.append('upload_file',files[i]);
+                    body.append('userId', userId);
+                    body.append('extractMetadata', true);
+                    const token = localStorage.getItem("token");
+                    const headers={"Authorization":"Token " + token};
+                    try{
+                      await axios({
+                        url: 'https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/image',
+                        method: 'post',
+                        withCredentials:true,
+                        data: body,
+                        headers
+                      });
+                    }catch(e){
+                      console.log(e);
+                    }
+                  }
+                }
+                this.setMode('private');
+                this.uploadLoading = false;
+              },
+            close() {
+                this.$emit('close');
+                this.showModal=false;
+                this.imageId = "";
+                this.src = "";
+                this.comment = '';
+                this.comments = [];
+            },
+            closeOutsideModal(event) {
+              if(!event.target.closest('.prompt-modal-image-tag')){
+                this.$emit('close');
+                this.showModal=false;
+                this.imageId = "";
+                this.src = "";
+                this.comment = '';
+                this.comments = [];
+              }
+            },
+            onEscapeKey(event) {
+              if (event.keyCode === 27 || event.key === 'Escape') {
+                  // Perform action when "Esc" key is pressed
+                  this.close();
+              }
+              // Perform action when "Esc" key is pressed
+            },
+            formatNumber(num) {
+                if (num >= 1000000) {
+                  return (num / 1000000).toFixed(1) + 'm';
+                } else if (num >= 1000) {
+                  return (num / 1000).toFixed(1) + 'k';
+                } else {
+                  return num.toString();
+                }
+            },
+            openModal(item){
+              this.showModal = true;
+              this.currentItem = item;
+              this.metadata = item.metadata;
+              this.src = item.url;
+              this.imageId = item.id;
+              item.total_clicks += 1;
+              this.addImageClick();
+              this.fetchComments();
+            },
+            addImageClick(){
+              const userId = localStorage.getItem("userId");
+              const imageId = this.imageId;
+              const token = localStorage.getItem("token");
+              const headers={"Authorization":"Token " + token};
+              axios.post(
+                  'https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/click',
+                  {
+                    userId,
+                    imageId,
+                  },
+                  {
+                    headers
+                  }
+                  )
+              .then(response => {
+                
+              }).catch(error => {
+                  this.loading = false;
+                  console.log(error)
+              })
+            },
+            sendToTextToImage(){
+              const userId = localStorage.getItem("userId");
+              const imageId = this.imageId;
+              const token = localStorage.getItem("token");
+              const headers={"Authorization":"Token " + token};
+              this.currentItem.total_sendto = this.currentItem.total_sendto + 1;
+              axios.post(
+                  'https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/send_to',
+                  {
+                    userId,
+                    imageId,
+                    sendType: "SEND_TO_TEXT",
+                  },{
+                    headers
+                  }
+                  )
+              .then(response => {
+                this.sendToTextProcess();
+              }).catch(error => {
+                  this.loading = false;
+                  console.log(error)
+              })
+            },
+            copyClipboard(){
+              const userId = localStorage.getItem("userId");
+              const imageId = this.imageId;
+              const token = localStorage.getItem("token");
+              const headers={"Authorization":"Token " + token};
+              axios.post(
+                  'https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/send_to',
+                  {
+                    userId,
+                    imageId,
+                    sendType: "COPY_TO_CLIPBOARD",
+                  },{
+                    headers
+                  }
+                  )
+              .then(response => {
+                let filteredObject = '';
+                for (const [k, v] of Object.entries(this.metadata)) {
+                  if (/^[A-Z]/.test(k)) {
+                    filteredObject += `${k}:${v},`;
+                  }
+                }
+                this.currentItem.total_sendto = this.currentItem.total_sendto + 1;
+                copyToClipboard(filteredObject);
+              }).catch(error => {
+                  this.loading = false;
+                  console.log(error)
+              });
+
+            },
+            sendToTextProcess(){
+              const textPrompt = gradioApp().querySelector('#txt2img_prompt').getElementsByTagName( 'textarea' );
+              if(textPrompt&&textPrompt.length> 0){
+                window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_prompt')[0].props.value = this.metadata.prompt;
+                textPrompt[0].value = this.metadata.prompt;
+              }
+              const negativePrompt = gradioApp().querySelector('#txt2img_neg_prompt').getElementsByTagName( 'textarea' );
+              if(negativePrompt&&negativePrompt.length> 0){
+                window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_neg_prompt')[0].props.value = this.metadata.negative_prompt;
+                negativePrompt[0].value = this.metadata.negative_prompt;
+              }
+              try{
+                const samplerList = gradioApp().querySelector( '#txt2img_sampling' ).getElementsByTagName('span');
+                if(samplerList&&samplerList.length> 1){
+                  window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_sampling')[0].props.value = this.metadata.sampler;
+                  samplerList[1].textContent = this.metadata.sampler;
+                }
+              }catch(e){
+              }
+
+              try{
+                const textImageStepsList = gradioApp().querySelector( '#txt2img_steps' ).getElementsByTagName('input');
+                if(textImageStepsList&&textImageStepsList.length> 1){
+                  window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_steps')[0].props.value = parseInt(this.metadata.n_iter);
+                  textImageStepsList[0].value = this.metadata.n_iter;
+                  textImageStepsList[1].value = this.metadata.n_iter;
+                }
+              }catch(e){
+              }
+              try{
+                window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_width')[0].props.value = parseFloat(this.metadata['Size-1']);
+                const textWidthList = gradioApp().querySelector( '#txt2img_width' ).getElementsByTagName('input');
+                if(textWidthList&&textWidthList.length> 1){
+                  textWidthList[0].value = this.metadata['Size-1'];
+                  textWidthList[1].value = this.metadata['Size-1'];
+                }
+              }catch(e){
+              }
+              try{
+                window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_height')[0].props.value = parseFloat(this.metadata['Size-2']);
+                const textWidthList = gradioApp().querySelector( '#txt2img_height' ).getElementsByTagName('input');
+                if(textWidthList&&textWidthList.length> 1){
+                  textWidthList[0].value = this.metadata['Size-2'];
+                  textWidthList[1].value = this.metadata['Size-2'];
+                }
+              }catch(e){
+              }
+              try{
+                window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_cfg_scale')[0].props.value = parseFloat(this.metadata['cfg_scale']);
+                const cfgScaleList = gradioApp().querySelector( '#txt2img_cfg_scale' ).getElementsByTagName('input');
+                if(cfgScaleList&&cfgScaleList.length> 1){
+                  cfgScaleList[0].value = this.metadata['cfg_scale'];
+                  cfgScaleList[1].value = this.metadata['cfg_scale'];
+                }
+              }catch(e){
+              }
+              try{
+                const steps = this.metadata['Hires upscaler'];
+                if(steps){
+                  window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_enable_hr')[0].props.value = true;
+                  gradioApp().querySelector( '#txt2img_enable_hr' ).getElementsByTagName('input')[0].checked = true;
+                  try{
+                    const originalHires = gradioApp().querySelector( '#txt2img_hires_fix' );
+                    originalHires.classList.remove('hidden');
+                  }catch(e){
+                    
+                  }
+                  const scalerMethod = this.metadata['Hires upscaler'];
+                  const containUpscaler = window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_hr_upscaler')[0].props.choices.includes(scalerMethod);
+                  if(containUpscaler){
+                    window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_hr_upscaler')[0].props.value = this.metadata['Hires upscaler'];
+                    const hr_span = gradioApp().querySelector( '#txt2img_hr_upscaler' ).getElementsByTagName('span');
+                    hr_span[0].textContent = this.metadata['Hires upscaler'];
+
+                    const cfgScaleList = gradioApp().querySelector( '#txt2img_enable_hr' ).getElementsByTagName('input');
+                  if(cfgScaleList&&cfgScaleList.length> 1){
+                    cfgScaleList[0].value = this.metadata['cfg_scale'];
+                    cfgScaleList[1].value = this.metadata['cfg_scale'];
+                  }
+                }  
+                try{
+                  window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_hires_steps')[0].props.value = parseInt(this.metadata['Hires steps']);
+                  const cfgScaleList = gradioApp().querySelector( '#txt2img_hires_steps' ).getElementsByTagName('input');
+                  if(cfgScaleList&&cfgScaleList.length> 1){
+                    cfgScaleList[0].value = this.metadata['Hires steps'];
+                    cfgScaleList[1].value = this.metadata['Hires steps'];
+                  }
+                }catch(e){
+                  console.log(e);
+                  console.log('errorhr0');
+                }
+                try{
+                  window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_hr_scale')[0].props.value = parseFloat(this.metadata['Hires upscale']);
+                  const cfgScaleList = gradioApp().querySelector( '#txt2img_hr_scale' ).getElementsByTagName('input');
+                  if(cfgScaleList&&cfgScaleList.length> 1){
+                    cfgScaleList[0].value = this.metadata['Hires upscale'];
+                    cfgScaleList[1].value = this.metadata['Hires upscale'];
+                  }
+                }catch(e){
+                  console.log(e);
+                  console.log('errorhr');
+                }
+                try{
+                  const hiresValue1 = parseInt(this.metadata['Hires resize-1']);
+                  console.log(hiresValue1);
+                  if(hiresValue1 && hiresValue1 > 0){
+                      window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_hr_resize_x')[0].props.value = parseInt(this.metadata['Hires resize-1']);
+                    const cfgScaleList = gradioApp().querySelector( '#txt2img_hr_resize_x' ).getElementsByTagName('input');
+                    if(cfgScaleList&&cfgScaleList.length> 1){
+                      cfgScaleList[0].value = this.metadata['Hires resize-1'];
+                      cfgScaleList[1].value = this.metadata['Hires resize-1'];
+                    }
+                  }
+                }catch(e){
+                  console.log(e);
+                  console.log('errorhr2');
+                }
+                try{
+                  const hiresValue2 = parseInt(this.metadata['Hires resize-2']);
+                  console.log(hiresValue2);
+                  if(hiresValue2 > 0){
+                    window.gradio_config.components.filter(el=>el.props.elem_id=='txt2img_hr_resize_y')[0].props.value = parseInt(this.metadata['Hires resize-2']);
+                    const cfgScaleList = gradioApp().querySelector( '#txt2img_hr_resize_y' ).getElementsByTagName('input');
+                    if(cfgScaleList&&cfgScaleList.length> 1){
+                      cfgScaleList[0].value = this.metadata['Hires resize-2'];
+                      cfgScaleList[1].value = this.metadata['Hires resize-2'];
+                    }
+                  }
+                }catch(e){
+                  console.log(e);
+                  console.log('errorhr3');
+                }
+                }
+              
+                
+
+              }catch(e){
+                console.log(e);
+                console.log('errorhr4');
+              }
+              this.close();
+              switch_to_txt2img();
+            },
+            sendToImageToImage(){
+              const userId = localStorage.getItem("userId");
+              const imageId = this.imageId;
+              const token = localStorage.getItem("token");
+              const headers={"Authorization":"Token " + token};
+              axios.post(
+                  'https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/send_to',
+                  {
+                    userId,
+                    imageId,
+                    sendType: "SEND_TO_IMAGE",
+                  },{
+                    headers
+                  }
+              )
+              .then(response => {
+                var xhr = new XMLHttpRequest();
+                var self = this;
+                xhr.onload = function() {
+                    var reader = new FileReader();
+                    reader.onloadend = function() {
+                      self.sendToImageToImageProcess(reader.result);
+                    }
+                    reader.readAsDataURL(xhr.response);
+                };
+                xhr.open('GET', this.src);
+                xhr.responseType = 'blob';
+                xhr.send();
+                this.currentItem.total_sendto = this.currentItem.total_sendto + 1;
+                
+              }).catch(error => {
+                  this.loading = false;
+                  console.log(error)
+              })
+            },
+            sendToImageToImageProcess(date64Image){
+              try{
+                window.gradio_config.components.filter(el=>el.props.elem_id=='img2img_image')[0].props.value = date64Image;
+              }catch(e){
+
+              }
+              try{
+                const textPrompt = gradioApp().querySelector('#img2img_prompt').getElementsByTagName( 'textarea' );
+                if(textPrompt&&textPrompt.length> 0){
+                  window.gradio_config.components.filter(el=>el.props.elem_id=='img2img_prompt')[0].props.value = this.metadata.prompt;
+                  textPrompt[0].value = this.metadata.prompt;
+                }
+              }catch(e){
+                console.log('Error prompt img2img');
+                console.log(e);
+
+              }
+              try{
+                const negativePrompt = gradioApp().querySelector('#img2img_neg_prompt').getElementsByTagName( 'textarea' );
+                if(negativePrompt&&negativePrompt.length> 0){
+                  window.gradio_config.components.filter(el=>el.props.elem_id=='img2img_neg_prompt')[0].props.value = this.metadata.negative_prompt;
+                  negativePrompt[0].value = this.metadata.negative_prompt;
+                }
+              }catch(e){
+                console.log('Error neg img2img');
+                console.log(e);
+
+              }
+              
+              
+              try{
+                const samplerList = gradioApp().querySelector( '#img2img_sampling' ).getElementsByTagName('span');
+                if(samplerList&&samplerList.length> 1){
+                  window.gradio_config.components.filter(el=>el.props.elem_id=='img2img_sampling')[0].props.value = this.metadata.sampler;
+                  samplerList[1].textContent = this.metadata.sampler;
+                }
+              }catch(e){
+              }
+
+              try{
+                const textImageStepsList = gradioApp().querySelector( '#img2img_steps' ).getElementsByTagName('input');
+                if(textImageStepsList&&textImageStepsList.length> 1){
+                  window.gradio_config.components.filter(el=>el.props.elem_id=='img2img_steps')[0].props.value = parseInt(this.metadata.n_iter);
+                  textImageStepsList[0].value = this.metadata.n_iter;
+                  textImageStepsList[1].value = this.metadata.n_iter;
+                }
+              }catch(e){
+                console.log('Error steps img2img');
+                console.log(e);
+
+              }
+              try{
+                window.gradio_config.components.filter(el=>el.props.elem_id=='img2img_width')[0].props.value = parseInt(this.metadata['Size-1']);
+                const textWidthList = gradioApp().querySelector( '#img2img_width' ).getElementsByTagName('input');
+                if(textWidthList&&textWidthList.length> 1){
+                  textWidthList[0].value = this.metadata['Size-1'];
+                  textWidthList[1].value = this.metadata['Size-1'];
+                }
+              }catch(e){
+                console.log('Error width img2img');
+                console.log(e);
+              }
+              try{
+                window.gradio_config.components.filter(el=>el.props.elem_id=='img2img_height')[0].props.value = parseInt(this.metadata['Size-2']);
+                const textWidthList = gradioApp().querySelector( '#img2img_height' ).getElementsByTagName('input');
+                if(textWidthList&&textWidthList.length> 1){
+                  textWidthList[0].value = this.metadata['Size-2'];
+                  textWidthList[1].value = this.metadata['Size-2'];
+                }
+              }catch(e){
+                console.log('Error width height2img');
+                console.log(e);
+              }
+              this.close();
+              switch_to_img2img();
+            },
+            addToWishList(item){
+                const userId = localStorage.getItem("userId");
+                const token = localStorage.getItem("token");
+                const headers={"Authorization":"Token " + token};
+                item.liked = true;
+                item.likecount = item.likecount + 1;
+                axios.post(
+                  `https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/like`,
+                  {
+                    userId, 
+                    imageId: item.id,
+                  }, 
+                  {
+                    headers
+                  }
+                )
+            },
+            handleTextareaInput(event) {
+              let textarea = event.target;
+              textarea.style.height = 'auto';
+              textarea.style.height = (textarea.scrollHeight) + 'px';
+              comment = event.target.text;
+            },
+            deleteWishList(item){
+              const userId = localStorage.getItem("userId");
+              item.liked = false;
+              item.likecount = item.likecount -1 ;
+              const token = localStorage.getItem("token");
+              const headers={"Authorization":"Token " + token};
+              axios.delete(
+                `https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/like`,
+                {data: {
+                  userId, 
+                  imageId: item.id,
+                },
+                headers
+              }, 
+              )
+            },
+            makeSaveButtonVisible(){
+              this.showCommentWrite = true;
+            },
+            hideSaveButtonVisible(){
+              this.comment = '';
+              this.showCommentWrite = false;
+              const textArea = gradioApp().querySelector("#modal-writer-type");
+              textArea.style.height = 'auto';
+              textArea.style.height = (textarea.scrollHeight) + 'px';
+            },
+            writeComment(){
+              if(this.comment.length > 0){
+                const userId = localStorage.getItem("userId");
+                const token = localStorage.getItem("token");
+                const headers={"Authorization":"Token " + token};
+                axios.post(
+                  `https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/comment`,
+                  {
+                    userId, 
+                    imageId: this.currentItem.id,
+                    text: this.comment,
+                  },{
+                    headers
+                  } 
+                ).then((res)=>{
+                  this.fetchComments();
+                  this.showCommentWrite = false;
+                  this.comment = '';
+                  this.currentItem.total_comments = this.currentItem.total_comments + 1;
+                }).catch((e)=>{
+                  console.log(e);
+                });
+
+              }else{
+                return false;
+              }
+            },
+            fetchComments(){
+              const token = localStorage.getItem("token");
+              const headers={"Authorization":"Token " + token};
+              axios.get(
+                `https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/comments/${this.currentItem.id}`,
+                {
+                  headers
+                }
+              ).then((res)=>{
+                this.comments = res.data;
+                this.$forceUpdate();
+              }).catch((e)=>{
+                console.log(e);
+              })
+            },
+              setOrder(event){
+                this.page = 1;
+                this.order = event.target.value;
+                this.fetchImages();
+              },
+              setPublic(event){
+                this.page = 1;
+                this.mode = event.target.value;
+                this.fetchImages();
+              },
+              setMode(mode){
+                this.page = 1;
+                this.mode = mode;
+                this.fetchImages();
+              },
+              fetchSearchProfile(){
+                const params = {
+                  nickname: this.searchNickname,
+                };
+                axios.get(
+                  `https://newtypev3-server-vjiloyvjvq-an.a.run.app/user/profile_nickname`,
+                  {
+                    params
+                  }
+                ).then((res)=>{
+                  this.searchProfile = res.data.profile;
+                  this.$forceUpdate();
+                }).catch((e)=>{
+                  console.log(e);
+                })
+              },
+              fetchNewImage(){
+                this.page = 1;
+                this.fetchImages();
+              },
+              fetchImages(){
+                this.loading = true;
+                if(this.page == -1){
+                  this.loading = false;
+                  return false;
+                }
+                const userId = localStorage.getItem("userId");
+                const defaultParams = {
+                  page: this.page,
+                  batch_size: 100, 
+                  prompt: this.prompt,
+                  filter_nsfw: this.filterNsfw,
+                };
+                if(this.mode == 'latest'){
+                  defaultParams['order'] = 'created_at';
+                }
+                if(this.mode == 'nicknameSearch'){
+                  defaultParams['nickname'] = this.searchNickname;
+                  defaultParams['order'] = 'created_at';
+                }
+                if(this.animeTag){
+                  defaultParams['image_tag'] = 'ANIME';
+                }
+                if(this.realTag){
+                  defaultParams['image_tag'] = 'REAL';
+                }
+                if(this.mode == 'likes'){
+                  defaultParams['order'] = 'likes';
+                }
+                if(this.mode == 'favorite'){
+                  defaultParams['only_liked_pictures'] = true;
+                  defaultParams['order'] = 'likes_created';
+                }
+                if(this.mode == 'private'){
+                  defaultParams['only_my_pictures'] = true;
+                  defaultParams['order'] = 'created_at';
+                }
+                const token = localStorage.getItem("token");
+                const headers={"Authorization":"Token " + token};
+
+                axios.get(
+                  `https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/images/${userId}`,
+                  {params: defaultParams, headers}, 
+                )
+                .then(response => {
+                  if(this.page == 1){
+                    this.imageList = response.data;
+                  }else{
+                    this.imageList = [...this.imageList, ...response.data];
+                  }
+                  this.sortedDataList = reorderData(this.imageList, 4);
+                  if(response.data.length < 100){
+                    this.page = -1;
+                  }
+                  this.loading = false;
+                })
+                .catch(error => {
+                  this.loading = false;
+                  console.log(error)
+                })
+              
+              }
+            },
+            mounted(){
+              var self = this;
+              waitForElementToDisplay('#feed-header', function(){
+              window.addEventListener('scroll', e => {
+                const listElem = gradioApp().querySelector("#columns");
+                const tabElem = gradioApp().querySelector("#tab_newtype_tab");
+                const tabNotVisible = tabElem && tabElem.style && tabElem.style.display && tabElem.style.display == 'none';
+                if(listElem&&listElem.clientHeight * 0.8 <= window.scrollY  && self.loading == false && self.page > 0 && !tabNotVisible) {
+                  self.page = self.page + 1;
+                  self.fetchImages();
+                }
+              }, false);          
+              },1000,9000);
+              
+            },
+            beforeDestroy(){
+              
+            },
+            created() {
+              this.fetchImages();
+              const userId = localStorage.getItem("userId");
+              const token = localStorage.getItem("token");
+              const headers={"Authorization":"Token " + token};
+              axios.get(
+                  `https://newtypev3-server-vjiloyvjvq-an.a.run.app/user/profile/${userId}`,
+                  {
+                      headers
+                  }
+                  )
+              .then(response => {
+                  const dataObject = response.data;;
+                  this.likeCount = dataObject.likecount; 
+                  this.nickname = dataObject.nickname; 
+                  this.profile = dataObject.profile;
+                  this.userId = userId;
+              }).catch(error => {
+                  this.loading = false;
+                  console.log(error)
+              })
+            },
+        };
+        const fetchApp =  Vue.createApp(imageModel);
+        if (document.querySelector("#vue-elem") != null) {
+          fetchApp.mount('#vue-elem');
+        }else{
+          fetchApp.mount(document.getElementsByTagName("gradio-app")[0].shadowRoot.getElementById("vue-elem"));
+        }
+        // const fileInput = gradioApp().querySelector('#feed-image-upload');
+        // fileInput.addEventListener("change", function() {
+        //   fileInput.dispatchEvent(new CustomEvent("on_file_upload"));
+        //   setTimeout(() =>{
+        //     const el = gradioApp().querySelector(
+        //       '#feed-search-btn'
+        //     );
+        //     el.click();
+        //   }, 2000);
+        // });
+        console.log("mount success");
+    },1000,9000);

sd_feed/javascript/loradownload.js

@@ -0,0 +1,55 @@
+
+waitForElementToDisplay(
+  "#lora-search-table",
+  function(){
+      const loraModel = {
+          data() {
+              return {
+                  loraList: [],
+                  loraString: '',
+              }
+          },
+          methods: {
+              setUrlData(url, fname){
+                const textArea = gradioApp().querySelector("#lora-download-url")
+                textArea.value = url;
+                const fnameInput = gradioApp().querySelector("#lora-filename")
+                fnameInput.value = fname;
+                const downloadBtn = gradioApp().querySelector('#lora_download_btn');
+                downloadBtn.click();
+              }
+          },
+          mounted(){
+            var self = this;
+            const textArea = gradioApp().querySelector("#table-data").querySelector('textarea');
+            const searchLoraBtn = gradioApp().querySelector("#lora_search_button");
+            const tableArea = gradioApp().querySelector("#table-data");
+            searchLoraBtn.addEventListener('click', (e)=>{
+
+            const interval = 500; // 1 second
+            const duration = 15000; // 15 seconds
+            let count = 0;
+            const timer = setInterval(() => {
+                // Perform your desired actions here            
+                count += interval;
+                const textArea = gradioApp().querySelector("#table-data").querySelector('textarea');
+                const textAreaValue = textArea.value;
+                let newJson;
+                try{
+                    newJson = JSON.parse(textAreaValue.replace('(','[').replace(',)', ')').replace(')',']').replaceAll("'", '"'));
+                }catch(e){
+                    console.log(e);
+                    newJson = [];
+                }            
+                if (textAreaValue !== self.loraString||count >= duration) {
+                    self.loraString = textAreaValue;
+                    self.loraList = newJson;
+                    clearInterval(timer);
+                }
+            }, interval);
+            });
+          },
+      };
+      const loraApp =  Vue.createApp(loraModel);
+      loraApp.mount(gradioApp().querySelector("#lora-search-table"));
+  },1000,9000);

sd_feed/javascript/userprofile.js

@@ -0,0 +1,74 @@
+waitForElementToDisplay(
+    "#feed-profile-tab",
+    function(){
+        const userModel = {
+            data() {
+                return {
+                    userId: '',
+                    nickname: '',
+                    likeCount: 0,
+                    editMode: false,
+                    loading: false,
+                }
+            },
+            methods: {
+                close() {},
+                mounted(){},
+                beforeDestroy(){},
+                setEditMode(){
+                    this.editMode = !this.editMode;
+                },
+                saveNickname(){
+                    const userId = localStorage.getItem("userId");
+                    const token = localStorage.getItem("token");
+                    const headers={"Authorization":"Token " + token};
+                    axios.post(
+                        `https://newtypev3-server-vjiloyvjvq-an.a.run.app/user/profile`,
+                        {
+                            userId,
+                            nickname: this.nickname,
+                        }, 
+                        {
+                            headers
+                        }
+                      )
+                      .then(response => {
+                        this.editMode = false;        
+                      })
+                      .catch(error => {
+                        this.loading = false;
+                        alert(error.response.data.detail);
+                      })
+                },
+               
+            },
+            created() {
+                const userId = localStorage.getItem("userId");
+                const token = localStorage.getItem("token");
+                const headers={"Authorization":"Token " + token};
+                axios.get(
+                    `https://newtypev3-server-vjiloyvjvq-an.a.run.app/user/profile/${userId}`,
+                    {
+                        headers
+                    }
+                    )
+                .then(response => {
+                    const dataObject = response.data;;
+                    this.likeCount = dataObject.likecount; 
+                    this.nickname = dataObject.nickname; 
+                    this.userId = userId;
+                }).catch(error => {
+                    this.loading = false;
+                    console.log(error)
+                })
+            },
+        };
+        const fetchApp =  Vue.createApp(userModel);
+        if (document.querySelector("#feed-profile-tab") != null) {
+          fetchApp.mount('#feed-profile-tab');
+        }else{
+          fetchApp.mount(document.getElementsByTagName("gradio-app")[0].shadowRoot.getElementById("feed-profile-tab"));
+        }
+
+        console.log("mount success");
+    },1000,9000);

sd_feed/newtype_v3/images.py

@@ -0,0 +1,279 @@
+import json
+import os
+from typing import Dict, List, Optional
+import re
+import base64
+from io import BytesIO
+from tempfile import _TemporaryFileWrapper
+import traceback
+
+from modules.images import read_info_from_image
+from newtype_v3.users import create_user
+from newtype_v3.utils.string_utils import return_string_dict
+from newtype_v3.locs import DEFAULT_LOC
+
+from PIL import Image
+from modules.processing import StableDiffusionProcessing
+import requests
+
+
+def dumper(obj):
+    try:
+        return obj.toJSON()
+    except Exception:
+        try:
+            return obj.__dict__
+        except Exception:
+            return str(obj)
+    
+
+def create_image(img: Image, user_id: str, process: StableDiffusionProcessing, info: Optional[str] = None, token: str = ''):
+    try:
+        if info:
+            data_dict = return_string_dict(info)
+        else:
+            applied_to_hires = (
+                getattr(process, 'applied_old_hires_behavior_to')
+                if hasattr(process, 'applied_old_hires_behavior_to')
+                else None
+            )
+            enable_hr = (
+                getattr(process, 'enable_hr')
+                if hasattr(process, 'enable_hr')
+                else None
+            )
+            
+            data_dict = {
+                'all_negative_prompts': process.all_negative_prompts, 
+                'all_prompts': process.all_prompts,
+                'all_seeds': process.all_seeds, 
+                'all_subseeds': process.all_subseeds, 
+                'applied_old_hires_behavior_to': applied_to_hires,
+                'batch_size': process.batch_size, 
+                'cfg_scale': process.cfg_scale, 
+                'color_corrections': process.color_corrections,
+                'ddim_discretize': process.ddim_discretize,
+                'denoising_strength': process.denoising_strength,
+                'disable_extra_networks': process.disable_extra_networks,
+                'do_not_reload_embeddings': process.do_not_reload_embeddings, 
+                'do_not_save_grid': process.do_not_save_grid, 
+                'do_not_save_samples': process.do_not_save_samples,
+                'enable_hr': enable_hr, 
+                'eta': process.eta, 
+                'extra_generation_params': process.extra_generation_params, 
+                'height': process.height,
+                'iteration': process.iteration, 
+                'n_iter': process.n_iter, 
+                'negative_prompt': process.negative_prompt, 
+                'outpath_grids': process.outpath_grids,
+                'outpath_samples': process.outpath_samples,
+                'overlay_images': process.overlay_images,
+                'override_settings': process.override_settings, 
+                'override_settings_restore_afterwards': 
+                process.override_settings_restore_afterwards, 
+                'process': process.paste_to,
+                'prompt': process.prompt, 
+                'prompt_for_display': process.prompt_for_display, 
+                'restore_faces': process.restore_faces, 
+                's_churn': process.s_churn, 
+                's_noise': process.s_noise, 
+                's_tmax': process.s_tmax, 
+                's_tmin': process.s_tmin,
+                'sampler_name': process.sampler_name, 
+                'sampler_noise_scheduler_override': 
+                process.sampler_noise_scheduler_override,
+                'script_args': process.script_args, 
+                'scripts': [x.title() for x in process.scripts.scripts], 
+                'sd_model': process.sd_model.sd_checkpoint_info.title,
+                'seed': process.seed, 
+                'seed_resize_from_h': process.seed_resize_from_h, 
+                'seed_resize_from_w': process.seed_resize_from_w, 
+                'steps': process.steps, 
+                'styles': process.styles, 
+                'subseed': process.subseed, 
+                'subseed_strength': process.subseed_strength, 
+            }
+            for row in [
+                'hr_resize_x',
+                'hr_resize_y',
+                'hr_scale',
+                'hr_second_pass_steps',
+                'hr_upscale_to_x',
+                'hr_upscale_to_y',
+                'hr_upscaler',
+                'tiling',
+                'truncate_x',
+                'truncate_y',
+                'width'
+            ]:
+                value = (
+                    getattr(process, row)
+                    if hasattr(process, row)
+                    else None
+                )
+                data_dict[row] = value
+        img.save(f'{DEFAULT_LOC}/temp.png', "PNG", optimize=True)
+        img.seek(0)
+        _ = requests.post(
+            'https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/image',
+            data={
+                'userId': user_id,
+                'metadata': json.dumps(data_dict, default=dumper)
+            },
+            files={
+                'upload_file': open(f'{DEFAULT_LOC}/temp.png', 'rb')
+            },
+            stream=True,
+            headers={
+                'Authorization': f'Token {token}'
+            }
+        )
+        os.remove(f'{DEFAULT_LOC}/temp.png')
+    except Exception as e:
+        print(e)
+        pass
+
+
+def create_image_from_string(img_string: str, user_id: str, token: str = ''):
+    try:
+        remove_image = False
+        if img_string.startswith("http") or img_string.startswith("file="):
+            file_loc = (
+                re.search(r'file=(.*)', img_string).group(1)
+                .replace("%20", ' ')
+            )
+            img = Image.open(file_loc)
+            text_info, extra_items = read_info_from_image(img)
+            data_dict = return_string_dict(text_info)
+            data_dict.update(extra_items)
+        else:
+            file_loc = f'{DEFAULT_LOC}/temp.png'
+            img_string = img_string.split(",")[1]
+            img = Image.open(BytesIO(base64.b64decode(img_string)))
+            img.save(file_loc, "PNG", optimize=True)
+            img.seek(0)
+            remove_image = True
+            
+        _ = requests.post(
+            'https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/image',
+            data={
+                'userId': user_id,
+                'metadata': json.dumps(data_dict, default=dumper)
+            },
+            files={
+                'upload_file': open(file_loc, 'rb')
+            },
+            stream=True,
+            headers={
+                'Authorization': f'Token {token}'
+            }
+        )
+        try:
+            if remove_image:
+                os.remove(file_loc)
+        except Exception:
+            pass
+        return True
+    except Exception as e:
+        print(e, traceback.format_exc())
+        return False
+
+
+def create_image_to_image_file(
+    img_string: str, original_img_string: str, user_id: str,
+    token: str = ''
+):
+    try:
+        remove_image = False
+        if img_string.startswith("http") or img_string.startswith("file="):
+            file_loc = (
+                re.search(r'file=(.*)', img_string).group(1)
+                .replace("%20", ' ')
+            )
+            img = Image.open(file_loc)
+            text_info, extra_items = read_info_from_image(img)
+            data_dict = return_string_dict(text_info)
+            data_dict.update(extra_items)
+        else:
+            file_loc = f'{DEFAULT_LOC}/temp.png'
+            img_string = img_string.split(",")[1]
+            img = Image.open(BytesIO(base64.b64decode(img_string)))
+            img.save(file_loc, "PNG", optimize=True)
+            img.seek(0)
+            remove_image = True
+        origin_loc = f'{DEFAULT_LOC}/temp2.png'
+        original_img_string = original_img_string.split(',')[1]
+        img = Image.open(BytesIO(base64.b64decode(original_img_string)))
+        img.save(origin_loc, "PNG", optimize=True)
+        _ = requests.post(
+            'https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/image',
+            data={
+                'userId': user_id,
+                'metadata': json.dumps(data_dict, default=dumper)
+            },
+            files={
+                'upload_file': open(file_loc, 'rb'),
+                'img_to_img_file': open(origin_loc, 'rb')
+            },
+            stream=True,
+            headers={
+                'Authorization': f'Token {token}'
+            }
+        )
+        try:
+            if remove_image:
+                os.remove(file_loc)
+            os.remove(origin_loc)
+        except Exception:
+            pass
+        return True
+    except Exception as e:
+        print('Image to image save error', e)
+        return False
+
+
+def create_image_from_img(img: Image, user_id: str, data_dict: Dict, token: str = ''):
+    try:
+        file_loc = f'{DEFAULT_LOC}/temp.png'
+        img.save(file_loc, "PNG", optimize=True)
+        img.seek(0)
+        _ = requests.post(
+            'https://newtypev3-server-vjiloyvjvq-an.a.run.app/image/image',
+            data={
+                'userId': user_id,
+                'metadata': json.dumps(data_dict, default=dumper)
+            },
+            files={
+                'upload_file': open(file_loc, 'rb')
+            },
+            stream=True,
+            headers={
+                'Authorization': f'Token {token}'
+            }
+        )
+        os.remove(file_loc)
+    except Exception as e:
+        print(e)
+        pass
+
+
+def create_multiple_file_uploads(
+    data: List[_TemporaryFileWrapper]
+):
+    user_dict = create_user()
+    user_id = user_dict.get('userId')
+    for d in data:
+        try:
+            image = Image.open(BytesIO(d))
+            textinfo, items = read_info_from_image(image)
+            if textinfo is not None:
+                data = return_string_dict(textinfo)
+                data.update(items)
+            else:
+                data = {}
+            create_image_from_img(image, user_id, data)
+        except Exception as e:
+            print(e)
+            pass
+    return []
+

sd_feed/newtype_v3/locs.py

@@ -0,0 +1,10 @@
+import os
+
+
+is_exists = os.path.exists('/content/repository/extensions-builtin')
+is_exists_seconds = os.path.exists('/content/drive/MyDrive/SD/extensions/sd_feed/javascript')
+SD_ROOT = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
+if is_exists and is_exists_seconds:
+    DEFAULT_LOC = '/content/drive/MyDrive/SD/extensions/sd_feed'
+else:
+    DEFAULT_LOC = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))

sd_feed/newtype_v3/lora.py

@@ -0,0 +1,32 @@
+import re
+import traceback
+from typing import List, Dict
+import requests
+from newtype_v3.users import create_user_headers
+
+
+def lora_string_list(prompt: str) -> List[str]:
+    data_list = []
+    for x in re.findall(r'\<lora\:.+?.\>', prompt):
+        data_list.append(x.split(':')[1])
+    return data_list
+
+
+def search_lora_list(lora_list: List[str]) -> List[Dict]:
+    try:
+        headers, user_dict = create_user_headers()
+        result_list = requests.post(
+            'https://newtypev3-server-vjiloyvjvq-an.a.run.app/lora/loras',
+            json={
+                'loraList': lora_list,
+                'userId': user_dict.get("userId")
+            },
+            headers=headers
+        )
+        result = result_list.json()
+        if type(result) is list:
+            return result
+        raise ValueError(result)
+    except Exception as e:
+        print(e, traceback.format_exc())
+        return False

sd_feed/newtype_v3/lora_types.py

@@ -0,0 +1,7 @@
+
+
+SIMILAR: str = "SIMILAR"
+SAME: str = "SAME"
+NOT_FOUND: str = "NOT_FOUND"
+ALREADY_EXISTS: str = "ALREADY_EXSITS"
+

sd_feed/newtype_v3/newtype_v3.py

@@ -0,0 +1,293 @@
+import logging
+from pathlib import Path
+from typing import List, Tuple
+import requests
+import os
+
+import gradio as gr
+
+from modules import call_queue
+from modules import scripts, script_callbacks
+from modules.script_callbacks import ImageSaveParams
+from modules import shared
+
+from newtype_v3.locs import DEFAULT_LOC
+from newtype_v3.lora import search_lora_list, lora_string_list
+from . import lora_types
+from .users import create_user, create_user_headers
+from .images import (
+    create_image, create_image_from_string, create_image_to_image_file
+)
+
+
+logger = logging.getLogger(__name__)
+
+loaded_count = 0
+
+base_dir = Path(scripts.basedir())
+all_btns: List[Tuple[gr.Button, ...]] = []
+share_image = False
+
+
+def search_lora(prompt: str) -> List[str]:
+    if type(prompt) is not str:
+        prompt = prompt[0]
+    search_list = lora_string_list(prompt)
+    try:
+        list_of_dict = search_lora_list(search_list)
+    except Exception:
+        list_of_dict = []
+    fname_list = os.listdir(shared.cmd_opts.lora_dir)
+    fname_text_list = [
+        x.replace(".safetensors", '').replace('.ckpt', '').replace(".pt", '') 
+        for x in fname_list
+    ]
+    searched_list = [
+        x.get('fileName').replace(".safetensors", '').replace('.ckpt', '').replace(".pt", '') 
+        for x in list_of_dict
+    ]
+    not_found_list = [{
+        'fileName': x,
+        'downloadUrl': '',
+        'status': lora_types.NOT_FOUND
+    } for x in search_list 
+        if x.replace(".safetensor", '').replace('.ckpt', '').replace(".pt", '')
+        not in fname_text_list
+        and 
+        x not in searched_list
+    ]
+    list_of_dict = list(filter(lambda x: x.get('fileName') not in fname_list, list_of_dict))
+    list_of_dict = [
+        {
+            'fileName': x.get('fileName'), 'downloadUrl': x.get('downloadUrl'),
+            'status': lora_types.SAME,
+        } 
+        for x in list_of_dict
+    ]
+    return list_of_dict + not_found_list
+
+
+def set_share_image(x):
+    global share_image
+    share_image = x
+
+
+def save_files(image_file: str, outer_html) -> List[str]:
+    user_dict = create_user()
+    userId = user_dict.get('userId') 
+    result = create_image_from_string(
+        image_file,
+        userId,
+    )
+    result_string = "Success" if result else "Failed"
+    return [
+        result_string
+    ]
+
+
+def download_lora(link, fname):
+    # Code to handle file download
+    response = requests.get(link)
+    with open(f'{shared.cmd_opts.lora_dir}/{fname}', 'wb') as f:
+        f.write(response.content)
+    try:
+        headers, user_dict = create_user_headers()
+        _ = requests.post(
+            'https://newtypev3-server-vjiloyvjvq-an.a.run.app/lora/download',
+            json={
+                'userId': user_dict.get("userId")
+            },
+            headers=headers
+        )
+    except Exception:
+        pass
+    return ["Success"]
+
+
+def save_imgto_img_file(
+    image_file: str, original_img_string: str, outer_html: str
+):
+    user_dict = create_user()
+    userId = user_dict.get('userId')
+    token = user_dict.get('token', '') 
+    result = create_image_to_image_file(
+        image_file,
+        original_img_string,
+        userId,
+        token=token
+    )
+    result_string = "Success" if result else "Failed"
+    return [result_string]
+
+
+def register_png_posturl() -> None:
+    def on_img_save_url(image_save_params: ImageSaveParams) -> None:
+        try:
+            global share_image
+            if share_image:
+                user_dict = create_user()
+                userId = user_dict.get('userId') 
+                create_image(
+                    image_save_params.image, 
+                    userId,
+                    image_save_params.p,
+                    info=image_save_params.pnginfo.get("parameters") if image_save_params.pnginfo else None,
+                    token=user_dict.get('token', ''),
+                )
+        except Exception:
+            logger.exception("Error save data")
+    script_callbacks.on_image_saved(on_img_save_url)
+
+
+class Script(scripts.Script):
+    def __init__(self):
+        global loaded_count
+
+        loaded_count += 1
+
+        # This is a hack to make sure that the script is only loaded once
+        # Auto1111 calls the script twice, once for the txt2img and once for 
+        # img2img
+        # These callbacks should only be registered once.
+
+        # When the Reload UI button in the settings tab is pressed, the script 
+        # is loaded twice again
+        # Therefore we only register callbacks every second time the script is 
+        # loaded
+        if loaded_count % 2 == 0:
+            return
+        register_png_posturl()
+
+    def title(self):
+        return "Feed"
+
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+
+    def process(
+        self,
+        p,
+        *args,
+        **kwargs
+    ):
+        return p
+
+    def after_component(self, component, **kwargs):
+        if isinstance(component, gr.HTML):
+            if component.elem_id in {'html_info_txt2img'}:
+                with open(f'{DEFAULT_LOC}/html/potd.html', encoding='utf-8') as f:
+                    gr.HTML(
+                        f.read(), 
+                        elem_id="best-product-tab", 
+                    )
+
+        if component.elem_id in {'txt2img_override_settings'}:
+            with gr.Accordion('Lora Downloads', open=True):
+                with gr.Column():
+                    with gr.Row():
+                        lora_save = gr.Button(
+                            'Search Lora', elem_id='lora_search_button'
+                        )
+                        # JS에서 브라우저에있는데이터를 save_files함수로 전송
+                        js_string = """(a)=>{
+                            const container = gradioApp().querySelector('#txt2img_prompt');
+                            const textArea = container.getElementsByTagName("textarea")[0];
+                            return [textArea.value];
+                        }"""
+                        txtArea = gr.TextArea(
+                            '', 
+                            elem_id="table-data", 
+                            visible=False
+                        )
+                        lora_download = gr.Button(
+                            '', elem_id='lora_download_btn',
+                            visible=False
+                        )
+                    download_results = gr.Textbox(
+                        "",
+                        label="",
+                        visible=True
+                    )
+                    with open(f'{DEFAULT_LOC}/html/lora.html', encoding='utf-8') as f:
+                        gr.HTML(f.read(), elem_id="lora-elem")
+                    lora_save.click(
+                        fn=call_queue.wrap_gradio_call(search_lora),
+                        _js=js_string,
+                        inputs=[lora_save],
+                        outputs=txtArea,
+                        show_progress=True,
+                    )
+                    download_js_string = """(a)=>{
+                        const container = gradioApp().querySelector('#lora-download-url');
+                        const lorafname = gradioApp().querySelector('#lora-filename');
+                        return [container.value, lorafname.value];
+                    }"""
+                    lora_download.click(
+                        fn=call_queue.wrap_gradio_call(download_lora),
+                        _js=download_js_string,
+                        inputs=[lora_download, lora_download],
+                        outputs=[download_results],
+                        show_progress=True,
+                    )
+
+        if isinstance(component, gr.Gallery):
+            if component.elem_id in {'txt2img_gallery'}:
+                with gr.Accordion('Feed Settings', open=True):
+                    with gr.Column():
+                        with gr.Row():
+                            save = gr.Button(
+                                'Share', elem_id='save_txt2_image'
+                            )
+                            with open(f'{DEFAULT_LOC}/html/profile.html', encoding='utf-8') as f:
+                                gr.HTML(f.read(), elem_id="feed-profile-tab")
+                        # JS에서 브라우저에있는데이터를 save_files함수로 전송
+                        js_string = """(a)=>{
+                            const el = gradioApp().querySelector('#txt2img_gallery');
+                            const imgList = el.getElementsByTagName('img');
+                            const htmlInfoTxtImg = gradioApp().querySelector('#html_info_txt2img');
+                            return [imgList[0]['src'], htmlInfoTxtImg.outerHTML];
+                        }"""
+                        txt_to_txt_result = gr.Textbox(
+                            "",
+                            label="",
+                            visible=True
+                        )
+                        save.click(
+                            fn=call_queue.wrap_gradio_call(save_files),
+                            _js=js_string,
+                            inputs=[save, save],
+                            outputs=[txt_to_txt_result],
+                            show_progress=True,
+                        )
+            if component.elem_id in {'img2img_gallery'}:
+                with gr.Accordion('Feed Settings', open=True):
+                    with gr.Column():
+                        save = gr.Button('Share', elem_id='save_img2_image')
+                        img_to_img_result = gr.Textbox(
+                            "",
+                            label="",
+                            visible=True
+                        )
+                        # JS에서 브라우저에있는데이터를 save_files함수로 전송
+                        js_string = """(a)=>{
+                            const el = gradioApp().querySelector('#img2img_gallery');
+                            const elOrigin = gradioApp().querySelector("#img2img_image");
+                            const imgList = el.getElementsByTagName('img');
+                            const originImgList = elOrigin.getElementsByTagName('img');
+                            const htmlInfoTxtImg = gradioApp().querySelector('#html_info_img2img');
+                            return [imgList[0]['src'], originImgList[0]['src'], htmlInfoTxtImg.outerHTML];
+                        }"""
+                        save.click(
+                            fn=call_queue.wrap_gradio_call(
+                                save_imgto_img_file
+                            ),
+                            _js=js_string,
+                            inputs=[save, save, save],
+                            outputs=[
+                                img_to_img_result
+                            ],
+                            show_progress=True,
+                        )
+
+    def ui(self, is_img2img):
+        return []

sd_feed/newtype_v3/tabs/__init__.py

@@ -0,0 +1,6 @@
+from . import feed
+
+
+__all__ = [
+    feed,    
+]

sd_feed/newtype_v3/tabs/feed.py

@@ -0,0 +1,26 @@
+import gradio as gr
+
+from newtype_v3.users import create_user
+from newtype_v3.locs import DEFAULT_LOC
+
+
+def on_ui_tabs():
+    user_dict = create_user()
+    userId = user_dict.get("userId")
+    token = user_dict.get("token")
+    script_html = f"""
+        <script>window.localStorage.setItem("userId", "{userId}");</script>
+        <script>window.localStorage.setItem("token", "{token}");</script>
+    """
+    iframe_html = (
+        f"""<iframe style="width: 100%; height: 0px" srcdoc='{script_html}'>
+        </iframe>"""
+    )
+    with gr.Blocks() as newtype_tab:
+        with gr.Group(elem_id="dynamic-prompting"):
+            with gr.Row():
+                with gr.Column():
+                    gr.HTML(iframe_html, elem_id="html_id")
+                    with open(f'{DEFAULT_LOC}/html/feedtab.html', encoding='utf-8') as f:
+                        gr.HTML(f.read(), elem_id="vue-elem")
+        return (newtype_tab, "Feed", "newtype_tab"),

sd_feed/newtype_v3/users.py

@@ -0,0 +1,48 @@
+import json
+from typing import Dict, Tuple
+import uuid
+import requests
+from newtype_v3.locs import DEFAULT_LOC
+
+def create_user() -> Dict:
+    try:
+        with open(f'{DEFAULT_LOC}/user.json') as f:
+            user_dict = json.load(f)
+            user_id = user_dict.get('userId')
+            if not user_dict.get("token"):
+                post_dict = {
+                    'userId': user_id,
+                    'useAuth': True,
+                }
+                response = requests.post(
+                    'https://newtypev3-server-vjiloyvjvq-an.a.run.app/user/user', 
+                    json=post_dict
+                )
+                token = response.json().get('token')
+                user_dict['token'] = token
+                with open(f'{DEFAULT_LOC}/user.json', 'w') as f:
+                    json.dump(user_dict, f, sort_keys=True, indent=4)
+            return user_dict
+    except Exception:
+        userId = str(uuid.uuid4())
+        user_dict = {
+            'userId': userId,
+            'useAuth': True,
+        }
+        response = requests.post(
+            'https://newtypev3-server-vjiloyvjvq-an.a.run.app/user/user', 
+            json=user_dict
+        )
+        token = response.json().get('token')
+        user_dict['token'] = token
+        with open(f'{DEFAULT_LOC}/user.json', 'w') as f:
+            json.dump(user_dict, f, sort_keys=True, indent=4)
+        return user_dict
+
+
+def create_user_headers() -> Tuple[Dict, Dict]:
+    user_dict = create_user()
+    headers = {
+        "Authorization": f"Token {user_dict['token']}",
+    }
+    return headers, user_dict