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@@ -33,3 +33,12 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+IP_Composer/assets/age/kid.png filter=lfs diff=lfs merge=lfs -text
+IP_Composer/assets/age/old.png filter=lfs diff=lfs merge=lfs -text
+IP_Composer/assets/emotions/joyful.png filter=lfs diff=lfs merge=lfs -text
+IP_Composer/assets/objects/mug.png filter=lfs diff=lfs merge=lfs -text
+IP_Composer/assets/objects/plate.png filter=lfs diff=lfs merge=lfs -text
+IP_Composer/assets/patterns/pebble.png filter=lfs diff=lfs merge=lfs -text
+IP_Composer/assets/patterns/splash.png filter=lfs diff=lfs merge=lfs -text
+IP_Composer/assets/people/boy.png filter=lfs diff=lfs merge=lfs -text
+IP_Composer/assets/people/woman.png filter=lfs diff=lfs merge=lfs -text

IP_Composer/IP_Adapter/ip_adapter/__init__.py

@@ -0,0 +1,9 @@
+from .ip_adapter import IPAdapter, IPAdapterPlus, IPAdapterPlusXL, IPAdapterXL, IPAdapterFull
+
+__all__ = [
+    "IPAdapter",
+    "IPAdapterPlus",
+    "IPAdapterPlusXL",
+    "IPAdapterXL",
+    "IPAdapterFull",
+]

IP_Composer/IP_Adapter/ip_adapter/attention_processor.py

@@ -0,0 +1,568 @@
+# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class AttnProcessor(nn.Module):
+    r"""
+    Default processor for performing attention-related computations.
+    """
+
+    def __init__(
+        self,
+        hidden_size=None,
+        cross_attention_dim=None,
+    ):
+        super().__init__()
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+        *args,
+        **kwargs,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        query = attn.head_to_batch_dim(query)
+        key = attn.head_to_batch_dim(key)
+        value = attn.head_to_batch_dim(value)
+
+        attention_probs = attn.get_attention_scores(query, key, attention_mask)
+        hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class IPAttnProcessor(nn.Module):
+    r"""
+    Attention processor for IP-Adapater.
+    Args:
+        hidden_size (`int`):
+            The hidden size of the attention layer.
+        cross_attention_dim (`int`):
+            The number of channels in the `encoder_hidden_states`.
+        scale (`float`, defaults to 1.0):
+            the weight scale of image prompt.
+        num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
+            The context length of the image features.
+    """
+
+    def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):
+        super().__init__()
+
+        self.hidden_size = hidden_size
+        self.cross_attention_dim = cross_attention_dim
+        self.scale = scale
+        self.num_tokens = num_tokens
+
+        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+        *args,
+        **kwargs,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        else:
+            # get encoder_hidden_states, ip_hidden_states
+            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
+            encoder_hidden_states, ip_hidden_states = (
+                encoder_hidden_states[:, :end_pos, :],
+                encoder_hidden_states[:, end_pos:, :],
+            )
+            if attn.norm_cross:
+                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        query = attn.head_to_batch_dim(query)
+        key = attn.head_to_batch_dim(key)
+        value = attn.head_to_batch_dim(value)
+
+        attention_probs = attn.get_attention_scores(query, key, attention_mask)
+        hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # for ip-adapter
+        ip_key = self.to_k_ip(ip_hidden_states)
+        ip_value = self.to_v_ip(ip_hidden_states)
+
+        ip_key = attn.head_to_batch_dim(ip_key)
+        ip_value = attn.head_to_batch_dim(ip_value)
+
+        ip_attention_probs = attn.get_attention_scores(query, ip_key, None)
+        self.attn_map = ip_attention_probs
+        ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)
+        ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)
+
+        hidden_states = hidden_states + self.scale * ip_hidden_states
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class AttnProcessor2_0(torch.nn.Module):
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+    """
+
+    def __init__(
+        self,
+        hidden_size=None,
+        cross_attention_dim=None,
+    ):
+        super().__init__()
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+        *args,
+        **kwargs,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class IPAttnProcessor2_0(torch.nn.Module):
+    r"""
+    Attention processor for IP-Adapater for PyTorch 2.0.
+    Args:
+        hidden_size (`int`):
+            The hidden size of the attention layer.
+        cross_attention_dim (`int`):
+            The number of channels in the `encoder_hidden_states`.
+        scale (`float`, defaults to 1.0):
+            the weight scale of image prompt.
+        num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
+            The context length of the image features.
+    """
+
+    def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):
+        super().__init__()
+
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+        self.hidden_size = hidden_size
+        self.cross_attention_dim = cross_attention_dim
+        self.scale = scale
+        self.num_tokens = num_tokens
+
+        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+        *args,
+        **kwargs,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        else:
+            # get encoder_hidden_states, ip_hidden_states
+            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
+            encoder_hidden_states, ip_hidden_states = (
+                encoder_hidden_states[:, :end_pos, :],
+                encoder_hidden_states[:, end_pos:, :],
+            )
+            if attn.norm_cross:
+                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # for ip-adapter
+        ip_key = self.to_k_ip(ip_hidden_states)
+        ip_value = self.to_v_ip(ip_hidden_states)
+
+        ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        ip_hidden_states = F.scaled_dot_product_attention(
+            query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
+        )
+        with torch.no_grad():
+            self.attn_map = query @ ip_key.transpose(-2, -1).softmax(dim=-1)
+            #print(self.attn_map.shape)
+
+        ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        ip_hidden_states = ip_hidden_states.to(query.dtype)
+
+        hidden_states = hidden_states + self.scale * ip_hidden_states
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+## for controlnet
+class CNAttnProcessor:
+    r"""
+    Default processor for performing attention-related computations.
+    """
+
+    def __init__(self, num_tokens=4):
+        self.num_tokens = num_tokens
+
+    def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None, temb=None, *args, **kwargs,):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        else:
+            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
+            encoder_hidden_states = encoder_hidden_states[:, :end_pos]  # only use text
+            if attn.norm_cross:
+                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        query = attn.head_to_batch_dim(query)
+        key = attn.head_to_batch_dim(key)
+        value = attn.head_to_batch_dim(value)
+
+        attention_probs = attn.get_attention_scores(query, key, attention_mask)
+        hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class CNAttnProcessor2_0:
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+    """
+
+    def __init__(self, num_tokens=4):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+        self.num_tokens = num_tokens
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+        *args,
+        **kwargs,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        else:
+            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
+            encoder_hidden_states = encoder_hidden_states[:, :end_pos]  # only use text
+            if attn.norm_cross:
+                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states

IP_Composer/IP_Adapter/ip_adapter/ip_adapter.py

@@ -0,0 +1,420 @@
+import os
+from typing import List
+
+import torch
+from diffusers import StableDiffusionPipeline
+from diffusers.pipelines.controlnet import MultiControlNetModel
+from PIL import Image
+from safetensors import safe_open
+from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
+
+from .utils import is_torch2_available, get_generator
+
+if is_torch2_available():
+    from .attention_processor import (
+        AttnProcessor2_0 as AttnProcessor,
+    )
+    from .attention_processor import (
+        CNAttnProcessor2_0 as CNAttnProcessor,
+    )
+    from .attention_processor import (
+        IPAttnProcessor2_0 as IPAttnProcessor,
+    )
+else:
+    from .attention_processor import AttnProcessor, CNAttnProcessor, IPAttnProcessor
+from .resampler import Resampler
+
+
+class ImageProjModel(torch.nn.Module):
+    """Projection Model"""
+
+    def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
+        super().__init__()
+
+        self.generator = None
+        self.cross_attention_dim = cross_attention_dim
+        self.clip_extra_context_tokens = clip_extra_context_tokens
+        self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
+        self.norm = torch.nn.LayerNorm(cross_attention_dim)
+
+    def forward(self, image_embeds):
+        embeds = image_embeds
+        clip_extra_context_tokens = self.proj(embeds).reshape(
+            -1, self.clip_extra_context_tokens, self.cross_attention_dim
+        )
+        clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
+        return clip_extra_context_tokens
+
+
+class MLPProjModel(torch.nn.Module):
+    """SD model with image prompt"""
+    def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024):
+        super().__init__()
+        
+        self.proj = torch.nn.Sequential(
+            torch.nn.Linear(clip_embeddings_dim, clip_embeddings_dim),
+            torch.nn.GELU(),
+            torch.nn.Linear(clip_embeddings_dim, cross_attention_dim),
+            torch.nn.LayerNorm(cross_attention_dim)
+        )
+        
+    def forward(self, image_embeds):
+        clip_extra_context_tokens = self.proj(image_embeds)
+        return clip_extra_context_tokens
+
+
+class IPAdapter:
+    def __init__(self, sd_pipe, image_encoder_repo, image_encoder_subfolder, ip_ckpt, device, num_tokens=4):
+        self.device = device
+        self.ip_ckpt = ip_ckpt
+        self.num_tokens = num_tokens
+
+        self.pipe = sd_pipe.to(self.device)
+        self.set_ip_adapter()
+
+        # load image encoder
+        self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(image_encoder_repo, subfolder=image_encoder_subfolder).to(
+            self.device, dtype=torch.float16
+        )
+        self.clip_image_processor = CLIPImageProcessor()
+        # image proj model
+        self.image_proj_model = self.init_proj()
+
+        self.load_ip_adapter()
+
+    def init_proj(self):
+        image_proj_model = ImageProjModel(
+            cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
+            clip_embeddings_dim=self.image_encoder.config.projection_dim,
+            clip_extra_context_tokens=self.num_tokens,
+        ).to(self.device, dtype=torch.float16)
+        return image_proj_model
+
+    def set_ip_adapter(self):
+        unet = self.pipe.unet
+        attn_procs = {}
+        for name in unet.attn_processors.keys():
+            cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
+            if name.startswith("mid_block"):
+                hidden_size = unet.config.block_out_channels[-1]
+            elif name.startswith("up_blocks"):
+                block_id = int(name[len("up_blocks.")])
+                hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
+            elif name.startswith("down_blocks"):
+                block_id = int(name[len("down_blocks.")])
+                hidden_size = unet.config.block_out_channels[block_id]
+            if cross_attention_dim is None:
+                attn_procs[name] = AttnProcessor()
+            else:
+                attn_procs[name] = IPAttnProcessor(
+                    hidden_size=hidden_size,
+                    cross_attention_dim=cross_attention_dim,
+                    scale=1.0,
+                    num_tokens=self.num_tokens,
+                ).to(self.device, dtype=torch.float16)
+        unet.set_attn_processor(attn_procs)
+        if hasattr(self.pipe, "controlnet"):
+            if isinstance(self.pipe.controlnet, MultiControlNetModel):
+                for controlnet in self.pipe.controlnet.nets:
+                    controlnet.set_attn_processor(CNAttnProcessor(num_tokens=self.num_tokens))
+            else:
+                self.pipe.controlnet.set_attn_processor(CNAttnProcessor(num_tokens=self.num_tokens))
+
+    def load_ip_adapter(self):
+        if os.path.splitext(self.ip_ckpt)[-1] == ".safetensors":
+            state_dict = {"image_proj": {}, "ip_adapter": {}}
+            with safe_open(self.ip_ckpt, framework="pt", device="cpu") as f:
+                for key in f.keys():
+                    if key.startswith("image_proj."):
+                        state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
+                    elif key.startswith("ip_adapter."):
+                        state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
+        else:
+            state_dict = torch.load(self.ip_ckpt, map_location="cpu")
+        self.image_proj_model.load_state_dict(state_dict["image_proj"])
+        ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
+        ip_layers.load_state_dict(state_dict["ip_adapter"])
+
+    @torch.inference_mode()
+    def get_image_embeds(self, pil_image=None, clip_image_embeds=None):
+        if pil_image is not None:
+            if isinstance(pil_image, Image.Image):
+                pil_image = [pil_image]
+            clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
+            clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
+        else:
+            clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
+        image_prompt_embeds = self.image_proj_model(clip_image_embeds)
+        uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(clip_image_embeds))
+        return image_prompt_embeds, uncond_image_prompt_embeds
+
+    def set_scale(self, scale):
+        for attn_processor in self.pipe.unet.attn_processors.values():
+            if isinstance(attn_processor, IPAttnProcessor):
+                attn_processor.scale = scale
+
+    def generate(
+        self,
+        pil_image=None,
+        clip_image_embeds=None,
+        prompt=None,
+        negative_prompt=None,
+        scale=1.0,
+        num_samples=4,
+        seed=None,
+        guidance_scale=7.5,
+        num_inference_steps=30,
+        **kwargs,
+    ):
+        self.set_scale(scale)
+
+        if pil_image is not None:
+            num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
+        else:
+            num_prompts = clip_image_embeds.size(0)
+
+        if prompt is None:
+            prompt = "best quality, high quality"
+        if negative_prompt is None:
+            negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
+
+        if not isinstance(prompt, List):
+            prompt = [prompt] * num_prompts
+        if not isinstance(negative_prompt, List):
+            negative_prompt = [negative_prompt] * num_prompts
+
+        image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(
+            pil_image=pil_image, clip_image_embeds=clip_image_embeds
+        )
+        bs_embed, seq_len, _ = image_prompt_embeds.shape
+        image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
+        image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
+        uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
+        uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
+
+        with torch.inference_mode():
+            prompt_embeds_, negative_prompt_embeds_ = self.pipe.encode_prompt(
+                prompt,
+                device=self.device,
+                num_images_per_prompt=num_samples,
+                do_classifier_free_guidance=True,
+                negative_prompt=negative_prompt,
+            )
+            prompt_embeds = torch.cat([prompt_embeds_, image_prompt_embeds], dim=1)
+            negative_prompt_embeds = torch.cat([negative_prompt_embeds_, uncond_image_prompt_embeds], dim=1)
+
+        generator = get_generator(seed, self.device)
+
+        images = self.pipe(
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            generator=generator,
+            **kwargs,
+        ).images
+
+        return images
+
+
+class IPAdapterXL(IPAdapter):
+    """SDXL"""
+
+    def generate(
+        self,
+        pil_image=None,
+        clip_image_embeds=None,
+        prompt=None,
+        negative_prompt=None,
+        scale=1.0,
+        num_samples=4,
+        seed=None,
+        num_inference_steps=30,
+        **kwargs,
+    ):
+        self.set_scale(scale)
+
+        if pil_image is not None:
+            num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
+        else:
+            num_prompts = clip_image_embeds.size(0)
+
+        if prompt is None:
+            prompt = "best quality, high quality"
+        if negative_prompt is None:
+            negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
+
+        if not isinstance(prompt, List):
+            prompt = [prompt] * num_prompts
+        if not isinstance(negative_prompt, List):
+            negative_prompt = [negative_prompt] * num_prompts
+
+        image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(pil_image, clip_image_embeds=clip_image_embeds)
+        bs_embed, seq_len, _ = image_prompt_embeds.shape
+        image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
+        image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
+        uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
+        uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
+
+        with torch.inference_mode():
+            (
+                prompt_embeds,
+                negative_prompt_embeds,
+                pooled_prompt_embeds,
+                negative_pooled_prompt_embeds,
+            ) = self.pipe.encode_prompt(
+                prompt,
+                num_images_per_prompt=num_samples,
+                do_classifier_free_guidance=True,
+                negative_prompt=negative_prompt,
+            )
+            prompt_embeds = torch.cat([prompt_embeds, image_prompt_embeds], dim=1)
+            negative_prompt_embeds = torch.cat([negative_prompt_embeds, uncond_image_prompt_embeds], dim=1)
+
+        self.generator = get_generator(seed, self.device)
+        
+        images = self.pipe(
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            num_inference_steps=num_inference_steps,
+            generator=self.generator,
+            **kwargs,
+        ).images
+
+        return images
+
+
+class IPAdapterPlus(IPAdapter):
+    """IP-Adapter with fine-grained features"""
+
+    def init_proj(self):
+        image_proj_model = Resampler(
+            dim=self.pipe.unet.config.cross_attention_dim,
+            depth=4,
+            dim_head=64,
+            heads=12,
+            num_queries=self.num_tokens,
+            embedding_dim=self.image_encoder.config.hidden_size,
+            output_dim=self.pipe.unet.config.cross_attention_dim,
+            ff_mult=4,
+        ).to(self.device, dtype=torch.float16)
+        return image_proj_model
+
+    @torch.inference_mode()
+    def get_image_embeds(self, pil_image=None, clip_image_embeds=None):
+        if isinstance(pil_image, Image.Image):
+            pil_image = [pil_image]
+        clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
+        clip_image = clip_image.to(self.device, dtype=torch.float16)
+        clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
+        image_prompt_embeds = self.image_proj_model(clip_image_embeds)
+        uncond_clip_image_embeds = self.image_encoder(
+            torch.zeros_like(clip_image), output_hidden_states=True
+        ).hidden_states[-2]
+        uncond_image_prompt_embeds = self.image_proj_model(uncond_clip_image_embeds)
+        return image_prompt_embeds, uncond_image_prompt_embeds
+
+
+class IPAdapterFull(IPAdapterPlus):
+    """IP-Adapter with full features"""
+
+    def init_proj(self):
+        image_proj_model = MLPProjModel(
+            cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
+            clip_embeddings_dim=self.image_encoder.config.hidden_size,
+        ).to(self.device, dtype=torch.float16)
+        return image_proj_model
+
+
+class IPAdapterPlusXL(IPAdapter):
+    """SDXL"""
+
+    def init_proj(self):
+        image_proj_model = Resampler(
+            dim=1280,
+            depth=4,
+            dim_head=64,
+            heads=20,
+            num_queries=self.num_tokens,
+            embedding_dim=self.image_encoder.config.hidden_size,
+            output_dim=self.pipe.unet.config.cross_attention_dim,
+            ff_mult=4,
+        ).to(self.device, dtype=torch.float16)
+        return image_proj_model
+
+    @torch.inference_mode()
+    def get_image_embeds(self, pil_image):
+        if isinstance(pil_image, Image.Image):
+            pil_image = [pil_image]
+        clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
+        clip_image = clip_image.to(self.device, dtype=torch.float16)
+        clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
+        image_prompt_embeds = self.image_proj_model(clip_image_embeds)
+        uncond_clip_image_embeds = self.image_encoder(
+            torch.zeros_like(clip_image), output_hidden_states=True
+        ).hidden_states[-2]
+        uncond_image_prompt_embeds = self.image_proj_model(uncond_clip_image_embeds)
+        return image_prompt_embeds, uncond_image_prompt_embeds
+
+    def generate(
+        self,
+        pil_image,
+        prompt=None,
+        negative_prompt=None,
+        scale=1.0,
+        num_samples=4,
+        seed=None,
+        num_inference_steps=30,
+        **kwargs,
+    ):
+        self.set_scale(scale)
+
+        num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
+
+        if prompt is None:
+            prompt = "best quality, high quality"
+        if negative_prompt is None:
+            negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
+
+        if not isinstance(prompt, List):
+            prompt = [prompt] * num_prompts
+        if not isinstance(negative_prompt, List):
+            negative_prompt = [negative_prompt] * num_prompts
+
+        image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(pil_image)
+        bs_embed, seq_len, _ = image_prompt_embeds.shape
+        image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
+        image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
+        uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
+        uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
+
+        with torch.inference_mode():
+            (
+                prompt_embeds,
+                negative_prompt_embeds,
+                pooled_prompt_embeds,
+                negative_pooled_prompt_embeds,
+            ) = self.pipe.encode_prompt(
+                prompt,
+                num_images_per_prompt=num_samples,
+                do_classifier_free_guidance=True,
+                negative_prompt=negative_prompt,
+            )
+            prompt_embeds = torch.cat([prompt_embeds, image_prompt_embeds], dim=1)
+            negative_prompt_embeds = torch.cat([negative_prompt_embeds, uncond_image_prompt_embeds], dim=1)
+
+        generator = get_generator(seed, self.device)
+
+        images = self.pipe(
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            num_inference_steps=num_inference_steps,
+            generator=generator,
+            **kwargs,
+        ).images
+
+        return images

IP_Composer/IP_Adapter/ip_adapter/resampler.py

@@ -0,0 +1,158 @@
+# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
+# and https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py
+
+import math
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+from einops.layers.torch import Rearrange
+
+
+# FFN
+def FeedForward(dim, mult=4):
+    inner_dim = int(dim * mult)
+    return nn.Sequential(
+        nn.LayerNorm(dim),
+        nn.Linear(dim, inner_dim, bias=False),
+        nn.GELU(),
+        nn.Linear(inner_dim, dim, bias=False),
+    )
+
+
+def reshape_tensor(x, heads):
+    bs, length, width = x.shape
+    # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
+    x = x.view(bs, length, heads, -1)
+    # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
+    x = x.transpose(1, 2)
+    # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
+    x = x.reshape(bs, heads, length, -1)
+    return x
+
+
+class PerceiverAttention(nn.Module):
+    def __init__(self, *, dim, dim_head=64, heads=8):
+        super().__init__()
+        self.scale = dim_head**-0.5
+        self.dim_head = dim_head
+        self.heads = heads
+        inner_dim = dim_head * heads
+
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+    def forward(self, x, latents):
+        """
+        Args:
+            x (torch.Tensor): image features
+                shape (b, n1, D)
+            latent (torch.Tensor): latent features
+                shape (b, n2, D)
+        """
+        x = self.norm1(x)
+        latents = self.norm2(latents)
+
+        b, l, _ = latents.shape
+
+        q = self.to_q(latents)
+        kv_input = torch.cat((x, latents), dim=-2)
+        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+
+        q = reshape_tensor(q, self.heads)
+        k = reshape_tensor(k, self.heads)
+        v = reshape_tensor(v, self.heads)
+
+        # attention
+        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
+        weight = (q * scale) @ (k * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        out = weight @ v
+
+        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
+
+        return self.to_out(out)
+
+
+class Resampler(nn.Module):
+    def __init__(
+        self,
+        dim=1024,
+        depth=8,
+        dim_head=64,
+        heads=16,
+        num_queries=8,
+        embedding_dim=768,
+        output_dim=1024,
+        ff_mult=4,
+        max_seq_len: int = 257,  # CLIP tokens + CLS token
+        apply_pos_emb: bool = False,
+        num_latents_mean_pooled: int = 0,  # number of latents derived from mean pooled representation of the sequence
+    ):
+        super().__init__()
+        self.pos_emb = nn.Embedding(max_seq_len, embedding_dim) if apply_pos_emb else None
+
+        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
+
+        self.proj_in = nn.Linear(embedding_dim, dim)
+
+        self.proj_out = nn.Linear(dim, output_dim)
+        self.norm_out = nn.LayerNorm(output_dim)
+
+        self.to_latents_from_mean_pooled_seq = (
+            nn.Sequential(
+                nn.LayerNorm(dim),
+                nn.Linear(dim, dim * num_latents_mean_pooled),
+                Rearrange("b (n d) -> b n d", n=num_latents_mean_pooled),
+            )
+            if num_latents_mean_pooled > 0
+            else None
+        )
+
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                nn.ModuleList(
+                    [
+                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
+                        FeedForward(dim=dim, mult=ff_mult),
+                    ]
+                )
+            )
+
+    def forward(self, x):
+        if self.pos_emb is not None:
+            n, device = x.shape[1], x.device
+            pos_emb = self.pos_emb(torch.arange(n, device=device))
+            x = x + pos_emb
+
+        latents = self.latents.repeat(x.size(0), 1, 1)
+
+        x = self.proj_in(x)
+
+        if self.to_latents_from_mean_pooled_seq:
+            meanpooled_seq = masked_mean(x, dim=1, mask=torch.ones(x.shape[:2], device=x.device, dtype=torch.bool))
+            meanpooled_latents = self.to_latents_from_mean_pooled_seq(meanpooled_seq)
+            latents = torch.cat((meanpooled_latents, latents), dim=-2)
+
+        for attn, ff in self.layers:
+            latents = attn(x, latents) + latents
+            latents = ff(latents) + latents
+
+        latents = self.proj_out(latents)
+        return self.norm_out(latents)
+
+
+def masked_mean(t, *, dim, mask=None):
+    if mask is None:
+        return t.mean(dim=dim)
+
+    denom = mask.sum(dim=dim, keepdim=True)
+    mask = rearrange(mask, "b n -> b n 1")
+    masked_t = t.masked_fill(~mask, 0.0)
+
+    return masked_t.sum(dim=dim) / denom.clamp(min=1e-5)

IP_Composer/IP_Adapter/ip_adapter/utils.py

@@ -0,0 +1,93 @@
+import torch
+import torch.nn.functional as F
+import numpy as np
+from PIL import Image
+
+attn_maps = {}
+def hook_fn(name):
+    def forward_hook(module, input, output):
+        if hasattr(module.processor, "attn_map"):
+            attn_maps[name] = module.processor.attn_map
+            del module.processor.attn_map
+
+    return forward_hook
+
+def register_cross_attention_hook(unet):
+    for name, module in unet.named_modules():
+        if name.split('.')[-1].startswith('attn2'):
+            module.register_forward_hook(hook_fn(name))
+
+    return unet
+
+def upscale(attn_map, target_size):
+    attn_map = torch.mean(attn_map, dim=0)
+    attn_map = attn_map.permute(1,0)
+    temp_size = None
+
+    for i in range(0,5):
+        scale = 2 ** i
+        if ( target_size[0] // scale ) * ( target_size[1] // scale) == attn_map.shape[1]*64:
+            temp_size = (target_size[0]//(scale*8), target_size[1]//(scale*8))
+            break
+
+    assert temp_size is not None, "temp_size cannot is None"
+
+    attn_map = attn_map.view(attn_map.shape[0], *temp_size)
+
+    attn_map = F.interpolate(
+        attn_map.unsqueeze(0).to(dtype=torch.float32),
+        size=target_size,
+        mode='bilinear',
+        align_corners=False
+    )[0]
+
+    attn_map = torch.softmax(attn_map, dim=0)
+    return attn_map
+def get_net_attn_map(image_size, batch_size=2, instance_or_negative=False, detach=True):
+
+    idx = 0 if instance_or_negative else 1
+    net_attn_maps = []
+
+    for name, attn_map in attn_maps.items():
+        attn_map = attn_map.cpu() if detach else attn_map
+        attn_map = torch.chunk(attn_map, batch_size)[idx].squeeze()
+        attn_map = upscale(attn_map, image_size) 
+        net_attn_maps.append(attn_map) 
+
+    net_attn_maps = torch.mean(torch.stack(net_attn_maps,dim=0),dim=0)
+
+    return net_attn_maps
+
+def attnmaps2images(net_attn_maps):
+
+    #total_attn_scores = 0
+    images = []
+
+    for attn_map in net_attn_maps:
+        attn_map = attn_map.cpu().numpy()
+        #total_attn_scores += attn_map.mean().item()
+
+        normalized_attn_map = (attn_map - np.min(attn_map)) / (np.max(attn_map) - np.min(attn_map)) * 255
+        normalized_attn_map = normalized_attn_map.astype(np.uint8)
+        #print("norm: ", normalized_attn_map.shape)
+        image = Image.fromarray(normalized_attn_map)
+
+        #image = fix_save_attn_map(attn_map)
+        images.append(image)
+
+    #print(total_attn_scores)
+    return images
+def is_torch2_available():
+    return hasattr(F, "scaled_dot_product_attention")
+
+def get_generator(seed, device):
+
+    if seed is not None:
+        if isinstance(seed, list):
+            generator = [torch.Generator(device).manual_seed(seed_item) for seed_item in seed]
+        else:
+            generator = torch.Generator(device).manual_seed(seed)
+    else:
+        generator = None
+
+    return generator

IP_Composer/README.md

@@ -0,0 +1,48 @@
+## **1. Composition Generation script**  
+
+### **Running the Script**  
+
+Use the following command:  
+
+```bash
+python generate_compositions.py --config path/to/config.json --create_grids
+```
+
+### Parameters
+- `--config`: Path to the configuration JSON file.
+- `--create_grids`: (Optional) Enable grid creation for visualization of the results.
+
+### Configuration File
+
+The configuration file should be a JSON file containing the following keys:
+
+### Explanation of Config Keys
+
+- `input_dir_base`: Path to the directory containing the base images.
+- `input_dirs_concepts`: List of paths to directories containing concept images.
+- `all_embeds_paths`: List of `.npy` files containing precomputed embeddings for the concepts. The order should match `input_dirs_concepts`.
+- `ranks`: List of integers specifying the rank for each concept’s projection matrix. The order should match `input_dirs_concepts`.
+- `output_base_dir`: Path to store the generated images.
+- `prompt` (optional): Additional text prompt.
+- `scale` (optional): Scale parameter passed to IP Adapter.
+- `seed` (optional): Random seed.
+- `num_samples` (optional): Number of images to generate per combination.
+
+
+## 2. Text Embeddings Script
+
+This repository also includes a script for generating text embeddings using CLIP. The script takes a CSV file containing text descriptions and outputs a `.npy` file with the corresponding embeddings.
+
+### Running the Script
+
+Use the following command:
+
+```bash
+python generate_text_embeddings.py --input_csv path/to/descriptions.csv --output_file path/to/output.npy --batch_size 100 --device cuda:0
+```
+
+### Parameters
+- `--input_csv`: Path to the input CSV file containing text descriptions.
+- `--output_file`: Path to save the output `.npy` file.
+- `--batch_size`: (Optional) Batch size for processing embeddings (default: 100).
+- `--device`: (Optional) Device to run the model on.

IP_Composer/create_grids.py

@@ -0,0 +1,206 @@
+import argparse
+import os
+import json
+import itertools
+from PIL import Image, ImageDraw, ImageFont
+
+def wrap_text(text, max_width, draw, font):
+    """
+    Wrap the text to fit within the given width by breaking it into lines.
+    """
+    lines = []
+    words = text.split(' ')
+    current_line = []
+
+    for word in words:
+        current_line.append(word)
+        line_width = draw.textbbox((0, 0), ' '.join(current_line), font=font)[2]
+        if line_width > max_width:
+            current_line.pop()
+            lines.append(' '.join(current_line))
+            current_line = [word]
+
+    if current_line:
+        lines.append(' '.join(current_line))
+
+    return lines
+
+def image_grid_with_titles(imgs, rows, cols, top_titles, left_titles, margin=20):
+    assert len(imgs) == rows * cols
+    assert len(top_titles) == cols
+    assert len(left_titles) == rows
+
+    imgs = [img.resize((256, 256)) for img in imgs]
+    w, h = imgs[0].size
+
+    title_height = 50
+    title_width = 120
+
+    grid_width = cols * (w + margin) + title_width + margin
+    grid_height = rows * (h + margin) + title_height + margin
+
+    grid = Image.new('RGB', size=(grid_width, grid_height), color='white')
+    draw = ImageDraw.Draw(grid)
+
+    try:
+        font = ImageFont.truetype("arial.ttf", 20)
+    except IOError:
+        font = ImageFont.load_default()
+
+    for i, title in enumerate(top_titles):
+        wrapped_title = wrap_text(title, w, draw, font)
+        total_text_height = sum([draw.textbbox((0, 0), line, font=font)[3] for line in wrapped_title])
+        y_offset = (title_height - total_text_height) // 2
+
+        for line in wrapped_title:
+            text_width = draw.textbbox((0, 0), line, font=font)[2]
+            x_offset = ((i * (w + margin)) + title_width + margin + (w - text_width) // 2)
+            draw.text((x_offset, y_offset), line, fill="black", font=font)
+            y_offset += draw.textbbox((0, 0), line, font=font)[3]
+
+    for i, title in enumerate(left_titles):
+        wrapped_title = wrap_text(title, title_width - 10, draw, font)
+        total_text_height = sum([draw.textbbox((0, 0), line, font=font)[3] for line in wrapped_title])
+        y_offset = (i * (h + margin)) + title_height + (h - total_text_height) // 2 + margin
+
+        for line in wrapped_title:
+            text_width = draw.textbbox((0, 0), line, font=font)[2]
+            x_offset = (title_width - text_width) // 2
+            draw.text((x_offset, y_offset), line, fill="black", font=font)
+            y_offset += draw.textbbox((0, 0), line, font=font)[3]
+
+    for i, img in enumerate(imgs):
+        x_pos = (i % cols) * (w + margin) + title_width + margin
+        y_pos = (i // cols) * (h + margin) + title_height + margin
+        grid.paste(img, box=(x_pos, y_pos))
+
+    return grid
+
+def create_grids(config):
+    num_samples = config["num_samples"]
+    concept_dirs = config["input_dirs_concepts"]
+    output_base_dir = config["output_base_dir"]
+    output_grid_dir = os.path.join(output_base_dir, "grids")
+
+    os.makedirs(output_grid_dir, exist_ok=True)
+
+    base_images = os.listdir(config["input_dir_base"])
+
+    if len(concept_dirs) == 1:
+        # Special case: Single concept
+        last_concept_dir = concept_dirs[0]
+        last_concept_images = os.listdir(last_concept_dir)
+
+        top_titles = ["Base Image", "Concept 1"] + ["Samples"] + [""] * (num_samples - 1)
+        left_titles = ["" for i in range(len(last_concept_images))]
+
+        def load_image(path):
+            return Image.open(path) if os.path.exists(path) else Image.new("RGB", (256, 256), color="white")
+
+        for base_image in base_images:
+            base_image_path = os.path.join(config["input_dir_base"], base_image)
+            images = []
+
+            for last_image in last_concept_images:
+                last_image_path = os.path.join(last_concept_dir, last_image)
+                row_images = [load_image(base_image_path), load_image(last_image_path)]
+
+                # Add generated samples for the current row
+                sample_dir = os.path.join(output_base_dir, f"{base_image}_to_{last_image}")
+                if os.path.exists(sample_dir):
+                    sample_images = sorted(os.listdir(sample_dir))
+                    row_images.extend([load_image(os.path.join(sample_dir, sample_image)) for sample_image in sample_images])
+
+                images.extend(row_images)
+
+            # Fill empty spaces to match the grid dimensions
+            total_required = len(left_titles) * len(top_titles)
+            if len(images) < total_required:
+                images.extend([Image.new("RGB", (256, 256), color="white")] * (total_required - len(images)))
+
+            # Create the grid
+            grid = image_grid_with_titles(
+                imgs=images,
+                rows=len(left_titles),
+                cols=len(top_titles),
+                top_titles=top_titles,
+                left_titles=left_titles
+            )
+
+            # Save the grid
+            grid_save_path = os.path.join(output_grid_dir, f"grid_base_{base_image}_concept1.png")
+            grid.save(grid_save_path)
+            print(f"Grid saved at {grid_save_path}")
+
+    else:
+        # General case: Multiple concepts
+        fixed_concepts = concept_dirs[:-1]
+        last_concept_dir = concept_dirs[-1]
+        last_concept_images = os.listdir(last_concept_dir)
+
+        top_titles = ["Base Image"] + [f"Concept {i+1}" for i in range(len(fixed_concepts))] + ["Last Concept"] + ["Samples"] + [""] * (num_samples - 1)
+        left_titles = ["" for i in range(len(last_concept_images))]
+
+        def load_image(path):
+            return Image.open(path) if os.path.exists(path) else Image.new("RGB", (256, 256), color="white")
+
+        fixed_concept_images = [os.listdir(concept_dir) for concept_dir in fixed_concepts]
+
+        for base_image in base_images:
+            base_image_path = os.path.join(config["input_dir_base"], base_image)
+            fixed_combinations = itertools.product(*fixed_concept_images)
+
+            for fixed_combination in fixed_combinations:
+                images = []
+
+                # Build fixed combination row
+                fixed_images = [load_image(base_image_path)]
+                for concept_dir, concept_image in zip(fixed_concepts, fixed_combination):
+                    concept_image_path = os.path.join(concept_dir, concept_image)
+                    fixed_images.append(load_image(concept_image_path))
+
+                # Iterate over last concept for rows
+                for last_image in last_concept_images:
+                    last_image_path = os.path.join(last_concept_dir, last_image)
+                    row_images = fixed_images + [load_image(last_image_path)]
+
+                    # Add generated samples for the current row
+                    sample_dir = os.path.join(output_base_dir, f"{base_image}_to_" + "_".join([f"{concept_image}" for concept_image in fixed_combination]) + f"_{last_image}")
+                    if os.path.exists(sample_dir):
+                        sample_images = sorted(os.listdir(sample_dir))
+                        row_images.extend([load_image(os.path.join(sample_dir, sample_image)) for sample_image in sample_images])
+
+                    images.extend(row_images)
+
+                # Fill empty spaces to match the grid dimensions
+                total_required = len(left_titles) * len(top_titles)
+                if len(images) < total_required:
+                    images.extend([Image.new("RGB", (256, 256), color="white")] * (total_required - len(images)))
+
+                # Create the grid
+                grid = image_grid_with_titles(
+                    imgs=images,
+                    rows=len(left_titles),
+                    cols=len(top_titles),
+                    top_titles=top_titles,
+                    left_titles=left_titles
+                )
+
+                # Save the grid
+                grid_save_path = os.path.join(output_grid_dir, f"grid_base_{base_image}_combo_{'_'.join(map(str, fixed_combination))}.png")
+                grid.save(grid_save_path)
+                print(f"Grid saved at {grid_save_path}")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Create image grids based on a configuration file.")
+    parser.add_argument("config_path", type=str, help="Path to the configuration JSON file.")
+    args = parser.parse_args()
+
+    # Load the configuration
+    with open(args.config_path, 'r') as f:
+        config = json.load(f)
+
+    if "num_samples" not in config:
+        config["num_samples"] = 4
+
+    create_grids(config)

IP_Composer/example_config.json

@@ -0,0 +1,18 @@
+{
+    "input_dir_base": "<input_base_directory>",
+    "input_dirs_concepts": [
+        "<concept_1_images_directory>",
+        "<concept_2_images_directory>",
+        "<concept_3_images_directory>"
+    ],
+    "all_embeds_paths": [
+        "<concept_1_text_embeddings_path>",
+        "<concept_2_text_embeddings_path>",
+        "<concept_3_text_embeddings_path>"
+    ],
+    "ranks": [30, 30, 30],
+    "output_base_dir": "<results_output_base_directory>",
+    "seed": 420,
+    "prompt": null,
+    "scale": 1.0
+}

IP_Composer/example_configs/age_emotions.json

@@ -0,0 +1,16 @@
+{
+    "input_dir_base": "assets/people",
+    "input_dirs_concepts": [
+        "assets/age",
+        "assets/emotions"
+    ],
+    "all_embeds_paths": [
+        "text_embeddings/age_descriptions.npy",
+        "text_embeddings/emotion_descriptions.npy"
+    ],
+    "ranks": [30, 30],
+    "output_base_dir": "assets/age_emotions_results",
+    "seed": 420,
+    "prompt": null,
+    "scale": 1.0
+}

IP_Composer/example_configs/patterns.json

@@ -0,0 +1,14 @@
+{
+    "input_dir_base": "assets/objects",
+    "input_dirs_concepts": [
+        "assets/patterns"
+    ],
+    "all_embeds_paths": [
+        "text_embeddings/pattern_descriptions.npy"
+    ],
+    "ranks": [100],
+    "output_base_dir": "assets/patterns_results",
+    "seed": 420,
+    "prompt": null,
+    "scale": 1.0
+}

IP_Composer/generate_compositions.py

@@ -0,0 +1,160 @@
+import os
+import json
+import torch
+import gc
+import numpy as np
+from PIL import Image
+from diffusers import StableDiffusionXLPipeline
+import open_clip
+from huggingface_hub import hf_hub_download
+from IP_Adapter.ip_adapter import IPAdapterXL
+from perform_swap import compute_dataset_embeds_svd, get_modified_images_embeds_composition
+from create_grids import create_grids
+import argparse
+
+def save_images(output_dir, image_list):
+    os.makedirs(output_dir, exist_ok=True)
+    for i, img in enumerate(image_list):
+        img.save(os.path.join(output_dir, f"sample_{i + 1}.png"))
+
+def get_image_embeds(pil_image, model, preprocess, device):
+    image = preprocess(pil_image)[np.newaxis, :, :, :]
+    with torch.no_grad():
+        embeds = model.encode_image(image.to(device))
+    return embeds.cpu().detach().numpy()
+
+def process_combo(
+    image_embeds_base,
+    image_names_base,
+    concept_embeds,
+    concept_names,
+    projection_matrices,
+    ip_model,
+    output_base_dir,
+    num_samples=4,
+    seed=420,
+    prompt=None,
+    scale=1.0
+):
+    for base_embed, base_name in zip(image_embeds_base, image_names_base):
+        # Generate all combinations of concept embeddings
+        for combo_indices in np.ndindex(*(len(embeds) for embeds in concept_embeds)):
+            concept_combo_names = [concept_names[c][idx] for c, idx in enumerate(combo_indices)]
+            combo_dir = os.path.join(
+                output_base_dir,
+                f"{base_name}_to_" + "_".join(concept_combo_names)
+            )
+            if os.path.exists(combo_dir):
+                print(f"Directory {combo_dir} already exists. Skipping...")
+                continue
+
+            projections_data = [
+                {
+                    "embed": concept_embeds[c][idx],
+                    "projection_matrix": projection_matrices[c]
+                }
+                for c, idx in enumerate(combo_indices)
+            ]
+
+            modified_images = get_modified_images_embeds_composition(
+                base_embed, projections_data, ip_model, prompt=prompt, scale=scale, num_samples=num_samples, seed=seed
+            )
+            save_images(combo_dir, modified_images)
+            del modified_images
+            torch.cuda.empty_cache()
+            gc.collect()
+
+def main(config_path, should_create_grids):
+    with open(config_path, 'r') as f:
+        config = json.load(f)
+
+    if "prompt" not in config:
+        config["prompt"] = None
+    
+    if "scale" not in config:
+        config["scale"] = 1.0 if config["prompt"] is None else 0.6
+
+    if "seed" not in config:
+        config["seed"] = 420
+
+    if "num_samples" not in config:
+        config["num_samples"] = 4
+
+
+    base_model_path = "stabilityai/stable-diffusion-xl-base-1.0"
+
+    pipe = StableDiffusionXLPipeline.from_pretrained(
+        base_model_path,
+        torch_dtype=torch.float16,
+        add_watermarker=False,
+    )
+
+    image_encoder_repo = 'h94/IP-Adapter'
+    image_encoder_subfolder = 'models/image_encoder'
+
+    ip_ckpt = hf_hub_download('h94/IP-Adapter', subfolder="sdxl_models", filename='ip-adapter_sdxl_vit-h.bin')
+    device = "cuda"
+
+    ip_model = IPAdapterXL(pipe, image_encoder_repo, image_encoder_subfolder, ip_ckpt, device)
+
+    device = 'cuda:0'
+    model, _, preprocess = open_clip.create_model_and_transforms('hf-hub:laion/CLIP-ViT-H-14-laion2B-s32B-b79K')
+    model.to(device)
+
+    # Get base image embeddings
+    image_files_base = [os.path.join(config["input_dir_base"], f) for f in os.listdir(config["input_dir_base"]) if f.lower().endswith(('png', 'jpg', 'jpeg'))]
+    image_embeds_base = []
+    image_names_base = []
+    for path in image_files_base:
+        img_name = os.path.basename(path)
+        image_names_base.append(img_name)
+        image_embeds_base.append(get_image_embeds(Image.open(path).convert("RGB"), model, preprocess, device))
+
+    # Handle n concepts
+    concept_dirs = config["input_dirs_concepts"]
+    concept_embeds = []
+    concept_names = []
+    projection_matrices = []
+
+    for concept_dir, embeds_path, rank in zip(concept_dirs, config["all_embeds_paths"], config["ranks"]):
+        image_files = [os.path.join(concept_dir, f) for f in os.listdir(concept_dir) if f.lower().endswith(('png', 'jpg', 'jpeg'))]
+        embeds = []
+        names = []
+        for path in image_files:
+            img_name = os.path.basename(path)
+            names.append(img_name)
+            embeds.append(get_image_embeds(Image.open(path).convert("RGB"), model, preprocess, device))
+        concept_embeds.append(embeds)
+        concept_names.append(names)
+
+        with open(embeds_path, "rb") as f:
+            all_embeds_in = np.load(f)
+        projection_matrix = compute_dataset_embeds_svd(all_embeds_in, rank)
+        projection_matrices.append(projection_matrix)
+
+
+    # Process combinations
+    process_combo(
+        image_embeds_base,
+        image_names_base,
+        concept_embeds,
+        concept_names,
+        projection_matrices,
+        ip_model,
+        config["output_base_dir"],
+        config["num_samples"],
+        config["seed"],
+        config["prompt"],
+        config["scale"]
+    )
+
+    # generate grids
+    if should_create_grids:
+        create_grids(config)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Process images using embeddings and configurations.")
+    parser.add_argument("--config", type=str, required=True, help="Path to the configuration JSON file.")
+    parser.add_argument("--create_grids", action="store_true", help="Enable grid creation")
+    args = parser.parse_args()
+    main(args.config, args.create_grids)

IP_Composer/generate_text_embeddings.py

@@ -0,0 +1,60 @@
+import os
+import sys
+import torch
+import numpy as np
+import csv
+import argparse
+import open_clip
+
+def load_descriptions(file_path):
+    """Load descriptions from a CSV file."""
+    descriptions = []
+    with open(file_path, 'r') as file:
+        csv_reader = csv.reader(file)
+        next(csv_reader)  # Skip the header
+        for row in csv_reader:
+            descriptions.append(row[0])
+    return descriptions
+
+def generate_embeddings(descriptions, model, tokenizer, device, batch_size):
+    """Generate text embeddings in batches."""
+    final_embeddings = []
+    for i in range(0, len(descriptions), batch_size):
+        batch_desc = descriptions[i:i + batch_size]
+        texts = tokenizer(batch_desc).to(device)
+        batch_embeddings = model.encode_text(texts)
+        batch_embeddings = batch_embeddings.detach().cpu().numpy()
+        final_embeddings.append(batch_embeddings)
+        del texts, batch_embeddings
+        torch.cuda.empty_cache()
+    return np.vstack(final_embeddings)
+
+def save_embeddings(output_file, embeddings):
+    """Save embeddings to a .npy file."""
+    np.save(output_file, embeddings)
+
+def main():
+    parser = argparse.ArgumentParser(description="Generate text embeddings using CLIP.")
+    parser.add_argument("--input_csv", type=str, required=True, help="Path to the input CSV file containing text descriptions.")
+    parser.add_argument("--output_file", type=str, required=True, help="Path to save the output .npy file.")
+    parser.add_argument("--batch_size", type=int, default=100, help="Batch size for processing embeddings.")
+    parser.add_argument("--device", type=str, default="cuda:0", help="Device to run the model on (e.g., 'cuda:0' or 'cpu').")
+    
+    args = parser.parse_args()
+
+    # Load the CLIP model and tokenizer
+    model, _, _ = open_clip.create_model_and_transforms('hf-hub:laion/CLIP-ViT-H-14-laion2B-s32B-b79K')
+    model.to(args.device)
+    tokenizer = open_clip.get_tokenizer('hf-hub:laion/CLIP-ViT-H-14-laion2B-s32B-b79K')
+
+    # Load descriptions from CSV
+    descriptions = load_descriptions(args.input_csv)
+
+    # Generate embeddings
+    embeddings = generate_embeddings(descriptions, model, tokenizer, args.device, args.batch_size)
+
+    # Save embeddings to output file
+    save_embeddings(args.output_file, embeddings)
+
+if __name__ == "__main__":
+    main()

IP_Composer/perform_swap.py

@@ -0,0 +1,38 @@
+import torch
+import numpy as np
+
+def compute_dataset_embeds_svd(all_embeds, rank):
+
+    # Perform SVD on the combined matrix
+    u, s, vh = np.linalg.svd(all_embeds, full_matrices=False)
+
+    # Select the top `rank` singular vectors to construct the projection matrix
+    vh = vh[:rank]  # Top `rank` right singular vectors
+    projection_matrix = vh.T @ vh  # Shape: (feature_dim, feature_dim)
+
+    return projection_matrix
+
+def get_embedding_composition(embed, projections_data):
+    # Initialize the combined embedding with the input embed
+    combined_embeds = embed.copy()
+
+    for proj_data in projections_data:
+
+        # Add the combined projection to the result
+        combined_embeds -= embed @ proj_data["projection_matrix"]
+        combined_embeds += proj_data["embed"] @ proj_data["projection_matrix"]
+
+    return combined_embeds
+
+
+def get_modified_images_embeds_composition(embed, projections_data, ip_model, prompt=None, scale=1.0, num_samples=3, seed=420):
+    
+    final_embeds = get_embedding_composition(embed, projections_data)
+    clip_embeds = torch.from_numpy(final_embeds)
+
+    images = ip_model.generate(clip_image_embeds=clip_embeds, prompt=prompt, num_samples=num_samples, num_inference_steps=50, seed=seed, guidance_scale=7.5, scale=scale)
+    return images
+
+
+
+

IP_Composer/text_datasets/age_descriptions.csv

@@ -0,0 +1,100 @@
+Description
+A picture of a newborn
+A picture of an infant
+A picture of a toddler
+A picture of a young child
+A picture of a preschooler
+A picture of a school-age child
+A picture of an elementary schooler
+A picture of a preteen
+A picture of a middle schooler
+A picture of a teenager
+A picture of a high schooler
+A picture of a young adult
+A picture of a college student
+A picture of a recent graduate
+A picture of an early-career professional
+A picture of a mid-20s individual
+A picture of a late-20s individual
+A picture of a 30-something adult
+A picture of an early-30s person
+A picture of a mid-30s person
+A picture of a late-30s person
+A picture of a 40-something adult
+A picture of an early-40s individual
+A picture of a mid-40s person
+A picture of a late-40s individual
+A picture of a 50-something adult
+A picture of an early-50s person
+A picture of a mid-50s individual
+A picture of a late-50s person
+A picture of a 60-something adult
+A picture of an early-60s individual
+A picture of a mid-60s person
+A picture of a late-60s person
+A picture of a retiree
+A picture of a 70-something adult
+A picture of an early-70s individual
+A picture of a mid-70s person
+A picture of a late-70s individual
+A picture of an 80-something adult
+A picture of an early-80s individual
+A picture of a mid-80s person
+A picture of a late-80s individual
+A picture of a 90-something adult
+A picture of an early-90s individual
+A picture of a mid-90s person
+A picture of a late-90s individual
+A picture of a centenarian
+A picture of a very elderly person
+A picture of an old person
+A picture of a baby
+A picture of a kindergartener
+A picture of a primary schooler
+A picture of a young teenager
+A picture of a high school sophomore
+A picture of a high school junior
+A picture of a high school senior
+A picture of a college freshman
+A picture of a college sophomore
+A picture of a college junior
+A picture of a college senior
+A picture of a graduate student
+A picture of a doctoral candidate
+A picture of a young professional
+A picture of a recent homeowner
+A picture of a newlywed
+A picture of a first-time parent
+A picture of a person in their early 40s
+A picture of a person in their late 40s
+A picture of a seasoned professional
+A picture of a mid-career worker
+A picture of a midlife individual
+A picture of a person nearing retirement
+A picture of a retired individual
+A picture of a grandparent
+A picture of a person in their 50s
+A picture of a person in their 60s
+A picture of a senior citizen
+A picture of a senior retiree
+A picture of a person enjoying their golden years
+A picture of an elderly neighbor
+A picture of a great-grandparent
+A picture of a person in their mid-70s
+A picture of a person in their early 80s
+A picture of a person in their late 80s
+A picture of a nonagenarian
+A picture of a lively 90-year-old
+A picture of a spry centenarian
+A picture of a very old individual
+A picture of a youthful senior
+A picture of a seasoned elder
+A picture of a person in their mid-30s
+A picture of a person approaching 40
+A picture of a person in their late 60s
+A picture of a person in their late 70s
+A picture of a vibrant elder
+A picture of a wise senior
+A picture of a long-lived individual
+A picture of a respected elder
+A picture of a person well past 100

IP_Composer/text_datasets/animal_fur_descriptions.csv

@@ -0,0 +1,284 @@
+Description
+a picture of an animal with striped fur
+a picture of an animal with spotted fur
+a picture of an animal with solid black fur
+a picture of an animal with solid white fur
+a picture of an animal with brown fur with lighter patches
+a picture of an animal with patchy fur with multiple colors
+a picture of an animal with striped fur in contrasting colors
+a picture of an animal with spotted fur with large dots
+a picture of an animal with thick fur with a gray gradient
+a picture of an animal with golden fur with dark spots
+a picture of an animal with white fur with subtle streaks
+a picture of an animal with brown fur with dark stripes
+a picture of an animal with mottled fur with blended tones
+a picture of an animal with shiny black fur with smooth texture
+a picture of an animal with fluffy white fur with a soft appearance
+a picture of an animal with short fur with a brindle pattern
+a picture of an animal with long fur with a wavy texture
+a picture of an animal with curly fur with tight loops
+a picture of an animal with patchy fur with dark and light areas
+a picture of an animal with speckled fur with small spots
+a picture of an animal with golden fur with subtle gradients
+a picture of an animal with thick fur with warm brown tones
+a picture of an animal with black and white fur in bold patterns
+a picture of an animal with gray fur with silver accents
+a picture of an animal with shaggy fur with uneven patches
+a picture of an animal with cream-colored fur with faint markings
+a picture of an animal with dark brown fur with soft streaks
+a picture of an animal with yellowish fur with muted tones
+a picture of an animal with gray fur with fine speckles
+a picture of an animal with dense fur with a marbled effect
+a picture of an animal with soft fur with sandy hues
+a picture of an animal with orange fur with white patches
+a picture of an animal with fluffy fur with blended tones
+a picture of an animal with smooth fur with natural highlights
+a picture of an animal with tufted fur with layered textures
+a picture of an animal with thick fur with subtle color variations
+a picture of an animal with silky fur with a glossy finish
+a picture of an animal with short fur with a salt-and-pepper look
+a picture of an animal with coarse fur with distinct bands
+a picture of an animal with soft fur with faint gray patterns
+a picture of an animal with striped fur with alternating shades
+a picture of an animal with spotted fur with intricate details
+a picture of an animal with golden fur with textured layers
+a picture of an animal with black fur with subtle brown undertones
+a picture of an animal with brown fur with warm highlights
+a picture of an animal with fluffy fur with snowy white shades
+a picture of an animal with gray fur with smooth gradients
+a picture of an animal with shaggy fur with rough textures
+a picture of an animal with tufted fur with blended colors
+a picture of an animal with spotted fur with irregular shapes
+a picture of an animal with striped fur with bold contrasts
+a picture of an animal with white fur with pale tones
+a picture of an animal with soft gray fur with faint streaks
+a picture of an animal with striped fur with organic patterns
+a picture of an animal with smooth fur with earthy hues
+a picture of an animal with patchy fur with soft transitions
+a picture of an animal with dense fur with contrasting patches
+a picture of an animal with mottled fur with natural colors
+a picture of an animal with black fur with glossy highlights
+a picture of an animal with short fur with blended stripes
+a picture of an animal with shiny fur with reflective tones
+a picture of an animal with wavy fur with soft layers
+a picture of an animal with tufted fur with warm gradients
+a picture of an animal with soft fur with golden accents
+a picture of an animal with coarse fur with dark and light contrasts
+a picture of an animal with gray fur with natural highlights
+a picture of an animal with fluffy fur with a soft yellow hue
+a picture of an animal with smooth fur with subtle textures
+a picture of an animal with dense black fur with even tones
+a picture of an animal with spotted fur with balanced patterns
+a picture of an animal with striped fur with symmetrical designs
+a picture of an animal with curly fur with soft spirals
+a picture of an animal with shaggy brown fur with varied streaks
+a picture of an animal with fluffy fur with cool silver tones
+a picture of an animal with smooth fur with warm orange hues
+"a picture of an animal with spotted fur with small, even dots"
+a picture of an animal with striped fur with clean lines
+a picture of an animal with tufted fur with balanced layers
+a picture of an animal with dense gray fur with subtle patterns
+a picture of an animal with shiny black fur with muted undertones
+a picture of an animal with coarse fur with mixed colors
+a picture of an animal with fluffy white fur with soft highlights
+a picture of an animal with curly brown fur with gentle waves
+a picture of an animal with silky fur with refined layers
+a picture of an animal with soft fur with delicate streaks
+a picture of an animal with dense fur with earthy gradients
+a picture of an animal with golden fur with soft textures
+a picture of an animal with shiny black fur with natural tones
+a picture of an animal with tufted fur with warm shades
+a picture of an animal with wavy fur with blended gradients
+a picture of an animal with spotted fur with organic shapes
+a picture of an animal with striped fur with varied tones
+a picture of an animal with shaggy fur with rugged patterns
+a picture of an animal with soft gray fur with pale highlights
+a picture of an animal with fluffy fur with muted contrasts
+a picture of an animal with dense fur with layered colors
+a picture of an animal with patchy fur with random patterns
+a picture of an animal with striped fur with subtle shading
+a picture of an animal with spotted fur with varying sizes
+a picture of an animal with soft fur with muted gradients
+a picture of an animal with thick fur with blended tones
+a picture of an animal with curly fur with natural spirals
+a picture of an animal with shaggy fur with uneven textures
+a picture of an animal with fluffy fur with a glossy finish
+a picture of an animal with smooth fur with contrasting bands
+a picture of an animal with coarse fur with earthy hues
+a picture of an animal with tufted fur with soft tips
+a picture of an animal with dense fur with a gradient effect
+a picture of an animal with short fur with crisp patterns
+a picture of an animal with wavy fur with delicate layers
+a picture of an animal with mottled fur with abstract designs
+a picture of an animal with soft fur with warm highlights
+a picture of an animal with striped fur with organic shapes
+a picture of an animal with spotted fur with irregular dots
+a picture of an animal with fluffy fur with soft undertones
+a picture of an animal with shiny fur with natural variations
+a picture of an animal with silky fur with smooth textures
+a picture of an animal with coarse fur with bold contrasts
+a picture of an animal with striped fur with sharp lines
+a picture of an animal with spotted fur with faded edges
+a picture of an animal with dense fur with intricate details
+a picture of an animal with short fur with sleek patterns
+a picture of an animal with fluffy fur with faint streaks
+a picture of an animal with smooth fur with vibrant tones
+a picture of an animal with curly fur with subtle twists
+a picture of an animal with soft fur with delicate transitions
+a picture of an animal with tufted fur with rough layers
+a picture of an animal with striped fur with layered tones
+a picture of an animal with spotted fur with balanced contrasts
+a picture of an animal with dense fur with fine textures
+a picture of an animal with shiny fur with highlighted streaks
+a picture of an animal with coarse fur with mixed tones
+a picture of an animal with fluffy fur with soft gradients
+a picture of an animal with smooth fur with pale colors
+a picture of an animal with mottled fur with warm hues
+a picture of an animal with spotted fur with clean shapes
+a picture of an animal with dense fur with vibrant highlights
+a picture of an animal with tufted fur with blended layers
+a picture of an animal with shaggy fur with uneven bands
+a picture of an animal with soft fur with rich textures
+a picture of an animal with striped fur with flowing patterns
+a picture of an animal with spotted fur with soft gradients
+a picture of an animal with short fur with sleek finishes
+a picture of an animal with coarse fur with detailed shading
+a picture of an animal with dense fur with natural hues
+a picture of an animal with shiny fur with faint glimmers
+a picture of an animal with fluffy fur with even textures
+a picture of an animal with smooth fur with layered gradients
+a picture of an animal with curly fur with soft highlights
+a picture of an animal with spotted fur with gentle contrasts
+a picture of an animal with striped fur with harmonious shades
+a picture of an animal with shaggy fur with bold patterns
+a picture of an animal with tufted fur with subtle differences
+a picture of an animal with fluffy fur with varied lengths
+a picture of an animal with mottled fur with abstract colors
+a picture of an animal with dense fur with light transitions
+a picture of an animal with soft fur with smooth finishes
+a picture of an animal with striped fur with natural flows
+a picture of an animal with spotted fur with detailed edges
+a picture of an animal with coarse fur with soft contrasts
+a picture of an animal with fluffy fur with blended shades
+a picture of an animal with smooth fur with reflective tones
+a picture of an animal with curly fur with rounded shapes
+a picture of an animal with striped fur with sharp contrasts
+a picture of an animal with shiny fur with glowing highlights
+a picture of an animal with tufted fur with rich textures
+a picture of an animal with dense fur with earthy contrasts
+a picture of an animal with soft fur with blended gradients
+a picture of an animal with spotted fur with flowing designs
+a picture of an animal with striped fur with faint streaks
+a picture of an animal with fluffy fur with warm undertones
+a picture of an animal with smooth fur with natural gloss
+a picture of an animal with coarse fur with rough patterns
+a picture of an animal with curly fur with mixed loops
+a picture of an animal with striped fur with wavy designs
+a picture of an animal with dense fur with intricate shading
+a picture of an animal with spotted fur with tiny marks
+a picture of an animal with soft fur with delicate details
+a picture of an animal with shiny fur with varied tones
+a picture of an animal with tufted fur with unique patterns
+a picture of an animal with fluffy fur with golden hints
+a picture of an animal with striped fur with subtle highlights
+a picture of an animal with coarse fur with angular designs
+a picture of an animal with smooth fur with gentle transitions
+a picture of an animal with curly fur with unique curls
+a picture of an animal with striped fur with bold strokes
+a picture of an animal with spotted fur with organic edges
+a picture of an animal with shaggy fur with layered hues
+a picture of an animal with soft fur with radiant tones
+a picture of an animal with soft fur with light streaks
+a picture of an animal with striped fur with gentle curves
+a picture of an animal with spotted fur with random patterns
+a picture of an animal with smooth fur with layered highlights
+a picture of an animal with dense fur with contrasting textures
+a picture of an animal with fluffy fur with muted colors
+a picture of an animal with shaggy fur with blended tones
+a picture of an animal with curly fur with fine spirals
+a picture of an animal with striped fur with faint gradients
+a picture of an animal with coarse fur with rich details
+a picture of an animal with short fur with smooth contrasts
+a picture of an animal with wavy fur with flowing lines
+a picture of an animal with spotted fur with blurred edges
+a picture of an animal with tufted fur with soft gradients
+a picture of an animal with fluffy fur with warm colors
+a picture of an animal with striped fur with deep contrasts
+a picture of an animal with spotted fur with tiny details
+a picture of an animal with dense fur with delicate streaks
+a picture of an animal with shiny fur with pale reflections
+a picture of an animal with coarse fur with jagged patterns
+a picture of an animal with soft fur with natural transitions
+a picture of an animal with mottled fur with uneven shades
+a picture of an animal with striped fur with vibrant hues
+a picture of an animal with spotted fur with soft edges
+a picture of an animal with curly fur with intricate shapes
+a picture of an animal with dense fur with light accents
+a picture of an animal with shaggy fur with bold strokes
+a picture of an animal with striped fur with layered gradients
+a picture of an animal with spotted fur with irregular sizes
+a picture of an animal with smooth fur with crisp textures
+a picture of an animal with wavy fur with balanced tones
+a picture of an animal with tufted fur with faint streaks
+a picture of an animal with shiny fur with glowing accents
+a picture of an animal with fluffy fur with delicate shades
+a picture of an animal with coarse fur with angular contrasts
+a picture of an animal with dense fur with soft flows
+a picture of an animal with striped fur with rhythmic designs
+a picture of an animal with spotted fur with blurred transitions
+a picture of an animal with soft fur with subtle textures
+a picture of an animal with short fur with clean gradients
+a picture of an animal with mottled fur with natural shapes
+a picture of an animal with shaggy fur with vibrant highlights
+a picture of an animal with curly fur with rounded spirals
+a picture of an animal with dense fur with earthy shades
+a picture of an animal with striped fur with soft lines
+a picture of an animal with spotted fur with faded tones
+a picture of an animal with fluffy fur with gentle transitions
+a picture of an animal with shiny fur with smooth gradients
+a picture of an animal with coarse fur with rough streaks
+a picture of an animal with tufted fur with unique layers
+a picture of an animal with spotted fur with intricate textures
+a picture of an animal with dense fur with subtle contrasts
+a picture of an animal with smooth fur with muted tones
+a picture of an animal with curly fur with overlapping loops
+a picture of an animal with striped fur with soft gradients
+a picture of an animal with shaggy fur with warm hues
+a picture of an animal with fluffy fur with rich highlights
+a picture of an animal with soft fur with faint transitions
+a picture of an animal with mottled fur with irregular colors
+a picture of an animal with striped fur with alternating bands
+a picture of an animal with spotted fur with light edges
+a picture of an animal with coarse fur with bold designs
+a picture of an animal with dense fur with natural tones
+a picture of an animal with wavy fur with faint contrasts
+a picture of an animal with short fur with glowing highlights
+a picture of an animal with striped fur with unique patterns
+a picture of an animal with shiny fur with smooth textures
+a picture of an animal with fluffy fur with subtle hues
+a picture of an animal with spotted fur with layered tones
+a picture of an animal with curly fur with fine details
+a picture of an animal with dense fur with blended gradients
+a picture of an animal with striped fur with bold tones
+a picture of an animal with soft fur with muted streaks
+a picture of an animal with mottled fur with soft edges
+a picture of an animal with tufted fur with natural flows
+a picture of an animal with striped fur with crisp lines
+a picture of an animal with spotted fur with random contrasts
+a picture of an animal with shiny fur with warm highlights
+a picture of an animal with wavy fur with deep gradients
+a picture of an animal with fluffy fur with intricate textures
+a picture of an animal with striped fur with gradual changes
+a picture of an animal with curly fur with delicate spirals
+a picture of an animal with dense fur with light gradients
+a picture of an animal with shaggy fur with subtle tones
+a picture of an animal with striped fur with flowing textures
+a picture of an animal with spotted fur with organic patterns
+a picture of an animal with coarse fur with jagged edges
+a picture of an animal with short fur with even transitions
+a picture of an animal with soft fur with radiant hues
+a picture of an animal with wavy fur with smooth streaks
+a picture of an animal with fluffy fur with natural shades
+a picture of an animal with striped fur with earthy gradients
+a picture of an animal with shiny fur with polished contrasts
+a picture of an animal with spotted fur with faint tones

IP_Composer/text_datasets/emotion_descriptions.csv

@@ -0,0 +1,171 @@
+Emotion Description
+a photo of a person feeling joyful
+a photo of a person feeling sorrowful
+a photo of a person feeling enraged
+a photo of a person feeling astonished
+a photo of a person feeling disgusted
+a photo of a person feeling terrified
+a photo of a person feeling thrilled
+a photo of a person feeling nervous
+a photo of a person feeling tranquil
+a photo of a person feeling perplexed
+a photo of a person feeling resolute
+a photo of a person feeling exasperated
+a photo of a person feeling optimistic
+a photo of a person feeling accomplished
+a photo of a person feeling serene
+a photo of a person feeling exhausted
+a photo of a person feeling remorseful
+a photo of a person feeling satisfied
+a photo of a person feeling inquisitive
+a photo of a person feeling culpable
+a photo of a person feeling ecstatic
+a photo of a person feeling melancholic
+a photo of a person feeling irate
+a photo of a person feeling awestruck
+a photo of a person feeling repulsed
+a photo of a person feeling apprehensive
+a photo of a person feeling euphoric
+a photo of a person feeling uneasy
+a photo of a person feeling composed
+a photo of a person feeling baffled
+a photo of a person feeling driven
+a photo of a person feeling vexed
+a photo of a person feeling enthusiastic
+a photo of a person feeling gratified
+a photo of a person feeling meditative
+a photo of a person feeling fatigued
+a photo of a person feeling regretful
+a photo of a person feeling peaceful
+a photo of a person feeling intrigued
+a photo of a person feeling embarrassed
+a photo of a person feeling jubilant
+a photo of a person feeling gloomy
+a photo of a person feeling furious
+a photo of a person feeling shocked
+a photo of a person feeling revolted
+a photo of a person feeling hesitant
+a photo of a person feeling elated
+a photo of a person feeling restless
+a photo of a person feeling balanced
+a photo of a person feeling mystified
+a photo of a person feeling inspired
+a photo of a person feeling irritated
+a photo of a person feeling encouraged
+a photo of a person feeling rewarded
+a photo of a person feeling contemplative
+a photo of a person feeling weary
+a photo of a person feeling penitent
+a photo of a person feeling content
+a photo of a person feeling curious
+a photo of a person feeling disheartened
+a photo of a person feeling wrathful
+a photo of a person feeling flabbergasted
+a photo of a person feeling nauseated
+a photo of a person feeling worried
+a photo of a person feeling exhilarated
+a photo of a person feeling tense
+a photo of a person feeling poised
+a photo of a person feeling bewildered
+a photo of a person feeling ambitious
+a photo of a person feeling perturbed
+a photo of a person feeling hopeful
+a photo of a person feeling thankful
+a photo of a person feeling introspective
+a photo of a person feeling sleepy
+a photo of a person feeling repentant
+a photo of a person feeling harmonious
+a photo of a person feeling fascinated
+a photo of a person feeling mortified
+a photo of a person feeling distraught
+a photo of a person feeling livid
+a photo of a person feeling astounded
+a photo of a person feeling appalled
+a photo of a person feeling panicked
+a photo of a person feeling overjoyed
+a photo of a person feeling edgy
+a photo of a person feeling centered
+a photo of a person feeling puzzled
+a photo of a person feeling resourceful
+a photo of a person feeling bothered
+a photo of a person feeling upbeat
+a photo of a person feeling fulfilled
+a photo of a person feeling thoughtful
+a photo of a person feeling drained
+a photo of a person feeling guilty
+a photo of a person feeling mellow
+a photo of a person feeling nostalgic
+a photo of a person feeling reflective
+a photo of a person feeling amused
+a photo of a person feeling adventurous
+a photo of a person feeling bashful
+a photo of a person feeling blissful
+a photo of a person feeling bold
+a photo of a person feeling cautious
+a photo of a person feeling compassionate
+a photo of a person feeling conflicted
+a photo of a person feeling contented
+a photo of a person feeling courageous
+a photo of a person feeling creative
+a photo of a person feeling defeated
+a photo of a person feeling delighted
+a photo of a person feeling determined
+a photo of a person feeling dignified
+a photo of a person feeling disillusioned
+a photo of a person feeling envious
+a photo of a person feeling foolish
+a photo of a person feeling forgiving
+a photo of a person feeling free
+a photo of a person feeling generous
+a photo of a person feeling grateful
+a photo of a person feeling humble
+a photo of a person feeling imaginative
+a photo of a person feeling independent
+a photo of a person feeling jealous
+a photo of a person feeling kindhearted
+a photo of a person feeling lonely
+a photo of a person feeling mischievous
+a photo of a person feeling open-minded
+a photo of a person feeling overwhelmed
+a photo of a person feeling patient
+a photo of a person feeling perceptive
+a photo of a person feeling secure
+a photo of a person feeling self-assured
+a photo of a person feeling sentimental
+a photo of a person feeling sensitive
+a photo of a person feeling skeptical
+a photo of a person feeling stubborn
+a photo of a person feeling supportive
+a photo of a person feeling triumphant
+a photo of a person feeling alienated
+a photo of a person feeling apologetic
+a photo of a person feeling appreciative
+a photo of a person feeling assertive
+a photo of a person feeling bereft
+a photo of a person feeling brave
+a photo of a person feeling charmed
+a photo of a person feeling cheerful
+a photo of a person feeling cooperative
+a photo of a person feeling crushed
+a photo of a person feeling decisive
+a photo of a person feeling disoriented
+a photo of a person feeling distressed
+a photo of a person feeling doubtful
+a photo of a person feeling empathetic
+a photo of a person feeling empowered
+a photo of a person feeling enlightened
+a photo of a person feeling exuberant
+a photo of a person feeling gentle
+a photo of a person feeling giddy
+a photo of a person feeling humbled
+a photo of a person feeling hurt
+a photo of a person feeling invigorated
+a photo of a person feeling liberated
+a photo of a person feeling loyal
+a photo of a person feeling misunderstood
+a photo of a person feeling pleased
+a photo of a person feeling radiant
+a photo of a person feeling relieved
+a photo of a person feeling self-conscious
+a photo of a person feeling tender
+a photo of a person feeling validated

IP_Composer/text_datasets/floor_descriptions.csv

@@ -0,0 +1,197 @@
+Description
+a photo with wooden flooring
+a photo with marble flooring
+a photo with tile flooring
+a photo with laminate flooring
+a photo with vinyl flooring
+a photo with concrete flooring
+a photo with granite flooring
+a photo with parquet flooring
+a photo with carpeted flooring
+a photo with bamboo flooring
+a photo with terrazzo flooring
+a photo with stone flooring
+a photo with slate flooring
+a photo with hardwood flooring
+a photo with engineered wood flooring
+a photo with luxury vinyl plank flooring
+a photo with cork flooring
+a photo with epoxy flooring
+a photo with ceramic tile flooring
+a photo with porcelain tile flooring
+a photo with quartz flooring
+a photo with linoleum flooring
+a photo with rubber flooring
+a photo with stained concrete flooring
+a photo with polished concrete flooring
+a photo with herringbone wood flooring
+a photo with chevron wood flooring
+a photo with flagstone flooring
+a photo with travertine flooring
+a photo with pebble flooring
+a photo with mosaic tile flooring
+a photo with hand-scraped wood flooring
+a photo with distressed wood flooring
+a photo with reclaimed wood flooring
+a photo with saltillo tile flooring
+a photo with granite tile flooring
+a photo with natural stone flooring
+a photo with rough concrete flooring
+a photo with sealed concrete flooring
+a photo with patterned tile flooring
+a photo with checkerboard tile flooring
+a photo with hexagonal tile flooring
+a photo with penny tile flooring
+a photo with decorative vinyl flooring
+a photo with woven vinyl flooring
+a photo with matte finish wood flooring
+a photo with high-gloss wood flooring
+a photo with scored concrete flooring
+a photo with textured stone flooring
+a photo with flagstone pavers flooring
+a photo with antique wood flooring
+a photo with dark stained wood flooring
+a photo with light stained wood flooring
+a photo with weathered wood flooring
+a photo with brick flooring
+a photo with sandstone flooring
+a photo with slab concrete flooring
+a photo with terra cotta tile flooring
+a photo with marble mosaic flooring
+a photo with onyx tile flooring
+a photo with grouted tile flooring
+a photo with seamless epoxy flooring
+a photo with faux wood tile flooring
+a photo with natural pebble flooring
+a photo with river rock flooring
+a photo with decorative carpet flooring
+a photo with patterned laminate flooring
+a photo with smooth laminate flooring
+a photo with hand-painted tile flooring
+a photo with antique brick flooring
+a photo with whitewashed wood flooring
+a photo with black marble flooring
+a photo with gray slate flooring
+a photo with textured laminate flooring
+a photo with woven bamboo flooring
+a photo with chevron tile flooring
+a photo with vintage hardwood flooring
+a photo with weathered plank flooring
+a photo with etched concrete flooring
+a photo with geometric tile flooring
+a photo with gradient epoxy flooring
+a photo with custom mosaic flooring
+a photo with recycled glass tile flooring
+a photo with micro-topped concrete flooring
+a photo with sealed sandstone flooring
+a photo with rustic plank flooring
+a photo with industrial concrete flooring
+a photo with stenciled wood flooring
+a photo with bleached wood flooring
+a photo with poured epoxy flooring
+a photo with multi-colored tile flooring
+a photo with floral patterned tile flooring
+a photo with textured carpet flooring
+a photo with luxury vinyl sheet flooring
+a photo with antique mosaic tile flooring
+a photo with textured bamboo flooring
+a photo with custom patterned flooring
+a photo with light gray laminate flooring
+a photo with handcrafted wood flooring
+a photo with mixed material flooring
+a photo with polished stone flooring
+a photo with textured wood flooring
+a photo with distressed bamboo flooring
+a photo with etched marble flooring
+a photo with handcrafted stone flooring
+a photo with pebble mosaic flooring
+a photo with luxury tile flooring
+a photo with hand-painted wood flooring
+a photo with decorative stone flooring
+a photo with custom concrete flooring
+a photo with patterned epoxy flooring
+a photo with layered laminate flooring
+a photo with smooth vinyl flooring
+a photo with antique wooden plank flooring
+a photo with chevron patterned tile flooring
+a photo with reclaimed tile flooring
+a photo with natural slate flooring
+a photo with high-gloss laminate flooring
+a photo with rustic concrete flooring
+a photo with herringbone tile flooring
+a photo with marble inlay flooring
+a photo with vintage parquet flooring
+a photo with hand-scraped hardwood flooring
+a photo with stamped concrete flooring
+a photo with gradient vinyl flooring
+a photo with colored pebble flooring
+a photo with textured quartz flooring
+a photo with decorative terrazzo flooring
+a photo with custom wooden flooring
+a photo with carved stone flooring
+a photo with etched tile flooring
+a photo with layered bamboo flooring
+a photo with vintage vinyl flooring
+a photo with matte finish concrete flooring
+a photo with patterned carpet flooring
+a photo with luxury porcelain flooring
+a photo with printed vinyl flooring
+a photo with woven carpet flooring
+a photo with engineered laminate flooring
+a photo with geometric parquet flooring
+a photo with stenciled concrete flooring
+a photo with antique marble flooring
+a photo with decorative ceramic flooring
+a photo with frosted glass tile flooring
+a photo with herringbone bamboo flooring
+a photo with antique slate flooring
+a photo with high-contrast tile flooring
+a photo with vintage mosaic flooring
+a photo with patterned marble flooring
+a photo with reclaimed hardwood flooring
+a photo with smooth cork flooring
+a photo with distressed cork flooring
+a photo with faux stone laminate flooring
+a photo with printed bamboo flooring
+a photo with colorful tile flooring
+a photo with luxury stone flooring
+a photo with custom laminate flooring
+a photo with marbled vinyl flooring
+a photo with natural sandstone flooring
+a photo with handcrafted terrazzo flooring
+a photo with etched glass tile flooring
+a photo with pale wood flooring
+a photo with deep stained wood flooring
+a photo with sculpted stone flooring
+a photo with neutral tile flooring
+a photo with high-shine tile flooring
+a photo with checkerboard wood flooring
+a photo with luxury chevron flooring
+a photo with patterned porcelain flooring
+a photo with speckled terrazzo flooring
+a photo with rustic stone flooring
+a photo with multi-toned vinyl flooring
+a photo with reclaimed plank flooring
+a photo with high-shine concrete flooring
+a photo with decorative herringbone flooring
+a photo with matte laminate flooring
+a photo with vintage stone flooring
+a photo with modern tile flooring
+a photo with polished pebble flooring
+a photo with decorative mosaic flooring
+a photo with luxury cork flooring
+a photo with engraved tile flooring
+a photo with etched plank flooring
+a photo with gradient stone flooring
+a photo with industrial stone flooring
+a photo with patterned terrazzo flooring
+a photo with printed carpet flooring
+a photo with worn wood flooring
+a photo with custom bamboo flooring
+a photo with light oak flooring
+a photo with gray concrete flooring
+a photo with warm toned wood flooring
+a photo with high-shine vinyl flooring
+a photo with layered wood flooring
+a photo with carved marble flooring
+a photo with intricate tile flooring

IP_Composer/text_datasets/flower_descriptions.csv

@@ -0,0 +1,102 @@
+Description
+A picture with a rose
+A picture with a tulip
+A picture with a daffodil
+A picture with a sunflower
+A picture with a daisy
+A picture with a lily
+A picture with a marigold
+A picture with a peony
+A picture with a chrysanthemum
+A picture with a carnation
+A picture with a lavender
+A picture with a hydrangea
+A picture with an iris
+A picture with a jasmine flower
+A picture with a poppy
+A picture with a hibiscus
+A picture with a geranium
+A picture with a petunia
+A picture with a pansy
+A picture with a zinnia
+A picture with a camellia
+A picture with a magnolia
+A picture with a snapdragon
+A picture with a begonia
+A picture with a morning glory
+A picture with a wisteria
+A picture with a bougainvillea
+A picture with a anemone
+A picture with a gladiolus
+A picture with a calla lily
+A picture with a gardenia
+A picture with a azalea
+A picture with a foxglove
+A picture with a primrose
+A picture with a cyclamen
+A picture with a alyssum
+A picture with a bluebell
+A picture with a buttercup
+A picture with a clover flower
+A picture with a cosmos flower
+A picture with a dahlia
+A picture with a delphinium
+A picture with a forget-me-not
+A picture with a freesia
+A picture with a goldenrod
+A picture with a hollyhock
+A picture with a honeysuckle flower
+A picture with a lantana
+A picture with a larkspur
+A picture with a lobelia
+A picture with a nasturtium
+A picture with an orchid
+A picture with a petal of phlox
+A picture with a plumeria flower
+A picture with a salvia
+A picture with a scabiosa
+A picture with a sedum
+A picture with a statice flower
+A picture with a sweet pea
+A picture with a trillium flower
+A picture with a verbena
+A picture with a viburnum flower
+A picture with a vinca flower
+A picture with a yarrow
+A picture with a yucca flower
+A picture with a amaryllis
+A picture with a arum lily
+A picture with a bird of paradise
+A picture with a bleeding heart flower
+A picture with a blue lotus
+A picture with a bottlebrush
+A picture with a bridal wreath
+A picture with a cockscomb
+A picture with a crocus
+A picture with a cypress vine flower
+A picture with a echinacea
+A picture with a fuchsia
+A picture with a gentian
+A picture with a globe amaranth
+A picture with a heather flower
+A picture with a hosta
+A picture with a hyacinth
+A picture with a ixora
+A picture with a jacaranda flower
+A picture with a kalanchoe
+A picture with a lisianthus
+A picture with a monkshood
+A picture with a nightshade flower
+A picture with a oleander
+A picture with a passionflower
+A picture with a quaking grass flower
+A picture with a rudbeckia
+A picture with a saffron crocus
+A picture with a scarlet pimpernel
+A picture with a scilla
+A picture with a sea holly
+A picture with a spider flower
+A picture with a stargazer lily
+A picture with a stock flower
+A picture with a tansy
+A picture with a wallflower

IP_Composer/text_datasets/fruit_vegetable_descriptions.csv

@@ -0,0 +1,142 @@
+Description
+A photo of a apple
+A photo of a banana
+A photo of a cherry
+A photo of a grape
+A photo of a orange
+A photo of a peach
+A photo of a pear
+A photo of a plum
+A photo of a strawberry
+A photo of a watermelon
+A photo of a blueberry
+A photo of a kiwi
+A photo of a mango
+A photo of a pineapple
+A photo of a raspberry
+A photo of a blackberry
+A photo of a fig
+A photo of a pomegranate
+A photo of a papaya
+A photo of a coconut
+A photo of a carrot
+A photo of a broccoli
+A photo of a cucumber
+A photo of a tomato
+A photo of a potato
+A photo of a onion
+A photo of a pepper
+A photo of a spinach
+A photo of a lettuce
+A photo of a celery
+A photo of a corn
+A photo of a pea
+A photo of a bean
+A photo of a zucchini
+A photo of a eggplant
+A photo of a pumpkin
+A photo of a garlic
+A photo of a ginger
+A photo of a radish
+A photo of a turnip
+A photo of a cauliflower
+A photo of a kale
+A photo of a cabbage
+A photo of a artichoke
+A photo of a asparagus
+A photo of a beet
+A photo of a brussels sprout
+A photo of a leek
+A photo of a mushroom
+A photo of a parsnip
+A photo of a squash
+A photo of a sweet potato
+A photo of a yam
+A photo of a chard
+A photo of a okra
+A photo of a avocado
+A photo of a starfruit
+A photo of a guava
+A photo of a lychee
+A photo of a durian
+A photo of a dragon fruit
+A photo of a passion fruit
+A photo of a tamarind
+A photo of a jackfruit
+A photo of a persimmon
+A photo of a cranberry
+A photo of a gooseberry
+A photo of a mulberry
+A photo of a rhubarb
+A photo of a melon
+A photo of a grapefruit
+A photo of a lime
+A photo of a lemon
+A photo of a nectarine
+A photo of a apricot
+A photo of a olive
+A photo of a date
+A photo of a pluot
+A photo of a cantaloupe
+A photo of a honeydew
+A photo of a chili pepper
+A photo of a bell pepper
+A photo of a jalapeno
+A photo of a habanero
+A photo of a scallion
+A photo of a shallot
+A photo of a watercress
+A photo of a bok choy
+A photo of a endive
+A photo of a radicchio
+A photo of a boysenberry
+A photo of a huckleberry
+A photo of a cactus pear
+A photo of a currant
+A photo of a elderberry
+A photo of a marionberry
+A photo of a persian lime
+A photo of a blood orange
+A photo of a mandarin orange
+A photo of a tangelo
+A photo of a ugli fruit
+A photo of a cherimoya
+A photo of a soursop
+A photo of a sapodilla
+A photo of a longan
+A photo of a rambutan
+A photo of a quince
+A photo of a medlar
+A photo of a feijoa
+A photo of a jabuticaba
+A photo of a malanga
+A photo of a taro
+A photo of a yuca
+A photo of a jicama
+A photo of a fennel
+A photo of a horseradish
+A photo of a water chestnut
+A photo of a bamboo shoot
+A photo of a lotus root
+A photo of a chicory
+A photo of a kohlrabi
+A photo of a mustard greens
+A photo of a dandelion greens
+A photo of a collard greens
+A photo of a turnip greens
+A photo of a purslane
+A photo of a salsify
+A photo of a seaweed
+A photo of a wakame
+A photo of a nori
+A photo of a chayote
+A photo of a amaranth
+A photo of a breadfruit
+A photo of a ackee
+A photo of a sapote
+A photo of a mamey
+A photo of a cupuacu
+A photo of a acai berry
+A photo of a miracle fruit
+A photo of a tamarillo
+A photo of a rose apple

IP_Composer/text_datasets/fur_descriptions.csv

@@ -0,0 +1,284 @@
+Description
+a picture of striped fur
+a picture of spotted fur
+a picture of solid black fur
+a picture of solid white fur
+a picture of brown fur with lighter patches
+a picture of patchy fur with multiple colors
+a picture of striped fur in contrasting colors
+a picture of spotted fur with large dots
+a picture of thick fur with a gray gradient
+a picture of golden fur with dark spots
+a picture of white fur with subtle streaks
+a picture of brown fur with dark stripes
+a picture of mottled fur with blended tones
+a picture of shiny black fur with smooth texture
+a picture of fluffy white fur with a soft appearance
+a picture of short fur with a brindle pattern
+a picture of long fur with a wavy texture
+a picture of curly fur with tight loops
+a picture of patchy fur with dark and light areas
+a picture of speckled fur with small spots
+a picture of golden fur with subtle gradients
+a picture of thick fur with warm brown tones
+a picture of black and white fur in bold patterns
+a picture of gray fur with silver accents
+a picture of shaggy fur with uneven patches
+a picture of cream-colored fur with faint markings
+a picture of dark brown fur with soft streaks
+a picture of yellowish fur with muted tones
+a picture of gray fur with fine speckles
+a picture of dense fur with a marbled effect
+a picture of soft fur with sandy hues
+a picture of orange fur with white patches
+a picture of fluffy fur with blended tones
+a picture of smooth fur with natural highlights
+a picture of tufted fur with layered textures
+a picture of thick fur with subtle color variations
+a picture of silky fur with a glossy finish
+a picture of short fur with a salt-and-pepper look
+a picture of coarse fur with distinct bands
+a picture of soft fur with faint gray patterns
+a picture of striped fur with alternating shades
+a picture of spotted fur with intricate details
+a picture of golden fur with textured layers
+a picture of black fur with subtle brown undertones
+a picture of brown fur with warm highlights
+a picture of fluffy fur with snowy white shades
+a picture of gray fur with smooth gradients
+a picture of shaggy fur with rough textures
+a picture of tufted fur with blended colors
+a picture of spotted fur with irregular shapes
+a picture of striped fur with bold contrasts
+a picture of white fur with pale tones
+a picture of soft gray fur with faint streaks
+a picture of striped fur with organic patterns
+a picture of smooth fur with earthy hues
+a picture of patchy fur with soft transitions
+a picture of dense fur with contrasting patches
+a picture of mottled fur with natural colors
+a picture of black fur with glossy highlights
+a picture of short fur with blended stripes
+a picture of shiny fur with reflective tones
+a picture of wavy fur with soft layers
+a picture of tufted fur with warm gradients
+a picture of soft fur with golden accents
+a picture of coarse fur with dark and light contrasts
+a picture of gray fur with natural highlights
+a picture of fluffy fur with a soft yellow hue
+a picture of smooth fur with subtle textures
+a picture of dense black fur with even tones
+a picture of spotted fur with balanced patterns
+a picture of striped fur with symmetrical designs
+a picture of curly fur with soft spirals
+a picture of shaggy brown fur with varied streaks
+a picture of fluffy fur with cool silver tones
+a picture of smooth fur with warm orange hues
+"a picture of spotted fur with small, even dots"
+a picture of striped fur with clean lines
+a picture of tufted fur with balanced layers
+a picture of dense gray fur with subtle patterns
+a picture of shiny black fur with muted undertones
+a picture of coarse fur with mixed colors
+a picture of fluffy white fur with soft highlights
+a picture of curly brown fur with gentle waves
+a picture of silky fur with refined layers
+a picture of soft fur with delicate streaks
+a picture of dense fur with earthy gradients
+a picture of golden fur with soft textures
+a picture of shiny black fur with natural tones
+a picture of tufted fur with warm shades
+a picture of wavy fur with blended gradients
+a picture of spotted fur with organic shapes
+a picture of striped fur with varied tones
+a picture of shaggy fur with rugged patterns
+a picture of soft gray fur with pale highlights
+a picture of fluffy fur with muted contrasts
+a picture of dense fur with layered colors
+a picture of patchy fur with random patterns
+a picture of striped fur with subtle shading
+a picture of spotted fur with varying sizes
+a picture of soft fur with muted gradients
+a picture of thick fur with blended tones
+a picture of curly fur with natural spirals
+a picture of shaggy fur with uneven textures
+a picture of fluffy fur with a glossy finish
+a picture of smooth fur with contrasting bands
+a picture of coarse fur with earthy hues
+a picture of tufted fur with soft tips
+a picture of dense fur with a gradient effect
+a picture of short fur with crisp patterns
+a picture of wavy fur with delicate layers
+a picture of mottled fur with abstract designs
+a picture of soft fur with warm highlights
+a picture of striped fur with organic shapes
+a picture of spotted fur with irregular dots
+a picture of fluffy fur with soft undertones
+a picture of shiny fur with natural variations
+a picture of silky fur with smooth textures
+a picture of coarse fur with bold contrasts
+a picture of striped fur with sharp lines
+a picture of spotted fur with faded edges
+a picture of dense fur with intricate details
+a picture of short fur with sleek patterns
+a picture of fluffy fur with faint streaks
+a picture of smooth fur with vibrant tones
+a picture of curly fur with subtle twists
+a picture of soft fur with delicate transitions
+a picture of tufted fur with rough layers
+a picture of striped fur with layered tones
+a picture of spotted fur with balanced contrasts
+a picture of dense fur with fine textures
+a picture of shiny fur with highlighted streaks
+a picture of coarse fur with mixed tones
+a picture of fluffy fur with soft gradients
+a picture of smooth fur with pale colors
+a picture of mottled fur with warm hues
+a picture of spotted fur with clean shapes
+a picture of dense fur with vibrant highlights
+a picture of tufted fur with blended layers
+a picture of shaggy fur with uneven bands
+a picture of soft fur with rich textures
+a picture of striped fur with flowing patterns
+a picture of spotted fur with soft gradients
+a picture of short fur with sleek finishes
+a picture of coarse fur with detailed shading
+a picture of dense fur with natural hues
+a picture of shiny fur with faint glimmers
+a picture of fluffy fur with even textures
+a picture of smooth fur with layered gradients
+a picture of curly fur with soft highlights
+a picture of spotted fur with gentle contrasts
+a picture of striped fur with harmonious shades
+a picture of shaggy fur with bold patterns
+a picture of tufted fur with subtle differences
+a picture of fluffy fur with varied lengths
+a picture of mottled fur with abstract colors
+a picture of dense fur with light transitions
+a picture of soft fur with smooth finishes
+a picture of striped fur with natural flows
+a picture of spotted fur with detailed edges
+a picture of coarse fur with soft contrasts
+a picture of fluffy fur with blended shades
+a picture of smooth fur with reflective tones
+a picture of curly fur with rounded shapes
+a picture of striped fur with sharp contrasts
+a picture of shiny fur with glowing highlights
+a picture of tufted fur with rich textures
+a picture of dense fur with earthy contrasts
+a picture of soft fur with blended gradients
+a picture of spotted fur with flowing designs
+a picture of striped fur with faint streaks
+a picture of fluffy fur with warm undertones
+a picture of smooth fur with natural gloss
+a picture of coarse fur with rough patterns
+a picture of curly fur with mixed loops
+a picture of striped fur with wavy designs
+a picture of dense fur with intricate shading
+a picture of spotted fur with tiny marks
+a picture of soft fur with delicate details
+a picture of shiny fur with varied tones
+a picture of tufted fur with unique patterns
+a picture of fluffy fur with golden hints
+a picture of striped fur with subtle highlights
+a picture of coarse fur with angular designs
+a picture of smooth fur with gentle transitions
+a picture of curly fur with unique curls
+a picture of striped fur with bold strokes
+a picture of spotted fur with organic edges
+a picture of shaggy fur with layered hues
+a picture of soft fur with radiant tones
+a picture of soft fur with light streaks
+a picture of striped fur with gentle curves
+a picture of spotted fur with random patterns
+a picture of smooth fur with layered highlights
+a picture of dense fur with contrasting textures
+a picture of fluffy fur with muted colors
+a picture of shaggy fur with blended tones
+a picture of curly fur with fine spirals
+a picture of striped fur with faint gradients
+a picture of coarse fur with rich details
+a picture of short fur with smooth contrasts
+a picture of wavy fur with flowing lines
+a picture of spotted fur with blurred edges
+a picture of tufted fur with soft gradients
+a picture of fluffy fur with warm colors
+a picture of striped fur with deep contrasts
+a picture of spotted fur with tiny details
+a picture of dense fur with delicate streaks
+a picture of shiny fur with pale reflections
+a picture of coarse fur with jagged patterns
+a picture of soft fur with natural transitions
+a picture of mottled fur with uneven shades
+a picture of striped fur with vibrant hues
+a picture of spotted fur with soft edges
+a picture of curly fur with intricate shapes
+a picture of dense fur with light accents
+a picture of shaggy fur with bold strokes
+a picture of striped fur with layered gradients
+a picture of spotted fur with irregular sizes
+a picture of smooth fur with crisp textures
+a picture of wavy fur with balanced tones
+a picture of tufted fur with faint streaks
+a picture of shiny fur with glowing accents
+a picture of fluffy fur with delicate shades
+a picture of coarse fur with angular contrasts
+a picture of dense fur with soft flows
+a picture of striped fur with rhythmic designs
+a picture of spotted fur with blurred transitions
+a picture of soft fur with subtle textures
+a picture of short fur with clean gradients
+a picture of mottled fur with natural shapes
+a picture of shaggy fur with vibrant highlights
+a picture of curly fur with rounded spirals
+a picture of dense fur with earthy shades
+a picture of striped fur with soft lines
+a picture of spotted fur with faded tones
+a picture of fluffy fur with gentle transitions
+a picture of shiny fur with smooth gradients
+a picture of coarse fur with rough streaks
+a picture of tufted fur with unique layers
+a picture of spotted fur with intricate textures
+a picture of dense fur with subtle contrasts
+a picture of smooth fur with muted tones
+a picture of curly fur with overlapping loops
+a picture of striped fur with soft gradients
+a picture of shaggy fur with warm hues
+a picture of fluffy fur with rich highlights
+a picture of soft fur with faint transitions
+a picture of mottled fur with irregular colors
+a picture of striped fur with alternating bands
+a picture of spotted fur with light edges
+a picture of coarse fur with bold designs
+a picture of dense fur with natural tones
+a picture of wavy fur with faint contrasts
+a picture of short fur with glowing highlights
+a picture of striped fur with unique patterns
+a picture of shiny fur with smooth textures
+a picture of fluffy fur with subtle hues
+a picture of spotted fur with layered tones
+a picture of curly fur with fine details
+a picture of dense fur with blended gradients
+a picture of striped fur with bold tones
+a picture of soft fur with muted streaks
+a picture of mottled fur with soft edges
+a picture of tufted fur with natural flows
+a picture of striped fur with crisp lines
+a picture of spotted fur with random contrasts
+a picture of shiny fur with warm highlights
+a picture of wavy fur with deep gradients
+a picture of fluffy fur with intricate textures
+a picture of striped fur with gradual changes
+a picture of curly fur with delicate spirals
+a picture of dense fur with light gradients
+a picture of shaggy fur with subtle tones
+a picture of striped fur with flowing textures
+a picture of spotted fur with organic patterns
+a picture of coarse fur with jagged edges
+a picture of short fur with even transitions
+a picture of soft fur with radiant hues
+a picture of wavy fur with smooth streaks
+a picture of fluffy fur with natural shades
+a picture of striped fur with earthy gradients
+a picture of shiny fur with polished contrasts
+a picture of spotted fur with faint tones

IP_Composer/text_datasets/furniture_descriptions.csv

@@ -0,0 +1,318 @@
+Description
+a photo with a wooden table
+a photo with a sofa
+a photo with a armchair
+a photo with a coffee table
+a photo with a bookshelf
+a photo with a bed frame
+a photo with a rocking chair
+a photo with a TV stand
+a photo with a dressing table
+a photo with a bench
+a photo with a office chair
+a photo with a sectional sofa
+a photo with a bar stool
+a photo with a dining table
+a photo with a writing desk
+a photo with a recliner chair
+a photo with a patio chair
+a photo with a camping chair
+a photo with a bunk bed
+a photo with a console table
+a photo with a kitchen cabinet
+a photo with a ottoman
+a photo with a egg chair
+a photo with a display cabinet
+a photo with a lounge chair
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IP_Composer/text_datasets/material_descriptions.csv

@@ -0,0 +1,154 @@
+Description
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IP_Composer/text_datasets/outfit_color_descriptions.csv

@@ -0,0 +1,103 @@
+Description
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IP_Composer/text_datasets/outfit_descriptions.csv

@@ -0,0 +1,289 @@
+Outfit Description
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+a picture of a person wearing jogger pants
+a picture of a person wearing a quilted jacket
+a picture of a person wearing a one-piece swimsuit
+a picture of a person wearing a halter top
+a picture of a person wearing over-the-knee boots
+a picture of a person wearing a pea coat
+a picture of a person wearing a baseball cap
+a picture of a person wearing a scarf and mittens
+a picture of a person wearing a faux fur coat
+a picture of a person wearing a high-low dress
+a picture of a person wearing a puff-sleeve blouse
+a picture of a person wearing linen trousers
+a picture of a person wearing platform heels
+a picture of a person wearing a tulle skirt
+a picture of a person wearing a baseball jacket
+a picture of a person wearing a silk robe
+a picture of a person wearing cargo shorts
+a picture of a person wearing a bucket hat
+a picture of a person wearing an asymmetrical dress
+a picture of a person wearing a mock neck top
+a picture of a person wearing a biker jacket
+a picture of a person wearing plaid trousers
+a picture of a person wearing a long cardigan
+a picture of a person wearing a smocked blouse
+a picture of a person wearing a tiered skirt
+a picture of a person wearing a mermaid gown
+a picture of a person wearing a drawstring hoodie
+a picture of a person wearing a parka
+a picture of a person wearing a satin midi dress
+a picture of a person wearing chelsea boots
+a picture of a person wearing a leather pencil skirt
+a picture of a person wearing a crochet top
+a picture of a person wearing a denim jumpsuit
+a picture of a person wearing knee-high socks
+a picture of a person wearing an aviator jacket
+a picture of a person wearing a houndstooth blazer
+a picture of a person wearing a faux leather skirt
+a picture of a person wearing a wrap-around scarf
+a picture of a person wearing tapered sweatpants
+a picture of a person wearing a cold-shoulder blouse
+a picture of a person wearing a vintage band tee
+a picture of a person wearing floral print trousers
+a picture of a person wearing espadrille sandals
+a picture of a person wearing a puffed sleeve dress
+a picture of a person wearing a mock turtleneck
+a picture of a person wearing a camo jacket
+a picture of a person wearing a reversible bomber jacket
+a picture of a person wearing a leather trench coat
+a picture of a person wearing a fringed poncho
+a picture of a person wearing a satin bomber jacket
+a picture of a person wearing a belted trench coat
+a picture of a person wearing a floral maxi dress
+a picture of a person wearing high-rise mom jeans
+a picture of a person wearing a cropped leather jacket
+a picture of a person wearing a slouchy beanie
+a picture of a person wearing a denim skater dress
+a picture of a person wearing patent leather boots
+a picture of a person wearing a puff jacket
+a picture of a person wearing metallic leggings
+a picture of a person wearing a button-down dress
+a picture of a person wearing an oversized flannel shirt
+a picture of a person wearing a beret and trench coat
+a picture of a person wearing a two-tone varsity jacket
+a picture of a person wearing joggers and a cropped hoodie
+a picture of a person wearing a tiered maxi dress
+a picture of a person wearing a sleeveless denim jacket
+a picture of a person wearing ripped boyfriend jeans
+a picture of a person wearing a halter neck midi dress
+a picture of a person wearing a sherpa-lined coat
+a picture of a person wearing a double-breasted blazer
+a picture of a person wearing culottes and a crop top
+a picture of a person wearing a silk bomber jacket
+a picture of a person wearing slim-fit chinos
+a picture of a person wearing a pleated mini skirt
+a picture of a person wearing a belted jumpsuit
+a picture of a person wearing leather platform boots
+a picture of a person wearing a cotton sundress
+a picture of a person wearing a polka-dot wrap top
+a picture of a person wearing cargo jogger pants
+a picture of a person wearing a ribbed knit sweater
+a picture of a person wearing a faux fur vest
+a picture of a person wearing an embroidered tunic
+a picture of a person wearing a satin blouse
+a picture of a person wearing tie-dye leggings
+a picture of a person wearing a hooded rain jacket
+a picture of a person wearing a square-neck blouse
+a picture of a person wearing a velvet midi skirt
+a picture of a person wearing slouchy over-the-knee boots
+a picture of a person wearing a pleated wrap skirt
+a picture of a person wearing a quilted gilet
+a picture of a person wearing a flowy chiffon dress
+a picture of a person wearing a tiered tulle skirt
+a picture of a person wearing a shirtdress with rolled sleeves
+a picture of a person wearing embroidered loafers
+a picture of a person wearing a split-hem maxi skirt
+a picture of a person wearing a one-shoulder jumpsuit
+a picture of a person wearing a velvet off-shoulder dress
+a picture of a person wearing cropped wide-leg pants
+a picture of a person wearing a belted shirt dress
+a picture of a person wearing a two-piece matching set
+a picture of a person wearing ruched leggings
+a picture of a person wearing high-waisted culottes
+a picture of a person wearing a metallic crop top
+a picture of a person wearing a sleeveless puffer jacket
+a picture of a person wearing a drawstring utility jacket
+a picture of a person wearing a draped cowl-neck top
+a picture of a person wearing color-block track pants
+a picture of a person wearing a boxy cropped jacket
+a picture of a person wearing a sequined shift dress
+a picture of a person wearing a retro windbreaker
+a picture of a person wearing a crochet halter top
+a picture of a person wearing tapered high-waist trousers
+a picture of a person wearing a bold floral blouse
+a picture of a person wearing high-rise biker shorts
+a picture of a person wearing a polka-dot midi dress
+a picture of a person wearing pointed-toe stilettos
+a picture of a person wearing a lace-up peasant blouse
+a picture of a person wearing a smocked empire waist dress
+a picture of a person wearing a ruched bodycon skirt
+a picture of a person wearing a fleece-lined hoodie
+a picture of a person wearing slouchy straight-leg jeans
+a picture of a person wearing a boat-neck knit sweater
+a picture of a person wearing a flared bell-sleeve top
+a picture of a person wearing lace-trim satin shorts
+a picture of a person wearing cropped paperbag pants
+a picture of a person wearing an oversized cashmere scarf
+a picture of a person wearing a wool toggle coat
+a picture of a person wearing a cross-back jumpsuit
+a picture of a person wearing a color-block poncho
+a picture of a person wearing pleated high-rise shorts
+a picture of a person wearing a wide-brim sun hat
+a picture of a person wearing a maxi coat and knee boots
+a picture of a person wearing a mock neck shift dress
+a picture of a person wearing a cape-sleeve midi dress
+a picture of a person wearing embroidered cropped trousers
+a picture of a person wearing a sweetheart neckline dress
+a picture of a person wearing a pleated skater skirt
+a picture of a person wearing layered gold necklaces
+a picture of a person wearing a keyhole back dress
+a picture of a person wearing a fitted mock neck bodysuit
+a picture of a person wearing wool plaid culottes
+a picture of a person wearing a chain belt and high heels
+a picture of a person wearing a knit wrap-front top
+a picture of a person wearing a puffer vest and leggings

IP_Composer/text_datasets/pattern_descriptions.csv

@@ -0,0 +1,284 @@
+Description
+an object with a striped pattern
+an object with a series of evenly spaced polka dot pattern
+an object with a swirling paisley pattern
+an object with a classic checkered pattern
+an object with a gradient color shift with seamless blending pattern
+an object with a repeating geometric diamond pattern
+an object with a floral with intricate petals pattern
+an object with zigzag line pattern
+an object with a spiral that radiates outward pattern
+an object with abstract, overlapping shape pattern
+an object with a symmetrical mandala pattern
+an object with a mosaic of tiny, colorful tile pattern
+an object with a lattice made of interwoven line pattern
+an object with crosshatched line in parallel arrays pattern
+an object with hexagonal honeycomb cell pattern
+an object with triangular tessellation in alternating colors pattern
+an object with concentric circle creating a target effect pattern
+an object with a kaleidoscopic, mirrored pattern
+an object with wave-like undulations running across pattern
+an object with overlapping rectangular block pattern
+an object with nested circular designs with a layered effect pattern
+an object with a plaid with intersecting bands pattern
+an object with a texture resembling woven thread pattern
+an object with a camouflage with organic shapes pattern
+an object with intricate, interlocking carving pattern
+an object with a stippled texture with dense points pattern
+an object with a fractal design with self-repeating motifs pattern
+an object with concentric oval creating depth pattern
+an object with a grid of evenly spaced square pattern
+an object with checkerboard in alternating shades pattern
+an object with interlocking loop forming a chain pattern
+an object with tribal-inspired symmetrical motif pattern
+an object with brushstroke in a random layout pattern
+an object with a leafy vine spreading outward pattern
+an object with a digital glitch creating sharp angular shapes pattern
+an object with a starburst radiating from the center pattern
+an object with crystalline shards forming a fractured design pattern
+an object with a metallic surface with linear groove pattern
+an object with gradient stripe in a subtle fade pattern
+an object with a textured resembling raindrops pattern
+an object with a frosted glass effect with subtle opacity pattern
+an object with woodgrain with natural variations pattern
+an object with marbled swirls in complementary colors pattern
+an object with a cosmic with scattered stars pattern
+an object with watercolor textures with flowing gradients pattern
+an object with splatter resembling paint drops pattern
+an object with a shadow-like gradient overlay pattern
+an object with a tiled of repeating shapes pattern
+an object with embossed textures with raised details pattern
+an object with a holographic rainbow sheen pattern
+an object with a vaporwave grid with neon accents pattern
+an object with retro-inspired concentric designs pattern
+an object with a futuristic circuit board pattern
+an object with a 3D illusion of depth and layers pattern
+an object with blotchy, smeared color patches pattern
+an object with scribbled with hand-drawn lines pattern
+an object with a chainmail of interlocking rings pattern
+an object with woven texture mimicking basketry pattern
+an object with a shattered glass with sharp edges pattern
+an object with a pixel grid with random gaps pattern
+an object with barcode-like stripes in alternating widths pattern
+an object with neon light streaks in gradient hues pattern
+an object with ornate Gothic-inspired flourishes pattern
+an object with sinuous swirls forming an endless loop pattern
+an object with chalkboard-style scribbled pattern
+an object with earthy, textured with rough edges pattern
+an object with splattered ink forming irregular spots pattern
+an object with a perforated surface with uniform holes pattern
+an object with carved stone with deep grooves pattern
+an object with a rain-splattered glass texture pattern
+an object with a glowing ring-like halo pattern
+an object with vibrant overlapping gradient pattern
+an object with a weathered, rustic pattern
+an object with ripple radiating outward pattern
+an object with a cracked desert floor pattern
+an object with smoky, swirling resembling clouds pattern
+an object with a nebula-like cosmic pattern
+an object with a lava lamp of floating blobs pattern
+an object with blurred, bokeh-style light spots pattern
+an object with stitched thread in crisscross designs pattern
+an object with a repeating chainlink pattern
+an object with a cobblestone of irregular shapes pattern
+an object with woven mat-like pattern
+an object with a papercut with layered shapes pattern
+an object with torn edge mimicking paper pattern
+an object with a dripping paint pattern
+an object with a heatmap with smooth gradient transitions pattern
+an object with fingerprint-like swirling line pattern
+an object with layered transparencies in subtle gradients pattern
+an object with a constellation connecting stars pattern
+an object with a patchwork quilt of geometric shapes pattern
+an object with burned paper with charred edges pattern
+an object with cracked surface resembling aged walls pattern
+an object with a braided rope-like pattern
+an object with pixel art with blocky shapes pattern
+an object with glowing outline in dark backgrounds pattern
+an object with frosted window with random swirls pattern
+an object with ancient scroll-like with fine detail pattern
+an object with dots pattern
+an object with stripes pattern
+an object with waves pattern
+an object with checks pattern
+an object with grids pattern
+an object with triangles pattern
+an object with circles pattern
+an object with squares pattern
+an object with lines pattern
+an object with hexagons pattern
+an object with stars pattern
+an object with arrows pattern
+an object with hearts pattern
+an object with flowers pattern
+an object with spirals pattern
+an object with swirls pattern
+an object with zigzags pattern
+an object with curves pattern
+an object with hatches pattern
+an object with chevrons pattern
+an object with polka dots pattern
+an object with pinstripes pattern
+an object with crosses pattern
+an object with diamonds pattern
+an object with chains pattern
+an object with plaids pattern
+an object with loops pattern
+an object with slashes pattern
+an object with shards pattern
+an object with dots and dashes pattern
+an object with waves and ripples pattern
+an object with connected lines pattern
+an object with dashed lines pattern
+an object with arcs pattern
+an object with dots and circles pattern
+an object with parallel lines pattern
+an object with overlapping shapes pattern
+an object with bold stripes pattern
+an object with fine lines pattern
+an object with tiny triangles pattern
+an object with interlocking shapes pattern
+an object with random dots pattern
+an object with wavy lines pattern
+an object with crosshatch pattern
+an object with clouds pattern
+an object with abstract lines pattern
+an object with simple dots pattern
+an object with tiny stars pattern
+an object with rays pattern
+an object with petals pattern
+an object with leaves pattern
+an object with small squares pattern
+an object with thin stripes pattern
+an object with angled lines pattern
+an object with stacked lines pattern
+an object with bubbles pattern
+an object with fish scales pattern
+an object with small grids pattern
+an object with honeycombs pattern
+an object with dotted grids pattern
+an object with gradient stripes pattern
+an object with overlapping circles pattern
+an object with linked squares pattern
+an object with tiny arrows pattern
+an object with bold dots pattern
+an object with curly lines pattern
+an object with rounded squares pattern
+an object with broken lines pattern
+an object with double lines pattern
+an object with jagged lines pattern
+an object with random lines pattern
+an object with thin waves pattern
+an object with layered circles pattern
+an object with bold zigzags pattern
+an object with connected dots pattern
+an object with dashed grids pattern
+an object with tiny hexagons pattern
+an object with tiled shapes pattern
+an object with fine grids pattern
+an object with linked chains pattern
+an object with scattered dots pattern
+an object with angled grids pattern
+an object with block shapes pattern
+an object with angled waves pattern
+an object with crisscross lines pattern
+an object with random triangles pattern
+an object with angled stripes pattern
+an object with simple shapes pattern
+an object with line clusters pattern
+an object with staggered lines pattern
+an object with patterned grids pattern
+an object with gradient circles pattern
+an object with tiny diamonds pattern
+an object with scattered stars pattern
+an object with linked patterns pattern
+an object with basic dots pattern
+an object with diagonal lines pattern
+an object with zigzag stripes pattern
+an object with circular dots pattern
+an object with oval shapes pattern
+an object with wavy grids pattern
+an object with crisscross hatches pattern
+an object with parallel stripes pattern
+an object with thin checks pattern
+an object with wide stripes pattern
+an object with tiny grids pattern
+an object with spotted shapes pattern
+an object with spiral dots pattern
+an object with layered grids pattern
+an object with jagged edges pattern
+an object with overlapping lines pattern
+an object with gradient dots pattern
+an object with small rectangles pattern
+an object with offset squares pattern
+an object with scattered circles pattern
+an object with shaded squares pattern
+an object with alternating lines pattern
+an object with dotted stripes pattern
+an object with wavy edges pattern
+an object with tiny bubbles pattern
+an object with striped hexagons pattern
+an object with colorful checks pattern
+an object with linked diamonds pattern
+an object with angled dots pattern
+an object with repeated squares pattern
+an object with offset grids pattern
+an object with staggered shapes pattern
+an object with shaded triangles pattern
+an object with angled zigzags pattern
+an object with scattered arrows pattern
+an object with linked rectangles pattern
+an object with broken grids pattern
+an object with curved lines pattern
+an object with tiny arcs pattern
+an object with overlapping rectangles pattern
+an object with angled shapes pattern
+an object with wide grids pattern
+an object with outlined shapes pattern
+an object with offset triangles pattern
+an object with hollow circles pattern
+an object with streaked lines pattern
+an object with blurred dots pattern
+an object with faint stripes pattern
+an object with tinted shapes pattern
+an object with offset hexagons pattern
+an object with overlapping triangles pattern
+an object with thin grids pattern
+an object with layered stripes pattern
+an object with split diamonds pattern
+an object with gradient waves pattern
+an object with striped bubbles pattern
+an object with angled crosses pattern
+an object with tiny loops pattern
+an object with fuzzy lines pattern
+an object with streaked grids pattern
+an object with hazy circles pattern
+an object with offset lines pattern
+an object with striped stars pattern
+an object with wavy circles pattern
+an object with repeated stars pattern
+an object with angled diamonds pattern
+an object with layered lines pattern
+an object with offset diamonds pattern
+an object with hollow squares pattern
+an object with linked loops pattern
+an object with layered triangles pattern
+an object with scattered bubbles pattern
+an object with random grids pattern
+an object with crossed lines pattern
+an object with layered arcs pattern
+an object with blurry shapes pattern
+an object with faint dots pattern
+an object with striped shapes pattern
+an object with dotted waves pattern
+an object with offset bubbles pattern
+an object with curved grids pattern
+an object with striped arcs pattern
+an object with scattered hexagons pattern
+an object with layered bubbles pattern
+an object with streaked circles pattern
+an object with shaded arcs pattern
+an object with tiny zigzags pattern
+an object with random stars pattern
+an object with faint bubbles pattern
+an object with striped grids pattern

IP_Composer/text_datasets/pattern_descriptions_no_obj.csv

@@ -0,0 +1,284 @@
+Description
+a striped pattern
+a series of evenly spaced polka dot pattern
+a swirling paisley pattern
+a classic checkered pattern
+a gradient color shift with seamless blending pattern
+a repeating geometric diamond pattern
+a floral with intricate petals pattern
+zigzag line pattern
+a spiral that radiates outward pattern
+abstract, overlapping shape pattern
+a symmetrical mandala pattern
+a mosaic of tiny, colorful tile pattern
+a lattice made of interwoven line pattern
+crosshatched line in parallel arrays pattern
+hexagonal honeycomb cell pattern
+triangular tessellation in alternating colors pattern
+concentric circle creating a target effect pattern
+a kaleidoscopic, mirrored pattern
+wave-like undulations running across pattern
+overlapping rectangular block pattern
+nested circular designs with a layered effect pattern
+a plaid with intersecting bands pattern
+a texture resembling woven thread pattern
+a camouflage with organic shapes pattern
+intricate, interlocking carving pattern
+a stippled texture with dense points pattern
+a fractal design with self-repeating motifs pattern
+concentric oval creating depth pattern
+a grid of evenly spaced square pattern
+checkerboard in alternating shades pattern
+interlocking loop forming a chain pattern
+tribal-inspired symmetrical motif pattern
+brushstroke in a random layout pattern
+a leafy vine spreading outward pattern
+a digital glitch creating sharp angular shapes pattern
+a starburst radiating from the center pattern
+crystalline shards forming a fractured design pattern
+a metallic surface with linear groove pattern
+gradient stripe in a subtle fade pattern
+a textured resembling raindrops pattern
+a frosted glass effect with subtle opacity pattern
+woodgrain with natural variations pattern
+marbled swirls in complementary colors pattern
+a cosmic with scattered stars pattern
+watercolor textures with flowing gradients pattern
+splatter resembling paint drops pattern
+a shadow-like gradient overlay pattern
+a tiled of repeating shapes pattern
+embossed textures with raised details pattern
+a holographic rainbow sheen pattern
+a vaporwave grid with neon accents pattern
+retro-inspired concentric designs pattern
+a futuristic circuit board pattern
+a 3D illusion of depth and layers pattern
+blotchy, smeared color patches pattern
+scribbled with hand-drawn lines pattern
+a chainmail of interlocking rings pattern
+woven texture mimicking basketry pattern
+a shattered glass with sharp edges pattern
+a pixel grid with random gaps pattern
+barcode-like stripes in alternating widths pattern
+neon light streaks in gradient hues pattern
+ornate Gothic-inspired flourishes pattern
+sinuous swirls forming an endless loop pattern
+chalkboard-style scribbled pattern
+earthy, textured with rough edges pattern
+splattered ink forming irregular spots pattern
+a perforated surface with uniform holes pattern
+carved stone with deep grooves pattern
+a rain-splattered glass texture pattern
+a glowing ring-like halo pattern
+vibrant overlapping gradient pattern
+a weathered, rustic pattern
+ripple radiating outward pattern
+a cracked desert floor pattern
+smoky, swirling resembling clouds pattern
+a nebula-like cosmic pattern
+a lava lamp of floating blobs pattern
+blurred, bokeh-style light spots pattern
+stitched thread in crisscross designs pattern
+a repeating chainlink pattern
+a cobblestone of irregular shapes pattern
+woven mat-like pattern
+a papercut with layered shapes pattern
+torn edge mimicking paper pattern
+a dripping paint pattern
+a heatmap with smooth gradient transitions pattern
+fingerprint-like swirling line pattern
+layered transparencies in subtle gradients pattern
+a constellation connecting stars pattern
+a patchwork quilt of geometric shapes pattern
+burned paper with charred edges pattern
+cracked surface resembling aged walls pattern
+a braided rope-like pattern
+pixel art with blocky shapes pattern
+glowing outline in dark backgrounds pattern
+frosted window with random swirls pattern
+ancient scroll-like with fine detail pattern
+dots pattern
+stripes pattern
+waves pattern
+checks pattern
+grids pattern
+triangles pattern
+circles pattern
+squares pattern
+lines pattern
+hexagons pattern
+stars pattern
+arrows pattern
+hearts pattern
+flowers pattern
+spirals pattern
+swirls pattern
+zigzags pattern
+curves pattern
+hatches pattern
+chevrons pattern
+polka dots pattern
+pinstripes pattern
+crosses pattern
+diamonds pattern
+chains pattern
+plaids pattern
+loops pattern
+slashes pattern
+shards pattern
+dots and dashes pattern
+waves and ripples pattern
+connected lines pattern
+dashed lines pattern
+arcs pattern
+dots and circles pattern
+parallel lines pattern
+overlapping shapes pattern
+bold stripes pattern
+fine lines pattern
+tiny triangles pattern
+interlocking shapes pattern
+random dots pattern
+wavy lines pattern
+crosshatch pattern
+clouds pattern
+abstract lines pattern
+simple dots pattern
+tiny stars pattern
+rays pattern
+petals pattern
+leaves pattern
+small squares pattern
+thin stripes pattern
+angled lines pattern
+stacked lines pattern
+bubbles pattern
+fish scales pattern
+small grids pattern
+honeycombs pattern
+dotted grids pattern
+gradient stripes pattern
+overlapping circles pattern
+linked squares pattern
+tiny arrows pattern
+bold dots pattern
+curly lines pattern
+rounded squares pattern
+broken lines pattern
+double lines pattern
+jagged lines pattern
+random lines pattern
+thin waves pattern
+layered circles pattern
+bold zigzags pattern
+connected dots pattern
+dashed grids pattern
+tiny hexagons pattern
+tiled shapes pattern
+fine grids pattern
+linked chains pattern
+scattered dots pattern
+angled grids pattern
+block shapes pattern
+angled waves pattern
+crisscross lines pattern
+random triangles pattern
+angled stripes pattern
+simple shapes pattern
+line clusters pattern
+staggered lines pattern
+patterned grids pattern
+gradient circles pattern
+tiny diamonds pattern
+scattered stars pattern
+linked patterns pattern
+basic dots pattern
+diagonal lines pattern
+zigzag stripes pattern
+circular dots pattern
+oval shapes pattern
+wavy grids pattern
+crisscross hatches pattern
+parallel stripes pattern
+thin checks pattern
+wide stripes pattern
+tiny grids pattern
+spotted shapes pattern
+spiral dots pattern
+layered grids pattern
+jagged edges pattern
+overlapping lines pattern
+gradient dots pattern
+small rectangles pattern
+offset squares pattern
+scattered circles pattern
+shaded squares pattern
+alternating lines pattern
+dotted stripes pattern
+wavy edges pattern
+tiny bubbles pattern
+striped hexagons pattern
+colorful checks pattern
+linked diamonds pattern
+angled dots pattern
+repeated squares pattern
+offset grids pattern
+staggered shapes pattern
+shaded triangles pattern
+angled zigzags pattern
+scattered arrows pattern
+linked rectangles pattern
+broken grids pattern
+curved lines pattern
+tiny arcs pattern
+overlapping rectangles pattern
+angled shapes pattern
+wide grids pattern
+outlined shapes pattern
+offset triangles pattern
+hollow circles pattern
+streaked lines pattern
+blurred dots pattern
+faint stripes pattern
+tinted shapes pattern
+offset hexagons pattern
+overlapping triangles pattern
+thin grids pattern
+layered stripes pattern
+split diamonds pattern
+gradient waves pattern
+striped bubbles pattern
+angled crosses pattern
+tiny loops pattern
+fuzzy lines pattern
+streaked grids pattern
+hazy circles pattern
+offset lines pattern
+striped stars pattern
+wavy circles pattern
+repeated stars pattern
+angled diamonds pattern
+layered lines pattern
+offset diamonds pattern
+hollow squares pattern
+linked loops pattern
+layered triangles pattern
+scattered bubbles pattern
+random grids pattern
+crossed lines pattern
+layered arcs pattern
+blurry shapes pattern
+faint dots pattern
+striped shapes pattern
+dotted waves pattern
+offset bubbles pattern
+curved grids pattern
+striped arcs pattern
+scattered hexagons pattern
+layered bubbles pattern
+streaked circles pattern
+shaded arcs pattern
+tiny zigzags pattern
+random stars pattern
+faint bubbles pattern
+striped grids pattern

IP_Composer/text_datasets/times_of_day_descriptions.csv

@@ -0,0 +1,292 @@
+Description
+A picture taken at sunrise
+A picture taken at sunset
+A picture taken at dawn
+A picture taken at dusk
+A picture taken at midday
+A picture taken at midnight
+A picture taken under the morning sun
+A picture taken in the early afternoon
+A picture taken in the late afternoon
+A picture taken under the evening sky
+A picture taken during a starry night
+A picture taken during a cloudy morning
+A picture taken during a foggy dawn
+A picture taken on a clear summer evening
+A picture taken under the golden hour
+A picture taken under moonlight
+A picture taken on a rainy morning
+A picture taken on a snowy evening
+A picture taken under a vibrant sunset
+A picture taken during a thunderstorm
+A picture taken during twilight
+A picture taken under a crimson sky
+A picture taken on a windy afternoon
+A picture taken at the break of dawn
+A picture taken under the pale moonlight
+A picture taken in the blue hour
+A picture taken during the afternoon glow
+A picture taken under the shadow of clouds
+A picture taken during the first light
+A picture taken under the noonday sun
+A picture taken in the pink hues of sunrise
+A picture taken in the orange hues of sunset
+A picture taken during a calm night
+A picture taken under the shimmering stars
+A picture taken in the pre-dawn light
+A picture taken under the evening glow
+A picture taken on a frosty morning
+A picture taken on a sunny afternoon
+A picture taken under a lavender sky
+A picture taken during a quiet dawn
+A picture taken on a humid evening
+A picture taken under the fiery sunset
+A picture taken in the afternoon shadows
+A picture taken on a crisp morning
+A picture taken during a moonlit night
+A picture taken in the soft morning light
+A picture taken under a silver moon
+A picture taken during a vibrant sunrise
+A picture taken under the twilight glow
+A picture taken in the golden dusk
+A picture taken during the shadowy afternoon
+A picture taken under a blazing sun
+A picture taken at the darkest hour
+A picture taken during the sunrise mist
+A picture taken under a sapphire sky
+A picture taken at the first rays of dawn
+A picture taken in the orange twilight
+A picture taken during a hazy morning
+A picture taken under a dramatic sunset
+A picture taken on a serene evening
+A picture taken under the noonday glare
+A picture taken during a tranquil dawn
+A picture taken on a breezy afternoon
+A picture taken during a rainy dusk
+A picture taken under the evening breeze
+A picture taken in the warm evening light
+A picture taken under the glowing embers of sunset
+A picture taken during a late-night storm
+A picture taken under the glowing horizon
+A picture taken in the rosy dawn
+A picture taken during the soft afternoon
+A picture taken on a quiet morning
+A picture taken under the autumnal dusk
+A picture taken in the winter twilight
+A picture taken on a summer morning
+A picture taken under the early morning fog
+A picture taken during the golden morning
+A picture taken on a hazy dusk
+A picture taken in the spring sunrise
+A picture taken under the glowing winter moon
+A picture taken during the last light of day
+A picture taken under the summer solstice sun
+A picture taken in the icy morning air
+A picture taken during a luminous dusk
+A picture taken under a moonlit frost
+A picture taken on a rainy dawn
+A picture taken in the glow of the aurora
+A picture taken under the winter stars
+A picture taken during a chilly sunset
+A picture taken in the arctic twilight
+A picture taken under the gentle afternoon sun
+A picture taken during the last hour of sunlight
+A picture taken under the misty evening glow
+A picture taken in the coral hues of dawn
+A picture taken during a still morning
+A picture taken under the cobalt evening sky
+A picture taken in the amber light of sunset
+A picture taken at high noon
+A picture taken at early twilight
+A picture taken in the moonlit forest
+A picture taken during the blue dawn
+A picture taken on a misty evening
+A picture taken during the golden sunrise
+A picture taken under a starlit sky
+A picture taken on a stormy afternoon
+A picture taken during the hazy sunset
+A picture taken under a glowing evening sky
+A picture taken during a windy morning
+A picture taken in the glowing morning haze
+A picture taken during a frosty dawn
+A picture taken under the clear afternoon sky
+A picture taken on a peaceful night
+A picture taken during the orange dusk
+A picture taken on a rainy night
+A picture taken in the foggy evening
+A picture taken under a bright moon
+A picture taken on a calm afternoon
+A picture taken during a clear winter night
+A picture taken under the springtime sun
+A picture taken at the height of dawn
+A picture taken during the shimmering twilight
+A picture taken under a sapphire morning
+A picture taken on a breezy evening
+A picture taken at the morning break
+A picture taken during a fiery dusk
+A picture taken under the blazing summer sun
+A picture taken in the luminous dawn
+A picture taken during a clear night
+A picture taken on a frosty evening
+A picture taken at the edge of twilight
+A picture taken in the glow of sunrise
+A picture taken during the last rays of light
+A picture taken under the gentle starlight
+A picture taken on a golden afternoon
+A picture taken in the shimmering evening
+A picture taken during the misty sunrise
+A picture taken under the pale dawn
+A picture taken on a shadowy dusk
+A picture taken under the orange morning
+A picture taken in the silver evening light
+A picture taken during the early sunrise
+A picture taken under the radiant midday sun
+A picture taken in the autumn dawn
+A picture taken during the stormy dusk
+A picture taken under the first light of morning
+A picture taken on a calm winter night
+A picture taken during the soft golden hour
+A picture taken on a bright afternoon
+A picture taken in the gentle morning glow
+A picture taken under the hazy twilight
+A picture taken in the serene afternoon
+A picture taken during a warm summer dawn
+A picture taken under the clear winter skies
+A picture taken in the shimmering morning light
+A picture taken at the edge of night
+A picture taken in the blue morning haze
+A picture taken under the vibrant noonday sun
+A picture taken in the glowing sunset haze
+A picture taken during the frosty twilight
+A picture taken under the faint moonlight
+A picture taken on a stormy evening
+A picture taken during the golden evening glow
+A picture taken under the brilliant stars
+A picture taken in the radiant morning
+A picture taken during a misty dawn
+A picture taken under the glowing clouds of sunrise
+A picture taken in the soft evening twilight
+A picture taken at the heart of the night
+A picture taken in the gleaming afternoon sun
+A picture taken under the golden sky
+A picture taken in the deep blue dusk
+A picture taken during the shimmering aurora
+A picture taken under the morning frost
+A picture taken during the silver dusk
+A picture taken in the summer twilight
+A picture taken under the warm morning light
+A picture taken during the radiant sunset
+A picture taken in the quiet afternoon glow
+A picture taken under the soft light of dawn
+A picture taken during the hazy golden hour
+A picture taken under the sparkling morning frost
+A picture taken in the violet dusk
+A picture taken under the crisp winter sun
+A picture taken in the serene spring sunrise
+A picture taken during the tranquil night
+A picture taken in the glowing evening warmth
+A picture taken under the star-filled sky
+A picture taken in the pink morning haze
+A picture taken under the glowing sunrise hues
+A picture taken in the quiet golden hour
+A picture taken during the icy morning glow
+A picture taken under the vibrant evening light
+A picture taken in the early evening shadows
+A picture taken under the pale sunset glow
+A picture taken in the luminous starlight
+A picture taken at the golden crest of dawn
+A picture taken under the vibrant sunset glow
+A picture taken during a radiant evening
+A picture taken on a tranquil dawn
+A picture taken in the fading afternoon light
+A picture taken during the shimmering sunset
+A picture taken under the darkening sky
+A picture taken in the glowing starlight
+A picture taken during the crimson sunset
+A picture taken under the twilight shadows
+A picture taken in the soft golden sunlight
+A picture taken during the windy dusk
+A picture taken under the glowing horizon at dawn
+A picture taken in the cool morning mist
+A picture taken during the vibrant twilight
+A picture taken under the pale morning light
+A picture taken in the shimmering moonlit haze
+A picture taken under the radiant evening stars
+A picture taken in the autumnal morning
+A picture taken during the bright snowy sunrise
+A picture taken under the glowing clouds of twilight
+A picture taken in the golden glow of morning
+A picture taken under the serene night sky
+A picture taken in the fiery dusk haze
+A picture taken during the pale winter sunrise
+A picture taken under the morning sky's soft glow
+A picture taken in the violet evening
+A picture taken under the crisp, clear winter dusk
+A picture taken in the tranquil twilight
+A picture taken during the pink morning glow
+A picture taken under the icy shimmer of dawn
+A picture taken in the warmth of the evening glow
+A picture taken during a cool starry night
+A picture taken under the fiery crimson sky
+A picture taken in the last light of the day
+A picture taken under the fading golden light
+A picture taken in the pale, icy morning glow
+A picture taken under the soft pink sunrise
+A picture taken in the vibrant summer twilight
+A picture taken under the radiant afternoon sun
+A picture taken in the winter morning frost
+A picture taken under the golden stars of dusk
+A picture taken in the serene pink evening
+A picture taken under the last glow of the horizon
+A picture taken in the crisp blue twilight
+A picture taken under the glowing silver moon
+A picture taken in the frosty golden morning light
+A picture taken during the warm autumn dusk
+A picture taken under the summer evening breeze
+A picture taken in the hazy golden sunrise
+A picture taken under the cold glow of moonlight
+A picture taken in the fiery orange sunset glow
+A picture taken under the icy dawn sky
+A picture taken during the radiant pink twilight
+A picture taken in the calm after a summer rain
+A picture taken under the serene evening stars
+A picture taken in the warm afternoon breeze
+A picture taken under the pale violet evening glow
+A picture taken in the shimmering morning dew
+A picture taken under the misty dawn clouds
+A picture taken during the silver moonlit dawn
+A picture taken in the bright blue midday sky
+A picture taken under the pale orange horizon
+A picture taken in the shimmering snow-covered dusk
+A picture taken during the tranquil autumn morning
+A picture taken under the glowing morning clouds
+A picture taken in the shadow of the dawn
+A picture taken under the silver starlit glow
+A picture taken in the quiet glow of the morning sun
+A picture taken under the serene golden dawn light
+A picture taken in the gentle blue of early morning
+A picture taken under the warm glow of twilight
+A picture taken in the crisp air of winter sunrise
+A picture taken under the vibrant hues of evening
+A picture taken in the tranquil calm of dawn
+A picture taken under the icy blue evening light
+A picture taken in the warm glow of dusk
+A picture taken under the shimmering pink and orange sky
+A picture taken in the golden hour before sunset
+A picture taken under the serene violet morning haze
+A picture taken in the last rays of golden light
+A picture taken under the deep blue winter twilight
+A picture taken in the frosty pink dawn haze
+A picture taken under the glow of an evening aurora
+A picture taken in the crisp, clear evening sky
+A picture taken under the faint shimmer of dawn
+A picture taken in the vivid glow of a summer morning
+A picture taken under the orange hues of autumn sunset
+A picture taken in the silver and blue of night
+A picture taken under the golden shimmer of twilight
+A picture taken in the warm air of spring sunrise
+A picture taken under the fading light of the day
+A picture taken in the pink and purple evening hues
+A picture taken under the vibrant morning sunlight
+A picture taken in the glow of the first light of dawn
+A picture taken under the shimmering stars of midnight

IP_Composer/text_datasets/vehicle_color_descriptions.csv

@@ -0,0 +1,1521 @@
+Vehicle Description
+A picture with a red sports car.
+A picture with a blue sports car.
+A picture with a green sports car.
+A picture with a yellow sports car.
+A picture with a black sports car.
+A picture with a white sports car.
+A picture with a orange sports car.
+A picture with a purple sports car.
+A picture with a red sedan.
+A picture with a blue sedan.
+A picture with a green sedan.
+A picture with a yellow sedan.
+A picture with a black sedan.
+A picture with a white sedan.
+A picture with a orange sedan.
+A picture with a purple sedan.
+A picture with a red school bus.
+A picture with a blue school bus.
+A picture with a green school bus.
+A picture with a yellow school bus.
+A picture with a black school bus.
+A picture with a white school bus.
+A picture with a orange school bus.
+A picture with a purple school bus.
+A picture with a red pickup truck.
+A picture with a blue pickup truck.
+A picture with a green pickup truck.
+A picture with a yellow pickup truck.
+A picture with a black pickup truck.
+A picture with a white pickup truck.
+A picture with a orange pickup truck.
+A picture with a purple pickup truck.
+A picture with a red convertible.
+A picture with a blue convertible.
+A picture with a green convertible.
+A picture with a yellow convertible.
+A picture with a black convertible.
+A picture with a white convertible.
+A picture with a orange convertible.
+A picture with a purple convertible.
+A picture with a red SUV.
+A picture with a blue SUV.
+A picture with a green SUV.
+A picture with a yellow SUV.
+A picture with a black SUV.
+A picture with a white SUV.
+A picture with a orange SUV.
+A picture with a purple SUV.
+A picture with a red classic car.
+A picture with a blue classic car.
+A picture with a green classic car.
+A picture with a yellow classic car.
+A picture with a black classic car.
+A picture with a white classic car.
+A picture with a orange classic car.
+A picture with a purple classic car.
+A picture with a red minivan.
+A picture with a blue minivan.
+A picture with a green minivan.
+A picture with a yellow minivan.
+A picture with a black minivan.
+A picture with a white minivan.
+A picture with a orange minivan.
+A picture with a purple minivan.
+A picture with a red motorcycle.
+A picture with a blue motorcycle.
+A picture with a green motorcycle.
+A picture with a yellow motorcycle.
+A picture with a black motorcycle.
+A picture with a white motorcycle.
+A picture with a orange motorcycle.
+A picture with a purple motorcycle.
+A picture with a red delivery van.
+A picture with a blue delivery van.
+A picture with a green delivery van.
+A picture with a yellow delivery van.
+A picture with a black delivery van.
+A picture with a white delivery van.
+A picture with a orange delivery van.
+A picture with a purple delivery van.
+A picture with a red vintage truck.
+A picture with a blue vintage truck.
+A picture with a green vintage truck.
+A picture with a yellow vintage truck.
+A picture with a black vintage truck.
+A picture with a white vintage truck.
+A picture with a orange vintage truck.
+A picture with a purple vintage truck.
+A picture with a red limousine.
+A picture with a blue limousine.
+A picture with a green limousine.
+A picture with a yellow limousine.
+A picture with a black limousine.
+A picture with a white limousine.
+A picture with a orange limousine.
+A picture with a purple limousine.
+A picture with a red hatchback.
+A picture with a blue hatchback.
+A picture with a green hatchback.
+A picture with a yellow hatchback.
+A picture with a black hatchback.
+A picture with a white hatchback.
+A picture with a orange hatchback.
+A picture with a purple hatchback.
+A picture with a red coupe.
+A picture with a blue coupe.
+A picture with a green coupe.
+A picture with a yellow coupe.
+A picture with a black coupe.
+A picture with a white coupe.
+A picture with a orange coupe.
+A picture with a purple coupe.
+A picture with a red dune buggy.
+A picture with a blue dune buggy.
+A picture with a green dune buggy.
+A picture with a yellow dune buggy.
+A picture with a black dune buggy.
+A picture with a white dune buggy.
+A picture with a orange dune buggy.
+A picture with a purple dune buggy.
+A picture with a red racecar.
+A picture with a blue racecar.
+A picture with a green racecar.
+A picture with a yellow racecar.
+A picture with a black racecar.
+A picture with a white racecar.
+A picture with a orange racecar.
+A picture with a purple racecar.
+A picture with a red electric car.
+A picture with a blue electric car.
+A picture with a green electric car.
+A picture with a yellow electric car.
+A picture with a black electric car.
+A picture with a white electric car.
+A picture with a orange electric car.
+A picture with a purple electric car.
+A picture with a red luxury SUV.
+A picture with a blue luxury SUV.
+A picture with a green luxury SUV.
+A picture with a yellow luxury SUV.
+A picture with a black luxury SUV.
+A picture with a white luxury SUV.
+A picture with a orange luxury SUV.
+A picture with a purple luxury SUV.
+A picture with a red police car.
+A picture with a blue police car.
+A picture with a green police car.
+A picture with a yellow police car.
+A picture with a black police car.
+A picture with a white police car.
+A picture with a orange police car.
+A picture with a purple police car.
+A picture with a red military jeep.
+A picture with a blue military jeep.
+A picture with a green military jeep.
+A picture with a yellow military jeep.
+A picture with a black military jeep.
+A picture with a white military jeep.
+A picture with a orange military jeep.
+A picture with a purple military jeep.
+A picture with a red taxi cab.
+A picture with a blue taxi cab.
+A picture with a green taxi cab.
+A picture with a yellow taxi cab.
+A picture with a black taxi cab.
+A picture with a white taxi cab.
+A picture with a orange taxi cab.
+A picture with a purple taxi cab.
+A picture with a red camper van.
+A picture with a blue camper van.
+A picture with a green camper van.
+A picture with a yellow camper van.
+A picture with a black camper van.
+A picture with a white camper van.
+A picture with a orange camper van.
+A picture with a purple camper van.
+A picture with a red firetruck.
+A picture with a blue firetruck.
+A picture with a green firetruck.
+A picture with a yellow firetruck.
+A picture with a black firetruck.
+A picture with a white firetruck.
+A picture with a orange firetruck.
+A picture with a purple firetruck.
+A picture with a red tow truck.
+A picture with a blue tow truck.
+A picture with a green tow truck.
+A picture with a yellow tow truck.
+A picture with a black tow truck.
+A picture with a white tow truck.
+A picture with a orange tow truck.
+A picture with a purple tow truck.
+A picture with a red cargo truck.
+A picture with a blue cargo truck.
+A picture with a green cargo truck.
+A picture with a yellow cargo truck.
+A picture with a black cargo truck.
+A picture with a white cargo truck.
+A picture with a orange cargo truck.
+A picture with a purple cargo truck.
+A picture with a red garbage truck.
+A picture with a blue garbage truck.
+A picture with a green garbage truck.
+A picture with a yellow garbage truck.
+A picture with a black garbage truck.
+A picture with a white garbage truck.
+A picture with a orange garbage truck.
+A picture with a purple garbage truck.
+A picture with a red roadster.
+A picture with a blue roadster.
+A picture with a green roadster.
+A picture with a yellow roadster.
+A picture with a black roadster.
+A picture with a white roadster.
+A picture with a orange roadster.
+A picture with a purple roadster.
+A picture with a red armored vehicle.
+A picture with a blue armored vehicle.
+A picture with a green armored vehicle.
+A picture with a yellow armored vehicle.
+A picture with a black armored vehicle.
+A picture with a white armored vehicle.
+A picture with a orange armored vehicle.
+A picture with a purple armored vehicle.
+A picture with a red antique car.
+A picture with a blue antique car.
+A picture with a green antique car.
+A picture with a yellow antique car.
+A picture with a black antique car.
+A picture with a white antique car.
+A picture with a orange antique car.
+A picture with a purple antique car.
+A picture with a red tractor.
+A picture with a blue tractor.
+A picture with a green tractor.
+A picture with a yellow tractor.
+A picture with a black tractor.
+A picture with a white tractor.
+A picture with a orange tractor.
+A picture with a purple tractor.
+A picture with a red RV.
+A picture with a blue RV.
+A picture with a green RV.
+A picture with a yellow RV.
+A picture with a black RV.
+A picture with a white RV.
+A picture with a orange RV.
+A picture with a purple RV.
+A picture with a red ambulance.
+A picture with a blue ambulance.
+A picture with a green ambulance.
+A picture with a yellow ambulance.
+A picture with a black ambulance.
+A picture with a white ambulance.
+A picture with a orange ambulance.
+A picture with a purple ambulance.
+A picture with a red hearse.
+A picture with a blue hearse.
+A picture with a green hearse.
+A picture with a yellow hearse.
+A picture with a black hearse.
+A picture with a white hearse.
+A picture with a orange hearse.
+A picture with a purple hearse.
+A picture with a red race bike.
+A picture with a blue race bike.
+A picture with a green race bike.
+A picture with a yellow race bike.
+A picture with a black race bike.
+A picture with a white race bike.
+A picture with a orange race bike.
+A picture with a purple race bike.
+A picture with a red quad bike.
+A picture with a blue quad bike.
+A picture with a green quad bike.
+A picture with a yellow quad bike.
+A picture with a black quad bike.
+A picture with a white quad bike.
+A picture with a orange quad bike.
+A picture with a purple quad bike.
+A picture with a red scooter.
+A picture with a blue scooter.
+A picture with a green scooter.
+A picture with a yellow scooter.
+A picture with a black scooter.
+A picture with a white scooter.
+A picture with a orange scooter.
+A picture with a purple scooter.
+A picture with a red luxury car.
+A picture with a blue luxury car.
+A picture with a green luxury car.
+A picture with a yellow luxury car.
+A picture with a black luxury car.
+A picture with a white luxury car.
+A picture with a orange luxury car.
+A picture with a purple luxury car.
+A picture with a red stretch limousine.
+A picture with a blue stretch limousine.
+A picture with a green stretch limousine.
+A picture with a yellow stretch limousine.
+A picture with a black stretch limousine.
+A picture with a white stretch limousine.
+A picture with a orange stretch limousine.
+A picture with a purple stretch limousine.
+A picture with a red tank truck.
+A picture with a blue tank truck.
+A picture with a green tank truck.
+A picture with a yellow tank truck.
+A picture with a black tank truck.
+A picture with a white tank truck.
+A picture with a orange tank truck.
+A picture with a purple tank truck.
+A picture with a red custom bike.
+A picture with a blue custom bike.
+A picture with a green custom bike.
+A picture with a yellow custom bike.
+A picture with a black custom bike.
+A picture with a white custom bike.
+A picture with a orange custom bike.
+A picture with a purple custom bike.
+A picture with a red snowmobile.
+A picture with a blue snowmobile.
+A picture with a green snowmobile.
+A picture with a yellow snowmobile.
+A picture with a black snowmobile.
+A picture with a white snowmobile.
+A picture with a orange snowmobile.
+A picture with a purple snowmobile.
+A picture with a red ATV.
+A picture with a blue ATV.
+A picture with a green ATV.
+A picture with a yellow ATV.
+A picture with a black ATV.
+A picture with a white ATV.
+A picture with a orange ATV.
+A picture with a purple ATV.
+A picture with a red three-wheeler.
+A picture with a blue three-wheeler.
+A picture with a green three-wheeler.
+A picture with a yellow three-wheeler.
+A picture with a black three-wheeler.
+A picture with a white three-wheeler.
+A picture with a orange three-wheeler.
+A picture with a purple three-wheeler.
+A picture with a red ice cream truck.
+A picture with a blue ice cream truck.
+A picture with a green ice cream truck.
+A picture with a yellow ice cream truck.
+A picture with a black ice cream truck.
+A picture with a white ice cream truck.
+A picture with a orange ice cream truck.
+A picture with a purple ice cream truck.
+A picture with a red crane truck.
+A picture with a blue crane truck.
+A picture with a green crane truck.
+A picture with a yellow crane truck.
+A picture with a black crane truck.
+A picture with a white crane truck.
+A picture with a orange crane truck.
+A picture with a purple crane truck.
+A picture with a red hybrid SUV.
+A picture with a blue hybrid SUV.
+A picture with a green hybrid SUV.
+A picture with a yellow hybrid SUV.
+A picture with a black hybrid SUV.
+A picture with a white hybrid SUV.
+A picture with a orange hybrid SUV.
+A picture with a purple hybrid SUV.
+A picture with a red cruiser.
+A picture with a blue cruiser.
+A picture with a green cruiser.
+A picture with a yellow cruiser.
+A picture with a black cruiser.
+A picture with a white cruiser.
+A picture with a orange cruiser.
+A picture with a purple cruiser.
+A picture with a red farm truck.
+A picture with a blue farm truck.
+A picture with a green farm truck.
+A picture with a yellow farm truck.
+A picture with a black farm truck.
+A picture with a white farm truck.
+A picture with a orange farm truck.
+A picture with a purple farm truck.
+A picture with a red sports bike.
+A picture with a blue sports bike.
+A picture with a green sports bike.
+A picture with a yellow sports bike.
+A picture with a black sports bike.
+A picture with a white sports bike.
+A picture with a orange sports bike.
+A picture with a purple sports bike.
+A picture with a red hybrid car.
+A picture with a blue hybrid car.
+A picture with a green hybrid car.
+A picture with a yellow hybrid car.
+A picture with a black hybrid car.
+A picture with a white hybrid car.
+A picture with a orange hybrid car.
+A picture with a purple hybrid car.
+A picture with a red monster truck.
+A picture with a blue monster truck.
+A picture with a green monster truck.
+A picture with a yellow monster truck.
+A picture with a black monster truck.
+A picture with a white monster truck.
+A picture with a orange monster truck.
+A picture with a purple monster truck.
+A picture with a red off-road vehicle.
+A picture with a blue off-road vehicle.
+A picture with a green off-road vehicle.
+A picture with a yellow off-road vehicle.
+A picture with a black off-road vehicle.
+A picture with a white off-road vehicle.
+A picture with a orange off-road vehicle.
+A picture with a purple off-road vehicle.
+A picture with a red custom van.
+A picture with a blue custom van.
+A picture with a green custom van.
+A picture with a yellow custom van.
+A picture with a black custom van.
+A picture with a white custom van.
+A picture with a orange custom van.
+A picture with a purple custom van.
+A picture with a red jet ski trailer.
+A picture with a blue jet ski trailer.
+A picture with a green jet ski trailer.
+A picture with a yellow jet ski trailer.
+A picture with a black jet ski trailer.
+A picture with a white jet ski trailer.
+A picture with a orange jet ski trailer.
+A picture with a purple jet ski trailer.
+A picture with a red ladder truck.
+A picture with a blue ladder truck.
+A picture with a green ladder truck.
+A picture with a yellow ladder truck.
+A picture with a black ladder truck.
+A picture with a white ladder truck.
+A picture with a orange ladder truck.
+A picture with a purple ladder truck.
+A picture with a red racing SUV.
+A picture with a blue racing SUV.
+A picture with a green racing SUV.
+A picture with a yellow racing SUV.
+A picture with a black racing SUV.
+A picture with a white racing SUV.
+A picture with a orange racing SUV.
+A picture with a purple racing SUV.
+A picture with a red touring bus.
+A picture with a blue touring bus.
+A picture with a green touring bus.
+A picture with a yellow touring bus.
+A picture with a black touring bus.
+A picture with a white touring bus.
+A picture with a orange touring bus.
+A picture with a purple touring bus.
+A picture with a red electric van.
+A picture with a blue electric van.
+A picture with a green electric van.
+A picture with a yellow electric van.
+A picture with a black electric van.
+A picture with a white electric van.
+A picture with a orange electric van.
+A picture with a purple electric van.
+A picture with a red compact car.
+A picture with a blue compact car.
+A picture with a green compact car.
+A picture with a yellow compact car.
+A picture with a black compact car.
+A picture with a white compact car.
+A picture with a orange compact car.
+A picture with a purple compact car.
+A picture with a red autonomous shuttle bus.
+A picture with a blue autonomous shuttle bus.
+A picture with a green autonomous shuttle bus.
+A picture with a yellow autonomous shuttle bus.
+A picture with a black autonomous shuttle bus.
+A picture with a white autonomous shuttle bus.
+A picture with a orange autonomous shuttle bus.
+A picture with a purple autonomous shuttle bus.
+A picture with a red construction truck.
+A picture with a blue construction truck.
+A picture with a green construction truck.
+A picture with a yellow construction truck.
+A picture with a black construction truck.
+A picture with a white construction truck.
+A picture with a orange construction truck.
+A picture with a purple construction truck.
+A picture with a red bank truck.
+A picture with a blue bank truck.
+A picture with a green bank truck.
+A picture with a yellow bank truck.
+A picture with a black bank truck.
+A picture with a white bank truck.
+A picture with a orange bank truck.
+A picture with a purple bank truck.
+A picture with a red city bus.
+A picture with a blue city bus.
+A picture with a green city bus.
+A picture with a yellow city bus.
+A picture with a black city bus.
+A picture with a white city bus.
+A picture with a orange city bus.
+A picture with a purple city bus.
+A picture with a red station wagon.
+A picture with a blue station wagon.
+A picture with a green station wagon.
+A picture with a yellow station wagon.
+A picture with a black station wagon.
+A picture with a white station wagon.
+A picture with a orange station wagon.
+A picture with a purple station wagon.
+A picture with a red golf cart.
+A picture with a blue golf cart.
+A picture with a green golf cart.
+A picture with a yellow golf cart.
+A picture with a black golf cart.
+A picture with a white golf cart.
+A picture with a orange golf cart.
+A picture with a purple golf cart.
+A picture with a red dirt bike.
+A picture with a blue dirt bike.
+A picture with a green dirt bike.
+A picture with a yellow dirt bike.
+A picture with a black dirt bike.
+A picture with a white dirt bike.
+A picture with a orange dirt bike.
+A picture with a purple dirt bike.
+A picture with a red off-road jeep.
+A picture with a blue off-road jeep.
+A picture with a green off-road jeep.
+A picture with a yellow off-road jeep.
+A picture with a black off-road jeep.
+A picture with a white off-road jeep.
+A picture with a orange off-road jeep.
+A picture with a purple off-road jeep.
+A picture with a red solar car.
+A picture with a blue solar car.
+A picture with a green solar car.
+A picture with a yellow solar car.
+A picture with a black solar car.
+A picture with a white solar car.
+A picture with a orange solar car.
+A picture with a purple solar car.
+A picture with a red drift car.
+A picture with a blue drift car.
+A picture with a green drift car.
+A picture with a yellow drift car.
+A picture with a black drift car.
+A picture with a white drift car.
+A picture with a orange drift car.
+A picture with a purple drift car.
+A picture with a red patrol vehicle.
+A picture with a blue patrol vehicle.
+A picture with a green patrol vehicle.
+A picture with a yellow patrol vehicle.
+A picture with a black patrol vehicle.
+A picture with a white patrol vehicle.
+A picture with a orange patrol vehicle.
+A picture with a purple patrol vehicle.
+A picture with a red supercar.
+A picture with a blue supercar.
+A picture with a green supercar.
+A picture with a yellow supercar.
+A picture with a black supercar.
+A picture with a white supercar.
+A picture with a orange supercar.
+A picture with a purple supercar.
+A picture with a red tricycle.
+A picture with a blue tricycle.
+A picture with a green tricycle.
+A picture with a yellow tricycle.
+A picture with a black tricycle.
+A picture with a white tricycle.
+A picture with a orange tricycle.
+A picture with a purple tricycle.
+A picture with a red cargo van.
+A picture with a blue cargo van.
+A picture with a green cargo van.
+A picture with a yellow cargo van.
+A picture with a black cargo van.
+A picture with a white cargo van.
+A picture with a orange cargo van.
+A picture with a purple cargo van.
+A picture with a red camper trailer.
+A picture with a blue camper trailer.
+A picture with a green camper trailer.
+A picture with a yellow camper trailer.
+A picture with a black camper trailer.
+A picture with a white camper trailer.
+A picture with a orange camper trailer.
+A picture with a purple camper trailer.
+A picture with a red RV with stripes.
+A picture with a blue RV with stripes.
+A picture with a green RV with stripes.
+A picture with a yellow RV with stripes.
+A picture with a black RV with stripes.
+A picture with a white RV with stripes.
+A picture with a orange RV with stripes.
+A picture with a purple RV with stripes.
+A picture with a red delivery scooter.
+A picture with a blue delivery scooter.
+A picture with a green delivery scooter.
+A picture with a yellow delivery scooter.
+A picture with a black delivery scooter.
+A picture with a white delivery scooter.
+A picture with a orange delivery scooter.
+A picture with a purple delivery scooter.
+A picture with a red luxury limousine.
+A picture with a blue luxury limousine.
+A picture with a green luxury limousine.
+A picture with a yellow luxury limousine.
+A picture with a black luxury limousine.
+A picture with a white luxury limousine.
+A picture with a orange luxury limousine.
+A picture with a purple luxury limousine.
+A picture with a red compact sedan.
+A picture with a blue compact sedan.
+A picture with a green compact sedan.
+A picture with a yellow compact sedan.
+A picture with a black compact sedan.
+A picture with a white compact sedan.
+A picture with a orange compact sedan.
+A picture with a purple compact sedan.
+A picture with a red concept car.
+A picture with a blue concept car.
+A picture with a green concept car.
+A picture with a yellow concept car.
+A picture with a black concept car.
+A picture with a white concept car.
+A picture with a orange concept car.
+A picture with a purple concept car.
+A picture with a red autonomous vehicle.
+A picture with a blue autonomous vehicle.
+A picture with a green autonomous vehicle.
+A picture with a yellow autonomous vehicle.
+A picture with a black autonomous vehicle.
+A picture with a white autonomous vehicle.
+A picture with a orange autonomous vehicle.
+A picture with a purple autonomous vehicle.
+A picture with a red VW Beetle.
+A picture with a blue VW Beetle.
+A picture with a green VW Beetle.
+A picture with a yellow VW Beetle.
+A picture with a black VW Beetle.
+A picture with a white VW Beetle.
+A picture with a orange VW Beetle.
+A picture with a purple VW Beetle.
+A picture with a red beverage truck.
+A picture with a blue beverage truck.
+A picture with a green beverage truck.
+A picture with a yellow beverage truck.
+A picture with a black beverage truck.
+A picture with a white beverage truck.
+A picture with a orange beverage truck.
+A picture with a purple beverage truck.
+A picture with a red food truck.
+A picture with a blue food truck.
+A picture with a green food truck.
+A picture with a yellow food truck.
+A picture with a black food truck.
+A picture with a white food truck.
+A picture with a orange food truck.
+A picture with a purple food truck.
+A picture with a red cargo trailer.
+A picture with a blue cargo trailer.
+A picture with a green cargo trailer.
+A picture with a yellow cargo trailer.
+A picture with a black cargo trailer.
+A picture with a white cargo trailer.
+A picture with a orange cargo trailer.
+A picture with a purple cargo trailer.
+A picture with a red bulldozer.
+A picture with a blue bulldozer.
+A picture with a green bulldozer.
+A picture with a yellow bulldozer.
+A picture with a black bulldozer.
+A picture with a white bulldozer.
+A picture with a orange bulldozer.
+A picture with a purple bulldozer.
+A picture with a red tram car.
+A picture with a blue tram car.
+A picture with a green tram car.
+A picture with a yellow tram car.
+A picture with a black tram car.
+A picture with a white tram car.
+A picture with a orange tram car.
+A picture with a purple tram car.
+A picture with a red bus.
+A picture with a blue bus.
+A picture with a green bus.
+A picture with a yellow bus.
+A picture with a black bus.
+A picture with a white bus.
+A picture with a orange bus.
+A picture with a purple bus.
+A picture with a red taxi.
+A picture with a blue taxi.
+A picture with a green taxi.
+A picture with a yellow taxi.
+A picture with a black taxi.
+A picture with a white taxi.
+A picture with a orange taxi.
+A picture with a purple taxi.
+A picture with a red custom jeep.
+A picture with a blue custom jeep.
+A picture with a green custom jeep.
+A picture with a yellow custom jeep.
+A picture with a black custom jeep.
+A picture with a white custom jeep.
+A picture with a orange custom jeep.
+A picture with a purple custom jeep.
+A picture with a red chopper bike.
+A picture with a blue chopper bike.
+A picture with a green chopper bike.
+A picture with a yellow chopper bike.
+A picture with a black chopper bike.
+A picture with a white chopper bike.
+A picture with a orange chopper bike.
+A picture with a purple chopper bike.
+A picture with a red hovercraft.
+A picture with a blue hovercraft.
+A picture with a green hovercraft.
+A picture with a yellow hovercraft.
+A picture with a black hovercraft.
+A picture with a white hovercraft.
+A picture with a orange hovercraft.
+A picture with a purple hovercraft.
+A picture with a red snowplow.
+A picture with a blue snowplow.
+A picture with a green snowplow.
+A picture with a yellow snowplow.
+A picture with a black snowplow.
+A picture with a white snowplow.
+A picture with a orange snowplow.
+A picture with a purple snowplow.
+A picture with a red golf buggy.
+A picture with a blue golf buggy.
+A picture with a green golf buggy.
+A picture with a yellow golf buggy.
+A picture with a black golf buggy.
+A picture with a white golf buggy.
+A picture with a orange golf buggy.
+A picture with a purple golf buggy.
+A picture with a red submarine.
+A picture with a blue submarine.
+A picture with a green submarine.
+A picture with a yellow submarine.
+A picture with a black submarine.
+A picture with a white submarine.
+A picture with a orange submarine.
+A picture with a purple submarine.
+A picture with a red space shuttle.
+A picture with a blue space shuttle.
+A picture with a green space shuttle.
+A picture with a yellow space shuttle.
+A picture with a black space shuttle.
+A picture with a white space shuttle.
+A picture with a orange space shuttle.
+A picture with a purple space shuttle.
+A picture with a red cable car.
+A picture with a blue cable car.
+A picture with a green cable car.
+A picture with a yellow cable car.
+A picture with a black cable car.
+A picture with a white cable car.
+A picture with a orange cable car.
+A picture with a purple cable car.
+A picture with a red rickshaw.
+A picture with a blue rickshaw.
+A picture with a green rickshaw.
+A picture with a yellow rickshaw.
+A picture with a black rickshaw.
+A picture with a white rickshaw.
+A picture with a orange rickshaw.
+A picture with a purple rickshaw.
+A picture with a red rowing boat.
+A picture with a blue rowing boat.
+A picture with a green rowing boat.
+A picture with a yellow rowing boat.
+A picture with a black rowing boat.
+A picture with a white rowing boat.
+A picture with a orange rowing boat.
+A picture with a purple rowing boat.
+A picture with a red kayak.
+A picture with a blue kayak.
+A picture with a green kayak.
+A picture with a yellow kayak.
+A picture with a black kayak.
+A picture with a white kayak.
+A picture with a orange kayak.
+A picture with a purple kayak.
+A picture with a red canoe.
+A picture with a blue canoe.
+A picture with a green canoe.
+A picture with a yellow canoe.
+A picture with a black canoe.
+A picture with a white canoe.
+A picture with a orange canoe.
+A picture with a purple canoe.
+A picture with a red yacht.
+A picture with a blue yacht.
+A picture with a green yacht.
+A picture with a yellow yacht.
+A picture with a black yacht.
+A picture with a white yacht.
+A picture with a orange yacht.
+A picture with a purple yacht.
+A picture with a red fishing boat.
+A picture with a blue fishing boat.
+A picture with a green fishing boat.
+A picture with a yellow fishing boat.
+A picture with a black fishing boat.
+A picture with a white fishing boat.
+A picture with a orange fishing boat.
+A picture with a purple fishing boat.
+A picture with a red cruise ship.
+A picture with a blue cruise ship.
+A picture with a green cruise ship.
+A picture with a yellow cruise ship.
+A picture with a black cruise ship.
+A picture with a white cruise ship.
+A picture with a orange cruise ship.
+A picture with a purple cruise ship.
+A picture with a red sailboat.
+A picture with a blue sailboat.
+A picture with a green sailboat.
+A picture with a yellow sailboat.
+A picture with a black sailboat.
+A picture with a white sailboat.
+A picture with a orange sailboat.
+A picture with a purple sailboat.
+A picture with a red dinghy.
+A picture with a blue dinghy.
+A picture with a green dinghy.
+A picture with a yellow dinghy.
+A picture with a black dinghy.
+A picture with a white dinghy.
+A picture with a orange dinghy.
+A picture with a purple dinghy.
+A picture with a red paddle boat.
+A picture with a blue paddle boat.
+A picture with a green paddle boat.
+A picture with a yellow paddle boat.
+A picture with a black paddle boat.
+A picture with a white paddle boat.
+A picture with a orange paddle boat.
+A picture with a purple paddle boat.
+A picture with a red jet ski.
+A picture with a blue jet ski.
+A picture with a green jet ski.
+A picture with a yellow jet ski.
+A picture with a black jet ski.
+A picture with a white jet ski.
+A picture with a orange jet ski.
+A picture with a purple jet ski.
+A picture with a red amphibious vehicle.
+A picture with a blue amphibious vehicle.
+A picture with a green amphibious vehicle.
+A picture with a yellow amphibious vehicle.
+A picture with a black amphibious vehicle.
+A picture with a white amphibious vehicle.
+A picture with a orange amphibious vehicle.
+A picture with a purple amphibious vehicle.
+A picture with a red glider.
+A picture with a blue glider.
+A picture with a green glider.
+A picture with a yellow glider.
+A picture with a black glider.
+A picture with a white glider.
+A picture with a orange glider.
+A picture with a purple glider.
+A picture with a red light aircraft.
+A picture with a blue light aircraft.
+A picture with a green light aircraft.
+A picture with a yellow light aircraft.
+A picture with a black light aircraft.
+A picture with a white light aircraft.
+A picture with a orange light aircraft.
+A picture with a purple light aircraft.
+A picture with a red hot air balloon.
+A picture with a blue hot air balloon.
+A picture with a green hot air balloon.
+A picture with a yellow hot air balloon.
+A picture with a black hot air balloon.
+A picture with a white hot air balloon.
+A picture with a orange hot air balloon.
+A picture with a purple hot air balloon.
+A picture with a red helicopter.
+A picture with a blue helicopter.
+A picture with a green helicopter.
+A picture with a yellow helicopter.
+A picture with a black helicopter.
+A picture with a white helicopter.
+A picture with a orange helicopter.
+A picture with a purple helicopter.
+A picture with a red monorail.
+A picture with a blue monorail.
+A picture with a green monorail.
+A picture with a yellow monorail.
+A picture with a black monorail.
+A picture with a white monorail.
+A picture with a orange monorail.
+A picture with a purple monorail.
+A picture with a red steam train.
+A picture with a blue steam train.
+A picture with a green steam train.
+A picture with a yellow steam train.
+A picture with a black steam train.
+A picture with a white steam train.
+A picture with a orange steam train.
+A picture with a purple steam train.
+A picture with a red freight train.
+A picture with a blue freight train.
+A picture with a green freight train.
+A picture with a yellow freight train.
+A picture with a black freight train.
+A picture with a white freight train.
+A picture with a orange freight train.
+A picture with a purple freight train.
+A picture with a red bullet train.
+A picture with a blue bullet train.
+A picture with a green bullet train.
+A picture with a yellow bullet train.
+A picture with a black bullet train.
+A picture with a white bullet train.
+A picture with a orange bullet train.
+A picture with a purple bullet train.
+A picture with a red tram.
+A picture with a blue tram.
+A picture with a green tram.
+A picture with a yellow tram.
+A picture with a black tram.
+A picture with a white tram.
+A picture with a orange tram.
+A picture with a purple tram.
+A picture with a red subway car.
+A picture with a blue subway car.
+A picture with a green subway car.
+A picture with a yellow subway car.
+A picture with a black subway car.
+A picture with a white subway car.
+A picture with a orange subway car.
+A picture with a purple subway car.
+A picture with a red trolleybus.
+A picture with a blue trolleybus.
+A picture with a green trolleybus.
+A picture with a yellow trolleybus.
+A picture with a black trolleybus.
+A picture with a white trolleybus.
+A picture with a orange trolleybus.
+A picture with a purple trolleybus.
+A picture with a red caravan.
+A picture with a blue caravan.
+A picture with a green caravan.
+A picture with a yellow caravan.
+A picture with a black caravan.
+A picture with a white caravan.
+A picture with a orange caravan.
+A picture with a purple caravan.
+A picture with a red utility van.
+A picture with a blue utility van.
+A picture with a green utility van.
+A picture with a yellow utility van.
+A picture with a black utility van.
+A picture with a white utility van.
+A picture with a orange utility van.
+A picture with a purple utility van.
+A picture with a red towable RV.
+A picture with a blue towable RV.
+A picture with a green towable RV.
+A picture with a yellow towable RV.
+A picture with a black towable RV.
+A picture with a white towable RV.
+A picture with a orange towable RV.
+A picture with a purple towable RV.
+A picture with a red microcar.
+A picture with a blue microcar.
+A picture with a green microcar.
+A picture with a yellow microcar.
+A picture with a black microcar.
+A picture with a white microcar.
+A picture with a orange microcar.
+A picture with a purple microcar.
+A picture with a red electric scooter.
+A picture with a blue electric scooter.
+A picture with a green electric scooter.
+A picture with a yellow electric scooter.
+A picture with a black electric scooter.
+A picture with a white electric scooter.
+A picture with a orange electric scooter.
+A picture with a purple electric scooter.
+A picture with a red all-terrain vehicle.
+A picture with a blue all-terrain vehicle.
+A picture with a green all-terrain vehicle.
+A picture with a yellow all-terrain vehicle.
+A picture with a black all-terrain vehicle.
+A picture with a white all-terrain vehicle.
+A picture with a orange all-terrain vehicle.
+A picture with a purple all-terrain vehicle.
+A picture with a red off-road buggy.
+A picture with a blue off-road buggy.
+A picture with a green off-road buggy.
+A picture with a yellow off-road buggy.
+A picture with a black off-road buggy.
+A picture with a white off-road buggy.
+A picture with a orange off-road buggy.
+A picture with a purple off-road buggy.
+A picture with a red personal watercraft.
+A picture with a blue personal watercraft.
+A picture with a green personal watercraft.
+A picture with a yellow personal watercraft.
+A picture with a black personal watercraft.
+A picture with a white personal watercraft.
+A picture with a orange personal watercraft.
+A picture with a purple personal watercraft.
+A picture with a red moped.
+A picture with a blue moped.
+A picture with a green moped.
+A picture with a yellow moped.
+A picture with a black moped.
+A picture with a white moped.
+A picture with a orange moped.
+A picture with a purple moped.
+A picture with a red minibike.
+A picture with a blue minibike.
+A picture with a green minibike.
+A picture with a yellow minibike.
+A picture with a black minibike.
+A picture with a white minibike.
+A picture with a orange minibike.
+A picture with a purple minibike.
+A picture with a red bicycle.
+A picture with a blue bicycle.
+A picture with a green bicycle.
+A picture with a yellow bicycle.
+A picture with a black bicycle.
+A picture with a white bicycle.
+A picture with a orange bicycle.
+A picture with a purple bicycle.
+A picture with a red unicycle.
+A picture with a blue unicycle.
+A picture with a green unicycle.
+A picture with a yellow unicycle.
+A picture with a black unicycle.
+A picture with a white unicycle.
+A picture with a orange unicycle.
+A picture with a purple unicycle.
+A picture with a red segway.
+A picture with a blue segway.
+A picture with a green segway.
+A picture with a yellow segway.
+A picture with a black segway.
+A picture with a white segway.
+A picture with a orange segway.
+A picture with a purple segway.
+A picture with a red self-driving car.
+A picture with a blue self-driving car.
+A picture with a green self-driving car.
+A picture with a yellow self-driving car.
+A picture with a black self-driving car.
+A picture with a white self-driving car.
+A picture with a orange self-driving car.
+A picture with a purple self-driving car.
+A picture with a red ride-on lawnmower.
+A picture with a blue ride-on lawnmower.
+A picture with a green ride-on lawnmower.
+A picture with a yellow ride-on lawnmower.
+A picture with a black ride-on lawnmower.
+A picture with a white ride-on lawnmower.
+A picture with a orange ride-on lawnmower.
+A picture with a purple ride-on lawnmower.
+A picture with a red tractor-trailer.
+A picture with a blue tractor-trailer.
+A picture with a green tractor-trailer.
+A picture with a yellow tractor-trailer.
+A picture with a black tractor-trailer.
+A picture with a white tractor-trailer.
+A picture with a orange tractor-trailer.
+A picture with a purple tractor-trailer.
+A picture with a red semi-truck.
+A picture with a blue semi-truck.
+A picture with a green semi-truck.
+A picture with a yellow semi-truck.
+A picture with a black semi-truck.
+A picture with a white semi-truck.
+A picture with a orange semi-truck.
+A picture with a purple semi-truck.
+A picture with a red lowboy truck.
+A picture with a blue lowboy truck.
+A picture with a green lowboy truck.
+A picture with a yellow lowboy truck.
+A picture with a black lowboy truck.
+A picture with a white lowboy truck.
+A picture with a orange lowboy truck.
+A picture with a purple lowboy truck.
+A picture with a red box truck.
+A picture with a blue box truck.
+A picture with a green box truck.
+A picture with a yellow box truck.
+A picture with a black box truck.
+A picture with a white box truck.
+A picture with a orange box truck.
+A picture with a purple box truck.
+A picture with a red refrigerated truck.
+A picture with a blue refrigerated truck.
+A picture with a green refrigerated truck.
+A picture with a yellow refrigerated truck.
+A picture with a black refrigerated truck.
+A picture with a white refrigerated truck.
+A picture with a orange refrigerated truck.
+A picture with a purple refrigerated truck.
+A picture with a red cement mixer.
+A picture with a blue cement mixer.
+A picture with a green cement mixer.
+A picture with a yellow cement mixer.
+A picture with a black cement mixer.
+A picture with a white cement mixer.
+A picture with a orange cement mixer.
+A picture with a purple cement mixer.
+A picture with a red dump truck.
+A picture with a blue dump truck.
+A picture with a green dump truck.
+A picture with a yellow dump truck.
+A picture with a black dump truck.
+A picture with a white dump truck.
+A picture with a orange dump truck.
+A picture with a purple dump truck.
+A picture with a red flatbed truck.
+A picture with a blue flatbed truck.
+A picture with a green flatbed truck.
+A picture with a yellow flatbed truck.
+A picture with a black flatbed truck.
+A picture with a white flatbed truck.
+A picture with a orange flatbed truck.
+A picture with a purple flatbed truck.
+A picture with a red log carrier.
+A picture with a blue log carrier.
+A picture with a green log carrier.
+A picture with a yellow log carrier.
+A picture with a black log carrier.
+A picture with a white log carrier.
+A picture with a orange log carrier.
+A picture with a purple log carrier.
+A picture with a red mining truck.
+A picture with a blue mining truck.
+A picture with a green mining truck.
+A picture with a yellow mining truck.
+A picture with a black mining truck.
+A picture with a white mining truck.
+A picture with a orange mining truck.
+A picture with a purple mining truck.
+A picture with a red tank.
+A picture with a blue tank.
+A picture with a green tank.
+A picture with a yellow tank.
+A picture with a black tank.
+A picture with a white tank.
+A picture with a orange tank.
+A picture with a purple tank.
+A picture with a red troop carrier.
+A picture with a blue troop carrier.
+A picture with a green troop carrier.
+A picture with a yellow troop carrier.
+A picture with a black troop carrier.
+A picture with a white troop carrier.
+A picture with a orange troop carrier.
+A picture with a purple troop carrier.
+A picture with a red fire engine.
+A picture with a blue fire engine.
+A picture with a green fire engine.
+A picture with a yellow fire engine.
+A picture with a black fire engine.
+A picture with a white fire engine.
+A picture with a orange fire engine.
+A picture with a purple fire engine.
+A picture with a red rescue boat.
+A picture with a blue rescue boat.
+A picture with a green rescue boat.
+A picture with a yellow rescue boat.
+A picture with a black rescue boat.
+A picture with a white rescue boat.
+A picture with a orange rescue boat.
+A picture with a purple rescue boat.
+A picture with a red lifeboat.
+A picture with a blue lifeboat.
+A picture with a green lifeboat.
+A picture with a yellow lifeboat.
+A picture with a black lifeboat.
+A picture with a white lifeboat.
+A picture with a orange lifeboat.
+A picture with a purple lifeboat.
+A picture with a red pontoon boat.
+A picture with a blue pontoon boat.
+A picture with a green pontoon boat.
+A picture with a yellow pontoon boat.
+A picture with a black pontoon boat.
+A picture with a white pontoon boat.
+A picture with a orange pontoon boat.
+A picture with a purple pontoon boat.
+A picture with a red scooter board.
+A picture with a blue scooter board.
+A picture with a green scooter board.
+A picture with a yellow scooter board.
+A picture with a black scooter board.
+A picture with a white scooter board.
+A picture with a orange scooter board.
+A picture with a purple scooter board.
+A picture with a red hoverboard.
+A picture with a blue hoverboard.
+A picture with a green hoverboard.
+A picture with a yellow hoverboard.
+A picture with a black hoverboard.
+A picture with a white hoverboard.
+A picture with a orange hoverboard.
+A picture with a purple hoverboard.
+A picture with a red electric skateboard.
+A picture with a blue electric skateboard.
+A picture with a green electric skateboard.
+A picture with a yellow electric skateboard.
+A picture with a black electric skateboard.
+A picture with a white electric skateboard.
+A picture with a orange electric skateboard.
+A picture with a purple electric skateboard.
+A picture with a red kick scooter.
+A picture with a blue kick scooter.
+A picture with a green kick scooter.
+A picture with a yellow kick scooter.
+A picture with a black kick scooter.
+A picture with a white kick scooter.
+A picture with a orange kick scooter.
+A picture with a purple kick scooter.
+A picture with a red powered parachute.
+A picture with a blue powered parachute.
+A picture with a green powered parachute.
+A picture with a yellow powered parachute.
+A picture with a black powered parachute.
+A picture with a white powered parachute.
+A picture with a orange powered parachute.
+A picture with a purple powered parachute.
+A picture with a red paraglider.
+A picture with a blue paraglider.
+A picture with a green paraglider.
+A picture with a yellow paraglider.
+A picture with a black paraglider.
+A picture with a white paraglider.
+A picture with a orange paraglider.
+A picture with a purple paraglider.
+A picture with a red airship.
+A picture with a blue airship.
+A picture with a green airship.
+A picture with a yellow airship.
+A picture with a black airship.
+A picture with a white airship.
+A picture with a orange airship.
+A picture with a purple airship.
+A picture with a red blimp.
+A picture with a blue blimp.
+A picture with a green blimp.
+A picture with a yellow blimp.
+A picture with a black blimp.
+A picture with a white blimp.
+A picture with a orange blimp.
+A picture with a purple blimp.
+A picture with a red dirigible.
+A picture with a blue dirigible.
+A picture with a green dirigible.
+A picture with a yellow dirigible.
+A picture with a black dirigible.
+A picture with a white dirigible.
+A picture with a orange dirigible.
+A picture with a purple dirigible.
+A picture with a red crane.
+A picture with a blue crane.
+A picture with a green crane.
+A picture with a yellow crane.
+A picture with a black crane.
+A picture with a white crane.
+A picture with a orange crane.
+A picture with a purple crane.
+A picture with a red excavator.
+A picture with a blue excavator.
+A picture with a green excavator.
+A picture with a yellow excavator.
+A picture with a black excavator.
+A picture with a white excavator.
+A picture with a orange excavator.
+A picture with a purple excavator.
+A picture with a red front loader.
+A picture with a blue front loader.
+A picture with a green front loader.
+A picture with a yellow front loader.
+A picture with a black front loader.
+A picture with a white front loader.
+A picture with a orange front loader.
+A picture with a purple front loader.
+A picture with a red grader.
+A picture with a blue grader.
+A picture with a green grader.
+A picture with a yellow grader.
+A picture with a black grader.
+A picture with a white grader.
+A picture with a orange grader.
+A picture with a purple grader.
+A picture with a red road roller.
+A picture with a blue road roller.
+A picture with a green road roller.
+A picture with a yellow road roller.
+A picture with a black road roller.
+A picture with a white road roller.
+A picture with a orange road roller.
+A picture with a purple road roller.
+A picture with a red paver.
+A picture with a blue paver.
+A picture with a green paver.
+A picture with a yellow paver.
+A picture with a black paver.
+A picture with a white paver.
+A picture with a orange paver.
+A picture with a purple paver.
+A picture with a red cherry picker.
+A picture with a blue cherry picker.
+A picture with a green cherry picker.
+A picture with a yellow cherry picker.
+A picture with a black cherry picker.
+A picture with a white cherry picker.
+A picture with a orange cherry picker.
+A picture with a purple cherry picker.
+A picture with a red scissor lift.
+A picture with a blue scissor lift.
+A picture with a green scissor lift.
+A picture with a yellow scissor lift.
+A picture with a black scissor lift.
+A picture with a white scissor lift.
+A picture with a orange scissor lift.
+A picture with a purple scissor lift.
+A picture with a red forklift.
+A picture with a blue forklift.
+A picture with a green forklift.
+A picture with a yellow forklift.
+A picture with a black forklift.
+A picture with a white forklift.
+A picture with a orange forklift.
+A picture with a purple forklift.
+A picture with a red warehouse tug.
+A picture with a blue warehouse tug.
+A picture with a green warehouse tug.
+A picture with a yellow warehouse tug.
+A picture with a black warehouse tug.
+A picture with a white warehouse tug.
+A picture with a orange warehouse tug.
+A picture with a purple warehouse tug.
+A picture with a red skid-steer loader.
+A picture with a blue skid-steer loader.
+A picture with a green skid-steer loader.
+A picture with a yellow skid-steer loader.
+A picture with a black skid-steer loader.
+A picture with a white skid-steer loader.
+A picture with a orange skid-steer loader.
+A picture with a purple skid-steer loader.
+A picture with a red street sweeper.
+A picture with a blue street sweeper.
+A picture with a green street sweeper.
+A picture with a yellow street sweeper.
+A picture with a black street sweeper.
+A picture with a white street sweeper.
+A picture with a orange street sweeper.
+A picture with a purple street sweeper.
+A picture with a red garbage compactor.
+A picture with a blue garbage compactor.
+A picture with a green garbage compactor.
+A picture with a yellow garbage compactor.
+A picture with a black garbage compactor.
+A picture with a white garbage compactor.
+A picture with a orange garbage compactor.
+A picture with a purple garbage compactor.
+A picture with a red auto rickshaw.
+A picture with a blue auto rickshaw.
+A picture with a green auto rickshaw.
+A picture with a yellow auto rickshaw.
+A picture with a black auto rickshaw.
+A picture with a white auto rickshaw.
+A picture with a orange auto rickshaw.
+A picture with a purple auto rickshaw.
+A picture with a red electric trike.
+A picture with a blue electric trike.
+A picture with a green electric trike.
+A picture with a yellow electric trike.
+A picture with a black electric trike.
+A picture with a white electric trike.
+A picture with a orange electric trike.
+A picture with a purple electric trike.
+A picture with a red luxury yacht.
+A picture with a blue luxury yacht.
+A picture with a green luxury yacht.
+A picture with a yellow luxury yacht.
+A picture with a black luxury yacht.
+A picture with a white luxury yacht.
+A picture with a orange luxury yacht.
+A picture with a purple luxury yacht.
+A picture with a red speedboat.
+A picture with a blue speedboat.
+A picture with a green speedboat.
+A picture with a yellow speedboat.
+A picture with a black speedboat.
+A picture with a white speedboat.
+A picture with a orange speedboat.
+A picture with a purple speedboat.
+A picture with a red deep-sea trawler.
+A picture with a blue deep-sea trawler.
+A picture with a green deep-sea trawler.
+A picture with a yellow deep-sea trawler.
+A picture with a black deep-sea trawler.
+A picture with a white deep-sea trawler.
+A picture with a orange deep-sea trawler.
+A picture with a purple deep-sea trawler.
+A picture with a red ferry.
+A picture with a blue ferry.
+A picture with a green ferry.
+A picture with a yellow ferry.
+A picture with a black ferry.
+A picture with a white ferry.
+A picture with a orange ferry.
+A picture with a purple ferry.
+A picture with a red hovertrain.
+A picture with a blue hovertrain.
+A picture with a green hovertrain.
+A picture with a yellow hovertrain.
+A picture with a black hovertrain.
+A picture with a white hovertrain.
+A picture with a orange hovertrain.
+A picture with a purple hovertrain.
+A picture with a red mountain bike.
+A picture with a blue mountain bike.
+A picture with a green mountain bike.
+A picture with a yellow mountain bike.
+A picture with a black mountain bike.
+A picture with a white mountain bike.
+A picture with a orange mountain bike.
+A picture with a purple mountain bike.
+A picture with a red road bike.
+A picture with a blue road bike.
+A picture with a green road bike.
+A picture with a yellow road bike.
+A picture with a black road bike.
+A picture with a white road bike.
+A picture with a orange road bike.
+A picture with a purple road bike.
+A picture with a red fixie bike.
+A picture with a blue fixie bike.
+A picture with a green fixie bike.
+A picture with a yellow fixie bike.
+A picture with a black fixie bike.
+A picture with a white fixie bike.
+A picture with a orange fixie bike.
+A picture with a purple fixie bike.
+A picture with a red electric mountain bike.
+A picture with a blue electric mountain bike.
+A picture with a green electric mountain bike.
+A picture with a yellow electric mountain bike.
+A picture with a black electric mountain bike.
+A picture with a white electric mountain bike.
+A picture with a orange electric mountain bike.
+A picture with a purple electric mountain bike.
+A picture with a red cargo bike.
+A picture with a blue cargo bike.
+A picture with a green cargo bike.
+A picture with a yellow cargo bike.
+A picture with a black cargo bike.
+A picture with a white cargo bike.
+A picture with a orange cargo bike.
+A picture with a purple cargo bike.
+A picture with a red delivery trike.
+A picture with a blue delivery trike.
+A picture with a green delivery trike.
+A picture with a yellow delivery trike.
+A picture with a black delivery trike.
+A picture with a white delivery trike.
+A picture with a orange delivery trike.
+A picture with a purple delivery trike.
+A picture with a red recumbent bike.
+A picture with a blue recumbent bike.
+A picture with a green recumbent bike.
+A picture with a yellow recumbent bike.
+A picture with a black recumbent bike.
+A picture with a white recumbent bike.
+A picture with a orange recumbent bike.
+A picture with a purple recumbent bike.
+A picture with a red pedal car.
+A picture with a blue pedal car.
+A picture with a green pedal car.
+A picture with a yellow pedal car.
+A picture with a black pedal car.
+A picture with a white pedal car.
+A picture with a orange pedal car.
+A picture with a purple pedal car.
+A picture with a red soapbox derby car.
+A picture with a blue soapbox derby car.
+A picture with a green soapbox derby car.
+A picture with a yellow soapbox derby car.
+A picture with a black soapbox derby car.
+A picture with a white soapbox derby car.
+A picture with a orange soapbox derby car.
+A picture with a purple soapbox derby car.
+A picture with a red snowmobile sled.
+A picture with a blue snowmobile sled.
+A picture with a green snowmobile sled.
+A picture with a yellow snowmobile sled.
+A picture with a black snowmobile sled.
+A picture with a white snowmobile sled.
+A picture with a orange snowmobile sled.
+A picture with a purple snowmobile sled.
+A picture with a red rock crawler.
+A picture with a blue rock crawler.
+A picture with a green rock crawler.
+A picture with a yellow rock crawler.
+A picture with a black rock crawler.
+A picture with a white rock crawler.
+A picture with a orange rock crawler.
+A picture with a purple rock crawler.

IP_Composer/text_datasets/vehicle_descriptions.csv

@@ -0,0 +1,191 @@
+Vehicle Description
+A picture with a sports car.
+A picture with a sedan.
+A picture with a school bus.
+A picture with a pickup truck.
+A picture with a convertible.
+A picture with a SUV.
+A picture with a classic car.
+A picture with a minivan.
+A picture with a motorcycle.
+A picture with a delivery van.
+A picture with a vintage truck.
+A picture with a limousine.
+A picture with a hatchback.
+A picture with a coupe.
+A picture with a dune buggy.
+A picture with a racecar.
+A picture with a electric car.
+A picture with a luxury SUV.
+A picture with a police car.
+A picture with a military jeep.
+A picture with a taxi cab.
+A picture with a camper van.
+A picture with a firetruck.
+A picture with a tow truck.
+A picture with a cargo truck.
+A picture with a garbage truck.
+A picture with a roadster.
+A picture with a armored vehicle.
+A picture with a antique car.
+A picture with a tractor.
+A picture with a RV.
+A picture with a ambulance.
+A picture with a hearse.
+A picture with a race bike.
+A picture with a quad bike.
+A picture with a scooter.
+A picture with a luxury car.
+A picture with a stretch limousine.
+A picture with a tank truck.
+A picture with a custom bike.
+A picture with a snowmobile.
+A picture with a ATV.
+A picture with a three-wheeler.
+A picture with a ice cream truck.
+A picture with a crane truck.
+A picture with a hybrid SUV.
+A picture with a cruiser.
+A picture with a farm truck.
+A picture with a sports bike.
+A picture with a hybrid car.
+A picture with a monster truck.
+A picture with a off-road vehicle.
+A picture with a custom van.
+A picture with a jet ski trailer.
+A picture with a ladder truck.
+A picture with a racing SUV.
+A picture with a touring bus.
+A picture with a electric van.
+A picture with a compact car.
+A picture with a autonomous shuttle bus.
+A picture with a construction truck.
+A picture with a bank truck.
+A picture with a city bus.
+A picture with a station wagon.
+A picture with a golf cart.
+A picture with a dirt bike.
+A picture with a off-road jeep.
+A picture with a solar car.
+A picture with a drift car.
+A picture with a patrol vehicle.
+A picture with a supercar.
+A picture with a tricycle.
+A picture with a cargo van.
+A picture with a camper trailer.
+A picture with a RV with stripes.
+A picture with a delivery scooter.
+A picture with a luxury limousine.
+A picture with a compact sedan.
+A picture with a concept car.
+A picture with a autonomous vehicle.
+A picture with a VW Beetle.
+A picture with a beverage truck.
+A picture with a food truck.
+A picture with a cargo trailer.
+A picture with a bulldozer.
+A picture with a tram car.
+A picture with a bus.
+A picture with a taxi.
+A picture with a custom jeep.
+A picture with a chopper bike.
+A picture with a hovercraft.
+A picture with a snowplow.
+A picture with a golf buggy.
+A picture with a submarine.
+A picture with a space shuttle.
+A picture with a cable car.
+A picture with a rickshaw.
+A picture with a rowing boat.
+A picture with a kayak.
+A picture with a canoe.
+A picture with a yacht.
+A picture with a fishing boat.
+A picture with a cruise ship.
+A picture with a sailboat.
+A picture with a dinghy.
+A picture with a paddle boat.
+A picture with a jet ski.
+A picture with a amphibious vehicle.
+A picture with a glider.
+A picture with a light aircraft.
+A picture with a hot air balloon.
+A picture with a helicopter.
+A picture with a monorail.
+A picture with a steam train.
+A picture with a freight train.
+A picture with a bullet train.
+A picture with a tram.
+A picture with a subway car.
+A picture with a trolleybus.
+A picture with a caravan.
+A picture with a utility van.
+A picture with a towable RV.
+A picture with a microcar.
+A picture with a electric scooter.
+A picture with a all-terrain vehicle.
+A picture with a off-road buggy.
+A picture with a personal watercraft.
+A picture with a moped.
+A picture with a minibike.
+A picture with a bicycle.
+A picture with a unicycle.
+A picture with a segway.
+A picture with a self-driving car.
+A picture with a ride-on lawnmower.
+A picture with a tractor-trailer.
+A picture with a semi-truck.
+A picture with a lowboy truck.
+A picture with a box truck.
+A picture with a refrigerated truck.
+A picture with a cement mixer.
+A picture with a dump truck.
+A picture with a flatbed truck.
+A picture with a log carrier.
+A picture with a mining truck.
+A picture with a tank.
+A picture with a troop carrier.
+A picture with a fire engine.
+A picture with a rescue boat.
+A picture with a lifeboat.
+A picture with a pontoon boat.
+A picture with a scooter board.
+A picture with a hoverboard.
+A picture with a electric skateboard.
+A picture with a kick scooter.
+A picture with a powered parachute.
+A picture with a paraglider.
+A picture with a airship.
+A picture with a blimp.
+A picture with a dirigible.
+A picture with a crane.
+A picture with a excavator.
+A picture with a front loader.
+A picture with a grader.
+A picture with a road roller.
+A picture with a paver.
+A picture with a cherry picker.
+A picture with a scissor lift.
+A picture with a forklift.
+A picture with a warehouse tug.
+A picture with a skid-steer loader.
+A picture with a street sweeper.
+A picture with a garbage compactor.
+A picture with a auto rickshaw.
+A picture with a electric trike.
+A picture with a luxury yacht.
+A picture with a speedboat.
+A picture with a deep-sea trawler.
+A picture with a ferry.
+A picture with a hovertrain.
+A picture with a mountain bike.
+A picture with a road bike.
+A picture with a fixie bike.
+A picture with a electric mountain bike.
+A picture with a cargo bike.
+A picture with a delivery trike.
+A picture with a recumbent bike.
+A picture with a pedal car.
+A picture with a soapbox derby car.
+A picture with a snowmobile sled.
+A picture with a rock crawler.

IP_Composer/text_embeddings/age_descriptions.npy

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IP_Composer/text_embeddings/animal_fur_descriptions.npy

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IP_Composer/text_embeddings/deterioration_descriptions.npy

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IP_Composer/text_embeddings/dog_descriptions.npy

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IP_Composer/text_embeddings/emotion_descriptions.npy

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IP_Composer/text_embeddings/floor_descriptions.npy

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IP_Composer/text_embeddings/flower_descriptions.npy

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IP_Composer/text_embeddings/fruit_vegetable_descriptions.npy

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