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"remote: Enumerating objects: 2368, done.\u001b[K\n", - "remote: Total 2368 (delta 0), reused 0 (delta 0), pack-reused 2368 (from 1)\u001b[K\n", - "Receiving objects: 100% (2368/2368), 18.35 MiB | 6.72 MiB/s, done.\n", - "Resolving deltas: 100% (417/417), done.\n", - "Updating files: 100% (1301/1301), done.\n", - "Filtering content: 100% (578/578), 2.21 GiB | 39.94 MiB/s, done.\n" + "remote: Enumerating objects: 2380, done.\u001b[K\n", + "remote: Counting objects: 100% (498/498), done.\u001b[K\n", + "remote: Compressing objects: 100% (223/223), done.\u001b[K\n", + "remote: Total 2380 (delta 326), reused 355 (delta 266), pack-reused 1882 (from 1)\u001b[K\n", + "Receiving objects: 100% (2380/2380), 23.62 MiB | 7.15 MiB/s, done.\n", + "Resolving deltas: 100% (326/326), done.\n", + "Updating files: 100% (1302/1302), done.\n", + "Filtering content: 100% (578/578), 2.21 GiB | 36.30 MiB/s, done.\n" ] } ] @@ -3059,7 +323,7 @@ "base_uri": "https://localhost:8080/" } }, - "execution_count": 7, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -3342,7 +606,7 @@ "metadata": { "id": "xc-PbIYF428y" }, - "execution_count": 9, + "execution_count": null, "outputs": [] }, { @@ -3418,7 +682,7 @@ "base_uri": "https://localhost:8080/" } }, - "execution_count": 10, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -3874,7 +1138,7 @@ { "cell_type": "code", "source": [ - "# @title Make your own text_encodings .safetensor file for later use (using GPU is recommended to speed things up , but not required)\n", + "# @title Do the same but for image encodings (if urls exist)\n", "import json\n", "import pandas as pd\n", "import os\n", @@ -3882,6 +1146,8 @@ "import torch\n", "from safetensors.torch import save_file\n", "import json\n", + "from PIL import Image\n", + "import requests\n", "\n", "# Determine if this notebook is running on Colab or Kaggle\n", "#Use https://www.kaggle.com/ if Google Colab GPU is busy\n", @@ -3905,24 +1171,27 @@ "root_output_folder = home_directory + 'output/'\n", "output_folder = root_output_folder + 'fusion/'\n", "root_filename = 'prompts'\n", + "root_filename_links = 'links'\n", "NUM_FILES = 1\n", "#--------#\n", "\n", - "\n", - "\n", "# Setup environment\n", "def my_mkdirs(folder):\n", " if os.path.exists(folder)==False:\n", " os.makedirs(folder)\n", "#--------#\n", "output_folder_text = output_folder + 'text/'\n", - "output_folder_text = output_folder + 'text/'\n", + "output_folder_images = output_folder + 'images/'\n", "output_folder_text_encodings = output_folder + 'text_encodings/'\n", - "target_raw = target + 'raw/text/'\n", + "output_folder_image_encodings = output_folder + 'image_encodings/'\n", + "target_raw_text = target + 'raw/text/'\n", + "target_raw_images = target + 'raw/images/'\n", "%cd {home_directory}\n", "my_mkdirs(output_folder)\n", "my_mkdirs(output_folder_text)\n", + "my_mkdirs(output_folder_images)\n", "my_mkdirs(output_folder_text_encodings)\n", + "my_mkdirs(output_folder_image_encodings)\n", "#-------#\n", "\n", "\n", @@ -3934,113 +1203,149 @@ "model = CLIPModel.from_pretrained(\"openai/clip-vit-large-patch14\").to(device)\n", "#---------#\n", "for file_index in range(NUM_FILES + 1):\n", - " if (file_index < 1): continue\n", - "\n", - " # Assign name of JSON file to read\n", - " filename = f'{root_filename}{file_index}'\n", - " if NUM_FILES == 1 : filename = f'{root_filename}'\n", - " #--------#\n", + " if (file_index < 1): continue\n", "\n", - " # Read {filename}.json\n", - " %cd {target_raw}\n", - " with open(filename + '.json', 'r') as f:\n", - " data = json.load(f)\n", - " _df = pd.DataFrame({'count': data})['count']\n", - " prompts = {\n", - " key : value.replace(\"\",\" \") for key, value in _df.items()\n", - " }\n", - " index = 0\n", - " for key in prompts:\n", - " index = index + 1\n", - " #----------#\n", - " NUM_ITEMS = index\n", - " #------#\n", + " # Assign name of JSON file to read\n", + " filename = f'{root_filename}{file_index}'\n", + " if NUM_FILES == 1 : filename = f'{root_filename}'\n", + " #--------#\n", "\n", - " # Calculate text_encoding for .json file contents and results as .db file\n", - " names_dict = {}\n", - " text_encoding_dict = {}\n", - " segments = {}\n", - " index = 0;\n", - " subby = 1;\n", - " NUM_HEADERS = 2\n", - " CHUNKS_SIZE = 300\n", - " _filename = ''\n", - " for _index in range(NUM_ITEMS):\n", - " if not (f'{_index}' in prompts) : continue\n", - " if (prompts[f'{_index}']==\"SKIP\") : continue\n", - " if (index % 100 == 0) : print(index)\n", - " if (index == 0 and _index>0) : index = index + 2 #make space for headers\n", - " if (_index % (CHUNKS_SIZE-NUM_HEADERS) == 0 and _index > 0) :\n", + " # Assign name of JSON file to read\n", + " filename_links = f'{root_filename_links}{file_index}'\n", + " if NUM_FILES == 1 : filename_links = f'{root_filename_links}'\n", + " #--------#\n", "\n", - " # Write headers in the .json\n", - " names_dict[f'{0}'] = f'{_index}'\n", - " names_dict[f'{1}'] = f'{filename}-{subby}'\n", + " # Read {filename}.json\n", + " %cd {target_raw_text}\n", + " with open(filename + '.json', 'r') as f:\n", + " data = json.load(f)\n", + " _df = pd.DataFrame({'count': data})['count']\n", + " prompts = {\n", + " key : value.replace(\"\",\" \") for key, value in _df.items()\n", + " }\n", + " index = 0\n", + " for key in prompts:\n", + " index = index + 1\n", + " #----------#\n", + " NUM_ITEMS = index\n", + " #------#\n", "\n", - " # Encode the headers into text_encoding\n", - " inputs = tokenizer(text = '' + names_dict[f'{0}'], padding=True,truncation=True, return_tensors=\"pt\").to(device)\n", - " text_features = model.get_text_features(**inputs).to(device)\n", - " text_features = text_features/text_features.norm(p=2, dim=-1, keepdim=True)\n", - " text_encoding_dict[f'{0}'] = text_features.to(torch.device('cpu'))\n", - " inputs = tokenizer(text = '' + names_dict[f'{1}'], padding=True,truncation=True, return_tensors=\"pt\").to(device)\n", - " text_features = model.get_text_features(**inputs).to(device)\n", - " text_features = text_features/text_features.norm(p=2, dim=-1, keepdim=True)\n", - " text_encoding_dict[f'{1}'] = text_features.to(torch.device('cpu'))\n", - " #-------#\n", + " # Read image_urls\n", + " %cd {target_raw_images}\n", + " with open(filename_links + '.json', 'r') as f:\n", + " data = json.load(f)\n", + " _df = pd.DataFrame({'count': data})['count']\n", + " image_urls = {\n", + " key : value.replace(\"\",\" \") for key, value in _df.items()\n", + " }\n", + " index = 0\n", + " for key in image_urls:\n", + " index = index + 1\n", + " #----------#\n", + " NUM_ITEMS2 = index\n", + " #------#\n", "\n", - " # Write .json\n", - " _filename = f'{filename}-{subby}.json'\n", - " %cd {output_folder_text}\n", - " print(f'Saving segment {_filename} to {output_folder_text}...')\n", - " with open(_filename, 'w') as f:\n", - " json.dump(names_dict, f)\n", - " #-------#\n", + " if (NUM_ITEMS != NUM_ITEMS2) :\n", + " print(f\"NUM_ITEMS (text) : {NUM_ITEMS}\")\n", + " print(f\"NUM_ITEMS (links) : {NUM_ITEMS2}\")\n", "\n", - " # Write .safetensors\n", - " _filename = f'{filename}-{subby}.safetensors'\n", - " %cd {output_folder_text_encodings}\n", - " print(f'Saving segment {_filename} to {output_folder_text_encodings}...')\n", - " save_file(text_encoding_dict, _filename)\n", - " #--------#\n", + " # Calculate text_encoding for .json file contents and results as .db file\n", + " NUM_HEADERS = 2\n", + " CHUNKS_SIZE = 20\n", + " START_AT = 0 #<---Use this is job was aborted and you wish to continue where you left of. Set the value to 0 otherwise\n", + " #--------#\n", + " names_dict = {}\n", + " image_encoding_dict = {}\n", + " text_encoding_dict = {}\n", + " segments = {}\n", + " index = 0;\n", + " subby = 1;\n", + " _filename = ''\n", "\n", - " #Iterate\n", - " subby = subby + 1\n", - " segments[f'{subby}'] = _filename\n", - " text_encoding_dict = {}\n", - " names_dict = {}\n", - " index = 0\n", - " #------#\n", - " #------#\n", - " else: index = index + 1\n", - " #--------#\n", - " inputs = tokenizer(text = '' + prompts[f'{_index}'], padding=True,truncation=True, return_tensors=\"pt\").to(device)\n", - " text_features = model.get_text_features(**inputs).to(device)\n", - " text_features = text_features/text_features.norm(p=2, dim=-1, keepdim=True)\n", - " text_encoding_dict[f'{index}'] = text_features.to(torch.device('cpu'))\n", - " names_dict[f'{index}'] = prompts[f'{_index}']\n", + " print(f'processing batch no {subby}....')\n", + " print(f'----------')\n", + " for _index in range(NUM_ITEMS2):\n", + " if not (f'{_index}' in prompts) : continue\n", + " if (prompts[f'{_index}']==\"SKIP\") : continue\n", + " if (index % 100 == 0) : print(index)\n", + " if (index == 0 and _index>0) : index = index + 2 #make space for headers\n", + " if (index % (CHUNKS_SIZE-NUM_HEADERS)> 0 or _index <= 0) :\n", + " index = index + 1\n", + " else:\n", + " if index=0.43.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba) (0.43.0)\n", + "Requirement already satisfied: numpy<2.1,>=1.22 in /usr/local/lib/python3.10/dist-packages (from numba) (1.26.4)\n" + ] + } + ] }, { "cell_type": "code", @@ -4076,6 +1920,8 @@ "import torch\n", "from safetensors.torch import save_file\n", "import json\n", + "from PIL import Image\n", + "import requests\n", "\n", "# Determine if this notebook is running on Colab or Kaggle\n", "#Use https://www.kaggle.com/ if Google Colab GPU is busy\n", @@ -4098,20 +1944,18 @@ "target = home_directory + 'text-to-image-prompts/fusion/'\n", "root_output_folder = home_directory + 'output/'\n", "output_folder = root_output_folder + 'fusion/'\n", - "root_filename = 'links'\n", + "root_filename = 'prompts'\n", + "root_filename_links = 'links'\n", "NUM_FILES = 1\n", "#--------#\n", "\n", - "\n", - "\n", "# Setup environment\n", "def my_mkdirs(folder):\n", " if os.path.exists(folder)==False:\n", " os.makedirs(folder)\n", "#--------#\n", - "output_folder_images = output_folder + 'images/'\n", "output_folder_text = output_folder + 'text/'\n", - "output_folder_images_urls = output_folder + 'images/urls/'\n", + "output_folder_images = output_folder + 'images/'\n", "output_folder_text_encodings = output_folder + 'text_encodings/'\n", "output_folder_image_encodings = output_folder + 'image_encodings/'\n", "target_raw_text = target + 'raw/text/'\n", @@ -4119,7 +1963,7 @@ "%cd {home_directory}\n", "my_mkdirs(output_folder)\n", "my_mkdirs(output_folder_text)\n", - "my_mkdirs(output_folder_text_urls)\n", + "my_mkdirs(output_folder_images)\n", "my_mkdirs(output_folder_text_encodings)\n", "my_mkdirs(output_folder_image_encodings)\n", "#-------#\n", @@ -4133,137 +1977,184 @@ "model = CLIPModel.from_pretrained(\"openai/clip-vit-large-patch14\").to(device)\n", "#---------#\n", "for file_index in range(NUM_FILES + 1):\n", - " if (file_index < 1): continue\n", - "\n", - " # Assign name of JSON file to read\n", - " filename = f'{root_filename}{file_index}'\n", - " if NUM_FILES == 1 : filename = f'{root_filename}'\n", - " #--------#\n", + " if (file_index < 1): continue\n", "\n", - " # Read {filename}.json\n", - " %cd {target_raw_images}\n", - " with open('links.json', 'r') as f:\n", - " data = json.load(f)\n", - " _df = pd.DataFrame({'count': data})['count']\n", - " image_urls = {\n", - " key : value.replace(\"\",\" \") for key, value in _df.items()\n", - " }\n", - " index = 0\n", - " for key in image_urls:\n", - " index = index + 1\n", - " #----------#\n", - " NUM_ITEMS = index\n", - " #------#\n", + " # Assign name of JSON file to read\n", + " filename = f'{root_filename}{file_index}'\n", + " if NUM_FILES == 1 : filename = f'{root_filename}'\n", + " #--------#\n", "\n", - " # Calculate text_encoding for .json file contents and results as .db file\n", - " names_dict = {}\n", - " image_encoding_dict = {}\n", - " segments = {}\n", - " index = 0;\n", - " subby = 1;\n", - " NUM_HEADERS = 2\n", - " CHUNKS_SIZE = 500\n", - " _filename = ''\n", - " for _index in range(NUM_ITEMS):\n", - " if not (f'{_index}' in prompts) : continue\n", - " if (prompts[f'{_index}']==\"SKIP\") : continue\n", - " if (index % 100 == 0) : print(index)\n", - " if (index == 0 and _index>0) : index = index + 2 #make space for headers\n", - " if (_index % (CHUNKS_SIZE-NUM_HEADERS) == 0 and _index > 0) :\n", + " # Assign name of JSON file to read\n", + " filename_links = f'{root_filename_links}{file_index}'\n", + " if NUM_FILES == 1 : filename_links = f'{root_filename_links}'\n", + " #--------#\n", "\n", - " # Write headers in the .json\n", - " names_dict[f'{0}'] = f'{_index}'\n", - " names_dict[f'{1}'] = f'{filename}-{subby}'\n", + " # Read {filename}.json\n", + " %cd {target_raw_text}\n", + " with open(filename + '.json', 'r') as f:\n", + " data = json.load(f)\n", + " _df = pd.DataFrame({'count': data})['count']\n", + " prompts = {\n", + " key : value.replace(\"\",\" \") for key, value in _df.items()\n", + " }\n", + " index = 0\n", + " for key in prompts:\n", + " index = index + 1\n", + " #----------#\n", + " NUM_ITEMS = index\n", + " #------#\n", "\n", - " # Encode the headers into text_encoding\n", - " inputs = tokenizer(text = '' + names_dict[f'{0}'], padding=True,truncation=True, return_tensors=\"pt\").to(device)\n", - " text_features = model.get_text_features(**inputs).to(device)\n", - " text_features = text_features/text_features.norm(p=2, dim=-1, keepdim=True)\n", - " image_encoding_dict[f'{0}'] = text_features.to(torch.device('cpu'))\n", - " inputs = tokenizer(text = '' + names_dict[f'{1}'], padding=True,truncation=True, return_tensors=\"pt\").to(device)\n", - " text_features = model.get_text_features(**inputs).to(device)\n", - " text_features = text_features/text_features.norm(p=2, dim=-1, keepdim=True)\n", - " image_encoding_dict[f'{1}'] = text_features.to(torch.device('cpu'))\n", - " #-------#\n", + " # Read image_urls\n", + " %cd {target_raw_images}\n", + " with open(filename_links + '.json', 'r') as f:\n", + " data = json.load(f)\n", + " _df = pd.DataFrame({'count': data})['count']\n", + " image_urls = {\n", + " key : value.replace(\"\",\" \") for key, value in _df.items()\n", + " }\n", + " index = 0\n", + " for key in image_urls:\n", + " index = index + 1\n", + " #----------#\n", + " NUM_ITEMS2 = index\n", + " #------#\n", "\n", - " Write .json\n", - " _filename = f'{filename}-{subby}.json'\n", - " %cd {output_folder_images}\n", - " print(f'Saving segment {_filename} to {output_folder_images}...')\n", - " with open(_filename, 'w') as f:\n", - " json.dump(names_dict, f)\n", - " #-------#\n", + " if (NUM_ITEMS != NUM_ITEMS2) :\n", + " print(f\"NUM_ITEMS (text) : {NUM_ITEMS}\")\n", + " print(f\"NUM_ITEMS (links) : {NUM_ITEMS2}\")\n", "\n", - " # Write .safetensors\n", - " _filename = f'{filename}-{subby}.safetensors'\n", - " %cd {output_folder_image_encodings}\n", - " print(f'Saving segment {_filename} to {output_folder_image_encodings}...')\n", - " save_file(image_encoding_dict, _filename)\n", - " #--------#\n", + " # Calculate text_encoding for .json file contents and results as .db file\n", + " NUM_HEADERS = 2\n", + " CHUNKS_SIZE = 20\n", + " START_AT = 0 #<---Use this is job was aborted and you wish to continue where you left of. Set the value to 0 otherwise\n", + " #--------#\n", + " names_dict = {}\n", + " image_encoding_dict = {}\n", + " segments = {}\n", + " index = 0;\n", + " subby = 1;\n", + " _filename = ''\n", "\n", - " #Iterate\n", - " subby = subby + 1\n", - " segments[f'{subby}'] = _filename\n", - " image_encoding_dict = {}\n", - " names_dict = {}\n", - " index = 0\n", - " #------#\n", - " #------#\n", - " else: index = index + 1\n", - " #--------#\n", + " print(f'processing batch no {subby}....')\n", + " print(f'----------')\n", + " for _index in range(NUM_ITEMS2):\n", + " if not (f'{_index}' in prompts) : continue\n", + " if (prompts[f'{_index}']==\"SKIP\") : continue\n", + " if (index % 100 == 0) : print(index)\n", + " if (index == 0 and _index>0) : index = index + 2 #make space for headers\n", + " if (index % (CHUNKS_SIZE-NUM_HEADERS)> 0 or _index <= 0) :\n", + " index = index + 1\n", + " else:\n", + " if index\",\" \") for key, value in _df.items()\n", + " }\n", + " index = 0\n", + " for key in prompts:\n", + " index = index + 1\n", + " #----------#\n", + " NUM_ITEMS = index\n", + " #------#\n", "\n", - " # Read {filename}.json for text prompts\n", - " %cd {target_raw_text}\n", - " with open(filename + '.json', 'r') as f:\n", - " data = json.load(f)\n", - " _df = pd.DataFrame({'count': data})['count']\n", - " prompts = {\n", - " key : value.replace(\"\",\" \") for key, value in _df.items()\n", - " }\n", - " index = 0\n", - " for key in prompts:\n", - " index = index + 1\n", - " #----------#\n", - " NUM_ITEMS = index\n", - " #------#\n", "\n", - " # Read {filename}.json for image urls\n", - " %cd {target_raw_images}\n", - " with open('links.json', 'r') as f:\n", - " data = json.load(f)\n", - " _df = pd.DataFrame({'count': data})['count']\n", - " urls = {\n", - " key : value.replace(\"\",\" \") for key, value in _df.items()\n", - " }\n", - " #-------#\n", + "\n", + " # Read image_urls\n", + " %cd {target_raw_images}\n", + " with open('links.json', 'r') as f:\n", + " data = json.load(f)\n", + " _df = pd.DataFrame({'count': data})['count']\n", + " image_urls = {\n", + " key : value.replace(\"\",\" \") for key, value in _df.items()\n", + " }\n", + " index = 0\n", + " for key in image_urls:\n", + " index = index + 1\n", + " #----------#\n", + " NUM_ITEMS = index\n", + " #------#\n", "\n", " # Calculate text_encoding for .json file contents and results as .db file\n", - " names_dict = {}\n", - " text_encoding_dict = {}\n", - " image_encoding_dict = {}\n", - " segments = {}\n", - " index = 0;\n", - " subby = 1;\n", - " NUM_HEADERS = 2\n", - " CHUNKS_SIZE = 1000\n", - " _filename = ''\n", - " #from google.colab.patches import cv2_imshow\n", + " names_dict = {}\n", + " image_encoding_dict = {}\n", + " segments = {}\n", + " index = 0;\n", + " subby = 1;\n", + " NUM_HEADERS = 2\n", + " CHUNKS_SIZE = 500\n", + " _filename = ''\n", + " for _index in range(NUM_ITEMS):\n", + " if not (f'{_index}' in prompts) : continue\n", + " if (prompts[f'{_index}']==\"SKIP\") : continue\n", + " if (index % 100 == 0) : print(index)\n", + " if (index == 0 and _index>0) : index = index + 2 #make space for headers\n", + " if (_index % (CHUNKS_SIZE-NUM_HEADERS) == 0 and _index > 0) :\n", "\n", - " for _index in range(NUM_ITEMS):\n", - " if not (f'{_index}' in prompts) : continue\n", - " if (prompts[f'{_index}']==\"SKIP\") : continue\n", - " if (index % 100 == 0) : print(index)\n", - " if (index == 0 and _index>0) : index = index + 2 #make space for headers\n", - " if (_index % (CHUNKS_SIZE-NUM_HEADERS) == 0 and _index > 0) :\n", + " # Write headers in the .json\n", + " names_dict[f'{0}'] = f'{_index}'\n", + " names_dict[f'{1}'] = f'{filename}-{subby}'\n", "\n", - " # Write headers in the .json\n", - " names_dict[f'{0}'] = f'{_index}'\n", - " names_dict[f'{1}'] = f'{filename}-{subby}'\n", + " # Encode the headers into text_encoding\n", + " inputs = tokenizer(text = '' + names_dict[f'{0}'], padding=True,truncation=True, return_tensors=\"pt\").to(device)\n", + " text_features = model.get_text_features(**inputs).to(device)\n", + " text_features = text_features/text_features.norm(p=2, dim=-1, keepdim=True)\n", + " image_encoding_dict[f'{0}'] = text_features.to(torch.device('cpu'))\n", + " inputs = tokenizer(text = '' + names_dict[f'{1}'], padding=True,truncation=True, return_tensors=\"pt\").to(device)\n", + " text_features = model.get_text_features(**inputs).to(device)\n", + " text_features = text_features/text_features.norm(p=2, dim=-1, keepdim=True)\n", + " image_encoding_dict[f'{1}'] = text_features.to(torch.device('cpu'))\n", + " #-------#\n", "\n", - " # Encode the headers into text_encoding and image_encoding\n", - " inputs = tokenizer(text = '' + names_dict[f'{0}'], padding=True,truncation=True, return_tensors=\"pt\").to(device)\n", - " text_features = model.get_text_features(**inputs).to(device)\n", - " text_features = text_features/text_features.norm(p=2, dim=-1, keepdim=True)\n", - " text_encoding_dict[f'{0}'] = text_features.to(torch.device('cpu'))\n", - " #image_encoding_dict[f'{0}'] = text_features.to(torch.device('cpu'))\n", - " inputs = tokenizer(text = '' + names_dict[f'{1}'], padding=True,truncation=True, return_tensors=\"pt\").to(device)\n", - " text_features = model.get_text_features(**inputs).to(device)\n", - " text_features = text_features/text_features.norm(p=2, dim=-1, keepdim=True)\n", - " text_encoding_dict[f'{1}'] = text_features.to(torch.device('cpu'))\n", - " #image_encoding_dict[f'{1}'] = text_features.to(torch.device('cpu'))\n", - " #-------#\n", + " Write .json\n", + " _filename = f'{filename}-{subby}.json'\n", + " %cd {output_folder_images}\n", + " print(f'Saving segment {_filename} to {output_folder_images}...')\n", + " with open(_filename, 'w') as f:\n", + " json.dump(names_dict, f)\n", + " #-------#\n", "\n", - " # Write .json\n", - " _filename = f'{filename}-{subby}.json'\n", - " %cd {output_folder_text}\n", - " print(f'Saving segment {_filename} to {output_folder_text}...')\n", - " with open(_filename, 'w') as f:\n", - " json.dump(names_dict, f)\n", - " #-------#\n", + " # Write .safetensors\n", + " _filename = f'{filename}-{subby}.safetensors'\n", + " %cd {output_folder_image_encodings}\n", + " print(f'Saving segment {_filename} to {output_folder_image_encodings}...')\n", + " save_file(image_encoding_dict, _filename)\n", + " #--------#\n", "\n", - " # Write .safetensors for text\n", - " _filename = f'{filename}-{subby}.safetensors'\n", - " %cd {output_folder_text_encodings}\n", - " print(f'Saving segment {_filename} to {output_folder_text_encodings}...')\n", - " save_file(text_encoding_dict, _filename)\n", - " #--------#\n", + " #Iterate\n", + " subby = subby + 1\n", + " segments[f'{subby}'] = _filename\n", + " image_encoding_dict = {}\n", + " names_dict = {}\n", + " index = 0\n", + " #------#\n", + " #------#\n", + " else: index = index + 1\n", + " #--------#\n", "\n", - " # Write .safetensors for images\n", - " #_filename = f'{filename}-{subby}.safetensors'\n", - " #%cd {output_folder_image_encodings}\n", - " #print(f'Saving segment {_filename} to {output_folder_image_encodings}...')\n", - " #save_file(image_encoding_dict, _filename)\n", - " #--------#\n", "\n", - " #Iterate\n", - " subby = subby + 1\n", - " segments[f'{subby}'] = _filename\n", - " text_encoding_dict = {}\n", - " image_encoding_dict = {}\n", - " names_dict = {}\n", - " index = 0\n", - " #------#\n", - " else:\n", - " index = index + 1\n", - " #--------#\n", + " inputs = tokenizer(text = '' + prompts[f'{_index}'], padding=True,truncation=True, return_tensors=\"pt\").to(device)\n", + " text_features = model.get_text_features(**inputs).to(device)\n", + " text_features = text_features/text_features.norm(p=2, dim=-1, keepdim=True)\n", + " text_encoding_dict[f'{index}'] = text_features.to(torch.device('cpu'))\n", "\n", - " #----text-encodings----#\n", - " inputs = tokenizer(text = '' + prompts[f'{_index}'], padding=True,truncation=True, return_tensors=\"pt\").to(device)\n", - " text_features = model.get_text_features(**inputs).to(device)\n", - " text_features = text_features/text_features.norm(p=2, dim=-1, keepdim=True)\n", - " text_encoding_dict[f'{index}'] = text_features.to(torch.device('cpu'))\n", - " names_dict[f'{index}'] = prompts[f'{_index}']\n", - " #-----#\n", "\n", - " #---image-encodings---#\n", - " if False:\n", - " image_url = urls[f'{_index}']\n", - " with Image.open(requests.get(image_url, stream=True).raw) as image_A :\n", - " inputs = processor(images=image_A, return_tensors=\"pt\").to(device)\n", - " image_features = model.get_image_features(**inputs).to(device)\n", - " image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True).to(device)\n", - " #--------#\n", - " #image_encoding_dict[f'{index}'] = text_features.to(torch.device('cpu'))\n", + " names_dict[f'{index}'] = prompts[f'{_index}']\n", + " continue\n", + " #-----#\n", " #-----#\n", - " continue\n", - " #-----#\n", - " #-----#\n", " # Write headers in the .json\n", " names_dict[f'{0}'] = f'{_index}'\n", " names_dict[f'{1}'] = f'{filename}-{subby}'\n", "\n", - " # Encode the headers into text_encoding and image_encoding\n", + " # Encode the headers into text_encoding\n", " inputs = tokenizer(text = '' + names_dict[f'{0}'], padding=True,truncation=True, return_tensors=\"pt\").to(device)\n", " text_features = model.get_text_features(**inputs).to(device)\n", " text_features = text_features/text_features.norm(p=2, dim=-1, keepdim=True)\n", " text_encoding_dict[f'{0}'] = text_features.to(torch.device('cpu'))\n", - " #image_encoding_dict[f'{0}'] = text_features.to(torch.device('cpu'))\n", " inputs = tokenizer(text = '' + names_dict[f'{1}'], padding=True,truncation=True, return_tensors=\"pt\").to(device)\n", " text_features = model.get_text_features(**inputs).to(device)\n", " text_features = text_features/text_features.norm(p=2, dim=-1, keepdim=True)\n", " text_encoding_dict[f'{1}'] = text_features.to(torch.device('cpu'))\n", - " #image_encoding_dict[f'{1}'] = text_features.to(torch.device('cpu'))\n", " #-------#\n", "\n", " # Write .json\n", @@ -4484,300 +2367,28 @@ " json.dump(names_dict, f)\n", " #-------#\n", "\n", - " # Write .safetensors for text\n", + " # Write .safetensors\n", " _filename = f'{filename}-{subby}.safetensors'\n", " %cd {output_folder_text_encodings}\n", " print(f'Saving segment {_filename} to {output_folder_text_encodings}...')\n", " save_file(text_encoding_dict, _filename)\n", " #--------#\n", "\n", - " # Write .safetensors for images\n", - " #_filename = f'{filename}-{subby}.safetensors'\n", - " #%cd {output_folder_image_encodings}\n", - " #print(f'Saving segment {_filename} to {output_folder_image_encodings}...')\n", - " #save_file(image_encoding_dict, _filename)\n", - " #--------#\n", - "\n", " #Iterate\n", " subby = subby + 1\n", " segments[f'{subby}'] = _filename\n", " text_encoding_dict = {}\n", - " image_encoding_dict = {}\n", " names_dict = {}\n", " index = 0\n", " #------#\n", " #----#" ], "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 976, - "referenced_widgets": [ - "ecd90f093b8a4d9d890e9994dd125f73", - "75bd795ba1d54cfbb3f7a96d6dfe26fc", - 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"61f23e35323e4d3f956971e049cea55a", - "a18dc75a05a3440d80bca2441bdf32c6", - "31b0d34881f64a1fb47725e326f71b7b", - "cefbca57ef744fc5afe83674350850df", - "4ea40c0e14d94a8abbb2f0242350e604" - ] - }, - "id": "SDKl21yzsyuo", - "outputId": "27c66241-4338-4c33-c8fb-4c9c92001d18" + "id": "Sy5K7c-IDcic", + "cellView": "form" }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "/content\n", - "/content\n", - "Cloning into 'text-to-image-prompts'...\n", - "remote: Enumerating objects: 2351, done.\u001b[K\n", - "remote: Counting objects: 100% (2348/2348), done.\u001b[K\n", - "remote: Compressing objects: 100% (1923/1923), done.\u001b[K\n", - "remote: Total 2351 (delta 415), reused 2245 (delta 369), pack-reused 3 (from 1)\u001b[K\n", - "Receiving objects: 100% (2351/2351), 18.24 MiB | 8.82 MiB/s, done.\n", - "Resolving deltas: 100% (415/415), done.\n", - "Filtering content: 100% (572/572), 2.20 GiB | 65.28 MiB/s, done.\n", - "/content\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "tokenizer_config.json: 0%| | 0.00/905 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 168\u001b[0m \u001b[0;31m#----text-encodings----#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m''\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mprompts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf'{_index}'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtruncation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_tensors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"pt\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 170\u001b[0;31m \u001b[0mtext_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_text_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 171\u001b[0m \u001b[0mtext_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtext_features\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mtext_features\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 172\u001b[0m \u001b[0mtext_encoding_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf'{index}'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtext_features\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cpu'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/clip/modeling_clip.py\u001b[0m in \u001b[0;36mget_text_features\u001b[0;34m(self, input_ids, attention_mask, position_ids, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 1184\u001b[0m \u001b[0mreturn_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreturn_dict\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreturn_dict\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_return_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1186\u001b[0;31m text_outputs = self.text_model(\n\u001b[0m\u001b[1;32m 1187\u001b[0m \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1188\u001b[0m \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1560\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1561\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1564\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/clip/modeling_clip.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states)\u001b[0m\n\u001b[1;32m 513\u001b[0m \u001b[0mhidden_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 514\u001b[0m \u001b[0mhidden_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivation_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 515\u001b[0;31m \u001b[0mhidden_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 516\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mhidden_states\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 517\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1551\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1552\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1553\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1555\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1560\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1561\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1564\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 2.00 MiB. GPU 0 has a total capacity of 14.75 GiB of which 1.06 MiB is free. Process 31920 has 14.74 GiB memory in use. Of the allocated memory 14.54 GiB is allocated by PyTorch, and 85.99 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)" - ] - } - ] + "execution_count": null, + "outputs": [] }, { "cell_type": "code", @@ -4799,10 +2410,421 @@ "!zip -r {zip_dest} {root_output_folder}" ], "metadata": { - "id": "V4YCpmWlkPMG" + "id": "V4YCpmWlkPMG", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "157c08eb-f323-49ce-8120-cead51d3a279" }, - "execution_count": null, - "outputs": [] + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content\n", + "/content\n", + " adding: content/output/ (stored 0%)\n", + " adding: content/output/fusion/ (stored 0%)\n", + " adding: content/output/fusion/text_encodings/ (stored 0%)\n", + " adding: content/output/fusion/text_encodings/prompts-9.safetensors (deflated 38%)\n", + " adding: content/output/fusion/text_encodings/prompts-41.safetensors (deflated 18%)\n", + " adding: 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