diff --git "a/joy_caption_alpha_one_jupyter.ipynb" "b/joy_caption_alpha_one_jupyter.ipynb" --- "a/joy_caption_alpha_one_jupyter.ipynb" +++ "b/joy_caption_alpha_one_jupyter.ipynb" @@ -10,7 +10,7 @@ "id": "kLmXfHXy80BY", "outputId": "d99480a2-f2a2-43ec-fe12-fce0a740bae3" }, - "execution_count": 1, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -23,13 +23,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "id": "VjYy0F2gZIPR", "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "bedb6702-9231-45b1-f071-904527abd4b9" + "outputId": "ecb00576-852b-4494-cbb9-7d0909a3b738" }, "outputs": [ { @@ -40,11 +40,11 @@ " libaria2-0 libc-ares2\n", "The following NEW packages will be installed:\n", " aria2 libaria2-0 libc-ares2\n", - "0 upgraded, 3 newly installed, 0 to remove and 49 not upgraded.\n", + "0 upgraded, 3 newly installed, 0 to remove and 18 not upgraded.\n", "Need to get 1,513 kB of archives.\n", "After this operation, 5,441 kB of additional disk space will be used.\n", "Selecting previously unselected package libc-ares2:amd64.\n", - "(Reading database ... 124565 files and directories currently installed.)\n", + "(Reading database ... 124950 files and directories currently installed.)\n", "Preparing to unpack .../libc-ares2_1.18.1-1ubuntu0.22.04.3_amd64.deb ...\n", "Unpacking libc-ares2:amd64 (1.18.1-1ubuntu0.22.04.3) ...\n", "Selecting previously unselected package libaria2-0:amd64.\n", @@ -57,30 +57,30 @@ "Setting up libaria2-0:amd64 (1.36.0-1) ...\n", "Setting up aria2 (1.36.0-1) ...\n", "Processing triggers for man-db (2.10.2-1) ...\n", - "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n", + "Processing triggers for libc-bin (2.35-0ubuntu3.8) ...\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n", "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n", + "/sbin/ldconfig.real: /usr/local/lib/libhwloc.so.15 is not a symbolic link\n", "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n", + "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_opencl.so.0 is not a symbolic link\n", "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n", "\n", - "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_opencl.so.0 is not a symbolic link\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n", "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n", "\n", - "/sbin/ldconfig.real: /usr/local/lib/libumf.so.0 is not a symbolic link\n", + "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_level_zero.so.0 is not a symbolic link\n", "\n", - "/sbin/ldconfig.real: /usr/local/lib/libur_loader.so.0 is not a symbolic link\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n", "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtcm.so.1 is not a symbolic link\n", + "/sbin/ldconfig.real: /usr/local/lib/libur_loader.so.0 is not a symbolic link\n", "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n", + "/sbin/ldconfig.real: /usr/local/lib/libumf.so.0 is not a symbolic link\n", "\n", - "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_level_zero.so.0 is not a symbolic link\n", + "/sbin/ldconfig.real: /usr/local/lib/libtcm.so.1 is not a symbolic link\n", "\n", - "/sbin/ldconfig.real: /usr/local/lib/libhwloc.so.15 is not a symbolic link\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n", "\n", "/sbin/ldconfig.real: /usr/local/lib/libtcm_debug.so.1 is not a symbolic link\n", "\n", @@ -88,7 +88,7 @@ "Download Results:\n", "gid |stat|avg speed |path/URI\n", "======+====+===========+=======================================================\n", - "1f6e49|\u001b[1;32mOK\u001b[0m | 106KiB/s|/content/joy/text_model/adapter_config.json\n", + "55e009|\u001b[1;32mOK\u001b[0m | 91KiB/s|/content/joy/text_model/adapter_config.json\n", "\n", "Status Legend:\n", "(OK):download completed.\n", @@ -96,7 +96,7 @@ "Download Results:\n", "gid |stat|avg speed |path/URI\n", "======+====+===========+=======================================================\n", - "ec6147|\u001b[1;32mOK\u001b[0m | 42MiB/s|/content/joy/text_model/adapter_model.safetensors\n", + "293d1a|\u001b[1;32mOK\u001b[0m | 77MiB/s|/content/joy/text_model/adapter_model.safetensors\n", "\n", "Status Legend:\n", "(OK):download completed.\n", @@ -104,7 +104,7 @@ "Download Results:\n", "gid |stat|avg speed |path/URI\n", "======+====+===========+=======================================================\n", - "ce51a4|\u001b[1;32mOK\u001b[0m | 171MiB/s|/content/joy/clip_model.pt\n", + "dd9439|\u001b[1;32mOK\u001b[0m | 85MiB/s|/content/joy/clip_model.pt\n", "\n", "Status Legend:\n", "(OK):download completed.\n", @@ -112,7 +112,7 @@ "Download Results:\n", "gid |stat|avg speed |path/URI\n", "======+====+===========+=======================================================\n", - "a87c89|\u001b[1;32mOK\u001b[0m | 89KiB/s|/content/joy/config.yaml\n", + "e49830|\u001b[1;32mOK\u001b[0m | 53KiB/s|/content/joy/config.yaml\n", "\n", "Status Legend:\n", "(OK):download completed.\n", @@ -120,43 +120,53 @@ "Download Results:\n", "gid |stat|avg speed |path/URI\n", "======+====+===========+=======================================================\n", - "079f74|\u001b[1;32mOK\u001b[0m | 51MiB/s|/content/joy/image_adapter.pt\n", + "f76217|\u001b[1;32mOK\u001b[0m | 46MiB/s|/content/joy/image_adapter.pt\n", "\n", "Status Legend:\n", "(OK):download completed.\n", "Requirement already satisfied: peft in /usr/local/lib/python3.11/dist-packages (0.14.0)\n", "Collecting bitsandbytes\n", - " Downloading bitsandbytes-0.45.0-py3-none-manylinux_2_24_x86_64.whl.metadata (2.9 kB)\n", + " Downloading bitsandbytes-0.45.1-py3-none-manylinux_2_24_x86_64.whl.metadata (5.8 kB)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from peft) (1.26.4)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from peft) (24.2)\n", "Requirement already satisfied: psutil in /usr/local/lib/python3.11/dist-packages (from peft) (5.9.5)\n", "Requirement already satisfied: pyyaml in /usr/local/lib/python3.11/dist-packages (from peft) (6.0.2)\n", - "Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.11/dist-packages (from peft) (2.5.1+cu121)\n", + "Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.11/dist-packages (from peft) (2.5.1+cu124)\n", "Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages 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satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.11/dist-packages (from transformers->peft) (0.21.0)\n", @@ -165,10 +175,70 @@ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (3.10)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (2.3.0)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (2024.12.14)\n", - "Downloading bitsandbytes-0.45.0-py3-none-manylinux_2_24_x86_64.whl (69.1 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.1/69.1 MB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hInstalling collected packages: bitsandbytes\n", - "Successfully installed bitsandbytes-0.45.0\n" + "Downloading 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"\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, bitsandbytes\n", + " Attempting uninstall: nvidia-nvjitlink-cu12\n", + " Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n", + " Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n", + " Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n", + " Attempting uninstall: nvidia-curand-cu12\n", + " Found existing installation: nvidia-curand-cu12 10.3.6.82\n", + " Uninstalling nvidia-curand-cu12-10.3.6.82:\n", + " Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n", + " Attempting uninstall: nvidia-cufft-cu12\n", + " Found existing installation: nvidia-cufft-cu12 11.2.3.61\n", + " Uninstalling nvidia-cufft-cu12-11.2.3.61:\n", + " Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n", + " Attempting uninstall: nvidia-cuda-runtime-cu12\n", + " Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n", + " Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n", + " Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n", + " Attempting uninstall: nvidia-cuda-nvrtc-cu12\n", + " Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n", + " Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n", + " Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n", + " Attempting uninstall: nvidia-cuda-cupti-cu12\n", + " Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n", + " Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n", + " Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n", + " Attempting uninstall: nvidia-cublas-cu12\n", + " Found existing installation: nvidia-cublas-cu12 12.5.3.2\n", + " Uninstalling nvidia-cublas-cu12-12.5.3.2:\n", + " Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n", + " Attempting uninstall: nvidia-cusparse-cu12\n", + " Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n", + " Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n", + " Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n", + " Attempting uninstall: nvidia-cudnn-cu12\n", + " Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n", + " Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n", + " Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n", + " Attempting uninstall: nvidia-cusolver-cu12\n", + " Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n", + " Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n", + " Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n", + "Successfully installed bitsandbytes-0.45.1 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n" ] } ], @@ -185,227 +255,239 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optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, { "output_type": "display_data", "data": { @@ -415,7 +497,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "b8bb9e2634654ee2a72e69988c129d9e" + "model_id": "aa6bcb20909c4dabb4e50cbe669d2e59" } }, "metadata": {} @@ -429,7 +511,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "abee79db89a1406b880a1d716885127d" + "model_id": "eaad4156f51542809864313ae0ca6d4b" } }, "metadata": {} @@ -443,7 +525,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "c67638f86f5c488f9bdddf74da6a8ab6" + "model_id": "a39c5b146c084e83ab577dce631369b7" } }, "metadata": {} @@ -457,7 +539,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "00391a7b433c47e3a6fc7767b4c8c249" + "model_id": "20f386c28e3b4b5c8b2a1d473694f184" } }, "metadata": {} @@ -471,7 +553,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "291c08e5dd514e6d9e56a99cbfd4f3f2" + "model_id": "b10c589bfcd9473fbbe32a165d9984a9" } }, "metadata": {} @@ -485,7 +567,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "2c2d344215034590a21daf70024fc291" + "model_id": "8cc627f0f2c44d6aac739435d84d18e0" } }, "metadata": {} @@ -499,7 +581,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "767d48df537d467c94c0551d73a9710d" + "model_id": "a5855c924dc743c39b0a60c155fdda96" } }, "metadata": {} @@ -508,7 +590,7 @@ "output_type": "stream", "name": "stderr", "text": [ - ":70: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + ":70: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " checkpoint = torch.load(\"/content/joy/clip_model.pt\", map_location='cpu')\n" ] }, @@ -521,7 +603,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "2422294b7eff41c99e25aeb2c598e34d" + "model_id": "9b2cb8292d53406d97fd89bb0d53aaaa" } }, "metadata": {} @@ -535,7 +617,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "0f495441b3d24f06a74d81b2a397ae70" + "model_id": "2900e012356645278c77b9d2c2a1f7ff" } }, "metadata": {} @@ -549,7 +631,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "8ce208addaa841e9ae6b86e4d5a31d16" + "model_id": "8451f18d92e44e6f97b2293b6aa39d49" } }, "metadata": {} @@ -563,7 +645,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "50faa3180ff1460896bfe13a342ea35d" + "model_id": "3556f5edde894b63ae368fd1fb816592" } }, "metadata": {} @@ -584,7 +666,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "f21f9d263a1f412d82d6f44eef5e0b8e" + "model_id": "8cc27d8c393f4d42949f45eff03460d7" } }, "metadata": {} @@ -598,7 +680,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "c610c5d5d7984394ba13702e83b99a53" + "model_id": "bab4df97ae5f45a19630bb99c2fc612a" } }, "metadata": {} @@ -612,7 +694,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "ea76f3af8e324592af04c8efe8aedd3a" + "model_id": "907e2db2f8a443af81ad31a4e6b802dc" } }, "metadata": {} @@ -626,7 +708,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "581d54fdfb124936b112e26b61785943" + "model_id": "b64252230d0a49ba8d7a619b9a4dc2db" } }, "metadata": {} @@ -640,7 +722,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "f84dab3b23bb4d4db2f0964dc4591c30" + "model_id": "815e9798057a4479b207785f98139e21" } }, "metadata": {} @@ -654,7 +736,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "fd1ba10dbccb4f4e85391e4462e37ea4" + "model_id": "cdfb0aeb78ae45b7a8371d55117eddcf" } }, "metadata": {} @@ -668,7 +750,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "5bfb13a9844b4ecb9c3ed30d7cac28bc" + "model_id": "43cbb633025845e7a6fec832cde23d1a" } }, "metadata": {} @@ -682,7 +764,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "add06b1c33f144499e0b4ae4d0fbad32" + "model_id": "2f4d3f3967614cb69300ce77c4e41e4c" } }, "metadata": {} @@ -691,7 +773,7 @@ "output_type": "stream", "name": "stderr", "text": [ - ":83: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + ":83: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " image_adapter.load_state_dict(torch.load(\"/content/joy/image_adapter.pt\", map_location=\"cpu\"))\n" ] } @@ -855,6 +937,7 @@ "\n", "tgt_folder = '/content/'\n", "suffixes = ['.png', '.jpeg' , '.webp' , '.jpg']\n", + "num = 1\n", "for filename in os.listdir(tgt_folder):\n", " for suffix in suffixes:\n", " if not filename.find(suffix)>-1: continue\n", @@ -862,589 +945,438 @@ " %cd {home_directory}\n", " input_image = Image.open(f\"{filename}\").convert('RGB')\n", " %cd {tgt_directory}\n", - " num = int(f'{filename}'.replace(suffix,''))\n", - " input_image.save(f'{num-1}.png', \"PNG\")" + " input_image.save(f'{162 + num}.png', \"PNG\")\n", + " num = num+1" ], "metadata": { "id": "yVnuCVjNNdim", - "outputId": "f04c5cdd-48df-4afd-d3cf-b43332061064", + "outputId": "a8ab6e34-a4c9-4c4a-8144-8f6d76f86ae8", "colab": { "base_uri": "https://localhost:8080/" } }, - "execution_count": 9, + "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "/content\n", - "99.png\n", + "2025-01-29 11.35.10_1.jpg\n", "/content\n", "/content/tmp\n", - "130.png\n", + "2025-01-29 11.15.21.webp\n", "/content\n", "/content/tmp\n", - "45.png\n", + "2025-01-29 11.01.18_6.jpg\n", "/content\n", "/content/tmp\n", - "16.png\n", + "2025-01-29 11.01.18_5.jpg\n", "/content\n", "/content/tmp\n", - "139.png\n", + "2012-05-15 22.22.14.jpg\n", "/content\n", "/content/tmp\n", - "5.png\n", + "2025-01-29 11.01.18_3.jpg\n", "/content\n", "/content/tmp\n", - "88.png\n", + "2025-01-29 11.01.23_3.jpg\n", "/content\n", "/content/tmp\n", - "154.png\n", + "2025-01-29 11.01.21_4.jpg\n", "/content\n", "/content/tmp\n", - "9.png\n", + "2025-01-29 11.01.20_2.jpg\n", "/content\n", "/content/tmp\n", - "77.png\n", + "2025-01-29 11.01.21_1.jpg\n", "/content\n", "/content/tmp\n", - "35.png\n", + "2013-01-14 01.03.06.jpg\n", "/content\n", "/content/tmp\n", - "90.png\n", + "2025-01-18 04.07.57_9.jpg\n", "/content\n", "/content/tmp\n", - "136.png\n", + "2025-01-29 11.01.18_1.jpg\n", "/content\n", "/content/tmp\n", - "38.png\n", + "2025-01-29 11.01.23_1.jpg\n", "/content\n", "/content/tmp\n", - "103.png\n", + "2025-01-29 11.01.22_2.jpg\n", "/content\n", "/content/tmp\n", - "122.png\n", + "2025-01-29 11.35.10_3.jpg\n", "/content\n", "/content/tmp\n", - "34.png\n", + "2025-01-29 11.01.23_2.jpg\n", "/content\n", "/content/tmp\n", - "113.png\n", + "2025-01-29 11.35.10_5.jpg\n", "/content\n", "/content/tmp\n", - "33.png\n", + "2025-01-29 11.01.22.jpg\n", "/content\n", "/content/tmp\n", - "97.png\n", + "2025-01-29 11.01.18_9.jpg\n", "/content\n", "/content/tmp\n", - "39.png\n", + "2025-01-29 11.01.20_1.jpg\n", "/content\n", "/content/tmp\n", - "133.png\n", + "2025-01-29 11.01.21_5.jpg\n", "/content\n", "/content/tmp\n", - "36.png\n", + "2025-01-29 11.01.18_4.jpg\n", "/content\n", "/content/tmp\n", - "63.png\n", + "2025-01-29 11.07.42.jpg\n", "/content\n", "/content/tmp\n", - "6.png\n", + "2025-01-29 11.35.10_2.jpg\n", "/content\n", "/content/tmp\n", - "72.png\n", + "2025-01-29 11.17.17.jpg\n", "/content\n", "/content/tmp\n", - "110.png\n", + "2025-01-18 04.07.55.jpg\n", "/content\n", "/content/tmp\n", - "53.png\n", + "2025-01-29 11.01.21_6.jpg\n", "/content\n", "/content/tmp\n", - "81.png\n", + "2025-01-29 11.08.28.jpg\n", "/content\n", "/content/tmp\n", - "62.png\n", + "2025-01-29 11.01.18.jpg\n", "/content\n", "/content/tmp\n", - "95.png\n", + "2025-01-29 11.13.36.jpg\n", "/content\n", "/content/tmp\n", - "80.png\n", + "2025-01-29 11.01.22_4.jpg\n", "/content\n", "/content/tmp\n", - "151.png\n", + "2025-01-29 11.01.18_7.jpg\n", "/content\n", "/content/tmp\n", - "100.png\n", + "2025-01-29 11.12.03.jpg\n", "/content\n", "/content/tmp\n", - "123.png\n", + "2025-01-29 11.01.21_3.jpg\n", "/content\n", "/content/tmp\n", - "3.png\n", + "2025-01-29 11.01.24_3.jpg\n", "/content\n", "/content/tmp\n", - "161.png\n", + "2025-01-29 11.01.18_2.jpg\n", "/content\n", "/content/tmp\n", - "156.png\n", + "2025-01-29 11.01.20.jpg\n", "/content\n", "/content/tmp\n", - "104.png\n", + "2025-01-29 11.08.20.jpg\n", "/content\n", "/content/tmp\n", - "52.png\n", + "2025-01-29 11.01.21.jpg\n", "/content\n", "/content/tmp\n", - "27.png\n", + "2025-01-29 11.35.10_4.jpg\n", "/content\n", "/content/tmp\n", - "153.png\n", + "2025-01-29 11.01.24.jpg\n", "/content\n", "/content/tmp\n", - "87.png\n", + "2025-01-29 11.01.24_1.jpg\n", "/content\n", "/content/tmp\n", - "117.png\n", + "2025-01-29 11.01.24_2.jpg\n", "/content\n", "/content/tmp\n", - "37.png\n", + "2025-01-29 11.01.22_1.jpg\n", "/content\n", "/content/tmp\n", - "18.png\n", + "2025-01-29 11.01.24_4.jpg\n", "/content\n", "/content/tmp\n", - "124.png\n", + "2025-01-29 11.01.22_3.jpg\n", "/content\n", "/content/tmp\n", - "29.png\n", + "2025-01-29 11.01.23.jpg\n", "/content\n", "/content/tmp\n", - "94.png\n", + "2025-01-29 11.35.10.jpg\n", "/content\n", "/content/tmp\n", - "158.png\n", + "2025-01-29 11.01.22_5.jpg\n", "/content\n", "/content/tmp\n", - "68.png\n", + "2025-01-29 11.01.18_8.jpg\n", "/content\n", "/content/tmp\n", - "89.png\n", + "2025-01-29 11.01.21_2.jpg\n", "/content\n", - "/content/tmp\n", - "86.png\n", + "/content/tmp\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "%cd /content/\n", + "import shutil\n", + "shutil.make_archive('prompts', 'zip', '/content/tmp')" + ], + "metadata": { + "id": "0C4KHOeN7V_O", + "outputId": "c8559382-6a1d-4df2-84e0-a78017f7e9e4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'/content/prompts.zip'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 10 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from PIL import Image\n", + "\n", + "# Open an existing image\n", + "#image = Image.open('new_york_city.jpg')\n", + "\n", + "# Save the image in a different format\n", + "\n", + "\n", + "suffix = 'png'\n", + "for number in range(300):\n", + " try:\n", + " %cd /content/\n", + " input_image = Image.open(f\"/content/tmp/{number+1}.{suffix}\").convert('RGB')\n", + " caption = stream_chat(input_image, \"descriptive\", \"formal\", \"any\")\n", + " %cd /content/tmp\n", + " f = open(f\"{number+1}.txt\", \"w\")\n", + " f.write(f'{caption}')\n", + " f.close()\n", + " print(f\"...\\n\\n...caption for {number+1}.{suffix}\\n\\n...\")\n", + " print(caption)\n", + " except:\n", + " continue\n", + "#----#\n" + ], + "metadata": { + "id": "NbiUlfjD3iwB", + "outputId": "74750d45-5853-4736-8d96-a75157c372a0", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ "/content\n", - "/content/tmp\n", - "11.png\n", "/content\n", - "/content/tmp\n", - "46.png\n", "/content\n", - "/content/tmp\n", - "73.png\n", "/content\n", - "/content/tmp\n", - "57.png\n", "/content\n", - "/content/tmp\n", - "107.png\n", "/content\n", - "/content/tmp\n", - "24.png\n", "/content\n", - "/content/tmp\n", - "7.png\n", "/content\n", - "/content/tmp\n", - "162.png\n", "/content\n", - "/content/tmp\n", - "125.png\n", "/content\n", - "/content/tmp\n", - "64.png\n", "/content\n", - "/content/tmp\n", - "41.png\n", "/content\n", - "/content/tmp\n", - "150.png\n", "/content\n", - "/content/tmp\n", - "51.png\n", "/content\n", - "/content/tmp\n", - "98.png\n", "/content\n", - "/content/tmp\n", - "66.png\n", "/content\n", - "/content/tmp\n", - "134.png\n", "/content\n", - "/content/tmp\n", - "61.png\n", "/content\n", - "/content/tmp\n", - "137.png\n", "/content\n", - "/content/tmp\n", - "85.png\n", "/content\n", - "/content/tmp\n", - "101.png\n", "/content\n", - "/content/tmp\n", - "23.png\n", "/content\n", - 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"/content/tmp\n", - "12.png\n", "/content\n", - "/content/tmp\n", - "71.png\n", "/content\n", - "/content/tmp\n", - "116.png\n", "/content\n", - "/content/tmp\n", - "26.png\n", "/content\n", - "/content/tmp\n", - "19.png\n", "/content\n", - "/content/tmp\n", - "28.png\n", "/content\n", - "/content/tmp\n", - "17.png\n", "/content\n", - "/content/tmp\n", - "59.png\n", "/content\n", - "/content/tmp\n", - "82.png\n", "/content\n", - "/content/tmp\n", - "48.png\n", "/content\n", - "/content/tmp\n", - "157.png\n", "/content\n", - "/content/tmp\n", - "31.png\n", "/content\n", - "/content/tmp\n", - "132.png\n", "/content\n", - "/content/tmp\n", - "79.png\n", "/content\n", - "/content/tmp\n", - "111.png\n", "/content\n", - "/content/tmp\n", - "146.png\n", "/content\n", - "/content/tmp\n", - "75.png\n", "/content\n", - "/content/tmp\n", - "50.png\n", "/content\n", - "/content/tmp\n", - "15.png\n", "/content\n", - "/content/tmp\n", - "58.png\n", "/content\n", - "/content/tmp\n", - "96.png\n", "/content\n", - "/content/tmp\n", - "21.png\n", "/content\n", - "/content/tmp\n", - "159.png\n", "/content\n", - "/content/tmp\n", - "160.png\n", "/content\n", - "/content/tmp\n", - "105.png\n", "/content\n", - "/content/tmp\n", - "144.png\n", "/content\n", - "/content/tmp\n" + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "/content\n", + "Prompt: Describe the image in 400 words\n" ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "%cd /content/\n", - "import shutil\n", - "shutil.make_archive('prompts', 'zip', '/content/tmp')" - ], - "metadata": { - "id": "0C4KHOeN7V_O", - "outputId": "c8559382-6a1d-4df2-84e0-a78017f7e9e4", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 53 - } - }, - "execution_count": 10, - "outputs": [ + }, { "output_type": "stream", - "name": "stdout", + "name": "stderr", "text": [ - "/content\n" + "/usr/local/lib/python3.11/dist-packages/bitsandbytes/autograd/_functions.py:315: UserWarning: MatMul8bitLt: inputs will be cast from torch.bfloat16 to float16 during quantization\n", + " warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n" ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'/content/prompts.zip'" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 10 } ] }, - { - "cell_type": "code", - "source": [ - "from PIL import Image\n", - "\n", - "# Open an existing image\n", - "image = Image.open('new_york_city.jpg')\n", - "\n", - "# Save the image in a different format\n", - "\n", - "\n", - "suffix = 'png'\n", - "for number in range(164):\n", - " if number<84:continue\n", - " try:\n", - " %cd /content/\n", - " input_image = Image.open(f\"/content/{number+1}.{suffix}\").convert('RGB')\n", - " caption = stream_chat(input_image, \"descriptive\", \"formal\", \"any\")\n", - " %cd /content/tmp\n", - " f = open(f\"{number+1}.txt\", \"w\")\n", - " f.write(f'{caption}')\n", - " f.close()\n", - " print(f\"...\\n\\n...caption for {number+1}.{suffix}\\n\\n...\")\n", - " print(caption)\n", - " except:\n", - " continue\n", - "#----#\n" - ], - "metadata": { - "id": "NbiUlfjD3iwB" - }, - "execution_count": null, - "outputs": [] - }, { "cell_type": "code", "source": [ @@ -1460,7 +1392,7 @@ "height": 53 } }, - "execution_count": 7, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -1500,7 +1432,7 @@ "base_uri": "https://localhost:8080/" } }, - "execution_count": 1, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -4238,7 +4170,8 @@ ], "metadata": { "colab": { - "provenance": [] + "provenance": [], + "gpuType": "T4" }, "kernelspec": { "display_name": "Python 3", @@ -4249,7 +4182,7 @@ }, "widgets": { "application/vnd.jupyter.widget-state+json": { - "b8bb9e2634654ee2a72e69988c129d9e": { + "aa6bcb20909c4dabb4e50cbe669d2e59": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -4264,14 +4197,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_9d76b0209327475285d33c3a807dd1dc", - "IPY_MODEL_1e96f0fdbf98422fb804c09fa0d8628f", - "IPY_MODEL_d3f12f02049b43d09a2d7065c389c5f9" + "IPY_MODEL_76bc7cdb2b104e45a4003d6c22984ede", + "IPY_MODEL_1e061aa7debb41d79ebef10055081c12", + "IPY_MODEL_66675c37ea4b49149ead2b347ebaa537" ], - "layout": "IPY_MODEL_a9c9fb4914964163b46aabbea13dd263" + "layout": "IPY_MODEL_c3051c4febeb4037bc69d05d43b8cda0" } }, - 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"style": "IPY_MODEL_c43fac14080148ea8de9a835ef2c9653", + "style": "IPY_MODEL_4eb4542532f14ea785ef664dcc22f212", "value": "tokenizer_config.json: 100%" } }, - "b8030d625d334c789e698d1b7019e2a4": { + "610c1bad13154c8cbb724a86da4a8abd": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", @@ -4650,15 +4583,15 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_b7b2761d0c1b42fb8ce336878eeecd70", + "layout": "IPY_MODEL_9cec881e50da4f88a730d9408a02068b", "max": 711, "min": 0, "orientation": "horizontal", - "style": "IPY_MODEL_2e17b2b321f04fa498c17f3402b0357d", + "style": "IPY_MODEL_6d934ad9eb41426abcedcdbc24e52045", "value": 711 } }, - "03b046cd3add4e9ca1fd84c82daf80a2": { + "1a2748ed8b0843158e487295cd277a37": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -4673,13 +4606,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_bfacf25d7efe4ffeaba20ecc083281eb", + "layout": "IPY_MODEL_68e9cabe2c2b4dfda49e527d7897dba6", "placeholder": "​", - "style": "IPY_MODEL_3d58c6ba77b74c39963c94ca494718e5", - "value": " 711/711 [00:00<00:00, 60.6kB/s]" + "style": "IPY_MODEL_28bd944a6eb34cbd897ca24df9376aed", + "value": " 711/711 [00:00<00:00, 28.3kB/s]" } }, - "3370e1a438914b9097ccaf1d683dcba7": { + "ce84c5b61fe0437588958bba178fb421": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -4731,7 +4664,7 @@ "width": null } }, - "ee37c418271047449742220c7f1b84fb": { + "9c94abf070634379bf79a850189a7e20": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -4783,7 +4716,7 @@ "width": null } }, - "c43fac14080148ea8de9a835ef2c9653": { + "4eb4542532f14ea785ef664dcc22f212": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -4798,7 +4731,7 @@ "description_width": "" } }, - 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"layout": "IPY_MODEL_f0f0726c26d04e299623fa92f6c7aee9", + "layout": "IPY_MODEL_c228a09ca0c44e97bb84fe87f25ec165", "max": 798330, "min": 0, "orientation": "horizontal", - "style": "IPY_MODEL_4498475a7e764ecd9a072a05eb2d934b", + "style": "IPY_MODEL_2a374055a6bb4412a493c35958c7e860", "value": 798330 } }, - "43ad821b3dfe454db270a67bb6943824": { + "42cc71df9faf4659aefd3ba24dc9e670": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -5015,13 +4948,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_d652c7e96c6d43fdbfe446a99004b516", + "layout": "IPY_MODEL_b8ba56c2bd4346379da6d7628a1a6fc9", "placeholder": "​", - "style": "IPY_MODEL_60df4b2e287b4eae9e9f46c68a2ad110", - "value": " 798k/798k [00:00<00:00, 6.54MB/s]" + "style": "IPY_MODEL_07ae6aed5cbd439b990a18e934fbcf2f", + "value": " 798k/798k [00:00<00:00, 8.85MB/s]" } }, - "64ecb51e18d746099f0a2d028a5687f7": { + "0d474dbef7d4443e8acedf7a7b79c86b": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -5073,7 +5006,7 @@ "width": null } }, - 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