diff --git "a/Joycaption_Alpha_One.ipynb" "b/Joycaption_Alpha_One.ipynb"
--- "a/Joycaption_Alpha_One.ipynb"
+++ "b/Joycaption_Alpha_One.ipynb"
@@ -1 +1 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Joycaption_Alpha_One.ipynb","timestamp":1740657473013},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Joycaption_Alpha_One.ipynb","timestamp":1739796923572},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Joycaption_Alpha_One.ipynb","timestamp":1739735627072}],"gpuType":"T4"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"Dwr7gk5OwuGC"},"outputs":[],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","source":["!apt -y install -qq aria2\n","!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/joy-caption-alpha-one/raw/main/text_model/adapter_config.json -d /content/joy/text_model -o adapter_config.json\n","!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/joy-caption-alpha-one/resolve/main/text_model/adapter_model.safetensors -d /content/joy/text_model -o adapter_model.safetensors\n","!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/joy-caption-alpha-one/resolve/main/clip_model.pt -d /content/joy -o clip_model.pt\n","!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/joy-caption-alpha-one/raw/main/config.yaml -d /content/joy -o config.yaml\n","!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/joy-caption-alpha-one/resolve/main/image_adapter.pt -d /content/joy -o image_adapter.pt\n","\n","!pip install peft bitsandbytes\n","from huggingface_hub import InferenceClient\n","from torch import nn\n","from transformers import AutoModel, AutoProcessor, AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast, AutoModelForCausalLM\n","import torch\n","import torch.amp.autocast_mode\n","from PIL import Image\n","import os\n","import torchvision.transforms.functional as TVF\n","\n","CLIP_PATH = \"google/siglip-so400m-patch14-384\"\n","MODEL_PATH = \"unsloth/Meta-Llama-3.1-8B-bnb-4bit\"\n","CAPTION_TYPE_MAP = {\n","    (\"descriptive\", \"formal\", False, False): [\"Describe the image in 400 words\"],\n","    (\"descriptive\", \"formal\", False, True): [\"Write a descriptive caption for this image in a formal tone within {word_count} words.\"],\n","    (\"descriptive\", \"formal\", True, False): [\"Write a {length} descriptive caption for this image in a formal tone.\"],\n","    (\"descriptive\", \"informal\", False, False): [\"Write a descriptive caption for this image in a casual tone.\"],\n","    (\"descriptive\", \"informal\", False, True): [\"Write a descriptive caption for this image in a casual tone within {word_count} words.\"],\n","    (\"descriptive\", \"informal\", True, False): [\"Write a {length} descriptive caption for this image in a casual tone.\"],\n","    (\"training_prompt\", \"formal\", False, False): [\"Write a stable diffusion prompt for this image.\"],\n","    (\"training_prompt\", \"formal\", False, True): [\"Write a stable diffusion prompt for this image within {word_count} words.\"],\n","    (\"training_prompt\", \"formal\", True, False): [\"Write a {length} stable diffusion prompt for this image.\"],\n","    (\"rng-tags\", \"formal\", False, False): [\"Write a list of Booru tags for this image.\"],\n","    (\"rng-tags\", \"formal\", False, True): [\"Write a list of Booru tags for this image within {word_count} words.\"],\n","    (\"rng-tags\", \"formal\", True, False): [\"Write a {length} list of Booru tags for this image.\"],\n","}\n","\n","class ImageAdapter(nn.Module):\n","\tdef __init__(self, input_features: int, output_features: int, ln1: bool, pos_emb: bool, num_image_tokens: int, deep_extract: bool):\n","\t\tsuper().__init__()\n","\t\tself.deep_extract = deep_extract\n","\t\tif self.deep_extract:\n","\t\t\tinput_features = input_features * 5\n","\t\tself.linear1 = nn.Linear(input_features, output_features)\n","\t\tself.activation = nn.GELU()\n","\t\tself.linear2 = nn.Linear(output_features, output_features)\n","\t\tself.ln1 = nn.Identity() if not ln1 else nn.LayerNorm(input_features)\n","\t\tself.pos_emb = None if not pos_emb else nn.Parameter(torch.zeros(num_image_tokens, input_features))\n","\t\tself.other_tokens = nn.Embedding(3, output_features)\n","\t\tself.other_tokens.weight.data.normal_(mean=0.0, std=0.02)   # Matches HF's implementation of llama3\n","\tdef forward(self, vision_outputs: torch.Tensor):\n","\t\tif self.deep_extract:\n","\t\t\tx = torch.concat((\n","\t\t\t\tvision_outputs[-2],\n","\t\t\t\tvision_outputs[3],\n","\t\t\t\tvision_outputs[7],\n","\t\t\t\tvision_outputs[13],\n","\t\t\t\tvision_outputs[20],\n","\t\t\t), dim=-1)\n","\t\t\tassert len(x.shape) == 3, f\"Expected 3, got {len(x.shape)}\"  # batch, tokens, features\n","\t\t\tassert x.shape[-1] == vision_outputs[-2].shape[-1] * 5, f\"Expected {vision_outputs[-2].shape[-1] * 5}, got {x.shape[-1]}\"\n","\t\telse:\n","\t\t\tx = vision_outputs[-2]\n","\t\tx = self.ln1(x)\n","\t\tif self.pos_emb is not None:\n","\t\t\tassert x.shape[-2:] == self.pos_emb.shape, f\"Expected {self.pos_emb.shape}, got {x.shape[-2:]}\"\n","\t\t\tx = x + self.pos_emb\n","\t\tx = self.linear1(x)\n","\t\tx = self.activation(x)\n","\t\tx = self.linear2(x)\n","\t\tother_tokens = self.other_tokens(torch.tensor([0, 1], device=self.other_tokens.weight.device).expand(x.shape[0], -1))\n","\t\tassert other_tokens.shape == (x.shape[0], 2, x.shape[2]), f\"Expected {(x.shape[0], 2, x.shape[2])}, got {other_tokens.shape}\"\n","\t\tx = torch.cat((other_tokens[:, 0:1], x, other_tokens[:, 1:2]), dim=1)\n","\t\treturn x\n","\tdef get_eot_embedding(self):\n","\t\treturn self.other_tokens(torch.tensor([2], device=self.other_tokens.weight.device)).squeeze(0)\n","\n","clip_processor = AutoProcessor.from_pretrained(CLIP_PATH)\n","clip_model = AutoModel.from_pretrained(CLIP_PATH)\n","clip_model = clip_model.vision_model\n","checkpoint = torch.load(\"/content/joy/clip_model.pt\", map_location='cpu')\n","checkpoint = {k.replace(\"_orig_mod.module.\", \"\"): v for k, v in checkpoint.items()}\n","clip_model.load_state_dict(checkpoint)\n","# del checkpoint\n","clip_model.eval()\n","clip_model.requires_grad_(False)\n","clip_model.to(\"cuda\")\n","tokenizer = AutoTokenizer.from_pretrained(f'{MODEL_PATH}')\n","#tokenizer = AutoTokenizer.from_pretrained(\"unsloth/Meta-Llama-3.1-8B-bnb-4bit\")\n","assert isinstance(tokenizer, PreTrainedTokenizer) or isinstance(tokenizer, PreTrainedTokenizerFast), f\"Tokenizer is of type {type(tokenizer)}\"\n","text_model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, load_in_8bit=True, device_map=\"auto\", torch_dtype=torch.bfloat16)\n","text_model.load_adapter(\"/content/joy/text_model\")\n","text_model.eval()\n","image_adapter = ImageAdapter(clip_model.config.hidden_size, text_model.config.hidden_size, False, False, 38, False)\n","image_adapter.load_state_dict(torch.load(\"/content/joy/image_adapter.pt\", map_location=\"cpu\"))\n","image_adapter.eval()\n","image_adapter.to(\"cuda\")\n","\n","@torch.no_grad()\n","def stream_chat(input_image: Image.Image, caption_type: str, caption_tone: str, caption_length: str | int) -> str:\n","    torch.cuda.empty_cache()\n","    length = None if caption_length == \"any\" else caption_length\n","    if isinstance(length, str):\n","        try:\n","            length = int(length)\n","        except ValueError:\n","            pass\n","    if caption_type == \"rng-tags\" or caption_type == \"training_prompt\":\n","        caption_tone = \"formal\"\n","    prompt_key = (caption_type, caption_tone, isinstance(length, str), isinstance(length, int))\n","    if prompt_key not in CAPTION_TYPE_MAP:\n","        raise ValueError(f\"Invalid caption type: {prompt_key}\")\n","    prompt_str = CAPTION_TYPE_MAP[prompt_key][0].format(length=length, word_count=length)\n","    print(f\"Prompt: {prompt_str}\")\n","    image = input_image.resize((384, 384), Image.LANCZOS)\n","    pixel_values = TVF.pil_to_tensor(image).unsqueeze(0) / 255.0\n","    pixel_values = TVF.normalize(pixel_values, [0.5], [0.5])\n","    pixel_values = pixel_values.to('cuda')\n","    prompt = tokenizer.encode(prompt_str, return_tensors='pt', padding=False, truncation=False, add_special_tokens=False)\n","    with torch.amp.autocast_mode.autocast('cuda', enabled=True):\n","        vision_outputs = clip_model(pixel_values=pixel_values, output_hidden_states=True)\n","        image_features = vision_outputs.hidden_states\n","        embedded_images = image_adapter(image_features)\n","        embedded_images = embedded_images.to('cuda')\n","    prompt_embeds = text_model.model.embed_tokens(prompt.to('cuda'))\n","    assert prompt_embeds.shape == (1, prompt.shape[1], text_model.config.hidden_size), f\"Prompt shape is {prompt_embeds.shape}, expected {(1, prompt.shape[1], text_model.config.hidden_size)}\"\n","    embedded_bos = text_model.model.embed_tokens(torch.tensor([[tokenizer.bos_token_id]], device=text_model.device, dtype=torch.int64))\n","    eot_embed = image_adapter.get_eot_embedding().unsqueeze(0).to(dtype=text_model.dtype)\n","    inputs_embeds = torch.cat([\n","        embedded_bos.expand(embedded_images.shape[0], -1, -1),\n","        embedded_images.to(dtype=embedded_bos.dtype),\n","        prompt_embeds.expand(embedded_images.shape[0], -1, -1),\n","        eot_embed.expand(embedded_images.shape[0], -1, -1),\n","    ], dim=1)\n","    input_ids = torch.cat([\n","        torch.tensor([[tokenizer.bos_token_id]], dtype=torch.long),\n","        torch.zeros((1, embedded_images.shape[1]), dtype=torch.long),\n","        prompt,\n","        torch.tensor([[tokenizer.convert_tokens_to_ids(\"<|eot_id|>\")]], dtype=torch.long),\n","    ], dim=1).to('cuda')\n","    attention_mask = torch.ones_like(input_ids)\n","    generate_ids = text_model.generate(input_ids, inputs_embeds=inputs_embeds, attention_mask=attention_mask, max_new_tokens=300, do_sample=True, suppress_tokens=None)   # Uses the default which is temp=0.6, top_p=0.9\n","    generate_ids = generate_ids[:, input_ids.shape[1]:]\n","    if generate_ids[0][-1] == tokenizer.eos_token_id or generate_ids[0][-1] == tokenizer.convert_tokens_to_ids(\"<|eot_id|>\"):\n","        generate_ids = generate_ids[:, :-1]\n","    caption = tokenizer.batch_decode(generate_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0]\n","    caption = f'{caption.strip()}'.replace('Prompt: Describe the image in 400 words','')\n","    return caption"],"metadata":{"id":"0zaheBIsw_dc"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import os\n","from PIL import Image\n","home_directory = '/content/'\n","using_Kaggle = os.environ.get('KAGGLE_URL_BASE','')\n","if using_Kaggle : home_directory = '/kaggle/working/'\n","%cd {home_directory}\n","\n","def my_mkdirs(folder):\n","  if os.path.exists(folder)==False:\n","    os.makedirs(folder)\n","\n","\n","tgt_folder = f'/content/tmp/'\n","my_mkdirs(f'{tgt_folder}')\n","\n","\n","src_folder = '/content/'\n","suffixes = ['.png', '.jpeg' , '.webp' , '.jpg']\n","num = 1\n","for filename in os.listdir(src_folder):\n","  for suffix in suffixes:\n","    if not filename.find(suffix)>-1: continue\n","    print(filename)\n","    %cd {src_folder}\n","    input_image = Image.open(f\"{filename}\").convert('RGB')\n","    caption = stream_chat(input_image, \"descriptive\", \"formal\", \"any\")\n","    print(f\"...\\n\\n...caption for {filename}\\n\\n...\")\n","    print(caption)\n","    #---------#\n","    %cd {tgt_folder}\n","    f = open(f\"{num}.txt\", \"w\")\n","    f.write(f'{caption}')\n","    f.close()\n","    input_image.save(f'{num}.png', \"PNG\")\n","    num = num+1"],"metadata":{"id":"J811UZU6xZEo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","!pip install pyminizip\n","import pyminizip\n","compression_level = 2 # 1-9\n","pyminizip.compress(f\"{tgt_folder}\", \"content/drive/MyDrive/wabbo.zip\", \"bananzers88\", compression_level)\n","\n","#'/content/drive/MyDrive/my_dataset'\n","#, format='zip', root_dir=f'{tgt_folder}')\n"],"metadata":{"id":"5EztLCjkPq4U"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","from google.colab import runtime\n","#runtime.unassign() #Disconnect from runtime"],"metadata":{"id":"kM4TpfdB1amt"},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
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at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","source":["!apt -y install -qq aria2\n","!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/joy-caption-alpha-one/raw/main/text_model/adapter_config.json -d /content/joy/text_model -o adapter_config.json\n","!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/joy-caption-alpha-one/resolve/main/text_model/adapter_model.safetensors -d /content/joy/text_model -o adapter_model.safetensors\n","!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/joy-caption-alpha-one/resolve/main/clip_model.pt -d /content/joy -o clip_model.pt\n","!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/joy-caption-alpha-one/raw/main/config.yaml -d /content/joy -o config.yaml\n","!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/joy-caption-alpha-one/resolve/main/image_adapter.pt -d /content/joy -o image_adapter.pt\n","\n","!pip install peft bitsandbytes\n","from huggingface_hub import InferenceClient\n","from torch import nn\n","from transformers import AutoModel, AutoProcessor, AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast, AutoModelForCausalLM\n","import torch\n","import torch.amp.autocast_mode\n","from PIL import Image\n","import os\n","import torchvision.transforms.functional as TVF\n","\n","CLIP_PATH = \"google/siglip-so400m-patch14-384\"\n","MODEL_PATH = \"unsloth/Meta-Llama-3.1-8B-bnb-4bit\"\n","CAPTION_TYPE_MAP = {\n","    (\"descriptive\", \"formal\", False, False): [\"Describe the image in 400 words\"],\n","    (\"descriptive\", \"formal\", False, True): [\"Write a descriptive caption for this image in a formal tone within {word_count} words.\"],\n","    (\"descriptive\", \"formal\", True, False): [\"Write a {length} descriptive caption for this image in a formal tone.\"],\n","    (\"descriptive\", \"informal\", False, False): [\"Write a descriptive caption for this image in a casual tone.\"],\n","    (\"descriptive\", \"informal\", False, True): [\"Write a descriptive caption for this image in a casual tone within {word_count} words.\"],\n","    (\"descriptive\", \"informal\", True, False): [\"Write a {length} descriptive caption for this image in a casual tone.\"],\n","    (\"training_prompt\", \"formal\", False, False): [\"Write a stable diffusion prompt for this image.\"],\n","    (\"training_prompt\", \"formal\", False, True): [\"Write a stable diffusion prompt for this image within {word_count} words.\"],\n","    (\"training_prompt\", \"formal\", True, False): [\"Write a {length} stable diffusion prompt for this image.\"],\n","    (\"rng-tags\", \"formal\", False, False): [\"Write a list of Booru tags for this image.\"],\n","    (\"rng-tags\", \"formal\", False, True): [\"Write a list of Booru tags for this image within {word_count} words.\"],\n","    (\"rng-tags\", \"formal\", True, False): [\"Write a {length} list of Booru tags for this image.\"],\n","}\n","\n","class ImageAdapter(nn.Module):\n","\tdef __init__(self, input_features: int, output_features: int, ln1: bool, pos_emb: bool, num_image_tokens: int, deep_extract: bool):\n","\t\tsuper().__init__()\n","\t\tself.deep_extract = deep_extract\n","\t\tif self.deep_extract:\n","\t\t\tinput_features = input_features * 5\n","\t\tself.linear1 = nn.Linear(input_features, output_features)\n","\t\tself.activation = nn.GELU()\n","\t\tself.linear2 = nn.Linear(output_features, output_features)\n","\t\tself.ln1 = nn.Identity() if not ln1 else nn.LayerNorm(input_features)\n","\t\tself.pos_emb = None if not pos_emb else nn.Parameter(torch.zeros(num_image_tokens, input_features))\n","\t\tself.other_tokens = nn.Embedding(3, output_features)\n","\t\tself.other_tokens.weight.data.normal_(mean=0.0, std=0.02)   # Matches HF's implementation of llama3\n","\tdef forward(self, vision_outputs: torch.Tensor):\n","\t\tif self.deep_extract:\n","\t\t\tx = torch.concat((\n","\t\t\t\tvision_outputs[-2],\n","\t\t\t\tvision_outputs[3],\n","\t\t\t\tvision_outputs[7],\n","\t\t\t\tvision_outputs[13],\n","\t\t\t\tvision_outputs[20],\n","\t\t\t), dim=-1)\n","\t\t\tassert len(x.shape) == 3, f\"Expected 3, got {len(x.shape)}\"  # batch, tokens, features\n","\t\t\tassert x.shape[-1] == vision_outputs[-2].shape[-1] * 5, f\"Expected {vision_outputs[-2].shape[-1] * 5}, got {x.shape[-1]}\"\n","\t\telse:\n","\t\t\tx = vision_outputs[-2]\n","\t\tx = self.ln1(x)\n","\t\tif self.pos_emb is not None:\n","\t\t\tassert x.shape[-2:] == self.pos_emb.shape, f\"Expected {self.pos_emb.shape}, got {x.shape[-2:]}\"\n","\t\t\tx = x + self.pos_emb\n","\t\tx = self.linear1(x)\n","\t\tx = self.activation(x)\n","\t\tx = self.linear2(x)\n","\t\tother_tokens = self.other_tokens(torch.tensor([0, 1], device=self.other_tokens.weight.device).expand(x.shape[0], -1))\n","\t\tassert other_tokens.shape == (x.shape[0], 2, x.shape[2]), f\"Expected {(x.shape[0], 2, x.shape[2])}, got {other_tokens.shape}\"\n","\t\tx = torch.cat((other_tokens[:, 0:1], x, other_tokens[:, 1:2]), dim=1)\n","\t\treturn x\n","\tdef get_eot_embedding(self):\n","\t\treturn self.other_tokens(torch.tensor([2], device=self.other_tokens.weight.device)).squeeze(0)\n","\n","clip_processor = AutoProcessor.from_pretrained(CLIP_PATH)\n","clip_model = AutoModel.from_pretrained(CLIP_PATH)\n","clip_model = clip_model.vision_model\n","checkpoint = torch.load(\"/content/joy/clip_model.pt\", map_location='cpu')\n","checkpoint = {k.replace(\"_orig_mod.module.\", \"\"): v for k, v in checkpoint.items()}\n","clip_model.load_state_dict(checkpoint)\n","# del checkpoint\n","clip_model.eval()\n","clip_model.requires_grad_(False)\n","clip_model.to(\"cuda\")\n","tokenizer = AutoTokenizer.from_pretrained(f'{MODEL_PATH}')\n","#tokenizer = AutoTokenizer.from_pretrained(\"unsloth/Meta-Llama-3.1-8B-bnb-4bit\")\n","assert isinstance(tokenizer, PreTrainedTokenizer) or isinstance(tokenizer, PreTrainedTokenizerFast), f\"Tokenizer is of type {type(tokenizer)}\"\n","text_model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, load_in_8bit=True, device_map=\"auto\", torch_dtype=torch.bfloat16)\n","text_model.load_adapter(\"/content/joy/text_model\")\n","text_model.eval()\n","image_adapter = ImageAdapter(clip_model.config.hidden_size, text_model.config.hidden_size, False, False, 38, False)\n","image_adapter.load_state_dict(torch.load(\"/content/joy/image_adapter.pt\", map_location=\"cpu\"))\n","image_adapter.eval()\n","image_adapter.to(\"cuda\")\n","\n","@torch.no_grad()\n","def stream_chat(input_image: Image.Image, caption_type: str, caption_tone: str, caption_length: str | int) -> str:\n","    torch.cuda.empty_cache()\n","    length = None if caption_length == \"any\" else caption_length\n","    if isinstance(length, str):\n","        try:\n","            length = int(length)\n","        except ValueError:\n","            pass\n","    if caption_type == \"rng-tags\" or caption_type == \"training_prompt\":\n","        caption_tone = \"formal\"\n","    prompt_key = (caption_type, caption_tone, isinstance(length, str), isinstance(length, int))\n","    if prompt_key not in CAPTION_TYPE_MAP:\n","        raise ValueError(f\"Invalid caption type: {prompt_key}\")\n","    prompt_str = CAPTION_TYPE_MAP[prompt_key][0].format(length=length, word_count=length)\n","    print(f\"Prompt: {prompt_str}\")\n","    image = input_image.resize((384, 384), Image.LANCZOS)\n","    pixel_values = TVF.pil_to_tensor(image).unsqueeze(0) / 255.0\n","    pixel_values = TVF.normalize(pixel_values, [0.5], [0.5])\n","    pixel_values = pixel_values.to('cuda')\n","    prompt = tokenizer.encode(prompt_str, return_tensors='pt', padding=False, truncation=False, add_special_tokens=False)\n","    with torch.amp.autocast_mode.autocast('cuda', enabled=True):\n","        vision_outputs = clip_model(pixel_values=pixel_values, output_hidden_states=True)\n","        image_features = vision_outputs.hidden_states\n","        embedded_images = image_adapter(image_features)\n","        embedded_images = embedded_images.to('cuda')\n","    prompt_embeds = text_model.model.embed_tokens(prompt.to('cuda'))\n","    assert prompt_embeds.shape == (1, prompt.shape[1], text_model.config.hidden_size), f\"Prompt shape is {prompt_embeds.shape}, expected {(1, prompt.shape[1], text_model.config.hidden_size)}\"\n","    embedded_bos = text_model.model.embed_tokens(torch.tensor([[tokenizer.bos_token_id]], device=text_model.device, dtype=torch.int64))\n","    eot_embed = image_adapter.get_eot_embedding().unsqueeze(0).to(dtype=text_model.dtype)\n","    inputs_embeds = torch.cat([\n","        embedded_bos.expand(embedded_images.shape[0], -1, -1),\n","        embedded_images.to(dtype=embedded_bos.dtype),\n","        prompt_embeds.expand(embedded_images.shape[0], -1, -1),\n","        eot_embed.expand(embedded_images.shape[0], -1, -1),\n","    ], dim=1)\n","    input_ids = torch.cat([\n","        torch.tensor([[tokenizer.bos_token_id]], dtype=torch.long),\n","        torch.zeros((1, embedded_images.shape[1]), dtype=torch.long),\n","        prompt,\n","        torch.tensor([[tokenizer.convert_tokens_to_ids(\"<|eot_id|>\")]], dtype=torch.long),\n","    ], dim=1).to('cuda')\n","    attention_mask = torch.ones_like(input_ids)\n","    generate_ids = text_model.generate(input_ids, inputs_embeds=inputs_embeds, attention_mask=attention_mask, max_new_tokens=300, do_sample=True, suppress_tokens=None)   # Uses the default which is temp=0.6, top_p=0.9\n","    generate_ids = generate_ids[:, input_ids.shape[1]:]\n","    if generate_ids[0][-1] == tokenizer.eos_token_id or generate_ids[0][-1] == tokenizer.convert_tokens_to_ids(\"<|eot_id|>\"):\n","        generate_ids = generate_ids[:, :-1]\n","    caption = tokenizer.batch_decode(generate_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0]\n","    caption = f'{caption.strip()}'.replace('Prompt: Describe the image in 400 words','')\n","    return caption"],"metadata":{"id":"0zaheBIsw_dc","outputId":"d4a10e32-d390-4543-b05c-6dd236b126d2","colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["ad1d3cd62e3b449aad13c6976a9a7ee9","b82c69c2bc1f49ec92166775de75fb57","97b36bdba43f40b595eba0d5d077b49c","ee5558ee6da14a8eb3cd522f64649ec4","df5284d0a54e4b7aaaf25a72bbd6f7c6","24a75d90cc4b43ad8b4587db24844f65","726b502df490414985143264cc6d88f9","c1691740ee4345cd93c152678ebe762c","9f895ca8a418476ca9776e371fa0ac58","325e6aa551d244f7bf1c0b343ec3682f","9e85e7efdf6444d18e50f63390b4562d","74640fd827424d6d8cf9cdcde3f3470b","b3122dc261d34163a86793aab2895c42","b4e250686116433bb587d1e2a7dfc3db","7534e910eb6647b7943063bc2c214f05","dbd5645879b8455fac79354b36b5f312","233578d09a754a069bf31d4f5f1d9260","87a7bfed1f534a79a079c6629be89797","6f20455dd24c4677a519d061f6a9db07","d69c68d6d54f44e38020345bd921fb8b","59ceecc622a64fe183f7330069e1c15f","547103d143014c41b79e8e8f2fa2e6fc","fd26ad2f999a4888ab37bebf81782a5b","f23564ef6d1644cb80b4fda69bfa858d","698e8e8f69264625b4c979a4b84e7f3d","3acbaefee32e41a6bd57fa88324bb825","a6fa712a0a9846bba39b0b834f91b4ab","78e75af5560448ac9d08df638c0daa41","676e9ddcf09a486a92676686256a1004","bd4938e41a3d4c4b9740cc487e701b2c","13f084fdf8d74f169ba09bf107344bdc","1c19e1ec619f4a97bb7e46087c6385cc","6fab45d9a1bc417a9d4bf7f1a9ec6f43","fac7b9cdb04543fea94f958771abde2e","ec170bc47e044f3eb0d04e9ce4651b55","87bc9c81b48f45ef891d5a6aacf99354","1a0a8d3737e34687abe0174b88f82799","c2a045cf2cc44715bf08c5d2acf8ee40","c338b1293d0243a6a1f9a1dfa3836db1","0f6bdcc270c645e89461786a2b9b2272","4018f9fa088a4ce2b787f777c4a3e072","c5365a97277741dfb88fbf24dd0a08f5","a765d914f000438d9e46d4a332ae33ea","e122c20655f048a58814619fa95b49b9","82b76417ee17487e869989b8ae399f7f","220c92cdf3fe492881d10a7ffa7ea6ae","d3eb08cddfa04f7f8beeb0245e721540","e10c7efc346d471ea4c4903f498e68c0","ea100309137141879a93a86da49be717","92b4658ef564490db8c6c495cc1cfdf5","d9e0d8c94c0d4decac3a29ed5eba56bc","a76055145c8248c3a7c33b355414ef2f","b0d053a67fe24c83a8d52154b3be1fa4","9732a846442142479a534c5783a4fbb4","d5245b5bc68c4febb02a027f29676992","a0c1d481e9ce4604b932fff59b292659","5eddf38b5ec34097a4f77495871626b2","313d76a7df414e9aa947214a6f730b3c","85adcd08b26446b490e1963fdad6975f","7036e248c9964f68a869feea748057a7","2862bc77c9e344c0b317c763bf22f35d","f4a098448e6f48059b4942dd2fef51d5","68cae6b39db0472e98a93a518ffa8a22","28c1bc57d3fd4587bcd1239c3a189c87","6bfbc369e2c749cea4c8d24d16d39013","eed6ef208bc344908d8590480ae902c7","514e23cc92e94b1683ebec6b52513fcc","d7471df4cc3a410d9bd962bdffdf0070","b11ec411344a4a108e4e57a053c135ed","bfe9295f8a78457589f12de73f371e30","9e83e9e403b94c1faffd33d2203b4f30","d84b83c57d774b7690dc556a371becc0","42b42b7c0e3144c997656b36256aab4a","579c369bb72b4a60944071c6030749dd","fffb31589f274abeae3ec766cb83fd77","50e8bce1c42346a3be367d626031dd7c","919208f38bbc4f0fbe7e722b35700690","7c5b294c4e264bd082d7ecfa0450c406","93e39138082b4c78afebd7ce0367ddc6","ce071d8499a4487ca39ed6c230dbe84a","a57438274bc7490eaae0213828aef719","8ef34ce560684f3cafbea704d1836155","afff5777abfe48a5b25e97d3281c7096","1ebf092d1f7240aba4ef5a346c29977b","736c853aeb014482ba7f344a9e456896","2299d4a3f6e0494d9a043172fbbeedca","e0426e62d29a4f318f75ce9dca6f53d8","8b92d4bb16a744a69b15bbb343a91b5d","1d4ccff85534420888b0096ecaa5c440","bfa1bf0840e147d8bf4baccb399ef844","594a838f30e54fa5b17d5f9b2163161e","bfcf9232aa664fa7a80986be30a0aa02","65820b5ca08e411eb97d7d0559e6cfbc","1e9e402b248b4be2a6618b778e0117a7","70b81627e0b14bd690586a3776f52165","9abfb1960b2344318a728b49f47cb3ef","5350d9730707457ca0459c8066f4d633","e7c1daa9932b42f1876b676252144bc4","34e389de350648c880ee0a6fd19dd9aa","8e31f7042b1b474096c40e0d8bf09821","7782eb7258284dd5aaa66d959712c9ff","1bfe2aee737c43a582f588b3a2cfc707","a2c51c618be9495bbe06fbb4f95cb29b","f1a0e5b56f37417c9689f096ac196c49","3e89d6f0e7154a5fa4faaf949c07104d","160e7c314a4e496896f0fdeec60c7896","b3cb58c37744466994f17ff7d041b4d0","4cd7102b962043359316f4b7cca7cb65","fdcd3d33d1b447c58276271bf8f7ad3a","57f92654577c4a06af80bc5dc3d1d375","e6f55a78abb04c618cdf8b7ec689f2a6","1481509b6e9f4df0bd7b6b171c28662f","16a041154f2848fb82471d374cc9c6d4","20dc97916d77454294365ffc10a323e5","a584317667e149578f2ef716851261a7","0889cc6a18344a17a8b4f01476edb4cc","7b286a7acba5407d8098392f9df41eef","d1494bb0647b4fbabc48f1c83161e4f6","2c3ddf85245d42cab570af821c187d97","726e15b6c5cb4ff2b2b8eb47bb4245d3","f7f99ad64ba248d092523fcc56ad121b","f165cd92337b46168c01a932a9a37a46","64011a0da8ca4527b8ae3c5181c1fcaa","e5f4f37bce5c48c5ba3e6f62d123b3c9","80a3a962001b4e18b0fb762fc1b89021","a4b1506571d54e50880b9068ebee028d","b51d11caaadc4130bc2a868793463756","ac40c82b04434ef2888d9a7551f2bd0f","d8afdb611f324959bbed3e5003630aa8","53d53928546b41009f31701f466784f7","443699fc5dfb49378e34fe5d8a5872b4","0f03bee49b3c43f2bb61404953f1473e"]}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["The following additional packages will be installed:\n","  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 29 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 ... 124947 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","Preparing to unpack .../libaria2-0_1.36.0-1_amd64.deb ...\n","Unpacking libaria2-0:amd64 (1.36.0-1) ...\n","Selecting previously unselected package aria2.\n","Preparing to unpack .../aria2_1.36.0-1_amd64.deb ...\n","Unpacking aria2 (1.36.0-1) ...\n","Setting up libc-ares2:amd64 (1.18.1-1ubuntu0.22.04.3) ...\n","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 (2.35-0ubuntu3.8) ...\n","/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n","\n","/sbin/ldconfig.real: /usr/local/lib/libhwloc.so.15 is not a symbolic link\n","\n","/sbin/ldconfig.real: /usr/local/lib/libumf.so.0 is not a symbolic link\n","\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/libtbbbind_2_5.so.3 is not a symbolic link\n","\n","/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n","\n","/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n","\n","/sbin/ldconfig.real: /usr/local/lib/libtcm.so.1 is not a symbolic link\n","\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/libtcm_debug.so.1 is not a symbolic link\n","\n","/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 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","\n","/sbin/ldconfig.real: /usr/local/lib/libur_loader.so.0 is not a symbolic link\n","\n","\n","Download Results:\n","gid   |stat|avg speed  |path/URI\n","======+====+===========+=======================================================\n","b0a51a|\u001b[1;32mOK\u001b[0m  |    91KiB/s|/content/joy/text_model/adapter_config.json\n","\n","Status Legend:\n","(OK):download completed.\n","\u001b[0m\n","Download Results:\n","gid   |stat|avg speed  |path/URI\n","======+====+===========+=======================================================\n","c5aeb3|\u001b[1;32mOK\u001b[0m  |   100MiB/s|/content/joy/text_model/adapter_model.safetensors\n","\n","Status Legend:\n","(OK):download completed.\n","\u001b[0m\n","Download Results:\n","gid   |stat|avg speed  |path/URI\n","======+====+===========+=======================================================\n","b3fccd|\u001b[1;32mOK\u001b[0m  |   160MiB/s|/content/joy/clip_model.pt\n","\n","Status Legend:\n","(OK):download completed.\n","\n","Download Results:\n","gid   |stat|avg speed  |path/URI\n","======+====+===========+=======================================================\n","c4ca5f|\u001b[1;32mOK\u001b[0m  |   115KiB/s|/content/joy/config.yaml\n","\n","Status Legend:\n","(OK):download completed.\n","\u001b[0m\n","Download Results:\n","gid   |stat|avg speed  |path/URI\n","======+====+===========+=======================================================\n","d1bf95|\u001b[1;32mOK\u001b[0m  |   116MiB/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-0.45.3-py3-none-manylinux_2_24_x86_64.whl.metadata (5.0 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+cu124)\n","Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (from peft) (4.48.3)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from peft) (4.67.1)\n","Requirement already satisfied: accelerate>=0.21.0 in /usr/local/lib/python3.11/dist-packages (from peft) (1.3.0)\n","Requirement already satisfied: safetensors in /usr/local/lib/python3.11/dist-packages (from peft) 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torch>=1.13.0->peft)\n","  Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cuda-runtime-cu12==12.4.127 (from torch>=1.13.0->peft)\n","  Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cuda-cupti-cu12==12.4.127 (from torch>=1.13.0->peft)\n","  Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch>=1.13.0->peft)\n","  Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cublas-cu12==12.4.5.8 (from torch>=1.13.0->peft)\n","  Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cufft-cu12==11.2.1.3 (from torch>=1.13.0->peft)\n","  Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Collecting 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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-0.45.3 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"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["preprocessor_config.json:   0%|          | 0.00/368 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ad1d3cd62e3b449aad13c6976a9a7ee9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/711 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"74640fd827424d6d8cf9cdcde3f3470b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["spiece.model:   0%|          | 0.00/798k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fd26ad2f999a4888ab37bebf81782a5b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/409 [00:00<?, 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?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"514e23cc92e94b1683ebec6b52513fcc"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["<ipython-input-2-d0c33a306178>:78: 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"]},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/50.6k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7c5b294c4e264bd082d7ecfa0450c406"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/17.2M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1d4ccff85534420888b0096ecaa5c440"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/459 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8e31f7042b1b474096c40e0d8bf09821"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/1.52k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e6f55a78abb04c618cdf8b7ec689f2a6"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n","Unused kwargs: ['_load_in_4bit', '_load_in_8bit', 'quant_method']. These kwargs are not used in <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>.\n","/usr/local/lib/python3.11/dist-packages/transformers/quantizers/auto.py:195: UserWarning: You passed `quantization_config` or equivalent parameters to `from_pretrained` but the model you're loading already has a `quantization_config` attribute. The `quantization_config` from the model will be used.\n","  warnings.warn(warning_msg)\n"]},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/5.70G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f165cd92337b46168c01a932a9a37a46"}},"metadata":{}}]},{"cell_type":"code","source":["import os\n","from PIL import Image\n","home_directory = '/content/'\n","using_Kaggle = os.environ.get('KAGGLE_URL_BASE','')\n","if using_Kaggle : home_directory = '/kaggle/working/'\n","%cd {home_directory}\n","\n","def my_mkdirs(folder):\n","  if os.path.exists(folder)==False:\n","    os.makedirs(folder)\n","\n","\n","tgt_folder = f'/content/tmp/'\n","my_mkdirs(f'{tgt_folder}')\n","\n","\n","src_folder = '/content/'\n","suffixes = ['.png', '.jpeg' , '.webp' , '.jpg']\n","num = 1\n","for filename in os.listdir(src_folder):\n","  for suffix in suffixes:\n","    if not filename.find(suffix)>-1: continue\n","    print(filename)\n","    %cd {src_folder}\n","    input_image = Image.open(f\"{filename}\").convert('RGB')\n","    caption = stream_chat(input_image, \"descriptive\", \"formal\", \"any\")\n","    print(f\"...\\n\\n...caption for {filename}\\n\\n...\")\n","    print(caption)\n","    #---------#\n","    %cd {tgt_folder}\n","    f = open(f\"{num}.txt\", \"w\")\n","    f.write(f'{caption}')\n","    f.close()\n","    input_image.save(f'{num}.png', \"PNG\")\n","    num = num+1"],"metadata":{"id":"J811UZU6xZEo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import shutil\n","shutil.make_archive('/content/tmp', 'zip', '/content/tmp')"],"metadata":{"id":"5EztLCjkPq4U"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","from google.colab import runtime\n","#runtime.unassign() #Disconnect from runtime"],"metadata":{"id":"kM4TpfdB1amt"},"execution_count":null,"outputs":[]}]}
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