Upload Joycaption_Alpha_One.ipynb

Joycaption_Alpha_One.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "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\"\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(MODEL_PATH, use_fast=False)\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/drive/MyDrive/tmp/'\n",
+        "my_mkdirs(f'{tgt_folder}')\n",
+        "\n",
+        "\n",
+        "src_folder = '/content/drive/MyDrive/wild party/'\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}.{suffix}\\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": [
+        "from google.colab import runtime\n",
+        "runtime.unassign()"
+      ],
+      "metadata": {
+        "id": "kM4TpfdB1amt"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}