Upload joy_caption_jupyter (1).ipynb

joy_caption_jupyter (1).ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VjYy0F2gZIPR"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install gradio bitsandbytes transformers==4.43.3\n",
+        "\n",
+        "!wget https://huggingface.co/spaces/fancyfeast/joy-caption-pre-alpha/resolve/main/wpkklhc6/image_adapter.pt -O /content/image_adapter.pt\n",
+        "!wget https://huggingface.co/spaces/fancyfeast/joy-caption-pre-alpha/raw/main/wpkklhc6/config.yaml -O /content/config.yaml\n",
+        "\n",
+        "import gradio as gr\n",
+        "from huggingface_hub import InferenceClient\n",
+        "from torch import nn\n",
+        "from transformers import AutoModel, AutoProcessor, AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast, AutoModelForCausalLM\n",
+        "from pathlib import Path\n",
+        "import torch\n",
+        "import torch.amp.autocast_mode\n",
+        "from PIL import Image\n",
+        "import os\n",
+        "\n",
+        "CLIP_PATH = \"google/siglip-so400m-patch14-384\"\n",
+        "VLM_PROMPT = \"A descriptive caption for this image:\\n\"\n",
+        "# MODEL_PATH = \"unsloth/Meta-Llama-3.1-8B\"\n",
+        "MODEL_PATH = \"unsloth/Meta-Llama-3.1-8B-bnb-4bit\"\n",
+        "CHECKPOINT_PATH = Path(\"wpkklhc6\")\n",
+        "\n",
+        "class ImageAdapter(nn.Module):\n",
+        "\tdef __init__(self, input_features: int, output_features: int):\n",
+        "\t\tsuper().__init__()\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",
+        "\n",
+        "\tdef forward(self, vision_outputs: torch.Tensor):\n",
+        "\t\tx = self.linear1(vision_outputs)\n",
+        "\t\tx = self.activation(x)\n",
+        "\t\tx = self.linear2(x)\n",
+        "\t\treturn x\n",
+        "\n",
+        "# Load CLIP\n",
+        "print(\"Loading CLIP\")\n",
+        "clip_processor = AutoProcessor.from_pretrained(CLIP_PATH)\n",
+        "clip_model = AutoModel.from_pretrained(CLIP_PATH)\n",
+        "clip_model = clip_model.vision_model\n",
+        "clip_model.eval()\n",
+        "clip_model.requires_grad_(False)\n",
+        "clip_model.to(\"cuda\")\n",
+        "\n",
+        "# Tokenizer\n",
+        "print(\"Loading tokenizer\")\n",
+        "tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, load_in_4bit=True, use_fast=False)\n",
+        "assert isinstance(tokenizer, PreTrainedTokenizer) or isinstance(tokenizer, PreTrainedTokenizerFast), f\"Tokenizer is of type {type(tokenizer)}\"\n",
+        "\n",
+        "# LLM\n",
+        "print(\"Loading LLM\")\n",
+        "text_model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, load_in_4bit=True, device_map=\"auto\", torch_dtype=torch.float16)\n",
+        "text_model.eval()\n",
+        "\n",
+        "# Image Adapter\n",
+        "print(\"Loading image adapter\")\n",
+        "image_adapter = ImageAdapter(clip_model.config.hidden_size, text_model.config.hidden_size)\n",
+        "image_adapter.load_state_dict(torch.load(\"/content/image_adapter.pt\", map_location=\"cpu\"))\n",
+        "image_adapter.eval()\n",
+        "image_adapter.to(\"cuda\")\n",
+        "\n",
+        "@torch.inference_mode()\n",
+        "def stream_chat(input_image: Image.Image):\n",
+        "\ttorch.cuda.empty_cache()\n",
+        "\n",
+        "\t# Preprocess image\n",
+        "\timage = clip_processor(images=input_image, return_tensors='pt').pixel_values\n",
+        "\timage = image.to('cuda')\n",
+        "\n",
+        "\t# Tokenize the prompt\n",
+        "\tprompt = tokenizer.encode(VLM_PROMPT, return_tensors='pt', padding=False, truncation=False, add_special_tokens=False)\n",
+        "\n",
+        "\t# Embed image\n",
+        "\twith torch.amp.autocast_mode.autocast('cuda', enabled=True):\n",
+        "\t\tvision_outputs = clip_model(pixel_values=image, output_hidden_states=True)\n",
+        "\t\timage_features = vision_outputs.hidden_states[-2]\n",
+        "\t\tembedded_images = image_adapter(image_features)\n",
+        "\t\tembedded_images = embedded_images.to('cuda')\n",
+        "\n",
+        "\t# Embed prompt\n",
+        "\tprompt_embeds = text_model.model.embed_tokens(prompt.to('cuda'))\n",
+        "\tassert 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",
+        "\tembedded_bos = text_model.model.embed_tokens(torch.tensor([[tokenizer.bos_token_id]], device=text_model.device, dtype=torch.int64))\n",
+        "\n",
+        "\t# Construct prompts\n",
+        "\tinputs_embeds = torch.cat([\n",
+        "\t\tembedded_bos.expand(embedded_images.shape[0], -1, -1),\n",
+        "\t\tembedded_images.to(dtype=embedded_bos.dtype),\n",
+        "\t\tprompt_embeds.expand(embedded_images.shape[0], -1, -1),\n",
+        "\t], dim=1)\n",
+        "\n",
+        "\tinput_ids = torch.cat([\n",
+        "\t\ttorch.tensor([[tokenizer.bos_token_id]], dtype=torch.long),\n",
+        "\t\ttorch.zeros((1, embedded_images.shape[1]), dtype=torch.long),\n",
+        "\t\tprompt,\n",
+        "\t], dim=1).to('cuda')\n",
+        "\tattention_mask = torch.ones_like(input_ids)\n",
+        "\n",
+        "\t#generate_ids = text_model.generate(input_ids, inputs_embeds=inputs_embeds, attention_mask=attention_mask, max_new_tokens=300, do_sample=False, suppress_tokens=None)\n",
+        "\tgenerate_ids = text_model.generate(input_ids, inputs_embeds=inputs_embeds, attention_mask=attention_mask, max_new_tokens=300, do_sample=True, top_k=10, temperature=0.5, suppress_tokens=None)\n",
+        "\n",
+        "\t# Trim off the prompt\n",
+        "\tgenerate_ids = generate_ids[:, input_ids.shape[1]:]\n",
+        "\tif generate_ids[0][-1] == tokenizer.eos_token_id:\n",
+        "\t\tgenerate_ids = generate_ids[:, :-1]\n",
+        "\n",
+        "\tcaption = tokenizer.batch_decode(generate_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0]\n",
+        "\n",
+        "\treturn caption.strip()\n",
+        "\n",
+        "\n",
+        "with gr.Blocks(css=\".gradio-container {max-width: 544px !important}\", analytics_enabled=False) as demo:\n",
+        "\twith gr.Row():\n",
+        "\t\twith gr.Column():\n",
+        "\t\t\tinput_image = gr.Image(type=\"pil\", label=\"Input Image\")\n",
+        "\t\t\trun_button = gr.Button(\"Caption\")\n",
+        "\t\t\toutput_caption = gr.Textbox(label=\"Caption\")\n",
+        "\trun_button.click(fn=stream_chat, inputs=[input_image], outputs=[output_caption])\n",
+        "\n",
+        "demo.queue().launch(share=True, inline=False, debug=True)"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}