{ "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 }