{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "411c59b3-f177-4a10-8925-d931ce572eaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, StableDiffusionInpaintPipelineLegacy, DDIMScheduler, AutoencoderKL\n",
    "from PIL import Image\n",
    "\n",
    "from ip_adapter import IPAdapterPlus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6b6dc69c-192d-4d74-8b1e-f0d9ccfbdb49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/modelzoo/IP-Adapter\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "current_dir = os.getcwd()\n",
    "print(current_dir)\n",
    "\n",
    "base_model_path = f\"{current_dir}/pretrained_models/sd1.5/Realistic_Vision_v4.0_noVAE\"\n",
    "vae_model_path = f\"{current_dir}/pretrained_models/sd1.5/sd-vae-ft-mse\"\n",
    "image_encoder_path = f\"{current_dir}/pretrained_models/models/image_encoder\"\n",
    "ip_ckpt = f\"{current_dir}/pretrained_models/models/ip-adapter-plus_sd15.safetensors\"\n",
    "device = \"cuda\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "63ec542f-8474-4f38-9457-073425578073",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "An error occurred while trying to fetch /home/modelzoo/IP-Adapter/pretrained_models/sd1.5/sd-vae-ft-mse: Error no file named diffusion_pytorch_model.safetensors found in directory /home/modelzoo/IP-Adapter/pretrained_models/sd1.5/sd-vae-ft-mse.\n",
      "Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  return self.fget.__get__(instance, owner)()\n"
     ]
    }
   ],
   "source": [
    "def image_grid(imgs, rows, cols):\n",
    "    assert len(imgs) == rows*cols\n",
    "\n",
    "    w, h = imgs[0].size\n",
    "    grid = Image.new('RGB', size=(cols*w, rows*h))\n",
    "    grid_w, grid_h = grid.size\n",
    "    \n",
    "    for i, img in enumerate(imgs):\n",
    "        grid.paste(img, box=(i%cols*w, i//cols*h))\n",
    "    return grid\n",
    "\n",
    "noise_scheduler = DDIMScheduler(\n",
    "    num_train_timesteps=1000,\n",
    "    beta_start=0.00085,\n",
    "    beta_end=0.012,\n",
    "    beta_schedule=\"scaled_linear\",\n",
    "    clip_sample=False,\n",
    "    set_alpha_to_one=False,\n",
    "    steps_offset=1,\n",
    ")\n",
    "vae = AutoencoderKL.from_pretrained(vae_model_path).to(dtype=torch.float16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3849f9d0-5f68-4a49-9190-69dd50720cae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "31b18de091d6467294e5fff83ead0351",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading pipeline components...:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load SD pipeline\n",
    "pipe = StableDiffusionPipeline.from_pretrained(\n",
    "    base_model_path,\n",
    "    torch_dtype=torch.float16,\n",
    "    scheduler=noise_scheduler,\n",
    "    vae=vae,\n",
    "    feature_extractor=None,\n",
    "    safety_checker=None\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ec09e937-3904-4d8e-a559-9066502ded36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "[encoded data, 10624 characters]",
      "image/png": "[encoded data, 72888 characters]",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=256x256>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read image prompt\n",
    "image = Image.open(\"assets/images/statue.png\")\n",
    "image.resize((256, 256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a23de3d2-169e-470b-8012-960e3d07b04b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load ip-adapter\n",
    "ip_model = IPAdapterPlus(pipe, image_encoder_path, ip_ckpt, device, num_tokens=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d83df45f-717d-4bb3-a5fd-0ea30930a431",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "380900e5571040d58d60ab134dcf5490",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": "[encoded data, 138316 characters]",
      "image/png": "[encoded data, 1144300 characters]",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=2048x512>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# only image prompt\n",
    "images = ip_model.generate(pil_image=image, num_samples=4, num_inference_steps=50, seed=42)\n",
    "grid = image_grid(images, 1, 4)\n",
    "grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b77f52de-a9e4-44e1-aeec-8165414f1273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "471035d3730143bd98ac44bf050e1684",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": "[encoded data, 161016 characters]",
      "image/png": "[encoded data, 2181608 characters]",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=2048x512>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multimodal prompts\n",
    "images = ip_model.generate(pil_image=image, num_samples=4, num_inference_steps=50, seed=42,\n",
    "        prompt=\"best quality, high quality, wearing a hat on the beach\", scale=0.6)\n",
    "grid = image_grid(images, 1, 4)\n",
    "grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5d3d874a-49b2-4c7e-ad58-b0ecc085c1fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e26a277d47a42e59b3d2b54e0af378a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": "[encoded data, 233692 characters]",
      "image/png": "[encoded data, 2548456 characters]",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=2048x512>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multimodal prompts\n",
    "images = ip_model.generate(pil_image=image, num_samples=4, num_inference_steps=50, seed=42,\n",
    "        prompt=\"best quality, high quality, wearing sunglasses in a garden\", scale=0.6)\n",
    "grid = image_grid(images, 1, 4)\n",
    "grid"
   ]
  }
 ],
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