zizi-conditional-vast.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ab0a059f-ddb3-4e7c-a0cc-97ebf37bad7c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -q torch torchvision diffusers pillow accelerate matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "21859818-de73-4eb8-b6fb-20bf7a078c80",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import os\n",
    "import json\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "from torchvision import transforms\n",
    "from PIL import Image\n",
    "from diffusers import DDPMScheduler\n",
    "from diffusers import DDPMPipeline\n",
    "from diffusers.optimization import get_cosine_schedule_with_warmup\n",
    "from diffusers import UNet2DConditionModel\n",
    "from diffusers import UNet2DModel\n",
    "\n",
    "from accelerate import Accelerator\n",
    "from tqdm.auto import tqdm\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d31812f8-cfd9-473f-b9c0-0c38eff6439e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c16586c5-d436-45e5-bdd0-c31a1efcdf95",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"mps\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "123f4655-5390-453e-8cdb-73b2140d69cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "\n",
    "@dataclass\n",
    "class TrainingConfig:\n",
    "    image_size = 128  # the generated image resolution\n",
    "    train_batch_size = 8\n",
    "    eval_batch_size = 4  # how many images to sample during evaluation\n",
    "    num_epochs = 50\n",
    "    gradient_accumulation_steps = 1\n",
    "    learning_rate = 1e-4\n",
    "    lr_warmup_steps = 500\n",
    "    save_image_epochs = 10\n",
    "    save_model_epochs = 10\n",
    "    mixed_precision = \"fp16\"  # `no` for float32, `fp16` for automatic mixed precision\n",
    "    output_dir = \"output/zizi-test-128\"  # the model name locally and on the HF Hub\n",
    "    overwrite_output_dir = True  # overwrite the old model when re-running the notebook\n",
    "    seed = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fbc0e594-29c7-4440-9a53-4463daa1a57a",
   "metadata": {},
   "outputs": [],
   "source": [
    "config = TrainingConfig()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6b90bb28-4e60-4e94-a9a3-3459476cd0d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConditionalZiziDataset(Dataset):\n",
    "    def __init__(self, input_dir, pose_dir):\n",
    "        self.input_dir = input_dir\n",
    "        self.pose_dir = pose_dir\n",
    "        self.input_files = os.listdir(input_dir)\n",
    "        self.preprocess = transforms.Compose(\n",
    "            [\n",
    "                transforms.Resize((config.image_size, config.image_size)),\n",
    "                transforms.ToTensor(),\n",
    "                transforms.Normalize([0.5], [0.5])\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.input_files)\n",
    "\n",
    "    def _get_img(self, idx):\n",
    "        img_dir = os.path.join(self.input_dir, self.input_files[idx])\n",
    "        return self.preprocess(Image.open(img_dir).convert(\"RGB\"))\n",
    "\n",
    "    def _get_json(self, idx):\n",
    "        input_split = os.path.splitext(self.input_files[idx])\n",
    "        pose_json_name = input_split[0] + \"_keypoints.json\"\n",
    "        json_dir = os.path.join(self.pose_dir, pose_json_name)\n",
    "        with open(json_dir, 'r') as f:\n",
    "            data = json.load(f)\n",
    "        return torch.tensor(data['people'][0]['pose_keypoints_2d'])\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        if isinstance(idx, slice):\n",
    "            # Get the start, stop, and step from the slice\n",
    "            return [self[ii] for ii in range(*idx.indices(len(self)))]\n",
    "\n",
    "        input_img = self._get_img(idx)\n",
    "        json_data = self._get_json(idx)\n",
    "\n",
    "        return {\n",
    "            \"images\": input_img,\n",
    "            \"poses\": json_data\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0f7babfe-c38e-4db7-aa42-659dfae02b1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = ConditionalZiziDataset(\"data/meth-pose/test_img\", \"data/meth-pose/test_openpose\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "665828b7-057f-4a27-83c4-00252b89cb80",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=config.train_batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6f9bd954-6692-4d32-9c55-93b1578fdc22",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = UNet2DConditionModel(\n",
    "    sample_size=config.image_size,\n",
    "    in_channels=3,\n",
    "    out_channels=3,\n",
    "    layers_per_block=2,\n",
    "    encoder_hid_dim=75,\n",
    "    cross_attention_dim=512,\n",
    "    block_out_channels=(128, 256, 512, 512),\n",
    "    mid_block_scale_factor=1,\n",
    "    down_block_types=(\n",
    "        \"CrossAttnDownBlock2D\",\n",
    "        \"CrossAttnDownBlock2D\",\n",
    "        \"CrossAttnDownBlock2D\",\n",
    "        \"DownBlock2D\"\n",
    "    ),\n",
    "    up_block_types=(\n",
    "        \"UpBlock2D\",\n",
    "        \"CrossAttnUpBlock2D\",\n",
    "        \"CrossAttnUpBlock2D\",\n",
    "        \"CrossAttnUpBlock2D\"\n",
    "    )\n",
    ").to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3d57392c-e2df-4afe-a3f2-2cb2686cf557",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sample_image = dataset[0][\"images\"].unsqueeze(0).to(device)\n",
    "sample_pose = dataset[0][\"poses\"].unsqueeze(0).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "307e0971-57c0-4504-889e-75379e300127",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=128x128>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noise_scheduler = DDPMScheduler(num_train_timesteps=1000)\n",
    "noise = torch.randn(sample_image.shape).to(device)\n",
    "timesteps = torch.LongTensor([50]).to(device)\n",
    "noisy_image = noise_scheduler.add_noise(sample_image, noise, timesteps)\n",
    "\n",
    "Image.fromarray(((noisy_image.cpu().permute(0, 2, 3, 1) + 1.0) * 127.5).type(torch.uint8).numpy()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "27f7f9f6-efb5-4c8a-b256-ecaa0f80ae40",
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "MPS backend out of memory (MPS allocated: 30.12 GB, other allocations: 82.93 MB, max allowed: 36.27 GB). Tried to allocate 8.00 GB on private pool. Use PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0 to disable upper limit for memory allocations (may cause system failure).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m noise_pred \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnoisy_image\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimesteps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_pose\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mview\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m75\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39msample\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/jupyter/lib/python3.9/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/jupyter/lib/python3.9/site-packages/diffusers/models/unet_2d_condition.py:905\u001b[0m, in \u001b[0;36mUNet2DConditionModel.forward\u001b[0;34m(self, sample, timestep, encoder_hidden_states, class_labels, timestep_cond, attention_mask, cross_attention_kwargs, added_cond_kwargs, down_block_additional_residuals, mid_block_additional_residual, encoder_attention_mask, return_dict)\u001b[0m\n\u001b[1;32m    903\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m downsample_block \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdown_blocks:\n\u001b[1;32m    904\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(downsample_block, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_cross_attention\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m downsample_block\u001b[38;5;241m.\u001b[39mhas_cross_attention:\n\u001b[0;32m--> 905\u001b[0m         sample, res_samples \u001b[38;5;241m=\u001b[39m \u001b[43mdownsample_block\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    906\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    907\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtemb\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43memb\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    908\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    909\u001b[0m \u001b[43m            \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    910\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcross_attention_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcross_attention_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    911\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    912\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    913\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    914\u001b[0m         sample, res_samples \u001b[38;5;241m=\u001b[39m downsample_block(hidden_states\u001b[38;5;241m=\u001b[39msample, temb\u001b[38;5;241m=\u001b[39memb)\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/jupyter/lib/python3.9/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/jupyter/lib/python3.9/site-packages/diffusers/models/unet_2d_blocks.py:993\u001b[0m, in \u001b[0;36mCrossAttnDownBlock2D.forward\u001b[0;34m(self, hidden_states, temb, encoder_hidden_states, attention_mask, cross_attention_kwargs, encoder_attention_mask)\u001b[0m\n\u001b[1;32m    991\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    992\u001b[0m         hidden_states \u001b[38;5;241m=\u001b[39m resnet(hidden_states, temb)\n\u001b[0;32m--> 993\u001b[0m         hidden_states \u001b[38;5;241m=\u001b[39m \u001b[43mattn\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    994\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    995\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    996\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcross_attention_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcross_attention_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    997\u001b[0m \u001b[43m            \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    998\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    999\u001b[0m \u001b[43m            \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1000\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m   1002\u001b[0m     output_states \u001b[38;5;241m=\u001b[39m output_states \u001b[38;5;241m+\u001b[39m (hidden_states,)\n\u001b[1;32m   1004\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdownsamplers \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/jupyter/lib/python3.9/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/jupyter/lib/python3.9/site-packages/diffusers/models/transformer_2d.py:291\u001b[0m, in \u001b[0;36mTransformer2DModel.forward\u001b[0;34m(self, hidden_states, encoder_hidden_states, timestep, class_labels, cross_attention_kwargs, attention_mask, encoder_attention_mask, return_dict)\u001b[0m\n\u001b[1;32m    289\u001b[0m \u001b[38;5;66;03m# 2. Blocks\u001b[39;00m\n\u001b[1;32m    290\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m block \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransformer_blocks:\n\u001b[0;32m--> 291\u001b[0m     hidden_states \u001b[38;5;241m=\u001b[39m \u001b[43mblock\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    292\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    293\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    294\u001b[0m \u001b[43m        \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    295\u001b[0m \u001b[43m        \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    296\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimestep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimestep\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    297\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcross_attention_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcross_attention_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    298\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclass_labels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclass_labels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    299\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    301\u001b[0m \u001b[38;5;66;03m# 3. Output\u001b[39;00m\n\u001b[1;32m    302\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_input_continuous:\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/jupyter/lib/python3.9/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/jupyter/lib/python3.9/site-packages/diffusers/models/attention.py:154\u001b[0m, in \u001b[0;36mBasicTransformerBlock.forward\u001b[0;34m(self, hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, timestep, cross_attention_kwargs, class_labels)\u001b[0m\n\u001b[1;32m    150\u001b[0m     norm_hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnorm1(hidden_states)\n\u001b[1;32m    152\u001b[0m cross_attention_kwargs \u001b[38;5;241m=\u001b[39m cross_attention_kwargs \u001b[38;5;28;01mif\u001b[39;00m cross_attention_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m {}\n\u001b[0;32m--> 154\u001b[0m attn_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mattn1\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    155\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnorm_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    156\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43monly_cross_attention\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    157\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    158\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcross_attention_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    159\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    160\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muse_ada_layer_norm_zero:\n\u001b[1;32m    161\u001b[0m     attn_output \u001b[38;5;241m=\u001b[39m gate_msa\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m attn_output\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/jupyter/lib/python3.9/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/jupyter/lib/python3.9/site-packages/diffusers/models/attention_processor.py:321\u001b[0m, in \u001b[0;36mAttention.forward\u001b[0;34m(self, hidden_states, encoder_hidden_states, attention_mask, **cross_attention_kwargs)\u001b[0m\n\u001b[1;32m    317\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, hidden_states, encoder_hidden_states\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, attention_mask\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcross_attention_kwargs):\n\u001b[1;32m    318\u001b[0m     \u001b[38;5;66;03m# The `Attention` class can call different attention processors / attention functions\u001b[39;00m\n\u001b[1;32m    319\u001b[0m     \u001b[38;5;66;03m# here we simply pass along all tensors to the selected processor class\u001b[39;00m\n\u001b[1;32m    320\u001b[0m     \u001b[38;5;66;03m# For standard processors that are defined here, `**cross_attention_kwargs` is empty\u001b[39;00m\n\u001b[0;32m--> 321\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocessor\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    322\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    323\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    324\u001b[0m \u001b[43m        \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    325\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    326\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcross_attention_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    327\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/jupyter/lib/python3.9/site-packages/diffusers/models/attention_processor.py:1129\u001b[0m, in \u001b[0;36mAttnProcessor2_0.__call__\u001b[0;34m(self, attn, hidden_states, encoder_hidden_states, attention_mask, temb)\u001b[0m\n\u001b[1;32m   1125\u001b[0m value \u001b[38;5;241m=\u001b[39m value\u001b[38;5;241m.\u001b[39mview(batch_size, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, attn\u001b[38;5;241m.\u001b[39mheads, head_dim)\u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# the output of sdp = (batch, num_heads, seq_len, head_dim)\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;66;03m# TODO: add support for attn.scale when we move to Torch 2.1\u001b[39;00m\n\u001b[0;32m-> 1129\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscaled_dot_product_attention\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1130\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattn_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdropout_p\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_causal\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\n\u001b[1;32m   1131\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1133\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m hidden_states\u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m)\u001b[38;5;241m.\u001b[39mreshape(batch_size, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, attn\u001b[38;5;241m.\u001b[39mheads \u001b[38;5;241m*\u001b[39m head_dim)\n\u001b[1;32m   1134\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m hidden_states\u001b[38;5;241m.\u001b[39mto(query\u001b[38;5;241m.\u001b[39mdtype)\n",
      "\u001b[0;31mRuntimeError\u001b[0m: MPS backend out of memory (MPS allocated: 30.12 GB, other allocations: 82.93 MB, max allowed: 36.27 GB). Tried to allocate 8.00 GB on private pool. Use PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0 to disable upper limit for memory allocations (may cause system failure)."
     ]
    }
   ],
   "source": [
    "noise_pred = model(noisy_image, timesteps, sample_pose.view((1, 1, 75))).sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3b3192e8-1dc3-458a-bb5c-ebea74321eba",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = F.mse_loss(noise_pred, noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3de757e1-b805-429e-94ee-464f4814a09e",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate)\n",
    "lr_scheduler = get_cosine_schedule_with_warmup(\n",
    "    optimizer=optimizer,\n",
    "    num_warmup_steps=config.lr_warmup_steps,\n",
    "    num_training_steps=(len(train_dataloader) * config.num_epochs),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d13d345d-46f2-4341-b303-05e8e7bf2bf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_grid(images, rows, cols):\n",
    "    w, h = images[0].size\n",
    "    grid = Image.new(\"RGB\", size=(cols * w, rows * h))\n",
    "    for i, image in enumerate(images):\n",
    "        grid.paste(image, box=(i % cols * w, i // cols * h))\n",
    "    return grid\n",
    "\n",
    "\n",
    "def evaluate(config, epoch, pipeline):\n",
    "    # Sample some images from random noise (this is the backward diffusion process).\n",
    "    # The default pipeline output type is `List[PIL.Image]`\n",
    "    images = pipeline(\n",
    "        batch_size=config.eval_batch_size,\n",
    "        generator=torch.manual_seed(config.seed),\n",
    "    ).images\n",
    "\n",
    "    # Make a grid out of the images\n",
    "    image_grid = make_grid(images, rows=4, cols=4)\n",
    "\n",
    "    # Save the images\n",
    "    test_dir = os.path.join(config.output_dir, \"samples\")\n",
    "    os.makedirs(test_dir, exist_ok=True)\n",
    "    image_grid.save(f\"{test_dir}/{epoch:04d}.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d4384ce4-56ef-4e65-a7c0-26a618ac4ac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_loop(config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler):\n",
    "    # Initialize accelerator and tensorboard logging\n",
    "    accelerator = Accelerator(\n",
    "        mixed_precision=config.mixed_precision,\n",
    "        gradient_accumulation_steps=config.gradient_accumulation_steps,\n",
    "        log_with=\"tensorboard\",\n",
    "        project_dir=os.path.join(config.output_dir, \"logs\"),\n",
    "    )\n",
    "    if accelerator.is_main_process:\n",
    "        os.makedirs(config.output_dir, exist_ok=True)\n",
    "        accelerator.init_trackers(\"train_example\")\n",
    "\n",
    "    # Prepare everything\n",
    "    # There is no specific order to remember, you just need to unpack the\n",
    "    # objects in the same order you gave them to the prepare method.\n",
    "    model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n",
    "        model, optimizer, train_dataloader, lr_scheduler\n",
    "    )\n",
    "\n",
    "    global_step = 0\n",
    "\n",
    "    # Now you train the model\n",
    "    for epoch in range(config.num_epochs):\n",
    "        progress_bar = tqdm(total=len(train_dataloader), disable=not accelerator.is_local_main_process)\n",
    "        progress_bar.set_description(f\"Epoch {epoch}\")\n",
    "\n",
    "        for step, batch in enumerate(train_dataloader):\n",
    "            clean_images = batch[\"images\"]\n",
    "            # Sample noise to add to the images\n",
    "            noise = torch.randn(clean_images.shape).to(clean_images.device)\n",
    "            bs = clean_images.shape[0]\n",
    "\n",
    "            # Sample a random timestep for each image\n",
    "            timesteps = torch.randint(\n",
    "                0, noise_scheduler.config.num_train_timesteps, (bs,), device=clean_images.device\n",
    "            ).long()\n",
    "\n",
    "            # Add noise to the clean images according to the noise magnitude at each timestep\n",
    "            # (this is the forward diffusion process)\n",
    "            noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)\n",
    "\n",
    "            poses = batch[\"poses\"].reshape((config.train_batch_size, 1, 75))\n",
    "\n",
    "            with accelerator.accumulate(model):\n",
    "                # Predict the noise residual\n",
    "                noise_pred = model(noisy_images, timesteps, poses, return_dict=False)[0]\n",
    "                loss = F.mse_loss(noise_pred, noise)\n",
    "                accelerator.backward(loss)\n",
    "\n",
    "                accelerator.clip_grad_norm_(model.parameters(), 1.0)\n",
    "                optimizer.step()\n",
    "                lr_scheduler.step()\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "            progress_bar.update(1)\n",
    "            logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0], \"step\": global_step}\n",
    "            progress_bar.set_postfix(**logs)\n",
    "            accelerator.log(logs, step=global_step)\n",
    "            global_step += 1\n",
    "\n",
    "        # After each epoch you optionally sample some demo images with evaluate() and save the model\n",
    "        if accelerator.is_main_process:\n",
    "            pipeline = DDPMPipeline(unet=accelerator.unwrap_model(model), scheduler=noise_scheduler)\n",
    "\n",
    "            if (epoch + 1) % config.save_image_epochs == 0 or epoch == config.num_epochs - 1:\n",
    "                evaluate(config, epoch, pipeline)\n",
    "\n",
    "            if (epoch + 1) % config.save_model_epochs == 0 or epoch == config.num_epochs - 1:\n",
    "                pipeline.save_pretrained(config.output_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "089a4cde-0c2b-404d-86be-fa7e6c585a29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Launching training on one GPU.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/accelerate/accelerator.py:371: UserWarning: `log_with=tensorboard` was passed but no supported trackers are currently installed.\n",
      "  warnings.warn(f\"`log_with={log_with}` was passed but no supported trackers are currently installed.\")\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d13b5fc7488c452aab557e34e9e2f786",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/543 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[17], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01maccelerate\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m notebook_launcher\n\u001b[1;32m      3\u001b[0m args \u001b[38;5;241m=\u001b[39m (config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)\n\u001b[0;32m----> 5\u001b[0m \u001b[43mnotebook_launcher\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_loop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_processes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/accelerate/launchers.py:156\u001b[0m, in \u001b[0;36mnotebook_launcher\u001b[0;34m(function, args, num_processes, mixed_precision, use_port)\u001b[0m\n\u001b[1;32m    154\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    155\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLaunching training on CPU.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 156\u001b[0m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[16], line 48\u001b[0m, in \u001b[0;36mtrain_loop\u001b[0;34m(config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)\u001b[0m\n\u001b[1;32m     46\u001b[0m noise_pred \u001b[38;5;241m=\u001b[39m model(noisy_images, timesteps, poses, return_dict\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m     47\u001b[0m loss \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39mmse_loss(noise_pred, noise)\n\u001b[0;32m---> 48\u001b[0m \u001b[43maccelerator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloss\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     50\u001b[0m accelerator\u001b[38;5;241m.\u001b[39mclip_grad_norm_(model\u001b[38;5;241m.\u001b[39mparameters(), \u001b[38;5;241m1.0\u001b[39m)\n\u001b[1;32m     51\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mstep()\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/accelerate/accelerator.py:1851\u001b[0m, in \u001b[0;36mAccelerator.backward\u001b[0;34m(self, loss, **kwargs)\u001b[0m\n\u001b[1;32m   1849\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m   1850\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscaler \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1851\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscaler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscale\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloss\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1852\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1853\u001b[0m     loss\u001b[38;5;241m.\u001b[39mbackward(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/_tensor.py:487\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    477\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    478\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m    479\u001b[0m         Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m    480\u001b[0m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    485\u001b[0m         inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m    486\u001b[0m     )\n\u001b[0;32m--> 487\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    488\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[1;32m    489\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/autograd/__init__.py:200\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    195\u001b[0m     retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m    197\u001b[0m \u001b[38;5;66;03m# The reason we repeat same the comment below is that\u001b[39;00m\n\u001b[1;32m    198\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m    199\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 200\u001b[0m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m    201\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    202\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from accelerate import notebook_launcher\n",
    "\n",
    "args = (config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)\n",
    "\n",
    "notebook_launcher(train_loop, args, num_processes=1)"
   ]
  }
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