pnp_sketch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import argparse, os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"7\"\n",
    "import torch\n",
    "import numpy as np\n",
    "from omegaconf import OmegaConf\n",
    "from PIL import Image\n",
    "from tqdm import tqdm\n",
    "from einops import rearrange\n",
    "from pytorch_lightning import seed_everything\n",
    "try:\n",
    "    from torch import autocast\n",
    "except:\n",
    "    from torch.cuda.amp import autocast\n",
    "from contextlib import nullcontext\n",
    "import json\n",
    "from torchvision import transforms\n",
    "import logging\n",
    "from pnp_utils import check_safety\n",
    "\n",
    "from pnp_ldm.util import instantiate_from_config\n",
    "from pnp_ldm.models.diffusion.ddim import DDIMSampler\n",
    "import collections\n",
    "\n",
    "memory_storage = collections.defaultdict(dict)\n",
    "\n",
    "def load_img(path):\n",
    "    image = Image.open(path).convert(\"RGB\")\n",
    "    x, y = image.size\n",
    "    print(f\"loaded input image of size ({x}, {y}) from {path}\")\n",
    "    h = w = 512\n",
    "    image = transforms.CenterCrop(min(x,y))(image)\n",
    "    image = image.resize((w, h), resample=Image.Resampling.LANCZOS)\n",
    "    image = np.array(image).astype(np.float32) / 255.0\n",
    "    image = image[None].transpose(0, 3, 1, 2)\n",
    "    image = torch.from_numpy(image)\n",
    "    return 2.*image - 1.\n",
    "\n",
    "\n",
    "def load_model_from_config(config, ckpt, verbose=False):\n",
    "    print(f\"Loading model from {ckpt}\")\n",
    "    pl_sd = torch.load(ckpt, map_location=\"cpu\")\n",
    "    if \"global_step\" in pl_sd:\n",
    "        print(f\"Global Step: {pl_sd['global_step']}\")\n",
    "    sd = pl_sd[\"state_dict\"]\n",
    "    model = instantiate_from_config(config.model)\n",
    "    m, u = model.load_state_dict(sd, strict=False)\n",
    "    if len(m) > 0 and verbose:\n",
    "        print(\"missing keys:\")\n",
    "        print(m)\n",
    "    if len(u) > 0 and verbose:\n",
    "        print(\"unexpected keys:\")\n",
    "        print(u)\n",
    "\n",
    "    model.cuda()\n",
    "    model.eval()\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Options:\n",
    "    def __init__(self):\n",
    "        self.config = \"./configs/pnp_refine_all.yaml\"\n",
    "        self.img_dir_path = \"/hdd3/junyi/DiffSketch/data\"\n",
    "        self.text_dir_path = \"PATH_TO_CLASS_INFO\"\n",
    "        self.ddim_eta = 0.0\n",
    "        self.save_all_features = True\n",
    "        self.H = 512\n",
    "        self.W = 512\n",
    "        self.C = 4\n",
    "        self.f = 8\n",
    "        self.model_config = \"configs/stable-diffusion/v1-inference.yaml\"\n",
    "        self.ckpt = \"models/ldm/stable-diffusion-v1/model.ckpt\"\n",
    "        self.precision = \"autocast\"\n",
    "        self.check_safety = False\n",
    "\n",
    "opt = Options()\n",
    "img_path=\"/hdd3/junyi/DiffSketch/experiments/sketch_79/refined/INJECTION_T_40_STEPS_50_NP-ALPHA_0.75_SCHEDULE_linear_NP_line_drawing,_simple,_smooth_sample_0.png\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model from models/ldm/stable-diffusion-v1/model.ckpt\n",
      "Global Step: 840000\n",
      "LatentDiffusion: Running in eps-prediction mode\n",
      "DiffusionWrapper has 859.52 M params.\n",
      "making attention of type 'vanilla' with 512 in_channels\n",
      "Working with z of shape (1, 4, 32, 32) = 4096 dimensions.\n",
      "making attention of type 'vanilla' with 512 in_channels\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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'vision_model.encoder.layers.7.self_attn.out_proj.bias', 'vision_model.encoder.layers.19.layer_norm1.bias', 'vision_model.encoder.layers.12.self_attn.q_proj.bias', 'vision_model.encoder.layers.4.self_attn.k_proj.bias', 'vision_model.encoder.layers.6.self_attn.out_proj.weight', 'vision_model.encoder.layers.9.mlp.fc2.bias', 'vision_model.pre_layrnorm.weight', 'vision_model.encoder.layers.4.layer_norm2.bias', 'vision_model.encoder.layers.22.mlp.fc1.weight', 'vision_model.encoder.layers.17.self_attn.q_proj.weight']\n",
      "- This IS expected if you are initializing CLIPTextModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing CLIPTextModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "\n",
    "setup_config = OmegaConf.load(\"./configs/pnp/setup.yaml\")\n",
    "model_config = OmegaConf.load(f\"{opt.model_config}\")\n",
    "model = load_model_from_config(model_config, f\"{opt.ckpt}\")\n",
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "model = model.to(device)\n",
    "unet_model = model.model.diffusion_model\n",
    "sampler = DDIMSampler(model)\n",
    "\n",
    "img_dir_path=opt.img_dir_path\n",
    "img_paths=[]\n",
    "\n",
    "# iterate through all images in the directory\n",
    "for root, dirs, files in os.walk(img_dir_path):\n",
    "    for file in files:\n",
    "        if file.endswith(\".jpg\") or file.endswith(\".png\"):\n",
    "            path = os.path.join(root, file)\n",
    "            img_paths.append(path)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/junyi/miniconda3/envs/od/lib/python3.9/site-packages/pytorch_lightning/utilities/seed.py:60: UserWarning: -1 is not in bounds, numpy accepts from 0 to 4294967295\n",
      "  rank_zero_warn(f\"{seed} is not in bounds, numpy accepts from {min_seed_value} to {max_seed_value}\")\n",
      "Global seed set to 467327626\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded input image of size (512, 512) from /hdd3/junyi/DiffSketch/experiments/sketch_79/refined/INJECTION_T_40_STEPS_50_NP-ALPHA_0.75_SCHEDULE_linear_NP_line_drawing,_simple,_smooth_sample_0.png\n",
      "Running DDIM inversion with 999 timesteps\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DDIM Inversion: 100%|██████████| 999/999 [01:19<00:00, 12.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape for DDIM sampling is (1, 4, 64, 64), eta 0.0\n",
      "Running DDIM Sampling with 999 timesteps\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DDIM Sampler:   0%|          | 2/999 [00:00<01:36, 10.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected timesteps for ddim sampler: [  1  21  41  61  81 101 121 141 161 181 201 221 241 261 281 301 321 341\n",
      " 361 381 401 421 441 461 481 501 521 541 561 581 601 621 641 661 681 701\n",
      " 721 741 761 781 801 821 841 861 881 901 921 941 961 981]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "ename": "OutOfMemoryError",
     "evalue": "CUDA out of memory. Tried to allocate 1024.00 MiB (GPU 0; 23.69 GiB total capacity; 20.23 GiB already allocated; 225.50 MiB free; 21.88 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOutOfMemoryError\u001b[0m                          Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[46], line 91\u001b[0m\n\u001b[1;32m     89\u001b[0m     z_enc \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mrandn([\u001b[39m1\u001b[39m, opt\u001b[39m.\u001b[39mC, opt\u001b[39m.\u001b[39mH \u001b[39m/\u001b[39m\u001b[39m/\u001b[39m opt\u001b[39m.\u001b[39mf, opt\u001b[39m.\u001b[39mW \u001b[39m/\u001b[39m\u001b[39m/\u001b[39m opt\u001b[39m.\u001b[39mf], device\u001b[39m=\u001b[39mdevice)\n\u001b[1;32m     90\u001b[0m torch\u001b[39m.\u001b[39msave(z_enc, \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00moutpath\u001b[39m}\u001b[39;00m\u001b[39m/z_enc.pt\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m---> 91\u001b[0m samples_ddim, _ \u001b[39m=\u001b[39m sampler\u001b[39m.\u001b[39;49msample(S\u001b[39m=\u001b[39;49mexp_config\u001b[39m.\u001b[39;49mconfig\u001b[39m.\u001b[39;49mddim_steps,\n\u001b[1;32m     92\u001b[0m                 conditioning\u001b[39m=\u001b[39;49mc,\n\u001b[1;32m     93\u001b[0m                 batch_size\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m,\n\u001b[1;32m     94\u001b[0m                 shape\u001b[39m=\u001b[39;49mshape,\n\u001b[1;32m     95\u001b[0m                 verbose\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m     96\u001b[0m                 unconditional_guidance_scale\u001b[39m=\u001b[39;49mexp_config\u001b[39m.\u001b[39;49mconfig\u001b[39m.\u001b[39;49mscale,\n\u001b[1;32m     97\u001b[0m                 unconditional_conditioning\u001b[39m=\u001b[39;49muc,\n\u001b[1;32m     98\u001b[0m                 eta\u001b[39m=\u001b[39;49mopt\u001b[39m.\u001b[39;49mddim_eta,\n\u001b[1;32m     99\u001b[0m                 x_T\u001b[39m=\u001b[39;49mz_enc,\n\u001b[1;32m    100\u001b[0m                 img_callback\u001b[39m=\u001b[39;49mddim_sampler_callback,\n\u001b[1;32m    101\u001b[0m                 callback_ddim_timesteps\u001b[39m=\u001b[39;49msave_feature_timesteps,\n\u001b[1;32m    102\u001b[0m                 outpath\u001b[39m=\u001b[39;49moutpath)\n\u001b[1;32m    104\u001b[0m x_samples_ddim \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mdecode_first_stage(samples_ddim)\n\u001b[1;32m    105\u001b[0m x_samples_ddim \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mclamp((x_samples_ddim \u001b[39m+\u001b[39m \u001b[39m1.0\u001b[39m) \u001b[39m/\u001b[39m \u001b[39m2.0\u001b[39m, \u001b[39mmin\u001b[39m\u001b[39m=\u001b[39m\u001b[39m0.0\u001b[39m, \u001b[39mmax\u001b[39m\u001b[39m=\u001b[39m\u001b[39m1.0\u001b[39m)\n",
      "File \u001b[0;32m~/miniconda3/envs/od/lib/python3.9/site-packages/torch/autograd/grad_mode.py:27\u001b[0m, in \u001b[0;36m_DecoratorContextManager.__call__.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m     25\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdecorate_context\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m     26\u001b[0m     \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mclone():\n\u001b[0;32m---> 27\u001b[0m         \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m/hdd3/junyi/DiffSketch/pnp_ldm/models/diffusion/ddim.py:117\u001b[0m, in \u001b[0;36mDDIMSampler.sample\u001b[0;34m(self, S, batch_size, shape, conditioning, negative_conditioning, callback, normals_sequence, img_callback, quantize_x0, eta, mask, x0, temperature, noise_dropout, score_corrector, corrector_kwargs, verbose, x_T, log_every_t, unconditional_guidance_scale, unconditional_conditioning, injected_features, strength, callback_ddim_timesteps, negative_prompt_alpha, negative_prompt_schedule, **kwargs)\u001b[0m\n\u001b[1;32m    114\u001b[0m size \u001b[39m=\u001b[39m (batch_size, C, H, W)\n\u001b[1;32m    115\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mData shape for DDIM sampling is \u001b[39m\u001b[39m{\u001b[39;00msize\u001b[39m}\u001b[39;00m\u001b[39m, eta \u001b[39m\u001b[39m{\u001b[39;00meta\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[0;32m--> 117\u001b[0m samples, intermediates \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mddim_sampling(conditioning, size,\n\u001b[1;32m    118\u001b[0m                                             negative_conditioning\u001b[39m=\u001b[39;49mnegative_conditioning,\n\u001b[1;32m    119\u001b[0m                                             callback\u001b[39m=\u001b[39;49mcallback,\n\u001b[1;32m    120\u001b[0m                                             img_callback\u001b[39m=\u001b[39;49mimg_callback,\n\u001b[1;32m    121\u001b[0m                                             quantize_denoised\u001b[39m=\u001b[39;49mquantize_x0,\n\u001b[1;32m    122\u001b[0m                                             mask\u001b[39m=\u001b[39;49mmask, x0\u001b[39m=\u001b[39;49mx0,\n\u001b[1;32m    123\u001b[0m                                             ddim_use_original_steps\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m    124\u001b[0m                                             noise_dropout\u001b[39m=\u001b[39;49mnoise_dropout,\n\u001b[1;32m    125\u001b[0m                                             temperature\u001b[39m=\u001b[39;49mtemperature,\n\u001b[1;32m    126\u001b[0m                                             score_corrector\u001b[39m=\u001b[39;49mscore_corrector,\n\u001b[1;32m    127\u001b[0m                                             corrector_kwargs\u001b[39m=\u001b[39;49mcorrector_kwargs,\n\u001b[1;32m    128\u001b[0m                                             x_T\u001b[39m=\u001b[39;49mx_T,\n\u001b[1;32m    129\u001b[0m                                             log_every_t\u001b[39m=\u001b[39;49mlog_every_t,\n\u001b[1;32m    130\u001b[0m                                             unconditional_guidance_scale\u001b[39m=\u001b[39;49munconditional_guidance_scale,\n\u001b[1;32m    131\u001b[0m                                             unconditional_conditioning\u001b[39m=\u001b[39;49munconditional_conditioning,\n\u001b[1;32m    132\u001b[0m                                             injected_features\u001b[39m=\u001b[39;49minjected_features,\n\u001b[1;32m    133\u001b[0m                                             callback_ddim_timesteps\u001b[39m=\u001b[39;49mcallback_ddim_timesteps,\n\u001b[1;32m    134\u001b[0m                                             negative_prompt_alpha\u001b[39m=\u001b[39;49mnegative_prompt_alpha,\n\u001b[1;32m    135\u001b[0m                                             negative_prompt_schedule\u001b[39m=\u001b[39;49mnegative_prompt_schedule,\n\u001b[1;32m    136\u001b[0m                                             )\n\u001b[1;32m    137\u001b[0m \u001b[39mreturn\u001b[39;00m samples, intermediates\n",
      "File \u001b[0;32m~/miniconda3/envs/od/lib/python3.9/site-packages/torch/autograd/grad_mode.py:27\u001b[0m, in \u001b[0;36m_DecoratorContextManager.__call__.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m     25\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdecorate_context\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m     26\u001b[0m     \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mclone():\n\u001b[0;32m---> 27\u001b[0m         \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m/hdd3/junyi/DiffSketch/pnp_ldm/models/diffusion/ddim.py:185\u001b[0m, in \u001b[0;36mDDIMSampler.ddim_sampling\u001b[0;34m(self, cond, shape, negative_conditioning, x_T, ddim_use_original_steps, callback, timesteps, quantize_denoised, mask, x0, img_callback, log_every_t, temperature, noise_dropout, score_corrector, corrector_kwargs, unconditional_guidance_scale, unconditional_conditioning, injected_features, callback_ddim_timesteps, negative_prompt_alpha, negative_prompt_schedule)\u001b[0m\n\u001b[1;32m    182\u001b[0m injected_features_i \u001b[39m=\u001b[39m injected_features[i]\\\n\u001b[1;32m    183\u001b[0m     \u001b[39mif\u001b[39;00m (injected_features \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \u001b[39mlen\u001b[39m(injected_features) \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m) \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    184\u001b[0m negative_prompt_alpha_i \u001b[39m=\u001b[39m negative_prompt_alpha_schedule[i]\n\u001b[0;32m--> 185\u001b[0m outs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mp_sample_ddim(img, cond, ts, index\u001b[39m=\u001b[39;49mindex, use_original_steps\u001b[39m=\u001b[39;49mddim_use_original_steps,\n\u001b[1;32m    186\u001b[0m                           negative_conditioning\u001b[39m=\u001b[39;49mnegative_conditioning,\n\u001b[1;32m    187\u001b[0m                           quantize_denoised\u001b[39m=\u001b[39;49mquantize_denoised, temperature\u001b[39m=\u001b[39;49mtemperature,\n\u001b[1;32m    188\u001b[0m                           noise_dropout\u001b[39m=\u001b[39;49mnoise_dropout, score_corrector\u001b[39m=\u001b[39;49mscore_corrector,\n\u001b[1;32m    189\u001b[0m                           corrector_kwargs\u001b[39m=\u001b[39;49mcorrector_kwargs,\n\u001b[1;32m    190\u001b[0m                           unconditional_guidance_scale\u001b[39m=\u001b[39;49munconditional_guidance_scale,\n\u001b[1;32m    191\u001b[0m                           unconditional_conditioning\u001b[39m=\u001b[39;49munconditional_conditioning,\n\u001b[1;32m    192\u001b[0m                           injected_features\u001b[39m=\u001b[39;49minjected_features_i,\n\u001b[1;32m    193\u001b[0m                           negative_prompt_alpha\u001b[39m=\u001b[39;49mnegative_prompt_alpha_i\n\u001b[1;32m    194\u001b[0m                           )\n\u001b[1;32m    196\u001b[0m img, pred_x0 \u001b[39m=\u001b[39m outs\n\u001b[1;32m    197\u001b[0m \u001b[39mif\u001b[39;00m step \u001b[39min\u001b[39;00m callback_ddim_timesteps_list:\n",
      "File \u001b[0;32m~/miniconda3/envs/od/lib/python3.9/site-packages/torch/autograd/grad_mode.py:27\u001b[0m, in \u001b[0;36m_DecoratorContextManager.__call__.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m     25\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdecorate_context\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m     26\u001b[0m     \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mclone():\n\u001b[0;32m---> 27\u001b[0m         \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m/hdd3/junyi/DiffSketch/pnp_ldm/models/diffusion/ddim.py:242\u001b[0m, in \u001b[0;36mDDIMSampler.p_sample_ddim\u001b[0;34m(self, x, c, t, index, negative_conditioning, repeat_noise, use_original_steps, quantize_denoised, temperature, noise_dropout, score_corrector, corrector_kwargs, unconditional_guidance_scale, unconditional_conditioning, injected_features, negative_prompt_alpha)\u001b[0m\n\u001b[1;32m    240\u001b[0m     t_in \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mcat([t] \u001b[39m*\u001b[39m \u001b[39m2\u001b[39m)\n\u001b[1;32m    241\u001b[0m     c_in \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mcat([unconditional_conditioning, c])\n\u001b[0;32m--> 242\u001b[0m     e_t_uncond, e_t \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmodel\u001b[39m.\u001b[39;49mapply_model(x_in, t_in, c_in)\u001b[39m.\u001b[39mchunk(\u001b[39m2\u001b[39m)\n\u001b[1;32m    243\u001b[0m     e_t \u001b[39m=\u001b[39m e_t_uncond \u001b[39m+\u001b[39m unconditional_guidance_scale \u001b[39m*\u001b[39m (e_t \u001b[39m-\u001b[39m e_t_uncond) \u001b[39mif\u001b[39;00m unconditional_guidance_scale\u001b[39m!=\u001b[39m\u001b[39m1\u001b[39m \u001b[39melse\u001b[39;00m e_t\n\u001b[1;32m    245\u001b[0m \u001b[39mif\u001b[39;00m score_corrector \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/hdd3/junyi/DiffSketch/pnp_ldm/models/diffusion/ddpm.py:996\u001b[0m, in \u001b[0;36mLatentDiffusion.apply_model\u001b[0;34m(self, x_noisy, t, cond, return_ids, injected_features)\u001b[0m\n\u001b[1;32m    993\u001b[0m     x_recon \u001b[39m=\u001b[39m fold(o) \u001b[39m/\u001b[39m normalization\n\u001b[1;32m    995\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 996\u001b[0m     x_recon \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmodel(x_noisy,\n\u001b[1;32m    997\u001b[0m                          t,\n\u001b[1;32m    998\u001b[0m                          \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcond,\n\u001b[1;32m    999\u001b[0m                          injected_features\u001b[39m=\u001b[39;49minjected_features\n\u001b[1;32m   1000\u001b[0m                          )\n\u001b[1;32m   1002\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(x_recon, \u001b[39mtuple\u001b[39m) \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m return_ids:\n\u001b[1;32m   1003\u001b[0m     \u001b[39mreturn\u001b[39;00m x_recon[\u001b[39m0\u001b[39m]\n",
      "File \u001b[0;32m~/miniconda3/envs/od/lib/python3.9/site-packages/torch/nn/modules/module.py:1194\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1190\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1191\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1192\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1193\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1194\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1195\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1196\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "File \u001b[0;32m/hdd3/junyi/DiffSketch/pnp_ldm/models/diffusion/ddpm.py:1431\u001b[0m, in \u001b[0;36mDiffusionWrapper.forward\u001b[0;34m(self, x, t, c_concat, c_crossattn, injected_features)\u001b[0m\n\u001b[1;32m   1429\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconditioning_key \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mcrossattn\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[1;32m   1430\u001b[0m     cc \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mcat(c_crossattn, \u001b[39m1\u001b[39m)\n\u001b[0;32m-> 1431\u001b[0m     out \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdiffusion_model(x,\n\u001b[1;32m   1432\u001b[0m                                t,\n\u001b[1;32m   1433\u001b[0m                                context\u001b[39m=\u001b[39;49mcc,\n\u001b[1;32m   1434\u001b[0m                                injected_features\u001b[39m=\u001b[39;49minjected_features)\n\u001b[1;32m   1435\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconditioning_key \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mhybrid\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[1;32m   1436\u001b[0m     xc \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mcat([x] \u001b[39m+\u001b[39m c_concat, dim\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n",
      "File \u001b[0;32m~/miniconda3/envs/od/lib/python3.9/site-packages/torch/nn/modules/module.py:1194\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1190\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1191\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1192\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1193\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1194\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1195\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1196\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "File \u001b[0;32m/hdd3/junyi/DiffSketch/pnp_ldm/modules/diffusionmodules/openaimodel.py:784\u001b[0m, in \u001b[0;36mUNetModel.forward\u001b[0;34m(self, x, timesteps, context, y, injected_features, **kwargs)\u001b[0m\n\u001b[1;32m    781\u001b[0m         out_layers_injected \u001b[39m=\u001b[39m injected_features[out_layers_feature_key]\n\u001b[1;32m    783\u001b[0m     h \u001b[39m=\u001b[39m th\u001b[39m.\u001b[39mcat([h, hs\u001b[39m.\u001b[39mpop()], dim\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n\u001b[0;32m--> 784\u001b[0m     h \u001b[39m=\u001b[39m module(h,\n\u001b[1;32m    785\u001b[0m                emb,\n\u001b[1;32m    786\u001b[0m                context,\n\u001b[1;32m    787\u001b[0m                self_attn_q_injected\u001b[39m=\u001b[39;49mself_attn_q_injected,\n\u001b[1;32m    788\u001b[0m                self_attn_k_injected\u001b[39m=\u001b[39;49mself_attn_k_injected,\n\u001b[1;32m    789\u001b[0m                out_layers_injected\u001b[39m=\u001b[39;49mout_layers_injected)\n\u001b[1;32m    790\u001b[0m     module_i \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m    792\u001b[0m h \u001b[39m=\u001b[39m h\u001b[39m.\u001b[39mtype(x\u001b[39m.\u001b[39mdtype)\n",
      "File \u001b[0;32m~/miniconda3/envs/od/lib/python3.9/site-packages/torch/nn/modules/module.py:1194\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1190\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1191\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1192\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1193\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1194\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1195\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1196\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "File \u001b[0;32m/hdd3/junyi/DiffSketch/pnp_ldm/modules/diffusionmodules/openaimodel.py:93\u001b[0m, in \u001b[0;36mTimestepEmbedSequential.forward\u001b[0;34m(self, x, emb, context, self_attn_q_injected, self_attn_k_injected, out_layers_injected)\u001b[0m\n\u001b[1;32m     91\u001b[0m     x \u001b[39m=\u001b[39m layer(x, emb)\n\u001b[1;32m     92\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(layer, SpatialTransformer):\n\u001b[0;32m---> 93\u001b[0m     x \u001b[39m=\u001b[39m layer(x,\n\u001b[1;32m     94\u001b[0m               context,\n\u001b[1;32m     95\u001b[0m               self_attn_q_injected,\n\u001b[1;32m     96\u001b[0m               self_attn_k_injected)\n\u001b[1;32m     97\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     98\u001b[0m     x \u001b[39m=\u001b[39m layer(x)\n",
      "File \u001b[0;32m~/miniconda3/envs/od/lib/python3.9/site-packages/torch/nn/modules/module.py:1194\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1190\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1191\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1192\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1193\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1194\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1195\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1196\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "File \u001b[0;32m/hdd3/junyi/DiffSketch/pnp_ldm/modules/attention.py:304\u001b[0m, in \u001b[0;36mSpatialTransformer.forward\u001b[0;34m(self, x, context, self_attn_q_injected, self_attn_k_injected)\u001b[0m\n\u001b[1;32m    302\u001b[0m x \u001b[39m=\u001b[39m rearrange(x, \u001b[39m'\u001b[39m\u001b[39mb c h w -> b (h w) c\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m    303\u001b[0m \u001b[39mfor\u001b[39;00m block \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransformer_blocks:\n\u001b[0;32m--> 304\u001b[0m     x \u001b[39m=\u001b[39m block(x,\n\u001b[1;32m    305\u001b[0m               context\u001b[39m=\u001b[39;49mcontext,\n\u001b[1;32m    306\u001b[0m               self_attn_q_injected\u001b[39m=\u001b[39;49mself_attn_q_injected,\n\u001b[1;32m    307\u001b[0m               self_attn_k_injected\u001b[39m=\u001b[39;49mself_attn_k_injected)\n\u001b[1;32m    308\u001b[0m x \u001b[39m=\u001b[39m rearrange(x, \u001b[39m'\u001b[39m\u001b[39mb (h w) c -> b c h w\u001b[39m\u001b[39m'\u001b[39m, h\u001b[39m=\u001b[39mh, w\u001b[39m=\u001b[39mw)\n\u001b[1;32m    309\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mproj_out(x)\n",
      "File \u001b[0;32m~/miniconda3/envs/od/lib/python3.9/site-packages/torch/nn/modules/module.py:1194\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1190\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1191\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1192\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1193\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1194\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1195\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1196\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "File \u001b[0;32m/hdd3/junyi/DiffSketch/pnp_ldm/modules/attention.py:242\u001b[0m, in \u001b[0;36mBasicTransformerBlock.forward\u001b[0;34m(self, x, context, self_attn_q_injected, self_attn_k_injected)\u001b[0m\n\u001b[1;32m    236\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m,\n\u001b[1;32m    237\u001b[0m             x,\n\u001b[1;32m    238\u001b[0m             context\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[1;32m    239\u001b[0m             self_attn_q_injected\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[1;32m    240\u001b[0m             self_attn_k_injected\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m\n\u001b[1;32m    241\u001b[0m             ):\n\u001b[0;32m--> 242\u001b[0m     \u001b[39mreturn\u001b[39;00m checkpoint(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_forward, (x,\n\u001b[1;32m    243\u001b[0m                                       context,\n\u001b[1;32m    244\u001b[0m                                       self_attn_q_injected,\n\u001b[1;32m    245\u001b[0m                                       self_attn_k_injected), \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mparameters(), \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcheckpoint)\n",
      "File \u001b[0;32m/hdd3/junyi/DiffSketch/pnp_ldm/modules/diffusionmodules/util.py:114\u001b[0m, in \u001b[0;36mcheckpoint\u001b[0;34m(func, inputs, params, flag)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[39mif\u001b[39;00m flag:\n\u001b[1;32m    113\u001b[0m     args \u001b[39m=\u001b[39m \u001b[39mtuple\u001b[39m(inputs) \u001b[39m+\u001b[39m \u001b[39mtuple\u001b[39m(params)\n\u001b[0;32m--> 114\u001b[0m     \u001b[39mreturn\u001b[39;00m CheckpointFunction\u001b[39m.\u001b[39;49mapply(func, \u001b[39mlen\u001b[39;49m(inputs), \u001b[39m*\u001b[39;49margs)\n\u001b[1;32m    115\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    116\u001b[0m     \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39minputs)\n",
      "File \u001b[0;32m/hdd3/junyi/DiffSketch/pnp_ldm/modules/diffusionmodules/util.py:127\u001b[0m, in \u001b[0;36mCheckpointFunction.forward\u001b[0;34m(ctx, run_function, length, *args)\u001b[0m\n\u001b[1;32m    124\u001b[0m ctx\u001b[39m.\u001b[39minput_params \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(args[length:])\n\u001b[1;32m    126\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mno_grad():\n\u001b[0;32m--> 127\u001b[0m     output_tensors \u001b[39m=\u001b[39m ctx\u001b[39m.\u001b[39;49mrun_function(\u001b[39m*\u001b[39;49mctx\u001b[39m.\u001b[39;49minput_tensors)\n\u001b[1;32m    128\u001b[0m \u001b[39mreturn\u001b[39;00m output_tensors\n",
      "File \u001b[0;32m/hdd3/junyi/DiffSketch/pnp_ldm/modules/attention.py:252\u001b[0m, in \u001b[0;36mBasicTransformerBlock._forward\u001b[0;34m(self, x, context, self_attn_q_injected, self_attn_k_injected)\u001b[0m\n\u001b[1;32m    247\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_forward\u001b[39m(\u001b[39mself\u001b[39m,\n\u001b[1;32m    248\u001b[0m              x,\n\u001b[1;32m    249\u001b[0m              context\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[1;32m    250\u001b[0m              self_attn_q_injected\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[1;32m    251\u001b[0m              self_attn_k_injected\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m--> 252\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mattn1(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnorm1(x),\n\u001b[1;32m    253\u001b[0m                    q_injected\u001b[39m=\u001b[39;49mself_attn_q_injected,\n\u001b[1;32m    254\u001b[0m                    k_injected\u001b[39m=\u001b[39;49mself_attn_k_injected) \u001b[39m+\u001b[39m x\n\u001b[1;32m    255\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mattn2(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnorm2(x), context\u001b[39m=\u001b[39mcontext) \u001b[39m+\u001b[39m x\n\u001b[1;32m    256\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mff(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnorm3(x)) \u001b[39m+\u001b[39m x\n",
      "File \u001b[0;32m~/miniconda3/envs/od/lib/python3.9/site-packages/torch/nn/modules/module.py:1194\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1190\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1191\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1192\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1193\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1194\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1195\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1196\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "File \u001b[0;32m/hdd3/junyi/DiffSketch/pnp_ldm/modules/attention.py:215\u001b[0m, in \u001b[0;36mCrossAttention.forward\u001b[0;34m(self, x, context, mask, q_injected, k_injected)\u001b[0m\n\u001b[1;32m    212\u001b[0m     sim\u001b[39m.\u001b[39mmasked_fill_(\u001b[39m~\u001b[39mmask, max_neg_value)\n\u001b[1;32m    214\u001b[0m \u001b[39m# attention, what we cannot get enough of\u001b[39;00m\n\u001b[0;32m--> 215\u001b[0m attn \u001b[39m=\u001b[39m sim\u001b[39m.\u001b[39;49msoftmax(dim\u001b[39m=\u001b[39;49m\u001b[39m-\u001b[39;49m\u001b[39m1\u001b[39;49m)\n\u001b[1;32m    217\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mattn \u001b[39m=\u001b[39m attn\n\u001b[1;32m    219\u001b[0m out \u001b[39m=\u001b[39m einsum(\u001b[39m'\u001b[39m\u001b[39mb i j, b j d -> b i d\u001b[39m\u001b[39m'\u001b[39m, attn, v)\n",
      "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 1024.00 MiB (GPU 0; 23.69 GiB total capacity; 20.23 GiB already allocated; 225.50 MiB free; 21.88 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF"
     ]
    }
   ],
   "source": [
    "exp_config = OmegaConf.load(opt.config)\n",
    "experiment_name =  exp_config.config.experiment_name+img_path.split(\"/\")[-1].split(\".\")[0] # experiment_name + \"01.jpg\"\n",
    "exp_path_root = setup_config.config.exp_path_root\n",
    "\n",
    "exp_config.config.seed = -1\n",
    "exp_config.config.prompt = \"\"\n",
    "exp_config.config.scale = 1.0\n",
    "\n",
    "seed = exp_config.config.seed \n",
    "seed_everything(seed)\n",
    "\n",
    "save_feature_timesteps = exp_config.config.ddim_steps if exp_config.config.init_img == '' else exp_config.config.save_feature_timesteps\n",
    "\n",
    "outpath = f\"{exp_path_root}/{experiment_name}\"\n",
    "\n",
    "# if os.path.exists(outpath):\n",
    "#     continue\n",
    "\n",
    "callback_timesteps_to_save = [save_feature_timesteps]\n",
    "if os.path.exists(outpath):\n",
    "    logging.warning(\"Experiment directory already exists, previously saved content will be overriden\")\n",
    "    if exp_config.config.init_img != '':\n",
    "        with open(os.path.join(outpath, \"args.json\"), \"r\") as f:\n",
    "            args = json.load(f)\n",
    "        callback_timesteps_to_save = args[\"save_feature_timesteps\"] + callback_timesteps_to_save\n",
    "\n",
    "\n",
    "sample_path = os.path.join(outpath, \"inversion\")\n",
    "os.makedirs(outpath, exist_ok=True)\n",
    "os.makedirs(sample_path, exist_ok=True)\n",
    "\n",
    "# save parse_args in experiment dir\n",
    "with open(os.path.join(outpath, \"args.json\"), \"w\") as f:\n",
    "    args_to_save = OmegaConf.to_container(exp_config.config)\n",
    "    args_to_save[\"save_feature_timesteps\"] = callback_timesteps_to_save\n",
    "    json.dump(args_to_save, f)\n",
    "\n",
    "def ddim_sampler_callback(pred_x0, x_t, i):\n",
    "    save_feature_maps_callback(i)\n",
    "    # save_sampled_img(pred_x0, i, predicted_samples_path)\n",
    "\n",
    "def save_feature_maps(blocks, i, feature_type=\"input_block\"):\n",
    "    block_idx = 0\n",
    "    for block in tqdm(blocks, desc=\"Saving input blocks feature maps\"):\n",
    "        if not opt.save_all_features and block_idx < 4:\n",
    "            block_idx += 1\n",
    "            continue\n",
    "        if \"ResBlock\" in str(type(block[0])):\n",
    "            if opt.save_all_features or block_idx == 4:\n",
    "                save_feature_map(block[0].in_layers_features, f\"{feature_type}_{block_idx}_in_layers_features_time_{i}\")\n",
    "                save_feature_map(block[0].out_layers_features, f\"{feature_type}_{block_idx}_out_layers_features_time_{i}\")\n",
    "        if len(block) > 1 and \"SpatialTransformer\" in str(type(block[1])):\n",
    "            save_feature_map(block[1].transformer_blocks[0].attn1.k, f\"{feature_type}_{block_idx}_self_attn_k_time_{i}\")\n",
    "            save_feature_map(block[1].transformer_blocks[0].attn1.q, f\"{feature_type}_{block_idx}_self_attn_q_time_{i}\")\n",
    "        block_idx += 1\n",
    "\n",
    "def save_feature_maps_callback(i):\n",
    "    if opt.save_all_features:\n",
    "        save_feature_maps(unet_model.input_blocks, i, \"input_block\")\n",
    "    save_feature_maps(unet_model.output_blocks , i, \"output_block\")\n",
    "\n",
    "def save_feature_map(feature_map, filename):\n",
    "    memory_storage[filename] = feature_map\n",
    "\n",
    "# cls_name=\"cat\" # TODO, pass the cls_name from text\n",
    "cls_name = None\n",
    "\n",
    "assert exp_config.config.prompt is not None\n",
    "prompts = [exp_config.config.prompt.replace('object', cls_name)] if cls_name else [exp_config.config.prompt]\n",
    "\n",
    "precision_scope = autocast if opt.precision==\"autocast\" else nullcontext\n",
    "with torch.no_grad():\n",
    "    with precision_scope(\"cuda\"):\n",
    "        with model.ema_scope():\n",
    "            uc = model.get_learned_conditioning([\"\"])\n",
    "            if isinstance(prompts, tuple):\n",
    "                prompts = list(prompts)\n",
    "            c = model.get_learned_conditioning(prompts)\n",
    "            shape = [opt.C, opt.H // opt.f, opt.W // opt.f]\n",
    "\n",
    "            z_enc = None\n",
    "            if exp_config.config.init_img != '':\n",
    "                assert os.path.isfile(img_path)\n",
    "                init_image = load_img(img_path).to(device)\n",
    "                init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image))\n",
    "                ddim_inversion_steps = 999\n",
    "                z_enc, _ = sampler.encode_ddim(init_latent, num_steps=ddim_inversion_steps, conditioning=c,unconditional_conditioning=uc,unconditional_guidance_scale=exp_config.config.scale)\n",
    "            else:\n",
    "                z_enc = torch.randn([1, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)\n",
    "            torch.save(z_enc, f\"{outpath}/z_enc.pt\")\n",
    "            samples_ddim, _ = sampler.sample(S=exp_config.config.ddim_steps,\n",
    "                            conditioning=c,\n",
    "                            batch_size=1,\n",
    "                            shape=shape,\n",
    "                            verbose=False,\n",
    "                            unconditional_guidance_scale=exp_config.config.scale,\n",
    "                            unconditional_conditioning=uc,\n",
    "                            eta=opt.ddim_eta,\n",
    "                            x_T=z_enc,\n",
    "                            img_callback=ddim_sampler_callback,\n",
    "                            callback_ddim_timesteps=save_feature_timesteps,\n",
    "                            outpath=outpath)\n",
    "\n",
    "            x_samples_ddim = model.decode_first_stage(samples_ddim)\n",
    "            x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)\n",
    "            x_samples_ddim = x_samples_ddim.cpu().permute(0, 2, 3, 1).numpy()\n",
    "            if opt.check_safety:\n",
    "                x_samples_ddim = check_safety(x_samples_ddim)\n",
    "            x_image_torch = torch.from_numpy(x_samples_ddim).permute(0, 3, 1, 2)\n",
    "\n",
    "            sample_idx = 0\n",
    "            for x_sample in x_image_torch:\n",
    "                x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')\n",
    "                img = Image.fromarray(x_sample.astype(np.uint8))\n",
    "                img.save(os.path.join(sample_path, f\"{sample_idx}.png\"))\n",
    "                sample_idx += 1\n",
    "\n",
    "print(f\"Sampled images and extracted features saved in: {outpath}\")\n",
    "exp_path_root_config = OmegaConf.load(\"./configs/pnp/setup.yaml\")\n",
    "exp_path_root = exp_path_root_config.config.exp_path_root"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 3533965183\n",
      "loading source experiment features: 100%|██████████| 50/50 [00:00<00:00, 74818.12it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape for DDIM sampling is (1, 4, 64, 64), eta 0.0\n",
      "Running DDIM Sampling with 50 timesteps\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DDIM Sampler: 100%|██████████| 50/50 [00:09<00:00,  5.20it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=512x512>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inversion done, start refinement\n",
    "exp_config.scale=20\n",
    "exp_config.feature_injection_threshold=40\n",
    "# read seed from args.json of source experiment\n",
    "with open(os.path.join(exp_path_root, experiment_name, \"args.json\"), \"r\") as f:\n",
    "    args = json.load(f)\n",
    "    seed = args[\"seed\"]\n",
    "    source_prompt = args[\"prompt\"]\n",
    "negative_prompt = source_prompt if exp_config.negative_prompt is None else exp_config.negative_prompt\n",
    "\n",
    "seed_everything(seed)\n",
    "possible_ddim_steps = args[\"save_feature_timesteps\"]\n",
    "assert exp_config.num_ddim_sampling_steps in possible_ddim_steps or exp_config.num_ddim_sampling_steps is None, f\"possible sampling steps for this experiment are: {possible_ddim_steps}; for {exp_config.num_ddim_sampling_steps} steps, run 'run_features_extraction.py' with save_feature_timesteps = {exp_config.num_ddim_sampling_steps}\"\n",
    "ddim_steps = exp_config.num_ddim_sampling_steps if exp_config.num_ddim_sampling_steps is not None else possible_ddim_steps[-1]\n",
    "\n",
    "sampler = DDIMSampler(model)\n",
    "sampler.make_schedule(ddim_num_steps=ddim_steps, ddim_eta=opt.ddim_eta, verbose=False) \n",
    "\n",
    "seed = torch.initial_seed()\n",
    "opt.seed = seed\n",
    "\n",
    "outpaths = [os.path.join(f\"{exp_path_root}/{experiment_name}/refined\")]\n",
    "out_label = f\"INJECTION_T_{exp_config.feature_injection_threshold}_STEPS_{ddim_steps}\"\n",
    "out_label += f\"_NP-ALPHA_{exp_config.negative_prompt_alpha}_SCHEDULE_{exp_config.negative_prompt_schedule}_NP_{negative_prompt.replace(' ', '_')}\"\n",
    "\n",
    "predicted_samples_paths = [os.path.join(outpath, f\"predicted_samples_{out_label}\") for outpath in outpaths]\n",
    "for i in range(len(outpaths)):\n",
    "    os.makedirs(outpaths[i], exist_ok=True)\n",
    "    os.makedirs(predicted_samples_paths[i], exist_ok=True)\n",
    "    # save args in experiment dir\n",
    "    with open(os.path.join(outpaths[i], \"args.json\"), \"w\") as f:\n",
    "        json.dump(OmegaConf.to_container(exp_config), f)\n",
    "\n",
    "def ddim_sampler_callback(pred_x0, xt, i):\n",
    "    # save_sampled_img(pred_x0, i, predicted_samples_paths)\n",
    "    pass\n",
    "\n",
    "def load_feature_map(filename):\n",
    "    return memory_storage[filename]\n",
    "\n",
    "def load_target_features():\n",
    "    self_attn_output_block_indices = [4,5,6,7,8,9,10,11]\n",
    "    out_layers_output_block_indices = [1,2,3,4,5,6,7,8,9,10,11]\n",
    "    output_block_self_attn_map_injection_thresholds = [ddim_steps // 2] * len(self_attn_output_block_indices)\n",
    "    feature_injection_thresholds = [exp_config.feature_injection_threshold]\n",
    "    target_features = []\n",
    "\n",
    "    time_range = np.flip(sampler.ddim_timesteps)\n",
    "    total_steps = sampler.ddim_timesteps.shape[0]\n",
    "\n",
    "    iterator = tqdm(time_range, desc=\"loading source experiment features\", total=total_steps)\n",
    "\n",
    "    for i, t in enumerate(iterator):\n",
    "        current_features = {}\n",
    "        for (output_block_idx, output_block_self_attn_map_injection_threshold) in zip(self_attn_output_block_indices, output_block_self_attn_map_injection_thresholds):\n",
    "            if i <= int(output_block_self_attn_map_injection_threshold):\n",
    "                output_q = load_feature_map(f\"output_block_{output_block_idx}_self_attn_q_time_{t}\")\n",
    "                output_k = load_feature_map(f\"output_block_{output_block_idx}_self_attn_k_time_{t}\")\n",
    "                current_features[f'output_block_{output_block_idx}_self_attn_q'] = output_q\n",
    "                current_features[f'output_block_{output_block_idx}_self_attn_k'] = output_k\n",
    "\n",
    "        for (output_block_idx, feature_injection_threshold) in zip(out_layers_output_block_indices, feature_injection_thresholds):\n",
    "            if i <= int(feature_injection_threshold):\n",
    "                output = load_feature_map(f\"output_block_{output_block_idx}_out_layers_features_time_{t}\")\n",
    "                current_features[f'output_block_{output_block_idx}_out_layers'] = output\n",
    "\n",
    "        target_features.append(current_features)\n",
    "\n",
    "    return target_features # for one timestep, attn: (16, patch**2, dim); out_layers: (2, 1280, 16, 16)\n",
    "\n",
    "batch_size = len(exp_config.prompts)\n",
    "prompts = [\"a photo of a face, extremely detailed, sketch style, high quality, sketchy, grayscale, black hair\"]\n",
    "assert prompts is not None\n",
    "\n",
    "start_code_path = f\"{exp_path_root}/{experiment_name}/z_enc.pt\"\n",
    "start_code = torch.load(start_code_path).cuda() if os.path.exists(start_code_path) else None\n",
    "if start_code is not None:\n",
    "    start_code = start_code.repeat(batch_size, 1, 1, 1)\n",
    "\n",
    "precision_scope = autocast if opt.precision==\"autocast\" else nullcontext\n",
    "injected_features = load_target_features()\n",
    "unconditional_prompt = \"\"\n",
    "with torch.no_grad():\n",
    "    with precision_scope(\"cuda\"):\n",
    "        with model.ema_scope():\n",
    "            uc = None\n",
    "            nc = None\n",
    "            if exp_config.scale != 1.0:\n",
    "                uc = model.get_learned_conditioning(batch_size * [unconditional_prompt])\n",
    "                nc = model.get_learned_conditioning(batch_size * [negative_prompt])\n",
    "            if not isinstance(prompts, list):\n",
    "                prompts = list(prompts)\n",
    "            c = model.get_learned_conditioning(prompts)\n",
    "            shape = [opt.C, opt.H // opt.f, opt.W // opt.f]\n",
    "            samples_ddim, _ = sampler.sample(S=ddim_steps,\n",
    "                                            conditioning=c,\n",
    "                                            negative_conditioning=nc,\n",
    "                                            batch_size=len(prompts),\n",
    "                                            shape=shape,\n",
    "                                            verbose=False,\n",
    "                                            unconditional_guidance_scale=exp_config.scale,\n",
    "                                            unconditional_conditioning=uc,\n",
    "                                            eta=opt.ddim_eta,\n",
    "                                            x_T=start_code,\n",
    "                                            img_callback=ddim_sampler_callback,\n",
    "                                            injected_features=injected_features,\n",
    "                                            negative_prompt_alpha=exp_config.negative_prompt_alpha,\n",
    "                                            negative_prompt_schedule=exp_config.negative_prompt_schedule,\n",
    "                                            )\n",
    "\n",
    "            x_samples_ddim = model.decode_first_stage(samples_ddim)\n",
    "            x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)\n",
    "            x_samples_ddim = x_samples_ddim.cpu().permute(0, 2, 3, 1).numpy()\n",
    "            if opt.check_safety:\n",
    "                x_samples_ddim = check_safety(x_samples_ddim)\n",
    "            x_image_torch = torch.from_numpy(x_samples_ddim).permute(0, 3, 1, 2)\n",
    "\n",
    "            sample_idx = 0\n",
    "            for k, x_sample in enumerate(x_image_torch):\n",
    "                x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')\n",
    "                img = Image.fromarray(x_sample.astype(np.uint8))\n",
    "                # img.save(os.path.join(outpaths[k], f\"{out_label}_sample_{sample_idx}.png\"))\n",
    "img"
   ]
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