FIFO CogVideoX.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa86af0a-c983-4d5c-809e-24b20ca96045",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'diffusers'...\n",
      "remote: Enumerating objects: 71242, done.\u001b[K\n",
      "remote: Counting objects: 100% (1016/1016), done.\u001b[K\n",
      "remote: Compressing objects: 100% (551/551), done.\u001b[K\n",
      "remote: Total 71242 (delta 651), reused 642 (delta 382), pack-reused 70226 (from 1)\u001b[K\n",
      "Receiving objects: 100% (71242/71242), 50.40 MiB | 17.57 MiB/s, done.\n",
      "Resolving deltas: 100% (52546/52546), done.\n",
      "Updating files: 100% (1579/1579), done.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/huggingface/diffusers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a4ab2d21-c633-43bb-9466-edf0ee782fdc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[?25hInstalling collected packages: sentencepiece, tqdm, safetensors, regex, protobuf, fsspec, huggingface_hub, tokenizers, accelerate, transformers\n",
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      "    Found existing installation: fsspec 2023.4.0\n",
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      "Successfully installed accelerate-0.34.2 fsspec-2024.9.0 huggingface_hub-0.25.1 protobuf-5.28.2 regex-2024.9.11 safetensors-0.4.5 sentencepiece-0.2.0 tokenizers-0.20.0 tqdm-4.66.5 transformers-4.45.1\n",
      "\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[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install huggingface_hub accelerate tokenizers protobuf transformers sentencepiece"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e286f688-7e6c-4799-b4fc-fc2648a2199f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing ./diffusers\n",
      "  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: importlib-metadata in /usr/lib/python3/dist-packages (from diffusers==0.31.0.dev0) (4.6.4)\n",
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      "Building wheels for collected packages: diffusers\n",
      "  Building wheel for diffusers (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for diffusers: filename=diffusers-0.31.0.dev0-py3-none-any.whl size=2799557 sha256=6838be53f6c510b6059227d426566906dd0bdd2cb5303f5a25b2c7899ee91e7f\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-bjspwkn5/wheels/0f/20/c8/51c98dd92d3452e41884e999b329516f339b1c440542fc729e\n",
      "Successfully built diffusers\n",
      "Installing collected packages: diffusers\n",
      "Successfully installed diffusers-0.31.0.dev0\n",
      "\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[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install ./diffusers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7c14e6ca-000a-4d25-8f91-11c4a4c63159",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e2e51130efc24a16823e2b34a10b4d20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading pipeline components...:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5fc8593000c94c4aaa086f45136a691f",
       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %env CUDA_LAUNCH_BLOCKING=1\n",
    "import torch\n",
    "from pipeline_cogvideox import CogVideoXPipeline\n",
    "from transformer_cogvideox_v2 import CogVideoXTransformer3DModel\n",
    "from sampler_cogvideox import CogVideoXDDIMScheduler\n",
    "\n",
    "prompt = \"A detailed wooden toy ship with intricately carved masts and sails is seen gliding smoothly over a plush, blue carpet that mimics the waves of the sea. The ship's hull is painted a rich brown, with tiny windows. The carpet, soft and textured, provides a perfect backdrop, resembling an oceanic expanse. Surrounding the ship are various other toys and children's items, hinting at a playful environment. The scene captures the innocence and imagination of childhood, with the toy ship's journey symbolizing endless adventures in a whimsical, indoor setting.\"\n",
    "\n",
    "transformer = CogVideoXTransformer3DModel.from_pretrained(\n",
    "    \"THUDM/CogVideoX-2b\",\n",
    "    torch_dtype=torch.float16,\n",
    "    subfolder=\"transformer\"\n",
    ")\n",
    "scheduler = CogVideoXDDIMScheduler.from_pretrained(\n",
    "    \"THUDM/CogVideoX-2b\",\n",
    "    torch_dtype=torch.float16,\n",
    "    subfolder=\"scheduler\"\n",
    ")\n",
    "pipe = CogVideoXPipeline.from_pretrained(\n",
    "    \"THUDM/CogVideoX-2b\",\n",
    "    torch_dtype=torch.float16,\n",
    "    transformer=transformer,\n",
    "    scheduler=scheduler\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "df5bf0c9-42b8-4e1f-9bdc-9b31287c0cbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CogVideoXPipeline {\n",
       "  \"_class_name\": \"CogVideoXPipeline\",\n",
       "  \"_diffusers_version\": \"0.31.0.dev0\",\n",
       "  \"_name_or_path\": \"THUDM/CogVideoX-2b\",\n",
       "  \"scheduler\": [\n",
       "    \"sampler_cogvideox\",\n",
       "    \"CogVideoXDDIMScheduler\"\n",
       "  ],\n",
       "  \"text_encoder\": [\n",
       "    \"transformers\",\n",
       "    \"T5EncoderModel\"\n",
       "  ],\n",
       "  \"tokenizer\": [\n",
       "    \"transformers\",\n",
       "    \"T5Tokenizer\"\n",
       "  ],\n",
       "  \"transformer\": [\n",
       "    \"transformer_cogvideox_v2\",\n",
       "    \"CogVideoXTransformer3DModel\"\n",
       "  ],\n",
       "  \"vae\": [\n",
       "    \"diffusers\",\n",
       "    \"AutoencoderKLCogVideoX\"\n",
       "  ]\n",
       "}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "74108d0b-bcca-45a1-9c9a-8bd1cccd6677",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 12, 16, 60, 90)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d30f72705c7403dbde7473a4a93f9c5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/96 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([989, 989, 989, 989, 989, 989, 989, 989, 989, 989, 989, 989],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999],\n",
      "       device='cuda:0') a_t tensor([[[[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([979, 979, 979, 979, 979, 979, 979, 979, 979, 979, 979, 979],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([989, 989, 989, 989, 989, 989, 989, 989, 989, 989, 989, 989],\n",
      "       device='cuda:0') a_t tensor([[[[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([968, 968, 968, 968, 968, 968, 968, 968, 968, 968, 968, 968],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([978, 978, 978, 978, 978, 978, 978, 978, 978, 978, 978, 978],\n",
      "       device='cuda:0') a_t tensor([[[[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([958, 958, 958, 958, 958, 958, 958, 958, 958, 958, 958, 958],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([968, 968, 968, 968, 968, 968, 968, 968, 968, 968, 968, 968],\n",
      "       device='cuda:0') a_t tensor([[[[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([947, 947, 947, 947, 947, 947, 947, 947, 947, 947, 947, 947],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([957, 957, 957, 957, 957, 957, 957, 957, 957, 957, 957, 957],\n",
      "       device='cuda:0') a_t tensor([[[[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]],\n",
      "\n",
      "\n",
      "         [[[1.0000]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([937, 937, 937, 937, 937, 937, 937, 937, 937, 937, 937, 937],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([947, 947, 947, 947, 947, 947, 947, 947, 947, 947, 947, 947],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([927, 927, 927, 927, 927, 927, 927, 927, 927, 927, 927, 927],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([937, 937, 937, 937, 937, 937, 937, 937, 937, 937, 937, 937],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([916, 916, 916, 916, 916, 916, 916, 916, 916, 916, 916, 916],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([926, 926, 926, 926, 926, 926, 926, 926, 926, 926, 926, 926],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([906, 906, 906, 906, 906, 906, 906, 906, 906, 906, 906, 906],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([916, 916, 916, 916, 916, 916, 916, 916, 916, 916, 916, 916],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([895, 895, 895, 895, 895, 895, 895, 895, 895, 895, 895, 895],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([905, 905, 905, 905, 905, 905, 905, 905, 905, 905, 905, 905],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([885, 885, 885, 885, 885, 885, 885, 885, 885, 885, 885, 885],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([895, 895, 895, 895, 895, 895, 895, 895, 895, 895, 895, 895],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]],\n",
      "\n",
      "\n",
      "         [[[0.9999]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([874, 874, 874, 874, 874, 874, 874, 874, 874, 874, 874, 874],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([884, 884, 884, 884, 884, 884, 884, 884, 884, 884, 884, 884],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([874, 874, 874, 874, 874, 874, 874, 874, 874, 874, 874, 874],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([854, 854, 854, 854, 854, 854, 854, 854, 854, 854, 854, 854],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([843, 843, 843, 843, 843, 843, 843, 843, 843, 843, 843, 843],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([853, 853, 853, 853, 853, 853, 853, 853, 853, 853, 853, 853],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]],\n",
      "\n",
      "\n",
      "         [[[0.9998]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([833, 833, 833, 833, 833, 833, 833, 833, 833, 833, 833, 833],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([843, 843, 843, 843, 843, 843, 843, 843, 843, 843, 843, 843],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([822, 822, 822, 822, 822, 822, 822, 822, 822, 822, 822, 822],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([832, 832, 832, 832, 832, 832, 832, 832, 832, 832, 832, 832],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([812, 812, 812, 812, 812, 812, 812, 812, 812, 812, 812, 812],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([822, 822, 822, 822, 822, 822, 822, 822, 822, 822, 822, 822],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]],\n",
      "\n",
      "\n",
      "         [[[0.9997]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([802, 802, 802, 802, 802, 802, 802, 802, 802, 802, 802, 802],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([812, 812, 812, 812, 812, 812, 812, 812, 812, 812, 812, 812],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([791, 791, 791, 791, 791, 791, 791, 791, 791, 791, 791, 791],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([801, 801, 801, 801, 801, 801, 801, 801, 801, 801, 801, 801],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]],\n",
      "\n",
      "\n",
      "         [[[0.9996]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([781, 781, 781, 781, 781, 781, 781, 781, 781, 781, 781, 781],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([791, 791, 791, 791, 791, 791, 791, 791, 791, 791, 791, 791],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([770, 770, 770, 770, 770, 770, 770, 770, 770, 770, 770, 770],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([780, 780, 780, 780, 780, 780, 780, 780, 780, 780, 780, 780],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]],\n",
      "\n",
      "\n",
      "         [[[0.9995]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([760, 760, 760, 760, 760, 760, 760, 760, 760, 760, 760, 760],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([770, 770, 770, 770, 770, 770, 770, 770, 770, 770, 770, 770],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([749, 749, 749, 749, 749, 749, 749, 749, 749, 749, 749, 749],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([759, 759, 759, 759, 759, 759, 759, 759, 759, 759, 759, 759],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]],\n",
      "\n",
      "\n",
      "         [[[0.9994]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([739, 739, 739, 739, 739, 739, 739, 739, 739, 739, 739, 739],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([749, 749, 749, 749, 749, 749, 749, 749, 749, 749, 749, 749],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([729, 729, 729, 729, 729, 729, 729, 729, 729, 729, 729, 729],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([739, 739, 739, 739, 739, 739, 739, 739, 739, 739, 739, 739],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]],\n",
      "\n",
      "\n",
      "         [[[0.9993]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([718, 718, 718, 718, 718, 718, 718, 718, 718, 718, 718, 718],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([728, 728, 728, 728, 728, 728, 728, 728, 728, 728, 728, 728],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9992]]],\n",
      "\n",
      "\n",
      "         [[[0.9992]]],\n",
      "\n",
      "\n",
      "         [[[0.9992]]],\n",
      "\n",
      "\n",
      "         [[[0.9992]]],\n",
      "\n",
      "\n",
      "         [[[0.9992]]],\n",
      "\n",
      "\n",
      "         [[[0.9992]]],\n",
      "\n",
      "\n",
      "         [[[0.9992]]],\n",
      "\n",
      "\n",
      "         [[[0.9992]]],\n",
      "\n",
      "\n",
      "         [[[0.9992]]],\n",
      "\n",
      "\n",
      "         [[[0.9992]]],\n",
      "\n",
      "\n",
      "         [[[0.9992]]],\n",
      "\n",
      "\n",
      "         [[[0.9992]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([708, 708, 708, 708, 708, 708, 708, 708, 708, 708, 708, 708],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([718, 718, 718, 718, 718, 718, 718, 718, 718, 718, 718, 718],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([697, 697, 697, 697, 697, 697, 697, 697, 697, 697, 697, 697],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([707, 707, 707, 707, 707, 707, 707, 707, 707, 707, 707, 707],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]],\n",
      "\n",
      "\n",
      "         [[[0.9991]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([687, 687, 687, 687, 687, 687, 687, 687, 687, 687, 687, 687],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([697, 697, 697, 697, 697, 697, 697, 697, 697, 697, 697, 697],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9990]]],\n",
      "\n",
      "\n",
      "         [[[0.9990]]],\n",
      "\n",
      "\n",
      "         [[[0.9990]]],\n",
      "\n",
      "\n",
      "         [[[0.9990]]],\n",
      "\n",
      "\n",
      "         [[[0.9990]]],\n",
      "\n",
      "\n",
      "         [[[0.9990]]],\n",
      "\n",
      "\n",
      "         [[[0.9990]]],\n",
      "\n",
      "\n",
      "         [[[0.9990]]],\n",
      "\n",
      "\n",
      "         [[[0.9990]]],\n",
      "\n",
      "\n",
      "         [[[0.9990]]],\n",
      "\n",
      "\n",
      "         [[[0.9990]]],\n",
      "\n",
      "\n",
      "         [[[0.9990]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([677, 677, 677, 677, 677, 677, 677, 677, 677, 677, 677, 677],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([687, 687, 687, 687, 687, 687, 687, 687, 687, 687, 687, 687],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9989]]],\n",
      "\n",
      "\n",
      "         [[[0.9989]]],\n",
      "\n",
      "\n",
      "         [[[0.9989]]],\n",
      "\n",
      "\n",
      "         [[[0.9989]]],\n",
      "\n",
      "\n",
      "         [[[0.9989]]],\n",
      "\n",
      "\n",
      "         [[[0.9989]]],\n",
      "\n",
      "\n",
      "         [[[0.9989]]],\n",
      "\n",
      "\n",
      "         [[[0.9989]]],\n",
      "\n",
      "\n",
      "         [[[0.9989]]],\n",
      "\n",
      "\n",
      "         [[[0.9989]]],\n",
      "\n",
      "\n",
      "         [[[0.9989]]],\n",
      "\n",
      "\n",
      "         [[[0.9989]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 666],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([676, 676, 676, 676, 676, 676, 676, 676, 676, 676, 676, 676],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9988]]],\n",
      "\n",
      "\n",
      "         [[[0.9988]]],\n",
      "\n",
      "\n",
      "         [[[0.9988]]],\n",
      "\n",
      "\n",
      "         [[[0.9988]]],\n",
      "\n",
      "\n",
      "         [[[0.9988]]],\n",
      "\n",
      "\n",
      "         [[[0.9988]]],\n",
      "\n",
      "\n",
      "         [[[0.9988]]],\n",
      "\n",
      "\n",
      "         [[[0.9988]]],\n",
      "\n",
      "\n",
      "         [[[0.9988]]],\n",
      "\n",
      "\n",
      "         [[[0.9988]]],\n",
      "\n",
      "\n",
      "         [[[0.9988]]],\n",
      "\n",
      "\n",
      "         [[[0.9988]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([656, 656, 656, 656, 656, 656, 656, 656, 656, 656, 656, 656],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 666, 666],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9987]]],\n",
      "\n",
      "\n",
      "         [[[0.9987]]],\n",
      "\n",
      "\n",
      "         [[[0.9987]]],\n",
      "\n",
      "\n",
      "         [[[0.9987]]],\n",
      "\n",
      "\n",
      "         [[[0.9987]]],\n",
      "\n",
      "\n",
      "         [[[0.9987]]],\n",
      "\n",
      "\n",
      "         [[[0.9987]]],\n",
      "\n",
      "\n",
      "         [[[0.9987]]],\n",
      "\n",
      "\n",
      "         [[[0.9987]]],\n",
      "\n",
      "\n",
      "         [[[0.9987]]],\n",
      "\n",
      "\n",
      "         [[[0.9987]]],\n",
      "\n",
      "\n",
      "         [[[0.9987]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([645, 645, 645, 645, 645, 645, 645, 645, 645, 645, 645, 645],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([655, 655, 655, 655, 655, 655, 655, 655, 655, 655, 655, 655],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9986]]],\n",
      "\n",
      "\n",
      "         [[[0.9986]]],\n",
      "\n",
      "\n",
      "         [[[0.9986]]],\n",
      "\n",
      "\n",
      "         [[[0.9986]]],\n",
      "\n",
      "\n",
      "         [[[0.9986]]],\n",
      "\n",
      "\n",
      "         [[[0.9986]]],\n",
      "\n",
      "\n",
      "         [[[0.9986]]],\n",
      "\n",
      "\n",
      "         [[[0.9986]]],\n",
      "\n",
      "\n",
      "         [[[0.9986]]],\n",
      "\n",
      "\n",
      "         [[[0.9986]]],\n",
      "\n",
      "\n",
      "         [[[0.9986]]],\n",
      "\n",
      "\n",
      "         [[[0.9986]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([635, 635, 635, 635, 635, 635, 635, 635, 635, 635, 635, 635],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([645, 645, 645, 645, 645, 645, 645, 645, 645, 645, 645, 645],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9985]]],\n",
      "\n",
      "\n",
      "         [[[0.9985]]],\n",
      "\n",
      "\n",
      "         [[[0.9985]]],\n",
      "\n",
      "\n",
      "         [[[0.9985]]],\n",
      "\n",
      "\n",
      "         [[[0.9985]]],\n",
      "\n",
      "\n",
      "         [[[0.9985]]],\n",
      "\n",
      "\n",
      "         [[[0.9985]]],\n",
      "\n",
      "\n",
      "         [[[0.9985]]],\n",
      "\n",
      "\n",
      "         [[[0.9985]]],\n",
      "\n",
      "\n",
      "         [[[0.9985]]],\n",
      "\n",
      "\n",
      "         [[[0.9985]]],\n",
      "\n",
      "\n",
      "         [[[0.9985]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([624, 624, 624, 624, 624, 624, 624, 624, 624, 624, 624, 624],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([634, 634, 634, 634, 634, 634, 634, 634, 634, 634, 634, 634],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9984]]],\n",
      "\n",
      "\n",
      "         [[[0.9984]]],\n",
      "\n",
      "\n",
      "         [[[0.9984]]],\n",
      "\n",
      "\n",
      "         [[[0.9984]]],\n",
      "\n",
      "\n",
      "         [[[0.9984]]],\n",
      "\n",
      "\n",
      "         [[[0.9984]]],\n",
      "\n",
      "\n",
      "         [[[0.9984]]],\n",
      "\n",
      "\n",
      "         [[[0.9984]]],\n",
      "\n",
      "\n",
      "         [[[0.9984]]],\n",
      "\n",
      "\n",
      "         [[[0.9984]]],\n",
      "\n",
      "\n",
      "         [[[0.9984]]],\n",
      "\n",
      "\n",
      "         [[[0.9984]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([614, 614, 614, 614, 614, 614, 614, 614, 614, 614, 614, 614],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([624, 624, 624, 624, 624, 624, 624, 624, 624, 624, 624, 624],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9983]]],\n",
      "\n",
      "\n",
      "         [[[0.9983]]],\n",
      "\n",
      "\n",
      "         [[[0.9983]]],\n",
      "\n",
      "\n",
      "         [[[0.9983]]],\n",
      "\n",
      "\n",
      "         [[[0.9983]]],\n",
      "\n",
      "\n",
      "         [[[0.9983]]],\n",
      "\n",
      "\n",
      "         [[[0.9983]]],\n",
      "\n",
      "\n",
      "         [[[0.9983]]],\n",
      "\n",
      "\n",
      "         [[[0.9983]]],\n",
      "\n",
      "\n",
      "         [[[0.9983]]],\n",
      "\n",
      "\n",
      "         [[[0.9983]]],\n",
      "\n",
      "\n",
      "         [[[0.9983]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([604, 604, 604, 604, 604, 604, 604, 604, 604, 604, 604, 604],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([614, 614, 614, 614, 614, 614, 614, 614, 614, 614, 614, 614],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9982]]],\n",
      "\n",
      "\n",
      "         [[[0.9982]]],\n",
      "\n",
      "\n",
      "         [[[0.9982]]],\n",
      "\n",
      "\n",
      "         [[[0.9982]]],\n",
      "\n",
      "\n",
      "         [[[0.9982]]],\n",
      "\n",
      "\n",
      "         [[[0.9982]]],\n",
      "\n",
      "\n",
      "         [[[0.9982]]],\n",
      "\n",
      "\n",
      "         [[[0.9982]]],\n",
      "\n",
      "\n",
      "         [[[0.9982]]],\n",
      "\n",
      "\n",
      "         [[[0.9982]]],\n",
      "\n",
      "\n",
      "         [[[0.9982]]],\n",
      "\n",
      "\n",
      "         [[[0.9982]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([593, 593, 593, 593, 593, 593, 593, 593, 593, 593, 593, 593],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([603, 603, 603, 603, 603, 603, 603, 603, 603, 603, 603, 603],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9980]]],\n",
      "\n",
      "\n",
      "         [[[0.9980]]],\n",
      "\n",
      "\n",
      "         [[[0.9980]]],\n",
      "\n",
      "\n",
      "         [[[0.9980]]],\n",
      "\n",
      "\n",
      "         [[[0.9980]]],\n",
      "\n",
      "\n",
      "         [[[0.9980]]],\n",
      "\n",
      "\n",
      "         [[[0.9980]]],\n",
      "\n",
      "\n",
      "         [[[0.9980]]],\n",
      "\n",
      "\n",
      "         [[[0.9980]]],\n",
      "\n",
      "\n",
      "         [[[0.9980]]],\n",
      "\n",
      "\n",
      "         [[[0.9980]]],\n",
      "\n",
      "\n",
      "         [[[0.9980]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([583, 583, 583, 583, 583, 583, 583, 583, 583, 583, 583, 583],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([593, 593, 593, 593, 593, 593, 593, 593, 593, 593, 593, 593],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9979]]],\n",
      "\n",
      "\n",
      "         [[[0.9979]]],\n",
      "\n",
      "\n",
      "         [[[0.9979]]],\n",
      "\n",
      "\n",
      "         [[[0.9979]]],\n",
      "\n",
      "\n",
      "         [[[0.9979]]],\n",
      "\n",
      "\n",
      "         [[[0.9979]]],\n",
      "\n",
      "\n",
      "         [[[0.9979]]],\n",
      "\n",
      "\n",
      "         [[[0.9979]]],\n",
      "\n",
      "\n",
      "         [[[0.9979]]],\n",
      "\n",
      "\n",
      "         [[[0.9979]]],\n",
      "\n",
      "\n",
      "         [[[0.9979]]],\n",
      "\n",
      "\n",
      "         [[[0.9979]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([572, 572, 572, 572, 572, 572, 572, 572, 572, 572, 572, 572],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([582, 582, 582, 582, 582, 582, 582, 582, 582, 582, 582, 582],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9978]]],\n",
      "\n",
      "\n",
      "         [[[0.9978]]],\n",
      "\n",
      "\n",
      "         [[[0.9978]]],\n",
      "\n",
      "\n",
      "         [[[0.9978]]],\n",
      "\n",
      "\n",
      "         [[[0.9978]]],\n",
      "\n",
      "\n",
      "         [[[0.9978]]],\n",
      "\n",
      "\n",
      "         [[[0.9978]]],\n",
      "\n",
      "\n",
      "         [[[0.9978]]],\n",
      "\n",
      "\n",
      "         [[[0.9978]]],\n",
      "\n",
      "\n",
      "         [[[0.9978]]],\n",
      "\n",
      "\n",
      "         [[[0.9978]]],\n",
      "\n",
      "\n",
      "         [[[0.9978]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([562, 562, 562, 562, 562, 562, 562, 562, 562, 562, 562, 562],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([572, 572, 572, 572, 572, 572, 572, 572, 572, 572, 572, 572],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9976]]],\n",
      "\n",
      "\n",
      "         [[[0.9976]]],\n",
      "\n",
      "\n",
      "         [[[0.9976]]],\n",
      "\n",
      "\n",
      "         [[[0.9976]]],\n",
      "\n",
      "\n",
      "         [[[0.9976]]],\n",
      "\n",
      "\n",
      "         [[[0.9976]]],\n",
      "\n",
      "\n",
      "         [[[0.9976]]],\n",
      "\n",
      "\n",
      "         [[[0.9976]]],\n",
      "\n",
      "\n",
      "         [[[0.9976]]],\n",
      "\n",
      "\n",
      "         [[[0.9976]]],\n",
      "\n",
      "\n",
      "         [[[0.9976]]],\n",
      "\n",
      "\n",
      "         [[[0.9976]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([552, 552, 552, 552, 552, 552, 552, 552, 552, 552, 552, 552],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([562, 562, 562, 562, 562, 562, 562, 562, 562, 562, 562, 562],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9975]]],\n",
      "\n",
      "\n",
      "         [[[0.9975]]],\n",
      "\n",
      "\n",
      "         [[[0.9975]]],\n",
      "\n",
      "\n",
      "         [[[0.9975]]],\n",
      "\n",
      "\n",
      "         [[[0.9975]]],\n",
      "\n",
      "\n",
      "         [[[0.9975]]],\n",
      "\n",
      "\n",
      "         [[[0.9975]]],\n",
      "\n",
      "\n",
      "         [[[0.9975]]],\n",
      "\n",
      "\n",
      "         [[[0.9975]]],\n",
      "\n",
      "\n",
      "         [[[0.9975]]],\n",
      "\n",
      "\n",
      "         [[[0.9975]]],\n",
      "\n",
      "\n",
      "         [[[0.9975]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([541, 541, 541, 541, 541, 541, 541, 541, 541, 541, 541, 541],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([551, 551, 551, 551, 551, 551, 551, 551, 551, 551, 551, 551],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9973]]],\n",
      "\n",
      "\n",
      "         [[[0.9973]]],\n",
      "\n",
      "\n",
      "         [[[0.9973]]],\n",
      "\n",
      "\n",
      "         [[[0.9973]]],\n",
      "\n",
      "\n",
      "         [[[0.9973]]],\n",
      "\n",
      "\n",
      "         [[[0.9973]]],\n",
      "\n",
      "\n",
      "         [[[0.9973]]],\n",
      "\n",
      "\n",
      "         [[[0.9973]]],\n",
      "\n",
      "\n",
      "         [[[0.9973]]],\n",
      "\n",
      "\n",
      "         [[[0.9973]]],\n",
      "\n",
      "\n",
      "         [[[0.9973]]],\n",
      "\n",
      "\n",
      "         [[[0.9973]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([531, 531, 531, 531, 531, 531, 531, 531, 531, 531, 531, 531],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([541, 541, 541, 541, 541, 541, 541, 541, 541, 541, 541, 541],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9971]]],\n",
      "\n",
      "\n",
      "         [[[0.9971]]],\n",
      "\n",
      "\n",
      "         [[[0.9971]]],\n",
      "\n",
      "\n",
      "         [[[0.9971]]],\n",
      "\n",
      "\n",
      "         [[[0.9971]]],\n",
      "\n",
      "\n",
      "         [[[0.9971]]],\n",
      "\n",
      "\n",
      "         [[[0.9971]]],\n",
      "\n",
      "\n",
      "         [[[0.9971]]],\n",
      "\n",
      "\n",
      "         [[[0.9971]]],\n",
      "\n",
      "\n",
      "         [[[0.9971]]],\n",
      "\n",
      "\n",
      "         [[[0.9971]]],\n",
      "\n",
      "\n",
      "         [[[0.9971]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([520, 520, 520, 520, 520, 520, 520, 520, 520, 520, 520, 520],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([530, 530, 530, 530, 530, 530, 530, 530, 530, 530, 530, 530],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9969]]],\n",
      "\n",
      "\n",
      "         [[[0.9969]]],\n",
      "\n",
      "\n",
      "         [[[0.9969]]],\n",
      "\n",
      "\n",
      "         [[[0.9969]]],\n",
      "\n",
      "\n",
      "         [[[0.9969]]],\n",
      "\n",
      "\n",
      "         [[[0.9969]]],\n",
      "\n",
      "\n",
      "         [[[0.9969]]],\n",
      "\n",
      "\n",
      "         [[[0.9969]]],\n",
      "\n",
      "\n",
      "         [[[0.9969]]],\n",
      "\n",
      "\n",
      "         [[[0.9969]]],\n",
      "\n",
      "\n",
      "         [[[0.9969]]],\n",
      "\n",
      "\n",
      "         [[[0.9969]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([510, 510, 510, 510, 510, 510, 510, 510, 510, 510, 510, 510],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([520, 520, 520, 520, 520, 520, 520, 520, 520, 520, 520, 520],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9968]]],\n",
      "\n",
      "\n",
      "         [[[0.9968]]],\n",
      "\n",
      "\n",
      "         [[[0.9968]]],\n",
      "\n",
      "\n",
      "         [[[0.9968]]],\n",
      "\n",
      "\n",
      "         [[[0.9968]]],\n",
      "\n",
      "\n",
      "         [[[0.9968]]],\n",
      "\n",
      "\n",
      "         [[[0.9968]]],\n",
      "\n",
      "\n",
      "         [[[0.9968]]],\n",
      "\n",
      "\n",
      "         [[[0.9968]]],\n",
      "\n",
      "\n",
      "         [[[0.9968]]],\n",
      "\n",
      "\n",
      "         [[[0.9968]]],\n",
      "\n",
      "\n",
      "         [[[0.9968]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([499, 499, 499, 499, 499, 499, 499, 499, 499, 499, 499, 499],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([509, 509, 509, 509, 509, 509, 509, 509, 509, 509, 509, 509],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9966]]],\n",
      "\n",
      "\n",
      "         [[[0.9966]]],\n",
      "\n",
      "\n",
      "         [[[0.9966]]],\n",
      "\n",
      "\n",
      "         [[[0.9966]]],\n",
      "\n",
      "\n",
      "         [[[0.9966]]],\n",
      "\n",
      "\n",
      "         [[[0.9966]]],\n",
      "\n",
      "\n",
      "         [[[0.9966]]],\n",
      "\n",
      "\n",
      "         [[[0.9966]]],\n",
      "\n",
      "\n",
      "         [[[0.9966]]],\n",
      "\n",
      "\n",
      "         [[[0.9966]]],\n",
      "\n",
      "\n",
      "         [[[0.9966]]],\n",
      "\n",
      "\n",
      "         [[[0.9966]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([489, 489, 489, 489, 489, 489, 489, 489, 489, 489, 489, 489],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([499, 499, 499, 499, 499, 499, 499, 499, 499, 499, 499, 499],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9963]]],\n",
      "\n",
      "\n",
      "         [[[0.9963]]],\n",
      "\n",
      "\n",
      "         [[[0.9963]]],\n",
      "\n",
      "\n",
      "         [[[0.9963]]],\n",
      "\n",
      "\n",
      "         [[[0.9963]]],\n",
      "\n",
      "\n",
      "         [[[0.9963]]],\n",
      "\n",
      "\n",
      "         [[[0.9963]]],\n",
      "\n",
      "\n",
      "         [[[0.9963]]],\n",
      "\n",
      "\n",
      "         [[[0.9963]]],\n",
      "\n",
      "\n",
      "         [[[0.9963]]],\n",
      "\n",
      "\n",
      "         [[[0.9963]]],\n",
      "\n",
      "\n",
      "         [[[0.9963]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([489, 489, 489, 489, 489, 489, 489, 489, 489, 489, 489, 489],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9961]]],\n",
      "\n",
      "\n",
      "         [[[0.9961]]],\n",
      "\n",
      "\n",
      "         [[[0.9961]]],\n",
      "\n",
      "\n",
      "         [[[0.9961]]],\n",
      "\n",
      "\n",
      "         [[[0.9961]]],\n",
      "\n",
      "\n",
      "         [[[0.9961]]],\n",
      "\n",
      "\n",
      "         [[[0.9961]]],\n",
      "\n",
      "\n",
      "         [[[0.9961]]],\n",
      "\n",
      "\n",
      "         [[[0.9961]]],\n",
      "\n",
      "\n",
      "         [[[0.9961]]],\n",
      "\n",
      "\n",
      "         [[[0.9961]]],\n",
      "\n",
      "\n",
      "         [[[0.9961]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([468, 468, 468, 468, 468, 468, 468, 468, 468, 468, 468, 468],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([478, 478, 478, 478, 478, 478, 478, 478, 478, 478, 478, 478],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9959]]],\n",
      "\n",
      "\n",
      "         [[[0.9959]]],\n",
      "\n",
      "\n",
      "         [[[0.9959]]],\n",
      "\n",
      "\n",
      "         [[[0.9959]]],\n",
      "\n",
      "\n",
      "         [[[0.9959]]],\n",
      "\n",
      "\n",
      "         [[[0.9959]]],\n",
      "\n",
      "\n",
      "         [[[0.9959]]],\n",
      "\n",
      "\n",
      "         [[[0.9959]]],\n",
      "\n",
      "\n",
      "         [[[0.9959]]],\n",
      "\n",
      "\n",
      "         [[[0.9959]]],\n",
      "\n",
      "\n",
      "         [[[0.9959]]],\n",
      "\n",
      "\n",
      "         [[[0.9959]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([458, 458, 458, 458, 458, 458, 458, 458, 458, 458, 458, 458],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([468, 468, 468, 468, 468, 468, 468, 468, 468, 468, 468, 468],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9956]]],\n",
      "\n",
      "\n",
      "         [[[0.9956]]],\n",
      "\n",
      "\n",
      "         [[[0.9956]]],\n",
      "\n",
      "\n",
      "         [[[0.9956]]],\n",
      "\n",
      "\n",
      "         [[[0.9956]]],\n",
      "\n",
      "\n",
      "         [[[0.9956]]],\n",
      "\n",
      "\n",
      "         [[[0.9956]]],\n",
      "\n",
      "\n",
      "         [[[0.9956]]],\n",
      "\n",
      "\n",
      "         [[[0.9956]]],\n",
      "\n",
      "\n",
      "         [[[0.9956]]],\n",
      "\n",
      "\n",
      "         [[[0.9956]]],\n",
      "\n",
      "\n",
      "         [[[0.9956]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([447, 447, 447, 447, 447, 447, 447, 447, 447, 447, 447, 447],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([457, 457, 457, 457, 457, 457, 457, 457, 457, 457, 457, 457],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9954]]],\n",
      "\n",
      "\n",
      "         [[[0.9954]]],\n",
      "\n",
      "\n",
      "         [[[0.9954]]],\n",
      "\n",
      "\n",
      "         [[[0.9954]]],\n",
      "\n",
      "\n",
      "         [[[0.9954]]],\n",
      "\n",
      "\n",
      "         [[[0.9954]]],\n",
      "\n",
      "\n",
      "         [[[0.9954]]],\n",
      "\n",
      "\n",
      "         [[[0.9954]]],\n",
      "\n",
      "\n",
      "         [[[0.9954]]],\n",
      "\n",
      "\n",
      "         [[[0.9954]]],\n",
      "\n",
      "\n",
      "         [[[0.9954]]],\n",
      "\n",
      "\n",
      "         [[[0.9954]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([437, 437, 437, 437, 437, 437, 437, 437, 437, 437, 437, 437],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([447, 447, 447, 447, 447, 447, 447, 447, 447, 447, 447, 447],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9951]]],\n",
      "\n",
      "\n",
      "         [[[0.9951]]],\n",
      "\n",
      "\n",
      "         [[[0.9951]]],\n",
      "\n",
      "\n",
      "         [[[0.9951]]],\n",
      "\n",
      "\n",
      "         [[[0.9951]]],\n",
      "\n",
      "\n",
      "         [[[0.9951]]],\n",
      "\n",
      "\n",
      "         [[[0.9951]]],\n",
      "\n",
      "\n",
      "         [[[0.9951]]],\n",
      "\n",
      "\n",
      "         [[[0.9951]]],\n",
      "\n",
      "\n",
      "         [[[0.9951]]],\n",
      "\n",
      "\n",
      "         [[[0.9951]]],\n",
      "\n",
      "\n",
      "         [[[0.9951]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([427, 427, 427, 427, 427, 427, 427, 427, 427, 427, 427, 427],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([437, 437, 437, 437, 437, 437, 437, 437, 437, 437, 437, 437],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9948]]],\n",
      "\n",
      "\n",
      "         [[[0.9948]]],\n",
      "\n",
      "\n",
      "         [[[0.9948]]],\n",
      "\n",
      "\n",
      "         [[[0.9948]]],\n",
      "\n",
      "\n",
      "         [[[0.9948]]],\n",
      "\n",
      "\n",
      "         [[[0.9948]]],\n",
      "\n",
      "\n",
      "         [[[0.9948]]],\n",
      "\n",
      "\n",
      "         [[[0.9948]]],\n",
      "\n",
      "\n",
      "         [[[0.9948]]],\n",
      "\n",
      "\n",
      "         [[[0.9948]]],\n",
      "\n",
      "\n",
      "         [[[0.9948]]],\n",
      "\n",
      "\n",
      "         [[[0.9948]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([426, 426, 426, 426, 426, 426, 426, 426, 426, 426, 426, 426],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9945]]],\n",
      "\n",
      "\n",
      "         [[[0.9945]]],\n",
      "\n",
      "\n",
      "         [[[0.9945]]],\n",
      "\n",
      "\n",
      "         [[[0.9945]]],\n",
      "\n",
      "\n",
      "         [[[0.9945]]],\n",
      "\n",
      "\n",
      "         [[[0.9945]]],\n",
      "\n",
      "\n",
      "         [[[0.9945]]],\n",
      "\n",
      "\n",
      "         [[[0.9945]]],\n",
      "\n",
      "\n",
      "         [[[0.9945]]],\n",
      "\n",
      "\n",
      "         [[[0.9945]]],\n",
      "\n",
      "\n",
      "         [[[0.9945]]],\n",
      "\n",
      "\n",
      "         [[[0.9945]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([406, 406, 406, 406, 406, 406, 406, 406, 406, 406, 406, 406],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9942]]],\n",
      "\n",
      "\n",
      "         [[[0.9942]]],\n",
      "\n",
      "\n",
      "         [[[0.9942]]],\n",
      "\n",
      "\n",
      "         [[[0.9942]]],\n",
      "\n",
      "\n",
      "         [[[0.9942]]],\n",
      "\n",
      "\n",
      "         [[[0.9942]]],\n",
      "\n",
      "\n",
      "         [[[0.9942]]],\n",
      "\n",
      "\n",
      "         [[[0.9942]]],\n",
      "\n",
      "\n",
      "         [[[0.9942]]],\n",
      "\n",
      "\n",
      "         [[[0.9942]]],\n",
      "\n",
      "\n",
      "         [[[0.9942]]],\n",
      "\n",
      "\n",
      "         [[[0.9942]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([395, 395, 395, 395, 395, 395, 395, 395, 395, 395, 395, 395],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([405, 405, 405, 405, 405, 405, 405, 405, 405, 405, 405, 405],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9938]]],\n",
      "\n",
      "\n",
      "         [[[0.9938]]],\n",
      "\n",
      "\n",
      "         [[[0.9938]]],\n",
      "\n",
      "\n",
      "         [[[0.9938]]],\n",
      "\n",
      "\n",
      "         [[[0.9938]]],\n",
      "\n",
      "\n",
      "         [[[0.9938]]],\n",
      "\n",
      "\n",
      "         [[[0.9938]]],\n",
      "\n",
      "\n",
      "         [[[0.9938]]],\n",
      "\n",
      "\n",
      "         [[[0.9938]]],\n",
      "\n",
      "\n",
      "         [[[0.9938]]],\n",
      "\n",
      "\n",
      "         [[[0.9938]]],\n",
      "\n",
      "\n",
      "         [[[0.9938]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([385, 385, 385, 385, 385, 385, 385, 385, 385, 385, 385, 385],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([395, 395, 395, 395, 395, 395, 395, 395, 395, 395, 395, 395],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9934]]],\n",
      "\n",
      "\n",
      "         [[[0.9934]]],\n",
      "\n",
      "\n",
      "         [[[0.9934]]],\n",
      "\n",
      "\n",
      "         [[[0.9934]]],\n",
      "\n",
      "\n",
      "         [[[0.9934]]],\n",
      "\n",
      "\n",
      "         [[[0.9934]]],\n",
      "\n",
      "\n",
      "         [[[0.9934]]],\n",
      "\n",
      "\n",
      "         [[[0.9934]]],\n",
      "\n",
      "\n",
      "         [[[0.9934]]],\n",
      "\n",
      "\n",
      "         [[[0.9934]]],\n",
      "\n",
      "\n",
      "         [[[0.9934]]],\n",
      "\n",
      "\n",
      "         [[[0.9934]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([374, 374, 374, 374, 374, 374, 374, 374, 374, 374, 374, 374],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([384, 384, 384, 384, 384, 384, 384, 384, 384, 384, 384, 384],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9930]]],\n",
      "\n",
      "\n",
      "         [[[0.9930]]],\n",
      "\n",
      "\n",
      "         [[[0.9930]]],\n",
      "\n",
      "\n",
      "         [[[0.9930]]],\n",
      "\n",
      "\n",
      "         [[[0.9930]]],\n",
      "\n",
      "\n",
      "         [[[0.9930]]],\n",
      "\n",
      "\n",
      "         [[[0.9930]]],\n",
      "\n",
      "\n",
      "         [[[0.9930]]],\n",
      "\n",
      "\n",
      "         [[[0.9930]]],\n",
      "\n",
      "\n",
      "         [[[0.9930]]],\n",
      "\n",
      "\n",
      "         [[[0.9930]]],\n",
      "\n",
      "\n",
      "         [[[0.9930]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([364, 364, 364, 364, 364, 364, 364, 364, 364, 364, 364, 364],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([374, 374, 374, 374, 374, 374, 374, 374, 374, 374, 374, 374],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9926]]],\n",
      "\n",
      "\n",
      "         [[[0.9926]]],\n",
      "\n",
      "\n",
      "         [[[0.9926]]],\n",
      "\n",
      "\n",
      "         [[[0.9926]]],\n",
      "\n",
      "\n",
      "         [[[0.9926]]],\n",
      "\n",
      "\n",
      "         [[[0.9926]]],\n",
      "\n",
      "\n",
      "         [[[0.9926]]],\n",
      "\n",
      "\n",
      "         [[[0.9926]]],\n",
      "\n",
      "\n",
      "         [[[0.9926]]],\n",
      "\n",
      "\n",
      "         [[[0.9926]]],\n",
      "\n",
      "\n",
      "         [[[0.9926]]],\n",
      "\n",
      "\n",
      "         [[[0.9926]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([354, 354, 354, 354, 354, 354, 354, 354, 354, 354, 354, 354],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([364, 364, 364, 364, 364, 364, 364, 364, 364, 364, 364, 364],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9922]]],\n",
      "\n",
      "\n",
      "         [[[0.9922]]],\n",
      "\n",
      "\n",
      "         [[[0.9922]]],\n",
      "\n",
      "\n",
      "         [[[0.9922]]],\n",
      "\n",
      "\n",
      "         [[[0.9922]]],\n",
      "\n",
      "\n",
      "         [[[0.9922]]],\n",
      "\n",
      "\n",
      "         [[[0.9922]]],\n",
      "\n",
      "\n",
      "         [[[0.9922]]],\n",
      "\n",
      "\n",
      "         [[[0.9922]]],\n",
      "\n",
      "\n",
      "         [[[0.9922]]],\n",
      "\n",
      "\n",
      "         [[[0.9922]]],\n",
      "\n",
      "\n",
      "         [[[0.9922]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([343, 343, 343, 343, 343, 343, 343, 343, 343, 343, 343, 343],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([353, 353, 353, 353, 353, 353, 353, 353, 353, 353, 353, 353],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9917]]],\n",
      "\n",
      "\n",
      "         [[[0.9917]]],\n",
      "\n",
      "\n",
      "         [[[0.9917]]],\n",
      "\n",
      "\n",
      "         [[[0.9917]]],\n",
      "\n",
      "\n",
      "         [[[0.9917]]],\n",
      "\n",
      "\n",
      "         [[[0.9917]]],\n",
      "\n",
      "\n",
      "         [[[0.9917]]],\n",
      "\n",
      "\n",
      "         [[[0.9917]]],\n",
      "\n",
      "\n",
      "         [[[0.9917]]],\n",
      "\n",
      "\n",
      "         [[[0.9917]]],\n",
      "\n",
      "\n",
      "         [[[0.9917]]],\n",
      "\n",
      "\n",
      "         [[[0.9917]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([333, 333, 333, 333, 333, 333, 333, 333, 333, 333, 333, 333],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([343, 343, 343, 343, 343, 343, 343, 343, 343, 343, 343, 343],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9912]]],\n",
      "\n",
      "\n",
      "         [[[0.9912]]],\n",
      "\n",
      "\n",
      "         [[[0.9912]]],\n",
      "\n",
      "\n",
      "         [[[0.9912]]],\n",
      "\n",
      "\n",
      "         [[[0.9912]]],\n",
      "\n",
      "\n",
      "         [[[0.9912]]],\n",
      "\n",
      "\n",
      "         [[[0.9912]]],\n",
      "\n",
      "\n",
      "         [[[0.9912]]],\n",
      "\n",
      "\n",
      "         [[[0.9912]]],\n",
      "\n",
      "\n",
      "         [[[0.9912]]],\n",
      "\n",
      "\n",
      "         [[[0.9912]]],\n",
      "\n",
      "\n",
      "         [[[0.9912]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([322, 322, 322, 322, 322, 322, 322, 322, 322, 322, 322, 322],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([332, 332, 332, 332, 332, 332, 332, 332, 332, 332, 332, 332],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9906]]],\n",
      "\n",
      "\n",
      "         [[[0.9906]]],\n",
      "\n",
      "\n",
      "         [[[0.9906]]],\n",
      "\n",
      "\n",
      "         [[[0.9906]]],\n",
      "\n",
      "\n",
      "         [[[0.9906]]],\n",
      "\n",
      "\n",
      "         [[[0.9906]]],\n",
      "\n",
      "\n",
      "         [[[0.9906]]],\n",
      "\n",
      "\n",
      "         [[[0.9906]]],\n",
      "\n",
      "\n",
      "         [[[0.9906]]],\n",
      "\n",
      "\n",
      "         [[[0.9906]]],\n",
      "\n",
      "\n",
      "         [[[0.9906]]],\n",
      "\n",
      "\n",
      "         [[[0.9906]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([312, 312, 312, 312, 312, 312, 312, 312, 312, 312, 312, 312],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([322, 322, 322, 322, 322, 322, 322, 322, 322, 322, 322, 322],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9900]]],\n",
      "\n",
      "\n",
      "         [[[0.9900]]],\n",
      "\n",
      "\n",
      "         [[[0.9900]]],\n",
      "\n",
      "\n",
      "         [[[0.9900]]],\n",
      "\n",
      "\n",
      "         [[[0.9900]]],\n",
      "\n",
      "\n",
      "         [[[0.9900]]],\n",
      "\n",
      "\n",
      "         [[[0.9900]]],\n",
      "\n",
      "\n",
      "         [[[0.9900]]],\n",
      "\n",
      "\n",
      "         [[[0.9900]]],\n",
      "\n",
      "\n",
      "         [[[0.9900]]],\n",
      "\n",
      "\n",
      "         [[[0.9900]]],\n",
      "\n",
      "\n",
      "         [[[0.9900]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([302, 302, 302, 302, 302, 302, 302, 302, 302, 302, 302, 302],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([312, 312, 312, 312, 312, 312, 312, 312, 312, 312, 312, 312],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9894]]],\n",
      "\n",
      "\n",
      "         [[[0.9894]]],\n",
      "\n",
      "\n",
      "         [[[0.9894]]],\n",
      "\n",
      "\n",
      "         [[[0.9894]]],\n",
      "\n",
      "\n",
      "         [[[0.9894]]],\n",
      "\n",
      "\n",
      "         [[[0.9894]]],\n",
      "\n",
      "\n",
      "         [[[0.9894]]],\n",
      "\n",
      "\n",
      "         [[[0.9894]]],\n",
      "\n",
      "\n",
      "         [[[0.9894]]],\n",
      "\n",
      "\n",
      "         [[[0.9894]]],\n",
      "\n",
      "\n",
      "         [[[0.9894]]],\n",
      "\n",
      "\n",
      "         [[[0.9894]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([291, 291, 291, 291, 291, 291, 291, 291, 291, 291, 291, 291],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([301, 301, 301, 301, 301, 301, 301, 301, 301, 301, 301, 301],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9887]]],\n",
      "\n",
      "\n",
      "         [[[0.9887]]],\n",
      "\n",
      "\n",
      "         [[[0.9887]]],\n",
      "\n",
      "\n",
      "         [[[0.9887]]],\n",
      "\n",
      "\n",
      "         [[[0.9887]]],\n",
      "\n",
      "\n",
      "         [[[0.9887]]],\n",
      "\n",
      "\n",
      "         [[[0.9887]]],\n",
      "\n",
      "\n",
      "         [[[0.9887]]],\n",
      "\n",
      "\n",
      "         [[[0.9887]]],\n",
      "\n",
      "\n",
      "         [[[0.9887]]],\n",
      "\n",
      "\n",
      "         [[[0.9887]]],\n",
      "\n",
      "\n",
      "         [[[0.9887]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([281, 281, 281, 281, 281, 281, 281, 281, 281, 281, 281, 281],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([291, 291, 291, 291, 291, 291, 291, 291, 291, 291, 291, 291],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9880]]],\n",
      "\n",
      "\n",
      "         [[[0.9880]]],\n",
      "\n",
      "\n",
      "         [[[0.9880]]],\n",
      "\n",
      "\n",
      "         [[[0.9880]]],\n",
      "\n",
      "\n",
      "         [[[0.9880]]],\n",
      "\n",
      "\n",
      "         [[[0.9880]]],\n",
      "\n",
      "\n",
      "         [[[0.9880]]],\n",
      "\n",
      "\n",
      "         [[[0.9880]]],\n",
      "\n",
      "\n",
      "         [[[0.9880]]],\n",
      "\n",
      "\n",
      "         [[[0.9880]]],\n",
      "\n",
      "\n",
      "         [[[0.9880]]],\n",
      "\n",
      "\n",
      "         [[[0.9880]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9872]]],\n",
      "\n",
      "\n",
      "         [[[0.9872]]],\n",
      "\n",
      "\n",
      "         [[[0.9872]]],\n",
      "\n",
      "\n",
      "         [[[0.9872]]],\n",
      "\n",
      "\n",
      "         [[[0.9872]]],\n",
      "\n",
      "\n",
      "         [[[0.9872]]],\n",
      "\n",
      "\n",
      "         [[[0.9872]]],\n",
      "\n",
      "\n",
      "         [[[0.9872]]],\n",
      "\n",
      "\n",
      "         [[[0.9872]]],\n",
      "\n",
      "\n",
      "         [[[0.9872]]],\n",
      "\n",
      "\n",
      "         [[[0.9872]]],\n",
      "\n",
      "\n",
      "         [[[0.9872]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9864]]],\n",
      "\n",
      "\n",
      "         [[[0.9864]]],\n",
      "\n",
      "\n",
      "         [[[0.9864]]],\n",
      "\n",
      "\n",
      "         [[[0.9864]]],\n",
      "\n",
      "\n",
      "         [[[0.9864]]],\n",
      "\n",
      "\n",
      "         [[[0.9864]]],\n",
      "\n",
      "\n",
      "         [[[0.9864]]],\n",
      "\n",
      "\n",
      "         [[[0.9864]]],\n",
      "\n",
      "\n",
      "         [[[0.9864]]],\n",
      "\n",
      "\n",
      "         [[[0.9864]]],\n",
      "\n",
      "\n",
      "         [[[0.9864]]],\n",
      "\n",
      "\n",
      "         [[[0.9864]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([249, 249, 249, 249, 249, 249, 249, 249, 249, 249, 249, 249],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([259, 259, 259, 259, 259, 259, 259, 259, 259, 259, 259, 259],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9855]]],\n",
      "\n",
      "\n",
      "         [[[0.9855]]],\n",
      "\n",
      "\n",
      "         [[[0.9855]]],\n",
      "\n",
      "\n",
      "         [[[0.9855]]],\n",
      "\n",
      "\n",
      "         [[[0.9855]]],\n",
      "\n",
      "\n",
      "         [[[0.9855]]],\n",
      "\n",
      "\n",
      "         [[[0.9855]]],\n",
      "\n",
      "\n",
      "         [[[0.9855]]],\n",
      "\n",
      "\n",
      "         [[[0.9855]]],\n",
      "\n",
      "\n",
      "         [[[0.9855]]],\n",
      "\n",
      "\n",
      "         [[[0.9855]]],\n",
      "\n",
      "\n",
      "         [[[0.9855]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([249, 249, 249, 249, 249, 249, 249, 249, 249, 249, 249, 249],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9845]]],\n",
      "\n",
      "\n",
      "         [[[0.9845]]],\n",
      "\n",
      "\n",
      "         [[[0.9845]]],\n",
      "\n",
      "\n",
      "         [[[0.9845]]],\n",
      "\n",
      "\n",
      "         [[[0.9845]]],\n",
      "\n",
      "\n",
      "         [[[0.9845]]],\n",
      "\n",
      "\n",
      "         [[[0.9845]]],\n",
      "\n",
      "\n",
      "         [[[0.9845]]],\n",
      "\n",
      "\n",
      "         [[[0.9845]]],\n",
      "\n",
      "\n",
      "         [[[0.9845]]],\n",
      "\n",
      "\n",
      "         [[[0.9845]]],\n",
      "\n",
      "\n",
      "         [[[0.9845]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([229, 229, 229, 229, 229, 229, 229, 229, 229, 229, 229, 229],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9835]]],\n",
      "\n",
      "\n",
      "         [[[0.9835]]],\n",
      "\n",
      "\n",
      "         [[[0.9835]]],\n",
      "\n",
      "\n",
      "         [[[0.9835]]],\n",
      "\n",
      "\n",
      "         [[[0.9835]]],\n",
      "\n",
      "\n",
      "         [[[0.9835]]],\n",
      "\n",
      "\n",
      "         [[[0.9835]]],\n",
      "\n",
      "\n",
      "         [[[0.9835]]],\n",
      "\n",
      "\n",
      "         [[[0.9835]]],\n",
      "\n",
      "\n",
      "         [[[0.9835]]],\n",
      "\n",
      "\n",
      "         [[[0.9835]]],\n",
      "\n",
      "\n",
      "         [[[0.9835]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([218, 218, 218, 218, 218, 218, 218, 218, 218, 218, 218, 218],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([228, 228, 228, 228, 228, 228, 228, 228, 228, 228, 228, 228],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9823]]],\n",
      "\n",
      "\n",
      "         [[[0.9823]]],\n",
      "\n",
      "\n",
      "         [[[0.9823]]],\n",
      "\n",
      "\n",
      "         [[[0.9823]]],\n",
      "\n",
      "\n",
      "         [[[0.9823]]],\n",
      "\n",
      "\n",
      "         [[[0.9823]]],\n",
      "\n",
      "\n",
      "         [[[0.9823]]],\n",
      "\n",
      "\n",
      "         [[[0.9823]]],\n",
      "\n",
      "\n",
      "         [[[0.9823]]],\n",
      "\n",
      "\n",
      "         [[[0.9823]]],\n",
      "\n",
      "\n",
      "         [[[0.9823]]],\n",
      "\n",
      "\n",
      "         [[[0.9823]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([208, 208, 208, 208, 208, 208, 208, 208, 208, 208, 208, 208],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([218, 218, 218, 218, 218, 218, 218, 218, 218, 218, 218, 218],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9811]]],\n",
      "\n",
      "\n",
      "         [[[0.9811]]],\n",
      "\n",
      "\n",
      "         [[[0.9811]]],\n",
      "\n",
      "\n",
      "         [[[0.9811]]],\n",
      "\n",
      "\n",
      "         [[[0.9811]]],\n",
      "\n",
      "\n",
      "         [[[0.9811]]],\n",
      "\n",
      "\n",
      "         [[[0.9811]]],\n",
      "\n",
      "\n",
      "         [[[0.9811]]],\n",
      "\n",
      "\n",
      "         [[[0.9811]]],\n",
      "\n",
      "\n",
      "         [[[0.9811]]],\n",
      "\n",
      "\n",
      "         [[[0.9811]]],\n",
      "\n",
      "\n",
      "         [[[0.9811]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([207, 207, 207, 207, 207, 207, 207, 207, 207, 207, 207, 207],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9796]]],\n",
      "\n",
      "\n",
      "         [[[0.9796]]],\n",
      "\n",
      "\n",
      "         [[[0.9796]]],\n",
      "\n",
      "\n",
      "         [[[0.9796]]],\n",
      "\n",
      "\n",
      "         [[[0.9796]]],\n",
      "\n",
      "\n",
      "         [[[0.9796]]],\n",
      "\n",
      "\n",
      "         [[[0.9796]]],\n",
      "\n",
      "\n",
      "         [[[0.9796]]],\n",
      "\n",
      "\n",
      "         [[[0.9796]]],\n",
      "\n",
      "\n",
      "         [[[0.9796]]],\n",
      "\n",
      "\n",
      "         [[[0.9796]]],\n",
      "\n",
      "\n",
      "         [[[0.9796]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9782]]],\n",
      "\n",
      "\n",
      "         [[[0.9782]]],\n",
      "\n",
      "\n",
      "         [[[0.9782]]],\n",
      "\n",
      "\n",
      "         [[[0.9782]]],\n",
      "\n",
      "\n",
      "         [[[0.9782]]],\n",
      "\n",
      "\n",
      "         [[[0.9782]]],\n",
      "\n",
      "\n",
      "         [[[0.9782]]],\n",
      "\n",
      "\n",
      "         [[[0.9782]]],\n",
      "\n",
      "\n",
      "         [[[0.9782]]],\n",
      "\n",
      "\n",
      "         [[[0.9782]]],\n",
      "\n",
      "\n",
      "         [[[0.9782]]],\n",
      "\n",
      "\n",
      "         [[[0.9782]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9766]]],\n",
      "\n",
      "\n",
      "         [[[0.9766]]],\n",
      "\n",
      "\n",
      "         [[[0.9766]]],\n",
      "\n",
      "\n",
      "         [[[0.9766]]],\n",
      "\n",
      "\n",
      "         [[[0.9766]]],\n",
      "\n",
      "\n",
      "         [[[0.9766]]],\n",
      "\n",
      "\n",
      "         [[[0.9766]]],\n",
      "\n",
      "\n",
      "         [[[0.9766]]],\n",
      "\n",
      "\n",
      "         [[[0.9766]]],\n",
      "\n",
      "\n",
      "         [[[0.9766]]],\n",
      "\n",
      "\n",
      "         [[[0.9766]]],\n",
      "\n",
      "\n",
      "         [[[0.9766]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9746]]],\n",
      "\n",
      "\n",
      "         [[[0.9746]]],\n",
      "\n",
      "\n",
      "         [[[0.9746]]],\n",
      "\n",
      "\n",
      "         [[[0.9746]]],\n",
      "\n",
      "\n",
      "         [[[0.9746]]],\n",
      "\n",
      "\n",
      "         [[[0.9746]]],\n",
      "\n",
      "\n",
      "         [[[0.9746]]],\n",
      "\n",
      "\n",
      "         [[[0.9746]]],\n",
      "\n",
      "\n",
      "         [[[0.9746]]],\n",
      "\n",
      "\n",
      "         [[[0.9746]]],\n",
      "\n",
      "\n",
      "         [[[0.9746]]],\n",
      "\n",
      "\n",
      "         [[[0.9746]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9726]]],\n",
      "\n",
      "\n",
      "         [[[0.9726]]],\n",
      "\n",
      "\n",
      "         [[[0.9726]]],\n",
      "\n",
      "\n",
      "         [[[0.9726]]],\n",
      "\n",
      "\n",
      "         [[[0.9726]]],\n",
      "\n",
      "\n",
      "         [[[0.9726]]],\n",
      "\n",
      "\n",
      "         [[[0.9726]]],\n",
      "\n",
      "\n",
      "         [[[0.9726]]],\n",
      "\n",
      "\n",
      "         [[[0.9726]]],\n",
      "\n",
      "\n",
      "         [[[0.9726]]],\n",
      "\n",
      "\n",
      "         [[[0.9726]]],\n",
      "\n",
      "\n",
      "         [[[0.9726]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9702]]],\n",
      "\n",
      "\n",
      "         [[[0.9702]]],\n",
      "\n",
      "\n",
      "         [[[0.9702]]],\n",
      "\n",
      "\n",
      "         [[[0.9702]]],\n",
      "\n",
      "\n",
      "         [[[0.9702]]],\n",
      "\n",
      "\n",
      "         [[[0.9702]]],\n",
      "\n",
      "\n",
      "         [[[0.9702]]],\n",
      "\n",
      "\n",
      "         [[[0.9702]]],\n",
      "\n",
      "\n",
      "         [[[0.9702]]],\n",
      "\n",
      "\n",
      "         [[[0.9702]]],\n",
      "\n",
      "\n",
      "         [[[0.9702]]],\n",
      "\n",
      "\n",
      "         [[[0.9702]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9676]]],\n",
      "\n",
      "\n",
      "         [[[0.9676]]],\n",
      "\n",
      "\n",
      "         [[[0.9676]]],\n",
      "\n",
      "\n",
      "         [[[0.9676]]],\n",
      "\n",
      "\n",
      "         [[[0.9676]]],\n",
      "\n",
      "\n",
      "         [[[0.9676]]],\n",
      "\n",
      "\n",
      "         [[[0.9676]]],\n",
      "\n",
      "\n",
      "         [[[0.9676]]],\n",
      "\n",
      "\n",
      "         [[[0.9676]]],\n",
      "\n",
      "\n",
      "         [[[0.9676]]],\n",
      "\n",
      "\n",
      "         [[[0.9676]]],\n",
      "\n",
      "\n",
      "         [[[0.9676]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9644]]],\n",
      "\n",
      "\n",
      "         [[[0.9644]]],\n",
      "\n",
      "\n",
      "         [[[0.9644]]],\n",
      "\n",
      "\n",
      "         [[[0.9644]]],\n",
      "\n",
      "\n",
      "         [[[0.9644]]],\n",
      "\n",
      "\n",
      "         [[[0.9644]]],\n",
      "\n",
      "\n",
      "         [[[0.9644]]],\n",
      "\n",
      "\n",
      "         [[[0.9644]]],\n",
      "\n",
      "\n",
      "         [[[0.9644]]],\n",
      "\n",
      "\n",
      "         [[[0.9644]]],\n",
      "\n",
      "\n",
      "         [[[0.9644]]],\n",
      "\n",
      "\n",
      "         [[[0.9644]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9610]]],\n",
      "\n",
      "\n",
      "         [[[0.9610]]],\n",
      "\n",
      "\n",
      "         [[[0.9610]]],\n",
      "\n",
      "\n",
      "         [[[0.9610]]],\n",
      "\n",
      "\n",
      "         [[[0.9610]]],\n",
      "\n",
      "\n",
      "         [[[0.9610]]],\n",
      "\n",
      "\n",
      "         [[[0.9610]]],\n",
      "\n",
      "\n",
      "         [[[0.9610]]],\n",
      "\n",
      "\n",
      "         [[[0.9610]]],\n",
      "\n",
      "\n",
      "         [[[0.9610]]],\n",
      "\n",
      "\n",
      "         [[[0.9610]]],\n",
      "\n",
      "\n",
      "         [[[0.9610]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104],\n",
      "       device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9570]]],\n",
      "\n",
      "\n",
      "         [[[0.9570]]],\n",
      "\n",
      "\n",
      "         [[[0.9570]]],\n",
      "\n",
      "\n",
      "         [[[0.9570]]],\n",
      "\n",
      "\n",
      "         [[[0.9570]]],\n",
      "\n",
      "\n",
      "         [[[0.9570]]],\n",
      "\n",
      "\n",
      "         [[[0.9570]]],\n",
      "\n",
      "\n",
      "         [[[0.9570]]],\n",
      "\n",
      "\n",
      "         [[[0.9570]]],\n",
      "\n",
      "\n",
      "         [[[0.9570]]],\n",
      "\n",
      "\n",
      "         [[[0.9570]]],\n",
      "\n",
      "\n",
      "         [[[0.9570]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93], device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103],\n",
      "       device='cuda:0') a_t tensor([[[[[0.9518]]],\n",
      "\n",
      "\n",
      "         [[[0.9518]]],\n",
      "\n",
      "\n",
      "         [[[0.9518]]],\n",
      "\n",
      "\n",
      "         [[[0.9518]]],\n",
      "\n",
      "\n",
      "         [[[0.9518]]],\n",
      "\n",
      "\n",
      "         [[[0.9518]]],\n",
      "\n",
      "\n",
      "         [[[0.9518]]],\n",
      "\n",
      "\n",
      "         [[[0.9518]]],\n",
      "\n",
      "\n",
      "         [[[0.9518]]],\n",
      "\n",
      "\n",
      "         [[[0.9518]]],\n",
      "\n",
      "\n",
      "         [[[0.9518]]],\n",
      "\n",
      "\n",
      "         [[[0.9518]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83], device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93], device='cuda:0') a_t tensor([[[[[0.9461]]],\n",
      "\n",
      "\n",
      "         [[[0.9461]]],\n",
      "\n",
      "\n",
      "         [[[0.9461]]],\n",
      "\n",
      "\n",
      "         [[[0.9461]]],\n",
      "\n",
      "\n",
      "         [[[0.9461]]],\n",
      "\n",
      "\n",
      "         [[[0.9461]]],\n",
      "\n",
      "\n",
      "         [[[0.9461]]],\n",
      "\n",
      "\n",
      "         [[[0.9461]]],\n",
      "\n",
      "\n",
      "         [[[0.9461]]],\n",
      "\n",
      "\n",
      "         [[[0.9461]]],\n",
      "\n",
      "\n",
      "         [[[0.9461]]],\n",
      "\n",
      "\n",
      "         [[[0.9461]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72], device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82], device='cuda:0') a_t tensor([[[[[0.9382]]],\n",
      "\n",
      "\n",
      "         [[[0.9382]]],\n",
      "\n",
      "\n",
      "         [[[0.9382]]],\n",
      "\n",
      "\n",
      "         [[[0.9382]]],\n",
      "\n",
      "\n",
      "         [[[0.9382]]],\n",
      "\n",
      "\n",
      "         [[[0.9382]]],\n",
      "\n",
      "\n",
      "         [[[0.9382]]],\n",
      "\n",
      "\n",
      "         [[[0.9382]]],\n",
      "\n",
      "\n",
      "         [[[0.9382]]],\n",
      "\n",
      "\n",
      "         [[[0.9382]]],\n",
      "\n",
      "\n",
      "         [[[0.9382]]],\n",
      "\n",
      "\n",
      "         [[[0.9382]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62], device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72], device='cuda:0') a_t tensor([[[[[0.9289]]],\n",
      "\n",
      "\n",
      "         [[[0.9289]]],\n",
      "\n",
      "\n",
      "         [[[0.9289]]],\n",
      "\n",
      "\n",
      "         [[[0.9289]]],\n",
      "\n",
      "\n",
      "         [[[0.9289]]],\n",
      "\n",
      "\n",
      "         [[[0.9289]]],\n",
      "\n",
      "\n",
      "         [[[0.9289]]],\n",
      "\n",
      "\n",
      "         [[[0.9289]]],\n",
      "\n",
      "\n",
      "         [[[0.9289]]],\n",
      "\n",
      "\n",
      "         [[[0.9289]]],\n",
      "\n",
      "\n",
      "         [[[0.9289]]],\n",
      "\n",
      "\n",
      "         [[[0.9289]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52], device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62], device='cuda:0') a_t tensor([[[[[0.9168]]],\n",
      "\n",
      "\n",
      "         [[[0.9168]]],\n",
      "\n",
      "\n",
      "         [[[0.9168]]],\n",
      "\n",
      "\n",
      "         [[[0.9168]]],\n",
      "\n",
      "\n",
      "         [[[0.9168]]],\n",
      "\n",
      "\n",
      "         [[[0.9168]]],\n",
      "\n",
      "\n",
      "         [[[0.9168]]],\n",
      "\n",
      "\n",
      "         [[[0.9168]]],\n",
      "\n",
      "\n",
      "         [[[0.9168]]],\n",
      "\n",
      "\n",
      "         [[[0.9168]]],\n",
      "\n",
      "\n",
      "         [[[0.9168]]],\n",
      "\n",
      "\n",
      "         [[[0.9168]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41], device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51], device='cuda:0') a_t tensor([[[[[0.8978]]],\n",
      "\n",
      "\n",
      "         [[[0.8978]]],\n",
      "\n",
      "\n",
      "         [[[0.8978]]],\n",
      "\n",
      "\n",
      "         [[[0.8978]]],\n",
      "\n",
      "\n",
      "         [[[0.8978]]],\n",
      "\n",
      "\n",
      "         [[[0.8978]]],\n",
      "\n",
      "\n",
      "         [[[0.8978]]],\n",
      "\n",
      "\n",
      "         [[[0.8978]]],\n",
      "\n",
      "\n",
      "         [[[0.8978]]],\n",
      "\n",
      "\n",
      "         [[[0.8978]]],\n",
      "\n",
      "\n",
      "         [[[0.8978]]],\n",
      "\n",
      "\n",
      "         [[[0.8978]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31], device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41], device='cuda:0') a_t tensor([[[[[0.8716]]],\n",
      "\n",
      "\n",
      "         [[[0.8716]]],\n",
      "\n",
      "\n",
      "         [[[0.8716]]],\n",
      "\n",
      "\n",
      "         [[[0.8716]]],\n",
      "\n",
      "\n",
      "         [[[0.8716]]],\n",
      "\n",
      "\n",
      "         [[[0.8716]]],\n",
      "\n",
      "\n",
      "         [[[0.8716]]],\n",
      "\n",
      "\n",
      "         [[[0.8716]]],\n",
      "\n",
      "\n",
      "         [[[0.8716]]],\n",
      "\n",
      "\n",
      "         [[[0.8716]]],\n",
      "\n",
      "\n",
      "         [[[0.8716]]],\n",
      "\n",
      "\n",
      "         [[[0.8716]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20], device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30], device='cuda:0') a_t tensor([[[[[0.8213]]],\n",
      "\n",
      "\n",
      "         [[[0.8213]]],\n",
      "\n",
      "\n",
      "         [[[0.8213]]],\n",
      "\n",
      "\n",
      "         [[[0.8213]]],\n",
      "\n",
      "\n",
      "         [[[0.8213]]],\n",
      "\n",
      "\n",
      "         [[[0.8213]]],\n",
      "\n",
      "\n",
      "         [[[0.8213]]],\n",
      "\n",
      "\n",
      "         [[[0.8213]]],\n",
      "\n",
      "\n",
      "         [[[0.8213]]],\n",
      "\n",
      "\n",
      "         [[[0.8213]]],\n",
      "\n",
      "\n",
      "         [[[0.8213]]],\n",
      "\n",
      "\n",
      "         [[[0.8213]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10], device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20], device='cuda:0') a_t tensor([[[[[0.7215]]],\n",
      "\n",
      "\n",
      "         [[[0.7215]]],\n",
      "\n",
      "\n",
      "         [[[0.7215]]],\n",
      "\n",
      "\n",
      "         [[[0.7215]]],\n",
      "\n",
      "\n",
      "         [[[0.7215]]],\n",
      "\n",
      "\n",
      "         [[[0.7215]]],\n",
      "\n",
      "\n",
      "         [[[0.7215]]],\n",
      "\n",
      "\n",
      "         [[[0.7215]]],\n",
      "\n",
      "\n",
      "         [[[0.7215]]],\n",
      "\n",
      "\n",
      "         [[[0.7215]]],\n",
      "\n",
      "\n",
      "         [[[0.7215]]],\n",
      "\n",
      "\n",
      "         [[[0.7215]]]]], device='cuda:0', dtype=torch.float64)\n",
      "latents torch.Size([2, 12, 16, 60, 90])\n",
      "torch.Size([24])\n",
      "ada torch.Size([24, 512]) torch.Size([2, 16200, 1920])\n",
      "noise_pred torch.Size([1, 12, 16, 60, 90]) latents torch.Size([1, 12, 16, 60, 90])\n",
      "prev_timestep tensor([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], device='cuda:0')\n",
      "sample torch.Size([1, 12, 16, 60, 90]) timestep torch.Size([12])\n",
      "broadcast_shape (1, 12, 1, 1, 1)\n",
      "sample torch.Size([1, 12, 16, 60, 90]) a_t torch.Size([1, 12, 1, 1, 1]) b_t torch.Size([1, 12, 1, 1, 1])\n",
      "t tensor([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9], device='cuda:0') a_t tensor([[[[[0.]]],\n",
      "\n",
      "\n",
      "         [[[0.]]],\n",
      "\n",
      "\n",
      "         [[[0.]]],\n",
      "\n",
      "\n",
      "         [[[0.]]],\n",
      "\n",
      "\n",
      "         [[[0.]]],\n",
      "\n",
      "\n",
      "         [[[0.]]],\n",
      "\n",
      "\n",
      "         [[[0.]]],\n",
      "\n",
      "\n",
      "         [[[0.]]],\n",
      "\n",
      "\n",
      "         [[[0.]]],\n",
      "\n",
      "\n",
      "         [[[0.]]],\n",
      "\n",
      "\n",
      "         [[[0.]]],\n",
      "\n",
      "\n",
      "         [[[0.]]]]], device='cuda:0', dtype=torch.float64)\n",
      "torch.Size([1, 12, 16, 60, 90])\n",
      "latents passed to vae torch.Size([1, 16, 12, 60, 90])\n"
     ]
    }
   ],
   "source": [
    "video = pipe.generate_video(\n",
    "    prompt=prompt,\n",
    "    num_videos_per_prompt=1,\n",
    "    num_inference_steps=96,\n",
    "    num_frames=48,\n",
    "    guidance_scale=6,\n",
    "    height=480,\n",
    "    width=720,\n",
    "    generator=torch.Generator(device=\"cuda\").manual_seed(42),\n",
    "    latents_dir='./latents',\n",
    "    save_latents=True\n",
    ").frames[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f470a386-e0bf-42a2-8504-21d131563e44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=720x480>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acc1a7b3-1e5c-4df5-bc6b-28bc8c75ac6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "60 * 90 / 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "666638ee-a97c-4860-a633-fcf8bd68cf66",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.index_select(pipe.scheduler.alphas_cumprod, 0, torch.tensor([0, 700]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89140d33-fa6e-4a32-8d93-d22cd1170363",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "from IPython.display import display, Image as IPyImage\n",
    "\n",
    "# Assuming `image_list` is your list of PIL images\n",
    "# Example: creating some dummy images for the demonstration\n",
    "\n",
    "# Save images as a GIF to a file\n",
    "gif_path = 'my_animation.gif'\n",
    "video[0].save(gif_path, save_all=True, append_images=video[1:], loop=0, duration=500)\n",
    "\n",
    "# Display the GIF in Jupyter notebook\n",
    "display(IPyImage(filename=gif_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "731c19bd-ae1c-46e9-9d35-6f97ebcc4656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "150a9c77-33a2-40ff-9825-2411124b110f",
   "metadata": {},
   "source": [
    "FIFO config:\n",
    "    num_partitions = 2\n",
    "    partition_Size = 16 #num of video frames to denoise in one go\n",
    "    f = partition_size // vae_temporal_scale_factor\n",
    "    total_num_frames = 1000 #num of total video frames\n",
    "    enable_lookahead_denoising = True\n",
    "Steps:\n",
    "    1. prepare latents. latent frames count = num_partitions * partition_size * 2(if look_ahead else 1)\n",
    "    2. latents shape = [2, 256 * f, 1920]\n",
    "       encoder_hidden_states = [2, 226, 512]\n",
    "       timesteps = [2, 512] -> [2*f, 512]\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8e07f113-0833-4fe1-83aa-27202b24fa90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21600"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51edd037-1169-45ab-9ec9-420f0795489a",
   "metadata": {},
   "outputs": [],
   "source": [
    "FIFO Steps:\n",
    "    1. Prepare the initial noise\n",
    "    2. Prepare the latents, prompt embeds etc\n",
    "    3. Prepare the num of times fifo needs to run to get the total num frames\n",
    "    4. For each iteration, get the output frame, updated latents and write them to file. Consider the vae scale factor\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c4dc1e97-19d7-46e4-ba75-7a8dd2374cee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7863ab623e8442db9b7995bbf2d489e1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "scheduler/scheduler_config.json:   0%|          | 0.00/482 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f400b21df5e64e418b1f1bb7f1ec9c95",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sampler_cogvideox import CogVideoXDDIMScheduler\n",
    "scheduler = CogVideoXDDIMScheduler.from_pretrained(\n",
    "    \"THUDM/CogVideoX-2b\",\n",
    "    torch_dtype=torch.bfloat16,\n",
    "    subfolder=\"scheduler\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5597378b-0adc-4294-9932-41d207287ee7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import inspect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6d37561e-1120-4022-9209-482ddd526a81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Signature (model_output: torch.Tensor, timestep: int, sample: torch.Tensor, eta: float = 0.0, use_clipped_model_output: bool = False, generator=None, variance_noise: Optional[torch.Tensor] = None, return_dict: bool = True) -> Union[sampler_cogvideox.DDIMSchedulerOutput, Tuple]>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inspect.signature(scheduler.step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "94ef3a06-3fb9-4d18-8dc0-4aa03b07440c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting argparse\n",
      "  Downloading argparse-1.4.0-py2.py3-none-any.whl.metadata (2.8 kB)\n",
      "Downloading argparse-1.4.0-py2.py3-none-any.whl (23 kB)\n",
      "Installing collected packages: argparse\n",
      "Successfully installed argparse-1.4.0\n",
      "\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[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install argparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e106f11b-b37d-487c-82ae-c502877d0af6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a9f82d115ae0451e96da6df8604338de",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading pipeline components...:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ad3e5f5f80324abb9e3a9eb0e8bd1d3e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model loading\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "usage: ipykernel_launcher.py [-h] [--model_path MODEL_PATH]\n",
      "                             [--version {None,65x512x512,221x512x512,513x512x512}]\n",
      "                             [--num_frames NUM_FRAMES] [--height HEIGHT]\n",
      "                             [--width WIDTH] [--guidance_scale GUIDANCE_SCALE]\n",
      "                             [--num_sampling_steps NUM_SAMPLING_STEPS]\n",
      "                             [--queue_length QUEUE_LENGTH] [--fps FPS]\n",
      "                             [--text_prompt TEXT_PROMPT [TEXT_PROMPT ...]]\n",
      "                             [--video_length VIDEO_LENGTH]\n",
      "                             [--new_video_length NEW_VIDEO_LENGTH]\n",
      "                             [--num_partitions NUM_PARTITIONS]\n",
      "                             [--lookahead_denoising] [--output_dir OUTPUT_DIR]\n",
      "                             [--save_frames]\n",
      "ipykernel_launcher.py: error: argument --video_length/-f: invalid int value: '/root/.local/share/jupyter/runtime/kernel-e6e7f97c-fa1a-4522-871e-877e32177a8e.json'\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'tuple' object has no attribute 'tb_frame'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m/usr/lib/python3.10/argparse.py:2507\u001b[0m, in \u001b[0;36mArgumentParser._get_value\u001b[0;34m(self, action, arg_string)\u001b[0m\n\u001b[1;32m   2506\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 2507\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mtype_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg_string\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2509\u001b[0m \u001b[38;5;66;03m# ArgumentTypeErrors indicate errors\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: '/root/.local/share/jupyter/runtime/kernel-e6e7f97c-fa1a-4522-871e-877e32177a8e.json'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mArgumentError\u001b[0m                             Traceback (most recent call last)",
      "File \u001b[0;32m/usr/lib/python3.10/argparse.py:1878\u001b[0m, in \u001b[0;36mArgumentParser.parse_known_args\u001b[0;34m(self, args, namespace)\u001b[0m\n\u001b[1;32m   1877\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1878\u001b[0m     namespace, args \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_known_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnamespace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1879\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ArgumentError:\n",
      "File \u001b[0;32m/usr/lib/python3.10/argparse.py:2091\u001b[0m, in \u001b[0;36mArgumentParser._parse_known_args\u001b[0;34m(self, arg_strings, namespace)\u001b[0m\n\u001b[1;32m   2090\u001b[0m     \u001b[38;5;66;03m# consume the next optional and any arguments for it\u001b[39;00m\n\u001b[0;32m-> 2091\u001b[0m     start_index \u001b[38;5;241m=\u001b[39m \u001b[43mconsume_optional\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_index\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2093\u001b[0m \u001b[38;5;66;03m# consume any positionals following the last Optional\u001b[39;00m\n",
      "File \u001b[0;32m/usr/lib/python3.10/argparse.py:2031\u001b[0m, in \u001b[0;36mArgumentParser._parse_known_args.<locals>.consume_optional\u001b[0;34m(start_index)\u001b[0m\n\u001b[1;32m   2030\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m action, args, option_string \u001b[38;5;129;01min\u001b[39;00m action_tuples:\n\u001b[0;32m-> 2031\u001b[0m     \u001b[43mtake_action\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moption_string\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2032\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m stop\n",
      "File \u001b[0;32m/usr/lib/python3.10/argparse.py:1939\u001b[0m, in \u001b[0;36mArgumentParser._parse_known_args.<locals>.take_action\u001b[0;34m(action, argument_strings, option_string)\u001b[0m\n\u001b[1;32m   1938\u001b[0m seen_actions\u001b[38;5;241m.\u001b[39madd(action)\n\u001b[0;32m-> 1939\u001b[0m argument_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_values\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margument_strings\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1941\u001b[0m \u001b[38;5;66;03m# error if this argument is not allowed with other previously\u001b[39;00m\n\u001b[1;32m   1942\u001b[0m \u001b[38;5;66;03m# seen arguments, assuming that actions that use the default\u001b[39;00m\n\u001b[1;32m   1943\u001b[0m \u001b[38;5;66;03m# value don't really count as \"present\"\u001b[39;00m\n",
      "File \u001b[0;32m/usr/lib/python3.10/argparse.py:2474\u001b[0m, in \u001b[0;36mArgumentParser._get_values\u001b[0;34m(self, action, arg_strings)\u001b[0m\n\u001b[1;32m   2473\u001b[0m arg_string, \u001b[38;5;241m=\u001b[39m arg_strings\n\u001b[0;32m-> 2474\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43marg_string\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2475\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_value(action, value)\n",
      "File \u001b[0;32m/usr/lib/python3.10/argparse.py:2520\u001b[0m, in \u001b[0;36mArgumentParser._get_value\u001b[0;34m(self, action, arg_string)\u001b[0m\n\u001b[1;32m   2519\u001b[0m     msg \u001b[38;5;241m=\u001b[39m _(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minvalid \u001b[39m\u001b[38;5;132;01m%(type)s\u001b[39;00m\u001b[38;5;124m value: \u001b[39m\u001b[38;5;132;01m%(value)r\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m-> 2520\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ArgumentError(action, msg \u001b[38;5;241m%\u001b[39m args)\n\u001b[1;32m   2522\u001b[0m \u001b[38;5;66;03m# return the converted value\u001b[39;00m\n",
      "\u001b[0;31mArgumentError\u001b[0m: argument --video_length/-f: invalid int value: '/root/.local/share/jupyter/runtime/kernel-e6e7f97c-fa1a-4522-871e-877e32177a8e.json'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mSystemExit\u001b[0m                                Traceback (most recent call last)",
      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
      "Cell \u001b[0;32mIn[2], line 194\u001b[0m\n\u001b[1;32m    192\u001b[0m parser\u001b[38;5;241m.\u001b[39madd_argument(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m--save_frames\u001b[39m\u001b[38;5;124m\"\u001b[39m, action\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstore_true\u001b[39m\u001b[38;5;124m'\u001b[39m, default\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m--> 194\u001b[0m args \u001b[38;5;241m=\u001b[39m \u001b[43mparser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    196\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m args\u001b[38;5;241m.\u001b[39mnum_frames \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m4\u001b[39m\u001b[38;5;241m*\u001b[39margs\u001b[38;5;241m.\u001b[39mvideo_length \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m3\u001b[39m\n",
      "File \u001b[0;32m/usr/lib/python3.10/argparse.py:1845\u001b[0m, in \u001b[0;36mArgumentParser.parse_args\u001b[0;34m(self, args, namespace)\u001b[0m\n\u001b[1;32m   1844\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mparse_args\u001b[39m(\u001b[38;5;28mself\u001b[39m, args\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, namespace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m-> 1845\u001b[0m     args, argv \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_known_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnamespace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1846\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m argv:\n",
      "File \u001b[0;32m/usr/lib/python3.10/argparse.py:1881\u001b[0m, in \u001b[0;36mArgumentParser.parse_known_args\u001b[0;34m(self, args, namespace)\u001b[0m\n\u001b[1;32m   1880\u001b[0m         err \u001b[38;5;241m=\u001b[39m _sys\u001b[38;5;241m.\u001b[39mexc_info()[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m-> 1881\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merror\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43merr\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1882\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m/usr/lib/python3.10/argparse.py:2606\u001b[0m, in \u001b[0;36mArgumentParser.error\u001b[0;34m(self, message)\u001b[0m\n\u001b[1;32m   2605\u001b[0m args \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprog\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprog, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmessage\u001b[39m\u001b[38;5;124m'\u001b[39m: message}\n\u001b[0;32m-> 2606\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m%(prog)s\u001b[39;49;00m\u001b[38;5;124;43m: error: \u001b[39;49m\u001b[38;5;132;43;01m%(message)s\u001b[39;49;00m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/lib/python3.10/argparse.py:2593\u001b[0m, in \u001b[0;36mArgumentParser.exit\u001b[0;34m(self, status, message)\u001b[0m\n\u001b[1;32m   2592\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_print_message(message, _sys\u001b[38;5;241m.\u001b[39mstderr)\n\u001b[0;32m-> 2593\u001b[0m \u001b[43m_sys\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstatus\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mSystemExit\u001b[0m: 2",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py:2119\u001b[0m, in \u001b[0;36mInteractiveShell.showtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2116\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exception_only:\n\u001b[1;32m   2117\u001b[0m     stb \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAn exception has occurred, use \u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mtb to see \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   2118\u001b[0m            \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthe full traceback.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m-> 2119\u001b[0m     stb\u001b[38;5;241m.\u001b[39mextend(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mInteractiveTB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_exception_only\u001b[49m\u001b[43m(\u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2120\u001b[0m \u001b[43m                                                     \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m   2121\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2123\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcontains_exceptiongroup\u001b[39m(val):\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py:710\u001b[0m, in \u001b[0;36mListTB.get_exception_only\u001b[0;34m(self, etype, value)\u001b[0m\n\u001b[1;32m    702\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_exception_only\u001b[39m(\u001b[38;5;28mself\u001b[39m, etype, value):\n\u001b[1;32m    703\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Only print the exception type and message, without a traceback.\u001b[39;00m\n\u001b[1;32m    704\u001b[0m \n\u001b[1;32m    705\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    708\u001b[0m \u001b[38;5;124;03m    value : exception value\u001b[39;00m\n\u001b[1;32m    709\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 710\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mListTB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstructured_traceback\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py:568\u001b[0m, in \u001b[0;36mListTB.structured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, context)\u001b[0m\n\u001b[1;32m    565\u001b[0m     chained_exc_ids\u001b[38;5;241m.\u001b[39madd(\u001b[38;5;28mid\u001b[39m(exception[\u001b[38;5;241m1\u001b[39m]))\n\u001b[1;32m    566\u001b[0m     chained_exceptions_tb_offset \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m    567\u001b[0m     out_list \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 568\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstructured_traceback\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    569\u001b[0m \u001b[43m            \u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    570\u001b[0m \u001b[43m            \u001b[49m\u001b[43mevalue\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    571\u001b[0m \u001b[43m            \u001b[49m\u001b[43m(\u001b[49m\u001b[43metb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchained_exc_ids\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore\u001b[39;49;00m\n\u001b[1;32m    572\u001b[0m \u001b[43m            \u001b[49m\u001b[43mchained_exceptions_tb_offset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    573\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    574\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    575\u001b[0m         \u001b[38;5;241m+\u001b[39m chained_exception_message\n\u001b[1;32m    576\u001b[0m         \u001b[38;5;241m+\u001b[39m out_list)\n\u001b[1;32m    578\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out_list\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py:1435\u001b[0m, in \u001b[0;36mAutoFormattedTB.structured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1433\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1434\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtb \u001b[38;5;241m=\u001b[39m etb\n\u001b[0;32m-> 1435\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mFormattedTB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstructured_traceback\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1436\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43metb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtb_offset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumber_of_lines_of_context\u001b[49m\n\u001b[1;32m   1437\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py:1326\u001b[0m, in \u001b[0;36mFormattedTB.structured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1323\u001b[0m mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmode\n\u001b[1;32m   1324\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mode \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose_modes:\n\u001b[1;32m   1325\u001b[0m     \u001b[38;5;66;03m# Verbose modes need a full traceback\u001b[39;00m\n\u001b[0;32m-> 1326\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVerboseTB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstructured_traceback\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1327\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtb_offset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumber_of_lines_of_context\u001b[49m\n\u001b[1;32m   1328\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1329\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMinimal\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m   1330\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ListTB\u001b[38;5;241m.\u001b[39mget_exception_only(\u001b[38;5;28mself\u001b[39m, etype, value)\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py:1173\u001b[0m, in \u001b[0;36mVerboseTB.structured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1164\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstructured_traceback\u001b[39m(\n\u001b[1;32m   1165\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   1166\u001b[0m     etype: \u001b[38;5;28mtype\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1170\u001b[0m     number_of_lines_of_context: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m5\u001b[39m,\n\u001b[1;32m   1171\u001b[0m ):\n\u001b[1;32m   1172\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return a nice text document describing the traceback.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1173\u001b[0m     formatted_exception \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mformat_exception_as_a_whole\u001b[49m\u001b[43m(\u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43metb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumber_of_lines_of_context\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1174\u001b[0m \u001b[43m                                                           \u001b[49m\u001b[43mtb_offset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1176\u001b[0m     colors \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mColors  \u001b[38;5;66;03m# just a shorthand + quicker name lookup\u001b[39;00m\n\u001b[1;32m   1177\u001b[0m     colorsnormal \u001b[38;5;241m=\u001b[39m colors\u001b[38;5;241m.\u001b[39mNormal  \u001b[38;5;66;03m# used a lot\u001b[39;00m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py:1063\u001b[0m, in \u001b[0;36mVerboseTB.format_exception_as_a_whole\u001b[0;34m(self, etype, evalue, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[1;32m   1060\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tb_offset, \u001b[38;5;28mint\u001b[39m)\n\u001b[1;32m   1061\u001b[0m head \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprepare_header(\u001b[38;5;28mstr\u001b[39m(etype), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlong_header)\n\u001b[1;32m   1062\u001b[0m records \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m-> 1063\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_records\u001b[49m\u001b[43m(\u001b[49m\u001b[43metb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumber_of_lines_of_context\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtb_offset\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m etb \u001b[38;5;28;01melse\u001b[39;00m []\n\u001b[1;32m   1064\u001b[0m )\n\u001b[1;32m   1066\u001b[0m frames \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m   1067\u001b[0m skipped \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py:1131\u001b[0m, in \u001b[0;36mVerboseTB.get_records\u001b[0;34m(self, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[1;32m   1129\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m cf \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[1;32m   1130\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1131\u001b[0m         mod \u001b[38;5;241m=\u001b[39m inspect\u001b[38;5;241m.\u001b[39mgetmodule(\u001b[43mcf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtb_frame\u001b[49m)\n\u001b[1;32m   1132\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m mod \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[1;32m   1133\u001b[0m             mod_name \u001b[38;5;241m=\u001b[39m mod\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'tb_frame'"
     ]
    }
   ],
   "source": [
    "### IMPORTS\n",
    "\n",
    "import torch\n",
    "import argparse\n",
    "from pipeline_cogvideox import CogVideoXPipeline\n",
    "from transformer_cogvideox_v2 import CogVideoXTransformer3DModel\n",
    "from sampler_cogvideox import CogVideoXDDIMScheduler\n",
    "\n",
    "from tqdm import trange, tqdm\n",
    "from torchvision.utils import save_image\n",
    "\n",
    "\n",
    "checkpoint = \"THUDM/CogVideoX-2b\"\n",
    "dtype = torch.bfloat16\n",
    "device = \"cuda\"\n",
    "\n",
    "### INIT\n",
    "transformer = CogVideoXTransformer3DModel.from_pretrained(\n",
    "    checkpoint,\n",
    "    torch_dtype=dtype,\n",
    "    subfolder=\"transformer\"\n",
    ")\n",
    "scheduler = CogVideoXDDIMScheduler.from_pretrained(\n",
    "    checkpoint,\n",
    "    torch_dtype=dtype,\n",
    "    subfolder=\"scheduler\"\n",
    ")\n",
    "pipe = CogVideoXPipeline.from_pretrained(\n",
    "    checkpoint,\n",
    "    torch_dtype=dtype,\n",
    "    transformer=transformer,\n",
    "    scheduler=scheduler\n",
    ")\n",
    "\n",
    "print(\"model loading\")\n",
    "### FIFO Pipeline\n",
    "\n",
    "def shift_latents(latents, scheduler):\n",
    "    # shift latents\n",
    "    latents[:,:,:-1] = latents[:,:,1:].clone()\n",
    "\n",
    "    # add new noise to the last frame\n",
    "    latents[:,:,-1] = torch.randn_like(latents[:,:,-1]) * scheduler.init_noise_sigma\n",
    "\n",
    "    return latents\n",
    "\n",
    "\n",
    "\n",
    "def main(args):\n",
    "    # args.video_length a.k.a f = 16 or 48 need to check if to use video frames or video latents\n",
    "    # args.height\n",
    "    # args.width\n",
    "    # args.new_video_length #total num of frames in final video\n",
    "    # args.video_length #num of frames per iteration to be processed/denoised a.k.a partition size\n",
    "    # args.num_partitions\n",
    "    # args.queue_length = args.video_length * args.num_partitions\n",
    "    # args.num_sampling_steps = args.video_length * args.num_partitions\n",
    "    # args.lookahead_denoising\n",
    "    # args.max_queue_length = num of latents to keep in memory before\n",
    "    generator = torch.Generator('cuda').manual_seed(0)\n",
    "    fifo_video_frames = []\n",
    "    fifo_first_latents = []\n",
    "\n",
    "    new_video_length = (args.new_video_length - 1) // self.vae_scale_factor_temporal + 1\n",
    "    video_length = (args.video_length - 1) // self.vae_scale_factor_temporal + 1\n",
    "    \n",
    "    timesteps = pipe.scheduler.timesteps\n",
    "    timesteps = torch.flip(timesteps, [0])\n",
    "    if args.lookahead_denoising:\n",
    "        timesteps = torch.cat([torch.full((video_length//2,), timesteps[0]).to(timesteps.device), timesteps])\n",
    "\n",
    "    ## check this again\n",
    "    max_queue_length = len(timesteps)\n",
    "    bsz = 1\n",
    "    ch = pipe.transformer.config.in_channels\n",
    "    h = args.height // pipe.vae.vae_scale_factor[1]\n",
    "    w = args.width // pipe.vae.vae_scale_factor[2]\n",
    "    init_noise_shape = [bsz, ch, max_queue_length, h, w]\n",
    "    ## check this again - END\n",
    "\n",
    "    latents = self.prepare_latents(\n",
    "            batch_size * num_videos_per_prompt,\n",
    "            pipe.transformer.config.in_channels,\n",
    "            # num_frames,\n",
    "            max_queue_length,\n",
    "            args.height,\n",
    "            args.width,\n",
    "            dtype,\n",
    "            device,\n",
    "            generator,\n",
    "            None,\n",
    "        )\n",
    "\n",
    "    latents = randn_tensor(init_noise_shape, device=pipe.text_encoder.device, dtype=torch.float16)\n",
    "    \n",
    "    num_vae = (new_video_length - 1) // (video_length-1)\n",
    "    if (new_video_length - 1) % (video_length-1) != 0:\n",
    "        num_vae += 1\n",
    "    \n",
    "    num_iterations = num_vae * (video_length-1) + 1 + args.queue_length\n",
    "\n",
    "    for i in trange(num_iterations):\n",
    "        num_inference_steps_per_gpu = video_length\n",
    "        curr_timesteps = timesteps.clone()\n",
    "\n",
    "        if i < max_queue_length-1:\n",
    "            curr_timesteps[:-i-1] = curr_timesteps[-i-1]\n",
    "\n",
    "        for rank in reversed(range(2 * args.num_partitions if args.lookahead_denoising else args.num_partitions)):\n",
    "            if args.lookahead_denoising:\n",
    "                start_idx = (rank // 2) * num_inference_steps_per_gpu + (rank % 2) * (num_inference_steps_per_gpu // 2)\n",
    "            else:\n",
    "                start_idx = rank * num_inference_steps_per_gpu\n",
    "            midpoint_idx = start_idx + num_inference_steps_per_gpu // 2 + (rank % 2)\n",
    "            end_idx = start_idx + num_inference_steps_per_gpu\n",
    "            \n",
    "            t = curr_timesteps[start_idx:end_idx]\n",
    "            print(\"timesteps shape\", t.shape)\n",
    "            input_latents = latents[:,:,start_idx:end_idx].clone()\n",
    "\n",
    "            output_latents, first_latent, first_frame = pipe.fifo_onestep(prompt,\n",
    "                                    video_length=args.video_length,\n",
    "                                    height=args.height,\n",
    "                                    width=args.width,\n",
    "                                    num_inference_steps=args.num_sampling_steps,\n",
    "                                    guidance_scale=args.guidance_scale,\n",
    "                                    enable_temporal_attentions=not args.force_images,\n",
    "                                    num_images_per_prompt=1,\n",
    "                                    mask_feature=True,\n",
    "                                    latents=input_latents,\n",
    "                                    timesteps=t,\n",
    "                                    save_frames=args.save_frames,\n",
    "                                    )\n",
    "\n",
    "            if args.lookahead_denoising:\n",
    "                latents[:,:,midpoint_idx:end_idx] = output_latents[:,:,-(end_idx-midpoint_idx):]\n",
    "            else:\n",
    "                latents[:,:,start_idx:end_idx] = output_latents\n",
    "            del output_latents\n",
    "\n",
    "        latents = shift_latents(latents, videogen_pipeline.scheduler)\n",
    "\n",
    "        if i >= max_queue_length:\n",
    "            if args.save_frames:\n",
    "                output_path = os.path.join(fifo_dir, f\"frame_{i:04d}.png\")\n",
    "                imageio.mimwrite(output_path, first_frame, quality=9)  # highest quality is 10, lowest is 0\n",
    "            fifo_first_latents.append(first_latent)\n",
    "        fifo_vae_video_frames = []\n",
    "        \n",
    "    for i in range(num_vae):\n",
    "        target_latents = torch.cat(fifo_first_latents[i*(args.video_length-1):(i+1)*(args.video_length-1)+1], dim=2)\n",
    "        video = videogen_pipeline.decode_latents(target_latents)[0]\n",
    "\n",
    "        if i == 0:\n",
    "            fifo_vae_video_frames.append(video)\n",
    "        else:\n",
    "            fifo_vae_video_frames.append(video[1:])\n",
    "    \n",
    "    if num_vae > 0:\n",
    "        fifo_vae_video_frames = torch.cat(fifo_vae_video_frames, dim=0)\n",
    "        if args.output_dir is None:\n",
    "            output_vae_path = os.path.join(output_dir, \"fifo_vae.mp4\")\n",
    "        else:\n",
    "            output_vae_path = os.path.join(args.output_dir, f\"{prompt_save}.mp4\")\n",
    "        imageio.mimwrite(output_vae_path, fifo_vae_video_frames, fps=args.fps, quality=9)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    parser = argparse.ArgumentParser()\n",
    "    parser.add_argument(\"--model_path\", type=str, default='LanguageBind/Open-Sora-Plan-v1.1.0')\n",
    "    parser.add_argument(\"--version\", type=str, default=None, choices=[None, '65x512x512', '221x512x512', '513x512x512'])\n",
    "    parser.add_argument(\"--num_frames\", type=int, default=65)\n",
    "    parser.add_argument(\"--height\", type=int, default=480)\n",
    "    parser.add_argument(\"--width\", type=int, default=720)\n",
    "    # parser.add_argument(\"--ae\", type=str, default='CausalVAEModel_4x8x8')\n",
    "    # parser.add_argument(\"--text_encoder_name\", type=str, default='DeepFloyd/t5-v1_1-xxl')\n",
    "    parser.add_argument(\"--guidance_scale\", type=float, default=7.5)\n",
    "    # parser.add_argument(\"--sample_method\", type=str, default=\"DDPM\")\n",
    "    parser.add_argument(\"--num_sampling_steps\", type=int, default=50)\n",
    "    parser.add_argument(\"--queue_length\", type=int, default=17)\n",
    "    parser.add_argument(\"--fps\", type=int, default=8)\n",
    "    # parser.add_argument(\"--run_time\", type=int, default=0)\n",
    "    parser.add_argument(\"--text_prompt\", nargs='+')\n",
    "    # parser.add_argument('--force_images', action='store_true')\n",
    "    # parser.add_argument('--tile_overlap_factor', type=float, default=0.25)\n",
    "    # parser.add_argument('--enable_tiling', action='store_true')\n",
    "    # parser.add_argument(\"--cache_dir\", type=str, default=\"./cache\")\n",
    "    parser.add_argument(\"--video_length\", \"-f\", type=int, default=17)\n",
    "    parser.add_argument(\"--new_video_length\", \"-N\", type=int, default=None)\n",
    "    parser.add_argument(\"--num_partitions\", \"-n\", type=int, default=4)\n",
    "    parser.add_argument(\"--lookahead_denoising\", \"-ld\", action='store_false', default=True)\n",
    "    parser.add_argument(\"--output_dir\", type=str, default=None, help=\"custom output directory\")\n",
    "    parser.add_argument(\"--save_frames\", action='store_true', default=False)\n",
    "\n",
    "    args = parser.parse_args()\n",
    "\n",
    "    assert args.num_frames == 4*args.video_length - 3\n",
    "\n",
    "    args.queue_length = args.video_length * args.num_partitions\n",
    "    args.num_sampling_steps = args.video_length * args.num_partitions\n",
    "\n",
    "    main(args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4fcad4b8-3c32-4eef-88d5-23b8649b3aba",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f497213997ce4b93959859e08e9b932c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading pipeline components...:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b9f8b820a1c044a4aceb2bacd9846098",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "from diffusers import CogVideoXPipeline\n",
    "from diffusers.utils import export_to_video\n",
    "\n",
    "prompt = \"A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance.\"\n",
    "\n",
    "pipe = CogVideoXPipeline.from_pretrained(\n",
    "    \"THUDM/CogVideoX-2b\",\n",
    "    torch_dtype=torch.float16\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3becec10-b380-49b6-9b06-e1aa39aa5e8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CogVideoXPipeline {\n",
       "  \"_class_name\": \"CogVideoXPipeline\",\n",
       "  \"_diffusers_version\": \"0.31.0.dev0\",\n",
       "  \"_name_or_path\": \"THUDM/CogVideoX-2b\",\n",
       "  \"scheduler\": [\n",
       "    \"diffusers\",\n",
       "    \"CogVideoXDDIMScheduler\"\n",
       "  ],\n",
       "  \"text_encoder\": [\n",
       "    \"transformers\",\n",
       "    \"T5EncoderModel\"\n",
       "  ],\n",
       "  \"tokenizer\": [\n",
       "    \"transformers\",\n",
       "    \"T5Tokenizer\"\n",
       "  ],\n",
       "  \"transformer\": [\n",
       "    \"diffusers\",\n",
       "    \"CogVideoXTransformer3DModel\"\n",
       "  ],\n",
       "  \"vae\": [\n",
       "    \"diffusers\",\n",
       "    \"AutoencoderKLCogVideoX\"\n",
       "  ]\n",
       "}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.to('cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "07153ca5-1b99-432a-9073-fbfaa993939d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2207c71141e04d5a8cdc2adc9333c1f7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "video = pipe(\n",
    "    prompt=prompt,\n",
    "    num_videos_per_prompt=1,\n",
    "    num_inference_steps=50,\n",
    "    num_frames=5,\n",
    "    guidance_scale=6,\n",
    "    generator=torch.Generator(device=\"cuda\").manual_seed(42),\n",
    "    output_type='latent',\n",
    ").frames[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c215d930-5ecc-4138-91e7-95d3d40f95c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.float16"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c48765f8-aa5b-4a47-9efe-3d586cf3e94f",
   "metadata": {},
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    out = pipe.decode_latents(video.unsqueeze(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5e0e8f05-f1ea-47e1-b5de-86ea4db5e63a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 3, 8, 480, 720])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8264e23a-fafb-4f98-a2ea-1e31d101ffab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e5b7be59-8155-41ff-bdb6-8861f65e49b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2fa0de3e0a0f47888fe848a5a0166a05",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading pipeline components...:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b141876423de4fb88967923362c155ad",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "from diffusers import CogVideoXPipeline\n",
    "from diffusers.utils import export_to_video\n",
    "\n",
    "prompt = \"A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance.\"\n",
    "\n",
    "pipe = CogVideoXPipeline.from_pretrained(\n",
    "    \"THUDM/CogVideoX-2b\",\n",
    "    torch_dtype=torch.float16\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2271608b-5abe-4286-96cf-700dcba5df1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe.vae = pipe.vae.to('cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "34dd523f-5f55-46f7-9737-6f9936d214f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "tensor = torch.randn((1,1,16,60,90)).to('cuda').half()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5bcaceff-8fca-4f42-8987-0d1edd378ebf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 16, 1, 60, 90])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensor.permute(0,2,1,3,4).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a01fc84a-661f-4675-ac62-a32e7170732e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe.vae.enable_tiling()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9f3afdf2-0127-4cea-bcac-d03f2bc27e77",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 16, 2, 60, 90])\n",
      "frame_batch_size 2\n",
      "num_batches 1\n"
     ]
    },
    {
     "ename": "OutOfMemoryError",
     "evalue": "CUDA out of memory. Tried to allocate 132.00 MiB. GPU 0 has a total capacty of 44.34 GiB of which 86.81 MiB is free. Process 660831 has 44.25 GiB memory in use. Of the allocated memory 43.35 GiB is allocated by PyTorch, and 587.99 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large 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[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mpipe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode_latents\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvideo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munsqueeze\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/diffusers/pipelines/cogvideo/pipeline_cogvideox.py:350\u001b[0m, in \u001b[0;36mCogVideoXPipeline.decode_latents\u001b[0;34m(self, latents)\u001b[0m\n\u001b[1;32m    347\u001b[0m latents \u001b[38;5;241m=\u001b[39m latents\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m4\u001b[39m)  \u001b[38;5;66;03m# [batch_size, num_channels, num_frames, height, width]\u001b[39;00m\n\u001b[1;32m    348\u001b[0m latents \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvae_scaling_factor_image \u001b[38;5;241m*\u001b[39m latents\n\u001b[0;32m--> 350\u001b[0m frames \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvae\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlatents\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39msample\n\u001b[1;32m    351\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m frames\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/diffusers/utils/accelerate_utils.py:46\u001b[0m, in \u001b[0;36mapply_forward_hook.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_hf_hook\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_hf_hook, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpre_forward\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m     45\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpre_forward(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m---> 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\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",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py:1279\u001b[0m, in \u001b[0;36mAutoencoderKLCogVideoX.decode\u001b[0;34m(self, z, return_dict)\u001b[0m\n\u001b[1;32m   1277\u001b[0m     decoded \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat(decoded_slices)\n\u001b[1;32m   1278\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1279\u001b[0m     decoded \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_decode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mz\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39msample\n\u001b[1;32m   1281\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m return_dict:\n\u001b[1;32m   1282\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m (decoded,)\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py:1250\u001b[0m, in \u001b[0;36mAutoencoderKLCogVideoX._decode\u001b[0;34m(self, z, return_dict)\u001b[0m\n\u001b[1;32m   1248\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpost_quant_conv \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[1;32m   1249\u001b[0m         z_intermediate \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpost_quant_conv(z_intermediate)\n\u001b[0;32m-> 1250\u001b[0m     z_intermediate, conv_cache \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecoder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mz_intermediate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconv_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconv_cache\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1251\u001b[0m     dec\u001b[38;5;241m.\u001b[39mappend(z_intermediate)\n\u001b[1;32m   1253\u001b[0m dec \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat(dec, dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\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[43m_call_impl\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",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\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   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\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   1525\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   1526\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-> 1527\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   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py:970\u001b[0m, in \u001b[0;36mCogVideoXDecoder3D.forward\u001b[0;34m(self, sample, temb, conv_cache)\u001b[0m\n\u001b[1;32m    968\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, up_block \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mup_blocks):\n\u001b[1;32m    969\u001b[0m         conv_cache_key \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mup_block_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 970\u001b[0m         hidden_states, new_conv_cache[conv_cache_key] \u001b[38;5;241m=\u001b[39m \u001b[43mup_block\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    971\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconv_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconv_cache\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconv_cache_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    972\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    974\u001b[0m \u001b[38;5;66;03m# 3. Post-process\u001b[39;00m\n\u001b[1;32m    975\u001b[0m hidden_states, new_conv_cache[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnorm_out\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnorm_out(\n\u001b[1;32m    976\u001b[0m     hidden_states, sample, conv_cache\u001b[38;5;241m=\u001b[39mconv_cache\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnorm_out\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    977\u001b[0m )\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\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[43m_call_impl\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",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\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   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\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   1525\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   1526\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-> 1527\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   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py:647\u001b[0m, in \u001b[0;36mCogVideoXUpBlock3D.forward\u001b[0;34m(self, hidden_states, temb, zq, conv_cache)\u001b[0m\n\u001b[1;32m    639\u001b[0m         hidden_states, new_conv_cache[conv_cache_key] \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39mcheckpoint\u001b[38;5;241m.\u001b[39mcheckpoint(\n\u001b[1;32m    640\u001b[0m             create_custom_forward(resnet),\n\u001b[1;32m    641\u001b[0m             hidden_states,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    644\u001b[0m             conv_cache\u001b[38;5;241m=\u001b[39mconv_cache\u001b[38;5;241m.\u001b[39mget(conv_cache_key),\n\u001b[1;32m    645\u001b[0m         )\n\u001b[1;32m    646\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 647\u001b[0m         hidden_states, new_conv_cache[conv_cache_key] \u001b[38;5;241m=\u001b[39m \u001b[43mresnet\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    648\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mzq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconv_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconv_cache\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconv_cache_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    649\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    651\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mupsamplers \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[1;32m    652\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m upsampler \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mupsamplers:\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\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[43m_call_impl\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",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\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   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\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   1525\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   1526\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-> 1527\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   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py:296\u001b[0m, in \u001b[0;36mCogVideoXResnetBlock3D.forward\u001b[0;34m(self, inputs, temb, zq, conv_cache)\u001b[0m\n\u001b[1;32m    293\u001b[0m     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    295\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnonlinearity(hidden_states)\n\u001b[0;32m--> 296\u001b[0m hidden_states, new_conv_cache[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconv1\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconv_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconv_cache\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mconv1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    298\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m temb \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[1;32m    299\u001b[0m     hidden_states \u001b[38;5;241m=\u001b[39m hidden_states \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtemb_proj(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnonlinearity(temb))[:, :, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m]\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\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[43m_call_impl\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",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\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   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\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   1525\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   1526\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-> 1527\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   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py:134\u001b[0m, in \u001b[0;36mCogVideoXCausalConv3d.forward\u001b[0;34m(self, inputs, conv_cache)\u001b[0m\n\u001b[1;32m    131\u001b[0m conv_cache \u001b[38;5;241m=\u001b[39m inputs[:, :, \u001b[38;5;241m-\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtime_kernel_size \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m :]\u001b[38;5;241m.\u001b[39mclone()\n\u001b[1;32m    133\u001b[0m padding_2d \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwidth_pad, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwidth_pad, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mheight_pad, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mheight_pad)\n\u001b[0;32m--> 134\u001b[0m inputs \u001b[38;5;241m=\u001b[39m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpad\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpadding_2d\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mconstant\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    136\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconv(inputs)\n\u001b[1;32m    137\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m output, conv_cache\n",
      "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 132.00 MiB. GPU 0 has a total capacty of 44.34 GiB of which 86.81 MiB is free. Process 660831 has 44.25 GiB memory in use. Of the allocated memory 43.35 GiB is allocated by PyTorch, and 587.99 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF"
     ]
    }
   ],
   "source": [
    "out = pipe.decode_latents(video.unsqueeze(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3abd5593-8ebf-4484-b70f-ac4505e33de7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 2, 16, 60, 90])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video.unsqueeze(0).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1d1f1cad-18dc-4832-b63f-c5af7259a788",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sun Sep 29 15:30:16 2024       \n",
      "+-----------------------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 550.90.07              Driver Version: 550.90.07      CUDA Version: 12.4     |\n",
      "|-----------------------------------------+------------------------+----------------------+\n",
      "| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |\n",
      "|                                         |                        |               MIG M. |\n",
      "|=========================================+========================+======================|\n",
      "|   0  NVIDIA A40                     On  |   00000000:57:00.0 Off |                    0 |\n",
      "|  0%   39C    P0             76W /  300W |   32855MiB /  46068MiB |      0%      Default |\n",
      "|                                         |                        |                  N/A |\n",
      "+-----------------------------------------+------------------------+----------------------+\n",
      "                                                                                         \n",
      "+-----------------------------------------------------------------------------------------+\n",
      "| Processes:                                                                              |\n",
      "|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |\n",
      "|        ID   ID                                                               Usage      |\n",
      "|=========================================================================================|\n",
      "+-----------------------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  }
 ],
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