pred_diffusion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "from inspect import isfunction\n",
    "from functools import partial\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm.auto import tqdm\n",
    "from einops import rearrange\n",
    "\n",
    "import torch\n",
    "from torch import nn, einsum\n",
    "import torch.nn.functional as F\n",
    "from torchvision.transforms import Compose, ToTensor, Lambda, ToPILImage, CenterCrop, Resize,Normalize\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from models.resnet_attn import Unet\n",
    "image_size = 128\n",
    "channels = 3\n",
    "model = Unet(\n",
    "    dim=128,\n",
    "    channels=3,\n",
    "    resnet_block_groups=8,\n",
    "    use_convnext=False,\n",
    "    dim_mults=(1, 2, 4,)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean=torch.tensor([0.4850, 0.4560, 0.4060])\n",
    "std=torch.tensor([0.2290, 0.2240, 0.2250])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "postprocess = Compose([\n",
    "     Lambda(lambda t: (t.cpu() * std.reshape(3,1,1))+mean.reshape(3,1,1)),\n",
    "     Lambda(lambda t: t.permute(1, 2, 0)), # CHW to HWC\n",
    "     Lambda(lambda t: t * 255.),\n",
    "     Lambda(lambda t: t.cpu().numpy().astype(np.uint8)),\n",
    "     ToPILImage(),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def cos_diffusion_schedule(diffusion_times):\n",
    "    # diffusion times -> angles\n",
    "    min_signal_rate = torch.tensor(0.02).to(diffusion_times)\n",
    "    max_signal_rate = torch.tensor(0.95).to(diffusion_times)\n",
    "    start_angle = torch.acos(max_signal_rate)\n",
    "    end_angle = torch.acos(min_signal_rate)\n",
    "    diffusion_angles = start_angle + diffusion_times * (end_angle - start_angle)\n",
    "    # angles -> signal and noise rates\n",
    "    signal_rates = torch.cos(diffusion_angles).to(diffusion_times)\n",
    "    noise_rates = torch.sin(diffusion_angles).to(diffusion_times)\n",
    "    # note that their squared sum is always: sin^2(x) + cos^2(x) = 1\n",
    "    return noise_rates, signal_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pytorch_lightning as pl\n",
    "from ema_pytorch import EMA\n",
    "\n",
    "class Diffusion(pl.LightningModule):\n",
    "\n",
    "    def __init__(self, model, lr, weight_decay):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.model = model\n",
    "        self.lr = lr\n",
    "        self.weight_decay = weight_decay\n",
    "        \n",
    "        self.ema_network = EMA (\n",
    "        self.model,\n",
    "        beta = 0.999,              # exponential moving average factor\n",
    "        update_after_step = 1,    # only after this number of .update() calls will it start updating\n",
    "        update_every = 1,          # how often to actually update, to save on compute (updates every 10th .update() call)\n",
    "        )\n",
    "        self.gidx=1\n",
    "    \n",
    "    \n",
    "    def denoise(self,noisy_images, noise_rates, signal_rates,training=True):\n",
    "        # predict noise component and calculate the image component using it\n",
    "        if training:\n",
    "            network = self.model\n",
    "        else:\n",
    "            network = self.ema_network\n",
    "        pred_noises = network(noisy_images, noise_rates.reshape((noise_rates.shape[0]),)**2)\n",
    "        pred_images = (noisy_images - noise_rates * pred_noises) / signal_rates\n",
    "        return pred_noises, pred_images\n",
    "    \n",
    "    \n",
    "    def forward(self, noisy_images, noisy_rates):\n",
    "        outputs = self.model(noisy_images, noisy_rates)\n",
    "        return outputs\n",
    "    \n",
    "    def losses(self,noise,predicted_noise, loss_type=\"l1\"):\n",
    "        if loss_type == 'l1':\n",
    "            loss = F.l1_loss(noise, predicted_noise)\n",
    "        elif loss_type == 'l2':\n",
    "            loss = F.mse_loss(noise, predicted_noise)\n",
    "        elif loss_type == \"huber\":\n",
    "            loss = F.smooth_l1_loss(noise, predicted_noise)\n",
    "        else:\n",
    "            raise NotImplementedError()\n",
    "        return loss\n",
    "    \n",
    "    @torch.no_grad()\n",
    "    def get_noise(self,images):\n",
    "        noises = torch.randn((batch_size,channels, image_size, image_size)).to(images)\n",
    "        diffusion_times = torch.rand((batch_size,1,1,1)).to(images) \n",
    "        noise_rates, signal_rates = cos_diffusion_schedule(diffusion_times)\n",
    "        noisy_images = signal_rates * images + noise_rates * noises\n",
    "        return noises,noise_rates,noisy_images,signal_rates\n",
    "        \n",
    "     \n",
    "    def common_step(self, batch, batch_idx):\n",
    "        images = batch[\"pixel_values\"]\n",
    "        noises,noise_rates, noisy_images,signal_rates=self.get_noise(images)\n",
    "        pred_noises, pred_images = self.denoise(noisy_images, noise_rates, signal_rates)\n",
    "        noise_loss = self.losses(noises, pred_noises)\n",
    "        image_loss = self.losses(images, pred_images)\n",
    "        return noise_loss,{\"NoiseLoss\":noise_loss,\"ImageLoss\":image_loss}\n",
    "    \n",
    "    def training_step(self, batch, batch_idx):\n",
    "        loss, loss_dict = self.common_step(batch, batch_idx)     \n",
    "        # logs metrics for each training_step,\n",
    "        # and the average across the epoch\n",
    "        self.log(\"training_loss\", loss)\n",
    "        for k,v in loss_dict.items():\n",
    "            self.log(\"train_\" + k, v.item())\n",
    "        self.ema_network.update()\n",
    "        return loss\n",
    "    \n",
    "    @torch.no_grad()\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        loss, loss_dict = self.common_step(batch, batch_idx)     \n",
    "        self.log(\"validation_loss\", loss)\n",
    "        for k,v in loss_dict.items():\n",
    "            self.log(\"validation_\" + k, v.item())\n",
    "        return loss\n",
    "    \n",
    "    def validation_epoch_end(self,validation_step_outputs):\n",
    "        self.eval()\n",
    "        images=generate(self,24,200,device=\"cuda\")\n",
    "        rows=len(images)//4\n",
    "        cols=len(images)//rows\n",
    "        plt=plot_images(images,rows,cols)\n",
    "        plt.savefig(os.path.join(\"./lightning_logs/simple_unet_celeb\",f\"{self.gidx}\"+\"_res.png\"))\n",
    "        self.train()\n",
    "        self.gidx+=1\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = torch.optim.AdamW(self.model.parameters(), lr=self.lr,weight_decay=self.weight_decay)\n",
    "        return optimizer\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        return train_dataloader\n",
    "\n",
    "    def val_dataloader(self):\n",
    "        return val_dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ckpt_file_path=\"lightning_logs/resnet_attnention/checkpoints/flowers_diffusion-epoch=197-validation_loss=0.1135.ckpt\"\n",
    "DModel= Diffusion.load_from_checkpoint(ckpt_file_path,model=model, lr=1e-3, weight_decay=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Diffusion(\n",
       "  (model): Unet(\n",
       "    (init_conv): Conv2d(3, 84, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "    (time_mlp): Sequential(\n",
       "      (0): SinusoidalPositionEmbeddings()\n",
       "      (1): Linear(in_features=128, out_features=512, bias=True)\n",
       "      (2): GELU()\n",
       "      (3): Linear(in_features=512, out_features=512, bias=True)\n",
       "    )\n",
       "    (downs): ModuleList(\n",
       "      (0): ModuleList(\n",
       "        (0): ResnetBlock(\n",
       "          (mlp): Sequential(\n",
       "            (0): SiLU()\n",
       "            (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "          )\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(84, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Conv2d(84, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "        )\n",
       "        (1): ResnetBlock(\n",
       "          (mlp): Sequential(\n",
       "            (0): SiLU()\n",
       "            (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "          )\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Identity()\n",
       "        )\n",
       "        (2): Residual(\n",
       "          (fn): PreNorm(\n",
       "            (fn): LinearAttention(\n",
       "              (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "              (to_out): Sequential(\n",
       "                (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "                (1): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "              )\n",
       "            )\n",
       "            (norm): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "          )\n",
       "        )\n",
       "        (3): Conv2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "      )\n",
       "      (1): ModuleList(\n",
       "        (0): ResnetBlock(\n",
       "          (mlp): Sequential(\n",
       "            (0): SiLU()\n",
       "            (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "          )\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "        )\n",
       "        (1): ResnetBlock(\n",
       "          (mlp): Sequential(\n",
       "            (0): SiLU()\n",
       "            (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "          )\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Identity()\n",
       "        )\n",
       "        (2): Residual(\n",
       "          (fn): PreNorm(\n",
       "            (fn): LinearAttention(\n",
       "              (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "              (to_out): Sequential(\n",
       "                (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "                (1): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "              )\n",
       "            )\n",
       "            (norm): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "          )\n",
       "        )\n",
       "        (3): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "      )\n",
       "      (2): ModuleList(\n",
       "        (0): ResnetBlock(\n",
       "          (mlp): Sequential(\n",
       "            (0): SiLU()\n",
       "            (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        )\n",
       "        (1): ResnetBlock(\n",
       "          (mlp): Sequential(\n",
       "            (0): SiLU()\n",
       "            (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Identity()\n",
       "        )\n",
       "        (2): Residual(\n",
       "          (fn): PreNorm(\n",
       "            (fn): LinearAttention(\n",
       "              (to_qkv): Conv2d(512, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "              (to_out): Sequential(\n",
       "                (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "                (1): GroupNorm(1, 512, eps=1e-05, affine=True)\n",
       "              )\n",
       "            )\n",
       "            (norm): GroupNorm(1, 512, eps=1e-05, affine=True)\n",
       "          )\n",
       "        )\n",
       "        (3): Identity()\n",
       "      )\n",
       "    )\n",
       "    (ups): ModuleList(\n",
       "      (0): ModuleList(\n",
       "        (0): ResnetBlock(\n",
       "          (mlp): Sequential(\n",
       "            (0): SiLU()\n",
       "            (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "          )\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "        )\n",
       "        (1): ResnetBlock(\n",
       "          (mlp): Sequential(\n",
       "            (0): SiLU()\n",
       "            (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "          )\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Identity()\n",
       "        )\n",
       "        (2): Residual(\n",
       "          (fn): PreNorm(\n",
       "            (fn): LinearAttention(\n",
       "              (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "              (to_out): Sequential(\n",
       "                (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "                (1): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "              )\n",
       "            )\n",
       "            (norm): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "          )\n",
       "        )\n",
       "        (3): ConvTranspose2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "      )\n",
       "      (1): ModuleList(\n",
       "        (0): ResnetBlock(\n",
       "          (mlp): Sequential(\n",
       "            (0): SiLU()\n",
       "            (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "          )\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "        )\n",
       "        (1): ResnetBlock(\n",
       "          (mlp): Sequential(\n",
       "            (0): SiLU()\n",
       "            (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "          )\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Identity()\n",
       "        )\n",
       "        (2): Residual(\n",
       "          (fn): PreNorm(\n",
       "            (fn): LinearAttention(\n",
       "              (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "              (to_out): Sequential(\n",
       "                (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "                (1): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "              )\n",
       "            )\n",
       "            (norm): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "          )\n",
       "        )\n",
       "        (3): ConvTranspose2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "      )\n",
       "    )\n",
       "    (mid_block1): ResnetBlock(\n",
       "      (mlp): Sequential(\n",
       "        (0): SiLU()\n",
       "        (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "      )\n",
       "      (block1): Block(\n",
       "        (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "        (act): SiLU()\n",
       "      )\n",
       "      (block2): Block(\n",
       "        (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "        (act): SiLU()\n",
       "      )\n",
       "      (res_conv): Identity()\n",
       "    )\n",
       "    (mid_attn): Residual(\n",
       "      (fn): PreNorm(\n",
       "        (fn): Attention(\n",
       "          (to_qkv): Conv2d(512, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (to_out): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        )\n",
       "        (norm): GroupNorm(1, 512, eps=1e-05, affine=True)\n",
       "      )\n",
       "    )\n",
       "    (mid_block2): ResnetBlock(\n",
       "      (mlp): Sequential(\n",
       "        (0): SiLU()\n",
       "        (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "      )\n",
       "      (block1): Block(\n",
       "        (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "        (act): SiLU()\n",
       "      )\n",
       "      (block2): Block(\n",
       "        (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "        (act): SiLU()\n",
       "      )\n",
       "      (res_conv): Identity()\n",
       "    )\n",
       "    (final_conv): Sequential(\n",
       "      (0): ResnetBlock(\n",
       "        (block1): Block(\n",
       "          (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (block2): Block(\n",
       "          (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (res_conv): Identity()\n",
       "      )\n",
       "      (1): Conv2d(128, 3, kernel_size=(1, 1), stride=(1, 1))\n",
       "    )\n",
       "  )\n",
       "  (ema_network): EMA(\n",
       "    (online_model): Unet(\n",
       "      (init_conv): Conv2d(3, 84, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "      (time_mlp): Sequential(\n",
       "        (0): SinusoidalPositionEmbeddings()\n",
       "        (1): Linear(in_features=128, out_features=512, bias=True)\n",
       "        (2): GELU()\n",
       "        (3): Linear(in_features=512, out_features=512, bias=True)\n",
       "      )\n",
       "      (downs): ModuleList(\n",
       "        (0): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(84, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv2d(84, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "                  (1): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "                )\n",
       "              )\n",
       "              (norm): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "            )\n",
       "          )\n",
       "          (3): Conv2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "        )\n",
       "        (1): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "                  (1): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "                )\n",
       "              )\n",
       "              (norm): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "            )\n",
       "          )\n",
       "          (3): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "        )\n",
       "        (2): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv2d(512, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "                  (1): GroupNorm(1, 512, eps=1e-05, affine=True)\n",
       "                )\n",
       "              )\n",
       "              (norm): GroupNorm(1, 512, eps=1e-05, affine=True)\n",
       "            )\n",
       "          )\n",
       "          (3): Identity()\n",
       "        )\n",
       "      )\n",
       "      (ups): ModuleList(\n",
       "        (0): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "                  (1): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "                )\n",
       "              )\n",
       "              (norm): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "            )\n",
       "          )\n",
       "          (3): ConvTranspose2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "        )\n",
       "        (1): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "                  (1): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "                )\n",
       "              )\n",
       "              (norm): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "            )\n",
       "          )\n",
       "          (3): ConvTranspose2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "        )\n",
       "      )\n",
       "      (mid_block1): ResnetBlock(\n",
       "        (mlp): Sequential(\n",
       "          (0): SiLU()\n",
       "          (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (block1): Block(\n",
       "          (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (block2): Block(\n",
       "          (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (res_conv): Identity()\n",
       "      )\n",
       "      (mid_attn): Residual(\n",
       "        (fn): PreNorm(\n",
       "          (fn): Attention(\n",
       "            (to_qkv): Conv2d(512, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (to_out): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (norm): GroupNorm(1, 512, eps=1e-05, affine=True)\n",
       "        )\n",
       "      )\n",
       "      (mid_block2): ResnetBlock(\n",
       "        (mlp): Sequential(\n",
       "          (0): SiLU()\n",
       "          (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (block1): Block(\n",
       "          (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (block2): Block(\n",
       "          (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (res_conv): Identity()\n",
       "      )\n",
       "      (final_conv): Sequential(\n",
       "        (0): ResnetBlock(\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Identity()\n",
       "        )\n",
       "        (1): Conv2d(128, 3, kernel_size=(1, 1), stride=(1, 1))\n",
       "      )\n",
       "    )\n",
       "    (ema_model): Unet(\n",
       "      (init_conv): Conv2d(3, 84, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "      (time_mlp): Sequential(\n",
       "        (0): SinusoidalPositionEmbeddings()\n",
       "        (1): Linear(in_features=128, out_features=512, bias=True)\n",
       "        (2): GELU()\n",
       "        (3): Linear(in_features=512, out_features=512, bias=True)\n",
       "      )\n",
       "      (downs): ModuleList(\n",
       "        (0): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(84, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv2d(84, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "                  (1): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "                )\n",
       "              )\n",
       "              (norm): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "            )\n",
       "          )\n",
       "          (3): Conv2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "        )\n",
       "        (1): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "                  (1): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "                )\n",
       "              )\n",
       "              (norm): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "            )\n",
       "          )\n",
       "          (3): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "        )\n",
       "        (2): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv2d(512, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "                  (1): GroupNorm(1, 512, eps=1e-05, affine=True)\n",
       "                )\n",
       "              )\n",
       "              (norm): GroupNorm(1, 512, eps=1e-05, affine=True)\n",
       "            )\n",
       "          )\n",
       "          (3): Identity()\n",
       "        )\n",
       "      )\n",
       "      (ups): ModuleList(\n",
       "        (0): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "                  (1): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "                )\n",
       "              )\n",
       "              (norm): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "            )\n",
       "          )\n",
       "          (3): ConvTranspose2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "        )\n",
       "        (1): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "                  (1): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "                )\n",
       "              )\n",
       "              (norm): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "            )\n",
       "          )\n",
       "          (3): ConvTranspose2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "        )\n",
       "      )\n",
       "      (mid_block1): ResnetBlock(\n",
       "        (mlp): Sequential(\n",
       "          (0): SiLU()\n",
       "          (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (block1): Block(\n",
       "          (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (block2): Block(\n",
       "          (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (res_conv): Identity()\n",
       "      )\n",
       "      (mid_attn): Residual(\n",
       "        (fn): PreNorm(\n",
       "          (fn): Attention(\n",
       "            (to_qkv): Conv2d(512, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (to_out): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (norm): GroupNorm(1, 512, eps=1e-05, affine=True)\n",
       "        )\n",
       "      )\n",
       "      (mid_block2): ResnetBlock(\n",
       "        (mlp): Sequential(\n",
       "          (0): SiLU()\n",
       "          (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (block1): Block(\n",
       "          (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (block2): Block(\n",
       "          (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (res_conv): Identity()\n",
       "      )\n",
       "      (final_conv): Sequential(\n",
       "        (0): ResnetBlock(\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Identity()\n",
       "        )\n",
       "        (1): Conv2d(128, 3, kernel_size=(1, 1), stride=(1, 1))\n",
       "      )\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Diffusion(\n",
       "  (model): Unet(\n",
       "    (init_conv): Conv2d(3, 84, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "    (time_mlp): Sequential(\n",
       "      (0): SinusoidalPositionEmbeddings()\n",
       "      (1): Linear(in_features=128, out_features=512, bias=True)\n",
       "      (2): GELU()\n",
       "      (3): Linear(in_features=512, out_features=512, bias=True)\n",
       "    )\n",
       "    (downs): ModuleList(\n",
       "      (0): ModuleList(\n",
       "        (0): ResnetBlock(\n",
       "          (mlp): Sequential(\n",
       "            (0): SiLU()\n",
       "            (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "          )\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(84, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Conv2d(84, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "        )\n",
       "        (1): ResnetBlock(\n",
       "          (mlp): Sequential(\n",
       "            (0): SiLU()\n",
       "            (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "          )\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Identity()\n",
       "        )\n",
       "        (2): Residual(\n",
       "          (fn): PreNorm(\n",
       "            (fn): LinearAttention(\n",
       "              (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "              (to_out): Sequential(\n",
       "                (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "                (1): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "              )\n",
       "            )\n",
       "            (norm): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "          )\n",
       "        )\n",
       "        (3): Conv2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "      )\n",
       "      (1): ModuleList(\n",
       "        (0): ResnetBlock(\n",
       "          (mlp): Sequential(\n",
       "            (0): SiLU()\n",
       "            (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "          )\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "        )\n",
       "        (1): ResnetBlock(\n",
       "          (mlp): Sequential(\n",
       "            (0): SiLU()\n",
       "            (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "          )\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Identity()\n",
       "        )\n",
       "        (2): Residual(\n",
       "          (fn): PreNorm(\n",
       "            (fn): LinearAttention(\n",
       "              (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "              (to_out): Sequential(\n",
       "                (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "                (1): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "              )\n",
       "            )\n",
       "            (norm): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "          )\n",
       "        )\n",
       "        (3): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "      )\n",
       "      (2): ModuleList(\n",
       "        (0): ResnetBlock(\n",
       "          (mlp): Sequential(\n",
       "            (0): SiLU()\n",
       "            (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        )\n",
       "        (1): ResnetBlock(\n",
       "          (mlp): Sequential(\n",
       "            (0): SiLU()\n",
       "            (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Identity()\n",
       "        )\n",
       "        (2): Residual(\n",
       "          (fn): PreNorm(\n",
       "            (fn): LinearAttention(\n",
       "              (to_qkv): Conv2d(512, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "              (to_out): Sequential(\n",
       "                (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "                (1): GroupNorm(1, 512, eps=1e-05, affine=True)\n",
       "              )\n",
       "            )\n",
       "            (norm): GroupNorm(1, 512, eps=1e-05, affine=True)\n",
       "          )\n",
       "        )\n",
       "        (3): Identity()\n",
       "      )\n",
       "    )\n",
       "    (ups): ModuleList(\n",
       "      (0): ModuleList(\n",
       "        (0): ResnetBlock(\n",
       "          (mlp): Sequential(\n",
       "            (0): SiLU()\n",
       "            (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "          )\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "        )\n",
       "        (1): ResnetBlock(\n",
       "          (mlp): Sequential(\n",
       "            (0): SiLU()\n",
       "            (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "          )\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Identity()\n",
       "        )\n",
       "        (2): Residual(\n",
       "          (fn): PreNorm(\n",
       "            (fn): LinearAttention(\n",
       "              (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "              (to_out): Sequential(\n",
       "                (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "                (1): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "              )\n",
       "            )\n",
       "            (norm): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "          )\n",
       "        )\n",
       "        (3): ConvTranspose2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "      )\n",
       "      (1): ModuleList(\n",
       "        (0): ResnetBlock(\n",
       "          (mlp): Sequential(\n",
       "            (0): SiLU()\n",
       "            (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "          )\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "        )\n",
       "        (1): ResnetBlock(\n",
       "          (mlp): Sequential(\n",
       "            (0): SiLU()\n",
       "            (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "          )\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Identity()\n",
       "        )\n",
       "        (2): Residual(\n",
       "          (fn): PreNorm(\n",
       "            (fn): LinearAttention(\n",
       "              (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "              (to_out): Sequential(\n",
       "                (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "                (1): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "              )\n",
       "            )\n",
       "            (norm): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "          )\n",
       "        )\n",
       "        (3): ConvTranspose2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "      )\n",
       "    )\n",
       "    (mid_block1): ResnetBlock(\n",
       "      (mlp): Sequential(\n",
       "        (0): SiLU()\n",
       "        (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "      )\n",
       "      (block1): Block(\n",
       "        (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "        (act): SiLU()\n",
       "      )\n",
       "      (block2): Block(\n",
       "        (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "        (act): SiLU()\n",
       "      )\n",
       "      (res_conv): Identity()\n",
       "    )\n",
       "    (mid_attn): Residual(\n",
       "      (fn): PreNorm(\n",
       "        (fn): Attention(\n",
       "          (to_qkv): Conv2d(512, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (to_out): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        )\n",
       "        (norm): GroupNorm(1, 512, eps=1e-05, affine=True)\n",
       "      )\n",
       "    )\n",
       "    (mid_block2): ResnetBlock(\n",
       "      (mlp): Sequential(\n",
       "        (0): SiLU()\n",
       "        (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "      )\n",
       "      (block1): Block(\n",
       "        (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "        (act): SiLU()\n",
       "      )\n",
       "      (block2): Block(\n",
       "        (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "        (act): SiLU()\n",
       "      )\n",
       "      (res_conv): Identity()\n",
       "    )\n",
       "    (final_conv): Sequential(\n",
       "      (0): ResnetBlock(\n",
       "        (block1): Block(\n",
       "          (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (block2): Block(\n",
       "          (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (res_conv): Identity()\n",
       "      )\n",
       "      (1): Conv2d(128, 3, kernel_size=(1, 1), stride=(1, 1))\n",
       "    )\n",
       "  )\n",
       "  (ema_network): EMA(\n",
       "    (online_model): Unet(\n",
       "      (init_conv): Conv2d(3, 84, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "      (time_mlp): Sequential(\n",
       "        (0): SinusoidalPositionEmbeddings()\n",
       "        (1): Linear(in_features=128, out_features=512, bias=True)\n",
       "        (2): GELU()\n",
       "        (3): Linear(in_features=512, out_features=512, bias=True)\n",
       "      )\n",
       "      (downs): ModuleList(\n",
       "        (0): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(84, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv2d(84, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "                  (1): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "                )\n",
       "              )\n",
       "              (norm): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "            )\n",
       "          )\n",
       "          (3): Conv2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "        )\n",
       "        (1): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "                  (1): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "                )\n",
       "              )\n",
       "              (norm): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "            )\n",
       "          )\n",
       "          (3): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "        )\n",
       "        (2): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv2d(512, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "                  (1): GroupNorm(1, 512, eps=1e-05, affine=True)\n",
       "                )\n",
       "              )\n",
       "              (norm): GroupNorm(1, 512, eps=1e-05, affine=True)\n",
       "            )\n",
       "          )\n",
       "          (3): Identity()\n",
       "        )\n",
       "      )\n",
       "      (ups): ModuleList(\n",
       "        (0): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "                  (1): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "                )\n",
       "              )\n",
       "              (norm): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "            )\n",
       "          )\n",
       "          (3): ConvTranspose2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "        )\n",
       "        (1): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "                  (1): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "                )\n",
       "              )\n",
       "              (norm): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "            )\n",
       "          )\n",
       "          (3): ConvTranspose2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "        )\n",
       "      )\n",
       "      (mid_block1): ResnetBlock(\n",
       "        (mlp): Sequential(\n",
       "          (0): SiLU()\n",
       "          (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (block1): Block(\n",
       "          (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (block2): Block(\n",
       "          (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (res_conv): Identity()\n",
       "      )\n",
       "      (mid_attn): Residual(\n",
       "        (fn): PreNorm(\n",
       "          (fn): Attention(\n",
       "            (to_qkv): Conv2d(512, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (to_out): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (norm): GroupNorm(1, 512, eps=1e-05, affine=True)\n",
       "        )\n",
       "      )\n",
       "      (mid_block2): ResnetBlock(\n",
       "        (mlp): Sequential(\n",
       "          (0): SiLU()\n",
       "          (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (block1): Block(\n",
       "          (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (block2): Block(\n",
       "          (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (res_conv): Identity()\n",
       "      )\n",
       "      (final_conv): Sequential(\n",
       "        (0): ResnetBlock(\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Identity()\n",
       "        )\n",
       "        (1): Conv2d(128, 3, kernel_size=(1, 1), stride=(1, 1))\n",
       "      )\n",
       "    )\n",
       "    (ema_model): Unet(\n",
       "      (init_conv): Conv2d(3, 84, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "      (time_mlp): Sequential(\n",
       "        (0): SinusoidalPositionEmbeddings()\n",
       "        (1): Linear(in_features=128, out_features=512, bias=True)\n",
       "        (2): GELU()\n",
       "        (3): Linear(in_features=512, out_features=512, bias=True)\n",
       "      )\n",
       "      (downs): ModuleList(\n",
       "        (0): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(84, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv2d(84, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "                  (1): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "                )\n",
       "              )\n",
       "              (norm): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "            )\n",
       "          )\n",
       "          (3): Conv2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "        )\n",
       "        (1): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "                  (1): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "                )\n",
       "              )\n",
       "              (norm): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "            )\n",
       "          )\n",
       "          (3): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "        )\n",
       "        (2): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv2d(512, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "                  (1): GroupNorm(1, 512, eps=1e-05, affine=True)\n",
       "                )\n",
       "              )\n",
       "              (norm): GroupNorm(1, 512, eps=1e-05, affine=True)\n",
       "            )\n",
       "          )\n",
       "          (3): Identity()\n",
       "        )\n",
       "      )\n",
       "      (ups): ModuleList(\n",
       "        (0): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "                  (1): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "                )\n",
       "              )\n",
       "              (norm): GroupNorm(1, 256, eps=1e-05, affine=True)\n",
       "            )\n",
       "          )\n",
       "          (3): ConvTranspose2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "        )\n",
       "        (1): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=512, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "                  (1): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "                )\n",
       "              )\n",
       "              (norm): GroupNorm(1, 128, eps=1e-05, affine=True)\n",
       "            )\n",
       "          )\n",
       "          (3): ConvTranspose2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "        )\n",
       "      )\n",
       "      (mid_block1): ResnetBlock(\n",
       "        (mlp): Sequential(\n",
       "          (0): SiLU()\n",
       "          (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (block1): Block(\n",
       "          (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (block2): Block(\n",
       "          (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (res_conv): Identity()\n",
       "      )\n",
       "      (mid_attn): Residual(\n",
       "        (fn): PreNorm(\n",
       "          (fn): Attention(\n",
       "            (to_qkv): Conv2d(512, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (to_out): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (norm): GroupNorm(1, 512, eps=1e-05, affine=True)\n",
       "        )\n",
       "      )\n",
       "      (mid_block2): ResnetBlock(\n",
       "        (mlp): Sequential(\n",
       "          (0): SiLU()\n",
       "          (1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (block1): Block(\n",
       "          (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (block2): Block(\n",
       "          (proj): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (norm): GroupNorm(8, 512, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (res_conv): Identity()\n",
       "      )\n",
       "      (final_conv): Sequential(\n",
       "        (0): ResnetBlock(\n",
       "          (block1): Block(\n",
       "            (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (block2): Block(\n",
       "            (proj): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "            (act): SiLU()\n",
       "          )\n",
       "          (res_conv): Identity()\n",
       "        )\n",
       "        (1): Conv2d(128, 3, kernel_size=(1, 1), stride=(1, 1))\n",
       "      )\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DModel.eval().cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def reverse_diffusion(model,initial_noise, diffusion_steps,device=\"cuda\"):\n",
    "    # reverse diffusion = sampling\n",
    "    #print(next(model.parameters()).device)\n",
    "    num_images = initial_noise.shape[0]\n",
    "    step_size = 1.0 / diffusion_steps\n",
    "    # important line:\n",
    "    # at the first sampling step, the \"noisy image\" is pure noise\n",
    "    # but its signal rate is assumed to be nonzero (min_signal_rate)\n",
    "    next_noisy_images = initial_noise\n",
    "    all_preds=[]\n",
    "    for step in tqdm(range(diffusion_steps)):\n",
    "        noisy_images = next_noisy_images\n",
    "        # separate the current noisy image to its components\n",
    "        diffusion_times = torch.ones((num_images,1,1,1)).to(device) - step * step_size\n",
    "        diffusion_times=diffusion_times.to(initial_noise)\n",
    "        noise_rates, signal_rates = cos_diffusion_schedule(diffusion_times)\n",
    "        pred_noises, pred_images = model.denoise(noisy_images, noise_rates, signal_rates,training=False)\n",
    "        # remix the predicted components using the next signal and noise rates\n",
    "        next_diffusion_times = diffusion_times - step_size\n",
    "        next_noise_rates, next_signal_rates = cos_diffusion_schedule(next_diffusion_times)\n",
    "        next_noisy_images = (next_signal_rates * pred_images + next_noise_rates * pred_noises)\n",
    "        all_preds.append(pred_images)\n",
    "        # this new noisy image will be used in the next step\n",
    "        #next_noisy_images=pred_images\n",
    "    # Last step\n",
    "    #diffusion_times = torch.zeros((num_images,1,1,1)).to(device)\n",
    "    #diffusion_times=diffusion_times.to(initial_noise)\n",
    "    #noise_rates, signal_rates = cos_diffusion_schedule(diffusion_times)\n",
    "    #pred_noises, pred_images = model.denoise(pred_images, noise_rates, signal_rates,training=False)\n",
    "    \n",
    "    return all_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def generate(model,num_images, diffusion_steps,device=\"cuda\"):\n",
    "    # noise -> images -> denormalized images\n",
    "    initial_noise =torch.randn((num_images,channels, image_size, image_size)).to(device)\n",
    "    #initial_noise = tf.random.normal(shape=(num_images, image_size, image_size, 3))\n",
    "    generated_images = reverse_diffusion(model,initial_noise, diffusion_steps)\n",
    "    ims=[]\n",
    "    for i in range(len(generated_images)):\n",
    "        steps=[]\n",
    "        images_steps=generated_images[i]\n",
    "        for j in range(images_steps.shape[0]):\n",
    "            steps.append(postprocess(images_steps[j]))\n",
    "        ims.append(steps)\n",
    "    return ims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6494d0ff5b8446388a35f974e0cc99d1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6494d0ff5b8446388a35f974e0cc99d1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "images=generate(DModel,4,200,device=\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(images[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_images(images,num_rows=3, num_cols=6):\n",
    "    plt.figure(figsize=(num_cols * 2.0, num_rows * 2.0))\n",
    "    for row in range(num_rows):\n",
    "        for col in range(num_cols):\n",
    "            index = row * num_cols + col\n",
    "            plt.subplot(num_rows, num_cols, index + 1)\n",
    "            plt.imshow(images[index])\n",
    "            plt.axis(\"off\")\n",
    "    plt.tight_layout()\n",
    "    #plt.show()\n",
    "    #plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x144 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x144 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rows=len(images[-1])//4\n",
    "cols=len(images[-1])//rows\n",
    "plot_images(images[-1],rows,cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pick index and plot images from diffusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx=2\n",
    "animate_imgs=[]\n",
    "for step_imgs in images:\n",
    "    im = plt.imshow(step_imgs[idx], animated=True)\n",
    "    animate_imgs.append([im])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MovieWriter ffmpeg unavailable; using Pillow instead.\n",
      "MovieWriter ffmpeg unavailable; using Pillow instead.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.animation as animation\n",
    "fig = plt.figure()\n",
    "animate = animation.ArtistAnimation(fig, animate_imgs, interval=200, blit=True, repeat_delay=1000)\n",
    "animate.save('diffusion.gif')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(animate_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/bash: /opt/conda/bin/conda: Permission denied\n",
      "/bin/bash: /opt/conda/bin/conda: Permission denied\n"
     ]
    }
   ],
   "source": [
    "! sudo chmod -R 755\n",
    "!conda install -c conda-forge ffmpeg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}