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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "68fece49",
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import matplotlib.pyplot as plt\n",
"\n",
"class DoubleConv(nn.Module):\n",
" def __init__(self, in_channels, out_channels, mid_channels=None, residual=False):\n",
" super().__init__()\n",
" self.residual = residual\n",
" if not mid_channels:\n",
" mid_channels = out_channels\n",
" self.double_conv = nn.Sequential(\n",
" nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False),\n",
" nn.GroupNorm(1, mid_channels),\n",
" nn.GELU(),\n",
" nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False),\n",
" nn.GroupNorm(1, out_channels),\n",
" )\n",
"\n",
" def forward(self, x):\n",
" if self.residual:\n",
" return F.gelu(x + self.double_conv(x))\n",
" else:\n",
" return self.double_conv(x)\n",
"\n",
"class Down(nn.Module):\n",
" def __init__(self, in_channels, out_channels, emb_dim=256):\n",
" super().__init__()\n",
" self.maxpool_conv = nn.Sequential(\n",
" nn.MaxPool2d(2),\n",
" DoubleConv(in_channels, in_channels, residual=True),\n",
" DoubleConv(in_channels, out_channels),\n",
" )\n",
"\n",
" self.emb_layer = nn.Sequential(\n",
" nn.SiLU(),\n",
" nn.Linear(\n",
" emb_dim,\n",
" out_channels\n",
" ),\n",
" )\n",
"\n",
" def forward(self, x, t):\n",
" x = self.maxpool_conv(x)\n",
" emb = self.emb_layer(t)[:, :, None, None].repeat(1, 1, x.shape[-2], x.shape[-1])\n",
" return x + emb\n",
"\n",
"class Up(nn.Module):\n",
" def __init__(self, in_channels, out_channels, emb_dim=256):\n",
" super().__init__()\n",
"\n",
" self.up = nn.Upsample(scale_factor=2, mode=\"bilinear\", align_corners=True)\n",
" self.conv = nn.Sequential(\n",
" DoubleConv(in_channels, in_channels, residual=True),\n",
" DoubleConv(in_channels, out_channels, in_channels // 2),\n",
" )\n",
"\n",
" self.emb_layer = nn.Sequential(\n",
" nn.SiLU(),\n",
" nn.Linear(\n",
" emb_dim,\n",
" out_channels\n",
" ),\n",
" )\n",
"\n",
" def forward(self, x, skip_x, t):\n",
" x = self.up(x)\n",
" x = torch.cat([skip_x, x], dim=1)\n",
" x = self.conv(x)\n",
" emb = self.emb_layer(t)[:, :, None, None].repeat(1, 1, x.shape[-2], x.shape[-1])\n",
" return x + emb\n",
"\n",
"class UNet(nn.Module):\n",
" def __init__(self, c_in=3, c_out=3, time_dim=256, device=\"cuda\"):\n",
" super().__init__()\n",
" self.device = device\n",
" self.time_dim = time_dim\n",
"\n",
" self.inc = DoubleConv(c_in, 64)\n",
" self.down1 = Down(64, 128)\n",
" self.down2 = Down(128, 256)\n",
" self.down3 = Down(256, 256)\n",
"\n",
" self.bot1 = DoubleConv(256, 512)\n",
" self.bot2 = DoubleConv(512, 512)\n",
" self.bot3 = DoubleConv(512, 256)\n",
"\n",
" self.up1 = Up(512, 128)\n",
" self.up2 = Up(256, 64)\n",
" self.up3 = Up(128, 64)\n",
" self.outc = nn.Conv2d(64, c_out, kernel_size=1)\n",
"\n",
" def positional_encoding(self, t, channels):\n",
" inv_freq = 1.0 / (\n",
" 10000\n",
" ** (torch.arange(0, channels, 2, device=self.device).float() / channels)\n",
" )\n",
" pos_enc_a = torch.sin(t.repeat(1, channels // 2) * inv_freq)\n",
" pos_enc_b = torch.cos(t.repeat(1, channels // 2) * inv_freq)\n",
" pos_enc = torch.cat([pos_enc_a, pos_enc_b], dim=-1)\n",
" return pos_enc\n",
"\n",
" def forward(self, image, t):\n",
" t = t.unsqueeze(-1).type(torch.float)\n",
" t = self.positional_encoding(t, self.time_dim)\n",
"\n",
" x1 = self.inc(image)\n",
" x2 = self.down1(x1, t)\n",
" x3 = self.down2(x2, t)\n",
" x4 = self.down3(x3, t)\n",
"\n",
" x4 = self.bot1(x4)\n",
" # x4 = self.bot2(x4)\n",
" x4 = self.bot3(x4)\n",
"\n",
" x = self.up1(x4, x3, t)\n",
" x = self.up2(x, x2, t)\n",
" x = self.up3(x, x1, t)\n",
" output = self.outc(x)\n",
" return output\n",
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
"model = UNet(device = device).to(device)\n",
"model.load_state_dict(torch.load('Model_Saved_States/diffusion_64.pth'))\n",
"img_size = 64\n",
"class Diffusion():\n",
" def __init__(self, time_steps = 500, beta_start = 0.0001, beta_stop = 0.02, image_size = 64, device = device):\n",
" self.time_steps = time_steps\n",
" self.beta_start = beta_start\n",
" self.beta_stop = beta_stop\n",
" self.img_size = image_size\n",
" self.device = device\n",
"\n",
" self.beta = self.beta_schedule()\n",
" self.beta = self.beta.to(device)\n",
" self.alpha = 1 - self.beta\n",
" self.alpha = self.alpha.to(device)\n",
" self.alpha_hat = torch.cumprod(self.alpha, dim = 0).to(device)\n",
"\n",
"\n",
" def beta_schedule(self):\n",
" return torch.linspace(self.beta_start, self.beta_stop, self.time_steps)\n",
"\n",
" def noise_images(self, images, t):\n",
" sqrt_alpha_hat = torch.sqrt(self.alpha_hat[t])[:, None, None, None,]\n",
" sqrt_one_minus_alpha_hat = torch.sqrt(1 - self.alpha_hat[t])[:, None, None, None,]\n",
" noises = torch.randn_like(images)\n",
" noised_images = sqrt_alpha_hat * images + sqrt_one_minus_alpha_hat * noises\n",
" return noised_images, noises\n",
"\n",
" def random_timesteps(self, n):\n",
" return torch.randint(low=1, high=self.time_steps, size=(n,))\n",
"\n",
" def generate_samples(self, model, n):\n",
" with torch.no_grad():\n",
" x = torch.randn((n, 3, self.img_size, self.img_size)).to(self.device)\n",
" for i in range(self.time_steps - 1, 1, -1):\n",
" t = (torch.ones(n) * i).long().to(self.device)\n",
" predicted_noise = model(x, t)\n",
" alpha = self.alpha[t][:, None, None, None]\n",
" alpha_hat = self.alpha_hat[t][:, None, None, None]\n",
" beta = self.beta[t][:, None, None, None]\n",
" if i > 1:\n",
" noise = torch.randn_like(x)\n",
" else:\n",
" noise = torch.zeros_like(x)\n",
" x = 1 / torch.sqrt(alpha) * (x - ((1 - alpha) / (torch.sqrt(1 - alpha_hat))) * predicted_noise) + torch.sqrt(beta) * noise\n",
"\n",
" return (x[0].cpu().numpy().transpose(1, 2, 0) / 255)\n",
" #show_images\n",
"\n",
"diffusion = Diffusion()\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "a80516cd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7867\n",
"Running on public URL: https://080248f8c7c14eec1e.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"https://080248f8c7c14eec1e.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"def greet(n):\n",
" image = diffusion.generate_samples(model, n = 1)\n",
" image = (np.clip(image * 255, -1, 1) + 1) / 2\n",
" plt.imshow(image)\n",
" return image\n",
"\n",
"iface = gr.Interface(fn=greet, inputs=\"number\", outputs=\"image\")\n",
"iface.launch(share = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cc6f5064",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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