File size: 4,587 Bytes
7b4c6f4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
{
"cells": [
{
"cell_type": "markdown",
"source": [
"### 🚀 For an interactive experience, head over to our [demo platform](https://var.vision/demo) and dive right in! 🌟"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"################## 1. Download checkpoints and build models\n",
"import os\n",
"import os.path as osp\n",
"import torch, torchvision\n",
"import random\n",
"import numpy as np\n",
"import PIL.Image as PImage, PIL.ImageDraw as PImageDraw\n",
"setattr(torch.nn.Linear, 'reset_parameters', lambda self: None) # disable default parameter init for faster speed\n",
"setattr(torch.nn.LayerNorm, 'reset_parameters', lambda self: None) # disable default parameter init for faster speed\n",
"from models import VQVAE, build_vae_var\n",
"\n",
"MODEL_DEPTH = 16 # TODO: =====> please specify MODEL_DEPTH <=====\n",
"assert MODEL_DEPTH in {16, 20, 24, 30}\n",
"\n",
"\n",
"# download checkpoint\n",
"hf_home = 'https://huggingface.co/FoundationVision/var/resolve/main'\n",
"vae_ckpt, var_ckpt = 'vae_ch160v4096z32.pth', f'var_d{MODEL_DEPTH}.pth'\n",
"if not osp.exists(vae_ckpt): os.system(f'wget {hf_home}/{vae_ckpt}')\n",
"if not osp.exists(var_ckpt): os.system(f'wget {hf_home}/{var_ckpt}')\n",
"\n",
"# build vae, var\n",
"patch_nums = (1, 2, 3, 4, 5, 6, 8, 10, 13, 16)\n",
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
"if 'vae' not in globals() or 'var' not in globals():\n",
" vae, var = build_vae_var(\n",
" V=4096, Cvae=32, ch=160, share_quant_resi=4, # hard-coded VQVAE hyperparameters\n",
" device=device, patch_nums=patch_nums,\n",
" num_classes=1000, depth=MODEL_DEPTH, shared_aln=False,\n",
" )\n",
"\n",
"# load checkpoints\n",
"vae.load_state_dict(torch.load(vae_ckpt, map_location='cpu'), strict=True)\n",
"var.load_state_dict(torch.load(var_ckpt, map_location='cpu'), strict=True)\n",
"vae.eval(), var.eval()\n",
"for p in vae.parameters(): p.requires_grad_(False)\n",
"for p in var.parameters(): p.requires_grad_(False)\n",
"print(f'prepare finished.')"
],
"metadata": {
"collapsed": false,
"is_executing": true
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"############################# 2. Sample with classifier-free guidance\n",
"\n",
"# set args\n",
"seed = 0 #@param {type:\"number\"}\n",
"torch.manual_seed(seed)\n",
"num_sampling_steps = 250 #@param {type:\"slider\", min:0, max:1000, step:1}\n",
"cfg = 4 #@param {type:\"slider\", min:1, max:10, step:0.1}\n",
"class_labels = (980, 980, 437, 437, 22, 22, 562, 562) #@param {type:\"raw\"}\n",
"more_smooth = False # True for more smooth output\n",
"\n",
"# seed\n",
"torch.manual_seed(seed)\n",
"random.seed(seed)\n",
"np.random.seed(seed)\n",
"torch.backends.cudnn.deterministic = True\n",
"torch.backends.cudnn.benchmark = False\n",
"\n",
"# run faster\n",
"tf32 = True\n",
"torch.backends.cudnn.allow_tf32 = bool(tf32)\n",
"torch.backends.cuda.matmul.allow_tf32 = bool(tf32)\n",
"torch.set_float32_matmul_precision('high' if tf32 else 'highest')\n",
"\n",
"# sample\n",
"B = len(class_labels)\n",
"label_B: torch.LongTensor = torch.tensor(class_labels, device=device)\n",
"with torch.inference_mode():\n",
" with torch.autocast('cuda', enabled=True, dtype=torch.float16, cache_enabled=True): # using bfloat16 can be faster\n",
" recon_B3HW = var.autoregressive_infer_cfg(B=B, label_B=label_B, cfg=cfg, top_k=900, top_p=0.95, g_seed=seed, more_smooth=more_smooth)\n",
"\n",
"chw = torchvision.utils.make_grid(recon_B3HW, nrow=8, padding=0, pad_value=1.0)\n",
"chw = chw.permute(1, 2, 0).mul_(255).cpu().numpy()\n",
"chw = PImage.fromarray(chw.astype(np.uint8))\n",
"chw.show()\n"
],
"metadata": {
"collapsed": false
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|