Spaces:
Running
Running
File size: 7,409 Bytes
891b88f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 |
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from PIL import Image\n",
"from IPython.display import display\n",
"import torch as th\n",
"\n",
"from glide_text2im.download import load_checkpoint\n",
"from glide_text2im.model_creation import (\n",
" create_model_and_diffusion,\n",
" model_and_diffusion_defaults,\n",
" model_and_diffusion_defaults_upsampler\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# This notebook supports both CPU and GPU.\n",
"# On CPU, generating one sample may take on the order of 20 minutes.\n",
"# On a GPU, it should be under a minute.\n",
"\n",
"has_cuda = th.cuda.is_available()\n",
"device = th.device('cpu' if not has_cuda else 'cuda')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create base model.\n",
"options = model_and_diffusion_defaults()\n",
"options['use_fp16'] = has_cuda\n",
"options['timestep_respacing'] = '100' # use 100 diffusion steps for fast sampling\n",
"model, diffusion = create_model_and_diffusion(**options)\n",
"model.eval()\n",
"if has_cuda:\n",
" model.convert_to_fp16()\n",
"model.to(device)\n",
"model.load_state_dict(load_checkpoint('base', device))\n",
"print('total base parameters', sum(x.numel() for x in model.parameters()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create upsampler model.\n",
"options_up = model_and_diffusion_defaults_upsampler()\n",
"options_up['use_fp16'] = has_cuda\n",
"options_up['timestep_respacing'] = 'fast27' # use 27 diffusion steps for very fast sampling\n",
"model_up, diffusion_up = create_model_and_diffusion(**options_up)\n",
"model_up.eval()\n",
"if has_cuda:\n",
" model_up.convert_to_fp16()\n",
"model_up.to(device)\n",
"model_up.load_state_dict(load_checkpoint('upsample', device))\n",
"print('total upsampler parameters', sum(x.numel() for x in model_up.parameters()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def show_images(batch: th.Tensor):\n",
" \"\"\" Display a batch of images inline. \"\"\"\n",
" scaled = ((batch + 1)*127.5).round().clamp(0,255).to(th.uint8).cpu()\n",
" reshaped = scaled.permute(2, 0, 3, 1).reshape([batch.shape[2], -1, 3])\n",
" display(Image.fromarray(reshaped.numpy()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Sampling parameters\n",
"prompt = \"an oil painting of a corgi\"\n",
"batch_size = 1\n",
"guidance_scale = 3.0\n",
"\n",
"# Tune this parameter to control the sharpness of 256x256 images.\n",
"# A value of 1.0 is sharper, but sometimes results in grainy artifacts.\n",
"upsample_temp = 0.997"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##############################\n",
"# Sample from the base model #\n",
"##############################\n",
"\n",
"# Create the text tokens to feed to the model.\n",
"tokens = model.tokenizer.encode(prompt)\n",
"tokens, mask = model.tokenizer.padded_tokens_and_mask(\n",
" tokens, options['text_ctx']\n",
")\n",
"\n",
"# Create the classifier-free guidance tokens (empty)\n",
"full_batch_size = batch_size * 2\n",
"uncond_tokens, uncond_mask = model.tokenizer.padded_tokens_and_mask(\n",
" [], options['text_ctx']\n",
")\n",
"\n",
"# Pack the tokens together into model kwargs.\n",
"model_kwargs = dict(\n",
" tokens=th.tensor(\n",
" [tokens] * batch_size + [uncond_tokens] * batch_size, device=device\n",
" ),\n",
" mask=th.tensor(\n",
" [mask] * batch_size + [uncond_mask] * batch_size,\n",
" dtype=th.bool,\n",
" device=device,\n",
" ),\n",
")\n",
"\n",
"# Create a classifier-free guidance sampling function\n",
"def model_fn(x_t, ts, **kwargs):\n",
" half = x_t[: len(x_t) // 2]\n",
" combined = th.cat([half, half], dim=0)\n",
" model_out = model(combined, ts, **kwargs)\n",
" eps, rest = model_out[:, :3], model_out[:, 3:]\n",
" cond_eps, uncond_eps = th.split(eps, len(eps) // 2, dim=0)\n",
" half_eps = uncond_eps + guidance_scale * (cond_eps - uncond_eps)\n",
" eps = th.cat([half_eps, half_eps], dim=0)\n",
" return th.cat([eps, rest], dim=1)\n",
"\n",
"# Sample from the base model.\n",
"model.del_cache()\n",
"samples = diffusion.p_sample_loop(\n",
" model_fn,\n",
" (full_batch_size, 3, options[\"image_size\"], options[\"image_size\"]),\n",
" device=device,\n",
" clip_denoised=True,\n",
" progress=True,\n",
" model_kwargs=model_kwargs,\n",
" cond_fn=None,\n",
")[:batch_size]\n",
"model.del_cache()\n",
"\n",
"# Show the output\n",
"show_images(samples)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##############################\n",
"# Upsample the 64x64 samples #\n",
"##############################\n",
"\n",
"tokens = model_up.tokenizer.encode(prompt)\n",
"tokens, mask = model_up.tokenizer.padded_tokens_and_mask(\n",
" tokens, options_up['text_ctx']\n",
")\n",
"\n",
"# Create the model conditioning dict.\n",
"model_kwargs = dict(\n",
" # Low-res image to upsample.\n",
" low_res=((samples+1)*127.5).round()/127.5 - 1,\n",
"\n",
" # Text tokens\n",
" tokens=th.tensor(\n",
" [tokens] * batch_size, device=device\n",
" ),\n",
" mask=th.tensor(\n",
" [mask] * batch_size,\n",
" dtype=th.bool,\n",
" device=device,\n",
" ),\n",
")\n",
"\n",
"# Sample from the base model.\n",
"model_up.del_cache()\n",
"up_shape = (batch_size, 3, options_up[\"image_size\"], options_up[\"image_size\"])\n",
"up_samples = diffusion_up.ddim_sample_loop(\n",
" model_up,\n",
" up_shape,\n",
" noise=th.randn(up_shape, device=device) * upsample_temp,\n",
" device=device,\n",
" clip_denoised=True,\n",
" progress=True,\n",
" model_kwargs=model_kwargs,\n",
" cond_fn=None,\n",
")[:batch_size]\n",
"model_up.del_cache()\n",
"\n",
"# Show the output\n",
"show_images(up_samples)"
]
}
],
"metadata": {
"interpreter": {
"hash": "e7d6e62d90e7e85f9a0faa7f0b1d576302d7ae6108e9fe361594f8e1c8b05781"
},
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|