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Add ESGD-M optimizer #18

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Add ESGD-M optimizer #18

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13 changes: 8 additions & 5 deletions StyleGAN3+CLIP.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/ouhenio/StyleGAN3-CLIP-notebooks/blob/save-images-with-custom-name/StyleGAN3%2BCLIP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/ouhenio/StyleGAN3-CLIP-notebooks/blob/add-esgd-m-optimizer-option/StyleGAN3%2BCLIP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
Expand Down Expand Up @@ -102,12 +102,14 @@
"#!pip install --upgrade https://download.pytorch.org/whl/nightly/cu111/torch-1.11.0.dev20211012%2Bcu111-cp37-cp37m-linux_x86_64.whl https://download.pytorch.org/whl/nightly/cu111/torchvision-0.12.0.dev20211012%2Bcu111-cp37-cp37m-linux_x86_64.whl\n",
"!git clone https://github.com/NVlabs/stylegan3\n",
"!git clone https://github.com/openai/CLIP\n",
"!git clone https://github.com/crowsonkb/esgd.git\n",
"!pip install -e ./CLIP\n",
"!pip install einops ninja\n",
"\n",
"import sys\n",
"sys.path.append('./CLIP')\n",
"sys.path.append('./stylegan3')\n",
"sys.path.append('./esgd')\n",
"\n",
"import io\n",
"import os, time, glob\n",
Expand All @@ -123,6 +125,7 @@
"import clip\n",
"import unicodedata\n",
"import re\n",
"from esgd import ESGD\n",
"from tqdm.notebook import tqdm\n",
"from torchvision.transforms import Compose, Resize, ToTensor, Normalize\n",
"from IPython.display import display\n",
Expand Down Expand Up @@ -369,8 +372,7 @@
{
"cell_type": "code",
"metadata": {
"id": "QXoRP4SHzJ6i",
"cellView": "form"
"id": "QXoRP4SHzJ6i"
},
"source": [
"#@markdown #**Run the model** 🚀\n",
Expand Down Expand Up @@ -408,15 +410,16 @@
"\n",
" # Sampling loop\n",
" q_ema = q\n",
" opt = torch.optim.AdamW([q], lr=0.03, betas=(0.0,0.999))\n",
" opt = ESGD([q], lr=1, betas=(0.9, 0.999), v=0.9)\n",
" # opt = torch.optim.AdamW([q], lr=0.03, betas=(0.0,0.999))\n",
" loop = tqdm(range(steps))\n",
" for i in loop:\n",
" opt.zero_grad()\n",
" w = q * w_stds\n",
" image = G.synthesis(w + G.mapping.w_avg, noise_mode='const')\n",
" embed = embed_image(image.add(1).div(2))\n",
" loss = prompts_dist_loss(embed, targets, spherical_dist_loss).mean()\n",
" loss.backward()\n",
" loss.backward(create_graph=opt.should_create_graph())\n",
" opt.step()\n",
" loop.set_postfix(loss=loss.item(), q_magnitude=q.std().item())\n",
"\n",
Expand Down