mirror of
https://github.com/openai/shap-e.git
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first commit
This commit is contained in:
Alex Nichol
93
shap_e/examples/encode_model.ipynb
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93
shap_e/examples/encode_model.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"\n",
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"from shap_e.models.download import load_model\n",
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"from shap_e.util.data_util import load_or_create_multimodal_batch\n",
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"from shap_e.util.notebooks import create_pan_cameras, decode_latent_images, gif_widget"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"xm = load_model('transmitter', device=device)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"model_path = \"example_data/cactus/object.obj\"\n",
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"\n",
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"# This may take a few minutes, since it requires rendering the model twice\n",
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"# in two different modes.\n",
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"batch = load_or_create_multimodal_batch(\n",
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" device,\n",
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" model_path=model_path,\n",
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" mv_light_mode=\"basic\",\n",
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" mv_image_size=256,\n",
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" cache_dir=\"example_data/cactus/cached\",\n",
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" verbose=True, # this will show Blender output during renders\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"with torch.no_grad():\n",
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" latent = xm.encoder.encode_to_bottleneck(batch)\n",
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"\n",
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" render_mode = 'stf' # you can change this to 'nerf'\n",
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" size = 128 # recommended that you lower resolution when using nerf\n",
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"\n",
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" cameras = create_pan_cameras(size, device)\n",
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" images = decode_latent_images(xm, latent, cameras, rendering_mode=render_mode)\n",
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" display(gif_widget(images))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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52
shap_e/examples/example_data/cactus/material.mtl
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52
shap_e/examples/example_data/cactus/material.mtl
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newmtl mat0
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Ka 0.0000 0.7000 0.0000
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Kd 0.0000 0.7000 0.0000
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Ks 0.0000 0.0000 0.0000
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newmtl mat1
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Ka 0.6600 0.4400 0.2000
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Kd 0.6600 0.4400 0.2000
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Ks 0.0000 0.0000 0.0000
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newmtl mat2
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Ka 0.3000 0.3000 0.3000
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Kd 0.3000 0.3000 0.3000
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Ks 0.0000 0.0000 0.0000
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newmtl mat3
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Ka 0.0000 0.5000 0.0000
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Kd 0.0000 0.5000 0.0000
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Ks 0.0000 0.0000 0.0000
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newmtl mat4
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Ka 0.0000 0.5667 0.0000
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Kd 0.0000 0.5667 0.0000
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Ks 0.0000 0.0000 0.0000
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newmtl mat5
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Ka 0.5400 0.3933 0.2333
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Kd 0.5400 0.3933 0.2333
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Ks 0.0000 0.0000 0.0000
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newmtl mat6
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Ka 0.0000 0.6333 0.0000
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Kd 0.0000 0.6333 0.0000
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Ks 0.0000 0.0000 0.0000
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newmtl mat7
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Ka 0.2000 0.3667 0.2000
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Kd 0.2000 0.3667 0.2000
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Ks 0.0000 0.0000 0.0000
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newmtl mat8
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Ka 0.4200 0.3467 0.2667
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Kd 0.4200 0.3467 0.2667
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Ks 0.0000 0.0000 0.0000
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newmtl mat9
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Ka 0.1000 0.4333 0.1000
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Kd 0.1000 0.4333 0.1000
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Ks 0.0000 0.0000 0.0000
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newmtl mat10
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Ka 0.1000 0.5667 0.1000
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Kd 0.1000 0.5667 0.1000
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Ks 0.0000 0.0000 0.0000
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newmtl mat11
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Ka 0.2000 0.4333 0.2000
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Kd 0.2000 0.4333 0.2000
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Ks 0.0000 0.0000 0.0000
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newmtl mat12
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Ka 0.1000 0.5000 0.1000
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Kd 0.1000 0.5000 0.1000
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Ks 0.0000 0.0000 0.0000
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774994
shap_e/examples/example_data/cactus/object.obj
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774994
shap_e/examples/example_data/cactus/object.obj
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File diff suppressed because it is too large
Load Diff
BIN
shap_e/examples/example_data/corgi.png
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shap_e/examples/example_data/corgi.png
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108
shap_e/examples/sample_image_to_3d.ipynb
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108
shap_e/examples/sample_image_to_3d.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "964ccced",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"\n",
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"from shap_e.diffusion.sample import sample_latents\n",
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"from shap_e.diffusion.gaussian_diffusion import diffusion_from_config\n",
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"from shap_e.models.download import load_model, load_config\n",
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"from shap_e.util.notebooks import create_pan_cameras, decode_latent_images, gif_widget\n",
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"from shap_e.util.image_util import load_image"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8eed3a76",
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"metadata": {},
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"outputs": [],
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"source": [
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"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2d922637",
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"metadata": {},
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"outputs": [],
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"source": [
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"xm = load_model('transmitter', device=device)\n",
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"model = load_model('image300M', device=device)\n",
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"diffusion = diffusion_from_config(load_config('diffusion'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "53d329d0",
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"metadata": {},
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"outputs": [],
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"source": [
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"batch_size = 4\n",
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"guidance_scale = 3.0\n",
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"\n",
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"image = load_image(\"example_data/corgi.png\")\n",
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"\n",
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"latents = sample_latents(\n",
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" batch_size=batch_size,\n",
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" model=model,\n",
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" diffusion=diffusion,\n",
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" guidance_scale=guidance_scale,\n",
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" model_kwargs=dict(images=[image] * batch_size),\n",
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" progress=True,\n",
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" clip_denoised=True,\n",
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" use_fp16=True,\n",
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" use_karras=True,\n",
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" karras_steps=64,\n",
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" sigma_min=1e-3,\n",
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" sigma_max=160,\n",
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" s_churn=0,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "633da2ec",
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"metadata": {},
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"outputs": [],
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"source": [
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"render_mode = 'nerf' # you can change this to 'stf' for mesh rendering\n",
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"size = 64 # this is the size of the renders; higher values take longer to render.\n",
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"\n",
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"cameras = create_pan_cameras(size, device)\n",
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"for i, latent in enumerate(latents):\n",
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" images = decode_latent_images(xm, latent, cameras, rendering_mode=render_mode)\n",
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" display(gif_widget(images))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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106
shap_e/examples/sample_text_to_3d.ipynb
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106
shap_e/examples/sample_text_to_3d.ipynb
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@@ -0,0 +1,106 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "964ccced",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"\n",
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"from shap_e.diffusion.sample import sample_latents\n",
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"from shap_e.diffusion.gaussian_diffusion import diffusion_from_config\n",
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"from shap_e.models.download import load_model, load_config\n",
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"from shap_e.util.notebooks import create_pan_cameras, decode_latent_images, gif_widget"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8eed3a76",
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"metadata": {},
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"outputs": [],
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"source": [
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"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2d922637",
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"metadata": {},
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"outputs": [],
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"source": [
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"xm = load_model('transmitter', device=device)\n",
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"model = load_model('text300M', device=device)\n",
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"diffusion = diffusion_from_config(load_config('diffusion'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "53d329d0",
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"metadata": {},
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"outputs": [],
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"source": [
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"batch_size = 4\n",
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"guidance_scale = 15.0\n",
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"prompt = \"a shark\"\n",
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"\n",
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"latents = sample_latents(\n",
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" batch_size=batch_size,\n",
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" model=model,\n",
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" diffusion=diffusion,\n",
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" guidance_scale=guidance_scale,\n",
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" model_kwargs=dict(texts=[prompt] * batch_size),\n",
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" progress=True,\n",
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" clip_denoised=True,\n",
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" use_fp16=True,\n",
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" use_karras=True,\n",
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" karras_steps=64,\n",
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" sigma_min=1e-3,\n",
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" sigma_max=160,\n",
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" s_churn=0,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "633da2ec",
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"metadata": {},
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"outputs": [],
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"source": [
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"render_mode = 'nerf' # you can change this to 'stf'\n",
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"size = 64 # this is the size of the renders; higher values take longer to render.\n",
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"\n",
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"cameras = create_pan_cameras(size, device)\n",
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"for i, latent in enumerate(latents):\n",
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" images = decode_latent_images(xm, latent, cameras, rendering_mode=render_mode)\n",
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" display(gif_widget(images))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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