| [Project Page](https://sherwinbahmani.github.io/tc4d/) | [Paper](https://arxiv.org/abs/2403.17920) | [User Study Template](https://github.com/victor-rong/video-generation-study) |
- **This code is forked from [threestudio](https://github.com/threestudio-project/threestudio).**
## Installation
### Install threestudio
**This part is the same as original threestudio. Skip it if you already have installed the environment.**
- You must have an NVIDIA graphics card with at least 24 GB VRAM and have [CUDA](https://developer.nvidia.com/cuda-downloads) installed.
- Install `Python >= 3.8`.
- (Optional, Recommended) Create a virtual environment:
```sh
python3 -m virtualenv venv
. venv/bin/activate
# Newer pip versions, e.g. pip-23.x, can be much faster than old versions, e.g. pip-20.x.
# For instance, it caches the wheels of git packages to avoid unnecessarily rebuilding them later.
python3 -m pip install --upgrade pip
```
- Install `PyTorch >= 1.12`. We have tested on `torch1.12.1+cu113` and `torch2.0.0+cu118`, but other versions should also work fine.
```sh
# torch1.12.1+cu113
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
# or torch2.0.0+cu118
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
```
- (Optional, Recommended) Install ninja to speed up the compilation of CUDA extensions:
```sh
pip install ninja
```
- Install dependencies:
```sh
pip install -r requirements.txt
```
### Install MVDream
MVDream multi-view diffusion model is provided in a different codebase. Install it by:
```sh
git clone https://github.com/bytedance/MVDream extern/MVDream
pip install -e extern/MVDream
```
## Quickstart
Our model is trained in 3 stages and there are three different config files for every stage. Training has to be resumed after finishing a stage.
```sh
seed=0
gpu=0
exp_root_dir=/path/to
# Trajectory-conditioned generation
scene_setup_path=configs_prompts/a_deer_walking.yaml
# Stage 1
python launch.py --config configs/tc4d_stage_1.yaml --train --gpu $gpu exp_root_dir=$exp_root_dir seed=$seed system.prompt_processor.prompt="a deer walking" system.scene_setup_path=$scene_setup_path
# Stage 2
ckpt=/path/to/tc4d_stage_1/a_deer_walking@timestamp/ckpts/last.ckpt
python launch.py --config configs/tc4d_stage_2.yaml --train --gpu $gpu exp_root_dir=$exp_root_dir seed=$seed system.prompt_processor.prompt="a deer walking" system.scene_setup_path=$scene_setup_path system.weights=$ckpt
# Stage 3
ckpt=/path/to/tc4d_stage_2/a_deer_walking@timestamp/ckpts/last.ckpt
python launch.py --config configs/tc4d_stage_3.yaml --train --gpu $gpu exp_root_dir=$exp_root_dir seed=$seed system.prompt_processor.prompt="a deer walking" system.scene_setup_path=$scene_setup_path system.weights=$ckpt
# Compositional 4D Scene after training multiple stage 3 trajectory-conditioned prompts
# Add ckpts in the compositional config and define the trajectory list, see configs_comp for examples used in the paper
scene_setup_path=configs_comp/comp0.yaml
ckpt=/path/to/tc4d_stage_2/a_deer_walking@timestamp/ckpts/last.ckpt # Just a dummy input, overwritten by ckpts specified in the comp0.yaml
python launch.py --config configs/tc4d_stage_3.yaml --test --gpu $gpu exp_root_dir=$exp_root_dir seed=$seed system.prompt_processor.prompt="a deer walking" system.scene_setup_path=$scene_setup_path system.weights=$ckpt
# Render high resolution videos used in the paper and project page after training
ckpt=/path/to/tc4d_stage_3/a_deer_walking@timestamp/ckpts/last.ckpt
python launch.py --config configs/tc4d_stage_3_eval.yaml --test --gpu $gpu exp_root_dir=$exp_root_dir seed=$seed system.prompt_processor.prompt="a deer walking" system.scene_setup_path=$scene_setup_path system.weights=$ckpt
```
## Memory Usage
We tested our code on a A100 80 GB, but the memory can be reduced to 24 GB by adjusting following:
- Reduce system.renderer.base_renderer.train_max_nums to fit into the given memory. This reduces the number of sampled points which are part of the backpropagation
- Set system.guidance_video.low_ram_vae to a number between 1 and 16. This saves memory by only backpropagating through low_ram_vae number of frames instead of all 16.
Note that the convergence speed can be slower when reducing these parameters, the quality should not degrade however.
We provide 24 GB and 40 GB configs for stage 3, replace --config configs/tc4d_stage_3.yaml in the training command with: tc4d_stage_3_24_gb.yaml or tc4d_stage_3_40_gb.yaml
## Credits
This code is built on the [threestudio-project](https://github.com/threestudio-project/threestudio), [MVDream-threestudio](https://github.com/bytedance/MVDream-threestudio), [4D-fy-threestudio](https://github.com/sherwinbahmani/4dfy), and [VideoCrafter](https://github.com/AILab-CVC/VideoCrafter). Thanks to the maintainers for their contribution to the community!
## Citing
If you find TC4D helpful, please consider citing:
```
@article{bah2024tc4d,
title={TC4D: Trajectory-Conditioned Text-to-4D Generation},
author={Bahmani, Sherwin and Liu, Xian and Yifan, Wang and Skorokhodov, Ivan and Rong, Victor and Liu, Ziwei and Liu, Xihui and Park, Jeong Joon and Tulyakov, Sergey and Wetzstein, Gordon and Tagliasacchi, Andrea and Lindell, David B.},
journal={arXiv},
year={2024}
}
```
================================================
FILE: configs/tc4d_stage_1.yaml
================================================
name: "tc4d_stage_1"
tag: "${rmspace:${system.prompt_processor.prompt},_}"
exp_root_dir: "outputs"
seed: 0
data_type: "single-multiview-combined-camera-datamodule"
data:
multi_rate_perc: 1.0
multi_rate: null
single_view:
batch_size: [1,1]
width: [64, 256]
height: [64, 256]
resolution_milestones: [5000]
camera_distance_range: [1.5, 2.0]
fovy_range: [15, 60]
elevation_range: [0, 30]
camera_perturb: 0.
center_perturb: 0.
up_perturb: 0.
eval_camera_distance: 2.0
eval_fovy_deg: 40.
static: true
eval_height: 256
eval_width: 256
num_frames: 16
multi_view:
batch_size: [8,4] # must be dividable by n_view
n_view: 4
# 0-4999: 64x64, >=5000: 256x256
width: [64, 256]
height: [64, 256]
resolution_milestones: [5000]
camera_distance_range: [1.5, 2.0]
fovy_range: [15, 60]
elevation_range: [0, 30]
camera_perturb: 0.
center_perturb: 0.
up_perturb: 0.
n_val_views: 4
eval_camera_distance: 2.0
eval_fovy_deg: 40.
relative_radius: false
eval_height: ${data.single_view.eval_height}
eval_width: ${data.single_view.eval_width}
system_type: "tc4d-system"
system:
multi_rate_perc: ${data.multi_rate_perc}
stage: coarse
geometry_type: "implicit-volume"
geometry:
radius: 1.0
normal_type: "finite_difference"
density_bias: "blob_magic3d"
density_activation: softplus
density_blob_scale: 10.
density_blob_std: 0.5
pos_encoding_config:
otype: HashGridSpatialTimeDeform
n_levels: 16
n_features_per_level: 2
log2_hashmap_size: 19
base_resolution: 16
per_level_scale: 1.447269237440378 # max resolution 4096
static: ${data.single_view.static}
num_frames: ${data.single_view.num_frames}
compute_elastic_loss: false
compute_rigidity_loss: false
rigidity_loss_time: false
compute_divergence_loss: false
div_type: "l1"
rigidity_loss_std: 0.0
elastic_loss_alpha: 1.0
time_encoding_config:
otype: ProgressiveBandHashGrid
type: 'Hash'
n_levels: 8
n_features_per_level: 2
log2_hashmap_size: 19
base_resolution: 4
per_level_scale: 1.447269237440378
start_level: 4
start_step: 0
update_steps: 500
time_network_config:
otype: "VanillaMLP"
n_neurons: 64
n_hidden_layers: 2
material_type: "no-material"
material:
n_output_dims: 3
color_activation: sigmoid
background_type: "neural-environment-map-background"
background:
color_activation: sigmoid
random_aug: false
renderer_type: "stable-nerf-volume-renderer-multi"
renderer:
base_renderer_type: "mask-nerf-volume-renderer-multi"
base_renderer:
radius: ${system.geometry.radius}
num_samples_per_ray: 512
train_max_nums: 2000000
block_nums: [3,3]
prompt_processor_type: "stable-diffusion-prompt-processor"
prompt_processor:
pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base"
prompt: ???
front_threshold: 30.
back_threshold: 30.
prompt_processor_type_multi_view: "stable-diffusion-prompt-processor"
prompt_processor_multi_view:
pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base"
prompt: ???
negative_prompt: "ugly, bad anatomy, blurry, pixelated obscure, unnatural colors, poor lighting, dull, and unclear, cropped, lowres, low quality, artifacts, duplicate, morbid, mutilated, poorly drawn face, deformed, dehydrated, bad proportions"
front_threshold: 30.
back_threshold: 30.
guidance_type: "stable-diffusion-vsd-guidance"
guidance:
pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base"
pretrained_model_name_or_path_lora: "stabilityai/stable-diffusion-2-1"
guidance_scale: 7.5
min_step_percent: 0.02
max_step_percent: 0.98
max_step_percent_annealed: 0.5
anneal_start_step: 8000
guidance_type_multi_view: "multiview-diffusion-guidance"
guidance_multi_view:
model_name: "sd-v2.1-base-4view"
ckpt_path: null # path to a pre-downloaded checkpoint file (null for loading from URL)
guidance_scale: 50.0
min_step_percent: [0, 0.98, 0.02, 8000] # (start_iter, start_val, end_val, end_iter)
max_step_percent: [0, 0.98, 0.50, 8000]
recon_loss: true
recon_std_rescale: 0.5
# prompt_processor_type_video: "zeroscope-prompt-processor"
# prompt_processor_video:
# pretrained_model_name_or_path: "cerspense/zeroscope_v2_576w"
# prompt: ???
# guidance_type_video: "zeroscope-guidance"
# guidance_video:
# pretrained_model_name_or_path: "cerspense/zeroscope_v2_576w"
# guidance_scale: 100.
# weighting_strategy: sds
loggers:
wandb:
enable: false
project: "threestudio"
loss:
lambda_sds: 1.
lambda_sds_video: 0.0
lambda_vsd: 1.
lambda_lora: 1.
lambda_orient: 0.
lambda_sparsity: 100.
lambda_opaque: 0.
lambda_z_variance: 0.
lambda_tv: 0.
lambda_deformation: 0.
lambda_elastic: 0.0
lambda_divergence: 0.0
lambda_rigidity: 0.0
optimizer:
name: AdamW
args:
betas: [0.9, 0.99]
eps: 1.e-15
params:
renderer.renderers.0.geometry.encoding:
lr: 0.01
renderer.renderers.0.geometry.density_network:
lr: 0.001
renderer.renderers.0.geometry.feature_network:
lr: 0.001
background:
lr: 0.001
background_scene:
lr: 0.001
guidance_single_view:
lr: 0.0001
trainer:
max_steps: 10000
log_every_n_steps: 1
num_sanity_val_steps: 0
val_check_interval: 1000
enable_progress_bar: true
precision: 16-mixed
checkpoint:
save_last: true
save_top_k: 0
every_n_train_steps: ${trainer.max_steps}
================================================
FILE: configs/tc4d_stage_2.yaml
================================================
name: "tc4d_stage_2"
tag: "${rmspace:${system.prompt_processor.prompt},_}"
exp_root_dir: "outputs"
seed: 0
data_type: "single-multiview-combined-camera-datamodule"
data:
multi_rate_perc: 0.5
multi_rate: null
single_view:
batch_size: [1,1]
width: [256, 256]
height: [256, 256]
resolution_milestones: [5000]
camera_distance_range: [1.5, 2.0]
fovy_range: [15, 60]
elevation_range: [0, 30]
camera_perturb: 0.
center_perturb: 0.
up_perturb: 0.
eval_camera_distance: 2.0
eval_fovy_deg: 40.
static: true
eval_height: 256
eval_width: 256
num_frames: 16
multi_view:
batch_size: [4,4] # must be dividable by n_view
n_view: 4
# 0-4999: 64x64, >=5000: 256x256
width: [256, 256]
height: [256, 256]
resolution_milestones: [5000]
camera_distance_range: [1.5, 2.0]
fovy_range: [15, 60]
elevation_range: [0, 30]
camera_perturb: 0.
center_perturb: 0.
up_perturb: 0.
n_val_views: 4
eval_camera_distance: 2.0
eval_fovy_deg: 40.
relative_radius: false
eval_height: ${data.single_view.eval_height}
eval_width: ${data.single_view.eval_width}
system_type: "tc4d-system"
system:
multi_rate_perc: ${data.multi_rate_perc}
stage: coarse
geometry_type: "implicit-volume"
geometry:
radius: 1.0
normal_type: "finite_difference"
density_bias: "blob_magic3d"
density_activation: softplus
density_blob_scale: 10.
density_blob_std: 0.5
pos_encoding_config:
otype: HashGridSpatialTimeDeform
n_levels: 16
n_features_per_level: 2
log2_hashmap_size: 19
base_resolution: 16
per_level_scale: 1.447269237440378 # max resolution 4096
static: ${data.single_view.static}
num_frames: ${data.single_view.num_frames}
compute_elastic_loss: false
compute_rigidity_loss: false
rigidity_loss_time: false
compute_divergence_loss: false
div_type: "l1"
rigidity_loss_std: 0.0
elastic_loss_alpha: 1.0
time_encoding_config:
otype: ProgressiveBandHashGrid
type: 'Hash'
n_levels: 8
n_features_per_level: 2
log2_hashmap_size: 19
base_resolution: 4
per_level_scale: 1.447269237440378 # max resolution 233
start_level: 4
start_step: 0
update_steps: 500
time_network_config:
otype: "VanillaMLP"
n_neurons: 64
n_hidden_layers: 2
material_type: "no-material"
material:
n_output_dims: 3
color_activation: sigmoid
background_type: "neural-environment-map-background"
background:
color_activation: sigmoid
random_aug: false
renderer_type: "stable-nerf-volume-renderer-multi"
renderer:
base_renderer_type: "mask-nerf-volume-renderer-multi"
base_renderer:
radius: ${system.geometry.radius}
num_samples_per_ray: 512
train_max_nums: 2000000
block_nums: [3,3]
prompt_processor_type: "stable-diffusion-prompt-processor"
prompt_processor:
pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base"
prompt: ???
front_threshold: 30.
back_threshold: 30.
prompt_processor_type_multi_view: "stable-diffusion-prompt-processor"
prompt_processor_multi_view:
pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base"
prompt: ???
negative_prompt: "ugly, bad anatomy, blurry, pixelated obscure, unnatural colors, poor lighting, dull, and unclear, cropped, lowres, low quality, artifacts, duplicate, morbid, mutilated, poorly drawn face, deformed, dehydrated, bad proportions"
front_threshold: 30.
back_threshold: 30.
guidance_type: "stable-diffusion-vsd-guidance"
guidance:
pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base"
pretrained_model_name_or_path_lora: "stabilityai/stable-diffusion-2-1"
guidance_scale: 7.5
min_step_percent: 0.02
max_step_percent: 0.98
max_step_percent_annealed: 0.5
anneal_start_step: 8000
guidance_type_multi_view: "multiview-diffusion-guidance"
guidance_multi_view:
model_name: "sd-v2.1-base-4view"
ckpt_path: null # path to a pre-downloaded checkpoint file (null for loading from URL)
guidance_scale: 50.0
min_step_percent: [0, 0.02, 0.02, 8000] # (start_iter, start_val, end_val, end_iter)
max_step_percent: [0, 0.50, 0.50, 8000]
recon_loss: true
recon_std_rescale: 0.5
# prompt_processor_type_video: "zeroscope-prompt-processor"
# prompt_processor_video:
# pretrained_model_name_or_path: "cerspense/zeroscope_v2_576w"
# prompt: ???
# guidance_type_video: "zeroscope-guidance"
# guidance_video:
# pretrained_model_name_or_path: "cerspense/zeroscope_v2_576w"
# guidance_scale: 100.
# weighting_strategy: sds
loggers:
wandb:
enable: false
project: "threestudio"
loss:
lambda_sds: 1.
lambda_sds_video: 0.0
lambda_vsd: 1.
lambda_lora: 1.
lambda_orient: 0.
lambda_sparsity: 100.
lambda_z_variance: 0.
lambda_opaque: 0.
lambda_tv: 0.
lambda_deformation: 0.
lambda_elastic: 0.0
lambda_divergence: 0.0
lambda_rigidity: 0.0
optimizer:
name: AdamW
args:
betas: [0.9, 0.99]
eps: 1.e-15
params:
renderer.renderers.0.geometry.encoding:
lr: 0.01
renderer.renderers.0.geometry.density_network:
lr: 0.001
renderer.renderers.0.geometry.feature_network:
lr: 0.001
background:
lr: 0.001
background_scene:
lr: 0.001
guidance_single_view:
lr: 0.0001
trainer:
max_steps: 20000
log_every_n_steps: 1
num_sanity_val_steps: 0
val_check_interval: 1000
enable_progress_bar: true
precision: 16-mixed
checkpoint:
save_last: true
save_top_k: 0
every_n_train_steps: ${trainer.max_steps}
================================================
FILE: configs/tc4d_stage_3.yaml
================================================
name: "tc4d_stage_3"
tag: "${rmspace:${system.prompt_processor.prompt},_}"
exp_root_dir: "outputs"
seed: 0
data_type: "single-multiview-combined-camera-datamodule"
data:
multi_rate_perc: 0.0
multi_rate: null
single_view:
batch_size: [1,1]
# 0-4999: 64x64, >=5000: 512x512
# this drastically reduces VRAM usage as empty space is pruned in early training
width: [256, 256]
height: [256, 256]
resolution_milestones: [5000]
camera_distance_range: [1.5, 2.0]
fovy_range: [15, 60]
elevation_range: [0, 30]
camera_perturb: 0.
center_perturb: 0.
up_perturb: 0.
eval_camera_distance: 2.0
eval_fovy_deg: 40.
static: false
num_frames: 16
simultan: true
simultan_vid_mod_perc: 1.0
simultan_vid_mod: null
width_vid: 128
height_vid: 80
sample_rand_frames: t0_sub
num_frames_factor: 4
# eval_height: 1024
# eval_width: 1024
eval_height: 512
eval_width: 512
frame_range: 1.0
scene_single_obj: true
scene_single_obj_static: true
multi_obj: true
num_objs: 1
scene_iters: 9999999999 # currently: always render object in non canonical pose but depending on trajectory
scene_iters_freq: 1
multi_view:
batch_size: [4,4] # must be dividable by n_view
n_view: 4
width: [256, 256]
height: [256, 256]
resolution_milestones: [5000]
camera_distance_range: [1.5, 2.0]
fovy_range: [15, 60]
elevation_range: [0, 30]
camera_perturb: 0.
center_perturb: 0.
up_perturb: 0.
n_val_views: 4
eval_camera_distance: 2.0
eval_fovy_deg: 40.
relative_radius: false
num_frames: ${data.single_view.num_frames}
sample_rand_frames: ${data.single_view.sample_rand_frames}
eval_height: ${data.single_view.eval_height}
eval_width: ${data.single_view.eval_width}
multi_obj: ${data.single_view.multi_obj}
num_objs: ${data.single_view.num_objs}
scene_iters: ${data.single_view.scene_iters}
scene_iters_freq: ${data.single_view.scene_iters_freq}
frame_range: ${data.single_view.frame_range}
system_type: "tc4d-system"
system:
weights_ignore_modules: ["renderer.base_renderer.estimators", "renderer.base_renderer.obj_trajs", "geometry.0.encoding.encoding.time_network", "geometry.0.encoding.encoding.encoding_time"]
multi_obj: ${data.single_view.multi_obj}
multi_rate_perc: ${data.multi_rate_perc}
simultan_vid_mod_perc: ${data.single_view.simultan_vid_mod_perc}
use_traj_length_frame_range: true
traj_length_frame_range: 0.3
stage: coarse
geometry_type: "implicit-volume"
geometry:
radius: 1.0
normal_type: "finite_difference"
density_bias: "blob_magic3d"
density_activation: softplus
density_blob_scale: 10.
density_blob_std: 0.5
pos_encoding_config:
otype: HashGridSpatialTimeDeform
n_levels: 16
n_features_per_level: 2
log2_hashmap_size: 19
base_resolution: 16
per_level_scale: 1.447269237440378 # max resolution 4096
static: ${data.single_view.static}
num_frames: ${data.single_view.num_frames}
compute_divergence_loss: false
div_type: 'l2'
compute_elastic_loss: false
elastic_loss_alpha: 1.0
compute_rigidity_loss: false
rigidity_loss_time: true
rigidity_loss_std: 0.001
time_encoding_config:
otype: Grid
type: 'Hash'
n_levels: 8
n_features_per_level: 2
log2_hashmap_size: 19
base_resolution: 4
per_level_scale: 1.447269237440378
start_level: 4
start_step: 0
update_steps: 500
time_network_config:
otype: "VanillaMLP"
n_neurons: 64
n_hidden_layers: 2
anneal_density_blob_std_config:
min_anneal_step: 0
max_anneal_step: 5000
start_val: ${system.geometry.density_blob_std}
end_val: 0.5
material_type: "no-material"
material:
n_output_dims: 3
color_activation: sigmoid
background_type: "neural-environment-map-background"
background:
color_activation: sigmoid
random_aug: false
renderer_type: "stable-nerf-volume-renderer-multi"
renderer:
base_renderer_type: "mask-nerf-volume-renderer-multi"
base_renderer:
radius: ${system.geometry.radius}
num_samples_per_ray: 512
occ_frame_updates: true
occ_n: 16
occ_ema_decay: 0.9
occ_ema_decay_init_zero: true
occ_thre: 0.5
occ_thre_post_init: 0.5
# A100 (80GB)
train_max_nums: 700000
block_nums: [3,3]
simultan: ${data.single_view.simultan}
prompt_processor_type: "stable-diffusion-prompt-processor"
prompt_processor:
pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base"
prompt: ???
front_threshold: 30.
back_threshold: 30.
prompt_processor_type_multi_view: "stable-diffusion-prompt-processor"
prompt_processor_multi_view:
pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base"
prompt: ???
negative_prompt: "ugly, bad anatomy, blurry, pixelated obscure, unnatural colors, poor lighting, dull, and unclear, cropped, lowres, low quality, artifacts, duplicate, morbid, mutilated, poorly drawn face, deformed, dehydrated, bad proportions"
front_threshold: 30.
back_threshold: 30.
guidance_type: "stable-diffusion-vsd-guidance"
guidance:
pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base"
pretrained_model_name_or_path_lora: "stabilityai/stable-diffusion-2-1"
guidance_scale: 7.5
min_step_percent: 0.02
max_step_percent: 0.5
max_step_percent_annealed: 0.5
anneal_start_step: 5000
guidance_type_multi_view: "multiview-diffusion-guidance"
guidance_multi_view:
model_name: "sd-v2.1-base-4view"
ckpt_path: null # path to a pre-downloaded checkpoint file (null for loading from URL)
guidance_scale: 50.0
min_step_percent: [0, 0.02, 0.02, 8000] # (start_iter, start_val, end_val, end_iter)
max_step_percent: [0, 0.5, 0.5, 8000]
recon_loss: true
recon_std_rescale: 0.5
prompt_processor_type_video: "videocrafter-prompt-processor"
prompt_processor_video:
config: threestudio/models/guidance/videocrafter/configs/inference_t2v_512_v2.0.yaml
# Checkpoint https://huggingface.co/VideoCrafter/VideoCrafter2/blob/main/model.ckpt
pretrained_model_name_or_path: "VideoCrafter/VideoCrafter2"
negative_prompt: "low motion, static statue, not moving, no motion"
prompt: ???
guidance_type_video: "videocrafter-guidance"
guidance_video:
config: ${system.prompt_processor_video.config}
pretrained_model_name_or_path: ${system.prompt_processor_video.pretrained_model_name_or_path}
guidance_scale: 100.
weighting_strategy: sds
use_hifa: false
width_vid: ${data.single_view.width_vid}
height_vid: ${data.single_view.height_vid}
motion_amp_scale: 1.
half_precision_weights: false
fps: 8
min_step_percent: [0, 0.02, 0.02, 5000] # (start_iter, start_val, end_val, end_iter)
max_step_percent: [0, 0.98, 0.5, 5000]
# Set a number between 1 and 16, this saves memory by only backpropagating through low_ram_vae number of frames instead of all 16
# low_ram_vae: 16
loggers:
wandb:
enable: false
project: "threestudio"
loss:
lambda_sds: 1.
lambda_sds_video: 1.0
lambda_vsd: 1.
lambda_lora: 1.
lambda_orient: 0.
lambda_sparsity: 0.
lambda_opaque: 0.
lambda_z_variance: 0.
lambda_tv: 0.
lambda_deformation: 100.0
lambda_elastic: 0.0
lambda_rigidity: 0.0
lambda_divergence: 0.0
optimizer:
name: AdamW
args:
betas: [0.9, 0.99]
eps: 1.e-15
params:
renderer.renderers.0.geometry.density_network:
lr: 0.0
renderer.renderers.0.geometry.feature_network:
lr: 0.0
renderer.renderers.0.geometry.encoding.encoding.encoding:
lr: 0.0
renderer.renderers.0.geometry.encoding.encoding.encoding_time:
lr: 0.001
renderer.renderers.0.geometry.encoding.encoding.time_network:
lr: 0.0001
background:
lr: 0.0
background_scene:
lr: 0.0
trainer:
max_steps: 30000
log_every_n_steps: 1
num_sanity_val_steps: 0
val_check_interval: 1000
enable_progress_bar: true
precision: 16-mixed
checkpoint:
save_last: true
save_top_k: 0
every_n_train_steps: ${trainer.max_steps}
================================================
FILE: configs/tc4d_stage_3_24_gb.yaml
================================================
name: "tc4d_stage_3"
tag: "${rmspace:${system.prompt_processor.prompt},_}"
exp_root_dir: "outputs"
seed: 0
data_type: "single-multiview-combined-camera-datamodule"
data:
multi_rate_perc: 0.0
multi_rate: null
single_view:
batch_size: [1,1]
# 0-4999: 64x64, >=5000: 512x512
# this drastically reduces VRAM usage as empty space is pruned in early training
width: [256, 256]
height: [256, 256]
resolution_milestones: [5000]
camera_distance_range: [1.5, 2.0]
fovy_range: [15, 60]
elevation_range: [0, 30]
camera_perturb: 0.
center_perturb: 0.
up_perturb: 0.
eval_camera_distance: 2.0
eval_fovy_deg: 40.
static: false
num_frames: 16
simultan: true
simultan_vid_mod_perc: 1.0
simultan_vid_mod: null
width_vid: 128
height_vid: 80
sample_rand_frames: t0_sub
num_frames_factor: 4
# eval_height: 1024
# eval_width: 1024
eval_height: 512
eval_width: 512
frame_range: 1.0
scene_single_obj: true
scene_single_obj_static: true
multi_obj: true
num_objs: 1
scene_iters: 9999999999 # currently: always render object in non canonical pose but depending on trajectory
scene_iters_freq: 1
multi_view:
batch_size: [4,4] # must be dividable by n_view
n_view: 4
width: [256, 256]
height: [256, 256]
resolution_milestones: [5000]
camera_distance_range: [1.5, 2.0]
fovy_range: [15, 60]
elevation_range: [0, 30]
camera_perturb: 0.
center_perturb: 0.
up_perturb: 0.
n_val_views: 4
eval_camera_distance: 2.0
eval_fovy_deg: 40.
relative_radius: false
num_frames: ${data.single_view.num_frames}
sample_rand_frames: ${data.single_view.sample_rand_frames}
eval_height: ${data.single_view.eval_height}
eval_width: ${data.single_view.eval_width}
multi_obj: ${data.single_view.multi_obj}
num_objs: ${data.single_view.num_objs}
scene_iters: ${data.single_view.scene_iters}
scene_iters_freq: ${data.single_view.scene_iters_freq}
frame_range: ${data.single_view.frame_range}
system_type: "tc4d-system"
system:
weights_ignore_modules: ["renderer.base_renderer.estimators", "renderer.base_renderer.obj_trajs", "geometry.0.encoding.encoding.time_network", "geometry.0.encoding.encoding.encoding_time"]
multi_obj: ${data.single_view.multi_obj}
multi_rate_perc: ${data.multi_rate_perc}
simultan_vid_mod_perc: ${data.single_view.simultan_vid_mod_perc}
use_traj_length_frame_range: true
traj_length_frame_range: 0.3
stage: coarse
geometry_type: "implicit-volume"
geometry:
radius: 1.0
normal_type: "finite_difference"
density_bias: "blob_magic3d"
density_activation: softplus
density_blob_scale: 10.
density_blob_std: 0.5
pos_encoding_config:
otype: HashGridSpatialTimeDeform
n_levels: 16
n_features_per_level: 2
log2_hashmap_size: 19
base_resolution: 16
per_level_scale: 1.447269237440378 # max resolution 4096
static: ${data.single_view.static}
num_frames: ${data.single_view.num_frames}
compute_divergence_loss: false
div_type: 'l2'
compute_elastic_loss: false
elastic_loss_alpha: 1.0
compute_rigidity_loss: false
rigidity_loss_time: true
rigidity_loss_std: 0.001
time_encoding_config:
otype: Grid
type: 'Hash'
n_levels: 8
n_features_per_level: 2
log2_hashmap_size: 19
base_resolution: 4
per_level_scale: 1.447269237440378
start_level: 4
start_step: 0
update_steps: 500
time_network_config:
otype: "VanillaMLP"
n_neurons: 64
n_hidden_layers: 2
anneal_density_blob_std_config:
min_anneal_step: 0
max_anneal_step: 5000
start_val: ${system.geometry.density_blob_std}
end_val: 0.5
material_type: "no-material"
material:
n_output_dims: 3
color_activation: sigmoid
background_type: "neural-environment-map-background"
background:
color_activation: sigmoid
random_aug: false
renderer_type: "stable-nerf-volume-renderer-multi"
renderer:
base_renderer_type: "mask-nerf-volume-renderer-multi"
base_renderer:
radius: ${system.geometry.radius}
num_samples_per_ray: 512
occ_frame_updates: true
occ_n: 16
occ_ema_decay: 0.9
occ_ema_decay_init_zero: true
occ_thre: 0.5
occ_thre_post_init: 0.5
# A100 (80GB)
train_max_nums: 60000
block_nums: [3,3]
simultan: ${data.single_view.simultan}
prompt_processor_type: "stable-diffusion-prompt-processor"
prompt_processor:
pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base"
prompt: ???
front_threshold: 30.
back_threshold: 30.
prompt_processor_type_multi_view: "stable-diffusion-prompt-processor"
prompt_processor_multi_view:
pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base"
prompt: ???
negative_prompt: "ugly, bad anatomy, blurry, pixelated obscure, unnatural colors, poor lighting, dull, and unclear, cropped, lowres, low quality, artifacts, duplicate, morbid, mutilated, poorly drawn face, deformed, dehydrated, bad proportions"
front_threshold: 30.
back_threshold: 30.
guidance_type: "stable-diffusion-vsd-guidance"
guidance:
pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base"
pretrained_model_name_or_path_lora: "stabilityai/stable-diffusion-2-1"
guidance_scale: 7.5
min_step_percent: 0.02
max_step_percent: 0.5
max_step_percent_annealed: 0.5
anneal_start_step: 5000
guidance_type_multi_view: "multiview-diffusion-guidance"
guidance_multi_view:
model_name: "sd-v2.1-base-4view"
ckpt_path: null # path to a pre-downloaded checkpoint file (null for loading from URL)
guidance_scale: 50.0
min_step_percent: [0, 0.02, 0.02, 8000] # (start_iter, start_val, end_val, end_iter)
max_step_percent: [0, 0.5, 0.5, 8000]
recon_loss: true
recon_std_rescale: 0.5
prompt_processor_type_video: "videocrafter-prompt-processor"
prompt_processor_video:
config: threestudio/models/guidance/videocrafter/configs/inference_t2v_512_v2.0.yaml
# Checkpoint https://huggingface.co/VideoCrafter/VideoCrafter2/blob/main/model.ckpt
pretrained_model_name_or_path: "VideoCrafter/VideoCrafter2"
negative_prompt: "low motion, static statue, not moving, no motion"
prompt: ???
guidance_type_video: "videocrafter-guidance"
guidance_video:
config: ${system.prompt_processor_video.config}
pretrained_model_name_or_path: ${system.prompt_processor_video.pretrained_model_name_or_path}
guidance_scale: 100.
weighting_strategy: sds
use_hifa: false
width_vid: ${data.single_view.width_vid}
height_vid: ${data.single_view.height_vid}
motion_amp_scale: 1.
half_precision_weights: false
fps: 8
min_step_percent: [0, 0.02, 0.02, 5000] # (start_iter, start_val, end_val, end_iter)
max_step_percent: [0, 0.98, 0.5, 5000]
# Set a number between 1 and 16, this saves memory by only backpropagating through low_ram_vae number of frames instead of all 16
low_ram_vae: 3
loggers:
wandb:
enable: false
project: "threestudio"
loss:
lambda_sds: 1.
lambda_sds_video: 1.0
lambda_vsd: 1.
lambda_lora: 1.
lambda_orient: 0.
lambda_sparsity: 0.
lambda_opaque: 0.
lambda_z_variance: 0.
lambda_tv: 0.
lambda_deformation: 100.0
lambda_elastic: 0.0
lambda_rigidity: 0.0
lambda_divergence: 0.0
optimizer:
name: AdamW
args:
betas: [0.9, 0.99]
eps: 1.e-15
params:
renderer.renderers.0.geometry.density_network:
lr: 0.0
renderer.renderers.0.geometry.feature_network:
lr: 0.0
renderer.renderers.0.geometry.encoding.encoding.encoding:
lr: 0.0
renderer.renderers.0.geometry.encoding.encoding.encoding_time:
lr: 0.001
renderer.renderers.0.geometry.encoding.encoding.time_network:
lr: 0.0001
background:
lr: 0.0
background_scene:
lr: 0.0
trainer:
max_steps: 30000
log_every_n_steps: 1
num_sanity_val_steps: 0
val_check_interval: 1000
enable_progress_bar: true
precision: 16-mixed
checkpoint:
save_last: true
save_top_k: 0
every_n_train_steps: ${trainer.max_steps}
================================================
FILE: configs/tc4d_stage_3_40_gb.yaml
================================================
name: "tc4d_stage_3"
tag: "${rmspace:${system.prompt_processor.prompt},_}"
exp_root_dir: "outputs"
seed: 0
data_type: "single-multiview-combined-camera-datamodule"
data:
multi_rate_perc: 0.0
multi_rate: null
single_view:
batch_size: [1,1]
# 0-4999: 64x64, >=5000: 512x512
# this drastically reduces VRAM usage as empty space is pruned in early training
width: [256, 256]
height: [256, 256]
resolution_milestones: [5000]
camera_distance_range: [1.5, 2.0]
fovy_range: [15, 60]
elevation_range: [0, 30]
camera_perturb: 0.
center_perturb: 0.
up_perturb: 0.
eval_camera_distance: 2.0
eval_fovy_deg: 40.
static: false
num_frames: 16
simultan: true
simultan_vid_mod_perc: 1.0
simultan_vid_mod: null
width_vid: 128
height_vid: 80
sample_rand_frames: t0_sub
num_frames_factor: 4
# eval_height: 1024
# eval_width: 1024
eval_height: 512
eval_width: 512
frame_range: 1.0
scene_single_obj: true
scene_single_obj_static: true
multi_obj: true
num_objs: 1
scene_iters: 9999999999 # currently: always render object in non canonical pose but depending on trajectory
scene_iters_freq: 1
multi_view:
batch_size: [4,4] # must be dividable by n_view
n_view: 4
width: [256, 256]
height: [256, 256]
resolution_milestones: [5000]
camera_distance_range: [1.5, 2.0]
fovy_range: [15, 60]
elevation_range: [0, 30]
camera_perturb: 0.
center_perturb: 0.
up_perturb: 0.
n_val_views: 4
eval_camera_distance: 2.0
eval_fovy_deg: 40.
relative_radius: false
num_frames: ${data.single_view.num_frames}
sample_rand_frames: ${data.single_view.sample_rand_frames}
eval_height: ${data.single_view.eval_height}
eval_width: ${data.single_view.eval_width}
multi_obj: ${data.single_view.multi_obj}
num_objs: ${data.single_view.num_objs}
scene_iters: ${data.single_view.scene_iters}
scene_iters_freq: ${data.single_view.scene_iters_freq}
frame_range: ${data.single_view.frame_range}
system_type: "tc4d-system"
system:
weights_ignore_modules: ["renderer.base_renderer.estimators", "renderer.base_renderer.obj_trajs", "geometry.0.encoding.encoding.time_network", "geometry.0.encoding.encoding.encoding_time"]
multi_obj: ${data.single_view.multi_obj}
multi_rate_perc: ${data.multi_rate_perc}
simultan_vid_mod_perc: ${data.single_view.simultan_vid_mod_perc}
use_traj_length_frame_range: true
traj_length_frame_range: 0.3
stage: coarse
geometry_type: "implicit-volume"
geometry:
radius: 1.0
normal_type: "finite_difference"
density_bias: "blob_magic3d"
density_activation: softplus
density_blob_scale: 10.
density_blob_std: 0.5
pos_encoding_config:
otype: HashGridSpatialTimeDeform
n_levels: 16
n_features_per_level: 2
log2_hashmap_size: 19
base_resolution: 16
per_level_scale: 1.447269237440378 # max resolution 4096
static: ${data.single_view.static}
num_frames: ${data.single_view.num_frames}
compute_divergence_loss: false
div_type: 'l2'
compute_elastic_loss: false
elastic_loss_alpha: 1.0
compute_rigidity_loss: false
rigidity_loss_time: true
rigidity_loss_std: 0.001
time_encoding_config:
otype: Grid
type: 'Hash'
n_levels: 8
n_features_per_level: 2
log2_hashmap_size: 19
base_resolution: 4
per_level_scale: 1.447269237440378
start_level: 4
start_step: 0
update_steps: 500
time_network_config:
otype: "VanillaMLP"
n_neurons: 64
n_hidden_layers: 2
anneal_density_blob_std_config:
min_anneal_step: 0
max_anneal_step: 5000
start_val: ${system.geometry.density_blob_std}
end_val: 0.5
material_type: "no-material"
material:
n_output_dims: 3
color_activation: sigmoid
background_type: "neural-environment-map-background"
background:
color_activation: sigmoid
random_aug: false
renderer_type: "stable-nerf-volume-renderer-multi"
renderer:
base_renderer_type: "mask-nerf-volume-renderer-multi"
base_renderer:
radius: ${system.geometry.radius}
num_samples_per_ray: 512
occ_frame_updates: true
occ_n: 16
occ_ema_decay: 0.9
occ_ema_decay_init_zero: true
occ_thre: 0.5
occ_thre_post_init: 0.5
# A100 (80GB)
train_max_nums: 600000
block_nums: [3,3]
simultan: ${data.single_view.simultan}
prompt_processor_type: "stable-diffusion-prompt-processor"
prompt_processor:
pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base"
prompt: ???
front_threshold: 30.
back_threshold: 30.
prompt_processor_type_multi_view: "stable-diffusion-prompt-processor"
prompt_processor_multi_view:
pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base"
prompt: ???
negative_prompt: "ugly, bad anatomy, blurry, pixelated obscure, unnatural colors, poor lighting, dull, and unclear, cropped, lowres, low quality, artifacts, duplicate, morbid, mutilated, poorly drawn face, deformed, dehydrated, bad proportions"
front_threshold: 30.
back_threshold: 30.
guidance_type: "stable-diffusion-vsd-guidance"
guidance:
pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base"
pretrained_model_name_or_path_lora: "stabilityai/stable-diffusion-2-1"
guidance_scale: 7.5
min_step_percent: 0.02
max_step_percent: 0.5
max_step_percent_annealed: 0.5
anneal_start_step: 5000
guidance_type_multi_view: "multiview-diffusion-guidance"
guidance_multi_view:
model_name: "sd-v2.1-base-4view"
ckpt_path: null # path to a pre-downloaded checkpoint file (null for loading from URL)
guidance_scale: 50.0
min_step_percent: [0, 0.02, 0.02, 8000] # (start_iter, start_val, end_val, end_iter)
max_step_percent: [0, 0.5, 0.5, 8000]
recon_loss: true
recon_std_rescale: 0.5
prompt_processor_type_video: "videocrafter-prompt-processor"
prompt_processor_video:
config: threestudio/models/guidance/videocrafter/configs/inference_t2v_512_v2.0.yaml
# Checkpoint https://huggingface.co/VideoCrafter/VideoCrafter2/blob/main/model.ckpt
pretrained_model_name_or_path: "VideoCrafter/VideoCrafter2"
negative_prompt: "low motion, static statue, not moving, no motion"
prompt: ???
guidance_type_video: "videocrafter-guidance"
guidance_video:
config: ${system.prompt_processor_video.config}
pretrained_model_name_or_path: ${system.prompt_processor_video.pretrained_model_name_or_path}
guidance_scale: 100.
weighting_strategy: sds
use_hifa: false
width_vid: ${data.single_view.width_vid}
height_vid: ${data.single_view.height_vid}
motion_amp_scale: 1.
half_precision_weights: false
fps: 8
min_step_percent: [0, 0.02, 0.02, 5000] # (start_iter, start_val, end_val, end_iter)
max_step_percent: [0, 0.98, 0.5, 5000]
# Set a number between 1 and 16, this saves memory by only backpropagating through low_ram_vae number of frames instead of all 16
low_ram_vae: 3
loggers:
wandb:
enable: false
project: "threestudio"
loss:
lambda_sds: 1.
lambda_sds_video: 1.0
lambda_vsd: 1.
lambda_lora: 1.
lambda_orient: 0.
lambda_sparsity: 0.
lambda_opaque: 0.
lambda_z_variance: 0.
lambda_tv: 0.
lambda_deformation: 100.0
lambda_elastic: 0.0
lambda_rigidity: 0.0
lambda_divergence: 0.0
optimizer:
name: AdamW
args:
betas: [0.9, 0.99]
eps: 1.e-15
params:
renderer.renderers.0.geometry.density_network:
lr: 0.0
renderer.renderers.0.geometry.feature_network:
lr: 0.0
renderer.renderers.0.geometry.encoding.encoding.encoding:
lr: 0.0
renderer.renderers.0.geometry.encoding.encoding.encoding_time:
lr: 0.001
renderer.renderers.0.geometry.encoding.encoding.time_network:
lr: 0.0001
background:
lr: 0.0
background_scene:
lr: 0.0
trainer:
max_steps: 30000
log_every_n_steps: 1
num_sanity_val_steps: 0
val_check_interval: 1000
enable_progress_bar: true
precision: 16-mixed
checkpoint:
save_last: true
save_top_k: 0
every_n_train_steps: ${trainer.max_steps}
================================================
FILE: configs/tc4d_stage_3_eval.yaml
================================================
name: "tc4d_stage_3_eval"
tag: "${rmspace:${system.prompt_processor.prompt},_}"
exp_root_dir: "outputs"
seed: 0
data_type: "single-multiview-combined-camera-datamodule"
data:
multi_rate_perc: 0.0
multi_rate: null
single_view:
batch_size: [1,1]
# 0-4999: 64x64, >=5000: 512x512
# this drastically reduces VRAM usage as empty space is pruned in early training
width: [256, 256]
height: [256, 256]
resolution_milestones: [5000]
camera_distance_range: [1.5, 2.0]
fovy_range: [15, 60]
elevation_range: [0, 30]
camera_perturb: 0.
center_perturb: 0.
up_perturb: 0.
eval_camera_distance: 2.0
eval_fovy_deg: 40.
static: false
num_frames: 16
simultan: true
simultan_vid_mod_perc: 1.0
simultan_vid_mod: null
width_vid: 128
height_vid: 80
sample_rand_frames: t0_sub
num_frames_factor: 8
eval_height: 1024
eval_width: 1024
frame_range: 1.0
scene_single_obj: true
scene_single_obj_static: true
multi_obj: true
num_objs: 1
scene_iters: 9999999999 # currently: always render object in non canonical pose but depending on trajectory
scene_iters_freq: 1
multi_view:
batch_size: [4,4] # must be dividable by n_view
n_view: 4
width: [256, 256]
height: [256, 256]
resolution_milestones: [5000]
camera_distance_range: [1.5, 2.0]
fovy_range: [15, 60]
elevation_range: [0, 30]
camera_perturb: 0.
center_perturb: 0.
up_perturb: 0.
n_val_views: 4
eval_camera_distance: 2.0
eval_fovy_deg: 40.
relative_radius: false
num_frames: ${data.single_view.num_frames}
sample_rand_frames: ${data.single_view.sample_rand_frames}
eval_height: ${data.single_view.eval_height}
eval_width: ${data.single_view.eval_width}
multi_obj: ${data.single_view.multi_obj}
num_objs: ${data.single_view.num_objs}
scene_iters: ${data.single_view.scene_iters}
scene_iters_freq: ${data.single_view.scene_iters_freq}
frame_range: ${data.single_view.frame_range}
system_type: "tc4d-system"
system:
multi_obj: ${data.single_view.multi_obj}
multi_rate_perc: ${data.multi_rate_perc}
simultan_vid_mod_perc: ${data.single_view.simultan_vid_mod_perc}
use_traj_length_frame_range: true
traj_length_frame_range: 0.3
stage: coarse
geometry_type: "implicit-volume"
geometry:
radius: 1.0
normal_type: "finite_difference"
density_bias: "blob_magic3d"
density_activation: softplus
density_blob_scale: 10.
density_blob_std: 0.5
pos_encoding_config:
otype: HashGridSpatialTimeDeform
n_levels: 16
n_features_per_level: 2
log2_hashmap_size: 19
base_resolution: 16
per_level_scale: 1.447269237440378 # max resolution 4096
static: ${data.single_view.static}
num_frames: ${data.single_view.num_frames}
compute_divergence_loss: false
div_type: 'l2'
compute_elastic_loss: false
elastic_loss_alpha: 1.0
compute_rigidity_loss: false
rigidity_loss_time: true
rigidity_loss_std: 0.001
time_encoding_config:
otype: Grid
type: 'Hash'
n_levels: 8
n_features_per_level: 2
log2_hashmap_size: 19
base_resolution: 4
per_level_scale: 1.447269237440378
start_level: 4
start_step: 0
update_steps: 500
time_network_config:
otype: "VanillaMLP"
n_neurons: 64
n_hidden_layers: 2
anneal_density_blob_std_config:
min_anneal_step: 0
max_anneal_step: 5000
start_val: ${system.geometry.density_blob_std}
end_val: 0.5
material_type: "no-material"
material:
n_output_dims: 3
color_activation: sigmoid
background_type: "solid-color-background"
background:
color: [0.5, 0.5, 0.5]
renderer_type: "stable-nerf-volume-renderer-multi"
renderer:
base_renderer_type: "mask-nerf-volume-renderer-multi"
base_renderer:
radius: ${system.geometry.radius}
num_samples_per_ray: 512
occ_frame_updates: true
occ_n: 16
occ_ema_decay: 0.9
occ_ema_decay_init_zero: true
occ_thre: 0.5
occ_thre_post_init: 0.5
# A100 (80GB)
train_max_nums: 700000
block_nums: [3,3]
simultan: ${data.single_view.simultan}
prompt_processor_type: "stable-diffusion-prompt-processor"
prompt_processor:
pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base"
prompt: ???
front_threshold: 30.
back_threshold: 30.
prompt_processor_type_multi_view: "stable-diffusion-prompt-processor"
prompt_processor_multi_view:
pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base"
prompt: ???
negative_prompt: "ugly, bad anatomy, blurry, pixelated obscure, unnatural colors, poor lighting, dull, and unclear, cropped, lowres, low quality, artifacts, duplicate, morbid, mutilated, poorly drawn face, deformed, dehydrated, bad proportions"
front_threshold: 30.
back_threshold: 30.
guidance_type: "stable-diffusion-vsd-guidance"
guidance:
pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base"
pretrained_model_name_or_path_lora: "stabilityai/stable-diffusion-2-1"
guidance_scale: 7.5
min_step_percent: 0.02
max_step_percent: 0.5
max_step_percent_annealed: 0.5
anneal_start_step: 5000
guidance_type_multi_view: "multiview-diffusion-guidance"
guidance_multi_view:
model_name: "sd-v2.1-base-4view"
ckpt_path: null # path to a pre-downloaded checkpoint file (null for loading from URL)
guidance_scale: 50.0
min_step_percent: [0, 0.02, 0.02, 8000] # (start_iter, start_val, end_val, end_iter)
max_step_percent: [0, 0.5, 0.5, 8000]
recon_loss: true
recon_std_rescale: 0.5
prompt_processor_type_video: "videocrafter-prompt-processor"
prompt_processor_video:
config: threestudio/models/guidance/videocrafter/configs/inference_t2v_512_v2.0.yaml
# Checkpoint https://huggingface.co/VideoCrafter/VideoCrafter2/blob/main/model.ckpt
pretrained_model_name_or_path: "VideoCrafter/VideoCrafter2"
negative_prompt: "low motion, static statue, not moving, no motion"
prompt: ???
guidance_type_video: "videocrafter-guidance"
guidance_video:
config: ${system.prompt_processor_video.config}
pretrained_model_name_or_path: ${system.prompt_processor_video.pretrained_model_name_or_path}
guidance_scale: 100.
weighting_strategy: sds
use_hifa: false
width_vid: ${data.single_view.width_vid}
height_vid: ${data.single_view.height_vid}
motion_amp_scale: 1.
half_precision_weights: false
fps: 8
min_step_percent: [0, 0.02, 0.02, 5000] # (start_iter, start_val, end_val, end_iter)
max_step_percent: [0, 0.98, 0.5, 5000]
loggers:
wandb:
enable: false
project: "threestudio"
loss:
lambda_sds: 1.
lambda_sds_video: 1.0
lambda_vsd: 1.
lambda_lora: 1.
lambda_orient: 0.
lambda_sparsity: 0.
lambda_opaque: 0.
lambda_z_variance: 0.
lambda_tv: 0.
lambda_deformation: 100.0
lambda_elastic: 0.0
lambda_rigidity: 0.0
lambda_divergence: 0.0
optimizer:
name: AdamW
args:
betas: [0.9, 0.99]
eps: 1.e-15
params:
renderer.renderers.0.geometry.density_network:
lr: 0.0
renderer.renderers.0.geometry.feature_network:
lr: 0.0
renderer.renderers.0.geometry.encoding.encoding.encoding:
lr: 0.0
renderer.renderers.0.geometry.encoding.encoding.encoding_time:
lr: 0.001
renderer.renderers.0.geometry.encoding.encoding.time_network:
lr: 0.0001
background:
lr: 0.0
background_scene:
lr: 0.0
trainer:
max_steps: 30000
log_every_n_steps: 1
num_sanity_val_steps: 0
val_check_interval: 1000
enable_progress_bar: true
precision: 16-mixed
checkpoint:
save_last: true
save_top_k: 0
every_n_train_steps: ${trainer.max_steps}
================================================
FILE: configs_comp/comp0.yaml
================================================
name: "comp0"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.4, 0.4, 0.4]
trajs:
- traj_type: spline-trajectory
coords: [[0.25, 0.5, 0.5], [0.5, 0.75, 0.5], [0.75, 0.5, 0.5], [0.5, 0.25, 0.5], [0.25, 0.5, 0.5]]
angle_offset: 0.
time_offset: 0.
# Object 1
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.25, 0.5, 0.5], [0.5, 0.75, 0.5], [0.75, 0.5, 0.5], [0.5, 0.25, 0.5], [0.25, 0.5, 0.5]]
angle_offset: 0.
time_offset: 0.33
# Object 2
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.25, 0.5, 0.5], [0.5, 0.75, 0.5], [0.75, 0.5, 0.5], [0.5, 0.25, 0.5], [0.25, 0.5, 0.5]]
angle_offset: 0.
time_offset: 0.66
# Object 3
-
proxy_size: [0.4, 0.4, 0.4]
trajs:
- traj_type: static-trajectory
pos_start: [0.5, 0.5, 0.5]
angle_offset: 90.
prompt_processor:
prompt: ["a bear walking", "an astronaut riding a horse", "deadpool riding a cow", "a firepit"]
# First train each object independently and use this to render different trained models together
checkpoints: [
/path/to/a_bear_walking@x/ckpts/last.ckpt,
/path/to/an_astronaut_riding_a_horse@x/ckpts/last.ckpt,
/path/to/deadpool_riding_a_cow@x/ckpts/last.ckpt,
/path/to/a_firepit@x/ckpts/last.ckpt
]
================================================
FILE: configs_comp/comp1.yaml
================================================
name: "comp1"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.25, 0.25, 0.5], [0.35, 0.6, 0.5], [0.5, 0.75, 0.5]]
angle_offset: 0.
# Object 1
-
proxy_size: [0.5, 0.5, 0.6]
trajs:
- traj_type: spline-trajectory
coords: [[0.75, 0.75, 0.5], [0.6, 0.35, 0.5], [0.25, 0.5, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["an elephant walking", "a giraffe walking"]
# First train each object independently and use this to render different trained models together
checkpoints: [
/path/to/an_elephant_walking@x/ckpts/last.ckpt,
/path/to/a_giraffe_walking@x/ckpts/last.ckpt,
]
================================================
FILE: configs_comp/comp2.yaml
================================================
name: "comp2"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.30, 0.30, 0.5], [0.30, 0.70, 0.5]]
angle_offset: 0.
# Object 1
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.70, 0.70, 0.5], [0.70, 0.30, 0.5]]
angle_offset: 0.
# Object 2
-
proxy_size: [0.6, 0.6, 0.6]
trajs:
- traj_type: spline-trajectory
coords: [[0.30, 0.70, 0.5], [0.70, 0.70, 0.5]]
angle_offset: 0.
# Object 3
-
proxy_size: [0.3, 0.3, 0.3]
trajs:
- traj_type: spline-trajectory
coords: [[0.70, 0.30, 0.5], [0.30, 0.30, 0.5]]
angle_offset: 0.
# Object 4
-
proxy_size: [0.27, 0.27, 0.27]
# proxy_size: [0.2, 0.2, 0.2]
trajs:
- traj_type: static-trajectory
pos_start: [0.5, 0.5, 0.52]
angle_offset: 90.
prompt_processor:
prompt: ["a tiger walking", "a rhinoceros walking", "a firehydrant", "assassin riding a cow", "a deer walking"]
# First train each object independently and use this to render different trained models together
checkpoints: [
/path/to/a_tiger_walking@x/ckpts/last.ckpt,
/path/to/a_rhinoceros_walking@x/ckpts/last.ckpt,
/path/to/assassin_riding_a_cow@x/ckpts/last.ckpt,
/path/to/a_deer_walking@x/ckpts/last.ckpt,
/path/to/water_spraying_out_of_a_firehydrant@x/ckpts/last.ckpt,
]
================================================
FILE: configs_comp/comp3.yaml
================================================
name: "comp3"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.75, 0.65, 0.4], [0.75, 0.35, 0.4]]
angle_offset: 0.
# Object 1
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.5, 0.3, 0.4], [0.3, 0.5, 0.4], [0.5, 0.7, 0.4]]
angle_offset: 0.
# Object 3
-
proxy_size: [0.4, 0.4, 0.4]
trajs:
- traj_type: spline-trajectory
coords: [[0.25, 0.5, 0.7], [0.5, 0.25, 0.7], [0.75, 0.5, 0.7], [0.5, 0.75, 0.7],[0.25, 0.5, 0.7]]
angle_offset: 0.
# Object 2
-
proxy_size: [0.7, 0.7, 0.7]
trajs:
- traj_type: static-trajectory
pos_start: [0.5, 0.5, 0.55]
angle_offset: 90.
prompt_processor:
prompt: ["a sheep running", "a goat walking", "a seagull flying", "a lamppost"]
# First train each object independently and use this to render different trained models together
checkpoints: [
/path/to/a_sheep_running@x/ckpts/last.ckpt,
/path/to/a_goat_walking@x/ckpts/last.ckpt,
/path/to/a_seagull_flying_with_fluttering_wings@x/ckpts/last.ckpt,
/path/to/a_lamppost@x/ckpts/last.ckpt,
]
================================================
FILE: configs_prompts/a_bear_walking.yaml
================================================
name: "a_bear_walking"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.5, 0.35, 0.5], [0.5, 0.65, 0.5]]
# coords: [[0.25, 0.25, 0.5], [0.35, 0.6, 0.5], [0.5, 0.75, 0.5]]
# coords: [[0.25, 0.5, 0.5], [0.5, 0.75, 0.5], [0.75, 0.5, 0.5], [0.5, 0.25, 0.5], [0.25, 0.5, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a bear walking"]
================================================
FILE: configs_prompts/a_camel_walking.yaml
================================================
name: "a_camel_walking"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.5, 0.35, 0.5], [0.5, 0.65, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a camel walking"]
================================================
FILE: configs_prompts/a_carp_swimming.yaml
================================================
name: "a_carp_swimming"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.3, 0.65, 0.5], [0.25, 0.52, 0.5], [0.25, 0.4, 0.5], [0.25, 0.3, 0.5], [0.45, 0.3, 0.5], [0.45, 0.7, 0.5], [0.4, 0.7, 0.5], [0.3, 0.65, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a carp swimming"]
================================================
FILE: configs_prompts/a_cat_walking.yaml
================================================
name: "a_cat_walking"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.65, 0.65, 0.65]
trajs:
- traj_type: spline-trajectory
coords: [[0.34, 0.6, 0.5], [0.4, 0.65, 0.5], [0.6, 0.6, 0.5], [0.7, 0.3, 0.5], [0.33, 0.51, 0.5], [0.34, 0.6, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a cat walking"]
================================================
FILE: configs_prompts/a_chihuahua_running.yaml
================================================
name: "a_chihuahua_running"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.5, 0.3, 0.5], [0.28, 0.55, 0.5], [0.4, 0.75, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a chihuahua running"]
================================================
FILE: configs_prompts/a_clown_fish_swimming.yaml
================================================
name: "a_clown_fish_swimming"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.3, 0.45, 0.5], [0.5, 0.75, 0.5], [0.7, 0.5, 0.5], [0.5, 0.25, 0.5], [0.3, 0.45, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a clown fish swimming"]
================================================
FILE: configs_prompts/a_corgi_running.yaml
================================================
name: "a_corgi_running"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.5, 0.3, 0.5], [0.6, 0.5, 0.5], [0.5, 0.7, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a corgi running"]
================================================
FILE: configs_prompts/a_deer_walking.yaml
================================================
name: "a_deer_walking"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
# coords: [[0.25, 0.5, 0.5], [0.5, 0.25, 0.5], [0.75, 0.5, 0.5], [0.5, 0.75, 0.5], [0.25, 0.5, 0.5]]
coords: [[0.5, 0.35, 0.5], [0.5, 0.65, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a deer walking"]
================================================
FILE: configs_prompts/a_dog_riding_a_skateboard.yaml
================================================
name: "a_dog_riding_a_skateboard"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.3, 0.45, 0.5], [0.35, 0.30, 0.5], [0.4, 0.3, 0.5], [0.55, 0.7, 0.5], [0.75, 0.45, 0.5], [0.55, 0.45, 0.5], [0.3, 0.45, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a dog riding a skateboard"]
================================================
FILE: configs_prompts/a_fox_walking.yaml
================================================
name: "a_fox_walking"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.65, 0.25, 0.5], [0.25, 0.65, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a fox walking"]
================================================
FILE: configs_prompts/a_german_shepherd_running.yaml
================================================
name: "a_german_shepherd_running"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.5, 0.3, 0.5], [0.3, 0.5, 0.5], [0.5, 0.7, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a german shepherd running"]
================================================
FILE: configs_prompts/a_giraffe_walking.yaml
================================================
name: "a_giraffe_walking"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.5, 0.35, 0.5], [0.5, 0.65, 0.5]]
# For compositional scene
# coords: [[0.75, 0.75, 0.5], [0.6, 0.35, 0.5], [0.25, 0.5, 0.5]]
angle_offset: 0.
prompt_processor:
# prompt: ["a giraffe"] # used in static stage
prompt: ["a giraffe walking"]
================================================
FILE: configs_prompts/a_girl_is_riding_a_bicycle.yaml
================================================
name: "a_girl_is_riding_a_bicycle"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.25, 0.5, 0.5], [0.5, 0.25, 0.5], [0.75, 0.5, 0.5], [0.5, 0.75, 0.5], [0.25, 0.5, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a girl is riding a bicycle"]
================================================
FILE: configs_prompts/a_goat_walking.yaml
================================================
name: "a_goat_walking"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.5, 0.3, 0.5], [0.3, 0.5, 0.5], [0.5, 0.7, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a goat walking"]
================================================
FILE: configs_prompts/a_hippo_walking.yaml
================================================
name: "a_hippo_walking"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.5, 0.3, 0.5], [0.25, 0.5, 0.5], [0.5, 0.7, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a hippo walking"]
================================================
FILE: configs_prompts/a_labrador_running.yaml
================================================
name: "a_labrador_running"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.5, 0.35, 0.5], [0.5, 0.65, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a labrador running"]
================================================
FILE: configs_prompts/a_lion_walking.yaml
================================================
name: "a_lion_walking"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.3, 0.3, 0.5], [0.3, 0.5, 0.5], [0.5, 0.6, 0.5], [0.7, 0.7, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a lion walking"]
================================================
FILE: configs_prompts/a_pigeon_flying.yaml
================================================
name: "a_pigeon_flying"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.5, 0.35, 0.5], [0.5, 0.65, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a pigeon flying"]
================================================
FILE: configs_prompts/a_rhinoceros_walking.yaml
================================================
name: "a_rhinoceros_walking"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
angle_offset: 0.
coords: [[0.5, 0.35, 0.5], [0.5, 0.65, 0.5]]
prompt_processor:
prompt: ["a rhinoceros walking"]
================================================
FILE: configs_prompts/a_seagull_flying.yaml
================================================
name: "a_seagull_flying"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.45, 0.25, 0.5], [0.30, 0.33, 0.5], [0.3, 0.4, 0.5], [0.75, 0.65, 0.5], [0.51, 0.75, 0.5], [0.45, 0.6, 0.5], [0.45, 0.5, 0.5], [0.45, 0.25, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a seagull flying"]
================================================
FILE: configs_prompts/a_shark_swimming.yaml
================================================
name: "a_shark_swimming"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.45, 0.25, 0.5], [0.30, 0.33, 0.5], [0.3, 0.4, 0.5], [0.75, 0.65, 0.5], [0.51, 0.75, 0.5], [0.45, 0.6, 0.5], [0.45, 0.5, 0.5], [0.45, 0.25, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a shark swimming"]
================================================
FILE: configs_prompts/a_sheep_running.yaml
================================================
name: "a_sheep_running"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.5, 0.35, 0.5], [0.5, 0.65, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a sheep running"]
================================================
FILE: configs_prompts/a_tiger_walking.yaml
================================================
name: "a_tiger_walking"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.5, 0.35, 0.5], [0.5, 0.65, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a tiger walking"]
================================================
FILE: configs_prompts/a_turtle_swimming.yaml
================================================
name: "a_turtle_swimming"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.3, 0.55, 0.5], [0.7, 0.7, 0.5], [0.75, 0.5, 0.5], [0.5, 0.25, 0.5], [0.25, 0.25, 0.5], [0.3, 0.55, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a turtle swimming"]
================================================
FILE: configs_prompts/a_unicorn_running.yaml
================================================
name: "a_unicorn_running"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.5, 0.7, 0.5], [0.5, 0.3, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a unicorn running"]
================================================
FILE: configs_prompts/a_wolf_running.yaml
================================================
name: "a_wolf_running"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.5, 0.35, 0.5], [0.5, 0.65, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["a wolf running"]
================================================
FILE: configs_prompts/an_astronaut_riding_a_horse.yaml
================================================
name: "an_astronaut_riding_a_horse"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
# Compositional scene
# coords: [[0.25, 0.5, 0.5], [0.5, 0.75, 0.5], [0.75, 0.5, 0.5], [0.5, 0.25, 0.5], [0.25, 0.5, 0.5]]
# Single
coords: [[0.5, 0.35, 0.5], [0.5, 0.65, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["an astronaut riding a horse"]
================================================
FILE: configs_prompts/an_eagle_flying.yaml
================================================
name: "an_eagle_flying"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.45, 0.25, 0.5], [0.30, 0.33, 0.5], [0.3, 0.4, 0.5], [0.75, 0.65, 0.5], [0.51, 0.75, 0.5], [0.45, 0.6, 0.5], [0.45, 0.5, 0.5], [0.45, 0.25, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["an eagle flying"]
================================================
FILE: configs_prompts/an_elephant_walking.yaml
================================================
name: "an_elephant_walking"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
# Single
coords: [[0.5, 0.35, 0.5], [0.5, 0.65, 0.5]]
# Compositional
# coords: [[0.25, 0.25, 0.5], [0.35, 0.6, 0.5], [0.5, 0.75, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["an elephant walking"]
================================================
FILE: configs_prompts/an_octopus_swimming.yaml
================================================
name: "an_octopus_swimming"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.65, 0.65, 0.65]
trajs:
# Object 0
- traj_type: spline-trajectory
coords: [[0.5, 0.5, 0.4], [0.5, 0.5, 0.6]]
angle_offset: 180.
prompt_processor:
prompt: ["an octopus swimming"]
================================================
FILE: configs_prompts/assassin_riding_a_cow.yaml
================================================
name: "assassin_riding_a_cow"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.6, 0.6, 0.6]
trajs:
- traj_type: spline-trajectory
coords: [[0.5, 0.35, 0.5], [0.5, 0.65, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["assassin riding a cow"]
================================================
FILE: configs_prompts/batman_riding_a_camel.yaml
================================================
name: "batman_riding_a_camel"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.7, 0.7, 0.7]
trajs:
- traj_type: spline-trajectory
coords: [[0.5, 0.35, 0.5], [0.5, 0.65, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["batman riding a camel"]
================================================
FILE: configs_prompts/deadpool_riding_a_cow.yaml
================================================
name: "deadpool_riding_a_cow"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
# Compositional
# coords: [[0.25, 0.5, 0.5], [0.5, 0.75, 0.5], [0.75, 0.5, 0.5], [0.5, 0.25, 0.5], [0.25, 0.5, 0.5]]
# Single
coords: [[0.5, 0.35, 0.5], [0.5, 0.65, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["deadpool riding a cow"]
================================================
FILE: configs_prompts/son_goku_riding_an_elephant.yaml
================================================
name: "son_goku_riding_an_elephant"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.65, 0.65, 0.65]
trajs:
- traj_type: spline-trajectory
coords: [[0.5, 0.35, 0.5], [0.4, 0.5, 0.5], [0.5, 0.65, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["son goku riding an elephant"]
================================================
FILE: configs_prompts/spiderman_riding_a_donkey.yaml
================================================
name: "spiderman_riding_a_donkey"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: spline-trajectory
coords: [[0.5, 0.35, 0.5], [0.5, 0.65, 0.5]]
angle_offset: 0.
prompt_processor:
prompt: ["spiderman riding a donkey, donkey running"]
================================================
FILE: configs_prompts_static/a_firepit.yaml
================================================
name: "a_firepit"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: static-trajectory
pos_start: [0.5, 0.5, 0.5]
angle_offset: 90.
prompt_processor:
prompt: ["a firepit with large flame"]
================================================
FILE: configs_prompts_static/a_lamppost.yaml
================================================
name: "a_lamppost"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: static-trajectory
pos_start: [0.5, 0.5, 0.5]
angle_offset: 90.
prompt_processor:
prompt: ["a lamppost"]
# Only train stage 1 and 2
================================================
FILE: configs_prompts_static/water_spraying_out_of_a_firehydrant.yaml
================================================
name: "water_spraying_out_of_a_firehydrant"
config_scene:
traj_kwargs:
# Object 0
-
proxy_size: [0.5, 0.5, 0.5]
trajs:
- traj_type: static-trajectory
pos_start: [0.5, 0.5, 0.5]
angle_offset: 90.
prompt_processor:
prompt: ["water spraying out of a firehydrant"]
================================================
FILE: launch.py
================================================
import argparse
import logging
import os
import shutil
import sys
class ColoredFilter(logging.Filter):
"""
A logging filter to add color to certain log levels.
"""
RESET = "\033[0m"
RED = "\033[31m"
GREEN = "\033[32m"
YELLOW = "\033[33m"
BLUE = "\033[34m"
MAGENTA = "\033[35m"
CYAN = "\033[36m"
COLORS = {
"WARNING": YELLOW,
"INFO": GREEN,
"DEBUG": BLUE,
"CRITICAL": MAGENTA,
"ERROR": RED,
}
RESET = "\x1b[0m"
def __init__(self):
super().__init__()
def filter(self, record):
if record.levelname in self.COLORS:
color_start = self.COLORS[record.levelname]
record.levelname = f"{color_start}[{record.levelname}]"
record.msg = f"{record.msg}{self.RESET}"
return True
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--config", required=True, help="path to config file")
parser.add_argument("--gpu", default="0", help="GPU(s) to be used")
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument("--train", action="store_true")
group.add_argument("--validate", action="store_true")
group.add_argument("--test", action="store_true")
group.add_argument("--export", action="store_true")
parser.add_argument(
"--verbose", action="store_true", help="if true, set logging level to DEBUG"
)
parser.add_argument(
"--typecheck",
action="store_true",
help="whether to enable dynamic type checking",
)
args, extras = parser.parse_known_args()
# set CUDA_VISIBLE_DEVICES then import pytorch-lightning
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
n_gpus = len(args.gpu.split(","))
import pytorch_lightning as pl
import torch
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
from pytorch_lightning.loggers import CSVLogger, TensorBoardLogger
from pytorch_lightning.utilities.rank_zero import rank_zero_only
if args.typecheck:
from jaxtyping import install_import_hook
install_import_hook("threestudio", "typeguard.typechecked")
import threestudio
from threestudio.systems.base import BaseSystem
from threestudio.utils.callbacks import (
CodeSnapshotCallback,
ConfigSnapshotCallback,
CustomProgressBar,
# ModelCheckpointCustom,
)
from threestudio.utils.config import ExperimentConfig, load_config
from threestudio.utils.typing import Optional
logger = logging.getLogger("pytorch_lightning")
if args.verbose:
logger.setLevel(logging.DEBUG)
for handler in logger.handlers:
if handler.stream == sys.stderr: # type: ignore
handler.setFormatter(logging.Formatter("%(levelname)s %(message)s"))
handler.addFilter(ColoredFilter())
# parse YAML config to OmegaConf
cfg: ExperimentConfig
cfg = load_config(args.config, cli_args=extras, n_gpus=n_gpus)
pl.seed_everything(cfg.seed)
dm = threestudio.find(cfg.data_type)(cfg.data)
system: BaseSystem = threestudio.find(cfg.system_type)(
cfg.system, resumed=cfg.resume is not None
)
system.set_save_dir(os.path.join(cfg.trial_dir, "save"))
callbacks = []
if args.train:
callbacks += [
ModelCheckpoint(
dirpath=os.path.join(cfg.trial_dir, "ckpts"), **cfg.checkpoint
),
LearningRateMonitor(logging_interval="step"),
CustomProgressBar(refresh_rate=1),
CodeSnapshotCallback(
os.path.join(cfg.trial_dir, "code"), use_version=False
),
ConfigSnapshotCallback(
args.config,
cfg,
os.path.join(cfg.trial_dir, "configs"),
use_version=False,
),
]
def write_to_text(file, lines):
with open(file, "w") as f:
for line in lines:
f.write(line + "\n")
loggers = []
if args.train:
# make tensorboard logging dir to suppress warning
rank_zero_only(
lambda: os.makedirs(os.path.join(cfg.trial_dir, "tb_logs"), exist_ok=True)
)()
loggers += [
TensorBoardLogger(cfg.trial_dir, name="tb_logs"),
CSVLogger(cfg.trial_dir, name="csv_logs"),
] + system.get_loggers()
rank_zero_only(
lambda: write_to_text(
os.path.join(cfg.trial_dir, "log.txt"),
["python " + " ".join(sys.argv), str(args)],
)
)()
trainer = Trainer(
callbacks=callbacks, logger=loggers, inference_mode=False, **cfg.trainer
)
def set_system_status(system: BaseSystem, ckpt_path: Optional[str]):
if ckpt_path is None:
return
ckpt = torch.load(ckpt_path, map_location="cpu")
system.set_resume_status(ckpt["epoch"], ckpt["global_step"])
if args.train:
trainer.fit(system, datamodule=dm, ckpt_path=cfg.resume)
trainer.test(system, datamodule=dm)
elif args.validate:
# manually set epoch and global_step as they cannot be automatically resumed
set_system_status(system, cfg.resume)
trainer.validate(system, datamodule=dm, ckpt_path=cfg.resume)
elif args.test:
# manually set epoch and global_step as they cannot be automatically resumed
set_system_status(system, cfg.resume)
trainer.test(system, datamodule=dm, ckpt_path=cfg.resume)
elif args.export:
set_system_status(system, cfg.resume)
trainer.predict(system, datamodule=dm, ckpt_path=cfg.resume)
if __name__ == "__main__":
main()
================================================
FILE: load/make_prompt_library.py
================================================
import json
dreamfusion_gallery_video_names = [
"a_20-sided_die_made_out_of_glass.mp4",
"a_bald_eagle_carved_out_of_wood.mp4",
"a_banana_peeling_itself.mp4",
"a_beagle_in_a_detective's_outfit.mp4",
"a_beautiful_dress_made_out_of_fruit,_on_a_mannequin._Studio_lighting,_high_quality,_high_resolution.mp4",
"a_beautiful_dress_made_out_of_garbage_bags,_on_a_mannequin._Studio_lighting,_high_quality,_high_resolution.mp4",
"a_beautiful_rainbow_fish.mp4",
"a_bichon_frise_wearing_academic_regalia.mp4",
"a_blue_motorcycle.mp4",
"a_blue_poison-dart_frog_sitting_on_a_water_lily.mp4",
"a_brightly_colored_mushroom_growing_on_a_log.mp4",
"a_bumblebee_sitting_on_a_pink_flower.mp4",
"a_bunch_of_colorful_marbles_spilling_out_of_a_red_velvet_bag.mp4",
"a_capybara_wearing_a_top_hat,_low_poly.mp4",
"a_cat_with_a_mullet.mp4",
"a_ceramic_lion.mp4",
"a_ceramic_upside_down_yellow_octopus_holding_a_blue_green_ceramic_cup.mp4",
"a_chihuahua_wearing_a_tutu.mp4",
"a_chimpanzee_holding_a_peeled_banana.mp4",
"a_chimpanzee_looking_through_a_telescope.mp4",
"a_chimpanzee_stirring_a_bubbling_purple_potion_in_a_cauldron.mp4",
"a_chimpanzee_with_a_big_grin.mp4",
"a_completely_destroyed_car.mp4",
"a_confused_beagle_sitting_at_a_desk_working_on_homework.mp4",
"a_corgi_taking_a_selfie.mp4",
"a_crab,_low_poly.mp4",
"a_crocodile_playing_a_drum_set.mp4",
"a_cute_steampunk_elephant.mp4",
"a_dachsund_dressed_up_in_a_hotdog_costume.mp4",
"a_delicious_hamburger.mp4",
"a_dragon-cat_hybrid.mp4",
"a_DSLR_photo_of_a_baby_dragon_drinking_boba.mp4",
"a_DSLR_photo_of_a_baby_dragon_hatching_out_of_a_stone_egg.mp4",
"a_DSLR_photo_of_a_baby_grand_piano_viewed_from_far_away.mp4",
"a_DSLR_photo_of_a_bagel_filled_with_cream_cheese_and_lox.mp4",
"a_DSLR_photo_of_a_bald_eagle.mp4",
"a_DSLR_photo_of_a_barbecue_grill_cooking_sausages_and_burger_patties.mp4",
"a_DSLR_photo_of_a_basil_plant.mp4",
"a_DSLR_photo_of_a_bear_dancing_ballet.mp4",
"a_DSLR_photo_of_a_bear_dressed_as_a_lumberjack.mp4",
"a_DSLR_photo_of_a_bear_dressed_in_medieval_armor.mp4",
"a_DSLR_photo_of_a_beautiful_violin_sitting_flat_on_a_table.mp4",
"a_DSLR_photo_of_a_blue_jay_standing_on_a_large_basket_of_rainbow_macarons.mp4",
"a_DSLR_photo_of_a_bulldozer_clearing_away_a_pile_of_snow.mp4",
"a_DSLR_photo_of_a_bulldozer.mp4",
"a_DSLR_photo_of_a_cake_covered_in_colorful_frosting_with_a_slice_being_taken_out,_high_resolution.mp4",
"a_DSLR_photo_of_a_candelabra_with_many_candles_on_a_red_velvet_tablecloth.mp4",
"a_DSLR_photo_of_a_car_made_out_of_cheese.mp4",
"a_DSLR_photo_of_A_car_made_out_of_sushi.mp4",
"a_DSLR_photo_of_a_car_made_out_pizza.mp4",
"a_DSLR_photo_of_a_cat_lying_on_its_side_batting_at_a_ball_of_yarn.mp4",
"a_DSLR_photo_of_a_cat_magician_making_a_white_dove_appear.mp4",
"a_DSLR_photo_of_a_cat_wearing_a_bee_costume.mp4",
"a_DSLR_photo_of_a_cat_wearing_a_lion_costume.mp4",
"a_DSLR_photo_of_a_cauldron_full_of_gold_coins.mp4",
"a_DSLR_photo_of_a_chimpanzee_dressed_like_Henry_VIII_king_of_England.mp4",
"a_DSLR_photo_of_a_chimpanzee_dressed_like_Napoleon_Bonaparte.mp4",
"a_DSLR_photo_of_a_chow_chow_puppy.mp4",
"a_DSLR_photo_of_a_Christmas_tree_with_donuts_as_decorations.mp4",
"a_DSLR_photo_of_a_chrome-plated_duck_with_a_golden_beak_arguing_with_an_angry_turtle_in_a_forest.mp4",
"a_DSLR_photo_of_a_classic_Packard_car.mp4",
"a_DSLR_photo_of_a_cocker_spaniel_wearing_a_crown.mp4",
"a_DSLR_photo_of_a_corgi_lying_on_its_back_with_its_tongue_lolling_out.mp4",
"a_DSLR_photo_of_a_corgi_puppy.mp4",
"a_DSLR_photo_of_a_corgi_sneezing.mp4",
"a_DSLR_photo_of_a_corgi_standing_up_drinking_boba.mp4",
"a_DSLR_photo_of_a_corgi_taking_a_selfie.mp4",
"a_DSLR_photo_of_a_corgi_wearing_a_beret_and_holding_a_baguette,_standing_up_on_two_hind_legs.mp4",
"a_DSLR_photo_of_a_covered_wagon.mp4",
"a_DSLR_photo_of_a_cracked_egg_with_the_yolk_spilling_out_on_a_wooden_table.mp4",
"a_DSLR_photo_of_a_cup_full_of_pens_and_pencils.mp4",
"a_DSLR_photo_of_a_dalmation_wearing_a_fireman's_hat.mp4",
"a_DSLR_photo_of_a_delicious_chocolate_brownie_dessert_with_ice_cream_on_the_side.mp4",
"a_DSLR_photo_of_a_delicious_croissant.mp4",
"a_DSLR_photo_of_A_DMC_Delorean_car.mp4",
"a_DSLR_photo_of_a_dog_made_out_of_salad.mp4",
"a_DSLR_photo_of_a_drum_set_made_of_cheese.mp4",
"a_DSLR_photo_of_a_drying_rack_covered_in_clothes.mp4",
"a_DSLR_photo_of_aerial_view_of_a_ruined_castle.mp4",
"a_DSLR_photo_of_a_football_helmet.mp4",
"a_DSLR_photo_of_a_fox_holding_a_videogame_controller.mp4",
"a_DSLR_photo_of_a_fox_taking_a_photograph_using_a_DSLR.mp4",
"a_DSLR_photo_of_a_frazer_nash_super_sport_car.mp4",
"a_DSLR_photo_of_a_frog_wearing_a_sweater.mp4",
"a_DSLR_photo_of_a_ghost_eating_a_hamburger.mp4",
"a_DSLR_photo_of_a_giant_worm_emerging_from_the_sand_in_the_middle_of_the_desert.mp4",
"a_DSLR_photo_of_a_goose_made_out_of_gold.mp4",
"a_DSLR_photo_of_a_green_monster_truck.mp4",
"a_DSLR_photo_of_a_group_of_dogs_eating_pizza.mp4",
"a_DSLR_photo_of_a_group_of_dogs_playing_poker.mp4",
"a_DSLR_photo_of_a_gummy_bear_playing_the_saxophone.mp4",
"a_DSLR_photo_of_a_hippo_wearing_a_sweater.mp4",
"a_DSLR_photo_of_a_humanoid_robot_holding_a_human_brain.mp4",
"a_DSLR_photo_of_a_humanoid_robot_playing_solitaire.mp4",
"a_DSLR_photo_of_a_humanoid_robot_playing_the_cello.mp4",
"a_DSLR_photo_of_a_humanoid_robot_using_a_laptop.mp4",
"a_DSLR_photo_of_a_humanoid_robot_using_a_rolling_pin_to_roll_out_dough.mp4",
"a_DSLR_photo_of_a_human_skull.mp4",
"a_DSLR_photo_of_a_kitten_standing_on_top_of_a_giant_tortoise.mp4",
"a_DSLR_photo_of_a_knight_chopping_wood.mp4",
"a_DSLR_photo_of_a_knight_holding_a_lance_and_sitting_on_an_armored_horse.mp4",
"a_DSLR_photo_of_a_koala_wearing_a_party_hat_and_blowing_out_birthday_candles_on_a_cake.mp4",
"a_DSLR_photo_of_a_lemur_taking_notes_in_a_journal.mp4",
"a_DSLR_photo_of_a_lion_reading_the_newspaper.mp4",
"a_DSLR_photo_of_a_mandarin_duck_swimming_in_a_pond.mp4",
"a_DSLR_photo_of_a_model_of_the_eiffel_tower_made_out_of_toothpicks.mp4",
"a_DSLR_photo_of_a_mouse_playing_the_tuba.mp4",
"a_DSLR_photo_of_a_mug_of_hot_chocolate_with_whipped_cream_and_marshmallows.mp4",
"a_DSLR_photo_of_an_adorable_piglet_in_a_field.mp4",
"a_DSLR_photo_of_an_airplane_taking_off_from_the_runway.mp4",
"a_DSLR_photo_of_an_astronaut_standing_on_the_surface_of_mars.mp4",
"a_DSLR_photo_of_an_eggshell_broken_in_two_with_an_adorable_chick_standing_next_to_it.mp4",
"a_DSLR_photo_of_an_elephant_skull.mp4",
"a_DSLR_photo_of_an_exercise_bike_in_a_well_lit_room.mp4",
"a_DSLR_photo_of_an_extravagant_mansion,_aerial_view.mp4",
"a_DSLR_photo_of_an_ice_cream_sundae.mp4",
"a_DSLR_photo_of_an_iguana_holding_a_balloon.mp4",
"a_DSLR_photo_of_an_intricate_and_complex_dish_from_a_michelin_star_restaurant.mp4",
"a_DSLR_photo_of_An_iridescent_steampunk_patterned_millipede_with_bison_horns.mp4",
"a_DSLR_photo_of_an_octopus_playing_the_piano.mp4",
"a_DSLR_photo_of_an_old_car_overgrown_by_vines_and_weeds.mp4",
"a_DSLR_photo_of_an_old_vintage_car.mp4",
"a_DSLR_photo_of_an_orangutan_making_a_clay_bowl_on_a_throwing_wheel.mp4",
"a_DSLR_photo_of_an_orc_forging_a_hammer_on_an_anvil.mp4",
"a_DSLR_photo_of_an_origami_motorcycle.mp4",
"a_DSLR_photo_of_an_ornate_silver_gravy_boat_sitting_on_a_patterned_tablecloth.mp4",
"a_DSLR_photo_of_an_overstuffed_pastrami_sandwich.mp4",
"a_DSLR_photo_of_an_unstable_rock_cairn_in_the_middle_of_a_stream.mp4",
"a_DSLR_photo_of_a_pair_of_headphones_sitting_on_a_desk.mp4",
"a_DSLR_photo_of_a_pair_of_tan_cowboy_boots,_studio_lighting,_product_photography.mp4",
"a_DSLR_photo_of_a_peacock_on_a_surfboard.mp4",
"a_DSLR_photo_of_a_pigeon_reading_a_book.mp4",
"a_DSLR_photo_of_a_piglet_sitting_in_a_teacup.mp4",
"a_DSLR_photo_of_a_pig_playing_a_drum_set.mp4",
"a_DSLR_photo_of_a_pile_of_dice_on_a_green_tabletop_next_to_some_playing_cards.mp4",
"a_DSLR_photo_of_a_pirate_collie_dog,_high_resolution.mp4",
"a_DSLR_photo_of_a_plate_of_fried_chicken_and_waffles_with_maple_syrup_on_them.mp4",
"a_DSLR_photo_of_a_plate_piled_high_with_chocolate_chip_cookies.mp4",
"a_DSLR_photo_of_a_plush_t-rex_dinosaur_toy,_studio_lighting,_high_resolution.mp4",
"a_DSLR_photo_of_a_plush_triceratops_toy,_studio_lighting,_high_resolution.mp4",
"a_DSLR_photo_of_a_pomeranian_dog.mp4",
"a_DSLR_photo_of_a_porcelain_dragon.mp4",
"a_DSLR_photo_of_a_praying_mantis_wearing_roller_skates.mp4",
"a_DSLR_photo_of_a_puffin_standing_on_a_rock.mp4",
"a_DSLR_photo_of_a_pug_made_out_of_metal.mp4",
"a_DSLR_photo_of_a_pug_wearing_a_bee_costume.mp4",
"a_DSLR_photo_of_a_quill_and_ink_sitting_on_a_desk.mp4",
"a_DSLR_photo_of_a_raccoon_stealing_a_pie.mp4",
"a_DSLR_photo_of_a_red_cardinal_bird_singing.mp4",
"a_DSLR_photo_of_a_red_convertible_car_with_the_top_down.mp4",
"a_DSLR_photo_of_a_red-eyed_tree_frog.mp4",
"a_DSLR_photo_of_a_red_pickup_truck_driving_across_a_stream.mp4",
"a_DSLR_photo_of_a_red_wheelbarrow_with_a_shovel_in_it.mp4",
"a_DSLR_photo_of_a_roast_turkey_on_a_platter.mp4",
"a_DSLR_photo_of_a_robot_and_dinosaur_playing_chess,_high_resolution.mp4",
"a_DSLR_photo_of_a_robot_arm_picking_up_a_colorful_block_from_a_table.mp4",
"a_DSLR_photo_of_a_robot_cat_knocking_over_a_chess_piece_on_a_board.mp4",
"a_DSLR_photo_of_a_robot_dinosaur.mp4",
"a_DSLR_photo_of_a_robot_made_out_of_vegetables.mp4",
"a_DSLR_photo_of_a_robot_stegosaurus.mp4",
"a_DSLR_photo_of_a_robot_tiger.mp4",
"a_DSLR_photo_of_a_rolling_pin_on_top_of_bread_dough.mp4",
"a_DSLR_photo_of_a_sheepdog_running.mp4",
"a_DSLR_photo_of_a_shiba_inu_playing_golf_wearing_tartan_golf_clothes_and_hat.mp4",
"a_DSLR_photo_of_a_shiny_silver_robot_cat.mp4",
"a_DSLR_photo_of_a_silverback_gorilla_holding_a_golden_trophy.mp4",
"a_DSLR_photo_of_a_silver_humanoid_robot_flipping_a_coin.mp4",
"a_DSLR_photo_of_a_small_cherry_tomato_plant_in_a_pot_with_a_few_red_tomatoes_growing_on_it.mp4",
"a_DSLR_photo_of_a_small_saguaro_cactus_planted_in_a_clay_pot.mp4",
"a_DSLR_photo_of_a_Space_Shuttle.mp4",
"a_DSLR_photo_of_a_squirrel_dressed_like_a_clown.mp4",
"a_DSLR_photo_of_a_squirrel_flying_a_biplane.mp4",
"a_DSLR_photo_of_a_squirrel_giving_a_lecture_writing_on_a_chalkboard.mp4",
"a_DSLR_photo_of_a_squirrel_holding_a_bowling_ball.mp4",
"a_DSLR_photo_of_a_squirrel-lizard_hybrid.mp4",
"a_DSLR_photo_of_a_squirrel_made_out_of_fruit.mp4",
"a_DSLR_photo_of_a_squirrel-octopus_hybrid.mp4",
"a_DSLR_photo_of_a_stack_of_pancakes_covered_in_maple_syrup.mp4",
"a_DSLR_photo_of_a_steam_engine_train,_high_resolution.mp4",
"a_DSLR_photo_of_a_steaming_basket_full_of_dumplings.mp4",
"a_DSLR_photo_of_a_steaming_hot_plate_piled_high_with_spaghetti_and_meatballs.mp4",
"a_DSLR_photo_of_a_steampunk_space_ship_designed_in_the_18th_century.mp4",
"a_DSLR_photo_of_a_straw_basket_with_a_cobra_coming_out_of_it.mp4",
"a_DSLR_photo_of_a_swan_and_its_cygnets_swimming_in_a_pond.mp4",
"a_DSLR_photo_of_a_tarantula,_highly_detailed.mp4",
"a_DSLR_photo_of_a_teal_moped.mp4",
"a_DSLR_photo_of_a_teapot_shaped_like_an_elephant_head_where_its_snout_acts_as_the_spout.mp4",
"a_DSLR_photo_of_a_teddy_bear_taking_a_selfie.mp4",
"a_DSLR_photo_of_a_terracotta_bunny.mp4",
"a_DSLR_photo_of_a_tiger_dressed_as_a_doctor.mp4",
"a_DSLR_photo_of_a_tiger_made_out_of_yarn.mp4",
"a_DSLR_photo_of_a_toilet_made_out_of_gold.mp4",
"a_DSLR_photo_of_a_toy_robot.mp4",
"a_DSLR_photo_of_a_train_engine_made_out_of_clay.mp4",
"a_DSLR_photo_of_a_tray_of_Sushi_containing_pugs.mp4",
"a_DSLR_photo_of_a_tree_stump_with_an_axe_buried_in_it.mp4",
"a_DSLR_photo_of_a_turtle_standing_on_its_hind_legs,_wearing_a_top_hat_and_holding_a_cane.mp4",
"a_DSLR_photo_of_a_very_beautiful_small_organic_sculpture_made_of_fine_clockwork_and_gears_with_tiny_ruby_bearings,_very_intricate,_caved,_curved._Studio_lighting,_High_resolution,_white_background.mp4",
"a_DSLR_photo_of_A_very_beautiful_tiny_human_heart_organic_sculpture_made_of_copper_wire_and_threaded_pipes,_very_intricate,_curved,_Studio_lighting,_high_resolution.mp4",
"a_DSLR_photo_of_a_very_cool_and_trendy_pair_of_sneakers,_studio_lighting.mp4",
"a_DSLR_photo_of_a_vintage_record_player.mp4",
"a_DSLR_photo_of_a_wine_bottle_and_full_wine_glass_on_a_chessboard.mp4",
"a_DSLR_photo_of_a_wooden_desk_and_chair_from_an_elementary_school.mp4",
"a_DSLR_photo_of_a_yorkie_dog_eating_a_donut.mp4",
"a_DSLR_photo_of_a_yorkie_dog_wearing_extremely_cool_sneakers.mp4",
"a_DSLR_photo_of_baby_elephant_jumping_on_a_trampoline.mp4",
"a_DSLR_photo_of_cat_wearing_virtual_reality_headset_in_renaissance_oil_painting_high_detail_caravaggio.mp4",
"a_DSLR_photo_of_edible_typewriter_made_out_of_vegetables.mp4",
"a_DSLR_photo_of_Mont_Saint-Michel,_France,_aerial_view.mp4",
"a_DSLR_photo_of_Mount_Fuji,_aerial_view.mp4",
"a_DSLR_photo_of_Neuschwanstein_Castle,_aerial_view.mp4",
"A_DSLR_photo_of___pyramid_shaped_burrito_with_a_slice_cut_out_of_it.mp4",
"a_DSLR_photo_of_the_Imperial_State_Crown_of_England.mp4",
"a_DSLR_photo_of_the_leaning_tower_of_Pisa,_aerial_view.mp4",
"a_DSLR_photo_of_the_Statue_of_Liberty,_aerial_view.mp4",
"a_DSLR_photo_of_Two_locomotives_playing_tug_of_war.mp4",
"a_DSLR_photo_of_two_macaw_parrots_sharing_a_milkshake_with_two_straws.mp4",
"a_DSLR_photo_of_Westminster_Abbey,_aerial_view.mp4",
"a_ficus_planted_in_a_pot.mp4",
"a_flower_made_out_of_metal.mp4",
"a_fluffy_cat_lying_on_its_back_in_a_patch_of_sunlight.mp4",
"a_fox_and_a_hare_tangoing_together.mp4",
"a_fox_holding_a_videogame_controller.mp4",
"a_fox_playing_the_cello.mp4",
"a_frazer_nash_super_sport_car.mp4",
"a_freshly_baked_loaf_of_sourdough_bread_on_a_cutting_board.mp4",
"a_goat_drinking_beer.mp4",
"a_golden_goblet,_low_poly.mp4",
"a_green_dragon_breathing_fire.mp4",
"a_green_tractor_farming_corn_fields.mp4",
"a_highland_cow.mp4",
"a_hotdog_in_a_tutu_skirt.mp4",
"a_humanoid_robot_laying_on_the_couch_while_on_a_laptop.mp4",
"a_humanoid_robot_playing_the_violin.mp4",
"a_humanoid_robot_sitting_looking_at_a_Go_board_with_some_pieces_on_it.mp4",
"a_human_skeleton_drinking_a_glass_of_red_wine.mp4",
"a_human_skull_with_a_vine_growing_through_one_of_the_eye_sockets.mp4",
"a_kitten_looking_at_a_goldfish_in_a_bowl.mp4",
"a_lemur_drinking_boba.mp4",
"a_lemur_taking_notes_in_a_journal.mp4",
"a_lionfish.mp4",
"a_llama_wearing_a_suit.mp4",
"a_marble_bust_of_a_mouse.mp4",
"a_metal_sculpture_of_a_lion's_head,_highly_detailed.mp4",
"a_mojito_in_a_beach_chair.mp4",
"a_monkey-rabbit_hybrid.mp4",
"an_airplane_made_out_of_wood.mp4",
"an_amigurumi_bulldozer.mp4",
"An_anthropomorphic_tomato_eating_another_tomato.mp4",
"an_astronaut_playing_the_violin.mp4",
"an_astronaut_riding_a_kangaroo.mp4",
"an_English_castle,_aerial_view.mp4",
"an_erupting_volcano,_aerial_view.mp4",
"a_nest_with_a_few_white_eggs_and_one_golden_egg.mp4",
"an_exercise_bike.mp4",
"an_iridescent_metal_scorpion.mp4",
"An_octopus_and_a_giraffe_having_cheesecake.mp4",
"an_octopus_playing_the_harp.mp4",
"an_old_vintage_car.mp4",
"an_opulent_couch_from_the_palace_of_Versailles.mp4",
"an_orange_road_bike.mp4",
"an_orangutan_holding_a_paint_palette_in_one_hand_and_a_paintbrush_in_the_other.mp4",
"an_orangutan_playing_accordion_with_its_hands_spread_wide.mp4",
"an_orangutan_using_chopsticks_to_eat_ramen.mp4",
"an_orchid_flower_planted_in_a_clay_pot.mp4",
"a_palm_tree,_low_poly_3d_model.mp4",
"a_panda_rowing_a_boat_in_a_pond.mp4",
"a_panda_wearing_a_necktie_and_sitting_in_an_office_chair.mp4",
"A_Panther_De_Ville_car.mp4",
"a_pig_wearing_a_backpack.mp4",
"a_plate_of_delicious_tacos.mp4",
"a_plush_dragon_toy.mp4",
"a_plush_toy_of_a_corgi_nurse.mp4",
"a_rabbit,_animated_movie_character,_high_detail_3d_model.mp4",
"a_rabbit_cutting_grass_with_a_lawnmower.mp4",
"a_red_eyed_tree_frog,_low_poly.mp4",
"a_red_panda.mp4",
"a_ripe_strawberry.mp4",
"a_roulette_wheel.mp4",
"a_shiny_red_stand_mixer.mp4",
"a_silver_platter_piled_high_with_fruits.mp4",
"a_sliced_loaf_of_fresh_bread.mp4",
"a_snail_on_a_leaf.mp4",
"a_spanish_galleon_sailing_on_the_open_sea.mp4",
"a_squirrel_dressed_like_Henry_VIII_king_of_England.mp4",
"a_squirrel_gesturing_in_front_of_an_easel_showing_colorful_pie_charts.mp4",
"a_squirrel_wearing_a_tuxedo_and_holding_a_conductor's_baton.mp4",
"a_team_of_butterflies_playing_soccer_on_a_field.mp4",
"a_teddy_bear_pushing_a_shopping_cart_full_of_fruits_and_vegetables.mp4",
"a_tiger_dressed_as_a_military_general.mp4",
"a_tiger_karate_master.mp4",
"a_tiger_playing_the_violin.mp4",
"a_tiger_waiter_at_a_fancy_restaurant.mp4",
"a_tiger_wearing_a_tuxedo.mp4",
"a_t-rex_roaring_up_into_the_air.mp4",
"a_turtle_standing_on_its_hind_legs,_wearing_a_top_hat_and_holding_a_cane.mp4",
"a_typewriter.mp4",
"a_walrus_smoking_a_pipe.mp4",
"a_wedge_of_cheese_on_a_silver_platter.mp4",
"a_wide_angle_DSLR_photo_of_a_colorful_rooster.mp4",
"a_wide_angle_DSLR_photo_of_a_humanoid_banana_sitting_at_a_desk_doing_homework.mp4",
"a_wide_angle_DSLR_photo_of_a_mythical_troll_stirring_a_cauldron.mp4",
"a_wide_angle_DSLR_photo_of_a_squirrel_in_samurai_armor_wielding_a_katana.mp4",
"a_wide_angle_zoomed_out_DSLR_photo_of_A_red_dragon_dressed_in_a_tuxedo_and_playing_chess._The_chess_pieces_are_fashioned_after_robots.mp4",
"a_wide_angle_zoomed_out_DSLR_photo_of_a_skiing_penguin_wearing_a_puffy_jacket.mp4",
"a_wide_angle_zoomed_out_DSLR_photo_of_zoomed_out_view_of_Tower_Bridge_made_out_of_gingerbread_and_candy.mp4",
"a_woolly_mammoth_standing_on_ice.mp4",
"a_yellow_schoolbus.mp4",
"a_zoomed_out_DSLR_photo_of_a_3d_model_of_an_adorable_cottage_with_a_thatched_roof.mp4",
"a_zoomed_out_DSLR_photo_of_a_baby_bunny_sitting_on_top_of_a_stack_of_pancakes.mp4",
"a_zoomed_out_DSLR_photo_of_a_baby_dragon.mp4",
"a_zoomed_out_DSLR_photo_of_a_baby_monkey_riding_on_a_pig.mp4",
"a_zoomed_out_DSLR_photo_of_a_badger_wearing_a_party_hat_and_blowing_out_birthday_candles_on_a_cake.mp4",
"a_zoomed_out_DSLR_photo_of_a_beagle_eating_a_donut.mp4",
"a_zoomed_out_DSLR_photo_of_a_bear_playing_electric_bass.mp4",
"a_zoomed_out_DSLR_photo_of_a_beautifully_carved_wooden_knight_chess_piece.mp4",
"a_zoomed_out_DSLR_photo_of_a_beautiful_suit_made_out_of_moss,_on_a_mannequin._Studio_lighting,_high_quality,_high_resolution.mp4",
"a_zoomed_out_DSLR_photo_of_a_blue_lobster.mp4",
"a_zoomed_out_DSLR_photo_of_a_blue_tulip.mp4",
"a_zoomed_out_DSLR_photo_of_a_bowl_of_cereal_and_milk_with_a_spoon_in_it.mp4",
"a_zoomed_out_DSLR_photo_of_a_brain_in_a_jar.mp4",
"a_zoomed_out_DSLR_photo_of_a_bulldozer_made_out_of_toy_bricks.mp4",
"a_zoomed_out_DSLR_photo_of_a_cake_in_the_shape_of_a_train.mp4",
"a_zoomed_out_DSLR_photo_of_a_chihuahua_lying_in_a_pool_ring.mp4",
"a_zoomed_out_DSLR_photo_of_a_chimpanzee_dressed_as_a_football_player.mp4",
"a_zoomed_out_DSLR_photo_of_a_chimpanzee_holding_a_cup_of_hot_coffee.mp4",
"a_zoomed_out_DSLR_photo_of_a_chimpanzee_wearing_headphones.mp4",
"a_zoomed_out_DSLR_photo_of_a_colorful_camping_tent_in_a_patch_of_grass.mp4",
"a_zoomed_out_DSLR_photo_of_a_complex_movement_from_an_expensive_watch_with_many_shiny_gears,_sitting_on_a_table.mp4",
"a_zoomed_out_DSLR_photo_of_a_construction_excavator.mp4",
"a_zoomed_out_DSLR_photo_of_a_corgi_wearing_a_top_hat.mp4",
"a_zoomed_out_DSLR_photo_of_a_corn_cob_and_a_banana_playing_poker.mp4",
"a_zoomed_out_DSLR_photo_of_a_dachsund_riding_a_unicycle.mp4",
"a_zoomed_out_DSLR_photo_of_a_dachsund_wearing_a_boater_hat.mp4",
"a_zoomed_out_DSLR_photo_of_a_few_pool_balls_sitting_on_a_pool_table.mp4",
"a_zoomed_out_DSLR_photo_of_a_fox_working_on_a_jigsaw_puzzle.mp4",
"a_zoomed_out_DSLR_photo_of_a_fresh_cinnamon_roll_covered_in_glaze.mp4",
"a_zoomed_out_DSLR_photo_of_a_green_tractor.mp4",
"a_zoomed_out_DSLR_photo_of_a_greyhound_dog_racing_down_the_track.mp4",
"a_zoomed_out_DSLR_photo_of_a_group_of_squirrels_rowing_crew.mp4",
"a_zoomed_out_DSLR_photo_of_a_gummy_bear_driving_a_convertible.mp4",
"a_zoomed_out_DSLR_photo_of_a_hermit_crab_with_a_colorful_shell.mp4",
"a_zoomed_out_DSLR_photo_of_a_hippo_biting_through_a_watermelon.mp4",
"a_zoomed_out_DSLR_photo_of_a_hippo_made_out_of_chocolate.mp4",
"a_zoomed_out_DSLR_photo_of_a_humanoid_robot_lying_on_a_couch_using_a_laptop.mp4",
"a_zoomed_out_DSLR_photo_of_a_humanoid_robot_sitting_on_a_chair_drinking_a_cup_of_coffee.mp4",
"a_zoomed_out_DSLR_photo_of_a_human_skeleton_relaxing_in_a_lounge_chair.mp4",
"a_zoomed_out_DSLR_photo_of_a_kangaroo_sitting_on_a_bench_playing_the_accordion.mp4",
"a_zoomed_out_DSLR_photo_of_a_kingfisher_bird.mp4",
"a_zoomed_out_DSLR_photo_of_a_ladybug.mp4",
"a_zoomed_out_DSLR_photo_of_a_lion's_mane_jellyfish.mp4",
"a_zoomed_out_DSLR_photo_of_a_lobster_playing_the_saxophone.mp4",
"a_zoomed_out_DSLR_photo_of_a_majestic_sailboat.mp4",
"a_zoomed_out_DSLR_photo_of_a_marble_bust_of_a_cat,_a_real_mouse_is_sitting_on_its_head.mp4",
"a_zoomed_out_DSLR_photo_of_a_marble_bust_of_a_fox_head.mp4",
"a_zoomed_out_DSLR_photo_of_a_model_of_a_house_in_Tudor_style.mp4",
"a_zoomed_out_DSLR_photo_of_a_monkey-rabbit_hybrid.mp4",
"a_zoomed_out_DSLR_photo_of_a_monkey_riding_a_bike.mp4",
"a_zoomed_out_DSLR_photo_of_a_mountain_goat_standing_on_a_boulder.mp4",
"a_zoomed_out_DSLR_photo_of_a_mouse_holding_a_candlestick.mp4",
"a_zoomed_out_DSLR_photo_of_an_adorable_kitten_lying_next_to_a_flower.mp4",
"a_zoomed_out_DSLR_photo_of_an_all-utility_vehicle_driving_across_a_stream.mp4",
"a_zoomed_out_DSLR_photo_of_an_amigurumi_motorcycle.mp4",
"a_zoomed_out_DSLR_photo_of_an_astronaut_chopping_vegetables_in_a_sunlit_kitchen.mp4",
"a_zoomed_out_DSLR_photo_of_an_egg_cracked_open_with_a_newborn_chick_hatching_out_of_it.mp4",
"a_zoomed_out_DSLR_photo_of_an_expensive_office_chair.mp4",
"a_zoomed_out_DSLR_photo_of_an_origami_bulldozer_sitting_on_the_ground.mp4",
"a_zoomed_out_DSLR_photo_of_an_origami_crane.mp4",
"a_zoomed_out_DSLR_photo_of_an_origami_hippo_in_a_river.mp4",
"a_zoomed_out_DSLR_photo_of_an_otter_lying_on_its_back_in_the_water_holding_a_flower.mp4",
"a_zoomed_out_DSLR_photo_of_a_pair_of_floating_chopsticks_picking_up_noodles_out_of_a_bowl_of_ramen.mp4",
"a_zoomed_out_DSLR_photo_of_a_panda_throwing_wads_of_cash_into_the_air.mp4",
"a_zoomed_out_DSLR_photo_of_a_panda_wearing_a_chef's_hat_and_kneading_bread_dough_on_a_countertop.mp4",
"a_zoomed_out_DSLR_photo_of_a_pigeon_standing_on_a_manhole_cover.mp4",
"a_zoomed_out_DSLR_photo_of_a_pig_playing_the_saxophone.mp4",
"a_zoomed_out_DSLR_photo_of_a_pile_of_dice_on_a_green_tabletop.mp4",
"a_zoomed_out_DSLR_photo_of_a_pita_bread_full_of_hummus_and_falafel_and_vegetables.mp4",
"a_zoomed_out_DSLR_photo_of_a_pug_made_out_of_modeling_clay.mp4",
"a_zoomed_out_DSLR_photo_of_A_punk_rock_squirrel_in_a_studded_leather_jacket_shouting_into_a_microphone_while_standing_on_a_stump_and_holding_a_beer.mp4",
"a_zoomed_out_DSLR_photo_of_a_rabbit_cutting_grass_with_a_lawnmower.mp4",
"a_zoomed_out_DSLR_photo_of_a_rabbit_digging_a_hole_with_a_shovel.mp4",
"a_zoomed_out_DSLR_photo_of_a_raccoon_astronaut_holding_his_helmet.mp4",
"a_zoomed_out_DSLR_photo_of_a_rainforest_bird_mating_ritual_dance.mp4",
"a_zoomed_out_DSLR_photo_of_a_recliner_chair.mp4",
"a_zoomed_out_DSLR_photo_of_a_red_rotary_telephone.mp4",
"a_zoomed_out_DSLR_photo_of_a_robot_couple_fine_dining.mp4",
"a_zoomed_out_DSLR_photo_of_a_rotary_telephone_carved_out_of_wood.mp4",
"a_zoomed_out_DSLR_photo_of_a_shiny_beetle.mp4",
"a_zoomed_out_DSLR_photo_of_a_silver_candelabra_sitting_on_a_red_velvet_tablecloth,_only_one_candle_is_lit.mp4",
"a_zoomed_out_DSLR_photo_of_a_squirrel_DJing.mp4",
"a_zoomed_out_DSLR_photo_of_a_squirrel_dressed_up_like_a_Victorian_woman.mp4",
"a_zoomed_out_DSLR_photo_of_a_table_with_dim_sum_on_it.mp4",
"a_zoomed_out_DSLR_photo_of_a_tiger_dressed_as_a_maid.mp4",
"a_zoomed_out_DSLR_photo_of_a_tiger_dressed_as_a_military_general.mp4",
"a_zoomed_out_DSLR_photo_of_a_tiger_eating_an_ice_cream_cone.mp4",
"a_zoomed_out_DSLR_photo_of_a_tiger_wearing_sunglasses_and_a_leather_jacket,_riding_a_motorcycle.mp4",
"a_zoomed_out_DSLR_photo_of_a_toad_catching_a_fly_with_its_tongue.mp4",
"a_zoomed_out_DSLR_photo_of_a_wizard_raccoon_casting_a_spell.mp4",
"a_zoomed_out_DSLR_photo_of_a_yorkie_dog_dressed_as_a_maid.mp4",
"a_zoomed_out_DSLR_photo_of_cats_wearing_eyeglasses.mp4",
"a_zoomed_out_DSLR_photo_of_miniature_schnauzer_wooden_sculpture,_high_quality_studio_photo.mp4",
"A_zoomed_out_DSLR_photo_of___phoenix_made_of_splashing_water_.mp4",
"a_zoomed_out_DSLR_photo_of_Sydney_opera_house,_aerial_view.mp4",
"a_zoomed_out_DSLR_photo_of_two_foxes_tango_dancing.mp4",
"a_zoomed_out_DSLR_photo_of_two_raccoons_playing_poker.mp4",
"Chichen_Itza,_aerial_view.mp4",
"__Coffee_cup_with_many_holes.mp4",
"fries_and_a_hamburger.mp4",
"__Luminescent_wild_horses.mp4",
"Michelangelo_style_statue_of_an_astronaut.mp4",
"Michelangelo_style_statue_of_dog_reading_news_on_a_cellphone.mp4",
"the_titanic,_aerial_view.mp4",
"two_gummy_bears_playing_dominoes.mp4",
"two_macaw_parrots_playing_chess.mp4",
"Wedding_dress_made_of_tentacles.mp4",
]
def main():
prompt_library = {
"dreamfusion": [
p.replace(".mp4", "").replace("_", " ")
for p in dreamfusion_gallery_video_names
]
}
with open("load/prompt_library.json", "w") as f:
json.dump(prompt_library, f, indent=2)
if __name__ == "__main__":
main()
================================================
FILE: load/prompt_library.json
================================================
{
"dreamfusion": [
"a 20-sided die made out of glass",
"a bald eagle carved out of wood",
"a banana peeling itself",
"a beagle in a detective's outfit",
"a beautiful dress made out of fruit, on a mannequin. Studio lighting, high quality, high resolution",
"a beautiful dress made out of garbage bags, on a mannequin. Studio lighting, high quality, high resolution",
"a beautiful rainbow fish",
"a bichon frise wearing academic regalia",
"a blue motorcycle",
"a blue poison-dart frog sitting on a water lily",
"a brightly colored mushroom growing on a log",
"a bumblebee sitting on a pink flower",
"a bunch of colorful marbles spilling out of a red velvet bag",
"a capybara wearing a top hat, low poly",
"a cat with a mullet",
"a ceramic lion",
"a ceramic upside down yellow octopus holding a blue green ceramic cup",
"a chihuahua wearing a tutu",
"a chimpanzee holding a peeled banana",
"a chimpanzee looking through a telescope",
"a chimpanzee stirring a bubbling purple potion in a cauldron",
"a chimpanzee with a big grin",
"a completely destroyed car",
"a confused beagle sitting at a desk working on homework",
"a corgi taking a selfie",
"a crab, low poly",
"a crocodile playing a drum set",
"a cute steampunk elephant",
"a dachsund dressed up in a hotdog costume",
"a delicious hamburger",
"a dragon-cat hybrid",
"a DSLR photo of a baby dragon drinking boba",
"a DSLR photo of a baby dragon hatching out of a stone egg",
"a DSLR photo of a baby grand piano viewed from far away",
"a DSLR photo of a bagel filled with cream cheese and lox",
"a DSLR photo of a bald eagle",
"a DSLR photo of a barbecue grill cooking sausages and burger patties",
"a DSLR photo of a basil plant",
"a DSLR photo of a bear dancing ballet",
"a DSLR photo of a bear dressed as a lumberjack",
"a DSLR photo of a bear dressed in medieval armor",
"a DSLR photo of a beautiful violin sitting flat on a table",
"a DSLR photo of a blue jay standing on a large basket of rainbow macarons",
"a DSLR photo of a bulldozer clearing away a pile of snow",
"a DSLR photo of a bulldozer",
"a DSLR photo of a cake covered in colorful frosting with a slice being taken out, high resolution",
"a DSLR photo of a candelabra with many candles on a red velvet tablecloth",
"a DSLR photo of a car made out of cheese",
"a DSLR photo of A car made out of sushi",
"a DSLR photo of a car made out pizza",
"a DSLR photo of a cat lying on its side batting at a ball of yarn",
"a DSLR photo of a cat magician making a white dove appear",
"a DSLR photo of a cat wearing a bee costume",
"a DSLR photo of a cat wearing a lion costume",
"a DSLR photo of a cauldron full of gold coins",
"a DSLR photo of a chimpanzee dressed like Henry VIII king of England",
"a DSLR photo of a chimpanzee dressed like Napoleon Bonaparte",
"a DSLR photo of a chow chow puppy",
"a DSLR photo of a Christmas tree with donuts as decorations",
"a DSLR photo of a chrome-plated duck with a golden beak arguing with an angry turtle in a forest",
"a DSLR photo of a classic Packard car",
"a DSLR photo of a cocker spaniel wearing a crown",
"a DSLR photo of a corgi lying on its back with its tongue lolling out",
"a DSLR photo of a corgi puppy",
"a DSLR photo of a corgi sneezing",
"a DSLR photo of a corgi standing up drinking boba",
"a DSLR photo of a corgi taking a selfie",
"a DSLR photo of a corgi wearing a beret and holding a baguette, standing up on two hind legs",
"a DSLR photo of a covered wagon",
"a DSLR photo of a cracked egg with the yolk spilling out on a wooden table",
"a DSLR photo of a cup full of pens and pencils",
"a DSLR photo of a dalmation wearing a fireman's hat",
"a DSLR photo of a delicious chocolate brownie dessert with ice cream on the side",
"a DSLR photo of a delicious croissant",
"a DSLR photo of A DMC Delorean car",
"a DSLR photo of a dog made out of salad",
"a DSLR photo of a drum set made of cheese",
"a DSLR photo of a drying rack covered in clothes",
"a DSLR photo of aerial view of a ruined castle",
"a DSLR photo of a football helmet",
"a DSLR photo of a fox holding a videogame controller",
"a DSLR photo of a fox taking a photograph using a DSLR",
"a DSLR photo of a frazer nash super sport car",
"a DSLR photo of a frog wearing a sweater",
"a DSLR photo of a ghost eating a hamburger",
"a DSLR photo of a giant worm emerging from the sand in the middle of the desert",
"a DSLR photo of a goose made out of gold",
"a DSLR photo of a green monster truck",
"a DSLR photo of a group of dogs eating pizza",
"a DSLR photo of a group of dogs playing poker",
"a DSLR photo of a gummy bear playing the saxophone",
"a DSLR photo of a hippo wearing a sweater",
"a DSLR photo of a humanoid robot holding a human brain",
"a DSLR photo of a humanoid robot playing solitaire",
"a DSLR photo of a humanoid robot playing the cello",
"a DSLR photo of a humanoid robot using a laptop",
"a DSLR photo of a humanoid robot using a rolling pin to roll out dough",
"a DSLR photo of a human skull",
"a DSLR photo of a kitten standing on top of a giant tortoise",
"a DSLR photo of a knight chopping wood",
"a DSLR photo of a knight holding a lance and sitting on an armored horse",
"a DSLR photo of a koala wearing a party hat and blowing out birthday candles on a cake",
"a DSLR photo of a lemur taking notes in a journal",
"a DSLR photo of a lion reading the newspaper",
"a DSLR photo of a mandarin duck swimming in a pond",
"a DSLR photo of a model of the eiffel tower made out of toothpicks",
"a DSLR photo of a mouse playing the tuba",
"a DSLR photo of a mug of hot chocolate with whipped cream and marshmallows",
"a DSLR photo of an adorable piglet in a field",
"a DSLR photo of an airplane taking off from the runway",
"a DSLR photo of an astronaut standing on the surface of mars",
"a DSLR photo of an eggshell broken in two with an adorable chick standing next to it",
"a DSLR photo of an elephant skull",
"a DSLR photo of an exercise bike in a well lit room",
"a DSLR photo of an extravagant mansion, aerial view",
"a DSLR photo of an ice cream sundae",
"a DSLR photo of an iguana holding a balloon",
"a DSLR photo of an intricate and complex dish from a michelin star restaurant",
"a DSLR photo of An iridescent steampunk patterned millipede with bison horns",
"a DSLR photo of an octopus playing the piano",
"a DSLR photo of an old car overgrown by vines and weeds",
"a DSLR photo of an old vintage car",
"a DSLR photo of an orangutan making a clay bowl on a throwing wheel",
"a DSLR photo of an orc forging a hammer on an anvil",
"a DSLR photo of an origami motorcycle",
"a DSLR photo of an ornate silver gravy boat sitting on a patterned tablecloth",
"a DSLR photo of an overstuffed pastrami sandwich",
"a DSLR photo of an unstable rock cairn in the middle of a stream",
"a DSLR photo of a pair of headphones sitting on a desk",
"a DSLR photo of a pair of tan cowboy boots, studio lighting, product photography",
"a DSLR photo of a peacock on a surfboard",
"a DSLR photo of a pigeon reading a book",
"a DSLR photo of a piglet sitting in a teacup",
"a DSLR photo of a pig playing a drum set",
"a DSLR photo of a pile of dice on a green tabletop next to some playing cards",
"a DSLR photo of a pirate collie dog, high resolution",
"a DSLR photo of a plate of fried chicken and waffles with maple syrup on them",
"a DSLR photo of a plate piled high with chocolate chip cookies",
"a DSLR photo of a plush t-rex dinosaur toy, studio lighting, high resolution",
"a DSLR photo of a plush triceratops toy, studio lighting, high resolution",
"a DSLR photo of a pomeranian dog",
"a DSLR photo of a porcelain dragon",
"a DSLR photo of a praying mantis wearing roller skates",
"a DSLR photo of a puffin standing on a rock",
"a DSLR photo of a pug made out of metal",
"a DSLR photo of a pug wearing a bee costume",
"a DSLR photo of a quill and ink sitting on a desk",
"a DSLR photo of a raccoon stealing a pie",
"a DSLR photo of a red cardinal bird singing",
"a DSLR photo of a red convertible car with the top down",
"a DSLR photo of a red-eyed tree frog",
"a DSLR photo of a red pickup truck driving across a stream",
"a DSLR photo of a red wheelbarrow with a shovel in it",
"a DSLR photo of a roast turkey on a platter",
"a DSLR photo of a robot and dinosaur playing chess, high resolution",
"a DSLR photo of a robot arm picking up a colorful block from a table",
"a DSLR photo of a robot cat knocking over a chess piece on a board",
"a DSLR photo of a robot dinosaur",
"a DSLR photo of a robot made out of vegetables",
"a DSLR photo of a robot stegosaurus",
"a DSLR photo of a robot tiger",
"a DSLR photo of a rolling pin on top of bread dough",
"a DSLR photo of a sheepdog running",
"a DSLR photo of a shiba inu playing golf wearing tartan golf clothes and hat",
"a DSLR photo of a shiny silver robot cat",
"a DSLR photo of a silverback gorilla holding a golden trophy",
"a DSLR photo of a silver humanoid robot flipping a coin",
"a DSLR photo of a small cherry tomato plant in a pot with a few red tomatoes growing on it",
"a DSLR photo of a small saguaro cactus planted in a clay pot",
"a DSLR photo of a Space Shuttle",
"a DSLR photo of a squirrel dressed like a clown",
"a DSLR photo of a squirrel flying a biplane",
"a DSLR photo of a squirrel giving a lecture writing on a chalkboard",
"a DSLR photo of a squirrel holding a bowling ball",
"a DSLR photo of a squirrel-lizard hybrid",
"a DSLR photo of a squirrel made out of fruit",
"a DSLR photo of a squirrel-octopus hybrid",
"a DSLR photo of a stack of pancakes covered in maple syrup",
"a DSLR photo of a steam engine train, high resolution",
"a DSLR photo of a steaming basket full of dumplings",
"a DSLR photo of a steaming hot plate piled high with spaghetti and meatballs",
"a DSLR photo of a steampunk space ship designed in the 18th century",
"a DSLR photo of a straw basket with a cobra coming out of it",
"a DSLR photo of a swan and its cygnets swimming in a pond",
"a DSLR photo of a tarantula, highly detailed",
"a DSLR photo of a teal moped",
"a DSLR photo of a teapot shaped like an elephant head where its snout acts as the spout",
"a DSLR photo of a teddy bear taking a selfie",
"a DSLR photo of a terracotta bunny",
"a DSLR photo of a tiger dressed as a doctor",
"a DSLR photo of a tiger made out of yarn",
"a DSLR photo of a toilet made out of gold",
"a DSLR photo of a toy robot",
"a DSLR photo of a train engine made out of clay",
"a DSLR photo of a tray of Sushi containing pugs",
"a DSLR photo of a tree stump with an axe buried in it",
"a DSLR photo of a turtle standing on its hind legs, wearing a top hat and holding a cane",
"a DSLR photo of a very beautiful small organic sculpture made of fine clockwork and gears with tiny ruby bearings, very intricate, caved, curved. Studio lighting, High resolution, white background",
"a DSLR photo of A very beautiful tiny human heart organic sculpture made of copper wire and threaded pipes, very intricate, curved, Studio lighting, high resolution",
"a DSLR photo of a very cool and trendy pair of sneakers, studio lighting",
"a DSLR photo of a vintage record player",
"a DSLR photo of a wine bottle and full wine glass on a chessboard",
"a DSLR photo of a wooden desk and chair from an elementary school",
"a DSLR photo of a yorkie dog eating a donut",
"a DSLR photo of a yorkie dog wearing extremely cool sneakers",
"a DSLR photo of baby elephant jumping on a trampoline",
"a DSLR photo of cat wearing virtual reality headset in renaissance oil painting high detail caravaggio",
"a DSLR photo of edible typewriter made out of vegetables",
"a DSLR photo of Mont Saint-Michel, France, aerial view",
"a DSLR photo of Mount Fuji, aerial view",
"a DSLR photo of Neuschwanstein Castle, aerial view",
"A DSLR photo of pyramid shaped burrito with a slice cut out of it",
"a DSLR photo of the Imperial State Crown of England",
"a DSLR photo of the leaning tower of Pisa, aerial view",
"a DSLR photo of the Statue of Liberty, aerial view",
"a DSLR photo of Two locomotives playing tug of war",
"a DSLR photo of two macaw parrots sharing a milkshake with two straws",
"a DSLR photo of Westminster Abbey, aerial view",
"a ficus planted in a pot",
"a flower made out of metal",
"a fluffy cat lying on its back in a patch of sunlight",
"a fox and a hare tangoing together",
"a fox holding a videogame controller",
"a fox playing the cello",
"a frazer nash super sport car",
"a freshly baked loaf of sourdough bread on a cutting board",
"a goat drinking beer",
"a golden goblet, low poly",
"a green dragon breathing fire",
"a green tractor farming corn fields",
"a highland cow",
"a hotdog in a tutu skirt",
"a humanoid robot laying on the couch while on a laptop",
"a humanoid robot playing the violin",
"a humanoid robot sitting looking at a Go board with some pieces on it",
"a human skeleton drinking a glass of red wine",
"a human skull with a vine growing through one of the eye sockets",
"a kitten looking at a goldfish in a bowl",
"a lemur drinking boba",
"a lemur taking notes in a journal",
"a lionfish",
"a llama wearing a suit",
"a marble bust of a mouse",
"a metal sculpture of a lion's head, highly detailed",
"a mojito in a beach chair",
"a monkey-rabbit hybrid",
"an airplane made out of wood",
"an amigurumi bulldozer",
"An anthropomorphic tomato eating another tomato",
"an astronaut playing the violin",
"an astronaut riding a kangaroo",
"an English castle, aerial view",
"an erupting volcano, aerial view",
"a nest with a few white eggs and one golden egg",
"an exercise bike",
"an iridescent metal scorpion",
"An octopus and a giraffe having cheesecake",
"an octopus playing the harp",
"an old vintage car",
"an opulent couch from the palace of Versailles",
"an orange road bike",
"an orangutan holding a paint palette in one hand and a paintbrush in the other",
"an orangutan playing accordion with its hands spread wide",
"an orangutan using chopsticks to eat ramen",
"an orchid flower planted in a clay pot",
"a palm tree, low poly 3d model",
"a panda rowing a boat in a pond",
"a panda wearing a necktie and sitting in an office chair",
"A Panther De Ville car",
"a pig wearing a backpack",
"a plate of delicious tacos",
"a plush dragon toy",
"a plush toy of a corgi nurse",
"a rabbit, animated movie character, high detail 3d model",
"a rabbit cutting grass with a lawnmower",
"a red eyed tree frog, low poly",
"a red panda",
"a ripe strawberry",
"a roulette wheel",
"a shiny red stand mixer",
"a silver platter piled high with fruits",
"a sliced loaf of fresh bread",
"a snail on a leaf",
"a spanish galleon sailing on the open sea",
"a squirrel dressed like Henry VIII king of England",
"a squirrel gesturing in front of an easel showing colorful pie charts",
"a squirrel wearing a tuxedo and holding a conductor's baton",
"a team of butterflies playing soccer on a field",
"a teddy bear pushing a shopping cart full of fruits and vegetables",
"a tiger dressed as a military general",
"a tiger karate master",
"a tiger playing the violin",
"a tiger waiter at a fancy restaurant",
"a tiger wearing a tuxedo",
"a t-rex roaring up into the air",
"a turtle standing on its hind legs, wearing a top hat and holding a cane",
"a typewriter",
"a walrus smoking a pipe",
"a wedge of cheese on a silver platter",
"a wide angle DSLR photo of a colorful rooster",
"a wide angle DSLR photo of a humanoid banana sitting at a desk doing homework",
"a wide angle DSLR photo of a mythical troll stirring a cauldron",
"a wide angle DSLR photo of a squirrel in samurai armor wielding a katana",
"a wide angle zoomed out DSLR photo of A red dragon dressed in a tuxedo and playing chess. The chess pieces are fashioned after robots",
"a wide angle zoomed out DSLR photo of a skiing penguin wearing a puffy jacket",
"a wide angle zoomed out DSLR photo of zoomed out view of Tower Bridge made out of gingerbread and candy",
"a woolly mammoth standing on ice",
"a yellow schoolbus",
"a zoomed out DSLR photo of a 3d model of an adorable cottage with a thatched roof",
"a zoomed out DSLR photo of a baby bunny sitting on top of a stack of pancakes",
"a zoomed out DSLR photo of a baby dragon",
"a zoomed out DSLR photo of a baby monkey riding on a pig",
"a zoomed out DSLR photo of a badger wearing a party hat and blowing out birthday candles on a cake",
"a zoomed out DSLR photo of a beagle eating a donut",
"a zoomed out DSLR photo of a bear playing electric bass",
"a zoomed out DSLR photo of a beautifully carved wooden knight chess piece",
"a zoomed out DSLR photo of a beautiful suit made out of moss, on a mannequin. Studio lighting, high quality, high resolution",
"a zoomed out DSLR photo of a blue lobster",
"a zoomed out DSLR photo of a blue tulip",
"a zoomed out DSLR photo of a bowl of cereal and milk with a spoon in it",
"a zoomed out DSLR photo of a brain in a jar",
"a zoomed out DSLR photo of a bulldozer made out of toy bricks",
"a zoomed out DSLR photo of a cake in the shape of a train",
"a zoomed out DSLR photo of a chihuahua lying in a pool ring",
"a zoomed out DSLR photo of a chimpanzee dressed as a football player",
"a zoomed out DSLR photo of a chimpanzee holding a cup of hot coffee",
"a zoomed out DSLR photo of a chimpanzee wearing headphones",
"a zoomed out DSLR photo of a colorful camping tent in a patch of grass",
"a zoomed out DSLR photo of a complex movement from an expensive watch with many shiny gears, sitting on a table",
"a zoomed out DSLR photo of a construction excavator",
"a zoomed out DSLR photo of a corgi wearing a top hat",
"a zoomed out DSLR photo of a corn cob and a banana playing poker",
"a zoomed out DSLR photo of a dachsund riding a unicycle",
"a zoomed out DSLR photo of a dachsund wearing a boater hat",
"a zoomed out DSLR photo of a few pool balls sitting on a pool table",
"a zoomed out DSLR photo of a fox working on a jigsaw puzzle",
"a zoomed out DSLR photo of a fresh cinnamon roll covered in glaze",
"a zoomed out DSLR photo of a green tractor",
"a zoomed out DSLR photo of a greyhound dog racing down the track",
"a zoomed out DSLR photo of a group of squirrels rowing crew",
"a zoomed out DSLR photo of a gummy bear driving a convertible",
"a zoomed out DSLR photo of a hermit crab with a colorful shell",
"a zoomed out DSLR photo of a hippo biting through a watermelon",
"a zoomed out DSLR photo of a hippo made out of chocolate",
"a zoomed out DSLR photo of a humanoid robot lying on a couch using a laptop",
"a zoomed out DSLR photo of a humanoid robot sitting on a chair drinking a cup of coffee",
"a zoomed out DSLR photo of a human skeleton relaxing in a lounge chair",
"a zoomed out DSLR photo of a kangaroo sitting on a bench playing the accordion",
"a zoomed out DSLR photo of a kingfisher bird",
"a zoomed out DSLR photo of a ladybug",
"a zoomed out DSLR photo of a lion's mane jellyfish",
"a zoomed out DSLR photo of a lobster playing the saxophone",
"a zoomed out DSLR photo of a majestic sailboat",
"a zoomed out DSLR photo of a marble bust of a cat, a real mouse is sitting on its head",
"a zoomed out DSLR photo of a marble bust of a fox head",
"a zoomed out DSLR photo of a model of a house in Tudor style",
"a zoomed out DSLR photo of a monkey-rabbit hybrid",
"a zoomed out DSLR photo of a monkey riding a bike",
"a zoomed out DSLR photo of a mountain goat standing on a boulder",
"a zoomed out DSLR photo of a mouse holding a candlestick",
"a zoomed out DSLR photo of an adorable kitten lying next to a flower",
"a zoomed out DSLR photo of an all-utility vehicle driving across a stream",
"a zoomed out DSLR photo of an amigurumi motorcycle",
"a zoomed out DSLR photo of an astronaut chopping vegetables in a sunlit kitchen",
"a zoomed out DSLR photo of an egg cracked open with a newborn chick hatching out of it",
"a zoomed out DSLR photo of an expensive office chair",
"a zoomed out DSLR photo of an origami bulldozer sitting on the ground",
"a zoomed out DSLR photo of an origami crane",
"a zoomed out DSLR photo of an origami hippo in a river",
"a zoomed out DSLR photo of an otter lying on its back in the water holding a flower",
"a zoomed out DSLR photo of a pair of floating chopsticks picking up noodles out of a bowl of ramen",
"a zoomed out DSLR photo of a panda throwing wads of cash into the air",
"a zoomed out DSLR photo of a panda wearing a chef's hat and kneading bread dough on a countertop",
"a zoomed out DSLR photo of a pigeon standing on a manhole cover",
"a zoomed out DSLR photo of a pig playing the saxophone",
"a zoomed out DSLR photo of a pile of dice on a green tabletop",
"a zoomed out DSLR photo of a pita bread full of hummus and falafel and vegetables",
"a zoomed out DSLR photo of a pug made out of modeling clay",
"a zoomed out DSLR photo of A punk rock squirrel in a studded leather jacket shouting into a microphone while standing on a stump and holding a beer",
"a zoomed out DSLR photo of a rabbit cutting grass with a lawnmower",
"a zoomed out DSLR photo of a rabbit digging a hole with a shovel",
"a zoomed out DSLR photo of a raccoon astronaut holding his helmet",
"a zoomed out DSLR photo of a rainforest bird mating ritual dance",
"a zoomed out DSLR photo of a recliner chair",
"a zoomed out DSLR photo of a red rotary telephone",
"a zoomed out DSLR photo of a robot couple fine dining",
"a zoomed out DSLR photo of a rotary telephone carved out of wood",
"a zoomed out DSLR photo of a shiny beetle",
"a zoomed out DSLR photo of a silver candelabra sitting on a red velvet tablecloth, only one candle is lit",
"a zoomed out DSLR photo of a squirrel DJing",
"a zoomed out DSLR photo of a squirrel dressed up like a Victorian woman",
"a zoomed out DSLR photo of a table with dim sum on it",
"a zoomed out DSLR photo of a tiger dressed as a maid",
"a zoomed out DSLR photo of a tiger dressed as a military general",
"a zoomed out DSLR photo of a tiger eating an ice cream cone",
"a zoomed out DSLR photo of a tiger wearing sunglasses and a leather jacket, riding a motorcycle",
"a zoomed out DSLR photo of a toad catching a fly with its tongue",
"a zoomed out DSLR photo of a wizard raccoon casting a spell",
"a zoomed out DSLR photo of a yorkie dog dressed as a maid",
"a zoomed out DSLR photo of cats wearing eyeglasses",
"a zoomed out DSLR photo of miniature schnauzer wooden sculpture, high quality studio photo",
"A zoomed out DSLR photo of phoenix made of splashing water ",
"a zoomed out DSLR photo of Sydney opera house, aerial view",
"a zoomed out DSLR photo of two foxes tango dancing",
"a zoomed out DSLR photo of two raccoons playing poker",
"Chichen Itza, aerial view",
" Coffee cup with many holes",
"fries and a hamburger",
" Luminescent wild horses",
"Michelangelo style statue of an astronaut",
"Michelangelo style statue of dog reading news on a cellphone",
"the titanic, aerial view",
"two gummy bears playing dominoes",
"two macaw parrots playing chess",
"Wedding dress made of tentacles"
]
}
================================================
FILE: load/shapes/README.md
================================================
# Shape Credits
- `animal.obj` - Ido Richardson
- `hand_prismatic.obj` - Ido Richardson
- `potion.obj` - Ido Richardson
- `blub.obj` - [Keenan's 3D Model Repository](https://www.cs.cmu.edu/~kmcrane/Projects/ModelRepository/)
- `nascar.obj` - [Princeton ModelNet](https://modelnet.cs.princeton.edu/)
- `cabin.obj` - [Princeton ModelNet](https://modelnet.cs.princeton.edu/)
- `teddy.obj` - [Gal Metzer](https://galmetzer.github.io/)
- `human.obj` - [TurboSquid](https://www.turbosquid.com/3d-models/3d-model-character-base/524860)
================================================
FILE: load/shapes/animal.obj
================================================
####
#
# OBJ File Generated by Meshlab
#
####
# Object animal_legs_head.obj
#
# Vertices: 1536
# Faces: 3068
#
####
mtllib ./animal_legs_head.obj.mtl
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FILE: load/shapes/cabin.obj
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f 1265//1265 1268//1268 1267//1267
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f 1334//1334 1369//1369 1368//1368
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f 1373//1373 1374//1374 1372//1372
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f 1342//1342 1376//1376 1375//1375
f 1333//1333 1376//1376 1342//1342
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f 1376//1376 1377//1377 1375//1375
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f 1368//1368 1378//1378 1376//1376
f 1368//1368 1370//1370 1378//1378
f 1378//1378 1379//1379 1377//1377
f 1378//1378 1380//1380 1379//1379
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f 1370//1370 1372//1372 1380//1380
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f 1380//1380 1382//1382 1381//1381
f 1372//1372 1382//1382 1380//1380
f 1372//1372 1374//1374 1382//1382
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f 1349//1349 1384//1384 1383//1383
f 1341//1341 1384//1384 1349//1349
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f 360//360 1822//1822 1821//1821
f 360//360 372//372 1822//1822
f 371//371 1823//1823 373//373
f 371//371 1821//1821 1823//1823
f 1822//1822 1824//1824 1821//1821
f 1821//1821 1825//1825 1823//1823
f 1821//1821 1824//1824 1825//1825
f 378//378 1826//1826 377//377
f 378//378 1827//1827 1826//1826
f 373//373 1827//1827 378//378
f 373//373 1823//1823 1827//1827
f 377//377 1828//1828 379//379
f 377//377 1826//1826 1828//1828
f 1827//1827 1829//1829 1826//1826
f 1827//1827 1830//1830 1829//1829
f 1823//1823 1830//1830 1827//1827
f 1823//1823 1825//1825 1830//1830
f 1826//1826 1831//1831 1828//1828
f 1826//1826 1829//1829 1831//1831
f 1822//1822 1832//1832 1824//1824
f 1824//1824 1833//1833 1825//1825
f 1824//1824 1832//1832 1833//1833
f 372//372 362//362 1834//1834
f 1830//1830 1835//1835 1829//1829
f 1830//1830 1836//1836 1835//1835
f 1825//1825 1836//1836 1830//1830
f 1825//1825 1833//1833 1836//1836
f 1829//1829 1837//1837 1831//1831
f 1829//1829 1835//1835 1837//1837
f 1836//1836 1838//1838 1835//1835
f 1836//1836 1839//1839 1838//1838
f 1833//1833 1839//1839 1836//1836
f 1835//1835 1840//1840 1837//1837
f 1835//1835 1838//1838 1840//1840
f 1822//1822 1841//1841 1832//1832
f 1822//1822 372//372 1841//1841
f 1832//1832 1842//1842 1833//1833
f 1841//1841 372//372 1843//1843
f 372//372 1834//1834 1843//1843
f 1839//1839 1844//1844 1838//1838
f 1833//1833 1842//1842 1839//1839
f 1838//1838 1845//1845 1840//1840
f 1838//1838 1844//1844 1845//1845
f 1839//1839 1846//1846 1844//1844
f 1842//1842 1846//1846 1839//1839
f 1841//1841 1847//1847 1832//1832
f 1832//1832 1847//1847 1842//1842
f 1843//1843 1834//1834 1848//1848
f 1846//1846 1849//1849 1844//1844
f 1844//1844 1850//1850 1845//1845
f 1844//1844 1849//1849 1850//1850
f 362//362 385//385 1851//1851
f 385//385 386//386 1852//1852
f 385//385 1852//1852 1851//1851
f 362//362 1851//1851 1834//1834
f 1851//1851 1852//1852 1853//1853
f 1852//1852 1854//1854 1853//1853
f 386//386 387//387 1852//1852
f 387//387 1855//1855 1852//1852
f 387//387 388//388 1855//1855
f 1852//1852 1855//1855 1854//1854
f 1854//1854 1855//1855 1856//1856
f 1834//1834 1851//1851 1857//1857
f 1851//1851 1853//1853 1857//1857
f 1853//1853 1854//1854 1858//1858
f 1834//1834 1857//1857 1848//1848
f 1857//1857 1853//1853 1859//1859
f 1853//1853 1860//1860 1859//1859
f 1853//1853 1858//1858 1860//1860
f 1854//1854 1856//1856 1861//1861
f 1854//1854 1861//1861 1858//1858
f 1858//1858 1861//1861 1862//1862
f 1847//1847 1863//1863 1842//1842
f 1841//1841 1843//1843 1864//1864
f 1843//1843 1848//1848 1864//1864
f 1846//1846 1865//1865 1849//1849
f 1846//1846 1866//1866 1865//1865
f 1842//1842 1866//1866 1846//1846
f 1842//1842 1863//1863 1866//1866
f 1849//1849 1865//1865 1850//1850
f 1866//1866 1867//1867 1865//1865
f 1866//1866 1868//1868 1867//1867
f 1863//1863 1868//1868 1866//1866
f 1865//1865 1869//1869 1850//1850
f 1865//1865 1867//1867 1869//1869
f 1841//1841 1870//1870 1847//1847
f 1841//1841 1864//1864 1870//1870
f 1870//1870 1871//1871 1847//1847
f 1847//1847 1871//1871 1863//1863
f 1864//1864 1848//1848 1872//1872
f 1868//1868 1873//1873 1867//1867
f 1867//1867 1874//1874 1869//1869
f 1867//1867 1873//1873 1874//1874
f 1870//1870 1864//1864 1875//1875
f 1871//1871 1876//1876 1863//1863
f 1864//1864 1872//1872 1875//1875
f 1868//1868 1877//1877 1873//1873
f 1863//1863 1877//1877 1868//1868
f 1863//1863 1876//1876 1877//1877
f 1873//1873 1878//1878 1874//1874
f 1877//1877 1879//1879 1873//1873
f 1873//1873 1879//1879 1878//1878
f 1870//1870 1880//1880 1871//1871
f 1870//1870 1881//1881 1880//1880
f 1870//1870 1882//1882 1881//1881
f 1870//1870 1875//1875 1882//1882
f 1871//1871 1880//1880 1876//1876
f 1875//1875 1883//1883 1882//1882
f 1875//1875 1872//1872 1883//1883
f 1881//1881 1884//1884 1880//1880
f 1881//1881 1885//1885 1884//1884
f 1881//1881 1886//1886 1885//1885
f 1881//1881 1882//1882 1886//1886
f 1880//1880 1887//1887 1876//1876
f 1880//1880 1884//1884 1887//1887
f 1882//1882 1888//1888 1886//1886
f 1882//1882 1883//1883 1888//1888
f 1877//1877 1889//1889 1879//1879
f 1876//1876 1889//1889 1877//1877
f 1876//1876 1887//1887 1889//1889
f 1848//1848 1857//1857 1890//1890
f 1857//1857 1859//1859 1890//1890
f 1848//1848 1890//1890 1872//1872
f 1890//1890 1859//1859 1891//1891
f 1859//1859 1860//1860 1891//1891
f 1858//1858 1862//1862 1860//1860
f 1862//1862 1861//1861 1892//1892
f 1860//1860 1893//1893 1891//1891
f 1860//1860 1862//1862 1893//1893
f 1862//1862 1892//1892 1893//1893
f 1872//1872 1890//1890 1894//1894
f 1890//1890 1895//1895 1894//1894
f 1890//1890 1891//1891 1895//1895
f 1872//1872 1894//1894 1883//1883
f 1894//1894 1895//1895 1896//1896
f 1883//1883 1897//1897 1888//1888
f 1883//1883 1894//1894 1897//1897
f 1894//1894 1896//1896 1897//1897
f 1896//1896 1895//1895 1898//1898
f 1891//1891 1899//1899 1895//1895
f 1891//1891 1893//1893 1899//1899
f 1895//1895 1900//1900 1898//1898
f 1895//1895 1899//1899 1900//1900
f 1899//1899 1901//1901 1900//1900
f 379//379 1902//1902 420//420
f 379//379 1828//1828 1902//1902
f 420//420 1903//1903 421//421
f 420//420 1902//1902 1903//1903
f 1828//1828 1904//1904 1902//1902
f 1828//1828 1831//1831 1904//1904
f 1902//1902 1905//1905 1903//1903
f 1902//1902 1904//1904 1905//1905
f 1903//1903 1905//1905 1906//1906
f 1831//1831 1907//1907 1904//1904
f 1831//1831 1837//1837 1907//1907
f 1904//1904 1908//1908 1905//1905
f 1904//1904 1907//1907 1908//1908
f 1837//1837 1909//1909 1907//1907
f 1837//1837 1840//1840 1909//1909
f 1907//1907 1909//1909 1908//1908
f 1905//1905 1910//1910 1906//1906
f 1905//1905 1908//1908 1910//1910
f 1908//1908 1911//1911 1910//1910
f 1908//1908 1909//1909 1911//1911
f 421//421 1912//1912 424//424
f 421//421 1903//1903 1912//1912
f 1903//1903 1906//1906 1912//1912
f 1912//1912 1906//1906 1913//1913
f 1906//1906 1910//1910 1913//1913
f 1840//1840 1914//1914 1909//1909
f 1840//1840 1845//1845 1914//1914
f 1909//1909 1914//1914 1915//1915
f 1909//1909 1915//1915 1911//1911
f 1914//1914 1917//1917 1915//1915
f 1845//1845 1918//1918 1914//1914
f 1845//1845 1850//1850 1918//1918
f 1914//1914 1918//1918 1917//1917
f 1917//1917 1919//1919 1915//1915
f 1915//1915 1919//1919 1916//1916
f 1911//1911 1915//1915 1916//1916
f 1916//1916 1919//1919 1921//1921
f 1916//1916 1921//1921 1911//1911
f 1911//1911 1921//1921 1920//1920
f 424//424 1912//1912 433//433
f 433//433 1912//1912 1922//1922
f 1912//1912 1913//1913 1922//1922
f 1922//1922 1913//1913 1923//1923
f 1913//1913 1910//1910 1924//1924
f 1910//1910 1911//1911 1925//1925
f 1910//1910 1925//1925 1924//1924
f 1913//1913 1924//1924 1923//1923
f 433//433 1922//1922 437//437
f 1922//1922 1923//1923 1926//1926
f 437//437 1922//1922 1927//1927
f 437//437 1927//1927 438//438
f 1922//1922 1926//1926 1927//1927
f 1923//1923 1928//1928 1926//1926
f 1923//1923 1924//1924 1928//1928
f 1924//1924 1925//1925 1929//1929
f 1924//1924 1929//1929 1928//1928
f 1911//1911 1920//1920 1925//1925
f 1925//1925 1920//1920 1930//1930
f 1920//1920 1921//1921 1931//1931
f 1920//1920 1931//1931 1930//1930
f 1925//1925 1930//1930 1929//1929
f 1930//1930 1932//1932 1929//1929
f 1929//1929 1932//1932 1933//1933
f 1933//1933 1932//1932 1934//1934
f 1850//1850 1869//1869 1918//1918
f 1869//1869 1935//1935 1918//1918
f 1869//1869 1874//1874 1935//1935
f 1918//1918 1935//1935 1917//1917
f 1935//1935 1936//1936 1917//1917
f 1917//1917 1936//1936 1919//1919
f 1936//1936 1937//1937 1919//1919
f 1919//1919 1937//1937 1921//1921
f 1937//1937 1938//1938 1921//1921
f 1921//1921 1938//1938 1931//1931
f 1874//1874 1878//1878 1935//1935
f 1878//1878 1939//1939 1935//1935
f 1935//1935 1940//1940 1936//1936
f 1935//1935 1939//1939 1940//1940
f 1936//1936 1940//1940 1937//1937
f 1937//1937 1940//1940 1941//1941
f 1940//1940 1942//1942 1941//1941
f 1937//1937 1941//1941 1938//1938
f 1938//1938 1941//1941 1943//1943
f 1941//1941 1944//1944 1943//1943
f 1930//1930 1931//1931 1945//1945
f 1930//1930 1946//1946 1932//1932
f 1930//1930 1945//1945 1946//1946
f 1938//1938 1947//1947 1931//1931
f 1931//1931 1947//1947 1945//1945
f 1932//1932 1946//1946 1948//1948
f 1932//1932 1948//1948 1934//1934
f 1946//1946 1949//1949 1948//1948
f 1938//1938 1943//1943 1947//1947
f 1947//1947 1943//1943 1950//1950
f 1947//1947 1950//1950 1945//1945
f 1945//1945 1949//1949 1946//1946
f 1945//1945 1950//1950 1949//1949
f 1949//1949 1952//1952 1948//1948
f 1950//1950 1951//1951 1949//1949
f 1949//1949 1951//1951 1952//1952
f 1951//1951 1953//1953 1952//1952
f 1954//1954 1956//1956 1955//1955
f 1954//1954 1957//1957 1956//1956
f 1954//1954 1958//1958 1957//1957
f 1954//1954 1959//1959 1958//1958
f 1954//1954 1960//1960 1959//1959
f 1954//1954 1955//1955 1960//1960
f 1957//1957 1961//1961 1956//1956
f 1957//1957 1962//1962 1961//1961
f 1957//1957 1963//1963 1962//1962
f 1957//1957 1958//1958 1963//1963
f 1955//1955 1965//1965 1964//1964
f 1955//1955 1956//1956 1965//1965
f 1955//1955 1966//1966 1960//1960
f 1955//1955 1964//1964 1966//1966
f 1956//1956 1967//1967 1965//1965
f 1956//1956 1961//1961 1967//1967
f 1959//1959 1968//1968 1958//1958
f 1959//1959 1969//1969 1968//1968
f 1959//1959 1970//1970 1969//1969
f 1959//1959 1960//1960 1970//1970
f 1958//1958 1971//1971 1963//1963
f 1958//1958 1968//1968 1971//1971
f 1960//1960 1972//1972 1970//1970
f 1960//1960 1966//1966 1972//1972
f 1969//1969 1973//1973 1968//1968
f 1969//1969 1974//1974 1973//1973
f 1969//1969 1975//1975 1974//1974
f 1969//1969 1970//1970 1975//1975
f 1968//1968 1976//1976 1971//1971
f 1968//1968 1973//1973 1976//1976
f 1970//1970 1977//1977 1975//1975
f 1970//1970 1972//1972 1977//1977
f 1885//1885 1978//1978 1884//1884
f 1885//1885 1962//1962 1978//1978
f 1885//1885 1961//1961 1962//1962
f 1885//1885 1886//1886 1961//1961
f 1884//1884 1979//1979 1887//1887
f 1884//1884 1978//1978 1979//1979
f 1886//1886 1967//1967 1961//1961
f 1886//1886 1888//1888 1967//1967
f 1962//1962 1980//1980 1978//1978
f 1962//1962 1963//1963 1980//1980
f 1978//1978 1981//1981 1979//1979
f 1978//1978 1980//1980 1981//1981
f 1887//1887 1979//1979 1889//1889
f 1879//1879 1982//1982 1878//1878
f 1889//1889 1983//1983 1879//1879
f 1889//1889 1984//1984 1983//1983
f 1979//1979 1984//1984 1889//1889
f 1979//1979 1981//1981 1984//1984
f 1879//1879 1985//1985 1982//1982
f 1879//1879 1983//1983 1985//1985
f 1963//1963 1986//1986 1980//1980
f 1963//1963 1971//1971 1986//1986
f 1980//1980 1987//1987 1981//1981
f 1980//1980 1986//1986 1987//1987
f 1971//1971 1988//1988 1986//1986
f 1971//1971 1976//1976 1988//1988
f 1986//1986 1989//1989 1987//1987
f 1986//1986 1988//1988 1989//1989
f 1984//1984 1990//1990 1983//1983
f 1984//1984 1991//1991 1990//1990
f 1981//1981 1991//1991 1984//1984
f 1981//1981 1987//1987 1991//1991
f 1983//1983 1992//1992 1985//1985
f 1983//1983 1990//1990 1992//1992
f 1987//1987 1993//1993 1991//1991
f 1987//1987 1989//1989 1993//1993
f 1991//1991 1992//1992 1990//1990
f 1991//1991 1994//1994 1992//1992
f 1991//1991 1995//1995 1994//1994
f 1991//1991 1993//1993 1995//1995
f 1996//1996 1998//1998 1997//1997
f 1996//1996 1999//1999 1998//1998
f 1974//1974 1997//1997 1973//1973
f 1974//1974 1996//1996 1997//1997
f 1974//1974 1999//1999 1996//1996
f 1974//1974 1975//1975 1999//1999
f 1997//1997 2001//2001 2000//2000
f 1997//1997 1998//1998 2001//2001
f 1973//1973 2000//2000 1976//1976
f 1973//1973 1997//1997 2000//2000
f 1975//1975 2002//2002 1999//1999
f 1975//1975 1977//1977 2002//2002
f 2003//2003 2005//2005 2004//2004
f 2003//2003 2006//2006 2005//2005
f 1999//1999 2004//2004 1998//1998
f 1999//1999 2003//2003 2004//2004
f 1999//1999 2006//2006 2003//2003
f 1999//1999 2002//2002 2006//2006
f 2004//2004 2008//2008 2007//2007
f 2004//2004 2005//2005 2008//2008
f 1998//1998 2007//2007 2001//2001
f 1998//1998 2004//2004 2007//2007
f 2000//2000 2010//2010 2009//2009
f 2000//2000 2001//2001 2010//2010
f 1976//1976 2009//2009 1988//1988
f 1976//1976 2000//2000 2009//2009
f 2009//2009 2012//2012 2011//2011
f 2009//2009 2010//2010 2012//2012
f 1988//1988 2011//2011 1989//1989
f 1988//1988 2009//2009 2011//2011
f 2007//2007 2014//2014 2013//2013
f 2007//2007 2008//2008 2014//2014
f 2001//2001 2013//2013 2010//2010
f 2001//2001 2007//2007 2013//2013
f 2013//2013 2016//2016 2015//2015
f 2013//2013 2014//2014 2016//2016
f 2010//2010 2015//2015 2012//2012
f 2010//2010 2013//2013 2015//2015
f 2011//2011 2018//2018 2017//2017
f 2011//2011 2012//2012 2018//2018
f 1989//1989 2017//2017 1993//1993
f 1989//1989 2011//2011 2017//2017
f 2017//2017 2020//2020 2019//2019
f 2017//2017 2018//2018 2020//2020
f 1993//1993 2019//2019 1995//1995
f 1993//1993 2017//2017 2019//2019
f 2015//2015 2022//2022 2021//2021
f 2015//2015 2016//2016 2022//2022
f 2012//2012 2021//2021 2018//2018
f 2012//2012 2015//2015 2021//2021
f 2021//2021 2024//2024 2023//2023
f 2021//2021 2022//2022 2024//2024
f 2018//2018 2023//2023 2020//2020
f 2018//2018 2021//2021 2023//2023
f 2025//2025 2027//2027 2026//2026
f 2025//2025 2028//2028 2027//2027
f 1964//1964 2028//2028 2025//2025
f 1964//1964 1965//1965 2028//2028
f 1964//1964 2026//2026 1966//1966
f 1964//1964 2025//2025 2026//2026
f 2028//2028 2029//2029 2027//2027
f 2028//2028 2030//2030 2029//2029
f 1965//1965 2030//2030 2028//2028
f 1965//1965 1967//1967 2030//2030
f 2026//2026 2032//2032 2031//2031
f 2026//2026 2027//2027 2032//2032
f 1966//1966 2031//2031 1972//1972
f 1966//1966 2026//2026 2031//2031
f 2027//2027 2033//2033 2032//2032
f 2027//2027 2029//2029 2033//2033
f 2031//2031 2035//2035 2034//2034
f 2031//2031 2032//2032 2035//2035
f 1972//1972 2034//2034 1977//1977
f 1972//1972 2031//2031 2034//2034
f 2032//2032 2036//2036 2035//2035
f 2032//2032 2033//2033 2036//2036
f 1888//1888 2030//2030 1967//1967
f 1888//1888 1897//1897 2030//2030
f 1897//1897 2037//2037 2030//2030
f 1897//1897 1896//1896 2037//2037
f 2030//2030 2038//2038 2029//2029
f 2030//2030 2037//2037 2038//2038
f 1896//1896 1898//1898 2037//2037
f 2037//2037 2039//2039 2038//2038
f 2037//2037 1898//1898 2039//2039
f 1898//1898 2040//2040 2039//2039
f 1898//1898 1900//1900 2040//2040
f 2029//2029 2041//2041 2033//2033
f 2029//2029 2038//2038 2041//2041
f 2033//2033 2042//2042 2036//2036
f 2033//2033 2041//2041 2042//2042
f 2038//2038 2043//2043 2041//2041
f 2038//2038 2039//2039 2043//2043
f 2039//2039 2044//2044 2043//2043
f 2039//2039 2040//2040 2044//2044
f 2041//2041 2045//2045 2042//2042
f 2041//2041 2043//2043 2045//2045
f 2043//2043 2046//2046 2045//2045
f 2043//2043 2044//2044 2046//2046
f 1900//1900 2047//2047 2040//2040
f 1900//1900 2048//2048 2047//2047
f 1900//1900 1901//1901 2048//2048
f 2040//2040 2049//2049 2044//2044
f 2040//2040 2047//2047 2049//2049
f 2047//2047 2048//2048 2049//2049
f 2044//2044 2050//2050 2046//2046
f 2044//2044 2049//2049 2050//2050
f 2049//2049 2051//2051 2050//2050
f 2049//2049 2048//2048 2051//2051
f 2048//2048 1901//1901 2052//2052
f 2048//2048 2053//2053 2051//2051
f 2048//2048 2052//2052 2053//2053
f 1977//1977 2054//2054 2002//2002
f 1977//1977 2034//2034 2054//2054
f 2034//2034 2056//2056 2055//2055
f 2034//2034 2035//2035 2056//2056
f 2034//2034 2057//2057 2054//2054
f 2034//2034 2055//2055 2057//2057
f 2035//2035 2042//2042 2056//2056
f 2035//2035 2036//2036 2042//2042
f 2006//2006 2058//2058 2005//2005
f 2006//2006 2059//2059 2058//2058
f 2002//2002 2059//2059 2006//2006
f 2002//2002 2054//2054 2059//2059
f 2005//2005 2060//2060 2008//2008
f 2005//2005 2058//2058 2060//2060
f 2059//2059 2061//2061 2058//2058
f 2059//2059 2062//2062 2061//2061
f 2054//2054 2062//2062 2059//2059
f 2054//2054 2057//2057 2062//2062
f 2058//2058 2063//2063 2060//2060
f 2058//2058 2061//2061 2063//2063
f 2055//2055 2065//2065 2064//2064
f 2055//2055 2056//2056 2065//2065
f 2055//2055 2066//2066 2057//2057
f 2055//2055 2064//2064 2066//2066
f 2056//2056 2045//2045 2065//2065
f 2056//2056 2042//2042 2045//2045
f 2064//2064 2068//2068 2067//2067
f 2064//2064 2065//2065 2068//2068
f 2064//2064 2069//2069 2066//2066
f 2064//2064 2067//2067 2069//2069
f 2065//2065 2046//2046 2068//2068
f 2065//2065 2045//2045 2046//2046
f 2062//2062 2070//2070 2061//2061
f 2062//2062 2071//2071 2070//2070
f 2057//2057 2071//2071 2062//2062
f 2057//2057 2066//2066 2071//2071
f 2061//2061 2072//2072 2063//2063
f 2061//2061 2070//2070 2072//2072
f 2071//2071 2073//2073 2070//2070
f 2071//2071 2074//2074 2073//2073
f 2066//2066 2074//2074 2071//2071
f 2066//2066 2069//2069 2074//2074
f 2070//2070 2075//2075 2072//2072
f 2070//2070 2073//2073 2075//2075
f 2008//2008 2076//2076 2014//2014
f 2008//2008 2060//2060 2076//2076
f 2014//2014 2077//2077 2016//2016
f 2014//2014 2076//2076 2077//2077
f 2060//2060 2078//2078 2076//2076
f 2060//2060 2063//2063 2078//2078
f 2076//2076 2079//2079 2077//2077
f 2076//2076 2078//2078 2079//2079
f 2016//2016 2077//2077 2022//2022
f 2022//2022 2080//2080 2024//2024
f 2022//2022 2077//2077 2080//2080
f 2077//2077 2079//2079 2080//2080
f 2063//2063 2081//2081 2078//2078
f 2063//2063 2072//2072 2081//2081
f 2078//2078 2081//2081 2079//2079
f 2072//2072 2082//2082 2081//2081
f 2072//2072 2075//2075 2082//2082
f 2081//2081 2082//2082 2083//2083
f 2079//2079 2081//2081 2084//2084
f 2081//2081 2083//2083 2084//2084
f 2085//2085 2087//2087 2086//2086
f 2085//2085 2088//2088 2087//2087
f 2067//2067 2088//2088 2085//2085
f 2067//2067 2068//2068 2088//2088
f 2067//2067 2086//2086 2069//2069
f 2067//2067 2085//2085 2086//2086
f 2088//2088 2089//2089 2087//2087
f 2088//2088 2050//2050 2089//2089
f 2068//2068 2050//2050 2088//2088
f 2068//2068 2046//2046 2050//2050
f 2074//2074 2090//2090 2073//2073
f 2074//2074 2091//2091 2090//2090
f 2086//2086 2090//2090 2091//2091
f 2086//2086 2087//2087 2090//2090
f 2069//2069 2091//2091 2074//2074
f 2069//2069 2086//2086 2091//2091
f 2073//2073 2092//2092 2075//2075
f 2073//2073 2090//2090 2092//2092
f 2087//2087 2092//2092 2090//2090
f 2087//2087 2089//2089 2092//2092
f 2050//2050 2093//2093 2089//2089
f 2050//2050 2051//2051 2093//2093
f 2075//2075 2094//2094 2082//2082
f 2075//2075 2092//2092 2094//2094
f 2082//2082 2095//2095 2083//2083
f 2082//2082 2094//2094 2095//2095
f 2092//2092 2096//2096 2094//2094
f 2092//2092 2097//2097 2096//2096
f 2089//2089 2097//2097 2092//2092
f 2089//2089 2093//2093 2097//2097
f 2094//2094 2098//2098 2095//2095
f 2094//2094 2096//2096 2098//2098
f 2051//2051 2099//2099 2093//2093
f 2051//2051 2053//2053 2099//2099
f 2053//2053 2100//2100 2099//2099
f 2097//2097 2101//2101 2096//2096
f 2097//2097 2102//2102 2101//2101
f 2093//2093 2102//2102 2097//2097
f 2093//2093 2099//2099 2102//2102
f 2096//2096 2103//2103 2098//2098
f 2096//2096 2101//2101 2103//2103
f 2102//2102 2104//2104 2101//2101
f 2102//2102 2105//2105 2104//2104
f 2099//2099 2105//2105 2102//2102
f 2099//2099 2100//2100 2105//2105
f 2101//2101 2106//2106 2103//2103
f 2101//2101 2104//2104 2106//2106
f 2098//2098 2103//2103 2095//2095
f 2103//2103 2106//2106 2107//2107
f 1878//1878 1982//1982 1939//1939
f 1939//1939 1942//1942 1940//1940
f 1982//1982 2108//2108 1939//1939
f 1982//1982 1985//1985 2108//2108
f 1939//1939 2109//2109 1942//1942
f 1939//1939 2108//2108 2109//2109
f 1942//1942 2109//2109 2110//2110
f 1942//1942 2110//2110 2111//2111
f 1985//1985 2112//2112 2108//2108
f 1985//1985 1992//1992 2112//2112
f 2108//2108 2113//2113 2109//2109
f 2108//2108 2112//2112 2113//2113
f 1994//1994 2112//2112 1992//1992
f 1994//1994 2114//2114 2112//2112
f 1994//1994 2115//2115 2114//2114
f 1994//1994 1995//1995 2115//2115
f 2114//2114 2113//2113 2112//2112
f 2114//2114 2116//2116 2113//2113
f 2114//2114 2117//2117 2116//2116
f 2114//2114 2115//2115 2117//2117
f 2109//2109 2118//2118 2110//2110
f 2109//2109 2113//2113 2118//2118
f 2110//2110 2119//2119 2111//2111
f 2110//2110 2118//2118 2119//2119
f 2116//2116 2118//2118 2113//2113
f 2116//2116 2120//2120 2118//2118
f 2116//2116 2121//2121 2120//2120
f 2116//2116 2117//2117 2121//2121
f 2120//2120 2119//2119 2118//2118
f 2120//2120 2122//2122 2119//2119
f 2120//2120 2123//2123 2122//2122
f 2120//2120 2121//2121 2123//2123
f 1942//1942 1944//1944 1941//1941
f 1942//1942 2124//2124 1944//1944
f 1942//1942 2111//2111 2124//2124
f 1944//1944 2124//2124 2125//2125
f 1944//1944 2126//2126 1943//1943
f 1944//1944 2125//2125 2127//2127
f 1944//1944 2128//2128 2126//2126
f 1944//1944 2127//2127 2128//2128
f 2111//2111 2129//2129 2124//2124
f 2111//2111 2119//2119 2129//2129
f 2124//2124 2130//2130 2125//2125
f 2124//2124 2129//2129 2130//2130
f 2122//2122 2129//2129 2119//2119
f 2122//2122 2131//2131 2129//2129
f 2122//2122 2132//2132 2131//2131
f 2122//2122 2123//2123 2132//2132
f 2131//2131 2130//2130 2129//2129
f 2131//2131 2133//2133 2130//2130
f 2131//2131 2134//2134 2133//2133
f 2131//2131 2132//2132 2134//2134
f 2125//2125 2135//2135 2127//2127
f 2125//2125 2130//2130 2135//2135
f 2127//2127 2136//2136 2128//2128
f 2127//2127 2135//2135 2136//2136
f 2133//2133 2135//2135 2130//2130
f 2133//2133 2137//2137 2135//2135
f 2133//2133 2138//2138 2137//2137
f 2133//2133 2134//2134 2138//2138
f 2137//2137 2136//2136 2135//2135
f 2137//2137 2139//2139 2136//2136
f 2137//2137 2140//2140 2139//2139
f 2137//2137 2138//2138 2140//2140
f 2019//2019 2142//2142 2141//2141
f 2019//2019 2020//2020 2142//2142
f 1995//1995 2141//2141 2115//2115
f 1995//1995 2019//2019 2141//2141
f 2141//2141 2144//2144 2143//2143
f 2141//2141 2142//2142 2144//2144
f 2115//2115 2143//2143 2117//2117
f 2115//2115 2141//2141 2143//2143
f 2023//2023 2146//2146 2145//2145
f 2023//2023 2024//2024 2146//2146
f 2020//2020 2145//2145 2142//2142
f 2020//2020 2023//2023 2145//2145
f 2145//2145 2148//2148 2147//2147
f 2145//2145 2146//2146 2148//2148
f 2142//2142 2147//2147 2144//2144
f 2142//2142 2145//2145 2147//2147
f 2143//2143 2150//2150 2149//2149
f 2143//2143 2144//2144 2150//2150
f 2117//2117 2149//2149 2121//2121
f 2117//2117 2143//2143 2149//2149
f 2149//2149 2152//2152 2151//2151
f 2149//2149 2150//2150 2152//2152
f 2121//2121 2151//2151 2123//2123
f 2121//2121 2149//2149 2151//2151
f 2147//2147 2154//2154 2153//2153
f 2147//2147 2148//2148 2154//2154
f 2144//2144 2153//2153 2150//2150
f 2144//2144 2147//2147 2153//2153
f 2153//2153 2156//2156 2155//2155
f 2153//2153 2154//2154 2156//2156
f 2150//2150 2155//2155 2152//2152
f 2150//2150 2153//2153 2155//2155
f 2151//2151 2158//2158 2157//2157
f 2151//2151 2152//2152 2158//2158
f 2123//2123 2157//2157 2132//2132
f 2123//2123 2151//2151 2157//2157
f 2157//2157 2160//2160 2159//2159
f 2157//2157 2158//2158 2160//2160
f 2132//2132 2159//2159 2134//2134
f 2132//2132 2157//2157 2159//2159
f 2155//2155 2162//2162 2161//2161
f 2155//2155 2156//2156 2162//2162
f 2152//2152 2161//2161 2158//2158
f 2152//2152 2155//2155 2161//2161
f 2161//2161 2164//2164 2163//2163
f 2161//2161 2162//2162 2164//2164
f 2158//2158 2163//2163 2160//2160
f 2158//2158 2161//2161 2163//2163
f 2159//2159 2166//2166 2165//2165
f 2159//2159 2160//2160 2166//2166
f 2134//2134 2165//2165 2138//2138
f 2134//2134 2159//2159 2165//2165
f 2165//2165 2168//2168 2167//2167
f 2165//2165 2166//2166 2168//2168
f 2138//2138 2167//2167 2140//2140
f 2138//2138 2165//2165 2167//2167
f 2163//2163 2170//2170 2169//2169
f 2163//2163 2164//2164 2170//2170
f 2160//2160 2169//2169 2166//2166
f 2160//2160 2163//2163 2169//2169
f 2169//2169 2172//2172 2171//2171
f 2169//2169 2170//2170 2172//2172
f 2166//2166 2171//2171 2168//2168
f 2166//2166 2169//2169 2171//2171
f 1943//1943 2126//2126 1950//1950
f 2126//2126 2128//2128 2173//2173
f 2126//2126 2173//2173 2174//2174
f 1950//1950 2126//2126 1951//1951
f 2126//2126 2174//2174 2175//2175
f 2126//2126 2175//2175 2176//2176
f 2128//2128 2177//2177 2173//2173
f 2128//2128 2136//2136 2177//2177
f 2173//2173 2178//2178 2174//2174
f 2173//2173 2177//2177 2178//2178
f 2139//2139 2177//2177 2136//2136
f 2139//2139 2179//2179 2177//2177
f 2139//2139 2180//2180 2179//2179
f 2139//2139 2140//2140 2180//2180
f 2179//2179 2178//2178 2177//2177
f 2179//2179 2181//2181 2178//2178
f 2179//2179 2182//2182 2181//2181
f 2179//2179 2180//2180 2182//2182
f 2174//2174 2183//2183 2175//2175
f 2174//2174 2178//2178 2183//2183
f 2175//2175 2184//2184 2176//2176
f 2175//2175 2183//2183 2184//2184
f 2181//2181 2183//2183 2178//2178
f 2181//2181 2185//2185 2183//2183
f 2181//2181 2186//2186 2185//2185
f 2181//2181 2182//2182 2186//2186
f 2185//2185 2184//2184 2183//2183
f 2185//2185 2187//2187 2184//2184
f 2185//2185 2188//2188 2187//2187
f 2185//2185 2186//2186 2188//2188
f 1951//1951 2126//2126 1953//1953
f 2126//2126 2176//2176 2189//2189
f 2126//2126 2190//2190 1953//1953
f 2126//2126 2189//2189 2190//2190
f 1953//1953 2190//2190 2191//2191
f 1953//1953 2191//2191 2192//2192
f 2176//2176 2193//2193 2189//2189
f 2176//2176 2184//2184 2193//2193
f 2189//2189 2194//2194 2190//2190
f 2189//2189 2193//2193 2194//2194
f 2187//2187 2193//2193 2184//2184
f 2187//2187 2195//2195 2193//2193
f 2187//2187 2196//2196 2195//2195
f 2187//2187 2188//2188 2196//2196
f 2195//2195 2194//2194 2193//2193
f 2195//2195 2197//2197 2194//2194
f 2195//2195 2198//2198 2197//2197
f 2195//2195 2196//2196 2198//2198
f 2190//2190 2199//2199 2191//2191
f 2190//2190 2194//2194 2199//2199
f 2191//2191 2200//2200 2192//2192
f 2191//2191 2199//2199 2200//2200
f 2197//2197 2199//2199 2194//2194
f 2197//2197 2201//2201 2199//2199
f 2197//2197 2202//2202 2201//2201
f 2197//2197 2198//2198 2202//2202
f 2201//2201 2200//2200 2199//2199
f 2201//2201 2203//2203 2200//2200
f 2201//2201 2204//2204 2203//2203
f 2201//2201 2202//2202 2204//2204
f 2167//2167 2206//2206 2205//2205
f 2167//2167 2168//2168 2206//2206
f 2140//2140 2205//2205 2180//2180
f 2140//2140 2167//2167 2205//2205
f 2205//2205 2208//2208 2207//2207
f 2205//2205 2206//2206 2208//2208
f 2180//2180 2207//2207 2182//2182
f 2180//2180 2205//2205 2207//2207
f 2171//2171 2210//2210 2209//2209
f 2171//2171 2172//2172 2210//2210
f 2168//2168 2209//2209 2206//2206
f 2168//2168 2171//2171 2209//2209
f 2209//2209 2212//2212 2211//2211
f 2209//2209 2210//2210 2212//2212
f 2206//2206 2211//2211 2208//2208
f 2206//2206 2209//2209 2211//2211
f 2207//2207 2214//2214 2213//2213
f 2207//2207 2208//2208 2214//2214
f 2182//2182 2213//2213 2186//2186
f 2182//2182 2207//2207 2213//2213
f 2213//2213 2216//2216 2215//2215
f 2213//2213 2214//2214 2216//2216
f 2186//2186 2215//2215 2188//2188
f 2186//2186 2213//2213 2215//2215
f 2211//2211 2218//2218 2217//2217
f 2211//2211 2212//2212 2218//2218
f 2208//2208 2217//2217 2214//2214
f 2208//2208 2211//2211 2217//2217
f 2217//2217 2220//2220 2219//2219
f 2217//2217 2218//2218 2220//2220
f 2214//2214 2219//2219 2216//2216
f 2214//2214 2217//2217 2219//2219
f 2215//2215 2222//2222 2221//2221
f 2215//2215 2216//2216 2222//2222
f 2188//2188 2221//2221 2196//2196
f 2188//2188 2215//2215 2221//2221
f 2221//2221 2224//2224 2223//2223
f 2221//2221 2222//2222 2224//2224
f 2196//2196 2223//2223 2198//2198
f 2196//2196 2221//2221 2223//2223
f 2219//2219 2226//2226 2225//2225
f 2219//2219 2220//2220 2226//2226
f 2216//2216 2225//2225 2222//2222
f 2216//2216 2219//2219 2225//2225
f 2225//2225 2228//2228 2227//2227
f 2225//2225 2226//2226 2228//2228
f 2222//2222 2227//2227 2224//2224
f 2222//2222 2225//2225 2227//2227
f 2223//2223 2230//2230 2229//2229
f 2223//2223 2224//2224 2230//2230
f 2198//2198 2229//2229 2202//2202
f 2198//2198 2223//2223 2229//2229
f 2229//2229 2232//2232 2231//2231
f 2229//2229 2230//2230 2232//2232
f 2202//2202 2231//2231 2204//2204
f 2202//2202 2229//2229 2231//2231
f 2227//2227 2234//2234 2233//2233
f 2227//2227 2228//2228 2234//2234
f 2224//2224 2233//2233 2230//2230
f 2224//2224 2227//2227 2233//2233
f 2233//2233 2236//2236 2235//2235
f 2233//2233 2234//2234 2236//2236
f 2230//2230 2235//2235 2232//2232
f 2230//2230 2233//2233 2235//2235
f 2024//2024 2237//2237 2146//2146
f 2024//2024 2080//2080 2237//2237
f 2146//2146 2237//2237 2148//2148
f 2080//2080 2238//2238 2237//2237
f 2080//2080 2079//2079 2238//2238
f 2148//2148 2237//2237 2154//2154
f 2154//2154 2237//2237 2156//2156
f 2079//2079 2084//2084 2238//2238
f 2084//2084 2083//2083 2239//2239
f 2084//2084 2239//2239 2240//2240
f 2238//2238 2084//2084 2241//2241
f 2084//2084 2242//2242 2241//2241
f 2084//2084 2240//2240 2242//2242
f 2241//2241 2242//2242 2243//2243
f 2156//2156 2244//2244 2162//2162
f 2156//2156 2237//2237 2244//2244
f 2162//2162 2245//2245 2164//2164
f 2162//2162 2244//2244 2245//2245
f 2237//2237 2246//2246 2244//2244
f 2237//2237 2238//2238 2246//2246
f 2244//2244 2246//2246 2245//2245
f 2164//2164 2247//2247 2170//2170
f 2164//2164 2245//2245 2247//2247
f 2170//2170 2247//2247 2172//2172
f 2245//2245 2248//2248 2247//2247
f 2245//2245 2246//2246 2248//2248
f 2247//2247 2248//2248 2249//2249
f 2238//2238 2241//2241 2246//2246
f 2246//2246 2241//2241 2250//2250
f 2241//2241 2243//2243 2251//2251
f 2241//2241 2252//2252 2250//2250
f 2241//2241 2251//2251 2252//2252
f 2246//2246 2253//2253 2248//2248
f 2246//2246 2250//2250 2253//2253
f 2248//2248 2254//2254 2249//2249
f 2248//2248 2253//2253 2254//2254
f 2253//2253 2255//2255 2254//2254
f 2253//2253 2250//2250 2255//2255
f 2083//2083 2256//2256 2239//2239
f 2083//2083 2095//2095 2256//2256
f 2239//2239 2257//2257 2240//2240
f 2239//2239 2256//2256 2257//2257
f 2095//2095 2258//2258 2256//2256
f 2256//2256 2259//2259 2257//2257
f 2256//2256 2258//2258 2259//2259
f 2240//2240 2260//2260 2242//2242
f 2240//2240 2257//2257 2260//2260
f 2242//2242 2261//2261 2243//2243
f 2242//2242 2260//2260 2261//2261
f 2257//2257 2262//2262 2260//2260
f 2257//2257 2259//2259 2262//2262
f 2260//2260 2263//2263 2261//2261
f 2260//2260 2262//2262 2263//2263
f 2095//2095 2264//2264 2258//2258
f 2095//2095 2103//2103 2264//2264
f 2258//2258 2264//2264 2259//2259
f 2103//2103 2265//2265 2264//2264
f 2103//2103 2107//2107 2265//2265
f 2259//2259 2266//2266 2262//2262
f 2259//2259 2264//2264 2266//2266
f 2262//2262 2267//2267 2263//2263
f 2262//2262 2266//2266 2267//2267
f 2264//2264 2268//2268 2266//2266
f 2264//2264 2265//2265 2268//2268
f 2266//2266 2269//2269 2267//2267
f 2266//2266 2268//2268 2269//2269
f 2243//2243 2270//2270 2251//2251
f 2243//2243 2261//2261 2270//2270
f 2251//2251 2271//2271 2252//2252
f 2251//2251 2270//2270 2271//2271
f 2261//2261 2272//2272 2270//2270
f 2261//2261 2263//2263 2272//2272
f 2270//2270 2273//2273 2271//2271
f 2270//2270 2272//2272 2273//2273
f 2250//2250 2274//2274 2255//2255
f 2250//2250 2252//2252 2274//2274
f 2252//2252 2275//2275 2274//2274
f 2252//2252 2271//2271 2275//2275
f 2263//2263 2276//2276 2272//2272
f 2263//2263 2267//2267 2276//2276
f 2272//2272 2277//2277 2273//2273
f 2272//2272 2276//2276 2277//2277
f 2267//2267 2278//2278 2276//2276
f 2267//2267 2269//2269 2278//2278
f 2276//2276 2279//2279 2277//2277
f 2276//2276 2278//2278 2279//2279
f 2273//2273 2280//2280 2271//2271
f 2273//2273 2277//2277 2280//2280
f 2271//2271 2281//2281 2275//2275
f 2271//2271 2280//2280 2281//2281
f 2277//2277 2282//2282 2280//2280
f 2277//2277 2279//2279 2282//2282
f 2280//2280 2283//2283 2281//2281
f 2280//2280 2282//2282 2283//2283
f 2172//2172 2247//2247 2210//2210
f 2210//2210 2284//2284 2212//2212
f 2210//2210 2247//2247 2284//2284
f 2247//2247 2249//2249 2285//2285
f 2247//2247 2286//2286 2284//2284
f 2247//2247 2285//2285 2286//2286
f 2212//2212 2287//2287 2218//2218
f 2212//2212 2284//2284 2287//2287
f 2218//2218 2288//2288 2220//2220
f 2218//2218 2287//2287 2288//2288
f 2284//2284 2286//2286 2287//2287
f 2287//2287 2289//2289 2288//2288
f 2287//2287 2286//2286 2289//2289
f 2249//2249 2290//2290 2285//2285
f 2249//2249 2254//2254 2290//2290
f 2285//2285 2291//2291 2286//2286
f 2285//2285 2290//2290 2291//2291
f 2254//2254 2292//2292 2290//2290
f 2254//2254 2255//2255 2292//2292
f 2290//2290 2293//2293 2291//2291
f 2290//2290 2292//2292 2293//2293
f 2286//2286 2291//2291 2294//2294
f 2286//2286 2295//2295 2289//2289
f 2286//2286 2294//2294 2295//2295
f 2291//2291 2296//2296 2294//2294
f 2291//2291 2293//2293 2296//2296
f 2294//2294 2297//2297 2295//2295
f 2294//2294 2296//2296 2297//2297
f 2220//2220 2288//2288 2226//2226
f 2226//2226 2288//2288 2228//2228
f 2228//2228 2298//2298 2234//2234
f 2228//2228 2288//2288 2298//2298
f 2234//2234 2299//2299 2236//2236
f 2234//2234 2298//2298 2299//2299
f 2288//2288 2289//2289 2298//2298
f 2298//2298 2300//2300 2299//2299
f 2298//2298 2289//2289 2300//2300
f 2289//2289 2301//2301 2300//2300
f 2289//2289 2295//2295 2301//2301
f 2295//2295 2297//2297 2302//2302
f 2295//2295 2302//2302 2301//2301
f 2255//2255 2303//2303 2292//2292
f 2255//2255 2274//2274 2303//2303
f 2292//2292 2304//2304 2293//2293
f 2292//2292 2303//2303 2304//2304
f 2274//2274 2305//2305 2303//2303
f 2274//2274 2275//2275 2305//2305
f 2303//2303 2306//2306 2304//2304
f 2303//2303 2305//2305 2306//2306
f 2293//2293 2307//2307 2296//2296
f 2293//2293 2304//2304 2307//2307
f 2296//2296 2308//2308 2297//2297
f 2296//2296 2307//2307 2308//2308
f 2304//2304 2309//2309 2307//2307
f 2304//2304 2306//2306 2309//2309
f 2307//2307 2310//2310 2308//2308
f 2307//2307 2309//2309 2310//2310
f 2275//2275 2311//2311 2305//2305
f 2275//2275 2281//2281 2311//2311
f 2305//2305 2312//2312 2306//2306
f 2305//2305 2311//2311 2312//2312
f 2281//2281 2313//2313 2311//2311
f 2281//2281 2283//2283 2313//2313
f 2311//2311 2314//2314 2312//2312
f 2311//2311 2313//2313 2314//2314
f 2306//2306 2315//2315 2309//2309
f 2306//2306 2312//2312 2315//2315
f 2309//2309 2316//2316 2310//2310
f 2309//2309 2315//2315 2316//2316
f 2312//2312 2317//2317 2315//2315
f 2312//2312 2314//2314 2317//2317
f 2315//2315 2318//2318 2316//2316
f 2315//2315 2317//2317 2318//2318
f 2297//2297 2308//2308 2319//2319
f 2308//2308 2320//2320 2319//2319
f 2308//2308 2310//2310 2320//2320
f 2319//2319 2320//2320 2321//2321
f 2297//2297 2319//2319 2302//2302
f 2319//2319 2321//2321 2322//2322
f 2310//2310 2323//2323 2320//2320
f 2310//2310 2316//2316 2323//2323
f 2320//2320 2323//2323 2321//2321
f 2316//2316 2324//2324 2323//2323
f 2316//2316 2318//2318 2324//2324
f 2323//2323 2325//2325 2321//2321
f 2323//2323 2324//2324 2325//2325
f 2321//2321 2326//2326 2322//2322
f 2321//2321 2327//2327 2326//2326
f 2321//2321 2325//2325 2327//2327
f 2326//2326 2328//2328 2322//2322
f 2326//2326 2327//2327 2328//2328
f 438//438 1927//1927 2329//2329
f 438//438 2329//2329 701//701
f 1927//1927 2330//2330 2329//2329
f 1927//1927 1926//1926 2330//2330
f 1926//1926 1928//1928 2331//2331
f 1928//1928 1929//1929 2331//2331
f 2331//2331 1929//1929 2332//2332
f 1926//1926 2331//2331 2330//2330
f 701//701 2329//2329 2333//2333
f 2329//2329 2330//2330 2333//2333
f 701//701 2333//2333 704//704
f 2333//2333 2330//2330 2334//2334
f 2330//2330 2331//2331 2335//2335
f 2331//2331 2332//2332 2335//2335
f 2335//2335 2332//2332 2336//2336
f 2330//2330 2335//2335 2334//2334
f 2335//2335 2336//2336 2337//2337
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f 2945//2945 3015//3015 2946//2946
f 2945//2945 3016//3016 3015//3015
f 2945//2945 2948//2948 3016//3016
f 2946//2946 3017//3017 3014//3014
f 2946//2946 3015//3015 3017//3017
f 3015//3015 3018//3018 3017//3017
f 3015//3015 3016//3016 3018//3018
f 2909//2909 3019//3019 2915//2915
f 2909//2909 3020//3020 3019//3019
f 2909//2909 3014//3014 3020//3020
f 2915//2915 3021//3021 2916//2916
f 2915//2915 3019//3019 3021//3021
f 3019//3019 3022//3022 3021//3021
f 3019//3019 3020//3020 3022//3022
f 3014//3014 3023//3023 3020//3020
f 3014//3014 3017//3017 3023//3023
f 3017//3017 3024//3024 3023//3023
f 3017//3017 3018//3018 3024//3024
f 3020//3020 3025//3025 3022//3022
f 3020//3020 3023//3023 3025//3025
f 3023//3023 3026//3026 3025//3025
f 3023//3023 3024//3024 3026//3026
f 2948//2948 3027//3027 3016//3016
f 2948//2948 2956//2956 3027//3027
f 2956//2956 3028//3028 3027//3027
f 2956//2956 2957//2957 3028//3028
f 3016//3016 3029//3029 3018//3018
f 3016//3016 3027//3027 3029//3029
f 3027//3027 3030//3030 3029//3029
f 3027//3027 3028//3028 3030//3030
f 2957//2957 3031//3031 3028//3028
f 2957//2957 2960//2960 3031//3031
f 2960//2960 3032//3032 3031//3031
f 2960//2960 2961//2961 3032//3032
f 3028//3028 3033//3033 3030//3030
f 3028//3028 3031//3031 3033//3033
f 3031//3031 3034//3034 3033//3033
f 3031//3031 3032//3032 3034//3034
f 3018//3018 3035//3035 3024//3024
f 3018//3018 3029//3029 3035//3035
f 3029//3029 3036//3036 3035//3035
f 3029//3029 3030//3030 3036//3036
f 3024//3024 3037//3037 3026//3026
f 3024//3024 3035//3035 3037//3037
f 3035//3035 3038//3038 3037//3037
f 3035//3035 3036//3036 3038//3038
f 3030//3030 3039//3039 3036//3036
f 3030//3030 3033//3033 3039//3039
f 3033//3033 3040//3040 3039//3039
f 3033//3033 3034//3034 3040//3040
f 3036//3036 3041//3041 3038//3038
f 3036//3036 3039//3039 3041//3041
f 3039//3039 3042//3042 3041//3041
f 3039//3039 3040//3040 3042//3042
f 2916//2916 3043//3043 2920//2920
f 2916//2916 3021//3021 3043//3043
f 3021//3021 3044//3044 3043//3043
f 3021//3021 3022//3022 3044//3044
f 2920//2920 3045//3045 2922//2922
f 2920//2920 3043//3043 3045//3045
f 3043//3043 3046//3046 3045//3045
f 3043//3043 3044//3044 3046//3046
f 3022//3022 3047//3047 3044//3044
f 3022//3022 3025//3025 3047//3047
f 3025//3025 3048//3048 3047//3047
f 3025//3025 3026//3026 3048//3048
f 3044//3044 3049//3049 3046//3046
f 3044//3044 3047//3047 3049//3049
f 3047//3047 3050//3050 3049//3049
f 3047//3047 3048//3048 3050//3050
f 2922//2922 3051//3051 2934//2934
f 2922//2922 3045//3045 3051//3051
f 3045//3045 3052//3052 3051//3051
f 3045//3045 3046//3046 3052//3052
f 2934//2934 3053//3053 2936//2936
f 2934//2934 3051//3051 3053//3053
f 3051//3051 3054//3054 3053//3053
f 3051//3051 3052//3052 3054//3054
f 3046//3046 3055//3055 3052//3052
f 3046//3046 3049//3049 3055//3055
f 3049//3049 3056//3056 3055//3055
f 3049//3049 3050//3050 3056//3056
f 3052//3052 3057//3057 3054//3054
f 3052//3052 3055//3055 3057//3057
f 3055//3055 3058//3058 3057//3057
f 3055//3055 3056//3056 3058//3058
f 3026//3026 3059//3059 3048//3048
f 3026//3026 3037//3037 3059//3059
f 3037//3037 3060//3060 3059//3059
f 3037//3037 3038//3038 3060//3060
f 3048//3048 3061//3061 3050//3050
f 3048//3048 3059//3059 3061//3061
f 3059//3059 3062//3062 3061//3061
f 3059//3059 3060//3060 3062//3062
f 3038//3038 3063//3063 3060//3060
f 3038//3038 3041//3041 3063//3063
f 3041//3041 3042//3042 3063//3063
f 3060//3060 3063//3063 3062//3062
f 3050//3050 3064//3064 3056//3056
f 3050//3050 3061//3061 3064//3064
f 3061//3061 3065//3065 3064//3064
f 3061//3061 3062//3062 3065//3065
f 3056//3056 3065//3065 3058//3058
f 3056//3056 3064//3064 3065//3065
f 3062//3062 3063//3063 3066//3066
f 3062//3062 3066//3066 3065//3065
f 2961//2961 3067//3067 3032//3032
f 2961//2961 3001//3001 3067//3067
f 3001//3001 3068//3068 3067//3067
f 3001//3001 3002//3002 3068//3068
f 3032//3032 3069//3069 3034//3034
f 3032//3032 3067//3067 3069//3069
f 3067//3067 3070//3070 3069//3069
f 3067//3067 3068//3068 3070//3070
f 3002//3002 3071//3071 3068//3068
f 3002//3002 3005//3005 3071//3071
f 3005//3005 3006//3006 3071//3071
f 3068//3068 3071//3071 3070//3070
f 3034//3034 3072//3072 3040//3040
f 3034//3034 3069//3069 3072//3072
f 3069//3069 3070//3070 3072//3072
f 3040//3040 3072//3072 3042//3042
f 3072//3072 3073//3073 3042//3042
f 3072//3072 3070//3070 3073//3073
f 3070//3070 3071//3071 3073//3073
f 3006//3006 3012//3012 3013//3013
f 3006//3006 3013//3013 3071//3071
f 3013//3013 3075//3075 3071//3071
f 3071//3071 3074//3074 3073//3073
f 3071//3071 3075//3075 3074//3074
f 3074//3074 3075//3075 3076//3076
f 3075//3075 3077//3077 3076//3076
f 3042//3042 3073//3073 3063//3063
f 3073//3073 3074//3074 3078//3078
f 3063//3063 3078//3078 3066//3066
f 3063//3063 3073//3073 3078//3078
f 3074//3074 3076//3076 3078//3078
f 3078//3078 3076//3076 3079//3079
f 3076//3076 3080//3080 3079//3079
f 1901//1901 2897//2897 3081//3081
f 2897//2897 3082//3082 3081//3081
f 2897//2897 2898//2898 3082//3082
f 1901//1901 3081//3081 2052//2052
f 2052//2052 3083//3083 2053//2053
f 3081//3081 3082//3082 3084//3084
f 2052//2052 3081//3081 3083//3083
f 3081//3081 3085//3085 3083//3083
f 3081//3081 3084//3084 3085//3085
f 2898//2898 3087//3087 3086//3086
f 2898//2898 2902//2902 3087//3087
f 2898//2898 3088//3088 3082//3082
f 2898//2898 3086//3086 3088//3088
f 3086//3086 3089//3089 3088//3088
f 3086//3086 3087//3087 3089//3089
f 2902//2902 3090//3090 3087//3087
f 2902//2902 2905//2905 3090//3090
f 2905//2905 3091//3091 3090//3090
f 2905//2905 2906//2906 3091//3091
f 3087//3087 3092//3092 3089//3089
f 3087//3087 3090//3090 3092//3092
f 3090//3090 3093//3093 3092//3092
f 3090//3090 3091//3091 3093//3093
f 3082//3082 3094//3094 3084//3084
f 3082//3082 3088//3088 3094//3094
f 3088//3088 3095//3095 3094//3094
f 3088//3088 3089//3089 3095//3095
f 3084//3084 3096//3096 3085//3085
f 3084//3084 3094//3094 3096//3096
f 3094//3094 3097//3097 3096//3096
f 3094//3094 3095//3095 3097//3097
f 3089//3089 3098//3098 3095//3095
f 3089//3089 3092//3092 3098//3098
f 3092//3092 3099//3099 3098//3098
f 3092//3092 3093//3093 3099//3099
f 3095//3095 3100//3100 3097//3097
f 3095//3095 3098//3098 3100//3100
f 3098//3098 3101//3101 3100//3100
f 3098//3098 3099//3099 3101//3101
f 2053//2053 3083//3083 2100//2100
f 3083//3083 3102//3102 2100//2100
f 3083//3083 3103//3103 3102//3102
f 3083//3083 3085//3085 3103//3103
f 2105//2105 3104//3104 2104//2104
f 2105//2105 3105//3105 3104//3104
f 2100//2100 3105//3105 2105//2105
f 2100//2100 3102//3102 3105//3105
f 2104//2104 3106//3106 2106//2106
f 2104//2104 3104//3104 3106//3106
f 3105//3105 3107//3107 3104//3104
f 3102//3102 3107//3107 3105//3105
f 3102//3102 3103//3103 3107//3107
f 2106//2106 3106//3106 2107//2107
f 3108//3108 3110//3110 3109//3109
f 3108//3108 3111//3111 3110//3110
f 3108//3108 3112//3112 3111//3111
f 3108//3108 3113//3113 3112//3112
f 3108//3108 3114//3114 3113//3113
f 3108//3108 3109//3109 3114//3114
f 3111//3111 3100//3100 3110//3110
f 3111//3111 3097//3097 3100//3100
f 3111//3111 3115//3115 3097//3097
f 3111//3111 3112//3112 3115//3115
f 3109//3109 3117//3117 3116//3116
f 3109//3109 3110//3110 3117//3117
f 3109//3109 3118//3118 3114//3114
f 3109//3109 3116//3116 3118//3118
f 3110//3110 3101//3101 3117//3117
f 3110//3110 3100//3100 3101//3101
f 3119//3119 3121//3121 3120//3120
f 3119//3119 3122//3122 3121//3121
f 3113//3113 3120//3120 3112//3112
f 3113//3113 3119//3119 3120//3120
f 3113//3113 3122//3122 3119//3119
f 3113//3113 3114//3114 3122//3122
f 3120//3120 3124//3124 3123//3123
f 3120//3120 3121//3121 3124//3124
f 3112//3112 3123//3123 3115//3115
f 3112//3112 3120//3120 3123//3123
f 3122//3122 3125//3125 3121//3121
f 3122//3122 3126//3126 3125//3125
f 3114//3114 3126//3126 3122//3122
f 3114//3114 3118//3118 3126//3126
f 3121//3121 3127//3127 3124//3124
f 3121//3121 3125//3125 3127//3127
f 3085//3085 3128//3128 3103//3103
f 3085//3085 3096//3096 3128//3128
f 3096//3096 3115//3115 3128//3128
f 3096//3096 3097//3097 3115//3115
f 3107//3107 3129//3129 3104//3104
f 3107//3107 3130//3130 3129//3129
f 3103//3103 3130//3130 3107//3107
f 3103//3103 3128//3128 3130//3130
f 3104//3104 3131//3131 3106//3106
f 3104//3104 3129//3129 3131//3131
f 3130//3130 3132//3132 3129//3129
f 3130//3130 3123//3123 3132//3132
f 3128//3128 3123//3123 3130//3130
f 3128//3128 3115//3115 3123//3123
f 3129//3129 3133//3133 3131//3131
f 3129//3129 3132//3132 3133//3133
f 3131//3131 3133//3133 3134//3134
f 3123//3123 3135//3135 3132//3132
f 3123//3123 3124//3124 3135//3135
f 3132//3132 3136//3136 3133//3133
f 3132//3132 3135//3135 3136//3136
f 3124//3124 3137//3137 3135//3135
f 3124//3124 3127//3127 3137//3137
f 3135//3135 3138//3138 3136//3136
f 3135//3135 3137//3137 3138//3138
f 3133//3133 3139//3139 3134//3134
f 3133//3133 3136//3136 3139//3139
f 3134//3134 3139//3139 3140//3140
f 3136//3136 3141//3141 3139//3139
f 3136//3136 3138//3138 3141//3141
f 3139//3139 3141//3141 3140//3140
f 3142//3142 3144//3144 3143//3143
f 3142//3142 3145//3145 3144//3144
f 3142//3142 3146//3146 3145//3145
f 3142//3142 3147//3147 3146//3146
f 3142//3142 3148//3148 3147//3147
f 3142//3142 3143//3143 3148//3148
f 3145//3145 3149//3149 3144//3144
f 3145//3145 3091//3091 3149//3149
f 3145//3145 3093//3093 3091//3091
f 3145//3145 3146//3146 3093//3093
f 3143//3143 3151//3151 3150//3150
f 3143//3143 3144//3144 3151//3151
f 3143//3143 3152//3152 3148//3148
f 3143//3143 3150//3150 3152//3152
f 3144//3144 3153//3153 3151//3151
f 3144//3144 3149//3149 3153//3153
f 3154//3154 3156//3156 3155//3155
f 3154//3154 3157//3157 3156//3156
f 3150//3150 3157//3157 3154//3154
f 3150//3150 3151//3151 3157//3157
f 3150//3150 3155//3155 3152//3152
f 3150//3150 3154//3154 3155//3155
f 3157//3157 3158//3158 3156//3156
f 3157//3157 3159//3159 3158//3158
f 3151//3151 3159//3159 3157//3157
f 3151//3151 3153//3153 3159//3159
f 3147//3147 3160//3160 3146//3146
f 3147//3147 3161//3161 3160//3160
f 3147//3147 3162//3162 3161//3161
f 3147//3147 3148//3148 3162//3162
f 3146//3146 3099//3099 3093//3093
f 3146//3146 3160//3160 3099//3099
f 3148//3148 3163//3163 3162//3162
f 3148//3148 3152//3152 3163//3163
f 3161//3161 3117//3117 3160//3160
f 3161//3161 3116//3116 3117//3117
f 3161//3161 3164//3164 3116//3116
f 3161//3161 3162//3162 3164//3164
f 3160//3160 3101//3101 3099//3099
f 3160//3160 3117//3117 3101//3101
f 3162//3162 3165//3165 3164//3164
f 3162//3162 3163//3163 3165//3165
f 3155//3155 3167//3167 3166//3166
f 3155//3155 3156//3156 3167//3167
f 3152//3152 3166//3166 3163//3163
f 3152//3152 3155//3155 3166//3166
f 3156//3156 3168//3168 3167//3167
f 3156//3156 3158//3158 3168//3168
f 3166//3166 3170//3170 3169//3169
f 3166//3166 3167//3167 3170//3170
f 3163//3163 3169//3169 3165//3165
f 3163//3163 3166//3166 3169//3169
f 3167//3167 3171//3171 3170//3170
f 3167//3167 3168//3168 3171//3171
f 2906//2906 3149//3149 3091//3091
f 2906//2906 2925//2925 3149//3149
f 2925//2925 3153//3153 3149//3149
f 2925//2925 2926//2926 3153//3153
f 2930//2930 3172//3172 3159//3159
f 2930//2930 2931//2931 3172//3172
f 2926//2926 3159//3159 3153//3153
f 2926//2926 2930//2930 3159//3159
f 2931//2931 3173//3173 3172//3172
f 2931//2931 2932//2932 3173//3173
f 3159//3159 3174//3174 3158//3158
f 3159//3159 3172//3172 3174//3174
f 3172//3172 3175//3175 3174//3174
f 3172//3172 3173//3173 3175//3175
f 2932//2932 3176//3176 3173//3173
f 2932//2932 2935//2935 3176//3176
f 2935//2935 3177//3177 3176//3176
f 2935//2935 2936//2936 3177//3177
f 3173//3173 3178//3178 3175//3175
f 3173//3173 3176//3176 3178//3178
f 3176//3176 3179//3179 3178//3178
f 3176//3176 3177//3177 3179//3179
f 3158//3158 3180//3180 3168//3168
f 3158//3158 3174//3174 3180//3180
f 3174//3174 3181//3181 3180//3180
f 3174//3174 3175//3175 3181//3181
f 3168//3168 3182//3182 3171//3171
f 3168//3168 3180//3180 3182//3182
f 3180//3180 3183//3183 3182//3182
f 3180//3180 3181//3181 3183//3183
f 3175//3175 3184//3184 3181//3181
f 3175//3175 3178//3178 3184//3184
f 3178//3178 3185//3185 3184//3184
f 3178//3178 3179//3179 3185//3185
f 3181//3181 3186//3186 3183//3183
f 3181//3181 3184//3184 3186//3186
f 3184//3184 3187//3187 3186//3186
f 3184//3184 3185//3185 3187//3187
f 3116//3116 3188//3188 3118//3118
f 3116//3116 3164//3164 3188//3188
f 3164//3164 3189//3189 3188//3188
f 3164//3164 3165//3165 3189//3189
f 3126//3126 3190//3190 3125//3125
f 3126//3126 3191//3191 3190//3190
f 3118//3118 3191//3191 3126//3126
f 3118//3118 3188//3188 3191//3191
f 3125//3125 3192//3192 3127//3127
f 3125//3125 3190//3190 3192//3192
f 3191//3191 3193//3193 3190//3190
f 3191//3191 3194//3194 3193//3193
f 3189//3189 3193//3193 3194//3194
f 3189//3189 3195//3195 3193//3193
f 3188//3188 3194//3194 3191//3191
f 3188//3188 3189//3189 3194//3194
f 3190//3190 3196//3196 3192//3192
f 3190//3190 3193//3193 3196//3196
f 3195//3195 3196//3196 3193//3193
f 3195//3195 3197//3197 3196//3196
f 3189//3189 3198//3198 3195//3195
f 3189//3189 3199//3199 3198//3198
f 3169//3169 3198//3198 3199//3199
f 3169//3169 3170//3170 3198//3198
f 3165//3165 3199//3199 3189//3189
f 3165//3165 3169//3169 3199//3199
f 3195//3195 3200//3200 3197//3197
f 3195//3195 3198//3198 3200//3200
f 3170//3170 3200//3200 3198//3198
f 3170//3170 3171//3171 3200//3200
f 3127//3127 3201//3201 3137//3137
f 3127//3127 3192//3192 3201//3201
f 3137//3137 3202//3202 3138//3138
f 3137//3137 3201//3201 3202//3202
f 3192//3192 3203//3203 3201//3201
f 3192//3192 3196//3196 3203//3203
f 3197//3197 3203//3203 3196//3196
f 3197//3197 3204//3204 3203//3203
f 3201//3201 3205//3205 3202//3202
f 3201//3201 3203//3203 3205//3205
f 3204//3204 3205//3205 3203//3203
f 3204//3204 3206//3206 3205//3205
f 3138//3138 3207//3207 3141//3141
f 3138//3138 3202//3202 3207//3207
f 3202//3202 3208//3208 3207//3207
f 3202//3202 3205//3205 3208//3208
f 3206//3206 3208//3208 3205//3205
f 3206//3206 3209//3209 3208//3208
f 3207//3207 3208//3208 3210//3210
f 3209//3209 3210//3210 3208//3208
f 3209//3209 3211//3211 3210//3210
f 3197//3197 3212//3212 3204//3204
f 3197//3197 3200//3200 3212//3212
f 3171//3171 3212//3212 3200//3200
f 3171//3171 3182//3182 3212//3212
f 3204//3204 3213//3213 3206//3206
f 3204//3204 3212//3212 3213//3213
f 3182//3182 3213//3213 3212//3212
f 3182//3182 3183//3183 3213//3213
f 3206//3206 3214//3214 3209//3209
f 3206//3206 3213//3213 3214//3214
f 3183//3183 3214//3214 3213//3213
f 3183//3183 3186//3186 3214//3214
f 3209//3209 3214//3214 3211//3211
f 3186//3186 3187//3187 3214//3214
f 2265//2265 3215//3215 2268//2268
f 2268//2268 3215//3215 2269//2269
f 2107//2107 3217//3217 2265//2265
f 2107//2107 3106//3106 3217//3217
f 2265//2265 3217//3217 3216//3216
f 2265//2265 3216//3216 3215//3215
f 2269//2269 3215//3215 2278//2278
f 2278//2278 3218//3218 2279//2279
f 2278//2278 3215//3215 3218//3218
f 2279//2279 3219//3219 2282//2282
f 2279//2279 3218//3218 3219//3219
f 2282//2282 3220//3220 2283//2283
f 2282//2282 3219//3219 3220//3220
f 3218//3218 3220//3220 3219//3219
f 3218//3218 3215//3215 3220//3220
f 3215//3215 3216//3216 3222//3222
f 3215//3215 3222//3222 3221//3221
f 3215//3215 3221//3221 3220//3220
f 3106//3106 3223//3223 3217//3217
f 3106//3106 3131//3131 3223//3223
f 3217//3217 3223//3223 3224//3224
f 3131//3131 3134//3134 3223//3223
f 3223//3223 3225//3225 3224//3224
f 3217//3217 3226//3226 3216//3216
f 3217//3217 3224//3224 3226//3226
f 3216//3216 3226//3226 3222//3222
f 3224//3224 3227//3227 3226//3226
f 3224//3224 3225//3225 3227//3227
f 3226//3226 3228//3228 3222//3222
f 3226//3226 3227//3227 3228//3228
f 3134//3134 3140//3140 3223//3223
f 3223//3223 3229//3229 3225//3225
f 3223//3223 3140//3140 3229//3229
f 3140//3140 3141//3141 3230//3230
f 3140//3140 3231//3231 3229//3229
f 3140//3140 3230//3230 3231//3231
f 3225//3225 3232//3232 3227//3227
f 3225//3225 3229//3229 3232//3232
f 3227//3227 3233//3233 3228//3228
f 3227//3227 3232//3232 3233//3233
f 3229//3229 3234//3234 3232//3232
f 3229//3229 3231//3231 3234//3234
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f 1042//1042 1045//1045 1050//1050
f 1023//1023 1020//1020 1042//1042
f 1052//1052 1051//1051 1053//1053
f 1051//1051 1054//1054 1053//1053
f 1052//1052 1021//1021 1051//1051
f 1053//1053 1055//1055 1052//1052
f 1053//1053 1056//1056 1057//1057
f 1056//1056 1058//1058 1057//1057
f 1053//1053 1054//1054 1056//1056
f 1057//1057 1055//1055 1053//1053
f 1051//1051 1030//1030 1059//1059
f 1030//1030 1031//1031 1059//1059
f 1051//1051 1021//1021 1030//1030
f 1059//1059 1054//1054 1051//1051
f 1052//1052 1060//1060 1026//1026
f 1060//1060 1028//1028 1026//1026
f 1052//1052 1055//1055 1060//1060
f 1026//1026 1021//1021 1052//1052
f 1062//1062 1061//1061 1063//1063
f 1061//1061 1064//1064 1063//1063
f 1062//1062 1065//1065 1061//1061
f 1063//1063 1066//1066 1062//1062
f 1063//1063 1067//1067 1068//1068
f 1067//1067 959//959 1068//1068
f 1063//1063 1064//1064 1067//1067
f 1068//1068 1066//1066 1063//1063
f 1061//1061 1069//1069 1070//1070
f 1069//1069 1031//1031 1070//1070
f 1061//1061 1065//1065 1069//1069
f 1070//1070 1064//1064 1061//1061
f 1062//1062 1071//1071 1072//1072
f 1071//1071 1073//1073 1072//1072
f 1062//1062 1066//1066 1071//1071
f 1072//1072 1065//1065 1062//1062
f 1075//1075 1074//1074 1076//1076
f 1074//1074 964//964 1076//1076
f 1075//1075 1066//1066 1074//1074
f 1076//1076 1077//1077 1075//1075
f 1076//1076 966//966 1078//1078
f 966//966 564//564 1078//1078
f 1076//1076 964//964 966//966
f 1078//1078 1077//1077 1076//1076
f 1074//1074 1068//1068 968//968
f 1068//1068 959//959 968//968
f 1074//1074 1066//1066 1068//1068
f 968//968 964//964 1074//1074
f 1075//1075 1079//1079 1071//1071
f 1079//1079 1073//1073 1071//1071
f 1075//1075 1077//1077 1079//1079
f 1071//1071 1066//1066 1075//1075
f 1081//1081 1080//1080 1082//1082
f 1080//1080 1036//1036 1082//1082
f 1081//1081 1064//1064 1080//1080
f 1082//1082 978//978 1081//1081
f 1082//1082 1038//1038 979//979
f 1038//1038 888//888 979//979
f 1082//1082 1036//1036 1038//1038
f 979//979 978//978 1082//1082
f 1080//1080 1070//1070 1040//1040
f 1070//1070 1031//1031 1040//1040
f 1080//1080 1064//1064 1070//1070
f 1040//1040 1036//1036 1080//1080
f 1081//1081 980//980 1067//1067
f 980//980 959//959 1067//1067
f 1081//1081 978//978 980//980
f 1067//1067 1064//1064 1081//1081
f 1084//1084 1083//1083 1085//1085
f 1083//1083 1086//1086 1085//1085
f 1084//1084 1065//1065 1083//1083
f 1085//1085 1054//1054 1084//1084
f 1085//1085 1087//1087 1056//1056
f 1087//1087 1058//1058 1056//1056
f 1085//1085 1086//1086 1087//1087
f 1056//1056 1054//1054 1085//1085
f 1083//1083 1072//1072 1088//1088
f 1072//1072 1073//1073 1088//1088
f 1083//1083 1065//1065 1072//1072
f 1088//1088 1086//1086 1083//1083
f 1084//1084 1059//1059 1069//1069
f 1059//1059 1031//1031 1069//1069
f 1084//1084 1054//1054 1059//1059
f 1069//1069 1065//1065 1084//1084
f 1090//1090 1089//1089 1091//1091
f 1089//1089 1092//1092 1091//1091
f 1090//1090 1093//1093 1089//1089
f 1091//1091 1094//1094 1090//1090
f 1091//1091 1095//1095 1096//1096
f 1095//1095 1097//1097 1096//1096
f 1091//1091 1092//1092 1095//1095
f 1096//1096 1094//1094 1091//1091
f 1089//1089 1098//1098 1099//1099
f 1098//1098 1025//1025 1099//1099
f 1089//1089 1093//1093 1098//1098
f 1099//1099 1092//1092 1089//1089
f 1090//1090 1100//1100 1101//1101
f 1100//1100 919//919 1101//1101
f 1090//1090 1094//1094 1100//1100
f 1101//1101 1093//1093 1090//1090
f 1103//1103 1102//1102 1104//1104
f 1102//1102 1105//1105 1104//1104
f 1103//1103 1094//1094 1102//1102
f 1104//1104 928//928 1103//1103
f 1104//1104 1106//1106 930//930
f 1106//1106 275//275 930//930
f 1104//1104 1105//1105 1106//1106
f 930//930 928//928 1104//1104
f 1102//1102 1096//1096 1107//1107
f 1096//1096 1097//1097 1107//1107
f 1102//1102 1094//1094 1096//1096
f 1107//1107 1105//1105 1102//1102
f 1103//1103 932//932 1100//1100
f 932//932 919//919 1100//1100
f 1103//1103 928//928 932//932
f 1100//1100 1094//1094 1103//1103
f 1109//1109 1108//1108 1110//1110
f 1108//1108 1045//1045 1110//1110
f 1109//1109 1092//1092 1108//1108
f 1110//1110 1111//1111 1109//1109
f 1110//1110 1047//1047 1112//1112
f 1047//1047 1048//1048 1112//1112
f 1110//1110 1045//1045 1047//1047
f 1112//1112 1111//1111 1110//1110
f 1108//1108 1099//1099 1050//1050
f 1099//1099 1025//1025 1050//1050
f 1108//1108 1092//1092 1099//1099
f 1050//1050 1045//1045 1108//1108
f 1109//1109 1113//1113 1095//1095
f 1113//1113 1097//1097 1095//1095
f 1109//1109 1111//1111 1113//1113
f 1095//1095 1092//1092 1109//1109
f 1115//1115 1114//1114 1116//1116
f 1114//1114 937//937 1116//1116
f 1115//1115 1093//1093 1114//1114
f 1116//1116 1035//1035 1115//1115
f 1116//1116 938//938 1037//1037
f 938//938 888//888 1037//1037
f 1116//1116 937//937 938//938
f 1037//1037 1035//1035 1116//1116
f 1114//1114 1101//1101 939//939
f 1101//1101 919//919 939//939
f 1114//1114 1093//1093 1101//1101
f 939//939 937//937 1114//1114
f 1115//1115 1039//1039 1098//1098
f 1039//1039 1025//1025 1098//1098
f 1115//1115 1035//1035 1039//1039
f 1098//1098 1093//1093 1115//1115
f 1118//1118 1117//1117 1119//1119
f 1117//1117 1120//1120 1119//1119
f 1118//1118 1121//1121 1117//1117
f 1119//1119 1122//1122 1118//1118
f 1119//1119 1123//1123 1124//1124
f 1123//1123 1125//1125 1124//1124
f 1119//1119 1120//1120 1123//1123
f 1124//1124 1122//1122 1119//1119
f 1117//1117 1126//1126 1127//1127
f 1126//1126 1028//1028 1127//1127
f 1117//1117 1121//1121 1126//1126
f 1127//1127 1120//1120 1117//1117
f 1118//1118 1128//1128 1129//1129
f 1128//1128 1130//1130 1129//1129
f 1118//1118 1122//1122 1128//1128
f 1129//1129 1121//1121 1118//1118
f 1132//1132 1131//1131 1133//1133
f 1131//1131 1134//1134 1133//1133
f 1132//1132 1122//1122 1131//1131
f 1133//1133 1135//1135 1132//1132
f 1133//1133 1136//1136 1137//1137
f 1136//1136 1138//1138 1137//1137
f 1133//1133 1134//1134 1136//1136
f 1137//1137 1135//1135 1133//1133
f 1131//1131 1124//1124 1139//1139
f 1124//1124 1125//1125 1139//1139
f 1131//1131 1122//1122 1124//1124
f 1139//1139 1134//1134 1131//1131
f 1132//1132 1140//1140 1128//1128
f 1140//1140 1130//1130 1128//1128
f 1132//1132 1135//1135 1140//1140
f 1128//1128 1122//1122 1132//1132
f 1142//1142 1141//1141 1143//1143
f 1141//1141 1055//1055 1143//1143
f 1142//1142 1120//1120 1141//1141
f 1143//1143 1144//1144 1142//1142
f 1143//1143 1057//1057 1145//1145
f 1057//1057 1058//1058 1145//1145
f 1143//1143 1055//1055 1057//1057
f 1145//1145 1144//1144 1143//1143
f 1141//1141 1127//1127 1060//1060
f 1127//1127 1028//1028 1060//1060
f 1141//1141 1120//1120 1127//1127
f 1060//1060 1055//1055 1141//1141
f 1142//1142 1146//1146 1123//1123
f 1146//1146 1125//1125 1123//1123
f 1142//1142 1144//1144 1146//1146
f 1123//1123 1120//1120 1142//1142
f 1148//1148 1147//1147 1149//1149
f 1147//1147 1150//1150 1149//1149
f 1148//1148 1121//1121 1147//1147
f 1149//1149 1044//1044 1148//1148
f 1149//1149 1151//1151 1046//1046
f 1151//1151 1048//1048 1046//1046
f 1149//1149 1150//1150 1151//1151
f 1046//1046 1044//1044 1149//1149
f 1147//1147 1129//1129 1152//1152
f 1129//1129 1130//1130 1152//1152
f 1147//1147 1121//1121 1129//1129
f 1152//1152 1150//1150 1147//1147
f 1148//1148 1049//1049 1126//1126
f 1049//1049 1028//1028 1126//1126
f 1148//1148 1044//1044 1049//1049
f 1126//1126 1121//1121 1148//1148
f 1154//1154 1153//1153 1155//1155
f 1153//1153 1156//1156 1155//1155
f 1154//1154 1157//1157 1153//1153
f 1155//1155 1158//1158 1154//1154
f 1155//1155 1159//1159 1160//1160
f 1159//1159 1161//1161 1160//1160
f 1155//1155 1156//1156 1159//1159
f 1160//1160 1158//1158 1155//1155
f 1153//1153 1162//1162 1163//1163
f 1162//1162 1164//1164 1163//1163
f 1153//1153 1157//1157 1162//1162
f 1163//1163 1156//1156 1153//1153
f 1154//1154 1165//1165 1166//1166
f 1165//1165 1167//1167 1166//1166
f 1154//1154 1158//1158 1165//1165
f 1166//1166 1157//1157 1154//1154
f 1169//1169 1168//1168 1170//1170
f 1168//1168 1171//1171 1170//1170
f 1169//1169 1158//1158 1168//1168
f 1170//1170 1172//1172 1169//1169
f 1170//1170 1173//1173 1174//1174
f 1173//1173 1175//1175 1174//1174
f 1170//1170 1171//1171 1173//1173
f 1174//1174 1172//1172 1170//1170
f 1168//1168 1160//1160 1176//1176
f 1160//1160 1161//1161 1176//1176
f 1168//1168 1158//1158 1160//1160
f 1176//1176 1171//1171 1168//1168
f 1169//1169 1177//1177 1165//1165
f 1177//1177 1167//1167 1165//1165
f 1169//1169 1172//1172 1177//1177
f 1165//1165 1158//1158 1169//1169
f 1179//1179 1178//1178 1180//1180
f 1178//1178 1181//1181 1180//1180
f 1179//1179 1156//1156 1178//1178
f 1180//1180 1182//1182 1179//1179
f 1180//1180 1183//1183 1184//1184
f 1183//1183 1185//1185 1184//1184
f 1180//1180 1181//1181 1183//1183
f 1184//1184 1182//1182 1180//1180
f 1178//1178 1163//1163 1186//1186
f 1163//1163 1164//1164 1186//1186
f 1178//1178 1156//1156 1163//1163
f 1186//1186 1181//1181 1178//1178
f 1179//1179 1187//1187 1159//1159
f 1187//1187 1161//1161 1159//1159
f 1179//1179 1182//1182 1187//1187
f 1159//1159 1156//1156 1179//1179
f 1189//1189 1188//1188 1190//1190
f 1188//1188 1191//1191 1190//1190
f 1189//1189 1157//1157 1188//1188
f 1190//1190 1192//1192 1189//1189
f 1190//1190 1193//1193 1194//1194
f 1193//1193 1195//1195 1194//1194
f 1190//1190 1191//1191 1193//1193
f 1194//1194 1192//1192 1190//1190
f 1188//1188 1166//1166 1196//1196
f 1166//1166 1167//1167 1196//1196
f 1188//1188 1157//1157 1166//1166
f 1196//1196 1191//1191 1188//1188
f 1189//1189 1197//1197 1162//1162
f 1197//1197 1164//1164 1162//1162
f 1189//1189 1192//1192 1197//1197
f 1162//1162 1157//1157 1189//1189
f 1199//1199 1198//1198 1200//1200
f 1198//1198 1201//1201 1200//1200
f 1199//1199 1202//1202 1198//1198
f 1200//1200 1203//1203 1199//1199
f 1200//1200 1204//1204 1205//1205
f 1204//1204 1206//1206 1205//1205
f 1200//1200 1201//1201 1204//1204
f 1205//1205 1203//1203 1200//1200
f 1198//1198 1207//1207 1208//1208
f 1207//1207 1167//1167 1208//1208
f 1198//1198 1202//1202 1207//1207
f 1208//1208 1201//1201 1198//1198
f 1199//1199 1209//1209 1210//1210
f 1209//1209 1211//1211 1210//1210
f 1199//1199 1203//1203 1209//1209
f 1210//1210 1202//1202 1199//1199
f 1213//1213 1212//1212 1214//1214
f 1212//1212 1215//1215 1214//1214
f 1213//1213 1203//1203 1212//1212
f 1214//1214 1216//1216 1213//1213
f 1214//1214 1217//1217 1218//1218
f 1217//1217 851//851 1218//1218
f 1214//1214 1215//1215 1217//1217
f 1218//1218 1216//1216 1214//1214
f 1212//1212 1205//1205 1219//1219
f 1205//1205 1206//1206 1219//1219
f 1212//1212 1203//1203 1205//1205
f 1219//1219 1215//1215 1212//1212
f 1213//1213 1220//1220 1209//1209
f 1220//1220 1211//1211 1209//1209
f 1213//1213 1216//1216 1220//1220
f 1209//1209 1203//1203 1213//1213
f 1222//1222 1221//1221 1223//1223
f 1221//1221 1172//1172 1223//1223
f 1222//1222 1201//1201 1221//1221
f 1223//1223 1224//1224 1222//1222
f 1223//1223 1174//1174 1225//1225
f 1174//1174 1175//1175 1225//1225
f 1223//1223 1172//1172 1174//1174
f 1225//1225 1224//1224 1223//1223
f 1221//1221 1208//1208 1177//1177
f 1208//1208 1167//1167 1177//1177
f 1221//1221 1201//1201 1208//1208
f 1177//1177 1172//1172 1221//1221
f 1222//1222 1226//1226 1204//1204
f 1226//1226 1206//1206 1204//1204
f 1222//1222 1224//1224 1226//1226
f 1204//1204 1201//1201 1222//1222
f 1228//1228 1227//1227 1229//1229
f 1227//1227 1230//1230 1229//1229
f 1228//1228 1202//1202 1227//1227
f 1229//1229 1191//1191 1228//1228
f 1229//1229 1231//1231 1193//1193
f 1231//1231 1195//1195 1193//1193
f 1229//1229 1230//1230 1231//1231
f 1193//1193 1191//1191 1229//1229
f 1227//1227 1210//1210 1232//1232
f 1210//1210 1211//1211 1232//1232
f 1227//1227 1202//1202 1210//1210
f 1232//1232 1230//1230 1227//1227
f 1228//1228 1196//1196 1207//1207
f 1196//1196 1167//1167 1207//1207
f 1228//1228 1191//1191 1196//1196
f 1207//1207 1202//1202 1228//1228
f 1234//1234 1233//1233 1235//1235
f 1233//1233 1236//1236 1235//1235
f 1234//1234 1237//1237 1233//1233
f 1235//1235 1238//1238 1234//1234
f 1235//1235 1239//1239 1240//1240
f 1239//1239 1241//1241 1240//1240
f 1235//1235 1236//1236 1239//1239
f 1240//1240 1238//1238 1235//1235
f 1233//1233 1242//1242 1243//1243
f 1242//1242 1161//1161 1243//1243
f 1233//1233 1237//1237 1242//1242
f 1243//1243 1236//1236 1233//1233
f 1234//1234 1244//1244 1245//1245
f 1244//1244 1246//1246 1245//1245
f 1234//1234 1238//1238 1244//1244
f 1245//1245 1237//1237 1234//1234
f 1248//1248 1247//1247 1249//1249
f 1247//1247 1250//1250 1249//1249
f 1248//1248 1238//1238 1247//1247
f 1249//1249 1251//1251 1248//1248
f 1249//1249 1252//1252 1253//1253
f 1252//1252 1002//1002 1253//1253
f 1249//1249 1250//1250 1252//1252
f 1253//1253 1251//1251 1249//1249
f 1247//1247 1240//1240 1254//1254
f 1240//1240 1241//1241 1254//1254
f 1247//1247 1238//1238 1240//1240
f 1254//1254 1250//1250 1247//1247
f 1248//1248 1255//1255 1244//1244
f 1255//1255 1246//1246 1244//1244
f 1248//1248 1251//1251 1255//1255
f 1244//1244 1238//1238 1248//1248
f 1257//1257 1256//1256 1258//1258
f 1256//1256 1182//1182 1258//1258
f 1257//1257 1236//1236 1256//1256
f 1258//1258 1259//1259 1257//1257
f 1258//1258 1184//1184 1260//1260
f 1184//1184 1185//1185 1260//1260
f 1258//1258 1182//1182 1184//1184
f 1260//1260 1259//1259 1258//1258
f 1256//1256 1243//1243 1187//1187
f 1243//1243 1161//1161 1187//1187
f 1256//1256 1236//1236 1243//1243
f 1187//1187 1182//1182 1256//1256
f 1257//1257 1261//1261 1239//1239
f 1261//1261 1241//1241 1239//1239
f 1257//1257 1259//1259 1261//1261
f 1239//1239 1236//1236 1257//1257
f 1263//1263 1262//1262 1264//1264
f 1262//1262 1265//1265 1264//1264
f 1263//1263 1237//1237 1262//1262
f 1264//1264 1171//1171 1263//1263
f 1264//1264 1266//1266 1173//1173
f 1266//1266 1175//1175 1173//1173
f 1264//1264 1265//1265 1266//1266
f 1173//1173 1171//1171 1264//1264
f 1262//1262 1245//1245 1267//1267
f 1245//1245 1246//1246 1267//1267
f 1262//1262 1237//1237 1245//1245
f 1267//1267 1265//1265 1262//1262
f 1263//1263 1176//1176 1242//1242
f 1176//1176 1161//1161 1242//1242
f 1263//1263 1171//1171 1176//1176
f 1242//1242 1237//1237 1263//1263
f 1269//1269 1268//1268 1270//1270
f 1268//1268 1271//1271 1270//1270
f 1269//1269 1272//1272 1268//1268
f 1270//1270 1273//1273 1269//1269
f 1270//1270 1274//1274 1275//1275
f 1274//1274 1276//1276 1275//1275
f 1270//1270 1271//1271 1274//1274
f 1275//1275 1273//1273 1270//1270
f 1268//1268 1277//1277 1278//1278
f 1277//1277 1164//1164 1278//1278
f 1268//1268 1272//1272 1277//1277
f 1278//1278 1271//1271 1268//1268
f 1269//1269 1279//1279 1280//1280
f 1279//1279 1281//1281 1280//1280
f 1269//1269 1273//1273 1279//1279
f 1280//1280 1272//1272 1269//1269
f 1283//1283 1282//1282 1284//1284
f 1282//1282 1285//1285 1284//1284
f 1283//1283 1273//1273 1282//1282
f 1284//1284 1286//1286 1283//1283
f 1284//1284 1287//1287 1288//1288
f 1287//1287 356//356 1288//1288
f 1284//1284 1285//1285 1287//1287
f 1288//1288 1286//1286 1284//1284
f 1282//1282 1275//1275 1289//1289
f 1275//1275 1276//1276 1289//1289
f 1282//1282 1273//1273 1275//1275
f 1289//1289 1285//1285 1282//1282
f 1283//1283 1290//1290 1279//1279
f 1290//1290 1281//1281 1279//1279
f 1283//1283 1286//1286 1290//1290
f 1279//1279 1273//1273 1283//1283
f 1292//1292 1291//1291 1293//1293
f 1291//1291 1192//1192 1293//1293
f 1292//1292 1271//1271 1291//1291
f 1293//1293 1294//1294 1292//1292
f 1293//1293 1194//1194 1295//1295
f 1194//1194 1195//1195 1295//1295
f 1293//1293 1192//1192 1194//1194
f 1295//1295 1294//1294 1293//1293
f 1291//1291 1278//1278 1197//1197
f 1278//1278 1164//1164 1197//1197
f 1291//1291 1271//1271 1278//1278
f 1197//1197 1192//1192 1291//1291
f 1292//1292 1296//1296 1274//1274
f 1296//1296 1276//1276 1274//1274
f 1292//1292 1294//1294 1296//1296
f 1274//1274 1271//1271 1292//1292
f 1298//1298 1297//1297 1299//1299
f 1297//1297 1300//1300 1299//1299
f 1298//1298 1272//1272 1297//1297
f 1299//1299 1181//1181 1298//1298
f 1299//1299 1301//1301 1183//1183
f 1301//1301 1185//1185 1183//1183
f 1299//1299 1300//1300 1301//1301
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f 1792//1792 843//843 1793//1793
f 1788//1788 1789//1789 1792//1792
f 1793//1793 1791//1791 1788//1788
f 1786//1786 1794//1794 1795//1795
f 1794//1794 1731//1731 1795//1795
f 1786//1786 1790//1790 1794//1794
f 1795//1795 1789//1789 1786//1786
f 1787//1787 1796//1796 1797//1797
f 1796//1796 1383//1383 1797//1797
f 1787//1787 1791//1791 1796//1796
f 1797//1797 1790//1790 1787//1787
f 1799//1799 1798//1798 1800//1800
f 1798//1798 848//848 1800//1800
f 1799//1799 1791//1791 1798//1798
f 1800//1800 1391//1391 1799//1799
f 1800//1800 850//850 1392//1392
f 850//850 851//851 1392//1392
f 1800//1800 848//848 850//850
f 1392//1392 1391//1391 1800//1800
f 1798//1798 1793//1793 853//853
f 1793//1793 843//843 853//853
f 1798//1798 1791//1791 1793//1793
f 853//853 848//848 1798//1798
f 1799//1799 1393//1393 1796//1796
f 1393//1393 1383//1383 1796//1796
f 1799//1799 1391//1391 1393//1393
f 1796//1796 1791//1791 1799//1799
f 1802//1802 1801//1801 1803//1803
f 1801//1801 1751//1751 1803//1803
f 1802//1802 1789//1789 1801//1801
f 1803//1803 863//863 1802//1802
f 1803//1803 1753//1753 864//864
f 1753//1753 761//761 864//864
f 1803//1803 1751//1751 1753//1753
f 864//864 863//863 1803//1803
f 1801//1801 1795//1795 1755//1755
f 1795//1795 1731//1731 1755//1755
f 1801//1801 1789//1789 1795//1795
f 1755//1755 1751//1751 1801//1801
f 1802//1802 865//865 1792//1792
f 865//865 843//843 1792//1792
f 1802//1802 863//863 865//865
f 1792//1792 1789//1789 1802//1802
f 1805//1805 1804//1804 1806//1806
f 1804//1804 1397//1397 1806//1806
f 1805//1805 1790//1790 1804//1804
f 1806//1806 1741//1741 1805//1805
f 1806//1806 1398//1398 1743//1743
f 1398//1398 1334//1334 1743//1743
f 1806//1806 1397//1397 1398//1398
f 1743//1743 1741//1741 1806//1806
f 1804//1804 1797//1797 1399//1399
f 1797//1797 1383//1383 1399//1399
f 1804//1804 1790//1790 1797//1797
f 1399//1399 1397//1397 1804//1804
f 1805//1805 1745//1745 1794//1794
f 1745//1745 1731//1731 1794//1794
f 1805//1805 1741//1741 1745//1745
f 1794//1794 1790//1790 1805//1805
f 1808//1808 1807//1807 1809//1809
f 1807//1807 1810//1810 1809//1809
f 1808//1808 1811//1811 1807//1807
f 1809//1809 1812//1812 1808//1808
f 1809//1809 1813//1813 1814//1814
f 1813//1813 131//131 1814//1814
f 1809//1809 1810//1810 1813//1813
f 1814//1814 1812//1812 1809//1809
f 1807//1807 1815//1815 1816//1816
f 1815//1815 1734//1734 1816//1816
f 1807//1807 1811//1811 1815//1815
f 1816//1816 1810//1810 1807//1807
f 1808//1808 1817//1817 1818//1818
f 1817//1817 810//810 1818//1818
f 1808//1808 1812//1812 1817//1817
f 1818//1818 1811//1811 1808//1808
f 1820//1820 1819//1819 1821//1821
f 1819//1819 136//136 1821//1821
f 1820//1820 1812//1812 1819//1819
f 1821//1821 818//818 1820//1820
f 1821//1821 138//138 819//819
f 138//138 139//139 819//819
f 1821//1821 136//136 138//138
f 819//819 818//818 1821//1821
f 1819//1819 1814//1814 141//141
f 1814//1814 131//131 141//141
f 1819//1819 1812//1812 1814//1814
f 141//141 136//136 1819//1819
f 1820//1820 820//820 1817//1817
f 820//820 810//810 1817//1817
f 1820//1820 818//818 820//820
f 1817//1817 1812//1812 1820//1820
f 1823//1823 1822//1822 1824//1824
f 1822//1822 1760//1760 1824//1824
f 1823//1823 1810//1810 1822//1822
f 1824//1824 151//151 1823//1823
f 1824//1824 1762//1762 152//152
f 1762//1762 33//33 152//152
f 1824//1824 1760//1760 1762//1762
f 152//152 151//151 1824//1824
f 1822//1822 1816//1816 1764//1764
f 1816//1816 1734//1734 1764//1764
f 1822//1822 1810//1810 1816//1816
f 1764//1764 1760//1760 1822//1822
f 1823//1823 153//153 1813//1813
f 153//153 131//131 1813//1813
f 1823//1823 151//151 153//153
f 1813//1813 1810//1810 1823//1823
f 1826//1826 1825//1825 1827//1827
f 1825//1825 824//824 1827//1827
f 1826//1826 1811//1811 1825//1825
f 1827//1827 1750//1750 1826//1826
f 1827//1827 825//825 1752//1752
f 825//825 761//761 1752//1752
f 1827//1827 824//824 825//825
f 1752//1752 1750//1750 1827//1827
f 1825//1825 1818//1818 826//826
f 1818//1818 810//810 826//826
f 1825//1825 1811//1811 1818//1818
f 826//826 824//824 1825//1825
f 1826//1826 1754//1754 1815//1815
f 1754//1754 1734//1734 1815//1815
f 1826//1826 1750//1750 1754//1754
f 1815//1815 1811//1811 1826//1826
f 1829//1829 1828//1828 1830//1830
f 1828//1828 1831//1831 1830//1830
f 1829//1829 1832//1832 1828//1828
f 1830//1830 1833//1833 1829//1829
f 1830//1830 1834//1834 1835//1835
f 1834//1834 1836//1836 1835//1835
f 1830//1830 1831//1831 1834//1834
f 1835//1835 1833//1833 1830//1830
f 1828//1828 1837//1837 1838//1838
f 1837//1837 1839//1839 1838//1838
f 1828//1828 1832//1832 1837//1837
f 1838//1838 1831//1831 1828//1828
f 1829//1829 1840//1840 1841//1841
f 1840//1840 1842//1842 1841//1841
f 1829//1829 1833//1833 1840//1840
f 1841//1841 1832//1832 1829//1829
f 1844//1844 1843//1843 1845//1845
f 1843//1843 1846//1846 1845//1845
f 1844//1844 1833//1833 1843//1843
f 1845//1845 1847//1847 1844//1844
f 1845//1845 1848//1848 1849//1849
f 1848//1848 195//195 1849//1849
f 1845//1845 1846//1846 1848//1848
f 1849//1849 1847//1847 1845//1845
f 1843//1843 1835//1835 1850//1850
f 1835//1835 1836//1836 1850//1850
f 1843//1843 1833//1833 1835//1835
f 1850//1850 1846//1846 1843//1843
f 1844//1844 1851//1851 1840//1840
f 1851//1851 1842//1842 1840//1840
f 1844//1844 1847//1847 1851//1851
f 1840//1840 1833//1833 1844//1844
f 1853//1853 1852//1852 1854//1854
f 1852//1852 1855//1855 1854//1854
f 1853//1853 1831//1831 1852//1852
f 1854//1854 1856//1856 1853//1853
f 1854//1854 1857//1857 1858//1858
f 1857//1857 908//908 1858//1858
f 1854//1854 1855//1855 1857//1857
f 1858//1858 1856//1856 1854//1854
f 1852//1852 1838//1838 1859//1859
f 1838//1838 1839//1839 1859//1859
f 1852//1852 1831//1831 1838//1838
f 1859//1859 1855//1855 1852//1852
f 1853//1853 1860//1860 1834//1834
f 1860//1860 1836//1836 1834//1834
f 1853//1853 1856//1856 1860//1860
f 1834//1834 1831//1831 1853//1853
f 1862//1862 1861//1861 1863//1863
f 1861//1861 1864//1864 1863//1863
f 1862//1862 1832//1832 1861//1861
f 1863//1863 1865//1865 1862//1862
f 1863//1863 1866//1866 1867//1867
f 1866//1866 1344//1344 1867//1867
f 1863//1863 1864//1864 1866//1866
f 1867//1867 1865//1865 1863//1863
f 1861//1861 1841//1841 1868//1868
f 1841//1841 1842//1842 1868//1868
f 1861//1861 1832//1832 1841//1841
f 1868//1868 1864//1864 1861//1861
f 1862//1862 1869//1869 1837//1837
f 1869//1869 1839//1839 1837//1837
f 1862//1862 1865//1865 1869//1869
f 1837//1837 1832//1832 1862//1862
f 1871//1871 1870//1870 1872//1872
f 1870//1870 1873//1873 1872//1872
f 1871//1871 1874//1874 1870//1870
f 1872//1872 1875//1875 1871//1871
f 1872//1872 1876//1876 1877//1877
f 1876//1876 210//210 1877//1877
f 1872//1872 1873//1873 1876//1876
f 1877//1877 1875//1875 1872//1872
f 1870//1870 1878//1878 1879//1879
f 1878//1878 1842//1842 1879//1879
f 1870//1870 1874//1874 1878//1878
f 1879//1879 1873//1873 1870//1870
f 1871//1871 1880//1880 1881//1881
f 1880//1880 1411//1411 1881//1881
f 1871//1871 1875//1875 1880//1880
f 1881//1881 1874//1874 1871//1871
f 1883//1883 1882//1882 1884//1884
f 1882//1882 214//214 1884//1884
f 1883//1883 1875//1875 1882//1882
f 1884//1884 1420//1420 1883//1883
f 1884//1884 215//215 1422//1422
f 215//215 103//103 1422//1422
f 1884//1884 214//214 215//215
f 1422//1422 1420//1420 1884//1884
f 1882//1882 1877//1877 216//216
f 1877//1877 210//210 216//216
f 1882//1882 1875//1875 1877//1877
f 216//216 214//214 1882//1882
f 1883//1883 1424//1424 1880//1880
f 1424//1424 1411//1411 1880//1880
f 1883//1883 1420//1420 1424//1424
f 1880//1880 1875//1875 1883//1883
f 1886//1886 1885//1885 1887//1887
f 1885//1885 1847//1847 1887//1887
f 1886//1886 1873//1873 1885//1885
f 1887//1887 223//223 1886//1886
f 1887//1887 1849//1849 224//224
f 1849//1849 195//195 224//224
f 1887//1887 1847//1847 1849//1849
f 224//224 223//223 1887//1887
f 1885//1885 1879//1879 1851//1851
f 1879//1879 1842//1842 1851//1851
f 1885//1885 1873//1873 1879//1879
f 1851//1851 1847//1847 1885//1885
f 1886//1886 225//225 1876//1876
f 225//225 210//210 1876//1876
f 1886//1886 223//223 225//225
f 1876//1876 1873//1873 1886//1886
f 1889//1889 1888//1888 1890//1890
f 1888//1888 1429//1429 1890//1890
f 1889//1889 1874//1874 1888//1888
f 1890//1890 1864//1864 1889//1889
f 1890//1890 1430//1430 1866//1866
f 1430//1430 1344//1344 1866//1866
f 1890//1890 1429//1429 1430//1430
f 1866//1866 1864//1864 1890//1890
f 1888//1888 1881//1881 1431//1431
f 1881//1881 1411//1411 1431//1431
f 1888//1888 1874//1874 1881//1881
f 1431//1431 1429//1429 1888//1888
f 1889//1889 1868//1868 1878//1878
f 1868//1868 1842//1842 1878//1878
f 1889//1889 1864//1864 1868//1868
f 1878//1878 1874//1874 1889//1889
f 1892//1892 1891//1891 1893//1893
f 1891//1891 1894//1894 1893//1893
f 1892//1892 1895//1895 1891//1891
f 1893//1893 1896//1896 1892//1892
f 1893//1893 1897//1897 1898//1898
f 1897//1897 924//924 1898//1898
f 1893//1893 1894//1894 1897//1897
f 1898//1898 1896//1896 1893//1893
f 1891//1891 1899//1899 1900//1900
f 1899//1899 1836//1836 1900//1900
f 1891//1891 1895//1895 1899//1899
f 1900//1900 1894//1894 1891//1891
f 1892//1892 1901//1901 1902//1902
f 1901//1901 262//262 1902//1902
f 1892//1892 1896//1896 1901//1901
f 1902//1902 1895//1895 1892//1892
f 1904//1904 1903//1903 1905//1905
f 1903//1903 929//929 1905//1905
f 1904//1904 1896//1896 1903//1903
f 1905//1905 271//271 1904//1904
f 1905//1905 931//931 273//273
f 931//931 275//275 273//273
f 1905//1905 929//929 931//931
f 273//273 271//271 1905//1905
f 1903//1903 1898//1898 933//933
f 1898//1898 924//924 933//933
f 1903//1903 1896//1896 1898//1898
f 933//933 929//929 1903//1903
f 1904//1904 276//276 1901//1901
f 276//276 262//262 1901//1901
f 1904//1904 271//271 276//276
f 1901//1901 1896//1896 1904//1904
f 1907//1907 1906//1906 1908//1908
f 1906//1906 1856//1856 1908//1908
f 1907//1907 1894//1894 1906//1906
f 1908//1908 943//943 1907//1907
f 1908//1908 1858//1858 944//944
f 1858//1858 908//908 944//944
f 1908//1908 1856//1856 1858//1858
f 944//944 943//943 1908//1908
f 1906//1906 1900//1900 1860//1860
f 1900//1900 1836//1836 1860//1860
f 1906//1906 1894//1894 1900//1900
f 1860//1860 1856//1856 1906//1906
f 1907//1907 945//945 1897//1897
f 945//945 924//924 1897//1897
f 1907//1907 943//943 945//945
f 1897//1897 1894//1894 1907//1907
f 1910//1910 1909//1909 1911//1911
f 1909//1909 281//281 1911//1911
f 1910//1910 1895//1895 1909//1909
f 1911//1911 1846//1846 1910//1910
f 1911//1911 282//282 1848//1848
f 282//282 195//195 1848//1848
f 1911//1911 281//281 282//282
f 1848//1848 1846//1846 1911//1911
f 1909//1909 1902//1902 283//283
f 1902//1902 262//262 283//283
f 1909//1909 1895//1895 1902//1902
f 283//283 281//281 1909//1909
f 1910//1910 1850//1850 1899//1899
f 1850//1850 1836//1836 1899//1899
f 1910//1910 1846//1846 1850//1850
f 1899//1899 1895//1895 1910//1910
f 1913//1913 1912//1912 1914//1914
f 1912//1912 1915//1915 1914//1914
f 1913//1913 1916//1916 1912//1912
f 1914//1914 1917//1917 1913//1913
f 1914//1914 1918//1918 1919//1919
f 1918//1918 1359//1359 1919//1919
f 1914//1914 1915//1915 1918//1918
f 1919//1919 1917//1917 1914//1914
f 1912//1912 1920//1920 1921//1921
f 1920//1920 1839//1839 1921//1921
f 1912//1912 1916//1916 1920//1920
f 1921//1921 1915//1915 1912//1912
f 1913//1913 1922//1922 1923//1923
f 1922//1922 989//989 1923//1923
f 1913//1913 1917//1917 1922//1922
f 1923//1923 1916//1916 1913//1913
f 1925//1925 1924//1924 1926//1926
f 1924//1924 1363//1363 1926//1926
f 1925//1925 1917//1917 1924//1924
f 1926//1926 998//998 1925//1925
f 1926//1926 1364//1364 1000//1000
f 1364//1364 1002//1002 1000//1000
f 1926//1926 1363//1363 1364//1364
f 1000//1000 998//998 1926//1926
f 1924//1924 1919//1919 1365//1365
f 1919//1919 1359//1359 1365//1365
f 1924//1924 1917//1917 1919//1919
f 1365//1365 1363//1363 1924//1924
f 1925//1925 1003//1003 1922//1922
f 1003//1003 989//989 1922//1922
f 1925//1925 998//998 1003//1003
f 1922//1922 1917//1917 1925//1925
f 1928//1928 1927//1927 1929//1929
f 1927//1927 1865//1865 1929//1929
f 1928//1928 1915//1915 1927//1927
f 1929//1929 1372//1372 1928//1928
f 1929//1929 1867//1867 1373//1373
f 1867//1867 1344//1344 1373//1373
f 1929//1929 1865//1865 1867//1867
f 1373//1373 1372//1372 1929//1929
f 1927//1927 1921//1921 1869//1869
f 1921//1921 1839//1839 1869//1869
f 1927//1927 1915//1915 1921//1921
f 1869//1869 1865//1865 1927//1927
f 1928//1928 1374//1374 1918//1918
f 1374//1374 1359//1359 1918//1918
f 1928//1928 1372//1372 1374//1374
f 1918//1918 1915//1915 1928//1928
f 1931//1931 1930//1930 1932//1932
f 1930//1930 1008//1008 1932//1932
f 1931//1931 1916//1916 1930//1930
f 1932//1932 1855//1855 1931//1931
f 1932//1932 1009//1009 1857//1857
f 1009//1009 908//908 1857//1857
f 1932//1932 1008//1008 1009//1009
f 1857//1857 1855//1855 1932//1932
f 1930//1930 1923//1923 1010//1010
f 1923//1923 989//989 1010//1010
f 1930//1930 1916//1916 1923//1923
f 1010//1010 1008//1008 1930//1930
f 1931//1931 1859//1859 1920//1920
f 1859//1859 1839//1839 1920//1920
f 1931//1931 1855//1855 1859//1859
f 1920//1920 1916//1916 1931//1931
f 1934//1934 1933//1933 1935//1935
f 1933//1933 1936//1936 1935//1935
f 1934//1934 1937//1937 1933//1933
f 1935//1935 1938//1938 1934//1934
f 1935//1935 1939//1939 1940//1940
f 1939//1939 1941//1941 1940//1940
f 1935//1935 1936//1936 1939//1939
f 1940//1940 1938//1938 1935//1935
f 1933//1933 1942//1942 1943//1943
f 1942//1942 1944//1944 1943//1943
f 1933//1933 1937//1937 1942//1942
f 1943//1943 1936//1936 1933//1933
f 1934//1934 1945//1945 1946//1946
f 1945//1945 1947//1947 1946//1946
f 1934//1934 1938//1938 1945//1945
f 1946//1946 1937//1937 1934//1934
f 1949//1949 1948//1948 1950//1950
f 1948//1948 1951//1951 1950//1950
f 1949//1949 1938//1938 1948//1948
f 1950//1950 1952//1952 1949//1949
f 1950//1950 1953//1953 1954//1954
f 1953//1953 43//43 1954//1954
f 1950//1950 1951//1951 1953//1953
f 1954//1954 1952//1952 1950//1950
f 1948//1948 1940//1940 1955//1955
f 1940//1940 1941//1941 1955//1955
f 1948//1948 1938//1938 1940//1940
f 1955//1955 1951//1951 1948//1948
f 1949//1949 1956//1956 1945//1945
f 1956//1956 1947//1947 1945//1945
f 1949//1949 1952//1952 1956//1956
f 1945//1945 1938//1938 1949//1949
f 1958//1958 1957//1957 1959//1959
f 1957//1957 1960//1960 1959//1959
f 1958//1958 1936//1936 1957//1957
f 1959//1959 1961//1961 1958//1958
f 1959//1959 1962//1962 1963//1963
f 1962//1962 621//621 1963//1963
f 1959//1959 1960//1960 1962//1962
f 1963//1963 1961//1961 1959//1959
f 1957//1957 1943//1943 1964//1964
f 1943//1943 1944//1944 1964//1964
f 1957//1957 1936//1936 1943//1943
f 1964//1964 1960//1960 1957//1957
f 1958//1958 1965//1965 1939//1939
f 1965//1965 1941//1941 1939//1939
f 1958//1958 1961//1961 1965//1965
f 1939//1939 1936//1936 1958//1958
f 1967//1967 1966//1966 1968//1968
f 1966//1966 1969//1969 1968//1968
f 1967//1967 1937//1937 1966//1966
f 1968//1968 1970//1970 1967//1967
f 1968//1968 1971//1971 1972//1972
f 1971//1971 1480//1480 1972//1972
f 1968//1968 1969//1969 1971//1971
f 1972//1972 1970//1970 1968//1968
f 1966//1966 1946//1946 1973//1973
f 1946//1946 1947//1947 1973//1973
f 1966//1966 1937//1937 1946//1946
f 1973//1973 1969//1969 1966//1966
f 1967//1967 1974//1974 1942//1942
f 1974//1974 1944//1944 1942//1942
f 1967//1967 1970//1970 1974//1974
f 1942//1942 1937//1937 1967//1967
f 1976//1976 1975//1975 1977//1977
f 1975//1975 1978//1978 1977//1977
f 1976//1976 1979//1979 1975//1975
f 1977//1977 1980//1980 1976//1976
f 1977//1977 1981//1981 1982//1982
f 1981//1981 59//59 1982//1982
f 1977//1977 1978//1978 1981//1981
f 1982//1982 1980//1980 1977//1977
f 1975//1975 1983//1983 1984//1984
f 1983//1983 1947//1947 1984//1984
f 1975//1975 1979//1979 1983//1983
f 1984//1984 1978//1978 1975//1975
f 1976//1976 1985//1985 1986//1986
f 1985//1985 1561//1561 1986//1986
f 1976//1976 1980//1980 1985//1985
f 1986//1986 1979//1979 1976//1976
f 1988//1988 1987//1987 1989//1989
f 1987//1987 64//64 1989//1989
f 1988//1988 1980//1980 1987//1987
f 1989//1989 1570//1570 1988//1988
f 1989//1989 66//66 1572//1572
f 66//66 67//67 1572//1572
f 1989//1989 64//64 66//66
f 1572//1572 1570//1570 1989//1989
f 1987//1987 1982//1982 69//69
f 1982//1982 59//59 69//69
f 1987//1987 1980//1980 1982//1982
f 69//69 64//64 1987//1987
f 1988//1988 1574//1574 1985//1985
f 1574//1574 1561//1561 1985//1985
f 1988//1988 1570//1570 1574//1574
f 1985//1985 1980//1980 1988//1988
f 1991//1991 1990//1990 1992//1992
f 1990//1990 1952//1952 1992//1992
f 1991//1991 1978//1978 1990//1990
f 1992//1992 79//79 1991//1991
f 1992//1992 1954//1954 80//80
f 1954//1954 43//43 80//80
f 1992//1992 1952//1952 1954//1954
f 80//80 79//79 1992//1992
f 1990//1990 1984//1984 1956//1956
f 1984//1984 1947//1947 1956//1956
f 1990//1990 1978//1978 1984//1984
f 1956//1956 1952//1952 1990//1990
f 1991//1991 81//81 1981//1981
f 81//81 59//59 1981//1981
f 1991//1991 79//79 81//81
f 1981//1981 1978//1978 1991//1991
f 1994//1994 1993//1993 1995//1995
f 1993//1993 1579//1579 1995//1995
f 1994//1994 1979//1979 1993//1993
f 1995//1995 1969//1969 1994//1994
f 1995//1995 1580//1580 1971//1971
f 1580//1580 1480//1480 1971//1971
f 1995//1995 1579//1579 1580//1580
f 1971//1971 1969//1969 1995//1995
f 1993//1993 1986//1986 1581//1581
f 1986//1986 1561//1561 1581//1581
f 1993//1993 1979//1979 1986//1986
f 1581//1581 1579//1579 1993//1993
f 1994//1994 1973//1973 1983//1983
f 1973//1973 1947//1947 1983//1983
f 1994//1994 1969//1969 1973//1973
f 1983//1983 1979//1979 1994//1994
f 1997//1997 1996//1996 1998//1998
f 1996//1996 1999//1999 1998//1998
f 1997//1997 2000//2000 1996//1996
f 1998//1998 2001//2001 1997//1997
f 1998//1998 2002//2002 2003//2003
f 2002//2002 637//637 2003//2003
f 1998//1998 1999//1999 2002//2002
f 2003//2003 2001//2001 1998//1998
f 1996//1996 2004//2004 2005//2005
f 2004//2004 1941//1941 2005//2005
f 1996//1996 2000//2000 2004//2004
f 2005//2005 1999//1999 1996//1996
f 1997//1997 2006//2006 2007//2007
f 2006//2006 126//126 2007//2007
f 1997//1997 2001//2001 2006//2006
f 2007//2007 2000//2000 1997//1997
f 2009//2009 2008//2008 2010//2010
f 2008//2008 642//642 2010//2010
f 2009//2009 2001//2001 2008//2008
f 2010//2010 135//135 2009//2009
f 2010//2010 644//644 137//137
f 644//644 139//139 137//137
f 2010//2010 642//642 644//644
f 137//137 135//135 2010//2010
f 2008//2008 2003//2003 646//646
f 2003//2003 637//637 646//646
f 2008//2008 2001//2001 2003//2003
f 646//646 642//642 2008//2008
f 2009//2009 140//140 2006//2006
f 140//140 126//126 2006//2006
f 2009//2009 135//135 140//140
f 2006//2006 2001//2001 2009//2009
f 2012//2012 2011//2011 2013//2013
f 2011//2011 1961//1961 2013//2013
f 2012//2012 1999//1999 2011//2011
f 2013//2013 656//656 2012//2012
f 2013//2013 1963//1963 657//657
f 1963//1963 621//621 657//657
f 2013//2013 1961//1961 1963//1963
f 657//657 656//656 2013//2013
f 2011//2011 2005//2005 1965//1965
f 2005//2005 1941//1941 1965//1965
f 2011//2011 1999//1999 2005//2005
f 1965//1965 1961//1961 2011//2011
f 2012//2012 658//658 2002//2002
f 658//658 637//637 2002//2002
f 2012//2012 656//656 658//658
f 2002//2002 1999//1999 2012//2012
f 2015//2015 2014//2014 2016//2016
f 2014//2014 145//145 2016//2016
f 2015//2015 2000//2000 2014//2014
f 2016//2016 1951//1951 2015//2015
f 2016//2016 146//146 1953//1953
f 146//146 43//43 1953//1953
f 2016//2016 145//145 146//146
f 1953//1953 1951//1951 2016//2016
f 2014//2014 2007//2007 147//147
f 2007//2007 126//126 147//147
f 2014//2014 2000//2000 2007//2007
f 147//147 145//145 2014//2014
f 2015//2015 1955//1955 2004//2004
f 1955//1955 1941//1941 2004//2004
f 2015//2015 1951//1951 1955//1955
f 2004//2004 2000//2000 2015//2015
f 2018//2018 2017//2017 2019//2019
f 2017//2017 2020//2020 2019//2019
f 2018//2018 2021//2021 2017//2017
f 2019//2019 2022//2022 2018//2018
f 2019//2019 2023//2023 2024//2024
f 2023//2023 1496//1496 2024//2024
f 2019//2019 2020//2020 2023//2023
f 2024//2024 2022//2022 2019//2019
f 2017//2017 2025//2025 2026//2026
f 2025//2025 1944//1944 2026//2026
f 2017//2017 2021//2021 2025//2025
f 2026//2026 2020//2020 2017//2017
f 2018//2018 2027//2027 2028//2028
f 2027//2027 702//702 2028//2028
f 2018//2018 2022//2022 2027//2027
f 2028//2028 2021//2021 2018//2018
f 2030//2030 2029//2029 2031//2031
f 2029//2029 1501//1501 2031//2031
f 2030//2030 2022//2022 2029//2029
f 2031//2031 711//711 2030//2030
f 2031//2031 1503//1503 713//713
f 1503//1503 715//715 713//713
f 2031//2031 1501//1501 1503//1503
f 713//713 711//711 2031//2031
f 2029//2029 2024//2024 1505//1505
f 2024//2024 1496//1496 1505//1505
f 2029//2029 2022//2022 2024//2024
f 1505//1505 1501//1501 2029//2029
f 2030//2030 716//716 2027//2027
f 716//716 702//702 2027//2027
f 2030//2030 711//711 716//716
f 2027//2027 2022//2022 2030//2030
f 2033//2033 2032//2032 2034//2034
f 2032//2032 1970//1970 2034//2034
f 2033//2033 2020//2020 2032//2032
f 2034//2034 1515//1515 2033//2033
f 2034//2034 1972//1972 1516//1516
f 1972//1972 1480//1480 1516//1516
f 2034//2034 1970//1970 1972//1972
f 1516//1516 1515//1515 2034//2034
f 2032//2032 2026//2026 1974//1974
f 2026//2026 1944//1944 1974//1974
f 2032//2032 2020//2020 2026//2026
f 1974//1974 1970//1970 2032//2032
f 2033//2033 1517//1517 2023//2023
f 1517//1517 1496//1496 2023//2023
f 2033//2033 1515//1515 1517//1517
f 2023//2023 2020//2020 2033//2033
f 2036//2036 2035//2035 2037//2037
f 2035//2035 721//721 2037//2037
f 2036//2036 2021//2021 2035//2035
f 2037//2037 1960//1960 2036//2036
f 2037//2037 722//722 1962//1962
f 722//722 621//621 1962//1962
f 2037//2037 721//721 722//722
f 1962//1962 1960//1960 2037//2037
f 2035//2035 2028//2028 723//723
f 2028//2028 702//702 723//723
f 2035//2035 2021//2021 2028//2028
f 723//723 721//721 2035//2035
f 2036//2036 1964//1964 2025//2025
f 1964//1964 1944//1944 2025//2025
f 2036//2036 1960//1960 1964//1964
f 2025//2025 2021//2021 2036//2036
f 2039//2039 2038//2038 2040//2040
f 2038//2038 2041//2041 2040//2040
f 2039//2039 2042//2042 2038//2038
f 2040//2040 2043//2043 2039//2039
f 2040//2040 2044//2044 2045//2045
f 2044//2044 2046//2046 2045//2045
f 2040//2040 2041//2041 2044//2044
f 2045//2045 2043//2043 2040//2040
f 2038//2038 2047//2047 2048//2048
f 2047//2047 2049//2049 2048//2048
f 2038//2038 2042//2042 2047//2047
f 2048//2048 2041//2041 2038//2038
f 2039//2039 2050//2050 2051//2051
f 2050//2050 2052//2052 2051//2051
f 2039//2039 2043//2043 2050//2050
f 2051//2051 2042//2042 2039//2039
f 2054//2054 2053//2053 2055//2055
f 2053//2053 2056//2056 2055//2055
f 2054//2054 2043//2043 2053//2053
f 2055//2055 2057//2057 2054//2054
f 2055//2055 2058//2058 2059//2059
f 2058//2058 1470//1470 2059//2059
f 2055//2055 2056//2056 2058//2058
f 2059//2059 2057//2057 2055//2055
f 2053//2053 2045//2045 2060//2060
f 2045//2045 2046//2046 2060//2060
f 2053//2053 2043//2043 2045//2045
f 2060//2060 2056//2056 2053//2053
f 2054//2054 2061//2061 2050//2050
f 2061//2061 2052//2052 2050//2050
f 2054//2054 2057//2057 2061//2061
f 2050//2050 2043//2043 2054//2054
f 2063//2063 2062//2062 2064//2064
f 2062//2062 2065//2065 2064//2064
f 2063//2063 2041//2041 2062//2062
f 2064//2064 2066//2066 2063//2063
f 2064//2064 2067//2067 2068//2068
f 2067//2067 1048//1048 2068//2068
f 2064//2064 2065//2065 2067//2067
f 2068//2068 2066//2066 2064//2064
f 2062//2062 2048//2048 2069//2069
f 2048//2048 2049//2049 2069//2069
f 2062//2062 2041//2041 2048//2048
f 2069//2069 2065//2065 2062//2062
f 2063//2063 2070//2070 2044//2044
f 2070//2070 2046//2046 2044//2044
f 2063//2063 2066//2066 2070//2070
f 2044//2044 2041//2041 2063//2063
f 2072//2072 2071//2071 2073//2073
f 2071//2071 2074//2074 2073//2073
f 2072//2072 2042//2042 2071//2071
f 2073//2073 2075//2075 2072//2072
f 2073//2073 2076//2076 2077//2077
f 2076//2076 185//185 2077//2077
f 2073//2073 2074//2074 2076//2076
f 2077//2077 2075//2075 2073//2073
f 2071//2071 2051//2051 2078//2078
f 2051//2051 2052//2052 2078//2078
f 2071//2071 2042//2042 2051//2051
f 2078//2078 2074//2074 2071//2071
f 2072//2072 2079//2079 2047//2047
f 2079//2079 2049//2049 2047//2047
f 2072//2072 2075//2075 2079//2079
f 2047//2047 2042//2042 2072//2072
f 2081//2081 2080//2080 2082//2082
f 2080//2080 2083//2083 2082//2082
f 2081//2081 2084//2084 2080//2080
f 2082//2082 2085//2085 2081//2081
f 2082//2082 2086//2086 2087//2087
f 2086//2086 1566//1566 2087//2087
f 2082//2082 2083//2083 2086//2086
f 2087//2087 2085//2085 2082//2082
f 2080//2080 2088//2088 2089//2089
f 2088//2088 2052//2052 2089//2089
f 2080//2080 2084//2084 2088//2088
f 2089//2089 2083//2083 2080//2080
f 2081//2081 2090//2090 2091//2091
f 2090//2090 234//234 2091//2091
f 2081//2081 2085//2085 2090//2090
f 2091//2091 2084//2084 2081//2081
f 2093//2093 2092//2092 2094//2094
f 2092//2092 1571//1571 2094//2094
f 2093//2093 2085//2085 2092//2092
f 2094//2094 242//242 2093//2093
f 2094//2094 1573//1573 243//243
f 1573//1573 67//67 243//243
f 2094//2094 1571//1571 1573//1573
f 243//243 242//242 2094//2094
f 2092//2092 2087//2087 1575//1575
f 2087//2087 1566//1566 1575//1575
f 2092//2092 2085//2085 2087//2087
f 1575//1575 1571//1571 2092//2092
f 2093//2093 244//244 2090//2090
f 244//244 234//234 2090//2090
f 2093//2093 242//242 244//244
f 2090//2090 2085//2085 2093//2093
f 2096//2096 2095//2095 2097//2097
f 2095//2095 2057//2057 2097//2097
f 2096//2096 2083//2083 2095//2095
f 2097//2097 1585//1585 2096//2096
f 2097//2097 2059//2059 1586//1586
f 2059//2059 1470//1470 1586//1586
f 2097//2097 2057//2057 2059//2059
f 1586//1586 1585//1585 2097//2097
f 2095//2095 2089//2089 2061//2061
f 2089//2089 2052//2052 2061//2061
f 2095//2095 2083//2083 2089//2089
f 2061//2061 2057//2057 2095//2095
f 2096//2096 1587//1587 2086//2086
f 1587//1587 1566//1566 2086//2086
f 2096//2096 1585//1585 1587//1587
f 2086//2086 2083//2083 2096//2096
f 2099//2099 2098//2098 2100//2100
f 2098//2098 248//248 2100//2100
f 2099//2099 2084//2084 2098//2098
f 2100//2100 2074//2074 2099//2099
f 2100//2100 249//249 2076//2076
f 249//249 185//185 2076//2076
f 2100//2100 248//248 249//249
f 2076//2076 2074//2074 2100//2100
f 2098//2098 2091//2091 250//250
f 2091//2091 234//234 250//250
f 2098//2098 2084//2084 2091//2091
f 250//250 248//248 2098//2098
f 2099//2099 2078//2078 2088//2088
f 2078//2078 2052//2052 2088//2088
f 2099//2099 2074//2074 2078//2078
f 2088//2088 2084//2084 2099//2099
f 2102//2102 2101//2101 2103//2103
f 2101//2101 2104//2104 2103//2103
f 2102//2102 2105//2105 2101//2101
f 2103//2103 2106//2106 2102//2102
f 2103//2103 2107//2107 2108//2108
f 2107//2107 1130//1130 2108//2108
f 2103//2103 2104//2104 2107//2107
f 2108//2108 2106//2106 2103//2103
f 2101//2101 2109//2109 2110//2110
f 2109//2109 2046//2046 2110//2110
f 2101//2101 2105//2105 2109//2109
f 2110//2110 2104//2104 2101//2101
f 2102//2102 2111//2111 2112//2112
f 2111//2111 1526//1526 2112//2112
f 2102//2102 2106//2106 2111//2111
f 2112//2112 2105//2105 2102//2102
f 2114//2114 2113//2113 2115//2115
f 2113//2113 1135//1135 2115//2115
f 2114//2114 2106//2106 2113//2113
f 2115//2115 1535//1535 2114//2114
f 2115//2115 1137//1137 1537//1537
f 1137//1137 1138//1138 1537//1537
f 2115//2115 1135//1135 1137//1137
f 1537//1537 1535//1535 2115//2115
f 2113//2113 2108//2108 1140//1140
f 2108//2108 1130//1130 1140//1140
f 2113//2113 2106//2106 2108//2108
f 1140//1140 1135//1135 2113//2113
f 2114//2114 1539//1539 2111//2111
f 1539//1539 1526//1526 2111//2111
f 2114//2114 1535//1535 1539//1539
f 2111//2111 2106//2106 2114//2114
f 2117//2117 2116//2116 2118//2118
f 2116//2116 2066//2066 2118//2118
f 2117//2117 2104//2104 2116//2116
f 2118//2118 1150//1150 2117//2117
f 2118//2118 2068//2068 1151//1151
f 2068//2068 1048//1048 1151//1151
f 2118//2118 2066//2066 2068//2068
f 1151//1151 1150//1150 2118//2118
f 2116//2116 2110//2110 2070//2070
f 2110//2110 2046//2046 2070//2070
f 2116//2116 2104//2104 2110//2110
f 2070//2070 2066//2066 2116//2116
f 2117//2117 1152//1152 2107//2107
f 1152//1152 1130//1130 2107//2107
f 2117//2117 1150//1150 1152//1152
f 2107//2107 2104//2104 2117//2117
f 2120//2120 2119//2119 2121//2121
f 2119//2119 1544//1544 2121//2121
f 2120//2120 2105//2105 2119//2119
f 2121//2121 2056//2056 2120//2120
f 2121//2121 1545//1545 2058//2058
f 1545//1545 1470//1470 2058//2058
f 2121//2121 1544//1544 1545//1545
f 2058//2058 2056//2056 2121//2121
f 2119//2119 2112//2112 1546//1546
f 2112//2112 1526//1526 1546//1546
f 2119//2119 2105//2105 2112//2112
f 1546//1546 1544//1544 2119//2119
f 2120//2120 2060//2060 2109//2109
f 2060//2060 2046//2046 2109//2109
f 2120//2120 2056//2056 2060//2060
f 2109//2109 2105//2105 2120//2120
f 2123//2123 2122//2122 2124//2124
f 2122//2122 2125//2125 2124//2124
f 2123//2123 2126//2126 2122//2122
f 2124//2124 2127//2127 2123//2123
f 2124//2124 2128//2128 2129//2129
f 2128//2128 267//267 2129//2129
f 2124//2124 2125//2125 2128//2128
f 2129//2129 2127//2127 2124//2124
f 2122//2122 2130//2130 2131//2131
f 2130//2130 2049//2049 2131//2131
f 2122//2122 2126//2126 2130//2130
f 2131//2131 2125//2125 2122//2122
f 2123//2123 2132//2132 2133//2133
f 2132//2132 1097//1097 2133//2133
f 2123//2123 2127//2127 2132//2132
f 2133//2133 2126//2126 2123//2123
f 2135//2135 2134//2134 2136//2136
f 2134//2134 272//272 2136//2136
f 2135//2135 2127//2127 2134//2134
f 2136//2136 1105//1105 2135//2135
f 2136//2136 274//274 1106//1106
f 274//274 275//275 1106//1106
f 2136//2136 272//272 274//274
f 1106//1106 1105//1105 2136//2136
f 2134//2134 2129//2129 277//277
f 2129//2129 267//267 277//277
f 2134//2134 2127//2127 2129//2129
f 277//277 272//272 2134//2134
f 2135//2135 1107//1107 2132//2132
f 1107//1107 1097//1097 2132//2132
f 2135//2135 1105//1105 1107//1107
f 2132//2132 2127//2127 2135//2135
f 2138//2138 2137//2137 2139//2139
f 2137//2137 2075//2075 2139//2139
f 2138//2138 2125//2125 2137//2137
f 2139//2139 287//287 2138//2138
f 2139//2139 2077//2077 288//288
f 2077//2077 185//185 288//288
f 2139//2139 2075//2075 2077//2077
f 288//288 287//287 2139//2139
f 2137//2137 2131//2131 2079//2079
f 2131//2131 2049//2049 2079//2079
f 2137//2137 2125//2125 2131//2131
f 2079//2079 2075//2075 2137//2137
f 2138//2138 289//289 2128//2128
f 289//289 267//267 2128//2128
f 2138//2138 287//287 289//289
f 2128//2128 2125//2125 2138//2138
f 2141//2141 2140//2140 2142//2142
f 2140//2140 1111//1111 2142//2142
f 2141//2141 2126//2126 2140//2140
f 2142//2142 2065//2065 2141//2141
f 2142//2142 1112//1112 2067//2067
f 1112//1112 1048//1048 2067//2067
f 2142//2142 1111//1111 1112//1112
f 2067//2067 2065//2065 2142//2142
f 2140//2140 2133//2133 1113//1113
f 2133//2133 1097//1097 1113//1113
f 2140//2140 2126//2126 2133//2133
f 1113//1113 1111//1111 2140//2140
f 2141//2141 2069//2069 2130//2130
f 2069//2069 2049//2049 2130//2130
f 2141//2141 2065//2065 2069//2069
f 2130//2130 2126//2126 2141//2141
f 2144//2144 2143//2143 2145//2145
f 2143//2143 2146//2146 2145//2145
f 2144//2144 2147//2147 2143//2143
f 2145//2145 2148//2148 2144//2144
f 2145//2145 2149//2149 2150//2150
f 2149//2149 2151//2151 2150//2150
f 2145//2145 2146//2146 2149//2149
f 2150//2150 2148//2148 2145//2145
f 2143//2143 2152//2152 2153//2153
f 2152//2152 2154//2154 2153//2153
f 2143//2143 2147//2147 2152//2152
f 2153//2153 2146//2146 2143//2143
f 2144//2144 2155//2155 2156//2156
f 2155//2155 2157//2157 2156//2156
f 2144//2144 2148//2148 2155//2155
f 2156//2156 2147//2147 2144//2144
f 2159//2159 2158//2158 2160//2160
f 2158//2158 2161//2161 2160//2160
f 2159//2159 2148//2148 2158//2158
f 2160//2160 2162//2162 2159//2159
f 2160//2160 2163//2163 2164//2164
f 2163//2163 332//332 2164//2164
f 2160//2160 2161//2161 2163//2163
f 2164//2164 2162//2162 2160//2160
f 2158//2158 2150//2150 2165//2165
f 2150//2150 2151//2151 2165//2165
f 2158//2158 2148//2148 2150//2150
f 2165//2165 2161//2161 2158//2158
f 2159//2159 2166//2166 2155//2155
f 2166//2166 2157//2157 2155//2155
f 2159//2159 2162//2162 2166//2166
f 2155//2155 2148//2148 2159//2159
f 2168//2168 2167//2167 2169//2169
f 2167//2167 2170//2170 2169//2169
f 2168//2168 2146//2146 2167//2167
f 2169//2169 2171//2171 2168//2168
f 2169//2169 2172//2172 2173//2173
f 2172//2172 771//771 2173//2173
f 2169//2169 2170//2170 2172//2172
f 2173//2173 2171//2171 2169//2169
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f 2153//2153 2154//2154 2174//2174
f 2167//2167 2146//2146 2153//2153
f 2174//2174 2170//2170 2167//2167
f 2168//2168 2175//2175 2149//2149
f 2175//2175 2151//2151 2149//2149
f 2168//2168 2171//2171 2175//2175
f 2149//2149 2146//2146 2168//2168
f 2177//2177 2176//2176 2178//2178
f 2176//2176 2179//2179 2178//2178
f 2177//2177 2147//2147 2176//2176
f 2178//2178 2180//2180 2177//2177
f 2178//2178 2181//2181 2182//2182
f 2181//2181 1195//1195 2182//2182
f 2178//2178 2179//2179 2181//2181
f 2182//2182 2180//2180 2178//2178
f 2176//2176 2156//2156 2183//2183
f 2156//2156 2157//2157 2183//2183
f 2176//2176 2147//2147 2156//2156
f 2183//2183 2179//2179 2176//2176
f 2177//2177 2184//2184 2152//2152
f 2184//2184 2154//2154 2152//2152
f 2177//2177 2180//2180 2184//2184
f 2152//2152 2147//2147 2177//2177
f 2186//2186 2185//2185 2187//2187
f 2185//2185 2188//2188 2187//2187
f 2186//2186 2189//2189 2185//2185
f 2187//2187 2190//2190 2186//2186
f 2187//2187 2191//2191 2192//2192
f 2191//2191 348//348 2192//2192
f 2187//2187 2188//2188 2191//2191
f 2192//2192 2190//2190 2187//2187
f 2185//2185 2193//2193 2194//2194
f 2193//2193 2157//2157 2194//2194
f 2185//2185 2189//2189 2193//2193
f 2194//2194 2188//2188 2185//2185
f 2186//2186 2195//2195 2196//2196
f 2195//2195 1276//1276 2196//2196
f 2186//2186 2190//2190 2195//2195
f 2196//2196 2189//2189 2186//2186
f 2198//2198 2197//2197 2199//2199
f 2197//2197 353//353 2199//2199
f 2198//2198 2190//2190 2197//2197
f 2199//2199 1285//1285 2198//2198
f 2199//2199 355//355 1287//1287
f 355//355 356//356 1287//1287
f 2199//2199 353//353 355//355
f 1287//1287 1285//1285 2199//2199
f 2197//2197 2192//2192 358//358
f 2192//2192 348//348 358//358
f 2197//2197 2190//2190 2192//2192
f 358//358 353//353 2197//2197
f 2198//2198 1289//1289 2195//2195
f 1289//1289 1276//1276 2195//2195
f 2198//2198 1285//1285 1289//1289
f 2195//2195 2190//2190 2198//2198
f 2201//2201 2200//2200 2202//2202
f 2200//2200 2162//2162 2202//2202
f 2201//2201 2188//2188 2200//2200
f 2202//2202 368//368 2201//2201
f 2202//2202 2164//2164 369//369
f 2164//2164 332//332 369//369
f 2202//2202 2162//2162 2164//2164
f 369//369 368//368 2202//2202
f 2200//2200 2194//2194 2166//2166
f 2194//2194 2157//2157 2166//2166
f 2200//2200 2188//2188 2194//2194
f 2166//2166 2162//2162 2200//2200
f 2201//2201 370//370 2191//2191
f 370//370 348//348 2191//2191
f 2201//2201 368//368 370//370
f 2191//2191 2188//2188 2201//2201
f 2204//2204 2203//2203 2205//2205
f 2203//2203 1294//1294 2205//2205
f 2204//2204 2189//2189 2203//2203
f 2205//2205 2179//2179 2204//2204
f 2205//2205 1295//1295 2181//2181
f 1295//1295 1195//1195 2181//2181
f 2205//2205 1294//1294 1295//1295
f 2181//2181 2179//2179 2205//2205
f 2203//2203 2196//2196 1296//1296
f 2196//2196 1276//1276 1296//1296
f 2203//2203 2189//2189 2196//2196
f 1296//1296 1294//1294 2203//2203
f 2204//2204 2183//2183 2193//2193
f 2183//2183 2157//2157 2193//2193
f 2204//2204 2179//2179 2183//2183
f 2193//2193 2189//2189 2204//2204
f 2207//2207 2206//2206 2208//2208
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# 5120 faces, 0 coords texture
# End of File
================================================
FILE: load/shapes/hand_prismatic.obj
================================================
# WaveFront *.obj file (generated by Autodesk ATF)
mtllib hand_b.mtl
g hand_b
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usemtl Steel_-_Satin
f 2/1/1 24/2/2 1/3/3
f 1/3/3 24/2/2 25/4/4
f 1/3/3 25/4/4 501/5/5
f 501/5/5 25/4/4 26/6/6
f 501/5/5 26/6/6 27/7/7
f 3/8/8 22/9/9 2/1/1
f 2/1/1 22/9/9 23/10/10
f 2/1/1 23/10/10 24/2/2
f 4/11/11 20/12/12 3/8/8
f 3/8/8 20/12/12 21/13/13
f 3/8/8 21/13/13 22/9/9
f 20/12/12 4/11/11 19/14/14
f 19/14/14 4/11/11 5/15/15
f 19/14/14 5/15/15 18/16/16
f 18/16/16 5/15/15 6/17/17
f 18/16/16 6/17/17 17/18/18
f 17/18/18 6/17/17 7/19/19
f 17/18/18 7/19/19 16/20/20
f 16/20/20 7/19/19 8/21/21
f 16/20/20 8/21/21 9/22/22
f 16/20/20 9/22/22 15/23/23
f 15/23/23 9/22/22 10/24/24
f 15/23/23 10/24/24 14/25/25
f 14/25/25 10/24/24 11/26/26
f 14/25/25 11/26/26 13/27/27
f 13/27/27 11/26/26 12/28/28
f 28/29/29 499/30/30 27/7/7
f 27/7/7 499/30/30 500/31/31
f 27/7/7 500/31/31 501/5/5
f 28/29/29 29/32/32 499/30/30
f 499/30/30 29/32/32 498/33/33
f 498/33/33 29/32/32 30/34/34
f 498/33/33 30/34/34 48/35/35
f 48/35/35 30/34/34 47/36/36
f 47/36/36 30/34/34 31/37/37
f 47/36/36 31/37/37 32/38/38
f 47/36/36 32/38/38 46/39/39
f 46/39/39 32/38/38 33/40/40
f 46/39/39 33/40/40 45/41/41
f 45/41/41 33/40/40 34/42/42
f 45/41/41 34/42/42 35/43/43
f 45/41/41 35/43/43 44/44/44
f 44/44/44 35/43/43 36/45/45
f 44/44/44 36/45/45 37/46/46
f 37/46/46 38/47/47 44/44/44
f 44/44/44 38/47/47 43/48/48
f 43/48/48 38/47/47 42/49/49
f 42/49/49 38/47/47 41/50/50
f 41/50/50 38/47/47 39/51/51
f 41/50/50 39/51/51 40/52/52
f 498/33/33 48/35/35 497/53/53
f 497/53/53 48/35/35 49/54/54
f 497/53/53 49/54/54 50/55/55
f 497/53/53 50/55/55 496/56/56
f 496/56/56 50/55/55 51/57/57
f 496/56/56 51/57/57 495/58/58
f 495/58/58 51/57/57 52/59/59
f 495/58/58 52/59/59 53/60/60
f 495/58/58 53/60/60 494/61/61
f 494/61/61 53/60/60 54/62/62
f 494/61/61 54/62/62 493/63/63
f 493/63/63 54/62/62 55/64/64
f 493/63/63 55/64/64 56/65/65
f 57/66/66 428/67/67 56/65/65
f 56/65/65 428/67/67 492/68/68
f 56/65/65 492/68/68 493/63/63
f 57/66/66 58/69/69 428/67/67
f 428/67/67 58/69/69 59/70/70
f 428/67/67 59/70/70 60/71/71
f 60/71/71 61/72/72 428/67/67
f 428/67/67 61/72/72 62/73/73
f 428/67/67 62/73/73 63/74/74
f 63/74/74 64/75/75 428/67/67
f 428/67/67 64/75/75 73/76/76
f 428/67/67 73/76/76 74/77/77
f 64/75/75 65/78/78 73/76/76
f 73/76/76 65/78/78 66/79/79
f 73/76/76 66/79/79 72/80/80
f 72/80/80 66/79/79 67/81/81
f 72/80/80 67/81/81 68/82/82
f 68/82/82 69/83/83 72/80/80
f 72/80/80 69/83/83 71/84/84
f 71/84/84 69/83/83 70/85/85
f 74/77/77 75/86/86 428/67/67
f 428/67/67 75/86/86 427/87/87
f 427/87/87 75/86/86 426/88/88
f 426/88/88 75/86/86 425/89/89
f 425/89/89 75/86/86 239/90/90
f 425/89/89 239/90/90 337/91/91
f 337/91/91 239/90/90 240/92/92
f 337/91/91 240/92/92 336/93/93
f 336/93/93 240/92/92 241/94/94
f 336/93/93 241/94/94 335/95/95
f 335/95/95 241/94/94 242/96/96
f 335/95/95 242/96/96 334/97/97
f 334/97/97 242/96/96 243/98/98
f 334/97/97 243/98/98 333/99/99
f 333/99/99 243/98/98 244/100/100
f 333/99/99 244/100/100 332/101/101
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f 331/102/102 244/100/100 245/103/103
f 331/102/102 245/103/103 330/104/104
f 330/104/104 245/103/103 329/105/105
f 329/105/105 245/103/103 246/106/106
f 329/105/105 246/106/106 328/107/107
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f 328/107/107 247/108/108 327/109/109
f 327/109/109 247/108/108 248/110/110
f 327/109/109 248/110/110 326/111/111
f 326/111/111 248/110/110 249/112/112
f 326/111/111 249/112/112 250/113/113
f 76/114/114 144/115/115 75/86/86
f 75/86/86 144/115/115 145/116/116
f 75/86/86 145/116/116 146/117/117
f 144/115/115 76/114/114 143/118/118
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f 142/120/120 77/119/119 78/121/121
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f 130/140/140 85/138/138 86/141/141
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f 129/142/142 86/141/141 87/143/143
f 129/142/142 87/143/143 128/144/144
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f 126/148/148 90/149/149 125/150/150
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f 122/156/156 93/155/155 94/157/157
f 122/156/156 94/157/157 121/158/158
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f 121/158/158 95/159/159 120/160/160
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f 119/162/162 97/163/163 118/164/164
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f 117/166/166 99/167/167 116/168/168
f 116/168/168 99/167/167 100/169/169
f 116/168/168 100/169/169 101/170/170
f 116/168/168 101/170/170 115/171/171
f 115/171/171 101/170/170 102/172/172
f 115/171/171 102/172/172 114/173/173
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# 1 groups
================================================
FILE: load/shapes/human.obj
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f 820/1057/3295 829/1066/3296 804/1004/3297 842/1116/3298
f 817/1054/3299 829/1066/3300 820/1057/3301 772/972/3302
s 2
f 830/1067/3303 834/1071/3304 1051/1288/3305 1053/1290/3306
s 4
f 1053/1290/3307 986/1223/3308 831/1068/3309 830/1067/3310
s 2
f 783/983/3311 844/1118/3312 833/1070/3313 832/1069/3314
f 844/1118/3315 965/1202/3316 1056/1293/3317 833/1070/3318
f 1051/1288/3319 834/1071/3320 833/1070/3321 1056/1293/3322
s 1
f 819/1056/3323 766/966/3324 765/965/3325 764/964/3326
f 841/1115/3327 819/1056/3328 764/964/3329 784/984/3330
f 809/1046/3331 823/1060/3332 752/952/3333 775/975/3334
f 752/952/3335 823/1060/3336 805/1005/3337
f 797/997/3338 1201/1420/3339 767/967/3340 826/1063/3341 804/1004/3342
f 771/971/3343 951/1188/3344 984/1221/3345 789/989/3346
s 2
f 834/1071/3347 832/1069/3348 833/1070/3349
s 1
f 831/1068/3350 986/1223/3351 1062/1299/3352 845/1119/3353
f 837/1074/3354 840/1077/3355 831/1068/3356
f 783/983/3357 831/1068/3358 840/1077/3359 846/1120/3360
f 848/1122/3361 844/1118/3362 783/983/3363 847/1121/3364
f 1069/1306/3365 965/1202/3366 844/1118/3367 848/1122/3368
f 845/1119/3369 837/1074/3370 831/1068/3371
f 841/1115/3372 826/1063/3373 819/1056/3374
s 4
f 832/1069/3375 830/1067/3376 831/1068/3377 783/983/3378
s 1
f 747/947/3379 855/1129/3380 854/1128/3381
f 855/1129/3382 747/947/3383 850/1124/3384 853/1127/3385
f 850/1124/3386 747/947/3387 793/993/3388 794/994/3389
f 854/1128/3390 749/949/3391 803/1003/3392 852/1126/3393
s 2
f 861/1135/3394 801/1001/3395 853/1127/3396 800/1000/3397
f 854/1128/3398 855/1129/3399 849/1123/3400 857/1131/3401 799/999/3402
f 855/1129/3403 853/1127/3404 801/1001/3405 862/1136/3406 849/1123/3407
s 4
f 859/1133/3408 749/949/3409 854/1128/3410 851/1125/3411
s 1
f 857/1131/3412 849/1123/3413 856/1130/3414 863/1137/3415
f 858/1132/3416 799/999/3417 857/1131/3418 863/1137/3419
f 859/1133/3420 851/1125/3421 858/1132/3422 863/1137/3423
f 856/1130/3424 749/949/3425 859/1133/3426 863/1137/3427
f 861/1135/3428 800/1000/3429 898/1172/3430 860/1134/3431 864/1138/3432
f 862/1136/3433 801/1001/3434 861/1135/3435 864/1138/3436
f 856/1130/3437 849/1123/3438 862/1136/3439 864/1138/3440
f 860/1134/3441 749/949/3442 856/1130/3443 864/1138/3444
s 2
f 851/1125/3445 854/1128/3446 858/1132/3447
f 858/1132/3448 854/1128/3449 799/999/3450
s 1
f 806/1006/3451 805/1005/3452 749/949/3453 860/1134/3454
f 788/988/3455 866/1140/3456 868/1142/3457 869/1143/3458
f 869/1143/3459 868/1142/3460 883/1157/3461 881/1155/3462
f 759/959/3463 870/1144/3464 868/1142/3465 866/1140/3466
f 868/1142/3467 870/1144/3468 884/1158/3469 883/1157/3470
f 879/1153/3471 884/1158/3472 870/1144/3473 877/1151/3474
f 874/1148/3475 776/976/3476 956/1193/3477 788/988/3478 869/1143/3479
f 776/976/3480 874/1148/3481 873/1147/3482 835/1072/3483
f 880/1154/3484 887/1161/3485 875/1149/3486 873/1147/3487
f 873/1147/3488 875/1149/3489 773/973/3490 835/1072/3491
f 773/973/3492 875/1149/3493 908/1182/3494
f 757/957/3495 867/1141/3496 871/1145/3497 910/1184/3498
f 878/1152/3499 876/1150/3500 872/1146/3501 885/1159/3502
f 876/1150/3503 816/1053/3504 772/972/3505 757/957/3506 872/1146/3507
f 877/1151/3508 876/1150/3509 878/1152/3510 879/1153/3511
f 775/975/3512 752/952/3513 876/1150/3514 877/1151/3515
f 876/1150/3516 752/952/3517 816/1053/3518
s 2
f 881/1155/3519 883/1157/3520 865/1139/3521
f 883/1157/3522 884/1158/3523 865/1139/3524
f 884/1158/3525 879/1153/3526 865/1139/3527
f 886/1160/3528 881/1155/3529 865/1139/3530
f 880/1154/3531 886/1160/3532 865/1139/3533
f 887/1161/3534 880/1154/3535 865/1139/3536
f 878/1152/3537 885/1159/3538 865/1139/3539
f 879/1153/3540 878/1152/3541 865/1139/3542
s 1
f 1206/1425/3543 1204/1423/3544 1179/1379/3545 706/1383/3546
f 1192/1409/3547 1203/1422/3548 1206/1425/3549 706/1383/3550
f 1200/1419/3551 1181/1381/3552 1194/1413/3553 1199/1418/3554
f 776/976/3555 835/1072/3556 969/1206/3557 992/1229/3558
f 775/975/3559 877/1151/3560 870/1144/3561 759/959/3562
f 782/982/3563 1001/1238/3564 1115/1352/3565 889/1163/3566
f 852/1126/3567 803/1003/3568 893/1167/3569 802/1002/3570
f 807/1007/3571 750/950/3572 893/1167/3573 808/1008/3574
f 756/956/3575 891/1165/3576 852/1126/3577 802/1002/3578
s 2
f 830/1067/3579 832/1069/3580 834/1071/3581
s 1
f 815/1052/3582 894/1168/3583 889/1163/3584 780/980/3585
f 896/1170/3586 815/1052/3587 895/1169/3588 825/1062/3589
f 815/1052/3590 896/1170/3591 894/1168/3592
f 888/1162/3593 807/1007/3594 808/1008/3595 781/981/3596
f 897/1171/3597 842/1116/3598 847/1121/3599
f 897/1171/3600 847/1121/3601 783/983/3602 846/1120/3603
f 800/1000/3604 797/997/3605 804/1004/3606 829/1066/3607
f 829/1066/3608 817/1054/3609 898/1172/3610 800/1000/3611
f 817/1054/3612 806/1006/3613 860/1134/3614 898/1172/3615
f 899/1173/3616 787/987/3617 782/982/3618 889/1163/3619
f 781/981/3620 787/987/3621 899/1173/3622 888/1162/3623
f 899/1173/3624 889/1163/3625 894/1168/3626 896/1170/3627
f 896/1170/3628 888/1162/3629 899/1173/3630
f 835/1072/3631 786/986/3632 778/978/3633
f 835/1072/3634 773/973/3635 786/986/3636
f 780/980/3637 1000/1237/3638 960/1197/3639 815/1052/3640
f 826/1063/3641 841/1115/3642 842/1116/3643 804/1004/3644
f 901/1175/3645 900/1174/3646 848/1122/3647 847/1121/3648
f 784/984/3649 789/989/3650 900/1174/3651 901/1175/3652
f 900/1174/3653 1129/1366/3654 1069/1306/3655 848/1122/3656
f 789/989/3657 984/1221/3658 1129/1366/3659 900/1174/3660
f 842/1116/3661 901/1175/3662 847/1121/3663
f 841/1115/3664 784/984/3665 901/1175/3666 842/1116/3667
f 874/1148/3668 886/1160/3669 880/1154/3670 873/1147/3671
f 902/1176/3672 1130/1367/3673 955/1192/3674 838/1075/3675
f 903/1177/3676 902/1176/3677 838/1075/3678 836/1073/3679
f 904/1178/3680 903/1177/3681 836/1073/3682 839/1076/3683
f 843/1117/3684 905/1179/3685 904/1178/3686 839/1076/3687
f 843/1117/3688 842/1116/3689 906/1180/3690 905/1179/3691
f 845/1119/3692 1062/1299/3693 1130/1367/3694 902/1176/3695
f 837/1074/3696 845/1119/3697 902/1176/3698 903/1177/3699
f 840/1077/3700 837/1074/3701 903/1177/3702 904/1178/3703
f 846/1120/3704 840/1077/3705 904/1178/3706 905/1179/3707
f 905/1179/3708 906/1180/3709 846/1120/3710
f 907/1181/3711 908/1182/3712 875/1149/3713 887/1161/3714
f 882/1156/3715 871/1145/3716 908/1182/3717 907/1181/3718
f 910/1184/3719 871/1145/3720 882/1156/3721 909/1183/3722
f 885/1159/3723 872/1146/3724 910/1184/3725 909/1183/3726
s 2
f 865/1139/3727 907/1181/3728 887/1161/3729
f 865/1139/3730 882/1156/3731 907/1181/3732
f 865/1139/3733 909/1183/3734 882/1156/3735
f 865/1139/3736 885/1159/3737 909/1183/3738
s 1
f 911/1009/3739 913/1011/3740 912/1010/3741
f 911/1009/3742 914/1012/3743 913/1011/3744
f 911/1009/3745 915/1013/3746 914/1012/3747
f 911/1009/3748 916/1014/3749 915/1013/3750
f 911/1009/3751 917/1015/3752 916/1014/3753
f 911/1009/3754 918/1016/3755 917/1015/3756
f 911/1009/3757 919/1017/3758 918/1016/3759
f 911/1009/3760 920/1018/3761 919/1017/3762
f 911/1009/3763 921/1019/3764 920/1018/3765
f 911/1009/3766 922/1020/3767 921/1019/3768
f 911/1009/3769 923/1021/3770 922/1020/3771
f 911/1009/3772 912/1010/3773 923/1021/3774
f 912/1010/3775 913/1011/3776 925/1023/3777 924/1022/3778
f 913/1011/3779 914/1012/3780 926/1024/3781 925/1023/3782
f 914/1012/3783 915/1013/3784 927/1025/3785 926/1024/3786
f 915/1013/3787 916/1014/3788 928/1026/3789 927/1025/3790
f 916/1014/3791 917/1015/3792 929/1027/3793 928/1026/3794
f 917/1015/3795 918/1016/3796 930/1028/3797 929/1027/3798
f 918/1016/3799 919/1017/3800 931/1029/3801 930/1028/3802
f 919/1017/3803 920/1018/3804 932/1030/3805 931/1029/3806
f 920/1018/3807 921/1019/3808 933/1031/3809 932/1030/3810
f 921/1019/3811 922/1020/3812 934/1032/3813 933/1031/3814
f 922/1020/3815 923/1021/3816 935/1033/3817 934/1032/3818
f 923/1021/3819 912/1010/3820 924/1022/3821 935/1033/3822
f 924/1022/3823 925/1023/3824 937/1035/3825 936/1034/3826
f 925/1023/3827 926/1024/3828 938/1036/3829 937/1035/3830
f 926/1024/3831 927/1025/3832 939/1037/3833 938/1036/3834
f 927/1025/3835 928/1026/3836 940/1038/3837 939/1037/3838
f 928/1026/3839 929/1027/3840 941/1039/3841 940/1038/3842
f 929/1027/3843 930/1028/3844 942/1040/3845 941/1039/3846
f 930/1028/3847 931/1029/3848 943/1041/3849 942/1040/3850
f 931/1029/3851 932/1030/3852 944/1042/3853 943/1041/3854
f 932/1030/3855 933/1031/3856 945/1043/3857 944/1042/3858
f 933/1031/3859 934/1032/3860 946/1044/3861 945/1043/3862
f 934/1032/3863 935/1033/3864 947/1045/3865 946/1044/3866
f 935/1033/3867 924/1022/3868 936/1034/3869 947/1045/3870
f 707/1387/3871 1178/1403/3872 1212/1431/3873 1213/1433/3874
f 1207/1426/3875 1184/1389/3876 697/1392/3877 978/1215/3878
f 1061/1298/3879 982/1219/3880 981/1218/3881 955/1192/3882
f 972/1209/3883 952/1189/3884 981/1218/3885 982/1219/3886
f 1061/1298/3887 1059/1296/3888 1005/1242/3889
f 1059/1296/3890 983/1220/3891 1005/1242/3892
f 983/1220/3893 972/1209/3894 982/1219/3895 1005/1242/3896
f 982/1219/3897 1061/1298/3898 1005/1242/3899
f 1066/1303/3900 1040/1277/3901 973/1210/3902 1067/1304/3903
f 966/1203/3904 973/1210/3905 1040/1277/3906 987/1224/3907
f 988/1225/3908 967/1204/3909 989/1226/3910 1092/1329/3911
f 989/1226/3912 973/1210/3913 966/1203/3914 1092/1329/3915
f 1063/1300/3916 989/1226/3917 967/1204/3918 1059/1296/3919
f 973/1210/3920 989/1226/3921 1063/1300/3922 1067/1304/3923
f 956/1193/3924 992/1229/3925 991/1228/3926
f 970/1207/3927 952/1189/3928 972/1209/3929 974/1211/3930
f 983/1220/3931 1059/1296/3932 967/1204/3933 993/1230/3934
f 974/1211/3935 972/1209/3936 983/1220/3937 993/1230/3938
f 988/1225/3939 1006/1243/3940 1191/1406/3941 993/1230/3942 967/1204/3943
f 1058/1295/3944 969/1206/3945 995/1232/3946 994/1231/3947
f 990/1227/3948 1031/1268/3949 996/1233/3950 968/1205/3951
f 996/1233/3952 971/1208/3953 1008/1245/3954 1091/1328/3955
f 1031/1268/3956 998/1235/3957 1007/1244/3958 996/1233/3959
f 1007/1244/3960 999/1236/3961 971/1208/3962 996/1233/3963
f 1114/1351/3964 997/1234/3965 1000/1237/3966 1115/1352/3967
f 971/1208/3968 1002/1239/3969 956/1193/3970 1008/1245/3971
f 999/1236/3972 1001/1238/3973 1002/1239/3974 971/1208/3975
f 1004/1241/3976 1009/1246/3977 985/1222/3978 975/1212/3979
f 985/1222/3980 951/1188/3981 977/1214/3982 1217/1437/3983
f 1198/1417/3984 975/1212/3985 985/1222/3986 1217/1437/3987
f 1015/1400/3988 948/1396/3989 1012/1249/3990 962/1199/3991
f 962/1199/3992 1012/1249/3993 1013/1250/3994 1014/1251/3995
f 1075/1312/3996 1010/1247/3997 1011/1248/3998 1014/1251/3999
f 1011/1248/4000 961/1198/4001 962/1199/4002 1014/1251/4003
f 961/1198/4004 957/1397/4005 1015/1400/4006 962/1199/4007
f 963/1200/4008 958/1398/4009 1016/1401/4010 1017/1254/4011
f 950/1187/4012 964/1201/4013 1018/1255/4014 1041/1278/4015
f 1018/1255/4016 963/1200/4017 1017/1254/4018 1041/1278/4019
f 1117/1354/4020 949/1186/4021 1013/1250/4022 1116/1353/4023
f 1118/1355/4024 1116/1353/4025 1013/1250/4026 1012/1249/4027
f 1118/1355/4028 963/1200/4029 1018/1255/4030 1116/1353/4031
f 1116/1353/4032 1018/1255/4033 964/1201/4034 1117/1354/4035
f 1119/1402/4036 1118/1355/4037 1012/1249/4038 948/1396/4039
f 1119/1402/4040 958/1398/4041 963/1200/4042 1118/1355/4043
f 1020/1257/4044 1078/1315/4045 1022/1259/4046 1019/1256/4047
f 1075/1312/4048 1078/1315/4049 1020/1257/4050 1010/1247/4051
f 954/1191/4052 1120/1357/4053 1024/1261/4054 1042/1279/4055
f 1024/1261/4056 964/1201/4057 950/1187/4058 1042/1279/4059
f 1077/1314/4060 1079/1316/4061 949/1186/4062 1117/1354/4063
f 1027/1264/4064 959/1196/4065 1036/1273/4066 1028/1265/4067
f 1025/1262/4068 1043/1280/4069 1027/1264/4070 953/1190/4071
f 1030/1267/4072 1043/1280/4073 1025/1262/4074 1120/1357/4075
f 1030/1267/4076 998/1235/4077 1031/1268/4078 1043/1280/4079
f 810/1395/4080 751/1394/4081 1032/1269/4082 1033/1270/4083
f 1017/1254/4084 1016/1401/4085 810/1395/4086 1033/1270/4087
f 1034/1271/4088 954/1191/4089 1042/1279/4090 1044/1281/4091
f 1042/1279/4092 950/1187/4093 1041/1278/4094 1044/1281/4095
f 1041/1278/4096 1017/1254/4097 1033/1270/4098 1044/1281/4099
f 1033/1270/4100 1032/1269/4101 1034/1271/4102 1044/1281/4103
f 1032/1269/4104 751/1393/4105 814/1051/4106 1122/1359/4107
f 1035/1272/4108 1122/1359/4109 814/1051/4110 960/1197/4111
f 1036/1273/4112 987/1224/4113 1037/1274/4114 1028/1265/4115
f 1029/1266/4116 1045/1282/4117 1123/1360/4118 1113/1350/4119
f 1029/1266/4120 954/1191/4121 1034/1271/4122 1045/1282/4123
f 1122/1359/4124 1045/1282/4125 1034/1271/4126 1032/1269/4127
f 979/1216/4128 1010/1247/4129 1020/1257/4130 1214/1434/4131
f 1020/1257/4132 1019/1256/4133 1209/1428/4134 1214/1434/4135
f 1038/1275/4136 961/1198/4137 1011/1248/4138 1215/1435/4139
f 1011/1248/4140 1010/1247/4141 979/1216/4142 1215/1435/4143
f 980/1399/4144 957/1397/4145 961/1198/4146 1038/1275/4147
f 976/1213/4148 1210/1429/4149 1046/1283/4150 1039/1276/4151
f 1040/1277/4152 1066/1303/4153 1026/1263/4154 1049/1286/4155
f 1037/1274/4156 987/1224/4157 1040/1277/4158 1049/1286/4159
s 2
f 1050/1287/4160 1053/1290/4161 1051/1288/4162 1057/1294/4163
s 4
f 1053/1290/4164 1050/1287/4165 1052/1289/4166 986/1223/4167
s 2
f 1003/1240/4168 1054/1291/4169 1055/1292/4170 1068/1305/4171
f 1068/1305/4172 1055/1292/4173 1056/1293/4174 965/1202/4175
f 1051/1288/4176 1056/1293/4177 1055/1292/4178 1057/1294/4179
s 1
f 1039/1276/4180 975/1212/4181 1198/1417/4182 976/1213/4183
f 1065/1302/4184 1004/1241/4185 975/1212/4186 1039/1276/4187
f 1031/1268/4188 990/1227/4189 959/1196/4190 1043/1280/4191
f 959/1196/4192 1027/1264/4193 1043/1280/4194
f 1046/1283/4195 1210/1429/4196 1209/1428/4197 1019/1256/4198 1026/1263/4199
f 985/1222/4200 1009/1246/4201 984/1221/4202 951/1188/4203
s 2
f 1057/1294/4204 1055/1292/4205 1054/1291/4206
s 1
f 1052/1289/4207 1070/1307/4208 1062/1299/4209 986/1223/4210
f 1060/1297/4211 1052/1289/4212 1064/1301/4213
f 1003/1240/4214 1071/1308/4215 1064/1301/4216 1052/1289/4217
f 1073/1310/4218 1072/1309/4219 1003/1240/4220 1068/1305/4221
f 1069/1306/4222 1073/1310/4223 1068/1305/4224 965/1202/4225
f 1070/1307/4226 1052/1289/4227 1060/1297/4228
f 1065/1302/4229 1039/1276/4230 1046/1283/4231
s 4
f 1054/1291/4232 1003/1240/4233 1052/1289/4234 1050/1287/4235
s 1
f 949/1186/4236 1079/1316/4237 1080/1317/4238
f 1080/1317/4239 1078/1315/4240 1075/1312/4241 949/1186/4242
f 1075/1312/4243 1014/1251/4244 1013/1250/4245 949/1186/4246
f 1079/1316/4247 1077/1314/4248 1025/1262/4249 953/1190/4250
s 2
f 1086/1323/4251 1022/1259/4252 1078/1315/4253 1023/1260/4254
f 1079/1316/4255 1021/1258/4256 1082/1319/4257 1074/1311/4258 1080/1317/4259
f 1080/1317/4260 1074/1311/4261 1087/1324/4262 1023/1260/4263 1078/1315/4264
s 4
f 1084/1321/4265 1076/1313/4266 1079/1316/4267 953/1190/4268
s 1
f 1082/1319/4269 1088/1325/4270 1081/1318/4271 1074/1311/4272
f 1083/1320/4273 1088/1325/4274 1082/1319/4275 1021/1258/4276
f 1084/1321/4277 1088/1325/4278 1083/1320/4279 1076/1313/4280
f 1081/1318/4281 1088/1325/4282 1084/1321/4283 953/1190/4284
f 1086/1323/4285 1089/1326/4286 1085/1322/4287 1125/1362/4288 1022/1259/4289
f 1087/1324/4290 1089/1326/4291 1086/1323/4292 1023/1260/4293
f 1081/1318/4294 1089/1326/4295 1087/1324/4296 1074/1311/4297
f 1085/1322/4298 1089/1326/4299 1081/1318/4300 953/1190/4301
s 2
f 1076/1313/4302 1083/1320/4303 1079/1316/4304
f 1083/1320/4305 1021/1258/4306 1079/1316/4307
s 1
f 1028/1265/4308 1085/1322/4309 953/1190/4310 1027/1264/4311
f 1008/1245/4312 1094/1331/4313 1093/1330/4314 1091/1328/4315
f 1094/1331/4316 1106/1343/4317 1108/1345/4318 1093/1330/4319
f 968/1205/4320 1091/1328/4321 1093/1330/4322 1095/1332/4323
f 1093/1330/4324 1108/1345/4325 1109/1346/4326 1095/1332/4327
f 1104/1341/4328 1102/1339/4329 1095/1332/4330 1109/1346/4331
f 1099/1336/4332 1094/1331/4333 1008/1245/4334 956/1193/4335 991/1228/4336
f 991/1228/4337 1058/1295/4338 1098/1335/4339 1099/1336/4340
f 1105/1342/4341 1098/1335/4342 1100/1337/4343 1112/1349/4344
f 1098/1335/4345 1058/1295/4346 988/1225/4347 1100/1337/4348
f 988/1225/4349 1092/1329/4350 1096/1333/4351 1137/1374/4352
f 966/1203/4353 1097/1334/4354 1139/1376/4355
f 1103/1340/4356 1110/1347/4357 1097/1334/4358 1101/1338/4359
f 1101/1338/4360 1097/1334/4361 966/1203/4362 987/1224/4363 1036/1273/4364
f 1102/1339/4365 1104/1341/4366 1103/1340/4367 1101/1338/4368
f 990/1227/4369 1102/1339/4370 1101/1338/4371 959/1196/4372
f 1101/1338/4373 1036/1273/4374 959/1196/4375
s 2
f 1106/1343/4376 1090/1327/4377 1108/1345/4378
f 1108/1345/4379 1090/1327/4380 1109/1346/4381
f 1109/1346/4382 1090/1327/4383 1104/1341/4384
f 1111/1348/4385 1090/1327/4386 1106/1343/4387
f 1105/1342/4388 1090/1327/4389 1111/1348/4390
f 1112/1349/4391 1090/1327/4392 1105/1342/4393
f 1103/1340/4394 1090/1327/4395 1110/1347/4396
f 1104/1341/4397 1090/1327/4398 1103/1340/4399
s 1
f 695/1412/4400 1047/1284/4401 978/1215/4402 697/1392/4403
f 1048/1285/4404 1186/1391/4405 707/1387/4406 1213/1433/4407
f 991/1228/4408 992/1229/4409 969/1206/4410 1058/1295/4411
f 990/1227/4412 968/1205/4413 1095/1332/4414 1102/1339/4415
f 999/1236/4416 1114/1351/4417 1115/1352/4418 1001/1238/4419
f 1077/1314/4420 1024/1261/4421 1120/1357/4422 1025/1262/4423
f 1029/1266/4424 1030/1267/4425 1120/1357/4426 954/1191/4427
f 964/1201/4428 1024/1261/4429 1077/1314/4430 1117/1354/4431
s 2
f 1050/1287/4432 1057/1294/4433 1054/1291/4434
s 1
f 1035/1272/4435 997/1234/4436 1114/1351/4437 1121/1358/4438
f 1123/1360/4439 1045/1282/4440 1122/1359/4441 1035/1272/4442
f 1035/1272/4443 1121/1358/4444 1123/1360/4445
f 1113/1350/4446 998/1235/4447 1030/1267/4448 1029/1266/4449
f 1124/1361/4450 1066/1303/4451 1135/1372/4452
f 1124/1361/4453 1071/1308/4454 1003/1240/4455 1072/1309/4456
f 1022/1259/4457 1049/1286/4458 1026/1263/4459 1019/1256/4460
f 1049/1286/4461 1022/1259/4462 1125/1362/4463 1037/1274/4464
f 1037/1274/4465 1125/1362/4466 1085/1322/4467 1028/1265/4468
f 999/1236/4469 1007/1244/4470 1126/1363/4471 1114/1351/4472
f 998/1235/4473 1113/1350/4474 1126/1363/4475 1007/1244/4476
f 1126/1363/4477 1123/1360/4478 1121/1358/4479 1114/1351/4480
f 1123/1360/4481 1126/1363/4482 1113/1350/4483
f 1058/1295/4484 994/1231/4485 1006/1243/4486
f 1058/1295/4487 1006/1243/4488 988/1225/4489
f 997/1234/4490 1035/1272/4491 960/1197/4492 1000/1237/4493
f 1046/1283/4494 1026/1263/4495 1066/1303/4496 1065/1302/4497
f 1128/1365/4498 1072/1309/4499 1073/1310/4500 1127/1364/4501
f 1004/1241/4502 1128/1365/4503 1127/1364/4504 1009/1246/4505
f 1127/1364/4506 1073/1310/4507 1069/1306/4508 1129/1366/4509
f 1009/1246/4510 1127/1364/4511 1129/1366/4512 984/1221/4513
f 1066/1303/4514 1072/1309/4515 1128/1365/4516
f 1065/1302/4517 1066/1303/4518 1128/1365/4519 1004/1241/4520
f 1099/1336/4521 1098/1335/4522 1105/1342/4523 1111/1348/4524
f 1131/1368/4525 1061/1298/4526 955/1192/4527 1130/1367/4528
f 1132/1369/4529 1059/1296/4530 1061/1298/4531 1131/1368/4532
f 1133/1370/4533 1063/1300/4534 1059/1296/4535 1132/1369/4536
f 1067/1304/4537 1063/1300/4538 1133/1370/4539 1134/1371/4540
f 1067/1304/4541 1134/1371/4542 1135/1372/4543 1066/1303/4544
f 1070/1307/4545 1131/1368/4546 1130/1367/4547 1062/1299/4548
f 1060/1297/4549 1132/1369/4550 1131/1368/4551 1070/1307/4552
f 1064/1301/4553 1133/1370/4554 1132/1369/4555 1060/1297/4556
f 1071/1308/4557 1134/1371/4558 1133/1370/4559 1064/1301/4560
f 1134/1371/4561 1071/1308/4562 1135/1372/4563
f 1136/1373/4564 1112/1349/4565 1100/1337/4566 1137/1374/4567
f 1107/1344/4568 1136/1373/4569 1137/1374/4570 1096/1333/4571
f 1139/1376/4572 1138/1375/4573 1107/1344/4574 1096/1333/4575
f 1110/1347/4576 1138/1375/4577 1139/1376/4578 1097/1334/4579
s 2
f 1090/1327/4580 1112/1349/4581 1136/1373/4582
f 1090/1327/4583 1136/1373/4584 1107/1344/4585
f 1090/1327/4586 1107/1344/4587 1138/1375/4588
f 1090/1327/4589 1138/1375/4590 1110/1347/4591
s 1
f 1140/1078/4592 1141/1079/4593 1142/1080/4594
f 1140/1078/4595 1142/1080/4596 1143/1081/4597
f 1140/1078/4598 1143/1081/4599 1144/1082/4600
f 1140/1078/4601 1144/1082/4602 1145/1083/4603
f 1140/1078/4604 1145/1083/4605 1146/1084/4606
f 1140/1078/4607 1146/1084/4608 1147/1085/4609
f 1140/1078/4610 1147/1085/4611 1148/1086/4612
f 1140/1078/4613 1148/1086/4614 1149/1087/4615
f 1140/1078/4616 1149/1087/4617 1150/1088/4618
f 1140/1078/4619 1150/1088/4620 1151/1089/4621
f 1140/1078/4622 1151/1089/4623 1152/1090/4624
f 1140/1078/4625 1152/1090/4626 1141/1079/4627
f 1141/1079/4628 1153/1091/4629 1154/1092/4630 1142/1080/4631
f 1142/1080/4632 1154/1092/4633 1155/1093/4634 1143/1081/4635
f 1143/1081/4636 1155/1093/4637 1156/1094/4638 1144/1082/4639
f 1144/1082/4640 1156/1094/4641 1157/1095/4642 1145/1083/4643
f 1145/1083/4644 1157/1095/4645 1158/1096/4646 1146/1084/4647
f 1146/1084/4648 1158/1096/4649 1159/1097/4650 1147/1085/4651
f 1147/1085/4652 1159/1097/4653 1160/1098/4654 1148/1086/4655
f 1148/1086/4656 1160/1098/4657 1161/1099/4658 1149/1087/4659
f 1149/1087/4660 1161/1099/4661 1162/1100/4662 1150/1088/4663
f 1150/1088/4664 1162/1100/4665 1163/1101/4666 1151/1089/4667
f 1151/1089/4668 1163/1101/4669 1164/1102/4670 1152/1090/4671
f 1152/1090/4672 1164/1102/4673 1153/1091/4674 1141/1079/4675
f 1153/1091/4676 1165/1103/4677 1166/1104/4678 1154/1092/4679
f 1154/1092/4680 1166/1104/4681 1167/1105/4682 1155/1093/4683
f 1155/1093/4684 1167/1105/4685 1168/1106/4686 1156/1094/4687
f 1156/1094/4688 1168/1106/4689 1169/1107/4690 1157/1095/4691
f 1157/1095/4692 1169/1107/4693 1170/1108/4694 1158/1096/4695
f 1158/1096/4696 1170/1108/4697 1171/1109/4698 1159/1097/4699
f 1159/1097/4700 1171/1109/4701 1172/1110/4702 1160/1098/4703
f 1160/1098/4704 1172/1110/4705 1173/1111/4706 1161/1099/4707
f 1161/1099/4708 1173/1111/4709 1174/1112/4710 1162/1100/4711
f 1162/1100/4712 1174/1112/4713 1175/1113/4714 1163/1101/4715
f 1163/1101/4716 1175/1113/4717 1176/1114/4718 1164/1102/4719
f 1164/1102/4720 1176/1114/4721 1165/1103/4722 1153/1091/4723
f 1188/1407/4724 1187/1404/4725 995/1232/4726 778/978/4727
f 1190/1405/4728 1188/1407/4729 778/978/4730 786/986/4731
f 763/963/4732 970/1207/4733 1187/1404/4734 1188/1407/4735
f 777/977/4736 763/963/4737 1188/1407/4738 1190/1405/4739
f 995/1232/4740 1187/1404/4741 1189/1408/4742 994/1231/4743
f 994/1231/4744 1189/1408/4745 1191/1406/4746 1006/1243/4747
f 1187/1404/4748 970/1207/4749 974/1211/4750 1189/1408/4751
f 1189/1408/4752 974/1211/4753 993/1230/4754 1191/1406/4755
f 869/1143/4756 881/1155/4757 886/1160/4758 874/1148/4759
f 1094/1331/4760 1099/1336/4761 1111/1348/4762 1106/1343/4763
f 1207/1426/4764 1218/1438/4765 1183/1388/4766 1184/1389/4767
f 1208/1427/4768 1196/1415/4769 1180/1380/4770 344/1386/4771
f 1192/1409/4772 1177/1377/4773 1203/1422/4774
f 1048/1285/4775 1211/1430/4776 696/1385/4777 1186/1391/4778
f 343/1410/4779 696/1385/4780 1211/1430/4781
f 1193/1411/4782 1205/1424/4783 1203/1422/4784 1177/1377/4785
f 695/1412/4786 1185/1390/4787 1047/1284/4788
f 1211/1430/4789 1047/1284/4790 1185/1390/4791 343/1410/4792
f 1182/1384/4793 1202/1421/4794 1205/1424/4795 708/1382/4796
f 1193/1411/4797 708/1382/4798 1205/1424/4799
f 1194/1413/4800 1180/1380/4801 1196/1415/4802 1199/1418/4803
f 1208/1427/4804 344/1386/4805 1195/1414/4806 1197/1416/4807
f 1195/1414/4808 1183/1388/4809 1218/1438/4810 1197/1416/4811
f 767/967/4812 1201/1420/4813 1202/1421/4814
f 978/1215/4815 1209/1428/4816 1210/1429/4817
f 1216/1436/4818 1196/1415/4819 1208/1427/4820 977/1214/4821
f 980/1217/4822 1212/1432/4823 1204/1423/4824 818/1055/4825
f 767/967/4826 1202/1421/4827 1200/1419/4828 766/966/4829
f 1205/1424/4830 827/1064/4831 768/968/4832 1203/1422/4833
f 1202/1421/4834 1201/1420/4835 827/1064/4836 1205/1424/4837
f 1206/1425/4838 828/1065/4839 818/1055/4840 1204/1423/4841
f 1203/1422/4842 768/968/4843 828/1065/4844 1206/1425/4845
f 1199/1418/4846 765/965/4847 766/966/4848 1200/1419/4849
f 977/1214/4850 1208/1427/4851 1197/1416/4852 1217/1437/4853
f 1038/1275/4854 1213/1433/4855 1212/1431/4856 980/1399/4857
f 976/1213/4858 1207/1426/4859 978/1215/4860 1210/1429/4861
f 1211/1430/4862 979/1216/4863 1214/1434/4864 1047/1284/4865
f 1047/1284/4866 1214/1434/4867 1209/1428/4868 978/1215/4869
f 1213/1433/4870 1038/1275/4871 1215/1435/4872 1048/1285/4873
f 1048/1285/4874 1215/1435/4875 979/1216/4876 1211/1430/4877
f 1207/1426/4878 976/1213/4879 1198/1417/4880 1218/1438/4881
f 765/965/4882 1199/1418/4883 1196/1415/4884 1216/1436/4885
f 1217/1437/4886 1197/1416/4887 1218/1438/4888 1198/1417/4889
f 1236/1458/4890 1222/1442/4891 1235/1456/4892 1266/1497/4893
f 1237/1460/4894 1241/1465/4895 1236/1458/4896 1266/1497/4897
f 1273/1488/4898 1220/1440/4899 1237/1457/4900 1266/1483/4901
f 1235/1455/4902 1219/1439/4903 1273/1488/4904 1266/1483/4905
f 1238/1461/4906 1241/1465/4907 1237/1460/4908 1267/1498/4909
f 1239/1462/4910 1223/1443/4911 1238/1461/4912 1267/1498/4913
f 1274/1489/4914 1221/1441/4915 1239/1459/4916 1267/1484/4917
f 1237/1457/4918 1220/1440/4919 1274/1489/4920 1267/1484/4921
f 1241/1465/4922 1224/1444/4923 1240/1463/4924 1236/1458/4925
f 1236/1458/4926 1240/1463/4927 1222/1442/4928
f 1243/1469/4929 1225/1445/4930 1242/1468/4931 1268/1499/4932
f 1243/1469/4933 1268/1499/4934 1226/1446/4935
f 1238/1461/4936 1223/1443/4937 1226/1446/4938 1268/1499/4939
f 1242/1468/4940 1241/1465/4941 1238/1461/4942 1268/1499/4943
f 1245/1470/4944 1228/1448/4945 1244/1464/4946 1269/1500/4947
f 1246/1471/4948 1252/1477/4949 1245/1470/4950 1269/1500/4951
f 1247/1472/4952 1230/1450/4953 1246/1471/4954 1269/1500/4955
f 1244/1464/4956 1227/1447/4957 1247/1472/4958 1269/1500/4959
f 1249/1467/4960 1229/1449/4961 1248/1466/4962 1270/1501/4963
f 1250/1475/4964 1231/1451/4965 1249/1467/4966 1270/1501/4967
f 1245/1470/4968 1252/1477/4969 1250/1475/4970 1270/1501/4971
f 1248/1466/4972 1228/1448/4973 1245/1470/4974 1270/1501/4975
f 1271/1502/4976 1251/1476/4977 1233/1453/4978 1282/1812/4979
f 1252/1477/4980 1232/1452/4981 1251/1476/4982 1271/1502/4983
f 1253/1478/4984 1231/1451/4985 1250/1475/4986 1272/1503/4987
f 1253/1478/4988 1272/1503/4989 1254/1479/4990
f 1271/1502/4991 1282/1812/4992 1254/1479/4993 1272/1503/4994
f 1250/1475/4995 1252/1477/4996 1271/1502/4997 1272/1503/4998
f 1256/1474/4999 1273/1488/5000 1219/1439/5001
f 1221/1441/5002 1274/1489/5003 1255/1473/5004
f 1244/1464/5005 1228/1448/5006 1220/1440/5007 1273/1488/5008
f 1256/1474/5009 1227/1447/5010 1244/1464/5011 1273/1488/5012
f 1248/1466/5013 1229/1449/5014 1255/1473/5015 1274/1489/5016
f 1220/1440/5017 1228/1448/5018 1248/1466/5019 1274/1489/5020
f 1257/1481/5021 1223/1443/5022 1239/1462/5023 1275/1561/5024
f 1239/1459/5025 1221/1441/5026 1255/1473/5027 1275/1490/5028
f 1226/1446/5029 1223/1443/5030 1257/1481/5031 1258/1482/5032
f 1249/1467/5033 1231/1451/5034 1257/1481/5035 1275/1561/5036
f 1255/1473/5037 1229/1449/5038 1249/1467/5039 1275/1490/5040
f 1257/1481/5041 1231/1451/5042 1253/1478/5043 1258/1482/5044
f 1260/1486/5045 1224/1444/5046 1259/1485/5047 1276/1735/5048
f 1259/1485/5049 1280/1779/5050 1261/1487/5051 1276/1735/5052
f 1251/1476/5053 1232/1452/5054 1260/1486/5055 1276/1735/5056
f 1261/1487/5057 1233/1453/5058 1251/1476/5059 1276/1735/5060
f 1262/1491/5061 1222/1442/5062 1240/1463/5063 1278/1777/5064
f 1262/1491/5065 1278/1777/5066 1234/1454/5067
f 1240/1463/5068 1224/1444/5069 1260/1486/5070 1278/1777/5071
f 1256/1474/5072 1219/1439/5073 1235/1455/5074 1277/1492/5075
f 1256/1474/5076 1277/1492/5077 1263/1480/5078
f 1262/1491/5079 1234/1454/5080 1263/1493/5081 1277/1737/5082
f 1235/1456/5083 1222/1442/5084 1262/1491/5085 1277/1737/5086
f 1234/1454/5087 1278/1777/5088 1264/1495/5089
f 1265/1496/5090 1230/1450/5091 1264/1495/5092 1278/1777/5093
f 1260/1486/5094 1232/1452/5095 1265/1496/5096 1278/1777/5097
f 1263/1480/5098 1279/1494/5099 1256/1474/5100
f 1247/1472/5101 1227/1447/5102 1256/1474/5103 1279/1494/5104
f 1264/1495/5105 1230/1450/5106 1247/1472/5107 1279/1778/5108
f 1263/1493/5109 1234/1454/5110 1264/1495/5111 1279/1778/5112
f 1259/1485/5113 1241/1465/5114 1242/1468/5115 1225/1445/5116 1280/1779/5117
f 1241/1465/5118 1259/1485/5119 1224/1444/5120
f 1265/1496/5121 1246/1471/5122 1230/1450/5123
f 1265/1496/5124 1232/1452/5125 1252/1477/5126 1246/1471/5127
f 1225/1445/5128 1593/1875/5129 1280/1779/5130
f 1233/1453/5131 1286/1822/5132 1282/1812/5133
f 1283/1814/5134 1593/1875/5135 1225/1445/5136 1243/1469/5137
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# 1482 polygons - 212 triangles
================================================
FILE: load/shapes/nascar.obj
================================================
####
#
# OBJ File Generated by Meshlab
#
####
# Object car_0016.obj
#
# Vertices: 3750
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#
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f 1061//1061 1043//1043 1065//1065
f 1065//1065 1062//1062 1066//1066
f 1061//1061 1065//1065 1064//1064
f 1046//1046 1045//1045 1067//1067
f 1044//1044 1046//1046 1067//1067
f 1067//1067 1045//1045 1068//1068
f 1044//1044 1067//1067 1062//1062
f 1062//1062 1068//1068 1069//1069
f 1062//1062 1067//1067 1068//1068
f 1062//1062 1069//1069 1066//1066
f 1069//1069 1068//1068 1070//1070
f 1013//1013 1072//1072 1071//1071
f 1013//1013 1016//1016 1072//1072
f 1013//1013 1071//1071 1015//1015
f 1014//1014 1073//1073 1016//1016
f 1014//1014 1018//1018 1073//1073
f 1072//1072 1075//1075 1074//1074
f 1016//1016 1075//1075 1072//1072
f 1016//1016 1073//1073 1075//1075
f 1071//1071 1074//1074 1076//1076
f 1072//1072 1074//1074 1071//1071
f 1071//1071 1076//1076 1015//1015
f 1018//1018 1024//1024 1077//1077
f 1018//1018 1077//1077 1073//1073
f 1075//1075 1079//1079 1078//1078
f 1075//1075 1080//1080 1079//1079
f 1073//1073 1080//1080 1075//1075
f 1073//1073 1077//1077 1080//1080
f 1076//1076 1082//1082 1081//1081
f 1076//1076 1074//1074 1082//1082
f 1076//1076 1083//1083 1015//1015
f 1076//1076 1081//1081 1083//1083
f 1075//1075 1078//1078 1084//1084
f 1074//1074 1075//1075 1084//1084
f 1082//1082 1084//1084 1085//1085
f 1084//1084 1078//1078 1085//1085
f 1074//1074 1084//1084 1082//1082
f 1085//1085 1087//1087 1086//1086
f 1085//1085 1088//1088 1087//1087
f 1078//1078 1088//1088 1085//1085
f 1078//1078 1079//1079 1088//1088
f 1015//1015 1083//1083 179//179
f 1081//1081 1082//1082 1089//1089
f 1081//1081 1089//1089 1083//1083
f 1089//1089 1091//1091 1090//1090
f 1089//1089 1082//1082 1091//1091
f 1089//1089 1090//1090 1083//1083
f 1085//1085 1092//1092 1082//1082
f 1085//1085 1086//1086 1092//1092
f 1091//1091 1082//1082 1093//1093
f 1082//1082 1092//1092 1093//1093
f 1083//1083 1090//1090 1094//1094
f 1086//1086 1095//1095 1092//1092
f 1086//1086 1087//1087 1095//1095
f 1093//1093 1097//1097 1096//1096
f 1093//1093 1092//1092 1097//1097
f 1095//1095 1097//1097 1092//1092
f 1095//1095 1098//1098 1097//1097
f 1099//1099 1091//1091 1100//1100
f 1090//1090 1091//1091 1099//1099
f 1090//1090 1099//1099 1094//1094
f 1099//1099 1102//1102 1101//1101
f 1099//1099 1100//1100 1102//1102
f 1094//1094 1099//1099 1101//1101
f 1091//1091 1096//1096 1100//1100
f 1093//1093 1096//1096 1091//1091
f 1100//1100 1103//1103 1102//1102
f 1100//1100 1096//1096 1103//1103
f 1096//1096 1104//1104 1103//1103
f 1096//1096 1105//1105 1104//1104
f 1096//1096 1097//1097 1105//1105
f 1106//1106 1108//1108 1107//1107
f 1106//1106 1109//1109 1108//1108
f 1106//1106 1054//1054 1109//1109
f 1106//1106 1110//1110 1054//1054
f 1106//1106 1111//1111 1110//1110
f 1106//1106 1107//1107 1111//1111
f 1080//1080 1112//1112 1079//1079
f 1077//1077 1112//1112 1080//1080
f 1077//1077 1107//1107 1112//1112
f 1077//1077 1111//1111 1107//1107
f 1107//1107 1113//1113 1112//1112
f 1107//1107 1108//1108 1113//1113
f 1024//1024 1111//1111 1077//1077
f 1024//1024 1110//1110 1111//1111
f 1024//1024 1114//1114 1110//1110
f 1024//1024 1025//1025 1114//1114
f 1110//1110 1055//1055 1054//1054
f 1110//1110 1114//1114 1055//1055
f 1109//1109 1050//1050 1108//1108
f 1109//1109 1052//1052 1050//1050
f 1109//1109 1054//1054 1052//1052
f 1050//1050 1115//1115 1108//1108
f 1050//1050 1051//1051 1115//1115
f 1108//1108 1116//1116 1113//1113
f 1116//1116 1118//1118 1117//1117
f 1108//1108 1118//1118 1116//1116
f 1108//1108 1115//1115 1118//1118
f 1088//1088 1119//1119 1087//1087
f 1079//1079 1119//1119 1088//1088
f 1079//1079 1112//1112 1119//1119
f 1112//1112 1120//1120 1119//1119
f 1112//1112 1113//1113 1120//1120
f 1120//1120 1116//1116 1121//1121
f 1113//1113 1116//1116 1120//1120
f 1116//1116 1117//1117 1121//1121
f 1025//1025 1122//1122 1114//1114
f 1025//1025 1026//1026 1122//1122
f 1114//1114 1057//1057 1055//1055
f 1114//1114 1122//1122 1057//1057
f 1026//1026 1123//1123 1122//1122
f 1026//1026 1028//1028 1123//1123
f 1122//1122 1059//1059 1057//1057
f 1122//1122 1123//1123 1059//1059
f 1063//1063 1064//1064 1115//1115
f 1051//1051 1063//1063 1115//1115
f 1118//1118 1124//1124 1117//1117
f 1115//1115 1124//1124 1118//1118
f 1115//1115 1064//1064 1124//1124
f 1064//1064 1125//1125 1124//1124
f 1065//1065 1066//1066 1126//1126
f 1064//1064 1065//1065 1126//1126
f 1125//1125 1126//1126 1127//1127
f 1126//1126 1066//1066 1127//1127
f 1064//1064 1126//1126 1125//1125
f 1117//1117 1124//1124 1128//1128
f 1128//1128 1124//1124 1129//1129
f 1125//1125 1129//1129 1124//1124
f 1066//1066 1130//1130 1127//1127
f 1066//1066 1069//1069 1130//1130
f 1069//1069 1070//1070 1131//1131
f 1131//1131 1132//1132 1130//1130
f 1069//1069 1131//1131 1130//1130
f 1125//1125 1133//1133 1129//1129
f 1125//1125 1127//1127 1133//1133
f 1129//1129 1133//1133 1134//1134
f 1133//1133 1127//1127 1134//1134
f 1127//1127 1135//1135 1134//1134
f 1127//1127 1130//1130 1135//1135
f 1095//1095 1136//1136 1098//1098
f 1087//1087 1136//1136 1095//1095
f 1087//1087 1119//1119 1136//1136
f 1119//1119 1137//1137 1136//1136
f 1119//1119 1120//1120 1137//1137
f 1136//1136 1138//1138 1098//1098
f 1136//1136 1137//1137 1138//1138
f 1098//1098 1138//1138 1097//1097
f 1120//1120 1121//1121 1137//1137
f 1137//1137 1140//1140 1139//1139
f 1137//1137 1121//1121 1140//1140
f 1117//1117 1141//1141 1140//1140
f 1121//1121 1117//1117 1140//1140
f 1105//1105 1142//1142 1104//1104
f 1105//1105 1143//1143 1142//1142
f 1097//1097 1143//1143 1105//1105
f 1097//1097 1138//1138 1143//1143
f 1143//1143 1144//1144 1142//1142
f 1138//1138 1144//1144 1143//1143
f 1138//1138 1137//1137 1144//1144
f 1144//1144 1145//1145 1142//1142
f 1137//1137 1139//1139 1144//1144
f 1144//1144 1146//1146 1145//1145
f 1144//1144 1147//1147 1146//1146
f 1139//1139 1147//1147 1144//1144
f 1139//1139 1140//1140 1147//1147
f 1117//1117 1128//1128 1141//1141
f 1128//1128 1148//1148 1141//1141
f 1129//1129 1148//1148 1128//1128
f 1129//1129 1134//1134 1148//1148
f 1141//1141 1150//1150 1149//1149
f 1148//1148 1150//1150 1141//1141
f 1148//1148 1151//1151 1150//1150
f 1148//1148 1152//1152 1151//1151
f 1148//1148 1134//1134 1152//1152
f 1134//1134 1135//1135 1152//1152
f 1147//1147 1153//1153 1146//1146
f 1141//1141 1153//1153 1147//1147
f 1141//1141 1149//1149 1153//1153
f 1140//1140 1141//1141 1147//1147
f 177//177 181//181 316//316
f 183//183 317//317 1154//1154
f 317//317 219//219 1154//1154
f 183//183 1155//1155 1027//1027
f 183//183 1154//1154 1155//1155
f 1154//1154 1058//1058 1155//1155
f 1154//1154 219//219 1058//1058
f 1027//1027 1123//1123 1028//1028
f 1027//1027 1155//1155 1123//1123
f 1155//1155 1059//1059 1123//1123
f 1155//1155 1058//1058 1059//1059
f 179//179 1083//1083 181//181
f 1094//1094 1101//1101 1156//1156
f 1102//1102 1158//1158 1157//1157
f 1101//1101 1102//1102 1157//1157
f 1101//1101 1157//1157 1156//1156
f 1094//1094 1156//1156 181//181
f 1157//1157 1160//1160 1159//1159
f 1157//1157 1158//1158 1160//1160
f 1156//1156 1159//1159 181//181
f 1156//1156 1157//1157 1159//1159
f 1102//1102 1161//1161 1158//1158
f 1102//1102 1103//1103 1161//1161
f 1103//1103 1104//1104 1161//1161
f 1161//1161 1104//1104 1162//1162
f 1158//1158 1163//1163 1160//1160
f 1161//1161 1163//1163 1158//1158
f 1161//1161 1162//1162 1163//1163
f 1083//1083 1094//1094 181//181
f 1159//1159 1165//1165 1164//1164
f 1159//1159 1160//1160 1165//1165
f 181//181 1159//1159 1164//1164
f 1163//1163 1162//1162 1166//1166
f 1160//1160 1167//1167 1165//1165
f 1160//1160 1163//1163 1167//1167
f 1163//1163 1166//1166 1167//1167
f 181//181 1168//1168 316//316
f 316//316 1169//1169 315//315
f 1169//1169 1171//1171 1170//1170
f 1169//1169 1172//1172 1171//1171
f 316//316 1172//1172 1169//1169
f 1169//1169 1174//1174 1173//1173
f 1169//1169 1170//1170 1174//1174
f 315//315 1169//1169 1173//1173
f 1170//1170 1176//1176 1175//1175
f 1170//1170 1171//1171 1176//1176
f 1175//1175 1176//1176 1177//1177
f 1170//1170 1178//1178 1174//1174
f 1170//1170 1175//1175 1178//1178
f 1175//1175 1179//1179 1178//1178
f 1172//1172 1180//1180 1171//1171
f 1172//1172 1181//1181 1180//1180
f 316//316 1168//1168 1172//1172
f 1171//1171 1182//1182 1176//1176
f 1171//1171 1180//1180 1182//1182
f 1180//1180 1183//1183 1182//1182
f 1180//1180 1181//1181 1183//1183
f 1176//1176 1184//1184 1177//1177
f 1176//1176 1185//1185 1184//1184
f 1176//1176 1186//1186 1185//1185
f 1176//1176 1182//1182 1186//1186
f 1183//1183 1188//1188 1187//1187
f 1182//1182 1187//1187 1186//1186
f 1182//1182 1183//1183 1187//1187
f 181//181 1164//1164 1168//1168
f 1172//1172 1189//1189 1181//1181
f 1172//1172 1190//1190 1189//1189
f 1172//1172 1191//1191 1190//1190
f 1168//1168 1191//1191 1172//1172
f 1181//1181 1192//1192 1183//1183
f 1181//1181 1189//1189 1192//1192
f 1183//1183 1193//1193 1188//1188
f 1183//1183 1192//1192 1193//1193
f 1189//1189 1194//1194 1192//1192
f 1189//1189 1190//1190 1194//1194
f 1192//1192 1195//1195 1193//1193
f 1192//1192 1194//1194 1195//1195
f 1191//1191 1196//1196 1190//1190
f 1191//1191 1197//1197 1196//1196
f 1190//1190 1198//1198 1194//1194
f 1190//1190 1196//1196 1198//1198
f 1196//1196 1199//1199 1198//1198
f 1196//1196 1197//1197 1199//1199
f 1194//1194 1200//1200 1195//1195
f 1194//1194 1201//1201 1200//1200
f 1194//1194 1202//1202 1201//1201
f 1194//1194 1198//1198 1202//1202
f 1199//1199 1204//1204 1203//1203
f 1199//1199 1205//1205 1204//1204
f 1198//1198 1203//1203 1202//1202
f 1198//1198 1199//1199 1203//1203
f 1130//1130 1132//1132 1206//1206
f 1130//1130 1206//1206 1135//1135
f 1104//1104 1142//1142 1208//1208
f 1104//1104 1208//1208 1162//1162
f 1145//1145 1146//1146 1209//1209
f 1208//1208 1210//1210 1162//1162
f 1208//1208 1142//1142 1210//1210
f 1162//1162 1210//1210 1166//1166
f 1210//1210 1145//1145 1211//1211
f 1142//1142 1145//1145 1210//1210
f 1145//1145 1209//1209 1211//1211
f 1211//1211 1209//1209 1212//1212
f 1152//1152 1213//1213 1151//1151
f 1152//1152 1135//1135 1213//1213
f 1135//1135 1206//1206 1214//1214
f 1213//1213 1216//1216 1215//1215
f 1214//1214 1206//1206 1216//1216
f 1135//1135 1216//1216 1213//1213
f 1135//1135 1214//1214 1216//1216
f 1146//1146 1217//1217 1209//1209
f 1146//1146 1153//1153 1217//1217
f 1153//1153 1149//1149 1217//1217
f 1217//1217 1218//1218 1209//1209
f 1149//1149 1219//1219 1217//1217
f 1151//1151 1213//1213 1219//1219
f 1150//1150 1219//1219 1149//1149
f 1150//1150 1151//1151 1219//1219
f 1217//1217 1220//1220 1218//1218
f 1217//1217 1219//1219 1220//1220
f 1213//1213 1220//1220 1219//1219
f 1213//1213 1215//1215 1220//1220
f 1215//1215 1222//1222 1221//1221
f 1215//1215 1216//1216 1222//1222
f 1209//1209 1218//1218 1223//1223
f 1209//1209 1224//1224 1212//1212
f 1209//1209 1223//1223 1224//1224
f 1218//1218 1220//1220 1225//1225
f 1215//1215 1225//1225 1220//1220
f 1215//1215 1221//1221 1225//1225
f 1225//1225 1227//1227 1226//1226
f 1225//1225 1221//1221 1227//1227
f 1222//1222 1227//1227 1221//1221
f 1222//1222 1228//1228 1227//1227
f 1225//1225 1226//1226 1229//1229
f 1218//1218 1229//1229 1223//1223
f 1218//1218 1225//1225 1229//1229
f 1175//1175 1231//1231 1230//1230
f 1175//1175 1177//1177 1231//1231
f 1175//1175 1230//1230 1179//1179
f 1230//1230 1233//1233 1232//1232
f 1230//1230 1231//1231 1233//1233
f 1232//1232 1233//1233 1234//1234
f 1230//1230 1235//1235 1179//1179
f 1230//1230 1232//1232 1235//1235
f 1232//1232 1236//1236 1235//1235
f 1232//1232 1234//1234 1236//1236
f 1177//1177 1184//1184 1231//1231
f 1231//1231 1237//1237 1233//1233
f 1231//1231 1184//1184 1237//1237
f 1233//1233 1238//1238 1234//1234
f 1233//1233 1237//1237 1238//1238
f 1236//1236 1240//1240 1239//1239
f 1236//1236 1238//1238 1240//1240
f 1234//1234 1238//1238 1236//1236
f 1239//1239 1242//1242 1241//1241
f 1239//1239 1240//1240 1242//1242
f 1241//1241 1242//1242 1243//1243
f 1188//1188 1193//1193 1244//1244
f 1193//1193 1200//1200 1244//1244
f 1193//1193 1195//1195 1200//1200
f 1205//1205 1245//1245 1204//1204
f 1212//1212 1224//1224 1247//1247
f 1247//1247 1224//1224 1248//1248
f 1223//1223 1249//1249 1224//1224
f 1223//1223 1229//1229 1249//1249
f 1226//1226 1249//1249 1229//1229
f 1224//1224 1249//1249 1250//1250
f 1250//1250 1249//1249 1251//1251
f 1252//1252 1254//1254 1253//1253
f 1164//1164 1254//1254 1252//1252
f 1164//1164 1165//1165 1254//1254
f 1164//1164 1252//1252 1168//1168
f 1165//1165 1253//1253 1254//1254
f 1165//1165 1167//1167 1253//1253
f 1252//1252 1253//1253 1191//1191
f 1168//1168 1252//1252 1191//1191
f 1197//1197 1255//1255 1199//1199
f 1197//1197 1191//1191 1255//1255
f 1253//1253 1255//1255 1191//1191
f 1199//1199 1256//1256 1205//1205
f 1199//1199 1255//1255 1256//1256
f 1253//1253 1256//1256 1255//1255
f 1167//1167 1211//1211 1253//1253
f 1167//1167 1210//1210 1211//1211
f 1166//1166 1210//1210 1167//1167
f 1205//1205 1256//1256 1245//1245
f 1253//1253 1245//1245 1256//1256
f 1253//1253 1211//1211 1245//1245
f 1204//1204 1245//1245 1246//1246
f 1246//1246 1245//1245 1257//1257
f 1211//1211 1257//1257 1245//1245
f 1211//1211 1212//1212 1257//1257
f 1212//1212 1258//1258 1257//1257
f 1212//1212 1247//1247 1258//1258
f 1247//1247 1259//1259 1258//1258
f 1247//1247 1248//1248 1259//1259
f 1257//1257 1261//1261 1260//1260
f 1257//1257 1258//1258 1261//1261
f 1246//1246 1257//1257 1260//1260
f 1258//1258 1262//1262 1261//1261
f 1258//1258 1259//1259 1262//1262
f 1250//1250 1251//1251 1248//1248
f 1224//1224 1250//1250 1248//1248
f 1263//1263 1262//1262 1264//1264
f 1259//1259 1264//1264 1262//1262
f 1259//1259 1248//1248 1264//1264
f 1248//1248 1265//1265 1264//1264
f 1248//1248 1251//1251 1265//1265
f 553//553 315//315 554//554
f 554//554 315//315 1266//1266
f 1173//1173 1174//1174 1267//1267
f 1174//1174 1178//1178 1267//1267
f 1268//1268 1270//1270 1269//1269
f 1268//1268 1271//1271 1270//1270
f 1268//1268 1272//1272 1271//1271
f 1268//1268 1273//1273 1272//1272
f 1268//1268 1269//1269 1273//1273
f 1274//1274 1186//1186 1275//1275
f 1274//1274 1185//1185 1186//1186
f 1274//1274 1276//1276 1185//1185
f 1274//1274 1271//1271 1276//1276
f 1274//1274 1270//1270 1271//1271
f 1274//1274 1275//1275 1270//1270
f 1185//1185 1277//1277 1184//1184
f 1185//1185 1276//1276 1277//1277
f 1275//1275 1187//1187 1278//1278
f 1275//1275 1186//1186 1187//1187
f 1275//1275 1278//1278 1270//1270
f 1271//1271 1272//1272 1276//1276
f 1173//1173 1267//1267 1279//1279
f 315//315 1173//1173 1279//1279
f 1267//1267 1178//1178 1280//1280
f 1281//1281 1269//1269 1282//1282
f 1281//1281 1273//1273 1269//1269
f 1281//1281 1283//1283 1273//1273
f 1281//1281 1284//1284 1283//1283
f 1281//1281 1282//1282 1284//1284
f 1283//1283 1272//1272 1273//1273
f 1283//1283 1285//1285 1272//1272
f 1283//1283 1286//1286 1285//1285
f 1283//1283 1284//1284 1286//1286
f 1284//1284 1287//1287 1286//1286
f 1269//1269 1270//1270 1288//1288
f 1269//1269 1288//1288 1289//1289
f 1278//1278 1291//1291 1290//1290
f 1278//1278 1187//1187 1291//1291
f 1278//1278 1290//1290 1270//1270
f 1187//1187 1244//1244 1291//1291
f 1187//1187 1188//1188 1244//1244
f 1290//1290 1201//1201 1292//1292
f 1290//1290 1291//1291 1201//1201
f 1290//1290 1292//1292 1270//1270
f 1291//1291 1200//1200 1201//1201
f 1291//1291 1244//1244 1200//1200
f 1288//1288 1270//1270 1293//1293
f 1288//1288 1296//1296 1294//1294
f 1288//1288 1293//1293 1296//1296
f 1288//1288 1295//1295 1289//1289
f 1288//1288 1294//1294 1295//1295
f 1292//1292 1202//1202 1297//1297
f 1292//1292 1201//1201 1202//1202
f 1292//1292 1293//1293 1270//1270
f 1292//1292 1297//1297 1293//1293
f 1297//1297 1203//1203 1298//1298
f 1297//1297 1202//1202 1203//1203
f 1297//1297 1296//1296 1293//1293
f 1297//1297 1298//1298 1296//1296
f 1282//1282 1289//1289 1299//1299
f 1282//1282 1269//1269 1289//1289
f 1282//1282 1299//1299 1284//1284
f 1299//1299 1300//1300 1284//1284
f 1284//1284 1301//1301 1287//1287
f 1284//1284 1300//1300 1301//1301
f 1302//1302 1295//1295 1303//1303
f 1299//1299 1295//1295 1302//1302
f 1299//1299 1289//1289 1295//1295
f 1299//1299 1302//1302 1300//1300
f 1302//1302 1305//1305 1304//1304
f 1302//1302 1303//1303 1305//1305
f 1300//1300 1302//1302 1304//1304
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f 1985//1985 1990//1990 1989//1989
f 1985//1985 1987//1987 1990//1990
f 1985//1985 1991//1991 1984//1984
f 1985//1985 1988//1988 1991//1991
f 1989//1989 1990//1990 1992//1992
f 1989//1989 1994//1994 1993//1993
f 1989//1989 1992//1992 1994//1994
f 1993//1993 1994//1994 1996//1996
f 1989//1989 1997//1997 1988//1988
f 1989//1989 1993//1993 1997//1997
f 1988//1988 1998//1998 1991//1991
f 1988//1988 1997//1997 1998//1998
f 1993//1993 1999//1999 1997//1997
f 1993//1993 1995//1995 1999//1999
f 1997//1997 2000//2000 1998//1998
f 1997//1997 1999//1999 2000//2000
f 2001//2001 1996//1996 2002//2002
f 2001//2001 1993//1993 1996//1996
f 2001//2001 2003//2003 1993//1993
f 2001//2001 2004//2004 2003//2003
f 2001//2001 2002//2002 2004//2004
f 1993//1993 2005//2005 1995//1995
f 1993//1993 2003//2003 2005//2005
f 2003//2003 2004//2004 2006//2006
f 1995//1995 2007//2007 1999//1999
f 1995//1995 2005//2005 2007//2007
f 2000//2000 2007//2007 2008//2008
f 1999//1999 2007//2007 2000//2000
f 2007//2007 2010//2010 2009//2009
f 2005//2005 2010//2010 2007//2007
f 1946//1946 1979//1979 2011//2011
f 1949//1949 1946//1946 2011//2011
f 1949//1949 2012//2012 1956//1956
f 1949//1949 2011//2011 2012//2012
f 1956//1956 2012//2012 1955//1955
f 2012//2012 2014//2014 2013//2013
f 1955//1955 2012//2012 2013//2013
f 1983//1983 2015//2015 1979//1979
f 1983//1983 2016//2016 2015//2015
f 1983//1983 1984//1984 2016//2016
f 1984//1984 1991//1991 2017//2017
f 2016//2016 2018//2018 2015//2015
f 2018//2018 2016//2016 2019//2019
f 1984//1984 2017//2017 2016//2016
f 2016//2016 2017//2017 2020//2020
f 1955//1955 2013//2013 1960//1960
f 1960//1960 2013//2013 1957//1957
f 2013//2013 2021//2021 1957//1957
f 2013//2013 2022//2022 2021//2021
f 2013//2013 2014//2014 2022//2022
f 1957//1957 2023//2023 1959//1959
f 2021//2021 2025//2025 2024//2024
f 1957//1957 2024//2024 2023//2023
f 1957//1957 2021//2021 2024//2024
f 2023//2023 2026//2026 1959//1959
f 2023//2023 2024//2024 2026//2026
f 2019//2019 2014//2014 2018//2018
f 2019//2019 2022//2022 2014//2014
f 2021//2021 2027//2027 2025//2025
f 2022//2022 2027//2027 2021//2021
f 2022//2022 2019//2019 2027//2027
f 2016//2016 2028//2028 2019//2019
f 2016//2016 2020//2020 2028//2028
f 2019//2019 2029//2029 2027//2027
f 2019//2019 2028//2028 2029//2029
f 3731//3731 2026//2026 2024//2024
f 3730//3730 2026//2026 3731//3731
f 2030//2030 2026//2026 3730//3730
f 2025//2025 3731//3731 2024//2024
f 3730//3730 3732//3732 2031//2031
f 3731//3731 2025//2025 3733//3733
f 3733//3733 2025//2025 2027//2027
f 3732//3732 2029//2029 2031//2031
f 3733//3733 2029//2029 3732//3732
f 2027//2027 2029//2029 3733//3733
f 1991//1991 1998//1998 2032//2032
f 1991//1991 2033//2033 2017//2017
f 1991//1991 2032//2032 2033//2033
f 2000//2000 2034//2034 2032//2032
f 1998//1998 2000//2000 2032//2032
f 2017//2017 2033//2033 2035//2035
f 2035//2035 2037//2037 2036//2036
f 2017//2017 2036//2036 2020//2020
f 2017//2017 2035//2035 2036//2036
f 2020//2020 2036//2036 2028//2028
f 2036//2036 2039//2039 2028//2028
f 2036//2036 2038//2038 2039//2039
f 2036//2036 2037//2037 2038//2038
f 2029//2029 2040//2040 2031//2031
f 2029//2029 2028//2028 2040//2040
f 2573//2573 3734//3734 2041//2041
f 2041//2041 3734//3734 2042//2042
f 3734//3734 2040//2040 2042//2042
f 3735//3735 2040//2040 3734//3734
f 2031//2031 2040//2040 3735//3735
f 2028//2028 2043//2043 2040//2040
f 2028//2028 2039//2039 2043//2043
f 2040//2040 2044//2044 2042//2042
f 2040//2040 2043//2043 2044//2044
f 2039//2039 2045//2045 2043//2043
f 2039//2039 2046//2046 2045//2045
f 2039//2039 2038//2038 2046//2046
f 2043//2043 2047//2047 2044//2044
f 2045//2045 2048//2048 2043//2043
f 2045//2045 2049//2049 2048//2048
f 2043//2043 2048//2048 2047//2047
f 2003//2003 2051//2051 2050//2050
f 2003//2003 2006//2006 2051//2051
f 2003//2003 2050//2050 2005//2005
f 2051//2051 2052//2052 2050//2050
f 2051//2051 2053//2053 2052//2052
f 2051//2051 2006//2006 2053//2053
f 2050//2050 2052//2052 2054//2054
f 2010//2010 2055//2055 2009//2009
f 2005//2005 2055//2055 2010//2010
f 2005//2005 2050//2050 2055//2055
f 2050//2050 2056//2056 2055//2055
f 2050//2050 2054//2054 2056//2056
f 2055//2055 2057//2057 2009//2009
f 2052//2052 2058//2058 2054//2054
f 2058//2058 2060//2060 2059//2059
f 2058//2058 2061//2061 2060//2060
f 2058//2058 2059//2059 2054//2054
f 2058//2058 2052//2052 2061//2061
f 2054//2054 2062//2062 2056//2056
f 2055//2055 2063//2063 2057//2057
f 2056//2056 2063//2063 2055//2055
f 2056//2056 2062//2062 2063//2063
f 2054//2054 2059//2059 2062//2062
f 2059//2059 2065//2065 2064//2064
f 2059//2059 2060//2060 2065//2065
f 2059//2059 2066//2066 2062//2062
f 2059//2059 2064//2064 2066//2066
f 2066//2066 2068//2068 2067//2067
f 2064//2064 2068//2068 2066//2066
f 2064//2064 2065//2065 2068//2068
f 2069//2069 2072//2072 2071//2071
f 2069//2069 2073//2073 2072//2072
f 2069//2069 2070//2070 2073//2073
f 2063//2063 2075//2075 2074//2074
f 2063//2063 2062//2062 2075//2075
f 2074//2074 2076//2076 2063//2063
f 2074//2074 2077//2077 2076//2076
f 2074//2074 2078//2078 2077//2077
f 2074//2074 2075//2075 2078//2078
f 2077//2077 2078//2078 2079//2079
f 2079//2079 2080//2080 2077//2077
f 2079//2079 2081//2081 2080//2080
f 2079//2079 2078//2078 2081//2081
f 2049//2049 2045//2045 2082//2082
f 2048//2048 2083//2083 2047//2047
f 2049//2049 2084//2084 2048//2048
f 2049//2049 2085//2085 2084//2084
f 2049//2049 2086//2086 2085//2085
f 2049//2049 2082//2082 2086//2086
f 2048//2048 2084//2084 2083//2083
f 2087//2087 2086//2086 2088//2088
f 2087//2087 2085//2085 2086//2086
f 2087//2087 2089//2089 2085//2085
f 2087//2087 2088//2088 2090//2090
f 2080//2080 2087//2087 2090//2090
f 2080//2080 2081//2081 2087//2087
f 2087//2087 2081//2081 2089//2089
f 2085//2085 2091//2091 2084//2084
f 2085//2085 2089//2089 2091//2091
f 2084//2084 2091//2091 2092//2092
f 2089//2089 2093//2093 2091//2091
f 2092//2092 2095//2095 2094//2094
f 2091//2091 2095//2095 2092//2092
f 2091//2091 2093//2093 2095//2095
f 2062//2062 2096//2096 2075//2075
f 2062//2062 2066//2066 2096//2096
f 2066//2066 2098//2098 2097//2097
f 2066//2066 2067//2067 2098//2098
f 2066//2066 2097//2097 2096//2096
f 2097//2097 2098//2098 2099//2099
f 2096//2096 2099//2099 2075//2075
f 2096//2096 2097//2097 2099//2099
f 2078//2078 2075//2075 2100//2100
f 2078//2078 2101//2101 2081//2081
f 2078//2078 2100//2100 2101//2101
f 2075//2075 2102//2102 2100//2100
f 2075//2075 2099//2099 2102//2102
f 2100//2100 2103//2103 2101//2101
f 2100//2100 2102//2102 2103//2103
f 2071//2071 2067//2067 2069//2069
f 2067//2067 2105//2105 2104//2104
f 2067//2067 2071//2071 2105//2105
f 2099//2099 2104//2104 2102//2102
f 2099//2099 2098//2098 2104//2104
f 2103//2103 2107//2107 2106//2106
f 2102//2102 2107//2107 2103//2103
f 2104//2104 2108//2108 2102//2102
f 2104//2104 2105//2105 2108//2108
f 2102//2102 2108//2108 2107//2107
f 2081//2081 2109//2109 2089//2089
f 2081//2081 2101//2101 2109//2109
f 2103//2103 2111//2111 2110//2110
f 2103//2103 2106//2106 2111//2111
f 2101//2101 2103//2103 2110//2110
f 2110//2110 2111//2111 2112//2112
f 2101//2101 2112//2112 2109//2109
f 2101//2101 2110//2110 2112//2112
f 2089//2089 2113//2113 2093//2093
f 2089//2089 2109//2109 2113//2113
f 2113//2113 2114//2114 2093//2093
f 2113//2113 2109//2109 2114//2114
f 2107//2107 2115//2115 2106//2106
f 2107//2107 2108//2108 2115//2115
f 2115//2115 2116//2116 2106//2106
f 2115//2115 2108//2108 2117//2117
f 2112//2112 2118//2118 2109//2109
f 2112//2112 2115//2115 2118//2118
f 2112//2112 2116//2116 2115//2115
f 2112//2112 2111//2111 2116//2116
f 2109//2109 2119//2119 2114//2114
f 2109//2109 2118//2118 2119//2119
f 2115//2115 2120//2120 2118//2118
f 2115//2115 2117//2117 2120//2120
f 2118//2118 2121//2121 2119//2119
f 2118//2118 2120//2120 2121//2121
f 1974//1974 2123//2123 2122//2122
f 1974//1974 2124//2124 2123//2123
f 1974//1974 2122//2122 1972//1972
f 1974//1974 1971//1971 2124//2124
f 1971//1971 1973//1973 2124//2124
f 2122//2122 2125//2125 1980//1980
f 2122//2122 2123//2123 2125//2125
f 1972//1972 1980//1980 1947//1947
f 1972//1972 2122//2122 1980//1980
f 2123//2123 2126//2126 2125//2125
f 1980//1980 1982//1982 1979//1979
f 1980//1980 2127//2127 1982//1982
f 1980//1980 2125//2125 2127//2127
f 2125//2125 2126//2126 2127//2127
f 2124//2124 2128//2128 2123//2123
f 2124//2124 2129//2129 2128//2128
f 1973//1973 2129//2129 2124//2124
f 2123//2123 2128//2128 2126//2126
f 2128//2128 2130//2130 2126//2126
f 2126//2126 2131//2131 2127//2127
f 2126//2126 2130//2130 2131//2131
f 2132//2132 2134//2134 2133//2133
f 2132//2132 1987//1987 2134//2134
f 2132//2132 1990//1990 1987//1987
f 2132//2132 1992//1992 1990//1990
f 2132//2132 2135//2135 1992//1992
f 2132//2132 2133//2133 2135//2135
f 2133//2133 2137//2137 2136//2136
f 2133//2133 2134//2134 2137//2137
f 2133//2133 2136//2136 2135//2135
f 2138//2138 2127//2127 2139//2139
f 2138//2138 1982//1982 2127//2127
f 2138//2138 1986//1986 1982//1982
f 2138//2138 1987//1987 1986//1986
f 2138//2138 2134//2134 1987//1987
f 2138//2138 2139//2139 2134//2134
f 2139//2139 2137//2137 2134//2134
f 2136//2136 2140//2140 2135//2135
f 2139//2139 2131//2131 2141//2141
f 2139//2139 2127//2127 2131//2131
f 2139//2139 2141//2141 2137//2137
f 1973//1973 1975//1975 2129//2129
f 2129//2129 2142//2142 2128//2128
f 2129//2129 2143//2143 2142//2142
f 1975//1975 2143//2143 2129//2129
f 1975//1975 1976//1976 2143//2143
f 2128//2128 2144//2144 2130//2130
f 2128//2128 2142//2142 2144//2144
f 2130//2130 2145//2145 2131//2131
f 2130//2130 2144//2144 2145//2145
f 1976//1976 2146//2146 2143//2143
f 2143//2143 2147//2147 2142//2142
f 2143//2143 2148//2148 2147//2147
f 2143//2143 2146//2146 2148//2148
f 2142//2142 2145//2145 2144//2144
f 2142//2142 2147//2147 2145//2145
f 2136//2136 2151//2151 2150//2150
f 2136//2136 2137//2137 2151//2151
f 2136//2136 2150//2150 2140//2140
f 2150//2150 2153//2153 2152//2152
f 2150//2150 2151//2151 2153//2153
f 2150//2150 2154//2154 2140//2140
f 2150//2150 2152//2152 2154//2154
f 2141//2141 2131//2131 2155//2155
f 2141//2141 2151//2151 2137//2137
f 2141//2141 2155//2155 2151//2151
f 2155//2155 2145//2145 2147//2147
f 2155//2155 2131//2131 2145//2145
f 2155//2155 2153//2153 2151//2151
f 2155//2155 2147//2147 2153//2153
f 2148//2148 2153//2153 2147//2147
f 2148//2148 2152//2152 2153//2153
f 2148//2148 2156//2156 2152//2152
f 2148//2148 2146//2146 2156//2156
f 2146//2146 2149//2149 2156//2156
f 2152//2152 2157//2157 2154//2154
f 2152//2152 2156//2156 2157//2157
f 2156//2156 2149//2149 2157//2157
f 2158//2158 2002//2002 2159//2159
f 2158//2158 2004//2004 2002//2002
f 2158//2158 2160//2160 2004//2004
f 2158//2158 2161//2161 2160//2160
f 2158//2158 2159//2159 2162//2162
f 2158//2158 2162//2162 2161//2161
f 2160//2160 2006//2006 2004//2004
f 2160//2160 2161//2161 2163//2163
f 2162//2162 2164//2164 2161//2161
f 2162//2162 2166//2166 2165//2165
f 2162//2162 2159//2159 2166//2166
f 2162//2162 2165//2165 2164//2164
f 2161//2161 2167//2167 2163//2163
f 2161//2161 2164//2164 2167//2167
f 2168//2168 2135//2135 2169//2169
f 2168//2168 1992//1992 2135//2135
f 2168//2168 1994//1994 1992//1992
f 2168//2168 1996//1996 1994//1994
f 2168//2168 2170//2170 1996//1996
f 2168//2168 2169//2169 2170//2170
f 1996//1996 2159//2159 2002//2002
f 1996//1996 2170//2170 2159//2159
f 2169//2169 2135//2135 2140//2140
f 2169//2169 2140//2140 2171//2171
f 2169//2169 2166//2166 2170//2170
f 2169//2169 2171//2171 2166//2166
f 2170//2170 2166//2166 2159//2159
f 2007//2007 2172//2172 2008//2008
f 2007//2007 2009//2009 2172//2172
f 2173//2173 2175//2175 2174//2174
f 2165//2165 2175//2175 2173//2173
f 2165//2165 2166//2166 2175//2175
f 2165//2165 2173//2173 2164//2164
f 2164//2164 2173//2173 2167//2167
f 2171//2171 2177//2177 2176//2176
f 2171//2171 2154//2154 2177//2177
f 2171//2171 2140//2140 2154//2154
f 2171//2171 2176//2176 2166//2166
f 2176//2176 2178//2178 2175//2175
f 2176//2176 2177//2177 2178//2178
f 2166//2166 2176//2176 2175//2175
f 2154//2154 2179//2179 2177//2177
f 2154//2154 2157//2157 2179//2179
f 2157//2157 2149//2149 2179//2179
f 2178//2178 2181//2181 2180//2180
f 2177//2177 2181//2181 2178//2178
f 2177//2177 2179//2179 2181//2181
f 2181//2181 2182//2182 2180//2180
f 2179//2179 2182//2182 2181//2181
f 2179//2179 2149//2149 2182//2182
f 2175//2175 2183//2183 2174//2174
f 2175//2175 2178//2178 2183//2183
f 2178//2178 2184//2184 2183//2183
f 2178//2178 2180//2180 2184//2184
f 2174//2174 2183//2183 2185//2185
f 2183//2183 2186//2186 2185//2185
f 2183//2183 2184//2184 2186//2186
f 2180//2180 2187//2187 2184//2184
f 2180//2180 2188//2188 2187//2187
f 2180//2180 2182//2182 2188//2188
f 2184//2184 2189//2189 2186//2186
f 2184//2184 2187//2187 2189//2189
f 2187//2187 2190//2190 2189//2189
f 2187//2187 2188//2188 2190//2190
f 1979//1979 2015//2015 2011//2011
f 2011//2011 2015//2015 2012//2012
f 2012//2012 2015//2015 2018//2018
f 2012//2012 2018//2018 2014//2014
f 2032//2032 2191//2191 2033//2033
f 2032//2032 2034//2034 2191//2191
f 2191//2191 2193//2193 2192//2192
f 2033//2033 2192//2192 2035//2035
f 2033//2033 2191//2191 2192//2192
f 2035//2035 2194//2194 2037//2037
f 2035//2035 2192//2192 2194//2194
f 2000//2000 2195//2195 2034//2034
f 2000//2000 2008//2008 2195//2195
f 2195//2195 2196//2196 2034//2034
f 2195//2195 2197//2197 2196//2196
f 2191//2191 2196//2196 2193//2193
f 2191//2191 2034//2034 2196//2196
f 2195//2195 2198//2198 2197//2197
f 2195//2195 2172//2172 2198//2198
f 2008//2008 2172//2172 2195//2195
f 2197//2197 2199//2199 2196//2196
f 2197//2197 2198//2198 2199//2199
f 2192//2192 2201//2201 2194//2194
f 2192//2192 2200//2200 2201//2201
f 2192//2192 2202//2202 2200//2200
f 2192//2192 2193//2193 2202//2202
f 2193//2193 2196//2196 2202//2202
f 2196//2196 2203//2203 2202//2202
f 2196//2196 2199//2199 2203//2203
f 2202//2202 2204//2204 2200//2200
f 2202//2202 2203//2203 2204//2204
f 2037//2037 2194//2194 2038//2038
f 2194//2194 2046//2046 2038//2038
f 2194//2194 2201//2201 2046//2046
f 2201//2201 2205//2205 2046//2046
f 2201//2201 2204//2204 2205//2205
f 2201//2201 2200//2200 2204//2204
f 2046//2046 2205//2205 2045//2045
f 1976//1976 2207//2207 2146//2146
f 2146//2146 2207//2207 2149//2149
f 2149//2149 2207//2207 2206//2206
f 2182//2182 2208//2208 2188//2188
f 2149//2149 2208//2208 2182//2182
f 2149//2149 2206//2206 2208//2208
f 2208//2208 2209//2209 2188//2188
f 2188//2188 2209//2209 2190//2190
f 2160//2160 2210//2210 2006//2006
f 2160//2160 2211//2211 2210//2210
f 2160//2160 2163//2163 2211//2211
f 2210//2210 2212//2212 2053//2053
f 2210//2210 2053//2053 2006//2006
f 2210//2210 2213//2213 2212//2212
f 2211//2211 2213//2213 2210//2210
f 2163//2163 2167//2167 2211//2211
f 2167//2167 2214//2214 2211//2211
f 2213//2213 2215//2215 2212//2212
f 2211//2211 2214//2214 2213//2213
f 2053//2053 2216//2216 2052//2052
f 2053//2053 2217//2217 2216//2216
f 2053//2053 2212//2212 2217//2217
f 2216//2216 2218//2218 2061//2061
f 2216//2216 2061//2061 2052//2052
f 2216//2216 2219//2219 2218//2218
f 2212//2212 2220//2220 2217//2217
f 2212//2212 2215//2215 2220//2220
f 2216//2216 2221//2221 2219//2219
f 2217//2217 2221//2221 2216//2216
f 2217//2217 2220//2220 2221//2221
f 2173//2173 2222//2222 2214//2214
f 2173//2173 2174//2174 2222//2222
f 2167//2167 2173//2173 2214//2214
f 2213//2213 2222//2222 2215//2215
f 2213//2213 2214//2214 2222//2222
f 2174//2174 2223//2223 2222//2222
f 2174//2174 2185//2185 2223//2223
f 2185//2185 2224//2224 2223//2223
f 2185//2185 2186//2186 2224//2224
f 2223//2223 2224//2224 2225//2225
f 2186//2186 2226//2226 2224//2224
f 2186//2186 2189//2189 2226//2226
f 2189//2189 2190//2190 2226//2226
f 2224//2224 2226//2226 2225//2225
f 2222//2222 2223//2223 2227//2227
f 2215//2215 2227//2227 2220//2220
f 2215//2215 2222//2222 2227//2227
f 2221//2221 2228//2228 2219//2219
f 2221//2221 2227//2227 2228//2228
f 2227//2227 2223//2223 2228//2228
f 2220//2220 2227//2227 2221//2221
f 2223//2223 2225//2225 2228//2228
f 2225//2225 2226//2226 2229//2229
f 2226//2226 2230//2230 2229//2229
f 2226//2226 2190//2190 2230//2230
f 2061//2061 2065//2065 2060//2060
f 2061//2061 2231//2231 2065//2065
f 2061//2061 2232//2232 2231//2231
f 2061//2061 2218//2218 2232//2232
f 2218//2218 2219//2219 2232//2232
f 2231//2231 2233//2233 2065//2065
f 2231//2231 2234//2234 2233//2233
f 2232//2232 2234//2234 2231//2231
f 2233//2233 2235//2235 2068//2068
f 2065//2065 2233//2233 2068//2068
f 2233//2233 2236//2236 2235//2235
f 2219//2219 2237//2237 2232//2232
f 2232//2232 2238//2238 2234//2234
f 2232//2232 2237//2237 2238//2238
f 2233//2233 2234//2234 2236//2236
f 2068//2068 2069//2069 2067//2067
f 2068//2068 2070//2070 2069//2069
f 2068//2068 2239//2239 2070//2070
f 2068//2068 2235//2235 2239//2239
f 2235//2235 2236//2236 2239//2239
f 2070//2070 2240//2240 2073//2073
f 2070//2070 2239//2239 2240//2240
f 2239//2239 2241//2241 2240//2240
f 2236//2236 2242//2242 2239//2239
f 2239//2239 2242//2242 2241//2241
f 2228//2228 2243//2243 2237//2237
f 2228//2228 2225//2225 2243//2243
f 2219//2219 2228//2228 2237//2237
f 2238//2238 2244//2244 2234//2234
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f 2982//2982 2988//2988 3116//3116
f 2982//2982 3121//3121 2981//2981
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f 2914//2914 2915//2915 2993//2993
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f 2950//2950 2992//2992 3125//3125
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f 3739//3739 3740//3740 2998//2998
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f 3113//3113 3004//3004 3001//3001
f 3113//3113 3126//3126 3004//3004
f 3008//3008 3137//3137 3002//3002
f 3008//3008 3120//3120 3137//3137
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f 3118//3118 3001//3001 3137//3137
f 3001//3001 3002//3002 3137//3137
f 3005//3005 3138//3138 3007//3007
f 3005//3005 3004//3004 3138//3138
f 3126//3126 3138//3138 3004//3004
f 3126//3126 3129//3129 3138//3138
f 3129//3129 3007//3007 3138//3138
f 3129//3129 3135//3135 3007//3007
f 3007//3007 3135//3135 3018//3018
f 3136//3136 3139//3139 3135//3135
f 3742//3742 3139//3139 3136//3136
f 3743//3743 3139//3139 3742//3742
f 3140//3140 3139//3139 3743//3743
f 3743//3743 3744//3744 3140//3140
f 3000//3000 3742//3742 2999//2999
f 2999//2999 3742//3742 3136//3136
f 3135//3135 3141//3141 3018//3018
f 3135//3135 3139//3139 3141//3141
f 3139//3139 3142//3142 3141//3141
f 3139//3139 3140//3140 3142//3142
f 3018//3018 3141//3141 3020//3020
f 3141//3141 3142//3142 3143//3143
f 3020//3020 3144//3144 3100//3100
f 3020//3020 3141//3141 3144//3144
f 3020//3020 3100//3100 3022//3022
f 3141//3141 3143//3143 3144//3144
f 2981//2981 3145//3145 3042//3042
f 2981//2981 3122//3122 3145//3145
f 3122//3122 3123//3123 3145//3145
f 3043//3043 3042//3042 3051//3051
f 3042//3042 3145//3145 3051//3051
f 3145//3145 3146//3146 3051//3051
f 3123//3123 2990//2990 3145//3145
f 3123//3123 3124//3124 2990//2990
f 3145//3145 3147//3147 3146//3146
f 3145//3145 2990//2990 3147//3147
f 2990//2990 3045//3045 3147//3147
f 3051//3051 3146//3146 3050//3050
f 3052//3052 3148//3148 3072//3072
f 3052//3052 3050//3050 3148//3148
f 3050//3050 3146//3146 3148//3148
f 3147//3147 3084//3084 3146//3146
f 3147//3147 3045//3045 3084//3084
f 3146//3146 3149//3149 3148//3148
f 3146//3146 3084//3084 3149//3149
f 3148//3148 3149//3149 3075//3075
f 3072//3072 3148//3148 3075//3075
f 3149//3149 3150//3150 3075//3075
f 3074//3074 3151//3151 3080//3080
f 3075//3075 3150//3150 3151//3151
f 3075//3075 3151//3151 3074//3074
f 3152//3152 3154//3154 3153//3153
f 3078//3078 3154//3154 3152//3152
f 3078//3078 3079//3079 3154//3154
f 3078//3078 3152//3152 3077//3077
f 3084//3084 3155//3155 3149//3149
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# 7500 faces, 0 coords texture
# End of File
================================================
FILE: load/shapes/potion.obj
================================================
# WaveFront *.obj file (generated by Autodesk ATF)
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vn 0.041052 0.080633 0.042580
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vn 0.040939 0.071060 0.057223
vn 0.039282 0.065680 0.064367
vn 0.036599 0.059942 0.071186
vn 0.032948 0.053878 0.077534
vn 0.028409 0.047521 0.083274
vn 0.023080 0.040906 0.088284
vn 0.017077 0.034068 0.092454
vn 0.010530 0.027044 0.095696
vn 0.003579 0.019873 0.097940
vn -0.003623 0.012594 0.099138
vn -0.010922 0.005247 0.099263
vn -0.018160 -0.002129 0.098314
vn -0.025179 -0.009494 0.096311
vn -0.031829 -0.016806 0.093298
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vn -0.048178 -0.038039 0.078942
vn -0.052034 -0.044754 0.072730
vn -0.054938 -0.051224 0.066015
vn -0.056827 -0.057416 0.058941
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vn -0.046125 -0.086955 0.017642
vn -0.041023 -0.090359 0.012342
vn -0.035199 -0.093271 0.007841
vn -0.028778 -0.095676 0.004235
vn 0.027631 0.084774 0.045276
vn 0.027044 0.080633 0.052601
vn 0.025394 0.076053 0.059758
vn 0.022718 0.071060 0.066591
vn 0.019073 0.065680 0.072955
vn 0.014539 0.059942 0.078712
vn 0.009214 0.053878 0.083739
vn 0.003213 0.047521 0.087928
vn -0.003333 0.040906 0.091190
vn -0.010283 0.034068 0.093454
vn -0.017487 0.027044 0.094672
vn -0.024789 0.019873 0.094819
vn -0.032031 0.012594 0.093890
vn -0.039057 0.005247 0.091908
vn -0.045714 -0.002129 0.088914
vn -0.051859 -0.009494 0.084974
vn -0.057358 -0.016806 0.080172
vn -0.062093 -0.024028 0.074613
vn -0.065961 -0.031118 0.068416
vn -0.068878 -0.038039 0.061715
vn -0.070781 -0.044754 0.054655
vn -0.071627 -0.051224 0.047388
vn -0.071398 -0.057416 0.040071
vn -0.070099 -0.063296 0.032861
vn -0.067755 -0.068831 0.025914
vn -0.064418 -0.073991 0.019381
vn -0.060158 -0.078749 0.013401
vn -0.055067 -0.083078 0.008104
vn 0.024253 0.084774 0.047172
vn 0.023132 0.080633 0.054435
vn 0.020965 0.076053 0.061452
vn 0.017796 0.071060 0.068072
vn 0.013697 0.065680 0.074152
vn 0.008754 0.059942 0.079563
vn 0.003077 0.053878 0.084188
vn -0.003213 0.047521 0.087928
vn -0.009980 0.040906 0.090703
vn -0.017077 0.034068 0.092454
vn -0.024351 0.027044 0.093143
vn -0.031644 0.019873 0.092756
vn -0.038799 0.012594 0.091302
vn -0.045661 0.005247 0.088812
vn -0.052082 -0.002129 0.085340
vn -0.057923 -0.009494 0.080962
vn -0.063057 -0.016806 0.075771
vn -0.067374 -0.024028 0.069881
vn -0.070779 -0.031118 0.063419
vn -0.073200 -0.038039 0.056523
vn -0.074582 -0.044754 0.049343
vn -0.074895 -0.051224 0.042033
vn -0.074133 -0.057416 0.034752
vn -0.072310 -0.063296 0.027656
vn -0.069466 -0.068831 0.020899
vn -0.065661 -0.073991 0.014627
vn -0.060976 -0.078749 0.008974
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vn -0.002371 0.076053 0.064886
vn -0.007695 0.071060 0.069938
vn -0.013697 0.065680 0.074152
vn -0.020246 0.059942 0.077441
vn -0.027201 0.053878 0.079732
vn -0.034412 0.047521 0.080979
vn -0.041724 0.040906 0.081153
vn -0.048978 0.034068 0.080253
vn -0.056018 0.027044 0.078298
vn -0.062691 0.019873 0.075332
vn -0.068855 0.012594 0.071417
vn -0.074375 0.005247 0.066640
vn -0.079132 -0.002129 0.061104
vn -0.083023 -0.009494 0.054928
vn -0.085965 -0.016806 0.048246
vn -0.087892 -0.024028 0.041202
vn -0.088764 -0.031118 0.033949
vn -0.088561 -0.038039 0.026644
vn -0.087287 -0.044754 0.019444
vn -0.084969 -0.051224 0.012505
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vn -0.002160 0.080633 0.059107
vn -0.007101 0.076053 0.064540
vn -0.012780 0.071060 0.069189
vn -0.019073 0.065680 0.072955
vn -0.025844 0.059942 0.075756
vn -0.032948 0.053878 0.077534
vn -0.040231 0.047521 0.078251
vn -0.047536 0.040906 0.077891
vn -0.054705 0.034068 0.076464
vn -0.061584 0.027044 0.074001
vn -0.068023 0.019873 0.070554
vn -0.073884 0.012594 0.066201
vn -0.079041 0.005247 0.061033
vn -0.083381 -0.002129 0.055164
vn -0.086811 -0.009494 0.048721
vn -0.089257 -0.016806 0.041842
vn -0.090665 -0.024028 0.034677
vn -0.091006 -0.031118 0.027380
vn -0.090270 -0.038039 0.020108
vn -0.088474 -0.044754 0.013021
vn -0.017126 0.084774 0.050200
vn -0.023132 0.080633 0.054435
vn -0.029689 0.076053 0.057745
vn -0.036653 0.071060 0.060059
vn -0.043876 0.065680 0.061327
vn -0.051202 0.059942 0.061525
vn -0.058472 0.053878 0.060648
vn -0.065530 0.047521 0.058715
vn -0.072225 0.040906 0.055770
vn -0.078411 0.034068 0.051876
vn -0.083955 0.027044 0.047118
vn -0.088739 0.019873 0.041599
vn -0.092658 0.012594 0.035439
vn -0.095628 0.005247 0.028770
vn -0.097585 -0.002129 0.021738
vn -0.098487 -0.009494 0.014494
vn -0.033929 0.084774 0.040770
vn -0.041052 0.080633 0.042580
vn -0.048358 0.076053 0.043329
vn -0.055689 0.071060 0.043002
vn -0.062889 0.065680 0.041607
vn -0.069802 0.059942 0.039175
vn -0.076279 0.053878 0.035758
vn -0.082181 0.047521 0.031432
vn -0.087382 0.040906 0.026289
vn -0.091769 0.034068 0.020442
vn -0.095248 0.027044 0.014018
vn -0.036815 0.084774 0.038185
vn -0.044051 0.080633 0.039470
vn -0.051392 0.076053 0.039683
vn -0.058680 0.071060 0.038822
vn -0.065758 0.065680 0.036905
vn -0.072475 0.059942 0.033975
vn -0.078686 0.053878 0.030095
vn -0.084256 0.047521 0.025349
vn -0.089068 0.040906 0.019841
vn -0.093016 0.034068 0.013689
vn -0.048026 0.084774 0.022514
vn -0.055244 0.080633 0.021129
vn -0.062177 0.076053 0.018706
vn -0.068676 0.071060 0.015298
vn -0.074603 0.065680 0.010979
usemtl Steel_-_Satin
f 61/1/1 1/2/2 62/3/3
f 62/3/3 1/2/2 2/4/4
f 62/3/3 2/4/4 116/5/5
f 116/5/5 2/4/4 3/6/6
f 116/5/5 3/6/6 115/7/7
f 115/7/7 3/6/6 4/8/8
f 115/7/7 4/8/8 114/9/9
f 114/9/9 4/8/8 5/10/10
f 114/9/9 5/10/10 113/11/11
f 113/11/11 5/10/10 6/12/12
f 113/11/11 6/12/12 112/13/13
f 112/13/13 6/12/12 7/14/14
f 112/13/13 7/14/14 111/15/15
f 111/15/15 7/14/14 8/16/16
f 111/15/15 8/16/16 110/17/17
f 110/17/17 8/16/16 9/18/18
f 110/17/17 9/18/18 109/19/19
f 109/19/19 9/18/18 10/20/20
f 109/19/19 10/20/20 108/21/21
f 108/21/21 10/20/20 11/22/22
f 108/21/21 11/22/22 12/23/23
f 108/21/21 12/23/23 107/24/24
f 107/24/24 12/23/23 13/25/25
f 107/24/24 13/25/25 106/26/26
f 106/26/26 13/25/25 14/27/27
f 106/26/26 14/27/27 105/28/28
f 105/28/28 14/27/27 15/29/29
f 105/28/28 15/29/29 104/30/30
f 104/30/30 15/29/29 16/31/31
f 104/30/30 16/31/31 103/32/32
f 103/32/32 16/31/31 17/33/33
f 103/32/32 17/33/33 102/34/34
f 102/34/34 17/33/33 18/35/35
f 102/34/34 18/35/35 101/36/36
f 101/36/36 18/35/35 19/37/37
f 101/36/36 19/37/37 100/38/38
f 100/38/38 19/37/37 20/39/39
f 100/38/38 20/39/39 99/40/40
f 99/40/40 20/39/39 21/41/41
f 99/40/40 21/41/41 22/42/42
f 99/40/40 22/42/42 98/43/43
f 98/43/43 22/42/42 23/44/44
f 98/43/43 23/44/44 97/45/45
f 97/45/45 23/44/44 24/46/46
f 97/45/45 24/46/46 96/47/47
f 96/47/47 24/46/46 25/48/48
f 96/47/47 25/48/48 95/49/49
f 95/49/49 25/48/48 26/50/50
f 95/49/49 26/50/50 94/51/51
f 94/51/51 26/50/50 27/52/52
f 94/51/51 27/52/52 93/53/53
f 93/53/53 27/52/52 28/54/54
f 93/53/53 28/54/54 92/55/55
f 92/55/55 28/54/54 29/56/56
f 92/55/55 29/56/56 91/57/57
f 91/57/57 29/56/56 30/58/58
f 91/57/57 30/58/58 90/59/59
f 90/59/59 30/58/58 31/60/60
f 90/59/59 31/60/60 89/61/61
f 89/61/61 31/60/60 32/62/62
f 89/61/61 32/62/62 33/63/63
f 89/61/61 33/63/63 88/64/64
f 88/64/64 33/63/63 34/65/65
f 88/64/64 34/65/65 87/66/66
f 87/66/66 34/65/65 35/67/67
f 87/66/66 35/67/67 86/68/68
f 86/68/68 35/67/67 36/69/69
f 86/68/68 36/69/69 85/70/70
f 85/70/70 36/69/69 37/71/71
f 85/70/70 37/71/71 84/72/72
f 84/72/72 37/71/71 38/73/73
f 84/72/72 38/73/73 83/74/74
f 83/74/74 38/73/73 39/75/75
f 83/74/74 39/75/75 82/76/76
f 82/76/76 39/75/75 40/77/77
f 82/76/76 40/77/77 81/78/78
f 81/78/78 40/77/77 41/79/79
f 81/78/78 41/79/79 80/80/80
f 80/80/80 41/79/79 42/81/81
f 80/80/80 42/81/81 43/82/82
f 80/80/80 43/82/82 79/83/83
f 79/83/83 43/82/82 44/84/84
f 79/83/83 44/84/84 78/85/85
f 78/85/85 44/84/84 45/86/86
f 78/85/85 45/86/86 77/87/87
f 77/87/87 45/86/86 46/88/88
f 77/87/87 46/88/88 76/89/89
f 76/89/89 46/88/88 47/90/90
f 76/89/89 47/90/90 75/91/91
f 75/91/91 47/90/90 48/92/92
f 75/91/91 48/92/92 74/93/93
f 74/93/93 48/92/92 49/94/94
f 74/93/93 49/94/94 73/95/95
f 73/95/95 49/94/94 50/96/96
f 73/95/95 50/96/96 72/97/97
f 72/97/97 50/96/96 51/98/98
f 72/97/97 51/98/98 71/99/99
f 71/99/99 51/98/98 52/100/100
f 71/99/99 52/100/100 53/101/101
f 71/99/99 53/101/101 70/102/102
f 70/102/102 53/101/101 54/103/103
f 70/102/102 54/103/103 69/104/104
f 69/104/104 54/103/103 55/105/105
f 69/104/104 55/105/105 68/106/106
f 68/106/106 55/105/105 56/107/107
f 68/106/106 56/107/107 67/108/108
f 67/108/108 56/107/107 57/109/109
f 67/108/108 57/109/109 66/110/110
f 66/110/110 57/109/109 58/111/111
f 66/110/110 58/111/111 65/112/112
f 65/112/112 58/111/111 59/113/113
f 65/112/112 59/113/113 64/114/114
f 64/114/114 59/113/113 60/115/115
f 64/114/114 60/115/115 63/116/116
f 63/116/116 60/115/115 61/1/1
f 63/116/116 61/1/1 62/3/3
f 61/117/117 117/118/118 1/119/119
f 1/119/119 117/118/118 118/120/120
f 1/119/119 118/120/120 2/121/121
f 2/121/121 118/120/120 119/122/122
f 2/121/121 119/122/122 3/123/123
f 3/123/123 119/122/122 120/124/124
f 3/123/123 120/124/124 4/125/125
f 4/125/125 120/124/124 121/126/126
f 4/125/125 121/126/126 5/127/127
f 5/127/127 121/126/126 122/128/128
f 5/127/127 122/128/128 6/129/129
f 6/129/129 122/128/128 123/130/130
f 6/129/129 123/130/130 7/131/131
f 7/131/131 123/130/130 124/132/132
f 7/131/131 124/132/132 8/133/133
f 8/133/133 124/132/132 125/134/134
f 8/133/133 125/134/134 9/135/135
f 9/135/135 125/134/134 126/136/136
f 9/135/135 126/136/136 10/137/137
f 10/137/137 126/136/136 127/138/138
f 10/137/137 127/138/138 11/139/139
f 11/139/139 127/138/138 128/140/140
f 11/139/139 128/140/140 12/141/141
f 12/141/141 128/140/140 129/142/142
f 12/141/141 129/142/142 13/143/143
f 13/143/143 129/142/142 130/144/144
f 13/143/143 130/144/144 14/145/145
f 14/145/145 130/144/144 131/146/146
f 14/145/145 131/146/146 15/147/147
f 15/147/147 131/146/146 132/148/148
f 15/147/147 132/148/148 16/149/149
f 16/149/149 132/148/148 133/150/150
f 16/149/149 133/150/150 17/151/151
f 17/151/151 133/150/150 134/152/152
f 17/151/151 134/152/152 18/153/153
f 18/153/153 134/152/152 135/154/154
f 18/153/153 135/154/154 19/155/155
f 19/155/155 135/154/154 136/156/156
f 19/155/155 136/156/156 20/157/157
f 20/157/157 136/156/156 137/158/158
f 20/157/157 137/158/158 21/159/159
f 21/159/159 137/158/158 138/160/160
f 21/159/159 138/160/160 22/161/161
f 22/161/161 138/160/160 139/162/162
f 22/161/161 139/162/162 23/163/163
f 23/163/163 139/162/162 140/164/164
f 23/163/163 140/164/164 24/165/165
f 24/165/165 140/164/164 141/166/166
f 24/165/165 141/166/166 25/167/167
f 25/167/167 141/166/166 142/168/168
f 25/167/167 142/168/168 26/169/169
f 26/169/169 142/168/168 143/170/170
f 26/169/169 143/170/170 27/171/171
f 27/171/171 143/170/170 144/172/172
f 27/171/171 144/172/172 28/173/173
f 28/173/173 144/172/172 145/174/174
f 28/173/173 145/174/174 29/175/175
f 29/175/175 145/174/174 146/176/176
f 29/175/175 146/176/176 30/177/177
f 30/177/177 146/176/176 147/178/178
f 30/177/177 147/178/178 31/179/179
f 31/180/179 147/181/178 148/182/180
f 31/180/179 148/182/180 32/183/181
f 32/183/181 148/182/180 149/184/182
f 32/183/181 149/184/182 33/185/183
f 33/185/183 149/184/182 150/186/184
f 33/185/183 150/186/184 34/187/185
f 34/187/185 150/186/184 151/188/186
f 34/187/185 151/188/186 35/189/187
f 35/189/187 151/188/186 152/190/188
f 35/189/187 152/190/188 36/191/189
f 36/191/189 152/190/188 153/192/190
f 36/191/189 153/192/190 37/193/191
f 37/193/191 153/192/190 154/194/192
f 37/193/191 154/194/192 38/195/193
f 38/195/193 154/194/192 155/196/194
f 38/195/193 155/196/194 39/197/195
f 39/197/195 155/196/194 156/198/196
f 39/197/195 156/198/196 40/199/197
f 40/199/197 156/198/196 157/200/198
f 40/199/197 157/200/198 41/201/199
f 41/201/199 157/200/198 158/202/200
f 41/201/199 158/202/200 42/203/201
f 42/203/201 158/202/200 159/204/202
f 42/203/201 159/204/202 43/205/203
f 43/205/203 159/204/202 160/206/204
f 43/205/203 160/206/204 44/207/205
f 44/207/205 160/206/204 161/208/206
f 44/207/205 161/208/206 45/209/207
f 45/209/207 161/208/206 162/210/208
f 45/209/207 162/210/208 46/211/209
f 46/211/209 162/210/208 163/212/210
f 46/211/209 163/212/210 47/213/211
f 47/213/211 163/212/210 164/214/212
f 47/213/211 164/214/212 48/215/213
f 48/215/213 164/214/212 165/216/214
f 48/215/213 165/216/214 49/217/215
f 49/217/215 165/216/214 166/218/216
f 49/217/215 166/218/216 50/219/217
f 50/219/217 166/218/216 167/220/218
f 50/219/217 167/220/218 51/221/219
f 51/221/219 167/220/218 168/222/220
f 51/221/219 168/222/220 52/223/221
f 52/223/221 168/222/220 169/224/222
f 52/223/221 169/224/222 53/225/223
f 53/225/223 169/224/222 170/226/224
f 53/225/223 170/226/224 54/227/225
f 54/227/225 170/226/224 171/228/226
f 54/227/225 171/228/226 55/229/227
f 55/229/227 171/228/226 172/230/228
f 55/229/227 172/230/228 56/231/229
f 56/231/229 172/230/228 173/232/230
f 56/231/229 173/232/230 57/233/231
f 57/233/231 173/232/230 174/234/232
f 57/233/231 174/234/232 58/235/233
f 58/235/233 174/234/232 175/236/234
f 58/235/233 175/236/234 59/237/235
f 59/237/235 175/236/234 176/238/236
f 59/237/235 176/238/236 60/239/237
f 60/239/237 176/238/236 177/240/238
f 60/239/237 177/240/238 61/117/117
f 61/117/117 177/240/238 117/118/118
f 118/241/239 117/242/240 146/243/241
f 146/243/241 117/242/240 177/244/242
f 146/243/241 177/244/242 147/245/243
f 147/245/243 177/244/242 176/246/244
f 147/245/243 176/246/244 175/247/245
f 175/247/245 174/248/246 147/245/243
f 147/245/243 174/248/246 173/249/247
f 147/245/243 173/249/247 172/250/248
f 172/250/248 171/251/249 147/245/243
f 147/245/243 171/251/249 170/252/250
f 147/245/243 170/252/250 169/253/251
f 169/253/251 168/254/252 147/245/243
f 147/245/243 168/254/252 167/255/253
f 147/245/243 167/255/253 166/256/254
f 166/256/254 165/257/255 147/245/243
f 147/245/243 165/257/255 164/258/256
f 147/245/243 164/258/256 163/259/257
f 163/259/257 162/260/258 147/245/243
f 147/245/243 162/260/258 161/261/259
f 147/245/243 161/261/259 160/262/260
f 160/262/260 159/263/261 147/245/243
f 147/245/243 159/263/261 158/264/262
f 147/245/243 158/264/262 157/265/263
f 157/265/263 156/266/264 147/245/243
f 147/245/243 156/266/264 155/267/265
f 147/245/243 155/267/265 154/268/266
f 154/268/266 153/269/267 147/245/243
f 147/245/243 153/269/267 152/270/268
f 147/245/243 152/270/268 151/271/269
f 151/271/269 150/272/270 147/245/243
f 147/245/243 150/272/270 149/273/271
f 147/245/243 149/273/271 148/274/272
f 118/241/239 146/243/241 119/275/273
f 119/275/273 146/243/241 145/276/274
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f 1956/1287/1266 1955/1286/1265 2030/1288/1267
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f 1955/1286/1265 2031/1344/1322 2030/1288/1267
f 2030/1288/1267 2031/1344/1322 1991/1345/1323
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f 3306/1966/1935 3307/1991/1960 3283/468/462
f 3306/1966/1935 3283/468/462 3284/470/464
f 2439/1968/1937 194/1967/1936 278/1992/1961
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# 3329 vertices
# 3687 texture params
# 3561 normals
# 6654 facets
# 1 groups
================================================
FILE: load/shapes/teddy.obj
================================================
# Blender v2.83.4 OBJ File: ''
# www.blender.org
mtllib teddy2.mtl
o teddy
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s 1
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usemtl Default_OBJ
s 1
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f 996//996 998//998 984//984
f 997//997 998//998 996//996
f 997//997 999//999 998//998
f 998//998 1000//1000 984//984
f 999//999 1000//1000 998//998
f 999//999 1003//1001 1000//1000
f 1000//1000 1001//1002 984//984
f 1002//1003 1001//1002 1000//1000
f 1002//1003 1000//1000 1003//1001
f 1004//1004 1006//1005 1003//1001
f 1004//1004 1003//1001 999//999
f 1005//1006 1002//1003 1003//1001
f 1005//1006 1003//1001 1006//1005
f 1007//1007 1009//1008 1006//1005
f 1007//1007 1006//1005 1004//1004
f 1008//1009 1005//1006 1006//1005
f 1008//1009 1006//1005 1009//1008
f 1010//1010 1012//1011 1009//1008
f 1010//1010 1009//1008 1007//1007
f 1011//1012 1008//1009 1009//1008
f 1011//1012 1009//1008 1012//1011
f 1013//1013 1015//1014 1012//1011
f 1013//1013 1012//1011 1010//1010
f 1014//1015 1011//1012 1012//1011
f 1014//1015 1012//1011 1015//1014
f 1016//1016 1018//1017 1015//1014
f 1016//1016 1015//1014 1013//1013
f 1017//1018 1014//1015 1015//1014
f 1017//1018 1015//1014 1018//1017
f 1019//1019 1021//1020 1018//1017
f 1019//1019 1018//1017 1016//1016
f 1020//1021 1017//1018 1018//1017
f 1020//1021 1018//1017 1021//1020
f 1022//1022 1021//1020 1019//1019
f 1022//1022 1020//1021 1021//1020
f 1024//1023 1022//1022 1023//1024
f 1022//1022 1019//1019 1023//1024
f 1022//1022 1025//1025 1020//1021
f 1024//1023 1025//1025 1022//1022
f 1024//1023 1026//1026 1025//1025
f 1026//1026 1027//1027 1025//1025
f 1025//1025 1027//1027 1028//1028
f 1025//1025 1028//1028 1020//1021
f 1028//1028 1017//1018 1020//1021
f 1028//1028 1030//1029 1017//1018
f 1027//1027 1029//1030 1028//1028
f 1029//1030 1030//1029 1028//1028
f 1030//1029 1014//1015 1017//1018
f 1030//1029 1032//1031 1014//1015
f 1029//1030 1031//1032 1030//1029
f 1031//1032 1032//1031 1030//1029
f 1032//1031 1011//1012 1014//1015
f 1032//1031 1034//1033 1011//1012
f 1031//1032 1033//1034 1032//1031
f 1033//1034 1034//1033 1032//1031
f 1034//1033 1008//1009 1011//1012
f 1034//1033 1036//1035 1008//1009
f 1033//1034 1035//1036 1034//1033
f 1035//1036 1036//1035 1034//1033
f 1036//1035 1005//1006 1008//1009
f 1036//1035 1038//1037 1005//1006
f 1035//1036 1037//1038 1036//1035
f 1037//1038 1038//1037 1036//1035
f 1038//1037 1002//1003 1005//1006
f 1038//1037 1040//1039 1002//1003
f 1037//1038 1039//1040 1038//1037
f 1039//1040 1040//1039 1038//1037
f 1040//1039 1001//1002 1002//1003
f 1040//1039 1042//1041 1001//1002
f 1039//1040 1041//1042 1040//1039
f 1041//1042 1042//1041 1040//1039
f 1001//1002 1042//1041 984//984
f 1042//1041 1043//1043 984//984
f 1041//1042 1043//1043 1042//1041
f 1043//1043 1045//1044 984//984
f 1044//1045 1045//1044 1043//1043
f 1044//1045 1043//1043 1041//1042
f 1045//1044 1047//1046 984//984
f 1046//1047 1047//1046 1045//1044
f 1046//1047 1045//1044 1044//1045
f 1047//1046 1049//1048 984//984
f 1048//1049 1049//1048 1047//1046
f 1048//1049 1047//1046 1046//1047
f 1049//1048 1051//1050 984//984
f 1050//1051 1051//1050 1049//1048
f 1050//1051 1049//1048 1048//1049
f 1051//1050 1053//1052 984//984
f 1052//1053 1053//1052 1051//1050
f 1052//1053 1051//1050 1050//1051
f 1053//1052 1054//1054 984//984
f 1052//1053 1054//1054 1053//1052
f 1052//1053 1055//1055 1054//1054
f 1054//1054 1056//1056 984//984
f 1055//1055 1056//1056 1054//1054
f 1055//1055 1057//1057 1056//1056
f 1056//1056 1058//1058 984//984
f 1057//1057 1058//1058 1056//1056
f 1057//1057 1059//1059 1058//1058
f 1058//1058 1060//1060 984//984
f 1059//1059 1060//1060 1058//1058
f 1059//1059 1061//1061 1060//1060
f 1060//1060 1062//1062 984//984
f 1061//1061 1062//1062 1060//1060
f 1061//1061 1064//1063 1062//1062
f 1062//1062 1063//1064 984//984
f 1064//1063 1063//1064 1062//1062
f 1065//1065 1066//1066 1064//1063
f 1065//1065 1064//1063 1061//1061
f 1066//1066 1067//1067 1064//1063
f 1064//1063 1067//1067 1063//1064
f 1066//1066 1068//1068 1067//1067
f 1068//1068 1069//1069 1067//1067
f 1067//1067 1069//1069 1070//1070
f 1067//1067 1070//1070 1063//1064
f 1063//1064 1070//1070 984//984
f 1070//1070 1071//1071 984//984
f 1069//1069 1071//1071 1070//1070
f 1071//1071 1074//1072 984//984
f 1069//1069 1073//1073 1071//1071
f 1072//1074 1074//1072 1071//1071
f 1072//1074 1071//1071 1073//1073
f 1074//1072 1076//1075 984//984
f 1075//1076 1076//1075 1074//1072
f 1075//1076 1074//1072 1072//1074
f 1076//1075 1078//1077 984//984
f 1077//1078 1078//1077 1076//1075
f 1077//1078 1076//1075 1075//1076
f 1078//1077 1080//1079 984//984
f 1079//1080 1080//1079 1078//1077
f 1079//1080 1078//1077 1077//1078
f 1080//1079 1082//1081 984//984
f 1081//1082 1082//1081 1080//1079
f 1081//1082 1080//1079 1079//1080
f 1082//1081 981//981 984//984
f 978//978 981//981 1082//1081
f 978//978 1082//1081 1081//1082
f 1083//1083 1081//1082 1079//1080
f 975//975 978//978 1081//1082
f 975//975 1081//1082 1083//1083
f 1085//1084 1083//1083 1084//1085
f 1083//1083 1079//1080 1084//1085
f 973//973 975//975 1083//1083
f 973//973 1083//1083 1085//1084
f 1087//1086 1085//1084 1086//1087
f 1085//1084 1084//1085 1086//1087
f 972//972 973//973 1085//1084
f 972//972 1085//1084 1087//1086
f 1090//1088 1087//1086 1088//1089
f 1087//1086 1086//1087 1088//1089
f 1089//1090 972//972 1087//1086
f 1089//1090 1087//1086 1090//1088
f 1093//1091 1090//1088 1091//1092
f 1090//1088 1088//1089 1091//1092
f 1092//1093 1089//1090 1090//1088
f 1092//1093 1090//1088 1093//1091
f 1096//1094 1093//1091 1094//1095
f 1093//1091 1091//1092 1094//1095
f 1095//1096 1092//1093 1093//1091
f 1095//1096 1093//1091 1096//1094
f 1097//1097 1099//1098 1096//1094
f 1097//1097 1096//1094 1098//1099
f 1096//1094 1094//1095 1098//1099
f 1099//1098 1095//1096 1096//1094
f 1100//1100 1101//1101 1099//1098
f 1100//1100 1099//1098 1097//1097
f 1101//1101 1102//1102 1099//1098
f 1099//1098 1102//1102 1095//1096
f 1101//1101 1103//1103 1102//1102
f 1103//1103 1104//1104 1102//1102
f 1102//1102 1104//1104 1105//1105
f 1102//1102 1105//1105 1095//1096
f 1105//1105 1092//1093 1095//1096
f 1105//1105 1107//1106 1092//1093
f 1104//1104 1106//1107 1105//1105
f 1106//1107 1107//1106 1105//1105
f 1107//1106 1089//1090 1092//1093
f 1107//1106 970//971 1089//1090
f 1106//1107 968//970 1107//1106
f 968//970 970//971 1107//1106
f 1108//1108 1111//1109 1106//1107
f 1108//1108 1106//1107 1109//1110
f 1104//1104 1109//1110 1106//1107
f 1106//1107 1110//1111 968//970
f 1106//1107 1111//1109 1110//1111
f 1112//1112 1114//1113 1111//1109
f 1112//1112 1111//1109 1108//1108
f 1111//1109 1113//1114 1110//1111
f 1111//1109 1114//1113 1113//1114
f 1115//1115 1117//1116 1114//1113
f 1115//1115 1114//1113 1112//1112
f 1114//1113 1116//1117 1113//1114
f 1114//1113 1117//1116 1116//1117
f 1118//1118 1120//1119 1117//1116
f 1118//1118 1117//1116 1115//1115
f 1117//1116 1119//1120 1116//1117
f 1117//1116 1120//1119 1119//1120
f 1121//1121 1123//1122 1120//1119
f 1121//1121 1120//1119 1118//1118
f 1120//1119 1122//1123 1119//1120
f 1120//1119 1123//1122 1122//1123
f 1124//1124 1126//1125 1123//1122
f 1124//1124 1123//1122 1121//1121
f 1123//1122 1125//1126 1122//1123
f 1123//1122 1126//1125 1125//1126
f 1023//1024 1019//1019 1126//1125
f 1023//1024 1126//1125 1124//1124
f 1126//1125 1016//1016 1125//1126
f 1126//1125 1019//1019 1016//1016
f 1122//1123 1125//1126 1127//1127
f 1125//1126 1016//1016 1013//1013
f 1125//1126 1013//1013 1127//1127
f 1122//1123 1127//1127 1128//1128
f 1128//1128 1127//1127 1129//1129
f 1127//1127 1013//1013 1010//1010
f 1127//1127 1010//1010 1129//1129
f 1128//1128 1129//1129 1130//1130
f 1130//1130 1129//1129 1131//1131
f 1129//1129 1010//1010 1007//1007
f 1129//1129 1007//1007 1131//1131
f 1130//1130 1131//1131 1132//1132
f 1132//1132 1131//1131 1133//1133
f 1131//1131 1007//1007 1004//1004
f 1131//1131 1004//1004 1133//1133
f 1132//1132 1133//1133 1134//1134
f 1134//1134 1133//1133 997//997
f 1133//1133 1004//1004 999//999
f 1133//1133 999//999 997//997
f 1135//1135 1132//1132 1134//1134
f 1135//1135 1134//1134 1136//1136
f 1136//1136 1134//1134 995//995
f 1134//1134 997//997 995//995
f 1137//1137 1135//1135 1136//1136
f 1137//1137 1136//1136 1138//1138
f 1138//1138 1136//1136 993//993
f 1136//1136 995//995 993//993
f 1139//1139 1137//1137 1138//1138
f 1139//1139 1138//1138 1140//1140
f 1140//1140 1138//1138 991//991
f 1138//1138 993//993 991//991
f 1141//1141 1139//1139 1140//1140
f 1141//1141 1140//1140 1142//1142
f 1142//1142 1140//1140 989//989
f 1140//1140 991//991 989//989
f 976//977 1141//1141 1142//1142
f 976//977 1142//1142 979//980
f 979//980 1142//1142 987//987
f 1142//1142 989//989 987//987
f 966//966 967//968 1141//1141
f 966//966 1141//1141 976//977
f 967//968 1139//1139 1141//1141
f 967//968 1143//1143 1139//1139
f 1143//1143 1137//1137 1139//1139
f 969//969 1144//1144 1143//1143
f 969//969 1143//1143 967//968
f 1144//1144 1145//1145 1143//1143
f 1143//1143 1145//1145 1137//1137
f 1144//1144 1146//1146 1145//1145
f 1146//1146 1147//1147 1145//1145
f 1145//1145 1147//1147 1135//1135
f 1145//1145 1135//1135 1137//1137
f 1146//1146 1148//1148 1147//1147
f 1148//1148 1130//1130 1147//1147
f 1147//1147 1130//1130 1132//1132
f 1147//1147 1132//1132 1135//1135
f 1116//1117 1119//1120 1148//1148
f 1116//1117 1148//1148 1146//1146
f 1119//1120 1128//1128 1148//1148
f 1148//1148 1128//1128 1130//1130
f 1113//1114 1116//1117 1146//1146
f 1113//1114 1146//1146 1144//1144
f 1110//1111 1113//1114 1144//1144
f 1110//1111 1144//1144 969//969
f 1119//1120 1122//1123 1128//1128
f 1150//1149 1124//1124 1121//1121
f 1149//1150 1023//1024 1124//1124
f 1149//1150 1124//1124 1150//1149
f 1153//1151 1150//1149 1151//1152
f 1150//1149 1121//1121 1151//1152
f 1152//1153 1149//1150 1150//1149
f 1152//1153 1150//1149 1153//1151
f 1156//1154 1153//1151 1154//1155
f 1153//1151 1151//1152 1154//1155
f 1155//1156 1152//1153 1153//1151
f 1155//1156 1153//1151 1156//1154
f 1159//1157 1156//1154 1157//1158
f 1156//1154 1154//1155 1157//1158
f 1158//1159 1155//1156 1156//1154
f 1158//1159 1156//1154 1159//1157
f 1162//1160 1159//1157 1160//1161
f 1159//1157 1157//1158 1160//1161
f 1161//1162 1158//1159 1159//1157
f 1161//1162 1159//1157 1162//1160
f 1165//1163 1162//1160 1163//1164
f 1162//1160 1160//1161 1163//1164
f 1164//1165 1161//1162 1162//1160
f 1164//1165 1162//1160 1165//1163
f 1166//1166 1164//1165 1165//1163
f 1166//1166 1165//1163 1167//1167
f 1165//1163 1163//1164 1167//1167
f 1166//1166 1167//1167 1168//1168
f 1167//1167 1169//1169 1168//1168
f 1167//1167 1163//1164 1169//1169
f 1168//1168 1169//1169 1170//1170
f 1169//1169 1171//1171 1170//1170
f 1169//1169 1172//1172 1171//1171
f 1163//1164 1172//1172 1169//1169
f 1172//1172 1173//1173 1171//1171
f 1172//1172 1174//1174 1173//1173
f 1163//1164 1160//1161 1172//1172
f 1160//1161 1174//1174 1172//1172
f 1174//1174 1175//1175 1173//1173
f 1174//1174 1176//1176 1175//1175
f 1160//1161 1157//1158 1174//1174
f 1157//1158 1176//1176 1174//1174
f 1176//1176 1177//1177 1175//1175
f 1176//1176 1178//1178 1177//1177
f 1157//1158 1154//1155 1176//1176
f 1154//1155 1178//1178 1176//1176
f 1178//1178 1115//1115 1177//1177
f 1178//1178 1118//1118 1115//1115
f 1154//1155 1151//1152 1178//1178
f 1151//1152 1118//1118 1178//1178
f 1175//1175 1177//1177 1179//1179
f 1177//1177 1112//1112 1179//1179
f 1177//1177 1115//1115 1112//1112
f 1181//1180 1179//1179 1180//1181
f 1179//1179 1108//1108 1180//1181
f 1179//1179 1112//1112 1108//1108
f 1175//1175 1179//1179 1181//1180
f 1183//1182 1181//1180 1182//1183
f 1181//1180 1180//1181 1182//1183
f 1173//1173 1175//1175 1181//1180
f 1173//1173 1181//1180 1183//1182
f 1185//1184 1183//1182 1184//1185
f 1183//1182 1182//1183 1184//1185
f 1171//1171 1173//1173 1183//1182
f 1171//1171 1183//1182 1185//1184
f 1187//1186 1185//1184 1186//1187
f 1185//1184 1184//1185 1186//1187
f 1170//1170 1171//1171 1185//1184
f 1170//1170 1185//1184 1187//1186
f 1190//1188 1187//1186 1188//1189
f 1187//1186 1186//1187 1188//1189
f 1189//1190 1170//1170 1187//1186
f 1189//1190 1187//1186 1190//1188
f 1166//1166 1190//1188 1191//1191
f 1190//1188 1188//1189 1191//1191
f 1166//1166 1189//1190 1190//1188
f 1166//1166 1191//1191 1192//1192
f 1191//1191 1193//1193 1192//1192
f 1191//1191 1188//1189 1193//1193
f 1195//1194 1192//1192 1193//1193
f 1188//1189 1194//1195 1193//1193
f 1196//1196 1193//1193 1194//1195
f 1195//1194 1193//1193 1196//1196
f 1197//1197 1195//1194 1196//1196
f 1197//1197 1196//1196 1198//1198
f 1198//1198 1196//1196 1199//1199
f 1196//1196 1194//1195 1199//1199
f 1198//1198 1199//1199 1200//1200
f 1200//1200 1199//1199 1201//1201
f 1199//1199 1202//1202 1201//1201
f 1199//1199 1194//1195 1202//1202
f 1186//1187 1184//1185 1202//1202
f 1186//1187 1202//1202 1194//1195
f 1184//1185 1203//1203 1202//1202
f 1201//1201 1202//1202 1203//1203
f 1184//1185 1182//1183 1203//1203
f 1182//1183 1204//1204 1203//1203
f 1205//1205 1203//1203 1204//1204
f 1201//1201 1203//1203 1205//1205
f 1206//1206 1201//1201 1205//1205
f 1206//1206 1205//1205 1207//1207
f 1207//1207 1205//1205 1208//1208
f 1205//1205 1204//1204 1208//1208
f 1207//1207 1208//1208 1103//1103
f 1103//1103 1208//1208 1104//1104
f 1208//1208 1204//1204 1109//1110
f 1208//1208 1109//1110 1104//1104
f 1209//1209 1206//1206 1207//1207
f 1209//1209 1207//1207 1210//1210
f 1210//1210 1207//1207 1103//1103
f 1211//1211 1209//1209 1210//1210
f 1211//1211 1210//1210 1212//1212
f 1212//1212 1210//1210 1101//1101
f 1210//1210 1103//1103 1101//1101
f 1213//1213 1211//1211 1212//1212
f 1213//1213 1212//1212 1214//1214
f 1214//1214 1212//1212 1100//1100
f 1212//1212 1101//1101 1100//1100
f 1215//1215 1213//1213 1214//1214
f 1215//1215 1214//1214 1216//1216
f 1216//1216 1214//1214 1217//1217
f 1214//1214 1100//1100 1217//1217
f 1216//1216 1217//1217 1218//1218
f 1218//1218 1217//1217 1219//1219
f 1217//1217 1100//1100 1097//1097
f 1217//1217 1097//1097 1219//1219
f 1218//1218 1219//1219 1220//1220
f 1220//1220 1219//1219 1221//1221
f 1219//1219 1097//1097 1098//1099
f 1219//1219 1098//1099 1221//1221
f 1220//1220 1221//1221 1222//1222
f 1221//1221 1223//1223 1222//1222
f 1221//1221 1224//1224 1223//1223
f 1098//1099 1224//1224 1221//1221
f 1224//1224 1225//1225 1223//1223
f 1224//1224 1226//1226 1225//1225
f 1098//1099 1094//1095 1224//1224
f 1094//1095 1226//1226 1224//1224
f 1226//1226 1227//1227 1225//1225
f 1226//1226 1228//1228 1227//1227
f 1094//1095 1091//1092 1226//1226
f 1091//1092 1228//1228 1226//1226
f 1228//1228 1229//1229 1227//1227
f 1228//1228 1230//1230 1229//1229
f 1091//1092 1088//1089 1228//1228
f 1088//1089 1230//1230 1228//1228
f 1230//1230 1231//1231 1229//1229
f 1230//1230 1232//1232 1231//1231
f 1088//1089 1086//1087 1230//1230
f 1086//1087 1232//1232 1230//1230
f 1232//1232 1075//1076 1231//1231
f 1232//1232 1077//1078 1075//1076
f 1086//1087 1084//1085 1232//1232
f 1084//1085 1077//1078 1232//1232
f 1229//1229 1231//1231 1233//1233
f 1231//1231 1072//1074 1233//1233
f 1231//1231 1075//1076 1072//1074
f 1235//1234 1233//1233 1234//1235
f 1233//1233 1073//1073 1234//1235
f 1233//1233 1072//1074 1073//1073
f 1229//1229 1233//1233 1235//1234
f 1237//1236 1235//1234 1236//1237
f 1235//1234 1234//1235 1236//1237
f 1227//1227 1229//1229 1235//1234
f 1227//1227 1235//1234 1237//1236
f 1239//1238 1237//1236 1238//1239
f 1237//1236 1236//1237 1238//1239
f 1225//1225 1227//1227 1237//1236
f 1225//1225 1237//1236 1239//1238
f 1241//1240 1239//1238 1240//1241
f 1239//1238 1238//1239 1240//1241
f 1223//1223 1225//1225 1239//1238
f 1223//1223 1239//1238 1241//1240
f 1243//1242 1241//1240 1242//1243
f 1241//1240 1240//1241 1242//1243
f 1222//1222 1223//1223 1241//1240
f 1222//1222 1241//1240 1243//1242
f 1244//1244 1246//1245 1243//1242
f 1244//1244 1243//1242 1245//1246
f 1243//1242 1242//1243 1245//1246
f 1246//1245 1222//1222 1243//1242
f 1247//1247 1248//1248 1246//1245
f 1247//1247 1246//1245 1244//1244
f 1248//1248 1220//1220 1246//1245
f 1246//1245 1220//1220 1222//1222
f 1249//1249 1250//1250 1248//1248
f 1249//1249 1248//1248 1247//1247
f 1250//1250 1218//1218 1248//1248
f 1248//1248 1218//1218 1220//1220
f 1251//1251 1252//1252 1250//1250
f 1251//1251 1250//1250 1249//1249
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f 1443//1443 1421//1421 1445//1445
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f 1446//1446 1445//1445 1026//1026
f 1445//1445 1421//1421 1419//1419
f 1445//1445 1419//1419 1026//1026
f 1444//1444 1446//1446 1152//1153
f 1446//1446 1149//1150 1152//1153
f 1446//1446 1024//1023 1149//1150
f 1446//1446 1026//1026 1024//1023
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f 1440//1440 1442//1442 1158//1159
f 1442//1442 1155//1156 1158//1159
f 1438//1438 1440//1440 1161//1162
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f 1438//1438 1161//1162 1164//1165
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f 1166//1166 1367//1367 1385//1386
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f 1166//1166 1271//1271 1364//1365
f 1271//1271 1273//1273 1364//1365
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f 1313//1314 1305//1305 1307//1307
f 1310//1311 1048//1049 1046//1047
f 1200//1200 1201//1201 1206//1206
f 1182//1183 1180//1181 1204//1204
f 1180//1181 1109//1110 1204//1204
f 1166//1166 1195//1194 1197//1197
f 1166//1166 1192//1192 1195//1194
f 1188//1189 1186//1187 1194//1195
f 1166//1166 1168//1168 1189//1190
f 1168//1168 1170//1170 1189//1190
f 1180//1181 1108//1108 1109//1110
f 1151//1152 1121//1121 1118//1118
f 1024//1023 1023//1024 1149//1150
f 968//970 1110//1111 969//969
f 970//971 972//972 1089//1090
f 1084//1085 1079//1080 1077//1078
f 979//980 987//987 982//983
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vn 0.5978 0.7017 0.3877
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vn 0.7323 0.4879 0.4750
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vn 0.3975 0.7398 0.5428
vn 0.1486 -0.9679 0.2029
vn 0.5012 0.5294 0.6845
vn 0.0862 -0.9893 0.1177
vn 0.5790 0.1986 0.7908
vn 0.0283 0.9989 0.0386
vn 0.0283 -0.9989 0.0386
vn 0.5790 -0.1986 0.7908
vn 0.0862 0.9893 0.1177
vn 0.5012 -0.5294 0.6845
vn 0.1486 0.9679 0.2029
vn 0.3975 -0.7398 0.5428
vn 0.2189 0.9288 0.2990
vn 0.3011 -0.8604 0.4112
vn 0.3011 0.8604 0.4112
vn 0.0324 0.9893 0.1422
vn 0.1884 -0.5294 0.8272
vn 0.0558 0.9679 0.2452
vn 0.1494 -0.7398 0.6560
vn 0.0823 0.9288 0.3613
vn 0.1132 -0.8604 0.4969
vn 0.1132 0.8604 0.4969
vn 0.0823 -0.9288 0.3613
vn 0.1494 0.7398 0.6560
vn 0.0558 -0.9679 0.2452
vn 0.1884 0.5294 0.8272
vn 0.0324 -0.9893 0.1422
vn 0.2177 0.1986 0.9556
vn 0.0106 0.9989 0.0466
vn 0.0106 -0.9989 0.0466
vn 0.2177 -0.1986 0.9556
vn -0.0990 0.7017 0.7056
vn -0.0387 -0.9604 0.2759
vn -0.1213 0.4879 0.8645
vn -0.0226 -0.9867 0.1609
vn -0.1367 0.1786 0.9744
vn -0.0074 0.9986 0.0529
vn -0.0074 -0.9986 0.0529
vn -0.1367 -0.1785 0.9744
vn -0.0226 0.9867 0.1609
vn -0.1213 -0.4879 0.8645
vn -0.0387 0.9604 0.2759
vn -0.0990 -0.7017 0.7056
vn -0.0565 0.9135 0.4029
vn -0.0766 -0.8341 0.5463
vn -0.0766 0.8341 0.5463
vn -0.0565 -0.9135 0.4029
vn -0.3639 -0.4314 0.8255
vn -0.1295 0.9471 0.2938
vn -0.3085 -0.6444 0.6997
vn -0.1863 0.8870 0.4225
vn -0.2467 -0.7912 0.5596
vn -0.2467 0.7912 0.5596
vn -0.1863 -0.8870 0.4225
vn -0.3085 0.6444 0.6997
vn -0.1295 -0.9471 0.2938
vn -0.3639 0.4314 0.8255
vn -0.0762 -0.9820 0.1729
vn -0.3986 0.1534 0.9042
vn -0.0252 0.9981 0.0571
vn -0.0252 -0.9981 0.0571
vn -0.3986 -0.1534 0.9042
vn -0.0762 0.9820 0.1729
vn -0.2121 -0.9304 0.2989
vn -0.5352 0.3806 0.7541
vn -0.1263 -0.9759 0.1779
vn -0.5737 0.1325 0.8083
vn -0.0419 0.9974 0.0590
vn -0.0419 -0.9974 0.0590
vn -0.5737 -0.1325 0.8083
vn -0.1263 0.9759 0.1779
vn -0.5352 -0.3806 0.7541
vn -0.2121 0.9304 0.2989
vn -0.4685 -0.5871 0.6601
vn -0.2995 0.8557 0.4220
vn -0.3867 -0.7440 0.5449
vn -0.3867 0.7440 0.5449
vn -0.2995 -0.8557 0.4220
vn -0.4685 0.5871 0.6601
vn -0.5863 -0.5396 0.6042
vn -0.3933 0.8252 0.4053
vn -0.4964 -0.7014 0.5115
vn -0.4964 0.7014 0.5115
vn -0.3933 -0.8252 0.4053
vn -0.5863 0.5396 0.6042
vn -0.2835 -0.9134 0.2922
vn -0.6545 0.3418 0.6744
vn -0.1708 -0.9695 0.1760
vn -0.6916 0.1173 0.7127
vn -0.0570 0.9966 0.0587
vn -0.0570 -0.9966 0.0587
vn -0.6916 -0.1173 0.7127
vn -0.1708 0.9695 0.1760
vn -0.6545 -0.3418 0.6744
vn -0.2835 0.9134 0.2922
vn -0.2083 -0.9636 0.1675
vn -0.7749 0.1071 0.6230
vn -0.0699 0.9960 0.0562
vn -0.0699 -0.9960 0.0562
vn -0.7749 -0.1071 0.6230
vn -0.2083 0.9636 0.1675
vn -0.7397 -0.3149 0.5947
vn -0.3422 0.8984 0.2751
vn -0.6729 -0.5046 0.5409
vn -0.4678 0.7998 0.3761
vn -0.5800 -0.6679 0.4663
vn -0.5800 0.6679 0.4663
vn -0.4678 -0.7998 0.3761
vn -0.6729 0.5046 0.5409
vn -0.3422 -0.8984 0.2751
vn -0.7397 0.3149 0.5947
vn -0.5244 0.7819 0.3371
vn -0.6426 -0.6453 0.4131
vn -0.6426 0.6453 0.4131
vn -0.5244 -0.7819 0.3371
vn -0.7370 0.4820 0.4738
vn -0.3877 -0.8875 0.2492
vn -0.8029 0.2980 0.5162
vn -0.2378 -0.9592 0.1529
vn -0.8369 0.1009 0.5380
vn -0.0801 0.9955 0.0515
vn -0.0801 -0.9955 0.0515
vn -0.8369 -0.1009 0.5380
vn -0.2378 0.9592 0.1529
vn -0.8029 -0.2980 0.5162
vn -0.3877 0.8875 0.2492
vn -0.7370 -0.4820 0.4738
vn -0.8851 0.0979 0.4550
vn -0.0873 0.9952 0.0449
vn -0.0873 -0.9952 0.0449
vn -0.8851 -0.0979 0.4550
vn -0.2584 0.9569 0.1328
vn -0.8512 -0.2900 0.4375
vn -0.4196 0.8817 0.2157
vn -0.7845 -0.4710 0.4033
vn -0.5646 0.7727 0.2902
vn -0.6878 -0.6340 0.3536
vn -0.6878 0.6340 0.3536
vn -0.5646 -0.7727 0.2902
vn -0.7845 0.4710 0.4033
vn -0.4196 -0.8817 0.2157
vn -0.8512 0.2900 0.4375
vn -0.2584 -0.9569 0.1328
vn -0.7179 -0.6340 0.2877
vn -0.7179 0.6340 0.2877
vn -0.5893 -0.7727 0.2361
vn -0.8188 0.4710 0.3281
vn -0.4379 -0.8817 0.1755
vn -0.8884 0.2900 0.3560
vn -0.2697 -0.9569 0.1081
vn -0.9238 0.0979 0.3702
vn -0.0911 0.9952 0.0365
vn -0.0911 -0.9952 0.0365
vn -0.9238 -0.0979 0.3702
vn -0.2697 0.9569 0.1081
vn -0.8884 -0.2900 0.3560
vn -0.4379 0.8817 0.1755
vn -0.8188 -0.4710 0.3281
vn -0.5893 0.7727 0.2361
vn -0.0914 0.9955 0.0267
vn -0.0914 -0.9955 0.0267
vn -0.9549 -0.1009 0.2793
vn -0.2713 0.9592 0.0794
vn -0.9162 -0.2980 0.2680
vn -0.4423 0.8875 0.1294
vn -0.8409 -0.4820 0.2460
vn -0.5983 0.7819 0.1750
vn -0.7332 -0.6453 0.2145
vn -0.7332 0.6453 0.2145
vn -0.5983 -0.7819 0.1750
vn -0.8409 0.4820 0.2460
vn -0.4423 -0.8875 0.1294
vn -0.9162 0.2980 0.2680
vn -0.2713 -0.9592 0.0794
vn -0.9549 0.1009 0.2793
vn -0.7324 0.6679 0.1324
vn -0.5907 -0.7998 0.1068
vn -0.8496 0.5046 0.1536
vn -0.4321 -0.8984 0.0781
vn -0.9340 0.3149 0.1688
vn -0.2630 -0.9636 0.0475
vn -0.9784 0.1071 0.1769
vn -0.0883 0.9960 0.0160
vn -0.0883 -0.9960 0.0160
vn -0.9784 -0.1071 0.1769
vn -0.2630 0.9636 0.0475
vn -0.9340 -0.3149 0.1688
vn -0.4321 0.8984 0.0781
vn -0.8496 -0.5046 0.1536
vn -0.5907 0.7998 0.1068
vn -0.7324 -0.6679 0.1324
vn -0.9916 -0.1173 0.0551
vn -0.2449 0.9695 0.0136
vn -0.9383 -0.3418 0.0522
vn -0.4065 0.9134 0.0226
vn -0.8406 -0.5396 0.0468
vn -0.5639 0.8252 0.0314
vn -0.7116 -0.7014 0.0396
vn -0.7116 0.7014 0.0396
vn -0.5639 -0.8252 0.0314
vn -0.8406 0.5396 0.0468
vn -0.4065 -0.9134 0.0226
vn -0.9383 0.3418 0.0522
vn -0.2449 -0.9695 0.0136
vn -0.9916 0.1173 0.0551
vn -0.0817 0.9966 0.0045
vn -0.0817 -0.9966 0.0045
vn -0.5150 -0.8557 -0.0504
vn -0.8056 0.5871 -0.0789
vn -0.3647 -0.9304 -0.0357
vn -0.9203 0.3806 -0.0901
vn -0.2171 -0.9759 -0.0213
vn -0.9865 0.1325 -0.0966
vn -0.0720 0.9974 -0.0071
vn -0.0720 -0.9974 -0.0071
vn -0.9865 -0.1325 -0.0966
vn -0.2171 0.9759 -0.0213
vn -0.9203 -0.3806 -0.0901
vn -0.3647 0.9304 -0.0357
vn -0.8056 -0.5871 -0.0789
vn -0.5150 0.8557 -0.0504
vn -0.6649 -0.7440 -0.0651
vn -0.6649 0.7440 -0.0651
vn -0.8620 -0.4314 -0.2662
vn -0.3068 0.9471 -0.0948
vn -0.7306 -0.6444 -0.2257
vn -0.4412 0.8870 -0.1363
vn -0.5843 -0.7912 -0.1805
vn -0.5843 0.7912 -0.1805
vn -0.4412 -0.8870 -0.1363
vn -0.7306 0.6444 -0.2257
vn -0.3068 -0.9471 -0.0948
vn -0.8620 0.4314 -0.2662
vn -0.1805 -0.9820 -0.0558
vn -0.9441 0.1534 -0.2916
vn -0.0596 0.9981 -0.0184
vn -0.0596 -0.9981 -0.0184
vn -0.9441 -0.1534 -0.2916
vn -0.1805 0.9820 -0.0558
vn -0.2338 -0.9604 -0.1516
vn -0.7323 0.4879 -0.4750
vn -0.1363 -0.9867 -0.0884
vn -0.8255 0.1786 -0.5355
vn -0.0448 0.9986 -0.0290
vn -0.0448 -0.9986 -0.0291
vn -0.8255 -0.1786 -0.5355
vn -0.1363 0.9867 -0.0884
vn -0.7323 -0.4879 -0.4750
vn -0.2338 0.9604 -0.1516
vn -0.5978 -0.7017 -0.3877
vn -0.3414 0.9135 -0.2214
vn -0.4628 -0.8341 -0.3002
vn -0.4628 0.8341 -0.3002
vn -0.3414 -0.9135 -0.2214
vn -0.5978 0.7017 -0.3877
vn -0.1486 0.9679 -0.2029
vn -0.3975 -0.7398 -0.5428
vn -0.2189 0.9288 -0.2990
vn -0.3011 -0.8604 -0.4112
vn -0.3011 0.8604 -0.4112
vn -0.2189 -0.9288 -0.2990
vn -0.3975 0.7398 -0.5428
vn -0.1486 -0.9679 -0.2029
vn -0.5012 0.5294 -0.6845
vn -0.0862 -0.9893 -0.1177
vn -0.5790 0.1986 -0.7908
vn -0.0283 0.9989 -0.0386
vn -0.0283 -0.9989 -0.0386
vn -0.5790 -0.1986 -0.7908
vn -0.0862 0.9893 -0.1177
vn -0.5012 -0.5294 -0.6845
vn -0.0558 -0.9679 -0.2452
vn 0.8255 0.1785 0.5355
vn -0.1367 0.1785 0.9744
vn -0.1367 -0.1786 0.9744
vn -0.8255 0.1785 -0.5355
vn -0.8255 -0.1785 -0.5355
usemtl None
s off
f 1937/1/1929 1948/2/1929 2406/3/1929
f 1931/4/1930 1940/5/1930 1941/6/1930
f 2406/3/1931 1949/7/1931 2407/8/1931
f 1931/4/1932 1942/9/1932 1932/10/1932
f 2407/8/1933 1950/11/1933 2408/12/1933
f 1932/10/1934 1943/13/1934 1933/14/1934
f 2408/12/1935 1951/15/1935 1938/16/1935
f 1933/14/1936 1944/17/1936 1934/18/1936
f 1938/16/1937 1952/19/1937 2409/20/1937
f 1934/18/1938 1945/21/1938 1935/22/1938
f 2409/20/1939 1953/23/1939 2410/24/1939
f 1935/22/1940 1946/25/1940 1936/26/1940
f 1929/27/1941 2225/28/1941 1939/29/1941
f 2134/30/1942 2410/24/1942 1953/23/1942
f 1936/26/1943 1947/31/1943 1937/1/1943
f 1929/27/1944 1940/5/1944 1930/32/1944
f 2134/33/1945 1953/23/1945 1968/34/1945
f 1947/31/1946 1961/35/1946 1962/36/1946
f 1939/29/1947 1955/37/1947 1940/5/1947
f 1948/2/1948 1962/36/1948 1963/38/1948
f 1940/5/1949 1956/39/1949 1941/6/1949
f 1948/2/1950 1964/40/1950 1949/7/1950
f 1941/6/1951 1957/41/1951 1942/9/1951
f 1950/11/1952 1964/40/1952 1965/42/1952
f 1942/9/1953 1958/43/1953 1943/13/1953
f 1950/11/1954 1966/44/1954 1951/15/1954
f 1943/13/1955 1959/45/1955 1944/17/1955
f 1951/15/1956 1967/46/1956 1952/19/1956
f 1944/17/1957 1960/47/1957 1945/21/1957
f 1952/19/1958 1968/34/1958 1953/23/1958
f 1945/21/1959 1961/35/1959 1946/25/1959
f 1939/29/1960 2225/48/1960 1954/49/1960
f 1965/42/1961 1981/50/1961 1966/44/1961
f 1958/43/1962 1974/51/1962 1959/45/1962
f 1966/44/1963 1982/52/1963 1967/46/1963
f 1960/47/1964 1974/51/1964 1975/53/1964
f 1967/46/1965 1983/54/1965 1968/34/1965
f 1960/47/1966 1976/55/1966 1961/35/1966
f 1954/49/1967 2225/56/1967 1969/57/1967
f 2134/58/1968 1968/34/1968 1983/54/1968
f 1961/35/1969 1977/59/1969 1962/36/1969
f 1954/49/1970 1970/60/1970 1955/37/1970
f 1962/36/1971 1978/61/1971 1963/38/1971
f 1955/37/1972 1971/62/1972 1956/39/1972
f 1964/40/1973 1978/61/1973 1979/63/1973
f 1956/39/1974 1972/64/1974 1957/41/1974
f 1965/42/1975 1979/63/1975 1980/65/1975
f 1957/41/1976 1973/66/1976 1958/43/1976
f 1969/57/1977 1985/67/1977 1970/60/1977
f 1977/59/1978 1993/68/1978 1978/61/1978
f 1970/60/1979 1986/69/1979 1971/62/1979
f 1979/63/1980 1993/68/1980 1994/70/1980
f 1971/62/1981 1987/71/1981 1972/64/1981
f 1979/63/1982 1995/72/1982 1980/65/1982
f 1972/64/1983 1988/73/1983 1973/66/1983
f 1980/65/1984 1996/74/1984 1981/50/1984
f 1973/66/1985 1989/75/1985 1974/51/1985
f 1981/50/1986 1997/76/1986 1982/52/1986
f 1974/51/1987 1990/77/1987 1975/53/1987
f 1982/52/1988 1998/78/1988 1983/54/1988
f 1976/55/1989 1990/77/1989 1991/79/1989
f 1969/57/1990 2225/80/1990 1984/81/1990
f 2134/82/1991 1983/54/1991 1998/78/1991
f 1976/55/1992 1992/83/1992 1977/59/1992
f 1988/73/1993 2004/84/1993 1989/75/1993
f 1997/76/1994 2011/85/1994 2012/86/1994
f 1989/75/1995 2005/87/1995 1990/77/1995
f 1997/76/1996 2013/88/1996 1998/78/1996
f 1991/79/1997 2005/87/1997 2006/89/1997
f 1984/81/1998 2225/90/1998 1999/91/1998
f 2134/92/1999 1998/78/1999 2013/88/1999
f 1991/79/2000 2007/93/2000 1992/83/2000
f 1984/81/2001 2000/94/2001 1985/67/2001
f 1992/83/2002 2008/95/2002 1993/68/2002
f 1985/67/2003 2001/96/2003 1986/69/2003
f 1994/70/2004 2008/95/2004 2009/97/2004
f 1986/69/2005 2002/98/2005 1987/71/2005
f 1994/70/2006 2010/99/2006 1995/72/2006
f 1988/73/2007 2002/98/2007 2003/100/2007
f 1996/74/2008 2010/99/2008 2011/85/2008
f 2007/93/2009 2023/101/2009 2008/95/2009
f 2000/94/2010 2016/102/2010 2001/96/2010
f 2009/97/2011 2023/101/2011 2024/103/2011
f 2001/96/2012 2017/104/2012 2002/98/2012
f 2010/99/2013 2024/103/2013 2025/105/2013
f 2003/100/2014 2017/104/2014 2018/106/2014
f 2010/99/2015 2026/107/2015 2011/85/2015
f 2003/100/2016 2019/108/2016 2004/84/2016
f 2012/86/2017 2026/107/2017 2027/109/2017
f 2005/87/2018 2019/108/2018 2020/110/2018
f 2012/86/2019 2028/111/2019 2013/88/2019
f 2005/87/2020 2021/112/2020 2006/89/2020
f 1999/91/2021 2225/113/2021 2014/114/2021
f 2134/115/2022 2013/88/2022 2028/111/2022
f 2006/89/2023 2022/116/2023 2007/93/2023
f 1999/91/2024 2015/117/2024 2000/94/2024
f 2026/107/2025 2042/118/2025 2027/109/2025
f 2020/110/2026 2034/119/2026 2035/120/2026
f 2028/111/2027 2042/118/2027 2043/121/2027
f 2020/110/2028 2036/122/2028 2021/112/2028
f 2014/114/2029 2225/123/2029 2029/124/2029
f 2134/125/2030 2028/111/2030 2043/121/2030
f 2021/112/2031 2037/126/2031 2022/116/2031
f 2014/114/2032 2030/127/2032 2015/117/2032
f 2022/116/2033 2038/128/2033 2023/101/2033
f 2015/117/2034 2031/129/2034 2016/102/2034
f 2024/103/2035 2038/128/2035 2039/130/2035
f 2017/104/2036 2031/129/2036 2032/131/2036
f 2025/105/2037 2039/130/2037 2040/132/2037
f 2017/104/2038 2033/133/2038 2018/106/2038
f 2025/105/2039 2041/134/2039 2026/107/2039
f 2018/106/2040 2034/119/2040 2019/108/2040
f 2030/127/2041 2046/135/2041 2031/129/2041
f 2039/130/2042 2053/136/2042 2054/137/2042
f 2031/129/2043 2047/138/2043 2032/131/2043
f 2039/130/2044 2055/139/2044 2040/132/2044
f 2032/131/2045 2048/140/2045 2033/133/2045
f 2041/134/2046 2055/139/2046 2056/141/2046
f 2033/133/2047 2049/142/2047 2034/119/2047
f 2042/118/2048 2056/141/2048 2057/143/2048
f 2035/120/2049 2049/142/2049 2050/144/2049
f 2043/121/2050 2057/143/2050 2058/145/2050
f 2035/120/2051 2051/146/2051 2036/122/2051
f 2029/124/2052 2225/147/2052 2044/148/2052
f 2134/149/2053 2043/121/2053 2058/145/2053
f 2037/126/2054 2051/146/2054 2052/150/2054
f 2029/124/2055 2045/151/2055 2030/127/2055
f 2037/126/2056 2053/136/2056 2038/128/2056
f 2050/144/2057 2064/152/2057 2065/153/2057
f 2058/145/2058 2072/154/2058 2073/155/2058
f 2050/144/2059 2066/156/2059 2051/146/2059
f 2044/148/2060 2225/157/2060 2059/158/2060
f 2134/159/2061 2058/145/2061 2073/155/2061
f 2051/146/2062 2067/160/2062 2052/150/2062
f 2045/151/2063 2059/158/2063 2060/161/2063
f 2052/150/2064 2068/162/2064 2053/136/2064
f 2045/151/2065 2061/163/2065 2046/135/2065
f 2054/137/2066 2068/162/2066 2069/164/2066
f 2046/135/2067 2062/165/2067 2047/138/2067
f 2054/137/2068 2070/166/2068 2055/139/2068
f 2048/140/2069 2062/165/2069 2063/167/2069
f 2056/141/2070 2070/166/2070 2071/168/2070
f 2048/140/2071 2064/152/2071 2049/142/2071
f 2056/141/2072 2072/154/2072 2057/143/2072
f 2069/164/2073 2083/169/2073 2084/170/2073
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f 2069/164/2075 2085/172/2075 2070/166/2075
f 2062/165/2076 2078/173/2076 2063/167/2076
f 2070/166/2077 2086/174/2077 2071/168/2077
f 2063/167/2078 2079/175/2078 2064/152/2078
f 2072/154/2079 2086/174/2079 2087/176/2079
f 2065/153/2080 2079/175/2080 2080/177/2080
f 2072/154/2081 2088/178/2081 2073/155/2081
f 2065/153/2082 2081/179/2082 2066/156/2082
f 2059/158/2083 2225/180/2083 2074/181/2083
f 2134/182/2084 2073/155/2084 2088/178/2084
f 2066/156/2085 2082/183/2085 2067/160/2085
f 2059/158/2086 2075/184/2086 2060/161/2086
f 2067/160/2087 2083/169/2087 2068/162/2087
f 2061/163/2088 2075/184/2088 2076/185/2088
f 2087/176/2089 2103/186/2089 2088/178/2089
f 2080/177/2090 2096/187/2090 2081/179/2090
f 2074/181/2091 2225/188/2091 2089/189/2091
f 2134/190/2092 2088/178/2092 2103/186/2092
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f 2074/181/2094 2090/192/2094 2075/184/2094
f 2082/183/2095 2098/193/2095 2083/169/2095
f 2075/184/2096 2091/194/2096 2076/185/2096
f 2084/170/2097 2098/193/2097 2099/195/2097
f 2076/185/2098 2092/196/2098 2077/171/2098
f 2084/170/2099 2100/197/2099 2085/172/2099
f 2078/173/2100 2092/196/2100 2093/198/2100
f 2086/174/2101 2100/197/2101 2101/199/2101
f 2078/173/2102 2094/200/2102 2079/175/2102
f 2086/174/2103 2102/201/2103 2087/176/2103
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vn -0.2175 0.9759 0.0174
vn -0.7285 0.4820 -0.4867
usemtl None
s off
f 2419/560/2447 2430/561/2447 2888/562/2447
f 2413/563/2448 2422/564/2448 2423/565/2448
f 2888/562/2449 2431/566/2449 2889/567/2449
f 2413/563/2450 2424/568/2450 2414/569/2450
f 2889/567/2451 2432/570/2451 2890/571/2451
f 2414/569/2452 2425/572/2452 2415/573/2452
f 2890/571/2453 2433/574/2453 2420/575/2453
f 2415/573/2454 2426/576/2454 2416/577/2454
f 2420/575/2455 2434/578/2455 2891/579/2455
f 2416/577/2456 2427/580/2456 2417/581/2456
f 2891/579/2457 2435/582/2457 2892/583/2457
f 2417/581/2458 2428/584/2458 2418/585/2458
f 2411/586/2459 2707/587/2459 2421/588/2459
f 2616/589/2460 2892/583/2460 2435/582/2460
f 2418/585/2461 2429/590/2461 2419/560/2461
f 2411/586/2462 2422/564/2462 2412/591/2462
f 2616/592/2463 2435/582/2463 2450/593/2463
f 2429/590/2464 2443/594/2464 2444/595/2464
f 2421/588/2465 2437/596/2465 2422/564/2465
f 2430/561/2466 2444/595/2466 2445/597/2466
f 2422/564/2467 2438/598/2467 2423/565/2467
f 2430/561/2468 2446/599/2468 2431/566/2468
f 2423/565/2469 2439/600/2469 2424/568/2469
f 2432/570/2470 2446/599/2470 2447/601/2470
f 2424/568/2471 2440/602/2471 2425/572/2471
f 2432/570/2472 2448/603/2472 2433/574/2472
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f 2433/574/2474 2449/605/2474 2434/578/2474
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f 2434/578/2476 2450/593/2476 2435/582/2476
f 2427/580/2477 2443/594/2477 2428/584/2477
f 2421/588/2478 2707/607/2478 2436/608/2478
f 2447/601/2479 2463/609/2479 2448/603/2479
f 2440/602/2480 2456/610/2480 2441/604/2480
f 2448/603/2481 2464/611/2481 2449/605/2481
f 2442/606/2482 2456/610/2482 2457/612/2482
f 2449/605/2483 2465/613/2483 2450/593/2483
f 2442/606/2484 2458/614/2484 2443/594/2484
f 2436/608/2485 2707/615/2485 2451/616/2485
f 2616/617/2486 2450/593/2486 2465/613/2486
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f 2444/595/2489 2460/620/2489 2445/597/2489
f 2437/596/2490 2453/621/2490 2438/598/2490
f 2446/599/2491 2460/620/2491 2461/622/2491
f 2438/598/2492 2454/623/2492 2439/600/2492
f 2447/601/2493 2461/622/2493 2462/624/2493
f 2439/600/2494 2455/625/2494 2440/602/2494
f 2451/616/2495 2467/626/2495 2452/619/2495
f 2459/618/2496 2475/627/2496 2460/620/2496
f 2452/619/2497 2468/628/2497 2453/621/2497
f 2461/622/2498 2475/627/2498 2476/629/2498
f 2453/621/2499 2469/630/2499 2454/623/2499
f 2461/622/2500 2477/631/2500 2462/624/2500
f 2454/623/2501 2470/632/2501 2455/625/2501
f 2462/624/2502 2478/633/2502 2463/609/2502
f 2455/625/2503 2471/634/2503 2456/610/2503
f 2463/609/2504 2479/635/2504 2464/611/2504
f 2456/610/2505 2472/636/2505 2457/612/2505
f 2464/611/2506 2480/637/2506 2465/613/2506
f 2458/614/2507 2472/636/2507 2473/638/2507
f 2451/616/2508 2707/639/2508 2466/640/2508
f 2616/641/2509 2465/613/2509 2480/637/2509
f 2458/614/2510 2474/642/2510 2459/618/2510
f 2470/632/2511 2486/643/2511 2471/634/2511
f 2479/635/2512 2493/644/2512 2494/645/2512
f 2471/634/2513 2487/646/2513 2472/636/2513
f 2479/635/2514 2495/647/2514 2480/637/2514
f 2473/638/2515 2487/646/2515 2488/648/2515
f 2466/640/2516 2707/649/2516 2481/650/2516
f 2616/651/2517 2480/637/2517 2495/647/2517
f 2473/638/2518 2489/652/2518 2474/642/2518
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f 2467/626/2521 2483/655/2521 2468/628/2521
f 2476/629/2522 2490/654/2522 2491/656/2522
f 2468/628/2523 2484/657/2523 2469/630/2523
f 2476/629/2524 2492/658/2524 2477/631/2524
f 2470/632/2525 2484/657/2525 2485/659/2525
f 2478/633/2526 2492/658/2526 2493/644/2526
f 2489/652/2527 2505/660/2527 2490/654/2527
f 2482/653/2528 2498/661/2528 2483/655/2528
f 2491/656/2529 2505/660/2529 2506/662/2529
f 2483/655/2530 2499/663/2530 2484/657/2530
f 2492/658/2531 2506/662/2531 2507/664/2531
f 2485/659/2532 2499/663/2532 2500/665/2532
f 2492/658/2533 2508/666/2533 2493/644/2533
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f 2494/645/2537 2510/670/2537 2495/647/2537
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f 2496/673/2547 2707/682/2547 2511/683/2547
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f 2499/663/2554 2513/688/2554 2514/690/2554
f 2507/664/2555 2521/689/2555 2522/691/2555
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f 2507/664/2557 2523/693/2557 2508/666/2557
f 2500/665/2558 2516/678/2558 2501/667/2558
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f 2521/689/2562 2537/698/2562 2522/691/2562
f 2514/690/2563 2530/699/2563 2515/692/2563
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f 2517/679/2567 2531/701/2567 2532/703/2567
f 2525/680/2568 2539/702/2568 2540/704/2568
f 2517/679/2569 2533/705/2569 2518/681/2569
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f 2519/685/2574 2535/695/2574 2520/687/2574
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f 2616/718/2579 2540/704/2579 2555/714/2579
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f 2530/699/2589 2546/711/2589 2531/701/2589
f 2538/700/2590 2554/713/2590 2539/702/2590
f 2551/723/2591 2565/728/2591 2566/729/2591
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================================================
FILE: load/tets/generate_tets.py
================================================
# Copyright (c) 2020-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
import os
import numpy as np
"""
This code segment shows how to use Quartet: https://github.com/crawforddoran/quartet,
to generate a tet grid
1) Download, compile and run Quartet as described in the link above. Example usage `quartet meshes/cube.obj 0.5 cube_5.tet`
2) Run the function below to generate a file `cube_32_tet.tet`
"""
def generate_tetrahedron_grid_file(res=32, root=".."):
frac = 1.0 / res
command = f"cd {root}; ./quartet meshes/cube.obj {frac} meshes/cube_{res}_tet.tet -s meshes/cube_boundary_{res}.obj"
os.system(command)
"""
This code segment shows how to convert from a quartet .tet file to compressed npz file
"""
def convert_from_quartet_to_npz(quartetfile="cube_32_tet.tet", npzfile="32_tets"):
file1 = open(quartetfile, "r")
header = file1.readline()
numvertices = int(header.split(" ")[1])
numtets = int(header.split(" ")[2])
print(numvertices, numtets)
# load vertices
vertices = np.loadtxt(quartetfile, skiprows=1, max_rows=numvertices)
print(vertices.shape)
# load indices
indices = np.loadtxt(
quartetfile, dtype=int, skiprows=1 + numvertices, max_rows=numtets
)
print(indices.shape)
np.savez_compressed(npzfile, vertices=vertices, indices=indices)
root = "/home/gyc/quartet"
for res in [300, 350, 400]:
generate_tetrahedron_grid_file(res, root)
convert_from_quartet_to_npz(
os.path.join(root, f"meshes/cube_{res}_tet.tet"), npzfile=f"{res}_tets"
)
================================================
FILE: load/zero123/download.sh
================================================
wget https://huggingface.co/cvlab/zero123-weights/resolve/main/105000.ckpt
================================================
FILE: load/zero123/sd-objaverse-finetune-c_concat-256.yaml
================================================
model:
base_learning_rate: 1.0e-04
target: extern.ldm_zero123.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "image_target"
cond_stage_key: "image_cond"
image_size: 32
channels: 4
cond_stage_trainable: false # Note: different from the one we trained before
conditioning_key: hybrid
monitor: val/loss_simple_ema
scale_factor: 0.18215
scheduler_config: # 10000 warmup steps
target: extern.ldm_zero123.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 100 ]
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
f_start: [ 1.e-6 ]
f_max: [ 1. ]
f_min: [ 1. ]
unet_config:
target: extern.ldm_zero123.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32 # unused
in_channels: 8
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
legacy: False
first_stage_config:
target: extern.ldm_zero123.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: extern.ldm_zero123.modules.encoders.modules.FrozenCLIPImageEmbedder
# data:
# target: extern.ldm_zero123.data.simple.ObjaverseDataModuleFromConfig
# params:
# root_dir: 'views_whole_sphere'
# batch_size: 192
# num_workers: 16
# total_view: 4
# train:
# validation: False
# image_transforms:
# size: 256
# validation:
# validation: True
# image_transforms:
# size: 256
# lightning:
# find_unused_parameters: false
# metrics_over_trainsteps_checkpoint: True
# modelcheckpoint:
# params:
# every_n_train_steps: 5000
# callbacks:
# image_logger:
# target: main.ImageLogger
# params:
# batch_frequency: 500
# max_images: 32
# increase_log_steps: False
# log_first_step: True
# log_images_kwargs:
# use_ema_scope: False
# inpaint: False
# plot_progressive_rows: False
# plot_diffusion_rows: False
# N: 32
# unconditional_scale: 3.0
# unconditional_label: [""]
# trainer:
# benchmark: True
# val_check_interval: 5000000 # really sorry
# num_sanity_val_steps: 0
# accumulate_grad_batches: 1
================================================
FILE: requirements-dev.txt
================================================
black
mypy
pylint
pre-commit
================================================
FILE: requirements.txt
================================================
lightning==2.0.0
omegaconf==2.3.0
jaxtyping
typeguard
git+https://github.com/KAIR-BAIR/nerfacc.git@v0.5.2
git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
diffusers
transformers
accelerate
opencv-python
tensorboard
matplotlib
imageio
imageio[ffmpeg]
trimesh
git+https://github.com/NVlabs/nvdiffrast.git
libigl
xatlas
trimesh[easy]
networkx
pysdf
# deepfloyd
xformers
bitsandbytes
sentencepiece
safetensors
huggingface_hub
libigl
xatlas
trimesh
networkx
PyMCubes
wandb
# for zero123
einops
kornia
taming-transformers-rom1504
git+https://github.com/openai/CLIP.git
av
open-clip-torch==2.24.0
================================================
FILE: threestudio/__init__.py
================================================
__modules__ = {}
def register(name):
def decorator(cls):
__modules__[name] = cls
return cls
return decorator
def find(name):
return __modules__[name]
### grammar sugar for logging utilities ###
import logging
logger = logging.getLogger("pytorch_lightning")
from pytorch_lightning.utilities.rank_zero import (
rank_zero_debug,
rank_zero_info,
rank_zero_only,
)
debug = rank_zero_debug
info = rank_zero_info
@rank_zero_only
def warn(*args, **kwargs):
logger.warn(*args, **kwargs)
from . import data, models, systems
================================================
FILE: threestudio/data/__init__.py
================================================
from . import co3d, image, uncond, multiview, random_multiview, single_multiview_combined
================================================
FILE: threestudio/data/co3d.py
================================================
import gzip
import json
import os
import warnings
from dataclasses import dataclass, field
from typing import List
import cv2
import numpy as np
import pytorch_lightning as pl
import torch
import torchvision.transforms.functional as TF
from PIL import Image
from torch.utils.data import DataLoader, Dataset, IterableDataset
from threestudio import register
from threestudio.data.uncond import (
RandomCameraDataModuleConfig,
RandomCameraDataset,
RandomCameraIterableDataset,
)
from threestudio.utils.config import parse_structured
from threestudio.utils.misc import get_rank
from threestudio.utils.ops import (
get_mvp_matrix,
get_projection_matrix,
get_ray_directions,
get_rays,
)
from threestudio.utils.typing import *
def _load_16big_png_depth(depth_png) -> np.ndarray:
with Image.open(depth_png) as depth_pil:
# the image is stored with 16-bit depth but PIL reads it as I (32 bit).
# we cast it to uint16, then reinterpret as float16, then cast to float32
depth = (
np.frombuffer(np.array(depth_pil, dtype=np.uint16), dtype=np.float16)
.astype(np.float32)
.reshape((depth_pil.size[1], depth_pil.size[0]))
)
return depth
def _load_depth(path, scale_adjustment) -> np.ndarray:
if not path.lower().endswith(".png"):
raise ValueError('unsupported depth file name "%s"' % path)
d = _load_16big_png_depth(path) * scale_adjustment
d[~np.isfinite(d)] = 0.0
return d[None] # fake feature channel
# Code adapted from https://github.com/eldar/snes/blob/473ff2b1f6/3rdparty/co3d/dataset/co3d_dataset.py
def _get_1d_bounds(arr):
nz = np.flatnonzero(arr)
return nz[0], nz[-1]
def get_bbox_from_mask(mask, thr, decrease_quant=0.05):
# bbox in xywh
masks_for_box = np.zeros_like(mask)
while masks_for_box.sum() <= 1.0:
masks_for_box = (mask > thr).astype(np.float32)
thr -= decrease_quant
if thr <= 0.0:
warnings.warn(f"Empty masks_for_bbox (thr={thr}) => using full image.")
x0, x1 = _get_1d_bounds(masks_for_box.sum(axis=-2))
y0, y1 = _get_1d_bounds(masks_for_box.sum(axis=-1))
return x0, y0, x1 - x0, y1 - y0
def get_clamp_bbox(bbox, box_crop_context=0.0, impath=""):
# box_crop_context: rate of expansion for bbox
# returns possibly expanded bbox xyxy as float
# increase box size
if box_crop_context > 0.0:
c = box_crop_context
bbox = bbox.astype(np.float32)
bbox[0] -= bbox[2] * c / 2
bbox[1] -= bbox[3] * c / 2
bbox[2] += bbox[2] * c
bbox[3] += bbox[3] * c
if (bbox[2:] <= 1.0).any():
warnings.warn(f"squashed image {impath}!!")
return None
# bbox[2:] = np.clip(bbox[2:], 2, )
bbox[2:] = np.maximum(bbox[2:], 2)
bbox[2:] += bbox[0:2] + 1 # convert to [xmin, ymin, xmax, ymax]
# +1 because upper bound is not inclusive
return bbox
def crop_around_box(tensor, bbox, impath=""):
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0.0, tensor.shape[-2])
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0.0, tensor.shape[-3])
bbox = bbox.round().astype(np.longlong)
return tensor[bbox[1] : bbox[3], bbox[0] : bbox[2], ...]
def resize_image(image, height, width, mode="bilinear"):
if image.shape[:2] == (height, width):
return image, 1.0, np.ones_like(image[..., :1])
image = torch.from_numpy(image).permute(2, 0, 1)
minscale = min(height / image.shape[-2], width / image.shape[-1])
imre = torch.nn.functional.interpolate(
image[None],
scale_factor=minscale,
mode=mode,
align_corners=False if mode == "bilinear" else None,
recompute_scale_factor=True,
)[0]
# pyre-fixme[19]: Expected 1 positional argument.
imre_ = torch.zeros(image.shape[0], height, width)
imre_[:, 0 : imre.shape[1], 0 : imre.shape[2]] = imre
# pyre-fixme[6]: For 2nd param expected `int` but got `Optional[int]`.
# pyre-fixme[6]: For 3rd param expected `int` but got `Optional[int]`.
mask = torch.zeros(1, height, width)
mask[:, 0 : imre.shape[1], 0 : imre.shape[2]] = 1.0
return imre_.permute(1, 2, 0).numpy(), minscale, mask.permute(1, 2, 0).numpy()
# Code adapted from https://github.com/POSTECH-CVLab/PeRFception/data_util/co3d.py
def similarity_from_cameras(c2w, fix_rot=False, radius=1.0):
"""
Get a similarity transform to normalize dataset
from c2w (OpenCV convention) cameras
:param c2w: (N, 4)
:return T (4,4) , scale (float)
"""
t = c2w[:, :3, 3]
R = c2w[:, :3, :3]
# (1) Rotate the world so that z+ is the up axis
# we estimate the up axis by averaging the camera up axes
ups = np.sum(R * np.array([0, -1.0, 0]), axis=-1)
world_up = np.mean(ups, axis=0)
world_up /= np.linalg.norm(world_up)
up_camspace = np.array([0.0, 0.0, 1.0])
c = (up_camspace * world_up).sum()
cross = np.cross(world_up, up_camspace)
skew = np.array(
[
[0.0, -cross[2], cross[1]],
[cross[2], 0.0, -cross[0]],
[-cross[1], cross[0], 0.0],
]
)
if c > -1:
R_align = np.eye(3) + skew + (skew @ skew) * 1 / (1 + c)
else:
# In the unlikely case the original data has y+ up axis,
# rotate 180-deg about x axis
R_align = np.array([[-1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]])
if fix_rot:
R_align = np.eye(3)
R = np.eye(3)
else:
R = R_align @ R
fwds = np.sum(R * np.array([0, 0.0, 1.0]), axis=-1)
t = (R_align @ t[..., None])[..., 0]
# (2) Recenter the scene using camera center rays
# find the closest point to the origin for each camera's center ray
nearest = t + (fwds * -t).sum(-1)[:, None] * fwds
# median for more robustness
translate = -np.median(nearest, axis=0)
# translate = -np.mean(t, axis=0) # DEBUG
transform = np.eye(4)
transform[:3, 3] = translate
transform[:3, :3] = R_align
# (3) Rescale the scene using camera distances
scale = radius / np.median(np.linalg.norm(t + translate, axis=-1))
return transform, scale
@dataclass
class Co3dDataModuleConfig:
root_dir: str = ""
batch_size: int = 1
height: int = 256
width: int = 256
load_preprocessed: bool = False
cam_scale_factor: float = 0.95
max_num_frames: int = 300
v2_mode: bool = True
use_mask: bool = True
box_crop: bool = True
box_crop_mask_thr: float = 0.4
box_crop_context: float = 0.3
train_num_rays: int = -1
train_views: Optional[list] = None
train_split: str = "train"
val_split: str = "val"
test_split: str = "test"
scale_radius: float = 1.0
use_random_camera: bool = True
random_camera: dict = field(default_factory=dict)
rays_noise_scale: float = 0.0
render_path: str = "circle"
class Co3dDatasetBase:
def setup(self, cfg, split):
self.split = split
self.rank = get_rank()
self.cfg: Co3dDataModuleConfig = cfg
if self.cfg.use_random_camera:
random_camera_cfg = parse_structured(
RandomCameraDataModuleConfig, self.cfg.get("random_camera", {})
)
if split == "train":
self.random_pose_generator = RandomCameraIterableDataset(
random_camera_cfg
)
else:
self.random_pose_generator = RandomCameraDataset(
random_camera_cfg, split
)
self.use_mask = self.cfg.use_mask
cam_scale_factor = self.cfg.cam_scale_factor
assert os.path.exists(self.cfg.root_dir), f"{self.cfg.root_dir} doesn't exist!"
cam_trans = np.diag(np.array([-1, -1, 1, 1], dtype=np.float32))
scene_number = self.cfg.root_dir.split("/")[-1]
json_path = os.path.join(self.cfg.root_dir, "..", "frame_annotations.jgz")
with gzip.open(json_path, "r") as fp:
all_frames_data = json.load(fp)
frame_data, images, intrinsics, extrinsics, image_sizes = [], [], [], [], []
masks = []
depths = []
for temporal_data in all_frames_data:
if temporal_data["sequence_name"] == scene_number:
frame_data.append(temporal_data)
self.all_directions = []
self.all_fg_masks = []
for frame in frame_data:
if "unseen" in frame["meta"]["frame_type"]:
continue
img = cv2.imread(
os.path.join(self.cfg.root_dir, "..", "..", frame["image"]["path"])
)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
# TODO: use estimated depth
depth = _load_depth(
os.path.join(self.cfg.root_dir, "..", "..", frame["depth"]["path"]),
frame["depth"]["scale_adjustment"],
)[0]
H, W = frame["image"]["size"]
image_size = np.array([H, W])
fxy = np.array(frame["viewpoint"]["focal_length"])
cxy = np.array(frame["viewpoint"]["principal_point"])
R = np.array(frame["viewpoint"]["R"])
T = np.array(frame["viewpoint"]["T"])
if self.cfg.v2_mode:
min_HW = min(W, H)
image_size_half = np.array([W * 0.5, H * 0.5], dtype=np.float32)
scale_arr = np.array([min_HW * 0.5, min_HW * 0.5], dtype=np.float32)
fxy_x = fxy * scale_arr
prp_x = np.array([W * 0.5, H * 0.5], dtype=np.float32) - cxy * scale_arr
cxy = (image_size_half - prp_x) / image_size_half
fxy = fxy_x / image_size_half
scale_arr = np.array([W * 0.5, H * 0.5], dtype=np.float32)
focal = fxy * scale_arr
prp = -1.0 * (cxy - 1.0) * scale_arr
pose = np.eye(4)
pose[:3, :3] = R
pose[:3, 3:] = -R @ T[..., None]
# original camera: x left, y up, z in (Pytorch3D)
# transformed camera: x right, y down, z in (OpenCV)
pose = pose @ cam_trans
intrinsic = np.array(
[
[focal[0], 0.0, prp[0], 0.0],
[0.0, focal[1], prp[1], 0.0],
[0.0, 0.0, 1.0, 0.0],
[0.0, 0.0, 0.0, 1.0],
]
)
if any([np.all(pose == _pose) for _pose in extrinsics]):
continue
image_sizes.append(image_size)
intrinsics.append(intrinsic)
extrinsics.append(pose)
images.append(img)
depths.append(depth)
self.all_directions.append(get_ray_directions(W, H, focal, prp))
# vis_utils.vis_depth_pcd([depth], [pose], intrinsic, [(img * 255).astype(np.uint8)])
if self.use_mask:
mask = np.array(
Image.open(
os.path.join(
self.cfg.root_dir, "..", "..", frame["mask"]["path"]
)
)
)
mask = mask.astype(np.float32) / 255.0 # (h, w)
else:
mask = torch.ones_like(img[..., 0])
self.all_fg_masks.append(mask)
intrinsics = np.stack(intrinsics)
extrinsics = np.stack(extrinsics)
image_sizes = np.stack(image_sizes)
self.all_directions = torch.stack(self.all_directions, dim=0)
self.all_fg_masks = np.stack(self.all_fg_masks, 0)
H_median, W_median = np.median(
np.stack([image_size for image_size in image_sizes]), axis=0
)
H_inlier = np.abs(image_sizes[:, 0] - H_median) / H_median < 0.1
W_inlier = np.abs(image_sizes[:, 1] - W_median) / W_median < 0.1
inlier = np.logical_and(H_inlier, W_inlier)
dists = np.linalg.norm(
extrinsics[:, :3, 3] - np.median(extrinsics[:, :3, 3], axis=0), axis=-1
)
med = np.median(dists)
good_mask = dists < (med * 5.0)
inlier = np.logical_and(inlier, good_mask)
if inlier.sum() != 0:
intrinsics = intrinsics[inlier]
extrinsics = extrinsics[inlier]
image_sizes = image_sizes[inlier]
images = [images[i] for i in range(len(inlier)) if inlier[i]]
depths = [depths[i] for i in range(len(inlier)) if inlier[i]]
self.all_directions = self.all_directions[inlier]
self.all_fg_masks = self.all_fg_masks[inlier]
extrinsics = np.stack(extrinsics)
T, sscale = similarity_from_cameras(extrinsics, radius=self.cfg.scale_radius)
extrinsics = T @ extrinsics
extrinsics[:, :3, 3] *= sscale * cam_scale_factor
depths = [depth * sscale * cam_scale_factor for depth in depths]
num_frames = len(extrinsics)
if self.cfg.max_num_frames < num_frames:
num_frames = self.cfg.max_num_frames
extrinsics = extrinsics[:num_frames]
intrinsics = intrinsics[:num_frames]
image_sizes = image_sizes[:num_frames]
images = images[:num_frames]
depths = depths[:num_frames]
self.all_directions = self.all_directions[:num_frames]
self.all_fg_masks = self.all_fg_masks[:num_frames]
if self.cfg.box_crop:
print("cropping...")
crop_masks = []
crop_imgs = []
crop_depths = []
crop_directions = []
crop_xywhs = []
max_sl = 0
for i in range(num_frames):
bbox_xywh = np.array(
get_bbox_from_mask(self.all_fg_masks[i], self.cfg.box_crop_mask_thr)
)
clamp_bbox_xywh = get_clamp_bbox(bbox_xywh, self.cfg.box_crop_context)
max_sl = max(clamp_bbox_xywh[2] - clamp_bbox_xywh[0], max_sl)
max_sl = max(clamp_bbox_xywh[3] - clamp_bbox_xywh[1], max_sl)
mask = crop_around_box(self.all_fg_masks[i][..., None], clamp_bbox_xywh)
img = crop_around_box(images[i], clamp_bbox_xywh)
depth = crop_around_box(depths[i][..., None], clamp_bbox_xywh)
# resize to the same shape
mask, _, _ = resize_image(mask, self.cfg.height, self.cfg.width)
depth, _, _ = resize_image(depth, self.cfg.height, self.cfg.width)
img, scale, _ = resize_image(img, self.cfg.height, self.cfg.width)
fx, fy, cx, cy = (
intrinsics[i][0, 0],
intrinsics[i][1, 1],
intrinsics[i][0, 2],
intrinsics[i][1, 2],
)
crop_masks.append(mask)
crop_imgs.append(img)
crop_depths.append(depth)
crop_xywhs.append(clamp_bbox_xywh)
crop_directions.append(
get_ray_directions(
self.cfg.height,
self.cfg.width,
(fx * scale, fy * scale),
(
(cx - clamp_bbox_xywh[0]) * scale,
(cy - clamp_bbox_xywh[1]) * scale,
),
)
)
# # pad all images to the same shape
# for i in range(num_frames):
# uh = (max_sl - crop_imgs[i].shape[0]) // 2 # h
# dh = max_sl - crop_imgs[i].shape[0] - uh
# lw = (max_sl - crop_imgs[i].shape[1]) // 2
# rw = max_sl - crop_imgs[i].shape[1] - lw
# crop_masks[i] = np.pad(crop_masks[i], pad_width=((uh, dh), (lw, rw), (0, 0)), mode='constant', constant_values=0.)
# crop_imgs[i] = np.pad(crop_imgs[i], pad_width=((uh, dh), (lw, rw), (0, 0)), mode='constant', constant_values=1.)
# crop_depths[i] = np.pad(crop_depths[i], pad_width=((uh, dh), (lw, rw), (0, 0)), mode='constant', constant_values=0.)
# fx, fy, cx, cy = intrinsics[i][0, 0], intrinsics[i][1, 1], intrinsics[i][0, 2], intrinsics[i][1, 2]
# crop_directions.append(get_ray_directions(max_sl, max_sl, (fx, fy), (cx - crop_xywhs[i][0] + lw, cy - crop_xywhs[i][1] + uh)))
# self.w, self.h = max_sl, max_sl
images = crop_imgs
depths = crop_depths
self.all_fg_masks = np.stack(crop_masks, 0)
self.all_directions = torch.from_numpy(np.stack(crop_directions, 0))
# self.width, self.height = self.w, self.h
self.all_c2w = torch.from_numpy(
(
extrinsics
@ np.diag(np.array([1, -1, -1, 1], dtype=np.float32))[None, ...]
)[..., :3, :4]
)
self.all_images = torch.from_numpy(np.stack(images, axis=0))
self.all_depths = torch.from_numpy(np.stack(depths, axis=0))
# self.all_c2w = []
# self.all_images = []
# for i in range(num_frames):
# # convert to: x right, y up, z back (OpenGL)
# c2w = torch.from_numpy(extrinsics[i] @ np.diag(np.array([1, -1, -1, 1], dtype=np.float32)))[:3, :4]
# self.all_c2w.append(c2w)
# img = torch.from_numpy(images[i])
# self.all_images.append(img)
# TODO: save data for fast loading next time
if self.cfg.load_preprocessed and os.path.exists(
self.cfg.root_dir, "nerf_preprocessed.npy"
):
pass
i_all = np.arange(num_frames)
if self.cfg.train_views is None:
i_test = i_all[::10]
i_val = i_test
i_train = np.array([i for i in i_all if not i in i_test])
else:
# use provided views
i_train = self.cfg.train_views
i_test = np.array([i for i in i_all if not i in i_train])
i_val = i_test
if self.split == "train":
print("[INFO] num of train views: ", len(i_train))
print("[INFO] train view ids = ", i_train)
i_split = {"train": i_train, "val": i_val, "test": i_all}
# if self.split == 'test':
# self.all_c2w = create_spheric_poses(self.all_c2w[:,:,3], n_steps=self.cfg.n_test_traj_steps)
# self.all_images = torch.zeros((self.cfg.n_test_traj_steps, self.h, self.w, 3), dtype=torch.float32)
# self.all_fg_masks = torch.zeros((self.cfg.n_test_traj_steps, self.h, self.w), dtype=torch.float32)
# self.directions = self.directions[0].to(self.rank)
# else:
self.all_images, self.all_c2w = (
self.all_images[i_split[self.split]],
self.all_c2w[i_split[self.split]],
)
self.all_directions = self.all_directions[i_split[self.split]].to(self.rank)
self.all_fg_masks = torch.from_numpy(self.all_fg_masks)[i_split[self.split]]
self.all_depths = self.all_depths[i_split[self.split]]
# if render_random_pose:
# render_poses = random_pose(extrinsics[i_all], 50)
# elif render_scene_interp:
# render_poses = pose_interp(extrinsics[i_all], interp_fac)
# render_poses = spherical_poses(sscale * cam_scale_factor * np.eye(4))
# near, far = 0., 1.
# ndc_coeffs = (-1., -1.)
self.all_c2w, self.all_images, self.all_fg_masks = (
self.all_c2w.float().to(self.rank),
self.all_images.float().to(self.rank),
self.all_fg_masks.float().to(self.rank),
)
# self.all_c2w, self.all_images, self.all_fg_masks = \
# self.all_c2w.float(), \
# self.all_images.float(), \
# self.all_fg_masks.float()
self.all_depths = self.all_depths.float().to(self.rank)
def get_all_images(self):
return self.all_images
class Co3dDataset(Dataset, Co3dDatasetBase):
def __init__(self, cfg, split):
self.setup(cfg, split)
def __len__(self):
if self.split == "test":
if self.cfg.render_path == "circle":
return len(self.random_pose_generator)
else:
return len(self.all_images)
else:
return len(self.random_pose_generator)
# return len(self.all_images)
def prepare_data(self, index):
# prepare batch data here
c2w = self.all_c2w[index]
light_positions = c2w[..., :3, -1]
directions = self.all_directions[index]
rays_o, rays_d = get_rays(
directions, c2w, keepdim=True, noise_scale=self.cfg.rays_noise_scale
)
rgb = self.all_images[index]
depth = self.all_depths[index]
mask = self.all_fg_masks[index]
# TODO: get projection matrix and mvp matrix
# proj_mtx = get_projection_matrix()
batch = {
"rays_o": rays_o,
"rays_d": rays_d,
"mvp_mtx": 0,
"camera_positions": c2w[..., :3, -1],
"light_positions": light_positions,
"elevation": 0,
"azimuth": 0,
"camera_distances": 0,
"rgb": rgb,
"depth": depth,
"mask": mask,
}
# c2w = self.all_c2w[index]
# return {
# 'index': index,
# 'c2w': c2w,
# 'light_positions': c2w[:3, -1],
# 'H': self.h,
# 'W': self.w
# }
return batch
def __getitem__(self, index):
if self.split == "test":
if self.cfg.render_path == "circle":
return self.random_pose_generator[index]
else:
return self.prepare_data(index)
else:
return self.random_pose_generator[index]
class Co3dIterableDataset(IterableDataset, Co3dDatasetBase):
def __init__(self, cfg, split):
self.setup(cfg, split)
self.idx = 0
self.image_perm = torch.randperm(len(self.all_images))
def __iter__(self):
while True:
yield {}
def collate(self, batch) -> Dict[str, Any]:
idx = self.image_perm[self.idx]
# prepare batch data here
c2w = self.all_c2w[idx][None]
light_positions = c2w[..., :3, -1]
directions = self.all_directions[idx][None]
rays_o, rays_d = get_rays(
directions, c2w, keepdim=True, noise_scale=self.cfg.rays_noise_scale
)
rgb = self.all_images[idx][None]
depth = self.all_depths[idx][None]
mask = self.all_fg_masks[idx][None]
if (
self.cfg.train_num_rays != -1
and self.cfg.train_num_rays < self.cfg.height * self.cfg.width
):
_, height, width, _ = rays_o.shape
x = torch.randint(
0, width, size=(self.cfg.train_num_rays,), device=rays_o.device
)
y = torch.randint(
0, height, size=(self.cfg.train_num_rays,), device=rays_o.device
)
rays_o = rays_o[:, y, x].unsqueeze(-2)
rays_d = rays_d[:, y, x].unsqueeze(-2)
directions = directions[:, y, x].unsqueeze(-2)
rgb = rgb[:, y, x].unsqueeze(-2)
mask = mask[:, y, x].unsqueeze(-2)
depth = depth[:, y, x].unsqueeze(-2)
# TODO: get projection matrix and mvp matrix
# proj_mtx = get_projection_matrix()
batch = {
"rays_o": rays_o,
"rays_d": rays_d,
"mvp_mtx": None,
"camera_positions": c2w[..., :3, -1],
"light_positions": light_positions,
"elevation": None,
"azimuth": None,
"camera_distances": None,
"rgb": rgb,
"depth": depth,
"mask": mask,
}
if self.cfg.use_random_camera:
batch["random_camera"] = self.random_pose_generator.collate(None)
# prepare batch data in system
# c2w = self.all_c2w[idx][None]
# batch = {
# 'index': torch.tensor([idx]),
# 'c2w': c2w,
# 'light_positions': c2w[..., :3, -1],
# 'H': self.h,
# 'W': self.w
# }
self.idx += 1
if self.idx == len(self.all_images):
self.idx = 0
self.image_perm = torch.randperm(len(self.all_images))
# self.idx = (self.idx + 1) % len(self.all_images)
return batch
@register("co3d-datamodule")
class Co3dDataModule(pl.LightningDataModule):
def __init__(self, cfg: Optional[Union[dict, DictConfig]] = None) -> None:
super().__init__()
self.cfg = parse_structured(Co3dDataModuleConfig, cfg)
def setup(self, stage=None):
if stage in [None, "fit"]:
self.train_dataset = Co3dIterableDataset(self.cfg, self.cfg.train_split)
if stage in [None, "fit", "validate"]:
self.val_dataset = Co3dDataset(self.cfg, self.cfg.val_split)
if stage in [None, "test", "predict"]:
self.test_dataset = Co3dDataset(self.cfg, self.cfg.test_split)
def prepare_data(self):
pass
def general_loader(self, dataset, batch_size, collate_fn=None) -> DataLoader:
sampler = None
return DataLoader(
dataset,
num_workers=0,
batch_size=batch_size,
# pin_memory=True,
collate_fn=collate_fn,
)
def train_dataloader(self):
return self.general_loader(
self.train_dataset, batch_size=1, collate_fn=self.train_dataset.collate
)
def val_dataloader(self):
return self.general_loader(self.val_dataset, batch_size=1)
def test_dataloader(self):
return self.general_loader(self.test_dataset, batch_size=1)
def predict_dataloader(self):
return self.general_loader(self.test_dataset, batch_size=1)
================================================
FILE: threestudio/data/image.py
================================================
import math
import os
from dataclasses import dataclass, field
import cv2
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset, IterableDataset
from threestudio import register
from threestudio.data.uncond import (
RandomCameraDataModuleConfig,
RandomCameraDataset,
RandomCameraIterableDataset,
)
from threestudio.utils.config import parse_structured
from threestudio.utils.misc import get_rank
from threestudio.utils.ops import (
get_mvp_matrix,
get_projection_matrix,
get_ray_directions,
get_rays,
)
from threestudio.utils.typing import *
@dataclass
class SingleImageDataModuleConfig:
height: int = 96
width: int = 96
default_elevation_deg: float = 0.0
default_azimuth_deg: float = -180.0
default_camera_distance: float = 1.2
default_fovy_deg: float = 60.0
image_path: str = ""
use_random_camera: bool = True
random_camera: dict = field(default_factory=dict)
rays_noise_scale: float = 2e-3
batch_size: int = 1
class SingleImageDataBase:
def setup(self, cfg, split):
self.split = split
self.rank = get_rank()
self.cfg: SingleImageDataModuleConfig = cfg
if self.cfg.use_random_camera:
random_camera_cfg = parse_structured(
RandomCameraDataModuleConfig, self.cfg.get("random_camera", {})
)
if split == "train":
self.random_pose_generator = RandomCameraIterableDataset(
random_camera_cfg
)
else:
self.random_pose_generator = RandomCameraDataset(
random_camera_cfg, split
)
# load image
assert os.path.exists(self.cfg.image_path)
rgba = cv2.cvtColor(
cv2.imread(self.cfg.image_path, cv2.IMREAD_UNCHANGED), cv2.COLOR_BGRA2RGBA
)
rgba = (
cv2.resize(
rgba, (self.cfg.width, self.cfg.height), interpolation=cv2.INTER_AREA
).astype(np.float32)
/ 255.0
)
rgb = rgba[..., :3] * rgba[..., 3:] + (1 - rgba[..., 3:])
self.rgb: Float[Tensor, "1 H W 3"] = (
torch.from_numpy(rgb).unsqueeze(0).contiguous().to(self.rank)
)
self.mask: Float[Tensor, "1 H W 1"] = (
torch.from_numpy(rgba[..., 3:] > 0.5).unsqueeze(0).to(self.rank)
)
print(
f"[INFO] single image dataset: load image {self.cfg.image_path} {self.rgb.shape}"
)
# load depth
depth_path = self.cfg.image_path.replace("_rgba.png", "_depth.png")
assert os.path.exists(depth_path)
depth = cv2.imread(depth_path, cv2.IMREAD_UNCHANGED)
depth = cv2.resize(
depth, (self.cfg.width, self.cfg.height), interpolation=cv2.INTER_AREA
)
self.depth: Float[Tensor, "1 H W 1"] = (
torch.from_numpy(depth.astype(np.float32) / 255.0)
.unsqueeze(0)
.to(self.rank)
)
print(
f"[INFO] single image dataset: load depth {depth_path} {self.depth.shape}"
)
elevation_deg = torch.FloatTensor([self.cfg.default_elevation_deg])
azimuth_deg = torch.FloatTensor([self.cfg.default_azimuth_deg])
camera_distance = torch.FloatTensor([self.cfg.default_camera_distance])
elevation = elevation_deg * math.pi / 180
azimuth = azimuth_deg * math.pi / 180
camera_position: Float[Tensor, "1 3"] = torch.stack(
[
camera_distance * torch.cos(elevation) * torch.cos(azimuth),
camera_distance * torch.cos(elevation) * torch.sin(azimuth),
camera_distance * torch.sin(elevation),
],
dim=-1,
)
center: Float[Tensor, "1 3"] = torch.zeros_like(camera_position)
up: Float[Tensor, "1 3"] = torch.as_tensor([0, 0, 1], dtype=torch.float32)[None]
light_position: Float[Tensor, "1 3"] = camera_position
lookat: Float[Tensor, "1 3"] = F.normalize(center - camera_position, dim=-1)
right: Float[Tensor, "1 3"] = F.normalize(torch.cross(lookat, up), dim=-1)
up = F.normalize(torch.cross(right, lookat), dim=-1)
c2w: Float[Tensor, "1 3 4"] = torch.cat(
[torch.stack([right, up, -lookat], dim=-1), camera_position[:, :, None]],
dim=-1,
)
# get directions by dividing directions_unit_focal by focal length
fovy = torch.deg2rad(torch.FloatTensor([self.cfg.default_fovy_deg]))
focal_length = 0.5 * self.cfg.height / torch.tan(0.5 * fovy)
directions_unit_focal = get_ray_directions(
H=self.cfg.height, W=self.cfg.width, focal=1.0
)
directions: Float[Tensor, "1 H W 3"] = directions_unit_focal[None]
directions[:, :, :, :2] = directions[:, :, :, :2] / focal_length
rays_o, rays_d = get_rays(
directions, c2w, keepdim=True, noise_scale=self.cfg.rays_noise_scale
)
proj_mtx: Float[Tensor, "4 4"] = get_projection_matrix(
fovy, self.cfg.width / self.cfg.height, 0.1, 100.0
) # FIXME: hard-coded near and far
mvp_mtx: Float[Tensor, "4 4"] = get_mvp_matrix(c2w, proj_mtx)
self.rays_o, self.rays_d = rays_o, rays_d
self.mvp_mtx = mvp_mtx
self.camera_position = camera_position
self.light_position = light_position
self.elevation_deg, self.azimuth_deg = elevation_deg, azimuth_deg
self.camera_distance = camera_distance
def get_all_images(self):
return self.rgb
class SingleImageIterableDataset(IterableDataset, SingleImageDataBase):
def __init__(self, cfg: Any, split: str) -> None:
super().__init__()
self.setup(cfg, split)
def collate(self, batch) -> Dict[str, Any]:
batch = {
"rays_o": self.rays_o,
"rays_d": self.rays_d,
"mvp_mtx": self.mvp_mtx,
"camera_positions": self.camera_position,
"light_positions": self.light_position,
"elevation": self.elevation_deg,
"azimuth": self.azimuth_deg,
"camera_distances": self.camera_distance,
"rgb": self.rgb,
"depth": self.depth,
"mask": self.mask,
}
if self.cfg.use_random_camera:
batch["random_camera"] = self.random_pose_generator.collate(None)
return batch
def __iter__(self):
while True:
yield {}
class SingleImageDataset(Dataset, SingleImageDataBase):
def __init__(self, cfg: Any, split: str) -> None:
super().__init__()
self.setup(cfg, split)
def __len__(self):
return len(self.random_pose_generator)
def __getitem__(self, index):
return self.random_pose_generator[index]
# if index == 0:
# return {
# 'rays_o': self.rays_o[0],
# 'rays_d': self.rays_d[0],
# 'mvp_mtx': self.mvp_mtx[0],
# 'camera_positions': self.camera_position[0],
# 'light_positions': self.light_position[0],
# 'elevation': self.elevation_deg[0],
# 'azimuth': self.azimuth_deg[0],
# 'camera_distances': self.camera_distance[0],
# 'rgb': self.rgb[0],
# 'depth': self.depth[0],
# 'mask': self.mask[0]
# }
# else:
# return self.random_pose_generator[index - 1]
@register("single-image-datamodule")
class SingleImageDataModule(pl.LightningDataModule):
cfg: SingleImageDataModuleConfig
def __init__(self, cfg: Optional[Union[dict, DictConfig]] = None) -> None:
super().__init__()
self.cfg = parse_structured(SingleImageDataModuleConfig, cfg)
def setup(self, stage=None) -> None:
if stage in [None, "fit"]:
self.train_dataset = SingleImageIterableDataset(self.cfg, "train")
if stage in [None, "fit", "validate"]:
self.val_dataset = SingleImageDataset(self.cfg, "val")
if stage in [None, "test", "predict"]:
self.test_dataset = SingleImageDataset(self.cfg, "test")
def prepare_data(self):
pass
def general_loader(self, dataset, batch_size, collate_fn=None) -> DataLoader:
return DataLoader(
dataset, num_workers=0, batch_size=batch_size, collate_fn=collate_fn
)
def train_dataloader(self) -> DataLoader:
return self.general_loader(
self.train_dataset,
batch_size=self.cfg.batch_size,
collate_fn=self.train_dataset.collate,
)
def val_dataloader(self) -> DataLoader:
return self.general_loader(self.val_dataset, batch_size=1)
def test_dataloader(self) -> DataLoader:
return self.general_loader(self.test_dataset, batch_size=1)
def predict_dataloader(self) -> DataLoader:
return self.general_loader(self.test_dataset, batch_size=1)
================================================
FILE: threestudio/data/multiview.py
================================================
import json
import math
import os
import random
from dataclasses import dataclass
import cv2
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn.functional as F
from scipy.spatial.transform import Rotation as Rot
from scipy.spatial.transform import Slerp
from torch.utils.data import DataLoader, Dataset, IterableDataset
from tqdm import tqdm
import threestudio
from threestudio import register
from threestudio.utils.config import parse_structured
from threestudio.utils.ops import get_mvp_matrix, get_ray_directions, get_rays
from threestudio.utils.typing import *
def convert_pose(C2W):
flip_yz = torch.eye(4)
flip_yz[1, 1] = -1
flip_yz[2, 2] = -1
C2W = torch.matmul(C2W, flip_yz)
return C2W
def convert_proj(K, H, W, near, far):
return [
[2 * K[0, 0] / W, -2 * K[0, 1] / W, (W - 2 * K[0, 2]) / W, 0],
[0, -2 * K[1, 1] / H, (H - 2 * K[1, 2]) / H, 0],
[0, 0, (-far - near) / (far - near), -2 * far * near / (far - near)],
[0, 0, -1, 0],
]
def inter_pose(pose_0, pose_1, ratio):
pose_0 = pose_0.detach().cpu().numpy()
pose_1 = pose_1.detach().cpu().numpy()
pose_0 = np.linalg.inv(pose_0)
pose_1 = np.linalg.inv(pose_1)
rot_0 = pose_0[:3, :3]
rot_1 = pose_1[:3, :3]
rots = Rot.from_matrix(np.stack([rot_0, rot_1]))
key_times = [0, 1]
slerp = Slerp(key_times, rots)
rot = slerp(ratio)
pose = np.diag([1.0, 1.0, 1.0, 1.0])
pose = pose.astype(np.float32)
pose[:3, :3] = rot.as_matrix()
pose[:3, 3] = ((1.0 - ratio) * pose_0 + ratio * pose_1)[:3, 3]
pose = np.linalg.inv(pose)
return pose
@dataclass
class MultiviewsDataModuleConfig:
dataroot: str = ""
train_downsample_resolution: int = 4
eval_downsample_resolution: int = 4
train_data_interval: int = 1
eval_data_interval: int = 1
batch_size: int = 1
eval_batch_size: int = 1
camera_layout: str = "around"
camera_distance: float = -1
eval_interpolation: Optional[Tuple[int, int, int]] = None # (0, 1, 30)
class MultiviewIterableDataset(IterableDataset):
def __init__(self, cfg: Any) -> None:
super().__init__()
self.cfg: MultiviewsDataModuleConfig = cfg
assert self.cfg.batch_size == 1
scale = self.cfg.train_downsample_resolution
camera_dict = json.load(
open(os.path.join(self.cfg.dataroot, "transforms.json"), "r")
)
assert camera_dict["camera_model"] == "OPENCV"
frames = camera_dict["frames"]
frames = frames[:: self.cfg.train_data_interval]
frames_proj = []
frames_c2w = []
frames_position = []
frames_direction = []
frames_img = []
self.frame_w = frames[0]["w"] // scale
self.frame_h = frames[0]["h"] // scale
threestudio.info("Loading frames...")
self.n_frames = len(frames)
c2w_list = []
for frame in tqdm(frames):
extrinsic: Float[Tensor, "4 4"] = torch.as_tensor(
frame["transform_matrix"], dtype=torch.float32
)
c2w = extrinsic
c2w_list.append(c2w)
c2w_list = torch.stack(c2w_list, dim=0)
if self.cfg.camera_layout == "around":
c2w_list[:, :3, 3] -= torch.mean(c2w_list[:, :3, 3], dim=0).unsqueeze(0)
elif self.cfg.camera_layout == "front":
assert self.cfg.camera_distance > 0
c2w_list[:, :3, 3] -= torch.mean(c2w_list[:, :3, 3], dim=0).unsqueeze(0)
z_vector = torch.zeros(c2w_list.shape[0], 3, 1)
z_vector[:, 2, :] = -1
rot_z_vector = c2w_list[:, :3, :3] @ z_vector
rot_z_vector = torch.mean(rot_z_vector, dim=0).unsqueeze(0)
c2w_list[:, :3, 3] -= rot_z_vector[:, :, 0] * self.cfg.camera_distance
else:
raise ValueError(
f"Unknown camera layout {self.cfg.camera_layout}. Now support only around and front."
)
for idx, frame in tqdm(enumerate(frames)):
intrinsic: Float[Tensor, "4 4"] = torch.eye(4)
intrinsic[0, 0] = frame["fl_x"] / scale
intrinsic[1, 1] = frame["fl_y"] / scale
intrinsic[0, 2] = frame["cx"] / scale
intrinsic[1, 2] = frame["cy"] / scale
frame_path = os.path.join(self.cfg.dataroot, frame["file_path"])
img = cv2.imread(frame_path)[:, :, ::-1].copy()
img = cv2.resize(img, (self.frame_w, self.frame_h))
img: Float[Tensor, "H W 3"] = torch.FloatTensor(img) / 255
frames_img.append(img)
direction: Float[Tensor, "H W 3"] = get_ray_directions(
self.frame_h,
self.frame_w,
(intrinsic[0, 0], intrinsic[1, 1]),
(intrinsic[0, 2], intrinsic[1, 2]),
use_pixel_centers=False,
)
c2w = c2w_list[idx]
camera_position: Float[Tensor, "3"] = c2w[:3, 3:].reshape(-1)
near = 0.1
far = 1000.0
proj = convert_proj(intrinsic, self.frame_h, self.frame_w, near, far)
proj: Float[Tensor, "4 4"] = torch.FloatTensor(proj)
frames_proj.append(proj)
frames_c2w.append(c2w)
frames_position.append(camera_position)
frames_direction.append(direction)
threestudio.info("Loaded frames.")
self.frames_proj: Float[Tensor, "B 4 4"] = torch.stack(frames_proj, dim=0)
self.frames_c2w: Float[Tensor, "B 4 4"] = torch.stack(frames_c2w, dim=0)
self.frames_position: Float[Tensor, "B 3"] = torch.stack(frames_position, dim=0)
self.frames_direction: Float[Tensor, "B H W 3"] = torch.stack(
frames_direction, dim=0
)
self.frames_img: Float[Tensor, "B H W 3"] = torch.stack(frames_img, dim=0)
self.rays_o, self.rays_d = get_rays(
self.frames_direction, self.frames_c2w, keepdim=True
)
self.mvp_mtx: Float[Tensor, "B 4 4"] = get_mvp_matrix(
self.frames_c2w, self.frames_proj
)
self.light_positions: Float[Tensor, "B 3"] = torch.zeros_like(
self.frames_position
)
def __iter__(self):
while True:
yield {}
def collate(self, batch):
index = torch.randint(0, self.n_frames, (1,)).item()
return {
"index": index,
"rays_o": self.rays_o[index : index + 1],
"rays_d": self.rays_d[index : index + 1],
"mvp_mtx": self.mvp_mtx[index : index + 1],
"c2w": self.frames_c2w[index : index + 1],
"camera_positions": self.frames_position[index : index + 1],
"light_positions": self.light_positions[index : index + 1],
"gt_rgb": self.frames_img[index : index + 1],
"height": self.frame_h,
"width": self.frame_w,
}
class MultiviewDataset(Dataset):
def __init__(self, cfg: Any, split: str) -> None:
super().__init__()
self.cfg: MultiviewsDataModuleConfig = cfg
assert self.cfg.eval_batch_size == 1
scale = 2**self.cfg.eval_downsample_resolution
camera_dict = json.load(
open(os.path.join(self.cfg.dataroot, "transforms.json"), "r")
)
assert camera_dict["camera_model"] == "OPENCV"
frames = camera_dict["frames"]
frames = frames[:: self.cfg.eval_data_interval]
frames_proj = []
frames_c2w = []
frames_position = []
frames_direction = []
frames_img = []
self.frame_w = frames[0]["w"] // scale
self.frame_h = frames[0]["h"] // scale
threestudio.info("Loading frames...")
self.n_frames = len(frames)
c2w_list = []
for frame in tqdm(frames):
extrinsic: Float[Tensor, "4 4"] = torch.as_tensor(
frame["transform_matrix"], dtype=torch.float32
)
c2w = extrinsic
c2w_list.append(c2w)
c2w_list = torch.stack(c2w_list, dim=0)
if self.cfg.camera_layout == "around":
c2w_list[:, :3, 3] -= torch.mean(c2w_list[:, :3, 3], dim=0).unsqueeze(0)
elif self.cfg.camera_layout == "front":
assert self.cfg.camera_distance > 0
c2w_list[:, :3, 3] -= torch.mean(c2w_list[:, :3, 3], dim=0).unsqueeze(0)
z_vector = torch.zeros(c2w_list.shape[0], 3, 1)
z_vector[:, 2, :] = -1
rot_z_vector = c2w_list[:, :3, :3] @ z_vector
rot_z_vector = torch.mean(rot_z_vector, dim=0).unsqueeze(0)
c2w_list[:, :3, 3] -= rot_z_vector[:, :, 0] * self.cfg.camera_distance
else:
raise ValueError(
f"Unknown camera layout {self.cfg.camera_layout}. Now support only around and front."
)
if not (self.cfg.eval_interpolation is None):
idx0 = self.cfg.eval_interpolation[0]
idx1 = self.cfg.eval_interpolation[1]
eval_nums = self.cfg.eval_interpolation[2]
frame = frames[idx0]
intrinsic: Float[Tensor, "4 4"] = torch.eye(4)
intrinsic[0, 0] = frame["fl_x"] / scale
intrinsic[1, 1] = frame["fl_y"] / scale
intrinsic[0, 2] = frame["cx"] / scale
intrinsic[1, 2] = frame["cy"] / scale
for ratio in np.linspace(0, 1, eval_nums):
img: Float[Tensor, "H W 3"] = torch.zeros(
(self.frame_h, self.frame_w, 3)
)
frames_img.append(img)
direction: Float[Tensor, "H W 3"] = get_ray_directions(
self.frame_h,
self.frame_w,
(intrinsic[0, 0], intrinsic[1, 1]),
(intrinsic[0, 2], intrinsic[1, 2]),
use_pixel_centers=False,
)
c2w = torch.FloatTensor(
inter_pose(c2w_list[idx0], c2w_list[idx1], ratio)
)
camera_position: Float[Tensor, "3"] = c2w[:3, 3:].reshape(-1)
near = 0.1
far = 1000.0
proj = convert_proj(intrinsic, self.frame_h, self.frame_w, near, far)
proj: Float[Tensor, "4 4"] = torch.FloatTensor(proj)
frames_proj.append(proj)
frames_c2w.append(c2w)
frames_position.append(camera_position)
frames_direction.append(direction)
else:
for idx, frame in tqdm(enumerate(frames)):
intrinsic: Float[Tensor, "4 4"] = torch.eye(4)
intrinsic[0, 0] = frame["fl_x"] / scale
intrinsic[1, 1] = frame["fl_y"] / scale
intrinsic[0, 2] = frame["cx"] / scale
intrinsic[1, 2] = frame["cy"] / scale
frame_path = os.path.join(self.cfg.dataroot, frame["file_path"])
img = cv2.imread(frame_path)[:, :, ::-1].copy()
img = cv2.resize(img, (self.frame_w, self.frame_h))
img: Float[Tensor, "H W 3"] = torch.FloatTensor(img) / 255
frames_img.append(img)
direction: Float[Tensor, "H W 3"] = get_ray_directions(
self.frame_h,
self.frame_w,
(intrinsic[0, 0], intrinsic[1, 1]),
(intrinsic[0, 2], intrinsic[1, 2]),
use_pixel_centers=False,
)
c2w = c2w_list[idx]
camera_position: Float[Tensor, "3"] = c2w[:3, 3:].reshape(-1)
near = 0.1
far = 1000.0
K = intrinsic
proj = [
[
2 * K[0, 0] / self.frame_w,
-2 * K[0, 1] / self.frame_w,
(self.frame_w - 2 * K[0, 2]) / self.frame_w,
0,
],
[
0,
-2 * K[1, 1] / self.frame_h,
(self.frame_h - 2 * K[1, 2]) / self.frame_h,
0,
],
[
0,
0,
(-far - near) / (far - near),
-2 * far * near / (far - near),
],
[0, 0, -1, 0],
]
proj: Float[Tensor, "4 4"] = torch.FloatTensor(proj)
frames_proj.append(proj)
frames_c2w.append(c2w)
frames_position.append(camera_position)
frames_direction.append(direction)
threestudio.info("Loaded frames.")
self.frames_proj: Float[Tensor, "B 4 4"] = torch.stack(frames_proj, dim=0)
self.frames_c2w: Float[Tensor, "B 4 4"] = torch.stack(frames_c2w, dim=0)
self.frames_position: Float[Tensor, "B 3"] = torch.stack(frames_position, dim=0)
self.frames_direction: Float[Tensor, "B H W 3"] = torch.stack(
frames_direction, dim=0
)
self.frames_img: Float[Tensor, "B H W 3"] = torch.stack(frames_img, dim=0)
self.rays_o, self.rays_d = get_rays(
self.frames_direction, self.frames_c2w, keepdim=True
)
self.mvp_mtx: Float[Tensor, "B 4 4"] = get_mvp_matrix(
self.frames_c2w, self.frames_proj
)
self.light_positions: Float[Tensor, "B 3"] = torch.zeros_like(
self.frames_position
)
def __len__(self):
return self.frames_proj.shape[0]
def __getitem__(self, index):
return {
"index": index,
"rays_o": self.rays_o[index],
"rays_d": self.rays_d[index],
"mvp_mtx": self.mvp_mtx[index],
"c2w": self.frames_c2w[index],
"camera_positions": self.frames_position[index],
"light_positions": self.light_positions[index],
"gt_rgb": self.frames_img[index],
}
def __iter__(self):
while True:
yield {}
def collate(self, batch):
batch = torch.utils.data.default_collate(batch)
batch.update({"height": self.frame_h, "width": self.frame_w})
return batch
@register("multiview-camera-datamodule")
class MultiviewDataModule(pl.LightningDataModule):
cfg: MultiviewsDataModuleConfig
def __init__(self, cfg: Optional[Union[dict, DictConfig]] = None) -> None:
super().__init__()
self.cfg = parse_structured(MultiviewsDataModuleConfig, cfg)
def setup(self, stage=None) -> None:
if stage in [None, "fit"]:
self.train_dataset = MultiviewIterableDataset(self.cfg)
if stage in [None, "fit", "validate"]:
self.val_dataset = MultiviewDataset(self.cfg, "val")
if stage in [None, "test", "predict"]:
self.test_dataset = MultiviewDataset(self.cfg, "test")
def prepare_data(self):
pass
def general_loader(self, dataset, batch_size, collate_fn=None) -> DataLoader:
return DataLoader(
dataset,
num_workers=1, # type: ignore
batch_size=batch_size,
collate_fn=collate_fn,
)
def train_dataloader(self) -> DataLoader:
return self.general_loader(
self.train_dataset, batch_size=None, collate_fn=self.train_dataset.collate
)
def val_dataloader(self) -> DataLoader:
return self.general_loader(
self.val_dataset, batch_size=1, collate_fn=self.val_dataset.collate
)
# return self.general_loader(self.train_dataset, batch_size=None, collate_fn=self.train_dataset.collate)
def test_dataloader(self) -> DataLoader:
return self.general_loader(
self.test_dataset, batch_size=1, collate_fn=self.test_dataset.collate
)
def predict_dataloader(self) -> DataLoader:
return self.general_loader(
self.test_dataset, batch_size=1, collate_fn=self.test_dataset.collate
)
================================================
FILE: threestudio/data/random_multiview.py
================================================
import math
import random
import os
from dataclasses import dataclass, field
import cv2
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset, IterableDataset
from threestudio import register
from threestudio.utils.base import Updateable
from threestudio.data.uncond import (
RandomCameraDataModuleConfig,
RandomCameraDataset,
RandomCameraIterableDataset,
)
from threestudio.utils.config import parse_structured
from threestudio.utils.misc import get_rank
from threestudio.utils.ops import (
get_mvp_matrix,
get_projection_matrix,
get_ray_directions,
get_rays,
)
from threestudio.utils.typing import *
@dataclass
class RandomMultiviewCameraDataModuleConfig(RandomCameraDataModuleConfig):
relative_radius: bool = True
n_view: int = 1
zoom_range: Tuple[float, float] = (1.0, 1.0)
class RandomMultiviewCameraIterableDataset(RandomCameraIterableDataset):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.zoom_range = self.cfg.zoom_range
def collate(self, batch) -> Dict[str, Any]:
assert self.batch_size % self.cfg.n_view == 0, f"batch_size ({self.batch_size}) must be dividable by n_view ({self.cfg.n_view})!"
real_batch_size = self.batch_size // self.cfg.n_view
# sample elevation angles
elevation_deg: Float[Tensor, "B"]
elevation: Float[Tensor, "B"]
if random.random() < 0.5:
# sample elevation angles uniformly with a probability 0.5 (biased towards poles)
elevation_deg = (
torch.rand(real_batch_size)
* (self.elevation_range[1] - self.elevation_range[0])
+ self.elevation_range[0]
).repeat_interleave(self.cfg.n_view, dim=0)
elevation = elevation_deg * math.pi / 180
else:
# otherwise sample uniformly on sphere
elevation_range_percent = [
(self.elevation_range[0] + 90.0) / 180.0,
(self.elevation_range[1] + 90.0) / 180.0,
]
# inverse transform sampling
elevation = torch.asin(
2
* (
torch.rand(real_batch_size)
* (elevation_range_percent[1] - elevation_range_percent[0])
+ elevation_range_percent[0]
)
- 1.0
).repeat_interleave(self.cfg.n_view, dim=0)
elevation_deg = elevation / math.pi * 180.0
# sample azimuth angles from a uniform distribution bounded by azimuth_range
azimuth_deg: Float[Tensor, "B"]
# ensures sampled azimuth angles in a batch cover the whole range
azimuth_deg = (
torch.rand(real_batch_size).reshape(-1,1) + torch.arange(self.cfg.n_view).reshape(1,-1)
).reshape(-1) / self.cfg.n_view * (
self.azimuth_range[1] - self.azimuth_range[0]
) + self.azimuth_range[
0
]
azimuth = azimuth_deg * math.pi / 180
######## Different from original ########
# sample fovs from a uniform distribution bounded by fov_range
fovy_deg: Float[Tensor, "B"] = (
torch.rand(real_batch_size)
* (self.fovy_range[1] - self.fovy_range[0])
+ self.fovy_range[0]
).repeat_interleave(self.cfg.n_view, dim=0)
fovy = fovy_deg * math.pi / 180
# sample distances from a uniform distribution bounded by distance_range
camera_distances: Float[Tensor, "B"] = (
torch.rand(real_batch_size)
* (self.camera_distance_range[1] - self.camera_distance_range[0])
+ self.camera_distance_range[0]
).repeat_interleave(self.cfg.n_view, dim=0)
if self.cfg.relative_radius:
scale = 1 / torch.tan(0.5 * fovy)
camera_distances = scale * camera_distances
# zoom in by decreasing fov after camera distance is fixed
zoom: Float[Tensor, "B"] = (
torch.rand(real_batch_size)
* (self.zoom_range[1] - self.zoom_range[0])
+ self.zoom_range[0]
).repeat_interleave(self.cfg.n_view, dim=0)
fovy = fovy * zoom
fovy_deg = fovy_deg * zoom
###########################################
# convert spherical coordinates to cartesian coordinates
# right hand coordinate system, x back, y right, z up
# elevation in (-90, 90), azimuth from +x to +y in (-180, 180)
camera_positions: Float[Tensor, "B 3"] = torch.stack(
[
camera_distances * torch.cos(elevation) * torch.cos(azimuth),
camera_distances * torch.cos(elevation) * torch.sin(azimuth),
camera_distances * torch.sin(elevation),
],
dim=-1,
)
# default scene center at origin
center: Float[Tensor, "B 3"] = torch.zeros_like(camera_positions)
# default camera up direction as +z
up: Float[Tensor, "B 3"] = torch.as_tensor([0, 0, 1], dtype=torch.float32)[
None, :
].repeat(self.batch_size, 1)
# sample camera perturbations from a uniform distribution [-camera_perturb, camera_perturb]
camera_perturb: Float[Tensor, "B 3"] = (
torch.rand(real_batch_size, 3) * 2 * self.cfg.camera_perturb
- self.cfg.camera_perturb
).repeat_interleave(self.cfg.n_view, dim=0)
camera_positions = camera_positions + camera_perturb
# sample center perturbations from a normal distribution with mean 0 and std center_perturb
center_perturb: Float[Tensor, "B 3"] = (
torch.randn(real_batch_size, 3) * self.cfg.center_perturb
).repeat_interleave(self.cfg.n_view, dim=0)
center = center + center_perturb
# sample up perturbations from a normal distribution with mean 0 and std up_perturb
up_perturb: Float[Tensor, "B 3"] = (
torch.randn(real_batch_size, 3) * self.cfg.up_perturb
).repeat_interleave(self.cfg.n_view, dim=0)
up = up + up_perturb
# sample light distance from a uniform distribution bounded by light_distance_range
light_distances: Float[Tensor, "B"] = (
torch.rand(real_batch_size)
* (self.cfg.light_distance_range[1] - self.cfg.light_distance_range[0])
+ self.cfg.light_distance_range[0]
).repeat_interleave(self.cfg.n_view, dim=0)
if self.cfg.light_sample_strategy == "dreamfusion":
# sample light direction from a normal distribution with mean camera_position and std light_position_perturb
light_direction: Float[Tensor, "B 3"] = F.normalize(
camera_positions
+ torch.randn(real_batch_size, 3).repeat_interleave(self.cfg.n_view, dim=0) * self.cfg.light_position_perturb,
dim=-1,
)
# get light position by scaling light direction by light distance
light_positions: Float[Tensor, "B 3"] = (
light_direction * light_distances[:, None]
)
elif self.cfg.light_sample_strategy == "magic3d":
# sample light direction within restricted angle range (pi/3)
local_z = F.normalize(camera_positions, dim=-1)
local_x = F.normalize(
torch.stack(
[local_z[:, 1], -local_z[:, 0], torch.zeros_like(local_z[:, 0])],
dim=-1,
),
dim=-1,
)
local_y = F.normalize(torch.cross(local_z, local_x, dim=-1), dim=-1)
rot = torch.stack([local_x, local_y, local_z], dim=-1)
light_azimuth = (
torch.rand(real_batch_size) * math.pi - 2 * math.pi
).repeat_interleave(self.cfg.n_view, dim=0) # [-pi, pi]
light_elevation = (
torch.rand(real_batch_size) * math.pi / 3 + math.pi / 6
).repeat_interleave(self.cfg.n_view, dim=0) # [pi/6, pi/2]
light_positions_local = torch.stack(
[
light_distances
* torch.cos(light_elevation)
* torch.cos(light_azimuth),
light_distances
* torch.cos(light_elevation)
* torch.sin(light_azimuth),
light_distances * torch.sin(light_elevation),
],
dim=-1,
)
light_positions = (rot @ light_positions_local[:, :, None])[:, :, 0]
else:
raise ValueError(
f"Unknown light sample strategy: {self.cfg.light_sample_strategy}"
)
lookat: Float[Tensor, "B 3"] = F.normalize(center - camera_positions, dim=-1)
right: Float[Tensor, "B 3"] = F.normalize(torch.cross(lookat, up), dim=-1)
up = F.normalize(torch.cross(right, lookat), dim=-1)
c2w3x4: Float[Tensor, "B 3 4"] = torch.cat(
[torch.stack([right, up, -lookat], dim=-1), camera_positions[:, :, None]],
dim=-1,
)
c2w: Float[Tensor, "B 4 4"] = torch.cat(
[c2w3x4, torch.zeros_like(c2w3x4[:, :1])], dim=1
)
c2w[:, 3, 3] = 1.0
# get directions by dividing directions_unit_focal by focal length
focal_length: Float[Tensor, "B"] = 0.5 * self.height / torch.tan(0.5 * fovy)
directions: Float[Tensor, "B H W 3"] = self.directions_unit_focal[
None, :, :, :
].repeat(self.batch_size, 1, 1, 1)
directions[:, :, :, :2] = (
directions[:, :, :, :2] / focal_length[:, None, None, None]
)
# Importance note: the returned rays_d MUST be normalized!
rays_o, rays_d = get_rays(directions, c2w, keepdim=True)
proj_mtx: Float[Tensor, "B 4 4"] = get_projection_matrix(
fovy, self.width / self.height, 0.1, 1000.0
) # FIXME: hard-coded near and far
mvp_mtx: Float[Tensor, "B 4 4"] = get_mvp_matrix(c2w, proj_mtx)
if self.cfg.sample_rand_frames == "fps_t0":
fps = torch.FloatTensor(1).uniform_(0, 1/self.cfg.num_frames).item()
t0 = torch.FloatTensor(1).uniform_(0, 1-self.cfg.num_frames*fps).item()
frame_times = torch.linspace(t0, t0+fps*self.cfg.num_frames, (self.cfg.num_frames+1))[:self.cfg.num_frames]
elif self.cfg.sample_rand_frames == "t0":
t0 = torch.FloatTensor(1).uniform_(0, 1/self.cfg.num_frames).item()
frame_times = torch.linspace(t0, t0+(self.cfg.num_frames-1)/self.cfg.num_frames, self.cfg.num_frames)
elif self.cfg.sample_rand_frames == "t0_sub":
t0 = torch.FloatTensor(1).uniform_(0, 1-self.cfg.frame_range+self.cfg.frame_range/self.cfg.num_frames).item()
frame_times = torch.linspace(t0, t0+(self.cfg.num_frames-1)*self.cfg.frame_range/self.cfg.num_frames, self.cfg.num_frames)
elif self.cfg.sample_rand_frames == "t0_sub_sample":
frame_length = (torch.rand(1) * (self.cfg.frame_length_range[1] - self.cfg.frame_length_range[0]) + self.cfg.frame_length_range[0]).item()
t0 = torch.FloatTensor(1).uniform_(0, 1-frame_length+frame_length/self.cfg.num_frames).item()
frame_times = torch.linspace(t0, t0+(self.cfg.num_frames-1)*frame_length/self.cfg.num_frames, self.cfg.num_frames)
else:
frame_times = torch.linspace(0.0, 1.0, self.cfg.num_frames)
if self.cfg.multi_obj:
nerf_to_render_idx = self.nerf_to_render_idx % (self.cfg.num_objs + self.cfg.scene_iters)
render_scene = nerf_to_render_idx >= self.cfg.num_objs
if render_scene:
if self.scene_idx % self.cfg.scene_iters_freq != 0:
self.nerf_to_render_idx += self.cfg.scene_iters
nerf_to_render_idx = self.nerf_to_render_idx % (self.cfg.num_objs + self.cfg.scene_iters)
render_scene = False
self.scene_idx += 1
self.nerf_to_render_idx += 1
else:
nerf_to_render_idx = 0
render_scene = False
if render_scene:
if self.cfg.scene_single_obj:
nerf_to_render_idx = self.scene_single_obj_idx % self.cfg.num_objs
self.scene_single_obj_idx += 1
else:
nerf_to_render_idx = None
return {
"rays_o": rays_o,
"rays_d": rays_d,
"mvp_mtx": mvp_mtx,
"camera_positions": camera_positions,
"c2w": c2w,
"light_positions": light_positions,
"elevation": elevation_deg,
"azimuth": azimuth_deg,
"camera_distances": camera_distances,
"height": self.height,
"width": self.width,
"fovy": fovy_deg,
"frame_times": frame_times,
"train_dynamic_camera": False,
"nerf_to_render_idx": nerf_to_render_idx,
"render_scene": render_scene,
}
@register("random-multiview-camera-datamodule")
class RandomMultiviewCameraDataModule(pl.LightningDataModule):
cfg: RandomMultiviewCameraDataModuleConfig
def __init__(self, cfg: Optional[Union[dict, DictConfig]] = None) -> None:
super().__init__()
self.cfg = parse_structured(RandomMultiviewCameraDataModuleConfig, cfg)
def setup(self, stage=None) -> None:
if stage in [None, "fit"]:
self.train_dataset = RandomMultiviewCameraIterableDataset(self.cfg)
if stage in [None, "fit", "validate"]:
self.val_dataset = RandomCameraDataset(self.cfg, "val")
if stage in [None, "test", "predict"]:
self.test_dataset = RandomCameraDataset(self.cfg, "test")
def prepare_data(self):
pass
def general_loader(self, dataset, batch_size, collate_fn=None) -> DataLoader:
return DataLoader(
dataset,
# very important to disable multi-processing if you want to change self attributes at runtime!
# (for example setting self.width and self.height in update_step)
num_workers=0, # type: ignore
batch_size=batch_size,
collate_fn=collate_fn,
)
def train_dataloader(self) -> DataLoader:
return self.general_loader(
self.train_dataset, batch_size=None, collate_fn=self.train_dataset.collate
)
def val_dataloader(self) -> DataLoader:
return self.general_loader(
self.val_dataset, batch_size=1, collate_fn=self.val_dataset.collate
)
# return self.general_loader(self.train_dataset, batch_size=None, collate_fn=self.train_dataset.collate)
def test_dataloader(self) -> DataLoader:
return self.general_loader(
self.test_dataset, batch_size=1, collate_fn=self.test_dataset.collate
)
def predict_dataloader(self) -> DataLoader:
return self.general_loader(
self.test_dataset, batch_size=1, collate_fn=self.test_dataset.collate
)
================================================
FILE: threestudio/data/single_multiview_combined.py
================================================
import math
import random
import os
from dataclasses import dataclass, field
import cv2
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset, IterableDataset
from threestudio import register
from threestudio.data.uncond import (
RandomCameraDataModuleConfig,
RandomCameraDataset,
RandomCameraIterableDataset,
)
from threestudio.data.random_multiview import (
RandomMultiviewCameraDataModuleConfig,
RandomMultiviewCameraIterableDataset
)
from threestudio.utils.config import parse_structured
from threestudio.utils.base import Updateable
from threestudio.utils.typing import *
class RandomSingleMultiViewCameraIterableDataset(IterableDataset, Updateable):
def __init__(self, cfg_single_view: Any, cfg_multi_view: Any, multi_rate: int = None, multi_rate_perc: int = None) -> None:
super().__init__()
self.cfg_single = parse_structured(RandomCameraDataModuleConfig, cfg_single_view)
self.cfg_multi = parse_structured(RandomMultiviewCameraDataModuleConfig, cfg_multi_view)
self.train_dataset_single = RandomCameraIterableDataset(self.cfg_single)
self.train_dataset_multi = RandomMultiviewCameraIterableDataset(self.cfg_multi)
self.idx = 0
self.multi_rate = multi_rate
self.multi_rate_perc = multi_rate_perc
def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):
self.train_dataset_single.update_step(epoch, global_step, on_load_weights)
self.train_dataset_multi.update_step(epoch, global_step, on_load_weights)
def __iter__(self):
while True:
yield {}
def collate(self, batch) -> Dict[str, Any]:
if self.multi_rate is not None:
multi = self.idx % self.multi_rate == 0
elif self.multi_rate_perc is not None:
multi = random.random() < self.multi_rate_perc
else:
multi = False
if multi:
batch = self.train_dataset_multi.collate(batch)
batch['single_view'] = False
batch['is_video'] = False
else:
batch = self.train_dataset_single.collate(batch)
batch['single_view'] = True
self.idx += 1
return batch
@register("single-multiview-combined-camera-datamodule")
class SingleMultiviewCombinedCameraDataModule(pl.LightningDataModule):
cfg: RandomMultiviewCameraDataModuleConfig
def __init__(self, cfg: Optional[Union[dict, DictConfig]] = None) -> None:
super().__init__()
self.cfg = cfg
self.cfg_multi = parse_structured(RandomMultiviewCameraDataModuleConfig, cfg.multi_view)
self.cfg_single = parse_structured(RandomCameraDataModuleConfig, cfg.single_view)
def setup(self, stage=None) -> None:
if stage in [None, "fit"]:
self.train_dataset = RandomSingleMultiViewCameraIterableDataset(self.cfg_single, self.cfg_multi, multi_rate=self.cfg.multi_rate, multi_rate_perc=self.cfg.multi_rate_perc)
if stage in [None, "fit", "validate"]:
self.val_dataset = RandomCameraDataset(self.cfg_single, "val")
if stage in [None, "test", "predict"]:
self.test_dataset = RandomCameraDataset(self.cfg_single, "test")
def prepare_data(self):
pass
def general_loader(self, dataset, batch_size, collate_fn=None) -> DataLoader:
return DataLoader(
dataset,
# very important to disable multi-processing if you want to change self attributes at runtime!
# (for example setting self.width and self.height in update_step)
num_workers=0, # type: ignore
batch_size=batch_size,
collate_fn=collate_fn,
)
def train_dataloader(self) -> DataLoader:
return self.general_loader(
self.train_dataset, batch_size=None, collate_fn=self.train_dataset.collate
)
def val_dataloader(self) -> DataLoader:
return self.general_loader(
self.val_dataset, batch_size=1, collate_fn=self.val_dataset.collate
)
def test_dataloader(self) -> DataLoader:
return self.general_loader(
self.test_dataset, batch_size=1, collate_fn=self.test_dataset.collate
)
def predict_dataloader(self) -> DataLoader:
return self.general_loader(
self.test_dataset, batch_size=1, collate_fn=self.test_dataset.collate
)
================================================
FILE: threestudio/data/uncond.py
================================================
import bisect
import math
import random
from dataclasses import dataclass, field
import pytorch_lightning as pl
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset, IterableDataset
import threestudio
from threestudio import register
from threestudio.utils.base import Updateable
from threestudio.utils.config import parse_structured
from threestudio.utils.misc import get_device
from threestudio.utils.ops import (
get_mvp_matrix,
get_projection_matrix,
get_ray_directions,
get_rays,
)
from threestudio.utils.typing import *
@dataclass
class RandomCameraDataModuleConfig:
# height and width should be Union[int, List[int]]
# but OmegaConf does not support Union of containers
height: Any = 64
width: Any = 64
resolution_milestones: List[int] = field(default_factory=lambda: [])
eval_height: int = 512
eval_width: int = 512
batch_size: int = 1
eval_batch_size: int = 1
n_val_views: int = 1
n_test_views: int = 120
elevation_range: Tuple[float, float] = (-10, 90)
azimuth_range: Tuple[float, float] = (-180, 180)
camera_distance_range: Tuple[float, float] = (1, 1.5)
fovy_range: Tuple[float, float] = (
40,
70,
) # in degrees, in vertical direction (along height)
camera_perturb: float = 0.1
center_perturb: float = 0.2
up_perturb: float = 0.02
light_position_perturb: float = 1.0
light_distance_range: Tuple[float, float] = (0.8, 1.5)
eval_elevation_deg: float = 15.0
eval_camera_distance: float = 1.5
eval_fovy_deg: float = 70.0
light_sample_strategy: str = "dreamfusion"
batch_uniform_azimuth: bool = True
# Dynamic
static: bool = True
num_frames: int = 1
sample_rand_frames: Optional[str] = None
frame_range: Optional[float] = 1.0
frame_length_range: Tuple[float, float] = (0.8, 1.0)
# Compositional
multi_obj: bool = False
scene_iters: int = 1
num_objs: int = 1
scene_iters_freq: int = 1
scene_single_obj: bool = False
scene_single_obj_static: Optional[bool] = False
# Simultaneous training
simultan: bool = False
simultan_vid_mod: Optional[int] = None
simultan_vid_mod_perc: Optional[float] = None
width_vid: int = 64
height_vid: int = 64
num_frames_factor: int = 1
train_dynamic_camera: Optional[bool] = False
num_test_loop_factor: int = 1
num_test_loop_static: int = 4
# Static simultaneous training
simultan_image: bool = False
width_alter: int = 64
height_alter: int = 64
test_traj: Optional[str] = None
class RandomCameraIterableDataset(IterableDataset, Updateable):
def __init__(self, cfg: Any) -> None:
super().__init__()
self.cfg: RandomCameraDataModuleConfig = cfg
self.heights: List[int] = (
[self.cfg.height] if isinstance(self.cfg.height, int) else self.cfg.height
)
self.widths: List[int] = (
[self.cfg.width] if isinstance(self.cfg.width, int) else self.cfg.width
)
self.batch_sizes: List[int] = (
[self.cfg.batch_size]
if isinstance(self.cfg.batch_size, int)
else self.cfg.batch_size
)
if (
len(self.heights) != 1
and len(self.widths) != 1
and len(self.batch_sizes) == 1
):
self.batch_sizes = self.batch_sizes*len(self.heights)
assert len(self.heights) == len(self.widths) == len(self.batch_sizes)
self.resolution_milestones: List[int]
if (
len(self.heights) == 1
and len(self.widths) == 1
and len(self.batch_sizes) == 1
):
if len(self.cfg.resolution_milestones) > 0:
threestudio.warn(
"Ignoring resolution_milestones since height and width are not changing"
)
self.resolution_milestones = [-1]
else:
assert len(self.heights) == len(self.cfg.resolution_milestones) + 1
self.resolution_milestones = [-1] + self.cfg.resolution_milestones
self.directions_unit_focals = [
get_ray_directions(H=height, W=width, focal=1.0)
for (height, width) in zip(self.heights, self.widths)
]
self.height: int = self.heights[0]
self.width: int = self.widths[0]
self.batch_size: int = self.batch_sizes[0]
self.directions_unit_focal = self.directions_unit_focals[0]
self.elevation_range = self.cfg.elevation_range
self.azimuth_range = self.cfg.azimuth_range
self.camera_distance_range = self.cfg.camera_distance_range
self.fovy_range = self.cfg.fovy_range
self.nerf_to_render_idx = 0 #1
self.scene_idx = 0
self.scene_single_obj_idx = 0
self.simultan_idx = 0
if self.cfg.simultan:
self.directions_unit_focals_vid = get_ray_directions(H=self.cfg.height_vid, W=self.cfg.width_vid, focal=1.0)
self.height_vid = self.cfg.height_vid
self.width_vid = self.cfg.width_vid
else:
self.directions_unit_focals_vid = None
self.height_vid = None
self.width_vid = None
if self.cfg.simultan_image:
self.height_alter = self.cfg.height_alter
self.width_alter = self.cfg.width_alter
self.directions_unit_focals_image = [
get_ray_directions(H=height, W=width, focal=1.0)
for (height, width) in zip(self.height_alter, self.width_alter)
]
else:
self.directions_unit_focals_image = None
self.height_alter = None
self.width_alter = None
if self.cfg.train_dynamic_camera:
self.elevation_range_delta = (-180./(16*self.cfg.num_frames), 180./(16*self.cfg.num_frames))
self.azimuth_range_delta = (-180./(2*self.cfg.num_frames), 180./(2*self.cfg.num_frames))
self.camera_distance_range_delta = lambda x: (
(self.camera_distance_range[0]-x)/self.cfg.num_frames, (self.camera_distance_range[1]-x)/self.cfg.num_frames
)
def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):
size_ind = bisect.bisect_right(self.resolution_milestones, global_step) - 1
self.height = self.heights[size_ind]
self.width = self.widths[size_ind]
self.batch_size = self.batch_sizes[size_ind]
self.directions_unit_focal = self.directions_unit_focals[size_ind]
if self.cfg.simultan_image and global_step % 2 == 0:
self.height = self.height_alter[size_ind]
self.width = self.width_alter[size_ind]
self.directions_unit_focal = self.directions_unit_focals_image[size_ind]
threestudio.debug(f"Training height: {self.height}, width: {self.width}")
def __iter__(self):
while True:
yield {}
def collate(self, batch) -> Dict[str, Any]:
# Simultaneous training
if self.cfg.simultan:
if self.cfg.simultan_vid_mod is not None:
is_video = self.simultan_idx % self.cfg.simultan_vid_mod == 0
elif self.cfg.simultan_vid_mod_perc is not None:
is_video = random.random() < self.cfg.simultan_vid_mod_perc
else:
is_video = False
else:
is_video = False
if is_video:
height = self.height_vid
width = self.width_vid
is_video = True
directions_unit_focal = self.directions_unit_focals_vid
else:
height = self.height
width = self.width
is_video = False
directions_unit_focal = self.directions_unit_focal
if self.cfg.simultan:
self.simultan_idx += 1
train_dynamic_camera = self.cfg.train_dynamic_camera and is_video
batch_size = self.batch_size
num_frames = self.cfg.num_frames
if train_dynamic_camera:
batch_factor = num_frames
else:
batch_factor = 1
batch_size = batch_size * batch_factor
# sample elevation angles
elevation_deg: Float[Tensor, "B"]
elevation: Float[Tensor, "B"]
if random.random() < 0.5:
# sample elevation angles uniformly with a probability 0.5 (biased towards poles)
elevation_deg = (
torch.rand(self.batch_size).repeat(batch_factor)
* (self.cfg.elevation_range[1] - self.cfg.elevation_range[0])
+ self.cfg.elevation_range[0]
)
elevation = elevation_deg * math.pi / 180
else:
# otherwise sample uniformly on sphere
elevation_range_percent = [
(self.cfg.elevation_range[0] + 90.0) / 180.0,
(self.cfg.elevation_range[1] + 90.0) / 180.0,
]
# inverse transform sampling
elevation = torch.asin(
2
* (
torch.rand(self.batch_size).repeat(batch_factor)
* (elevation_range_percent[1] - elevation_range_percent[0])
+ elevation_range_percent[0]
)
- 1.0
)
elevation_deg = elevation / math.pi * 180.0
if train_dynamic_camera:
elevation_delta_deg = (
torch.rand(self.batch_size)
* (self.elevation_range_delta[1] - self.elevation_range_delta[0])
+ self.elevation_range_delta[0]
) * torch.arange(num_frames)
elevation_deg = elevation_deg + elevation_delta_deg
elevation = elevation + elevation_delta_deg * math.pi / 180
# sample azimuth angles from a uniform distribution bounded by azimuth_range
azimuth_deg: Float[Tensor, "B"]
if self.cfg.batch_uniform_azimuth:
assert batch_factor == 1
# ensures sampled azimuth angles in a batch cover the whole range
azimuth_deg = (
torch.rand(self.batch_size) + torch.arange(self.batch_size)
) / self.batch_size * (
self.cfg.azimuth_range[1] - self.cfg.azimuth_range[0]
) + self.cfg.azimuth_range[
0
]
else:
# simple random sampling
azimuth_deg = (
torch.rand(self.batch_size).repeat(batch_factor)
* (self.cfg.azimuth_range[1] - self.cfg.azimuth_range[0])
+ self.cfg.azimuth_range[0]
)
azimuth = azimuth_deg * math.pi / 180
if train_dynamic_camera:
azimuth_delta_deg = (
torch.rand(self.batch_size)
* (self.azimuth_range_delta[1] - self.azimuth_range_delta[0])
+ self.azimuth_range_delta[0]
) * torch.arange(num_frames)
azimuth_deg = azimuth_deg + azimuth_delta_deg
azimuth = azimuth + elevation_delta_deg * math.pi / 180
# sample distances from a uniform distribution bounded by distance_range
camera_distances: Float[Tensor, "B"] = (
torch.rand(self.batch_size).repeat(batch_factor)
* (self.cfg.camera_distance_range[1] - self.cfg.camera_distance_range[0])
+ self.cfg.camera_distance_range[0]
)
if train_dynamic_camera:
camera_distance_range_delta = self.camera_distance_range_delta(camera_distances[0])
camera_distance_delta = (
torch.rand(self.batch_size)
* (camera_distance_range_delta[1] - camera_distance_range_delta[0])
+ camera_distance_range_delta[0]
) * torch.arange(num_frames)
camera_distances = camera_distances + camera_distance_delta
# convert spherical coordinates to cartesian coordinates
# right hand coordinate system, x back, y right, z up
# elevation in (-90, 90), azimuth from +x to +y in (-180, 180)
camera_positions: Float[Tensor, "B 3"] = torch.stack(
[
camera_distances * torch.cos(elevation) * torch.cos(azimuth),
camera_distances * torch.cos(elevation) * torch.sin(azimuth),
camera_distances * torch.sin(elevation),
],
dim=-1,
)
# default scene center at origin
center: Float[Tensor, "B 3"] = torch.zeros_like(camera_positions)
# default camera up direction as +z
up: Float[Tensor, "B 3"] = torch.as_tensor([0, 0, 1], dtype=torch.float32)[
None, :
].repeat(batch_size, 1)
# sample camera perturbations from a uniform distribution [-camera_perturb, camera_perturb]
camera_perturb: Float[Tensor, "B 3"] = (
torch.rand(self.batch_size, 3) * 2 * self.cfg.camera_perturb
- self.cfg.camera_perturb
).repeat(batch_factor, 1)
camera_positions = camera_positions + camera_perturb
# sample center perturbations from a normal distribution with mean 0 and std center_perturb
center_perturb: Float[Tensor, "B 3"] = (
torch.randn(self.batch_size, 3) * self.cfg.center_perturb
).repeat(batch_factor, 1)
center = center + center_perturb
# sample up perturbations from a normal distribution with mean 0 and std up_perturb
up_perturb: Float[Tensor, "B 3"] = (
torch.randn(self.batch_size, 3) * self.cfg.up_perturb
).repeat(batch_factor, 1)
up = up + up_perturb
# sample fovs from a uniform distribution bounded by fov_range
fovy_deg: Float[Tensor, "B"] = (
torch.rand(self.batch_size)
* (self.cfg.fovy_range[1] - self.cfg.fovy_range[0])
+ self.cfg.fovy_range[0]
).repeat(batch_factor)
fovy = fovy_deg * math.pi / 180
# sample light distance from a uniform distribution bounded by light_distance_range
light_distances: Float[Tensor, "B"] = (
torch.rand(self.batch_size)
* (self.cfg.light_distance_range[1] - self.cfg.light_distance_range[0])
+ self.cfg.light_distance_range[0]
).repeat(batch_factor)
if self.cfg.light_sample_strategy == "dreamfusion":
# sample light direction from a normal distribution with mean camera_position and std light_position_perturb
light_direction: Float[Tensor, "B 3"] = F.normalize(
camera_positions
+ torch.randn(self.batch_size, 3).repeat(batch_factor, 1) * self.cfg.light_position_perturb,
dim=-1,
)
# get light position by scaling light direction by light distance
light_positions: Float[Tensor, "B 3"] = (
light_direction * light_distances[:, None]
)
elif self.cfg.light_sample_strategy == "magic3d":
# sample light direction within restricted angle range (pi/3)
local_z = F.normalize(camera_positions, dim=-1)
local_x = F.normalize(
torch.stack(
[local_z[:, 1], -local_z[:, 0], torch.zeros_like(local_z[:, 0])],
dim=-1,
),
dim=-1,
)
local_y = F.normalize(torch.cross(local_z, local_x, dim=-1), dim=-1)
rot = torch.stack([local_x, local_y, local_z], dim=-1)
light_azimuth = (
torch.rand(self.batch_size) * math.pi - 2 * math.pi
).repeat(batch_factor) # [-pi, pi]
light_elevation = (
torch.rand(self.batch_size) * math.pi / 3 + math.pi / 6
).repeat(batch_factor) # [pi/6, pi/2]
light_positions_local = torch.stack(
[
light_distances
* torch.cos(light_elevation)
* torch.cos(light_azimuth),
light_distances
* torch.cos(light_elevation)
* torch.sin(light_azimuth),
light_distances * torch.sin(light_elevation),
],
dim=-1,
)
light_positions = (rot @ light_positions_local[:, :, None])[:, :, 0]
else:
raise ValueError(
f"Unknown light sample strategy: {self.cfg.light_sample_strategy}"
)
lookat: Float[Tensor, "B 3"] = F.normalize(center - camera_positions, dim=-1)
right: Float[Tensor, "B 3"] = F.normalize(torch.cross(lookat, up), dim=-1)
up = F.normalize(torch.cross(right, lookat), dim=-1)
c2w3x4: Float[Tensor, "B 3 4"] = torch.cat(
[torch.stack([right, up, -lookat], dim=-1), camera_positions[:, :, None]],
dim=-1,
)
c2w: Float[Tensor, "B 4 4"] = torch.cat(
[c2w3x4, torch.zeros_like(c2w3x4[:, :1])], dim=1
)
c2w[:, 3, 3] = 1.0
# get directions by dividing directions_unit_focal by focal length
focal_length: Float[Tensor, "B"] = 0.5 * height / torch.tan(0.5 * fovy)
directions: Float[Tensor, "B H W 3"] = directions_unit_focal[
None, :, :, :
].repeat(batch_size, 1, 1, 1)
directions[:, :, :, :2] = (
directions[:, :, :, :2] / focal_length[:, None, None, None]
)
# Importance note: the returned rays_d MUST be normalized!
rays_o, rays_d = get_rays(directions, c2w, keepdim=True)
proj_mtx: Float[Tensor, "B 4 4"] = get_projection_matrix(
fovy, width / height, 0.1, 1000.0
) # FIXME: hard-coded near and far
mvp_mtx: Float[Tensor, "B 4 4"] = get_mvp_matrix(c2w, proj_mtx)
# Dynamic
if self.cfg.sample_rand_frames == "fps_t0":
fps = torch.FloatTensor(1).uniform_(0, 1/num_frames).item()
t0 = torch.FloatTensor(1).uniform_(0, 1-num_frames*fps).item()
frame_times = torch.linspace(t0, t0+fps*num_frames, (num_frames+1))[:num_frames]
elif self.cfg.sample_rand_frames == "t0":
t0 = torch.FloatTensor(1).uniform_(0, 1/num_frames).item()
frame_times = torch.linspace(t0, t0+(num_frames-1)/num_frames, num_frames)
elif self.cfg.sample_rand_frames == "t0_sub":
t0 = torch.FloatTensor(1).uniform_(0, 1-self.cfg.frame_range+self.cfg.frame_range/self.cfg.num_frames).item()
frame_times = torch.linspace(t0, t0+(self.cfg.num_frames-1)*self.cfg.frame_range/self.cfg.num_frames, self.cfg.num_frames)
elif self.cfg.sample_rand_frames == "t0_sub_sample":
frame_length = (torch.rand(1) * (self.cfg.frame_length_range[1] - self.cfg.frame_length_range[0]) + self.cfg.frame_length_range[0]).item()
t0 = torch.FloatTensor(1).uniform_(0, 1-frame_length+frame_length/self.cfg.num_frames).item()
frame_times = torch.linspace(t0, t0+(self.cfg.num_frames-1)*frame_length/self.cfg.num_frames, self.cfg.num_frames)
else:
frame_times = torch.linspace(0.0, 1.0, num_frames)
# Compositional
if self.cfg.multi_obj:
nerf_to_render_idx = self.nerf_to_render_idx % (self.cfg.num_objs + self.cfg.scene_iters)
render_scene = nerf_to_render_idx >= self.cfg.num_objs
if render_scene:
if self.scene_idx % self.cfg.scene_iters_freq != 0:
self.nerf_to_render_idx += self.cfg.scene_iters
nerf_to_render_idx = self.nerf_to_render_idx % (self.cfg.num_objs + self.cfg.scene_iters)
render_scene = False
self.scene_idx += 1
self.nerf_to_render_idx += 1
else:
nerf_to_render_idx = 0
render_scene = False
if render_scene:
if self.cfg.scene_single_obj:
nerf_to_render_idx = self.scene_single_obj_idx % self.cfg.num_objs
self.scene_single_obj_idx += 1
if not is_video and self.cfg.scene_single_obj_static:
render_scene = False
else:
nerf_to_render_idx = None
return {
"rays_o": rays_o,
"rays_d": rays_d,
"mvp_mtx": mvp_mtx,
"camera_positions": camera_positions,
"c2w": c2w,
"light_positions": light_positions,
"elevation": elevation_deg,
"azimuth": azimuth_deg,
"camera_distances": camera_distances,
"height": height,
"width": width,
"frame_times": frame_times,
"frame_times_video": frame_times,
"nerf_to_render_idx": nerf_to_render_idx,
"render_scene": render_scene,
"is_video": is_video,
"train_dynamic_camera": train_dynamic_camera,
}
class RandomCameraDataset(Dataset):
def __init__(self, cfg: Any, split: str) -> None:
super().__init__()
self.cfg: RandomCameraDataModuleConfig = cfg
self.split = split
if split == "val":
self.n_views = self.cfg.n_val_views
else:
self.n_views = self.cfg.n_test_views
num_frames = self.cfg.num_frames
if self.cfg.static:
n_views_azimuth = self.n_views
else:
if split in ["val", "test"] and not self.cfg.static:
num_frames = num_frames*self.cfg.num_frames_factor
if self.split == "test":
if self.cfg.test_traj in ['motion_smooth', 'motion_smooth_full']:
self.n_views = num_frames*2
n_views_azimuth = self.n_views//8
if self.cfg.test_traj == 'motion_smooth':
self.n_views = self.n_views-4
num_frames_static = n_views_azimuth
else:
n_views_azimuth = num_frames*self.cfg.num_test_loop_factor
self.n_views = 2*n_views_azimuth*self.cfg.num_test_loop_static
num_frames_static = num_frames
elif self.split == "val":
n_views_azimuth = self.n_views
azimuth_deg: Float[Tensor, "B"]
if self.split == "val":
# make sure the first and last view are not the same
azimuth_deg = torch.linspace(0, 360.0, n_views_azimuth + 1)[: n_views_azimuth]
# else:
# azimuth_deg = torch.linspace(0, 360.0, n_views_azimuth
# )
elif self.split == "test":
if self.cfg.static:
azimuth_deg = torch.linspace(0, 360.0, n_views_azimuth)
else:
# assert n_views_azimuth % self.cfg.num_test_loop_static == 0
azimuth_deg = []
for i in range(self.cfg.num_test_loop_static):
if self.cfg.test_traj in ['motion_smooth', 'motion_smooth_full']:
azimuth_start = (self.cfg.num_test_loop_static-i)*90.
azimuth_end = (self.cfg.num_test_loop_static-i-1)*90.
else:
azimuth_start = i*90.
azimuth_end = (i+1)*90.
azimuth_static_deg_i = torch.full((num_frames_static,), azimuth_start)
azimuth_deg.append(azimuth_static_deg_i)
azimuth_dynamic_deg_i = torch.linspace(azimuth_start, azimuth_end, n_views_azimuth)
if self.cfg.test_traj == 'motion_smooth':
azimuth_dynamic_deg_i = azimuth_dynamic_deg_i[1:]
azimuth_deg.append(azimuth_dynamic_deg_i)
azimuth_deg = torch.cat(azimuth_deg)
elevation_deg: Float[Tensor, "B"] = torch.full_like(
azimuth_deg, self.cfg.eval_elevation_deg
)
camera_distances: Float[Tensor, "B"] = torch.full_like(
elevation_deg, self.cfg.eval_camera_distance
)
elevation = elevation_deg * math.pi / 180
azimuth = azimuth_deg * math.pi / 180
# convert spherical coordinates to cartesian coordinates
# right hand coordinate system, x back, y right, z up
# elevation in (-90, 90), azimuth from +x to +y in (-180, 180)
camera_positions: Float[Tensor, "B 3"] = torch.stack(
[
camera_distances * torch.cos(elevation) * torch.cos(azimuth),
camera_distances * torch.cos(elevation) * torch.sin(azimuth),
camera_distances * torch.sin(elevation),
],
dim=-1,
)
# default scene center at origin
center: Float[Tensor, "B 3"] = torch.zeros_like(camera_positions)
# default camera up direction as +z
up: Float[Tensor, "B 3"] = torch.as_tensor([0, 0, 1], dtype=torch.float32)[
None, :
].repeat(self.cfg.eval_batch_size, 1)
fovy_deg: Float[Tensor, "B"] = torch.full_like(
elevation_deg, self.cfg.eval_fovy_deg
)
fovy = fovy_deg * math.pi / 180
light_positions: Float[Tensor, "B 3"] = camera_positions
lookat: Float[Tensor, "B 3"] = F.normalize(center - camera_positions, dim=-1)
right: Float[Tensor, "B 3"] = F.normalize(torch.cross(lookat, up), dim=-1)
up = F.normalize(torch.cross(right, lookat), dim=-1)
c2w3x4: Float[Tensor, "B 3 4"] = torch.cat(
[torch.stack([right, up, -lookat], dim=-1), camera_positions[:, :, None]],
dim=-1,
)
c2w: Float[Tensor, "B 4 4"] = torch.cat(
[c2w3x4, torch.zeros_like(c2w3x4[:, :1])], dim=1
)
c2w[:, 3, 3] = 1.0
# get directions by dividing directions_unit_focal by focal length
focal_length: Float[Tensor, "B"] = (
0.5 * self.cfg.eval_height / torch.tan(0.5 * fovy)
)
directions_unit_focal = get_ray_directions(
H=self.cfg.eval_height, W=self.cfg.eval_width, focal=1.0
)
directions: Float[Tensor, "B H W 3"] = directions_unit_focal[
None, :, :, :
].repeat(self.n_views, 1, 1, 1)
directions[:, :, :, :2] = (
directions[:, :, :, :2] / focal_length[:, None, None, None]
)
rays_o, rays_d = get_rays(directions, c2w, keepdim=True)
proj_mtx: Float[Tensor, "B 4 4"] = get_projection_matrix(
fovy, self.cfg.eval_width / self.cfg.eval_height, 0.1, 1000.0
) # FIXME: hard-coded near and far
mvp_mtx: Float[Tensor, "B 4 4"] = get_mvp_matrix(c2w, proj_mtx)
if self.cfg.test_traj in ['motion_smooth', 'motion_smooth_full']:
frame_times = torch.linspace(0, 1.0, num_frames)
if self.cfg.test_traj == 'motion_smooth':
frame_times = frame_times[:num_frames-2]
else:
frame_times = frame_times[:num_frames]
frame_times = frame_times.repeat(math.ceil(self.n_views/num_frames))
else:
frame_times = torch.linspace(
0, 1.0, num_frames
).repeat(math.ceil(self.n_views/num_frames))
frame_times = frame_times[:self.n_views]
frame_times_video = torch.linspace(
0, 1.0, num_frames
)
self.rays_o, self.rays_d = rays_o, rays_d
self.mvp_mtx = mvp_mtx
self.proj_mtx = proj_mtx
self.focal_length = focal_length
self.c2w = c2w
self.camera_positions = camera_positions
self.light_positions = light_positions
self.elevation, self.azimuth = elevation, azimuth
self.elevation_deg, self.azimuth_deg = elevation_deg, azimuth_deg
self.camera_distances = camera_distances
self.frame_times = frame_times
self.frame_times_video = frame_times_video
def __len__(self):
return self.n_views
def __getitem__(self, index):
return {
"index": index,
"rays_o": self.rays_o[index],
"rays_d": self.rays_d[index],
"mvp_mtx": self.mvp_mtx[index],
"proj_mtx": self.proj_mtx[index],
"focal_length": self.focal_length[index],
"c2w": self.c2w[index],
"camera_positions": self.camera_positions[index],
"light_positions": self.light_positions[index],
"elevation": self.elevation_deg[index],
"azimuth": self.azimuth_deg[index],
"camera_distances": self.camera_distances[index],
"frame_times": self.frame_times[[index]],
"frame_times_video": self.frame_times_video,
"train_dynamic_camera": False,
"render_scene": self.cfg.multi_obj,
}
def collate(self, batch):
batch = torch.utils.data.default_collate(batch)
batch.update({"height": self.cfg.eval_height, "width": self.cfg.eval_width})
return batch
@register("random-camera-datamodule")
class RandomCameraDataModule(pl.LightningDataModule):
cfg: RandomCameraDataModuleConfig
def __init__(self, cfg: Optional[Union[dict, DictConfig]] = None) -> None:
super().__init__()
self.cfg = parse_structured(RandomCameraDataModuleConfig, cfg)
def setup(self, stage=None) -> None:
if stage in [None, "fit"]:
self.train_dataset = RandomCameraIterableDataset(self.cfg)
if stage in [None, "fit", "validate"]:
self.val_dataset = RandomCameraDataset(self.cfg, "val")
if stage in [None, "test", "predict"]:
self.test_dataset = RandomCameraDataset(self.cfg, "test")
def prepare_data(self):
pass
def general_loader(self, dataset, batch_size, collate_fn=None) -> DataLoader:
return DataLoader(
dataset,
# very important to disable multi-processing if you want to change self attributes at runtime!
# (for example setting self.width and self.height in update_step)
num_workers=0, # type: ignore
batch_size=batch_size,
collate_fn=collate_fn,
)
def train_dataloader(self) -> DataLoader:
return self.general_loader(
self.train_dataset, batch_size=None, collate_fn=self.train_dataset.collate
)
def val_dataloader(self) -> DataLoader:
return self.general_loader(
self.val_dataset, batch_size=1, collate_fn=self.val_dataset.collate
)
# return self.general_loader(self.train_dataset, batch_size=None, collate_fn=self.train_dataset.collate)
def test_dataloader(self) -> DataLoader:
return self.general_loader(
self.test_dataset, batch_size=1, collate_fn=self.test_dataset.collate
)
def predict_dataloader(self) -> DataLoader:
return self.general_loader(
self.test_dataset, batch_size=1, collate_fn=self.test_dataset.collate
)
================================================
FILE: threestudio/models/__init__.py
================================================
from . import (
background,
exporters,
geometry,
guidance,
materials,
prompt_processors,
renderers,
)
================================================
FILE: threestudio/models/background/__init__.py
================================================
from . import (
base,
neural_environment_map_background,
solid_color_background,
textured_background,
)
================================================
FILE: threestudio/models/background/base.py
================================================
import random
from dataclasses import dataclass, field
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio
from threestudio.utils.base import BaseModule
from threestudio.utils.typing import *
class BaseBackground(BaseModule):
@dataclass
class Config(BaseModule.Config):
pass
cfg: Config
def configure(self):
pass
def forward(self, dirs: Float[Tensor, "*B 3"]) -> Float[Tensor, "*B 3"]:
raise NotImplementedError
================================================
FILE: threestudio/models/background/neural_environment_map_background.py
================================================
import random
from dataclasses import dataclass, field
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio
from threestudio.models.background.base import BaseBackground
from threestudio.models.networks import get_encoding, get_mlp
from threestudio.utils.ops import get_activation
from threestudio.utils.typing import *
@threestudio.register("neural-environment-map-background")
class NeuralEnvironmentMapBackground(BaseBackground):
@dataclass
class Config(BaseBackground.Config):
n_output_dims: int = 3
color_activation: str = "sigmoid"
dir_encoding_config: dict = field(
default_factory=lambda: {"otype": "SphericalHarmonics", "degree": 3}
)
mlp_network_config: dict = field(
default_factory=lambda: {
"otype": "VanillaMLP",
"activation": "ReLU",
"n_neurons": 16,
"n_hidden_layers": 2,
}
)
random_aug: bool = False
random_aug_prob: float = 0.5
eval_color: Optional[Tuple[float, float, float]] = None
cfg: Config
def configure(self) -> None:
self.encoding = get_encoding(3, self.cfg.dir_encoding_config)
self.network = get_mlp(
self.encoding.n_output_dims,
self.cfg.n_output_dims,
self.cfg.mlp_network_config,
)
def forward(self, dirs: Float[Tensor, "*B 3"]) -> Float[Tensor, "*B 3"]:
if not self.training and self.cfg.eval_color is not None:
return torch.ones(*dirs.shape[:-1], self.cfg.n_output_dims).to(
dirs
) * torch.as_tensor(self.cfg.eval_color).to(dirs)
# viewdirs must be normalized before passing to this function
squeezed_dim = dirs.view(-1, 3).shape[0]
dirs = (dirs + 1.0) / 2.0 # (-1, 1) => (0, 1)
dirs_embd = self.encoding(dirs.view(-1, 3))
color = self.network(dirs_embd).view(*dirs.shape[:-1], self.cfg.n_output_dims)
color = get_activation(self.cfg.color_activation)(color)
if (
self.training
and self.cfg.random_aug
and random.random() < self.cfg.random_aug_prob
):
# use random background color with probability random_aug_prob
color = color * 0 + ( # prevent checking for unused parameters in DDP
torch.rand(self.cfg.n_output_dims)
.to(dirs)[None, :]
.expand(squeezed_dim, -1)
.view(*dirs.shape[:-1], -1)
)
return color
================================================
FILE: threestudio/models/background/solid_color_background.py
================================================
from dataclasses import dataclass, field
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio
from threestudio.models.background.base import BaseBackground
from threestudio.utils.typing import *
@threestudio.register("solid-color-background")
class SolidColorBackground(BaseBackground):
@dataclass
class Config(BaseBackground.Config):
n_output_dims: int = 3
color: Tuple = (1.0, 1.0, 1.0)
learned: bool = False
cfg: Config
def configure(self) -> None:
self.env_color: Float[Tensor, "Nc"]
if self.cfg.learned:
self.env_color = nn.Parameter(
torch.as_tensor(self.cfg.color, dtype=torch.float32)
)
else:
self.register_buffer(
"env_color", torch.as_tensor(self.cfg.color, dtype=torch.float32)
)
def forward(self, dirs: Float[Tensor, "*B 3"]) -> Float[Tensor, "*B Nc"]:
return (
torch.ones(*dirs.shape[:-1], self.cfg.n_output_dims).to(dirs)
* self.env_color
)
================================================
FILE: threestudio/models/background/textured_background.py
================================================
from dataclasses import dataclass, field
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio
from threestudio.models.background.base import BaseBackground
from threestudio.utils.ops import get_activation
from threestudio.utils.typing import *
@threestudio.register("textured-background")
class TexturedBackground(BaseBackground):
@dataclass
class Config(BaseBackground.Config):
n_output_dims: int = 3
height: int = 64
width: int = 64
color_activation: str = "sigmoid"
cfg: Config
def configure(self) -> None:
self.texture = nn.Parameter(
torch.randn((1, self.cfg.n_output_dims, self.cfg.height, self.cfg.width))
)
def spherical_xyz_to_uv(self, dirs: Float[Tensor, "*B 3"]) -> Float[Tensor, "*B 2"]:
x, y, z = dirs[..., 0], dirs[..., 1], dirs[..., 2]
xy = (x**2 + y**2) ** 0.5
u = torch.atan2(xy, z) / torch.pi
v = torch.atan2(y, x) / (torch.pi * 2) + 0.5
uv = torch.stack([u, v], -1)
return uv
def forward(self, dirs: Float[Tensor, "*B 3"]) -> Float[Tensor, "*B Nc"]:
dirs_shape = dirs.shape[:-1]
uv = self.spherical_xyz_to_uv(dirs)
uv = 2 * uv - 1 # rescale to [-1, 1] for grid_sample
uv = uv.reshape(1, -1, 1, 2)
color = (
F.grid_sample(
self.texture,
uv,
mode="bilinear",
padding_mode="reflection",
align_corners=False,
)
.reshape(self.cfg.n_output_dims, -1)
.T.reshape(*dirs_shape, self.cfg.n_output_dims)
)
color = get_activation(self.cfg.color_activation)(color)
return color
================================================
FILE: threestudio/models/estimators.py
================================================
from typing import Callable, List, Optional, Tuple
try:
from typing import Literal
except ImportError:
from typing_extensions import Literal
import torch
from nerfacc.data_specs import RayIntervals
from nerfacc.estimators.base import AbstractEstimator
from nerfacc.pdf import importance_sampling, searchsorted
from nerfacc.volrend import render_transmittance_from_density
from torch import Tensor
class ImportanceEstimator(AbstractEstimator):
def __init__(
self,
) -> None:
super().__init__()
@torch.no_grad()
def sampling(
self,
prop_sigma_fns: List[Callable],
prop_samples: List[int],
num_samples: int,
# rendering options
n_rays: int,
near_plane: float,
far_plane: float,
sampling_type: Literal["uniform", "lindisp"] = "uniform",
# training options
stratified: bool = False,
requires_grad: bool = False,
) -> Tuple[Tensor, Tensor]:
"""Sampling with CDFs from proposal networks.
Args:
prop_sigma_fns: Proposal network evaluate functions. It should be a list
of functions that take in samples {t_starts (n_rays, n_samples),
t_ends (n_rays, n_samples)} and returns the post-activation densities
(n_rays, n_samples).
prop_samples: Number of samples to draw from each proposal network. Should
be the same length as `prop_sigma_fns`.
num_samples: Number of samples to draw in the end.
n_rays: Number of rays.
near_plane: Near plane.
far_plane: Far plane.
sampling_type: Sampling type. Either "uniform" or "lindisp". Default to
"lindisp".
stratified: Whether to use stratified sampling. Default to `False`.
Returns:
A tuple of {Tensor, Tensor}:
- **t_starts**: The starts of the samples. Shape (n_rays, num_samples).
- **t_ends**: The ends of the samples. Shape (n_rays, num_samples).
"""
assert len(prop_sigma_fns) == len(prop_samples), (
"The number of proposal networks and the number of samples "
"should be the same."
)
cdfs = torch.cat(
[
torch.zeros((n_rays, 1), device=self.device),
torch.ones((n_rays, 1), device=self.device),
],
dim=-1,
)
intervals = RayIntervals(vals=cdfs)
for level_fn, level_samples in zip(prop_sigma_fns, prop_samples):
intervals, _ = importance_sampling(
intervals, cdfs, level_samples, stratified
)
t_vals = _transform_stot(
sampling_type, intervals.vals, near_plane, far_plane
)
t_starts = t_vals[..., :-1]
t_ends = t_vals[..., 1:]
with torch.set_grad_enabled(requires_grad):
sigmas = level_fn(t_starts, t_ends)
assert sigmas.shape == t_starts.shape
trans, _ = render_transmittance_from_density(t_starts, t_ends, sigmas)
cdfs = 1.0 - torch.cat([trans, torch.zeros_like(trans[:, :1])], dim=-1)
intervals, _ = importance_sampling(intervals, cdfs, num_samples, stratified)
t_vals_fine = _transform_stot(
sampling_type, intervals.vals, near_plane, far_plane
)
t_vals = torch.cat([t_vals, t_vals_fine], dim=-1)
t_vals, _ = torch.sort(t_vals, dim=-1)
t_starts_ = t_vals[..., :-1]
t_ends_ = t_vals[..., 1:]
return t_starts_, t_ends_
def _transform_stot(
transform_type: Literal["uniform", "lindisp"],
s_vals: torch.Tensor,
t_min: torch.Tensor,
t_max: torch.Tensor,
) -> torch.Tensor:
if transform_type == "uniform":
_contract_fn, _icontract_fn = lambda x: x, lambda x: x
elif transform_type == "lindisp":
_contract_fn, _icontract_fn = lambda x: 1 / x, lambda x: 1 / x
else:
raise ValueError(f"Unknown transform_type: {transform_type}")
s_min, s_max = _contract_fn(t_min), _contract_fn(t_max)
icontract_fn = lambda s: _icontract_fn(s * s_max + (1 - s) * s_min)
return icontract_fn(s_vals)
================================================
FILE: threestudio/models/exporters/__init__.py
================================================
from . import base, mesh_exporter
================================================
FILE: threestudio/models/exporters/base.py
================================================
from dataclasses import dataclass
import threestudio
from threestudio.models.background.base import BaseBackground
from threestudio.models.geometry.base import BaseImplicitGeometry
from threestudio.models.materials.base import BaseMaterial
from threestudio.utils.base import BaseObject
from threestudio.utils.typing import *
@dataclass
class ExporterOutput:
save_name: str
save_type: str
params: Dict[str, Any]
class Exporter(BaseObject):
@dataclass
class Config(BaseObject.Config):
save_video: bool = False
cfg: Config
def configure(
self,
geometry: BaseImplicitGeometry,
material: BaseMaterial,
background: BaseBackground,
) -> None:
@dataclass
class SubModules:
geometry: BaseImplicitGeometry
material: BaseMaterial
background: BaseBackground
self.sub_modules = SubModules(geometry, material, background)
@property
def geometry(self) -> BaseImplicitGeometry:
return self.sub_modules.geometry
@property
def material(self) -> BaseMaterial:
return self.sub_modules.material
@property
def background(self) -> BaseBackground:
return self.sub_modules.background
def __call__(self, *args, **kwargs) -> List[ExporterOutput]:
raise NotImplementedError
================================================
FILE: threestudio/models/exporters/mesh_exporter.py
================================================
from dataclasses import dataclass, field
import cv2
import numpy as np
import torch
import threestudio
from threestudio.models.background.base import BaseBackground
from threestudio.models.exporters.base import Exporter, ExporterOutput
from threestudio.models.geometry.base import BaseImplicitGeometry
from threestudio.models.materials.base import BaseMaterial
from threestudio.models.mesh import Mesh
from threestudio.utils.rasterize import NVDiffRasterizerContext
from threestudio.utils.typing import *
@threestudio.register("mesh-exporter")
class MeshExporter(Exporter):
@dataclass
class Config(Exporter.Config):
fmt: str = "obj-mtl" # in ['obj-mtl', 'obj'], TODO: fbx
save_name: str = "model"
save_normal: bool = False
save_uv: bool = True
save_texture: bool = True
texture_size: int = 1024
texture_format: str = "jpg"
xatlas_chart_options: dict = field(default_factory=dict)
xatlas_pack_options: dict = field(default_factory=dict)
context_type: str = "gl"
cfg: Config
def configure(
self,
geometry: BaseImplicitGeometry,
material: BaseMaterial,
background: BaseBackground,
) -> None:
super().configure(geometry, material, background)
self.ctx = NVDiffRasterizerContext(self.cfg.context_type, self.device)
def __call__(self) -> List[ExporterOutput]:
mesh: Mesh = self.geometry.isosurface()
if self.cfg.fmt == "obj-mtl":
return self.export_obj_with_mtl(mesh)
elif self.cfg.fmt == "obj":
return self.export_obj(mesh)
else:
raise ValueError(f"Unsupported mesh export format: {self.cfg.fmt}")
def export_obj_with_mtl(self, mesh: Mesh) -> List[ExporterOutput]:
params = {
"mesh": mesh,
"save_mat": True,
"save_normal": self.cfg.save_normal,
"save_uv": self.cfg.save_uv,
"save_vertex_color": False,
"map_Kd": None,
"map_Ks": None,
"map_Bump": None,
"map_format": self.cfg.texture_format,
}
if self.cfg.save_uv:
mesh.unwrap_uv(self.cfg.xatlas_chart_options, self.cfg.xatlas_pack_options)
if self.cfg.save_texture:
threestudio.info("Exporting textures ...")
assert self.cfg.save_uv, "save_uv must be True when save_texture is True"
# clip space transform
uv_clip = mesh.v_tex * 2.0 - 1.0
# pad to four component coordinate
uv_clip4 = torch.cat(
(
uv_clip,
torch.zeros_like(uv_clip[..., 0:1]),
torch.ones_like(uv_clip[..., 0:1]),
),
dim=-1,
)
# rasterize
rast, _ = self.ctx.rasterize_one(
uv_clip4, mesh.t_tex_idx, (self.cfg.texture_size, self.cfg.texture_size)
)
hole_mask = ~(rast[:, :, 3] > 0)
def uv_padding(image):
uv_padding_size = self.cfg.xatlas_pack_options.get("padding", 2)
inpaint_image = (
cv2.inpaint(
(image.detach().cpu().numpy() * 255).astype(np.uint8),
(hole_mask.detach().cpu().numpy() * 255).astype(np.uint8),
uv_padding_size,
cv2.INPAINT_TELEA,
)
/ 255.0
)
return torch.from_numpy(inpaint_image).to(image)
# Interpolate world space position
gb_pos, _ = self.ctx.interpolate_one(
mesh.v_pos, rast[None, ...], mesh.t_pos_idx
)
gb_pos = gb_pos[0]
# Sample out textures from MLP
geo_out = self.geometry.export(points=gb_pos)
mat_out = self.material.export(points=gb_pos, **geo_out)
threestudio.info(
"Perform UV padding on texture maps to avoid seams, may take a while ..."
)
if "normal" in geo_out:
params["map_Bump"] = uv_padding(geo_out["normal"])
if "albedo" in mat_out:
params["map_Kd"] = uv_padding(mat_out["albedo"])
else:
threestudio.warn(
"save_texture is True but no albedo texture found, using default white texture"
)
# TODO: map_Ks
return [
ExporterOutput(
save_name=f"{self.cfg.save_name}.obj", save_type="obj", params=params
)
]
def export_obj(self, mesh: Mesh) -> List[ExporterOutput]:
params = {
"mesh": mesh,
"save_mat": False,
"save_normal": self.cfg.save_normal,
"save_uv": self.cfg.save_uv,
"save_vertex_color": False,
"map_Kd": None,
"map_Ks": None,
"map_Bump": None,
"map_format": self.cfg.texture_format,
}
if self.cfg.save_uv:
mesh.unwrap_uv(self.cfg.xatlas_chart_options, self.cfg.xatlas_pack_options)
if self.cfg.save_texture:
threestudio.info("Exporting textures ...")
geo_out = self.geometry.export(points=mesh.v_pos)
mat_out = self.material.export(points=mesh.v_pos, **geo_out)
if "albedo" in mat_out:
mesh.set_vertex_color(mat_out["albedo"])
params["save_vertex_color"] = True
else:
threestudio.warn(
"save_texture is True but no albedo texture found, not saving vertex color"
)
return [
ExporterOutput(
save_name=f"{self.cfg.save_name}.obj", save_type="obj", params=params
)
]
================================================
FILE: threestudio/models/geometry/__init__.py
================================================
from . import base, implicit_sdf, implicit_volume, tetrahedra_sdf_grid, volume_grid
================================================
FILE: threestudio/models/geometry/base.py
================================================
from dataclasses import dataclass, field
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio
from threestudio.models.isosurface import (
IsosurfaceHelper,
MarchingCubeCPUHelper,
MarchingTetrahedraHelper,
)
from threestudio.models.mesh import Mesh
from threestudio.utils.base import BaseModule
from threestudio.utils.ops import chunk_batch, scale_tensor
from threestudio.utils.typing import *
def contract_to_unisphere(
x: Float[Tensor, "... 3"], bbox: Float[Tensor, "2 3"], unbounded: bool = False
) -> Float[Tensor, "... 3"]:
if unbounded:
x = scale_tensor(x, bbox, (0, 1))
x = x * 2 - 1 # aabb is at [-1, 1]
mag = x.norm(dim=-1, keepdim=True)
mask = mag.squeeze(-1) > 1
x[mask] = (2 - 1 / mag[mask]) * (x[mask] / mag[mask])
x = x / 4 + 0.5 # [-inf, inf] is at [0, 1]
else:
x = scale_tensor(x, bbox, (0, 1))
return x
class BaseGeometry(BaseModule):
@dataclass
class Config(BaseModule.Config):
pass
cfg: Config
@staticmethod
def create_from(
other: "BaseGeometry", cfg: Optional[Union[dict, DictConfig]] = None, **kwargs
) -> "BaseGeometry":
raise TypeError(
f"Cannot create {BaseGeometry.__name__} from {other.__class__.__name__}"
)
def export(self, *args, **kwargs) -> Dict[str, Any]:
return {}
class BaseImplicitGeometry(BaseGeometry):
@dataclass
class Config(BaseGeometry.Config):
radius: float = 1.0
isosurface: bool = True
isosurface_method: str = "mt"
isosurface_resolution: int = 128
isosurface_threshold: Union[float, str] = 0.0
isosurface_chunk: int = 0
isosurface_coarse_to_fine: bool = True
isosurface_deformable_grid: bool = False
isosurface_remove_outliers: bool = True
isosurface_outlier_n_faces_threshold: Union[int, float] = 0.01
cfg: Config
def configure(self) -> None:
self.bbox: Float[Tensor, "2 3"]
self.register_buffer(
"bbox",
torch.as_tensor(
[
[-self.cfg.radius, -self.cfg.radius, -self.cfg.radius],
[self.cfg.radius, self.cfg.radius, self.cfg.radius],
],
dtype=torch.float32,
),
)
self.isosurface_helper: Optional[IsosurfaceHelper] = None
self.unbounded: bool = False
def _initilize_isosurface_helper(self):
if self.cfg.isosurface and self.isosurface_helper is None:
if self.cfg.isosurface_method == "mc-cpu":
self.isosurface_helper = MarchingCubeCPUHelper(
self.cfg.isosurface_resolution
).to(self.device)
elif self.cfg.isosurface_method == "mt":
self.isosurface_helper = MarchingTetrahedraHelper(
self.cfg.isosurface_resolution,
f"load/tets/{self.cfg.isosurface_resolution}_tets.npz",
).to(self.device)
else:
raise AttributeError(
"Unknown isosurface method {self.cfg.isosurface_method}"
)
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
raise NotImplementedError
def forward_field(
self, points: Float[Tensor, "*N Di"]
) -> Tuple[Float[Tensor, "*N 1"], Optional[Float[Tensor, "*N 3"]]]:
# return the value of the implicit field, could be density / signed distance
# also return a deformation field if the grid vertices can be optimized
raise NotImplementedError
def forward_level(
self, field: Float[Tensor, "*N 1"], threshold: float
) -> Float[Tensor, "*N 1"]:
# return the value of the implicit field, where the zero level set represents the surface
raise NotImplementedError
def _isosurface(self, bbox: Float[Tensor, "2 3"], fine_stage: bool = False) -> Mesh:
def batch_func(x):
# scale to bbox as the input vertices are in [0, 1]
field, deformation = self.forward_field(
scale_tensor(
x.to(bbox.device), self.isosurface_helper.points_range, bbox
),
)
field = field.to(
x.device
) # move to the same device as the input (could be CPU)
if deformation is not None:
deformation = deformation.to(x.device)
return field, deformation
assert self.isosurface_helper is not None
field, deformation = chunk_batch(
batch_func,
self.cfg.isosurface_chunk,
self.isosurface_helper.grid_vertices,
)
threshold: float
if isinstance(self.cfg.isosurface_threshold, float):
threshold = self.cfg.isosurface_threshold
elif self.cfg.isosurface_threshold == "auto":
eps = 1.0e-5
threshold = field[field > eps].mean().item()
threestudio.info(
f"Automatically determined isosurface threshold: {threshold}"
)
else:
raise TypeError(
f"Unknown isosurface_threshold {self.cfg.isosurface_threshold}"
)
level = self.forward_level(field, threshold)
mesh: Mesh = self.isosurface_helper(level, deformation=deformation)
mesh.v_pos = scale_tensor(
mesh.v_pos, self.isosurface_helper.points_range, bbox
) # scale to bbox as the grid vertices are in [0, 1]
mesh.add_extra("bbox", bbox)
if self.cfg.isosurface_remove_outliers:
# remove outliers components with small number of faces
# only enabled when the mesh is not differentiable
mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)
return mesh
def isosurface(self) -> Mesh:
if not self.cfg.isosurface:
raise NotImplementedError(
"Isosurface is not enabled in the current configuration"
)
self._initilize_isosurface_helper()
if self.cfg.isosurface_coarse_to_fine:
threestudio.debug("First run isosurface to get a tight bounding box ...")
with torch.no_grad():
mesh_coarse = self._isosurface(self.bbox)
vmin, vmax = mesh_coarse.v_pos.amin(dim=0), mesh_coarse.v_pos.amax(dim=0)
vmin_ = (vmin - (vmax - vmin) * 0.1).max(self.bbox[0])
vmax_ = (vmax + (vmax - vmin) * 0.1).min(self.bbox[1])
threestudio.debug("Run isosurface again with the tight bounding box ...")
mesh = self._isosurface(torch.stack([vmin_, vmax_], dim=0), fine_stage=True)
else:
mesh = self._isosurface(self.bbox)
return mesh
class BaseExplicitGeometry(BaseGeometry):
@dataclass
class Config(BaseGeometry.Config):
radius: float = 1.0
cfg: Config
def configure(self) -> None:
self.bbox: Float[Tensor, "2 3"]
self.register_buffer(
"bbox",
torch.as_tensor(
[
[-self.cfg.radius, -self.cfg.radius, -self.cfg.radius],
[self.cfg.radius, self.cfg.radius, self.cfg.radius],
],
dtype=torch.float32,
),
)
================================================
FILE: threestudio/models/geometry/implicit_sdf.py
================================================
import os
from dataclasses import dataclass, field
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio
from threestudio.models.geometry.base import BaseImplicitGeometry, contract_to_unisphere
from threestudio.models.mesh import Mesh
from threestudio.models.networks import get_encoding, get_mlp
from threestudio.utils.misc import get_rank
from threestudio.utils.typing import *
@threestudio.register("implicit-sdf")
class ImplicitSDF(BaseImplicitGeometry):
@dataclass
class Config(BaseImplicitGeometry.Config):
n_input_dims: int = 3
n_feature_dims: int = 3
pos_encoding_config: dict = field(
default_factory=lambda: {
"otype": "HashGrid",
"n_levels": 16,
"n_features_per_level": 2,
"log2_hashmap_size": 19,
"base_resolution": 16,
"per_level_scale": 1.447269237440378,
}
)
mlp_network_config: dict = field(
default_factory=lambda: {
"otype": "VanillaMLP",
"activation": "ReLU",
"output_activation": "none",
"n_neurons": 64,
"n_hidden_layers": 1,
}
)
normal_type: Optional[
str
] = "finite_difference" # in ['pred', 'finite_difference']
finite_difference_normal_eps: float = 0.01
shape_init: Optional[str] = None
shape_init_params: Optional[Any] = None
shape_init_mesh_up: str = "+z"
shape_init_mesh_front: str = "+x"
force_shape_init: bool = False
sdf_bias: Union[float, str] = 0.0
sdf_bias_params: Optional[Any] = None
# no need to removal outlier for SDF
isosurface_remove_outliers: bool = False
cfg: Config
def configure(self) -> None:
super().configure()
self.encoding = get_encoding(
self.cfg.n_input_dims, self.cfg.pos_encoding_config
)
self.sdf_network = get_mlp(
self.encoding.n_output_dims, 1, self.cfg.mlp_network_config
)
if self.cfg.n_feature_dims > 0:
self.feature_network = get_mlp(
self.encoding.n_output_dims,
self.cfg.n_feature_dims,
self.cfg.mlp_network_config,
)
if self.cfg.normal_type == "pred":
self.normal_network = get_mlp(
self.encoding.n_output_dims, 3, self.cfg.mlp_network_config
)
if self.cfg.isosurface_deformable_grid:
assert (
self.cfg.isosurface_method == "mt"
), "isosurface_deformable_grid only works with mt"
self.deformation_network = get_mlp(
self.encoding.n_output_dims, 3, self.cfg.mlp_network_config
)
def initialize_shape(self) -> None:
if self.cfg.shape_init is None and not self.cfg.force_shape_init:
return
# do not initialize shape if weights are provided
if self.cfg.weights is not None and not self.cfg.force_shape_init:
return
if self.cfg.sdf_bias != 0.0:
threestudio.warn(
"shape_init and sdf_bias are both specified, which may lead to unexpected results."
)
get_gt_sdf: Callable[[Float[Tensor, "N 3"]], Float[Tensor, "N 1"]]
assert isinstance(self.cfg.shape_init, str)
if self.cfg.shape_init == "ellipsoid":
assert (
isinstance(self.cfg.shape_init_params, Sized)
and len(self.cfg.shape_init_params) == 3
)
size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return ((points_rand / size) ** 2).sum(
dim=-1, keepdim=True
).sqrt() - 1.0 # pseudo signed distance of an ellipsoid
get_gt_sdf = func
elif self.cfg.shape_init == "sphere":
assert isinstance(self.cfg.shape_init_params, float)
radius = self.cfg.shape_init_params
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius
get_gt_sdf = func
elif self.cfg.shape_init.startswith("mesh:"):
assert isinstance(self.cfg.shape_init_params, float)
mesh_path = self.cfg.shape_init[5:]
if not os.path.exists(mesh_path):
raise ValueError(f"Mesh file {mesh_path} does not exist.")
import trimesh
mesh = trimesh.load(mesh_path)
# move to center
centroid = mesh.vertices.mean(0)
mesh.vertices = mesh.vertices - centroid
# align to up-z and front-x
dirs = ["+x", "+y", "+z", "-x", "-y", "-z"]
dir2vec = {
"+x": np.array([1, 0, 0]),
"+y": np.array([0, 1, 0]),
"+z": np.array([0, 0, 1]),
"-x": np.array([-1, 0, 0]),
"-y": np.array([0, -1, 0]),
"-z": np.array([0, 0, -1]),
}
if (
self.cfg.shape_init_mesh_up not in dirs
or self.cfg.shape_init_mesh_front not in dirs
):
raise ValueError(
f"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}."
)
if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:
raise ValueError(
"shape_init_mesh_up and shape_init_mesh_front must be orthogonal."
)
z_, x_ = (
dir2vec[self.cfg.shape_init_mesh_up],
dir2vec[self.cfg.shape_init_mesh_front],
)
y_ = np.cross(z_, x_)
std2mesh = np.stack([x_, y_, z_], axis=0).T
mesh2std = np.linalg.inv(std2mesh)
# scaling
scale = np.abs(mesh.vertices).max()
mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params
mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T
from pysdf import SDF
sdf = SDF(mesh.vertices, mesh.faces)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
# add a negative signed here
# as in pysdf the inside of the shape has positive signed distance
return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(
points_rand
)[..., None]
get_gt_sdf = func
else:
raise ValueError(
f"Unknown shape initialization type: {self.cfg.shape_init}"
)
# Initialize SDF to a given shape when no weights are provided or force_shape_init is True
optim = torch.optim.Adam(self.parameters(), lr=1e-3)
from tqdm import tqdm
for _ in tqdm(
range(1000),
desc=f"Initializing SDF to a(n) {self.cfg.shape_init}:",
disable=get_rank() != 0,
):
points_rand = (
torch.rand((10000, 3), dtype=torch.float32).to(self.device) * 2.0 - 1.0
)
sdf_gt = get_gt_sdf(points_rand)
sdf_pred = self.forward_sdf(points_rand)
loss = F.mse_loss(sdf_pred, sdf_gt)
optim.zero_grad()
loss.backward()
optim.step()
def get_shifted_sdf(
self, points: Float[Tensor, "*N Di"], sdf: Float[Tensor, "*N 1"]
) -> Float[Tensor, "*N 1"]:
sdf_bias: Union[float, Float[Tensor, "*N 1"]]
if self.cfg.sdf_bias == "ellipsoid":
assert (
isinstance(self.cfg.sdf_bias_params, Sized)
and len(self.cfg.sdf_bias_params) == 3
)
size = torch.as_tensor(self.cfg.sdf_bias_params).to(points)
sdf_bias = ((points / size) ** 2).sum(
dim=-1, keepdim=True
).sqrt() - 1.0 # pseudo signed distance of an ellipsoid
elif self.cfg.sdf_bias == "sphere":
assert isinstance(self.cfg.sdf_bias_params, float)
radius = self.cfg.sdf_bias_params
sdf_bias = (points**2).sum(dim=-1, keepdim=True).sqrt() - radius
elif isinstance(self.cfg.sdf_bias, float):
sdf_bias = self.cfg.sdf_bias
else:
raise ValueError(f"Unknown sdf bias {self.cfg.sdf_bias}")
return sdf + sdf_bias
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
points_unscaled = points # points in the original scale
points = contract_to_unisphere(
points, self.bbox, self.unbounded
) # points normalized to (0, 1)
enc = self.encoding(points.view(-1, self.cfg.n_input_dims))
sdf = self.sdf_network(enc).view(*points.shape[:-1], 1)
sdf = self.get_shifted_sdf(points_unscaled, sdf)
output = {"sdf": sdf}
if self.cfg.n_feature_dims > 0:
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
output.update({"features": features})
if output_normal:
if self.cfg.normal_type == "finite_difference":
eps = self.cfg.finite_difference_normal_eps
offsets: Float[Tensor, "6 3"] = torch.as_tensor(
[
[eps, 0.0, 0.0],
[-eps, 0.0, 0.0],
[0.0, eps, 0.0],
[0.0, -eps, 0.0],
[0.0, 0.0, eps],
[0.0, 0.0, -eps],
]
).to(points_unscaled)
points_offset: Float[Tensor, "... 6 3"] = (
points_unscaled[..., None, :] + offsets
).clamp(-self.cfg.radius, self.cfg.radius)
sdf_offset: Float[Tensor, "... 6 1"] = self.forward_sdf(points_offset)
normal = (
0.5 * (sdf_offset[..., 0::2, 0] - sdf_offset[..., 1::2, 0]) / eps
)
normal = F.normalize(normal, dim=-1)
elif self.cfg.normal_type == "pred":
normal = self.normal_network(enc).view(*points.shape[:-1], 3)
normal = F.normalize(normal, dim=-1)
else:
raise AttributeError(f"Unknown normal type {self.cfg.normal_type}")
output.update({"normal": normal, "shading_normal": normal})
return output
def forward_sdf(self, points: Float[Tensor, "*N Di"]) -> Float[Tensor, "*N 1"]:
points_unscaled = points
points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)
sdf = self.sdf_network(
self.encoding(points.reshape(-1, self.cfg.n_input_dims))
).reshape(*points.shape[:-1], 1)
sdf = self.get_shifted_sdf(points_unscaled, sdf)
return sdf
def forward_field(
self, points: Float[Tensor, "*N Di"]
) -> Tuple[Float[Tensor, "*N 1"], Optional[Float[Tensor, "*N 3"]]]:
points_unscaled = points
points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)
enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))
sdf = self.sdf_network(enc).reshape(*points.shape[:-1], 1)
sdf = self.get_shifted_sdf(points_unscaled, sdf)
deformation: Optional[Float[Tensor, "*N 3"]] = None
if self.cfg.isosurface_deformable_grid:
deformation = self.deformation_network(enc).reshape(*points.shape[:-1], 3)
return sdf, deformation
def forward_level(
self, field: Float[Tensor, "*N 1"], threshold: float
) -> Float[Tensor, "*N 1"]:
return field - threshold
def export(self, points: Float[Tensor, "*N Di"], **kwargs) -> Dict[str, Any]:
out: Dict[str, Any] = {}
if self.cfg.n_feature_dims == 0:
return out
points_unscaled = points
points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)
enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
out.update(
{
"features": features,
}
)
return out
================================================
FILE: threestudio/models/geometry/implicit_volume.py
================================================
from dataclasses import dataclass, field
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio
from threestudio.models.geometry.base import (
BaseGeometry,
BaseImplicitGeometry,
contract_to_unisphere,
)
from threestudio.models.networks import get_encoding, get_mlp
from threestudio.utils.ops import get_activation
from threestudio.utils.typing import *
@threestudio.register("implicit-volume")
class ImplicitVolume(BaseImplicitGeometry):
@dataclass
class Config(BaseImplicitGeometry.Config):
n_input_dims: int = 3
n_feature_dims: int = 3
density_activation: Optional[str] = "softplus"
density_bias: Union[float, str] = "blob_magic3d"
density_blob_scale: float = 10.0
density_blob_std: float = 0.5
pos_encoding_config: dict = field(
default_factory=lambda: {
"otype": "HashGrid",
"n_levels": 16,
"n_features_per_level": 2,
"log2_hashmap_size": 19,
"base_resolution": 16,
"per_level_scale": 1.447269237440378,
}
)
mlp_network_config: dict = field(
default_factory=lambda: {
"otype": "VanillaMLP",
"activation": "ReLU",
"output_activation": "none",
"n_neurons": 64,
"n_hidden_layers": 1,
}
)
normal_type: Optional[
str
] = "finite_difference" # in ['pred', 'finite_difference']
finite_difference_normal_eps: float = 0.01
# automatically determine the threshold
isosurface_threshold: Union[float, str] = 25.0
anneal_density_blob_std_config: Optional[dict] = None
cfg: Config
def configure(self) -> None:
super().configure()
self.encoding = get_encoding(
self.cfg.n_input_dims, self.cfg.pos_encoding_config
)
self.density_network = get_mlp(
self.encoding.n_output_dims, 1, self.cfg.mlp_network_config
)
if self.cfg.n_feature_dims > 0:
self.feature_network = get_mlp(
self.encoding.n_output_dims,
self.cfg.n_feature_dims,
self.cfg.mlp_network_config,
)
if self.cfg.normal_type == "pred":
self.normal_network = get_mlp(
self.encoding.n_output_dims, 3, self.cfg.mlp_network_config
)
self.density_blob_std = self.cfg.density_blob_std
self.density_grid = None
def set_density_grid(self):
if self.density_grid is None:
x, y, z = (
torch.linspace(0, 1, 64),
torch.linspace(0, 1, 64),
torch.linspace(0, 1, 64),
)
x, y, z = torch.meshgrid(x, y, z, indexing="ij")
verts = torch.cat(
[x.reshape(-1, 1), y.reshape(-1, 1), z.reshape(-1, 1)], dim=-1
).reshape(-1, 3).to(self.device)
with torch.no_grad():
density = self.density_network(
self.encoding(verts.reshape(-1, self.cfg.n_input_dims), self.density_grid)
).reshape(*verts.shape[:-1], 1)
_, density = self.get_activated_density(verts, density)
self.density_grid = density.reshape(64, 64, 64).unsqueeze(0).detach()
def get_activated_density(
self, points: Float[Tensor, "*N Di"], density: Float[Tensor, "*N 1"]
) -> Tuple[Float[Tensor, "*N 1"], Float[Tensor, "*N 1"]]:
density_bias: Union[float, Float[Tensor, "*N 1"]]
if self.cfg.density_bias == "blob_dreamfusion":
# pre-activation density bias
density_bias = (
self.cfg.density_blob_scale
* torch.exp(
-0.5 * (points**2).sum(dim=-1) / self.density_blob_std**2
)[..., None]
)
elif self.cfg.density_bias == "blob_magic3d":
# pre-activation density bias
density_bias = (
self.cfg.density_blob_scale
* (
1
- torch.sqrt((points**2).sum(dim=-1)) / self.density_blob_std
)[..., None]
)
elif isinstance(self.cfg.density_bias, float):
density_bias = self.cfg.density_bias
else:
raise ValueError(f"Unknown density bias {self.cfg.density_bias}")
raw_density: Float[Tensor, "*N 1"] = density + density_bias
density = get_activation(self.cfg.density_activation)(raw_density)
return raw_density, density
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
grad_enabled = torch.is_grad_enabled()
if output_normal and self.cfg.normal_type == "analytic":
torch.set_grad_enabled(True)
points.requires_grad_(True)
points_unscaled = points # points in the original scale
points = contract_to_unisphere(
points, self.bbox, self.unbounded
) # points normalized to (0, 1)
enc, dx, elastic_loss, divergence_loss, dx_shifted = self.encoding(points.view(-1, self.cfg.n_input_dims), self.density_grid, out_all=True)
density = self.density_network(enc).view(*points.shape[:-1], 1)
raw_density, density = self.get_activated_density(points_unscaled, density)
output = {
"density": density,
"dx": dx,
"elastic_loss": elastic_loss,
"divergence_loss": divergence_loss,
"dx_shifted": dx_shifted,
}
if self.cfg.n_feature_dims > 0:
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
output.update({"features": features})
if output_normal:
if self.cfg.normal_type == "finite_difference":
# TODO: use raw density
eps = self.cfg.finite_difference_normal_eps
offsets: Float[Tensor, "6 3"] = torch.as_tensor(
[
[eps, 0.0, 0.0],
[-eps, 0.0, 0.0],
[0.0, eps, 0.0],
[0.0, -eps, 0.0],
[0.0, 0.0, eps],
[0.0, 0.0, -eps],
]
).to(points_unscaled)
points_offset: Float[Tensor, "... 6 3"] = (
points_unscaled[..., None, :] + offsets
).clamp(-self.cfg.radius, self.cfg.radius)
density_offset: Float[Tensor, "... 6 1"] = self.forward_density(
points_offset
)
normal = (
-0.5
* (density_offset[..., 0::2, 0] - density_offset[..., 1::2, 0])
/ eps
)
normal = F.normalize(normal, dim=-1)
elif self.cfg.normal_type == "pred":
normal = self.normal_network(enc).view(*points.shape[:-1], 3)
normal = F.normalize(normal, dim=-1)
elif self.cfg.normal_type == "analytic":
normal = -torch.autograd.grad(
density,
points_unscaled,
grad_outputs=torch.ones_like(density),
create_graph=True,
)[0]
normal = F.normalize(normal, dim=-1)
if not grad_enabled:
normal = normal.detach()
else:
raise AttributeError(f"Unknown normal type {self.cfg.normal_type}")
output.update({"normal": normal, "shading_normal": normal})
torch.set_grad_enabled(grad_enabled)
return output
def forward_density(self, points: Float[Tensor, "*N Di"]) -> Float[Tensor, "*N 1"]:
points_unscaled = points
points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)
density = self.density_network(
self.encoding(points.reshape(-1, self.cfg.n_input_dims), self.density_grid)
).reshape(*points.shape[:-1], 1)
_, density = self.get_activated_density(points_unscaled, density)
return density
def forward_field(
self, points: Float[Tensor, "*N Di"]
) -> Tuple[Float[Tensor, "*N 1"], Optional[Float[Tensor, "*N 3"]]]:
if self.cfg.isosurface_deformable_grid:
threestudio.warn(
f"{self.__class__.__name__} does not support isosurface_deformable_grid. Ignoring."
)
density = self.forward_density(points)
return density, None
def forward_level(
self, field: Float[Tensor, "*N 1"], threshold: float
) -> Float[Tensor, "*N 1"]:
return -(field - threshold)
def export(self, points: Float[Tensor, "*N Di"], **kwargs) -> Dict[str, Any]:
out: Dict[str, Any] = {}
if self.cfg.n_feature_dims == 0:
return out
points_unscaled = points
points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)
enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims), self.density_grid)
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
out.update(
{
"features": features,
}
)
return out
@staticmethod
@torch.no_grad()
def create_from(
other: BaseGeometry,
cfg: Optional[Union[dict, DictConfig]] = None,
copy_net: bool = True,
**kwargs,
) -> "ImplicitVolume":
if isinstance(other, ImplicitVolume):
instance = ImplicitVolume(cfg, **kwargs)
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.density_network.load_state_dict(other.density_network.state_dict())
if copy_net:
if (
instance.cfg.n_feature_dims > 0
and other.cfg.n_feature_dims == instance.cfg.n_feature_dims
):
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
if (
instance.cfg.normal_type == "pred"
and other.cfg.normal_type == "pred"
):
instance.normal_network.load_state_dict(
other.normal_network.state_dict()
)
return instance
else:
raise TypeError(
f"Cannot create {ImplicitVolume.__name__} from {other.__class__.__name__}"
)
def update_step(
self, epoch: int, global_step: int, on_load_weights: bool = False
) -> None:
if self.cfg.anneal_density_blob_std_config is not None:
min_step = self.cfg.anneal_density_blob_std_config.min_anneal_step
max_step = self.cfg.anneal_density_blob_std_config.max_anneal_step
if (
global_step >= min_step
and global_step <= max_step
):
end_val = self.cfg.anneal_density_blob_std_config.end_val
start_val = self.cfg.anneal_density_blob_std_config.start_val
self.density_blob_std = start_val + (global_step - min_step)*(end_val - start_val)/(max_step - min_step)
================================================
FILE: threestudio/models/geometry/tetrahedra_sdf_grid.py
================================================
from dataclasses import dataclass, field
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio
from threestudio.models.geometry.base import (
BaseExplicitGeometry,
BaseGeometry,
contract_to_unisphere,
)
from threestudio.models.geometry.implicit_sdf import ImplicitSDF
from threestudio.models.geometry.implicit_volume import ImplicitVolume
from threestudio.models.isosurface import MarchingTetrahedraHelper
from threestudio.models.mesh import Mesh
from threestudio.models.networks import get_encoding, get_mlp
from threestudio.utils.ops import scale_tensor
from threestudio.utils.typing import *
@threestudio.register("tetrahedra-sdf-grid")
class TetrahedraSDFGrid(BaseExplicitGeometry):
@dataclass
class Config(BaseExplicitGeometry.Config):
isosurface_resolution: int = 128
isosurface_deformable_grid: bool = True
isosurface_remove_outliers: bool = False
isosurface_outlier_n_faces_threshold: Union[int, float] = 0.01
n_input_dims: int = 3
n_feature_dims: int = 3
pos_encoding_config: dict = field(
default_factory=lambda: {
"otype": "HashGrid",
"n_levels": 16,
"n_features_per_level": 2,
"log2_hashmap_size": 19,
"base_resolution": 16,
"per_level_scale": 1.447269237440378,
}
)
mlp_network_config: dict = field(
default_factory=lambda: {
"otype": "VanillaMLP",
"activation": "ReLU",
"output_activation": "none",
"n_neurons": 64,
"n_hidden_layers": 1,
}
)
shape_init: Optional[str] = None
shape_init_params: Optional[Any] = None
force_shape_init: bool = False
geometry_only: bool = False
fix_geometry: bool = False
cfg: Config
def configure(self) -> None:
super().configure()
# this should be saved to state_dict, register as buffer
self.isosurface_bbox: Float[Tensor, "2 3"]
self.register_buffer("isosurface_bbox", self.bbox.clone())
self.isosurface_helper = MarchingTetrahedraHelper(
self.cfg.isosurface_resolution,
f"load/tets/{self.cfg.isosurface_resolution}_tets.npz",
)
self.sdf: Float[Tensor, "Nv 1"]
self.deformation: Optional[Float[Tensor, "Nv 3"]]
if not self.cfg.fix_geometry:
self.register_parameter(
"sdf",
nn.Parameter(
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
)
),
)
if self.cfg.isosurface_deformable_grid:
self.register_parameter(
"deformation",
nn.Parameter(
torch.zeros_like(self.isosurface_helper.grid_vertices)
),
)
else:
self.deformation = None
else:
self.register_buffer(
"sdf",
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
),
)
if self.cfg.isosurface_deformable_grid:
self.register_buffer(
"deformation",
torch.zeros_like(self.isosurface_helper.grid_vertices),
)
else:
self.deformation = None
if not self.cfg.geometry_only:
self.encoding = get_encoding(
self.cfg.n_input_dims, self.cfg.pos_encoding_config
)
self.feature_network = get_mlp(
self.encoding.n_output_dims,
self.cfg.n_feature_dims,
self.cfg.mlp_network_config,
)
self.mesh: Optional[Mesh] = None
def initialize_shape(self) -> None:
raise NotImplementedError
def isosurface(self) -> Mesh:
# return cached mesh if fix_geometry is True to save computation
if self.cfg.fix_geometry and self.mesh is not None:
return self.mesh
mesh = self.isosurface_helper(self.sdf, self.deformation)
mesh.v_pos = scale_tensor(
mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox
)
if self.cfg.isosurface_remove_outliers:
mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)
self.mesh = mesh
return mesh
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
if self.cfg.geometry_only:
return {}
assert (
output_normal == False
), f"Normal output is not supported for {self.__class__.__name__}"
points_unscaled = points # points in the original scale
points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1)
enc = self.encoding(points.view(-1, self.cfg.n_input_dims))
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
return {"features": features}
@staticmethod
@torch.no_grad()
def create_from(
other: BaseGeometry,
cfg: Optional[Union[dict, DictConfig]] = None,
copy_net: bool = True,
**kwargs,
) -> "TetrahedraSDFGrid":
if isinstance(other, TetrahedraSDFGrid):
instance = TetrahedraSDFGrid(cfg, **kwargs)
assert instance.cfg.isosurface_resolution == other.cfg.isosurface_resolution
instance.isosurface_bbox = other.isosurface_bbox.clone()
instance.sdf.data = other.sdf.data.clone()
if (
instance.cfg.isosurface_deformable_grid
and other.cfg.isosurface_deformable_grid
):
assert (
instance.deformation is not None and other.deformation is not None
)
instance.deformation.data = other.deformation.data.clone()
if (
not instance.cfg.geometry_only
and not other.cfg.geometry_only
and copy_net
):
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance
elif isinstance(other, ImplicitVolume):
instance = TetrahedraSDFGrid(cfg, **kwargs)
if other.cfg.isosurface_method != "mt":
other.cfg.isosurface_method = "mt"
threestudio.warn(
f"Override isosurface_method of the source geometry to 'mt'"
)
if other.cfg.isosurface_resolution != instance.cfg.isosurface_resolution:
other.cfg.isosurface_resolution = instance.cfg.isosurface_resolution
threestudio.warn(
f"Override isosurface_resolution of the source geometry to {instance.cfg.isosurface_resolution}"
)
mesh = other.isosurface()
instance.isosurface_bbox = mesh.extras["bbox"]
instance.sdf.data = (
mesh.extras["grid_level"].to(instance.sdf.data).clamp(-1, 1)
)
if not instance.cfg.geometry_only and copy_net:
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance
elif isinstance(other, ImplicitSDF):
instance = TetrahedraSDFGrid(cfg, **kwargs)
if other.cfg.isosurface_method != "mt":
other.cfg.isosurface_method = "mt"
threestudio.warn(
f"Override isosurface_method of the source geometry to 'mt'"
)
if other.cfg.isosurface_resolution != instance.cfg.isosurface_resolution:
other.cfg.isosurface_resolution = instance.cfg.isosurface_resolution
threestudio.warn(
f"Override isosurface_resolution of the source geometry to {instance.cfg.isosurface_resolution}"
)
mesh = other.isosurface()
instance.isosurface_bbox = mesh.extras["bbox"]
instance.sdf.data = mesh.extras["grid_level"].to(instance.sdf.data)
if (
instance.cfg.isosurface_deformable_grid
and other.cfg.isosurface_deformable_grid
):
assert instance.deformation is not None
instance.deformation.data = mesh.extras["grid_deformation"].to(
instance.deformation.data
)
if not instance.cfg.geometry_only and copy_net:
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance
else:
raise TypeError(
f"Cannot create {TetrahedraSDFGrid.__name__} from {other.__class__.__name__}"
)
def export(self, points: Float[Tensor, "*N Di"], **kwargs) -> Dict[str, Any]:
out: Dict[str, Any] = {}
if self.cfg.geometry_only or self.cfg.n_feature_dims == 0:
return out
points_unscaled = points
points = contract_to_unisphere(points_unscaled, self.bbox)
enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
out.update(
{
"features": features,
}
)
return out
================================================
FILE: threestudio/models/geometry/volume_grid.py
================================================
from dataclasses import dataclass, field
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio
from threestudio.models.geometry.base import BaseImplicitGeometry, contract_to_unisphere
from threestudio.utils.ops import get_activation
from threestudio.utils.typing import *
@threestudio.register("volume-grid")
class VolumeGrid(BaseImplicitGeometry):
@dataclass
class Config(BaseImplicitGeometry.Config):
grid_size: Tuple[int, int, int] = field(default_factory=lambda: (100, 100, 100))
n_feature_dims: int = 3
density_activation: Optional[str] = "softplus"
density_bias: Union[float, str] = "blob"
density_blob_scale: float = 5.0
density_blob_std: float = 0.5
normal_type: Optional[
str
] = "finite_difference" # in ['pred', 'finite_difference']
# automatically determine the threshold
isosurface_threshold: Union[float, str] = "auto"
cfg: Config
def configure(self) -> None:
super().configure()
self.grid_size = self.cfg.grid_size
self.grid = nn.Parameter(
torch.zeros(1, self.cfg.n_feature_dims + 1, *self.grid_size)
)
if self.cfg.density_bias == "blob":
self.register_buffer("density_scale", torch.tensor(0.0))
else:
self.density_scale = nn.Parameter(torch.tensor(0.0))
if self.cfg.normal_type == "pred":
self.normal_grid = nn.Parameter(torch.zeros(1, 3, *self.grid_size))
def get_density_bias(self, points: Float[Tensor, "*N Di"]):
if self.cfg.density_bias == "blob":
# density_bias: Float[Tensor, "*N 1"] = self.cfg.density_blob_scale * torch.exp(-0.5 * (points ** 2).sum(dim=-1) / self.cfg.density_blob_std ** 2)[...,None]
density_bias: Float[Tensor, "*N 1"] = (
self.cfg.density_blob_scale
* (
1
- torch.sqrt((points.detach() ** 2).sum(dim=-1))
/ self.cfg.density_blob_std
)[..., None]
)
return density_bias
elif isinstance(self.cfg.density_bias, float):
return self.cfg.density_bias
else:
raise AttributeError(f"Unknown density bias {self.cfg.density_bias}")
def get_trilinear_feature(
self, points: Float[Tensor, "*N Di"], grid: Float[Tensor, "1 Df G1 G2 G3"]
) -> Float[Tensor, "*N Df"]:
points_shape = points.shape[:-1]
df = grid.shape[1]
di = points.shape[-1]
out = F.grid_sample(
grid, points.view(1, 1, 1, -1, di), align_corners=False, mode="bilinear"
)
out = out.reshape(df, -1).T.reshape(*points_shape, df)
return out
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
points_unscaled = points # points in the original scale
points = contract_to_unisphere(
points, self.bbox, self.unbounded
) # points normalized to (0, 1)
points = points * 2 - 1 # convert to [-1, 1] for grid sample
out = self.get_trilinear_feature(points, self.grid)
density, features = out[..., 0:1], out[..., 1:]
density = density * torch.exp(self.density_scale) # exp scaling in DreamFusion
# breakpoint()
density = get_activation(self.cfg.density_activation)(
density + self.get_density_bias(points_unscaled)
)
output = {
"density": density,
"features": features,
}
if output_normal:
if self.cfg.normal_type == "finite_difference":
eps = 1.0e-3
offsets: Float[Tensor, "6 3"] = torch.as_tensor(
[
[eps, 0.0, 0.0],
[-eps, 0.0, 0.0],
[0.0, eps, 0.0],
[0.0, -eps, 0.0],
[0.0, 0.0, eps],
[0.0, 0.0, -eps],
]
).to(points_unscaled)
points_offset: Float[Tensor, "... 6 3"] = (
points_unscaled[..., None, :] + offsets
).clamp(-self.cfg.radius, self.cfg.radius)
density_offset: Float[Tensor, "... 6 1"] = self.forward_density(
points_offset
)
normal = (
-0.5
* (density_offset[..., 0::2, 0] - density_offset[..., 1::2, 0])
/ eps
)
normal = F.normalize(normal, dim=-1)
elif self.cfg.normal_type == "pred":
normal = self.get_trilinear_feature(points, self.normal_grid)
normal = F.normalize(normal, dim=-1)
else:
raise AttributeError(f"Unknown normal type {self.cfg.normal_type}")
output.update({"normal": normal, "shading_normal": normal})
return output
def forward_density(self, points: Float[Tensor, "*N Di"]) -> Float[Tensor, "*N 1"]:
points_unscaled = points
points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)
points = points * 2 - 1 # convert to [-1, 1] for grid sample
out = self.get_trilinear_feature(points, self.grid)
density = out[..., 0:1]
density = density * torch.exp(self.density_scale)
density = get_activation(self.cfg.density_activation)(
density + self.get_density_bias(points_unscaled)
)
return density
def forward_field(
self, points: Float[Tensor, "*N Di"]
) -> Tuple[Float[Tensor, "*N 1"], Optional[Float[Tensor, "*N 3"]]]:
if self.cfg.isosurface_deformable_grid:
threestudio.warn(
f"{self.__class__.__name__} does not support isosurface_deformable_grid. Ignoring."
)
density = self.forward_density(points)
return density, None
def forward_level(
self, field: Float[Tensor, "*N 1"], threshold: float
) -> Float[Tensor, "*N 1"]:
return -(field - threshold)
def export(self, points: Float[Tensor, "*N Di"], **kwargs) -> Dict[str, Any]:
out: Dict[str, Any] = {}
if self.cfg.n_feature_dims == 0:
return out
points_unscaled = points
points = contract_to_unisphere(points, self.bbox, self.unbounded)
points = points * 2 - 1 # convert to [-1, 1] for grid sample
features = self.get_trilinear_feature(points, self.grid)[..., 1:]
out.update(
{
"features": features,
}
)
return out
================================================
FILE: threestudio/models/guidance/__init__.py
================================================
from . import (
deep_floyd_guidance,
deep_floyd_vsd_guidance,
stable_diffusion_guidance,
stable_diffusion_vsd_guidance,
zero123_guidance,
video_stable_diffusion_guidance,
video_stable_diffusion_vsd_guidance,
multiview_diffusion_guidance,
zeroscope_guidance,
svd_guidance,
videocrafter_guidance,
)
================================================
FILE: threestudio/models/guidance/deep_floyd_guidance.py
================================================
from dataclasses import dataclass, field
import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers import IFPipeline
from diffusers.utils.import_utils import is_xformers_available
import threestudio
from threestudio.models.prompt_processors.base import PromptProcessorOutput
from threestudio.utils.base import BaseObject
from threestudio.utils.misc import C, parse_version
from threestudio.utils.typing import *
@threestudio.register("deep-floyd-guidance")
class DeepFloydGuidance(BaseObject):
@dataclass
class Config(BaseObject.Config):
pretrained_model_name_or_path: str = "DeepFloyd/IF-I-XL-v1.0"
# FIXME: xformers error
enable_memory_efficient_attention: bool = False
enable_sequential_cpu_offload: bool = False
enable_attention_slicing: bool = False
enable_channels_last_format: bool = True
guidance_scale: float = 20.0
grad_clip: Optional[
Any
] = None # field(default_factory=lambda: [0, 2.0, 8.0, 1000])
half_precision_weights: bool = True
min_step_percent: float = 0.02
max_step_percent: float = 0.98
weighting_strategy: str = "sds"
view_dependent_prompting: bool = True
cfg: Config
def configure(self) -> None:
threestudio.info(f"Loading Deep Floyd ...")
self.weights_dtype = (
torch.float16 if self.cfg.half_precision_weights else torch.float32
)
# Create model
self.pipe = IFPipeline.from_pretrained(
self.cfg.pretrained_model_name_or_path,
text_encoder=None,
safety_checker=None,
watermarker=None,
feature_extractor=None,
requires_safety_checker=False,
variant="fp16" if self.cfg.half_precision_weights else None,
torch_dtype=self.weights_dtype,
).to(self.device)
if self.cfg.enable_memory_efficient_attention:
if parse_version(torch.__version__) >= parse_version("2"):
threestudio.info(
"PyTorch2.0 uses memory efficient attention by default."
)
elif not is_xformers_available():
threestudio.warn(
"xformers is not available, memory efficient attention is not enabled."
)
else:
threestudio.warn(
f"Use DeepFloyd with xformers may raise error, see https://github.com/deep-floyd/IF/issues/52 to track this problem."
)
self.pipe.enable_xformers_memory_efficient_attention()
if self.cfg.enable_sequential_cpu_offload:
self.pipe.enable_sequential_cpu_offload()
if self.cfg.enable_attention_slicing:
self.pipe.enable_attention_slicing(1)
if self.cfg.enable_channels_last_format:
self.pipe.unet.to(memory_format=torch.channels_last)
self.unet = self.pipe.unet.eval()
for p in self.unet.parameters():
p.requires_grad_(False)
self.scheduler = self.pipe.scheduler
self.num_train_timesteps = self.scheduler.config.num_train_timesteps
self.min_step = int(self.num_train_timesteps * self.cfg.min_step_percent)
self.max_step = int(self.num_train_timesteps * self.cfg.max_step_percent)
self.alphas: Float[Tensor, "..."] = self.scheduler.alphas_cumprod.to(
self.device
)
self.grad_clip_val: Optional[float] = None
threestudio.info(f"Loaded Deep Floyd!")
@torch.cuda.amp.autocast(enabled=False)
def forward_unet(
self,
latents: Float[Tensor, "..."],
t: Float[Tensor, "..."],
encoder_hidden_states: Float[Tensor, "..."],
) -> Float[Tensor, "..."]:
input_dtype = latents.dtype
return self.unet(
latents.to(self.weights_dtype),
t.to(self.weights_dtype),
encoder_hidden_states=encoder_hidden_states.to(self.weights_dtype),
).sample.to(input_dtype)
def __call__(
self,
rgb: Float[Tensor, "B H W C"],
prompt_utils: PromptProcessorOutput,
elevation: Float[Tensor, "B"],
azimuth: Float[Tensor, "B"],
camera_distances: Float[Tensor, "B"],
rgb_as_latents=False,
**kwargs,
):
batch_size = rgb.shape[0]
rgb_BCHW = rgb.permute(0, 3, 1, 2)
assert rgb_as_latents == False, f"No latent space in {self.__class__.__name__}"
rgb_BCHW = rgb_BCHW * 2.0 - 1.0 # scale to [-1, 1] to match the diffusion range
latents = F.interpolate(
rgb_BCHW, (64, 64), mode="bilinear", align_corners=False
)
text_embeddings = prompt_utils.get_text_embeddings(
elevation, azimuth, camera_distances, self.cfg.view_dependent_prompting
)
# timestep ~ U(0.02, 0.98) to avoid very high/low noise level
t = torch.randint(
self.min_step,
self.max_step + 1,
[batch_size],
dtype=torch.long,
device=self.device,
)
# predict the noise residual with unet, NO grad!
with torch.no_grad():
# add noise
noise = torch.randn_like(latents) # TODO: use torch generator
latents_noisy = self.scheduler.add_noise(latents, noise, t)
# pred noise
latent_model_input = torch.cat([latents_noisy] * 2, dim=0)
noise_pred = self.forward_unet(
latent_model_input,
torch.cat([t] * 2),
encoder_hidden_states=text_embeddings,
) # (B, 6, 64, 64)
# perform guidance (high scale from paper!)
noise_pred_text, noise_pred_uncond = noise_pred.chunk(2)
noise_pred_text, predicted_variance = noise_pred_text.split(3, dim=1)
noise_pred_uncond, _ = noise_pred_uncond.split(3, dim=1)
noise_pred = noise_pred_text + self.cfg.guidance_scale * (
noise_pred_text - noise_pred_uncond
)
"""
# thresholding, experimental
if self.cfg.thresholding:
assert batch_size == 1
noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
noise_pred = custom_ddpm_step(self.scheduler,
noise_pred, int(t.item()), latents_noisy, **self.pipe.prepare_extra_step_kwargs(None, 0.0)
)
"""
if self.cfg.weighting_strategy == "sds":
# w(t), sigma_t^2
w = (1 - self.alphas[t]).view(-1, 1, 1, 1)
elif self.cfg.weighting_strategy == "uniform":
w = 1
elif self.cfg.weighting_strategy == "fantasia3d":
w = (self.alphas[t] ** 0.5 * (1 - self.alphas[t])).view(-1, 1, 1, 1)
else:
raise ValueError(
f"Unknown weighting strategy: {self.cfg.weighting_strategy}"
)
grad = w * (noise_pred - noise)
grad = torch.nan_to_num(grad)
# clip grad for stable training?
if self.grad_clip_val is not None:
grad = grad.clamp(-self.grad_clip_val, self.grad_clip_val)
# loss = SpecifyGradient.apply(latents, grad)
# SpecifyGradient is not straghtforward, use a reparameterization trick instead
target = (latents - grad).detach()
# d(loss)/d(latents) = latents - target = latents - (latents - grad) = grad
loss_sds = 0.5 * F.mse_loss(latents, target, reduction="sum") / batch_size
return {
"loss_sds": loss_sds,
"grad_norm": grad.norm(),
}
def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):
# clip grad for stable training as demonstrated in
# Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation
# http://arxiv.org/abs/2303.15413
if self.cfg.grad_clip is not None:
self.grad_clip_val = C(self.cfg.grad_clip, epoch, global_step)
"""
# used by thresholding, experimental
def custom_ddpm_step(ddpm, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor, generator=None, return_dict: bool = True):
self = ddpm
t = timestep
prev_t = self.previous_timestep(t)
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)
else:
predicted_variance = None
# 1. compute alphas, betas
alpha_prod_t = self.alphas_cumprod[t].item()
alpha_prod_t_prev = self.alphas_cumprod[prev_t].item() if prev_t >= 0 else 1.0
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
current_alpha_t = alpha_prod_t / alpha_prod_t_prev
current_beta_t = 1 - current_alpha_t
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
elif self.config.prediction_type == "sample":
pred_original_sample = model_output
elif self.config.prediction_type == "v_prediction":
pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
else:
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` or"
" `v_prediction` for the DDPMScheduler."
)
# 3. Clip or threshold "predicted x_0"
if self.config.thresholding:
pred_original_sample = self._threshold_sample(pred_original_sample)
elif self.config.clip_sample:
pred_original_sample = pred_original_sample.clamp(
-self.config.clip_sample_range, self.config.clip_sample_range
)
noise_thresholded = (sample - (alpha_prod_t ** 0.5) * pred_original_sample) / (beta_prod_t ** 0.5)
return noise_thresholded
"""
================================================
FILE: threestudio/models/guidance/deep_floyd_vsd_guidance.py
================================================
import random
from contextlib import contextmanager
from dataclasses import dataclass, field
import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers import (
DDPMScheduler,
DPMSolverMultistepScheduler,
IFPipeline,
UNet2DConditionModel,
)
from diffusers.loaders import AttnProcsLayers
from diffusers.models.attention_processor import (
LoRAAttnAddedKVProcessor,
LoRAAttnProcessor,
)
from diffusers.models.embeddings import TimestepEmbedding
from diffusers.utils.import_utils import is_xformers_available
import threestudio
from threestudio.models.prompt_processors.base import PromptProcessorOutput
from threestudio.utils.base import BaseModule
from threestudio.utils.misc import C, cleanup, parse_version
from threestudio.utils.typing import *
class ToWeightsDType(nn.Module):
def __init__(self, module: nn.Module, dtype: torch.dtype):
super().__init__()
self.module = module
self.dtype = dtype
def forward(self, x: Float[Tensor, "..."]) -> Float[Tensor, "..."]:
return self.module(x).to(self.dtype)
@threestudio.register("deep-floyd-vsd-guidance")
class DeepFloydVSDGuidance(BaseModule):
@dataclass
class Config(BaseModule.Config):
pretrained_model_name_or_path: str = "DeepFloyd/IF-I-XL-v1.0"
pretrained_model_name_or_path_lora: str = "DeepFloyd/IF-I-XL-v1.0"
enable_memory_efficient_attention: bool = False
enable_sequential_cpu_offload: bool = False
enable_attention_slicing: bool = False
enable_channels_last_format: bool = False
guidance_scale: float = 7.5
guidance_scale_lora: float = 1.0
half_precision_weights: bool = True
lora_cfg_training: bool = True
lora_n_timestamp_samples: int = 1
min_step_percent: float = 0.02
max_step_percent: float = 0.98
max_step_percent_annealed: float = 0.5
anneal_start_step: Optional[int] = 5000
view_dependent_prompting: bool = True
camera_condition_type: str = "extrinsics"
cfg: Config
def configure(self) -> None:
threestudio.info(f"Loading Deep Floyd ...")
self.weights_dtype = (
torch.float16 if self.cfg.half_precision_weights else torch.float32
)
pipe_kwargs = {
"text_encoder": None,
"safety_checker": None,
"watermarker": None,
"feature_extractor": None,
"requires_safety_checker": False,
"torch_dtype": self.weights_dtype,
"variant": "fp16" if self.cfg.half_precision_weights else None,
}
pipe_lora_kwargs = {
"text_encoder": None,
"safety_checker": None,
"watermarker": None,
"feature_extractor": None,
"requires_safety_checker": False,
"torch_dtype": self.weights_dtype,
"variant": "fp16" if self.cfg.half_precision_weights else None,
}
@dataclass
class SubModules:
pipe: IFPipeline
pipe_lora: IFPipeline
pipe = IFPipeline.from_pretrained(
self.cfg.pretrained_model_name_or_path,
**pipe_kwargs,
).to(self.device)
if (
self.cfg.pretrained_model_name_or_path
== self.cfg.pretrained_model_name_or_path_lora
):
self.single_model = True
pipe_lora = pipe
else:
self.single_model = False
pipe_lora = IFPipeline.from_pretrained(
self.cfg.pretrained_model_name_or_path_lora,
**pipe_lora_kwargs,
).to(self.device)
cleanup()
self.submodules = SubModules(pipe=pipe, pipe_lora=pipe_lora)
if self.cfg.enable_memory_efficient_attention:
if parse_version(torch.__version__) >= parse_version("2"):
threestudio.info(
"PyTorch2.0 uses memory efficient attention by default."
)
elif not is_xformers_available():
threestudio.warn(
"xformers is not available, memory efficient attention is not enabled."
)
else:
self.pipe.enable_xformers_memory_efficient_attention()
self.pipe_lora.enable_xformers_memory_efficient_attention()
if self.cfg.enable_sequential_cpu_offload:
self.pipe.enable_sequential_cpu_offload()
self.pipe_lora.enable_sequential_cpu_offload()
if self.cfg.enable_attention_slicing:
self.pipe.enable_attention_slicing(1)
self.pipe_lora.enable_attention_slicing(1)
if self.cfg.enable_channels_last_format:
self.pipe.unet.to(memory_format=torch.channels_last)
self.pipe_lora.unet.to(memory_format=torch.channels_last)
del self.pipe.text_encoder
if not self.single_model:
del self.pipe_lora.text_encoder
cleanup()
for p in self.unet.parameters():
p.requires_grad_(False)
for p in self.unet_lora.parameters():
p.requires_grad_(False)
# FIXME: hard-coded dims
self.camera_embedding = ToWeightsDType(
TimestepEmbedding(16, 2816), self.weights_dtype
)
self.unet_lora.class_embedding = self.camera_embedding
# set up LoRA layers
lora_attn_procs = {}
# down_blocks.1.attentions.0.processor
# down_blocks.1.attentions.1.processor
# down_blocks.1.attentions.2.processor
# down_blocks.2.attentions.0.processor
# down_blocks.2.attentions.1.processor
# down_blocks.2.attentions.2.processor
# down_blocks.3.attentions.0.processor
# down_blocks.3.attentions.1.processor
# down_blocks.3.attentions.2.processor
# up_blocks.0.attentions.0.processor
# up_blocks.0.attentions.1.processor
# up_blocks.0.attentions.2.processor
# up_blocks.0.attentions.3.processor
# up_blocks.1.attentions.0.processor
# up_blocks.1.attentions.1.processor
# up_blocks.1.attentions.2.processor
# up_blocks.1.attentions.3.processor
# up_blocks.2.attentions.0.processor
# up_blocks.2.attentions.1.processor
# up_blocks.2.attentions.2.processor
# up_blocks.2.attentions.3.processor
# mid_block.attentions.0.processor
for name in self.unet_lora.attn_processors.keys():
cross_attention_dim = self.unet_lora.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = self.unet_lora.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(self.unet_lora.config.block_out_channels))[
block_id
]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = self.unet_lora.config.block_out_channels[block_id]
lora_attn_procs[name] = LoRAAttnAddedKVProcessor(
hidden_size=hidden_size, cross_attention_dim=cross_attention_dim
)
self.unet_lora.set_attn_processor(lora_attn_procs)
self.lora_layers = AttnProcsLayers(self.unet_lora.attn_processors)
self.lora_layers._load_state_dict_pre_hooks.clear()
self.lora_layers._state_dict_hooks.clear()
self.scheduler = DDPMScheduler.from_pretrained(
self.cfg.pretrained_model_name_or_path,
subfolder="scheduler",
torch_dtype=self.weights_dtype,
)
self.scheduler_lora = DDPMScheduler.from_pretrained(
self.cfg.pretrained_model_name_or_path_lora,
subfolder="scheduler",
torch_dtype=self.weights_dtype,
)
self.scheduler_lora = self.scheduler_lora.__class__.from_config(
self.scheduler_lora.config, variance_type="fixed_small"
)
self.scheduler_sample = DPMSolverMultistepScheduler.from_config(
self.pipe.scheduler.config
)
self.scheduler_lora_sample = DPMSolverMultistepScheduler.from_config(
self.pipe_lora.scheduler.config, variance_type="fixed_small"
)
self.pipe.scheduler = self.scheduler
self.pipe_lora.scheduler = self.scheduler_lora
self.num_train_timesteps = self.scheduler.config.num_train_timesteps
self.min_step = int(self.num_train_timesteps * self.cfg.min_step_percent)
self.max_step = int(self.num_train_timesteps * self.cfg.max_step_percent)
self.alphas: Float[Tensor, "..."] = self.scheduler.alphas_cumprod.to(
self.device
)
threestudio.info(f"Loaded Deep Floyd!")
@property
def pipe(self):
return self.submodules.pipe
@property
def pipe_lora(self):
return self.submodules.pipe_lora
@property
def unet(self):
return self.submodules.pipe.unet
@property
def unet_lora(self):
return self.submodules.pipe_lora.unet
@torch.no_grad()
@torch.cuda.amp.autocast(enabled=False)
def _sample(
self,
pipe: IFPipeline,
sample_scheduler: DPMSolverMultistepScheduler,
text_embeddings: Float[Tensor, "BB N Nf"],
num_inference_steps: int,
guidance_scale: float,
num_images_per_prompt: int = 1,
height: Optional[int] = None,
width: Optional[int] = None,
class_labels: Optional[Float[Tensor, "BB 16"]] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
) -> Float[Tensor, "B H W 3"]:
height = height or pipe.unet.config.sample_size
width = width or pipe.unet.config.sample_size
batch_size = text_embeddings.shape[0] // 2
device = self.device
sample_scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = sample_scheduler.timesteps
num_channels_latents = pipe.unet.config.in_channels
latents = pipe.prepare_intermediate_images(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
self.weights_dtype,
device,
generator,
)
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2)
latent_model_input = sample_scheduler.scale_model_input(
latent_model_input, t
)
# predict the noise residual
if class_labels is None:
with self.disable_unet_class_embedding(pipe.unet) as unet:
noise_pred = unet(
latent_model_input,
t,
encoder_hidden_states=text_embeddings.to(self.weights_dtype),
cross_attention_kwargs=cross_attention_kwargs,
).sample
else:
noise_pred = pipe.unet(
latent_model_input,
t,
encoder_hidden_states=text_embeddings.to(self.weights_dtype),
class_labels=class_labels,
cross_attention_kwargs=cross_attention_kwargs,
).sample
noise_pred_text, noise_pred_uncond = noise_pred.chunk(2)
noise_pred_uncond, _ = noise_pred_uncond.split(3, dim=1)
noise_pred_text, _ = noise_pred_text.split(3, dim=1)
noise_pred = noise_pred_uncond + guidance_scale * (
noise_pred_text - noise_pred_uncond
)
# compute the previous noisy sample x_t -> x_t-1
latents = sample_scheduler.step(noise_pred, t, latents).prev_sample
images = (latents / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
images = images.permute(0, 2, 3, 1).float()
return images
def sample(
self,
prompt_utils: PromptProcessorOutput,
elevation: Float[Tensor, "B"],
azimuth: Float[Tensor, "B"],
camera_distances: Float[Tensor, "B"],
seed: int = 0,
**kwargs,
) -> Float[Tensor, "N H W 3"]:
# view-dependent text embeddings
text_embeddings_vd = prompt_utils.get_text_embeddings(
elevation,
azimuth,
camera_distances,
view_dependent_prompting=self.cfg.view_dependent_prompting,
)
cross_attention_kwargs = {"scale": 0.0} if self.single_model else None
generator = torch.Generator(device=self.device).manual_seed(seed)
return self._sample(
pipe=self.pipe,
sample_scheduler=self.scheduler_sample,
text_embeddings=text_embeddings_vd,
num_inference_steps=25,
guidance_scale=self.cfg.guidance_scale,
cross_attention_kwargs=cross_attention_kwargs,
generator=generator,
)
def sample_lora(
self,
prompt_utils: PromptProcessorOutput,
elevation: Float[Tensor, "B"],
azimuth: Float[Tensor, "B"],
camera_distances: Float[Tensor, "B"],
mvp_mtx: Float[Tensor, "B 4 4"],
c2w: Float[Tensor, "B 4 4"],
seed: int = 0,
**kwargs,
) -> Float[Tensor, "N H W 3"]:
# input text embeddings, view-independent
text_embeddings = prompt_utils.get_text_embeddings(
elevation, azimuth, camera_distances, view_dependent_prompting=False
)
if self.cfg.camera_condition_type == "extrinsics":
camera_condition = c2w
elif self.cfg.camera_condition_type == "mvp":
camera_condition = mvp_mtx
else:
raise ValueError(
f"Unknown camera_condition_type {self.cfg.camera_condition_type}"
)
B = elevation.shape[0]
camera_condition_cfg = torch.cat(
[
camera_condition.view(B, -1),
torch.zeros_like(camera_condition.view(B, -1)),
],
dim=0,
)
generator = torch.Generator(device=self.device).manual_seed(seed)
return self._sample(
sample_scheduler=self.scheduler_lora_sample,
pipe=self.pipe_lora,
text_embeddings=text_embeddings,
num_inference_steps=25,
guidance_scale=self.cfg.guidance_scale_lora,
class_labels=camera_condition_cfg,
cross_attention_kwargs={"scale": 1.0},
generator=generator,
)
@torch.cuda.amp.autocast(enabled=False)
def forward_unet(
self,
unet: UNet2DConditionModel,
latents: Float[Tensor, "..."],
t: Float[Tensor, "..."],
encoder_hidden_states: Float[Tensor, "..."],
class_labels: Optional[Float[Tensor, "B 16"]] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
) -> Float[Tensor, "..."]:
input_dtype = latents.dtype
return unet(
latents.to(self.weights_dtype),
t.to(self.weights_dtype),
encoder_hidden_states=encoder_hidden_states.to(self.weights_dtype),
class_labels=class_labels,
cross_attention_kwargs=cross_attention_kwargs,
).sample.to(input_dtype)
@contextmanager
def disable_unet_class_embedding(self, unet: UNet2DConditionModel):
class_embedding = unet.class_embedding
try:
unet.class_embedding = None
yield unet
finally:
unet.class_embedding = class_embedding
def compute_grad_vsd(
self,
latents: Float[Tensor, "B 3 64 64"],
text_embeddings_vd: Float[Tensor, "BB 77 768"],
text_embeddings: Float[Tensor, "BB 77 768"],
camera_condition: Float[Tensor, "B 4 4"],
):
B = latents.shape[0]
with torch.no_grad():
# random timestamp
t = torch.randint(
self.min_step,
self.max_step + 1,
[B],
dtype=torch.long,
device=self.device,
)
# add noise
noise = torch.randn_like(latents)
latents_noisy = self.scheduler.add_noise(latents, noise, t)
# pred noise
latent_model_input = torch.cat([latents_noisy] * 2, dim=0)
with self.disable_unet_class_embedding(self.unet) as unet:
cross_attention_kwargs = {"scale": 0.0} if self.single_model else None
noise_pred_pretrain = self.forward_unet(
unet,
latent_model_input,
torch.cat([t] * 2),
encoder_hidden_states=text_embeddings_vd,
cross_attention_kwargs=cross_attention_kwargs,
)
# use view-independent text embeddings in LoRA
noise_pred_est = self.forward_unet(
self.unet_lora,
latent_model_input,
torch.cat([t] * 2),
encoder_hidden_states=text_embeddings,
class_labels=torch.cat(
[
camera_condition.view(B, -1),
torch.zeros_like(camera_condition.view(B, -1)),
],
dim=0,
),
cross_attention_kwargs={"scale": 1.0},
)
(
noise_pred_pretrain_text,
noise_pred_pretrain_uncond,
) = noise_pred_pretrain.chunk(2)
noise_pred_pretrain_text, _ = noise_pred_pretrain_text.split(3, dim=1)
noise_pred_pretrain_uncond, _ = noise_pred_pretrain_uncond.split(3, dim=1)
# NOTE: guidance scale definition here is aligned with diffusers, but different from other guidance
noise_pred_pretrain = noise_pred_pretrain_uncond + self.cfg.guidance_scale * (
noise_pred_pretrain_text - noise_pred_pretrain_uncond
)
# TODO: more general cases
assert self.scheduler.config.prediction_type == "epsilon"
if self.scheduler_lora.config.prediction_type == "v_prediction":
alphas_cumprod = self.scheduler_lora.alphas_cumprod.to(
device=latents_noisy.device, dtype=latents_noisy.dtype
)
alpha_t = alphas_cumprod[t] ** 0.5
sigma_t = (1 - alphas_cumprod[t]) ** 0.5
noise_pred_est = latent_model_input * torch.cat([sigma_t] * 2, dim=0).view(
-1, 1, 1, 1
) + noise_pred_est * torch.cat([alpha_t] * 2, dim=0).view(-1, 1, 1, 1)
(
noise_pred_est_text,
noise_pred_est_uncond,
) = noise_pred_est.chunk(2)
noise_pred_est_text, _ = noise_pred_est_text.split(3, dim=1)
noise_pred_est_uncond, _ = noise_pred_est_uncond.split(3, dim=1)
# NOTE: guidance scale definition here is aligned with diffusers, but different from other guidance
noise_pred_est = noise_pred_est_uncond + self.cfg.guidance_scale_lora * (
noise_pred_est_text - noise_pred_est_uncond
)
w = (1 - self.alphas[t]).view(-1, 1, 1, 1)
grad = w * (noise_pred_pretrain - noise_pred_est)
return grad
def train_lora(
self,
latents: Float[Tensor, "B 3 64 64"],
text_embeddings: Float[Tensor, "BB 77 768"],
camera_condition: Float[Tensor, "B 4 4"],
):
B = latents.shape[0]
latents = latents.detach().repeat(self.cfg.lora_n_timestamp_samples, 1, 1, 1)
t = torch.randint(
int(self.num_train_timesteps * 0.0),
int(self.num_train_timesteps * 1.0),
[B * self.cfg.lora_n_timestamp_samples],
dtype=torch.long,
device=self.device,
)
noise = torch.randn_like(latents)
noisy_latents = self.scheduler_lora.add_noise(latents, noise, t)
if self.scheduler_lora.config.prediction_type == "epsilon":
target = noise
elif self.scheduler_lora.config.prediction_type == "v_prediction":
target = self.scheduler_lora.get_velocity(latents, noise, t)
else:
raise ValueError(
f"Unknown prediction type {self.scheduler_lora.config.prediction_type}"
)
# use view-independent text embeddings in LoRA
text_embeddings, _ = text_embeddings.chunk(2)
if self.cfg.lora_cfg_training and random.random() < 0.1:
camera_condition = torch.zeros_like(camera_condition)
noise_pred = self.forward_unet(
self.unet_lora,
noisy_latents,
t,
encoder_hidden_states=text_embeddings.repeat(
self.cfg.lora_n_timestamp_samples, 1, 1
),
class_labels=camera_condition.view(B, -1).repeat(
self.cfg.lora_n_timestamp_samples, 1
),
cross_attention_kwargs={"scale": 1.0},
)
noise_pred, _ = noise_pred.split(3, dim=1)
return F.mse_loss(noise_pred.float(), target.float(), reduction="mean")
def forward(
self,
rgb: Float[Tensor, "B H W C"],
prompt_utils: PromptProcessorOutput,
elevation: Float[Tensor, "B"],
azimuth: Float[Tensor, "B"],
camera_distances: Float[Tensor, "B"],
mvp_mtx: Float[Tensor, "B 4 4"],
c2w: Float[Tensor, "B 4 4"],
rgb_as_latents=False,
**kwargs,
):
batch_size = rgb.shape[0]
rgb_BCHW = rgb.permute(0, 3, 1, 2)
assert rgb_as_latents == False, f"No latent space in {self.__class__.__name__}"
rgb_BCHW = rgb_BCHW * 2.0 - 1.0 # scale to [-1, 1] to match the diffusion range
latents = F.interpolate(
rgb_BCHW, (64, 64), mode="bilinear", align_corners=False
)
# view-dependent text embeddings
text_embeddings_vd = prompt_utils.get_text_embeddings(
elevation,
azimuth,
camera_distances,
view_dependent_prompting=self.cfg.view_dependent_prompting,
)
# input text embeddings, view-independent
text_embeddings = prompt_utils.get_text_embeddings(
elevation, azimuth, camera_distances, view_dependent_prompting=False
)
if self.cfg.camera_condition_type == "extrinsics":
camera_condition = c2w
elif self.cfg.camera_condition_type == "mvp":
camera_condition = mvp_mtx
else:
raise ValueError(
f"Unknown camera_condition_type {self.cfg.camera_condition_type}"
)
grad = self.compute_grad_vsd(
latents, text_embeddings_vd, text_embeddings, camera_condition
)
grad = torch.nan_to_num(grad)
# reparameterization trick
# d(loss)/d(latents) = latents - target = latents - (latents - grad) = grad
target = (latents - grad).detach()
loss_vsd = 0.5 * F.mse_loss(latents, target, reduction="sum") / batch_size
loss_lora = self.train_lora(latents, text_embeddings, camera_condition)
return {
"loss_vsd": loss_vsd,
"loss_lora": loss_lora,
"grad_norm": grad.norm(),
}
def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):
if (
self.cfg.anneal_start_step is not None
and global_step > self.cfg.anneal_start_step
):
self.max_step = int(
self.num_train_timesteps * self.cfg.max_step_percent_annealed
)
================================================
FILE: threestudio/models/guidance/multiview_diffusion_guidance.py
================================================
import sys
from dataclasses import dataclass, field
from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from mvdream.camera_utils import convert_opengl_to_blender, normalize_camera
from mvdream.model_zoo import build_model
import threestudio
from threestudio.models.prompt_processors.base import PromptProcessorOutput
from threestudio.utils.base import BaseModule
from threestudio.utils.misc import C, cleanup, parse_version
from threestudio.utils.typing import *
@threestudio.register("multiview-diffusion-guidance")
class MultiviewDiffusionGuidance(BaseModule):
@dataclass
class Config(BaseModule.Config):
model_name: str = "sd-v2.1-base-4view" # check mvdream.model_zoo.PRETRAINED_MODELS
ckpt_path: Optional[str] = None # path to local checkpoint (None for loading from url)
guidance_scale: float = 50.0
grad_clip: Optional[
Any
] = None # field(default_factory=lambda: [0, 2.0, 8.0, 1000])
half_precision_weights: bool = True
min_step_percent: float = 0.02
max_step_percent: float = 0.98
camera_condition_type: str = "rotation"
view_dependent_prompting: bool = False
n_view: int = 4
image_size: int = 256
recon_loss: bool = True
recon_std_rescale: float = 0.5
cfg: Config
def configure(self) -> None:
threestudio.info(f"Loading Multiview Diffusion ...")
self.model = build_model(self.cfg.model_name, ckpt_path=self.cfg.ckpt_path)
for p in self.model.parameters():
p.requires_grad_(False)
self.num_train_timesteps = 1000
min_step_percent = C(self.cfg.min_step_percent, 0, 0)
max_step_percent = C(self.cfg.max_step_percent, 0, 0)
self.min_step = int( self.num_train_timesteps * min_step_percent )
self.max_step = int( self.num_train_timesteps * max_step_percent )
self.grad_clip_val: Optional[float] = None
self.to(self.device)
threestudio.info(f"Loaded Multiview Diffusion!")
def get_camera_cond(self,
camera: Float[Tensor, "B 4 4"],
fovy = None,
):
# Note: the input of threestudio is already blender coordinate system
# camera = convert_opengl_to_blender(camera)
if self.cfg.camera_condition_type == "rotation": # normalized camera
camera = normalize_camera(camera)
camera = camera.flatten(start_dim=1)
else:
raise NotImplementedError(f"Unknown camera_condition_type={self.cfg.camera_condition_type}")
return camera
def encode_images(
self, imgs: Float[Tensor, "B 3 256 256"]
) -> Float[Tensor, "B 4 32 32"]:
imgs = imgs * 2.0 - 1.0
latents = self.model.get_first_stage_encoding(self.model.encode_first_stage(imgs))
return latents # [B, 4, 32, 32] Latent space image
def forward(
self,
rgb: Float[Tensor, "B H W C"],
prompt_utils: PromptProcessorOutput,
elevation: Float[Tensor, "B"],
azimuth: Float[Tensor, "B"],
camera_distances: Float[Tensor, "B"],
c2w: Float[Tensor, "B 4 4"],
rgb_as_latents: bool = False,
fovy = None,
timestep=None,
text_embeddings=None,
input_is_latent=False,
**kwargs,
):
batch_size = rgb.shape[0]
camera = c2w
rgb_BCHW = rgb.permute(0, 3, 1, 2)
if text_embeddings is None:
text_embeddings = prompt_utils.get_text_embeddings(
elevation, azimuth, camera_distances, self.cfg.view_dependent_prompting
)
if input_is_latent:
latents = rgb
else:
latents: Float[Tensor, "B 4 64 64"]
if rgb_as_latents:
latents = F.interpolate(rgb_BCHW, (64, 64), mode='bilinear', align_corners=False) * 2 - 1
else:
# interp to 512x512 to be fed into vae.
pred_rgb = F.interpolate(rgb_BCHW, (self.cfg.image_size, self.cfg.image_size), mode='bilinear', align_corners=False)
# encode image into latents with vae, requires grad!
latents = self.encode_images(pred_rgb)
# sample timestep
if timestep is None:
t = torch.randint(self.min_step, self.max_step + 1, [1], dtype=torch.long, device=latents.device)
else:
assert timestep >= 0 and timestep < self.num_train_timesteps
t = torch.full([1], timestep, dtype=torch.long, device=latents.device)
t_expand = t.repeat(text_embeddings.shape[0])
# predict the noise residual with unet, NO grad!
with torch.no_grad():
# add noise
noise = torch.randn_like(latents)
latents_noisy = self.model.q_sample(latents, t, noise)
# pred noise
latent_model_input = torch.cat([latents_noisy] * 2)
# save input tensors for UNet
if camera is not None:
camera = self.get_camera_cond(camera, fovy)
camera = camera.repeat(2,1).to(text_embeddings)
context = {"context": text_embeddings, "camera": camera, "num_frames": self.cfg.n_view}
else:
context = {"context": text_embeddings}
noise_pred = self.model.apply_model(latent_model_input, t_expand, context)
# perform guidance
noise_pred_text, noise_pred_uncond = noise_pred.chunk(2) # Note: flipped compared to stable-dreamfusion
noise_pred = noise_pred_uncond + self.cfg.guidance_scale * (noise_pred_text - noise_pred_uncond)
if self.cfg.recon_loss:
# reconstruct x0
latents_recon = self.model.predict_start_from_noise(latents_noisy, t, noise_pred)
# clip or rescale x0
if self.cfg.recon_std_rescale > 0:
latents_recon_nocfg = self.model.predict_start_from_noise(latents_noisy, t, noise_pred_text)
latents_recon_nocfg_reshape = latents_recon_nocfg.view(-1,self.cfg.n_view, *latents_recon_nocfg.shape[1:])
latents_recon_reshape = latents_recon.view(-1,self.cfg.n_view, *latents_recon.shape[1:])
factor = (latents_recon_nocfg_reshape.std([1,2,3,4],keepdim=True) + 1e-8) / (latents_recon_reshape.std([1,2,3,4],keepdim=True) + 1e-8)
latents_recon_adjust = latents_recon.clone() * factor.squeeze(1).repeat_interleave(self.cfg.n_view, dim=0)
latents_recon = self.cfg.recon_std_rescale * latents_recon_adjust + (1-self.cfg.recon_std_rescale) * latents_recon
# x0-reconstruction loss from Sec 3.2 and Appendix
loss = 0.5 * F.mse_loss(latents, latents_recon.detach(), reduction="sum") / latents.shape[0]
grad = torch.autograd.grad(loss, latents, retain_graph=True)[0]
else:
# Original SDS
# w(t), sigma_t^2
w = (1 - self.alphas_cumprod[t])
grad = w * (noise_pred - noise)
# clip grad for stable training?
if self.grad_clip_val is not None:
grad = grad.clamp(-self.grad_clip_val, self.grad_clip_val)
grad = torch.nan_to_num(grad)
target = (latents - grad).detach()
# d(loss)/d(latents) = latents - target = latents - (latents - grad) = grad
loss = 0.5 * F.mse_loss(latents, target, reduction="sum") / latents.shape[0]
return {
"loss_sds": loss,
"grad_norm": grad.norm(),
}
def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):
min_step_percent = C(self.cfg.min_step_percent, epoch, global_step)
max_step_percent = C(self.cfg.max_step_percent, epoch, global_step)
self.min_step = int( self.num_train_timesteps * min_step_percent )
self.max_step = int( self.num_train_timesteps * max_step_percent )
================================================
FILE: threestudio/models/guidance/stable_diffusion_guidance.py
================================================
from dataclasses import dataclass, field
import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers import DDIMScheduler, DDPMScheduler, StableDiffusionPipeline
from diffusers.utils.import_utils import is_xformers_available
import threestudio
from threestudio.models.prompt_processors.base import PromptProcessorOutput
from threestudio.utils.base import BaseObject
from threestudio.utils.misc import C, cleanup, parse_version
from threestudio.utils.typing import *
@threestudio.register("stable-diffusion-guidance")
class StableDiffusionGuidance(BaseObject):
@dataclass
class Config(BaseObject.Config):
pretrained_model_name_or_path: str = "runwayml/stable-diffusion-v1-5"
enable_memory_efficient_attention: bool = False
enable_sequential_cpu_offload: bool = False
enable_attention_slicing: bool = False
enable_channels_last_format: bool = False
guidance_scale: float = 100.0
grad_clip: Optional[
Any
] = None # field(default_factory=lambda: [0, 2.0, 8.0, 1000])
half_precision_weights: bool = True
min_step_percent: float = 0.02
max_step_percent: float = 0.98
max_step_percent_annealed: float = 0.5
anneal_start_step: Optional[int] = None
use_sjc: bool = False
var_red: bool = True
weighting_strategy: str = "sds"
token_merging: bool = False
token_merging_params: Optional[dict] = field(default_factory=dict)
view_dependent_prompting: bool = True
cfg: Config
def configure(self) -> None:
threestudio.info(f"Loading Stable Diffusion ...")
self.weights_dtype = (
torch.float16 if self.cfg.half_precision_weights else torch.float32
)
pipe_kwargs = {
"tokenizer": None,
"safety_checker": None,
"feature_extractor": None,
"requires_safety_checker": False,
"torch_dtype": self.weights_dtype,
}
self.pipe = StableDiffusionPipeline.from_pretrained(
self.cfg.pretrained_model_name_or_path,
**pipe_kwargs,
).to(self.device)
if self.cfg.enable_memory_efficient_attention:
if parse_version(torch.__version__) >= parse_version("2"):
threestudio.info(
"PyTorch2.0 uses memory efficient attention by default."
)
elif not is_xformers_available():
threestudio.warn(
"xformers is not available, memory efficient attention is not enabled."
)
else:
self.pipe.enable_xformers_memory_efficient_attention()
if self.cfg.enable_sequential_cpu_offload:
self.pipe.enable_sequential_cpu_offload()
if self.cfg.enable_attention_slicing:
self.pipe.enable_attention_slicing(1)
if self.cfg.enable_channels_last_format:
self.pipe.unet.to(memory_format=torch.channels_last)
del self.pipe.text_encoder
cleanup()
# Create model
self.vae = self.pipe.vae.eval()
self.unet = self.pipe.unet.eval()
for p in self.vae.parameters():
p.requires_grad_(False)
for p in self.unet.parameters():
p.requires_grad_(False)
if self.cfg.token_merging:
import tomesd
tomesd.apply_patch(self.unet, **self.cfg.token_merging_params)
if self.cfg.use_sjc:
# score jacobian chaining use DDPM
self.scheduler = DDPMScheduler.from_pretrained(
self.cfg.pretrained_model_name_or_path,
subfolder="scheduler",
torch_dtype=self.weights_dtype,
beta_start=0.00085,
beta_end=0.0120,
beta_schedule="scaled_linear",
)
else:
self.scheduler = DDIMScheduler.from_pretrained(
self.cfg.pretrained_model_name_or_path,
subfolder="scheduler",
torch_dtype=self.weights_dtype,
)
self.num_train_timesteps = self.scheduler.config.num_train_timesteps
self.min_step = int(self.num_train_timesteps * self.cfg.min_step_percent)
self.max_step = int(self.num_train_timesteps * self.cfg.max_step_percent)
self.alphas: Float[Tensor, "..."] = self.scheduler.alphas_cumprod.to(
self.device
)
if self.cfg.use_sjc:
# score jacobian chaining need mu
self.us: Float[Tensor, "..."] = torch.sqrt((1 - self.alphas) / self.alphas)
self.grad_clip_val: Optional[float] = None
threestudio.info(f"Loaded Stable Diffusion!")
@torch.cuda.amp.autocast(enabled=False)
def forward_unet(
self,
latents: Float[Tensor, "..."],
t: Float[Tensor, "..."],
encoder_hidden_states: Float[Tensor, "..."],
) -> Float[Tensor, "..."]:
input_dtype = latents.dtype
return self.unet(
latents.to(self.weights_dtype),
t.to(self.weights_dtype),
encoder_hidden_states=encoder_hidden_states.to(self.weights_dtype),
).sample.to(input_dtype)
@torch.cuda.amp.autocast(enabled=False)
def encode_images(
self, imgs: Float[Tensor, "B 3 512 512"]
) -> Float[Tensor, "B 4 64 64"]:
input_dtype = imgs.dtype
imgs = imgs * 2.0 - 1.0
posterior = self.vae.encode(imgs.to(self.weights_dtype)).latent_dist
latents = posterior.sample() * self.vae.config.scaling_factor
return latents.to(input_dtype)
@torch.cuda.amp.autocast(enabled=False)
def decode_latents(
self,
latents: Float[Tensor, "B 4 H W"],
latent_height: int = 64,
latent_width: int = 64,
) -> Float[Tensor, "B 3 512 512"]:
input_dtype = latents.dtype
latents = F.interpolate(
latents, (latent_height, latent_width), mode="bilinear", align_corners=False
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents.to(self.weights_dtype)).sample
image = (image * 0.5 + 0.5).clamp(0, 1)
return image.to(input_dtype)
def compute_grad_sds(
self,
latents: Float[Tensor, "B 4 64 64"],
text_embeddings: Float[Tensor, "BB 77 768"],
t: Int[Tensor, "B"],
):
# predict the noise residual with unet, NO grad!
with torch.no_grad():
# add noise
noise = torch.randn_like(latents) # TODO: use torch generator
latents_noisy = self.scheduler.add_noise(latents, noise, t)
# pred noise
latent_model_input = torch.cat([latents_noisy] * 2, dim=0)
noise_pred = self.forward_unet(
latent_model_input,
torch.cat([t] * 2),
encoder_hidden_states=text_embeddings,
)
# perform guidance (high scale from paper!)
noise_pred_text, noise_pred_uncond = noise_pred.chunk(2)
noise_pred = noise_pred_text + self.cfg.guidance_scale * (
noise_pred_text - noise_pred_uncond
)
if self.cfg.weighting_strategy == "sds":
# w(t), sigma_t^2
w = (1 - self.alphas[t]).view(-1, 1, 1, 1)
elif self.cfg.weighting_strategy == "uniform":
w = 1
elif self.cfg.weighting_strategy == "fantasia3d":
w = (self.alphas[t] ** 0.5 * (1 - self.alphas[t])).view(-1, 1, 1, 1)
else:
raise ValueError(
f"Unknown weighting strategy: {self.cfg.weighting_strategy}"
)
grad = w * (noise_pred - noise)
return grad
def compute_grad_sjc(
self,
latents: Float[Tensor, "B 4 64 64"],
text_embeddings: Float[Tensor, "BB 77 768"],
t: Int[Tensor, "B"],
):
sigma = self.us[t]
sigma = sigma.view(-1, 1, 1, 1)
# predict the noise residual with unet, NO grad!
with torch.no_grad():
# add noise
noise = torch.randn_like(latents) # TODO: use torch generator
y = latents
zs = y + sigma * noise
scaled_zs = zs / torch.sqrt(1 + sigma**2)
# pred noise
latent_model_input = torch.cat([scaled_zs] * 2, dim=0)
noise_pred = self.forward_unet(
latent_model_input,
torch.cat([t] * 2),
encoder_hidden_states=text_embeddings,
)
# perform guidance (high scale from paper!)
noise_pred_text, noise_pred_uncond = noise_pred.chunk(2)
noise_pred = noise_pred_text + self.cfg.guidance_scale * (
noise_pred_text - noise_pred_uncond
)
Ds = zs - sigma * noise_pred
if self.cfg.var_red:
grad = -(Ds - y) / sigma
else:
grad = -(Ds - zs) / sigma
return grad
def __call__(
self,
rgb: Float[Tensor, "B H W C"],
prompt_utils: PromptProcessorOutput,
elevation: Float[Tensor, "B"],
azimuth: Float[Tensor, "B"],
camera_distances: Float[Tensor, "B"],
rgb_as_latents=False,
**kwargs,
):
batch_size = rgb.shape[0]
rgb_BCHW = rgb.permute(0, 3, 1, 2)
latents: Float[Tensor, "B 4 64 64"]
if rgb_as_latents:
latents = F.interpolate(
rgb_BCHW, (64, 64), mode="bilinear", align_corners=False
)
else:
rgb_BCHW_512 = F.interpolate(
rgb_BCHW, (512, 512), mode="bilinear", align_corners=False
)
# encode image into latents with vae
latents = self.encode_images(rgb_BCHW_512)
text_embeddings = prompt_utils.get_text_embeddings(
elevation, azimuth, camera_distances, self.cfg.view_dependent_prompting
)
# timestep ~ U(0.02, 0.98) to avoid very high/low noise level
t = torch.randint(
self.min_step,
self.max_step + 1,
[batch_size],
dtype=torch.long,
device=self.device,
)
if self.cfg.use_sjc:
grad = self.compute_grad_sjc(latents, text_embeddings, t)
else:
grad = self.compute_grad_sds(latents, text_embeddings, t)
grad = torch.nan_to_num(grad)
# clip grad for stable training?
if self.grad_clip_val is not None:
grad = grad.clamp(-self.grad_clip_val, self.grad_clip_val)
# loss = SpecifyGradient.apply(latents, grad)
# SpecifyGradient is not straghtforward, use a reparameterization trick instead
target = (latents - grad).detach()
# d(loss)/d(latents) = latents - target = latents - (latents - grad) = grad
loss_sds = 0.5 * F.mse_loss(latents, target, reduction="sum") / batch_size
return {
"loss_sds": loss_sds,
"grad_norm": grad.norm(),
}
def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):
# clip grad for stable training as demonstrated in
# Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation
# http://arxiv.org/abs/2303.15413
if self.cfg.grad_clip is not None:
self.grad_clip_val = C(self.cfg.grad_clip, epoch, global_step)
# t annealing from ProlificDreamer
if (
self.cfg.anneal_start_step is not None
and global_step > self.cfg.anneal_start_step
):
self.max_step = int(
self.num_train_timesteps * self.cfg.max_step_percent_annealed
)
================================================
FILE: threestudio/models/guidance/stable_diffusion_vsd_guidance.py
================================================
import random
from contextlib import contextmanager
from dataclasses import dataclass, field
import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers import (
DDPMScheduler,
DPMSolverMultistepScheduler,
StableDiffusionPipeline,
UNet2DConditionModel,
)
from diffusers.loaders import AttnProcsLayers
from diffusers.models.attention_processor import LoRAAttnProcessor
from diffusers.models.embeddings import TimestepEmbedding
from diffusers.utils.import_utils import is_xformers_available
import threestudio
from threestudio.models.prompt_processors.base import PromptProcessorOutput
from threestudio.utils.base import BaseModule
from threestudio.utils.misc import C, cleanup, parse_version
from threestudio.utils.typing import *
class ToWeightsDType(nn.Module):
def __init__(self, module: nn.Module, dtype: torch.dtype):
super().__init__()
self.module = module
self.dtype = dtype
def forward(self, x: Float[Tensor, "..."]) -> Float[Tensor, "..."]:
return self.module(x).to(self.dtype)
@threestudio.register("stable-diffusion-vsd-guidance")
class StableDiffusionVSDGuidance(BaseModule):
@dataclass
class Config(BaseModule.Config):
pretrained_model_name_or_path: str = "stabilityai/stable-diffusion-2-1-base"
pretrained_model_name_or_path_lora: str = "stabilityai/stable-diffusion-2-1"
enable_memory_efficient_attention: bool = False
enable_sequential_cpu_offload: bool = False
enable_attention_slicing: bool = False
enable_channels_last_format: bool = False
guidance_scale: float = 7.5
guidance_scale_lora: float = 1.0
half_precision_weights: bool = True
lora_cfg_training: bool = True
lora_n_timestamp_samples: int = 1
min_step_percent: float = 0.02
max_step_percent: float = 0.98
max_step_percent_annealed: float = 0.5
anneal_start_step: Optional[int] = 5000
view_dependent_prompting: bool = True
camera_condition_type: str = "extrinsics"
cfg: Config
def configure(self) -> None:
threestudio.info(f"Loading Stable Diffusion ...")
self.weights_dtype = (
torch.float16 if self.cfg.half_precision_weights else torch.float32
)
pipe_kwargs = {
"tokenizer": None,
"safety_checker": None,
"feature_extractor": None,
"requires_safety_checker": False,
"torch_dtype": self.weights_dtype,
}
pipe_lora_kwargs = {
"tokenizer": None,
"safety_checker": None,
"feature_extractor": None,
"requires_safety_checker": False,
"torch_dtype": self.weights_dtype,
}
@dataclass
class SubModules:
pipe: StableDiffusionPipeline
pipe_lora: StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained(
self.cfg.pretrained_model_name_or_path,
**pipe_kwargs,
).to(self.device)
if (
self.cfg.pretrained_model_name_or_path
== self.cfg.pretrained_model_name_or_path_lora
):
self.single_model = True
pipe_lora = pipe
else:
self.single_model = False
pipe_lora = StableDiffusionPipeline.from_pretrained(
self.cfg.pretrained_model_name_or_path_lora,
**pipe_lora_kwargs,
).to(self.device)
del pipe_lora.vae
cleanup()
pipe_lora.vae = pipe.vae
self.submodules = SubModules(pipe=pipe, pipe_lora=pipe_lora)
if self.cfg.enable_memory_efficient_attention:
if parse_version(torch.__version__) >= parse_version("2"):
threestudio.info(
"PyTorch2.0 uses memory efficient attention by default."
)
elif not is_xformers_available():
threestudio.warn(
"xformers is not available, memory efficient attention is not enabled."
)
else:
self.pipe.enable_xformers_memory_efficient_attention()
self.pipe_lora.enable_xformers_memory_efficient_attention()
if self.cfg.enable_sequential_cpu_offload:
self.pipe.enable_sequential_cpu_offload()
self.pipe_lora.enable_sequential_cpu_offload()
if self.cfg.enable_attention_slicing:
self.pipe.enable_attention_slicing(1)
self.pipe_lora.enable_attention_slicing(1)
if self.cfg.enable_channels_last_format:
self.pipe.unet.to(memory_format=torch.channels_last)
self.pipe_lora.unet.to(memory_format=torch.channels_last)
del self.pipe.text_encoder
if not self.single_model:
del self.pipe_lora.text_encoder
cleanup()
for p in self.vae.parameters():
p.requires_grad_(False)
for p in self.unet.parameters():
p.requires_grad_(False)
for p in self.unet_lora.parameters():
p.requires_grad_(False)
# FIXME: hard-coded dims
self.camera_embedding = ToWeightsDType(
TimestepEmbedding(16, 1280), self.weights_dtype
)
self.unet_lora.class_embedding = self.camera_embedding
# set up LoRA layers
lora_attn_procs = {}
for name in self.unet_lora.attn_processors.keys():
cross_attention_dim = (
None
if name.endswith("attn1.processor")
else self.unet_lora.config.cross_attention_dim
)
if name.startswith("mid_block"):
hidden_size = self.unet_lora.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(self.unet_lora.config.block_out_channels))[
block_id
]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = self.unet_lora.config.block_out_channels[block_id]
lora_attn_procs[name] = LoRAAttnProcessor(
hidden_size=hidden_size, cross_attention_dim=cross_attention_dim
)
self.unet_lora.set_attn_processor(lora_attn_procs)
self.lora_layers = AttnProcsLayers(self.unet_lora.attn_processors)
self.lora_layers._load_state_dict_pre_hooks.clear()
self.lora_layers._state_dict_hooks.clear()
self.scheduler = DDPMScheduler.from_pretrained(
self.cfg.pretrained_model_name_or_path,
subfolder="scheduler",
torch_dtype=self.weights_dtype,
)
self.scheduler_lora = DDPMScheduler.from_pretrained(
self.cfg.pretrained_model_name_or_path_lora,
subfolder="scheduler",
torch_dtype=self.weights_dtype,
)
self.scheduler_sample = DPMSolverMultistepScheduler.from_config(
self.pipe.scheduler.config
)
self.scheduler_lora_sample = DPMSolverMultistepScheduler.from_config(
self.pipe_lora.scheduler.config
)
self.pipe.scheduler = self.scheduler
self.pipe_lora.scheduler = self.scheduler_lora
self.num_train_timesteps = self.scheduler.config.num_train_timesteps
self.min_step = int(self.num_train_timesteps * self.cfg.min_step_percent)
self.max_step = int(self.num_train_timesteps * self.cfg.max_step_percent)
self.alphas: Float[Tensor, "..."] = self.scheduler.alphas_cumprod.to(
self.device
)
# self.compute_grad_vsd_t = None
# self.compute_grad_vsd_noise = None
# self.train_lora_t = None
# self.train_lora_noise = None
# self.train_lora_camera_condition = None
threestudio.info(f"Loaded Stable Diffusion!")
@property
def pipe(self):
return self.submodules.pipe
@property
def pipe_lora(self):
return self.submodules.pipe_lora
@property
def unet(self):
return self.submodules.pipe.unet
@property
def unet_lora(self):
return self.submodules.pipe_lora.unet
@property
def vae(self):
return self.submodules.pipe.vae
@property
def vae_lora(self):
return self.submodules.pipe_lora.vae
@torch.no_grad()
@torch.cuda.amp.autocast(enabled=False)
def _sample(
self,
pipe: StableDiffusionPipeline,
sample_scheduler: DPMSolverMultistepScheduler,
text_embeddings: Float[Tensor, "BB N Nf"],
num_inference_steps: int,
guidance_scale: float,
num_images_per_prompt: int = 1,
height: Optional[int] = None,
width: Optional[int] = None,
class_labels: Optional[Float[Tensor, "BB 16"]] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
) -> Float[Tensor, "B H W 3"]:
vae_scale_factor = 2 ** (len(pipe.vae.config.block_out_channels) - 1)
height = height or pipe.unet.config.sample_size * vae_scale_factor
width = width or pipe.unet.config.sample_size * vae_scale_factor
batch_size = text_embeddings.shape[0] // 2
device = self.device
sample_scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = sample_scheduler.timesteps
num_channels_latents = pipe.unet.config.in_channels
latents = pipe.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
self.weights_dtype,
device,
generator,
)
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2)
latent_model_input = sample_scheduler.scale_model_input(
latent_model_input, t
)
# predict the noise residual
if class_labels is None:
with self.disable_unet_class_embedding(pipe.unet) as unet:
noise_pred = unet(
latent_model_input,
t,
encoder_hidden_states=text_embeddings.to(self.weights_dtype),
cross_attention_kwargs=cross_attention_kwargs,
).sample
else:
noise_pred = pipe.unet(
latent_model_input,
t,
encoder_hidden_states=text_embeddings.to(self.weights_dtype),
class_labels=class_labels,
cross_attention_kwargs=cross_attention_kwargs,
).sample
noise_pred_text, noise_pred_uncond = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (
noise_pred_text - noise_pred_uncond
)
# compute the previous noisy sample x_t -> x_t-1
latents = sample_scheduler.step(noise_pred, t, latents).prev_sample
latents = 1 / pipe.vae.config.scaling_factor * latents
images = pipe.vae.decode(latents).sample
images = (images / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
images = images.permute(0, 2, 3, 1).float()
return images
def sample(
self,
prompt_utils: PromptProcessorOutput,
elevation: Float[Tensor, "B"],
azimuth: Float[Tensor, "B"],
camera_distances: Float[Tensor, "B"],
seed: int = 0,
**kwargs,
) -> Float[Tensor, "N H W 3"]:
# view-dependent text embeddings
text_embeddings_vd = prompt_utils.get_text_embeddings(
elevation,
azimuth,
camera_distances,
view_dependent_prompting=self.cfg.view_dependent_prompting,
)
cross_attention_kwargs = {"scale": 0.0} if self.single_model else None
generator = torch.Generator(device=self.device).manual_seed(seed)
return self._sample(
pipe=self.pipe,
sample_scheduler=self.scheduler_sample,
text_embeddings=text_embeddings_vd,
num_inference_steps=25,
guidance_scale=self.cfg.guidance_scale,
cross_attention_kwargs=cross_attention_kwargs,
generator=generator,
)
def sample_lora(
self,
prompt_utils: PromptProcessorOutput,
elevation: Float[Tensor, "B"],
azimuth: Float[Tensor, "B"],
camera_distances: Float[Tensor, "B"],
mvp_mtx: Float[Tensor, "B 4 4"],
c2w: Float[Tensor, "B 4 4"],
seed: int = 0,
**kwargs,
) -> Float[Tensor, "N H W 3"]:
# input text embeddings, view-independent
text_embeddings = prompt_utils.get_text_embeddings(
elevation, azimuth, camera_distances, view_dependent_prompting=False
)
if self.cfg.camera_condition_type == "extrinsics":
camera_condition = c2w
elif self.cfg.camera_condition_type == "mvp":
camera_condition = mvp_mtx
else:
raise ValueError(
f"Unknown camera_condition_type {self.cfg.camera_condition_type}"
)
B = elevation.shape[0]
camera_condition_cfg = torch.cat(
[
camera_condition.view(B, -1),
torch.zeros_like(camera_condition.view(B, -1)),
],
dim=0,
)
generator = torch.Generator(device=self.device).manual_seed(seed)
return self._sample(
sample_scheduler=self.scheduler_lora_sample,
pipe=self.pipe_lora,
text_embeddings=text_embeddings,
num_inference_steps=25,
guidance_scale=self.cfg.guidance_scale_lora,
class_labels=camera_condition_cfg,
cross_attention_kwargs={"scale": 1.0},
generator=generator,
)
@torch.cuda.amp.autocast(enabled=False)
def forward_unet(
self,
unet: UNet2DConditionModel,
latents: Float[Tensor, "..."],
t: Float[Tensor, "..."],
encoder_hidden_states: Float[Tensor, "..."],
class_labels: Optional[Float[Tensor, "B 16"]] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
) -> Float[Tensor, "..."]:
input_dtype = latents.dtype
return unet(
latents.to(self.weights_dtype),
t.to(self.weights_dtype),
encoder_hidden_states=encoder_hidden_states.to(self.weights_dtype),
class_labels=class_labels,
cross_attention_kwargs=cross_attention_kwargs,
).sample.to(input_dtype)
@torch.cuda.amp.autocast(enabled=False)
def encode_images(
self, imgs: Float[Tensor, "B 3 512 512"]
) -> Float[Tensor, "B 4 64 64"]:
input_dtype = imgs.dtype
imgs = imgs * 2.0 - 1.0
posterior = self.vae.encode(imgs.to(self.weights_dtype)).latent_dist
latents = posterior.sample() * self.vae.config.scaling_factor
return latents.to(input_dtype)
@torch.cuda.amp.autocast(enabled=False)
def decode_latents(
self,
latents: Float[Tensor, "B 4 H W"],
latent_height: int = 64,
latent_width: int = 64,
) -> Float[Tensor, "B 3 512 512"]:
input_dtype = latents.dtype
latents = F.interpolate(
latents, (latent_height, latent_width), mode="bilinear", align_corners=False
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents.to(self.weights_dtype)).sample
image = (image * 0.5 + 0.5).clamp(0, 1)
return image.to(input_dtype)
@contextmanager
def disable_unet_class_embedding(self, unet: UNet2DConditionModel):
class_embedding = unet.class_embedding
try:
unet.class_embedding = None
yield unet
finally:
unet.class_embedding = class_embedding
def compute_grad_vsd(
self,
latents: Float[Tensor, "B 4 64 64"],
text_embeddings_vd: Float[Tensor, "BB 77 768"],
text_embeddings: Float[Tensor, "BB 77 768"],
camera_condition: Float[Tensor, "B 4 4"],
):
B = latents.shape[0]
with torch.no_grad():
# random timestamp
# if self.compute_grad_vsd_t is None:
# t = torch.randint(
# self.min_step,
# self.max_step + 1,
# [B],
# dtype=torch.long,
# device=self.device,
# )
# self.compute_grad_vsd_t = t
# else:
# t = self.compute_grad_vsd_t
t = torch.randint(
self.min_step,
self.max_step + 1,
[B],
dtype=torch.long,
device=self.device,
)
# add noise
# if self.compute_grad_vsd_noise is None:
# noise = torch.randn_like(latents)
# self.compute_grad_vsd_noise = noise
# else:
# noise = self.compute_grad_vsd_noise
noise = torch.randn_like(latents)
latents_noisy = self.scheduler.add_noise(latents, noise, t)
# pred noise
latent_model_input = torch.cat([latents_noisy] * 2, dim=0)
with self.disable_unet_class_embedding(self.unet) as unet:
cross_attention_kwargs = {"scale": 0.0} if self.single_model else None
noise_pred_pretrain = self.forward_unet(
unet,
latent_model_input,
torch.cat([t] * 2),
encoder_hidden_states=text_embeddings_vd,
cross_attention_kwargs=cross_attention_kwargs,
)
# use view-independent text embeddings in LoRA
noise_pred_est = self.forward_unet(
self.unet_lora,
latent_model_input,
torch.cat([t] * 2),
encoder_hidden_states=text_embeddings,
class_labels=torch.cat(
[
camera_condition.view(B, -1),
torch.zeros_like(camera_condition.view(B, -1)),
],
dim=0,
),
cross_attention_kwargs={"scale": 1.0},
)
(
noise_pred_pretrain_text,
noise_pred_pretrain_uncond,
) = noise_pred_pretrain.chunk(2)
# NOTE: guidance scale definition here is aligned with diffusers, but different from other guidance
noise_pred_pretrain = noise_pred_pretrain_uncond + self.cfg.guidance_scale * (
noise_pred_pretrain_text - noise_pred_pretrain_uncond
)
# TODO: more general cases
assert self.scheduler.config.prediction_type == "epsilon"
if self.scheduler_lora.config.prediction_type == "v_prediction":
alphas_cumprod = self.scheduler_lora.alphas_cumprod.to(
device=latents_noisy.device, dtype=latents_noisy.dtype
)
alpha_t = alphas_cumprod[t] ** 0.5
sigma_t = (1 - alphas_cumprod[t]) ** 0.5
noise_pred_est = latent_model_input * torch.cat([sigma_t] * 2, dim=0).view(
-1, 1, 1, 1
) + noise_pred_est * torch.cat([alpha_t] * 2, dim=0).view(-1, 1, 1, 1)
(
noise_pred_est_text,
noise_pred_est_uncond,
) = noise_pred_est.chunk(2)
# NOTE: guidance scale definition here is aligned with diffusers, but different from other guidance
noise_pred_est = noise_pred_est_uncond + self.cfg.guidance_scale_lora * (
noise_pred_est_text - noise_pred_est_uncond
)
w = (1 - self.alphas[t]).view(-1, 1, 1, 1)
grad = w * (noise_pred_pretrain - noise_pred_est)
return grad
def train_lora(
self,
latents: Float[Tensor, "B 4 64 64"],
text_embeddings: Float[Tensor, "BB 77 768"],
camera_condition: Float[Tensor, "B 4 4"],
):
B = latents.shape[0]
latents = latents.detach().repeat(self.cfg.lora_n_timestamp_samples, 1, 1, 1)
# if self.train_lora_t is None:
# t = torch.randint(
# int(self.num_train_timesteps * 0.0),
# int(self.num_train_timesteps * 1.0),
# [B * self.cfg.lora_n_timestamp_samples],
# dtype=torch.long,
# device=self.device,
# )
# self.train_lora_t = t
# else:
# t = self.train_lora_t
t = torch.randint(
int(self.num_train_timesteps * 0.0),
int(self.num_train_timesteps * 1.0),
[B * self.cfg.lora_n_timestamp_samples],
dtype=torch.long,
device=self.device,
)
# if self.train_lora_noise is None:
# noise = torch.randn_like(latents)
# self.train_lora_noise = noise
# else:
# noise = self.train_lora_noise
noise = torch.randn_like(latents)
noisy_latents = self.scheduler_lora.add_noise(latents, noise, t)
if self.scheduler_lora.config.prediction_type == "epsilon":
target = noise
elif self.scheduler_lora.config.prediction_type == "v_prediction":
target = self.scheduler_lora.get_velocity(latents, noise, t)
else:
raise ValueError(
f"Unknown prediction type {self.scheduler_lora.config.prediction_type}"
)
# use view-independent text embeddings in LoRA
text_embeddings, _ = text_embeddings.chunk(2)
# if self.train_lora_camera_condition is None:
# if self.cfg.lora_cfg_training and random.random() < 0.1:
# camera_condition = torch.zeros_like(camera_condition)
# self.train_lora_camera_condition = camera_condition
# else:
# camera_condition = self.train_lora_camera_condition
if self.cfg.lora_cfg_training and random.random() < 0.1:
camera_condition = torch.zeros_like(camera_condition)
noise_pred = self.forward_unet(
self.unet_lora,
noisy_latents,
t,
encoder_hidden_states=text_embeddings.repeat(
self.cfg.lora_n_timestamp_samples, 1, 1
),
class_labels=camera_condition.view(B, -1).repeat(
self.cfg.lora_n_timestamp_samples, 1
),
cross_attention_kwargs={"scale": 1.0},
)
return F.mse_loss(noise_pred.float(), target.float(), reduction="mean")
def get_latents(
self, rgb_BCHW: Float[Tensor, "B C H W"], rgb_as_latents=False
) -> Float[Tensor, "B 4 64 64"]:
if rgb_as_latents:
latents = F.interpolate(
rgb_BCHW, (64, 64), mode="bilinear", align_corners=False
)
else:
rgb_BCHW_512 = F.interpolate(
rgb_BCHW, (512, 512), mode="bilinear", align_corners=False
)
# encode image into latents with vae
latents = self.encode_images(rgb_BCHW_512)
return latents
def forward(
self,
rgb: Float[Tensor, "B H W C"],
prompt_utils: PromptProcessorOutput,
elevation: Float[Tensor, "B"],
azimuth: Float[Tensor, "B"],
camera_distances: Float[Tensor, "B"],
mvp_mtx: Float[Tensor, "B 4 4"],
c2w: Float[Tensor, "B 4 4"],
rgb_as_latents=False,
**kwargs,
):
batch_size = rgb.shape[0]
rgb_BCHW = rgb.permute(0, 3, 1, 2)
latents = self.get_latents(rgb_BCHW, rgb_as_latents=rgb_as_latents)
# view-dependent text embeddings
text_embeddings_vd = prompt_utils.get_text_embeddings(
elevation,
azimuth,
camera_distances,
view_dependent_prompting=self.cfg.view_dependent_prompting,
)
# input text embeddings, view-independent
text_embeddings = prompt_utils.get_text_embeddings(
elevation, azimuth, camera_distances, view_dependent_prompting=False
)
if self.cfg.camera_condition_type == "extrinsics":
camera_condition = c2w
elif self.cfg.camera_condition_type == "mvp":
camera_condition = mvp_mtx
else:
raise ValueError(
f"Unknown camera_condition_type {self.cfg.camera_condition_type}"
)
grad = self.compute_grad_vsd(
latents, text_embeddings_vd, text_embeddings, camera_condition
)
grad = torch.nan_to_num(grad)
# reparameterization trick
# d(loss)/d(latents) = latents - target = latents - (latents - grad) = grad
target = (latents - grad).detach()
loss_vsd = 0.5 * F.mse_loss(latents, target, reduction="sum") / batch_size
loss_lora = self.train_lora(latents, text_embeddings, camera_condition)
return {
"loss_vsd": loss_vsd,
"loss_lora": loss_lora,
"grad_norm": grad.norm(),
}
def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):
if (
self.cfg.anneal_start_step is not None
and global_step > self.cfg.anneal_start_step
):
self.max_step = int(
self.num_train_timesteps * self.cfg.max_step_percent_annealed
)
================================================
FILE: threestudio/models/guidance/svd_guidance.py
================================================
import torch
from diffusers import StableVideoDiffusionPipeline, DDIMScheduler
# from diffusers.src.diffusers.pipleines.stable_video_diffusion.pipeline_stable_video_diffusion import _resize_with_antialiasing
from diffusers.utils import load_image, export_to_video, export_to_gif
from PIL import Image
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from dataclasses import dataclass, field
import torch
from diffusers.utils.import_utils import is_xformers_available
import threestudio
from threestudio.utils.base import BaseObject
from threestudio.utils.typing import *
from threestudio.models.prompt_processors.base import PromptProcessorOutput
import numpy as np
@threestudio.register("svd-guidance")
class StableVideoDiffusionGuidance(BaseObject):
@dataclass
class Config(BaseObject.Config):
pretrained_model_name_or_path: str = None
enable_memory_efficient_attention: bool = False
enable_sequential_cpu_offload: bool = False
enable_attention_slicing: bool = False
enable_channels_last_format: bool = False
grad_clip: Optional[
Any
] = None
half_precision_weights: bool = True
min_guidance_scale: float = 1.0
max_guidance_scale: float = 3.0
min_step_percent: float = 0.02
max_step_percent: float = 0.98
max_step_percent_annealed: float = 0.98
anneal_start_step: Optional[int] = None
width: Optional[int] = 512
height: Optional[int] = 512
weighting_strategy: str = "sds"
step_ratio: Optional[float] = None
low_ram_vae: int = -1
cfg: Config
def configure(self) -> None:
threestudio.info(f"Loading Stable Video Diffusion ...")
self.dtype = (
torch.float16 if self.cfg.half_precision_weights else torch.float32
)
# Create model
pipe = StableVideoDiffusionPipeline.from_pretrained(
self.cfg.pretrained_model_name_or_path, torch_dtype=torch.float16, variant="fp16"
)
pipe.to(self.device)
self.pipe = pipe
self.num_train_timesteps = self.pipe.scheduler.config.num_train_timesteps if self.cfg.weighting_strategy == 'sds' else 25
self.pipe.scheduler.set_timesteps(self.num_train_timesteps, device=self.device) # set sigma for euler discrete scheduling
self.min_step = int(self.num_train_timesteps * self.cfg.min_step_percent)
self.max_step = int(self.num_train_timesteps * self.cfg.max_step_percent)
self.alphas = self.pipe.scheduler.alphas_cumprod.to(self.device) # for convenience
self.embeddings = None
self.image = None
self.target_cache = None
def encode_image(self, image):
image = image * 2 -1
if self.cfg.low_ram_vae > 0:
vnum = self.cfg.low_ram_vae
mask_vae = torch.randperm(image.shape[0]) < vnum
with torch.no_grad():
latents_mask = torch.cat(
[
self.pipe._encode_vae_image(
image[~mask_vae][i : i + 1],
self.device, num_videos_per_prompt=1, do_classifier_free_guidance=False,
)
for i in range(image.shape[0] - vnum)
],
dim=0,
)
latents = torch.cat(
[
self.pipe._encode_vae_image(
image[mask_vae][i : i + 1],
self.device, num_videos_per_prompt=1, do_classifier_free_guidance=False,
)
for i in range(vnum)
],
dim=0,
)
latents_full = torch.zeros(
image.shape[0],
*latents.shape[1:],
device=latents.device,
dtype=latents.dtype,
)
latents_full[~mask_vae] = latents_mask
latents_full[mask_vae] = latents
latents = self.pipe.vae.config.scaling_factor * latents_full
else:
latents = self.pipe._encode_vae_image(image, self.device, num_videos_per_prompt=1, do_classifier_free_guidance=False)
latents = self.pipe.vae.config.scaling_factor * latents
return latents
def embed_image(self, image, num_videos_per_prompt=1, do_classifier_free_guidance=True):
dtype = next(self.pipe.image_encoder.parameters()).dtype
# We normalize the image before resizing to match with the original implementation.
# Then we unnormalize it after resizing.
image = image * 2.0 - 1.0
image = self._resize_with_antialiasing(image, (224, 224))
image = (image + 1.0) / 2.0
# Normalize the image with for CLIP input
image = self.pipe.feature_extractor(
images=image,
do_normalize=True,
do_center_crop=False,
do_resize=False,
do_rescale=False,
return_tensors="pt",
).pixel_values
image = image.to(device=self.device, dtype=dtype)
image_embeddings = self.pipe.image_encoder(image).image_embeds
image_embeddings = image_embeddings.unsqueeze(1)
# duplicate image embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = image_embeddings.shape
image_embeddings = image_embeddings.repeat(1, num_videos_per_prompt, 1)
image_embeddings = image_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)
if do_classifier_free_guidance:
negative_image_embeddings = torch.zeros_like(image_embeddings)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
image_embeddings = torch.cat([negative_image_embeddings, image_embeddings])
return image_embeddings
def __call__(
self,
rgb: Float[Tensor, "B H W C"],
prompt_utils: PromptProcessorOutput,
rgb_as_latents: bool = False,
num_frames: int = 14,
**kwargs,
):
rgb = rgb.permute(0, 3, 1, 2)
batch_size = rgb.shape[0]
image_embeddings = self.embed_image(rgb[[0]])
# rgb = rgb.to(self.dtype)
# interp to 512x512 to be fed into vae.
rgb_512 = F.interpolate(rgb, (self.cfg.height, self.cfg.width), mode="bilinear", align_corners=False)
# encode image into latents with vae, requires grad!
# latents = self.pipe._encode_image(rgb_512, self.device, num_videos_per_prompt=1, do_classifier_free_guidance=True)
latents = self.encode_image(rgb_512.to(self.dtype))
latents = latents.unsqueeze(0)
image_cond = rgb_512[[0]]
if self.cfg.step_ratio is not None:
# dreamtime-like
# t = self.max_step - (self.max_step - self.min_step) * np.sqrt(self.cfg.step_ratio)
t = np.round((1 - self.cfg.step_ratio) * self.num_train_timesteps).clip(self.min_step, self.max_step)
t = torch.full((1,), t, dtype=torch.long, device=self.device)
else:
t = torch.randint(self.min_step, self.max_step + 1, (1,), dtype=torch.long, device=self.device)
w = (1 - self.alphas[t]).view(1, 1, 1, 1)
if self.cfg.weighting_strategy == 'sds':
# predict the noise residual with unet, NO grad!
with torch.no_grad():
t = self.num_train_timesteps - t.item()
# add noise
noise = torch.randn_like(latents)
latents_noisy = self.pipe.scheduler.add_noise(latents, noise, self.pipe.scheduler.timesteps[t:t+1]) # t=0 noise;t=999 clean
noise_pred = self.pipe(
image=image_cond,
image_embeddings=image_embeddings,
height=self.cfg.height,
width=self.cfg.width,
latents=latents_noisy,
output_type='noise',
denoise_beg=t,
denoise_end=t + 1,
min_guidance_scale=self.cfg.min_guidance_scale,
max_guidance_scale=self.cfg.max_guidance_scale,
num_frames=batch_size,
num_inference_steps=self.num_train_timesteps
).frames[0]
grad = w * (noise_pred - noise)
grad = torch.nan_to_num(grad)
target = (latents - grad).detach()
loss = 0.5 * F.mse_loss(latents.float(), target, reduction='sum') / latents.shape[1]
return {
"loss_sds_video": loss,
"grad_norm": grad.norm(),
}
def _resize_with_antialiasing(self, input, size, interpolation="bicubic", align_corners=True):
h, w = input.shape[-2:]
factors = (h / size[0], w / size[1])
# First, we have to determine sigma
# Taken from skimage: https://github.com/scikit-image/scikit-image/blob/v0.19.2/skimage/transform/_warps.py#L171
sigmas = (
max((factors[0] - 1.0) / 2.0, 0.001),
max((factors[1] - 1.0) / 2.0, 0.001),
)
# Now kernel size. Good results are for 3 sigma, but that is kind of slow. Pillow uses 1 sigma
# https://github.com/python-pillow/Pillow/blob/master/src/libImaging/Resample.c#L206
# But they do it in the 2 passes, which gives better results. Let's try 2 sigmas for now
ks = int(max(2.0 * 2 * sigmas[0], 3)), int(max(2.0 * 2 * sigmas[1], 3))
# Make sure it is odd
if (ks[0] % 2) == 0:
ks = ks[0] + 1, ks[1]
if (ks[1] % 2) == 0:
ks = ks[0], ks[1] + 1
input = self._gaussian_blur2d(input, ks, sigmas)
output = torch.nn.functional.interpolate(input, size=size, mode=interpolation, align_corners=align_corners)
return output
def _compute_padding(self, kernel_size):
"""Compute padding tuple."""
# 4 or 6 ints: (padding_left, padding_right,padding_top,padding_bottom)
# https://pytorch.org/docs/stable/nn.html#torch.nn.functional.pad
if len(kernel_size) < 2:
raise AssertionError(kernel_size)
computed = [k - 1 for k in kernel_size]
# for even kernels we need to do asymmetric padding :(
out_padding = 2 * len(kernel_size) * [0]
for i in range(len(kernel_size)):
computed_tmp = computed[-(i + 1)]
pad_front = computed_tmp // 2
pad_rear = computed_tmp - pad_front
out_padding[2 * i + 0] = pad_front
out_padding[2 * i + 1] = pad_rear
return out_padding
def _filter2d(self, input, kernel):
# prepare kernel
b, c, h, w = input.shape
tmp_kernel = kernel[:, None, ...].to(device=input.device, dtype=input.dtype)
tmp_kernel = tmp_kernel.expand(-1, c, -1, -1)
height, width = tmp_kernel.shape[-2:]
padding_shape: list[int] = self._compute_padding([height, width])
input = torch.nn.functional.pad(input, padding_shape, mode="reflect")
# kernel and input tensor reshape to align element-wise or batch-wise params
tmp_kernel = tmp_kernel.reshape(-1, 1, height, width)
input = input.view(-1, tmp_kernel.size(0), input.size(-2), input.size(-1))
# convolve the tensor with the kernel.
output = torch.nn.functional.conv2d(input, tmp_kernel, groups=tmp_kernel.size(0), padding=0, stride=1)
out = output.view(b, c, h, w)
return out
def _gaussian(self, window_size: int, sigma):
if isinstance(sigma, float):
sigma = torch.tensor([[sigma]])
batch_size = sigma.shape[0]
x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1)
if window_size % 2 == 0:
x = x + 0.5
gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0)))
return gauss / gauss.sum(-1, keepdim=True)
def _gaussian_blur2d(self, input, kernel_size, sigma):
if isinstance(sigma, tuple):
sigma = torch.tensor([sigma], dtype=input.dtype)
else:
sigma = sigma.to(dtype=input.dtype)
ky, kx = int(kernel_size[0]), int(kernel_size[1])
bs = sigma.shape[0]
kernel_x = self._gaussian(kx, sigma[:, 1].view(bs, 1))
kernel_y = self._gaussian(ky, sigma[:, 0].view(bs, 1))
out_x = self._filter2d(input, kernel_x[..., None, :])
out = self._filter2d(out_x, kernel_y[..., None])
return out
================================================
FILE: threestudio/models/guidance/video_stable_diffusion_guidance.py
================================================
from dataclasses import dataclass, field
import inspect
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers import DDIMScheduler, DDPMScheduler, StableDiffusionPipeline
from diffusers.utils.import_utils import is_xformers_available
import threestudio
from threestudio.models.prompt_processors.base import PromptProcessorOutput
from threestudio.utils.base import BaseObject
from threestudio.utils.misc import C, cleanup, parse_version
from threestudio.utils.typing import *
from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from transformers import CLIPTokenizer, CLIPTextModel
from diffusers.utils.torch_utils import randn_tensor
import numpy as np
def tensor2vid(video: torch.Tensor, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) -> List[np.ndarray]:
# This code is copied from https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78
# reshape to ncfhw
mean = torch.tensor(mean, device=video.device).reshape(1, -1, 1, 1, 1)
std = torch.tensor(std, device=video.device).reshape(1, -1, 1, 1, 1)
# unnormalize back to [0,1]
video = video.mul_(std).add_(mean)
video.clamp_(0, 1)
# prepare the final outputs
i, c, f, h, w = video.shape
images = video.permute(2, 3, 0, 4, 1).reshape(
f, h, i * w, c
) # 1st (frames, h, batch_size, w, c) 2nd (frames, h, batch_size * w, c)
images = images.unbind(dim=0) # prepare a list of indvidual (consecutive frames)
images = [(image.cpu().numpy() * 255).astype("uint8") for image in images] # f h w c
return images
@threestudio.register("zeroscope-diffusion-guidance")
class StableDiffusionGuidance(BaseObject):
@dataclass
class Config(BaseObject.Config):
pretrained_model_name_or_path: str = "/localscratch/sba229/.cache/huggingface/hub/576w"
enable_memory_efficient_attention: bool = False
enable_sequential_cpu_offload: bool = False
enable_attention_slicing: bool = False
enable_channels_last_format: bool = False
guidance_scale: float = 100.0
grad_clip: Optional[
Any
] = None # field(default_factory=lambda: [0, 2.0, 8.0, 1000])
half_precision_weights: bool = True
min_step_percent: float = 0.02
max_step_percent: float = 0.98
max_step_percent_annealed: float = 0.5
anneal_start_step: Optional[int] = None
use_sjc: bool = False
var_red: bool = True
weighting_strategy: str = "sds"
token_merging: bool = False
token_merging_params: Optional[dict] = field(default_factory=dict)
view_dependent_prompting: bool = True
cfg: Config
def configure(self) -> None:
threestudio.info(f"Loading Stable Diffusion ...")
self.weights_dtype = (
torch.float16 if self.cfg.half_precision_weights else torch.float32
)
pipe_kwargs = {
"tokenizer": None,
"safety_checker": None,
"feature_extractor": None,
"requires_safety_checker": False,
"torch_dtype": self.weights_dtype,
}
self.pipe = StableDiffusionPipeline.from_pretrained(
self.cfg.pretrained_model_name_or_path,
**pipe_kwargs,
).to(self.device)
# Extra modules
self.tokenizer = CLIPTokenizer.from_pretrained(
self.cfg.pretrained_model_name_or_path, subfolder="tokenizer",
torch_dtype=self.weights_dtype
)
self.text_encoder = CLIPTextModel.from_pretrained(
self.cfg.pretrained_model_name_or_path, subfolder="text_encoder",
torch_dtype=self.weights_dtype
)
self.text_encoder = self.text_encoder.to(self.device)
if self.cfg.enable_memory_efficient_attention:
if parse_version(torch.__version__) >= parse_version("2"):
threestudio.info(
"PyTorch2.0 uses memory efficient attention by default."
)
elif not is_xformers_available():
threestudio.warn(
"xformers is not available, memory efficient attention is not enabled."
)
else:
self.pipe.enable_xformers_memory_efficient_attention()
if self.cfg.enable_sequential_cpu_offload:
self.pipe.enable_sequential_cpu_offload()
if self.cfg.enable_attention_slicing:
self.pipe.enable_attention_slicing(1)
if self.cfg.enable_channels_last_format:
self.pipe.unet.to(memory_format=torch.channels_last)
# del self.pipe.text_encoder
cleanup()
# Create model
self.vae = self.pipe.vae.eval()
self.unet = self.pipe.unet.eval()
for p in self.vae.parameters():
p.requires_grad_(False)
for p in self.unet.parameters():
p.requires_grad_(False)
if self.cfg.token_merging:
import tomesd
tomesd.apply_patch(self.unet, **self.cfg.token_merging_params)
if self.cfg.use_sjc:
# score jacobian chaining use DDPM
self.scheduler = DDPMScheduler.from_pretrained(
self.cfg.pretrained_model_name_or_path,
subfolder="scheduler",
torch_dtype=self.weights_dtype,
beta_start=0.00085,
beta_end=0.0120,
beta_schedule="scaled_linear",
)
else:
self.scheduler = DDIMScheduler.from_pretrained(
self.cfg.pretrained_model_name_or_path,
subfolder="scheduler",
torch_dtype=self.weights_dtype,
)
self.num_train_timesteps = self.scheduler.config.num_train_timesteps
self.min_step = int(self.num_train_timesteps * self.cfg.min_step_percent)
self.max_step = int(self.num_train_timesteps * self.cfg.max_step_percent)
self.alphas: Float[Tensor, "..."] = self.scheduler.alphas_cumprod.to(
self.device
)
if self.cfg.use_sjc:
# score jacobian chaining need mu
self.us: Float[Tensor, "..."] = torch.sqrt((1 - self.alphas) / self.alphas)
self.grad_clip_val: Optional[float] = None
# Extra for latents
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
threestudio.info(f"Loaded Stable Diffusion!")
@torch.cuda.amp.autocast(enabled=False)
def forward_unet(
self,
latents: Float[Tensor, "..."],
t: Float[Tensor, "..."],
encoder_hidden_states: Float[Tensor, "..."],
) -> Float[Tensor, "..."]:
input_dtype = latents.dtype
return self.unet(
latents.to(self.weights_dtype),
t.to(self.weights_dtype),
encoder_hidden_states=encoder_hidden_states.to(self.weights_dtype),
).sample.to(input_dtype)
@torch.cuda.amp.autocast(enabled=False)
def encode_images(
self, imgs: Float[Tensor, "B 3 N 320 576"], normalize: bool = True
) -> Float[Tensor, "B 4 40 72"]:
# breakpoint()
if len(imgs.shape) == 4:
print("Only given an image an not video") # TODO: Prevent
imgs = imgs[:, :, None]
# breakpoint()
batch_size, channels, num_frames, height, width = imgs.shape
imgs = imgs.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
input_dtype = imgs.dtype
if normalize:
imgs = imgs * 2.0 - 1.0 # Check later if needed TODO, also check if clip needed of -1 1
# breakpoint()
posterior = self.vae.encode(imgs.to(self.weights_dtype)).latent_dist
latents = posterior.sample() * self.vae.config.scaling_factor
latents = (
latents[None, :]
.reshape(
(
batch_size,
num_frames,
-1,
)
+ latents.shape[2:]
)
.permute(0, 2, 1, 3, 4)
)
return latents.to(input_dtype)
# @torch.cuda.amp.autocast(enabled=False)
# def encode_images(
# self, imgs: Float[Tensor, "B 3 512 512"]
# ) -> Float[Tensor, "B 4 64 64"]:
# input_dtype = imgs.dtype
# imgs = imgs * 2.0 - 1.0
# posterior = self.vae.encode(imgs.to(self.weights_dtype)).latent_dist
# latents = posterior.sample() * self.vae.config.scaling_factor
# return latents.to(input_dtype)
# @torch.cuda.amp.autocast(enabled=False)
# def decode_latents(
# self,
# latents: Float[Tensor, "B 4 H W"],
# latent_height: int = 64,
# latent_width: int = 64,
# ) -> Float[Tensor, "B 3 512 512"]:
# breakpoint()
# input_dtype = latents.dtype
# latents = F.interpolate(
# latents, (latent_height, latent_width), mode="bilinear", align_corners=False
# )
# latents = 1 / self.vae.config.scaling_factor * latents
# image = self.vae.decode(latents.to(self.weights_dtype)).sample
# image = (image * 0.5 + 0.5).clamp(0, 1)
# return image.to(input_dtype)
@torch.cuda.amp.autocast(enabled=False)
def decode_latents(self, latents):
# TODO: Make decoding align with previous version
latents = 1 / self.vae.config.scaling_factor * latents
batch_size, channels, num_frames, height, width = latents.shape
latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
image = self.vae.decode(latents).sample
video = (
image[None, :]
.reshape(
(
batch_size,
num_frames,
-1,
)
+ image.shape[2:]
)
.permute(0, 2, 1, 3, 4)
)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
video = video.float()
return video
def compute_grad_sds(
self,
latents: Float[Tensor, "B 4 64 64"],
text_embeddings: Float[Tensor, "BB 77 768"],
t: Int[Tensor, "B"],
):
# predict the noise residual with unet, NO grad!
with torch.no_grad():
# add noise
noise = torch.randn_like(latents) # TODO: use torch generator
latents_noisy = self.scheduler.add_noise(latents, noise, t)
# pred noise
latent_model_input = torch.cat([latents_noisy] * 2, dim=0)
# breakpoint()
# latent_model_input = latent_model_input.unsqueeze(2) # REMOVE
# latent_model_input = latent_model_input.repeat(1,1,16,1,1) # REMOVE
# breakpoint()
noise_pred = self.forward_unet(
latent_model_input,
torch.cat([t] * 2),
encoder_hidden_states=text_embeddings,
)
# noise_pred = self.forward_unet(latent_model_input,torch.cat([t] * 2),encoder_hidden_states=text_embeddings,)
# breakpoint()
# noise_pred = noise_pred.squeeze(2) # REMOVE
# noise_pred = noise_pred[:,:,0] # REMOVE
# noise_pred = self.forward_unet(latent_model_input,torch.cat([t] * 2),encoder_hidden_states=text_embeddings,)
# perform guidance (high scale from paper!)
noise_pred_text, noise_pred_uncond = noise_pred.chunk(2)
noise_pred = noise_pred_text + self.cfg.guidance_scale * (
noise_pred_text - noise_pred_uncond
)
if self.cfg.weighting_strategy == "sds":
# w(t), sigma_t^2
w = (1 - self.alphas[t]).view(-1, 1, 1, 1)
elif self.cfg.weighting_strategy == "uniform":
w = 1
elif self.cfg.weighting_strategy == "fantasia3d":
w = (self.alphas[t] ** 0.5 * (1 - self.alphas[t])).view(-1, 1, 1, 1)
else:
raise ValueError(
f"Unknown weighting strategy: {self.cfg.weighting_strategy}"
)
grad = w * (noise_pred - noise)
return grad
def compute_grad_sjc(
self,
latents: Float[Tensor, "B 4 64 64"],
text_embeddings: Float[Tensor, "BB 77 768"],
t: Int[Tensor, "B"],
):
sigma = self.us[t]
sigma = sigma.view(-1, 1, 1, 1)
# predict the noise residual with unet, NO grad!
with torch.no_grad():
# add noise
noise = torch.randn_like(latents) # TODO: use torch generator
y = latents
zs = y + sigma * noise
scaled_zs = zs / torch.sqrt(1 + sigma**2)
# pred noise
latent_model_input = torch.cat([scaled_zs] * 2, dim=0)
noise_pred = self.forward_unet(
latent_model_input,
torch.cat([t] * 2),
encoder_hidden_states=text_embeddings,
)
# perform guidance (high scale from paper!)
noise_pred_text, noise_pred_uncond = noise_pred.chunk(2)
noise_pred = noise_pred_text + self.cfg.guidance_scale * (
noise_pred_text - noise_pred_uncond
)
Ds = zs - sigma * noise_pred
if self.cfg.var_red:
grad = -(Ds - y) / sigma
else:
grad = -(Ds - zs) / sigma
return grad
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def get_text_embeddings(
self,
prompt = "a firepit",
device="cuda",
num_images_per_prompt=1,
do_classifier_free_guidance=True,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
# breakpoint()
text_inputs = self.pipe.tokenizer(
prompt,
padding="max_length",
max_length=self.pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.pipe.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.pipe.tokenizer.batch_decode(
untruncated_ids[:, self.pipe.tokenizer.model_max_length - 1 : -1]
)
# breakpoint()
if False: #hasattr(self.pipe.text_encoder.config, "use_attention_mask") and self.pipe.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
# breakpoint()
prompt_embeds = self.pipe.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
# self.pipe.text_encoder(text_input_ids.to(device),attention_mask=attention_mask,)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.pipe.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.pipe.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.pipe.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.pipe.text_encoder.config, "use_attention_mask") and self.pipe.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.pipe.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.pipe.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
# breakpoint()
return prompt_embeds
def __call__(
self,
rgb: Float[Tensor, "B H W C"],
prompt_utils: PromptProcessorOutput,
elevation: Float[Tensor, "B"],
azimuth: Float[Tensor, "B"],
camera_distances: Float[Tensor, "B"],
rgb_as_latents=False,
num_frames=4,
**kwargs,
):
# breakpoint()
rgb_BCHW = rgb.permute(0, 3, 1, 2)
batch_size = rgb_BCHW.shape[0] // num_frames
latents: Float[Tensor, "B 4 64 64"]
if kwargs['train_dynamic_camera']:
elevation = elevation[[0]]
azimuth = azimuth[[0]]
camera_distances = camera_distances[[0]]
if rgb_as_latents:
latents = F.interpolate(
rgb_BCHW, (40, 72), mode="bilinear", align_corners=False
# rgb_BCHW, (32, 32), mode="bilinear", align_corners=False
)# TODO: Edit to 40, 72 instead of (64, 64)
else:
rgb_BCHW_512 = F.interpolate(
rgb_BCHW, (320, 576), mode="bilinear", align_corners=False
# rgb_BCHW, (256, 256), mode="bilinear", align_corners=False
) # Use 320, 576 instead of (512, 512)
# encode image into latents with vae
# breakpoint()
rgb_BCHW_512 = rgb_BCHW_512.permute(1, 0, 2, 3)[None]
latents = self.encode_images(rgb_BCHW_512)
text_embeddings = prompt_utils.get_text_embeddings(
elevation, azimuth, camera_distances, self.cfg.view_dependent_prompting
)
# text_embeddings = self.get_text_embeddings()
# timestep ~ U(0.02, 0.98) to avoid very high/low noise level
t = torch.randint(
self.min_step,
self.max_step + 1,
[batch_size],
dtype=torch.long,
device=self.device,
)
if self.cfg.use_sjc:
grad = self.compute_grad_sjc(latents, text_embeddings, t)
else:
grad = self.compute_grad_sds(latents, text_embeddings, t)
# breakpoint()
grad = torch.nan_to_num(grad)
# clip grad for stable training?
if self.grad_clip_val is not None:
grad = grad.clamp(-self.grad_clip_val, self.grad_clip_val)
# loss = SpecifyGradient.apply(latents, grad)
# SpecifyGradient is not straghtforward, use a reparameterization trick instead
target = (latents - grad).detach()
# d(loss)/d(latents) = latents - target = latents - (latents - grad) = grad
loss_sds = 0.5 * F.mse_loss(latents, target, reduction="sum") / batch_size
return {
"loss_sds_video": loss_sds,
"grad_norm": grad.norm(),
}
def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):
# clip grad for stable training as demonstrated in
# Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation
# http://arxiv.org/abs/2303.15413
if self.cfg.grad_clip is not None:
self.grad_clip_val = C(self.cfg.grad_clip, epoch, global_step)
# t annealing from ProlificDreamer
if (
self.cfg.anneal_start_step is not None
and global_step > self.cfg.anneal_start_step
):
self.max_step = int(
self.num_train_timesteps * self.cfg.max_step_percent_annealed
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
# breakpoint()
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
@torch.no_grad()
def gen_video(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_frames: int = 16,
num_inference_steps: int = 50,
guidance_scale: float = 9.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "np",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the video generation. If not defined, one has to pass `prompt_embeds`.
instead.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixels of the generated video.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated video.
num_frames (`int`, *optional*, defaults to 16):
The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds
amounts to 2 seconds of video.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality videos at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate videos that are closely linked to the text `prompt`,
usually at the expense of lower video quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the video generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for video
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`. Latents should be of shape
`(batch_size, num_channel, num_frames, height, width)`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"np"`):
The output format of the generate video. Choose between `torch.FloatTensor` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.TextToVideoSDPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
Examples:
Returns:
[`~pipelines.stable_diffusion.TextToVideoSDPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.TextToVideoSDPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated frames.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
num_images_per_prompt = 1
# 1. Check inputs. Raise error if not correct
# self.check_inputs(
# prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
# )
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self.device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
num_frames,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# breakpoint()
# predict the noise residual
# breakpoint()
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# reshape latents
bsz, channel, frames, width, height = latents.shape
latents = latents.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height)
noise_pred = noise_pred.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# reshape latents back
latents = latents[None, :].reshape(bsz, frames, channel, width, height).permute(0, 2, 1, 3, 4)
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
video_tensor = self.decode_latents(latents)
# breakpoint()
# video_tensor = F.interpolate(video_tensor.squeeze(0).permute(1,0,2,3), (512, 512), mode="bilinear", align_corners=False).permute(1,0,2,3).unsqueeze(0)
# latents = self.encode_images(video_tensor.half(), normalize=False)
# breakpoint()
# video_tensor = self.decode_latents(latents)
if output_type == "pt":
video = video_tensor
else:
video = tensor2vid(video_tensor)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
return video
def prepare_latents(
self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
):
shape = (
batch_size,
num_channels_latents,
num_frames,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def progress_bar(self, iterable=None, total=None):
if not hasattr(self, "_progress_bar_config"):
self._progress_bar_config = {}
elif not isinstance(self._progress_bar_config, dict):
raise ValueError(
f"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}."
)
if iterable is not None:
return tqdm(iterable, **self._progress_bar_config)
elif total is not None:
return tqdm(total=total, **self._progress_bar_config)
else:
raise ValueError("Either `total` or `iterable` has to be defined.")
================================================
FILE: threestudio/models/guidance/video_stable_diffusion_vsd_guidance.py
================================================
import random
import inspect
from tqdm import tqdm
from contextlib import contextmanager
from dataclasses import dataclass, field
import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers import (
DDPMScheduler,
DPMSolverMultistepScheduler,
StableDiffusionPipeline,
UNet2DConditionModel,
)
from diffusers.loaders import AttnProcsLayers
from diffusers.models.attention_processor import LoRAAttnProcessor
from diffusers.models.embeddings import TimestepEmbedding
from diffusers.utils.import_utils import is_xformers_available
import threestudio
from threestudio.models.prompt_processors.base import PromptProcessorOutput
from threestudio.utils.base import BaseModule
from threestudio.utils.misc import C, cleanup, parse_version
from threestudio.utils.typing import *
from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from transformers import CLIPTokenizer, CLIPTextModel
from diffusers.utils.torch_utils import randn_tensor
import numpy as np
def tensor2vid(video: torch.Tensor, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) -> List[np.ndarray]:
# This code is copied from https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78
# reshape to ncfhw
mean = torch.tensor(mean, device=video.device).reshape(1, -1, 1, 1, 1)
std = torch.tensor(std, device=video.device).reshape(1, -1, 1, 1, 1)
# unnormalize back to [0,1]
video = video.mul_(std).add_(mean)
video.clamp_(0, 1)
# prepare the final outputs
i, c, f, h, w = video.shape
images = video.permute(2, 3, 0, 4, 1).reshape(
f, h, i * w, c
) # 1st (frames, h, batch_size, w, c) 2nd (frames, h, batch_size * w, c)
images = images.unbind(dim=0) # prepare a list of indvidual (consecutive frames)
images = [(image.cpu().numpy() * 255).astype("uint8") for image in images] # f h w c
return images
class ToWeightsDType(nn.Module):
def __init__(self, module: nn.Module, dtype: torch.dtype):
super().__init__()
self.module = module
self.dtype = dtype
def forward(self, x: Float[Tensor, "..."]) -> Float[Tensor, "..."]:
return self.module(x).to(self.dtype)
@threestudio.register("zeroscope-stable-diffusion-vsd-guidance")
class StableDiffusionVSDGuidance(BaseModule):
@dataclass
class Config(BaseModule.Config):
pretrained_model_name_or_path: str = "/local-scratch/localhome/sba229/.cache/huggingface/hub/576w/"
pretrained_model_name_or_path_lora: str = "/local-scratch/localhome/sba229/.cache/huggingface/hub/576w/"
enable_memory_efficient_attention: bool = False
enable_sequential_cpu_offload: bool = False
enable_attention_slicing: bool = False
enable_channels_last_format: bool = False
guidance_scale: float = 7.5
guidance_scale_lora: float = 1.0
half_precision_weights: bool = True
lora_cfg_training: bool = True
lora_n_timestamp_samples: int = 1
min_step_percent: float = 0.02
max_step_percent: float = 0.98
max_step_percent_annealed: float = 0.5
anneal_start_step: Optional[int] = 5000
view_dependent_prompting: bool = True
camera_condition_type: str = "extrinsics"
cfg: Config
def configure(self) -> None:
threestudio.info(f"Loading Stable Diffusion ...")
self.weights_dtype = (
torch.float16 if self.cfg.half_precision_weights else torch.float32
)
pipe_kwargs = {
"tokenizer": None,
"safety_checker": None,
"feature_extractor": None,
"requires_safety_checker": False,
"torch_dtype": self.weights_dtype,
}
pipe_lora_kwargs = {
"tokenizer": None,
"safety_checker": None,
"feature_extractor": None,
"requires_safety_checker": False,
"torch_dtype": self.weights_dtype,
}
@dataclass
class SubModules:
pipe: StableDiffusionPipeline
pipe_lora: StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained(
self.cfg.pretrained_model_name_or_path,
**pipe_kwargs,
).to(self.device)
if (
self.cfg.pretrained_model_name_or_path
== self.cfg.pretrained_model_name_or_path_lora
):
self.single_model = True
pipe_lora = pipe
else:
self.single_model = False
pipe_lora = StableDiffusionPipeline.from_pretrained(
self.cfg.pretrained_model_name_or_path_lora,
**pipe_lora_kwargs,
).to(self.device)
del pipe_lora.vae
cleanup()
pipe_lora.vae = pipe.vae
self.submodules = SubModules(pipe=pipe, pipe_lora=pipe_lora)
# Extra modules
self.tokenizer = CLIPTokenizer.from_pretrained(
self.cfg.pretrained_model_name_or_path, subfolder="tokenizer",
torch_dtype=self.weights_dtype
)
self.text_encoder = CLIPTextModel.from_pretrained(
self.cfg.pretrained_model_name_or_path, subfolder="text_encoder",
torch_dtype=self.weights_dtype
)
self.text_encoder = self.text_encoder.to(self.device)
if self.cfg.enable_memory_efficient_attention:
if parse_version(torch.__version__) >= parse_version("2"):
threestudio.info(
"PyTorch2.0 uses memory efficient attention by default."
)
elif not is_xformers_available():
threestudio.warn(
"xformers is not available, memory efficient attention is not enabled."
)
else:
self.pipe.enable_xformers_memory_efficient_attention()
self.pipe_lora.enable_xformers_memory_efficient_attention()
if self.cfg.enable_sequential_cpu_offload:
self.pipe.enable_sequential_cpu_offload()
self.pipe_lora.enable_sequential_cpu_offload()
if self.cfg.enable_attention_slicing:
self.pipe.enable_attention_slicing(1)
self.pipe_lora.enable_attention_slicing(1)
if self.cfg.enable_channels_last_format:
self.pipe.unet.to(memory_format=torch.channels_last)
self.pipe_lora.unet.to(memory_format=torch.channels_last)
# del self.pipe.text_encoder
# if not self.single_model:
# del self.pipe_lora.text_encoder
cleanup()
for p in self.vae.parameters():
p.requires_grad_(False)
for p in self.unet.parameters():
p.requires_grad_(False)
for p in self.unet_lora.parameters():
p.requires_grad_(False)
# FIXME: hard-coded dims
self.camera_embedding = ToWeightsDType(
TimestepEmbedding(16, 1280), self.weights_dtype
)
self.unet_lora.class_embedding = self.camera_embedding
# set up LoRA layers
lora_attn_procs = {}
for name in self.unet_lora.attn_processors.keys():
cross_attention_dim = (
None
if name.endswith("attn1.processor")
else self.unet_lora.config.cross_attention_dim
)
if name.startswith("mid_block"):
hidden_size = self.unet_lora.config.block_out_channels[-1]
if "temp_attentions" in name:
cross_attention_dim = hidden_size
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(self.unet_lora.config.block_out_channels))[
block_id
]
if "temp_attentions" in name:
cross_attention_dim = hidden_size
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = self.unet_lora.config.block_out_channels[block_id]
if "temp_attentions" in name:
cross_attention_dim = hidden_size
elif name in ['transformer_in.transformer_blocks.0.attn1.processor']:
# TODO: Read cleanly for zeroscope
hidden_size = 512
elif name in ['transformer_in.transformer_blocks.0.attn2.processor']:
# TODO: Read cleanly for zeroscope
hidden_size = 512
cross_attention_dim = 512
else:
raise ValueError(f"Unknown {name} for lora")
lora_attn_procs[name] = LoRAAttnProcessor(
hidden_size=hidden_size, cross_attention_dim=cross_attention_dim
)
self.unet_lora.set_attn_processor(lora_attn_procs)
self.lora_layers = AttnProcsLayers(self.unet_lora.attn_processors)
self.lora_layers._load_state_dict_pre_hooks.clear()
self.lora_layers._state_dict_hooks.clear()
self.scheduler = DDPMScheduler.from_pretrained(
self.cfg.pretrained_model_name_or_path,
subfolder="scheduler",
torch_dtype=self.weights_dtype,
)
self.scheduler_lora = DDPMScheduler.from_pretrained(
self.cfg.pretrained_model_name_or_path_lora,
subfolder="scheduler",
torch_dtype=self.weights_dtype,
)
self.scheduler_sample = DPMSolverMultistepScheduler.from_config(
self.pipe.scheduler.config
)
self.scheduler_lora_sample = DPMSolverMultistepScheduler.from_config(
self.pipe_lora.scheduler.config
)
self.pipe.scheduler = self.scheduler
self.pipe_lora.scheduler = self.scheduler_lora
self.num_train_timesteps = self.scheduler.config.num_train_timesteps
self.min_step = int(self.num_train_timesteps * self.cfg.min_step_percent)
self.max_step = int(self.num_train_timesteps * self.cfg.max_step_percent)
self.alphas: Float[Tensor, "..."] = self.scheduler.alphas_cumprod.to(
self.device
)
# Extra for latents
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
threestudio.info(f"Loaded Stable Diffusion!")
@property
def pipe(self):
return self.submodules.pipe
@property
def pipe_lora(self):
return self.submodules.pipe_lora
@property
def unet(self):
return self.submodules.pipe.unet
@property
def unet_lora(self):
return self.submodules.pipe_lora.unet
@property
def vae(self):
return self.submodules.pipe.vae
@property
def vae_lora(self):
return self.submodules.pipe_lora.vae
@torch.no_grad()
@torch.cuda.amp.autocast(enabled=False)
def _sample(
self,
pipe: StableDiffusionPipeline,
sample_scheduler: DPMSolverMultistepScheduler,
text_embeddings: Float[Tensor, "BB N Nf"],
num_inference_steps: int,
guidance_scale: float,
num_images_per_prompt: int = 1,
height: Optional[int] = None,
width: Optional[int] = None,
class_labels: Optional[Float[Tensor, "BB 16"]] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
) -> Float[Tensor, "B H W 3"]:
vae_scale_factor = 2 ** (len(pipe.vae.config.block_out_channels) - 1)
height = height or pipe.unet.config.sample_size * vae_scale_factor
width = width or pipe.unet.config.sample_size * vae_scale_factor
batch_size = text_embeddings.shape[0] // 2
device = self.device
sample_scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = sample_scheduler.timesteps
num_channels_latents = pipe.unet.config.in_channels
latents = pipe.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
self.weights_dtype,
device,
generator,
)
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2)
latent_model_input = sample_scheduler.scale_model_input(
latent_model_input, t
)
# predict the noise residual
if class_labels is None:
with self.disable_unet_class_embedding(pipe.unet) as unet:
noise_pred = unet(
latent_model_input,
t,
encoder_hidden_states=text_embeddings.to(self.weights_dtype),
cross_attention_kwargs=cross_attention_kwargs,
).sample
else:
noise_pred = pipe.unet(
latent_model_input,
t,
encoder_hidden_states=text_embeddings.to(self.weights_dtype),
class_labels=class_labels,
cross_attention_kwargs=cross_attention_kwargs,
).sample
noise_pred_text, noise_pred_uncond = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (
noise_pred_text - noise_pred_uncond
)
# compute the previous noisy sample x_t -> x_t-1
latents = sample_scheduler.step(noise_pred, t, latents).prev_sample
latents = 1 / pipe.vae.config.scaling_factor * latents
images = pipe.vae.decode(latents).sample
images = (images / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
images = images.permute(0, 2, 3, 1).float()
return images
def sample(
self,
prompt_utils: PromptProcessorOutput,
elevation: Float[Tensor, "B"],
azimuth: Float[Tensor, "B"],
camera_distances: Float[Tensor, "B"],
seed: int = 0,
**kwargs,
) -> Float[Tensor, "N H W 3"]:
# view-dependent text embeddings
text_embeddings_vd = prompt_utils.get_text_embeddings(
elevation,
azimuth,
camera_distances,
view_dependent_prompting=self.cfg.view_dependent_prompting,
)
cross_attention_kwargs = {"scale": 0.0} if self.single_model else None
generator = torch.Generator(device=self.device).manual_seed(seed)
return self._sample(
pipe=self.pipe,
sample_scheduler=self.scheduler_sample,
text_embeddings=text_embeddings_vd,
num_inference_steps=25,
guidance_scale=self.cfg.guidance_scale,
cross_attention_kwargs=cross_attention_kwargs,
generator=generator,
)
def sample_lora(
self,
prompt_utils: PromptProcessorOutput,
elevation: Float[Tensor, "B"],
azimuth: Float[Tensor, "B"],
camera_distances: Float[Tensor, "B"],
mvp_mtx: Float[Tensor, "B 4 4"],
c2w: Float[Tensor, "B 4 4"],
seed: int = 0,
**kwargs,
) -> Float[Tensor, "N H W 3"]:
# input text embeddings, view-independent
text_embeddings = prompt_utils.get_text_embeddings(
elevation, azimuth, camera_distances, view_dependent_prompting=False
)
if self.cfg.camera_condition_type == "extrinsics":
camera_condition = c2w
elif self.cfg.camera_condition_type == "mvp":
camera_condition = mvp_mtx
else:
raise ValueError(
f"Unknown camera_condition_type {self.cfg.camera_condition_type}"
)
B = elevation.shape[0]
camera_condition_cfg = torch.cat(
[
camera_condition.view(B, -1),
torch.zeros_like(camera_condition.view(B, -1)),
],
dim=0,
)
generator = torch.Generator(device=self.device).manual_seed(seed)
return self._sample(
sample_scheduler=self.scheduler_lora_sample,
pipe=self.pipe_lora,
text_embeddings=text_embeddings,
num_inference_steps=25,
guidance_scale=self.cfg.guidance_scale_lora,
class_labels=camera_condition_cfg,
cross_attention_kwargs={"scale": 1.0},
generator=generator,
)
@torch.cuda.amp.autocast(enabled=False)
def forward_unet(
self,
unet: UNet2DConditionModel,
latents: Float[Tensor, "..."],
t: Float[Tensor, "..."],
encoder_hidden_states: Float[Tensor, "..."],
class_labels: Optional[Float[Tensor, "B 16"]] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
) -> Float[Tensor, "..."]:
input_dtype = latents.dtype
return unet(
latents.to(self.weights_dtype),
t.to(self.weights_dtype),
encoder_hidden_states=encoder_hidden_states.to(self.weights_dtype),
class_labels=class_labels,
cross_attention_kwargs=cross_attention_kwargs,
).sample.to(input_dtype)
# @torch.cuda.amp.autocast(enabled=False)
# def encode_images(
# self, imgs: Float[Tensor, "B 3 512 512"]
# ) -> Float[Tensor, "B 4 64 64"]:
# input_dtype = imgs.dtype
# imgs = imgs * 2.0 - 1.0
# posterior = self.vae.encode(imgs.to(self.weights_dtype)).latent_dist
# latents = posterior.sample() * self.vae.config.scaling_factor
# return latents.to(input_dtype)
@torch.cuda.amp.autocast(enabled=False)
def encode_images(
self, imgs: Float[Tensor, "B 3 N 320 576"], normalize: bool = True
) -> Float[Tensor, "B 4 40 72"]:
if len(imgs.shape) == 4:
print("Only given an image an not video") # TODO: Prevent
imgs = imgs[:, :, None]
batch_size, channels, num_frames, height, width = imgs.shape
imgs = imgs.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
input_dtype = imgs.dtype
if normalize:
imgs = imgs * 2.0 - 1.0 # Check later if needed TODO, also check if clip needed of -1 1
posterior = self.vae.encode(imgs.to(self.weights_dtype)).latent_dist
latents = posterior.sample() * self.vae.config.scaling_factor
latents = (
latents[None, :]
.reshape(
(
batch_size,
num_frames,
-1,
)
+ latents.shape[2:]
)
.permute(0, 2, 1, 3, 4)
)
return latents.to(input_dtype)
# @torch.cuda.amp.autocast(enabled=False)
# def decode_latents(
# self,
# latents: Float[Tensor, "B 4 H W"],
# latent_height: int = 64,
# latent_width: int = 64,
# ) -> Float[Tensor, "B 3 512 512"]:
# input_dtype = latents.dtype
# latents = F.interpolate(
# latents, (latent_height, latent_width), mode="bilinear", align_corners=False
# )
# latents = 1 / self.vae.config.scaling_factor * latents
# image = self.vae.decode(latents.to(self.weights_dtype)).sample
# image = (image * 0.5 + 0.5).clamp(0, 1)
# return image.to(input_dtype)
@torch.cuda.amp.autocast(enabled=False)
def decode_latents(self, latents):
# TODO: Make decoding align with previous version
latents = 1 / self.vae.config.scaling_factor * latents
batch_size, channels, num_frames, height, width = latents.shape
latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
image = self.vae.decode(latents).sample
video = (
image[None, :]
.reshape(
(
batch_size,
num_frames,
-1,
)
+ image.shape[2:]
)
.permute(0, 2, 1, 3, 4)
)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
video = video.float()
return video
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def get_text_embeddings(
self,
prompt = "a firepit",
device="cuda",
num_images_per_prompt=1,
do_classifier_free_guidance=True,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.pipe.tokenizer(
prompt,
padding="max_length",
max_length=self.pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.pipe.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.pipe.tokenizer.batch_decode(
untruncated_ids[:, self.pipe.tokenizer.model_max_length - 1 : -1]
)
if False: #hasattr(self.pipe.text_encoder.config, "use_attention_mask") and self.pipe.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.pipe.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
# self.pipe.text_encoder(text_input_ids.to(device),attention_mask=attention_mask,)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.pipe.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.pipe.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.pipe.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.pipe.text_encoder.config, "use_attention_mask") and self.pipe.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.pipe.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.pipe.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
@contextmanager
def disable_unet_class_embedding(self, unet: UNet2DConditionModel):
class_embedding = unet.class_embedding
try:
unet.class_embedding = None
yield unet
finally:
unet.class_embedding = class_embedding
def compute_grad_vsd(
self,
latents: Float[Tensor, "B 4 64 64"],
text_embeddings_vd: Float[Tensor, "BB 77 768"],
text_embeddings: Float[Tensor, "BB 77 768"],
camera_condition: Float[Tensor, "B 4 4"],
):
B = latents.shape[0]
with torch.no_grad():
# random timestamp
t = torch.randint(
self.min_step,
self.max_step + 1,
[B],
dtype=torch.long,
device=self.device,
)
# add noise
noise = torch.randn_like(latents)
latents_noisy = self.scheduler.add_noise(latents, noise, t)
# pred noise
latent_model_input = torch.cat([latents_noisy] * 2, dim=0)
with self.disable_unet_class_embedding(self.unet) as unet:
cross_attention_kwargs = {"scale": 0.0} if self.single_model else None
noise_pred_pretrain = self.forward_unet(
unet,
latent_model_input,
torch.cat([t] * 2),
encoder_hidden_states=text_embeddings_vd,
cross_attention_kwargs=cross_attention_kwargs,
)
# use view-independent text embeddings in LoRA
noise_pred_est = self.forward_unet(
self.unet_lora,
latent_model_input,
torch.cat([t] * 2),
encoder_hidden_states=text_embeddings,
class_labels=torch.cat(
[
camera_condition.view(B, -1),
torch.zeros_like(camera_condition.view(B, -1)),
],
dim=0,
),
cross_attention_kwargs={"scale": 1.0},
)
(
noise_pred_pretrain_text,
noise_pred_pretrain_uncond,
) = noise_pred_pretrain.chunk(2)
# NOTE: guidance scale definition here is aligned with diffusers, but different from other guidance
noise_pred_pretrain = noise_pred_pretrain_uncond + self.cfg.guidance_scale * (
noise_pred_pretrain_text - noise_pred_pretrain_uncond
)
# TODO: more general cases
assert self.scheduler.config.prediction_type == "epsilon"
if self.scheduler_lora.config.prediction_type == "v_prediction":
alphas_cumprod = self.scheduler_lora.alphas_cumprod.to(
device=latents_noisy.device, dtype=latents_noisy.dtype
)
alpha_t = alphas_cumprod[t] ** 0.5
sigma_t = (1 - alphas_cumprod[t]) ** 0.5
noise_pred_est = latent_model_input * torch.cat([sigma_t] * 2, dim=0).view(
-1, 1, 1, 1
) + noise_pred_est * torch.cat([alpha_t] * 2, dim=0).view(-1, 1, 1, 1)
(
noise_pred_est_text,
noise_pred_est_uncond,
) = noise_pred_est.chunk(2)
# NOTE: guidance scale definition here is aligned with diffusers, but different from other guidance
noise_pred_est = noise_pred_est_uncond + self.cfg.guidance_scale_lora * (
noise_pred_est_text - noise_pred_est_uncond
)
w = (1 - self.alphas[t]).view(-1, 1, 1, 1)
grad = w * (noise_pred_pretrain - noise_pred_est)
return grad
def train_lora(
self,
latents: Float[Tensor, "B 4 64 64"],
text_embeddings: Float[Tensor, "BB 77 768"],
camera_condition: Float[Tensor, "B 4 4"],
):
B = latents.shape[0]
latents = latents.detach().repeat(self.cfg.lora_n_timestamp_samples, 1, 1, 1, 1)
t = torch.randint(
int(self.num_train_timesteps * 0.0),
int(self.num_train_timesteps * 1.0),
[B * self.cfg.lora_n_timestamp_samples],
dtype=torch.long,
device=self.device,
)
noise = torch.randn_like(latents)
noisy_latents = self.scheduler_lora.add_noise(latents, noise, t)
if self.scheduler_lora.config.prediction_type == "epsilon":
target = noise
elif self.scheduler_lora.config.prediction_type == "v_prediction":
target = self.scheduler_lora.get_velocity(latents, noise, t)
else:
raise ValueError(
f"Unknown prediction type {self.scheduler_lora.config.prediction_type}"
)
# use view-independent text embeddings in LoRA
text_embeddings, _ = text_embeddings.chunk(2)
if self.cfg.lora_cfg_training and random.random() < 0.1:
camera_condition = torch.zeros_like(camera_condition)
noise_pred = self.forward_unet(
self.unet_lora,
noisy_latents,
t,
encoder_hidden_states=text_embeddings.repeat(
self.cfg.lora_n_timestamp_samples, 1, 1
),
class_labels=camera_condition.view(B, -1).repeat(
self.cfg.lora_n_timestamp_samples, 1
),
cross_attention_kwargs={"scale": 1.0},
)
return F.mse_loss(noise_pred.float(), target.float(), reduction="mean")
def get_latents(
self, rgb_BCHW: Float[Tensor, "B C H W"], rgb_as_latents=False
) -> Float[Tensor, "B 4 64 64"]:
if rgb_as_latents:
latents = F.interpolate(
rgb_BCHW, (64, 64), mode="bilinear", align_corners=False
)
else:
rgb_BCHW_512 = F.interpolate(
rgb_BCHW, (512, 512), mode="bilinear", align_corners=False
)
rgb_BCHW_512 = rgb_BCHW_512.permute(1, 0, 2, 3)[None]
# encode image into latents with vae
latents = self.encode_images(rgb_BCHW_512)
return latents
def forward(
self,
rgb: Float[Tensor, "B H W C"],
prompt_utils: PromptProcessorOutput,
elevation: Float[Tensor, "B"],
azimuth: Float[Tensor, "B"],
camera_distances: Float[Tensor, "B"],
mvp_mtx: Float[Tensor, "B 4 4"],
c2w: Float[Tensor, "B 4 4"],
rgb_as_latents=False,
num_frames=4,
**kwargs,
):
rgb_BCHW = rgb.permute(0, 3, 1, 2)
batch_size = rgb_BCHW.shape[0] // num_frames
latents = self.get_latents(rgb_BCHW, rgb_as_latents=rgb_as_latents)
# view-dependent text embeddings
text_embeddings_vd = prompt_utils.get_text_embeddings(
elevation,
azimuth,
camera_distances,
view_dependent_prompting=self.cfg.view_dependent_prompting,
)
# input text embeddings, view-independent
text_embeddings = prompt_utils.get_text_embeddings(
elevation, azimuth, camera_distances, view_dependent_prompting=False
)
if self.cfg.camera_condition_type == "extrinsics":
camera_condition = c2w
elif self.cfg.camera_condition_type == "mvp":
camera_condition = mvp_mtx
else:
raise ValueError(
f"Unknown camera_condition_type {self.cfg.camera_condition_type}"
)
grad = self.compute_grad_vsd(
latents, text_embeddings_vd, text_embeddings, camera_condition
)
grad = torch.nan_to_num(grad)
# reparameterization trick
# d(loss)/d(latents) = latents - target = latents - (latents - grad) = grad
target = (latents - grad).detach()
loss_vsd = 0.5 * F.mse_loss(latents, target, reduction="sum") / batch_size
loss_lora = self.train_lora(latents, text_embeddings, camera_condition)
return {
"loss_vsd": loss_vsd,
"loss_lora": loss_lora,
"grad_norm": grad.norm(),
}
def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):
if (
self.cfg.anneal_start_step is not None
and global_step > self.cfg.anneal_start_step
):
self.max_step = int(
self.num_train_timesteps * self.cfg.max_step_percent_annealed
)
================================================
FILE: threestudio/models/guidance/videocrafter/.gitignore
================================================
.DS_Store
*pyc
.vscode
__pycache__
*.egg-info
checkpoints
results
================================================
FILE: threestudio/models/guidance/videocrafter/License
================================================
This license applies to the source codes that are open sourced in connection with the VideoCrafter1.
Copyright (C) 2023 THL A29 Limited, a Tencent company.
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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================================================
FILE: threestudio/models/guidance/videocrafter/README.md
================================================
## ___***VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models***___
[](https://discord.gg/rrayYqZ4tf)
[](https://github.com/VideoCrafter/VideoCrafter)
### 🔥🔥 Our dedicated high-resolution I2V model is released at: :point_right:[DynamiCrafter](https://github.com/Doubiiu/DynamiCrafter)!!!
[](https://www.youtube.com/watch?v=0NfmIsNAg-g)
### 🔥The VideoCrafter2 Large improvements over VideoCrafter1 with limited data. Better Motion, Better Concept Combination!!!
Please Join us and create your own film on [Discord/Floor33](https://discord.gg/rrayYqZ4tf).
##### 🎥 Exquisite film, produced by VideoCrafter2, directed by Human
[](https://www.youtube.com/watch?v=TUsFkW0tK-s)
## 🔆 Introduction
🤗🤗🤗 VideoCrafter is an open-source video generation and editing toolbox for crafting video content.
It currently includes the Text2Video and Image2Video models:
### 1. Generic Text-to-video Generation
Click the GIF to access the high-resolution video.
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| "Tom Cruise's face reflects focus, his eyes filled with purpose and drive." | "A child excitedly swings on a rusty swing set, laughter filling the air." | "A young woman with glasses is jogging in the park wearing a pink headband." |
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| "With the style of van gogh, A young couple dances under the moonlight by the lake." | "A rabbit, low-poly game art style" | "Impressionist style, a yellow rubber duck floating on the wave on the sunset" |
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| "a black swan swims on the pond" | "a girl is riding a horse fast on grassland" | "a boy sits on a chair facing the sea" | "two galleons moving in the wind at sunset" |