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Repository: zju3dv/Coin3D
Branch: main
Commit: 3de63a14eb21
Files: 126
Total size: 1.9 MB
Directory structure:
gitextract_sa0or0_n/
├── CONDITION.md
├── README.md
├── blender_utils/
│ └── render_proxy.py
├── configs/
│ ├── coin3d_train.yaml
│ ├── ctrldemo.yaml
│ ├── nerf.yaml
│ ├── neus.yaml
│ └── syncdreamer.yaml
├── example/
│ ├── panda/
│ │ ├── mesh.obj
│ │ └── proxy.txt
│ ├── pumpkin/
│ │ ├── mesh.obj
│ │ └── proxy.txt
│ ├── teddybear/
│ │ ├── mesh.obj
│ │ └── proxy.txt
│ ├── toycar/
│ │ ├── mesh.obj
│ │ └── proxy.txt
│ └── turtle/
│ ├── mesh.obj
│ └── proxy.txt
├── externs/
│ ├── __init__.py
│ └── pvcnn/
│ └── modules/
│ ├── __init__.py
│ ├── ball_query.py
│ ├── frustum.py
│ ├── functional/
│ │ ├── __init__.py
│ │ ├── backend.py
│ │ ├── ball_query.py
│ │ ├── devoxelization.py
│ │ ├── grouping.py
│ │ ├── interpolatation.py
│ │ ├── loss.py
│ │ ├── sampling.py
│ │ ├── src/
│ │ │ ├── ball_query/
│ │ │ │ ├── ball_query.cpp
│ │ │ │ ├── ball_query.cu
│ │ │ │ ├── ball_query.cuh
│ │ │ │ └── ball_query.hpp
│ │ │ ├── bindings.cpp
│ │ │ ├── cuda_utils.cuh
│ │ │ ├── grouping/
│ │ │ │ ├── grouping.cpp
│ │ │ │ ├── grouping.cu
│ │ │ │ ├── grouping.cuh
│ │ │ │ └── grouping.hpp
│ │ │ ├── interpolate/
│ │ │ │ ├── neighbor_interpolate.cpp
│ │ │ │ ├── neighbor_interpolate.cu
│ │ │ │ ├── neighbor_interpolate.cuh
│ │ │ │ ├── neighbor_interpolate.hpp
│ │ │ │ ├── trilinear_devox.cpp
│ │ │ │ ├── trilinear_devox.cu
│ │ │ │ ├── trilinear_devox.cuh
│ │ │ │ └── trilinear_devox.hpp
│ │ │ ├── sampling/
│ │ │ │ ├── sampling.cpp
│ │ │ │ ├── sampling.cu
│ │ │ │ ├── sampling.cuh
│ │ │ │ └── sampling.hpp
│ │ │ ├── utils.hpp
│ │ │ └── voxelization/
│ │ │ ├── vox.cpp
│ │ │ ├── vox.cu
│ │ │ ├── vox.cuh
│ │ │ └── vox.hpp
│ │ └── voxelization.py
│ ├── loss.py
│ ├── pointnet.py
│ ├── pvconv.py
│ ├── se.py
│ ├── shared_mlp.py
│ └── voxelization.py
├── foreground_segment.py
├── generate.py
├── ldm/
│ ├── DPMPPScheduler.py
│ ├── base_utils.py
│ ├── data/
│ │ ├── __init__.py
│ │ ├── base.py
│ │ ├── coco.py
│ │ ├── control_sync_dreamer.py
│ │ ├── dummy.py
│ │ ├── imagenet.py
│ │ ├── inpainting/
│ │ │ ├── __init__.py
│ │ │ └── synthetic_mask.py
│ │ ├── laion.py
│ │ ├── lsun.py
│ │ ├── nerf_like.py
│ │ ├── simple.py
│ │ └── sync_dreamer.py
│ ├── lr_scheduler.py
│ ├── models/
│ │ ├── autoencoder.py
│ │ └── diffusion/
│ │ ├── __init__.py
│ │ ├── ctrldemo_sync_dreamer.py
│ │ ├── sync_dreamer.py
│ │ ├── sync_dreamer_attention.py
│ │ ├── sync_dreamer_network.py
│ │ └── sync_dreamer_utils.py
│ ├── modules/
│ │ ├── attention.py
│ │ ├── diffusionmodules/
│ │ │ ├── __init__.py
│ │ │ ├── model.py
│ │ │ ├── openaimodel.py
│ │ │ └── util.py
│ │ ├── distributions/
│ │ │ ├── __init__.py
│ │ │ └── distributions.py
│ │ ├── encoders/
│ │ │ ├── __init__.py
│ │ │ └── modules.py
│ │ └── x_transformer.py
│ ├── thirdp/
│ │ └── psp/
│ │ ├── helpers.py
│ │ ├── id_loss.py
│ │ └── model_irse.py
│ ├── typing.py
│ └── util.py
├── meta_info/
│ └── camera-16.pkl
├── misc.ipynb
├── raymarching/
│ ├── __init__.py
│ ├── backend.py
│ ├── raymarching.py
│ ├── setup.py
│ └── src/
│ ├── bindings.cpp
│ ├── raymarching.cu
│ └── raymarching.h
├── renderer/
│ ├── agg_net.py
│ ├── cost_reg_net.py
│ ├── dummy_dataset.py
│ ├── feature_net.py
│ ├── neus_networks.py
│ ├── ngp_renderer.py
│ └── renderer.py
├── requirements.txt
├── train_diffusion.py
├── train_renderer.py
└── workflow/
├── Coin3D_condition_workflow.json
├── Coin3D_condition_workflow_api.json
└── inference_comfyui_api.py
================================================
FILE CONTENTS
================================================
================================================
FILE: CONDITION.md
================================================
### Prepare condition image for inference
1. Rendering the proxy image
First you need to download [Blender](https://www.blender.org/) and unzip it to any directory. The version we use is [Blender-3.6](https://download.blender.org/release/Blender3.6/blender-3.6.12-linux-x64.tar.xz)
```
path/to/your/blender -b -P blender_utils/render_proxy.py -- --obj_path example/teddybear/mesh.obj
```
The example rendered result can be found in this [image](example/teddybear/condition.png). Optionally, if you use blender to construct a coarse proxy, setting a different base color for each primitive can improve the effect of extracting softedges in ControlNet.
2. Use ComfyUI and Controlnet to construct condition image
**We have prepared a [Workflow](workflow/Coin3D_condition_workflow.json) using Depth-Condition and Softedge-Condition. You can pull the workflow into the ComfyUI interface to use it. Users can also use other controlnet conditions as needed.**
The usage of ComfyUI can be found [here](https://github.com/comfyanonymous/ComfyUI).
To run the above workflow, you need to download the following pretrained models. Here, we use the [Disney](https://civitai.com/models/65203/disney-pixar-cartoon-type-a) style basemodel as an example.
```
mkdir interactive_workflow
cd interactive_workflow
git clone https://github.com/comfyanonymous/ComfyUI.git
cd custom_nodes
git clone https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb.git
git clone https://github.com/Fannovel16/comfyui_controlnet_aux.git
cd ../models/checkpoints/
wget https://huggingface.co/BitStarWalkin/RPG_models/resolve/main/disneyPixarCartoon_v10.safetensors
cd ../controlnet/
wget https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth.pth
wget https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_softedge.pth
```
Make sure ComfyUI has the following custom_nodes and pretrain models
:
```bash
ComfyUI
|-- models
|-- checkpoints
|--disneyPixarCartoon_v10.safetensors
|-- controlnet
|--control_v11f1p_sd15_depth.pth
|--control_v11p_sd15_softedge.pth
|-- custom_nodes
|-- ComfyUI_ADV_CLIP_emb
|-- comfyui_controlnet_aux
```
After loading the workflow, it will be as shown below.
<div align=center>
<img src="assets/example_condition_workflow.png" width="100%"/>
</div>
You need to **load the rendered proxy image** into the Load Image Node, **set Text Prompt** in the CLIP Text Encode (Advance) Node, and finally **adjust the appropriate ControlNet parameters** in the Apply ControlNet Node.
Apply ControlNet Parameters Explanation:
- `strength` The parameter controls the degree of influence that ControlNet has on the generated image. The value usually ranges from 0 to 1. The higher the value, the greater the influence that ControlNet has on the generated image; the lower the value, the less influence. For example, when strength is set to 1, ControlNet completely controls the generation process; when it is set to 0, ControlNet does not participate in the control.
- `start_percent` Specifies the point in the generation process (as a percentage) when ControlNet starts to take effect. For example, a value of 0.3 means ControlNet starts influencing at 30% of the process. **Normally this parameter is set to 0.**
- `end_percent` Specifies the point in the generation process (as a percentage) when ControlNet stops taking effect. For example, a value of 0.7 means ControlNet stops influencing at 70% of the process.
**ComfyUI API**
At the same time, you can also get the condition image through the ComfyUI API. Here is the example code based on the above [workflow](workflow/Coin3D_condition_workflow.json).
First, you need to run ComfyUI
```
python3 main.py --port 6621
```
After that, run
```
python3 inference_comfyui_api.py
```
### Acknowledgement
We deeply appreciate the authors of the following repositories for generously sharing their code, which we have extensively utilized. Their contributions have been invaluable to our work, and we are grateful for their openness and willingness to share their expertise. Our project has greatly benefited from their efforts and dedication.
- [ComfyUI](https://github.com/comfyanonymous/ComfyUI)
================================================
FILE: README.md
================================================
# Coin3D: Controllable and Interactive 3D Assets Generation with Proxy-Guided Conditioning
### [Project Page](https://zju3dv.github.io/coin3d/) | [Video](https://www.youtube.com/watch?v=d6p3LLbmOnI) | [Paper](https://arxiv.org/abs/2405.08054)
<div align=center>
<img src="media/coin3d_highlight.gif" width="100%"/>
</div>
### ToDo List
- [x] Inference code and pretrained models.
- [ ] Interactive workflow.
- [x] Training data.
- [ ] Blender Addons
### Preparation for inference
1. Install packages in `requirements.txt`. We test our model on a A100-80G GPU with 11.8 CUDA and 2.0.1 pytorch.
```angular2html
conda create -n coin3d
conda activate coin3d
pip install -r requirements.txt
```
2. Download checkpoints
```
mkdir ckpt
cd ckpt
wget https://huggingface.co/WenqiDong/Coin3D-v1/resolve/main/ViT-L-14.pt
wget https://huggingface.co/WenqiDong/Coin3D-v1/resolve/main/model.ckpt
```
### Inference
1. Make sure you have the following models.
```bash
Coin3D
|-- ckpt
|-- ViT-L-14.pt
|-- model.ckpt
```
2. We provide a workflow that uses a custom mesh and text prompt to generate the input image. You can refer to [this instruction](CONDITION.md).
3. (Optional) Make sure the input image has a white background. Here we refer to [SyncDreamer](https://github.com/liuyuan-pal/SyncDreamer) and use the following tools for foreground segmentation. Predict foreground mask as the alpha channel. We use [Paint3D](https://apps.microsoft.com/store/detail/paint-3d/9NBLGGH5FV99) to segment the foreground object interactively.
We also provide a script `foreground_segment.py` using `carvekit` to predict foreground masks and you need to first crop the object region before feeding it to `foreground_segment.py`. We may double check the predicted masks are correct or not.
```bash
python3 foreground_segment.py --input <image-file-to-input> --output <image-file-in-png-format-to-output>
```
3. Using coarse proxy to control 3D generation of multi-view images.
```bash
python3 generate.py \
--cfg configs/ctrldemo.yaml \
--ckpt ckpt/model.ckpt \
--input example/panda/input.png \
--input_proxy example/panda/proxy.txt \
--output output/custom \
--sample_num 1 \
--cfg_scale 2.0 \
--elevation 30 \
--ctrl_end_step 1.0 \
--sampler ddim_demo
```
Explanation:
- `--cfg` is the model configuration.
- `--ckpt` is the checkpoint to load.
- `--input` is the input image in the RGBA form. The alpha value means the foreground object mask.
- `--input_proxy` is the input coarse proxy. The proxy contains 256 points by default. [misc.ipynb](misc.ipynb) contains code for using the coarse mesh sampling proxy.
- `--output` is the output directory. Results would be saved to `output/custom/0.png` which contains 16 images of predefined viewpoints per `png` file.
- `--sample_num` is the number of instances we will generate.
- `--cfg_scale` is the *classifier-free-guidance*. `2.0` is OK for most cases.
- `--elevation` is the elevation angle of the input image in degree. Need to be set to 30.
- `--ctrl_end_step` is the timestamp of ending 3D control, from `0` to `1.0`, usually set to `0.6` to `1.0`.
4. Run a NeuS or a NeRF for 3D reconstruction.
```bash
# train a neus
python3 train_renderer.py -i output/custom/0.png \
-n custom-neus \
-b configs/neus.yaml \
-l output/renderer
# train a nerf
python3 train_renderer.py -i output/custom/0.png \
-n custom-nerf \
-b configs/nerf.yaml \
-l output/renderer
```
Explanation:
- `-i` contains the multiview images generated by SyncDreamer. Since SyncDreamer does not always produce good results, we may need to select a good generated image set (from `0.png` to `3.png`) for reconstruction.
- `-n` means the name. `-l` means the log dir. Results will be saved to `<log_dir>/<name>` i.e. `output/renderer/custom-neus` and `output/renderer/custom-nerf`.
### Dataset
We train the model on the [Objaverse LVIS](https://objaverse.allenai.org/docs/objaverse-1.0/) dataset. The preprocessed data can be found [here](https://huggingface.co/datasets/WenqiDong/Coin3D_Objaverse_LVIS). We use the script for rendering multi-view images in [SyncDreamer](https://github.com/liuyuan-pal/SyncDreamer). The script of object extraction proxy can refer to [misc](misc.ipynb).
### Training
Please note that you need to set the data directory location in the [config file](configs/coin3d_train.yaml).
```
target_dir: path/to/renderings-v1 # renderings of target views
input_dir: path/to/renderings-random # renderings of input views
proxy_dir: path/to/proxy_256 # proxys of target objects
```
```bash
python3 train_syncdreamer.py -b configs/coin3d_train.yaml \
--finetune_from ckpt/syncdreamer-pretrain.ckpt \
-l ./logs/coin3d \
-c ./ckpt/coin3d \
--gpus 0
```
## Acknowledgement
We deeply appreciate the authors of the following repositories for generously sharing their code, which we have extensively utilized. Their contributions have been invaluable to our work, and we are grateful for their openness and willingness to share their expertise. Our project has greatly benefited from their efforts and dedication.
- [SyncDreamer](https://github.com/liuyuan-pal/SyncDreamer)
- [latent-diffusion](https://github.com/CompVis/latent-diffusion)
- [threestudio](https://github.com/threestudio-project/threestudio)
- [pvcnn](https://github.com/mit-han-lab/pvcnn)
- [stable diffusion](https://github.com/CompVis/stable-diffusion)
- [zero123](https://github.com/cvlab-columbia/zero123)
- [COLMAP](https://colmap.github.io/)
- [NeuS](https://github.com/Totoro97/NeuS)
## Citation
If you find this repository useful in your project, please cite the following work. :)
```
@article{dong2024coin3d,
title={Coin3D: Controllable and Interactive 3D Assets Generation with Proxy-Guided Conditioning},
author={Dong, Wenqi and Yang, Bangbang and Ma, Lin and Liu, Xiao and Cui, Liyuan and Bao, Hujun and Ma, Yuewen and Cui, Zhaopeng},
year={2024},
eprint={2405.08054},
archivePrefix={arXiv},
primaryClass={cs.GR}
}
```
================================================
FILE: blender_utils/render_proxy.py
================================================
import argparse
import json
import math
import os
import random
import sys
import time
import urllib.request
from pathlib import Path
import bmesh
from mathutils import Vector, Matrix
import numpy as np
from io import BytesIO
import bpy
from mathutils import Vector
import pickle
import json
from urllib import request, parse
import random
import base64
def load_object(object_path: str) -> None:
"""Loads a 3D model into the scene."""
if object_path.endswith(".glb"):
bpy.ops.import_scene.gltf(filepath=object_path, merge_vertices=True)
elif object_path.endswith(".fbx"):
bpy.ops.import_scene.fbx(filepath=object_path)
elif object_path.endswith(".obj"):
bpy.ops.import_scene.obj(filepath=object_path)
else:
raise ValueError(f"Unsupported file type: {object_path}")
def az_el_to_points(azimuths, elevations):
x = np.cos(azimuths)*np.cos(elevations)
y = np.sin(azimuths)*np.cos(elevations)
z = np.sin(elevations)
return np.stack([x,y,z],-1) #
def points_to_az_el_dist(location):
dist = np.linalg.norm(location)
location /= dist
x, y, z = location
ele = np.arcsin(z)
azi = np.arctan2(y, x)
return azi, ele, dist
def set_camera(camera, az, el, dist):
# az, el in degree
distances = dist
azimuths = np.deg2rad(az).astype(np.float32)
elevations = np.deg2rad(el).astype(np.float32)
cam_pts = az_el_to_points(azimuths, elevations) * distances
x, y, z = cam_pts
camera.location = x, y, z
def get_camera(camera_name):
context = bpy.context
scene = context.scene
# 获取或创建Empty对象
empty = scene.objects.get("Empty")
if empty is None:
empty = bpy.data.objects.new("Empty", None)
scene.collection.objects.link(empty)
# 尝试获取相机
camera = scene.objects.get(camera_name)
if camera is None:
# 创建新的相机
camera_data = bpy.data.cameras.new(name=camera_name)
camera = bpy.data.objects.new(camera_name, camera_data)
context.collection.objects.link(camera)
camera.location = (0, 1.2, 0)
camera.data.lens = 35
camera.data.sensor_width = 32
constrs = camera.constraints.get('Track To', None)
if constrs is None:
cam_constraint = camera.constraints.new(type='TRACK_TO')
cam_constraint.track_axis = 'TRACK_NEGATIVE_Z'
cam_constraint.up_axis = 'UP_Y'
# 设置约束的目标
cam_constraint.target = empty
return camera
def init_global(context):
scene = context.scene
render = scene.render
cam = get_camera("Camera")
set_camera(camera=cam, az=0, el=30, dist=1.5)
bpy.context.scene.camera = cam
render.engine = "CYCLES"
render.image_settings.file_format = "PNG"
render.image_settings.color_mode = "RGBA"
render.resolution_x = 256
render.resolution_y = 256
render.resolution_percentage = 100
scene.cycles.device = "GPU"
scene.cycles.samples = 128
scene.cycles.diffuse_bounces = 1
scene.cycles.glossy_bounces = 1
scene.cycles.transparent_max_bounces = 3
scene.cycles.transmission_bounces = 3
scene.cycles.filter_width = 0.01
scene.cycles.use_denoising = True
scene.render.film_transparent = True
bpy.context.preferences.addons["cycles"].preferences.get_devices()
# Set the device_type
bpy.context.preferences.addons["cycles"].preferences.compute_device_type = "NONE" # or "OPENCL"
bpy.context.scene.cycles.tile_size = 8192
world_tree = bpy.context.scene.world.node_tree
back_node = world_tree.nodes['Background']
env_light = 0.5
back_node.inputs['Color'].default_value = Vector([env_light, env_light, env_light, 1.0])
back_node.inputs['Strength'].default_value = 1.0
def reset_scene() -> None:
"""Resets the scene to a clean state."""
# delete everything that isn't part of a camera or a light
for obj in bpy.data.objects:
if obj.type not in {"CAMERA", "LIGHT"}:
bpy.data.objects.remove(obj, do_unlink=True)
# delete all the materials
for material in bpy.data.materials:
bpy.data.materials.remove(material, do_unlink=True)
# delete all the textures
for texture in bpy.data.textures:
bpy.data.textures.remove(texture, do_unlink=True)
# delete all the images
for image in bpy.data.images:
bpy.data.images.remove(image, do_unlink=True)
def scene_root_objects():
for obj in bpy.context.scene.objects.values():
if not obj.parent and isinstance(obj.data, (bpy.types.Mesh, bpy.types.Light)):
yield obj
def scene_meshes():
for obj in bpy.context.scene.objects.values():
if isinstance(obj.data, (bpy.types.Mesh)):
yield obj
def selected_root_objects():
for obj in bpy.context.selected_objects:
if not obj.parent and isinstance(obj.data, (bpy.types.Mesh, bpy.types.Light)):
yield obj
def selected_meshes():
for obj in bpy.context.selected_objects:
if isinstance(obj.data, (bpy.types.Mesh)):
yield obj
def selected_objects_bbox(single_obj=None, ignore_matrix=False):
bbox_min = (math.inf,) * 3
bbox_max = (-math.inf,) * 3
found = False
for obj in scene_meshes() if single_obj is None else [single_obj]:
found = True
for coord in obj.bound_box:
coord = Vector(coord)
if not ignore_matrix:
coord = obj.matrix_world @ coord
bbox_min = tuple(min(x, y) for x, y in zip(bbox_min, coord))
bbox_max = tuple(max(x, y) for x, y in zip(bbox_max, coord))
if not found:
raise RuntimeError("no objects in scene to compute bounding box for")
return Vector(bbox_min), Vector(bbox_max)
def scene_bbox(single_obj=None, ignore_matrix=False):
bbox_min = (math.inf,) * 3
bbox_max = (-math.inf,) * 3
found = False
for obj in scene_meshes() if single_obj is None else [single_obj]:
found = True
for coord in obj.bound_box:
coord = Vector(coord)
if not ignore_matrix:
coord = obj.matrix_world @ coord
bbox_min = tuple(min(x, y) for x, y in zip(bbox_min, coord))
bbox_max = tuple(max(x, y) for x, y in zip(bbox_max, coord))
if not found:
raise RuntimeError("no objects in scene to compute bounding box for")
return Vector(bbox_min), Vector(bbox_max)
def normalize_scene():
bbox_min, bbox_max = scene_bbox()
scale = 1 / max(bbox_max - bbox_min)
for obj in scene_root_objects():
obj.scale = obj.scale * scale
obj.location=obj.location*scale
# Apply scale to matrix_world.
bpy.context.view_layer.update()
bbox_min, bbox_max = scene_bbox()
offset = -(bbox_min + bbox_max) / 2
for obj in scene_root_objects():
obj.matrix_world.translation += offset
bpy.ops.object.select_all(action="DESELECT")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Render mesh minimal scripts"
)
parser.add_argument('--obj_path', type=str)
parser.add_argument(
"--outdir",
default="./render_result",
type=str,
help="Path to save sampled data."
)
argv = sys.argv[sys.argv.index("--") + 1 :]
args = parser.parse_args(argv)
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
reset_scene()
init_global(bpy.context)
load_object(args.obj_path)
normalize_scene()
output_path = os.path.join(args.outdir, "condition.png")
bpy.context.scene.render.filepath = (output_path)
bpy.ops.render.render(write_still=True)
================================================
FILE: configs/coin3d_train.yaml
================================================
model:
base_learning_rate: 5.0e-05
target: ldm.models.diffusion.ctrldemo_sync_dreamer.CtrlDemo
params:
view_num: 16
image_size: 256
cfg_scale: 2.0
output_num: 8
batch_view_num: 4
finetune_unet: false
finetune_projection: false
drop_conditions: false
clip_image_encoder_path: ckpt/ViT-L-14.pt
feature_scale: 1
scheduler_config: # 10000 warmup steps
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 100 ]
cycle_lengths: [ 100000 ]
f_start: [ 0.02 ]
f_max: [ 1.0 ]
f_min: [ 1.0 ]
unet_config:
target: ldm.models.diffusion.sync_dreamer_attention.DepthWiseAttention
params:
volume_dims: [64, 128, 256, 512]
image_size: 32
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
data:
target: ldm.data.control_sync_dreamer.ControlSyncDreamerDataset
params:
target_dir: path/to/renderings-v1 # renderings of target views
input_dir: path/to/renderings-random # renderings of input views
proxy_dir: path/to/proxy_256 # renderings of input views
validation_dir: path/to/renderings-v1 # directory of validation data
uid_set_pkl: path/to/proxy_256/train.pkl # a list of uids
valid_uid_set_pkl: path/to/proxy_256/test.pkl # a list of uids
batch_size: 8 # batch size for a single gpu
num_workers: 8
lightning:
modelcheckpoint:
params:
every_n_train_steps: 1000 # we will save models every 1k steps
callbacks:
{}
trainer:
benchmark: True
val_check_interval: 100000 # we will run validation every 1k steps, the validation will output images to <log_dir>/<images>/val
num_sanity_val_steps: 0
check_val_every_n_epoch: null
# max_epochs: 10000
================================================
FILE: configs/ctrldemo.yaml
================================================
model:
base_learning_rate: 5.0e-05
target: ldm.models.diffusion.ctrldemo_sync_dreamer.CtrlDemo
params:
view_num: 16
image_size: 256
cfg_scale: 2.0
output_num: 8
batch_view_num: 4
finetune_unet: false
finetune_projection: false
drop_conditions: false
clip_image_encoder_path: ckpt/ViT-L-14.pt
scheduler_config: # 10000 warmup steps
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 100 ]
cycle_lengths: [ 100000 ]
f_start: [ 0.02 ]
f_max: [ 1.0 ]
f_min: [ 1.0 ]
unet_config:
target: ldm.models.diffusion.sync_dreamer_attention.DepthWiseAttention
params:
volume_dims: [64, 128, 256, 512]
image_size: 32
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
data: {}
lightning:
trainer: {}
================================================
FILE: configs/nerf.yaml
================================================
model:
base_lr: 1.0e-2
target: renderer.renderer.RendererTrainer
params:
total_steps: 2000
warm_up_steps: 100
train_batch_num: 40960
test_batch_num: 40960
renderer: ngp
cube_bound: 0.6
use_mask: true
lambda_rgb_loss: 0.5
lambda_mask_loss: 10.0
data:
target: renderer.dummy_dataset.DummyDataset
params: {}
callbacks:
save_interval: 5000
trainer:
val_check_interval: 500
max_steps: 2000
================================================
FILE: configs/neus.yaml
================================================
model:
base_lr: 5.0e-4
target: renderer.renderer.RendererTrainer
params:
total_steps: 2000
warm_up_steps: 100
train_batch_num: 3584
train_batch_fg_num: 512
test_batch_num: 4096
use_mask: true
lambda_rgb_loss: 0.5
lambda_mask_loss: 1.0
lambda_eikonal_loss: 0.1
use_warm_up: true
data:
target: renderer.dummy_dataset.DummyDataset
params: {}
callbacks:
save_interval: 500
trainer:
val_check_interval: 500
max_steps: 2000
================================================
FILE: configs/syncdreamer.yaml
================================================
model:
base_learning_rate: 5.0e-05
target: ldm.models.diffusion.sync_dreamer.SyncMultiviewDiffusion
params:
view_num: 16
image_size: 256
cfg_scale: 2.0
output_num: 8
batch_view_num: 4
finetune_unet: false
finetune_projection: false
drop_conditions: false
clip_image_encoder_path: ckpt/ViT-L-14.pt
scheduler_config: # 10000 warmup steps
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 100 ]
cycle_lengths: [ 100000 ]
f_start: [ 0.02 ]
f_max: [ 1.0 ]
f_min: [ 1.0 ]
unet_config:
target: ldm.models.diffusion.sync_dreamer_attention.DepthWiseAttention
params:
volume_dims: [64, 128, 256, 512]
image_size: 32
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
data: {}
lightning:
trainer: {}
================================================
FILE: example/panda/mesh.obj
================================================
# Blender 3.6.1
# www.blender.org
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f 1015/
gitextract_sa0or0_n/
├── CONDITION.md
├── README.md
├── blender_utils/
│ └── render_proxy.py
├── configs/
│ ├── coin3d_train.yaml
│ ├── ctrldemo.yaml
│ ├── nerf.yaml
│ ├── neus.yaml
│ └── syncdreamer.yaml
├── example/
│ ├── panda/
│ │ ├── mesh.obj
│ │ └── proxy.txt
│ ├── pumpkin/
│ │ ├── mesh.obj
│ │ └── proxy.txt
│ ├── teddybear/
│ │ ├── mesh.obj
│ │ └── proxy.txt
│ ├── toycar/
│ │ ├── mesh.obj
│ │ └── proxy.txt
│ └── turtle/
│ ├── mesh.obj
│ └── proxy.txt
├── externs/
│ ├── __init__.py
│ └── pvcnn/
│ └── modules/
│ ├── __init__.py
│ ├── ball_query.py
│ ├── frustum.py
│ ├── functional/
│ │ ├── __init__.py
│ │ ├── backend.py
│ │ ├── ball_query.py
│ │ ├── devoxelization.py
│ │ ├── grouping.py
│ │ ├── interpolatation.py
│ │ ├── loss.py
│ │ ├── sampling.py
│ │ ├── src/
│ │ │ ├── ball_query/
│ │ │ │ ├── ball_query.cpp
│ │ │ │ ├── ball_query.cu
│ │ │ │ ├── ball_query.cuh
│ │ │ │ └── ball_query.hpp
│ │ │ ├── bindings.cpp
│ │ │ ├── cuda_utils.cuh
│ │ │ ├── grouping/
│ │ │ │ ├── grouping.cpp
│ │ │ │ ├── grouping.cu
│ │ │ │ ├── grouping.cuh
│ │ │ │ └── grouping.hpp
│ │ │ ├── interpolate/
│ │ │ │ ├── neighbor_interpolate.cpp
│ │ │ │ ├── neighbor_interpolate.cu
│ │ │ │ ├── neighbor_interpolate.cuh
│ │ │ │ ├── neighbor_interpolate.hpp
│ │ │ │ ├── trilinear_devox.cpp
│ │ │ │ ├── trilinear_devox.cu
│ │ │ │ ├── trilinear_devox.cuh
│ │ │ │ └── trilinear_devox.hpp
│ │ │ ├── sampling/
│ │ │ │ ├── sampling.cpp
│ │ │ │ ├── sampling.cu
│ │ │ │ ├── sampling.cuh
│ │ │ │ └── sampling.hpp
│ │ │ ├── utils.hpp
│ │ │ └── voxelization/
│ │ │ ├── vox.cpp
│ │ │ ├── vox.cu
│ │ │ ├── vox.cuh
│ │ │ └── vox.hpp
│ │ └── voxelization.py
│ ├── loss.py
│ ├── pointnet.py
│ ├── pvconv.py
│ ├── se.py
│ ├── shared_mlp.py
│ └── voxelization.py
├── foreground_segment.py
├── generate.py
├── ldm/
│ ├── DPMPPScheduler.py
│ ├── base_utils.py
│ ├── data/
│ │ ├── __init__.py
│ │ ├── base.py
│ │ ├── coco.py
│ │ ├── control_sync_dreamer.py
│ │ ├── dummy.py
│ │ ├── imagenet.py
│ │ ├── inpainting/
│ │ │ ├── __init__.py
│ │ │ └── synthetic_mask.py
│ │ ├── laion.py
│ │ ├── lsun.py
│ │ ├── nerf_like.py
│ │ ├── simple.py
│ │ └── sync_dreamer.py
│ ├── lr_scheduler.py
│ ├── models/
│ │ ├── autoencoder.py
│ │ └── diffusion/
│ │ ├── __init__.py
│ │ ├── ctrldemo_sync_dreamer.py
│ │ ├── sync_dreamer.py
│ │ ├── sync_dreamer_attention.py
│ │ ├── sync_dreamer_network.py
│ │ └── sync_dreamer_utils.py
│ ├── modules/
│ │ ├── attention.py
│ │ ├── diffusionmodules/
│ │ │ ├── __init__.py
│ │ │ ├── model.py
│ │ │ ├── openaimodel.py
│ │ │ └── util.py
│ │ ├── distributions/
│ │ │ ├── __init__.py
│ │ │ └── distributions.py
│ │ ├── encoders/
│ │ │ ├── __init__.py
│ │ │ └── modules.py
│ │ └── x_transformer.py
│ ├── thirdp/
│ │ └── psp/
│ │ ├── helpers.py
│ │ ├── id_loss.py
│ │ └── model_irse.py
│ ├── typing.py
│ └── util.py
├── meta_info/
│ └── camera-16.pkl
├── misc.ipynb
├── raymarching/
│ ├── __init__.py
│ ├── backend.py
│ ├── raymarching.py
│ ├── setup.py
│ └── src/
│ ├── bindings.cpp
│ ├── raymarching.cu
│ └── raymarching.h
├── renderer/
│ ├── agg_net.py
│ ├── cost_reg_net.py
│ ├── dummy_dataset.py
│ ├── feature_net.py
│ ├── neus_networks.py
│ ├── ngp_renderer.py
│ └── renderer.py
├── requirements.txt
├── train_diffusion.py
├── train_renderer.py
└── workflow/
├── Coin3D_condition_workflow.json
├── Coin3D_condition_workflow_api.json
└── inference_comfyui_api.py
SYMBOL INDEX (1033 symbols across 70 files)
FILE: blender_utils/render_proxy.py
function load_object (line 23) | def load_object(object_path: str) -> None:
function az_el_to_points (line 34) | def az_el_to_points(azimuths, elevations):
function points_to_az_el_dist (line 40) | def points_to_az_el_dist(location):
function set_camera (line 48) | def set_camera(camera, az, el, dist):
function get_camera (line 60) | def get_camera(camera_name):
function init_global (line 90) | def init_global(context):
function reset_scene (line 126) | def reset_scene() -> None:
function scene_root_objects (line 142) | def scene_root_objects():
function scene_meshes (line 147) | def scene_meshes():
function selected_root_objects (line 152) | def selected_root_objects():
function selected_meshes (line 157) | def selected_meshes():
function selected_objects_bbox (line 162) | def selected_objects_bbox(single_obj=None, ignore_matrix=False):
function scene_bbox (line 178) | def scene_bbox(single_obj=None, ignore_matrix=False):
function normalize_scene (line 194) | def normalize_scene():
FILE: externs/pvcnn/modules/ball_query.py
class BallQuery (line 9) | class BallQuery(nn.Module):
method __init__ (line 10) | def __init__(self, radius, num_neighbors, include_coordinates=True):
method forward (line 16) | def forward(self, points_coords, centers_coords, points_features=None):
method extra_repr (line 32) | def extra_repr(self):
FILE: externs/pvcnn/modules/frustum.py
class FrustumPointNetLoss (line 11) | class FrustumPointNetLoss(nn.Module):
method __init__ (line 12) | def __init__(self, num_heading_angle_bins, num_size_templates, size_te...
method forward (line 27) | def forward(self, inputs, targets):
function get_box_corners_3d (line 92) | def get_box_corners_3d(centers, headings, sizes, with_flip=False):
FILE: externs/pvcnn/modules/functional/ball_query.py
function ball_query (line 8) | def ball_query(centers_coords, points_coords, radius, num_neighbors):
FILE: externs/pvcnn/modules/functional/devoxelization.py
class TrilinearDevoxelization (line 8) | class TrilinearDevoxelization(Function):
method forward (line 10) | def forward(ctx, features, coords, resolution, is_training=True):
method backward (line 30) | def backward(ctx, grad_output):
FILE: externs/pvcnn/modules/functional/grouping.py
class Grouping (line 8) | class Grouping(Function):
method forward (line 10) | def forward(ctx, features, indices):
method backward (line 25) | def backward(ctx, grad_output):
FILE: externs/pvcnn/modules/functional/interpolatation.py
class NeighborInterpolation (line 8) | class NeighborInterpolation(Function):
method forward (line 10) | def forward(ctx, points_coords, centers_coords, centers_features):
method backward (line 30) | def backward(ctx, grad_output):
FILE: externs/pvcnn/modules/functional/loss.py
function kl_loss (line 7) | def kl_loss(x, y):
function huber_loss (line 13) | def huber_loss(error, delta):
FILE: externs/pvcnn/modules/functional/sampling.py
class Gather (line 10) | class Gather(Function):
method forward (line 12) | def forward(ctx, features, indices):
method backward (line 28) | def backward(ctx, grad_output):
function furthest_point_sample (line 37) | def furthest_point_sample(coords, num_samples):
function logits_mask (line 51) | def logits_mask(coords, logits, num_points_per_object):
FILE: externs/pvcnn/modules/functional/src/ball_query/ball_query.cpp
function ball_query_forward (line 6) | at::Tensor ball_query_forward(at::Tensor centers_coords,
FILE: externs/pvcnn/modules/functional/src/bindings.cpp
function PYBIND11_MODULE (line 10) | PYBIND11_MODULE(_pvcnn_backend, m) {
FILE: externs/pvcnn/modules/functional/src/grouping/grouping.cpp
function grouping_forward (line 6) | at::Tensor grouping_forward(at::Tensor features, at::Tensor indices) {
function grouping_backward (line 26) | at::Tensor grouping_backward(at::Tensor grad_y, at::Tensor indices,
FILE: externs/pvcnn/modules/functional/src/interpolate/neighbor_interpolate.cpp
function three_nearest_neighbors_interpolate_forward (line 6) | std::vector<at::Tensor>
function three_nearest_neighbors_interpolate_backward (line 42) | at::Tensor three_nearest_neighbors_interpolate_backward(at::Tensor grad_y,
FILE: externs/pvcnn/modules/functional/src/interpolate/trilinear_devox.cpp
function trilinear_devoxelize_forward (line 18) | std::vector<at::Tensor>
function trilinear_devoxelize_backward (line 67) | at::Tensor trilinear_devoxelize_backward(const at::Tensor grad_y,
FILE: externs/pvcnn/modules/functional/src/sampling/sampling.cpp
function gather_features_forward (line 6) | at::Tensor gather_features_forward(at::Tensor features, at::Tensor indic...
function gather_features_backward (line 25) | at::Tensor gather_features_backward(at::Tensor grad_y, at::Tensor indices,
function furthest_point_sampling_forward (line 43) | at::Tensor furthest_point_sampling_forward(at::Tensor coords,
FILE: externs/pvcnn/modules/functional/src/voxelization/vox.cpp
function avg_voxelize_forward (line 17) | std::vector<at::Tensor> avg_voxelize_forward(const at::Tensor features,
function avg_voxelize_backward (line 54) | at::Tensor avg_voxelize_backward(const at::Tensor grad_y,
FILE: externs/pvcnn/modules/functional/voxelization.py
class AvgVoxelization (line 8) | class AvgVoxelization(Function):
method forward (line 10) | def forward(ctx, features, coords, resolution):
method backward (line 27) | def backward(ctx, grad_output):
FILE: externs/pvcnn/modules/loss.py
class KLLoss (line 8) | class KLLoss(nn.Module):
method forward (line 9) | def forward(self, x, y):
FILE: externs/pvcnn/modules/pointnet.py
class PointNetAModule (line 11) | class PointNetAModule(nn.Module):
method __init__ (line 12) | def __init__(self, in_channels, out_channels, include_coordinates=True):
method forward (line 32) | def forward(self, inputs):
method extra_repr (line 45) | def extra_repr(self):
class PointNetSAModule (line 49) | class PointNetSAModule(nn.Module):
method __init__ (line 50) | def __init__(self, num_centers, radius, num_neighbors, in_channels, ou...
method forward (line 80) | def forward(self, inputs):
method extra_repr (line 91) | def extra_repr(self):
class PointNetFPModule (line 95) | class PointNetFPModule(nn.Module):
method __init__ (line 96) | def __init__(self, in_channels, out_channels):
method forward (line 100) | def forward(self, inputs):
FILE: externs/pvcnn/modules/pvconv.py
class PVConv (line 11) | class PVConv(nn.Module):
method __init__ (line 12) | def __init__(self, in_channels, out_channels, kernel_size, resolution,...
method forward (line 33) | def forward(self, inputs):
class ProxyVoxelConv (line 41) | class ProxyVoxelConv(nn.Module):
method __init__ (line 42) | def __init__(self, in_channels, out_channels, kernel_size, resolution,...
method forward (line 51) | def forward(self, inputs):
FILE: externs/pvcnn/modules/se.py
class SE3d (line 6) | class SE3d(nn.Module):
method __init__ (line 7) | def __init__(self, channel, reduction=8):
method forward (line 16) | def forward(self, inputs):
FILE: externs/pvcnn/modules/shared_mlp.py
class SharedMLP (line 6) | class SharedMLP(nn.Module):
method __init__ (line 7) | def __init__(self, in_channels, out_channels, dim=1):
method forward (line 29) | def forward(self, inputs):
FILE: externs/pvcnn/modules/voxelization.py
class Voxelization (line 9) | class Voxelization(nn.Module):
method __init__ (line 10) | def __init__(self, resolution, normalize=True, eps=0):
method forward (line 16) | def forward(self, features, coords):
method extra_repr (line 28) | def extra_repr(self):
FILE: foreground_segment.py
class BackgroundRemoval (line 9) | class BackgroundRemoval:
method __init__ (line 10) | def __init__(self, device='cuda'):
method __call__ (line 26) | def __call__(self, image):
function process (line 33) | def process(image_path, mask_path):
FILE: generate.py
function load_model (line 18) | def load_model(cfg,ckpt,strict=True):
function main (line 27) | def main():
FILE: ldm/DPMPPScheduler.py
class DPMPPSchedulerOutput (line 13) | class DPMPPSchedulerOutput(BaseOutput):
class DPMPPScheduler (line 29) | class DPMPPScheduler(DPMSolverMultistepScheduler):
method step (line 30) | def step(
method reinit (line 105) | def reinit(self):
FILE: ldm/base_utils.py
function save_pickle (line 7) | def save_pickle(data, pkl_path):
function read_pickle (line 12) | def read_pickle(pkl_path):
function draw_epipolar_line (line 16) | def draw_epipolar_line(F, img0, img1, pt0, color):
function draw_epipolar_lines (line 29) | def draw_epipolar_lines(F, img0, img1,num=20):
function compute_F (line 45) | def compute_F(K1, K2, Rt0, Rt1=None):
function compute_dR_dt (line 58) | def compute_dR_dt(Rt0, Rt1):
function concat_images (line 65) | def concat_images(img0,img1,vert=False):
function concat_images_list (line 79) | def concat_images_list(*args,vert=False):
function pose_inverse (line 87) | def pose_inverse(pose):
function project_points (line 92) | def project_points(pts,RT,K):
function draw_keypoints (line 104) | def draw_keypoints(img, kps, colors=None, radius=2):
function output_points (line 116) | def output_points(fn,pts,colors=None):
function mask_depth_to_pts (line 125) | def mask_depth_to_pts(mask,depth,K,rgb=None):
function transform_points_pose (line 135) | def transform_points_pose(pts, pose):
function pose_apply (line 141) | def pose_apply(pose,pts):
function downsample_gaussian_blur (line 144) | def downsample_gaussian_blur(img, ratio):
FILE: ldm/data/base.py
class Txt2ImgIterableBaseDataset (line 7) | class Txt2ImgIterableBaseDataset(IterableDataset):
method __init__ (line 11) | def __init__(self, num_records=0, valid_ids=None, size=256):
method __len__ (line 20) | def __len__(self):
method __iter__ (line 24) | def __iter__(self):
class PRNGMixin (line 28) | class PRNGMixin(object):
method prng (line 35) | def prng(self):
FILE: ldm/data/coco.py
class CocoBase (line 11) | class CocoBase(Dataset):
method __init__ (line 13) | def __init__(self, size=None, dataroot="", datajson="", onehot_segment...
method year (line 95) | def year(self):
method __len__ (line 98) | def __len__(self):
method preprocess_image (line 101) | def preprocess_image(self, image_path, segmentation_path=None):
method __getitem__ (line 145) | def __getitem__(self, i):
class CocoImagesAndCaptionsTrain2017 (line 166) | class CocoImagesAndCaptionsTrain2017(CocoBase):
method __init__ (line 168) | def __init__(self, size, onehot_segmentation=False, use_stuffthing=Fal...
method get_split (line 175) | def get_split(self):
method year (line 178) | def year(self):
class CocoImagesAndCaptionsValidation2017 (line 182) | class CocoImagesAndCaptionsValidation2017(CocoBase):
method __init__ (line 184) | def __init__(self, size, onehot_segmentation=False, use_stuffthing=Fal...
method get_split (line 193) | def get_split(self):
method year (line 196) | def year(self):
class CocoImagesAndCaptionsTrain2014 (line 201) | class CocoImagesAndCaptionsTrain2014(CocoBase):
method __init__ (line 203) | def __init__(self, size, onehot_segmentation=False, use_stuffthing=Fal...
method get_split (line 212) | def get_split(self):
method year (line 215) | def year(self):
class CocoImagesAndCaptionsValidation2014 (line 218) | class CocoImagesAndCaptionsValidation2014(CocoBase):
method __init__ (line 220) | def __init__(self, size, onehot_segmentation=False, use_stuffthing=Fal...
method get_split (line 231) | def get_split(self):
method year (line 234) | def year(self):
FILE: ldm/data/control_sync_dreamer.py
class ControlSyncDreamerTrainData (line 23) | class ControlSyncDreamerTrainData(SyncDreamerTrainData):
method __init__ (line 24) | def __init__(self, target_dir, input_dir, proxy_dir, uid_set_pkl, imag...
method get_data_for_index (line 41) | def get_data_for_index(self, index):
class ControlSyncDreamerEvalData (line 69) | class ControlSyncDreamerEvalData(Dataset):
method __init__ (line 70) | def __init__(self, image_dir, proxy_dir, uid_set_pkl):
method __len__ (line 81) | def __len__(self):
method get_data_for_index (line 84) | def get_data_for_index(self, index):
method __getitem__ (line 92) | def __getitem__(self, index):
class ControlSyncDreamerDataset (line 95) | class ControlSyncDreamerDataset(SyncDreamerDataset):
method __init__ (line 96) | def __init__(self, target_dir, input_dir, validation_dir, proxy_dir, b...
method setup (line 112) | def setup(self, stage):
FILE: ldm/data/dummy.py
class DummyData (line 6) | class DummyData(Dataset):
method __init__ (line 7) | def __init__(self, length, size):
method __len__ (line 11) | def __len__(self):
method __getitem__ (line 14) | def __getitem__(self, i):
class DummyDataWithEmbeddings (line 21) | class DummyDataWithEmbeddings(Dataset):
method __init__ (line 22) | def __init__(self, length, size, emb_size):
method __len__ (line 27) | def __len__(self):
method __getitem__ (line 30) | def __getitem__(self, i):
FILE: ldm/data/imagenet.py
function synset2idx (line 20) | def synset2idx(path_to_yaml="data/index_synset.yaml"):
class ImageNetBase (line 26) | class ImageNetBase(Dataset):
method __init__ (line 27) | def __init__(self, config=None):
method __len__ (line 39) | def __len__(self):
method __getitem__ (line 42) | def __getitem__(self, i):
method _prepare (line 45) | def _prepare(self):
method _filter_relpaths (line 48) | def _filter_relpaths(self, relpaths):
method _prepare_synset_to_human (line 66) | def _prepare_synset_to_human(self):
method _prepare_idx_to_synset (line 74) | def _prepare_idx_to_synset(self):
method _prepare_human_to_integer_label (line 80) | def _prepare_human_to_integer_label(self):
method _load (line 93) | def _load(self):
class ImageNetTrain (line 134) | class ImageNetTrain(ImageNetBase):
method __init__ (line 145) | def __init__(self, process_images=True, data_root=None, **kwargs):
method _prepare (line 150) | def _prepare(self):
class ImageNetValidation (line 197) | class ImageNetValidation(ImageNetBase):
method __init__ (line 211) | def __init__(self, process_images=True, data_root=None, **kwargs):
method _prepare (line 216) | def _prepare(self):
class ImageNetSR (line 272) | class ImageNetSR(Dataset):
method __init__ (line 273) | def __init__(self, size=None,
method __len__ (line 336) | def __len__(self):
method __getitem__ (line 339) | def __getitem__(self, i):
class ImageNetSRTrain (line 375) | class ImageNetSRTrain(ImageNetSR):
method __init__ (line 376) | def __init__(self, **kwargs):
method get_base (line 379) | def get_base(self):
class ImageNetSRValidation (line 386) | class ImageNetSRValidation(ImageNetSR):
method __init__ (line 387) | def __init__(self, **kwargs):
method get_base (line 390) | def get_base(self):
FILE: ldm/data/inpainting/synthetic_mask.py
function gen_segment_mask (line 56) | def gen_segment_mask(mask, start, end, brush_width):
function gen_box_mask (line 66) | def gen_box_mask(mask, masked):
function gen_round_mask (line 72) | def gen_round_mask(mask, masked, radius):
function gen_large_mask (line 85) | def gen_large_mask(prng, img_h, img_w,
FILE: ldm/data/laion.py
class DataWithWings (line 24) | class DataWithWings(torch.utils.data.IterableDataset):
method __init__ (line 25) | def __init__(self, min_size, transform=None, target_transform=None):
method _compute_hash (line 50) | def _compute_hash(url, text):
method _add_tags (line 58) | def _add_tags(self, x):
method _punsafe_to_class (line 64) | def _punsafe_to_class(self, punsafe):
method _filter_predicate (line 67) | def _filter_predicate(self, x):
method __iter__ (line 73) | def __iter__(self):
function dict_collation_fn (line 77) | def dict_collation_fn(samples, combine_tensors=True, combine_scalars=True):
class WebDataModuleFromConfig (line 108) | class WebDataModuleFromConfig(pl.LightningDataModule):
method __init__ (line 109) | def __init__(self, tar_base, batch_size, train=None, validation=None,
method make_loader (line 125) | def make_loader(self, dataset_config, train=True):
method filter_size (line 188) | def filter_size(self, x):
method filter_keys (line 205) | def filter_keys(self, x):
method train_dataloader (line 211) | def train_dataloader(self):
method val_dataloader (line 214) | def val_dataloader(self):
method test_dataloader (line 217) | def test_dataloader(self):
class AddLR (line 224) | class AddLR(object):
method __init__ (line 225) | def __init__(self, factor, output_size, initial_size=None, image_key="...
method pt2np (line 231) | def pt2np(self, x):
method np2pt (line 235) | def np2pt(self, x):
method __call__ (line 239) | def __call__(self, sample):
class AddBW (line 250) | class AddBW(object):
method __init__ (line 251) | def __init__(self, image_key="jpg"):
method pt2np (line 254) | def pt2np(self, x):
method np2pt (line 258) | def np2pt(self, x):
method __call__ (line 262) | def __call__(self, sample):
class AddMask (line 273) | class AddMask(PRNGMixin):
method __init__ (line 274) | def __init__(self, mode="512train", p_drop=0.):
method __call__ (line 280) | def __call__(self, sample):
class AddEdge (line 294) | class AddEdge(PRNGMixin):
method __init__ (line 295) | def __init__(self, mode="512train", mask_edges=True):
method __call__ (line 304) | def __call__(self, sample):
function example00 (line 365) | def example00():
function example01 (line 397) | def example01():
function example02 (line 438) | def example02():
function example03 (line 457) | def example03():
function example04 (line 505) | def example04():
FILE: ldm/data/lsun.py
class LSUNBase (line 9) | class LSUNBase(Dataset):
method __init__ (line 10) | def __init__(self,
method __len__ (line 36) | def __len__(self):
method __getitem__ (line 39) | def __getitem__(self, i):
class LSUNChurchesTrain (line 62) | class LSUNChurchesTrain(LSUNBase):
method __init__ (line 63) | def __init__(self, **kwargs):
class LSUNChurchesValidation (line 67) | class LSUNChurchesValidation(LSUNBase):
method __init__ (line 68) | def __init__(self, flip_p=0., **kwargs):
class LSUNBedroomsTrain (line 73) | class LSUNBedroomsTrain(LSUNBase):
method __init__ (line 74) | def __init__(self, **kwargs):
class LSUNBedroomsValidation (line 78) | class LSUNBedroomsValidation(LSUNBase):
method __init__ (line 79) | def __init__(self, flip_p=0.0, **kwargs):
class LSUNCatsTrain (line 84) | class LSUNCatsTrain(LSUNBase):
method __init__ (line 85) | def __init__(self, **kwargs):
class LSUNCatsValidation (line 89) | class LSUNCatsValidation(LSUNBase):
method __init__ (line 90) | def __init__(self, flip_p=0., **kwargs):
FILE: ldm/data/nerf_like.py
function cartesian_to_spherical (line 11) | def cartesian_to_spherical(xyz):
function get_T (line 21) | def get_T(T_target, T_cond):
function get_spherical (line 32) | def get_spherical(T_target, T_cond):
class RTMV (line 43) | class RTMV(Dataset):
method __init__ (line 44) | def __init__(self, root_dir='datasets/RTMV/google_scanned',\
method __len__ (line 52) | def __len__(self):
method __getitem__ (line 55) | def __getitem__(self, idx):
method blend_rgba (line 79) | def blend_rgba(self, img):
class GSO (line 84) | class GSO(Dataset):
method __init__ (line 85) | def __init__(self, root_dir='datasets/GoogleScannedObjects',\
method __len__ (line 95) | def __len__(self):
method __getitem__ (line 98) | def __getitem__(self, idx):
method blend_rgba (line 123) | def blend_rgba(self, img):
class WILD (line 127) | class WILD(Dataset):
method __init__ (line 128) | def __init__(self, root_dir='data/nerf_wild',\
method __len__ (line 136) | def __len__(self):
method __getitem__ (line 139) | def __getitem__(self, idx):
method blend_rgba (line 163) | def blend_rgba(self, img):
FILE: ldm/data/simple.py
function make_transform_multi_folder_data (line 29) | def make_transform_multi_folder_data(paths, caption_files=None, **kwargs):
function make_nfp_data (line 33) | def make_nfp_data(base_path):
class VideoDataset (line 42) | class VideoDataset(Dataset):
method __init__ (line 43) | def __init__(self, root_dir, image_transforms, caption_file, offset=8,...
method __len__ (line 62) | def __len__(self):
method __getitem__ (line 65) | def __getitem__(self, index):
method _load_sample (line 73) | def _load_sample(self, index):
function make_tranforms (line 103) | def make_tranforms(image_transforms):
function make_multi_folder_data (line 113) | def make_multi_folder_data(paths, caption_files=None, **kwargs):
class NfpDataset (line 136) | class NfpDataset(Dataset):
method __init__ (line 137) | def __init__(self,
method __len__ (line 151) | def __len__(self):
method __getitem__ (line 155) | def __getitem__(self, index):
method _load_im (line 164) | def _load_im(self, filename):
class ObjaverseDataModuleFromConfig (line 168) | class ObjaverseDataModuleFromConfig(pl.LightningDataModule):
method __init__ (line 169) | def __init__(self, root_dir, batch_size, total_view, train=None, valid...
method train_dataloader (line 191) | def train_dataloader(self):
method val_dataloader (line 197) | def val_dataloader(self):
method test_dataloader (line 203) | def test_dataloader(self):
class ObjaverseData (line 208) | class ObjaverseData(Dataset):
method __init__ (line 209) | def __init__(self,
method __len__ (line 245) | def __len__(self):
method cartesian_to_spherical (line 248) | def cartesian_to_spherical(self, xyz):
method get_T (line 257) | def get_T(self, target_RT, cond_RT):
method load_im (line 274) | def load_im(self, path, color):
method __getitem__ (line 287) | def __getitem__(self, index):
method process_im (line 328) | def process_im(self, im):
class FolderData (line 332) | class FolderData(Dataset):
method __init__ (line 333) | def __init__(self,
method __len__ (line 376) | def __len__(self):
method __getitem__ (line 382) | def __getitem__(self, index):
method process_im (line 410) | def process_im(self, im):
class TransformDataset (line 415) | class TransformDataset():
method __init__ (line 416) | def __init__(self, ds, extra_label="sksbspic"):
method __getitem__ (line 426) | def __getitem__(self, index):
method __len__ (line 444) | def __len__(self):
function hf_dataset (line 447) | def hf_dataset(
class TextOnly (line 473) | class TextOnly(Dataset):
method __init__ (line 474) | def __init__(self, captions, output_size, image_key="image", caption_k...
method __len__ (line 490) | def __len__(self):
method __getitem__ (line 493) | def __getitem__(self, index):
method _load_caption_file (line 498) | def _load_caption_file(self, filename):
class IdRetreivalDataset (line 507) | class IdRetreivalDataset(FolderData):
method __init__ (line 508) | def __init__(self, ret_file, *args, **kwargs):
method __getitem__ (line 513) | def __getitem__(self, index):
FILE: ldm/data/sync_dreamer.py
class SyncDreamerTrainData (line 21) | class SyncDreamerTrainData(Dataset):
method __init__ (line 22) | def __init__(self, target_dir, input_dir, uid_set_pkl, image_size=256):
method __len__ (line 36) | def __len__(self):
method load_im (line 39) | def load_im(self, path):
method process_im (line 47) | def process_im(self, im):
method load_index (line 52) | def load_index(self, filename, index):
method get_data_for_index (line 57) | def get_data_for_index(self, index):
method __getitem__ (line 76) | def __getitem__(self, index):
class SyncDreamerEvalData (line 80) | class SyncDreamerEvalData(Dataset):
method __init__ (line 81) | def __init__(self, image_dir):
method __len__ (line 92) | def __len__(self):
method get_data_for_index (line 95) | def get_data_for_index(self, index):
method __getitem__ (line 100) | def __getitem__(self, index):
class SyncDreamerDataset (line 103) | class SyncDreamerDataset(pl.LightningDataModule):
method __init__ (line 104) | def __init__(self, target_dir, input_dir, validation_dir, batch_size, ...
method setup (line 116) | def setup(self, stage):
method train_dataloader (line 123) | def train_dataloader(self):
method val_dataloader (line 127) | def val_dataloader(self):
method test_dataloader (line 131) | def test_dataloader(self):
FILE: ldm/lr_scheduler.py
class LambdaWarmUpCosineScheduler (line 4) | class LambdaWarmUpCosineScheduler:
method __init__ (line 8) | def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_...
method schedule (line 17) | def schedule(self, n, **kwargs):
method __call__ (line 32) | def __call__(self, n, **kwargs):
class LambdaWarmUpCosineScheduler2 (line 36) | class LambdaWarmUpCosineScheduler2:
method __init__ (line 41) | def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths...
method find_in_interval (line 52) | def find_in_interval(self, n):
method schedule (line 59) | def schedule(self, n, **kwargs):
method __call__ (line 77) | def __call__(self, n, **kwargs):
class LambdaLinearScheduler (line 81) | class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):
method schedule (line 83) | def schedule(self, n, **kwargs):
FILE: ldm/models/autoencoder.py
class VQModel (line 14) | class VQModel(pl.LightningModule):
method __init__ (line 15) | def __init__(self,
method ema_scope (line 64) | def ema_scope(self, context=None):
method init_from_ckpt (line 78) | def init_from_ckpt(self, path, ignore_keys=list()):
method on_train_batch_end (line 92) | def on_train_batch_end(self, *args, **kwargs):
method encode (line 96) | def encode(self, x):
method encode_to_prequant (line 102) | def encode_to_prequant(self, x):
method decode (line 107) | def decode(self, quant):
method decode_code (line 112) | def decode_code(self, code_b):
method forward (line 117) | def forward(self, input, return_pred_indices=False):
method get_input (line 124) | def get_input(self, batch, k):
method training_step (line 142) | def training_step(self, batch, batch_idx, optimizer_idx):
method validation_step (line 164) | def validation_step(self, batch, batch_idx):
method _validation_step (line 170) | def _validation_step(self, batch, batch_idx, suffix=""):
method configure_optimizers (line 197) | def configure_optimizers(self):
method get_last_layer (line 230) | def get_last_layer(self):
method log_images (line 233) | def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
method to_rgb (line 255) | def to_rgb(self, x):
class VQModelInterface (line 264) | class VQModelInterface(VQModel):
method __init__ (line 265) | def __init__(self, embed_dim, *args, **kwargs):
method encode (line 269) | def encode(self, x):
method decode (line 274) | def decode(self, h, force_not_quantize=False):
class AutoencoderKL (line 285) | class AutoencoderKL(pl.LightningModule):
method __init__ (line 286) | def __init__(self,
method init_from_ckpt (line 313) | def init_from_ckpt(self, path, ignore_keys=list()):
method encode (line 324) | def encode(self, x):
method decode (line 330) | def decode(self, z):
method forward (line 335) | def forward(self, input, sample_posterior=True):
method get_input (line 344) | def get_input(self, batch, k):
method training_step (line 351) | def training_step(self, batch, batch_idx, optimizer_idx):
method validation_step (line 372) | def validation_step(self, batch, batch_idx):
method configure_optimizers (line 386) | def configure_optimizers(self):
method get_last_layer (line 397) | def get_last_layer(self):
method log_images (line 401) | def log_images(self, batch, only_inputs=False, **kwargs):
method to_rgb (line 417) | def to_rgb(self, x):
class IdentityFirstStage (line 426) | class IdentityFirstStage(torch.nn.Module):
method __init__ (line 427) | def __init__(self, *args, vq_interface=False, **kwargs):
method encode (line 431) | def encode(self, x, *args, **kwargs):
method decode (line 434) | def decode(self, x, *args, **kwargs):
method quantize (line 437) | def quantize(self, x, *args, **kwargs):
method forward (line 442) | def forward(self, x, *args, **kwargs):
FILE: ldm/models/diffusion/ctrldemo_sync_dreamer.py
class ControlSpatialVolumeNet (line 24) | class ControlSpatialVolumeNet(SpatialVolumeNet):
method __init__ (line 25) | def __init__(self, time_dim, view_dim, view_num,
method construct_spatial_volume (line 41) | def construct_spatial_volume(self, x, t_embed, v_embed, target_poses, ...
class CtrlDemo (line 100) | class CtrlDemo(SyncMultiviewDiffusion):
method __init__ (line 101) | def __init__(self, unet_config, scheduler_config,
method _init_sampler (line 136) | def _init_sampler(self, latent_size, sample_steps):
method prepare (line 139) | def prepare(self, batch):
method inference (line 145) | def inference(self, sampler, batch, cfg_scale, batch_view_num, return_...
method decode_latents (line 152) | def decode_latents(self, x_sample):
method get_target_view_feats (line 156) | def get_target_view_feats(self, x_input, spatial_volume, clip_embed, t...
method training_step (line 182) | def training_step(self, batch):
method configure_optimizers (line 213) | def configure_optimizers(self):
class CtrlDemoSampler (line 226) | class CtrlDemoSampler:
method __init__ (line 227) | def __init__(self, model: CtrlDemo, scheduler_steps, scheduler_name='d...
method parameterization (line 243) | def parameterization(self):
method from_pkl (line 254) | def from_pkl(cls, model, pkl_dir):
method set_ctrl3D_params (line 258) | def set_ctrl3D_params(self, ctrl3D_params_list, strength: float=1.0):
method concat_proxy (line 262) | def concat_proxy(self, proxys, inferenc_step):
method denoise_apply_impl (line 276) | def denoise_apply_impl(self, x_target_noisy, time_steps, noise_pred, i...
method denoise_apply (line 285) | def denoise_apply(self, x_target_noisy, input_info, v_embed, clip_embe...
method inference (line 328) | def inference(self, input_info, clip_embed, unconditional_scale=1.0, l...
method get_clip_feature (line 372) | def get_clip_feature(self, x_input, clip_embed, v_embed, target_index):
FILE: ldm/models/diffusion/sync_dreamer.py
function disabled_train (line 20) | def disabled_train(self, mode=True):
function disable_training_module (line 25) | def disable_training_module(module: nn.Module):
function repeat_to_batch (line 32) | def repeat_to_batch(tensor, B, VN):
class UNetWrapper (line 38) | class UNetWrapper(nn.Module):
method __init__ (line 39) | def __init__(self, diff_model_config, drop_conditions=False, drop_sche...
method drop (line 47) | def drop(self, cond, mask):
method get_trainable_parameters (line 53) | def get_trainable_parameters(self):
method get_drop_scheme (line 56) | def get_drop_scheme(self, B, device):
method forward (line 67) | def forward(self, x, t, clip_embed, volume_feats, x_concat, is_train=F...
method predict_with_unconditional_scale (line 104) | def predict_with_unconditional_scale(self, x, t, clip_embed, volume_fe...
method predict_for_threestudio (line 123) | def predict_for_threestudio(self, x, t, clip_embed, volume_feats, x_co...
method predict_with_unconditional_scale_mv (line 138) | def predict_with_unconditional_scale_mv(self, x, t, clip_embed, volume...
class SpatialVolumeNet (line 146) | class SpatialVolumeNet(nn.Module):
method __init__ (line 147) | def __init__(self, time_dim, view_dim, view_num,
method construct_spatial_volume (line 168) | def construct_spatial_volume(self, x, t_embed, v_embed, target_poses, ...
method construct_view_frustum_volume (line 214) | def construct_view_frustum_volume(self, spatial_volume, t_embed, v_emb...
class SyncMultiviewDiffusion (line 247) | class SyncMultiviewDiffusion(pl.LightningModule):
method __init__ (line 248) | def __init__(self, unet_config, scheduler_config,
method _init_clip_projection (line 287) | def _init_clip_projection(self):
method _init_multiview (line 296) | def _init_multiview(self):
method get_viewpoint_embedding (line 309) | def get_viewpoint_embedding(self, batch_size, elevation_ref):
method _init_first_stage (line 329) | def _init_first_stage(self):
method _init_clip_image_encoder (line 354) | def _init_clip_image_encoder(self):
method _init_schedule (line 358) | def _init_schedule(self):
method _init_time_step_embedding (line 382) | def _init_time_step_embedding(self):
method encode_first_stage (line 390) | def encode_first_stage(self, x, sample=True):
method decode_first_stage (line 398) | def decode_first_stage(self, z):
method prepare (line 403) | def prepare(self, batch):
method embed_time (line 421) | def embed_time(self, t):
method get_target_view_feats (line 426) | def get_target_view_feats(self, x_input, spatial_volume, clip_embed, t...
method training_step (line 451) | def training_step(self, batch):
method add_noise (line 482) | def add_noise(self, x_start, t):
method sample (line 498) | def sample(self, sampler, batch, cfg_scale, batch_view_num, return_int...
method decode_latents (line 520) | def decode_latents(self, x_sample):
method inference (line 524) | def inference(self, sampler, batch, cfg_scale, batch_view_num, return_...
method log_image (line 530) | def log_image(self, x_sample, batch, step, output_dir):
method validation_step (line 543) | def validation_step(self, batch, batch_idx):
method configure_optimizers (line 554) | def configure_optimizers(self):
class SyncDDIMSampler (line 575) | class SyncDDIMSampler:
method __init__ (line 576) | def __init__(self, model: SyncMultiviewDiffusion, ddim_num_steps, ddim...
method _make_schedule (line 584) | def _make_schedule(self, ddim_num_steps, ddim_discretize="uniform", d...
method denoise_apply_impl (line 602) | def denoise_apply_impl(self, x_target_noisy, index, noise_pred, is_ste...
method denoise_apply (line 628) | def denoise_apply(self, x_target_noisy, input_info, clip_embed, time_s...
method sample (line 669) | def sample(self, input_info, clip_embed, unconditional_scale=1.0, log_...
FILE: ldm/models/diffusion/sync_dreamer_attention.py
class DepthAttention (line 8) | class DepthAttention(nn.Module):
method __init__ (line 9) | def __init__(self, query_dim, context_dim, heads, dim_head, output_bia...
method forward (line 26) | def forward(self, x, context):
class DepthTransformer (line 50) | class DepthTransformer(nn.Module):
method __init__ (line 51) | def __init__(self, dim, n_heads, d_head, context_dim=None, checkpoint=...
method forward (line 75) | def forward(self, x, context=None):
method _forward (line 78) | def _forward(self, x, context):
class DepthWiseAttention (line 87) | class DepthWiseAttention(UNetModel):
method __init__ (line 88) | def __init__(self, volume_dims=(5,16,32,64), *args, **kwargs):
method forward (line 117) | def forward(self, x, timesteps=None, context=None, source_dict=None, *...
method get_trainable_parameters (line 140) | def get_trainable_parameters(self):
FILE: ldm/models/diffusion/sync_dreamer_network.py
class Image2DResBlockWithTV (line 6) | class Image2DResBlockWithTV(nn.Module):
method __init__ (line 7) | def __init__(self, dim, tdim, vdim):
method forward (line 21) | def forward(self, x, t, v):
class NoisyTargetViewEncoder (line 25) | class NoisyTargetViewEncoder(nn.Module):
method __init__ (line 26) | def __init__(self, time_embed_dim, viewpoint_dim, run_dim=16, output_d...
method forward (line 39) | def forward(self, x, t, v):
class SpatialUpTimeBlock (line 52) | class SpatialUpTimeBlock(nn.Module):
method __init__ (line 53) | def __init__(self, x_in_dim, t_in_dim, out_dim):
method forward (line 61) | def forward(self, x, t):
class SpatialTimeBlock (line 65) | class SpatialTimeBlock(nn.Module):
method __init__ (line 66) | def __init__(self, x_in_dim, t_in_dim, out_dim, stride):
method forward (line 74) | def forward(self, x, t):
class SpatialTime3DNet (line 78) | class SpatialTime3DNet(nn.Module):
method __init__ (line 79) | def __init__(self, time_dim=256, input_dim=128, dims=(32, 64, 128, 256)):
method forward (line 103) | def forward(self, x, t, control=None):
class ControlSpatialTime3DNet (line 135) | class ControlSpatialTime3DNet(nn.Module):
method __init__ (line 136) | def __init__(self, time_dim=256, input_dim=128, proxy_input_dim=3, dim...
method forward (line 169) | def forward(self, x, t, proxy_feature):
method make_zero_conv (line 198) | def make_zero_conv(self, dims, channels):
class FrustumTVBlock (line 201) | class FrustumTVBlock(nn.Module):
method __init__ (line 202) | def __init__(self, x_dim, t_dim, v_dim, out_dim, stride):
method forward (line 211) | def forward(self, x, t, v):
class FrustumTVUpBlock (line 215) | class FrustumTVUpBlock(nn.Module):
method __init__ (line 216) | def __init__(self, x_dim, t_dim, v_dim, out_dim):
method forward (line 225) | def forward(self, x, t, v):
class FrustumTV3DNet (line 229) | class FrustumTV3DNet(nn.Module):
method __init__ (line 230) | def __init__(self, in_dim, t_dim, v_dim, dims=(32, 64, 128, 256)):
method forward (line 247) | def forward(self, x, t, v):
FILE: ldm/models/diffusion/sync_dreamer_utils.py
function project_and_normalize (line 5) | def project_and_normalize(ref_grid, src_proj, length):
function project_ (line 22) | def project_(ref_grid, src_proj):
function construct_project_matrix (line 37) | def construct_project_matrix(x_ratio, y_ratio, Ks, poses):
function get_warp_coordinates (line 54) | def get_warp_coordinates(volume_xyz, warp_size, input_size, Ks, warp_pose):
function get_proxy_warp_coordinates (line 61) | def get_proxy_warp_coordinates(proxy_xyz, warp_size, input_size, Ks, war...
function create_target_volume (line 68) | def create_target_volume(depth_size, volume_size, input_image_size, pose...
function near_far_from_unit_sphere_using_camera_poses (line 106) | def near_far_from_unit_sphere_using_camera_poses(camera_poses):
FILE: ldm/modules/attention.py
function exists (line 12) | def exists(val):
function uniq (line 16) | def uniq(arr):
function default (line 20) | def default(val, d):
function max_neg_value (line 26) | def max_neg_value(t):
function init_ (line 30) | def init_(tensor):
class GEGLU (line 38) | class GEGLU(nn.Module):
method __init__ (line 39) | def __init__(self, dim_in, dim_out):
method forward (line 43) | def forward(self, x):
class ConvGEGLU (line 47) | class ConvGEGLU(nn.Module):
method __init__ (line 48) | def __init__(self, dim_in, dim_out):
method forward (line 52) | def forward(self, x):
class FeedForward (line 57) | class FeedForward(nn.Module):
method __init__ (line 58) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
method forward (line 73) | def forward(self, x):
function zero_module (line 77) | def zero_module(module):
function Normalize (line 86) | def Normalize(in_channels):
class LinearAttention (line 90) | class LinearAttention(nn.Module):
method __init__ (line 91) | def __init__(self, dim, heads=4, dim_head=32):
method forward (line 98) | def forward(self, x):
class SpatialSelfAttention (line 109) | class SpatialSelfAttention(nn.Module):
method __init__ (line 110) | def __init__(self, in_channels):
method forward (line 136) | def forward(self, x):
class CrossAttention (line 162) | class CrossAttention(nn.Module):
method __init__ (line 163) | def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, ...
method forward (line 180) | def forward(self, x, context=None, mask=None):
class BasicSpatialTransformer (line 210) | class BasicSpatialTransformer(nn.Module):
method __init__ (line 211) | def __init__(self, dim, n_heads, d_head, context_dim=None, checkpoint=...
method forward (line 233) | def forward(self, x, context=None):
method _forward (line 236) | def _forward(self, x, context):
class BasicTransformerBlock (line 253) | class BasicTransformerBlock(nn.Module):
method __init__ (line 254) | def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None,...
method forward (line 267) | def forward(self, x, context=None):
method _forward (line 270) | def _forward(self, x, context=None):
class ConvFeedForward (line 276) | class ConvFeedForward(nn.Module):
method __init__ (line 277) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
method forward (line 292) | def forward(self, x):
class SpatialTransformer (line 296) | class SpatialTransformer(nn.Module):
method __init__ (line 304) | def __init__(self, in_channels, n_heads, d_head,
method forward (line 330) | def forward(self, x, context=None):
FILE: ldm/modules/diffusionmodules/model.py
function get_timestep_embedding (line 12) | def get_timestep_embedding(timesteps, embedding_dim):
function nonlinearity (line 33) | def nonlinearity(x):
function Normalize (line 38) | def Normalize(in_channels, num_groups=32):
class Upsample (line 42) | class Upsample(nn.Module):
method __init__ (line 43) | def __init__(self, in_channels, with_conv):
method forward (line 53) | def forward(self, x):
class Downsample (line 60) | class Downsample(nn.Module):
method __init__ (line 61) | def __init__(self, in_channels, with_conv):
method forward (line 72) | def forward(self, x):
class ResnetBlock (line 82) | class ResnetBlock(nn.Module):
method __init__ (line 83) | def __init__(self, *, in_channels, out_channels=None, conv_shortcut=Fa...
method forward (line 121) | def forward(self, x, temb):
class LinAttnBlock (line 144) | class LinAttnBlock(LinearAttention):
method __init__ (line 146) | def __init__(self, in_channels):
class AttnBlock (line 150) | class AttnBlock(nn.Module):
method __init__ (line 151) | def __init__(self, in_channels):
method forward (line 178) | def forward(self, x):
function make_attn (line 205) | def make_attn(in_channels, attn_type="vanilla"):
class Model (line 216) | class Model(nn.Module):
method __init__ (line 217) | def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
method forward (line 316) | def forward(self, x, t=None, context=None):
method get_last_layer (line 364) | def get_last_layer(self):
class Encoder (line 368) | class Encoder(nn.Module):
method __init__ (line 369) | def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
method forward (line 434) | def forward(self, x):
class Decoder (line 462) | class Decoder(nn.Module):
method __init__ (line 463) | def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
method forward (line 535) | def forward(self, z):
class SimpleDecoder (line 571) | class SimpleDecoder(nn.Module):
method __init__ (line 572) | def __init__(self, in_channels, out_channels, *args, **kwargs):
method forward (line 594) | def forward(self, x):
class UpsampleDecoder (line 607) | class UpsampleDecoder(nn.Module):
method __init__ (line 608) | def __init__(self, in_channels, out_channels, ch, num_res_blocks, reso...
method forward (line 641) | def forward(self, x):
class LatentRescaler (line 655) | class LatentRescaler(nn.Module):
method __init__ (line 656) | def __init__(self, factor, in_channels, mid_channels, out_channels, de...
method forward (line 680) | def forward(self, x):
class MergedRescaleEncoder (line 692) | class MergedRescaleEncoder(nn.Module):
method __init__ (line 693) | def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
method forward (line 705) | def forward(self, x):
class MergedRescaleDecoder (line 711) | class MergedRescaleDecoder(nn.Module):
method __init__ (line 712) | def __init__(self, z_channels, out_ch, resolution, num_res_blocks, att...
method forward (line 722) | def forward(self, x):
class Upsampler (line 728) | class Upsampler(nn.Module):
method __init__ (line 729) | def __init__(self, in_size, out_size, in_channels, out_channels, ch_mu...
method forward (line 741) | def forward(self, x):
class Resize (line 747) | class Resize(nn.Module):
method __init__ (line 748) | def __init__(self, in_channels=None, learned=False, mode="bilinear"):
method forward (line 763) | def forward(self, x, scale_factor=1.0):
class FirstStagePostProcessor (line 770) | class FirstStagePostProcessor(nn.Module):
method __init__ (line 772) | def __init__(self, ch_mult:list, in_channels,
method instantiate_pretrained (line 807) | def instantiate_pretrained(self, config):
method encode_with_pretrained (line 816) | def encode_with_pretrained(self,x):
method forward (line 822) | def forward(self,x):
FILE: ldm/modules/diffusionmodules/openaimodel.py
function convert_module_to_f16 (line 25) | def convert_module_to_f16(x):
function convert_module_to_f32 (line 28) | def convert_module_to_f32(x):
class AttentionPool2d (line 33) | class AttentionPool2d(nn.Module):
method __init__ (line 38) | def __init__(
method forward (line 52) | def forward(self, x):
class TimestepBlock (line 63) | class TimestepBlock(nn.Module):
method forward (line 69) | def forward(self, x, emb):
class TimestepEmbedSequential (line 75) | class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
method forward (line 81) | def forward(self, x, emb, context=None):
class Upsample (line 92) | class Upsample(nn.Module):
method __init__ (line 101) | def __init__(self, channels, use_conv, dims=2, out_channels=None, padd...
method forward (line 110) | def forward(self, x):
class TransposedUpsample (line 122) | class TransposedUpsample(nn.Module):
method __init__ (line 124) | def __init__(self, channels, out_channels=None, ks=5):
method forward (line 131) | def forward(self,x):
class Downsample (line 135) | class Downsample(nn.Module):
method __init__ (line 144) | def __init__(self, channels, use_conv, dims=2, out_channels=None,paddi...
method forward (line 159) | def forward(self, x):
class ResBlock (line 164) | class ResBlock(TimestepBlock):
method __init__ (line 180) | def __init__(
method forward (line 244) | def forward(self, x, emb):
method _forward (line 256) | def _forward(self, x, emb):
class AttentionBlock (line 279) | class AttentionBlock(nn.Module):
method __init__ (line 286) | def __init__(
method forward (line 315) | def forward(self, x):
method _forward (line 319) | def _forward(self, x):
function count_flops_attn (line 328) | def count_flops_attn(model, _x, y):
class QKVAttentionLegacy (line 348) | class QKVAttentionLegacy(nn.Module):
method __init__ (line 353) | def __init__(self, n_heads):
method forward (line 357) | def forward(self, qkv):
method count_flops (line 376) | def count_flops(model, _x, y):
class QKVAttention (line 380) | class QKVAttention(nn.Module):
method __init__ (line 385) | def __init__(self, n_heads):
method forward (line 389) | def forward(self, qkv):
method count_flops (line 410) | def count_flops(model, _x, y):
class UNetModel (line 414) | class UNetModel(nn.Module):
method __init__ (line 444) | def __init__(
method convert_to_fp16 (line 729) | def convert_to_fp16(self):
method convert_to_fp32 (line 737) | def convert_to_fp32(self):
method forward (line 745) | def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
class EncoderUNetModel (line 780) | class EncoderUNetModel(nn.Module):
method __init__ (line 786) | def __init__(
method convert_to_fp16 (line 959) | def convert_to_fp16(self):
method convert_to_fp32 (line 966) | def convert_to_fp32(self):
method forward (line 973) | def forward(self, x, timesteps):
FILE: ldm/modules/diffusionmodules/util.py
function make_beta_schedule (line 21) | def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_e...
function make_ddim_timesteps (line 46) | def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_...
function make_ddim_sampling_parameters (line 63) | def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbos...
function betas_for_alpha_bar (line 77) | def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.9...
function extract_into_tensor (line 96) | def extract_into_tensor(a, t, x_shape):
function checkpoint (line 102) | def checkpoint(func, inputs, params, flag):
class CheckpointFunction (line 119) | class CheckpointFunction(torch.autograd.Function):
method forward (line 121) | def forward(ctx, run_function, length, *args):
method backward (line 131) | def backward(ctx, *output_grads):
function timestep_embedding (line 151) | def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=Fal...
function zero_module (line 174) | def zero_module(module):
function scale_module (line 183) | def scale_module(module, scale):
function mean_flat (line 192) | def mean_flat(tensor):
function normalization (line 199) | def normalization(channels):
class SiLU (line 209) | class SiLU(nn.Module):
method forward (line 210) | def forward(self, x):
class GroupNorm32 (line 214) | class GroupNorm32(nn.GroupNorm):
method forward (line 215) | def forward(self, x):
function conv_nd (line 218) | def conv_nd(dims, *args, **kwargs):
function linear (line 231) | def linear(*args, **kwargs):
function avg_pool_nd (line 238) | def avg_pool_nd(dims, *args, **kwargs):
class HybridConditioner (line 251) | class HybridConditioner(nn.Module):
method __init__ (line 253) | def __init__(self, c_concat_config, c_crossattn_config):
method forward (line 258) | def forward(self, c_concat, c_crossattn):
function noise_like (line 264) | def noise_like(shape, device, repeat=False):
FILE: ldm/modules/distributions/distributions.py
class AbstractDistribution (line 5) | class AbstractDistribution:
method sample (line 6) | def sample(self):
method mode (line 9) | def mode(self):
class DiracDistribution (line 13) | class DiracDistribution(AbstractDistribution):
method __init__ (line 14) | def __init__(self, value):
method sample (line 17) | def sample(self):
method mode (line 20) | def mode(self):
class DiagonalGaussianDistribution (line 24) | class DiagonalGaussianDistribution(object):
method __init__ (line 25) | def __init__(self, parameters, deterministic=False):
method sample (line 35) | def sample(self):
method kl (line 39) | def kl(self, other=None):
method nll (line 53) | def nll(self, sample, dims=[1,2,3]):
method mode (line 61) | def mode(self):
function normal_kl (line 65) | def normal_kl(mean1, logvar1, mean2, logvar2):
FILE: ldm/modules/encoders/modules.py
class AbstractEncoder (line 12) | class AbstractEncoder(nn.Module):
method __init__ (line 13) | def __init__(self):
method encode (line 16) | def encode(self, *args, **kwargs):
class IdentityEncoder (line 19) | class IdentityEncoder(AbstractEncoder):
method encode (line 21) | def encode(self, x):
class FaceClipEncoder (line 24) | class FaceClipEncoder(AbstractEncoder):
method __init__ (line 25) | def __init__(self, augment=True, retreival_key=None):
method forward (line 31) | def forward(self, img):
method encode (line 54) | def encode(self, img):
class FaceIdClipEncoder (line 61) | class FaceIdClipEncoder(AbstractEncoder):
method __init__ (line 62) | def __init__(self):
method forward (line 69) | def forward(self, img):
method encode (line 84) | def encode(self, img):
class ClassEmbedder (line 91) | class ClassEmbedder(nn.Module):
method __init__ (line 92) | def __init__(self, embed_dim, n_classes=1000, key='class'):
method forward (line 97) | def forward(self, batch, key=None):
class TransformerEmbedder (line 106) | class TransformerEmbedder(AbstractEncoder):
method __init__ (line 108) | def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, devic...
method forward (line 114) | def forward(self, tokens):
method encode (line 119) | def encode(self, x):
class BERTTokenizer (line 123) | class BERTTokenizer(AbstractEncoder):
method __init__ (line 125) | def __init__(self, device="cuda", vq_interface=True, max_length=77):
method forward (line 133) | def forward(self, text):
method encode (line 140) | def encode(self, text):
method decode (line 146) | def decode(self, text):
class BERTEmbedder (line 150) | class BERTEmbedder(AbstractEncoder):
method __init__ (line 152) | def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
method forward (line 163) | def forward(self, text):
method encode (line 171) | def encode(self, text):
function disabled_train (line 178) | def disabled_train(self, mode=True):
class FrozenT5Embedder (line 184) | class FrozenT5Embedder(AbstractEncoder):
method __init__ (line 186) | def __init__(self, version="google/t5-v1_1-large", device="cuda", max_...
method freeze (line 194) | def freeze(self):
method forward (line 200) | def forward(self, text):
method encode (line 209) | def encode(self, text):
class FrozenFaceEncoder (line 215) | class FrozenFaceEncoder(AbstractEncoder):
method __init__ (line 216) | def __init__(self, model_path, augment=False):
method forward (line 237) | def forward(self, img):
method encode (line 251) | def encode(self, img):
class FrozenCLIPEmbedder (line 254) | class FrozenCLIPEmbedder(AbstractEncoder):
method __init__ (line 256) | def __init__(self, version="openai/clip-vit-large-patch14", device="cu...
method freeze (line 264) | def freeze(self):
method forward (line 270) | def forward(self, text):
method encode (line 279) | def encode(self, text):
class ClipImageProjector (line 284) | class ClipImageProjector(AbstractEncoder):
method __init__ (line 288) | def __init__(self, version="openai/clip-vit-large-patch14", max_length...
method get_null_cond (line 301) | def get_null_cond(self, version, max_length):
method preprocess (line 307) | def preprocess(self, x):
method forward (line 317) | def forward(self, x):
method encode (line 327) | def encode(self, im):
class ProjectedFrozenCLIPEmbedder (line 330) | class ProjectedFrozenCLIPEmbedder(AbstractEncoder):
method __init__ (line 331) | def __init__(self, version="openai/clip-vit-large-patch14", device="cu...
method forward (line 336) | def forward(self, text):
method encode (line 340) | def encode(self, text):
class FrozenCLIPImageEmbedder (line 343) | class FrozenCLIPImageEmbedder(AbstractEncoder):
method __init__ (line 348) | def __init__(
method preprocess (line 363) | def preprocess(self, x):
method forward (line 373) | def forward(self, x):
method encode (line 381) | def encode(self, im):
class FrozenCLIPImageMutliEmbedder (line 387) | class FrozenCLIPImageMutliEmbedder(AbstractEncoder):
method __init__ (line 392) | def __init__(
method preprocess (line 409) | def preprocess(self, x):
method forward (line 423) | def forward(self, x):
method encode (line 440) | def encode(self, im):
class SpatialRescaler (line 443) | class SpatialRescaler(nn.Module):
method __init__ (line 444) | def __init__(self,
method forward (line 462) | def forward(self,x):
method encode (line 471) | def encode(self, x):
class LowScaleEncoder (line 479) | class LowScaleEncoder(nn.Module):
method __init__ (line 480) | def __init__(self, model_config, linear_start, linear_end, timesteps=1...
method register_schedule (line 490) | def register_schedule(self, beta_schedule="linear", timesteps=1000,
method q_sample (line 517) | def q_sample(self, x_start, t, noise=None):
method forward (line 522) | def forward(self, x):
method decode (line 532) | def decode(self, z):
FILE: ldm/modules/x_transformer.py
class AbsolutePositionalEmbedding (line 25) | class AbsolutePositionalEmbedding(nn.Module):
method __init__ (line 26) | def __init__(self, dim, max_seq_len):
method init_ (line 31) | def init_(self):
method forward (line 34) | def forward(self, x):
class FixedPositionalEmbedding (line 39) | class FixedPositionalEmbedding(nn.Module):
method __init__ (line 40) | def __init__(self, dim):
method forward (line 45) | def forward(self, x, seq_dim=1, offset=0):
function exists (line 54) | def exists(val):
function default (line 58) | def default(val, d):
function always (line 64) | def always(val):
function not_equals (line 70) | def not_equals(val):
function equals (line 76) | def equals(val):
function max_neg_value (line 82) | def max_neg_value(tensor):
function pick_and_pop (line 88) | def pick_and_pop(keys, d):
function group_dict_by_key (line 93) | def group_dict_by_key(cond, d):
function string_begins_with (line 102) | def string_begins_with(prefix, str):
function group_by_key_prefix (line 106) | def group_by_key_prefix(prefix, d):
function groupby_prefix_and_trim (line 110) | def groupby_prefix_and_trim(prefix, d):
class Scale (line 117) | class Scale(nn.Module):
method __init__ (line 118) | def __init__(self, value, fn):
method forward (line 123) | def forward(self, x, **kwargs):
class Rezero (line 128) | class Rezero(nn.Module):
method __init__ (line 129) | def __init__(self, fn):
method forward (line 134) | def forward(self, x, **kwargs):
class ScaleNorm (line 139) | class ScaleNorm(nn.Module):
method __init__ (line 140) | def __init__(self, dim, eps=1e-5):
method forward (line 146) | def forward(self, x):
class RMSNorm (line 151) | class RMSNorm(nn.Module):
method __init__ (line 152) | def __init__(self, dim, eps=1e-8):
method forward (line 158) | def forward(self, x):
class Residual (line 163) | class Residual(nn.Module):
method forward (line 164) | def forward(self, x, residual):
class GRUGating (line 168) | class GRUGating(nn.Module):
method __init__ (line 169) | def __init__(self, dim):
method forward (line 173) | def forward(self, x, residual):
class GEGLU (line 184) | class GEGLU(nn.Module):
method __init__ (line 185) | def __init__(self, dim_in, dim_out):
method forward (line 189) | def forward(self, x):
class FeedForward (line 194) | class FeedForward(nn.Module):
method __init__ (line 195) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
method forward (line 210) | def forward(self, x):
class Attention (line 215) | class Attention(nn.Module):
method __init__ (line 216) | def __init__(
method forward (line 268) | def forward(
class AttentionLayers (line 370) | class AttentionLayers(nn.Module):
method __init__ (line 371) | def __init__(
method forward (line 481) | def forward(
class Encoder (line 541) | class Encoder(AttentionLayers):
method __init__ (line 542) | def __init__(self, **kwargs):
class TransformerWrapper (line 548) | class TransformerWrapper(nn.Module):
method __init__ (line 549) | def __init__(
method init_ (line 595) | def init_(self):
method forward (line 598) | def forward(
FILE: ldm/thirdp/psp/helpers.py
class Flatten (line 12) | class Flatten(Module):
method forward (line 13) | def forward(self, input):
function l2_norm (line 17) | def l2_norm(input, axis=1):
class Bottleneck (line 23) | class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
function get_block (line 27) | def get_block(in_channel, depth, num_units, stride=2):
function get_blocks (line 31) | def get_blocks(num_layers):
class SEModule (line 58) | class SEModule(Module):
method __init__ (line 59) | def __init__(self, channels, reduction):
method forward (line 67) | def forward(self, x):
class bottleneck_IR (line 77) | class bottleneck_IR(Module):
method __init__ (line 78) | def __init__(self, in_channel, depth, stride):
method forward (line 93) | def forward(self, x):
class bottleneck_IR_SE (line 99) | class bottleneck_IR_SE(Module):
method __init__ (line 100) | def __init__(self, in_channel, depth, stride):
method forward (line 118) | def forward(self, x):
FILE: ldm/thirdp/psp/id_loss.py
class IDFeatures (line 7) | class IDFeatures(nn.Module):
method __init__ (line 8) | def __init__(self, model_path):
method forward (line 16) | def forward(self, x, crop=False):
FILE: ldm/thirdp/psp/model_irse.py
class Backbone (line 11) | class Backbone(Module):
method __init__ (line 12) | def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, ...
method forward (line 46) | def forward(self, x):
function IR_50 (line 53) | def IR_50(input_size):
function IR_101 (line 59) | def IR_101(input_size):
function IR_152 (line 65) | def IR_152(input_size):
function IR_SE_50 (line 71) | def IR_SE_50(input_size):
function IR_SE_101 (line 77) | def IR_SE_101(input_size):
function IR_SE_152 (line 83) | def IR_SE_152(input_size):
FILE: ldm/util.py
function normalize (line 26) | def normalize(vec):
function look_at (line 31) | def look_at(cam_location, point):
function az_el_to_points (line 54) | def az_el_to_points(azimuths, elevations):
function get_3x4_RT_matrix_from_az_el (line 60) | def get_3x4_RT_matrix_from_az_el(az, el, distance):
function pil_rectangle_crop (line 78) | def pil_rectangle_crop(im):
function add_margin (line 97) | def add_margin(pil_img, color=0, size=256):
function create_carvekit_interface (line 104) | def create_carvekit_interface():
function load_and_preprocess (line 121) | def load_and_preprocess(interface, input_im):
function log_txt_as_img (line 147) | def log_txt_as_img(wh, xc, size=10):
function ismap (line 171) | def ismap(x):
function isimage (line 177) | def isimage(x):
function exists (line 183) | def exists(x):
function default (line 187) | def default(val, d):
function mean_flat (line 193) | def mean_flat(tensor):
function count_params (line 201) | def count_params(model, verbose=False):
function instantiate_from_config (line 208) | def instantiate_from_config(config):
function get_obj_from_str (line 218) | def get_obj_from_str(string, reload=False):
class AdamWwithEMAandWings (line 226) | class AdamWwithEMAandWings(optim.Optimizer):
method __init__ (line 228) | def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, #...
method __setstate__ (line 249) | def __setstate__(self, state):
method step (line 255) | def step(self, closure=None):
function prepare_inputs (line 335) | def prepare_inputs(image_input, elevation_input, crop_size=-1, image_siz...
function prepare_proxy (line 364) | def prepare_proxy(proxy_path, start_view_index=0):
function save_pickle (line 376) | def save_pickle(data, pkl_path):
function read_pickle (line 381) | def read_pickle(pkl_path):
class Ctrl3DParams (line 387) | class Ctrl3DParams:
function sample_proxy (line 393) | def sample_proxy(object_dir, num_proxy=256, overwrite=False):
FILE: raymarching/backend.py
function find_cl_path (line 17) | def find_cl_path():
FILE: raymarching/raymarching.py
class _near_far_from_aabb (line 19) | class _near_far_from_aabb(Function):
method forward (line 22) | def forward(ctx, rays_o, rays_d, aabb, min_near=0.2):
class _sph_from_ray (line 52) | class _sph_from_ray(Function):
method forward (line 55) | def forward(ctx, rays_o, rays_d, radius):
class _morton3D (line 83) | class _morton3D(Function):
method forward (line 85) | def forward(ctx, coords):
class _morton3D_invert (line 106) | class _morton3D_invert(Function):
method forward (line 108) | def forward(ctx, indices):
class _packbits (line 129) | class _packbits(Function):
method forward (line 132) | def forward(ctx, grid, thresh, bitfield=None):
class _march_rays_train (line 161) | class _march_rays_train(Function):
method forward (line 164) | def forward(ctx, rays_o, rays_d, bound, density_bitfield, C, H, nears,...
class _composite_rays_train (line 238) | class _composite_rays_train(Function):
method forward (line 241) | def forward(ctx, sigmas, rgbs, deltas, rays, T_thresh=1e-4):
method backward (line 273) | def backward(ctx, grad_weights_sum, grad_depth, grad_image):
class _march_rays (line 297) | class _march_rays(Function):
method forward (line 300) | def forward(ctx, n_alive, n_step, rays_alive, rays_t, rays_o, rays_d, ...
class _composite_rays (line 351) | class _composite_rays(Function):
method forward (line 354) | def forward(ctx, n_alive, n_step, rays_alive, rays_t, sigmas, rgbs, de...
FILE: raymarching/setup.py
function find_cl_path (line 18) | def find_cl_path():
FILE: raymarching/src/bindings.cpp
function PYBIND11_MODULE (line 5) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
FILE: renderer/agg_net.py
function weights_init (line 5) | def weights_init(m):
class NeRF (line 11) | class NeRF(nn.Module):
method __init__ (line 12) | def __init__(self, vol_n=8+8, feat_ch=8+16+32+3, hid_n=64):
method forward (line 27) | def forward(self, vox_feat, img_feat_rgb_dir, source_img_mask):
class Agg (line 44) | class Agg(nn.Module):
method __init__ (line 45) | def __init__(self, feat_ch):
method masked_mean_var (line 58) | def masked_mean_var(self, img_feat_rgb, source_img_mask):
method forward (line 66) | def forward(self, img_feat_rgb_dir, source_img_mask):
FILE: renderer/cost_reg_net.py
class ConvBnReLU3D (line 3) | class ConvBnReLU3D(nn.Module):
method __init__ (line 4) | def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,...
method forward (line 10) | def forward(self, x):
class CostRegNet (line 13) | class CostRegNet(nn.Module):
method __init__ (line 14) | def __init__(self, in_channels, norm_act=nn.BatchNorm3d):
method forward (line 44) | def forward(self, x):
class MinCostRegNet (line 60) | class MinCostRegNet(nn.Module):
method __init__ (line 61) | def __init__(self, in_channels, norm_act=nn.BatchNorm3d):
method forward (line 84) | def forward(self, x):
FILE: renderer/dummy_dataset.py
class DummyDataset (line 5) | class DummyDataset(pl.LightningDataModule):
method __init__ (line 6) | def __init__(self,seed):
method setup (line 9) | def setup(self, stage):
method train_dataloader (line 16) | def train_dataloader(self):
method val_dataloader (line 19) | def val_dataloader(self):
method test_dataloader (line 22) | def test_dataloader(self):
class DummyData (line 25) | class DummyData(Dataset):
method __init__ (line 26) | def __init__(self,is_train):
method __len__ (line 29) | def __len__(self):
method __getitem__ (line 35) | def __getitem__(self, index):
FILE: renderer/feature_net.py
class ConvBnReLU (line 4) | class ConvBnReLU(nn.Module):
method __init__ (line 5) | def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,...
method forward (line 11) | def forward(self, x):
class FeatureNet (line 14) | class FeatureNet(nn.Module):
method __init__ (line 15) | def __init__(self, norm_act=nn.BatchNorm2d):
method _upsample_add (line 28) | def _upsample_add(self, x, y):
method forward (line 31) | def forward(self, x):
FILE: renderer/neus_networks.py
class Embedder (line 10) | class Embedder:
method __init__ (line 11) | def __init__(self, **kwargs):
method create_embedding_fn (line 15) | def create_embedding_fn(self):
method embed (line 39) | def embed(self, inputs):
function get_embedder (line 43) | def get_embedder(multires, input_dims=3):
class SDFNetwork (line 60) | class SDFNetwork(nn.Module):
method __init__ (line 61) | def __init__(self, d_in, d_out, d_hidden, n_layers, skip_in=(4,), mult...
method forward (line 113) | def forward(self, inputs):
method sdf (line 132) | def sdf(self, x):
method sdf_hidden_appearance (line 135) | def sdf_hidden_appearance(self, x):
method gradient (line 138) | def gradient(self, x):
method sdf_normal (line 152) | def sdf_normal(self, x):
class SDFNetworkWithFeature (line 166) | class SDFNetworkWithFeature(nn.Module):
method __init__ (line 167) | def __init__(self, cube, dp_in, df_in, d_out, d_hidden, n_layers, skip...
method forward (line 221) | def forward(self, points):
method sdf (line 247) | def sdf(self, x):
method sdf_hidden_appearance (line 250) | def sdf_hidden_appearance(self, x):
method gradient (line 253) | def gradient(self, x):
method sdf_normal (line 267) | def sdf_normal(self, x):
class VanillaMLP (line 282) | class VanillaMLP(nn.Module):
method __init__ (line 283) | def __init__(self, dim_in, dim_out, n_neurons, n_hidden_layers):
method forward (line 295) | def forward(self, x):
method make_linear (line 299) | def make_linear(self, dim_in, dim_out, is_first, is_last):
method make_activation (line 320) | def make_activation(self):
class SDFHashGridNetwork (line 327) | class SDFHashGridNetwork(nn.Module):
method __init__ (line 328) | def __init__(self, bound=0.5, feats_dim=13):
method forward (line 358) | def forward(self, x):
method sdf (line 369) | def sdf(self, x):
method gradient (line 372) | def gradient(self, x):
method sdf_normal (line 386) | def sdf_normal(self, x):
class RenderingFFNetwork (line 400) | class RenderingFFNetwork(nn.Module):
method __init__ (line 401) | def __init__(self, in_feats_dim=12):
method forward (line 422) | def forward(self, points, normals, view_dirs, feature_vectors):
class RenderingNetwork (line 433) | class RenderingNetwork(nn.Module):
method __init__ (line 434) | def __init__(self, d_feature, d_in, d_out, d_hidden,
method forward (line 463) | def forward(self, points, normals, view_dirs, feature_vectors):
class SingleVarianceNetwork (line 488) | class SingleVarianceNetwork(nn.Module):
method __init__ (line 489) | def __init__(self, init_val, activation='exp'):
method forward (line 494) | def forward(self, x):
method warp (line 501) | def warp(self, x, inv_s):
FILE: renderer/ngp_renderer.py
function custom_meshgrid (line 17) | def custom_meshgrid(*args):
function sample_pdf (line 24) | def sample_pdf(bins, weights, n_samples, det=False):
function plot_pointcloud (line 61) | def plot_pointcloud(pc, color=None):
class NGPRenderer (line 73) | class NGPRenderer(nn.Module):
method __init__ (line 74) | def __init__(self,
method forward (line 115) | def forward(self, x, d):
method density (line 119) | def density(self, x):
method color (line 122) | def color(self, x, d, mask=None, **kwargs):
method reset_extra_state (line 125) | def reset_extra_state(self):
method run (line 137) | def run(self, rays_o, rays_d, num_steps=128, upsample_steps=128, bg_co...
method run_cuda (line 268) | def run_cuda(self, rays_o, rays_d, dt_gamma=0, bg_color=None, perturb=...
method mark_untrained_grid (line 369) | def mark_untrained_grid(self, poses, intrinsic, S=64):
method update_extra_state (line 434) | def update_extra_state(self, decay=0.95, S=128):
method render (line 529) | def render(self, rays_o, rays_d, staged=False, max_ray_batch=4096, **k...
class _trunc_exp (line 543) | class _trunc_exp(Function):
method forward (line 546) | def forward(ctx, x):
method backward (line 552) | def backward(ctx, g):
class NGPNetwork (line 558) | class NGPNetwork(NGPRenderer):
method __init__ (line 559) | def __init__(self,
method forward (line 636) | def forward(self, x, d):
method density (line 669) | def density(self, x):
method color (line 691) | def color(self, x, d, mask=None, geo_feat=None, **kwargs):
FILE: renderer/renderer.py
function sample_pdf (line 25) | def sample_pdf(bins, weights, n_samples, det=True):
function near_far_from_sphere (line 59) | def near_far_from_sphere(rays_o, rays_d, radius=DEFAULT_RADIUS):
class BackgroundRemoval (line 67) | class BackgroundRemoval:
method __init__ (line 68) | def __init__(self, device='cuda'):
method __call__ (line 84) | def __call__(self, image):
class BaseRenderer (line 92) | class BaseRenderer(nn.Module):
method __init__ (line 93) | def __init__(self, train_batch_num, test_batch_num):
method render_impl (line 99) | def render_impl(self, ray_batch, is_train, step):
method render_with_loss (line 103) | def render_with_loss(self, ray_batch, is_train, step):
method render (line 106) | def render(self, ray_batch, is_train, step):
class NeuSRenderer (line 124) | class NeuSRenderer(BaseRenderer):
method __init__ (line 125) | def __init__(self, train_batch_num, test_batch_num, lambda_eikonal_los...
method get_vertex_colors (line 144) | def get_vertex_colors(self, vertices):
method upsample (line 165) | def upsample(self, rays_o, rays_d, z_vals, sdf, n_importance, inv_s):
method cat_z_vals (line 198) | def cat_z_vals(self, rays_o, rays_d, z_vals, new_z_vals, sdf, last=Fal...
method sample_depth (line 215) | def sample_depth(self, rays_o, rays_d, near, far, perturb):
method compute_sdf_alpha (line 243) | def compute_sdf_alpha(self, points, dists, dirs, cos_anneal_ratio, step):
method get_anneal_val (line 270) | def get_anneal_val(self, step):
method get_inner_mask (line 276) | def get_inner_mask(self, points):
method render_impl (line 279) | def render_impl(self, ray_batch, is_train, step):
method render_with_loss (line 323) | def render_with_loss(self, ray_batch, is_train, step):
class NeRFRenderer (line 351) | class NeRFRenderer(BaseRenderer):
method __init__ (line 352) | def __init__(self, train_batch_num, test_batch_num, bound=0.5, use_mas...
method render_impl (line 364) | def render_impl(self, ray_batch, is_train, step):
method render_with_loss (line 379) | def render_with_loss(self, ray_batch, is_train, step):
class RendererTrainer (line 398) | class RendererTrainer(pl.LightningModule):
method __init__ (line 399) | def __init__(self, image_path, total_steps, warm_up_steps, log_dir, tr...
method _construct_ray_batch (line 438) | def _construct_ray_batch(self, images_info):
method load_model (line 464) | def load_model(cfg, ckpt):
method _init_dataset (line 473) | def _init_dataset(self):
method _shuffle_train_batch (line 512) | def _shuffle_train_batch(self):
method _shuffle_train_fg_batch (line 518) | def _shuffle_train_fg_batch(self):
method training_step (line 525) | def training_step(self, batch, batch_idx):
method _slice_images_info (line 545) | def _slice_images_info(self, index):
method validation_step (line 549) | def validation_step(self, batch, batch_idx):
method configure_optimizers (line 576) | def configure_optimizers(self):
FILE: train_diffusion.py
function rank_zero_print (line 22) | def rank_zero_print(*args):
function get_parser (line 25) | def get_parser(**parser_kwargs):
function trainer_args (line 46) | def trainer_args(opt):
class SetupCallback (line 52) | class SetupCallback(Callback):
method __init__ (line 53) | def __init__(self, resume, logdir, ckptdir, cfgdir, config):
method on_fit_start (line 61) | def on_fit_start(self, trainer, pl_module):
class ImageLogger (line 74) | class ImageLogger(Callback):
method __init__ (line 75) | def __init__(self, batch_frequency, max_images, log_images_kwargs=None):
method log_to_logger (line 82) | def log_to_logger(self, pl_module, images, split):
method log_to_file (line 91) | def log_to_file(self, save_dir, split, images, global_step, current_ep...
method log_img (line 105) | def log_img(self, pl_module, batch, split="train"):
method check_frequency (line 128) | def check_frequency(self, check_idx):
method on_train_batch_end (line 134) | def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch...
method on_validation_batch_end (line 138) | def on_validation_batch_end(self, trainer, pl_module, outputs, batch, ...
class CUDACallback (line 144) | class CUDACallback(Callback):
method on_train_epoch_start (line 146) | def on_train_epoch_start(self, trainer, pl_module):
method on_train_epoch_end (line 152) | def on_train_epoch_end(self, trainer, pl_module):
function get_node_name (line 166) | def get_node_name(name, parent_name):
class ResumeCallBacks (line 174) | class ResumeCallBacks(Callback):
method on_train_start (line 175) | def on_train_start(self, trainer, pl_module):
function load_pretrain_stable_diffusion (line 178) | def load_pretrain_stable_diffusion(new_model, finetune_from):
function get_optional_dict (line 206) | def get_optional_dict(name, config):
FILE: train_renderer.py
class ResumeCallBacks (line 23) | class ResumeCallBacks(Callback):
method __init__ (line 24) | def __init__(self):
method on_train_start (line 27) | def on_train_start(self, trainer, pl_module):
function render_images (line 30) | def render_images(model, output,):
function extract_fields (line 78) | def extract_fields(bound_min, bound_max, resolution, query_func, batch_s...
function extract_geometry (line 98) | def extract_geometry(bound_min, bound_max, resolution, threshold, query_...
function extract_mesh (line 108) | def extract_mesh(model, output, resolution=512):
function main (line 119) | def main():
FILE: workflow/inference_comfyui_api.py
function queue_prompt (line 16) | def queue_prompt(prompt):
function get_image (line 22) | def get_image(filename, subfolder, folder_type):
function get_history (line 28) | def get_history(prompt_id):
function get_images (line 32) | def get_images(ws, prompt):
Condensed preview — 126 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (2,008K chars).
[
{
"path": "CONDITION.md",
"chars": 4237,
"preview": "### Prepare condition image for inference\n1. Rendering the proxy image\n\nFirst you need to download [Blender](https://www"
},
{
"path": "README.md",
"chars": 6290,
"preview": "# Coin3D: Controllable and Interactive 3D Assets Generation with Proxy-Guided Conditioning\n### [Project Page](https://zj"
},
{
"path": "blender_utils/render_proxy.py",
"chars": 7619,
"preview": "import argparse\nimport json\nimport math\nimport os\nimport random\nimport sys\nimport time\nimport urllib.request\nfrom pathli"
},
{
"path": "configs/coin3d_train.yaml",
"chars": 2051,
"preview": "model:\n base_learning_rate: 5.0e-05\n target: ldm.models.diffusion.ctrldemo_sync_dreamer.CtrlDemo\n params:\n view_nu"
},
{
"path": "configs/ctrldemo.yaml",
"chars": 1135,
"preview": "model:\n base_learning_rate: 5.0e-05\n target: ldm.models.diffusion.ctrldemo_sync_dreamer.CtrlDemo\n params:\n view_nu"
},
{
"path": "configs/nerf.yaml",
"chars": 441,
"preview": "model:\n base_lr: 1.0e-2\n target: renderer.renderer.RendererTrainer\n params:\n total_steps: 2000\n warm_up_steps: "
},
{
"path": "configs/neus.yaml",
"chars": 478,
"preview": "model:\n base_lr: 5.0e-4\n target: renderer.renderer.RendererTrainer\n params:\n total_steps: 2000\n warm_up_steps: "
},
{
"path": "configs/syncdreamer.yaml",
"chars": 1141,
"preview": "model:\n base_learning_rate: 5.0e-05\n target: ldm.models.diffusion.sync_dreamer.SyncMultiviewDiffusion\n params:\n vi"
},
{
"path": "example/panda/mesh.obj",
"chars": 320831,
"preview": "# Blender 3.6.1\n# www.blender.org\no Sphere\nv -0.090264 0.462556 -0.155620\nv -0.090264 0.401258 -0.226042\nv -0.090264 0.3"
},
{
"path": "example/panda/proxy.txt",
"chars": 19623,
"preview": "-5.550776502986174560e-02 8.231279402941545087e-02 -1.258323406064429106e-01\n-3.998699747023472251e-02 3.020038030973061"
},
{
"path": "example/pumpkin/mesh.obj",
"chars": 81747,
"preview": "# Blender 3.6.1\n# www.blender.org\no Cube.003\nv -0.229940 -0.199760 0.283841\nv -0.174661 -0.026964 0.192628\nv -0.229940 -"
},
{
"path": "example/pumpkin/proxy.txt",
"chars": 19650,
"preview": "-3.305625800631857847e-01 -3.582112260109803631e-02 1.225265727345806049e-01\n-2.891790282159942826e-01 -2.95431763102793"
},
{
"path": "example/teddybear/mesh.obj",
"chars": 320831,
"preview": "# Blender 3.6.1\n# www.blender.org\no Sphere\nv -0.090264 0.462556 -0.155620\nv -0.090264 0.401258 -0.226042\nv -0.090264 0.3"
},
{
"path": "example/teddybear/proxy.txt",
"chars": 19623,
"preview": "-5.550776502986174560e-02 8.231279402941545087e-02 -1.258323406064429106e-01\n-3.998699747023472251e-02 3.020038030973061"
},
{
"path": "example/toycar/mesh.obj",
"chars": 79581,
"preview": "# Blender v3.6.1 OBJ File: 'car.blend'\n# www.blender.org\nmtllib 0000_Collection.mtl\no Cylinder_Cylinder.004\nv 0.115303 -"
},
{
"path": "example/toycar/proxy.txt",
"chars": 19603,
"preview": "-3.008041751643567713e-02 -1.599077930202584819e-01 3.288800368815070208e-01\n1.013531047142105096e-01 -1.094049703092424"
},
{
"path": "example/turtle/mesh.obj",
"chars": 288812,
"preview": "# Blender v3.6.1 OBJ File: 'turbo.blend'\n# www.blender.org\nmtllib 0000_Collection.mtl\no Plane\nv -0.119246 -0.093755 0.34"
},
{
"path": "example/turtle/proxy.txt",
"chars": 19625,
"preview": "-1.860431563537060518e-01 -9.375499933958053589e-02 -8.959774656980318275e-02\n-2.498418255269513610e-01 -9.3754999339580"
},
{
"path": "externs/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "externs/pvcnn/modules/__init__.py",
"chars": 431,
"preview": "# from modules.ball_query import BallQuery\n# from modules.frustum import FrustumPointNetLoss\n# from modules.loss import "
},
{
"path": "externs/pvcnn/modules/ball_query.py",
"chars": 1404,
"preview": "import torch\nimport torch.nn as nn\n\nimport modules.functional as F\n\n__all__ = ['BallQuery']\n\n\nclass BallQuery(nn.Module)"
},
{
"path": "externs/pvcnn/modules/frustum.py",
"chars": 7340,
"preview": "import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nimport modules.functional as PF\n\n"
},
{
"path": "externs/pvcnn/modules/functional/__init__.py",
"chars": 479,
"preview": "# from modules.functional.ball_query import ball_query\nfrom externs.pvcnn.modules.functional.devoxelization import trili"
},
{
"path": "externs/pvcnn/modules/functional/backend.py",
"chars": 957,
"preview": "import os\n\nfrom torch.utils.cpp_extension import load\n\n_src_path = os.path.dirname(os.path.abspath(__file__))\n_backend ="
},
{
"path": "externs/pvcnn/modules/functional/ball_query.py",
"chars": 752,
"preview": "from torch.autograd import Function\n\nfrom modules.functional.backend import _backend\n\n__all__ = ['ball_query']\n\n\ndef bal"
},
{
"path": "externs/pvcnn/modules/functional/devoxelization.py",
"chars": 1499,
"preview": "from torch.autograd import Function\n\nfrom externs.pvcnn.modules.functional.backend import _backend\n\n__all__ = ['trilinea"
},
{
"path": "externs/pvcnn/modules/functional/grouping.py",
"chars": 978,
"preview": "from torch.autograd import Function\n\nfrom modules.functional.backend import _backend\n\n__all__ = ['grouping']\n\n\nclass Gro"
},
{
"path": "externs/pvcnn/modules/functional/interpolatation.py",
"chars": 1452,
"preview": "from torch.autograd import Function\n\nfrom modules.functional.backend import _backend\n\n__all__ = ['nearest_neighbor_inter"
},
{
"path": "externs/pvcnn/modules/functional/loss.py",
"chars": 484,
"preview": "import torch\nimport torch.nn.functional as F\n\n__all__ = ['kl_loss', 'huber_loss']\n\n\ndef kl_loss(x, y):\n x = F.softmax"
},
{
"path": "externs/pvcnn/modules/functional/sampling.py",
"chars": 3656,
"preview": "import numpy as np\nimport torch\nfrom torch.autograd import Function\n\nfrom modules.functional.backend import _backend\n\n__"
},
{
"path": "externs/pvcnn/modules/functional/src/ball_query/ball_query.cpp",
"chars": 943,
"preview": "#include \"ball_query.hpp\"\n#include \"ball_query.cuh\"\n\n#include \"../utils.hpp\"\n\nat::Tensor ball_query_forward(at::Tensor c"
},
{
"path": "externs/pvcnn/modules/functional/src/ball_query/ball_query.cu",
"chars": 2039,
"preview": "#include <math.h>\n#include <stdio.h>\n#include <stdlib.h>\n\n#include \"../cuda_utils.cuh\"\n\n/*\n Function: ball query\n Args"
},
{
"path": "externs/pvcnn/modules/functional/src/ball_query/ball_query.cuh",
"chars": 225,
"preview": "#ifndef _BALL_QUERY_CUH\n#define _BALL_QUERY_CUH\n\nvoid ball_query(int b, int n, int m, float r2, int u,\n c"
},
{
"path": "externs/pvcnn/modules/functional/src/ball_query/ball_query.hpp",
"chars": 276,
"preview": "#ifndef _BALL_QUERY_HPP\n#define _BALL_QUERY_HPP\n\n#include <torch/extension.h>\n\nat::Tensor ball_query_forward(at::Tensor "
},
{
"path": "externs/pvcnn/modules/functional/src/bindings.cpp",
"chars": 1694,
"preview": "#include <pybind11/pybind11.h>\n\n#include \"ball_query/ball_query.hpp\"\n#include \"grouping/grouping.hpp\"\n#include \"interpol"
},
{
"path": "externs/pvcnn/modules/functional/src/cuda_utils.cuh",
"chars": 1385,
"preview": "#ifndef _CUDA_UTILS_H\n#define _CUDA_UTILS_H\n\n#include <ATen/ATen.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cmath>\n"
},
{
"path": "externs/pvcnn/modules/functional/src/grouping/grouping.cpp",
"chars": 1325,
"preview": "#include \"grouping.hpp\"\n#include \"grouping.cuh\"\n\n#include \"../utils.hpp\"\n\nat::Tensor grouping_forward(at::Tensor feature"
},
{
"path": "externs/pvcnn/modules/functional/src/grouping/grouping.cu",
"chars": 3056,
"preview": "#include <stdio.h>\n#include <stdlib.h>\n\n#include \"../cuda_utils.cuh\"\n\n/*\n Function: grouping features of neighbors (for"
},
{
"path": "externs/pvcnn/modules/functional/src/grouping/grouping.cuh",
"chars": 301,
"preview": "#ifndef _GROUPING_CUH\n#define _GROUPING_CUH\n\nvoid grouping(int b, int c, int n, int m, int u, const float *features,\n "
},
{
"path": "externs/pvcnn/modules/functional/src/grouping/grouping.hpp",
"chars": 264,
"preview": "#ifndef _GROUPING_HPP\n#define _GROUPING_HPP\n\n#include <torch/extension.h>\n\nat::Tensor grouping_forward(at::Tensor featur"
},
{
"path": "externs/pvcnn/modules/functional/src/interpolate/neighbor_interpolate.cpp",
"chars": 2367,
"preview": "#include \"neighbor_interpolate.hpp\"\n#include \"neighbor_interpolate.cuh\"\n\n#include \"../utils.hpp\"\n\nstd::vector<at::Tensor"
},
{
"path": "externs/pvcnn/modules/functional/src/interpolate/neighbor_interpolate.cu",
"chars": 6580,
"preview": "#include <math.h>\n#include <stdio.h>\n#include <stdlib.h>\n\n#include \"../cuda_utils.cuh\"\n\n/*\n Function: three nearest nei"
},
{
"path": "externs/pvcnn/modules/functional/src/interpolate/neighbor_interpolate.cuh",
"chars": 819,
"preview": "#ifndef _NEIGHBOR_INTERPOLATE_CUH\n#define _NEIGHBOR_INTERPOLATE_CUH\n\nvoid three_nearest_neighbors_interpolate(int b, int"
},
{
"path": "externs/pvcnn/modules/functional/src/interpolate/neighbor_interpolate.hpp",
"chars": 661,
"preview": "#ifndef _NEIGHBOR_INTERPOLATE_HPP\n#define _NEIGHBOR_INTERPOLATE_HPP\n\n#include <torch/extension.h>\n#include <vector>\n\nstd"
},
{
"path": "externs/pvcnn/modules/functional/src/interpolate/trilinear_devox.cpp",
"chars": 3401,
"preview": "#include \"trilinear_devox.hpp\"\n#include \"trilinear_devox.cuh\"\n\n#include \"../utils.hpp\"\n\n/*\n Function: trilinear devoxel"
},
{
"path": "externs/pvcnn/modules/functional/src/interpolate/trilinear_devox.cu",
"chars": 6396,
"preview": "#include <stdio.h>\n#include <stdlib.h>\n\n#include \"../cuda_utils.cuh\"\n\n/*\n Function: trilinear devoxlization (forward)\n "
},
{
"path": "externs/pvcnn/modules/functional/src/interpolate/trilinear_devox.cuh",
"chars": 536,
"preview": "#ifndef _TRILINEAR_DEVOX_CUH\n#define _TRILINEAR_DEVOX_CUH\n\n// CUDA function declarations\nvoid trilinear_devoxelize(int b"
},
{
"path": "externs/pvcnn/modules/functional/src/interpolate/trilinear_devox.hpp",
"chars": 628,
"preview": "#ifndef _TRILINEAR_DEVOX_HPP\n#define _TRILINEAR_DEVOX_HPP\n\n#include <torch/torch.h>\n#include <vector>\n\nstd::vector<at::T"
},
{
"path": "externs/pvcnn/modules/functional/src/sampling/sampling.cpp",
"chars": 1966,
"preview": "#include \"sampling.hpp\"\n#include \"sampling.cuh\"\n\n#include \"../utils.hpp\"\n\nat::Tensor gather_features_forward(at::Tensor "
},
{
"path": "externs/pvcnn/modules/functional/src/sampling/sampling.cu",
"chars": 5802,
"preview": "#include <stdio.h>\n#include <stdlib.h>\n\n#include \"../cuda_utils.cuh\"\n\n/*\n Function: gather centers' features (forward)\n"
},
{
"path": "externs/pvcnn/modules/functional/src/sampling/sampling.cuh",
"chars": 449,
"preview": "#ifndef _SAMPLING_CUH\n#define _SAMPLING_CUH\n\nvoid gather_features(int b, int c, int n, int m, const float *features,\n "
},
{
"path": "externs/pvcnn/modules/functional/src/sampling/sampling.hpp",
"chars": 414,
"preview": "#ifndef _SAMPLING_HPP\n#define _SAMPLING_HPP\n\n#include <torch/extension.h>\n\nat::Tensor gather_features_forward(at::Tensor"
},
{
"path": "externs/pvcnn/modules/functional/src/utils.hpp",
"chars": 760,
"preview": "#ifndef _UTILS_HPP\n#define _UTILS_HPP\n\n#include <ATen/cuda/CUDAContext.h>\n#include <torch/extension.h>\n\n#define CHECK_CU"
},
{
"path": "externs/pvcnn/modules/functional/src/voxelization/vox.cpp",
"chars": 2540,
"preview": "#include \"vox.hpp\"\n#include \"vox.cuh\"\n\n#include \"../utils.hpp\"\n\n/*\n Function: average pool voxelization (forward)\n Arg"
},
{
"path": "externs/pvcnn/modules/functional/src/voxelization/vox.cu",
"chars": 4358,
"preview": "#include <stdio.h>\n#include <stdlib.h>\n\n#include \"../cuda_utils.cuh\"\n\n/*\n Function: get how many points in each voxel g"
},
{
"path": "externs/pvcnn/modules/functional/src/voxelization/vox.cuh",
"chars": 367,
"preview": "#ifndef _VOX_CUH\n#define _VOX_CUH\n\n// CUDA function declarations\nvoid avg_voxelize(int b, int c, int n, int r, int r2, i"
},
{
"path": "externs/pvcnn/modules/functional/src/voxelization/vox.hpp",
"chars": 471,
"preview": "#ifndef _VOX_HPP\n#define _VOX_HPP\n\n#include <torch/torch.h>\n#include <vector>\n\nstd::vector<at::Tensor> avg_voxelize_forw"
},
{
"path": "externs/pvcnn/modules/functional/voxelization.py",
"chars": 1389,
"preview": "from torch.autograd import Function\n\nfrom externs.pvcnn.modules.functional.backend import _backend\n\n__all__ = ['avg_voxe"
},
{
"path": "externs/pvcnn/modules/loss.py",
"chars": 163,
"preview": "import torch.nn as nn\n\nimport modules.functional as F\n\n__all__ = ['KLLoss']\n\n\nclass KLLoss(nn.Module):\n def forward(s"
},
{
"path": "externs/pvcnn/modules/pointnet.py",
"chars": 4581,
"preview": "import torch\nimport torch.nn as nn\n\nimport modules.functional as F\nfrom modules.ball_query import BallQuery\nfrom modules"
},
{
"path": "externs/pvcnn/modules/pvconv.py",
"chars": 2617,
"preview": "import torch.nn as nn\n\nimport externs.pvcnn.modules.functional as F\nfrom externs.pvcnn.modules.voxelization import Voxel"
},
{
"path": "externs/pvcnn/modules/se.py",
"chars": 522,
"preview": "import torch.nn as nn\n\n__all__ = ['SE3d']\n\n\nclass SE3d(nn.Module):\n def __init__(self, channel, reduction=8):\n "
},
{
"path": "externs/pvcnn/modules/shared_mlp.py",
"chars": 923,
"preview": "import torch.nn as nn\n\n__all__ = ['SharedMLP']\n\n\nclass SharedMLP(nn.Module):\n def __init__(self, in_channels, out_cha"
},
{
"path": "externs/pvcnn/modules/voxelization.py",
"chars": 1068,
"preview": "import torch\nimport torch.nn as nn\n\nimport externs.pvcnn.modules.functional as F\n\n__all__ = ['Voxelization']\n\n\nclass Vox"
},
{
"path": "foreground_segment.py",
"chars": 1534,
"preview": "import cv2\nimport argparse\nimport numpy as np\n\nimport torch\nfrom PIL import Image\n\n\nclass BackgroundRemoval:\n def __i"
},
{
"path": "generate.py",
"chars": 3716,
"preview": "import argparse\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nfrom omegaconf import OmegaConf\nfrom skimage.i"
},
{
"path": "ldm/DPMPPScheduler.py",
"chars": 4557,
"preview": "import math\nfrom typing import List, Optional, Tuple, Union\nfrom dataclasses import dataclass\nimport numpy as np\nimport "
},
{
"path": "ldm/base_utils.py",
"chars": 4837,
"preview": "import pickle\nimport numpy as np\nimport cv2\nfrom skimage.io import imread\n\n\ndef save_pickle(data, pkl_path):\n # os.sy"
},
{
"path": "ldm/data/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "ldm/data/base.py",
"chars": 1204,
"preview": "import os\nimport numpy as np\nfrom abc import abstractmethod\nfrom torch.utils.data import Dataset, ConcatDataset, ChainDa"
},
{
"path": "ldm/data/coco.py",
"chars": 11339,
"preview": "import os\nimport json\nimport albumentations\nimport numpy as np\nfrom PIL import Image\nfrom tqdm import tqdm\nfrom torch.ut"
},
{
"path": "ldm/data/control_sync_dreamer.py",
"chars": 5150,
"preview": "import pytorch_lightning as pl\nimport numpy as np\nimport torch\nimport PIL\nimport os\nfrom skimage.io import imread\nimport"
},
{
"path": "ldm/data/dummy.py",
"chars": 887,
"preview": "import numpy as np\nimport random\nimport string\nfrom torch.utils.data import Dataset, Subset\n\nclass DummyData(Dataset):\n "
},
{
"path": "ldm/data/imagenet.py",
"chars": 15565,
"preview": "import os, yaml, pickle, shutil, tarfile, glob\nimport cv2\nimport albumentations\nimport PIL\nimport numpy as np\nimport tor"
},
{
"path": "ldm/data/inpainting/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "ldm/data/inpainting/synthetic_mask.py",
"chars": 5121,
"preview": "from PIL import Image, ImageDraw\nimport numpy as np\n\nsettings = {\n \"256narrow\": {\n \"p_irr\": 1,\n \"min_n_"
},
{
"path": "ldm/data/laion.py",
"chars": 19135,
"preview": "import webdataset as wds\nimport kornia\nfrom PIL import Image\nimport io\nimport os\nimport torchvision\nfrom PIL import Imag"
},
{
"path": "ldm/data/lsun.py",
"chars": 3274,
"preview": "import os\nimport numpy as np\nimport PIL\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nfrom torchvision impo"
},
{
"path": "ldm/data/nerf_like.py",
"chars": 6534,
"preview": "from torch.utils.data import Dataset\nimport os\nimport json\nimport numpy as np\nimport torch\nimport imageio\nimport math\nim"
},
{
"path": "ldm/data/simple.py",
"chars": 18705,
"preview": "from typing import Dict\nimport webdataset as wds\nimport numpy as np\nfrom omegaconf import DictConfig, ListConfig\nimport "
},
{
"path": "ldm/data/sync_dreamer.py",
"chars": 5093,
"preview": "import pytorch_lightning as pl\nimport numpy as np\nimport torch\nimport PIL\nimport os\nfrom skimage.io import imread\nimport"
},
{
"path": "ldm/lr_scheduler.py",
"chars": 3882,
"preview": "import numpy as np\n\n\nclass LambdaWarmUpCosineScheduler:\n \"\"\"\n note: use with a base_lr of 1.0\n \"\"\"\n def __in"
},
{
"path": "ldm/models/autoencoder.py",
"chars": 17619,
"preview": "import torch\nimport pytorch_lightning as pl\nimport torch.nn.functional as F\nfrom contextlib import contextmanager\n\nfrom "
},
{
"path": "ldm/models/diffusion/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "ldm/models/diffusion/ctrldemo_sync_dreamer.py",
"chars": 19230,
"preview": "from pathlib import Path\n\nimport pytorch_lightning as pl\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional a"
},
{
"path": "ldm/models/diffusion/sync_dreamer.py",
"chars": 32795,
"preview": "from pathlib import Path\n\nimport pytorch_lightning as pl\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional a"
},
{
"path": "ldm/models/diffusion/sync_dreamer_attention.py",
"chars": 5807,
"preview": "import torch\nimport torch.nn as nn\n\nfrom ldm.modules.attention import default, zero_module, checkpoint\nfrom ldm.modules."
},
{
"path": "ldm/models/diffusion/sync_dreamer_network.py",
"chars": 9689,
"preview": "import torch\nimport torch.nn as nn\nfrom ldm.modules.attention import default, zero_module, checkpoint\nfrom ldm.modules.d"
},
{
"path": "ldm/models/diffusion/sync_dreamer_utils.py",
"chars": 5230,
"preview": "import torch\nfrom kornia import create_meshgrid\n\n\ndef project_and_normalize(ref_grid, src_proj, length):\n \"\"\"\n\n @p"
},
{
"path": "ldm/modules/attention.py",
"chars": 11691,
"preview": "from inspect import isfunction\nimport math\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn, einsum\nfro"
},
{
"path": "ldm/modules/diffusionmodules/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "ldm/modules/diffusionmodules/model.py",
"chars": 33409,
"preview": "# pytorch_diffusion + derived encoder decoder\nimport math\nimport torch\nimport torch.nn as nn\nimport numpy as np\nfrom ein"
},
{
"path": "ldm/modules/diffusionmodules/openaimodel.py",
"chars": 37257,
"preview": "from abc import abstractmethod\nfrom functools import partial\nimport math\nfrom typing import Iterable\n\nimport numpy as np"
},
{
"path": "ldm/modules/diffusionmodules/util.py",
"chars": 9561,
"preview": "# adopted from\n# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py\n# and\n#"
},
{
"path": "ldm/modules/distributions/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "ldm/modules/distributions/distributions.py",
"chars": 2970,
"preview": "import torch\nimport numpy as np\n\n\nclass AbstractDistribution:\n def sample(self):\n raise NotImplementedError()\n"
},
{
"path": "ldm/modules/encoders/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "ldm/modules/encoders/modules.py",
"chars": 21114,
"preview": "import torch\nimport torch.nn as nn\nimport numpy as np\nfrom functools import partial\nimport kornia\n\nfrom ldm.modules.x_tr"
},
{
"path": "ldm/modules/x_transformer.py",
"chars": 20168,
"preview": "\"\"\"shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers\"\"\"\nimport torch\nfrom torch import "
},
{
"path": "ldm/thirdp/psp/helpers.py",
"chars": 3604,
"preview": "# https://github.com/eladrich/pixel2style2pixel\n\nfrom collections import namedtuple\nimport torch\nfrom torch.nn import Co"
},
{
"path": "ldm/thirdp/psp/id_loss.py",
"chars": 847,
"preview": "# https://github.com/eladrich/pixel2style2pixel\nimport torch\nfrom torch import nn\nfrom ldm.thirdp.psp.model_irse import "
},
{
"path": "ldm/thirdp/psp/model_irse.py",
"chars": 2883,
"preview": "# https://github.com/eladrich/pixel2style2pixel\n\nfrom torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, D"
},
{
"path": "ldm/typing.py",
"chars": 253,
"preview": "# Basic types\nfrom typing import (\n Any,\n Callable,\n Dict,\n Iterable,\n List,\n Literal,\n NamedTuple,"
},
{
"path": "ldm/util.py",
"chars": 14068,
"preview": "import importlib\n\nimport torchvision\nimport torch\nfrom torch import optim\nimport numpy as np\nimport pickle\n\nfrom inspect"
},
{
"path": "misc.ipynb",
"chars": 105176,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\":"
},
{
"path": "raymarching/__init__.py",
"chars": 26,
"preview": "from .raymarching import *"
},
{
"path": "raymarching/backend.py",
"chars": 1361,
"preview": "import os\nfrom torch.utils.cpp_extension import load\n\n_src_path = os.path.dirname(os.path.abspath(__file__))\n\nnvcc_flags"
},
{
"path": "raymarching/raymarching.py",
"chars": 14712,
"preview": "import numpy as np\nimport time\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Function\nfrom torch.cuda.a"
},
{
"path": "raymarching/setup.py",
"chars": 2062,
"preview": "import os\nfrom setuptools import setup\nfrom torch.utils.cpp_extension import BuildExtension, CUDAExtension\n\n_src_path = "
},
{
"path": "raymarching/src/bindings.cpp",
"chars": 903,
"preview": "#include <torch/extension.h>\n\n#include \"raymarching.h\"\n\nPYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {\n // utils\n m.de"
},
{
"path": "raymarching/src/raymarching.cu",
"chars": 31326,
"preview": "#include <cuda.h>\n#include <cuda_fp16.h>\n#include <cuda_runtime.h>\n\n#include <ATen/cuda/CUDAContext.h>\n#include <torch/t"
},
{
"path": "raymarching/src/raymarching.h",
"chars": 2283,
"preview": "#pragma once\n\n#include <stdint.h>\n#include <torch/torch.h>\n\n\nvoid near_far_from_aabb(const at::Tensor rays_o, const at::"
},
{
"path": "renderer/agg_net.py",
"chars": 3741,
"preview": "import torch.nn.functional as F\nimport torch.nn as nn\nimport torch\n\ndef weights_init(m):\n if isinstance(m, nn.Linear)"
},
{
"path": "renderer/cost_reg_net.py",
"chars": 3565,
"preview": "import torch.nn as nn\n\nclass ConvBnReLU3D(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size=3, s"
},
{
"path": "renderer/dummy_dataset.py",
"chars": 1048,
"preview": "import pytorch_lightning as pl\nfrom torch.utils.data import Dataset\nimport webdataset as wds\nfrom torch.utils.data.distr"
},
{
"path": "renderer/feature_net.py",
"chars": 1766,
"preview": "import torch.nn as nn\nimport torch.nn.functional as F\n\nclass ConvBnReLU(nn.Module):\n def __init__(self, in_channels, "
},
{
"path": "renderer/neus_networks.py",
"chars": 17899,
"preview": "import math\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport tinycudann as "
},
{
"path": "renderer/ngp_renderer.py",
"chars": 29447,
"preview": "import math\nimport trimesh\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom p"
},
{
"path": "renderer/renderer.py",
"chars": 27082,
"preview": "import abc\nimport os\nfrom pathlib import Path\n\nimport cv2\nimport numpy as np\nimport pytorch_lightning as pl\nimport torch"
},
{
"path": "requirements.txt",
"chars": 344,
"preview": "pytorch_lightning==1.9.0\nPillow==10.0.0\nopencv-python\ntransformers\ntaming-transformers-rom1504 \ntqdm\nnumpy\nkornia\nwebdat"
},
{
"path": "train_diffusion.py",
"chars": 13663,
"preview": "import argparse, os, sys\nimport numpy as np\nimport time\nimport torch\nimport torch.nn as nn\nimport torchvision\nimport pyt"
},
{
"path": "train_renderer.py",
"chars": 7724,
"preview": "import argparse\n\nimport imageio\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom pathlib import Path"
},
{
"path": "workflow/Coin3D_condition_workflow.json",
"chars": 16529,
"preview": "{\n \"last_node_id\": 189,\n \"last_link_id\": 685,\n \"nodes\": [\n {\n \"id\": 5,\n \"type\": \"EmptyLatentImage\",\n "
},
{
"path": "workflow/Coin3D_condition_workflow_api.json",
"chars": 4598,
"preview": "{\n \"3\": {\n \"inputs\": {\n \"seed\": 934049932825402,\n \"steps\": 20,\n \"cfg\": 7,\n \"sampler_name\": \"dpmp"
},
{
"path": "workflow/inference_comfyui_api.py",
"chars": 3333,
"preview": "import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client)\nimport uuid\nimport json\n"
}
]
// ... and 1 more files (download for full content)
About this extraction
This page contains the full source code of the zju3dv/Coin3D GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 126 files (1.9 MB), approximately 930.8k tokens, and a symbol index with 1033 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.