diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..b6e4761 --- /dev/null +++ b/.gitignore @@ -0,0 +1,129 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ diff --git a/.python_dependencies/.gitignore b/.python_dependencies/.gitignore new file mode 100644 index 0000000..e69de29 diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..e72bfdd --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..ea0f9ff --- /dev/null +++ b/README.md @@ -0,0 +1,32 @@ +![DMT Meshes, subtitle: Generative 3D Meshes built-in to Blender](docs/assets/banner.png) + +[![Latest Release](https://flat.badgen.net/github/release/Firwork-Games-AI-Division/dmt-meshes)](https://github.com/Firework-Games-AI-Division/dmt-meshes/releases/latest) +[![Total Downloads](https://img.shields.io/github/downloads/Firework-Games-AI-Division/dmt-meshes/total?style=flat-square)](https://github.com/Firework-Games-AI-Division/dmt-meshes/releases/latest) + +* Create point clouds or meshes with a simple text prompt or image. + +# Installation +Download the [latest release](https://github.com/Firwork-Games-AI-Division/dmt-meshes/releases/latest) and follow the instructions there to get up and running. + +## [Setting Up](docs/SETUP.md) +Setup instructions for various platforms and configurations. + +## [Mesh Generation](docs/MESH_GENERATION.md) +Create point clouds or meshes with text prompts or images. Learn how to use the various configuration options to get exactly what you're looking for. + +# Contributing +After cloning the repository, there a few more steps you need to complete to setup your development environment: +We recommend the [Blender Development](https://marketplace.visualstudio.com/items?itemName=JacquesLucke.blender-development) extension for VS Code for debugging. If you just want to install manually though, you can put the `dmt_meshes` repo folder in Blender's addon directory. +3. After running the local add-on in Blender, setup the model weights like normal. +4. Install dependencies locally + * Open Blender's preferences window + * Enable *Interface* > *Display* > *Developer Extras* + * Then install dependencies for development under *Add-ons* > *DMT Meshes* > *Development Tools* + * This will download all pip dependencies for the selected platform into `.python_dependencies` + +# Credits + +Parts of code/scripts are inspired/borrowed from: + - [dream-textures](https://github.com/carson-katri/dream-textures/) + - [Point-E](https://github.com/openai/point-e) + - [DMTet](https://github.com/NVIDIAGameWorks/kaolin/) \ No newline at end of file diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..dffbd02 --- /dev/null +++ b/__init__.py @@ -0,0 +1,73 @@ +bl_info = { + "name": "DMT Meshes", + "author": "DMT Meshes contributors", + "description": "Use OpenAI Point-e to generate meshes from images or texts.", + "blender": (3, 0, 0), + "version": (0, 0, 1), + "location": "Image Editor -> Sidebar -> DMT", + "category": "Paint" +} + +from multiprocessing import current_process + +if current_process().name != "__actor__": + from .absolute_path import absolute_path + import sys + sys.path.insert(0, absolute_path(".python_dependencies")) + import bpy + from bpy.props import IntProperty, PointerProperty, \ + EnumProperty, StringProperty + import os + from .operators.dmt_meshes import DMTMesh, kill_generator + from .preferences import set_pc_list + from .generator_process.actions.utils import get_point_cloud + + module_name = os.path.basename(os.path.dirname(__file__)) + def clear_modules(): + for name in list(sys.modules.keys()): + if name.startswith(module_name) and name != module_name: + del sys.modules[name] + clear_modules() # keep before all addon imports + + from .classes import CLASSES, PREFERENCE_CLASSES + from .property_groups.dmt_prompt import DMTPrompt + + requirements_path_items = ( + (os.path.join('requirements', 'win-linux-cuda.txt'), 'Linux/Windows (CUDA)', 'Linux or Windows with NVIDIA GPU'), + ) + + def register(): + dt_op = bpy.ops + for name in DMTMesh.bl_idname.split("."): + dt_op = getattr(dt_op, name) + if hasattr(bpy.types, dt_op.idname()): + raise RuntimeError('Another instance of DMT Meshes is already running.') + + bpy.types.Scene.dmt_meshes_requirements_path = EnumProperty(name="Platform", + items=requirements_path_items, + description="Specifies which set of dependencies to install", + default=os.path.join('requirements', 'win-linux-cuda.txt')) + + for cls in PREFERENCE_CLASSES: + bpy.utils.register_class(cls) + + bpy.types.Scene.dmt_meshes_prompt = PointerProperty(type=DMTPrompt) + bpy.types.Scene.init_img = PointerProperty(name="Init Image", type=bpy.types.Image) + bpy.types.Scene.dmt_meshes_progress = IntProperty(name="", default=0, min=0, max=0) + bpy.types.Scene.dmt_meshes_info = StringProperty(name="Info") + bpy.types.Scene.point_cloud_results_selection = IntProperty(default=1) + + for cls in CLASSES: + bpy.utils.register_class(cls) + + set_pc_list('point_cloud_results', get_point_cloud('')) + + + def unregister(): + for cls in PREFERENCE_CLASSES: + bpy.utils.unregister_class(cls) + for cls in CLASSES: + bpy.utils.unregister_class(cls) + + kill_generator() + diff --git a/absolute_path.py b/absolute_path.py new file mode 100644 index 0000000..83f30b1 --- /dev/null +++ b/absolute_path.py @@ -0,0 +1,11 @@ +import os + +def absolute_path(component: str): + """ + Returns the absolute path to a file in the addon directory. + Alternative to `os.abspath` that works the same on macOS and Windows. + """ + return os.path.join(os.path.dirname(os.path.realpath(__file__)), component) + + +DATA_PATH = './data' \ No newline at end of file diff --git a/classes.py b/classes.py new file mode 100644 index 0000000..b428206 --- /dev/null +++ b/classes.py @@ -0,0 +1,20 @@ +from .operators.install_dependencies import InstallDependencies +from .operators.dmt_meshes import DMTMesh, ReleaseGenerator, CancelGenerator +from .ui.panels import dmt_meshes +from .preferences import OpenContributors, DMTMeshesPreferences, PointCloud +from .property_groups.dmt_prompt import DMTPrompt + +CLASSES = ( + DMTMesh, + ReleaseGenerator, + CancelGenerator, + dmt_meshes.SCENE_UL_pc_list, + *dmt_meshes.dmt_meshes_panels() +) +PREFERENCE_CLASSES = ( + PointCloud, + InstallDependencies, + OpenContributors, + DMTPrompt, + DMTMeshesPreferences +) \ No newline at end of file diff --git a/data/mesh/.gitignore b/data/mesh/.gitignore new file mode 100644 index 0000000..e69de29 diff --git a/data/pc/.gitignore b/data/pc/.gitignore new file mode 100644 index 0000000..e69de29 diff --git a/docs/MESH_GENERATION.md b/docs/MESH_GENERATION.md new file mode 100644 index 0000000..2f5fcd0 --- /dev/null +++ b/docs/MESH_GENERATION.md @@ -0,0 +1,41 @@ +# Mesh Generation +1. To open DMT Meshes, go to 3D Viewport +1. Ensure the sidebar is visible by pressing *N* or checking *View* > *Sidebar* +2. Select the *DMT* panel to open the interface + +Enter a prompt then click *Generate*. It can take anywhere from a few seconds to a few minutes to generate, depending on your GPU. + +## Pipeline +As an overview, the process of DMT Meshes is: text/image -> point cloud -> mesh. + +## Prompt + +You can provide a text to generate the point cloud/mesh. + +## Source image + +You can use an image to geneate point cloud/mesh. + +## Point cloud + +The point cloud generated from the text/image will be shown in this screen. You can select the point cloud to regenerate the mesh with different methods/parameters. + +## Advanced + +* Image to point cloud model - Pre-trained openAI model to generate mesh from an image. + +* Point cloud to mesh model - You can use either point cloud to mesh implementation from openAI or [DMTet](https://github.com/NVIDIAGameWorks/kaolin/blob/master/examples/tutorial/dmtet_tutorial.ipynb). + +* Steps, mesh rendering, mesh learning rate, regularization, gridres, multires - parameters for point cloud to mesh. Except gridres, other parameters are only for DMTet. We will revamp the UI in the next release. For more information about the parameters, please visit [here](https://github.com/NVIDIAGameWorks/kaolin/blob/master/examples/tutorial/dmtet_tutorial.ipynb). + +> **NOTE:** DMTet mode only supports gridres=128 + +## Run mode + +You can choose different run mode: text to mesh, image to mesh, text to point cloud, image to point cloud. + +# Use with Dream Textures + +After generating the mesh, you can use Dream Textures to project texture on it. + + diff --git a/docs/SETUP.md b/docs/SETUP.md new file mode 100644 index 0000000..29c1458 --- /dev/null +++ b/docs/SETUP.md @@ -0,0 +1,16 @@ +# Setting Up +Getting up and running is easy. Make sure you have several GBs of storage free, as the model weights and add-on consume a lot of storage space. + +In general, all of the instructions you need to setup will be given within Blender. However, if you want to see screenshots and more explanation this can be helpful. + +If you have any problems, you can open an issue. + +## Installation + +See the [release notes](https://github.com/Firework-Games-AI-Division/dmt-meshes/releases/latest) for the most recent version of DMT Meshes. There you will find a section titled "Choose Your Installation". Use the dropdowns to find the right version for your system. + +After you have the add-on installed in Blender, check the box in Blender preferences to enable it. Then follow the steps below to complete setup. + +> **NOTE:** The first run may take a few minutes as we download the pre-trained model from openai/point-e. If you want to see the progress, go to Windows -> Toggle System Console. + +> **DO NOT** try to install dependencies. Tools for doing so are intended for development. *Always* [download a prebuilt version](https://github.com/Firework-Games-AI-Division/dmt-meshes/releases/latest). \ No newline at end of file diff --git a/docs/assets/banner.png b/docs/assets/banner.png new file mode 100644 index 0000000..73bb733 Binary files /dev/null and b/docs/assets/banner.png differ diff --git a/generator_process/__init__.py b/generator_process/__init__.py new file mode 100644 index 0000000..cc567e0 --- /dev/null +++ b/generator_process/__init__.py @@ -0,0 +1,12 @@ +from .actor import Actor + +class Generator(Actor): + """ + The actor used for all background process. + """ + from .actions.text_to_pc import text_to_pc + from .actions.text_to_mesh import text_to_mesh + from .actions.image_to_pc import image_to_pc + from .actions.pc_to_mesh import pc_to_mesh + from .actions.image_to_mesh import image_to_mesh + from .actions.utils import choose_device, get_point_cloud diff --git a/generator_process/actions/__init__.py b/generator_process/actions/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/generator_process/actions/image_to_mesh.py b/generator_process/actions/image_to_mesh.py new file mode 100644 index 0000000..cc10966 --- /dev/null +++ b/generator_process/actions/image_to_mesh.py @@ -0,0 +1,226 @@ +def image_to_mesh( + self, + init_image, + image_to_pc_model='base1B', + pc_to_mesh_method='dmtet', + gridres=128, + lr=1e-3, + laplacian_weight=0.4, + steps=5000, + view_every=500, + multires=4, + **kwargs + ): + import torch + import numpy as np + from uuid import uuid4 + from .utils import MeshGenerationResult, StepPreviewMode + from tqdm.auto import tqdm + from pathlib import Path + from ...absolute_path import absolute_path, DATA_PATH + import point_e + from point_e.diffusion.configs import DIFFUSION_CONFIGS, diffusion_from_config + from point_e.diffusion.sampler import PointCloudSampler + from point_e.models.download import load_checkpoint + from point_e.models.configs import MODEL_CONFIGS, model_from_config + + from point_e.models.download import load_checkpoint + from point_e.models.configs import MODEL_CONFIGS, model_from_config + from point_e.util.pc_to_mesh import marching_cubes_mesh + from point_e.util.point_cloud import PointCloud + from point_e.util.dmtet.dmtet_network import Decoder + from point_e.util.dmtet.trianglemesh import sample_points + from point_e.util.dmtet.pointcloud import chamfer_distance + from point_e.util.dmtet.tetmesh import marching_tetrahedra + import os + + + def laplace_regularizer_const(mesh_verts, mesh_faces): + term = torch.zeros_like(mesh_verts) + norm = torch.zeros_like(mesh_verts[..., 0:1]) + + v0 = mesh_verts[mesh_faces[:, 0], :] + v1 = mesh_verts[mesh_faces[:, 1], :] + v2 = mesh_verts[mesh_faces[:, 2], :] + + term.scatter_add_(0, mesh_faces[:, 0:1].repeat(1,3), (v1 - v0) + (v2 - v0)) + term.scatter_add_(0, mesh_faces[:, 1:2].repeat(1,3), (v0 - v1) + (v2 - v1)) + term.scatter_add_(0, mesh_faces[:, 2:3].repeat(1,3), (v0 - v2) + (v1 - v2)) + + two = torch.ones_like(v0) * 2.0 + norm.scatter_add_(0, mesh_faces[:, 0:1], two) + norm.scatter_add_(0, mesh_faces[:, 1:2], two) + norm.scatter_add_(0, mesh_faces[:, 2:3], two) + + term = term / torch.clamp(norm, min=1.0) + + return torch.mean(term**2) + + + def loss_f(mesh_verts, mesh_faces, points, it): + pred_points = sample_points(mesh_verts.unsqueeze(0), mesh_faces, 50000)[0][0] + chamfer = chamfer_distance(pred_points.unsqueeze(0), points.unsqueeze(0)).mean() + if it > steps//2: + lap = laplace_regularizer_const(mesh_verts, mesh_faces) + return chamfer + lap * laplacian_weight + return chamfer + + + device = self.choose_device() + img = init_image + for _, model in MODEL_CONFIGS.items(): + if model['name'] in ['CLIPImagePointDiffusionTransformer', + 'CLIPImageGridPointDiffusionTransformer', + 'UpsamplePointDiffusionTransformer', + 'CLIPImageGridUpsamplePointDiffusionTransformer']: + model.update({'cache_dir': Path(absolute_path(DATA_PATH))/'pointe_cache'}) + + # image to pc + base_name = image_to_pc_model + base_model = model_from_config(MODEL_CONFIGS[base_name], device) + base_model.eval() + base_diffusion = diffusion_from_config(DIFFUSION_CONFIGS[base_name]) + + status = 'creating upsample model...' + print(status) + yield MeshGenerationResult( + None, + None, + 0, + False, + status + ) + upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device) + upsampler_model.eval() + upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample']) + + status = 'downloading base checkpoint...' + print(status) + yield MeshGenerationResult( + None, + None, + 0, + False, + status + ) + base_model.load_state_dict(load_checkpoint(base_name, device, cache_dir=Path(absolute_path(DATA_PATH))/'pointe_cache')) + + status = 'downloading upsampler checkpoint...' + print(status) + yield MeshGenerationResult( + None, + None, + 0, + False, + status + ) + upsampler_model.load_state_dict(load_checkpoint('upsample', device, cache_dir=Path(absolute_path(DATA_PATH))/'pointe_cache')) + sampler = PointCloudSampler( + device=device, + models=[base_model, upsampler_model], + diffusions=[base_diffusion, upsampler_diffusion], + num_points=[1024, 4096 - 1024], + aux_channels=['R', 'G', 'B'], + guidance_scale=[3.0, 3.0], + ) + # Produce a sample from the model. + samples = None + for x in tqdm(sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(images=[img]))): + samples = x + pc = sampler.output_to_point_clouds(samples)[0] + pc_path = Path(absolute_path(DATA_PATH))/'pc' + pc.save(pc_path/f'{uuid4()}.npz') + + if pc_to_mesh_method == 'dmtet': + if isinstance(pc, str): + pc = PointCloud.load(pc) + points = pc.coords + center = (points.max(0)[0] + points.min(0)[0]) / 2 + max_l = (points.max(0)[0] - points.min(0)[0]).max() + points = ((points - center) / max_l)* 0.9 + points = torch.from_numpy(points).to(device) + tet_verts = torch.tensor(np.load(os.path.join(point_e.__path__[0], 'util', 'dmtet', 'samples', f'{gridres}_verts.npz'))['data'], dtype=torch.float, device=device) + tets = torch.tensor(([np.load(os.path.join(point_e.__path__[0], 'util', 'dmtet', 'samples', f'{gridres}_tets_{i}.npz'))['data'] for i in range(4)]), dtype=torch.long, device=device).permute(1,0) + + # Initialize model and create optimizer + model = Decoder(multires=multires).to(device) + model.pre_train_sphere(1000) + + vars = [p for _, p in model.named_parameters()] + optimizer = torch.optim.Adam(vars, lr=lr) + scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda x: max(0.0, 10**(-x*0.0002))) # LR decay over time + + for i in range(steps): + pred = model(tet_verts) # predict SDF and per-vertex deformation + sdf, deform = pred[:, 0], pred[:, 1:] + verts_deformed = tet_verts + torch.tanh(deform) / gridres # constraint deformation to avoid flipping tets + mesh_verts, mesh_faces = marching_tetrahedra(verts_deformed.unsqueeze(0), tets, sdf.unsqueeze(0)) # running MT (batched) to extract surface mesh + mesh_verts, mesh_faces = mesh_verts[0], mesh_faces[0] + + loss = loss_f(mesh_verts, mesh_faces, points, i) + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + if (i) % view_every == 0: + print('Iteration {} - loss: {}, # of mesh vertices: {}, # of mesh faces: {}'.format(i, loss, mesh_verts.shape[0], mesh_faces.shape[0])) + match kwargs['step_preview_mode']: + case StepPreviewMode.NONE: + yield MeshGenerationResult( + None, + None, + i, + False + ) + case StepPreviewMode.FAST: + yield MeshGenerationResult( + mesh_verts.detach().cpu().numpy(), + mesh_faces.detach().cpu().numpy(), + i, + False + ) + yield MeshGenerationResult( + mesh_verts.detach().cpu().numpy(), + mesh_faces.detach().cpu().numpy(), + i, + True + ) + else: + status = 'creating SDF model...' + print(status) + yield MeshGenerationResult( + None, + None, + 0, + False, + status + ) + name = 'sdf' + model = model_from_config(MODEL_CONFIGS[name], device) + model.eval() + + status = 'loading SDF model...' + print(status) + yield MeshGenerationResult( + None, + None, + 0, + False, + status + ) + model.load_state_dict(load_checkpoint(name, device, cache_dir=Path(absolute_path(DATA_PATH))/'pointe_cache')) + + mesh = marching_cubes_mesh( + pc=pc, + model=model, + batch_size=4096, + grid_size=gridres, # increase to 128 for resolution used in evals + progress=True, + ) + + yield MeshGenerationResult( + mesh.verts, + mesh.faces, + 0, + True + ) diff --git a/generator_process/actions/image_to_pc.py b/generator_process/actions/image_to_pc.py new file mode 100644 index 0000000..bbea0d9 --- /dev/null +++ b/generator_process/actions/image_to_pc.py @@ -0,0 +1,90 @@ +def image_to_pc( + self, + init_image, + image_to_pc_model='base1B', + **kwargs + ): + import torch + import numpy as np + from uuid import uuid4 + from .utils import MeshGenerationResult + from tqdm.auto import tqdm + from pathlib import Path + from ...absolute_path import absolute_path, DATA_PATH + from point_e.diffusion.configs import DIFFUSION_CONFIGS, diffusion_from_config + from point_e.diffusion.sampler import PointCloudSampler + from point_e.models.download import load_checkpoint + from point_e.models.configs import MODEL_CONFIGS, model_from_config + + from point_e.models.download import load_checkpoint + from point_e.models.configs import MODEL_CONFIGS, model_from_config + + device = self.choose_device() + img = init_image + for _, model in MODEL_CONFIGS.items(): + if model['name'] in ['CLIPImagePointDiffusionTransformer', + 'CLIPImageGridPointDiffusionTransformer', + 'UpsamplePointDiffusionTransformer', + 'CLIPImageGridUpsamplePointDiffusionTransformer']: + model.update({'cache_dir': Path(absolute_path(DATA_PATH))/'pointe_cache'}) + # image to pc + base_name = image_to_pc_model #'base40M' # use base300M or base1B for better results + base_model = model_from_config(MODEL_CONFIGS[base_name], device) + base_model.eval() + base_diffusion = diffusion_from_config(DIFFUSION_CONFIGS[base_name]) + + status = 'creating upsample model...' + print(status) + yield MeshGenerationResult( + None, + None, + 0, + False, + status + ) + upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device) + upsampler_model.eval() + upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample']) + + status = 'downloading base checkpoint...' + print(status) + yield MeshGenerationResult( + None, + None, + 0, + False, + status + ) + base_model.load_state_dict(load_checkpoint(base_name, device, cache_dir=Path(absolute_path(DATA_PATH))/'pointe_cache')) + + status = 'downloading upsampler checkpoint...' + print(status) + yield MeshGenerationResult( + None, + None, + 0, + False, + status + ) + upsampler_model.load_state_dict(load_checkpoint('upsample', device, cache_dir=Path(absolute_path(DATA_PATH))/'pointe_cache')) + sampler = PointCloudSampler( + device=device, + models=[base_model, upsampler_model], + diffusions=[base_diffusion, upsampler_diffusion], + num_points=[1024, 4096 - 1024], + aux_channels=['R', 'G', 'B'], + guidance_scale=[3.0, 3.0], + ) + # Produce a sample from the model. + samples = None + for x in tqdm(sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(images=[img]))): + samples = x + pc = sampler.output_to_point_clouds(samples)[0] + pc_path = Path(absolute_path(DATA_PATH))/'pc' + pc.save(pc_path/f'{uuid4()}.npz') + yield MeshGenerationResult( + None, + None, + 0, + True + ) diff --git a/generator_process/actions/pc_to_mesh.py b/generator_process/actions/pc_to_mesh.py new file mode 100644 index 0000000..ee14dd4 --- /dev/null +++ b/generator_process/actions/pc_to_mesh.py @@ -0,0 +1,167 @@ +def pc_to_mesh( + self, + pc_idx, + pc_to_mesh_method='dmtet', + gridres=128, + lr=1e-3, + laplacian_weight=0.4, + steps=5000, + view_every=500, + multires=4, + **kwargs + ): + import os + import torch + import numpy as np + from .utils import MeshGenerationResult, StepPreviewMode + from tqdm.auto import tqdm + from pathlib import Path + from ...absolute_path import absolute_path, DATA_PATH + import point_e + from point_e.models.download import load_checkpoint + from point_e.models.configs import MODEL_CONFIGS, model_from_config + + from point_e.models.download import load_checkpoint + from point_e.models.configs import MODEL_CONFIGS, model_from_config + from point_e.util.pc_to_mesh import marching_cubes_mesh + from point_e.util.point_cloud import PointCloud + from point_e.util.dmtet.dmtet_network import Decoder + from point_e.util.dmtet.trianglemesh import sample_points + from point_e.util.dmtet.pointcloud import chamfer_distance + from point_e.util.dmtet.tetmesh import marching_tetrahedra + + def laplace_regularizer_const(mesh_verts, mesh_faces): + term = torch.zeros_like(mesh_verts) + norm = torch.zeros_like(mesh_verts[..., 0:1]) + + v0 = mesh_verts[mesh_faces[:, 0], :] + v1 = mesh_verts[mesh_faces[:, 1], :] + v2 = mesh_verts[mesh_faces[:, 2], :] + + term.scatter_add_(0, mesh_faces[:, 0:1].repeat(1,3), (v1 - v0) + (v2 - v0)) + term.scatter_add_(0, mesh_faces[:, 1:2].repeat(1,3), (v0 - v1) + (v2 - v1)) + term.scatter_add_(0, mesh_faces[:, 2:3].repeat(1,3), (v0 - v2) + (v1 - v2)) + + two = torch.ones_like(v0) * 2.0 + norm.scatter_add_(0, mesh_faces[:, 0:1], two) + norm.scatter_add_(0, mesh_faces[:, 1:2], two) + norm.scatter_add_(0, mesh_faces[:, 2:3], two) + + term = term / torch.clamp(norm, min=1.0) + + return torch.mean(term**2) + + + def loss_f(mesh_verts, mesh_faces, points, it): + pred_points = sample_points(mesh_verts.unsqueeze(0), mesh_faces, 50000)[0][0] + chamfer = chamfer_distance(pred_points.unsqueeze(0), points.unsqueeze(0)).mean() + if it > steps//2: + lap = laplace_regularizer_const(mesh_verts, mesh_faces) + return chamfer + lap * laplacian_weight + return chamfer + + for _, model in MODEL_CONFIGS.items(): + if model['name'] in ['CLIPImagePointDiffusionTransformer', + 'CLIPImageGridPointDiffusionTransformer', + 'UpsamplePointDiffusionTransformer', + 'CLIPImageGridUpsamplePointDiffusionTransformer']: + model.update({'cache_dir': Path(absolute_path(DATA_PATH))/'pointe_cache'}) + + device = self.choose_device() + pc_path = Path(absolute_path(DATA_PATH))/'pc' + pc_list = sorted(pc_path.glob('*.npz'), key=os.path.getmtime, reverse=True) + pc_filename = pc_list[pc_idx] + pc = PointCloud.load(str(pc_filename)) + if pc_to_mesh_method == 'dmtet': + if isinstance(pc, str): + pc = PointCloud.load(pc) + points = pc.coords + center = (points.max(0)[0] + points.min(0)[0]) / 2 + max_l = (points.max(0)[0] - points.min(0)[0]).max() + points = ((points - center) / max_l)* 0.9 + points = torch.from_numpy(points).to(device) + tet_verts = torch.tensor(np.load(os.path.join(point_e.__path__[0], 'util', 'dmtet', 'samples', f'{gridres}_verts.npz'))['data'], dtype=torch.float, device=device) + tets = torch.tensor(([np.load(os.path.join(point_e.__path__[0], 'util', 'dmtet', 'samples', f'{gridres}_tets_{i}.npz'))['data'] for i in range(4)]), dtype=torch.long, device=device).permute(1,0) + + # Initialize model and create optimizer + model = Decoder(multires=multires).to(device) + model.pre_train_sphere(1000) + + vars = [p for _, p in model.named_parameters()] + optimizer = torch.optim.Adam(vars, lr=lr) + scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda x: max(0.0, 10**(-x*0.0002))) # LR decay over time + + for i in range(steps): + pred = model(tet_verts) # predict SDF and per-vertex deformation + sdf, deform = pred[:, 0], pred[:, 1:] + verts_deformed = tet_verts + torch.tanh(deform) / gridres # constraint deformation to avoid flipping tets + mesh_verts, mesh_faces = marching_tetrahedra(verts_deformed.unsqueeze(0), tets, sdf.unsqueeze(0)) # running MT (batched) to extract surface mesh + mesh_verts, mesh_faces = mesh_verts[0], mesh_faces[0] + + loss = loss_f(mesh_verts, mesh_faces, points, i) + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + if (i) % view_every == 0: + print('Iteration {} - loss: {}, # of mesh vertices: {}, # of mesh faces: {}'.format(i, loss, mesh_verts.shape[0], mesh_faces.shape[0])) + match kwargs['step_preview_mode']: + case StepPreviewMode.NONE: + yield MeshGenerationResult( + None, + None, + i, + False + ) + case StepPreviewMode.FAST: + yield MeshGenerationResult( + mesh_verts.detach().cpu().numpy(), + mesh_faces.detach().cpu().numpy(), + i, + False + ) + yield MeshGenerationResult( + mesh_verts.detach().cpu().numpy(), + mesh_faces.detach().cpu().numpy(), + i, + True + ) + else: + status = 'creating SDF model...' + print(status) + yield MeshGenerationResult( + None, + None, + 0, + False, + status + ) + name = 'sdf' + model = model_from_config(MODEL_CONFIGS[name], device) + model.eval() + + status = 'loading SDF model...' + print(status) + yield MeshGenerationResult( + None, + None, + 0, + False, + status + ) + model.load_state_dict(load_checkpoint(name, device, cache_dir=Path(absolute_path(DATA_PATH))/'pointe_cache')) + + mesh = marching_cubes_mesh( + pc=pc, + model=model, + batch_size=4096, + grid_size=gridres, # increase to 128 for resolution used in evals + progress=True, + ) + + yield MeshGenerationResult( + mesh.verts, + mesh.faces, + 0, + True + ) diff --git a/generator_process/actions/text_to_mesh.py b/generator_process/actions/text_to_mesh.py new file mode 100644 index 0000000..9e68af9 --- /dev/null +++ b/generator_process/actions/text_to_mesh.py @@ -0,0 +1,225 @@ +def text_to_mesh( + self, + prompt, + text_to_pc_model='base40M-textvec', + pc_to_mesh_method='dmtet', + gridres=128, + lr=1e-3, + laplacian_weight=0.4, + steps=5000, + view_every=500, + multires=4, + **kwargs + ): + import torch + import numpy as np + from uuid import uuid4 + from .utils import MeshGenerationResult, StepPreviewMode + from tqdm.auto import tqdm + from pathlib import Path + from ...absolute_path import absolute_path, DATA_PATH + import point_e + from point_e.diffusion.configs import DIFFUSION_CONFIGS, diffusion_from_config + from point_e.diffusion.sampler import PointCloudSampler + from point_e.models.download import load_checkpoint + from point_e.models.configs import MODEL_CONFIGS, model_from_config + + from point_e.models.download import load_checkpoint + from point_e.models.configs import MODEL_CONFIGS, model_from_config + from point_e.util.pc_to_mesh import marching_cubes_mesh + from point_e.util.point_cloud import PointCloud + from point_e.util.dmtet.dmtet_network import Decoder + from point_e.util.dmtet.trianglemesh import sample_points + from point_e.util.dmtet.pointcloud import chamfer_distance + from point_e.util.dmtet.tetmesh import marching_tetrahedra + import os + + def laplace_regularizer_const(mesh_verts, mesh_faces): + term = torch.zeros_like(mesh_verts) + norm = torch.zeros_like(mesh_verts[..., 0:1]) + + v0 = mesh_verts[mesh_faces[:, 0], :] + v1 = mesh_verts[mesh_faces[:, 1], :] + v2 = mesh_verts[mesh_faces[:, 2], :] + + term.scatter_add_(0, mesh_faces[:, 0:1].repeat(1,3), (v1 - v0) + (v2 - v0)) + term.scatter_add_(0, mesh_faces[:, 1:2].repeat(1,3), (v0 - v1) + (v2 - v1)) + term.scatter_add_(0, mesh_faces[:, 2:3].repeat(1,3), (v0 - v2) + (v1 - v2)) + + two = torch.ones_like(v0) * 2.0 + norm.scatter_add_(0, mesh_faces[:, 0:1], two) + norm.scatter_add_(0, mesh_faces[:, 1:2], two) + norm.scatter_add_(0, mesh_faces[:, 2:3], two) + + term = term / torch.clamp(norm, min=1.0) + + return torch.mean(term**2) + + + def loss_f(mesh_verts, mesh_faces, points, it): + pred_points = sample_points(mesh_verts.unsqueeze(0), mesh_faces, 50000)[0][0] + chamfer = chamfer_distance(pred_points.unsqueeze(0), points.unsqueeze(0)).mean() + if it > steps//2: + lap = laplace_regularizer_const(mesh_verts, mesh_faces) + return chamfer + lap * laplacian_weight + return chamfer + + device = self.choose_device() + + for _, model in MODEL_CONFIGS.items(): + if model['name'] in ['CLIPImagePointDiffusionTransformer', + 'CLIPImageGridPointDiffusionTransformer', + 'UpsamplePointDiffusionTransformer', + 'CLIPImageGridUpsamplePointDiffusionTransformer']: + model.update({'cache_dir': Path(absolute_path(DATA_PATH))/'pointe_cache'}) + + # image to pc + base_name = text_to_pc_model #'base40M' # use base300M or base1B for better results + base_model = model_from_config(MODEL_CONFIGS[base_name], device) + base_model.eval() + base_diffusion = diffusion_from_config(DIFFUSION_CONFIGS[base_name]) + + status = 'creating upsample model...' + print(status) + yield MeshGenerationResult( + None, + None, + 0, + False, + status + ) + upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device) + upsampler_model.eval() + upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample']) + + status = 'downloading base checkpoint...' + print(status) + yield MeshGenerationResult( + None, + None, + 0, + False, + status + ) + base_model.load_state_dict(load_checkpoint(base_name, device, cache_dir=Path(absolute_path(DATA_PATH))/'pointe_cache')) + + status = 'downloading upsampler checkpoint...' + print(status) + yield MeshGenerationResult( + None, + None, + 0, + False, + status + ) + upsampler_model.load_state_dict(load_checkpoint('upsample', device, cache_dir=Path(absolute_path(DATA_PATH))/'pointe_cache')) + sampler = PointCloudSampler( + device=device, + models=[base_model, upsampler_model], + diffusions=[base_diffusion, upsampler_diffusion], + num_points=[1024, 4096 - 1024], + aux_channels=['R', 'G', 'B'], + guidance_scale=[3.0, 0.0], + model_kwargs_key_filter=('texts', ''), # Do not condition the upsampler at all + ) + # Produce a sample from the model. + samples = None + for x in tqdm(sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(texts=[prompt]))): + samples = x + pc = sampler.output_to_point_clouds(samples)[0] + pc_path = Path(absolute_path(DATA_PATH))/'pc' + pc.save(pc_path/f'{uuid4()}.npz') + if pc_to_mesh_method == 'dmtet': + if isinstance(pc, str): + pc = PointCloud.load(pc) + points = pc.coords + center = (points.max(0)[0] + points.min(0)[0]) / 2 + max_l = (points.max(0)[0] - points.min(0)[0]).max() + points = ((points - center) / max_l)* 0.9 + points = torch.from_numpy(points).to(device) + tet_verts = torch.tensor(np.load(os.path.join(point_e.__path__[0], 'util', 'dmtet', 'samples', f'{gridres}_verts.npz'))['data'], dtype=torch.float, device=device) + tets = torch.tensor(([np.load(os.path.join(point_e.__path__[0], 'util', 'dmtet', 'samples', f'{gridres}_tets_{i}.npz'))['data'] for i in range(4)]), dtype=torch.long, device=device).permute(1,0) + + # Initialize model and create optimizer + model = Decoder(multires=multires).to(device) + model.pre_train_sphere(1000) + + vars = [p for _, p in model.named_parameters()] + optimizer = torch.optim.Adam(vars, lr=lr) + scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda x: max(0.0, 10**(-x*0.0002))) # LR decay over time + + for i in range(steps): + pred = model(tet_verts) # predict SDF and per-vertex deformation + sdf, deform = pred[:, 0], pred[:, 1:] + verts_deformed = tet_verts + torch.tanh(deform) / gridres # constraint deformation to avoid flipping tets + mesh_verts, mesh_faces = marching_tetrahedra(verts_deformed.unsqueeze(0), tets, sdf.unsqueeze(0)) # running MT (batched) to extract surface mesh + mesh_verts, mesh_faces = mesh_verts[0], mesh_faces[0] + + loss = loss_f(mesh_verts, mesh_faces, points, i) + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + if (i) % view_every == 0: + print('Iteration {} - loss: {}, # of mesh vertices: {}, # of mesh faces: {}'.format(i, loss, mesh_verts.shape[0], mesh_faces.shape[0])) + match kwargs['step_preview_mode']: + case StepPreviewMode.NONE: + yield MeshGenerationResult( + None, + None, + i, + False + ) + case StepPreviewMode.FAST: + yield MeshGenerationResult( + mesh_verts.detach().cpu().numpy(), + mesh_faces.detach().cpu().numpy(), + i, + False + ) + yield MeshGenerationResult( + mesh_verts.detach().cpu().numpy(), + mesh_faces.detach().cpu().numpy(), + i, + True + ) + else: + status = 'creating SDF model...' + print(status) + yield MeshGenerationResult( + None, + None, + 0, + False, + status + ) + name = 'sdf' + model = model_from_config(MODEL_CONFIGS[name], device) + model.eval() + + status = 'loading SDF model...' + print(status) + yield MeshGenerationResult( + None, + None, + 0, + False, + status + ) + model.load_state_dict(load_checkpoint(name, device, cache_dir=Path(absolute_path(DATA_PATH))/'pointe_cache')) + + mesh = marching_cubes_mesh( + pc=pc, + model=model, + batch_size=4096, + grid_size=gridres, # increase to 128 for resolution used in evals + progress=True, + ) + + yield MeshGenerationResult( + mesh.verts, + mesh.faces, + 0, + True + ) + diff --git a/generator_process/actions/text_to_pc.py b/generator_process/actions/text_to_pc.py new file mode 100644 index 0000000..833bf2b --- /dev/null +++ b/generator_process/actions/text_to_pc.py @@ -0,0 +1,82 @@ +def text_to_pc( + self, + prompt, + text_to_pc_model='base40M-textvec', + **kwargs + ): + import torch + import numpy as np + from uuid import uuid4 + from .utils import MeshGenerationResult + from tqdm.auto import tqdm + from pathlib import Path + from ...absolute_path import absolute_path, DATA_PATH + from point_e.diffusion.configs import DIFFUSION_CONFIGS, diffusion_from_config + from point_e.diffusion.sampler import PointCloudSampler + from point_e.models.download import load_checkpoint + from point_e.models.configs import MODEL_CONFIGS, model_from_config + + from point_e.models.download import load_checkpoint + from point_e.models.configs import MODEL_CONFIGS, model_from_config + + device = self.choose_device() + for _, model in MODEL_CONFIGS.items(): + if model['name'] in ['CLIPImagePointDiffusionTransformer', + 'CLIPImageGridPointDiffusionTransformer', + 'UpsamplePointDiffusionTransformer', + 'CLIPImageGridUpsamplePointDiffusionTransformer']: + model.update({'cache_dir': Path(absolute_path(DATA_PATH))/'pointe_cache'}) + # image to pc + base_name = text_to_pc_model #'base40M' # use base300M or base1B for better results + base_model = model_from_config(MODEL_CONFIGS[base_name], device) + base_model.eval() + base_diffusion = diffusion_from_config(DIFFUSION_CONFIGS[base_name]) + + print('creating upsample model...') + upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device) + upsampler_model.eval() + upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample']) + + status = 'downloading base checkpoint...' + print(status) + yield MeshGenerationResult( + None, + None, + 0, + False, + status + ) + base_model.load_state_dict(load_checkpoint(base_name, device, cache_dir=Path(absolute_path(DATA_PATH))/'pointe_cache')) + + status = 'downloading upsampler checkpoint...' + print(status) + yield MeshGenerationResult( + None, + None, + 0, + False, + status + ) + upsampler_model.load_state_dict(load_checkpoint('upsample', device, cache_dir=Path(absolute_path(DATA_PATH))/'pointe_cache')) + sampler = PointCloudSampler( + device=device, + models=[base_model, upsampler_model], + diffusions=[base_diffusion, upsampler_diffusion], + num_points=[1024, 4096 - 1024], + aux_channels=['R', 'G', 'B'], + guidance_scale=[3.0, 0.0], + model_kwargs_key_filter=('texts', ''), # Do not condition the upsampler at all + ) + # Produce a sample from the model. + samples = None + for x in tqdm(sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(texts=[prompt]))): + samples = x + pc = sampler.output_to_point_clouds(samples)[0] + pc_path = Path(absolute_path(DATA_PATH))/'pc' + pc.save(pc_path/f'{uuid4()}.npz') + yield MeshGenerationResult( + None, + None, + 0, + True + ) diff --git a/generator_process/actions/utils.py b/generator_process/actions/utils.py new file mode 100644 index 0000000..0629155 --- /dev/null +++ b/generator_process/actions/utils.py @@ -0,0 +1,40 @@ +import enum +import os +from dataclasses import dataclass +from numpy.typing import NDArray +from pathlib import Path +from ...absolute_path import absolute_path, DATA_PATH + + +def choose_device(self) -> str: + """ + Automatically select which PyTorch device to use. + """ + import torch + if torch.cuda.is_available(): + return "cuda" + elif torch.backends.mps.is_available(): + return "mps" + else: + return "cpu" + + +def get_point_cloud(self): + pc_path = Path(absolute_path(DATA_PATH))/'pc' + pc_list = sorted(pc_path.glob('*.npz'), key=os.path.getmtime, reverse=True) + return [pc.name for pc in pc_list] + + +@dataclass +class MeshGenerationResult: + verts: NDArray | None + faces: NDArray | None + step: int + final: bool + status: str = '' + + +class StepPreviewMode(enum.Enum): + NONE = "None" + FAST = "Fast" + ACCURATE = "Accurate" \ No newline at end of file diff --git a/generator_process/actor.py b/generator_process/actor.py new file mode 100644 index 0000000..72c5200 --- /dev/null +++ b/generator_process/actor.py @@ -0,0 +1,317 @@ +from multiprocessing import Queue, Process, Lock, current_process, get_context +import multiprocessing.synchronize +import enum +import traceback +import threading +from typing import Type, TypeVar, Callable, Any, MutableSet, Generator +import site +import sys + +def _load_dependencies(): + from ..absolute_path import absolute_path + site.addsitedir(absolute_path(".python_dependencies")) + deps = sys.path.pop(-1) + sys.path.insert(0, deps) +if current_process().name == "__actor__": + _load_dependencies() + +class Future: + """ + Object that represents a value that has not completed processing, but will in the future. + Add callbacks to be notified when values become available, or use `.result()` and `.exception()` to wait for the value. + """ + _response_callbacks: MutableSet[Callable[['Future', Any], None]] = set() + _exception_callbacks: MutableSet[Callable[['Future', BaseException], None]] = set() + _done_callbacks: MutableSet[Callable[['Future'], None]] = set() + _responses: list = [] + _exception: BaseException | None = None + _done_event: threading.Event + done: bool = False + cancelled: bool = False + call_done_on_exception: bool = True + + def __init__(self): + self._response_callbacks = set() + self._exception_callbacks = set() + self._done_callbacks = set() + self._responses = [] + self._exception = None + self._done_event = threading.Event() + self.done = False + self.cancelled = False + self.call_done_on_exception = True + + def result(self): + """ + Get the result value (blocking). + """ + def _response(): + match len(self._responses): + case 0: + return None + case 1: + return self._responses[0] + case _: + return self._responses + if self._exception is not None: + raise self._exception + if self.done: + return _response() + else: + self._done_event.wait() + if self._exception is not None: + raise self._exception + return _response() + + def exception(self): + if self.done: + return self._exception + else: + self._done_event.wait() + return self._exception + + def cancel(self): + self.cancelled = True + + def _run_on_main_thread(self, func): + import bpy + bpy.app.timers.register(func) + + def add_response(self, response): + """ + Add a response value and notify all consumers. + """ + self._responses.append(response) + def run_callbacks(): + for response_callback in self._response_callbacks: + response_callback(self, response) + self._run_on_main_thread(run_callbacks) + + def set_exception(self, exception: BaseException): + """ + Set the exception. + """ + self._exception = exception + def run_callbacks(): + for exception_callback in self._exception_callbacks: + exception_callback(self, exception) + self._run_on_main_thread(run_callbacks) + + def set_done(self): + """ + Mark the future as done. + """ + assert not self.done + self.done = True + self._done_event.set() + if self._exception is None or self.call_done_on_exception: + def run_callbacks(): + for done_callback in self._done_callbacks: + done_callback(self) + self._run_on_main_thread(run_callbacks) + + def add_response_callback(self, callback: Callable[['Future', Any], None]): + """ + Add a callback to run whenever a response is received. + Will be called multiple times by generator functions. + """ + self._response_callbacks.add(callback) + + def add_exception_callback(self, callback: Callable[['Future', BaseException], None]): + """ + Add a callback to run when the future errors. + Will only be called once at the first exception. + """ + self._exception_callbacks.add(callback) + + def add_done_callback(self, callback: Callable[['Future'], None]): + """ + Add a callback to run when the future is marked as done. + Will only be called once. + """ + self._done_callbacks.add(callback) + +class ActorContext(enum.IntEnum): + """ + The context of an `Actor` object. + + One `Actor` instance is the `FRONTEND`, while the other instance is the backend, which runs in a separate process. + The `FRONTEND` sends messages to the `BACKEND`, which does work and returns a result. + """ + FRONTEND = 0 + BACKEND = 1 + +class Message: + """ + Represents a function signature with a method name, positonal arguments, and keyword arguments. + Note: All arguments must be picklable. + """ + + def __init__(self, method_name, args, kwargs): + self.method_name = method_name + self.args = args + self.kwargs = kwargs + + CANCEL = "__cancel__" + END = "__end__" + +def _start_backend(cls, message_queue, response_queue): + cls( + ActorContext.BACKEND, + message_queue=message_queue, + response_queue=response_queue + ).start() + +class TracedError(BaseException): + def __init__(self, base: BaseException, trace: str): + self.base = base + self.trace = trace + +T = TypeVar('T', bound='Actor') + +class Actor: + """ + Base class for specialized actors. + + Uses queues to send actions to a background process and receive a response. + Calls to any method declared by the frontend are automatically dispatched to the backend. + All function arguments must be picklable. + """ + + _message_queue: Queue + _response_queue: Queue + _lock: multiprocessing.synchronize.Lock + + _shared_instance = None + + # Methods that are not used for message passing, and should not be overridden in `_setup`. + _protected_methods = { + "start", + "close", + "is_alive", + "can_use", + "shared" + } + + def __init__(self, context: ActorContext, message_queue: Queue = None, response_queue: Queue = None): + self.context = context + self._message_queue = message_queue if message_queue is not None else get_context('spawn').Queue(maxsize=1) + self._response_queue = response_queue if response_queue is not None else get_context('spawn').Queue(maxsize=1) + self._setup() + self.__class__._shared_instance = self + + def _setup(self): + """ + Setup the Actor after initialization. + """ + match self.context: + case ActorContext.FRONTEND: + self._lock = Lock() + for name in filter(lambda name: callable(getattr(self, name)) and not name.startswith("_") and name not in self._protected_methods, dir(self)): + setattr(self, name, self._send(name)) + case ActorContext.BACKEND: + pass + + @classmethod + def shared(cls: Type[T]) -> T: + return cls._shared_instance or cls(ActorContext.FRONTEND).start() + + def start(self: T) -> T: + """ + Start the actor process. + """ + match self.context: + case ActorContext.FRONTEND: + self.process = get_context('spawn').Process(target=_start_backend, args=(self.__class__, self._message_queue, self._response_queue), name="__actor__", daemon=True) + self.process.start() + case ActorContext.BACKEND: + self._backend_loop() + return self + + def close(self): + """ + Stop the actor process. + """ + match self.context: + case ActorContext.FRONTEND: + self.process.terminate() + self._message_queue.close() + self._response_queue.close() + case ActorContext.BACKEND: + pass + + @classmethod + def shared_close(cls: Type[T]): + if cls._shared_instance is None: + return + cls._shared_instance.close() + cls._shared_instance = None + + def is_alive(self): + match self.context: + case ActorContext.FRONTEND: + return self.process.is_alive() + case ActorContext.BACKEND: + return True + + def can_use(self): + if result := self._lock.acquire(block=False): + self._lock.release() + return result + + def _backend_loop(self): + while True: + self._receive(self._message_queue.get()) + + def _receive(self, message: Message): + try: + response = getattr(self, message.method_name)(*message.args, **message.kwargs) + if isinstance(response, Generator): + for res in iter(response): + extra_message = None + try: + extra_message = self._message_queue.get(block=False) + except: + pass + if extra_message == Message.CANCEL: + break + self._response_queue.put(res) + else: + self._response_queue.put(response) + except Exception as e: + trace = traceback.format_exc() + self._response_queue.put(TracedError(e, trace)) + self._response_queue.put(Message.END) + + def _send(self, name): + def _send(*args, _block=False, **kwargs): + future = Future() + def _send_thread(future: Future): + self._lock.acquire() + self._message_queue.put(Message(name, args, kwargs)) + + while not future.done: + if future.cancelled: + self._message_queue.put(Message.CANCEL) + response = self._response_queue.get() + if response == Message.END: + future.set_done() + elif isinstance(response, TracedError): + response.base.__cause__ = Exception(response.trace) + future.set_exception(response.base) + elif isinstance(response, Exception): + future.set_exception(response) + else: + future.add_response(response) + + self._lock.release() + if _block: + _send_thread(future) + else: + thread = threading.Thread(target=_send_thread, args=(future,), daemon=True) + thread.start() + return future + return _send + + def __del__(self): + self.close() \ No newline at end of file diff --git a/operators/dmt_meshes.py b/operators/dmt_meshes.py new file mode 100644 index 0000000..06152c1 --- /dev/null +++ b/operators/dmt_meshes.py @@ -0,0 +1,165 @@ +import bpy +import hashlib +import numpy as np + +from ..generator_process import Generator +from ..generator_process.actions.utils import MeshGenerationResult + +from ..preferences import DMTMeshesPreferences, set_pc_list +import uuid + +def bpy_mesh(name, verts, faces, step, existing_mesh_object): + mesh = bpy.data.meshes.new(f'{name}_{step}') + mesh.from_pydata(verts, [], faces) + if not existing_mesh_object: + existing_mesh_object = bpy.data.objects.new(name, mesh) + bpy.context.scene.collection.objects.link(existing_mesh_object) + else: + existing_mesh = existing_mesh_object.data + existing_mesh_object.data = mesh + bpy.data.meshes.remove(existing_mesh) + return existing_mesh_object + +class DMTMesh(bpy.types.Operator): + bl_idname = "object.dmt_mesh" + bl_label = "DMT Mesh" + bl_description = "Generate a mesh with AI" + bl_options = {'REGISTER'} + + @classmethod + def poll(cls, context): + return Generator.shared().can_use() + + def execute(self, context): + screen = context.screen + scene = context.scene + generated_args = scene.dmt_meshes_prompt.generate_args() + generated_args['point_cloud_results_selection'] = scene.point_cloud_results_selection + init_image = None + if generated_args['use_init_img']: + match generated_args['init_img_src']: + case 'file': + init_image = scene.init_img + case 'open_editor': + for area in screen.areas: + if area.type == 'IMAGE_EDITOR': + if area.spaces.active.image is not None: + init_image = area.spaces.active.image + if init_image is not None: + init_image = np.flipud( + (np.array(init_image.pixels) * 255) + .astype(np.uint8) + .reshape((init_image.size[1], init_image.size[0], init_image.channels)) + ) + + # Setup the progress indicator + def step_progress_update(self, context): + if hasattr(context.area, "regions"): + for region in context.area.regions: + if region.type == "UI": + region.tag_redraw() + return None + bpy.types.Scene.dmt_meshes_progress = bpy.props.IntProperty(name="", + default=0, + min=0, + max=generated_args['steps'], + update=step_progress_update) + scene.dmt_meshes_info = "Starting..." + + last_data_block = None + mesh_name = str(uuid.uuid4()) + def step_callback(_, step_mesh: MeshGenerationResult): + nonlocal last_data_block + if step_mesh.final: + return + scene.dmt_meshes_progress = step_mesh.step + if step_mesh.status: + scene.dmt_meshes_info = step_mesh.status + if step_mesh.verts is not None and step_mesh.faces is not None: + last_data_block = bpy_mesh(mesh_name, + step_mesh.verts, + step_mesh.faces, + step_mesh.step, + last_data_block) + + def done_callback(future): + nonlocal last_data_block + if hasattr(gen, '_active_generation_future'): + del gen._active_generation_future + mesh_result: MeshGenerationResult | list = future.result() + set_pc_list('point_cloud_results', Generator.shared().get_point_cloud().result()) + if isinstance(mesh_result, list): + mesh_result = mesh_result[-1] + if mesh_result.verts is not None and mesh_result.faces is not None: + last_data_block = bpy_mesh(mesh_name, + mesh_result.verts, + mesh_result.faces, + mesh_result.step, + last_data_block) + scene.dmt_meshes_info = "" + scene.dmt_meshes_progress = 0 + + def exception_callback(_, exception): + scene.dmt_meshes_info = "" + scene.dmt_meshes_progress = 0 + if hasattr(gen, '_active_generation_future'): + del gen._active_generation_future + self.report({'ERROR'}, str(exception)) + raise exception + + gen = Generator.shared() + def generate_next(): + if generated_args['run_mode'] == 'image_to_mesh' and init_image is not None: + f = gen.image_to_mesh(init_image=init_image, **generated_args) + elif generated_args['run_mode'] == 'image_to_pc' and init_image is not None: + f = gen.image_to_pc(init_image=init_image, **generated_args) + elif generated_args['run_mode'] == 'text_to_pc': + f = gen.text_to_pc(**generated_args) + elif generated_args['run_mode'] == 'text_to_mesh': + f = gen.text_to_mesh(**generated_args) + elif generated_args['run_mode'] == 'pc_to_mesh': + f = gen.pc_to_mesh(pc_idx=generated_args['point_cloud_results_selection'], **generated_args) + + gen._active_generation_future = f + f.call_done_on_exception = False + f.add_response_callback(step_callback) + f.add_exception_callback(exception_callback) + f.add_done_callback(done_callback) + generate_next() + return {"FINISHED"} + +def kill_generator(context=bpy.context): + Generator.shared_close() + try: + context.scene.dmt_meshes_info = "" + context.scene.dmt_mehses_progress = 0 + except: + pass + +class ReleaseGenerator(bpy.types.Operator): + bl_idname = "shade.dmt_meshes_release_generator" + bl_label = "Release Generator" + bl_description = "Releases the generator class to free up VRAM" + bl_options = {'REGISTER'} + + def execute(self, context): + kill_generator(context) + return {'FINISHED'} + +class CancelGenerator(bpy.types.Operator): + bl_idname = "shade.dmt_meshes_stop_generator" + bl_label = "Cancel Generator" + bl_description = "Stops the generator without reloading everything next time" + bl_options = {'REGISTER'} + + @classmethod + def poll(cls, context): + gen = Generator.shared() + return hasattr(gen, "_active_generation_future") and gen._active_generation_future is not None and not gen._active_generation_future.cancelled and not gen._active_generation_future.done + + def execute(self, context): + gen = Generator.shared() + gen._active_generation_future.cancel() + context.scene.dmt_meshes_info = "" + context.scene.dmt_meshes_progress = 0 + return {'FINISHED'} \ No newline at end of file diff --git a/operators/install_dependencies.py b/operators/install_dependencies.py new file mode 100644 index 0000000..2d63138 --- /dev/null +++ b/operators/install_dependencies.py @@ -0,0 +1,157 @@ +import bpy +import os +import site +import sys +import sysconfig +import subprocess +import requests +import tarfile +from enum import IntEnum + +from ..absolute_path import absolute_path + +class PipInstall(IntEnum): + DEPENDENCIES = 1 + STANDARD = 2 + USER_SITE = 3 + +def install_pip(method = PipInstall.STANDARD): + """ + Installs pip if not already present. Please note that ensurepip.bootstrap() also calls pip, which adds the + environment variable PIP_REQ_TRACKER. After ensurepip.bootstrap() finishes execution, the directory doesn't exist + anymore. However, when subprocess is used to call pip, in order to install a package, the environment variables + still contain PIP_REQ_TRACKER with the now nonexistent path. This is a problem since pip checks if PIP_REQ_TRACKER + is set and if it is, attempts to use it as temp directory. This would result in an error because the + directory can't be found. Therefore, PIP_REQ_TRACKER needs to be removed from environment variables. + :return: + """ + + import ensurepip + + if method == PipInstall.DEPENDENCIES: + # ensurepip doesn't have a useful way of installing to a specific directory. + # root parameter can be used, but it just concatenates that to the beginning of + # where it decides to install to, causing a more complicated path to where it installs. + wheels = {} + for name, package in ensurepip._get_packages().items(): + if package.wheel_name: + whl = os.path.join(os.path.dirname(ensurepip.__file__), "_bundled", package.wheel_name) + else: + whl = package.wheel_path + wheels[name] = whl + pip_whl = os.path.join(wheels['pip'], 'pip') + subprocess.run([sys.executable, pip_whl, "install", *wheels.values(), "--upgrade", "--no-index", "--no-deps", "--no-cache-dir", "--target", absolute_path(".python_dependencies")], check=True) + return + + # STANDARD or USER_SITE + no_user = os.environ.get("PYTHONNOUSERSITE", None) + if method == PipInstall.STANDARD: + os.environ["PYTHONNOUSERSITE"] = "1" + else: + os.environ.pop("PYTHONNOUSERSITE", None) + try: + ensurepip.bootstrap(user=method==PipInstall.USER_SITE) + finally: + os.environ.pop("PIP_REQ_TRACKER", None) + if no_user: + os.environ["PYTHONNOUSERSITE"] = no_user + else: + os.environ.pop("PYTHONNOUSERSITE", None) + +def install_pip_any(*methods): + methods = methods or PipInstall + for method in methods: + print(f"Attempting to install pip: {PipInstall(method).name}") + try: + install_pip(method) + return method + except: + import traceback + traceback.print_exc() + +def get_pip_install(): + def run(pip): + if os.path.exists(pip): + try: + subprocess.run([sys.executable, pip, "--version"], check=True) + return True + except subprocess.CalledProcessError: + pass + return False + + if run(absolute_path(".python_dependencies/pip")): + return PipInstall.DEPENDENCIES + + # This seems to not raise CalledProcessError while debugging in vscode, but works fine in normal use. + # subprocess.run([sys.executable, "-s", "-m", "pip", "--version"], check=True) + # Best to check if the module directory exists first. + for path in site.getsitepackages(): + if run(os.path.join(path,"pip")): + return PipInstall.STANDARD + + if run(os.path.join(site.getusersitepackages(),"pip")): + return PipInstall.USER_SITE + +def install_and_import_requirements(requirements_txt=None, pip_install=PipInstall.STANDARD): + """ + Installs all modules in the 'requirements.txt' file. + """ + environ_copy = dict(os.environ) + if pip_install != PipInstall.USER_SITE: + environ_copy["PYTHONNOUSERSITE"] = "1" + if pip_install == PipInstall.DEPENDENCIES: + environ_copy["PYTHONPATH"] = absolute_path(".python_dependencies") + python_include_dir = sysconfig.get_paths()['include'] + if not os.path.exists(python_include_dir): + try: + os.makedirs(python_include_dir) + finally: + pass + if os.access(python_include_dir, os.W_OK): + print("downloading additional include files") + python_devel_tgz_path = absolute_path('python-devel.tgz') + response = requests.get(f"https://www.python.org/ftp/python/{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}/Python-{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}.tgz") + with open(python_devel_tgz_path, 'wb') as f: + f.write(response.content) + with tarfile.open(python_devel_tgz_path) as python_devel_tgz: + def members(tf): + prefix = f"Python-{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}/Include/" + l = len(prefix) + for member in tf.getmembers(): + if member.path.startswith(prefix): + member.path = member.path[l:] + yield member + python_devel_tgz.extractall(path=python_include_dir, members=members(python_devel_tgz)) + os.remove(python_devel_tgz_path) + else: + print(f"skipping include files, can't write to {python_include_dir}",file=sys.stderr) + + subprocess.run([sys.executable, "-m", "pip", "install", "-r", absolute_path(requirements_txt), "--upgrade", "--no-cache-dir", "--target", absolute_path('.python_dependencies'), "--no-user"], check=True, env=environ_copy, cwd=absolute_path("")) + environ_copy["PYTHONPATH"] = absolute_path(".python_dependencies") + subprocess.run([sys.executable, "-m", "pip", "install", "git+https://github.com/Firework-Games/AI-Division/point-e", "--target", absolute_path('.python_dependencies'), "--no-user"], check=True, env=environ_copy, cwd=absolute_path("")) + +class InstallDependencies(bpy.types.Operator): + bl_idname = "stable_diffusion.install_dependencies" + bl_label = "Install Dependencies" + bl_description = ("Downloads and installs the required python packages into the '.python_dependencies' directory of the addon.") + bl_options = {"REGISTER", "INTERNAL"} + + def execute(self, context): + # Open the console so we can watch the progress. + if sys.platform == 'win32': + bpy.ops.wm.console_toggle() + + try: + pip_install = get_pip_install() + if pip_install is None: + pip_install = install_pip_any() + if pip_install is None: + raise ImportError(f'Pip could not be installed. You may have to manually install pip into {absolute_path(".python_dependencies")}') + + install_and_import_requirements(requirements_txt=context.scene.dmt_meshes_requirements_path, pip_install=pip_install) + except (subprocess.CalledProcessError, ImportError) as err: + self.report({"ERROR"}, str(err)) + return {"CANCELLED"} + + return {"FINISHED"} + diff --git a/preferences.py b/preferences.py new file mode 100644 index 0000000..acf69da --- /dev/null +++ b/preferences.py @@ -0,0 +1,49 @@ +import bpy +import os +import webbrowser + +from .absolute_path import absolute_path +from bpy.props import CollectionProperty, StringProperty +from bpy_extras.io_utils import ImportHelper + +from .operators.install_dependencies import InstallDependencies + +class PointCloud(bpy.types.PropertyGroup): + bl_label = "Point Cloud" + bl_idname = "dmt_meshes.point_cloud" + + point_cloud: bpy.props.StringProperty(name="point_cloud") + +class OpenContributors(bpy.types.Operator): + bl_idname = "dmt_meshes.open_contributors" + bl_label = "See All Contributors" + + def execute(self, context): + webbrowser.open("https://github.com/Firework-Games-AI-Division/dmt-meshes/graphs/contributors") + return {"FINISHED"} + +class DMTMeshesPreferences(bpy.types.AddonPreferences): + bl_idname = __package__ + point_cloud_results: CollectionProperty(type=PointCloud) + + def draw(self, context): + layout = self.layout + has_dependencies = len(os.listdir(absolute_path(".python_dependencies"))) > 2 + + if context.preferences.view.show_developer_ui: # If 'Developer Extras' is enabled, show addon development tools + developer_box = layout.box() + developer_box.label(text="Development Tools", icon="CONSOLE") + developer_box.label(text="This section is for addon development only. You are seeing this because you have 'Developer Extras' enabled.") + developer_box.label(text="Do not use any operators in this section unless you are setting up a development environment.") + if has_dependencies: + warn_box = developer_box.box() + warn_box.label(text="Dependencies already installed. Only install below if you developing the addon", icon="CHECKMARK") + developer_box.prop(context.scene, 'dmt_meshes_requirements_path') + developer_box.operator(InstallDependencies.bl_idname, icon="CONSOLE") + +@staticmethod +def set_pc_list(l: str, point_clouds: list): + getattr(bpy.context.preferences.addons[__package__].preferences, l).clear() + for point_cloud in point_clouds: + m = getattr(bpy.context.preferences.addons[__package__].preferences, l).add() + m.point_cloud = point_cloud \ No newline at end of file diff --git a/property_groups/dmt_prompt.py b/property_groups/dmt_prompt.py new file mode 100644 index 0000000..6b68de1 --- /dev/null +++ b/property_groups/dmt_prompt.py @@ -0,0 +1,84 @@ +import bpy +from bpy.props import FloatProperty, IntProperty, EnumProperty, BoolProperty, StringProperty, IntVectorProperty +import os +import sys +from typing import _AnnotatedAlias +from ..generator_process.actions.utils import StepPreviewMode + +step_preview_mode_options = [(mode.value, mode.value, '') for mode in StepPreviewMode] + +init_image_sources = [ + ('file', 'File', '', 'IMAGE_DATA', 1), + ('open_editor', 'Open Image', '', 'TPAINT_HLT', 2), +] + +# text_to_pc_model = [ +# ('base40M-textvec', 'Text to Mesh', '', 1), +# ('prompt_to_pc', 'Text to Point Cloud', '', 2), +# ] + +image_to_pc_models = [ + ('base40M', '40M model', '', 1), + ('base300M', '300M model', '', 2), + ('base1B', '1B model', '', 3), +] + +pc_to_mesh_methods = [ + ('openAI', 'openAI', '', 1), + ('dmtet', 'dmtet', '', 2), +] + +run_modes = [ + ('text_to_mesh', 'Text to Mesh', '', 1), + ('text_to_pc', 'Text to Point Cloud', '', 2), + ('pc_to_mesh', 'Point Cloud to Mesh', '', 3), + ('image_to_pc', 'Image to Point Cloud', '', 4), + ('image_to_mesh', 'Image to Mesh', '', 5), +] + +attributes = { + # Prompt + #"prompt_structure": EnumProperty(name="Preset", items=prompt_structures_items, description="Fill in a few simple options to create interesting images quickly"), + #"use_negative_prompt": BoolProperty(name="Use Negative Prompt", default=False), + #"negative_prompt": StringProperty(name="Negative Prompt", description="The model will avoid aspects of the negative prompt"), + "prompt": StringProperty(name="Prompt", description="prompt for text to point cloud", default="A corgi"), + + # Size + # "width": IntProperty(name="Width", default=512, min=64, step=64), + # "height": IntProperty(name="Height", default=512, min=64, step=64), + + # Init Image + "use_init_img": BoolProperty(name="Use Init Image", default=False), + "init_img_src": EnumProperty(name=" ", items=init_image_sources, default="file"), + "fit": BoolProperty(name="Fit to width/height", default=True), + "use_init_img_color": BoolProperty(name="Color Correct", default=True), + + # Advanced + #"text_to_pc_model": , + "image_to_pc_model": EnumProperty(name="Image to PC model", items=image_to_pc_models, default="base1B"), + "pc_to_mesh_method": EnumProperty(name="PC to mesh method", items=pc_to_mesh_methods, default="openAI"), + + "steps": IntProperty(name="Steps", default=5000, min=1), + "view_every": IntProperty(name="Mesh rendering frequency", default=500, min=1), + "step_preview_mode": EnumProperty(name="Step Preview", description="Displays intermediate steps in the 3D Viewport. Disabling can speed up generation", items=step_preview_mode_options, default=1), + "lr": FloatProperty(name="Mesh learning rate", default=0.001, precision=10), + "laplacian_weight": FloatProperty(name="Regulization weight", default=0.3, description="Laplacian regulizer weight"), + "gridres": IntProperty(name="Gridres", default=128), + "multires": IntProperty(name="Multires", default=4), + + # Run mode + "run_mode": EnumProperty(name="Run Mode", items=run_modes, default="text_to_mesh") +} + +DMTPrompt = type('DMTPrompt', (bpy.types.PropertyGroup,), { + "bl_label": "DMTPrompt", + "bl_idname": "dmt_meshes.DMTPrompt", + "__annotations__": attributes, +}) + +def generate_args(self): + args = { key: getattr(self, key) for key in DMTPrompt.__annotations__ } + args['step_preview_mode'] = StepPreviewMode(args['step_preview_mode']) + return args + +DMTPrompt.generate_args = generate_args \ No newline at end of file diff --git a/requirements/win-linux-cuda.txt b/requirements/win-linux-cuda.txt new file mode 100644 index 0000000..61c9e67 --- /dev/null +++ b/requirements/win-linux-cuda.txt @@ -0,0 +1 @@ +git+https://github.com/Firework-Games-AI-Division/point-e@main diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ui/panels/dmt_meshes.py b/ui/panels/dmt_meshes.py new file mode 100644 index 0000000..e19de73 --- /dev/null +++ b/ui/panels/dmt_meshes.py @@ -0,0 +1,189 @@ +import bpy +from bpy.types import Panel +from bpy_extras.io_utils import ImportHelper +from ..space_types import SPACE_TYPES +from ...operators.dmt_meshes import DMTMesh, ReleaseGenerator, CancelGenerator +from ...preferences import DMTMeshesPreferences +import webbrowser +import os +import shutil + + +def dmt_meshes_panels(): + for space_type in SPACE_TYPES: + class DMTMeshPanel(Panel): + bl_label = "DMT Mesh" + bl_idname = f"DMT_PT_mesh_panel_{space_type}" + bl_category = "DMT" + bl_space_type = space_type + bl_region_type = "UI" + + @classmethod + def poll(self, context): + if self.bl_space_type == 'NODE_EDITOR': + return context.area.ui_type == "ShaderNodeTree" or context.area.ui_type == "CompositorNodeTree" + else: + return True + + def draw(self, context): + layout = self.layout + layout.use_property_split = True + layout.use_property_decorate = False + + + DMTMeshPanel.__name__ = f"DMT_PT_mesh_panel_{space_type}" + yield DMTMeshPanel + def get_prompt(context): + return context.scene.dmt_meshes_prompt + yield from create_panel(space_type, 'UI', DMTMeshPanel.bl_idname, prompt_panel, get_prompt) + yield from create_panel(space_type, 'UI', DMTMeshPanel.bl_idname, init_image_panel, get_prompt) + yield from create_panel(space_type, 'UI', DMTMeshPanel.bl_idname, point_cloud_panel, get_prompt) + yield from create_panel(space_type, 'UI', DMTMeshPanel.bl_idname, advanced_panel, get_prompt) + yield from create_panel(space_type, 'UI', DMTMeshPanel.bl_idname, run_mode_panel, get_prompt) + yield create_panel(space_type, 'UI', DMTMeshPanel.bl_idname, actions_panel, get_prompt) + + +def create_panel(space_type, region_type, parent_id, ctor, get_prompt, use_property_decorate=False): + class BasePanel(bpy.types.Panel): + bl_category = "DMT" + bl_space_type = space_type + bl_region_type = region_type + + class SubPanel(BasePanel): + bl_category = "DMT" + bl_space_type = space_type + bl_region_type = region_type + bl_parent_id = parent_id + + def draw(self, context): + self.layout.use_property_decorate = use_property_decorate + + return ctor(SubPanel, space_type, get_prompt) + + +def prompt_panel(sub_panel, space_type, get_prompt): + class PromptPanel(sub_panel): + """Create a subpanel for prompt input""" + bl_label = "Prompt" + bl_idname = f'DMT_PT_mesh_panel_prompt_{space_type}' + + def draw(self, context): + super().draw(context) + layout = self.layout + layout.use_property_split = True + layout.prop(get_prompt(context), "prompt", text="") + yield PromptPanel + +def init_image_panel(sub_panel, space_type, get_prompt): + class InitImagePanel(sub_panel): + """Create a subpanel for init image options""" + bl_idname = f"DMT_PT_mesh_panel_init_image_{space_type}" + bl_label = "Source Image" + bl_options = {'DEFAULT_CLOSED'} + + def draw_header(self, context): + self.layout.prop(get_prompt(context), "use_init_img", text="") + + def draw(self, context): + super().draw(context) + layout = self.layout + prompt = get_prompt(context) + layout.enabled = prompt.use_init_img + layout.prop(prompt, "init_img_src", expand=True) + if prompt.init_img_src == 'file': + layout.template_ID(context.scene, "init_img", open="image.open") + layout.use_property_split = True + yield InitImagePanel + + +def point_cloud_panel(sub_panel, space_type, get_prompt): + class PointCloudPanel(sub_panel): + """Create a subpanel for advanced options""" + bl_idname = f"DMT_PT_mesh_panel_point_cloud_{space_type}" + bl_label = "Point Cloud" + # bl_options = {'DEFAULT_CLOSED'} + + def draw(self, context): + super().draw(context) + layout = self.layout + layout.use_property_split = True + layout.template_list("SCENE_UL_pc_list", "dmt_meshes_point_cloud_results", + context.preferences.addons[DMTMeshesPreferences.bl_idname].preferences, + "point_cloud_results", context.scene, "point_cloud_results_selection") + yield PointCloudPanel + + +class SCENE_UL_pc_list(bpy.types.UIList): + def draw_item(self, context, layout, data, item, icon, active_data, active_propname): + if self.layout_type in {'DEFAULT', 'COMPACT'}: + layout.label(text=item.point_cloud, translate=False, icon_value=icon) + # 'GRID' layout type should be as compact as possible (typically a single icon!). + elif self.layout_type == 'GRID': + layout.alignment = 'CENTER' + layout.label(text=item.point_cloud, icon_value=icon) + + +def advanced_panel(sub_panel, space_type, get_prompt): + class AdvancedPanel(sub_panel): + """Create a subpanel for advanced options""" + bl_idname = f"DMT_PT_mesh_panel_advanced_{space_type}" + bl_label = "Advanced" + bl_options = {'DEFAULT_CLOSED'} + + def draw(self, context): + super().draw(context) + layout = self.layout + layout.use_property_split = True + layout.prop(get_prompt(context), "image_to_pc_model") + layout.prop(get_prompt(context), "pc_to_mesh_method") + + layout.prop(get_prompt(context), "steps") + layout.prop(get_prompt(context), "view_every") + layout.prop(get_prompt(context), "step_preview_mode") + layout.prop(get_prompt(context), "lr") + layout.prop(get_prompt(context), "laplacian_weight") + layout.prop(get_prompt(context), "gridres") + layout.prop(get_prompt(context), "multires") + yield AdvancedPanel + +def run_mode_panel(sub_panel, space_type, get_prompt): + class RunModePanel(sub_panel): + """Create a subpanel for advanced options""" + bl_idname = f"DMT_PT_mesh_panel_run_mode_{space_type}" + bl_label = "Run Mode" + + def draw(self, context): + super().draw(context) + layout = self.layout + layout.use_property_split = True + layout.prop(get_prompt(context), "run_mode") + yield RunModePanel + +def actions_panel(sub_panel, space_type, get_prompt): + class ActionsPanel(sub_panel): + """Create a subpanel for actions""" + bl_idname = f"DMT_PT_mesh_panel_actions_{space_type}" + bl_label = "Advanced" + bl_options = {'HIDE_HEADER'} + + def draw(self, context): + super().draw(context) + layout = self.layout + layout.use_property_split = True + + row = layout.row() + row.scale_y = 1.5 + if context.scene.dmt_meshes_progress <= 0: + if context.scene.dmt_meshes_info != "": + row.label(text=context.scene.dmt_meshes_info, icon="INFO") + else: + row.operator(DMTMesh.bl_idname, icon="PLAY", text="Generate") + else: + disabled_row = row.row() + disabled_row.use_property_split = True + disabled_row.prop(context.scene, 'dmt_meshes_progress', slider=True) + disabled_row.enabled = False + if CancelGenerator.poll(context): + row.operator(CancelGenerator.bl_idname, icon="CANCEL", text="") + row.operator(ReleaseGenerator.bl_idname, icon="X", text="") + return ActionsPanel \ No newline at end of file diff --git a/ui/space_types.py b/ui/space_types.py new file mode 100644 index 0000000..90a7b82 --- /dev/null +++ b/ui/space_types.py @@ -0,0 +1 @@ +SPACE_TYPES = {'VIEW_3D', 'IMAGE_EDITOR'} \ No newline at end of file diff --git a/version.py b/version.py new file mode 100644 index 0000000..e69de29