diff --git a/morpheus-worker/Dockerfile b/morpheus-worker/Dockerfile
index 72de7cc..1826edf 100644
--- a/morpheus-worker/Dockerfile
+++ b/morpheus-worker/Dockerfile
@@ -9,6 +9,7 @@ ARG BASE_IMAGE
COPY requirements.txt .
RUN pip install --upgrade pip
RUN pip install torch
+RUN pip install kaolin==0.15.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-2.1.1_cu121.html
RUN pip install -r requirements.txt
WORKDIR /opt
diff --git a/morpheus-worker/app/actors/common/sd_base.py b/morpheus-worker/app/actors/common/sd_base.py
index 8442c48..26400b6 100644
--- a/morpheus-worker/app/actors/common/sd_base.py
+++ b/morpheus-worker/app/actors/common/sd_base.py
@@ -4,6 +4,7 @@
from pathlib import Path
import torch
+
from app.settings.settings import get_settings
settings = get_settings()
@@ -16,7 +17,8 @@ def __init__(
pipeline: str = settings.default_pipeline,
model_id: str = settings.default_model,
scheduler: str = settings.default_scheduler,
- controlnet_id: str = None
+ controlnet_id: str = None,
+ dtype: torch.dtype = None
):
self.logger = logging.getLogger("ray")
self.generator = None
@@ -42,7 +44,9 @@ def __init__(
# Check the environment variable/settings file to determine if we should
# be using 16 bit or 32 bit precision when generating images. 16 bit
# will be faster, but 32 bit may have higher image quality.
- self.dtype = torch.float32 if settings.enable_float32 else torch.float16
+ self.dtype = (
+ dtype or torch.float32 if settings.enable_float32 else torch.float16
+ )
self.logger.info(
"Floating point precision during image generation: " + str(self.dtype)
)
diff --git a/morpheus-worker/app/actors/texturing3d/models.py b/morpheus-worker/app/actors/texturing3d/models.py
new file mode 100644
index 0000000..19c9784
--- /dev/null
+++ b/morpheus-worker/app/actors/texturing3d/models.py
@@ -0,0 +1,20 @@
+from pydantic import BaseModel, Field
+
+
+class Prompt(BaseModel):
+ prompt: str = Field(..., min_length=1)
+ negative_prompt: str = "bad, ugly"
+ width: int = 512
+ height: int = 512
+ num_inference_steps: int = 50
+ guidance_scale: int = 10
+ num_images_per_prompt: int = 1
+ generator: int = 42
+ strength: float = 0.75
+ num_views_3d: int = 8
+ front_positive_prompt: str = None
+ front_negative_prompt: str = None
+ not_front_positive_prompt: str = None
+ not_front_negative_prompt: str = None
+ inpainting_mode_3d: bool = False
+ no_inpainting_mode_3d: bool = False
diff --git a/morpheus-worker/app/actors/texturing3d/tasks.py b/morpheus-worker/app/actors/texturing3d/tasks.py
new file mode 100644
index 0000000..27471c5
--- /dev/null
+++ b/morpheus-worker/app/actors/texturing3d/tasks.py
@@ -0,0 +1,209 @@
+import shutil
+import tempfile
+import uuid
+from pathlib import Path
+
+from fastapi import UploadFile
+from loguru import logger
+
+from app.actors.texturing3d.models import Prompt
+from app.actors.texturing3d.texturing3D import Texturing3D
+from app.actors.texturing3d.utils.texture3d.configs.train_config import TrainConfig
+from app.actors.texturing3d.utils.texture3d.training.trainer import TEXTure
+from app.actors.texturing3d.utils.texture3d.utils import (
+ update_obj_mtl_reference,
+ update_mtl_texture_reference,
+)
+
+
+class FilesService:
+ def __init__(self):
+ pass
+
+ def upload_multiple_files_to_s3(self, images, user_bucket):
+ for image in images:
+ type(image)
+ return ["url1", "url2"]
+
+
+def create_upload_file(file_path: str) -> UploadFile:
+ path = Path(file_path)
+ upload_file = UploadFile(filename=path.name, file=path.open("rb"))
+ return upload_file
+
+
+class TexturingTask:
+ def __init__(self):
+ self.model = Texturing3D()
+ self.file_service = FilesService()
+
+ def generate_3d_texturing_output_task(
+ self, prompt: Prompt, model3d: any
+ ) -> list[str]:
+ try:
+ preview_image = None
+ temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".obj")
+ with open(temp_file.name, "wb") as f:
+ f.write(model3d)
+
+ # set the model parameters
+ cfg = TrainConfig()
+ cfg.guide.text = prompt.prompt
+ cfg.guide.guidance_scale = prompt.guidance_scale
+ cfg.guide.shape_path = temp_file.name
+ cfg.optim.seed = prompt.generator
+ cfg.log.log_images = False
+ cfg.render.n_views = prompt.num_views_3d
+
+ # set the prompt parameters
+ cfg.guide.neg_text = prompt.negative_prompt
+ cfg.guide.text_front = prompt.front_positive_prompt
+ cfg.guide.neg_text_front = prompt.front_negative_prompt
+ cfg.guide.text_not_front = prompt.not_front_positive_prompt
+ cfg.guide.neg_text_not_front = prompt.not_front_negative_prompt
+
+ # Initialize TEXTure method
+ texture_model = TEXTure(cfg=cfg, models=self.model, optimize_camera=True)
+
+ # Generate the texture
+ obj_name = f"{uuid.uuid4()}"
+ preview_image, obj_file, mtl_file, texture_file = texture_model.paint(
+ obj_name
+ )
+
+ # Upload generated images to s3.
+ logger.info("Uploading image(s) to s3 and getting the url(s)")
+ url_images = []
+ if preview_image:
+ url_images = self.upload_images_to_s3(
+ preview_image=preview_image,
+ obj_file=obj_file,
+ mtl_file=mtl_file,
+ texture_file=texture_file,
+ )
+ self.clean_up(texture_model, cfg, temp_file)
+ return url_images
+ except Exception as e:
+ logger.exception(e)
+
+ def edit_3d_texturing_output_task(
+ self,
+ prompt: Prompt,
+ model3d: any,
+ material: any,
+ texture: any,
+ camera_position: any,
+ mask: any,
+ ) -> list[str]:
+ try:
+ preview_image = None
+
+ # create filenames
+ obj_temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".obj")
+ obj_path = Path(obj_temp_file.name)
+ mtl_temp_file = obj_path.with_suffix(".mtl")
+ texture_temp_file = obj_path.with_suffix(".png")
+
+ # modify paths to mtl file in obj file
+ if material and texture:
+ model3d_updated = update_obj_mtl_reference(model3d, mtl_temp_file.name)
+ material_updated = update_mtl_texture_reference(
+ material, texture_temp_file.name
+ )
+
+ with open(obj_path, "wb") as f:
+ f.write(model3d_updated)
+
+ # Save the .mtl file to a temp file
+ with open(mtl_temp_file, "wb") as f:
+ f.write(material_updated)
+
+ # Save the .png texture file to a temp file
+ with open(texture_temp_file, "wb") as f:
+ f.write(texture)
+ else:
+ with open(obj_path, "wb") as f:
+ f.write(model3d)
+
+ # set the model parameters
+ cfg = TrainConfig()
+ cfg.guide.text = prompt.prompt
+ cfg.guide.guidance_scale = prompt.guidance_scale
+ cfg.guide.shape_path = obj_temp_file.name
+ cfg.optim.seed = prompt.generator
+ cfg.log.log_images = False
+ cfg.render.n_views = prompt.num_views_3d
+ cfg.guide.append_direction = False
+ if material and texture:
+ cfg.guide.initial_texture = texture_temp_file
+
+ # Initialize TEXTure method
+ texture_model = TEXTure(cfg=cfg, models=self.model, optimize_camera=False)
+
+ # Generate the texture
+ obj_name = f"{uuid.uuid4()}"
+ no_inpainting = prompt.no_inpainting_mode_3d
+ inpaint_only = prompt.inpainting_mode_3d
+ (
+ preview_image,
+ obj_file,
+ mtl_file,
+ texture_file,
+ ) = texture_model.paint_specific_viewpoint(
+ obj_name,
+ camera_position,
+ inpaint=(not no_inpainting),
+ inpaint_only=inpaint_only,
+ mask=mask,
+ )
+
+ # Upload generated images to s3.
+ logger.info("Uploading image(s) to s3 and getting the url(s)")
+ url_images = []
+ if preview_image:
+ url_images = self.upload_images_to_s3(
+ preview_image=preview_image,
+ obj_file=obj_file,
+ mtl_file=mtl_file,
+ texture_file=texture_file,
+ )
+ self.clean_up(texture_model, cfg, obj_temp_file)
+ return url_images
+ except Exception as e:
+ logger.exception(e)
+
+ def upload_images_to_s3(self, *, preview_image, obj_file, mtl_file, texture_file):
+ obj_upload_file = create_upload_file(file_path=obj_file)
+ texture_upload_file = create_upload_file(file_path=texture_file)
+ mtl_upload_file = create_upload_file(file_path=mtl_file)
+
+ files = [preview_image, obj_upload_file, texture_upload_file, mtl_upload_file]
+ url_images = self.file_service.upload_multiple_files_to_s3(
+ images=files, user_bucket="settings.images_temp_bucket"
+ )
+ return url_images
+
+ def clean_up(self, texture_model, cfg, obj_temp_file):
+ # Remove temporary files
+ temp_file_path = Path(obj_temp_file.name)
+ if temp_file_path.exists():
+ temp_file_path.unlink()
+
+ # Remove 3D experiment files
+ experiments_folder_path = cfg.log.exp_dir
+ if experiments_folder_path.exists() and experiments_folder_path.is_dir():
+ shutil.rmtree(experiments_folder_path)
+
+ # remove cache files
+ experiment_cache = texture_model.cache_path
+ if experiment_cache.exists() and experiment_cache.is_dir():
+ shutil.rmtree(experiment_cache)
+
+
+if __name__ == "__main__":
+ prompt = Prompt(
+ prompt="hulk",
+ )
+ model3d = open("utils/assets/object.obj", "rb").read()
+ task = TexturingTask()
+ task.generate_3d_texturing_output_task(prompt=prompt, model3d=model3d)
diff --git a/morpheus-worker/app/actors/texturing3d/texturing3D.py b/morpheus-worker/app/actors/texturing3d/texturing3D.py
new file mode 100644
index 0000000..56e8f5b
--- /dev/null
+++ b/morpheus-worker/app/actors/texturing3d/texturing3D.py
@@ -0,0 +1,40 @@
+import torch
+from diffusers import (
+ UNet2DConditionModel,
+)
+from loguru import logger
+
+from app.actors.common.sd_base import StableDiffusionAbstract
+
+
+class Texturing3DDepthDiffusion(StableDiffusionAbstract):
+ def __init__(self):
+ super().__init__(
+ pipeline="StableDiffusionDepth2ImgPipeline",
+ model_id="stabilityai/stable-diffusion-2-depth",
+ dtype=torch.float32,
+ )
+
+
+class Texturing3DInpaintingDiffusion(StableDiffusionAbstract):
+ def __init__(self):
+ super().__init__(
+ pipeline="StableDiffusionInpaintPipeline",
+ model_id="stabilityai/stable-diffusion-2-inpainting",
+ dtype=torch.float32,
+ )
+
+
+class Texturing3D:
+ def __init__(self):
+ self.depth_model_name = "stabilityai/stable-diffusion-2-depth"
+ self.inpainting_model_name = "stabilityai/stable-diffusion-2-inpainting"
+
+ self.depth_model = Texturing3DDepthDiffusion()
+ self.depth_model = self.depth_model.pipeline
+ self.inpainting_model = Texturing3DInpaintingDiffusion()
+ self.inpainting_model = self.inpainting_model.pipeline
+ self.inpaint_unet = UNet2DConditionModel.from_pretrained(
+ self.inpainting_model_name, subfolder="unet"
+ )
+ logger.info("models loaded")
diff --git a/morpheus-worker/app/actors/texturing3d/utils/__init__.py b/morpheus-worker/app/actors/texturing3d/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/morpheus-worker/app/actors/texturing3d/utils/assets/object.obj b/morpheus-worker/app/actors/texturing3d/utils/assets/object.obj
new file mode 100644
index 0000000..4daf9fc
--- /dev/null
+++ b/morpheus-worker/app/actors/texturing3d/utils/assets/object.obj
@@ -0,0 +1,73398 @@
+# 3ds Max Wavefront OBJ Exporter v0.97b - (c)2007 guruware
+# File Created: 21.08.2014 17:37:51
+
+mtllib FinalBaseMesh.mtl
+
+#
+# object Group1
+#
+
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+
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+f 24329//24328 24328//24327 24416//24415 24404//24403
+f 24416//24415 24417//24416 24418//24417 24415//24414
+f 24328//24327 24330//24329 24417//24416 24416//24415
+f 24417//24416 24419//24418 24420//24419 24418//24417
+f 24421//24420 24422//24421 24418//24417 24420//24419
+f 24420//24419 24423//24422 24424//24423 24421//24420
+f 24419//24418 24425//24424 24423//24422 24420//24419
+f 24330//24329 24322//24321 24419//24418 24417//24416
+f 24422//24421 24414//24413 24415//24414 24418//24417
+f 24413//24412 24414//24413 24426//24425 24427//24426
+f 24426//24425 24428//24427 24429//24428 24430//24429
+f 24431//24430 24429//24428 24428//24427 24432//24431
+f 24422//24421 24421//24420 24432//24431 24428//24427
+f 24428//24427 24426//24425 24414//24413 24422//24421
+f 24430//24429 24433//24432 24427//24426 24426//24425
+f 24433//24432 24430//24429 24434//24433 24435//24434
+f 24430//24429 24429//24428 24436//24435 24434//24433
+f 24427//24426 24437//24436 24411//24410 24413//24412
+f 24438//24437 24437//24436 21790//21789 21789//21788
+f 24433//24432 21790//21789 24437//24436 24427//24426
+f 24412//24411 24411//24410 24437//24436 24438//24437
+f 24408//24407 24405//24404 24407//24406 24409//24408
+f 24412//24411 24439//24438 24410//24409 24409//24408
+f 24439//24438 24440//24439 24441//24440 24410//24409
+f 24440//24439 21754//21753 21757//21756 24441//24440
+f 21754//21753 24440//24439 21793//21792 21755//21754
+f 21794//21793 21793//21792 24440//24439 24439//24438
+f 24439//24438 24412//24411 24438//24437 21794//21793
+f 24410//24409 24441//24440 20940//20939 20942//20941
+f 20941//20940 20940//20939 24441//24440 21757//21756
+f 24425//24424 24419//24418 24322//24321 24321//24320
+f 24442//24441 24425//24424 24321//24320 24323//24322
+f 24425//24424 24442//24441 24443//24442 24423//24422
+f 24444//24443 24424//24423 24423//24422 24443//24442
+f 24424//24423 24444//24443 24445//24444 24446//24445
+f 24446//24445 24445//24444 21597//21596 24447//24446
+f 24431//24430 24447//24446 21587//21586 21590//21589
+f 21588//21587 21587//21586 24447//24446 21597//21596
+f 24447//24446 24431//24430 24432//24431 24446//24445
+f 24421//24420 24424//24423 24446//24445 24432//24431
+f 21595//21594 21597//21596 24445//24444 24448//24447
+f 24448//24447 24445//24444 24444//24443 24449//24448
+f 24450//24449 24448//24447 24449//24448 24451//24450
+f 24450//24449 24451//24450 21650//21649 21653//21652
+f 21596//21595 21595//21594 24448//24447 24450//24449
+f 21596//21595 24450//24449 21653//21652 21646//21645
+f 21650//21649 24451//24450 24452//24451 21651//21650
+f 24453//24452 24452//24451 24451//24450 24449//24448
+f 24452//24451 24453//24452 24454//24453 24455//24454
+f 24442//24441 24454//24453 24453//24452 24443//24442
+f 24455//24454 24454//24453 24456//24455 24343//24342
+f 24454//24453 24442//24441 24323//24322 24456//24455
+f 24449//24448 24444//24443 24443//24442 24453//24452
+f 24455//24454 24349//24348 21651//21650 24452//24451
+f 24325//24324 24345//24344 24344//24343 24324//24323
+f 24324//24323 24344//24343 24343//24342 24456//24455
+f 21692//21691 24348//24347 24340//24339 24265//24264
+f 24456//24455 24323//24322 24319//24318 24324//24323
+f 24349//24348 24455//24454 24343//24342 24342//24341
+f 24349//24348 24347//24346 21652//21651 21651//21650
+f 21654//21653 21652//21651 24347//24346 24346//24345
+f 21640//21639 21649//21648 21654//21653 21693//21692
+f 21693//21692 21654//21653 24346//24345 21691//21690
+f 21691//21690 24346//24345 24348//24347 21692//21691
+f 21638//21637 21640//21639 21693//21692 21687//21686
+f 21594//21593 21596//21595 21646//21645 21645//21644
+f 20878//20877 20880//20879 20882//20881 21758//21757
+f 20878//20877 21758//21757 21752//21751 20879//20878
+f 20877//20876 21773//21772 21771//21770 20876//20875
+f 20879//20878 21759//21758 21773//21772 20877//20876
+f 24435//24434 21788//21787 21790//21789 24433//24432
+f 21788//21787 24435//24434 24457//24456 21785//21784
+f 24458//24457 21783//21782 21785//21784 24457//24456
+f 21784//21783 21783//21782 24458//24457 21609//21608
+f 21613//21612 21604//21603 21609//21608 24458//24457
+f 21613//21612 24458//24457 24457//24456 21614//21613
+f 21614//21613 24457//24456 24435//24434 24434//24433
+f 21789//21788 21791//21790 21794//21793 24438//24437
+f 21828//21827 21769//21768 21772//21771 21825//21824
+f 21825//21824 21772//21771 21803//21802 21826//21825
+f 21772//21771 21765//21764 21767//21766 21803//21802
+f 21590//21589 24436//24435 24429//24428 24431//24430
+f 24434//24433 24436//24435 21615//21614 21614//21613
+f 21589//21588 21615//21614 24436//24435 21590//21589
+f 21593//21592 21594//21593 21645//21644 21598//21597
+f 19661//19660 19663//19662 21713//21712 24459//24458
+f 19662//19661 19661//19660 24459//24458 24460//24459
+f 19643//19642 19662//19661 24460//24459 19644//19643
+f 19644//19643 24460//24459 21841//21840 19646//19645
+f 21839//21838 21841//21840 24460//24459 24459//24458
+f 24459//24458 21713//21712 21840//21839 21839//21838
+f 21841//21840 21836//21835 21838//21837 19646//19645
+f 21786//21785 21732//21731 21733//21732 21735//21734
+f 21732//21731 21786//21785 21782//21781 21728//21727
+f 21784//21783 21726//21725 21728//21727 21782//21781
+f 21726//21725 21784//21783 21609//21608 21607//21606
+f 19084//19083 19240//19239 19090//19089 19082//19081
+f 20239//20238 20327//20326 20326//20325 20237//20236
+f 20223//20222 20237//20236 20326//20325 20221//20220
+f 20217//20216 20228//20227 20214//20213 20211//20210
+f 19687//19686 19743//19742 19683//19682 19685//19684
+f 19684//19683 19683//19682 19743//19742 19738//19737
+f 20409//20408 20373//20372 20372//20371 20407//20406
+f 19337//19336 19339//19338 19305//19304 19306//19305
+f 19307//19306 19309//19308 19333//19332 19334//19333
+f 19306//19305 19307//19306 19334//19333 19337//19336
+f 19309//19308 19269//19268 19268//19267 19333//19332
+f 19270//19269 19267//19266 19272//19271 19285//19284
+f 24461//24460 19846//19845 19848//19847 19890//19889
+f 21871//21870 19845//19844 19846//19845 24461//24460
+f 24461//24460 19888//19887 21868//21867 21871//21870
+f 19888//19887 24461//24460 19890//19889 19886//19885
+f 21862//21861 19842//19841 19840//19839 19845//19844
+f 21869//21868 21868//21867 19888//19887 19887//19886
+# 24459 polygons
+
diff --git a/morpheus-worker/app/actors/texturing3d/utils/texture3d/__init__.py b/morpheus-worker/app/actors/texturing3d/utils/texture3d/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/morpheus-worker/app/actors/texturing3d/utils/texture3d/configs/__init__.py b/morpheus-worker/app/actors/texturing3d/utils/texture3d/configs/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/morpheus-worker/app/actors/texturing3d/utils/texture3d/configs/train_config.py b/morpheus-worker/app/actors/texturing3d/utils/texture3d/configs/train_config.py
new file mode 100644
index 0000000..430e132
--- /dev/null
+++ b/morpheus-worker/app/actors/texturing3d/utils/texture3d/configs/train_config.py
@@ -0,0 +1,186 @@
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Optional, Tuple, List
+
+
+@dataclass
+class RenderConfig:
+ """ Parameters for the Mesh Renderer """
+ # Grid size for rendering during painting
+ train_grid_size: int = 1200
+
+ # Grid size of evaluation
+ eval_grid_size: int = 1024
+
+ # training camera radius range
+ radius: float = 0.75
+
+ # Set [0,overhead_range] as the overhead region
+ overhead_range: float = 40
+
+ # Define the front angle region
+ front_range: float = 70
+
+ # The front offset, use to rotate shape from code
+ front_offset:float = 0.0
+
+ # Number of views to use
+ n_views: int = 8
+
+ # List of angles to render (override n_views)
+ view_angles: Optional[List[float]] = None
+
+ # Theta value for rendering during training
+ base_theta:float = 60
+
+ # Additional views to use before rotating around shape
+ views_before: List[Tuple[float,float]] = field(default_factory=list)
+
+ # Additional views to use after rotating around shape
+ views_after: List[Tuple[float, float]] = field(default_factory=[[180,30],[180,150]].copy)
+
+ # Whether to alternate between the rotating views from the different sides
+ alternate_views: bool = True
+
+@dataclass
+class GuideConfig:
+ """ Parameters defining the guidance """
+ # Guiding text prompt
+ text: str = ""
+
+ # Viewpoint-dependent prompts
+ text_front: Optional[str] = None
+ text_side: Optional[str] = None
+ text_back: Optional[str] = None
+ text_overhead: Optional[str] = None
+ text_bottom: Optional[str] = None
+ text_not_front: Optional[str] = None
+
+ # Negative viewpoint-dependent prompts
+ neg_text: Optional[str] = None
+ neg_text_front: Optional[str] = None
+ neg_text_side: Optional[str] = None
+ neg_text_back: Optional[str] = None
+ neg_text_overhead: Optional[str] = None
+ neg_text_bottom: Optional[str] = None
+ neg_text_not_front: Optional[str] = None
+
+ # The mesh to paint
+ shape_path: str = ""
+
+ # Append direction to text prompts
+ append_direction: bool = True
+
+ # A Textual-Inversion concept to use
+ concept_name: Optional[str] = None
+
+ # Path to the TI embedding
+ concept_path: Optional[Path] = None
+
+ # Lora path to load on top of the diffusion model
+ diffusion_lora_path: Optional[str] = None
+
+ # Scale of mesh in 1x1x1 cube
+ # shape_scale: float = 0.6
+ shape_scale: float = 1.0
+
+ # height of mesh
+ # dy: float = 0.5
+ dy: float = 0.0
+
+ # texture image resolution
+ texture_resolution: int = 1024
+
+ # texture mapping interpolation mode from texture image, options: 'nearest', 'bilinear', 'bicubic'
+ texture_interpolation_mode: str= 'bilinear'
+
+ # Guidance scale for score distillation
+ guidance_scale: float = 7.5
+
+ # Use inpainting in relevant iterations
+ use_inpainting: bool = True
+
+ # The texture before editing
+ reference_texture: Optional[Path] = None
+
+ # The edited texture
+ initial_texture: Optional[Path] = None
+
+ # Whether to use background color or image
+ use_background_color: bool = True
+
+ # Background image to use
+ background_img: str = 'https://github.com/TEXTurePaper/TEXTurePaper/blob/main/textures/brick_wall.png?raw=true'
+
+ # Threshold for defining refine regions
+ z_update_thr: float = 0.2
+
+ # Some more strict masking for projecting back
+ strict_projection: bool = True
+
+
+@dataclass
+class OptimConfig:
+ """ Parameters for the optimization process """
+
+ # Seed for experiment
+ seed: int = 0
+
+ # Learning rate for projection
+ lr: float = 1e-2
+
+ # For Diffusion model
+ min_timestep: float = 0.02
+
+ # For Diffusion model
+ max_timestep: float = 0.98
+
+ # For Diffusion model
+ no_noise: bool = False
+
+
+@dataclass
+class LogConfig:
+ """ Parameters for logging and saving """
+
+ # Experiment name
+ exp_name: str = '3d_texture'
+
+ # Experiment output dir
+ exp_root: Path = Path('experiments/')
+
+ # How many steps between save step
+ save_interval: int = 100
+
+ # Run only test
+ eval_only: bool = False
+
+ # Number of angles to sample for eval during training
+ eval_size: int = 10
+
+ # Number of angles to sample for eval after training
+ full_eval_size: int = 100
+
+ # Export a mesh
+ save_mesh: bool = True
+
+ # Whether to show intermediate diffusion visualizations
+ vis_diffusion_steps: bool = False
+
+ # Whether to log intermediate images
+ log_images: bool = True
+
+ @property
+ def exp_dir(self) -> Path:
+ return self.exp_root / self.exp_name
+
+
+@dataclass
+class TrainConfig:
+ """ The main configuration for the coach trainer """
+ log: LogConfig = field(default_factory=LogConfig)
+ render: RenderConfig = field(default_factory=RenderConfig)
+ optim: OptimConfig = field(default_factory=OptimConfig)
+ guide: GuideConfig = field(default_factory=GuideConfig)
+
+
diff --git a/morpheus-worker/app/actors/texturing3d/utils/texture3d/models/__init__.py b/morpheus-worker/app/actors/texturing3d/utils/texture3d/models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/morpheus-worker/app/actors/texturing3d/utils/texture3d/models/mesh.py b/morpheus-worker/app/actors/texturing3d/utils/texture3d/models/mesh.py
new file mode 100644
index 0000000..06ac5e8
--- /dev/null
+++ b/morpheus-worker/app/actors/texturing3d/utils/texture3d/models/mesh.py
@@ -0,0 +1,89 @@
+import copy
+
+import kaolin as kal
+import numpy as np
+import torch
+
+
+def triangulate_quads(faces):
+ # Convert PyTorch tensor to NumPy array for dtype compatibility
+ faces_np = faces.cpu().numpy()
+
+ n = faces_np.shape[0]
+ triangles = np.zeros((n * 2, 3), dtype=faces_np.dtype)
+
+ for i in range(n):
+ # Splitting each quad into two triangles
+ triangles[2 * i] = [faces_np[i, 0], faces_np[i, 1], faces_np[i, 2]]
+ triangles[2 * i + 1] = [faces_np[i, 0], faces_np[i, 2], faces_np[i, 3]]
+ return torch.from_numpy(triangles).to(faces.device)
+
+
+class Mesh:
+ def __init__(self, obj_path, device):
+ # from https://github.com/threedle/text2mesh
+
+ if ".obj" in obj_path:
+ try:
+ mesh = kal.io.obj.import_mesh(
+ obj_path, with_normals=True, with_materials=True
+ )
+ except:
+ mesh = kal.io.obj.import_mesh(
+ obj_path, with_normals=True, with_materials=False
+ )
+
+ elif ".off" in obj_path:
+ mesh = kal.io.off.import_mesh(obj_path)
+ else:
+ raise ValueError(f"{obj_path} extension not implemented in mesh reader.")
+
+ self.vertices = mesh.vertices.to(device)
+ faces = mesh.faces.to(device)
+ if faces.shape[1] == 4:
+ faces = triangulate_quads(faces)
+ self.faces = faces
+ self.normals, self.face_area = self.calculate_face_normals(
+ self.vertices, self.faces
+ )
+ self.ft = mesh.face_uvs_idx
+ self.vt = mesh.uvs
+
+ @staticmethod
+ def calculate_face_normals(vertices: torch.Tensor, faces: torch.Tensor):
+ """
+ calculate per face normals from vertices and faces
+ """
+ v0 = vertices[faces[:, 0]]
+ v1 = vertices[faces[:, 1]]
+ v2 = vertices[faces[:, 2]]
+ e0 = v1 - v0
+ e1 = v2 - v0
+ n = torch.cross(e0, e1, dim=-1)
+ twice_area = torch.norm(n, dim=-1)
+ n = n / twice_area[:, None]
+ return n, twice_area / 2
+
+ def standardize_mesh(self, inplace=False):
+ mesh = self if inplace else copy.deepcopy(self)
+
+ verts = mesh.vertices
+ center = verts.mean(dim=0)
+ verts -= center
+ scale = torch.std(torch.norm(verts, p=2, dim=1))
+ verts /= scale
+ mesh.vertices = verts
+ return mesh
+
+ def normalize_mesh(self, inplace=False, target_scale=1, dy=0):
+ mesh = self if inplace else copy.deepcopy(self)
+
+ verts = mesh.vertices
+ center = verts.mean(dim=0)
+ verts = verts - center
+ scale = torch.max(torch.norm(verts, p=2, dim=1))
+ verts = verts / scale
+ verts *= target_scale
+ verts[:, 1] += dy
+ mesh.vertices = verts
+ return mesh
diff --git a/morpheus-worker/app/actors/texturing3d/utils/texture3d/models/render.py b/morpheus-worker/app/actors/texturing3d/utils/texture3d/models/render.py
new file mode 100644
index 0000000..5c8ae03
--- /dev/null
+++ b/morpheus-worker/app/actors/texturing3d/utils/texture3d/models/render.py
@@ -0,0 +1,271 @@
+import kaolin as kal
+import numpy as np
+import torch
+
+
+class Renderer:
+ # from https://github.com/threedle/text2mesh
+
+ def __init__(self, device, dim=(224, 224), interpolation_mode="nearest"):
+ assert interpolation_mode in [
+ "nearest",
+ "bilinear",
+ "bicubic",
+ ], f"no interpolation mode {interpolation_mode}"
+
+ camera = kal.render.camera.generate_perspective_projection(np.pi / 3).to(device)
+
+ self.device = device
+ self.interpolation_mode = interpolation_mode
+ self.camera_projection = camera
+ self.dim = dim
+ self.background = torch.ones(dim).to(device).float()
+
+ @staticmethod
+ def get_camera_from_view(elev, azim, r=3.0, look_at_height=0.0):
+ x = r * torch.sin(elev) * torch.sin(azim)
+ y = r * torch.cos(elev)
+ z = r * torch.sin(elev) * torch.cos(azim)
+
+ pos = torch.tensor([x, y, z]).unsqueeze(0)
+ look_at = torch.zeros_like(pos)
+ look_at[:, 1] = look_at_height
+ direction = torch.tensor([0.0, 1.0, 0.0]).unsqueeze(0)
+
+ camera_proj = kal.render.camera.generate_transformation_matrix(
+ pos, look_at, direction
+ )
+ return camera_proj
+
+ def normalize_depth(self, depth_map):
+ assert depth_map.max() <= 0.0, "depth map should be negative"
+ object_mask = depth_map != 0
+ # depth_map[object_mask] = (depth_map[object_mask] - depth_map[object_mask].min()) / (
+ # depth_map[object_mask].max() - depth_map[object_mask].min())
+ # depth_map = depth_map ** 4
+ min_val = 0.5
+ depth_map[object_mask] = (
+ (1 - min_val)
+ * (depth_map[object_mask] - depth_map[object_mask].min())
+ / (depth_map[object_mask].max() - depth_map[object_mask].min())
+ ) + min_val
+ # depth_map = (depth_map - depth_map.min()) / (depth_map.max() - depth_map.min())
+ # depth_map[depth_map == 1] = 0 # Background gets largest value, set to 0
+
+ return depth_map
+
+ def render_single_view(
+ self,
+ mesh,
+ face_attributes,
+ elev=0,
+ azim=0,
+ radius=2,
+ look_at_height=0.0,
+ calc_depth=True,
+ dims=None,
+ background_type="none",
+ ):
+ dims = self.dim if dims is None else dims
+
+ camera_transform = self.get_camera_from_view(
+ torch.tensor(elev),
+ torch.tensor(azim),
+ r=radius,
+ look_at_height=look_at_height,
+ ).to(self.device)
+
+ (
+ face_vertices_camera,
+ face_vertices_image,
+ face_normals,
+ ) = kal.render.mesh.prepare_vertices(
+ mesh.vertices.to(self.device),
+ mesh.faces.to(self.device),
+ self.camera_projection,
+ camera_transform=camera_transform,
+ )
+
+ if calc_depth:
+ depth_map, _ = kal.render.mesh.rasterize(
+ dims[1],
+ dims[0],
+ face_vertices_camera[:, :, :, -1],
+ face_vertices_image,
+ face_vertices_camera[:, :, :, -1:],
+ )
+ depth_map = self.normalize_depth(depth_map)
+ else:
+ depth_map = torch.zeros(1, 64, 64, 1)
+
+ image_features, face_idx = kal.render.mesh.rasterize(
+ dims[1],
+ dims[0],
+ face_vertices_camera[:, :, :, -1],
+ face_vertices_image,
+ face_attributes,
+ )
+
+ mask = (face_idx > -1).float()[..., None]
+ if background_type == "white":
+ image_features += 1 * (1 - mask)
+ if background_type == "random":
+ image_features += torch.rand((1, 1, 1, 3)).to(self.device) * (1 - mask)
+
+ return (
+ image_features.permute(0, 3, 1, 2),
+ mask.permute(0, 3, 1, 2),
+ depth_map.permute(0, 3, 1, 2),
+ )
+
+ def render_single_view_texture(
+ self,
+ verts,
+ faces,
+ uv_face_attr,
+ texture_map,
+ elev=0,
+ azim=0,
+ radius=2,
+ look_at_height=0.0,
+ dims=None,
+ background_type="none",
+ render_cache=None,
+ ):
+ dims = self.dim if dims is None else dims
+
+ if render_cache is None:
+ camera_transform = self.get_camera_from_view(
+ torch.tensor(elev),
+ torch.tensor(azim),
+ r=radius,
+ look_at_height=look_at_height,
+ ).to(self.device)
+ (
+ face_vertices_camera,
+ face_vertices_image,
+ face_normals,
+ ) = kal.render.mesh.prepare_vertices(
+ verts.to(self.device),
+ faces.to(self.device),
+ self.camera_projection,
+ camera_transform=camera_transform,
+ )
+
+ depth_map, _ = kal.render.mesh.rasterize(
+ dims[1],
+ dims[0],
+ face_vertices_camera[:, :, :, -1],
+ face_vertices_image,
+ face_vertices_camera[:, :, :, -1:],
+ )
+ depth_map = self.normalize_depth(depth_map)
+
+ uv_features, face_idx = kal.render.mesh.rasterize(
+ dims[1],
+ dims[0],
+ face_vertices_camera[:, :, :, -1],
+ face_vertices_image,
+ uv_face_attr,
+ )
+ uv_features = uv_features.detach()
+
+ else:
+ face_normals, uv_features, face_idx, depth_map = (
+ render_cache["face_normals"],
+ render_cache["uv_features"],
+ render_cache["face_idx"],
+ render_cache["depth_map"],
+ )
+ mask = (face_idx > -1).float()[..., None]
+
+ image_features = kal.render.mesh.texture_mapping(
+ uv_features, texture_map, mode=self.interpolation_mode
+ )
+ image_features = image_features * mask
+ if background_type == "white":
+ image_features += 1 * (1 - mask)
+ elif background_type == "random":
+ image_features += torch.rand((1, 1, 1, 3)).to(self.device) * (1 - mask)
+
+ normals_image = face_normals[0][face_idx, :]
+
+ render_cache = {
+ "uv_features": uv_features,
+ "face_normals": face_normals,
+ "face_idx": face_idx,
+ "depth_map": depth_map,
+ }
+
+ return (
+ image_features.permute(0, 3, 1, 2),
+ mask.permute(0, 3, 1, 2),
+ depth_map.permute(0, 3, 1, 2),
+ normals_image.permute(0, 3, 1, 2),
+ render_cache,
+ )
+
+ def project_uv_single_view(
+ self,
+ verts,
+ faces,
+ uv_face_attr,
+ elev=0,
+ azim=0,
+ radius=2,
+ look_at_height=0.0,
+ dims=None,
+ background_type="none",
+ ):
+ # project the vertices and interpolate the uv coordinates
+
+ dims = self.dim if dims is None else dims
+
+ camera_transform = self.get_camera_from_view(
+ torch.tensor(elev),
+ torch.tensor(azim),
+ r=radius,
+ look_at_height=look_at_height,
+ ).to(self.device)
+ (
+ face_vertices_camera,
+ face_vertices_image,
+ face_normals,
+ ) = kal.render.mesh.prepare_vertices(
+ verts.to(self.device),
+ faces.to(self.device),
+ self.camera_projection,
+ camera_transform=camera_transform,
+ )
+
+ uv_features, face_idx = kal.render.mesh.rasterize(
+ dims[1],
+ dims[0],
+ face_vertices_camera[:, :, :, -1],
+ face_vertices_image,
+ uv_face_attr,
+ )
+ return face_vertices_image, face_vertices_camera, uv_features, face_idx
+
+ def project_single_view(
+ self, verts, faces, elev=0, azim=0, radius=2, look_at_height=0.0
+ ):
+ # only project the vertices
+ camera_transform = self.get_camera_from_view(
+ torch.tensor(elev),
+ torch.tensor(azim),
+ r=radius,
+ look_at_height=look_at_height,
+ ).to(self.device)
+ (
+ face_vertices_camera,
+ face_vertices_image,
+ face_normals,
+ ) = kal.render.mesh.prepare_vertices(
+ verts.to(self.device),
+ faces.to(self.device),
+ self.camera_projection,
+ camera_transform=camera_transform,
+ )
+
+ return face_vertices_image
diff --git a/morpheus-worker/app/actors/texturing3d/utils/texture3d/models/textured_mesh.py b/morpheus-worker/app/actors/texturing3d/utils/texture3d/models/textured_mesh.py
new file mode 100644
index 0000000..619953f
--- /dev/null
+++ b/morpheus-worker/app/actors/texturing3d/utils/texture3d/models/textured_mesh.py
@@ -0,0 +1,606 @@
+import os
+from pathlib import Path
+
+import kaolin as kal
+import numpy as np
+import requests
+import scipy
+import torch
+import torch.nn as nn
+from PIL import Image
+from loguru import logger
+from scipy import sparse
+from scipy.sparse.linalg import eigsh
+
+from app.utils.texture3d.configs.train_config import GuideConfig
+from app.utils.texture3d.models.mesh import Mesh
+from app.utils.texture3d.models.render import Renderer
+
+
+def build_cotan_laplacian_torch(
+ points_tensor: torch.Tensor, tris_tensor: torch.Tensor
+) -> np.ndarray:
+ tris, points = tris_tensor.cpu().numpy(), points_tensor.cpu().numpy()
+
+ a, b, c = (tris[:, 0], tris[:, 1], tris[:, 2])
+ A = np.take(points, a, axis=1)
+ B = np.take(points, b, axis=1)
+ C = np.take(points, c, axis=1)
+
+ eab, ebc, eca = (B - A, C - B, A - C)
+ eab = eab / np.linalg.norm(eab, axis=0)[None, :]
+ ebc = ebc / np.linalg.norm(ebc, axis=0)[None, :]
+ eca = eca / np.linalg.norm(eca, axis=0)[None, :]
+
+ alpha = np.arccos(-np.sum(eca * eab, axis=0))
+ beta = np.arccos(-np.sum(eab * ebc, axis=0))
+ gamma = np.arccos(-np.sum(ebc * eca, axis=0))
+
+ wab, wbc, wca = (1.0 / np.tan(gamma), 1.0 / np.tan(alpha), 1.0 / np.tan(beta))
+ rows = np.concatenate((a, b, a, b, b, c, b, c, c, a, c, a), axis=0)
+ cols = np.concatenate((a, b, b, a, b, c, c, b, c, a, a, c), axis=0)
+ vals = np.concatenate(
+ (wab, wab, -wab, -wab, wbc, wbc, -wbc, -wbc, wca, wca, -wca, -wca), axis=0
+ )
+ L = sparse.coo_matrix(
+ (vals, (rows, cols)), shape=(points.shape[1], points.shape[1]), dtype=float
+ ).tocsc()
+ return L
+
+
+def build_graph_laplacian_torch(tris_tensor: torch.Tensor) -> np.ndarray:
+ tris = tris_tensor.cpu().numpy()
+ n_verts = tris.max() + 1
+ v2v = [[] for _ in range(n_verts)]
+ for face in tris:
+ for i in face:
+ for j in face:
+ if i != j:
+ if j not in v2v[i]:
+ v2v[i].append(j)
+
+ valency = [len(x) for x in v2v]
+ I, J, vals = [], [], []
+ for i in range(n_verts):
+ I.append(i)
+ J.append(i)
+ vals.append(1)
+
+ for neighbor in v2v[i]:
+ I.append(i)
+ J.append(neighbor)
+ vals.append(-1 / valency[i])
+ L = sparse.csr_matrix((vals, (I, J)), shape=(n_verts, n_verts))
+ return L
+
+
+def eigen_problem(Lap, k=20, e=0.0) -> (torch.Tensor, torch.Tensor):
+ shift = 1e-4
+ eigenvalues, eigenvectors = eigsh(
+ Lap + shift * scipy.sparse.eye(Lap.shape[0]),
+ k=k + 1,
+ which="LM",
+ sigma=e,
+ tol=1e-3,
+ )
+ eigenvalues += shift
+
+ eigenvalues = eigenvalues[1:]
+ eigenvectors = eigenvectors[:, 1:]
+
+ return (
+ torch.from_numpy(eigenvalues).float(),
+ torch.from_numpy(eigenvectors.T).float(),
+ )
+
+
+def choose_multi_modal(n: int, k: int):
+ interval_length = n // k
+ n_intervals = n / interval_length
+ if n_intervals == int(n_intervals):
+ n_intervals = int(n_intervals)
+ else:
+ n_intervals = int(n_intervals) + 1
+ chosen_numbers = []
+ for i in range(n_intervals):
+ current_interval_length = min(interval_length, n - i * interval_length)
+ chosen_numbers.append(
+ np.random.choice(current_interval_length) + i * interval_length
+ )
+ return chosen_numbers
+
+
+class TexturedMeshModel(nn.Module):
+ def __init__(
+ self,
+ opt: GuideConfig,
+ render_grid_size=1024,
+ texture_resolution=1024,
+ initial_texture_path=None,
+ cache_path=None,
+ device=torch.device("cpu"),
+ augmentations=False,
+ augment_prob=0.5,
+ ):
+ super().__init__()
+ self.device = device
+ self.augmentations = augmentations
+ self.augment_prob = augment_prob
+ self.opt = opt
+ self.dy = self.opt.dy
+ self.mesh_scale = self.opt.shape_scale
+ self.texture_resolution = texture_resolution
+ if initial_texture_path is not None:
+ self.initial_texture_path = initial_texture_path
+ else:
+ self.initial_texture_path = self.opt.initial_texture
+ self.cache_path = cache_path
+ self.num_features = 3
+
+ self.renderer = Renderer(
+ device=self.device,
+ dim=(render_grid_size, render_grid_size),
+ interpolation_mode=self.opt.texture_interpolation_mode,
+ )
+ self.env_sphere, self.mesh = self.init_meshes()
+ self.default_color = [0.8, 0.1, 0.8]
+ self.background_sphere_colors, self.texture_img = self.init_paint()
+ self.meta_texture_img = nn.Parameter(torch.zeros_like(self.texture_img))
+ if self.opt.reference_texture:
+ base_texture = (
+ torch.Tensor(
+ np.array(
+ Image.open(self.opt.reference_texture).resize(
+ (self.texture_resolution, self.texture_resolution)
+ )
+ )
+ )
+ .permute(2, 0, 1)
+ .cuda()
+ .unsqueeze(0)
+ / 255.0
+ )
+ change_mask = (
+ (base_texture.to(self.device) - self.texture_img).abs().sum(axis=1)
+ > 0.1
+ ).float()
+ with torch.no_grad():
+ self.meta_texture_img[:, 1] = change_mask
+ self.vt, self.ft = self.init_texture_map()
+ self.mesh.normalize_mesh(inplace=True, target_scale=self.mesh_scale, dy=self.dy)
+
+ self.face_attributes = kal.ops.mesh.index_vertices_by_faces(
+ self.vt.unsqueeze(0), self.ft.long()
+ ).detach()
+
+ self.n_eigen_values = 20
+ self._L = None
+ self._eigenvalues = None
+ self._eigenvectors = None
+
+ @property
+ def L(self) -> np.ndarray:
+ if self._L is None:
+ self._L = build_cotan_laplacian_torch(self.mesh.vertices.T, self.mesh.faces)
+ return self._L
+
+ def eigens(self, k: int, e: float) -> (torch.Tensor, torch.Tensor):
+ if self._eigenvalues is None or self._eigenvectors is None:
+ self._eigenvalues, self._eigenvectors = eigen_problem(self.L, k, e)
+ self._eigenvalues, self._eigenvectors = self._eigenvalues.to(
+ self.device
+ ), self._eigenvectors.to(self.device)
+
+ return self._eigenvalues, self._eigenvectors
+
+ @staticmethod
+ def normalize_vertices(
+ vertices: torch.Tensor, mesh_scale: float = 1.0, dy: float = 0.0
+ ) -> torch.Tensor:
+ vertices -= vertices.mean(dim=0)[None, :]
+ vertices /= vertices.norm(dim=1).max()
+ vertices *= mesh_scale
+ vertices[:, 1] += dy
+ return vertices
+
+ def spectral_augmentations(self, vertices: torch.Tensor) -> torch.Tensor:
+ eigen_values, basis_functions = self.eigens(self.n_eigen_values, 0.0)
+ basis_functions /= (
+ basis_functions.max(dim=-1)[0][:, None]
+ - basis_functions.min(dim=-1)[0][:, None]
+ )
+
+ chosen_basis_function = choose_multi_modal(basis_functions.shape[0], 2)
+ coeffs = torch.zeros(basis_functions.shape[0]).to(self.device)
+ signs = (
+ (torch.rand(len(chosen_basis_function), device=self.device) > 0.5).float()
+ - 0.5
+ ) * 2
+ coeffs[chosen_basis_function] = signs
+
+ reconstructed = coeffs @ basis_functions.float()
+
+ directions = vertices / torch.norm(vertices, dim=1)[:, None]
+ deformed_v = vertices + 0.25 * reconstructed[:, None] * directions
+ return self.normalize_vertices(
+ deformed_v, mesh_scale=self.mesh_scale, dy=self.dy
+ )
+
+ def axis_augmentations(
+ self,
+ vertices: torch.Tensor,
+ stretch_factor: float = 1.6,
+ squish_factor: float = 0.7,
+ ):
+ axis_indices = np.arange(0, 3)
+ axis_indices = np.random.permutation(axis_indices)
+ stretch_axis = axis_indices[0]
+ squish_axis = axis_indices[1]
+
+ deformed_v = vertices.clone()
+ deformed_v[:, stretch_axis] *= stretch_factor
+ deformed_v[:, squish_axis] *= squish_factor
+ return self.normalize_vertices(
+ deformed_v, mesh_scale=self.mesh_scale, dy=self.dy
+ )
+
+ def augment_vertices(self):
+ verts = self.mesh.vertices.clone()
+ if np.random.rand() < 0.5:
+ verts = self.spectral_augmentations(verts)
+ if np.random.rand() < 0.5:
+ verts = self.axis_augmentations(verts)
+ return verts
+
+ def init_meshes(self, env_sphere_path="./mnt/shapes/env_sphere.obj"):
+ if not Path(env_sphere_path).exists():
+ model_url = "https://raw.githubusercontent.com/TEXTurePaper/TEXTurePaper/main/shapes/env_sphere.obj"
+ response = requests.get(model_url)
+ if response.status_code == 200:
+ os.makedirs(Path(env_sphere_path).parent, exist_ok=True)
+ with open(env_sphere_path, "wb") as f:
+ f.write(response.content)
+
+ env_sphere = Mesh(env_sphere_path, self.device)
+
+ mesh = Mesh(self.opt.shape_path, self.device)
+ # mesh.normalize_mesh(inplace=True, target_scale=self.mesh_scale, dy=self.dy)
+
+ return env_sphere, mesh
+
+ def zero_meta(self):
+ with torch.no_grad():
+ self.meta_texture_img[:] = 0
+
+ def init_paint(self, num_backgrounds=1):
+ # random color face attributes for background sphere
+ init_background_bases = torch.rand(num_backgrounds, 3).to(self.device)
+ modulated_init_background_bases_latent = (
+ init_background_bases[:, None, None, :] * 0.8
+ + 0.2
+ * torch.randn(
+ num_backgrounds,
+ self.env_sphere.faces.shape[0],
+ 3,
+ self.num_features,
+ dtype=torch.float32,
+ ).cuda()
+ )
+ background_sphere_colors = nn.Parameter(
+ modulated_init_background_bases_latent.cuda()
+ )
+
+ if self.initial_texture_path is not None:
+ texture = (
+ torch.Tensor(
+ np.array(
+ Image.open(self.initial_texture_path).resize(
+ (self.texture_resolution, self.texture_resolution)
+ )
+ )
+ )
+ .permute(2, 0, 1)
+ .cuda()
+ .unsqueeze(0)
+ / 255.0
+ )
+ else:
+ texture = (
+ torch.ones(
+ 1, 3, self.texture_resolution, self.texture_resolution
+ ).cuda()
+ * torch.Tensor(self.default_color).reshape(1, 3, 1, 1).cuda()
+ )
+ texture_img = nn.Parameter(texture)
+ return background_sphere_colors, texture_img
+
+ def invert_color(self, color: torch.Tensor) -> torch.Tensor:
+ # inverse linear approx to find latent
+ A = self.linear_rgb_estimator.T
+ regularizer = 1e-2
+
+ pinv = torch.pinverse(A.T @ A + regularizer * torch.eye(4).cuda()) @ A.T
+ if len(color) == 1 or type(color) is torch.Tensor:
+ init_color_in_latent = color @ pinv.T
+ else:
+ init_color_in_latent = pinv @ torch.tensor(list(color)).float().to(A.device)
+ return init_color_in_latent
+
+ def change_default_to_median(self):
+ diff = (
+ (
+ self.texture_img
+ - torch.tensor(self.default_color).view(1, 3, 1, 1).to(self.device)
+ )
+ .abs()
+ .sum(axis=1)
+ )
+ default_mask = (diff < 0.1).float().unsqueeze(0)
+ median_color = (
+ self.texture_img[0, :]
+ .reshape(3, -1)[:, default_mask.flatten() == 0]
+ .mean(axis=1)
+ )
+ with torch.no_grad():
+ self.texture_img.reshape(3, -1)[
+ :, default_mask.flatten() == 1
+ ] = median_color.reshape(-1, 1)
+
+ def init_texture_map(self):
+ cache_path = self.cache_path
+ if cache_path is None:
+ cache_exists_flag = False
+ else:
+ vt_cache, ft_cache = cache_path / "vt.pth", cache_path / "ft.pth"
+ cache_exists_flag = vt_cache.exists() and ft_cache.exists()
+
+ if (
+ self.mesh.vt is not None
+ and self.mesh.ft is not None
+ and self.mesh.vt.shape[0] > 0
+ and self.mesh.ft.min() > -1
+ ):
+ vt = self.mesh.vt.cuda()
+ ft = self.mesh.ft.cuda()
+ run_xatlas = not (
+ vt.shape[0] == self.mesh.vertices.shape[0]
+ and ft.shape[0] == self.mesh.faces.shape[0]
+ )
+ elif cache_exists_flag:
+ vt = torch.load(vt_cache).cuda()
+ ft = torch.load(ft_cache).cuda()
+ # run_xatlas = not (vt.shape[0] == self.mesh.vertices.shape[0] and ft.shape[0] == self.mesh.faces.shape[0])
+ run_xatlas = not (ft.shape[0] == self.mesh.faces.shape[0])
+ else:
+ run_xatlas = True
+
+ if run_xatlas:
+ logger.info(
+ "running xatlas to unwrap UVs for mesh: v={v_np.shape} f={f_np.shape}"
+ )
+ # unwrap uvs
+ import xatlas
+
+ print("-----------------------------------------------------------")
+ print(self.mesh.vertices.shape)
+ print(self.mesh.faces.shape)
+ print("-----------------------------------------------------------")
+ v_np = self.mesh.vertices.cpu().numpy()
+ f_np = self.mesh.faces.int().cpu().numpy()
+
+ print("-----------------------------------------------------------")
+ print(v_np.shape)
+ print(f_np.shape)
+
+ atlas = xatlas.Atlas()
+ atlas.add_mesh(v_np, f_np)
+ chart_options = xatlas.ChartOptions()
+ chart_options.max_iterations = 4
+ atlas.generate(chart_options=chart_options)
+ vmapping, ft_np, vt_np = atlas[0] # [N], [M, 3], [N, 2]
+
+ vt = torch.from_numpy(vt_np.astype(np.float32)).float().cuda()
+ ft = torch.from_numpy(ft_np.astype(np.int64)).int().cuda()
+ if cache_path is not None:
+ os.makedirs(cache_path, exist_ok=True)
+ torch.save(vt.cpu(), vt_cache)
+ torch.save(ft.cpu(), ft_cache)
+ return vt, ft
+
+ def forward(self, x):
+ raise NotImplementedError
+
+ def get_params(self):
+ return [self.background_sphere_colors, self.texture_img, self.meta_texture_img]
+
+ @torch.no_grad()
+ def export_mesh(self, path, save_name=None):
+ v, f = self.mesh.vertices, self.mesh.faces.int()
+ h0, w0 = 256, 256
+ ssaa, name = 1, ""
+
+ # v, f: torch Tensor
+ v_np = v.cpu().numpy() # [N, 3]
+ f_np = f.cpu().numpy() # [M, 3]
+
+ colors = self.texture_img.permute(0, 2, 3, 1).contiguous().clamp(0, 1)
+
+ colors = colors[0].cpu().detach().numpy()
+ colors = (colors * 255).astype(np.uint8)
+
+ vt_np = self.vt.detach().cpu().numpy()
+ ft_np = self.ft.detach().cpu().numpy()
+
+ colors = Image.fromarray(colors)
+
+ if ssaa > 1:
+ colors = colors.resize((w0, h0), Image.LINEAR)
+
+ texture_file = os.path.join(path, f"{name}{save_name}.png")
+ colors.save(texture_file)
+
+ # save obj (v, vt, f /)
+ obj_file = os.path.join(path, f"{name}{save_name}.obj")
+ mtl_file = os.path.join(path, f"{name}{save_name}.mtl")
+
+ logger.info("writing obj mesh to {obj_file}")
+ with open(obj_file, "w") as fp:
+ fp.write(f"mtllib {name}{save_name}.mtl \n")
+
+ logger.info("writing vertices {v_np.shape}")
+ for v in v_np:
+ fp.write(f"v {v[0]} {v[1]} {v[2]} \n")
+
+ logger.info("writing vertices texture coords {vt_np.shape}")
+ for v in vt_np:
+ # fp.write(f'vt {v[0]} {1 - v[1]} \n')
+ fp.write(f"vt {v[0]} {v[1]} \n")
+
+ logger.info("writing faces {f_np.shape}")
+ fp.write(f"usemtl mat0 \n")
+ print("-----------------------------------------------------------")
+ print(f_np)
+ print("-----------------------------------------------------------")
+ for i in range(len(f_np)):
+ fp.write(
+ f"f {f_np[i, 0] + 1}/{ft_np[i, 0] + 1} {f_np[i, 1] + 1}/{ft_np[i, 1] + 1} {f_np[i, 2] + 1}/{ft_np[i, 2] + 1} \n"
+ )
+
+ with open(mtl_file, "w") as fp:
+ fp.write(f"newmtl mat0 \n")
+ fp.write(f"Ka 1.000000 1.000000 1.000000 \n")
+ fp.write(f"Kd 1.000000 1.000000 1.000000 \n")
+ fp.write(f"Ks 0.000000 0.000000 0.000000 \n")
+ fp.write(f"Tr 1.000000 \n")
+ fp.write(f"illum 1 \n")
+ fp.write(f"Ns 0.000000 \n")
+ fp.write(f"map_Kd {name}{save_name}.png \n")
+
+ return [obj_file, mtl_file, texture_file]
+
+ def render(
+ self,
+ theta=None,
+ phi=None,
+ radius=None,
+ background=None,
+ use_meta_texture=False,
+ render_cache=None,
+ use_median=False,
+ dims=None,
+ ):
+ if render_cache is None:
+ assert theta is not None and phi is not None and radius is not None
+ background_sphere_colors = self.background_sphere_colors[
+ torch.randint(0, self.background_sphere_colors.shape[0], (1,))
+ ]
+ if use_meta_texture:
+ texture_img = self.meta_texture_img
+ else:
+ texture_img = self.texture_img
+
+ if self.augmentations:
+ augmented_vertices = self.augment_vertices()
+ else:
+ augmented_vertices = self.mesh.vertices
+
+ if use_median:
+ diff = (
+ (
+ texture_img
+ - torch.tensor(self.default_color).view(1, 3, 1, 1).to(self.device)
+ )
+ .abs()
+ .sum(axis=1)
+ )
+ default_mask = (diff < 0.1).float().unsqueeze(0)
+ median_color = (
+ texture_img[0, :]
+ .reshape(3, -1)[:, default_mask.flatten() == 0]
+ .mean(axis=1)
+ )
+ texture_img = texture_img.clone()
+ with torch.no_grad():
+ texture_img.reshape(3, -1)[
+ :, default_mask.flatten() == 1
+ ] = median_color.reshape(-1, 1)
+ background_type = "none"
+ use_render_back = False
+ if background is not None and type(background) == str:
+ background_type = background
+ use_render_back = True
+ (
+ pred_features,
+ mask,
+ depth,
+ normals,
+ render_cache,
+ ) = self.renderer.render_single_view_texture(
+ augmented_vertices,
+ self.mesh.faces,
+ self.face_attributes,
+ texture_img,
+ elev=theta,
+ azim=phi,
+ radius=radius,
+ look_at_height=self.dy,
+ render_cache=render_cache,
+ dims=dims,
+ background_type=background_type,
+ )
+
+ mask = mask.detach()
+
+ if use_render_back:
+ pred_map = pred_features
+ pred_back = pred_features
+ else:
+ if background is None:
+ pred_back, _, _ = self.renderer.render_single_view(
+ self.env_sphere,
+ background_sphere_colors,
+ elev=theta,
+ azim=phi,
+ radius=radius,
+ dims=dims,
+ look_at_height=self.dy,
+ calc_depth=False,
+ )
+ elif len(background.shape) == 1:
+ pred_back = torch.ones_like(pred_features) * background.reshape(
+ 1, 3, 1, 1
+ )
+ else:
+ pred_back = background
+
+ pred_map = pred_back * (1 - mask) + pred_features * mask
+
+ if not use_meta_texture:
+ pred_map = pred_map.clamp(0, 1)
+ pred_features = pred_features.clamp(0, 1)
+
+ return {
+ "image": pred_map,
+ "mask": mask,
+ "background": pred_back,
+ "foreground": pred_features,
+ "depth": depth,
+ "normals": normals,
+ "render_cache": render_cache,
+ "texture_map": texture_img,
+ }
+
+ def draw(self, theta, phi, radius, target_rgb):
+ # failed attempt to draw on the texture image
+
+ uv_features, face_idx = self.renderer.project_uv_single_view(
+ self.mesh.vertices,
+ self.mesh.faces,
+ self.face_attributes,
+ elev=theta,
+ azim=phi,
+ radius=radius,
+ look_at_height=self.dy,
+ )
+ unique_face_idx = torch.unique(face_idx)
+ print("")
diff --git a/morpheus-worker/app/actors/texturing3d/utils/texture3d/shapes/env_sphere.obj b/morpheus-worker/app/actors/texturing3d/utils/texture3d/shapes/env_sphere.obj
new file mode 100644
index 0000000..a7ca6e2
--- /dev/null
+++ b/morpheus-worker/app/actors/texturing3d/utils/texture3d/shapes/env_sphere.obj
@@ -0,0 +1,10260 @@
+####
+#
+# OBJ File Generated by Meshlab
+#
+####
+# Object env_sphere.obj
+#
+# Vertices: 2562
+# Faces: 5120
+#
+####
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+f 2553//2553 2546//2546 2543//2543
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+f 2549//2549 1644//1644 1650//1650
+f 2554//2554 2547//2547 2549//2549
+f 1650//1650 1648//1648 2554//2554
+f 2555//2555 1139//1139 2552//2552
+f 1139//1139 1125//1125 2552//2552
+f 2555//2555 1134//1134 1139//1139
+f 2552//2552 2547//2547 2555//2555
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+f 2557//2557 2495//2495 2559//2559
+f 2558//2558 2545//2545 2557//2557
+f 2559//2559 1657//1657 2558//2558
+f 2559//2559 2497//2497 1658//1658
+f 2497//2497 1629//1629 1658//1658
+f 2559//2559 2495//2495 2497//2497
+f 1658//1658 1657//1657 2559//2559
+f 2557//2557 2551//2551 2499//2499
+f 2551//2551 2469//2469 2499//2499
+f 2557//2557 2545//2545 2551//2551
+f 2499//2499 2495//2495 2557//2557
+f 2558//2558 1659//1659 2548//2548
+f 1659//1659 1644//1644 2548//2548
+f 2558//2558 1657//1657 1659//1659
+f 2548//2548 2545//2545 2558//2558
+f 2561//2561 2560//2560 2562//2562
+f 2560//2560 1144//1144 2562//2562
+f 2561//2561 2546//2546 2560//2560
+f 2562//2562 2485//2485 2561//2561
+f 2562//2562 1145//1145 2487//2487
+f 1145//1145 1058//1058 2487//2487
+f 2562//2562 1144//1144 1145//1145
+f 2487//2487 2485//2485 2562//2562
+f 2560//2560 2553//2553 1146//1146
+f 2553//2553 1125//1125 1146//1146
+f 2560//2560 2546//2546 2553//2553
+f 1146//1146 1144//1144 2560//2560
+f 2561//2561 2489//2489 2550//2550
+f 2489//2489 2469//2469 2550//2550
+f 2561//2561 2485//2485 2489//2489
+f 2550//2550 2546//2546 2561//2561
+# 5120 faces, 0 coords texture
+
+# End of File
diff --git a/morpheus-worker/app/actors/texturing3d/utils/texture3d/stable_diffusion_depth.py b/morpheus-worker/app/actors/texturing3d/utils/texture3d/stable_diffusion_depth.py
new file mode 100644
index 0000000..e99db5c
--- /dev/null
+++ b/morpheus-worker/app/actors/texturing3d/utils/texture3d/stable_diffusion_depth.py
@@ -0,0 +1,565 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from PIL import Image
+from diffusers import PNDMScheduler
+from loguru import logger
+from tqdm.auto import tqdm
+from transformers import logging
+
+from app.utils.texture3d.utils import seed_everything
+
+# suppress partial model loading warning
+logging.set_verbosity_error()
+
+
+class StableDiffusionTexture(nn.Module):
+ def __init__(
+ self,
+ device,
+ concept_name=None,
+ concept_path=None,
+ latent_mode=True,
+ min_timestep=0.02,
+ max_timestep=0.98,
+ no_noise=False,
+ use_inpaint=False,
+ lora_path=None,
+ models=None,
+ ):
+ super().__init__()
+
+ self.device = device
+ self.latent_mode = latent_mode
+ self.no_noise = no_noise
+ self.num_train_timesteps = 1000
+ self.min_step = int(self.num_train_timesteps * min_timestep)
+ self.max_step = int(self.num_train_timesteps * max_timestep)
+ self.use_inpaint = use_inpaint
+
+ # 1. Load the autoencoder model which will be used to decode the latents into image space.
+ self.vae = models.depth_model.vae.to(self.device)
+
+ # 2. Load the tokenizer and text encoder to tokenize and encode the text.
+ self.tokenizer = models.depth_model.tokenizer
+ self.text_encoder = models.depth_model.text_encoder.to(self.device)
+ self.image_encoder = None
+ self.image_processor = None
+
+ # 3. The UNet model for generating the latents.
+ self.unet = models.depth_model.unet.to(self.device)
+
+ if self.use_inpaint:
+ self.inpaint_unet = models.inpaint_unet.to(self.device)
+
+ # 4. Create a scheduler for inference
+ self.scheduler = PNDMScheduler(
+ beta_start=0.00085,
+ beta_end=0.012,
+ beta_schedule="scaled_linear",
+ num_train_timesteps=self.num_train_timesteps,
+ steps_offset=1,
+ skip_prk_steps=True,
+ )
+ self.alphas = self.scheduler.alphas_cumprod.to(self.device) # for convenience
+
+ if lora_path:
+ self.unet.load_attn_procs(lora_path)
+ # self.inpaint_unet.load_attn_procs(lora_path)
+ logger.info(f"\t successfully loaded Lora weights!")
+
+ logger.info(f"\t successfully loaded stable diffusion!")
+
+ def get_text_embeds(self, prompt, negative_prompt=None):
+ # Tokenize text and get embeddings
+ text_input = self.tokenizer(
+ prompt,
+ padding="max_length",
+ max_length=self.tokenizer.model_max_length,
+ truncation=False,
+ return_tensors="pt",
+ )
+ logger.info(prompt)
+ logger.info(text_input.input_ids)
+
+ with torch.no_grad():
+ text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
+
+ # Do the same for unconditional embeddings
+ if negative_prompt is None:
+ negative_prompt = [""] * len(prompt)
+ uncond_input = self.tokenizer(
+ negative_prompt,
+ padding="max_length",
+ max_length=self.tokenizer.model_max_length,
+ truncation=False,
+ return_tensors="pt",
+ )
+ max_tokens = text_input.input_ids.shape[1]
+ if uncond_input.input_ids.shape[1] > max_tokens:
+ uncond_input.input_ids = uncond_input.input_ids[:, :max_tokens]
+ uncond_input.attention_mask = uncond_input.attention_mask[:, :max_tokens]
+
+ with torch.no_grad():
+ uncond_embeddings = self.text_encoder(
+ uncond_input.input_ids.to(self.device)
+ )[0]
+
+ # Cat for final embeddings
+ text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+ return text_embeddings
+
+ def img2img_single_step(
+ self, text_embeddings, prev_latents, depth_mask, step, guidance_scale=100
+ ):
+ # input is 1 3 512 512
+ # depth_mask is 1 1 512 512
+ # text_embeddings is 2 512
+
+ def sample(prev_latents, depth_mask, step):
+ latent_model_input = torch.cat([prev_latents] * 2)
+ latent_model_input = self.scheduler.scale_model_input(
+ latent_model_input, step
+ )
+
+ latent_model_input_depth = torch.cat(
+ [latent_model_input, depth_mask], dim=1
+ )
+
+ # predict the noise residual
+ with torch.no_grad():
+ noise_pred = self.unet(
+ latent_model_input_depth,
+ step,
+ encoder_hidden_states=text_embeddings,
+ )["sample"]
+
+ noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+ noise_pred = noise_pred_uncond + guidance_scale * (
+ noise_pred_text - noise_pred_uncond
+ )
+
+ # compute the previous noisy sample x_t -> x_t-1
+ latents = self.scheduler.step(noise_pred, step, prev_latents)["prev_sample"]
+
+ return latents
+
+ depth_mask = F.interpolate(
+ depth_mask, size=(64, 64), mode="bicubic", align_corners=False
+ )
+ depth_mask = (
+ 2.0
+ * (depth_mask - depth_mask.min())
+ / (depth_mask.max() - depth_mask.min())
+ - 1.0
+ )
+
+ with torch.no_grad():
+ target_latents = sample(prev_latents, depth_mask, step=step)
+ return target_latents
+
+ def img2img_step(
+ self,
+ text_embeddings,
+ inputs,
+ depth_mask,
+ guidance_scale=100,
+ strength=0.5,
+ num_inference_steps=50,
+ update_mask=None,
+ latent_mode=False,
+ check_mask=None,
+ fixed_seed=None,
+ check_mask_iters=0.5,
+ intermediate_vis=False,
+ inpainting_only=False,
+ ):
+ # input is 1 3 512 512
+ # depth_mask is 1 1 512 512
+ # text_embeddings is 2 512
+ intermediate_results = []
+
+ def sample(
+ latents,
+ depth_mask,
+ strength,
+ num_inference_steps,
+ update_mask=None,
+ check_mask=None,
+ masked_latents=None,
+ inpainting_only=False,
+ ):
+ self.scheduler.set_timesteps(num_inference_steps)
+ noise = None
+ if latents is None:
+ # Last channel is reserved for depth
+ latents = torch.randn(
+ (
+ text_embeddings.shape[0] // 2,
+ self.unet.in_channels - 1,
+ depth_mask.shape[2],
+ depth_mask.shape[3],
+ ),
+ device=self.device,
+ )
+ timesteps = self.scheduler.timesteps
+ else:
+ # Strength has meaning only when latents are given
+ timesteps, num_inference_steps = self.get_timesteps(
+ num_inference_steps, strength
+ )
+ latent_timestep = timesteps[:1]
+ if fixed_seed is not None:
+ seed_everything(fixed_seed)
+ noise = torch.randn_like(latents)
+ if update_mask is not None:
+ # NOTE: I think we might want to use same noise?
+ gt_latents = latents
+ latents = torch.randn(
+ (
+ text_embeddings.shape[0] // 2,
+ self.unet.in_channels - 1,
+ depth_mask.shape[2],
+ depth_mask.shape[3],
+ ),
+ device=self.device,
+ )
+ else:
+ latents = self.scheduler.add_noise(latents, noise, latent_timestep)
+
+ depth_mask = torch.cat([depth_mask] * 2)
+
+ with torch.autocast("cuda"):
+ mask_iters = int(len(timesteps) * check_mask_iters)
+ min_inpaint_step = 10
+ max_inpaint_step = 20
+ for i, t in tqdm(enumerate(timesteps)):
+ is_inpaint_range = self.use_inpaint and (
+ min_inpaint_step < i < max_inpaint_step
+ )
+ if inpainting_only:
+ is_inpaint_range = True
+
+ if not is_inpaint_range and update_mask is not None:
+ noised_truth = self.scheduler.add_noise(gt_latents, noise, t)
+ if check_mask is not None and i < mask_iters:
+ curr_mask = check_mask
+ else:
+ curr_mask = update_mask
+ latents = latents * curr_mask + noised_truth * (1 - curr_mask)
+
+ # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
+ latent_model_input = torch.cat([latents] * 2)
+ latent_model_input = self.scheduler.scale_model_input(
+ latent_model_input, t
+ ) # NOTE: This does nothing
+
+ if is_inpaint_range:
+ latent_mask = torch.cat([update_mask] * 2)
+ latent_image = torch.cat([masked_latents] * 2)
+ latent_model_input_inpaint = torch.cat(
+ [latent_model_input, latent_mask, latent_image], dim=1
+ )
+ with torch.no_grad():
+ noise_pred_inpaint = self.inpaint_unet(
+ latent_model_input_inpaint,
+ t,
+ encoder_hidden_states=text_embeddings,
+ )["sample"]
+ noise_pred = noise_pred_inpaint
+ else:
+ latent_model_input_depth = torch.cat(
+ [latent_model_input, depth_mask], dim=1
+ )
+ # predict the noise residual
+ with torch.no_grad():
+ noise_pred = self.unet(
+ latent_model_input_depth,
+ t,
+ encoder_hidden_states=text_embeddings,
+ )["sample"]
+
+ # perform guidance
+ noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+ noise_pred = noise_pred_uncond + guidance_scale * (
+ noise_pred_text - noise_pred_uncond
+ )
+
+ if intermediate_vis:
+ vis_alpha_t = torch.sqrt(self.scheduler.alphas_cumprod)
+ vis_sigma_t = torch.sqrt(1 - self.scheduler.alphas_cumprod)
+ a_t, s_t = vis_alpha_t[t], vis_sigma_t[t]
+ vis_latents = (latents - s_t * noise) / a_t
+ vis_latents = 1 / 0.18215 * vis_latents
+ image = self.vae.decode(vis_latents).sample
+ image = (image / 2 + 0.5).clamp(0, 1)
+ image = image.cpu().permute(0, 2, 3, 1).numpy()
+ image = Image.fromarray(
+ (image[0] * 255).round().astype("uint8")
+ )
+ intermediate_results.append(image)
+ latents = self.scheduler.step(noise_pred, t, latents)["prev_sample"]
+
+ return latents
+
+ depth_mask = F.interpolate(
+ depth_mask, size=(64, 64), mode="bicubic", align_corners=False
+ )
+ masked_latents = None
+ if inputs is None:
+ latents = None
+ elif latent_mode:
+ latents = inputs
+ else:
+ pred_rgb_512 = F.interpolate(
+ inputs, (512, 512), mode="bilinear", align_corners=False
+ )
+ latents = self.encode_imgs(pred_rgb_512)
+ if self.use_inpaint:
+ update_mask_512 = F.interpolate(update_mask, (512, 512))
+ masked_inputs = pred_rgb_512 * (update_mask_512 < 0.5) + 0.5 * (
+ update_mask_512 >= 0.5
+ )
+ masked_latents = self.encode_imgs(masked_inputs)
+
+ if update_mask is not None:
+ update_mask = F.interpolate(update_mask, (64, 64), mode="nearest")
+ if check_mask is not None:
+ check_mask = F.interpolate(check_mask, (64, 64), mode="nearest")
+
+ depth_mask = (
+ 2.0
+ * (depth_mask - depth_mask.min())
+ / (depth_mask.max() - depth_mask.min())
+ - 1.0
+ )
+
+ with torch.no_grad():
+ target_latents = sample(
+ latents,
+ depth_mask,
+ strength=strength,
+ num_inference_steps=num_inference_steps,
+ update_mask=update_mask,
+ check_mask=check_mask,
+ masked_latents=masked_latents,
+ inpainting_only=inpainting_only,
+ )
+ target_rgb = self.decode_latents(target_latents)
+
+ if latent_mode:
+ return target_rgb, target_latents
+ else:
+ return target_rgb, intermediate_results
+
+ def train_step(self, text_embeddings, inputs, depth_mask, guidance_scale=100):
+ # interp to 512x512 to be fed into vae.
+ if not self.latent_mode:
+ # latents = F.interpolate(latents, (64, 64), mode='bilinear', align_corners=False)
+ pred_rgb_512 = F.interpolate(
+ inputs, (512, 512), mode="bilinear", align_corners=False
+ )
+ latents = self.encode_imgs(pred_rgb_512)
+ depth_mask = F.interpolate(
+ depth_mask, size=(64, 64), mode="bicubic", align_corners=False
+ )
+ else:
+ latents = inputs
+
+ depth_mask = (
+ 2.0
+ * (depth_mask - depth_mask.min())
+ / (depth_mask.max() - depth_mask.min())
+ - 1.0
+ )
+ depth_mask = torch.cat([depth_mask] * 2)
+
+ # timestep ~ U(0.02, 0.98) to avoid very high/low noise level
+ t = torch.randint(
+ self.min_step, self.max_step + 1, [1], dtype=torch.long, device=self.device
+ )
+
+ # predict the noise residual with unet, NO grad!
+ with torch.no_grad():
+ # add noise
+ if self.no_noise:
+ noise = torch.zeros_like(latents)
+ latents_noisy = latents
+ else:
+ noise = torch.randn_like(latents)
+ latents_noisy = self.scheduler.add_noise(latents, noise, t)
+ # pred noise
+ latent_model_input = torch.cat([latents_noisy] * 2)
+ # add depth
+ latent_model_input = torch.cat([latent_model_input, depth_mask], dim=1)
+ noise_pred = self.unet(
+ latent_model_input, t, encoder_hidden_states=text_embeddings
+ ).sample
+
+ # perform guidance (high scale from paper!)
+ noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+ noise_pred = noise_pred_uncond + guidance_scale * (
+ noise_pred_text - noise_pred_uncond
+ )
+
+ # w(t), alpha_t * sigma_t^2
+ w = 1 - self.alphas[t]
+ grad = w * (noise_pred - noise)
+
+ # clip grad for stable training?
+ # grad = grad.clamp(-1, 1)
+ grad = torch.nan_to_num(grad)
+
+ # manually backward, since we omitted an item in grad and cannot simply autodiff.
+ latents.backward(gradient=grad, retain_graph=True)
+
+ return 0
+
+ def produce_latents(
+ self,
+ text_embeddings,
+ depth_mask,
+ height=512,
+ width=512,
+ num_inference_steps=50,
+ guidance_scale=7.5,
+ latents=None,
+ strength=0.5,
+ ):
+ self.scheduler.set_timesteps(num_inference_steps)
+
+ if latents is None:
+ # Last chanel is reserved for depth
+ latents = torch.randn(
+ (
+ text_embeddings.shape[0] // 2,
+ self.unet.in_channels - 1,
+ height // 8,
+ width // 8,
+ ),
+ device=self.device,
+ )
+ timesteps = self.scheduler.timesteps
+ else:
+ # Strength has meaning only when latents are given
+ timesteps, num_inference_steps = self.get_timesteps(
+ num_inference_steps, strength
+ )
+ # Dont really have to tie the scheudler to the strength
+ latent_timestep = timesteps[:1]
+ noise = torch.randn_like(latents)
+ latents = self.scheduler.add_noise(latents, noise, latent_timestep)
+
+ depth_mask = torch.cat([depth_mask] * 2)
+ with torch.autocast("cuda"):
+ for i, t in tqdm(enumerate(timesteps)):
+ # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
+ latent_model_input = torch.cat([latents] * 2)
+ latent_model_input = self.scheduler.scale_model_input(
+ latent_model_input, t
+ ) # NOTE: This does nothing
+ latent_model_input = torch.cat([latent_model_input, depth_mask], dim=1)
+ # Depth should be added here
+
+ # predict the noise residual
+ with torch.no_grad():
+ noise_pred = self.unet(
+ latent_model_input, t, encoder_hidden_states=text_embeddings
+ )["sample"]
+
+ # perform guidance
+ noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+ noise_pred = noise_pred_uncond + guidance_scale * (
+ noise_pred_text - noise_pred_uncond
+ )
+
+ # compute the previous noisy sample x_t -> x_t-1
+ latents = self.scheduler.step(noise_pred, t, latents)["prev_sample"]
+
+ return latents
+
+ def decode_latents(self, latents):
+ # latents = F.interpolate(latents, (64, 64), mode='bilinear', align_corners=False)
+ latents = 1 / 0.18215 * latents
+
+ with torch.no_grad():
+ imgs = self.vae.decode(latents).sample
+
+ imgs = (imgs / 2 + 0.5).clamp(0, 1)
+
+ return imgs
+
+ def encode_imgs(self, imgs):
+ # imgs: [B, 3, H, W]
+
+ imgs = 2 * imgs - 1
+
+ posterior = self.vae.encode(imgs).latent_dist
+ latents = posterior.sample() * 0.18215
+
+ return latents
+
+ def get_timesteps(self, num_inference_steps, strength):
+ # get the original timestep using init_timestep
+ init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+
+ t_start = max(num_inference_steps - init_timestep, 0)
+ timesteps = self.scheduler.timesteps[t_start:]
+
+ return timesteps, num_inference_steps - t_start
+
+ def prompt_to_img(
+ self,
+ prompts,
+ depth_mask,
+ height=512,
+ width=512,
+ num_inference_steps=50,
+ guidance_scale=7.5,
+ latents=None,
+ strength=0.5,
+ ):
+ if isinstance(prompts, str):
+ prompts = [prompts]
+
+ # Prompts -> text embeds
+ text_embeds = self.get_text_embeds(prompts) # [2, 77, 768]
+ # new should be torch.Size([2, 77, 1024])
+
+ # depth is in range of 20-1500 of size 1x384x384, normalized to -1 to 1, mean was -0.6
+ # Resized to 64x64
+ depth_mask = (
+ 2.0
+ * (depth_mask - depth_mask.min())
+ / (depth_mask.max() - depth_mask.min())
+ - 1.0
+ )
+ depth_mask = F.interpolate(
+ depth_mask.unsqueeze(1),
+ size=(height // 8, width // 8),
+ mode="bicubic",
+ align_corners=False,
+ )
+
+ # Added as an extra channel to the latents
+
+ # Text embeds -> img latents
+ latents = self.produce_latents(
+ text_embeds,
+ depth_mask=depth_mask,
+ height=height,
+ width=width,
+ latents=latents,
+ num_inference_steps=num_inference_steps,
+ guidance_scale=guidance_scale,
+ strength=strength,
+ ) # [1, 4, 64, 64]
+
+ # Img latents -> imgs
+ imgs = self.decode_latents(latents) # [1, 3, 512, 512]
+
+ # Img to Numpy
+ imgs = imgs.detach().cpu().permute(0, 2, 3, 1).numpy()
+ imgs = (imgs * 255).round().astype("uint8")
+
+ return imgs
diff --git a/morpheus-worker/app/actors/texturing3d/utils/texture3d/step_00001_0000_rgb.jpg b/morpheus-worker/app/actors/texturing3d/utils/texture3d/step_00001_0000_rgb.jpg
new file mode 100644
index 0000000..b003d9a
Binary files /dev/null and b/morpheus-worker/app/actors/texturing3d/utils/texture3d/step_00001_0000_rgb.jpg differ
diff --git a/morpheus-worker/app/actors/texturing3d/utils/texture3d/training/__init__.py b/morpheus-worker/app/actors/texturing3d/utils/texture3d/training/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/morpheus-worker/app/actors/texturing3d/utils/texture3d/training/trainer.py b/morpheus-worker/app/actors/texturing3d/utils/texture3d/training/trainer.py
new file mode 100644
index 0000000..8f394f4
--- /dev/null
+++ b/morpheus-worker/app/actors/texturing3d/utils/texture3d/training/trainer.py
@@ -0,0 +1,983 @@
+import math
+from io import BytesIO
+from pathlib import Path
+from typing import Any, Dict, Union, List
+
+import cv2
+import einops
+import imageio
+import numpy as np
+import torch
+import torch.nn.functional as F
+from PIL import Image
+from loguru import logger
+from matplotlib import cm
+from torch import nn
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from app.utils.texture3d import utils
+from app.utils.texture3d.configs.train_config import TrainConfig
+from app.utils.texture3d.models.textured_mesh import TexturedMeshModel
+from app.utils.texture3d.stable_diffusion_depth import StableDiffusionTexture
+from app.utils.texture3d.training.views_dataset import ViewsDataset, MultiviewDataset
+from app.utils.texture3d.utils import make_path, tensor2numpy
+
+
+class TEXTure:
+ def __init__(self, cfg: TrainConfig, models=None, optimize_camera=False):
+ self.cfg = cfg
+ self.paint_step = 0
+ self.device = models.depth_model.device
+
+ utils.seed_everything(self.cfg.optim.seed)
+
+ # Make view_dirs
+ self.exp_path = make_path(self.cfg.log.exp_dir)
+ self.ckpt_path = make_path(self.exp_path / "checkpoints")
+ self.train_renders_path = make_path(self.exp_path / "vis" / "train")
+ self.eval_renders_path = make_path(self.exp_path / "vis" / "eval")
+ self.final_renders_path = make_path(self.exp_path / "results")
+
+ self.init_logger()
+ # pyrallis.dump(self.cfg, (self.exp_path / 'config.yaml').open('w'))
+
+ self.view_dirs = ["front", "left", "back", "right", "overhead", "bottom"]
+ self.mesh_model = self.init_mesh_model()
+
+ # Init specific diffusion models
+ self.diffusion = self.init_diffusion(models=models)
+
+ self.text_z, self.text_string = self.calc_text_embeddings()
+ self.dataloaders = self.init_dataloaders()
+
+ if optimize_camera:
+ # Create a camera distance for each view to make it as large as possible
+ self.cfg.render.radius_per_view = []
+
+ # Create a new list of the same size as concatenated_list
+ for i, data in enumerate(self.dataloaders["train"]):
+ view_radius = self.optimize_camera_distance_for_viewpoint(data)
+ self.cfg.render.radius_per_view.append(view_radius)
+ logger.info(
+ f"Camera distance for view {i} optimized to {view_radius} meters"
+ )
+
+ # reset dataloaders with radius per view
+ self.dataloaders = self.init_dataloaders()
+ logger.info(f"Successfully initialized {self.cfg.log.exp_name}")
+
+ def init_mesh_model(self) -> nn.Module:
+ cache_path = Path("cache") / Path(self.cfg.guide.shape_path).stem
+ self.cache_path = cache_path
+ cache_path.mkdir(parents=True, exist_ok=True)
+ model = TexturedMeshModel(
+ self.cfg.guide,
+ device=self.device,
+ render_grid_size=self.cfg.render.train_grid_size,
+ cache_path=cache_path,
+ texture_resolution=self.cfg.guide.texture_resolution,
+ augmentations=False,
+ )
+
+ model = model.to(self.device)
+ logger.info(
+ f"Loaded Mesh, #parameters: {sum([p.numel() for p in model.parameters() if p.requires_grad])}"
+ )
+ logger.info(model)
+ return model
+
+ def init_diffusion(self, models=None) -> Any:
+ diffusion_model = StableDiffusionTexture(
+ self.device,
+ concept_name=self.cfg.guide.concept_name,
+ concept_path=self.cfg.guide.concept_path,
+ latent_mode=False,
+ min_timestep=self.cfg.optim.min_timestep,
+ max_timestep=self.cfg.optim.max_timestep,
+ no_noise=self.cfg.optim.no_noise,
+ use_inpaint=True,
+ lora_path=self.cfg.guide.diffusion_lora_path,
+ models=models,
+ )
+
+ for p in diffusion_model.parameters():
+ p.requires_grad = False
+ return diffusion_model
+
+ def get_viewpoint_prompts(self, direction):
+ pos_text = [self.cfg.guide.text]
+ neg_text = []
+
+ # generic negative prompt
+ if self.cfg.guide.neg_text is not None:
+ neg_text.append(self.cfg.guide.neg_text)
+
+ # front viewpoint
+ if self.cfg.guide.text_front is not None and direction == "front":
+ pos_text.append(self.cfg.guide.text_front)
+
+ if self.cfg.guide.neg_text_front is not None and direction == "front":
+ neg_text.append(self.cfg.guide.neg_text_front)
+
+ # side viewpoint
+ if self.cfg.guide.text_side is not None and (
+ direction == "right" or direction == "left"
+ ):
+ pos_text.append(self.cfg.guide.text_side)
+
+ if self.cfg.guide.neg_text_side is not None and (
+ direction == "right" or direction == "left"
+ ):
+ neg_text.append(self.cfg.guide.neg_text_side)
+
+ # back viewpoint
+ if self.cfg.guide.text_back is not None and direction == "back":
+ pos_text.append(self.cfg.guide.text_back)
+
+ if self.cfg.guide.neg_text_back is not None and direction == "back":
+ neg_text.append(self.cfg.guide.neg_text_back)
+
+ # overhead viewpoint
+ if self.cfg.guide.text_overhead is not None and direction == "overhead":
+ pos_text.append(self.cfg.guide.text_overhead)
+
+ if self.cfg.guide.neg_text_overhead is not None and direction == "overhead":
+ neg_text.append(self.cfg.guide.neg_text_overhead)
+
+ # bottom viewpoint
+ if self.cfg.guide.text_bottom is not None and direction == "bottom":
+ pos_text.append(self.cfg.guide.text_bottom)
+
+ if self.cfg.guide.neg_text_bottom is not None and direction == "bottom":
+ neg_text.append(self.cfg.guide.neg_text_bottom)
+
+ # not front viewpoint
+ if self.cfg.guide.text_not_front is not None and direction != "front":
+ pos_text.append(self.cfg.guide.text_not_front)
+
+ if self.cfg.guide.neg_text_not_front is not None and direction != "front":
+ neg_text.append(self.cfg.guide.neg_text_not_front)
+
+ # append direction
+ dir_str = f"{direction} view"
+ pos_text.append(dir_str)
+
+ positive_prompt = ", ".join(pos_text)
+ if len(neg_text) > 0:
+ negative_prompt = ", ".join(neg_text)
+ else:
+ negative_prompt = None
+
+ return positive_prompt, negative_prompt
+
+ def calc_text_embeddings(self) -> Union[torch.Tensor, List[torch.Tensor]]:
+ ref_text = self.cfg.guide.text
+
+ if not self.cfg.guide.append_direction:
+ text_z = self.diffusion.get_text_embeds(
+ [ref_text], negative_prompt=self.cfg.guide.neg_text
+ )
+ text_string = ref_text
+ else:
+ text_z = []
+ text_string = []
+ for d in self.view_dirs:
+ # condition on viewpoint-dependent prompts
+ text, negative_prompt = self.get_viewpoint_prompts(d)
+
+ # text = ref_text.format(d)
+ text_string.append(text)
+ logger.info(text)
+ logger.info(negative_prompt)
+ text_z.append(
+ self.diffusion.get_text_embeds(
+ [text], negative_prompt=negative_prompt
+ )
+ )
+ return text_z, text_string
+
+ def init_dataloaders(self) -> Dict[str, DataLoader]:
+ init_train_dataloader = MultiviewDataset(
+ self.cfg.render, device=self.device
+ ).dataloader()
+ val_loader = ViewsDataset(
+ self.cfg.render, device=self.device, size=self.cfg.log.eval_size
+ ).dataloader()
+ # Will be used for creating the final video
+ val_large_loader = ViewsDataset(
+ self.cfg.render, device=self.device, size=self.cfg.log.full_eval_size
+ ).dataloader()
+ dataloaders = {
+ "train": init_train_dataloader,
+ "val": val_loader,
+ "val_large": val_large_loader,
+ }
+ return dataloaders
+
+ def init_logger(self):
+ logger.remove() # Remove default logger
+ log_format = (
+ "{time:YYYY-MM-DD HH:mm:ss} {message}"
+ )
+ logger.add(
+ lambda msg: tqdm.write(msg, end=""), colorize=True, format=log_format
+ )
+ logger.add(self.exp_path / "log.txt", colorize=False, format=log_format)
+
+ def paint(self, name: str = None):
+ logger.info("Starting training ^_^")
+ # Evaluate the initialization
+ self.evaluate(self.dataloaders["val"], self.eval_renders_path)
+ self.mesh_model.train()
+
+ pbar = tqdm(
+ total=len(self.dataloaders["train"]),
+ initial=self.paint_step,
+ bar_format="{desc}: {percentage:3.0f}% painting step {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]",
+ )
+
+ for data in self.dataloaders["train"]:
+ self.paint_step += 1
+ pbar.update(1)
+ self.paint_viewpoint(data)
+ self.evaluate(self.dataloaders["val"], self.eval_renders_path)
+ self.mesh_model.train()
+
+ self.mesh_model.change_default_to_median()
+ logger.info("Finished Painting ^_^")
+ logger.info("Saving the last result...")
+
+ # get preview image
+ self.mesh_model.eval()
+ preview_image = self.get_preview_image(self.dataloaders["val_large"])
+
+ # save 3d mesh and texture
+ save_path = make_path(self.exp_path / "mesh")
+ logger.info(f"Saving mesh to {save_path}")
+ obj_file, mtl_file, texture_file = self.mesh_model.export_mesh(
+ save_path, save_name=name
+ )
+
+ # self.full_eval()
+ logger.info("\tDone!")
+
+ return preview_image, obj_file, mtl_file, texture_file
+
+ def paint_specific_viewpoint(
+ self,
+ name: str = None,
+ camera_position: List[float] = None,
+ inpaint: bool = False,
+ inpaint_only: bool = False,
+ mask: bytes = None,
+ ):
+ # Convert camera positions from cartesian to spherical coordinates
+ x = camera_position[0]
+ y = camera_position[1]
+ z = camera_position[2]
+ r = math.sqrt(x**2 + y**2 + z**2)
+ elev = math.acos(y / r)
+ azim = math.atan2(x, z)
+ data = {"theta": elev, "phi": azim, "radius": r}
+
+ # paint step 2 and more are interpreted as inpainting steps
+ if inpaint:
+ self.paint_step = 2
+
+ # self.model.train()
+ self.paint_viewpoint(data, custom_mask=mask, inpainting_only=inpaint_only)
+
+ # Need to create a new paint_viewpoint function that takes in the mask to generate (dont use their basic trimap)
+
+ self.mesh_model.eval()
+ preview_image = self.get_image_from_camera_viewpoint(data)
+ # preview_image = self.get_preview_image(self.dataloaders['val_large'])
+
+ # save 3d mesh and texture
+ save_path = make_path(self.exp_path / "mesh")
+ logger.info(f"Saving mesh to {save_path}")
+ obj_file, mtl_file, texture_file = self.mesh_model.export_mesh(
+ save_path, save_name=name
+ )
+
+ logger.info("\tDone!")
+
+ return preview_image, obj_file, mtl_file, texture_file
+
+ def evaluate(
+ self, dataloader: DataLoader, save_path: Path, save_as_video: bool = False
+ ):
+ # self.mesh_model.change_default_to_median()
+
+ logger.info(
+ f"Evaluating and saving model, painting iteration #{self.paint_step}..."
+ )
+ self.mesh_model.eval()
+ save_path.mkdir(exist_ok=True)
+
+ if save_as_video:
+ all_preds = []
+ for i, data in enumerate(dataloader):
+ preds, textures, depths, normals = self.eval_render(data)
+
+ pred = tensor2numpy(preds[0])
+
+ if save_as_video:
+ all_preds.append(pred)
+ else:
+ Image.fromarray(pred).save(
+ save_path / f"step_{self.paint_step:05d}_{i:04d}_rgb.jpg"
+ )
+ Image.fromarray(
+ (cm.seismic(normals[0, 0].cpu().numpy())[:, :, :3] * 255).astype(
+ np.uint8
+ )
+ ).save(save_path / f"{self.paint_step:04d}_{i:04d}_normals_cache.jpg")
+ if self.paint_step == 0:
+ # Also save depths for debugging
+ torch.save(depths[0], save_path / f"{i:04d}_depth.pt")
+
+ # Texture map is the same, so just take the last result
+ texture = tensor2numpy(textures[0])
+ Image.fromarray(texture).save(
+ save_path / f"step_{self.paint_step:05d}_texture.png"
+ )
+
+ if save_as_video:
+ all_preds = np.stack(all_preds, axis=0)
+
+ dump_vid = lambda video, name: imageio.mimsave(
+ save_path / f"step_{self.paint_step:05d}_{name}.mp4",
+ video,
+ fps=25,
+ quality=8,
+ macro_block_size=1,
+ )
+
+ dump_vid(all_preds, "rgb")
+
+ logger.info("Done!")
+
+ def get_preview_image(self, dataloader: DataLoader):
+ self.mesh_model.eval()
+ data = next(iter(dataloader))
+ preds, _, _, _ = self.eval_render(data)
+ pred = tensor2numpy(preds[0])
+ preview_image = Image.fromarray(pred)
+ return preview_image
+
+ def get_image_from_camera_viewpoint(self, data):
+ self.mesh_model.eval()
+ preds, _, _, _ = self.eval_render(data)
+ pred = tensor2numpy(preds[0])
+ preview_image = Image.fromarray(pred)
+ return preview_image
+
+ def full_eval(self, output_dir: Path = None):
+ if output_dir is None:
+ output_dir = self.final_renders_path
+ self.evaluate(self.dataloaders["val_large"], output_dir, save_as_video=True)
+
+ if self.cfg.log.save_mesh:
+ save_path = make_path(self.exp_path / "mesh")
+ logger.info(f"Saving mesh to {save_path}")
+
+ self.mesh_model.export_mesh(save_path)
+
+ logger.info(f"\tDone!")
+
+ def is_object_bigger_than_view(self, mask: torch.Tensor, border_size: int = 5):
+ # Top border
+ if torch.any(mask[0, 0, :border_size, :] == 1):
+ return True
+
+ # Bottom border
+ if torch.any(mask[0, 0, -border_size:, :] == 1):
+ return True
+
+ # Left border
+ if torch.any(mask[0, 0, :, :border_size] == 1):
+ return True
+
+ # Right border
+ if torch.any(mask[0, 0, :, -border_size:] == 1):
+ return True
+
+ return False
+
+ def optimize_camera_distance_for_viewpoint(
+ self, data: Dict[str, Any], radius_increment=0.05, max_radius=2.0
+ ):
+ theta, phi, radius = data["theta"], data["phi"], data["radius"]
+
+ # If offset of phi was set from code
+ phi = phi - np.deg2rad(self.cfg.render.front_offset)
+ phi = float(phi + 2 * np.pi if phi < 0 else phi)
+ logger.info(
+ f"Camera at theta: {np.rad2deg(theta)}, phi: {np.rad2deg(phi)}, radius: {radius}"
+ )
+
+ # Set background image
+ background = torch.Tensor([0, 0.8, 0]).to(self.device)
+
+ # Increment the camera distance to object until the object if fully visible
+ try:
+ outputs = self.mesh_model.render(
+ theta=theta, phi=phi, radius=radius, background=background
+ )
+ move_camera_away = self.is_object_bigger_than_view(outputs["mask"])
+ except:
+ move_camera_away = True
+
+ while move_camera_away and radius <= max_radius:
+ radius += radius_increment
+ try:
+ outputs = self.mesh_model.render(
+ theta=theta, phi=phi, radius=radius, background=background
+ )
+ move_camera_away = self.is_object_bigger_than_view(outputs["mask"])
+ except:
+ move_camera_away = True
+
+ return radius
+
+ def paint_viewpoint(
+ self,
+ data: Dict[str, Any],
+ custom_mask: bytes = None,
+ inpainting_only: bool = False,
+ ):
+ logger.info(f"--- Painting step #{self.paint_step} ---")
+ theta, phi, radius = data["theta"], data["phi"], data["radius"]
+
+ # If offset of phi was set from code
+ phi = phi - np.deg2rad(self.cfg.render.front_offset)
+ phi = float(phi + 2 * np.pi if phi < 0 else phi)
+ logger.info(
+ f"Painting from theta: {np.rad2deg(theta)}, phi: {np.rad2deg(phi)}, radius: {radius}"
+ )
+
+ # Set background image
+ background = torch.Tensor([0, 0.8, 0]).to(self.device)
+
+ # Render from viewpoint
+ outputs = self.mesh_model.render(
+ theta=theta, phi=phi, radius=radius, background=background
+ )
+ render_cache = outputs["render_cache"]
+ rgb_render_raw = outputs[
+ "image"
+ ] # Render where missing values have special color
+ depth_render = outputs["depth"]
+ # Render again with the median value to use as rgb, we shouldn't have color leakage, but just in case
+ outputs = self.mesh_model.render(
+ background=background,
+ render_cache=render_cache,
+ use_median=self.paint_step > 1,
+ )
+ rgb_render = outputs["image"]
+ # Render meta texture map
+ meta_output = self.mesh_model.render(
+ background=torch.Tensor([0, 0, 0]).to(self.device),
+ use_meta_texture=True,
+ render_cache=render_cache,
+ )
+
+ z_normals = outputs["normals"][:, -1:, :, :].clamp(0, 1)
+ z_normals_cache = meta_output["image"].clamp(0, 1)
+ edited_mask = meta_output["image"].clamp(0, 1)[:, 1:2]
+
+ self.log_train_image(rgb_render, "rendered_input")
+ self.log_train_image(depth_render[0, 0], "depth", colormap=True)
+ self.log_train_image(z_normals[0, 0], "z_normals", colormap=True)
+ self.log_train_image(z_normals_cache[0, 0], "z_normals_cache", colormap=True)
+
+ # text embeddings
+ if self.cfg.guide.append_direction:
+ dirs = data["dir"] # [B,]
+ text_z = self.text_z[dirs]
+ text_string = self.text_string[dirs]
+ else:
+ text_z = self.text_z
+ text_string = self.text_string
+ logger.info(f"text: {text_string}")
+
+ if custom_mask:
+ custom_mask_image = Image.open(BytesIO(custom_mask))
+ custom_mask_tensor = torch.from_numpy(np.array(custom_mask_image)).to(
+ self.device
+ )
+ custom_mask_tensor = (
+ custom_mask_tensor[:, :, :1].permute(2, 0, 1).unsqueeze(0) / 255.0
+ )
+ mask_height, mask_width = outputs["mask"].shape[2], outputs["mask"].shape[3]
+ custom_mask_tensor = F.interpolate(
+ custom_mask_tensor, size=(mask_height, mask_width), mode="nearest"
+ )
+ outputs["mask"] = outputs["mask"] * custom_mask_tensor
+
+ # # TODO remove code below, just added to test coloring feature
+ # expanded_mask = outputs['mask'].expand_as(rgb_render)
+
+ # # Create a tensor of the same shape as rgb_render but with RGB values for red where the mask is 1
+ # red_tensor = torch.Tensor([1, 0, 0]).to(self.device).view(1, 3, 1, 1)
+ # red_colored_mask = red_tensor * expanded_mask
+
+ # # Apply 50% blending of the red color and rgb_render where the mask is 1
+ # alpha = 0.5
+ # rgb_render = alpha * rgb_render + (1 - alpha) * red_colored_mask
+
+ update_mask, generate_mask, refine_mask = self.calculate_trimap(
+ rgb_render_raw=rgb_render_raw,
+ depth_render=depth_render,
+ z_normals=z_normals,
+ z_normals_cache=z_normals_cache,
+ edited_mask=edited_mask,
+ mask=outputs["mask"],
+ )
+
+ update_ratio = float(
+ update_mask.sum() / (update_mask.shape[2] * update_mask.shape[3])
+ )
+ if self.cfg.guide.reference_texture is not None and update_ratio < 0.01:
+ logger.info(
+ f"Update ratio {update_ratio:.5f} is small for an editing step, skipping"
+ )
+ return
+
+ self.log_train_image(rgb_render * (1 - update_mask), name="masked_input")
+ self.log_train_image(rgb_render * refine_mask, name="refine_regions")
+
+ # Crop to inner region based on object mask
+ min_h, min_w, max_h, max_w = utils.get_nonzero_region(outputs["mask"][0, 0])
+ crop = lambda x: x[:, :, min_h:max_h, min_w:max_w]
+ cropped_rgb_render = crop(rgb_render)
+ cropped_depth_render = crop(depth_render)
+ cropped_update_mask = crop(update_mask)
+ self.log_train_image(cropped_rgb_render, name="cropped_input")
+
+ checker_mask = None
+ if self.paint_step > 1:
+ checker_mask = self.generate_checkerboard(
+ crop(update_mask), crop(refine_mask), crop(generate_mask)
+ )
+ self.log_train_image(
+ F.interpolate(cropped_rgb_render, (512, 512)) * (1 - checker_mask),
+ "checkerboard_input",
+ )
+ self.diffusion.use_inpaint = (
+ self.cfg.guide.use_inpainting and self.paint_step > 1
+ )
+
+ cropped_rgb_output, steps_vis = self.diffusion.img2img_step(
+ text_z,
+ cropped_rgb_render.detach(),
+ cropped_depth_render.detach(),
+ guidance_scale=self.cfg.guide.guidance_scale,
+ strength=1.0,
+ update_mask=cropped_update_mask,
+ fixed_seed=self.cfg.optim.seed,
+ check_mask=checker_mask,
+ intermediate_vis=self.cfg.log.vis_diffusion_steps,
+ inpainting_only=inpainting_only,
+ )
+ self.log_train_image(cropped_rgb_output, name="direct_output")
+ self.log_diffusion_steps(steps_vis)
+
+ cropped_rgb_output = F.interpolate(
+ cropped_rgb_output,
+ (cropped_rgb_render.shape[2], cropped_rgb_render.shape[3]),
+ mode="bilinear",
+ align_corners=False,
+ )
+
+ # Extend rgb_output to full image size
+ rgb_output = rgb_render.clone()
+ rgb_output[:, :, min_h:max_h, min_w:max_w] = cropped_rgb_output
+ self.log_train_image(rgb_output, name="full_output")
+
+ # Project back
+ object_mask = outputs["mask"]
+ fitted_pred_rgb, _ = self.project_back(
+ render_cache=render_cache,
+ background=background,
+ rgb_output=rgb_output,
+ object_mask=object_mask,
+ update_mask=update_mask,
+ z_normals=z_normals,
+ z_normals_cache=z_normals_cache,
+ )
+ self.log_train_image(fitted_pred_rgb, name="fitted")
+
+ return
+
+ def eval_render(self, data):
+ theta = data["theta"]
+ phi = data["phi"]
+ radius = data["radius"]
+ phi = phi - np.deg2rad(self.cfg.render.front_offset)
+ phi = float(phi + 2 * np.pi if phi < 0 else phi)
+ dim = self.cfg.render.eval_grid_size
+ outputs = self.mesh_model.render(
+ theta=theta, phi=phi, radius=radius, dims=(dim, dim), background="white"
+ )
+ z_normals = outputs["normals"][:, -1:, :, :].clamp(0, 1)
+ rgb_render = outputs["image"] # .permute(0, 2, 3, 1).contiguous().clamp(0, 1)
+ diff = (
+ (
+ rgb_render.detach()
+ - torch.tensor(self.mesh_model.default_color)
+ .view(1, 3, 1, 1)
+ .to(self.device)
+ )
+ .abs()
+ .sum(axis=1)
+ )
+ uncolored_mask = (diff < 0.1).float().unsqueeze(0)
+ rgb_render = (
+ rgb_render * (1 - uncolored_mask)
+ + utils.color_with_shade(
+ [0.85, 0.85, 0.85], z_normals=z_normals, light_coef=0.3
+ )
+ * uncolored_mask
+ )
+
+ outputs_with_median = self.mesh_model.render(
+ theta=theta,
+ phi=phi,
+ radius=radius,
+ dims=(dim, dim),
+ use_median=True,
+ render_cache=outputs["render_cache"],
+ )
+
+ meta_output = self.mesh_model.render(
+ theta=theta,
+ phi=phi,
+ radius=radius,
+ background=torch.Tensor([0, 0, 0]).to(self.device),
+ use_meta_texture=True,
+ render_cache=outputs["render_cache"],
+ )
+
+ pred_z_normals = meta_output["image"][:, :1].detach()
+ rgb_render = rgb_render.permute(0, 2, 3, 1).contiguous().clamp(0, 1).detach()
+ texture_rgb = (
+ outputs_with_median["texture_map"]
+ .permute(0, 2, 3, 1)
+ .contiguous()
+ .clamp(0, 1)
+ .detach()
+ )
+ depth_render = outputs["depth"].permute(0, 2, 3, 1).contiguous().detach()
+
+ return rgb_render, texture_rgb, depth_render, pred_z_normals
+
+ def calculate_trimap(
+ self,
+ rgb_render_raw: torch.Tensor,
+ depth_render: torch.Tensor,
+ z_normals: torch.Tensor,
+ z_normals_cache: torch.Tensor,
+ edited_mask: torch.Tensor,
+ mask: torch.Tensor,
+ ):
+ diff = (
+ (
+ rgb_render_raw.detach()
+ - torch.tensor(self.mesh_model.default_color)
+ .view(1, 3, 1, 1)
+ .to(self.device)
+ )
+ .abs()
+ .sum(axis=1)
+ )
+ exact_generate_mask = (diff < 0.1).float().unsqueeze(0)
+
+ # Extend mask
+ generate_mask = (
+ torch.from_numpy(
+ cv2.dilate(
+ exact_generate_mask[0, 0].detach().cpu().numpy(),
+ np.ones((19, 19), np.uint8),
+ )
+ )
+ .to(exact_generate_mask.device)
+ .unsqueeze(0)
+ .unsqueeze(0)
+ )
+
+ update_mask = generate_mask.clone()
+
+ object_mask = torch.ones_like(update_mask)
+ object_mask[depth_render == 0] = 0
+ object_mask = (
+ torch.from_numpy(
+ cv2.erode(
+ object_mask[0, 0].detach().cpu().numpy(), np.ones((7, 7), np.uint8)
+ )
+ )
+ .to(object_mask.device)
+ .unsqueeze(0)
+ .unsqueeze(0)
+ )
+
+ # Generate the refine mask based on the z normals, and the edited mask
+
+ refine_mask = torch.zeros_like(update_mask)
+ refine_mask[
+ z_normals > z_normals_cache[:, :1, :, :] + self.cfg.guide.z_update_thr
+ ] = 1
+ if self.cfg.guide.initial_texture is None:
+ refine_mask[z_normals_cache[:, :1, :, :] == 0] = 0
+ elif self.cfg.guide.reference_texture is not None:
+ refine_mask[edited_mask == 0] = 0
+ refine_mask = (
+ torch.from_numpy(
+ cv2.dilate(
+ refine_mask[0, 0].detach().cpu().numpy(),
+ np.ones((31, 31), np.uint8),
+ )
+ )
+ .to(mask.device)
+ .unsqueeze(0)
+ .unsqueeze(0)
+ )
+ refine_mask[mask == 0] = 0
+ # Don't use bad angles here
+ refine_mask[z_normals < 0.4] = 0
+ else:
+ # Update all regions inside the object
+ refine_mask[mask == 0] = 0
+
+ refine_mask = (
+ torch.from_numpy(
+ cv2.erode(
+ refine_mask[0, 0].detach().cpu().numpy(), np.ones((5, 5), np.uint8)
+ )
+ )
+ .to(mask.device)
+ .unsqueeze(0)
+ .unsqueeze(0)
+ )
+ refine_mask = (
+ torch.from_numpy(
+ cv2.dilate(
+ refine_mask[0, 0].detach().cpu().numpy(), np.ones((5, 5), np.uint8)
+ )
+ )
+ .to(mask.device)
+ .unsqueeze(0)
+ .unsqueeze(0)
+ )
+ update_mask[refine_mask == 1] = 1
+
+ update_mask[torch.bitwise_and(object_mask == 0, generate_mask == 0)] = 0
+
+ # Visualize trimap
+ if self.cfg.log.log_images:
+ trimap_vis = utils.color_with_shade(
+ color=[112 / 255.0, 173 / 255.0, 71 / 255.0], z_normals=z_normals
+ )
+ trimap_vis[mask.repeat(1, 3, 1, 1) == 0] = 1
+ trimap_vis = (
+ trimap_vis * (1 - exact_generate_mask)
+ + utils.color_with_shade(
+ [255 / 255.0, 22 / 255.0, 67 / 255.0],
+ z_normals=z_normals,
+ light_coef=0.7,
+ )
+ * exact_generate_mask
+ )
+
+ shaded_rgb_vis = rgb_render_raw.detach()
+ shaded_rgb_vis = (
+ shaded_rgb_vis * (1 - exact_generate_mask)
+ + utils.color_with_shade(
+ [0.85, 0.85, 0.85], z_normals=z_normals, light_coef=0.7
+ )
+ * exact_generate_mask
+ )
+
+ if self.paint_step > 1 or self.cfg.guide.initial_texture is not None:
+ refinement_color_shaded = utils.color_with_shade(
+ color=[91 / 255.0, 155 / 255.0, 213 / 255.0], z_normals=z_normals
+ )
+ only_old_mask_for_vis = (
+ torch.bitwise_and(refine_mask == 1, exact_generate_mask == 0)
+ .float()
+ .detach()
+ )
+ trimap_vis = trimap_vis * 0 + 1.0 * (
+ trimap_vis * (1 - only_old_mask_for_vis)
+ + refinement_color_shaded * only_old_mask_for_vis
+ )
+ self.log_train_image(shaded_rgb_vis, "shaded_input")
+ self.log_train_image(trimap_vis, "trimap")
+
+ return update_mask, generate_mask, refine_mask
+
+ def generate_checkerboard(
+ self, update_mask_inner, improve_z_mask_inner, update_mask_base_inner
+ ):
+ try:
+ checkerboard = torch.ones((1, 1, 64 // 2, 64 // 2)).to(self.device)
+ # Create a checkerboard grid
+ checkerboard[:, :, ::2, ::2] = 0
+ checkerboard[:, :, 1::2, 1::2] = 0
+ checkerboard = F.interpolate(checkerboard, (512, 512))
+ checker_mask = F.interpolate(update_mask_inner, (512, 512))
+ only_old_mask = F.interpolate(
+ torch.bitwise_and(
+ improve_z_mask_inner == 1, update_mask_base_inner == 0
+ ).float(),
+ (512, 512),
+ )
+ checker_mask[only_old_mask == 1] = checkerboard[only_old_mask == 1]
+ return checker_mask
+ except:
+ print("Error generating checkerboard mask")
+ return None
+
+ def project_back(
+ self,
+ render_cache: Dict[str, Any],
+ background: Any,
+ rgb_output: torch.Tensor,
+ object_mask: torch.Tensor,
+ update_mask: torch.Tensor,
+ z_normals: torch.Tensor,
+ z_normals_cache: torch.Tensor,
+ ):
+ object_mask = (
+ torch.from_numpy(
+ cv2.erode(
+ object_mask[0, 0].detach().cpu().numpy(), np.ones((5, 5), np.uint8)
+ )
+ )
+ .to(object_mask.device)
+ .unsqueeze(0)
+ .unsqueeze(0)
+ )
+ render_update_mask = object_mask.clone()
+
+ render_update_mask[update_mask == 0] = 0
+
+ blurred_render_update_mask = (
+ torch.from_numpy(
+ cv2.dilate(
+ render_update_mask[0, 0].detach().cpu().numpy(),
+ np.ones((25, 25), np.uint8),
+ )
+ )
+ .to(render_update_mask.device)
+ .unsqueeze(0)
+ .unsqueeze(0)
+ )
+ blurred_render_update_mask = utils.gaussian_blur(
+ blurred_render_update_mask, 21, 16
+ )
+
+ # Do not get out of the object
+ blurred_render_update_mask[object_mask == 0] = 0
+
+ if self.cfg.guide.strict_projection:
+ blurred_render_update_mask[blurred_render_update_mask < 0.5] = 0
+ # Do not use bad normals
+ z_was_better = (
+ z_normals + self.cfg.guide.z_update_thr < z_normals_cache[:, :1, :, :]
+ )
+ blurred_render_update_mask[z_was_better] = 0
+
+ render_update_mask = blurred_render_update_mask
+ self.log_train_image(rgb_output * render_update_mask, "project_back_input")
+
+ # Update the normals
+ z_normals_cache[:, 0, :, :] = torch.max(
+ z_normals_cache[:, 0, :, :], z_normals[:, 0, :, :]
+ )
+
+ optimizer = torch.optim.Adam(
+ self.mesh_model.get_params(),
+ lr=self.cfg.optim.lr,
+ betas=(0.9, 0.99),
+ eps=1e-15,
+ )
+ for _ in tqdm(range(200), desc="fitting mesh colors"):
+ optimizer.zero_grad()
+ outputs = self.mesh_model.render(
+ background=background, render_cache=render_cache
+ )
+ rgb_render = outputs["image"]
+
+ mask = render_update_mask.flatten()
+ masked_pred = rgb_render.reshape(1, rgb_render.shape[1], -1)[:, :, mask > 0]
+ masked_target = rgb_output.reshape(1, rgb_output.shape[1], -1)[
+ :, :, mask > 0
+ ]
+ masked_mask = mask[mask > 0]
+ loss = (
+ (masked_pred - masked_target.detach()).pow(2) * masked_mask
+ ).mean() + (
+ (masked_pred - masked_pred.detach()).pow(2) * (1 - masked_mask)
+ ).mean()
+
+ meta_outputs = self.mesh_model.render(
+ background=torch.Tensor([0, 0, 0]).to(self.device),
+ use_meta_texture=True,
+ render_cache=render_cache,
+ )
+ current_z_normals = meta_outputs["image"]
+ current_z_mask = meta_outputs["mask"].flatten()
+ masked_current_z_normals = current_z_normals.reshape(
+ 1, current_z_normals.shape[1], -1
+ )[:, :, current_z_mask == 1][:, :1]
+ masked_last_z_normals = z_normals_cache.reshape(
+ 1, z_normals_cache.shape[1], -1
+ )[:, :, current_z_mask == 1][:, :1]
+ loss += (
+ (masked_current_z_normals - masked_last_z_normals.detach())
+ .pow(2)
+ .mean()
+ )
+ loss.backward()
+ optimizer.step()
+
+ return rgb_render, current_z_normals
+
+ def log_train_image(self, tensor: torch.Tensor, name: str, colormap=False):
+ if self.cfg.log.log_images:
+ if colormap:
+ tensor = cm.seismic(tensor.detach().cpu().numpy())[:, :, :3]
+ else:
+ tensor = (
+ einops.rearrange(tensor, "(1) c h w -> h w c")
+ .detach()
+ .cpu()
+ .numpy()
+ )
+ Image.fromarray((tensor * 255).astype(np.uint8)).save(
+ self.train_renders_path / f"{self.paint_step:04d}_{name}.jpg"
+ )
+
+ def log_diffusion_steps(self, intermediate_vis: List[Image.Image]):
+ if len(intermediate_vis) > 0:
+ step_folder = (
+ self.train_renders_path / f"{self.paint_step:04d}_diffusion_steps"
+ )
+ step_folder.mkdir(exist_ok=True)
+ for k, intermedia_res in enumerate(intermediate_vis):
+ intermedia_res.save(step_folder / f"{k:02d}_diffusion_step.jpg")
+
+ def save_image(self, tensor: torch.Tensor, path: Path):
+ if self.cfg.log.log_images:
+ Image.fromarray(
+ (
+ einops.rearrange(tensor, "(1) c h w -> h w c")
+ .detach()
+ .cpu()
+ .numpy()
+ * 255
+ ).astype(np.uint8)
+ ).save(path)
diff --git a/morpheus-worker/app/actors/texturing3d/utils/texture3d/training/views_dataset.py b/morpheus-worker/app/actors/texturing3d/utils/texture3d/training/views_dataset.py
new file mode 100644
index 0000000..0a35b81
--- /dev/null
+++ b/morpheus-worker/app/actors/texturing3d/utils/texture3d/training/views_dataset.py
@@ -0,0 +1,258 @@
+import warnings
+
+import numpy as np
+import torch
+from loguru import logger
+from torch.utils.data import DataLoader
+
+from app.utils.texture3d.configs.train_config import RenderConfig
+from app.utils.texture3d.utils import get_view_direction
+
+
+def rand_poses(
+ size,
+ device,
+ radius_range=(1.0, 1.5),
+ theta_range=(0.0, 150.0),
+ phi_range=(0.0, 360.0),
+ angle_overhead=30.0,
+ angle_front=60.0,
+ biased_angles=True,
+):
+ if theta_range != (0.0, 180.0):
+ warnings.warn(
+ "theta_range is not (0.0, 180.0) in rand_poses\n Will use (0.0, 180.0) instead"
+ )
+
+ angle_overhead = np.deg2rad(angle_overhead)
+ angle_front = np.deg2rad(angle_front)
+
+ radius = (
+ torch.rand(size, device=device) * (radius_range[1] - radius_range[0])
+ + radius_range[0]
+ )
+
+ theta_range = np.deg2rad(theta_range)
+ phi_range = np.deg2rad(phi_range)
+
+ if biased_angles:
+ top_flag = np.random.rand() > 0.3 # 70% of the time, the camera is at the top
+ if top_flag:
+ x = 1 - torch.rand(size, device=device)
+ thetas = torch.acos(x)
+ phis = (
+ torch.rand(size, device=device) * (phi_range[1] - phi_range[0])
+ + phi_range[0]
+ )
+ else:
+ x = 1 - (torch.rand(size, device=device) + 1)
+ thetas = torch.acos(x)
+ phis = (
+ torch.rand(size, device=device) * (phi_range[1] - phi_range[0])
+ + phi_range[0]
+ )
+ else:
+ # logger.warning('Using old theta calc')
+ thetas = (
+ torch.rand(size, device=device) * (theta_range[1] - theta_range[0])
+ + theta_range[0]
+ )
+ # thetas = torch.acos(1-2*torch.rand(size, device=device))
+
+ phis = (
+ torch.rand(size, device=device) * (phi_range[1] - phi_range[0])
+ + phi_range[0]
+ )
+
+ dirs = get_view_direction(thetas, phis, angle_overhead, angle_front)
+
+ return dirs, thetas.item(), phis.item(), radius.item()
+
+
+def rand_modal_poses(
+ size,
+ device,
+ radius_range=(1.4, 1.6),
+ theta_range=(45.0, 90.0),
+ phi_range=(0.0, 360.0),
+ angle_overhead=30.0,
+ theta_range_overhead=(0.0, 20.0),
+ angle_front=60.0,
+):
+ theta_range = np.deg2rad(theta_range)
+ theta_range_overhead = np.deg2rad(theta_range_overhead)
+ phi_range = np.deg2rad(phi_range)
+ angle_overhead = np.deg2rad(angle_overhead)
+ angle_front = np.deg2rad(angle_front)
+
+ radius = (
+ torch.rand(size, device=device) * (radius_range[1] - radius_range[0])
+ + radius_range[0]
+ )
+
+ overhead_flag = torch.rand(1, device=device) > 0.85
+ if overhead_flag:
+ phis = (
+ torch.rand(size, device=device) * (phi_range[1] - phi_range[0])
+ + phi_range[0]
+ )
+ thetas = (
+ torch.rand(size, device=device)
+ * (theta_range_overhead[1] - theta_range_overhead[0])
+ + theta_range_overhead[0]
+ )
+ else:
+ phi_mods = np.deg2rad([0, 90, 180, 270])
+ pertube_magnitude = np.deg2rad(15)
+ rand_pertubations = torch.rand(size, device=device) * pertube_magnitude
+ phis = rand_pertubations + torch.from_numpy(
+ phi_mods[np.random.randint(0, 4, size)]
+ ).to(device)
+ thetas = (
+ torch.rand(size, device=device) * (theta_range[1] - theta_range[0])
+ + theta_range[0]
+ )
+
+ dirs = get_view_direction(thetas, phis, angle_overhead, angle_front)
+
+ return dirs, thetas.item(), phis.item(), radius.item()
+
+
+def circle_poses(
+ device, radius=1.25, theta=60.0, phi=0.0, angle_overhead=30.0, angle_front=60.0
+):
+ theta = np.deg2rad(theta)
+ phi = np.deg2rad(phi)
+ angle_overhead = np.deg2rad(angle_overhead)
+ angle_front = np.deg2rad(angle_front)
+
+ thetas = torch.FloatTensor([theta]).to(device)
+ phis = torch.FloatTensor([phi]).to(device)
+ dirs = get_view_direction(thetas, phis, angle_overhead, angle_front)
+
+ return dirs, thetas.item(), phis.item(), radius
+
+
+class MultiviewDataset:
+ def __init__(self, cfg: RenderConfig, device):
+ super().__init__()
+
+ self.cfg = cfg
+ self.device = device
+ self.type = type # train, val, tests
+
+ if hasattr(cfg, "radius_per_view"):
+ self.multiple_radius = True
+ else:
+ self.multiple_radius = False
+
+ # view angles are equidistant
+ if self.cfg.view_angles is None:
+ size = self.cfg.n_views
+ self.phis = [(index / size) * 360 for index in range(size)]
+ self.thetas = [self.cfg.base_theta for _ in range(size)]
+
+ # Alternate lists
+ alternate_lists = (
+ lambda l: [l[0]]
+ + [i for j in zip(l[1 : size // 2], l[-1 : size // 2 : -1]) for i in j]
+ + [l[size // 2]]
+ )
+ if self.cfg.alternate_views:
+ self.phis = alternate_lists(self.phis)
+ self.thetas = alternate_lists(self.thetas)
+ logger.info(f"phis: {self.phis}")
+
+ # view angles can be defined manually instead
+ else:
+ self.phis = self.cfg.view_angles
+ size = len(self.phis)
+ self.thetas = [self.cfg.base_theta for _ in range(size)]
+
+ # append overhead and bottom views
+ for phi, theta in self.cfg.views_before:
+ self.phis = [phi] + self.phis
+ self.thetas = [theta] + self.thetas
+ for phi, theta in self.cfg.views_after:
+ self.phis = self.phis + [phi]
+ self.thetas = self.thetas + [theta]
+
+ self.size = len(self.phis)
+
+ def collate(self, index):
+ # B = len(index) # always 1
+
+ # phi = (index[0] / self.size) * 360
+ phi = self.phis[index[0]]
+ theta = self.thetas[index[0]]
+
+ if self.multiple_radius:
+ radius = self.cfg.radius_per_view[index[0]]
+ else:
+ radius = self.cfg.radius
+
+ dirs, thetas, phis, radius = circle_poses(
+ self.device,
+ radius=radius,
+ theta=theta,
+ phi=phi,
+ angle_overhead=self.cfg.overhead_range,
+ angle_front=self.cfg.front_range,
+ )
+
+ data = {"dir": dirs, "theta": thetas, "phi": phis, "radius": radius}
+
+ return data
+
+ def dataloader(self):
+ loader = DataLoader(
+ list(range(self.size)),
+ batch_size=1,
+ collate_fn=self.collate,
+ shuffle=False,
+ num_workers=0,
+ )
+ loader._data = self # an ugly fix... we need to access dataset in trainer.
+ return loader
+
+
+class ViewsDataset:
+ def __init__(self, cfg: RenderConfig, device, size=100, base_radius=1.75):
+ super().__init__()
+
+ self.cfg = cfg
+ self.device = device
+ self.type = type # train, val, test
+ self.size = size
+
+ if hasattr(cfg, "radius_per_view"):
+ self.radius = max(cfg.radius_per_view)
+ else:
+ self.radius = base_radius
+
+ def collate(self, index):
+ # circle pose
+ phi = (index[0] / self.size) * 360
+ dirs, thetas, phis, radius = circle_poses(
+ self.device,
+ radius=self.radius,
+ theta=self.cfg.base_theta,
+ phi=phi,
+ angle_overhead=self.cfg.overhead_range,
+ angle_front=self.cfg.front_range,
+ )
+
+ data = {"dir": dirs, "theta": thetas, "phi": phis, "radius": radius}
+
+ return data
+
+ def dataloader(self):
+ loader = DataLoader(
+ list(range(self.size)),
+ batch_size=1,
+ collate_fn=self.collate,
+ shuffle=False,
+ num_workers=0,
+ )
+ loader._data = self # an ugly fix... we need to access dataset in trainer.
+ return loader
diff --git a/morpheus-worker/app/actors/texturing3d/utils/texture3d/utils.py b/morpheus-worker/app/actors/texturing3d/utils/texture3d/utils.py
new file mode 100644
index 0000000..0fa6b0a
--- /dev/null
+++ b/morpheus-worker/app/actors/texturing3d/utils/texture3d/utils.py
@@ -0,0 +1,169 @@
+import random
+import os
+from pathlib import Path
+from typing import List
+import re
+
+import numpy as np
+import torch
+import torchvision.transforms as T
+from PIL import Image
+from matplotlib import cm
+import torch.nn.functional as F
+
+
+def get_view_direction(thetas, phis, overhead, front):
+ # phis [B,]; thetas: [B,]
+ # front = 0 [0, front)
+ # side (left) = 1 [front, 180)
+ # back = 2 [180, 180+front)
+ # side (right) = 3 [180+front, 360)
+ # top = 4 [0, overhead]
+ # bottom = 5 [180-overhead, 180]
+ res = torch.zeros(thetas.shape[0], dtype=torch.long)
+ # first determine by phis
+
+ # res[(phis < front)] = 0
+ res[(phis >= (2 * np.pi - front / 2)) & (phis < front / 2)] = 0
+
+ # res[(phis >= front) & (phis < np.pi)] = 1
+ res[(phis >= front / 2) & (phis < (np.pi - front / 2))] = 1
+
+ # res[(phis >= np.pi) & (phis < (np.pi + front))] = 2
+ res[(phis >= (np.pi - front / 2)) & (phis < (np.pi + front / 2))] = 2
+
+ # res[(phis >= (np.pi + front))] = 3
+ res[(phis >= (np.pi + front / 2)) & (phis < (2 * np.pi - front / 2))] = 3
+ # override by thetas
+ res[thetas <= overhead] = 4
+ res[thetas >= (np.pi - overhead)] = 5
+ return res
+
+
+def tensor2numpy(tensor: torch.Tensor) -> np.ndarray:
+ tensor = tensor.detach().cpu().numpy()
+ tensor = (tensor * 255).astype(np.uint8)
+ return tensor
+
+
+def make_path(path: Path) -> Path:
+ path.mkdir(exist_ok=True, parents=True)
+ return path
+
+
+def save_colormap(tensor: torch.Tensor, path: Path):
+ Image.fromarray((cm.seismic(tensor.cpu().numpy())[:, :, :3] * 255).astype(np.uint8)).save(path)
+
+
+def seed_everything(seed):
+ max_seed = 2 ** 32 - 1
+ if seed > max_seed:
+ seed = seed % max_seed
+ random.seed(seed)
+ os.environ['PYTHONHASHSEED'] = str(seed)
+ np.random.seed(seed)
+ torch.manual_seed(seed)
+ torch.cuda.manual_seed(seed)
+ # torch.backends.cudnn.deterministic = True
+ # torch.backends.cudnn.benchmark = True
+
+
+def smooth_image(self, img: torch.Tensor, sigma: float) -> torch.Tensor:
+ """apply gaussian blur to an image tensor with shape [C, H, W]"""
+ img = T.GaussianBlur(kernel_size=(51, 51), sigma=(sigma, sigma))(img)
+ return img
+
+
+def get_nonzero_region(mask:torch.Tensor):
+ H, W = mask.shape
+
+ # Get the indices of the non-zero elements
+ nz_indices = mask.nonzero()
+ # Get the minimum and maximum indices along each dimension
+ min_h, max_h = nz_indices[:, 0].min(), nz_indices[:, 0].max()
+ min_w, max_w = nz_indices[:, 1].min(), nz_indices[:, 1].max()
+
+ # Calculate the size of the square region
+ size = max(max_h - min_h + 1, max_w - min_w + 1) * 1.1
+
+ # Calculate the upper left corner of the square region
+ h_start = max(0, min(min_h, max_h) - (size - (max_h - min_h + 1)) / 2)
+ w_start = max(0, min(min_w, max_w) - (size - (max_w - min_w + 1)) / 2)
+
+
+ min_h = int(h_start)
+ min_w = int(w_start)
+
+ # Clip the values so they don't exceed the tensor's boundaries
+ max_h = int(min(H, min_h + size))
+ max_w = int(min(W, min_w + size))
+
+ return min_h, min_w, max_h, max_w
+
+
+def gaussian_fn(M, std):
+ n = torch.arange(0, M) - (M - 1.0) / 2.0
+ sig2 = 2 * std * std
+ w = torch.exp(-n ** 2 / sig2)
+ return w
+
+
+def gkern(kernlen=256, std=128):
+ """Returns a 2D Gaussian kernel array."""
+ gkern1d = gaussian_fn(kernlen, std=std)
+ gkern2d = torch.outer(gkern1d, gkern1d)
+ return gkern2d
+
+
+def gaussian_blur(image:torch.Tensor, kernel_size:int, std:int) -> torch.Tensor:
+ gaussian_filter = gkern(kernel_size, std=std)
+ gaussian_filter /= gaussian_filter.sum()
+ image = F.conv2d(image, gaussian_filter.unsqueeze(0).unsqueeze(0).cuda(), padding=kernel_size // 2)
+ return image
+
+
+def color_with_shade(color: List[float],z_normals:torch.Tensor,light_coef=0.7):
+ normals_with_light = (light_coef + (1 - light_coef) * z_normals.detach())
+ shaded_color = torch.tensor(color).view(1, 3, 1, 1).to(
+ z_normals.device) * normals_with_light
+ return shaded_color
+
+
+def modify_png_path_in_mtl(mtl_path: str, new_png_name: str):
+ # Ensure the new name ends with .png
+ if not new_png_name.endswith('.png'):
+ new_png_name += '.png'
+
+ # Read the file
+ mtl_path = Path(mtl_path)
+ content = mtl_path.read_text()
+ lines = content.split('\n')
+ for index, line in enumerate(lines):
+ if line.startswith('map_Kd'):
+ # Replace the old PNG name with the new name
+ lines[index] = f'map_Kd {new_png_name}'
+
+ # Join the lines and write back to the file
+ modified_content = '\n'.join(lines)
+ mtl_path.write_text(modified_content)
+
+def update_mtl_texture_reference(mtl: bytes, texture_basename: str) -> bytes:
+ # Convert model3d from bytes to string.
+ mtl_content = mtl.decode('utf-8')
+
+ if "map_Kd" in mtl_content:
+ mtl_content = re.sub(r"map_Kd .+\n", f"map_Kd {texture_basename} \n", mtl_content)
+
+ return mtl_content.encode('utf-8')
+
+def update_obj_mtl_reference(model3d: bytes, mtl_basename: str) -> bytes:
+ # Convert model3d from bytes to string.
+ obj_content = model3d.decode('utf-8')
+
+ if "mtllib" in obj_content:
+ obj_content = re.sub(r"mtllib .+\n", f"mtllib {mtl_basename} \n", obj_content)
+ else:
+ # Insert new mtllib directive.
+ obj_content = f"mtllib {mtl_basename}\n" + obj_content
+
+ return obj_content.encode('utf-8')
diff --git a/morpheus-worker/app/settings/settings.py b/morpheus-worker/app/settings/settings.py
index 14de2bd..02a0556 100644
--- a/morpheus-worker/app/settings/settings.py
+++ b/morpheus-worker/app/settings/settings.py
@@ -43,3 +43,6 @@ class Config:
def get_settings():
settings = Settings()
return settings
+
+
+settings = get_settings()
diff --git a/morpheus-worker/requirements.txt b/morpheus-worker/requirements.txt
index f30108d..8885ca7 100644
--- a/morpheus-worker/requirements.txt
+++ b/morpheus-worker/requirements.txt
@@ -24,6 +24,14 @@ opencv-contrib-python-headless
scikit-image
scikit-learn
+# 3D texturing
+matplotlib
+einops
+imageio
+opencv-python
+torch-vision
+xatlas
+
# Text generation
dynamicprompts[attentiongrabber,magicprompt]