Add Craftman

.gitignore

@@ -161,13 +161,11 @@ cython_debug/
 
 # Ignore test outputs
 outputs/
+output/
 
 # Ignore folders only used for build dependencies
 _Pre_Builds/_Build_Dependencies
 _Pre_Builds/_Build_Wheels
 _Pre_Builds/_Libs
 
-models/
-output/
-
 .idea

Configs/Craftsman_config.yaml

@@ -0,0 +1,143 @@
+name: michelangelo-image-to-shape-diffusion/clip-mvrgb-modln-l256-e64-ne8-nd16-nl6-170k
+description: ''
+tag: michelangelo-aligned-autoencoder+n4096+noise0.0+pfeat3+zeroemb0.0+normembFalse+lr5e-05+qkvbiasFalse+nfreq8+ln_postTrue
+seed: 0
+use_timestamp: true
+timestamp: ''
+exp_root_dir: outputs
+exp_dir: outputs/michelangelo-image-to-shape-diffusion/clip-mvrgb-modln-l256-e64-ne8-nd16-nl6-170k
+trial_name: michelangelo-aligned-autoencoder+n4096+noise0.0+pfeat3+zeroemb0.0+normembFalse+lr5e-05+qkvbiasFalse+nfreq8+ln_postTrue
+trial_dir: outputs/michelangelo-image-to-shape-diffusion/clip-mvrgb-modln-l256-e64-ne8-nd16-nl6-170k/michelangelo-aligned-autoencoder+n4096+noise0.0+pfeat3+zeroemb0.0+normembFalse+lr5e-05+qkvbiasFalse+nfreq8+ln_postTrue
+n_gpus: 8
+resume: ./ckpts/3DNativeGeneration/michelangelo-image-to-shape-diffusion/clip-mvrgb-modln-l256-e64-ne8-nd16-nl6-170k.ckpt
+data_type: objaverse-datamodule
+data:
+  root_dir: data/objaverse_clean/cap3d_high_quality_170k_images
+  data_type: occupancy
+  n_samples: 4096
+  noise_sigma: 0.0
+  load_supervision: false
+  supervision_type: occupancy
+  n_supervision: 10000
+  load_image: true
+  image_data_path: data/objaverse_clean/raw_data/images/cap3d_high_quality_170k
+  image_type: mvrgb
+  idx:
+  - 0
+  - 4
+  - 8
+  - 12
+  - 16
+  n_views: 4
+  load_caption: false
+  rotate_points: false
+  batch_size: 32
+  num_workers: 16
+system_type: shape-diffusion-system
+system:
+  val_samples_json: val_data/mv_images/val_samples_rgb_mvimage.json
+  z_scale_factor: 1.0
+  guidance_scale: 7.5
+  num_inference_steps: 50
+  eta: 0.0
+  shape_model_type: michelangelo-aligned-autoencoder
+  shape_model:
+    num_latents: 256
+    embed_dim: 64
+    point_feats: 3
+    out_dim: 1
+    num_freqs: 8
+    include_pi: false
+    heads: 12
+    width: 768
+    num_encoder_layers: 8
+    num_decoder_layers: 16
+    use_ln_post: true
+    init_scale: 0.25
+    qkv_bias: false
+    use_flash: true
+    use_checkpoint: true
+  condition_model_type: clip-embedder
+  condition_model:
+    pretrained_model_name_or_path: openai/clip-vit-large-patch14
+    encode_camera: true
+    camera_embeds_dim: 32
+    n_views: 4
+    empty_embeds_ratio: 0.1
+    normalize_embeds: false
+    zero_uncond_embeds: true
+  denoiser_model_type: simple-denoiser
+  denoiser_model:
+    input_channels: 64
+    output_channels: 64
+    n_ctx: 256
+    width: 768
+    layers: 6
+    heads: 12
+    context_dim: 1024
+    init_scale: 1.0
+    skip_ln: true
+    use_checkpoint: true
+  noise_scheduler_type: diffusers.schedulers.DDPMScheduler
+  noise_scheduler:
+    num_train_timesteps: 1000
+    beta_start: 0.00085
+    beta_end: 0.012
+    beta_schedule: scaled_linear
+    variance_type: fixed_small
+    clip_sample: false
+  denoise_scheduler_type: diffusers.schedulers.DDIMScheduler
+  denoise_scheduler:
+    num_train_timesteps: 1000
+    beta_start: 0.00085
+    beta_end: 0.012
+    beta_schedule: scaled_linear
+    clip_sample: false
+    set_alpha_to_one: false
+    steps_offset: 1
+  loggers:
+    wandb:
+      enable: false
+      project: JiangXin
+      name: text-to-shape-diffusion+michelangelo-image-to-shape-diffusion/clip-mvrgb-modln-l256-e64-ne8-nd16-nl6-170k+michelangelo-aligned-autoencoder+n4096+noise0.0+pfeat3+zeroemb0.0+normembFalse+lr5e-05+qkvbiasFalse+nfreq8+ln_postTrue
+  loss:
+    loss_type: mse
+    lambda_diffusion: 1.0
+  optimizer:
+    name: AdamW
+    args:
+      lr: 5.0e-05
+      betas:
+      - 0.9
+      - 0.99
+      eps: 1.0e-06
+  scheduler:
+    name: SequentialLR
+    interval: step
+    schedulers:
+    - name: LinearLR
+      interval: step
+      args:
+        start_factor: 1.0e-06
+        end_factor: 1.0
+        total_iters: 5000
+    - name: CosineAnnealingLR
+      interval: step
+      args:
+        T_max: 5000
+        eta_min: 0.0
+    milestones:
+    - 5000
+trainer:
+  num_nodes: 2
+  max_epochs: 100000
+  log_every_n_steps: 5
+  num_sanity_val_steps: 1
+  check_val_every_n_epoch: 3
+  enable_progress_bar: true
+  precision: 16-mixed
+  strategy: ddp_find_unused_parameters_true
+checkpoint:
+  save_last: true
+  save_top_k: -1
+  every_n_train_steps: 5000

Gen_3D_Modules/CharacterGen/Stage_3D/lrm/models/mesh.py

@@ -194,7 +194,7 @@ def _compute_vertex_normal(self):
         v1 = self.v_pos[i1, :]
         v2 = self.v_pos[i2, :]
 
-        face_normals = torch.cross(v1 - v0, v2 - v0)
+        face_normals = torch.cross(v1 - v0, v2 - v0, dim=-1)
 
         # Splat face normals to vertices
         v_nrm = torch.zeros_like(self.v_pos)

Gen_3D_Modules/InstantMesh/models/geometry/render/neural_render.py

@@ -48,7 +48,7 @@ def compute_vertex_normal(v_pos, t_pos_idx):
     v1 = v_pos[i1, :]
     v2 = v_pos[i2, :]
 
-    face_normals = torch.cross(v1 - v0, v2 - v0)
+    face_normals = torch.cross(v1 - v0, v2 - v0, dim=-1)
 
     # Splat face normals to vertices
     v_nrm = torch.zeros_like(v_pos)

Gen_3D_Modules/Unique3D/mesh_reconstruction/remesh.py

@@ -350,9 +350,9 @@ def flip_edges(
         e1 = v[:,1]
         cl = v[:,2]
         cr = v[:,3]
-        n = torch.cross(e1,cl) + torch.cross(cr,e1) #sum of old normal vectors 
-        flip.logical_and_(torch.sum(n*torch.cross(cr,cl),dim=-1)>0) #first new face
-        flip.logical_and_(torch.sum(n*torch.cross(cl-e1,cr-e1),dim=-1)>0) #second new face
+        n = torch.cross(e1,cl,dim=-1) + torch.cross(cr,e1,dim=-1) #sum of old normal vectors 
+        flip.logical_and_(torch.sum(n*torch.cross(cr,cl,dim=-1),dim=-1)>0) #first new face
+        flip.logical_and_(torch.sum(n*torch.cross(cl-e1,cr-e1,dim=-1),dim=-1)>0) #second new face
 
     flip_edges_neighbors = edges_neighbors[flip] #E",4
     flip_edge_to_face = edge_to_face[candidates,:,0][flip] #E",2

Gen_3D_Modules/craftsman/__init__.py

@@ -0,0 +1,52 @@
+import importlib
+
+__modules__ = {}
+
+
+def register(name):
+    def decorator(cls):
+        if name in __modules__:
+            raise ValueError(
+                f"Module {name} already exists! Names of extensions conflict!"
+            )
+        else:
+            __modules__[name] = cls
+        return cls
+
+    return decorator
+
+
+def find(name):
+    if name in __modules__:
+        return __modules__[name]
+    else:
+        try:
+            module_string = ".".join(name.split(".")[:-1])
+            cls_name = name.split(".")[-1]
+            module = importlib.import_module(module_string, package=None)
+            return getattr(module, cls_name)
+        except Exception as e:
+            raise ValueError(f"Module {name} not found!")
+
+
+###  grammar sugar for logging utilities  ###
+import logging
+
+logger = logging.getLogger("pytorch_lightning")
+
+from pytorch_lightning.utilities.rank_zero import (
+    rank_zero_debug,
+    rank_zero_info,
+    rank_zero_only,
+)
+
+debug = rank_zero_debug
+info = rank_zero_info
+
+
+@rank_zero_only
+def warn(*args, **kwargs):
+    logger.warn(*args, **kwargs)
+
+
+from . import data, models, systems

Gen_3D_Modules/craftsman/data/__init__.py

@@ -0,0 +1,3 @@
+from . import (
+    objaverse
+)