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Craftsman_config.yaml
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Craftsman_config.yaml
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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