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model:
target: michelangelo.models.asl_diffusion.clip_asl_diffuser_pl_module.ClipASLDiffuser
params:
first_stage_config:
target: michelangelo.models.tsal.asl_pl_module.AlignedShapeAsLatentPLModule
params:
shape_module_cfg:
target: michelangelo.models.tsal.sal_perceiver.AlignedShapeLatentPerceiver
params:
num_latents: &num_latents 256
embed_dim: &embed_dim 64
point_feats: 3 # normal
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_checkpoint: false
aligned_module_cfg:
target: michelangelo.models.tsal.clip_asl_module.CLIPAlignedShapeAsLatentModule
params:
# clip_model_version: "./checkpoints/clip/clip-vit-large-patch14"
clip_model_version: "openai/clip-vit-large-patch14"
loss_cfg:
target: torch.nn.Identity
cond_stage_config:
target: michelangelo.models.conditional_encoders.encoder_factory.FrozenCLIPImageGridEmbedder
params:
# version: "./checkpoints/clip/clip-vit-large-patch14"
version: "openai/clip-vit-large-patch14"
zero_embedding_radio: 0.1
first_stage_key: "surface"
cond_stage_key: "image"
scale_by_std: false
denoiser_cfg:
target: michelangelo.models.asl_diffusion.asl_udt.ConditionalASLUDTDenoiser
params:
input_channels: *embed_dim
output_channels: *embed_dim
n_ctx: *num_latents
width: 768
layers: 6 # 2 * 6 + 1 = 13
heads: 12
context_dim: 1024
init_scale: 1.0
skip_ln: true
use_checkpoint: true
scheduler_cfg:
guidance_scale: 7.5
num_inference_steps: 50
eta: 0.0
noise:
target: diffusers.schedulers.DDPMScheduler
params:
num_train_timesteps: 1000
beta_start: 0.00085
beta_end: 0.012
beta_schedule: "scaled_linear"
variance_type: "fixed_small"
clip_sample: false
denoise:
target: diffusers.schedulers.DDIMScheduler
params:
num_train_timesteps: 1000
beta_start: 0.00085
beta_end: 0.012
beta_schedule: "scaled_linear"
clip_sample: false # clip sample to -1~1
set_alpha_to_one: false
steps_offset: 1
optimizer_cfg:
optimizer:
target: torch.optim.AdamW
params:
betas: [0.9, 0.99]
eps: 1.e-6
weight_decay: 1.e-2
scheduler:
target: michelangelo.utils.trainings.lr_scheduler.LambdaWarmUpCosineFactorScheduler
params:
warm_up_steps: 5000
f_start: 1.e-6
f_min: 1.e-3
f_max: 1.0
loss_cfg:
loss_type: "mse"
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