DreamCraft3D/load/zero123/sd-objaverse-finetune-c_concat-256.yaml
2023-12-17 00:15:09 +08:00

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model:
base_learning_rate: 1.0e-04
target: extern.ldm_zero123.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "image_target"
cond_stage_key: "image_cond"
image_size: 32
channels: 4
cond_stage_trainable: false # Note: different from the one we trained before
conditioning_key: hybrid
monitor: val/loss_simple_ema
scale_factor: 0.18215
scheduler_config: # 10000 warmup steps
target: extern.ldm_zero123.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 100 ]
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
f_start: [ 1.e-6 ]
f_max: [ 1. ]
f_min: [ 1. ]
unet_config:
target: extern.ldm_zero123.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32 # unused
in_channels: 8
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
legacy: False
first_stage_config:
target: extern.ldm_zero123.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: extern.ldm_zero123.modules.encoders.modules.FrozenCLIPImageEmbedder
# data:
# target: extern.ldm_zero123.data.simple.ObjaverseDataModuleFromConfig
# params:
# root_dir: 'views_whole_sphere'
# batch_size: 192
# num_workers: 16
# total_view: 4
# train:
# validation: False
# image_transforms:
# size: 256
# validation:
# validation: True
# image_transforms:
# size: 256
# lightning:
# find_unused_parameters: false
# metrics_over_trainsteps_checkpoint: True
# modelcheckpoint:
# params:
# every_n_train_steps: 5000
# callbacks:
# image_logger:
# target: main.ImageLogger
# params:
# batch_frequency: 500
# max_images: 32
# increase_log_steps: False
# log_first_step: True
# log_images_kwargs:
# use_ema_scope: False
# inpaint: False
# plot_progressive_rows: False
# plot_diffusion_rows: False
# N: 32
# unconditional_scale: 3.0
# unconditional_label: [""]
# trainer:
# benchmark: True
# val_check_interval: 5000000 # really sorry
# num_sanity_val_steps: 0
# accumulate_grad_batches: 1