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model:
base_learning_rate: 5.0e-05
target: ldm.models.diffusion.sync_dreamer.SyncMultiviewDiffusion
params:
view_num: 16
image_size: 256
cfg_scale: 2.0
output_num: 8
batch_view_num: 4
finetune_unet: false
finetune_projection: false
drop_conditions: false
clip_image_encoder_path: ckpt/ViT-L-14.pt
scheduler_config: # 10000 warmup steps
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 100 ]
cycle_lengths: [ 100000 ]
f_start: [ 0.02 ]
f_max: [ 1.0 ]
f_min: [ 1.0 ]
unet_config:
target: ldm.models.diffusion.sync_dreamer_attention.DepthWiseAttention
params:
volume_dims: [64, 128, 256, 512]
image_size: 32
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
data:
target: ldm.data.sync_dreamer.SyncDreamerDataset
params:
target_dir: training_examples/target # renderings of target views
input_dir: training_examples/input # renderings of input views
uid_set_pkl: training_examples/uid_set.pkl # a list of uids
validation_dir: validation_set # directory of validation data
batch_size: 24 # batch size for a single gpu
num_workers: 8
lightning:
modelcheckpoint:
params:
every_n_train_steps: 1000 # we will save models every 1k steps
callbacks:
{}
trainer:
benchmark: True
val_check_interval: 1000 # we will run validation every 1k steps, the validation will output images to <log_dir>/<images>/val
num_sanity_val_steps: 0
check_val_every_n_epoch: null
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