122 lines
3.0 KiB
YAML
122 lines
3.0 KiB
YAML
model:
|
|
base_learning_rate: 9.0e-07
|
|
target: ldm.models.diffusion.ddpm.LatentDiffusion
|
|
params:
|
|
reg_weight: 1.0
|
|
linear_start: 0.00085
|
|
linear_end: 0.0120
|
|
num_timesteps_cond: 1
|
|
log_every_t: 200
|
|
timesteps: 1000
|
|
first_stage_key: image
|
|
cond_stage_key: caption
|
|
image_size: 64
|
|
channels: 4
|
|
cond_stage_trainable: true # Note: different from the one we trained before
|
|
conditioning_key: crossattn
|
|
monitor: val/loss_simple_ema
|
|
scale_factor: 0.18215
|
|
use_ema: False
|
|
#embedding_reg_weight: 0.0
|
|
unfreeze_model: True
|
|
model_lr: 6.0e-7
|
|
# scheduler_config:
|
|
# target: ldm.lr_scheduler.LambdaLinearScheduler
|
|
# params:
|
|
# verbosity_interval: 200
|
|
# warm_up_steps: 5
|
|
# max_decay_steps: 100
|
|
# lr_start: 6.0e-7
|
|
# lr_max: 8.0e-7
|
|
# lr_min: 1.0e-7
|
|
|
|
# personalization_config:
|
|
# target: ldm.modules.embedding_manager.EmbeddingManager
|
|
# params:
|
|
# placeholder_strings: ["*"]
|
|
# initializer_words: ["sculpture"]
|
|
# per_image_tokens: false
|
|
# num_vectors_per_token: 1
|
|
# progressive_words: False
|
|
|
|
unet_config:
|
|
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
|
|
params:
|
|
image_size: 32 # unused
|
|
in_channels: 4
|
|
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: ldm.models.autoencoder.AutoencoderKL
|
|
params:
|
|
embed_dim: 4
|
|
monitor: val/rec_loss
|
|
ddconfig:
|
|
double_z: true
|
|
z_channels: 4
|
|
resolution: 512
|
|
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: ldm.modules.encoders.modules.FrozenCLIPEmbedder
|
|
|
|
data:
|
|
target: main.DataModuleFromConfig
|
|
params:
|
|
batch_size: 6
|
|
num_workers: 8 # should probably not exceed thread count on CPU, good idea to have more workers than batch_size
|
|
wrap: falsegit
|
|
train:
|
|
target: ldm.data.personalized_batch.PersonalizeBatchBase
|
|
params:
|
|
size: 512
|
|
set: train
|
|
repeats: 5
|
|
validation:
|
|
target: ldm.data.personalized.PersonalizedBase
|
|
params:
|
|
size: 512
|
|
set: val
|
|
repeats: 1
|
|
|
|
lightning:
|
|
modelcheckpoint:
|
|
params:
|
|
every_n_epochs: 1
|
|
callbacks:
|
|
image_logger:
|
|
target: main.ImageLogger
|
|
params:
|
|
batch_frequency: 500
|
|
max_images: 12
|
|
increase_log_steps: False
|
|
|
|
trainer:
|
|
benchmark: True
|
|
max_epochs: 5
|
|
#precision: 16 # need lightning 1.6+
|
|
#num_nodes: 2 # for multigpu
|
|
#check_val_every_n_epoch: 1
|