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model:
base_learning_rate: 1.0e-06
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0205
log_every_t: 100
timesteps: 1000
loss_type: l1
first_stage_key: image
cond_stage_key: masked_image
image_size: 64
channels: 3
concat_mode: true
monitor: val/loss
scheduler_config:
target: ldm.lr_scheduler.LambdaWarmUpCosineScheduler
params:
verbosity_interval: 0
warm_up_steps: 1000
max_decay_steps: 50000
lr_start: 0.001
lr_max: 0.1
lr_min: 0.0001
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 64
in_channels: 7
out_channels: 3
model_channels: 256
attention_resolutions:
- 8
- 4
- 2
num_res_blocks: 2
channel_mult:
- 1
- 2
- 3
- 4
num_heads: 8
resblock_updown: true
first_stage_config:
target: ldm.models.autoencoder.VQModelInterface
params:
embed_dim: 3
n_embed: 8192
monitor: val/rec_loss
ddconfig:
attn_type: none
double_z: false
z_channels: 3
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: ldm.modules.losses.contperceptual.DummyLoss
cond_stage_config: __is_first_stage__
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