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save_path: saved_standard_challenging_context32_nocond_cont_cont_all_cont_eval
model:
base_learning_rate: 8.0e-05
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.0015
linear_end: 0.0195
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: action_
scheduler_sampling_rate: 0.0
hybrid_key: c_concat
image_size: [64, 48]
channels: 3
cond_stage_trainable: false
conditioning_key: hybrid
monitor: val/loss_simple_ema
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: [64, 48]
in_channels: 48
out_channels: 16
model_channels: 512
attention_resolutions: []
num_res_blocks: 2
channel_mult:
- 1
- 2
num_head_channels: 32
use_spatial_transformer: false
transformer_depth: 1
temporal_encoder_config:
target: ldm.modules.encoders.temporal_encoder.TemporalEncoder
params:
input_channels: 16
hidden_size: 4096
num_layers: 1
dropout: 0.1
output_channels: 32
output_height: 48
output_width: 64
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 16
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 16
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: __is_unconditional__
data:
target: data.data_processing.datasets.DataModule
params:
batch_size: 8
num_workers: 1
wrap: false
shuffle: True
drop_last: True
pin_memory: True
prefetch_factor: 2
persistent_workers: True
train:
target: data.data_processing.datasets.ActionsData
params:
data_csv_path: desktop_sequences_filtered_with_desktop_1.5k.challenging.train.target_frames.csv
normalization: standard
context_length: 32
#validation:
# target: data.data_processing.datasets.ActionsData
# params:
lightning:
trainer:
benchmark: False
max_epochs: 6400
limit_val_batches: 0
accelerator: gpu
gpus: 1
accumulate_grad_batches: 999999
gradient_clip_val: 1
checkpoint_callback: True
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