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model:
target: lvdm.models.ddpm3d_cond.T2IAdapterStyleAS
params:
linear_start: 0.00085
linear_end: 0.012
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: video
cond_stage_key: caption
cond_stage_trainable: false
conditioning_key: crossattn
image_size: [64, 64]
channels: 4
#monitor: val/loss_simple
scale_by_std: false
scale_factor: 0.18215
# training related
use_ema: false
uncond_prob: 0.0
uncond_type: 'empty_seq'
scheduler_config:
target: utils.lr_scheduler.LambdaLRScheduler
interval: 'step'
frequency: 100
params:
start_step: 0
final_decay_ratio: 0.01
decay_steps: 20000
unet_config:
target: lvdm.modules.networks.openaimodel3d.UNet2DModel
params:
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
num_head_channels: 64 # need to fix for flash-attn
transformer_depth: 1
context_dim: 1024
use_linear: true
use_checkpoint: true
temporal_conv: false
temporal_attention: true
temporal_selfatt_only: true
use_relative_position: true
use_causal_attention: false
temporal_length: 16
addition_attention: true
first_stage_config:
target: lvdm.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: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder
params:
freeze: true
layer: "penultimate"
# version: checkpoints/open_clip/CLIP-ViT-H-14-laion2B-s32B-b79K/open_clip_pytorch_model.bin
style_stage_config:
target: lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedder
params:
# version: checkpoints/open_clip/CLIP-ViT-H-14-laion2B-s32B-b79K/open_clip_pytorch_model.bin
freeze: true
only_cls: false
use_proj: false
use_shuffle: false
mask_ratio: 0.0
adapter_config:
target: lvdm.modules.encoders.adapter.StyleAdapterDualAttnAS
cond_name: style
trainable: true
params:
scale: 1.0
use_norm: true
image_context_config:
target: lvdm.modules.encoders.adapter.StyleTransformer
params:
in_dim: 1280
out_dim: 1024
num_heads: 8
num_tokens: 8
n_layers: 3
scale_predictor_config:
target: lvdm.modules.encoders.adapter.ScaleEncoder
params:
in_dim: 1024
out_dim: 1
num_heads: 8
num_tokens: 16
n_layers: 2
# target: lvdm.modules.encoders.adapter.ImageContext
# params:
# width: 1024
# context_dim: 1024
# token_num: 4
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