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v1-finetune_unfrozen.yaml
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v1-finetune_unfrozen.yaml
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model:
base_learning_rate: 1.0e-06
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: 1.0e-6
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: 1
num_workers: 2
wrap: false
train:
target: ldm.data.personalized.PersonalizedBase
params:
size: 512
set: train
per_image_tokens: false
repeats: 100
reg:
target: ldm.data.personalized.PersonalizedBase
params:
size: 512
set: train
reg: true
per_image_tokens: false
repeats: 10
validation:
target: ldm.data.personalized.PersonalizedBase
params:
size: 512
set: val
per_image_tokens: false
repeats: 10
lightning:
modelcheckpoint:
params:
every_n_train_steps: 500
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 500
max_images: 8
increase_log_steps: False
trainer:
benchmark: True
max_steps: 800