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finetune.yaml
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finetune.yaml
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model:
base_learning_rate: 5.0e-05
target: ldm.models.diffusion.ddpm_edit.LatentDiffusion
params:
ckpt_path: [TODO]
linear_start: 0.00085
linear_end: 0.012
num_timesteps_cond: 1
log_every_t: 50
timesteps: 1000
first_stage_key: edited
cond_stage_key: edit
image_size: 32
channels: 4
cond_stage_trainable: false
conditioning_key: hybrid
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: true
load_ema: true
scheduler_config:
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps:
- 1000
cycle_lengths:
- 10000000000000
f_start:
- 1.0e-06
f_max:
- 1.0
f_min:
- 1.0
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32
in_channels: 8
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: 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: ldm.modules.encoders.modules.FrozenCLIPEmbedder
data:
target: main.DataModuleFromConfig
params:
batch_size: 16
num_workers: 2
train:
target: edit_dataset.EditDataset
params:
path: [TODO]
split: train
min_resize_res: 256
max_resize_res: 256
crop_res: 256
flip_prob: 0.5
validation:
target: edit_dataset.EditDataset
params:
path: [TODO]
split: val
min_resize_res: 256
max_resize_res: 256
crop_res: 256