2022-06-16 17:12:54 +02:00
|
|
|
model:
|
|
|
|
|
base_learning_rate: 1.0e-04
|
|
|
|
|
target: ldm.models.diffusion.ddpm.LatentDiffusion
|
|
|
|
|
params:
|
|
|
|
|
linear_start: 0.001
|
|
|
|
|
linear_end: 0.015
|
|
|
|
|
num_timesteps_cond: 1
|
|
|
|
|
log_every_t: 200
|
|
|
|
|
timesteps: 1000
|
|
|
|
|
first_stage_key: "jpg"
|
|
|
|
|
cond_stage_key: "txt"
|
|
|
|
|
image_size: 48
|
|
|
|
|
channels: 16
|
|
|
|
|
cond_stage_trainable: false # Note: different from the one we trained before
|
|
|
|
|
conditioning_key: crossattn
|
|
|
|
|
monitor: val/loss_simple_ema
|
|
|
|
|
scale_factor: 0.22765929 # magic number
|
|
|
|
|
|
2022-06-18 01:05:03 +02:00
|
|
|
ckpt_path: "/home/mchorse/stable-diffusion-ckpts/256f16-2022-06-15-216k-pruned.ckpt"
|
2022-06-16 17:12:54 +02:00
|
|
|
|
|
|
|
|
scheduler_config: # 10000 warmup steps
|
|
|
|
|
target: ldm.lr_scheduler.LambdaLinearScheduler
|
|
|
|
|
params:
|
|
|
|
|
warm_up_steps: [ 10000 ]
|
|
|
|
|
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
|
|
|
|
|
f_start: [ 1.e-6 ]
|
|
|
|
|
f_max: [ 1. ]
|
|
|
|
|
f_min: [ 1. ]
|
|
|
|
|
|
|
|
|
|
unet_config:
|
|
|
|
|
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
|
|
|
|
|
params:
|
|
|
|
|
image_size: 48 # not really needed
|
|
|
|
|
in_channels: 16
|
|
|
|
|
out_channels: 16
|
|
|
|
|
model_channels: 320 # TODO: scale model here
|
|
|
|
|
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: 16
|
|
|
|
|
monitor: val/rec_loss
|
|
|
|
|
ddconfig:
|
|
|
|
|
double_z: True
|
|
|
|
|
z_channels: 16
|
|
|
|
|
resolution: 256
|
|
|
|
|
in_channels: 3
|
|
|
|
|
out_ch: 3
|
|
|
|
|
ch: 128
|
|
|
|
|
ch_mult: [ 1,1,2,2,4 ] # num_down = len(ch_mult)-1
|
|
|
|
|
num_res_blocks: 2
|
|
|
|
|
attn_resolutions: [ 16 ]
|
|
|
|
|
dropout: 0.0
|
|
|
|
|
lossconfig:
|
|
|
|
|
target: torch.nn.Identity
|
|
|
|
|
|
|
|
|
|
cond_stage_config:
|
|
|
|
|
target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
data:
|
|
|
|
|
target: ldm.data.laion.WebDataModuleFromConfig
|
|
|
|
|
params:
|
|
|
|
|
tar_base: "pipe:aws s3 cp s3://s-datasets/laion5b/laion2B-data/"
|
2022-06-18 09:30:59 +02:00
|
|
|
batch_size: 6
|
2022-06-16 17:12:54 +02:00
|
|
|
num_workers: 4
|
|
|
|
|
multinode: True
|
|
|
|
|
min_size: 384 # TODO: experiment. Note: for 2B, images are stored at max 384 resolution
|
|
|
|
|
train:
|
|
|
|
|
shards: '{000000..231317}.tar -'
|
|
|
|
|
shuffle: 10000
|
|
|
|
|
image_key: jpg
|
|
|
|
|
image_transforms:
|
|
|
|
|
- target: torchvision.transforms.Resize
|
|
|
|
|
params:
|
|
|
|
|
size: 768
|
|
|
|
|
interpolation: 3
|
|
|
|
|
- target: torchvision.transforms.RandomCrop
|
|
|
|
|
params:
|
|
|
|
|
size: 768
|
|
|
|
|
|
|
|
|
|
# NOTE use enough shards to avoid empty validation loops in workers
|
|
|
|
|
validation:
|
|
|
|
|
shards: '{231318..231349}.tar -'
|
|
|
|
|
shuffle: 0
|
|
|
|
|
image_key: jpg
|
|
|
|
|
image_transforms:
|
|
|
|
|
- target: torchvision.transforms.Resize
|
|
|
|
|
params:
|
|
|
|
|
size: 768
|
|
|
|
|
interpolation: 3
|
|
|
|
|
- target: torchvision.transforms.CenterCrop
|
|
|
|
|
params:
|
|
|
|
|
size: 768
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
lightning:
|
2022-06-18 01:20:03 +02:00
|
|
|
find_unused_parameters: False
|
|
|
|
|
|
2022-06-16 17:12:54 +02:00
|
|
|
callbacks:
|
|
|
|
|
image_logger:
|
|
|
|
|
target: main.ImageLogger
|
|
|
|
|
params:
|
|
|
|
|
batch_frequency: 5000
|
|
|
|
|
max_images: 4
|
|
|
|
|
increase_log_steps: False
|
|
|
|
|
log_first_step: False
|
|
|
|
|
log_images_kwargs:
|
|
|
|
|
use_ema_scope: False
|
|
|
|
|
inpaint: False
|
|
|
|
|
plot_progressive_rows: False
|
|
|
|
|
plot_diffusion_rows: False
|
|
|
|
|
N: 4
|
|
|
|
|
unconditional_guidance_scale: 3.0
|
|
|
|
|
unconditional_guidance_label: [""]
|
|
|
|
|
|
|
|
|
|
trainer:
|
|
|
|
|
benchmark: True
|
|
|
|
|
val_check_interval: 5000000
|
|
|
|
|
num_sanity_val_steps: 0
|
|
|
|
|
accumulate_grad_batches: 2
|
