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Robin Rombach 2022-07-28 00:10:22 +02:00
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149
configs/stable-diffusion/inpainting/v1-finetune-for-inpainting-laion-aesthetic-larger-masks.yaml Normal file
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model:
base_learning_rate: 7.5e-05
target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
image_size: 64
channels: 4
cond_stage_trainable: false # Note: different from the one we trained before
conditioning_key: hybrid # important
monitor: val/loss_simple_ema
scale_factor: 0.18215
ckpt_path: "/fsx/stable-diffusion/stable-diffusion/checkpoints/v1pp/v1pp-flatlined-hr.ckpt"
scheduler_config: # 10000 warmup steps
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch
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: 32 # unused
in_channels: 9 # 4 data + 4 downscaled image + 1 mask
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: ldm.data.laion.WebDataModuleFromConfig
params:
tar_base: "__improvedaesthetic__"
batch_size: 2
num_workers: 4
multinode: True
min_size: 512
max_pwatermark: 0.8
train:
shards: '{00000..17279}.tar -'
shuffle: 10000
image_key: jpg
image_transforms:
- target: torchvision.transforms.Resize
params:
size: 512
interpolation: 3
- target: torchvision.transforms.RandomCrop
params:
size: 512
postprocess:
target: ldm.data.laion.AddMask
params:
mode: "512train-large"
# NOTE use enough shards to avoid empty validation loops in workers
validation:
shards: '{17280..17535}.tar -'
shuffle: 0
image_key: jpg
image_transforms:
- target: torchvision.transforms.Resize
params:
size: 512
interpolation: 3
- target: torchvision.transforms.CenterCrop
params:
size: 512
postprocess:
target: ldm.data.laion.AddMask
params:
mode: "512train-large"
lightning:
find_unused_parameters: False
modelcheckpoint:
params:
every_n_train_steps: 2000
callbacks:
image_logger:
target: main.ImageLogger
params:
disabled: False
batch_frequency: 1000
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: [""]
ddim_steps: 100 # todo check these out for inpainting,
ddim_eta: 1.0 # todo check these out for inpainting,
trainer:
benchmark: True
val_check_interval: 5000000 # really sorry
num_sanity_val_steps: 0
accumulate_grad_batches: 2

214
configs/stable-diffusion/upscaling/upscale-v1-with-f16.yaml Normal file
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model:
base_learning_rate: 5.0e-05
target: ldm.models.diffusion.ddpm.LatentUpscaleDiffusion
params:
low_scale_key: "lr"
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: 32
channels: 16
cond_stage_trainable: false
conditioning_key: "hybrid-adm"
monitor: val/loss_simple_ema
scale_factor: 0.22765929 # magic number
low_scale_config:
target: ldm.modules.encoders.modules.LowScaleEncoder
params:
scale_factor: 0.18215
linear_start: 0.00085
linear_end: 0.0120
timesteps: 1000
max_noise_level: 250
output_size: null
model_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ckpt_path: "/fsx/stable-diffusion/stable-diffusion/models/first_stage_models/kl-f8/model.ckpt"
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
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:
num_classes: 251 # timesteps for noise conditoining
image_size: 64 # not really needed
in_channels: 20
out_channels: 16
model_channels: 128
attention_resolutions: [ 8, 4, 2 ] # -> at 32, 16, 8
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 6, 8 ]
# -> res, ds: (64, 1), (32, 2), (16, 4), (6, 8), (4, 16)
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
ckpt_path: "/fsx/stable-diffusion/stable-diffusion/models/first_stage_models/kl-f16/model.ckpt"
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: # TODO: finetune here later
# target: ldm.data.laion.WebDataModuleFromConfig
# params:
# tar_base: "pipe:aws s3 cp s3://s-datasets/laion-high-resolution/"
# batch_size: 10
# num_workers: 4
# train:
# shards: '{00000..17279}.tar -'
# shuffle: 10000
# image_key: jpg
# image_transforms:
# - target: torchvision.transforms.Resize
# params:
# size: 1024
# interpolation: 3
# - target: torchvision.transforms.RandomCrop
# params:
# size: 1024
# postprocess:
# target: ldm.data.laion.AddLR
# params:
# factor: 2
#
# # NOTE use enough shards to avoid empty validation loops in workers
# validation:
# shards: '{17280..17535}.tar -'
# shuffle: 0
# image_key: jpg
# image_transforms:
# - target: torchvision.transforms.Resize
# params:
# size: 1024
# interpolation: 3
# - target: torchvision.transforms.CenterCrop
# params:
# size: 1024
# postprocess:
# target: ldm.data.laion.AddLR
# params:
# factor: 2
data:
target: ldm.data.laion.WebDataModuleFromConfig
params:
tar_base: "__improvedaesthetic__"
batch_size: 28
num_workers: 4
multinode: True
min_size: 512
train:
shards: '{00000..17279}.tar -'
shuffle: 10000
image_key: jpg
image_transforms:
- target: torchvision.transforms.Resize
params:
size: 512
interpolation: 3
- target: torchvision.transforms.RandomCrop
params:
size: 512
postprocess:
target: ldm.data.laion.AddLR
params:
factor: 2
# NOTE use enough shards to avoid empty validation loops in workers
validation:
shards: '{17280..17535}.tar -'
shuffle: 0
image_key: jpg
image_transforms:
- target: torchvision.transforms.Resize
params:
size: 512
interpolation: 3
- target: torchvision.transforms.CenterCrop
params:
size: 512
postprocess:
target: ldm.data.laion.AddLR
params:
factor: 2
lightning:
find_unused_parameters: False
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 1000
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 # really sorry
num_sanity_val_steps: 0
accumulate_grad_batches: 2