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model:
base_learning_rate: 1.0e-5
target: sgm.models.diffusion.DiffusionEngine
params:
scale_factor: 0.13025
disable_first_stage_autocast: True
no_cond_log: True
ckpt_config:
target: sgm.modules.checkpoint.CheckpointEngine
params:
ckpt_path: checkpoints/sd_xl_base_1.0.safetensors
pre_adapters:
- target: sgm.modules.checkpoint.Finetuner
params:
keys:
- model\.diffusion_model\.(input_blocks|middle_block|output_blocks)(\.[0-9])?\.[0-9]\.transformer_blocks\.[0-9]\.attn2\.(to_k|to_v)\.weight
- target: sgm.modules.checkpoint.Pruner
params:
keys:
- model\.diffusion_model\.label_emb\.0\.0\.weight
slices:
- ":, :1024"
print_sd_keys: False
print_model: False
scheduler_config:
target: sgm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 1000 ]
cycle_lengths: [ 10000000000000 ]
f_start: [ 1.e-6 ]
f_max: [ 1. ]
f_min: [ 1. ]
denoiser_config:
target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
params:
num_idx: 1000
scaling_config:
target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
discretization_config:
target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
network_config:
target: sgm.modules.diffusionmodules.openaimodel.UNetModel
params:
adm_in_channels: 1024 #2816
num_classes: sequential
use_checkpoint: True
in_channels: 4
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4 ]
num_head_channels: 64
use_linear_in_transformer: True
transformer_depth: [ 1, 2, 10 ] # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16
context_dim: 1664 #1280
spatial_transformer_attn_type: softmax-xformers
conditioner_config:
target: sgm.modules.GeneralConditioner
params:
emb_models:
# cross atn
- is_trainable: False
input_key: jpg
target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
params:
arch: ViT-bigG-14
version: laion2b_s39b_b160k
freeze: True
repeat_to_max_len: False
output_tokens: True
only_tokens: True
# vector cond
- is_trainable: False
input_key: original_size_as_tuple
target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
params:
outdim: 256 # multiplied by two
# vector cond
- is_trainable: False
input_key: crop_coords_top_left
target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
params:
outdim: 256 # multiplied by two
# # vector cond
# - is_trainable: False
# input_key: target_size_as_tuple
# target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
# params:
# outdim: 256 # multiplied by two
first_stage_config:
target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
attn_type: vanilla-xformers
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
loss_fn_config:
target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
params:
offset_noise_level: 0.04
sigma_sampler_config:
target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
params:
num_idx: 1000
discretization_config:
target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
loss_weighting_config:
target: sgm.modules.diffusionmodules.loss_weighting.EpsWeighting
sampler_config:
target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
params:
num_steps: 50
discretization_config:
target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
guider_config:
target: sgm.modules.diffusionmodules.guiders.VanillaCFG
params:
scale: 5.0
data:
target: sgm.data.dataset.StableDataModuleFromConfig
params:
train:
datapipeline:
urls:
- s3://stability-west/sddatasets/laiocosplitv1c/
pipeline_config:
shardshuffle: 10000
sample_shuffle: 10000
preprocessors:
- target: sdata.filters.SimpleKeyFilter
params:
keys: [txt, jpg]
- target: sdata.filters.AttributeFilter
params:
filter_dict:
SSCD_65: False
is_spawning: True
is_getty: True
decoders:
- pil
loader:
batch_size: 1
num_workers: 4
batched_transforms:
- target: sdata.mappers.MultiAspectCacher
params:
batch_size: 16
debug: False
crop_coords_key: crop_coords_top_left
target_size_key: target_size_as_tuple
original_size_key: original_size_as_tuple
max_pixels: 262144
lightning:
strategy:
target: pytorch_lightning.strategies.DDPStrategy
modelcheckpoint:
params:
every_n_train_steps: 100000
callbacks:
metrics_over_trainsteps_checkpoint:
params:
every_n_train_steps: 5000
image_logger:
target: sgm.modules.loggers.train_logging.SampleLogger
params:
disabled: False
enable_autocast: True
batch_frequency: 2000
max_images: 4
increase_log_steps: True
log_first_step: False
log_before_first_step: True
log_images_kwargs:
N: 4
num_steps:
- 50
ucg_keys: [ ]
trainer:
devices: 0,
benchmark: False
num_sanity_val_steps: 0
accumulate_grad_batches: 1
max_epochs: 1000
precision: 16
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