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imagenet-f8_cond.yaml
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imagenet-f8_cond.yaml
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model:
base_learning_rate: 1.0e-4
target: sgm.models.diffusion.DiffusionEngine
params:
scale_factor: 0.13025
disable_first_stage_autocast: True
log_keys:
- cls
scheduler_config:
target: sgm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [10000]
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:
use_checkpoint: True
in_channels: 4
out_channels: 4
model_channels: 256
attention_resolutions: [1, 2, 4]
num_res_blocks: 2
channel_mult: [1, 2, 4]
num_head_channels: 64
num_classes: sequential
adm_in_channels: 1024
transformer_depth: 1
context_dim: 1024
spatial_transformer_attn_type: softmax-xformers
conditioner_config:
target: sgm.modules.GeneralConditioner
params:
emb_models:
- is_trainable: True
input_key: cls
ucg_rate: 0.2
target: sgm.modules.encoders.modules.ClassEmbedder
params:
add_sequence_dim: True
embed_dim: 1024
n_classes: 1000
- is_trainable: False
ucg_rate: 0.2
input_key: original_size_as_tuple
target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
params:
outdim: 256
- is_trainable: False
input_key: crop_coords_top_left
ucg_rate: 0.2
target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
params:
outdim: 256
first_stage_config:
target: sgm.models.autoencoder.AutoencoderKL
params:
ckpt_path: CKPT_PATH
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:
loss_weighting_config:
target: sgm.modules.diffusionmodules.loss_weighting.EpsWeighting
sigma_sampler_config:
target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
params:
num_idx: 1000
discretization_config:
target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
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:
# USER: adapt this path the root of your custom dataset
- DATA_PATH
pipeline_config:
shardshuffle: 10000
sample_shuffle: 10000 # USER: you might wanna adapt depending on your available RAM
decoders:
- pil
postprocessors:
- target: sdata.mappers.TorchVisionImageTransforms
params:
key: jpg # USER: you might wanna adapt this for your custom dataset
transforms:
- target: torchvision.transforms.Resize
params:
size: 256
interpolation: 3
- target: torchvision.transforms.ToTensor
- target: sdata.mappers.Rescaler
- target: sdata.mappers.AddOriginalImageSizeAsTupleAndCropToSquare
params:
h_key: height # USER: you might wanna adapt this for your custom dataset
w_key: width # USER: you might wanna adapt this for your custom dataset
loader:
batch_size: 64
num_workers: 6
lightning:
modelcheckpoint:
params:
every_n_train_steps: 5000
callbacks:
metrics_over_trainsteps_checkpoint:
params:
every_n_train_steps: 25000
image_logger:
target: main.ImageLogger
params:
disabled: False
enable_autocast: False
batch_frequency: 1000
max_images: 8
increase_log_steps: True
log_first_step: False
log_images_kwargs:
use_ema_scope: False
N: 8
n_rows: 2
trainer:
devices: 0,
benchmark: True
num_sanity_val_steps: 0
accumulate_grad_batches: 1
max_epochs: 1000