stable-diffusion-finetune/configs/stable-diffusion/txt2img-v2-clip-encoder-improved_aesthetics-512-dev.yaml

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model:
base_learning_rate: 1.0e-04
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
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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: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
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: 64 # unused
in_channels: 4
out_channels: 4
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model_channels: 384
attention_resolutions: [ 8, 4, 2, 1 ]
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num_res_blocks: [ 2, 2, 2, 2 ]
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channel_mult: [ 1, 2, 4, 4 ]
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disable_self_attentions: [ False, False, False, False ] # converts the self-attention to a cross-attention layer if true
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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
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data: # TODO
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target: main.DataModuleFromConfig
params:
batch_size: 1
num_workers: 4
wrap: false
train:
target: ldm.data.dummy.DummyData
params:
length: 20000
size: [512, 512, 3]
validation:
target: ldm.data.dummy.DummyData
params:
length: 10000
size: [512, 512, 3]
#data:
# target: ldm.data.laion.WebDataModuleFromConfig
# params:
# tar_base: "pipe:aws s3 cp s3://s-datasets/laion-high-resolution/"
# batch_size: 4
# num_workers: 4
# multinode: True
# 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
#
# # 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
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
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batch_frequency: 5 # TODO
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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:
#replace_sampler_ddp: False
benchmark: True
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val_check_interval: 1000 # TODO: 5000000 # really sorry
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num_sanity_val_steps: 0
accumulate_grad_batches: 2