stable-diffusion-finetune/configs/stable-diffusion/dev_mn_dummy.yaml

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model:
base_learning_rate: 1.0e-04
target: ldm.models.diffusion.ddpm.LatentDiffusion
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: 32
channels: 4
cond_stage_trainable: true
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: 32
in_channels: 4
out_channels: 4
model_channels: 32 # 320 # TODO increase
attention_resolutions: [ ] # is equal to fixed spatial resolution: 32 , 16 , 8
num_res_blocks: 2
channel_mult: [ 1, ]
#num_head_channels: 32
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 32
use_checkpoint: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ckpt_path: "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
cond_stage_config:
target: ldm.modules.encoders.modules.BERTEmbedder
params:
n_embed: 32
n_layer: 1 #32 # TODO: increase
data:
target: main.DataModuleFromConfig
params:
batch_size: 4
num_workers: 4
wrap: false
train:
target: ldm.data.dummy.DummyData
params:
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length: 20000
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size: [256, 256, 3]
validation:
target: ldm.data.dummy.DummyData
params:
length: 10000
size: [256, 256, 3]
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
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batch_frequency: 500 # 5000
max_images: 8
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increase_log_steps: False
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log_first_step: False
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trainer:
#replace_sampler_ddp: False
benchmark: True
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val_check_interval: 1000 # every 20k training steps
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num_sanity_val_steps: 0