EveryDream / v1-finetune_blacklight.yaml
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model:
base_learning_rate: 1.0e-6
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: image
cond_stage_key: caption
image_size: 64
channels: 4
cond_stage_trainable: true # Note: different from the one we trained before
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False
unfreeze_model: True
model_lr: 1.0e-6
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32 # unused
in_channels: 4
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: 512
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: main.DataModuleFromConfig
params:
batch_size: 2 # prefer highest possible without getting CUDA Out of Memory error
num_workers: 8 # number of dataloader threads
wrap: falsegit
train:
target: ldm.data.every_dream.EveryDreamBatch
params:
repeats: 70 # for micro models with 20-50 training images of one subject, try 30-60 repeats with 3-4 epochs (max_epochs below)
flip_p: 0.5 # use 0.5 to randomly flip images each repeat, not recommended unless very low training data < 20
debug_level: 1 # 1 to print if images are dropped due to multiple-aspect ratio images
conditional_dropout: 0.15 # drop conditioning (caption) completely this often
validation:
target: ldm.data.ed_validate.EDValidateBatch
params:
repeats: 1
test:
target: ldm.data.ed_validate.EDValidateBatch
params:
repeats: 1
lightning:
modelcheckpoint:
params:
every_n_epochs: 1 # produce a ckpt every epoch, leave at 1!
#every_n_train_steps: 1400 # can only use epoch or train step checkpoints
save_top_k: 99 # save the best N ckpts according to loss, can reduce to save disk space but not recommended
save_last: False
filename: "{epoch:02d}-{step:05d}"
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 150
max_images: 16
increase_log_steps: False
trainer:
benchmark: True
max_epochs: 1 # suggest 3-4+ and adjust repeats above, this will give you a few ckpts to test
max_steps: 99000 # better to end on epochs not steps, especially with >500 images to ensure even distribution, but you can set this if you really want...
check_val_every_n_epoch: 1
gpus: 0,