DS-Fusion-Express / configs /finetune /finetune_generic.yaml
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model:
base_learning_rate: 1.0e-5
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: 32
channels: 4
cond_stage_trainable: False
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False
weight_disc: 0.01
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32
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: 1280
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.BERTEmbedder
params:
n_embed: 1280
n_layer: 32
device: "cuda"
# discriminator_config:
# target: ldm.modules.discriminator.Discriminator
# params:
# bnorm: True
# leakyparam: 0.2
# bias: False
# generic: True
data:
target: main.DataModuleFromConfig
params:
batch_size: 1
num_workers: 32
wrap: false
train:
target: ldm.data.rasterizer.Rasterizer
params:
img_size: 256
text: "R"
style_word: "DRAGON"
data_path: "data/cat"
alternate_glyph: None
num_samples: 2001
make_black: False
one_font: False
full_word: False
font_name: "Garuda-Bold.ttf"
just_use_style: false
use_alt: False
validation:
target: ldm.data.rasterizer.Rasterizer
params:
img_size: 256
text: "R"
style_word: "DRAGON"
data_path: "data/cat"
alternate_glyph: None
num_samples: 5
make_black: False
one_font: False
full_word: False
font_name: "Garuda-Bold.ttf"
just_use_style: false
use_alt: False
lightning:
modelcheckpoint:
params:
every_n_train_steps: 5000
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 1000
max_images: 1
increase_log_steps: False
trainer:
benchmark: True
max_steps: 500