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jamesWalker55/artstyle.yaml

Created December 22, 2022 01:30
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artstyle.yaml
model:
base_learning_rate: 5.0e-03
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
embedding_reg_weight: 0.0
#unfreeze_model: False
#model_lr: 1.0e-7
personalization_config:
target: ldm.modules.embedding_manager.EmbeddingManager
params:
placeholder_strings: ["*"] # * & ^ % @
initializer_words: ["illustration","shading","girl","face"]
per_image_tokens: false
num_vectors_per_token: 20
progressive_words: False
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
params:
penultimate: True
extended_mode: True
max_chunks: 3
data:
target: main.DataModuleFromConfig
params:
batch_size: 1
num_workers: 16
wrap: false
train:
target: ldm.data.personalized.PersonalizedBase
params:
size: 512
set: train
per_image_tokens: false
repeats: 100
templates:
- 'a painting in the style of {}'
- 'a rendering in the style of {}'
- 'a cropped painting in the style of {}'
- 'the painting in the style of {}'
- 'a clean painting in the style of {}'
- 'a dirty painting in the style of {}'
- 'a dark painting in the style of {}'
- 'a picture in the style of {}'
- 'a cool painting in the style of {}'
- 'a close-up painting in the style of {}'
- 'a bright painting in the style of {}'
- 'a cropped painting in the style of {}'
- 'a good painting in the style of {}'
- 'a close-up painting in the style of {}'
- 'a rendition in the style of {}'
- 'a nice painting in the style of {}'
- 'a small painting in the style of {}'
- 'a weird painting in the style of {}'
- 'a large painting in the style of {}'
- 'a photorealistic illustration in the style of {}'
- 'an anime illustration in the style of {}'
validation:
target: ldm.data.personalized.PersonalizedBase
params:
size: 512
set: val
per_image_tokens: false
repeats: 10
templates:
- 'a painting in the style of {}'
- 'a rendering in the style of {}'
- 'a cropped painting in the style of {}'
- 'the painting in the style of {}'
- 'a clean painting in the style of {}'
- 'a dirty painting in the style of {}'
- 'a dark painting in the style of {}'
- 'a picture in the style of {}'
- 'a cool painting in the style of {}'
- 'a close-up painting in the style of {}'
- 'a bright painting in the style of {}'
- 'a cropped painting in the style of {}'
- 'a good painting in the style of {}'
- 'a close-up painting in the style of {}'
- 'a rendition in the style of {}'
- 'a nice painting in the style of {}'
- 'a small painting in the style of {}'
- 'a weird painting in the style of {}'
- 'a large painting in the style of {}'
- 'a photorealistic illustration in the style of {}'
- 'an anime illustration in the style of {}'
lightning:
modelcheckpoint:
params:
every_n_train_steps: 100
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 100
max_images: 8
increase_log_steps: False
trainer:
benchmark: True
max_steps: 200000
find_unused_parameters: False
Raw
character.yaml
model:
base_learning_rate: 5.0e-03
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
embedding_reg_weight: 0.0
#unfreeze_model: False
#model_lr: 1.0e-7
personalization_config:
target: ldm.modules.embedding_manager.EmbeddingManager
params:
placeholder_strings: ["*"] # * & ^ % @
initializer_words: ["illustration","shading","girl","face"]
per_image_tokens: false
num_vectors_per_token: 20
progressive_words: False
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
params:
penultimate: True
extended_mode: True
max_chunks: 3
data:
target: main.DataModuleFromConfig
params:
batch_size: 1
num_workers: 16
wrap: false
train:
target: ldm.data.personalized.PersonalizedBase
params:
size: 512
set: train
per_image_tokens: false
repeats: 100
templates:
- 'a photo of a {}'
- 'a rendering of a {}'
- 'a cropped photo of the {}'
- 'the photo of a {}'
- 'a photo of a clean {}'
- 'a photo of a dirty {}'
- 'a dark photo of the {}'
- 'a photo of my {}'
- 'a photo of the cool {}'
- 'a close-up photo of a {}'
- 'a bright photo of the {}'
- 'a cropped photo of a {}'
- 'a photo of the {}'
- 'a good photo of the {}'
- 'a photo of one {}'
- 'a close-up photo of the {}'
- 'a rendition of the {}'
- 'a photo of the clean {}'
- 'a rendition of a {}'
- 'a photo of a nice {}'
- 'a good photo of a {}'
- 'a photo of the nice {}'
- 'a photo of the small {}'
- 'a photo of the weird {}'
- 'a photo of the large {}'
- 'a photo of a cool {}'
- 'a photo of a small {}'
validation:
target: ldm.data.personalized.PersonalizedBase
params:
size: 512
set: val
per_image_tokens: false
repeats: 10
templates:
- 'a photo of a {}'
- 'a rendering of a {}'
- 'a cropped photo of the {}'
- 'the photo of a {}'
- 'a photo of a clean {}'
- 'a photo of a dirty {}'
- 'a dark photo of the {}'
- 'a photo of my {}'
- 'a photo of the cool {}'
- 'a close-up photo of a {}'
- 'a bright photo of the {}'
- 'a cropped photo of a {}'
- 'a photo of the {}'
- 'a good photo of the {}'
- 'a photo of one {}'
- 'a close-up photo of the {}'
- 'a rendition of the {}'
- 'a photo of the clean {}'
- 'a rendition of a {}'
- 'a photo of a nice {}'
- 'a good photo of a {}'
- 'a photo of the nice {}'
- 'a photo of the small {}'
- 'a photo of the weird {}'
- 'a photo of the large {}'
- 'a photo of a cool {}'
- 'a photo of a small {}'
lightning:
modelcheckpoint:
params:
every_n_train_steps: 100
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 100
max_images: 8
increase_log_steps: False
trainer:
benchmark: True
max_steps: 200000
find_unused_parameters: False
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