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https://github.com/invoke-ai/InvokeAI
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9997fde144
This allows reliable distribution of the initial 'configs' directory with the Python package, and enables the configuration script to be running from anywhere, as long as the virtual environment is available on the sys.path
111 lines
2.6 KiB
YAML
111 lines
2.6 KiB
YAML
model:
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base_learning_rate: 5.0e-03
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target: ldm.models.diffusion.ddpm.LatentDiffusion
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params:
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linear_start: 0.00085
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linear_end: 0.0120
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num_timesteps_cond: 1
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log_every_t: 200
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timesteps: 1000
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first_stage_key: image
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cond_stage_key: caption
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image_size: 64
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channels: 4
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cond_stage_trainable: true # Note: different from the one we trained before
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conditioning_key: crossattn
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monitor: val/loss_simple_ema
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scale_factor: 0.18215
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use_ema: False
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embedding_reg_weight: 0.0
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personalization_config:
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target: ldm.modules.embedding_manager.EmbeddingManager
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params:
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placeholder_strings: ["*"]
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initializer_words: ["sculpture"]
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per_image_tokens: false
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num_vectors_per_token: 1
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progressive_words: False
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unet_config:
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target: ldm.modules.diffusionmodules.openaimodel.UNetModel
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params:
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image_size: 32 # unused
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in_channels: 4
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out_channels: 4
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model_channels: 320
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attention_resolutions: [ 4, 2, 1 ]
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num_res_blocks: 2
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channel_mult: [ 1, 2, 4, 4 ]
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num_heads: 8
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use_spatial_transformer: True
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transformer_depth: 1
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context_dim: 768
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use_checkpoint: True
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legacy: False
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first_stage_config:
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target: ldm.models.autoencoder.AutoencoderKL
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params:
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embed_dim: 4
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monitor: val/rec_loss
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ddconfig:
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double_z: true
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z_channels: 4
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resolution: 256
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in_channels: 3
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out_ch: 3
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ch: 128
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ch_mult:
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- 1
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- 2
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- 4
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- 4
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num_res_blocks: 2
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attn_resolutions: []
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dropout: 0.0
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lossconfig:
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target: torch.nn.Identity
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cond_stage_config:
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target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
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data:
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target: main.DataModuleFromConfig
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params:
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batch_size: 1
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num_workers: 2
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wrap: false
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train:
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target: ldm.data.personalized.PersonalizedBase
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params:
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size: 512
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set: train
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per_image_tokens: false
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repeats: 100
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validation:
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target: ldm.data.personalized.PersonalizedBase
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params:
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size: 512
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set: val
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per_image_tokens: false
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repeats: 10
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lightning:
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modelcheckpoint:
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params:
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every_n_train_steps: 500
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callbacks:
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image_logger:
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target: main.ImageLogger
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params:
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batch_frequency: 500
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max_images: 8
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increase_log_steps: False
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trainer:
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benchmark: True
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max_steps: 4000000
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# max_steps: 4000
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