mirror of
https://github.com/brycedrennan/imaginAIry
synced 2024-11-05 12:00:15 +00:00
5cc73f6087
- feature: finetuning your own image models - feature: image prep command. crops to face or other interesting parts of photo - fix: back-compat for hf_hub_download - feature: add prune-ckpt command - feature: allow specification of model config file
71 lines
1.9 KiB
YAML
71 lines
1.9 KiB
YAML
model:
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base_learning_rate: 1.0e-4
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target: imaginairy.modules.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: "txt"
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image_size: 64
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channels: 4
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cond_stage_trainable: false
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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 # we set this to false because this is an inference only config
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scheduler_config: # 10000 warm-up steps
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target: imaginairy.lr_scheduler.LambdaLinearScheduler
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params:
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warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch
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cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
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f_start: [ 1.e-6 ]
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f_max: [ 1. ]
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f_min: [ 1. ]
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unet_config:
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target: imaginairy.modules.diffusion.openaimodel.UNetModel
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params:
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use_checkpoint: True
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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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legacy: False
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first_stage_config:
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target: imaginairy.modules.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: imaginairy.modules.clip_embedders.FrozenCLIPEmbedder
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