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model:
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base_learning_rate: 5.0e-5 # set to target_lr by starting main.py with '--scale_lr False'
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target: ldm.models.diffusion.ddpm.LatentDiffusion
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params:
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linear_start: 0.0015
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linear_end: 0.0155
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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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loss_type: l1
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first_stage_key: "image"
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cond_stage_key: "image"
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image_size: 32
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channels: 4
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cond_stage_trainable: False
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concat_mode: False
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scale_by_std: True
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monitor: 'val/loss_simple_ema'
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scheduler_config: # 10000 warmup steps
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target: ldm.lr_scheduler.LambdaLinearScheduler
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params:
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warm_up_steps: [10000]
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cycle_lengths: [10000000000000]
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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: ldm.modules.diffusionmodules.openaimodel.UNetModel
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params:
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image_size: 32
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in_channels: 4
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out_channels: 4
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model_channels: 192
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attention_resolutions: [ 1, 2, 4, 8 ] # 32, 16, 8, 4
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num_res_blocks: 2
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channel_mult: [ 1,2,2,4,4 ] # 32, 16, 8, 4, 2
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num_heads: 8
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use_scale_shift_norm: True
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resblock_updown: True
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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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ckpt_path: "models/first_stage_models/kl-f8/model.ckpt"
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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: [ 1,2,4,4 ] # num_down = len(ch_mult)-1
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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: "__is_unconditional__"
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data:
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target: main.DataModuleFromConfig
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params:
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batch_size: 96
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num_workers: 5
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wrap: False
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train:
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target: ldm.data.lsun.LSUNChurchesTrain
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params:
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size: 256
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validation:
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target: ldm.data.lsun.LSUNChurchesValidation
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params:
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size: 256
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lightning:
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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: 5000
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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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