2022-05-30 15:09:42 +02:00
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model:
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base_learning_rate: 1.0e-04
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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: "jpg"
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cond_stage_key: "txt"
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image_size: 32
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channels: 4
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cond_stage_trainable: true
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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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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 ] # 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: 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: 32 # 320 # TODO increase
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attention_resolutions: [ ] # is equal to fixed spatial resolution: 32 , 16 , 8
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num_res_blocks: 2
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channel_mult: [ 1, ]
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#num_head_channels: 32
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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: 32
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use_checkpoint: 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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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:
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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.BERTEmbedder
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params:
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n_embed: 32
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n_layer: 1 #32 # TODO: increase
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data:
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target: main.DataModuleFromConfig
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params:
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batch_size: 4
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num_workers: 4
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wrap: false
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train:
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target: ldm.data.dummy.DummyData
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params:
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length: 20000
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size: [256, 256, 3]
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validation:
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target: ldm.data.dummy.DummyData
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params:
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length: 10000
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size: [256, 256, 3]
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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: 500 # 5000
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max_images: 8
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increase_log_steps: False
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2022-05-31 00:16:58 +02:00
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log_first_step: False
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2022-05-30 15:09:42 +02:00
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trainer:
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#replace_sampler_ddp: False
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benchmark: True
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2022-05-31 00:16:58 +02:00
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val_check_interval: 1000 # every 20k training steps
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2022-05-30 15:09:42 +02:00
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num_sanity_val_steps: 0
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