178 lines
4.9 KiB
YAML
178 lines
4.9 KiB
YAML
model:
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base_learning_rate: 1.0e-04
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target: ldm.models.diffusion.ddpm.LatentUpscaleDiffusion
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params:
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low_scale_key: "LR_image" # TODO: adapt
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linear_start: 0.001
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linear_end: 0.015
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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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#first_stage_key: "jpg" # TODO: use this later
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cond_stage_key: "caption"
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#cond_stage_key: "txt" # TODO: use this later
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image_size: 64
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channels: 16
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cond_stage_trainable: false
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conditioning_key: "hybrid-adm"
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monitor: val/loss_simple_ema
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scale_factor: 0.22765929 # magic number
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low_scale_config:
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target: ldm.modules.encoders.modules.LowScaleEncoder
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params:
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scale_factor: 0.18215
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linear_start: 0.00085
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linear_end: 0.0120
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timesteps: 1000
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max_noise_level: 100
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output_size: 64
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model_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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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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num_classes: 1000 # timesteps for noise conditoining
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image_size: 64 # not really needed
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in_channels: 20
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out_channels: 16
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model_channels: 32 # TODO: more
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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: 16
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monitor: val/rec_loss
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ckpt_path: "models/first_stage_models/kl-f16/model.ckpt"
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ddconfig:
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double_z: True
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z_channels: 16
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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: [ 1,1,2,2,4 ] # num_down = len(ch_mult)-1
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num_res_blocks: 2
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attn_resolutions: [ 16 ]
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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: ldm.data.laion.WebDataModuleFromConfig
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# params:
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# tar_base: "pipe:aws s3 cp s3://s-datasets/laion5b/laion2B-data/"
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# batch_size: 4
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# num_workers: 4
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# multinode: True
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# min_size: 256 # TODO: experiment. Note: for 2B, images are stored at max 384 resolution
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# train:
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# shards: '{000000..231317}.tar -'
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# shuffle: 10000
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# image_key: jpg
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# image_transforms:
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# - target: torchvision.transforms.Resize
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# params:
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# size: 1024
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# interpolation: 3
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# - target: torchvision.transforms.RandomCrop
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# params:
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# size: 1024
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#
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# # NOTE use enough shards to avoid empty validation loops in workers
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# validation:
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# shards: '{231318..231349}.tar -'
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# shuffle: 0
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# image_key: jpg
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# image_transforms:
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# - target: torchvision.transforms.Resize
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# params:
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# size: 1024
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# interpolation: 3
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# - target: torchvision.transforms.CenterCrop
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# params:
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# size: 1024
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data:
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target: main.DataModuleFromConfig
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params:
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batch_size: 8
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num_workers: 7
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wrap: false
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train:
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target: ldm.data.imagenet.ImageNetSRTrain
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params:
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size: 1024
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downscale_f: 4
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degradation: "cv_nearest"
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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: 10
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max_images: 4
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increase_log_steps: False
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log_first_step: False
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log_images_kwargs:
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sample: False
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use_ema_scope: False
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inpaint: False
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plot_progressive_rows: False
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plot_diffusion_rows: False
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N: 4
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#unconditional_guidance_scale: 3.0
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#unconditional_guidance_label: [""]
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trainer:
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benchmark: True
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# val_check_interval: 5000000 # really sorry # TODO: bring back in
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num_sanity_val_steps: 0
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accumulate_grad_batches: 1
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