138 lines
3.6 KiB
YAML
138 lines
3.6 KiB
YAML
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model:
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base_learning_rate: 8.e-05
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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: false # 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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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 # unused
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in_channels: 4
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out_channels: 4
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model_channels: 416
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attention_resolutions: [ 4, 2, 1 ]
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num_res_blocks: [ 2, 2, 2, 2 ]
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channel_mult: [ 1, 2, 4, 4 ]
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disable_self_attentions: [ False, False, False, False ] # converts the self-attention to a cross-attention layer if true
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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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ckpt_path: "/fsx/stable-diffusion/stable-diffusion/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.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: "__improvedaesthetic__"
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batch_size: 8
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num_workers: 4
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multinode: True
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train:
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shards: '{00000..17279}.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: 256
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interpolation: 3
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- target: torchvision.transforms.RandomCrop
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params:
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size: 256
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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: '{17280..17535}.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: 256
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interpolation: 3
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- target: torchvision.transforms.CenterCrop
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params:
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size: 256
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lightning:
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find_unused_parameters: false
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modelcheckpoint:
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params:
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every_n_train_steps: 5000
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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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disabled: True
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batch_frequency: 2500
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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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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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#replace_sampler_ddp: False
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benchmark: True
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val_check_interval: 5000000 # really sorry
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num_sanity_val_steps: 0
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accumulate_grad_batches: 1
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