134 lines
3.5 KiB
YAML
134 lines
3.5 KiB
YAML
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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: "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 # Note: different from the one we trained before
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conditioning_key: crossattn
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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: [ 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: 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: 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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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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ckpt_path: "models/first_stage_models/kl-f8/model.ckpt"
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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: ldm.modules.encoders.modules.FrozenCLIPEmbedder
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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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num_val_workers: 0 # Avoid a weird val dataloader issue
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train:
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target: ldm.data.simple.hf_dataset
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params:
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name: lambdalabs/pokemon-blip-captions
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image_transforms:
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- target: torchvision.transforms.Resize
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params:
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size: 512
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interpolation: 3
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- target: torchvision.transforms.RandomCrop
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params:
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size: 512
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- target: torchvision.transforms.RandomHorizontalFlip
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validation:
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target: ldm.data.simple.TextOnly
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params:
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captions:
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- "A pokemon with green eyes, large wings, and a hat"
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- "A cute bunny rabbit"
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- "Yoda"
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- "An epic landscape photo of a mountain"
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output_size: 512
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n_gpus: 2 # small hack to sure we see all our samples
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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: 2000
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save_top_k: -1
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monitor: null
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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: 2000
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max_images: 4
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increase_log_steps: False
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log_first_step: True
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log_all_val: True
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log_images_kwargs:
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use_ema_scope: True
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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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num_sanity_val_steps: 0
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accumulate_grad_batches: 1
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