model: base_learning_rate: 1.0e-04 target: ldm.models.diffusion.ddpm.LatentUpscaleDiffusion params: low_scale_key: "lr" linear_start: 0.001 linear_end: 0.015 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 16 cond_stage_trainable: false conditioning_key: "hybrid-adm" monitor: val/loss_simple_ema scale_factor: 0.22765929 # magic number low_scale_config: target: ldm.modules.encoders.modules.LowScaleEncoder params: scale_factor: 0.18215 linear_start: 0.00085 linear_end: 0.0120 timesteps: 1000 max_noise_level: 100 output_size: 64 model_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ckpt_path: "models/first_stage_models/kl-f8/model.ckpt" ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [ ] dropout: 0.0 lossconfig: target: torch.nn.Identity scheduler_config: # 10000 warmup steps target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 10000 ] cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: num_classes: 1000 # timesteps for noise conditoining image_size: 64 # not really needed in_channels: 20 out_channels: 16 model_channels: 32 # TODO: more attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_heads: 8 use_spatial_transformer: True transformer_depth: 1 context_dim: 768 use_checkpoint: True legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 16 monitor: val/rec_loss ckpt_path: "models/first_stage_models/kl-f16/model.ckpt" ddconfig: double_z: True z_channels: 16 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: [ 1,1,2,2,4 ] # num_down = len(ch_mult)-1 num_res_blocks: 2 attn_resolutions: [ 16 ] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenCLIPEmbedder data: target: ldm.data.laion.WebDataModuleFromConfig params: tar_base: "pipe:aws s3 cp s3://s-datasets/laion-high-resolution/" batch_size: 4 num_workers: 1 train: shards: '{00000..17279}.tar -' shuffle: 10000 image_key: jpg image_transforms: - target: torchvision.transforms.Resize params: size: 1024 interpolation: 3 - target: torchvision.transforms.RandomCrop params: size: 1024 postprocess: target: ldm.data.laion.AddLR params: factor: 4 # NOTE use enough shards to avoid empty validation loops in workers validation: shards: '{17280..17535}.tar -' shuffle: 0 image_key: jpg image_transforms: - target: torchvision.transforms.Resize params: size: 1024 interpolation: 3 - target: torchvision.transforms.CenterCrop params: size: 1024 postprocess: target: ldm.data.laion.AddLR params: factor: 4 lightning: callbacks: image_logger: target: main.ImageLogger params: batch_frequency: 10 max_images: 4 increase_log_steps: False log_first_step: False log_images_kwargs: use_ema_scope: False inpaint: False plot_progressive_rows: False plot_diffusion_rows: False N: 4 unconditional_guidance_scale: 3.0 unconditional_guidance_label: [""] trainer: benchmark: True val_check_interval: 5000000 # really sorry num_sanity_val_steps: 0 accumulate_grad_batches: 1