model: base_learning_rate: 1.0e-06 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.0155 log_every_t: 100 timesteps: 1000 loss_type: l2 first_stage_key: image cond_stage_key: LR_image image_size: 64 channels: 3 concat_mode: true cond_stage_trainable: false unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 64 in_channels: 6 out_channels: 3 model_channels: 160 attention_resolutions: - 16 - 8 num_res_blocks: 2 channel_mult: - 1 - 2 - 2 - 4 num_head_channels: 32 first_stage_config: target: ldm.models.autoencoder.VQModelInterface params: embed_dim: 3 n_embed: 8192 monitor: val/rec_loss ddconfig: double_z: false z_channels: 3 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Module # todo cond_stage_config: target: torch.nn.Identity data: target: cutlit.DataModuleFromConfig params: batch_size: 64 wrap: false num_workers: 12 train: target: ldm.data.openimages.SuperresOpenImagesAdvancedTrain params: size: 256 degradation: bsrgan_light downscale_f: 4 min_crop_f: 0.5 max_crop_f: 1.0 random_crop: true validation: target: ldm.data.openimages.SuperresOpenImagesAdvancedValidation params: size: 256 degradation: bsrgan_light downscale_f: 4 min_crop_f: 0.5 max_crop_f: 1.0 random_crop: true