model: base_learning_rate: 2.0e-06 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.0195 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: image image_size: 64 channels: 3 monitor: val/loss_simple_ema unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 64 in_channels: 3 out_channels: 3 model_channels: 224 attention_resolutions: # note: this isn\t actually the resolution but # the downsampling factor, i.e. this corresnponds to # attention on spatial resolution 8,16,32, as the # spatial reolution of the latents is 64 for f4 - 8 - 4 - 2 num_res_blocks: 2 channel_mult: - 1 - 2 - 3 - 4 num_head_channels: 32 first_stage_config: target: ldm.models.autoencoder.VQModelInterface params: embed_dim: 3 n_embed: 8192 ckpt_path: models/first_stage_models/vq-f4/model.ckpt 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.Identity cond_stage_config: __is_unconditional__ data: target: main.DataModuleFromConfig params: batch_size: 48 num_workers: 5 wrap: false train: target: taming.data.faceshq.CelebAHQTrain params: size: 256 validation: target: taming.data.faceshq.CelebAHQValidation params: size: 256 lightning: callbacks: image_logger: target: main.ImageLogger params: batch_frequency: 5000 max_images: 8 increase_log_steps: False trainer: benchmark: True