model: base_learning_rate: 4.0e-04 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "inpaint" cond_stage_key: "image" image_size: 64 channels: 4 cond_stage_trainable: true # Note: different from the one we trained before conditioning_key: crossattn monitor: val/loss_simple_ema u_cond_percent: 0.2 scale_factor: 0.18215 use_ema: False 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-1 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 # unused out_channels: 4 model_channels: 320 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 add_conv_in_front_of_unet: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss 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 cond_stage_config: target: ldm.modules.encoders.modules.FrozenCLIPEmbedder other_params: clip_weight: 1.0 arcface_path: "Other_dependencies/arcface/model_ir_se50.pth" multi_scale_ID: False # True was used for the previous training there is an issue Additional_config: Reconstruct_initial: False # scy: Target_CLIP_feat: True Source_CLIP_feat: True Reconstruct_DDIM_steps: 4