| model: | |
| base_learning_rate: 7.5e-05 | |
| target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion | |
| params: | |
| linear_start: 0.00085 | |
| linear_end: 0.0120 | |
| num_timesteps_cond: 1 | |
| log_every_t: 200 | |
| timesteps: 1000 | |
| first_stage_key: "jpg" | |
| cond_stage_key: "txt" | |
| image_size: 64 | |
| channels: 4 | |
| cond_stage_trainable: false # Note: different from the one we trained before | |
| conditioning_key: hybrid # important | |
| monitor: val/loss_simple_ema | |
| scale_factor: 0.18215 | |
| finetune_keys: null | |
| scheduler_config: # 10000 warmup steps | |
| target: ldm.lr_scheduler.LambdaLinearScheduler | |
| params: | |
| warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch | |
| 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: | |
| image_size: 32 # unused | |
| in_channels: 9 # 4 data + 4 downscaled image + 1 mask | |
| 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 | |
| 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 | |