| model: | |
| base_learning_rate: 1.0e-04 | |
| target: ldm.models.diffusion.ddpm.LatentUpscaleDiffusion | |
| params: | |
| parameterization: "v" | |
| low_scale_key: "lr" | |
| linear_start: 0.0001 | |
| linear_end: 0.02 | |
| num_timesteps_cond: 1 | |
| log_every_t: 200 | |
| timesteps: 1000 | |
| first_stage_key: "jpg" | |
| cond_stage_key: "txt" | |
| image_size: 128 | |
| channels: 4 | |
| cond_stage_trainable: false | |
| conditioning_key: "hybrid-adm" | |
| monitor: val/loss_simple_ema | |
| scale_factor: 0.08333 | |
| use_ema: False | |
| low_scale_config: | |
| target: ldm.modules.diffusionmodules.upscaling.ImageConcatWithNoiseAugmentation | |
| params: | |
| noise_schedule_config: # image space | |
| linear_start: 0.0001 | |
| linear_end: 0.02 | |
| max_noise_level: 350 | |
| unet_config: | |
| target: ldm.modules.diffusionmodules.openaimodel.UNetModel | |
| params: | |
| use_checkpoint: True | |
| num_classes: 1000 # timesteps for noise conditioning (here constant, just need one) | |
| image_size: 128 | |
| in_channels: 7 | |
| out_channels: 4 | |
| model_channels: 256 | |
| attention_resolutions: [ 2,4,8] | |
| num_res_blocks: 2 | |
| channel_mult: [ 1, 2, 2, 4] | |
| disable_self_attentions: [True, True, True, False] | |
| disable_middle_self_attn: False | |
| num_heads: 8 | |
| use_spatial_transformer: True | |
| transformer_depth: 1 | |
| context_dim: 1024 | |
| legacy: False | |
| use_linear_in_transformer: True | |
| first_stage_config: | |
| target: ldm.models.autoencoder.AutoencoderKL | |
| params: | |
| embed_dim: 4 | |
| ddconfig: | |
| # attn_type: "vanilla-xformers" this model needs efficient attention to be feasible on HR data, also the decoder seems to break in half precision (UNet is fine though) | |
| double_z: True | |
| z_channels: 4 | |
| resolution: 256 | |
| in_channels: 3 | |
| out_ch: 3 | |
| ch: 128 | |
| ch_mult: [ 1,2,4 ] # num_down = len(ch_mult)-1 | |
| num_res_blocks: 2 | |
| attn_resolutions: [ ] | |
| dropout: 0.0 | |
| lossconfig: | |
| target: torch.nn.Identity | |
| cond_stage_config: | |
| target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder | |
| params: | |
| freeze: True | |
| layer: "penultimate" | |