model: base_learning_rate: 1.0e-04 target: ldm.models.diffusion.ddpm.ImageEmbeddingConditionedLatentDiffusion params: embedding_dropout: 0.25 parameterization: "v" linear_start: 0.00085 linear_end: 0.0120 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 96 channels: 4 cond_stage_trainable: false conditioning_key: crossattn-adm scale_factor: 0.18215 monitor: val/loss_simple_ema use_ema: False embedder_config: target: ldm.modules.encoders.modules.ClipImageEmbedder params: model: "ViT-L/14" noise_aug_config: target: ldm.modules.encoders.modules.CLIPEmbeddingNoiseAugmentation params: clip_stats_path: "checkpoints/karlo_models/ViT-L-14_stats.th" timestep_dim: 768 noise_schedule_config: timesteps: 1000 beta_schedule: squaredcos_cap_v2 unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: num_classes: "sequential" adm_in_channels: 1536 use_checkpoint: True image_size: 32 # unused in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_head_channels: 64 # need to fix for flash-attn use_spatial_transformer: True use_linear_in_transformer: True transformer_depth: 1 context_dim: 1024 legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: attn_type: "vanilla-xformers" 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.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate"