model: base_learning_rate: 1.0e-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: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false # Note: different from the one we trained before conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False given_betas: [ 0.0008500218391418457, 0.0009172558784484863, 0.0009224414825439453, 0.0009275078773498535, 0.0009327530860900879, 0.0009377002716064453, 0.0009428262710571289, 0.0009481310844421387, 0.0009533166885375977, 0.0009582042694091797, 0.0009638071060180664, 0.0009693503379821777, 0.0009734034538269043, 0.0009794235229492188, 0.0009845495223999023, 0.0009899139404296875, 0.0009950995445251465, 0.0010003447532653809, 0.0010057687759399414, 0.0010110735893249512, 0.0010165572166442871, 0.001021742820739746, 0.001027226448059082, 0.0010326504707336426, 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ldm.modules.diffusionmodules.openaimodel.UNetModel params: 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_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