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model: |
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base_learning_rate: 1.0e-04 |
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target: ldm.models.diffusion.ddpm.LatentDiffusion |
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params: |
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parameterization: "v" |
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linear_start: 0.00085 |
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linear_end: 0.0120 |
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num_timesteps_cond: 1 |
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log_every_t: 200 |
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timesteps: 1000 |
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first_stage_key: "jpg" |
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cond_stage_key: "txt" |
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image_size: 64 |
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channels: 4 |
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cond_stage_trainable: false # Note: different from the one we trained before |
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conditioning_key: crossattn |
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monitor: val/loss_simple_ema |
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scale_factor: 0.18215 |
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use_ema: False |
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|
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scheduler_config: # 10000 warmup steps |
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target: ldm.lr_scheduler.LambdaLinearScheduler |
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params: |
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warm_up_steps: [ 10000 ] |
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cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases |
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f_start: [ 1.e-6 ] |
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f_max: [ 1. ] |
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f_min: [ 1. ] |
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|
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unet_config: |
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target: ldm.modules.diffusionmodules.openaimodel.UNetModel |
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params: |
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image_size: 32 # unused |
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in_channels: 4 |
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out_channels: 4 |
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model_channels: 320 |
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attention_resolutions: [ 4, 2, 1 ] |
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num_res_blocks: 2 |
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channel_mult: [ 1, 2, 4, 4 ] |
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num_heads: 8 |
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use_spatial_transformer: True |
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transformer_depth: 1 |
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context_dim: 768 |
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use_checkpoint: True |
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legacy: False |
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|
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first_stage_config: |
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target: ldm.models.autoencoder.AutoencoderKL |
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params: |
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embed_dim: 4 |
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monitor: val/rec_loss |
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ddconfig: |
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double_z: true |
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z_channels: 4 |
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resolution: 256 |
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in_channels: 3 |
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out_ch: 3 |
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ch: 128 |
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ch_mult: |
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- 1 |
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- 2 |
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- 4 |
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- 4 |
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num_res_blocks: 2 |
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attn_resolutions: [] |
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dropout: 0.0 |
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lossconfig: |
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target: torch.nn.Identity |
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|
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cond_stage_config: |
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target: ldm.modules.encoders.modules.FrozenCLIPEmbedder |