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Delete v1-inference.yaml

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  1. v1-inference.yaml +0 -70
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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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- 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