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+ model:
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+ base_learning_rate: 1.0e-4
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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
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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 # we set this to false because this is an inference only config
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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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+ use_checkpoint: True
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+ use_fp16: True
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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_head_channels: 64 # need to fix for flash-attn
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+ use_spatial_transformer: True
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+ use_linear_in_transformer: True
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+ transformer_depth: 1
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+ context_dim: 1024
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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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+ #attn_type: "vanilla-xformers"
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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.FrozenOpenCLIPEmbedder
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+ params:
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+ freeze: True
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+ layer: "penultimate"
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