stable-diffusion-webui

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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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    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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    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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    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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    cond_stage_config:
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      target: modules.xlmr.BertSeriesModelWithTransformation
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      params:
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        name: "XLMR-Large"