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automatic1111
GitHub Repository: automatic1111/stable-diffusion-webui
 Path: blob/master/configs/alt-diffusion-m18-inference.yaml
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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_head_channels: 64

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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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        use_checkpoint: False

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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_m18.BertSeriesModelWithTransformation

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      params:

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        name: "XLMR-Large"

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