unet/config.json

{
  "_class_name": "UNet2DModel",
  "_diffusers_version": "0.27.2",
  "act_fn": "silu",
  "add_attention": true,
  "attention_head_dim": null,
  "attn_norm_num_groups": null,
  "block_out_channels": [
    128,
    128,
    256,
    256,
    256,
    256,
    256
  ],
  "center_input_sample": true,
  "class_embed_type": null,
  "decay": 0.9999,
  "down_block_types": [
    "SkipDownBlock2D",
    "SkipDownBlock2D",
    "SkipDownBlock2D",
    "SkipDownBlock2D",
    "AttnSkipDownBlock2D",
    "SkipDownBlock2D",
    "SkipDownBlock2D"
  ],
  "downsample_padding": 1,
  "downsample_type": "conv",
  "dropout": 0.0,
  "flip_sin_to_cos": true,
  "freq_shift": 0,
  "in_channels": 3,
  "inv_gamma": 1.0,
  "layers_per_block": 2,
  "mid_block_scale_factor": 1.41421356237,
  "min_decay": 0.0,
  "norm_eps": 1e-06,
  "norm_num_groups": null,
  "num_class_embeds": 11,
  "num_train_timesteps": null,
  "optimization_step": 180000,
  "out_channels": 3,
  "power": 0.75,
  "resnet_time_scale_shift": "default",
  "sample_size": 64,
  "set_W_to_weight": false,
  "time_embedding_type": "fourier",
  "up_block_types": [
    "SkipUpBlock2D",
    "SkipUpBlock2D",
    "AttnSkipUpBlock2D",
    "SkipUpBlock2D",
    "SkipUpBlock2D",
    "SkipUpBlock2D",
    "SkipUpBlock2D"
  ],
  "update_after_step": 0,
  "upsample_type": "conv",
  "use_ema_warmup": true
}