vipaint config

configs/inpainting/lands_config_mountain.yaml

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+data:
+  name: ldm.data.imagenet.ImageNetValidation
+  seq: {'half': [200, 300], 'box': [300, 350], 'random': [400,500]} #[400,500] #[350, 450], #, 'val': "random" : [350, 450], half : , val: [0,50]
+  file_seq: None
+  file_name: data/sflckr_all_images.npz 
+  channels: 3
+  image_size: 512
+  latent_size: 128
+  latent_channels: 3
+
+autoencoder: models/first_stage_models/vq-f4/config.yaml
+diffusion: configs/latent-diffusion/semantic_synthesis512.yaml
+diffusion_model: models/ldm/semantic_synthesis512/model.ckpt
+working_dir: results/landscapes_box
+conditional_model: True
+  
+name: inpainting
+
+measurement:
+  operator:
+    in_shape: !!python/tuple [1, 3, 256, 256]
+    scale_factor: 4
+  
+  noise: 
+    name: gaussian
+    sigma: 0.05
+
+mask_opt:
+  mask_type: random #random
+  mask_len_range: !!python/tuple [64, 65]  # for box
+  mask_prob_range: !!python/tuple [0.2, 0.21]  # [0.3, 0.7] for random
+  image_size: 512
+
+mask_files: {'random': ./masks/masks_mountain.npz, "half": masks/mask_random_half_100_imagenet.npy, 
+              "box": masks/box_100_imagenet.npy } # validation files : {'random': masks/mask_20_imagenet.npy, "half": masks/mask_random_half_20_imagenet.npy } 
+
+posterior: "gauss" #hierarchical, gauss
+name: ldm.guided_diffusion.loss_vq.VQLPIPSWithDiscriminator
+
+# gauss: 
+#   first_stage: vq
+#   unconditional_guidance_scale: 1
+#   eta: 0.2 
+#   beta: 4500 
+#   batch_size: 1
+#   iterations: 100
+#   t_steps_hierarchy:  [550] 
+#   rho: 7
+#   lr_init_gamma: 0.01
+#   mean_scale : 1
+#   mean_scale_top: 0.8
+
+hierarchical: 
+  first_stage: vq
+  unconditional_guidance_scale: 3
+  eta: 0.2 
+  beta_1: 45 #70 #700, prior
+  beta_2: 55 #70 #700, posterior
+  recon: 45
+  batch_size: 1
+  iterations: 100 #250
+  t_steps_hierarchy: [550, 400]  #  500, 450,  500, 450, 500, 450, 
+  rho: 7
+  lr_init_gamma: 0.01
+  mean_scale : 1
+  mean_scale_top: 0.8 
+
+init:
+  var_scale: 0.6
+  prior_scale: 6 # 4
+
+sampling:
+  method: ps
+  scale:  2
+  n_samples: 1
+  unconditional_guidance_scale: 3