dev configs for v2 run

configs/stable-diffusion/txt2img-v2-clip-encoder-improved_aesthetics-256-dev.yaml

@@ -0,0 +1,149 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 32
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 10000 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 384
+        attention_resolutions: [ 8, 4, 2, 1 ]
+        num_res_blocks: [ 2, 2, 2, 2 ]
+        channel_mult: [ 1, 2, 4, 4 ]
+        disable_self_attentions: [ False, False, False, False ]  # converts the self-attention to a cross-attention layer if true
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+
+
+data:  # TODO
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 8
+    num_workers: 4
+    wrap: false
+    train:
+      target: ldm.data.dummy.DummyData
+      params:
+        length: 20000
+        size: [256, 256, 3]
+    validation:
+      target: ldm.data.dummy.DummyData
+      params:
+        length: 10000
+        size: [256, 256, 3]
+
+#data:
+#  target: ldm.data.laion.WebDataModuleFromConfig
+#  params:
+#    tar_base: "pipe:aws s3 cp s3://s-datasets/laion-high-resolution/"
+#    batch_size: 4
+#    num_workers: 4
+#    multinode: True
+#    train:
+#      shards: '{00000..17279}.tar -'
+#      shuffle: 10000
+#      image_key: jpg
+#      image_transforms:
+#      - target: torchvision.transforms.Resize
+#        params:
+#          size: 512
+#          interpolation: 3
+#      - target: torchvision.transforms.RandomCrop
+#        params:
+#          size: 512
+#
+#    # NOTE use enough shards to avoid empty validation loops in workers
+#    validation:
+#      shards: '{17280..17535}.tar -'
+#      shuffle: 0
+#      image_key: jpg
+#      image_transforms:
+#      - target: torchvision.transforms.Resize
+#        params:
+#          size: 512
+#          interpolation: 3
+#      - target: torchvision.transforms.CenterCrop
+#        params:
+#          size: 512
+
+
+lightning:
+  callbacks:
+    image_logger:
+      target: main.ImageLogger
+      params:
+        batch_frequency: 5  # TODO
+        max_images: 4
+        increase_log_steps: False
+        log_first_step: False
+        log_images_kwargs:
+          use_ema_scope: False
+          inpaint: False
+          plot_progressive_rows: False
+          plot_diffusion_rows: False
+          N: 4
+          unconditional_guidance_scale: 3.0
+          unconditional_guidance_label: [""]
+
+  trainer:
+    #replace_sampler_ddp: False
+    benchmark: True
+    val_check_interval: 200  # TODO: 5000000 # really sorry
+    num_sanity_val_steps: 0
+    accumulate_grad_batches: 2

configs/stable-diffusion/txt2img-v2-clip-encoder-improved_aesthetics-512-dev.yaml

@@ -2,7 +2,7 @@ model:
   base_learning_rate: 1.0e-04
   target: ldm.models.diffusion.ddpm.LatentDiffusion
   params:
-    #ckpt_path: "/home/mchorse/stable-diffusion-ckpts/256pretrain-2022-06-09.ckpt"
+
     linear_start: 0.00085
     linear_end: 0.0120
     num_timesteps_cond: 1
@@ -34,10 +34,9 @@ model:
         out_channels: 4
         model_channels: 384
         attention_resolutions: [ 8, 4, 2, 1 ]
-        num_res_blocks: [ 2, 2, 2, 5 ]
+        num_res_blocks: [ 2, 2, 2, 2 ]
         channel_mult: [ 1, 2, 4, 4 ]
         disable_self_attentions: [ False, False, False, False ]  # converts the self-attention to a cross-attention layer if true
-        num_attention_blocks: [1, 1, 1, 3]
         num_heads: 8
         use_spatial_transformer: True
         transformer_depth: 1
@@ -72,7 +71,7 @@ model:
       target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
 
 
-data:
+data:  # TODO
   target: main.DataModuleFromConfig
   params:
     batch_size: 1
@@ -129,7 +128,7 @@ lightning:
     image_logger:
       target: main.ImageLogger
       params:
-        batch_frequency: 5000
+        batch_frequency: 5  # TODO
         max_images: 4
         increase_log_steps: False
         log_first_step: False
@@ -145,6 +144,6 @@ lightning:
   trainer:
     #replace_sampler_ddp: False
     benchmark: True
-    val_check_interval: 1000 # TODO: 1e10 # really sorry
+    val_check_interval: 1000 # TODO: 5000000 # really sorry
     num_sanity_val_steps: 0
     accumulate_grad_batches: 2