debug with dummy data

configs/stable-diffusion/dev_mn_dummy.yaml

@@ -0,0 +1,108 @@
+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: true
+    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
+        in_channels: 4
+        out_channels: 4
+        model_channels: 32 # 320   # TODO increase
+        attention_resolutions: [ ]  # is equal to fixed spatial resolution: 32 , 16 , 8
+        num_res_blocks: 2
+        channel_mult: [ 1, ]
+        #num_head_channels: 32
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 32
+        use_checkpoint: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ckpt_path: "models/first_stage_models/kl-f8/model.ckpt"
+        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.BERTEmbedder
+      params:
+        n_embed: 32
+        n_layer: 1 #32 # TODO: increase
+
+
+data:
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 4
+    num_workers: 4
+    wrap: false
+    train:
+      target: ldm.data.dummy.DummyData
+      params:
+        length: 10000
+        size: [256, 256, 3]
+    validation:
+      target: ldm.data.dummy.DummyData
+      params:
+        length: 10000
+        size: [256, 256, 3]
+
+
+lightning:
+  callbacks:
+    image_logger:
+      target: main.ImageLogger
+      params:
+        batch_frequency: 5000 # 5000
+        max_images: 0
+        increase_log_steps: False
+        log_first_step: True
+
+
+  trainer:
+    #replace_sampler_ddp: False
+    benchmark: True
+    num_sanity_val_steps: 0

ldm/data/dummy.py

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+import numpy as np
+import random
+import string
+from torch.utils.data import Dataset, Subset
+
+class DummyData(Dataset):
+    def __init__(self, length, size):
+        self.length = length
+        self.size = size
+
+    def __len__(self):
+        return self.length
+
+    def __getitem__(self, i):
+        x = np.random.randn(*self.size)
+        letters = string.ascii_lowercase
+        y = ''.join(random.choice(string.ascii_lowercase) for i in range(10))
+        return {"jpg": x, "txt": y}