Merge branch 'main' of github.com:pesser/stable-diffusion into main

configs/stable-diffusion/txt2img-1p4B-multinode-clip-encoder-high-res-512-inference.yaml

@@ -0,0 +1,69 @@
+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: 64
+    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: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        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

configs/stable-diffusion/txt2img-upscale-clip-encoder-f16-1024.yaml

@@ -1,5 +1,5 @@
 model:
-  base_learning_rate: 1.0e-04
+  base_learning_rate: 5.0e-05
   target: ldm.models.diffusion.ddpm.LatentUpscaleDiffusion
   params:
     low_scale_key: "lr"
@@ -66,10 +66,11 @@ model:
         image_size: 64    # not really needed
         in_channels: 20
         out_channels: 16
-        model_channels: 192
-        attention_resolutions: [ 4, 2, 1 ]
+        model_channels: 96
+        attention_resolutions: [ 8, 4, 2 ] # -> at 32, 16, 8
         num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
+        channel_mult: [ 1, 2, 4, 8, 8 ]
+        # -> res, ds: (64, 1), (32, 2), (16, 4), (8, 8), (4, 16)
         num_heads: 8
         use_spatial_transformer: True
         transformer_depth: 1
@@ -105,7 +106,7 @@ data:
   target: ldm.data.laion.WebDataModuleFromConfig
   params:
     tar_base: "pipe:aws s3 cp s3://s-datasets/laion-high-resolution/"
-    batch_size: 8
+    batch_size: 10
     num_workers: 4
     train:
       shards: '{00000..17279}.tar -'
@@ -143,6 +144,8 @@ data:
                       factor: 4
 
 lightning:
+  find_unused_parameters: False
+
   callbacks:
     image_logger:
       target: main.ImageLogger

ldm/models/diffusion/ddpm.py

@@ -1556,7 +1556,7 @@ class LatentUpscaleDiffusion(LatentDiffusion):
                     uc[k] = [uc_tmp]
                 elif k == "c_adm":
                     assert isinstance(c[k], torch.Tensor)
-                    uc[k] = torch.ones_like(c[k]) * (self.low_scale_model.max_max_noise_level-1)
+                    uc[k] = torch.ones_like(c[k]) * (self.low_scale_model.max_noise_level-1)
                 elif isinstance(c[k], list):
                     uc[k] = [torch.zeros_like(c[k][i]) for i in range(len(c[k]))]
                 else:

ldm/modules/attention.py

@@ -253,9 +253,9 @@ class SpatialTransformer(nn.Module):
         x_in = x
         x = self.norm(x)
         x = self.proj_in(x)
-        x = rearrange(x, 'b c h w -> b (h w) c')
+        x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
         for block in self.transformer_blocks:
             x = block(x, context=context)
-        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
+        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
         x = self.proj_out(x)
-        return x + x_in
+        return x + x_in

main.py

@@ -759,6 +759,9 @@ if __name__ == "__main__":
             del callbacks_cfg['ignore_keys_callback']
 
         trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]
+        if not lightning_config.get("find_unused_parameters", True):
+            from pytorch_lightning.plugins import DDPPlugin
+            trainer_kwargs["plugins"] = DDPPlugin(find_unused_parameters=False)
 
         trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs)
         trainer.logdir = logdir  ###