support lr creation in laion

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

@@ -0,0 +1,166 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentUpscaleDiffusion
+  params:
+    low_scale_key: "lr"
+    linear_start: 0.001
+    linear_end: 0.015
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 16
+    cond_stage_trainable: false
+    conditioning_key: "hybrid-adm"
+    monitor: val/loss_simple_ema
+    scale_factor: 0.22765929   # magic number
+
+    low_scale_config:
+      target: ldm.modules.encoders.modules.LowScaleEncoder
+      params:
+        linear_start: 0.00085
+        linear_end: 0.0120
+        timesteps: 1000
+        max_noise_level: 250
+        output_size: 64
+        model_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
+
+    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:
+        num_classes: 1000  #  timesteps for noise conditoining
+        image_size: 64    # not really needed
+        in_channels: 20
+        out_channels: 16
+        model_channels: 32 # TODO: more
+        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: 16
+        monitor: val/rec_loss
+        ckpt_path: "models/first_stage_models/kl-f16/model.ckpt"
+        ddconfig:
+          double_z: True
+          z_channels: 16
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult: [ 1,1,2,2,4 ]  # num_down = len(ch_mult)-1
+          num_res_blocks: 2
+          attn_resolutions: [ 16 ]
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+
+
+data:
+  target: ldm.data.laion.WebDataModuleFromConfig
+  params:
+    tar_base: "pipe:aws s3 cp s3://s-datasets/laion-high-resolution/"
+    batch_size: 4
+    num_workers: 1
+    train:
+      shards: '{00000..17279}.tar -'
+      shuffle: 10000
+      image_key: jpg
+      image_transforms:
+      - target: torchvision.transforms.Resize
+        params:
+          size: 1024
+          interpolation: 3
+      - target: torchvision.transforms.RandomCrop
+        params:
+          size: 1024
+      postprocess:
+              target: ldm.data.laion.AddLR
+              params:
+                      factor: 4
+
+    # 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: 1024
+          interpolation: 3
+      - target: torchvision.transforms.CenterCrop
+        params:
+          size: 1024
+      postprocess:
+              target: ldm.data.laion.AddLR
+              params:
+                      factor: 4
+
+lightning:
+  callbacks:
+    image_logger:
+      target: main.ImageLogger
+      params:
+        batch_frequency: 10
+        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:
+    benchmark: True
+    val_check_interval: 5000000  # really sorry
+    num_sanity_val_steps: 0
+    accumulate_grad_batches: 1

ldm/data/laion.py

@@ -138,6 +138,11 @@ class WebDataModuleFromConfig(pl.LightningDataModule):
         img_key = dataset_config.get('image_key', 'jpeg')
         transform_dict.update({img_key: image_transforms})
 
+        if 'postprocess' in dataset_config:
+            postprocess = instantiate_from_config(dataset_config['postprocess'])
+        else:
+            postprocess = None
+
         shuffle = dataset_config.get('shuffle', 0)
         shardshuffle = shuffle > 0
 
@@ -156,8 +161,12 @@ class WebDataModuleFromConfig(pl.LightningDataModule):
                 .decode('pil', handler=wds.warn_and_continue)
                 .select(self.filter_size)
                 .map_dict(**transform_dict, handler=wds.warn_and_continue)
+                )
+        if postprocess is not None:
+            dset = dset.map(postprocess)
+        dset = (dset
                 .batched(self.batch_size, partial=False,
-                         collation_fn=dict_collation_fn)
+                    collation_fn=dict_collation_fn)
                 )
 
         loader = wds.WebLoader(dset, batch_size=None, shuffle=False,
@@ -189,6 +198,29 @@ class WebDataModuleFromConfig(pl.LightningDataModule):
         return self.make_loader(self.test, train=False)
 
 
+from ldm.modules.image_degradation import degradation_fn_bsr_light
+
+class AddLR(object):
+    def __init__(self, factor):
+        self.factor = factor
+
+    def pt2np(self, x):
+        x = ((x+1.0)*127.5).clamp(0, 255).to(dtype=torch.uint8).detach().cpu().numpy()
+        return x
+
+    def np2pt(self, x):
+        x = torch.from_numpy(x)/127.5-1.0
+        return x
+
+    def __call__(self, sample):
+        # sample['jpg'] is tensor hwc in [-1, 1] at this point
+        x = self.pt2np(sample['jpg'])
+        x = degradation_fn_bsr_light(x, sf=self.factor)['image']
+        x = self.np2pt(x)
+        sample['lr'] = x
+        return sample
+
+
 def example00():
     url = "pipe:aws s3 cp s3://s-datasets/laion5b/laion2B-data/000000.tar -"
     dataset = wds.WebDataset(url)
@@ -270,7 +302,8 @@ if __name__ == "__main__":
     from torch.utils.data import DataLoader, RandomSampler, Sampler, SequentialSampler
     from pytorch_lightning.trainer.supporters import CombinedLoader, CycleIterator
 
-    config = OmegaConf.load("configs/stable-diffusion/txt2img-1p4B-multinode-clip-encoder-high-res-512.yaml")
+    #config = OmegaConf.load("configs/stable-diffusion/txt2img-1p4B-multinode-clip-encoder-high-res-512.yaml")
+    config = OmegaConf.load("configs/stable-diffusion/txt2img-upscale-clip-encoder-f16-1024.yaml")
     datamod = WebDataModuleFromConfig(**config["data"]["params"])
     dataloader = datamod.train_dataloader()