support lr creation in laion
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Patrick Esser
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@ -0,0 +1,166 @@
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model:
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base_learning_rate: 1.0e-04
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target: ldm.models.diffusion.ddpm.LatentUpscaleDiffusion
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params:
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low_scale_key: "lr"
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linear_start: 0.001
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linear_end: 0.015
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num_timesteps_cond: 1
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log_every_t: 200
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timesteps: 1000
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first_stage_key: "jpg"
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cond_stage_key: "txt"
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image_size: 64
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channels: 16
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cond_stage_trainable: false
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conditioning_key: "hybrid-adm"
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monitor: val/loss_simple_ema
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scale_factor: 0.22765929 # magic number
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low_scale_config:
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target: ldm.modules.encoders.modules.LowScaleEncoder
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params:
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linear_start: 0.00085
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linear_end: 0.0120
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timesteps: 1000
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max_noise_level: 250
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output_size: 64
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model_config:
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target: ldm.models.autoencoder.AutoencoderKL
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params:
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embed_dim: 4
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monitor: val/rec_loss
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ckpt_path: "models/first_stage_models/kl-f8/model.ckpt"
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ddconfig:
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double_z: true
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z_channels: 4
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resolution: 256
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in_channels: 3
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out_ch: 3
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ch: 128
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ch_mult:
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- 1
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- 2
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- 4
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- 4
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num_res_blocks: 2
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attn_resolutions: [ ]
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dropout: 0.0
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lossconfig:
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target: torch.nn.Identity
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scheduler_config: # 10000 warmup steps
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target: ldm.lr_scheduler.LambdaLinearScheduler
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params:
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warm_up_steps: [ 10000 ]
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cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
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f_start: [ 1.e-6 ]
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f_max: [ 1. ]
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f_min: [ 1. ]
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unet_config:
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target: ldm.modules.diffusionmodules.openaimodel.UNetModel
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params:
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num_classes: 1000 # timesteps for noise conditoining
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image_size: 64 # not really needed
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in_channels: 20
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out_channels: 16
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model_channels: 32 # TODO: more
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attention_resolutions: [ 4, 2, 1 ]
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num_res_blocks: 2
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channel_mult: [ 1, 2, 4, 4 ]
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num_heads: 8
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use_spatial_transformer: True
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transformer_depth: 1
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context_dim: 768
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use_checkpoint: True
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legacy: False
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first_stage_config:
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target: ldm.models.autoencoder.AutoencoderKL
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params:
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embed_dim: 16
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monitor: val/rec_loss
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ckpt_path: "models/first_stage_models/kl-f16/model.ckpt"
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ddconfig:
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double_z: True
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z_channels: 16
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resolution: 256
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in_channels: 3
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out_ch: 3
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ch: 128
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ch_mult: [ 1,1,2,2,4 ] # num_down = len(ch_mult)-1
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num_res_blocks: 2
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attn_resolutions: [ 16 ]
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dropout: 0.0
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lossconfig:
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target: torch.nn.Identity
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cond_stage_config:
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target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
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data:
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target: ldm.data.laion.WebDataModuleFromConfig
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params:
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tar_base: "pipe:aws s3 cp s3://s-datasets/laion-high-resolution/"
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batch_size: 4
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num_workers: 1
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train:
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shards: '{00000..17279}.tar -'
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shuffle: 10000
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image_key: jpg
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image_transforms:
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- target: torchvision.transforms.Resize
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params:
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size: 1024
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interpolation: 3
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- target: torchvision.transforms.RandomCrop
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params:
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size: 1024
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postprocess:
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target: ldm.data.laion.AddLR
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params:
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factor: 4
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# NOTE use enough shards to avoid empty validation loops in workers
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validation:
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shards: '{17280..17535}.tar -'
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shuffle: 0
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image_key: jpg
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image_transforms:
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- target: torchvision.transforms.Resize
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params:
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size: 1024
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interpolation: 3
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- target: torchvision.transforms.CenterCrop
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params:
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size: 1024
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postprocess:
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target: ldm.data.laion.AddLR
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params:
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factor: 4
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lightning:
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callbacks:
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image_logger:
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target: main.ImageLogger
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params:
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batch_frequency: 10
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max_images: 4
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increase_log_steps: False
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log_first_step: False
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log_images_kwargs:
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use_ema_scope: False
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inpaint: False
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plot_progressive_rows: False
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plot_diffusion_rows: False
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N: 4
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unconditional_guidance_scale: 3.0
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unconditional_guidance_label: [""]
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trainer:
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benchmark: True
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val_check_interval: 5000000 # really sorry
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num_sanity_val_steps: 0
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accumulate_grad_batches: 1
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@ -138,6 +138,11 @@ class WebDataModuleFromConfig(pl.LightningDataModule):
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img_key = dataset_config.get('image_key', 'jpeg')
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transform_dict.update({img_key: image_transforms})
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if 'postprocess' in dataset_config:
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postprocess = instantiate_from_config(dataset_config['postprocess'])
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else:
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postprocess = None
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shuffle = dataset_config.get('shuffle', 0)
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shardshuffle = shuffle > 0
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@ -156,8 +161,12 @@ class WebDataModuleFromConfig(pl.LightningDataModule):
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.decode('pil', handler=wds.warn_and_continue)
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.select(self.filter_size)
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.map_dict(**transform_dict, handler=wds.warn_and_continue)
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)
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if postprocess is not None:
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dset = dset.map(postprocess)
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dset = (dset
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.batched(self.batch_size, partial=False,
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collation_fn=dict_collation_fn)
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collation_fn=dict_collation_fn)
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)
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loader = wds.WebLoader(dset, batch_size=None, shuffle=False,
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@ -189,6 +198,29 @@ class WebDataModuleFromConfig(pl.LightningDataModule):
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return self.make_loader(self.test, train=False)
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from ldm.modules.image_degradation import degradation_fn_bsr_light
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class AddLR(object):
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def __init__(self, factor):
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self.factor = factor
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def pt2np(self, x):
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x = ((x+1.0)*127.5).clamp(0, 255).to(dtype=torch.uint8).detach().cpu().numpy()
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return x
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def np2pt(self, x):
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x = torch.from_numpy(x)/127.5-1.0
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return x
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def __call__(self, sample):
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# sample['jpg'] is tensor hwc in [-1, 1] at this point
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x = self.pt2np(sample['jpg'])
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x = degradation_fn_bsr_light(x, sf=self.factor)['image']
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x = self.np2pt(x)
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sample['lr'] = x
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return sample
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def example00():
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url = "pipe:aws s3 cp s3://s-datasets/laion5b/laion2B-data/000000.tar -"
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dataset = wds.WebDataset(url)
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@ -270,7 +302,8 @@ if __name__ == "__main__":
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from torch.utils.data import DataLoader, RandomSampler, Sampler, SequentialSampler
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from pytorch_lightning.trainer.supporters import CombinedLoader, CycleIterator
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config = OmegaConf.load("configs/stable-diffusion/txt2img-1p4B-multinode-clip-encoder-high-res-512.yaml")
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#config = OmegaConf.load("configs/stable-diffusion/txt2img-1p4B-multinode-clip-encoder-high-res-512.yaml")
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config = OmegaConf.load("configs/stable-diffusion/txt2img-upscale-clip-encoder-f16-1024.yaml")
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datamod = WebDataModuleFromConfig(**config["data"]["params"])
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dataloader = datamod.train_dataloader()
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