forked from mrq/DL-Art-School
150 lines
4.3 KiB
YAML
150 lines
4.3 KiB
YAML
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name: train_unet_diffusion
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use_tb_logger: true
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model: extensibletrainer
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scale: 1
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gpu_ids: [0]
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start_step: -1
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checkpointing_enabled: true # If using the UNet architecture, this is pretty much required.
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fp16: false
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wandb: false # Set to true to enable wandb logging.
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force_start_step: -1
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datasets:
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train:
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name: imgset5
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n_workers: 4
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batch_size: 256 # The OpenAI paper uses this batch size for 256px generation. The UNet model uses attention, which benefits from large batch sizes.
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mode: imagefolder
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rgb_n1_to_1: true
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paths: <insert path to a folder full of 256x256 tiled images here>
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target_size: 256
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scale: 2
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fixed_corruptions: [ jpeg-broad, gaussian_blur ] # This model is trained to correct JPEG artifacts and blurring.
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random_corruptions: [ none ]
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num_corrupts_per_image: 1
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corruption_blur_scale: 1
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corrupt_before_downsize: false
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networks:
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generator:
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type: generator
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which_model_G: unet_diffusion
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args:
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image_size: 256
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in_channels: 3
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num_corruptions: 2
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model_channels: 192
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out_channels: 6
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num_res_blocks: 2
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attention_resolutions: [8,16]
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dropout: 0
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channel_mult: [1,1,2,2,4,4] # These will need to be reduced if you lower the operating resolution.
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num_heads: 4
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num_heads_upsample: -1
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use_scale_shift_norm: true
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#### path
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path:
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#pretrain_model_generator: <Insert pretrained generator here>
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strict_load: true
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#resume_state: <Insert resume training_state here to resume existing training>
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steps:
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generator:
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training: generator
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optimizer: adamw
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optimizer_params:
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lr: !!float 3e-4 # Hyperparameters from OpenAI paper.
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weight_decay: 0
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beta1: 0.9
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beta2: 0.9999
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injectors:
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diffusion:
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type: gaussian_diffusion
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in: hq
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generator: generator
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beta_schedule:
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schedule_name: linear
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num_diffusion_timesteps: 4000
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diffusion_args:
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model_mean_type: epsilon
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model_var_type: learned_range
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loss_type: mse
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sampler_type: uniform
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model_input_keys:
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low_res: lq
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corruption_factor: corruption_entropy
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out: loss
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out_key_vb_loss: vb_loss
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out_key_x_start: x_start_pred
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losses:
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diffusion_loss:
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type: direct
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weight: 1
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key: loss
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var_loss:
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type: direct
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weight: 1
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key: vb_loss
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train:
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niter: 500000
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warmup_iter: -1
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mega_batch_factor: 32 # This is massive. Expect ~60sec/step on a RTX3090 at 90%+ memory utilization. I recommend using multiple GPUs to train this network.
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ema_rate: .999
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val_freq: 500
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default_lr_scheme: MultiStepLR
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gen_lr_steps: [ 50000, 100000, 150000 ]
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lr_gamma: 0.5
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eval:
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evaluators:
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# Validation for this network is a special FID computation that compares the full resolution images from the specified
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# dataset to the same images, downsampled and corrupted then fed through the network.
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fid:
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type: sr_diffusion_fid
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for: generator # Unused for this evaluator.
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batch_size: 8
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dataset:
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name: sr_fid_set
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mode: imagefolder
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rgb_n1_to_1: true
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paths: <insert path to a folder of 128-512 validation images here, drawn from your dataset>
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target_size: 256
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scale: 2
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fixed_corruptions: [ jpeg-broad, gaussian_blur ]
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random_corruptions: [ none ]
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num_corrupts_per_image: 1
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corruption_blur_scale: 1
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corrupt_before_downsize: false
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random_seed: 1234
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diffusion_params:
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type: gaussian_diffusion_inference
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generator: generator
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use_ema_model: true
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output_batch_size: 8
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output_scale_factor: 2
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respaced_timestep_spacing: 50
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undo_n1_to_1: true
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beta_schedule:
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schedule_name: linear
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num_diffusion_timesteps: 4000
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diffusion_args:
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model_mean_type: epsilon
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model_var_type: learned_range
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loss_type: mse
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model_input_keys:
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low_res: lq
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corruption_factor: corruption_entropy
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out: sample # Unused
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logger:
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print_freq: 30
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save_checkpoint_freq: 500
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visuals: [x_start_pred, hq, lq]
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visual_debug_rate: 500
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reverse_n1_to_1: true
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reverse_imagenet_norm: false
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