forked from mrq/DL-Art-School
78 lines
2.1 KiB
YAML
78 lines
2.1 KiB
YAML
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#### general settings
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name: test_diffusion_unet
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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
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fp16: false
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wandb: false
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datasets:
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train:
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name: my_inference_images
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n_workers: 0
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batch_size: 1
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mode: imagefolder
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rgb_n1_to_1: true
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disable_flip: true
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force_square: false
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paths: <low resolution images you want to upsample>
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scale: 1
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skip_lq: true
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fixed_parameters:
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# Specify correction factors here. For networks trained with the paired training configuration, the first number
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# is a JPEG correction factor, and the second number is a deblurring factor. Testing shows that if you attempt to
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# deblur too far, you get extremely distorted images. It's actually pretty cool - the network clearly knows how
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# much deblurring is appropriate.
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corruption_entropy: [.2, .5]
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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]
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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: <Your model (or EMA) path>
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strict_load: true
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steps:
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generator:
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training: generator
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injectors:
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visual_debug:
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type: gaussian_diffusion_inference
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generator: generator
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output_batch_size: 1
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output_scale_factor: 2
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respaced_timestep_spacing: 50 # This can be tweaked to perform inference faster or slower. 50-200 seems to be the sweet spot. At 4000 steps, the quality is actually worse often.
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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: hq
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corruption_factor: corruption_entropy
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out: sample
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eval:
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output_state: sample
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