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Dear Author,
thanks for sharing great works.
I wonder how many epochs do you recommand to fine tunning?
I have about 100 images of cutom traning datasets.
I'm training the data as below.
while checking the log images during training, Image quality at 6000 epochs seems not really good.
Should I train more epochs? or change some other parameters..?
sf: 4 model: base_learning_rate: 5.0e-05 target: ldm.models.diffusion.ddpm.LatentDiffusionSRTextWT params: # parameterization: "v" linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: image cond_stage_key: caption image_size: 512 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 use_ema: False # for training only ckpt_path : '/data2/LHC/StableSR/stablesr_000117.ckpt' unfrozen_diff: False random_size: False time_replace: 1000 use_usm: True #P2 weighting, we do not use in final version p2_gamma: ~ p2_k: ~ # ignore_keys: [] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModelDualcondV2 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_head_channels: 64 use_spatial_transformer: True use_linear_in_transformer: True transformer_depth: 1 context_dim: 1024 use_checkpoint: False legacy: False semb_channels: 256 first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: # for training only ckpt_path: '/data2/LHC/StableSR/stablesr_000117.ckpt' embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 512 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.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate" structcond_stage_config: target: ldm.modules.diffusionmodules.openaimodel.EncoderUNetModelWT params: image_size: 96 in_channels: 4 model_channels: 256 out_channels: 256 num_res_blocks: 2 attention_resolutions: [ 4, 2, 1 ] dropout: 0 channel_mult: [ 1, 1, 2, 2 ] conv_resample: True dims: 2 use_checkpoint: False use_fp16: False num_heads: 4 num_head_channels: -1 num_heads_upsample: -1 use_scale_shift_norm: False resblock_updown: False use_new_attention_order: False degradation: # the first degradation process resize_prob: [0.2, 0.7, 0.1] # up, down, keep resize_range: [0.3, 1.5] gaussian_noise_prob: 0.5 noise_range: [1, 15] poisson_scale_range: [0.05, 2.0] gray_noise_prob: 0.4 jpeg_range: [60, 95] # the second degradation process second_blur_prob: 0.5 resize_prob2: [0.3, 0.4, 0.3] # up, down, keep resize_range2: [0.6, 1.2] gaussian_noise_prob2: 0.5 noise_range2: [1, 12] poisson_scale_range2: [0.05, 1.0] gray_noise_prob2: 0.4 jpeg_range2: [60, 100] gt_size: 512 no_degradation_prob: 0.01 data: target: main.DataModuleFromConfig params: batch_size: 6 num_workers: 10 wrap: false train: target: basicsr.data.realesrgan_dataset.RealESRGANDataset params: queue_size: 180 gt_path: '/data2/LHC/StableSR/StableSR/DNA_512/train' face_gt_path: '/mnt/lustre/share/jywang/dataset/FFHQ/1024/' num_face: 10000 crop_size: 512 io_backend: type: disk blur_kernel_size: 21 kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'] kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03] sinc_prob: 0.1 blur_sigma: [0.2, 1.5] betag_range: [0.5, 2.0] betap_range: [1, 1.5] blur_kernel_size2: 11 kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'] kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03] sinc_prob2: 0.1 blur_sigma2: [0.2, 1.0] betag_range2: [0.5, 2.0] betap_range2: [1, 1.5] final_sinc_prob: 0.8 gt_size: 512 use_hflip: True use_rot: False validation: target: basicsr.data.realesrgan_dataset.RealESRGANDataset params: gt_path: '/data2/LHC/StableSR/StableSR/DNA_512/val' crop_size: 512 io_backend: type: disk blur_kernel_size: 21 kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'] kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03] sinc_prob: 0.1 blur_sigma: [0.2, 1.5] betag_range: [0.5, 2.0] betap_range: [1, 1.5] blur_kernel_size2: 11 kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'] kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03] sinc_prob2: 0.1 blur_sigma2: [0.2, 1.0] betag_range2: [0.5, 2.0] betap_range2: [1, 1.5] final_sinc_prob: 0.8 gt_size: 512 use_hflip: True use_rot: False test_data: target: main.DataModuleFromConfig params: batch_size: 1 num_workers: 6 wrap: false test: target: basicsr.data.realesrgan_dataset.RealESRGANDataset params: gt_path: '/data2/LHC/StableSR/StableSR/DNA_512/test' crop_size: 512 io_backend: type: disk blur_kernel_size: 21 kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'] kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03] sinc_prob: 0.1 blur_sigma: [0.2, 1.5] betag_range: [0.5, 2.0] betap_range: [1, 1.5] blur_kernel_size2: 11 kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'] kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03] sinc_prob2: 0.1 blur_sigma2: [0.2, 1.0] betag_range2: [0.5, 2.0] betap_range2: [1, 1.5] final_sinc_prob: 0.8 gt_size: 512 use_hflip: True use_rot: True lightning: modelcheckpoint: params: every_n_train_steps: 1500 callbacks: image_logger: target: main.ImageLogger params: batch_frequency: 1500 max_images: 4 increase_log_steps: False trainer: benchmark: True max_steps: 100000 accumulate_grad_batches: 4
The text was updated successfully, but these errors were encountered:
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Dear Author,
thanks for sharing great works.
I wonder how many epochs do you recommand to fine tunning?
I have about 100 images of cutom traning datasets.
I'm training the data as below.
while checking the log images during training, Image quality at 6000 epochs seems not really good.
Should I train more epochs? or change some other parameters..?
The text was updated successfully, but these errors were encountered: