106 lines
3.1 KiB
YAML
106 lines
3.1 KiB
YAML
model:
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base_learning_rate: 1.0e-6
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target: ldm.models.diffusion.ddpm.LatentDiffusion
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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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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: image
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cond_stage_key: caption
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image_size: 64
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channels: 4
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cond_stage_trainable: true
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conditioning_key: crossattn
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monitor: val/loss_simple_ema
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scale_factor: 0.18215
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use_ema: False
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unfreeze_model: True
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model_lr: 1.0e-6
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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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image_size: 32 # unused
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in_channels: 4
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out_channels: 4
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model_channels: 320
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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: 4
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monitor: val/rec_loss
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ddconfig:
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double_z: true
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z_channels: 4
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resolution: 512
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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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cond_stage_config:
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target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
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data:
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target: main.DataModuleFromConfig
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params:
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batch_size: 4 # prefer highest possible without getting CUDA Out of Memory error
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num_workers: 8
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wrap: falsegit
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train:
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target: ldm.data.every_dream.EveryDreamBatch
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params:
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repeats: 10 # rough suggestions: 5 with 5000+ images, 15 for 1000 images, use micro yaml for <100
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debug_level: 1 # 1 to print if images are dropped due to multiple-aspect ratio image batching
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validation:
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target: ldm.data.ed_validate.EDValidateBatch
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params:
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repeats: 1
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test:
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target: ldm.data.ed_validate.EDValidateBatch
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params:
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repeats: 0.2
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lightning:
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modelcheckpoint:
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params:
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every_n_epochs: 1 # produce a ckpt every epoch, leave 1!
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#every_n_train_steps: 1400 # can only use epoch or train step checkpoints
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save_top_k: 4 # save the best N ckpts according to loss, can reduce to save disk space but suggest at LEAST 2, more if you have max_epochs below higher!
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save_last: True
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filename: "{epoch:02d}-{step:05d}"
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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: 250
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max_images: 16
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increase_log_steps: False
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trainer:
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benchmark: True
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max_epochs: 4 # better to run several epochs and test your checkpoints! Try 4-5, you get a checkpoint every epoch to test!
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max_steps: 99000 # better to end on epochs not steps, especially with >500 images to ensure even distribution, but you can set this if you really want...
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check_val_every_n_epoch: 1
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gpus: 0, |