| model: | |
| base_learning_rate: 1.0e-6 | |
| target: ldm.models.diffusion.ddpm.LatentDiffusion | |
| params: | |
| 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: 64 | |
| channels: 4 | |
| cond_stage_trainable: true | |
| conditioning_key: crossattn | |
| monitor: val/loss_simple_ema | |
| scale_factor: 0.18215 | |
| use_ema: False | |
| unfreeze_model: True | |
| model_lr: 1.0e-6 | |
| unet_config: | |
| target: ldm.modules.diffusionmodules.openaimodel.UNetModel | |
| 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_heads: 8 | |
| use_spatial_transformer: True | |
| transformer_depth: 1 | |
| context_dim: 768 | |
| use_checkpoint: True | |
| legacy: False | |
| first_stage_config: | |
| target: ldm.models.autoencoder.AutoencoderKL | |
| params: | |
| 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.FrozenCLIPEmbedder | |
| data: | |
| target: main.DataModuleFromConfig | |
| params: | |
| batch_size: 4 # prefer highest possible without getting CUDA Out of Memory error, A100 40GB =~20 80GB= ~48 | |
| num_workers: 8 | |
| wrap: falsegit | |
| train: | |
| target: ldm.data.every_dream.EveryDreamBatch | |
| params: | |
| repeats: 5 # rough suggestions: 5 with 5000+ images, 15 for 1000 images, use micro yaml for <100 | |
| debug_level: 1 # 1 to print if images are dropped due to multiple-aspect ratio image batching | |
| conditional_dropout: 0.04 # experimental, likelihood to drop the caption, may help with poorly captioned images | |
| resolution: 512 # use 512 for 24GB, can use 576, 640, 704, 768, on higher VRAM cards only.. | |
| validation: | |
| target: ldm.data.ed_validate.EDValidateBatch | |
| params: | |
| repeats: 0.5 | |
| test: | |
| target: ldm.data.ed_validate.EDValidateBatch | |
| params: | |
| repeats: 0.2 | |
| lightning: | |
| modelcheckpoint: | |
| params: | |
| every_n_epochs: 1 # produce a ckpt every epoch, leave 1! | |
| #every_n_train_steps: 1400 # can only use epoch or train step checkpoints | |
| save_top_k: 6 # 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! | |
| save_last: True | |
| filename: "{epoch:02d}-{step:05d}" | |
| callbacks: | |
| image_logger: | |
| target: main.ImageLogger | |
| params: | |
| batch_frequency: 200 | |
| max_images: 16 | |
| increase_log_steps: False | |
| trainer: | |
| benchmark: True | |
| max_epochs: 6 # better to run several epochs and test your checkpoints! Try 4-5, you get a checkpoint every epoch to test! | |
| 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... | |
| check_val_every_n_epoch: 1 | |
| gpus: 0, |