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- model:
- base_learning_rate: 2.0e-06
- target: ldm.models.diffusion.ddpm.LatentDiffusion
- params:
- linear_start: 0.0015
- linear_end: 0.0195
- num_timesteps_cond: 1
- log_every_t: 200
- timesteps: 1000
- first_stage_key: image
- image_size: 64
- channels: 3
- monitor: val/loss_simple_ema
- unet_config:
- target: ldm.modules.diffusionmodules.openaimodel.UNetModel
- params:
- image_size: 64
- in_channels: 3
- out_channels: 3
- model_channels: 224
- attention_resolutions:
- # note: this isn\t actually the resolution but
- # the downsampling factor, i.e. this corresnponds to
- # attention on spatial resolution 8,16,32, as the
- # spatial reolution of the latents is 64 for f4
- - 8
- - 4
- - 2
- num_res_blocks: 2
- channel_mult:
- - 1
- - 2
- - 3
- - 4
- num_head_channels: 32
- first_stage_config:
- target: ldm.models.autoencoder.VQModelInterface
- params:
- ckpt_path: configs/first_stage_models/vq-f4/model.yaml
- embed_dim: 3
- n_embed: 8192
- ddconfig:
- double_z: false
- z_channels: 3
- resolution: 256
- in_channels: 3
- out_ch: 3
- ch: 128
- ch_mult:
- - 1
- - 2
- - 4
- num_res_blocks: 2
- attn_resolutions: []
- dropout: 0.0
- lossconfig:
- target: torch.nn.Identity
- cond_stage_config: __is_unconditional__
- data:
- target: main.DataModuleFromConfig
- params:
- batch_size: 48
- num_workers: 5
- wrap: false
- train:
- target: ldm.data.lsun.LSUNBedroomsTrain
- params:
- size: 256
- validation:
- target: ldm.data.lsun.LSUNBedroomsValidation
- params:
- size: 256
- lightning:
- callbacks:
- image_logger:
- target: main.ImageLogger
- params:
- batch_frequency: 5000
- max_images: 8
- increase_log_steps: False
- trainer:
- benchmark: True
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