98 lines
2.3 KiB
YAML
98 lines
2.3 KiB
YAML
model:
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base_learning_rate: 1.0e-06
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target: ldm.models.diffusion.ddpm.LatentDiffusion
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params:
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linear_start: 0.0015
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linear_end: 0.0195
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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: class_label
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image_size: 32
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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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unet_config:
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target: ldm.modules.diffusionmodules.openaimodel.UNetModel
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params:
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image_size: 32
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in_channels: 4
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out_channels: 4
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model_channels: 256
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attention_resolutions:
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#note: this isn\t actually the resolution but
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# the downsampling factor, i.e. this corresnponds to
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# attention on spatial resolution 8,16,32, as the
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# spatial reolution of the latents is 32 for f8
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- 4
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- 2
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- 1
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num_res_blocks: 2
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channel_mult:
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- 1
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- 2
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- 4
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num_head_channels: 32
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use_spatial_transformer: true
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transformer_depth: 1
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context_dim: 512
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first_stage_config:
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target: ldm.models.autoencoder.VQModelInterface
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params:
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embed_dim: 4
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n_embed: 16384
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ckpt_path: configs/first_stage_models/vq-f8/model.yaml
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ddconfig:
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double_z: false
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z_channels: 4
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resolution: 256
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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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- 2
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- 4
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num_res_blocks: 2
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attn_resolutions:
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- 32
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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.ClassEmbedder
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params:
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embed_dim: 512
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key: class_label
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data:
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target: main.DataModuleFromConfig
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params:
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batch_size: 64
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num_workers: 12
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wrap: false
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train:
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target: ldm.data.imagenet.ImageNetTrain
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params:
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config:
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size: 256
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validation:
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target: ldm.data.imagenet.ImageNetValidation
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params:
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config:
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size: 256
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lightning:
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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: 5000
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max_images: 8
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increase_log_steps: False
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trainer:
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benchmark: True |