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Running
on
T4
Running
on
T4
| 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: | |
| embed_dim: 3 | |
| n_embed: 8192 | |
| ckpt_path: configs/first_stage_models/vq-f4/model.yaml | |
| 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: 42 | |
| num_workers: 5 | |
| wrap: false | |
| train: | |
| target: taming.data.faceshq.FFHQTrain | |
| params: | |
| size: 256 | |
| validation: | |
| target: taming.data.faceshq.FFHQValidation | |
| params: | |
| size: 256 | |
| lightning: | |
| callbacks: | |
| image_logger: | |
| target: main.ImageLogger | |
| params: | |
| batch_frequency: 5000 | |
| max_images: 8 | |
| increase_log_steps: False | |
| trainer: | |
| benchmark: True |