model: base_learning_rate: 1.0e-04 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: "txt" image_size: 64 channels: 4 cond_stage_trainable: false # Note: different from the one we trained before conditioning_key: crossattn scale_factor: 0.18215 scheduler_config: # 10000 warmup steps target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] 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 ckpt_path: "models/first_stage_models/kl-f8/model.ckpt" params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 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 num_workers: 4 num_val_workers: 0 # Avoid a weird val dataloader issue train: target: ldm.data.simple.hf_dataset params: name: Transform21/Godardv1 image_transforms: - target: torchvision.transforms.Resize params: size: 512 interpolation: 3 - target: torchvision.transforms.RandomCrop params: size: 512 - target: torchvision.transforms.RandomHorizontalFlip validation: target: ldm.data.simple.TextOnly params: captions: - "A man sits in a bed next to a woman" - "A ship on the ocean" - "A dog stands on the asphalt" - "A face" output_size: 512 n_gpus: 2 # small hack to sure we see all our samples lightning: find_unused_parameters: False modelcheckpoint: params: every_n_train_steps: 2000 save_top_k: -1 monitor: null callbacks: image_logger: target: main.ImageLogger params: batch_frequency: 2000 max_images: 4 increase_log_steps: False log_first_step: True log_all_val: True log_images_kwargs: use_ema_scope: True inpaint: False plot_progressive_rows: False plot_diffusion_rows: False N: 4 unconditional_guidance_scale: 3.0 unconditional_guidance_label: [""] trainer: benchmark: True num_sanity_val_steps: 0 accumulate_grad_batches: 1