Delete tatto.yaml

tatto.yaml

@@ -1,133 +0,0 @@
-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: FineTunedModel.simple.hf_dataset
-      params:
-        name: "FineTunedModel/dataset"
-        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:
-        - "Rick and Morty tatoo"
-        - "ship and sea"
-        - "moon sphere"
-        - "cat and heart"
-        output_size: 512
-        n_gpus: 4 # 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