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item5/config_single_chunk.yaml

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+# Pretrained diffusers model path.
+pretrained_model_path: "ckpts/stable-video-diffusion-img2vid"
+# The folder where your training outputs will be placed.
+output_dir: "./outputs"
+seed: 23
+num_steps: 25
+# Xformers must be installed for best memory savings and performance (< Pytorch 2.0)
+enable_xformers_memory_efficient_attention: True
+# Use scaled dot product attention (Only available with >= Torch 2.0)
+enable_torch_2_attn: True
+
+use_sarp: true
+
+use_motion_lora: true
+train_motion_lora_only: false
+retrain_motion_lora: false
+
+use_inversed_latents: true
+use_attention_matching: true
+use_consistency_attention_control: false
+dtype: fp16
+
+save_last_frames: True
+load_from_last_frames_latents: 
+
+# data_params
+data_params:
+  video_path: "../datasets/svdedit/item5/blackswan.mp4"
+  keyframe_paths:
+    - "../datasets/svdedit/item5/flamingo.png"
+  start_t: 0
+  end_t: -1
+  sample_fps: 4
+  chunk_size: 14
+  overlay_size: 1
+  normalize: true
+  output_fps: 4
+  save_sampled_frame: true
+  output_res: [576, 1024]
+  pad_to_fit: false
+
+train_motion_lora_params:
+  cache_latents: true
+  cached_latent_dir: null #/path/to/cached_latents
+  lora_rank: 32
+  # Use LoRA for the UNET model.
+  use_unet_lora: True
+  # LoRA Dropout. This parameter adds the probability of randomly zeros out elements. Helps prevent overfitting.
+  # See: https://pytorch.org/docs/stable/generated/torch.nn.Dropout.html
+  lora_unet_dropout: 0.1
+  # The only time you want this off is if you're doing full LoRA training.
+  save_pretrained_model: False
+  # Learning rate for AdamW
+  learning_rate: 5e-4
+  # Weight decay. Higher = more regularization. Lower = closer to dataset.
+  adam_weight_decay: 1e-2
+  # Maximum number of train steps. Model is saved after training.
+  max_train_steps: 300
+  # Saves a model every nth step.
+  checkpointing_steps: 50
+  # How many steps to do for validation if sample_preview is enabled.
+  validation_steps: 50
+  # Whether or not we want to use mixed precision with accelerate
+  mixed_precision: "fp16"
+  # Trades VRAM usage for speed. You lose roughly 20% of training speed, but save a lot of VRAM.
+  # If you need to save more VRAM, it can also be enabled for the text encoder, but reduces speed x2.
+  gradient_checkpointing: True
+  image_encoder_gradient_checkpointing: True
+
+  train_data:
+    # The width and height in which you want your training data to be resized to.
+    width: 896
+    height: 512
+    # This will find the closest aspect ratio to your input width and height. 
+    # For example, 512x512 width and height with a video of resolution 1280x720 will be resized to 512x256
+    use_data_aug: ~ #"controlnet"
+    pad_to_fit: false
+
+  validation_data:
+    # Whether or not to sample preview during training (Requires more VRAM).
+    sample_preview: True
+    # The number of frames to sample during validation.
+    num_frames: 14
+    # Height and width of validation sample.
+    width: 1024
+    height: 576
+    pad_to_fit: false
+    # scale of spatial LoRAs, default is 0
+    spatial_scale: 0
+    # scale of noise prior, i.e. the scale of inversion noises
+    noise_prior:
+      #- 0.0 
+      - 1.0
+
+sarp_params:
+  sarp_noise_scale: 0.005
+
+attention_matching_params:
+  best_checkpoint_index: 250
+  lora_scale: 1.0
+  # lora path
+  lora_dir: "./cache/item5/train_motion_lora"
+  max_guidance_scale: 2.5
+
+  disk_store: True
+  load_attention_store: "./cache/item5/attention_store/"
+  registered_modules:
+    BasicTransformerBlock:
+      - "attn1"
+      #- "attn2"
+    TemporalBasicTransformerBlock:
+      - "attn1"
+      #- "attn2"
+  control_mode: 
+    spatial_self: "masked_copy"
+    temporal_self: "copy_v2"
+  cross_replace_steps: 0.0
+  temporal_self_replace_steps: 1.0
+  spatial_self_replace_steps: 1.0
+  spatial_attention_chunk_size: 1
+
+  params:
+    edit0:
+      temporal_step_thr: [0.4, 0.5]
+      mask_thr: [0.35, 0.35]
+
+long_video_params:
+  mode: "skip-interval"
+  registered_modules:
+    BasicTransformerBlock:
+      #- "attn1"
+      #- "attn2"
+    TemporalBasicTransformerBlock:
+      - "attn1"
+      #- "attn2"
+      
+