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I2VEdit-pretrained-videos

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I2VEdit-pretrained-videos
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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: 
  # - "..path.."

# data_params
data_params:
  video_path: "../datasets/svdedit/item4/rocket.mp4"
  keyframe_paths:
    - "../datasets/svdedit/item4/blue_aircraft.png"
    - "../datasets/svdedit/item4/black_aircraft.png"
  start_t: 0
  end_t: 2
  sample_fps: 7
  chunk_size: 14
  overlay_size: 1
  normalize: true
  output_fps: 7
  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: 50
  lora_scale: 1.0
  # lora path
  lora_dir: "./cache/item4/train_motion_lora"
  max_guidance_scale: 1.5

  disk_store: True
  load_attention_store: "./cache/item4/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: [0.5, 0.8]
      mask_thr: [0.35, 0.35]
    edit1:
      temporal_step_thr: [0.5, 0.8]
      mask_thr: [0.35, 0.35]

long_video_params:
  mode: "skip-interval"
  registered_modules:
    BasicTransformerBlock:
      #- "attn1"
      #- "attn2"
    TemporalBasicTransformerBlock:
      - "attn1"
      #- "attn2"