Delete item2/source_and_edits/config_multi_chunks.yaml

item2/source_and_edits/config_multi_chunks.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
-
-# data_params
-data_params:
-  video_path: "../datasets/svdedit/item2/sora.mp4"
-  keyframe_paths:
-    - "../datasets/svdedit/item2/sora_kimono.png"
-  start_t: 0
-  end_t: 4
-  sample_fps: 7
-  chunk_size: 16
-  overlay_size: 1
-  normalize: true
-  output_fps: 7
-  save_sampled_frame: true
-  output_res: [576, 1024]
-  pad_to_fit: True
-  begin_clip_id: 0
-  end_clip_id: 2
-
-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: True
-
-  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: True
-    # 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: "./i2vedit_2024-05-19T01-39-37/train_motion_lora"
-  max_guidance_scale: 2.0
-
-  disk_store: True
-  load_attention_store: "./cache/item2/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
-  temporal_step_thr: [0.5, 0.8]
-  spatial_self_replace_steps: 1.0
-  mask_thr: [0.35, 0.35]
-  spatial_attention_chunk_size: 1
-
-long_video_params:
-  mode: "skip-interval"
-  registered_modules:
-    BasicTransformerBlock:
-      #- "attn1"
-      #- "attn2"
-    TemporalBasicTransformerBlock:
-      - "attn1"
-      #- "attn2"
-      
-