# Pretrained diffusers model path. pretrained_model_path: "ckpts/stable-video-diffusion-img2vid" # The folder where your training outputs will be placed. output_dir: "./sig_girl" 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: true dtype: fp16 visualize_attention_store: false visualize_attention_store_steps: [0, 5, 10, 15, 20, 24] save_last_frames: True load_from_last_frames_latents: # data_params data_params: video_path: "../datasets/svdedit/item3/source.mp4" keyframe_paths: - "../datasets/svdedit/item3/cyberpunk.png" - "../datasets/svdedit/item3/hat.png" - "../datasets/svdedit/item3/shinkai.png" - "../datasets/svdedit/item3/tshirt.png" start_t: 0 end_t: -1 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: 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: 250 # Saves a model every nth step. checkpointing_steps: 250 # How many steps to do for validation if sample_preview is enabled. validation_steps: 300 # 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/item3/train_motion_lora/" max_guidance_scale: 2.0 disk_store: True load_attention_store: "./cache/item3/attention_store" load_consistency_attention_store: "./cache/item3/consistency_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] edit1: temporal_step_thr: [0.5, 0.8] mask_thr: [0.35, 0.35] edit2: temporal_step_thr: [0.5, 0.8] mask_thr: [0.35, 0.35] edit3: 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"