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This-and-That
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# Model Setting 
pretrained_model_name_or_path: stabilityai/stable-video-diffusion-img2vid   # stabilityai/pretrained
load_unet_path: ../saved_weights/v4_VL_paper/checkpoint-99000        # None/specific path    This is for pretrained-UNet path
load_controlnet_path:   # None/specific path    For checkpoint loaded from pretrained-Controlnet Path
video_seq_length: 14
process_fps: 7
train_noise_aug_strength: 0.1
scheduler: EDM      
conditioning_dropout_prob: 0.1   


# Dataset Setting
data_loader_type: thisthat          # thisthat
dataset_name: Bridge                # Bridge
dataset_path: [../sanity_check/bridge_v1_TT14, ../sanity_check/bridge_v2_TT14]      # ../Bridge_filter_flow, ../Bridge_v2_filter_flow/]
output_dir: checkpoints/img2video
height: 256                 # Ratio that is functional: 256:384  576:1024  320:448  320:576  512:640  448:640
width: 384                  # It is said that the height and width should be a scale of 64      
dataloader_num_workers: 4   # For Debug, it only needs 1  
flip_aug_prob: 0.45         # Whether we flip the GT and cond vertically
# No acceleration_tolerance, since TT dataset already filter those out


# Text setting
use_text: True                              # If this is True, we will use text value
pretrained_tokenizer_name_or_path: stabilityai/stable-diffusion-2-1-base      # Use SD 2.1
empty_prompts_proportion: 0.0   
mix_ambiguous: False                         # Whether we mix ambiguous prompt for "this" and "that"          


# Mask setting
mask_unet_vae: False        # Whether we use mask to map latents to be zero padding
mask_controlnet_vae: False
mask_proportion: 0.0


# Condition Setting
conditioning_channels: 3    # Usually it is 3
num_points_left:    # 1     # For flow: You can only choose one between flow_select_rate and num_points_left; num_points_left should be higher priority    
flow_select_rate: 0.99      # For flow
threshold_factor: 0.2       # For flow
dilate: True                # Traj must be True for dilate
inner_conditioning_scale: 1.0    # Conditioning scale for the internal value, defauly is starting from 1.0
outer_conditioning_scale: 1.0    # Outer Conditioning Scale for whole conditioning trainable copy  这里有点意思，直接不小心设定成2.0了


# Motion setting
motion_bucket_id: 200
dataset_motion_mean: 25       # For 14 fps, it is N(25, 10)
dataset_motion_std: 10        # For 25 fps, it is N(18, 7)
svd_motion_mean: 180
svd_motion_std: 30



# Training setting
resume_from_checkpoint: False     # latest/False
num_train_iters: 30100            # Will automatically choose the checkpoints
partial_finetune: False           # Whether we just tune some params to speed up
train_batch_size: 1               # This is the batch size per GPU
checkpointing_steps: 3000
validation_step: 300
logging_name: logging
seed: 42
validation_img_folder: datasets/validation_TT14
validation_store_folder: validation_videos
checkpoints_total_limit: 15


# Noise Strength
noise_mean: 0.5       # Regular Img2Video: (0.7, 1.6); Text2Video: (0.5, 1.4)
noise_std: 1.4


# Inference
num_inference_steps: 25
use_instructpix2pix: False          # Whether we will use the instructPix2Pix mode, which involves 3 inputs; it may needs tuning to have better result at the end.
inference_noise_aug_strength: 0.1
inference_max_guidance_scale: 3.0   # Take training and testing at different scenario
inference_guess_mode: False         # Whether we use guess mode in the contorlnet
image_guidance_scale: 2.5           # Empirically, 2.5 is the best value    Seems not using this now


# Learning Rate and Optimizer
learning_rate: 5e-6           # 5e-6 is the LR we test that is just right
scale_lr: False               # TODO: Is it needed to scale the learning rate?         
adam_beta1: 0.9
adam_beta2: 0.999
use_8bit_adam: True           # Need this to save more memory
adam_weight_decay: 1e-2
adam_epsilon: 1e-08
lr_warmup_steps: 500                            
lr_decay_scale: 0.5


# Other Setting
mixed_precision: fp16
gradient_accumulation_steps: 1    # ????
gradient_checkpointing: 1         # ????
report_to: tensorboard