Upload item3/config_multi_chunks.yaml with huggingface_hub
Browse files- item3/config_multi_chunks.yaml +151 -0
item3/config_multi_chunks.yaml
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# Pretrained diffusers model path.
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pretrained_model_path: "ckpts/stable-video-diffusion-img2vid"
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# The folder where your training outputs will be placed.
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output_dir: "./sig_girl"
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seed: 23
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num_steps: 25
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# Xformers must be installed for best memory savings and performance (< Pytorch 2.0)
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enable_xformers_memory_efficient_attention: True
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# Use scaled dot product attention (Only available with >= Torch 2.0)
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enable_torch_2_attn: True
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use_sarp: true
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use_motion_lora: true
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train_motion_lora_only: false
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retrain_motion_lora: false
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use_inversed_latents: true
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use_attention_matching: true
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use_consistency_attention_control: true
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dtype: fp16
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visualize_attention_store: false
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visualize_attention_store_steps: [0, 5, 10, 15, 20, 24]
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save_last_frames: True
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load_from_last_frames_latents:
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# data_params
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data_params:
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video_path: "../datasets/svdedit/item3/source.mp4"
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keyframe_paths:
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- "../datasets/svdedit/item3/cyberpunk.png"
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- "../datasets/svdedit/item3/hat.png"
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- "../datasets/svdedit/item3/shinkai.png"
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- "../datasets/svdedit/item3/tshirt.png"
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start_t: 0
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end_t: -1
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sample_fps: 7
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chunk_size: 16
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overlay_size: 1
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normalize: true
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output_fps: 7
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save_sampled_frame: true
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output_res: [576, 1024]
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pad_to_fit: false
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train_motion_lora_params:
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cache_latents: true
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cached_latent_dir: null #/path/to/cached_latents
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lora_rank: 32
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# Use LoRA for the UNET model.
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use_unet_lora: True
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# LoRA Dropout. This parameter adds the probability of randomly zeros out elements. Helps prevent overfitting.
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# See: https://pytorch.org/docs/stable/generated/torch.nn.Dropout.html
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lora_unet_dropout: 0.1
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# The only time you want this off is if you're doing full LoRA training.
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save_pretrained_model: False
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# Learning rate for AdamW
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learning_rate: 5e-4
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# Weight decay. Higher = more regularization. Lower = closer to dataset.
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adam_weight_decay: 1e-2
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# Maximum number of train steps. Model is saved after training.
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max_train_steps: 250
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# Saves a model every nth step.
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checkpointing_steps: 250
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# How many steps to do for validation if sample_preview is enabled.
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validation_steps: 300
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# Whether or not we want to use mixed precision with accelerate
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mixed_precision: "fp16"
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# Trades VRAM usage for speed. You lose roughly 20% of training speed, but save a lot of VRAM.
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# If you need to save more VRAM, it can also be enabled for the text encoder, but reduces speed x2.
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gradient_checkpointing: True
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image_encoder_gradient_checkpointing: True
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train_data:
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# The width and height in which you want your training data to be resized to.
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width: 896
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height: 512
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# This will find the closest aspect ratio to your input width and height.
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# For example, 512x512 width and height with a video of resolution 1280x720 will be resized to 512x256
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use_data_aug: ~ #"controlnet"
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pad_to_fit: false
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validation_data:
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# Whether or not to sample preview during training (Requires more VRAM).
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sample_preview: True
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# The number of frames to sample during validation.
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num_frames: 14
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# Height and width of validation sample.
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width: 1024
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height: 576
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pad_to_fit: false
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# scale of spatial LoRAs, default is 0
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spatial_scale: 0
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# scale of noise prior, i.e. the scale of inversion noises
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noise_prior:
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#- 0.0
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- 1.0
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sarp_params:
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sarp_noise_scale: 0.005
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attention_matching_params:
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best_checkpoint_index: 250
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lora_scale: 1.0
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# lora path
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lora_dir: "./cache/item3/train_motion_lora/"
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max_guidance_scale: 2.0
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disk_store: True
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load_attention_store: "./cache/item3/attention_store"
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load_consistency_attention_store: "./cache/item3/consistency_attention_store"
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registered_modules:
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BasicTransformerBlock:
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- "attn1"
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#- "attn2"
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TemporalBasicTransformerBlock:
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- "attn1"
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#- "attn2"
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control_mode:
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spatial_self: "masked_copy"
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temporal_self: "copy_v2"
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cross_replace_steps: 0.0
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temporal_self_replace_steps: 1.0
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spatial_self_replace_steps: 1.0
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spatial_attention_chunk_size: 1
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params:
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edit0:
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temporal_step_thr: [0.4, 0.5]
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mask_thr: [0.35, 0.35]
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edit1:
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temporal_step_thr: [0.5, 0.8]
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mask_thr: [0.35, 0.35]
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edit2:
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temporal_step_thr: [0.5, 0.8]
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mask_thr: [0.35, 0.35]
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edit3:
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temporal_step_thr: [0.5, 0.8]
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mask_thr: [0.35, 0.35]
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long_video_params:
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mode: "skip-interval"
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registered_modules:
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BasicTransformerBlock:
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#- "attn1"
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#- "attn2"
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TemporalBasicTransformerBlock:
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- "attn1"
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#- "attn2"
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