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github.com/Netflix/void-model
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Functions
673 in github.com/Netflix/void-model
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Functions
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4
Function
animate_camera
Animate camera along trajectory.
data_generation/render_paired_videos_blender_quadmask.py:895
Method
apply_to
(self, text_encoder, unet, apply_text_encoder=True, apply_unet=True)
videox_fun/utils/lora_utils.py:255
Method
attention_kwargs
(self)
videox_fun/pipeline/pipeline_cogvideox_fun.py:584
Method
attention_kwargs
(self)
videox_fun/pipeline/pipeline_wan_fun.py:374
Method
attention_kwargs
(self)
videox_fun/pipeline/pipeline_cogvideox_fun_inpaint.py:721
Method
attn_processors
r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with
videox_fun/models/cogvideox_transformer3d.py:470
Method
clear_all_frames
Clear all points for current video
VLM-MASK-REASONER/point_selector_gui.py:426
Method
clear_current_frame
Clear points for current frame only
VLM-MASK-REASONER/point_selector_gui.py:419
Method
clear_trajectory
Clear drawn trajectory
VLM-MASK-REASONER/stage3b_trajectory_gui.py:264
Function
collate_fn
(examples)
scripts/cogvideox_fun/train.py:1114
Function
collate_fn
(examples)
scripts/cogvideox_fun/train_warped_noise.py:1132
Method
compute_point_penetration_batched
Fully batched computation of point penetration using Kaolin's optimized operations. Note: Kaolin's check_sign doesn't suppor
data_generation/human_model/human_model.py:331
Method
compute_rel_l1_distance
(prev: torch.Tensor, cur: torch.Tensor)
videox_fun/models/cache_utils.py:62
Method
copy_black_from_previous
Copy ONLY black component from previous frame, preserving grey in current frame
VLM-MASK-REASONER/edit_quadmask.py:633
Method
copy_grey_from_previous
Copy ONLY grey component from previous frame, preserving black in current frame
VLM-MASK-REASONER/edit_quadmask.py:667
Function
create_armature_from_bones
Create Blender armature from bone hierarchy.
data_generation/render_paired_videos_blender_quadmask.py:761
Method
create_custom_forward
(module)
videox_fun/models/cogvideox_vae.py:502
Method
create_custom_forward
(module)
videox_fun/models/cogvideox_vae.py:604
Method
create_custom_forward
(module)
videox_fun/models/cogvideox_vae.py:718
Method
create_custom_forward
(module)
videox_fun/models/cogvideox_vae.py:855
Method
create_custom_forward
(module)
videox_fun/models/cogvideox_vae.py:1021
Method
create_custom_forward
(module)
videox_fun/models/cogvideox_transformer3d.py:625
Method
create_forward
(*inputs)
videox_fun/models/cogvideox_vae.py:503
Method
create_forward
(*inputs)
videox_fun/models/cogvideox_vae.py:605
Method
create_forward
(*inputs)
videox_fun/models/cogvideox_vae.py:719
Method
create_modules
( is_unet: bool, root_module: torch.nn.Module, target_replace_modules: Lis
videox_fun/utils/lora_utils.py:186
Function
create_network
( multiplier: float, network_dim: Optional[int], network_alpha: Optional[float], text_encoder:
videox_fun/utils/lora_utils.py:341
Function
create_object_from_vertices
Create Blender object from vertices and faces.
data_generation/render_paired_videos_blender_quadmask.py:805
Method
custom_forward
(*inputs)
videox_fun/models/cogvideox_vae.py:856
Method
custom_forward
(*inputs)
videox_fun/models/cogvideox_vae.py:1022
Method
custom_forward
(*inputs)
videox_fun/models/cogvideox_transformer3d.py:626
Method
disable_auto_split_process
(self)
videox_fun/models/cogvideox_vae.py:1282
Method
disable_slicing
r""" Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing decoding in one st
videox_fun/models/cogvideox_vae.py:1257
Method
disable_tiling
r""" Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing decoding in one step
videox_fun/models/cogvideox_vae.py:1243
Method
enable_auto_split_process
(self)
videox_fun/models/cogvideox_vae.py:1276
Method
enable_gradient_checkpointing
(self)
videox_fun/utils/lora_utils.py:309
Method
enable_slicing
r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in sev
videox_fun/models/cogvideox_vae.py:1250
Method
enable_tiling
r""" Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and en
videox_fun/models/cogvideox_vae.py:1207
Method
enumerate_params
(loras)
videox_fun/utils/lora_utils.py:289
Function
erode_video_mask
(video_mask, width=5)
videox_fun/utils/utils.py:492
Function
extract_videojam_motion_from_video
Standalone function to extract VideoJAM motion from a single video file. IMPORTANT: For training, set target_resolution to match your traini
videox_fun/utils/optical_flow_utils.py:707
Method
first_frame
Go to first frame
VLM-MASK-REASONER/edit_quadmask.py:777
Method
first_frame
Jump to first frame
VLM-MASK-REASONER/point_selector_gui.py:444
Method
first_video
Jump to first video
VLM-MASK-REASONER/point_selector_gui.py:482
Method
fn_recursive_add_processors
(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor])
videox_fun/models/cogvideox_transformer3d.py:479
Method
fn_recursive_attn_processor
(name: str, module: torch.nn.Module, processor)
videox_fun/models/cogvideox_transformer3d.py:515
Method
forward
Forward pass to compute deformed vertices and joint positions Args: pose_params: Dict of bone_name -> tensor [batch_size,
data_generation/human_model/human_model.py:262
Method
forward
(self, x, *args, **kwargs)
videox_fun/utils/lora_utils.py:81
Method
forward
(self, embed)
videox_fun/reward/improved_aesthetic_predictor.py:28
Method
forward
(self, pixel_values)
videox_fun/reward/improved_aesthetic_predictor.py:45
Method
forward
( self, pixel_values: torch.FloatTensor | None = None, labels: torch.Tensor | None = N
videox_fun/reward/aesthetic_predictor_v2_5/siglip_v2_5.py:58
Method
forward
(self, text_inputs=None, image_inputs=None, condition_inputs=None)
videox_fun/reward/MPS/trainer/models/clip_model.py:116
Method
forward
(self, x)
videox_fun/reward/MPS/trainer/models/cross_modeling.py:24
Method
forward
(self, x, *args, **kwargs)
videox_fun/reward/MPS/trainer/models/cross_modeling.py:35
Method
forward
(self, max_seq_len, *, device)
videox_fun/reward/MPS/trainer/models/cross_modeling.py:49
Method
forward
(self, x)
videox_fun/reward/MPS/trainer/models/cross_modeling.py:70
Method
forward
einstein notation b - batch h - heads n, i, j - sequence length (base sequence length, source, target) d - fe
videox_fun/reward/MPS/trainer/models/cross_modeling.py:110
Method
forward
einstein notation b - batch h - heads n, i, j - sequence length (base sequence length, source, target) d - fe
videox_fun/reward/MPS/trainer/models/cross_modeling.py:207
Method
forward
( self, query_tokens, context_tokens, mask )
videox_fun/reward/MPS/trainer/models/cross_modeling.py:281
Method
forward
(self, input: torch.Tensor)
videox_fun/models/cogvideox_vae.py:45
Method
forward
(self, inputs: torch.Tensor, conv_cache: Optional[torch.Tensor] = None)
videox_fun/models/cogvideox_vae.py:136
Method
forward
( self, f: torch.Tensor, zq: torch.Tensor, conv_cache: Optional[Dict[str, torch.Tensor]] = None )
videox_fun/models/cogvideox_vae.py:177
Method
forward
(self, inputs: torch.Tensor)
videox_fun/models/cogvideox_vae.py:237
Method
forward
( self, inputs: torch.Tensor, temb: Optional[torch.Tensor] = None, zq: Optiona
videox_fun/models/cogvideox_vae.py:363
Method
forward
r"""Forward method of the `CogVideoXDownBlock3D` class.
videox_fun/models/cogvideox_vae.py:485
Method
forward
r"""Forward method of the `CogVideoXMidBlock3D` class.
videox_fun/models/cogvideox_vae.py:587
Method
forward
r"""The forward method of the `CogVideoXEncoder3D` class.
videox_fun/models/cogvideox_vae.py:840
Method
forward
r"""The forward method of the `CogVideoXDecoder3D` class.
videox_fun/models/cogvideox_vae.py:1006
Method
forward
( self, sample: torch.Tensor, sample_posterior: bool = False, return_dict: boo
videox_fun/models/cogvideox_vae.py:1632
Method
forward
r""" Args: text_embeds (`torch.Tensor`): Input text embeddings. Expected shape: (batch_size, seq_length, embedding
videox_fun/models/cogvideox_transformer3d.py:126
Method
forward
( self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, temb
videox_fun/models/cogvideox_transformer3d.py:258
Method
forward
( self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, time
videox_fun/models/cogvideox_transformer3d.py:568
Method
from_pretrained
( cls, pretrained_model_path, subfolder=None, transformer_additional_kwargs={}, low_cpu_mem_us
videox_fun/models/cogvideox_transformer3d.py:685
Method
fuse_qkv_projections
Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value) are fused. For cross-att
videox_fun/models/cogvideox_transformer3d.py:529
Method
fuse_qkv_projections
r"""Enables fused QKV projections.
videox_fun/pipeline/pipeline_cogvideox_fun.py:518
Method
fuse_qkv_projections
r"""Enables fused QKV projections.
videox_fun/pipeline/pipeline_cogvideox_fun_inpaint.py:655
Method
generate
(self, datas)
videox_fun/api/api_multi_nodes.py:45
Method
generate
( self, diffusion_transformer_dropdown, base_model_dropdown, lora_model_dropdo
videox_fun/ui/wan_fun_ui.py:134
Method
generate
( self, diffusion_transformer_dropdown, base_model_dropdown, lora_model_dropdo
videox_fun/ui/cogvideox_fun_ui.py:105
Function
generate_camera_trajectory
Generate smooth linear camera movement (left or right).
data_generation/render_paired_videos_blender_quadmask.py:860
Function
generate_timestep_with_lognorm
(low, high, shape, device="cpu", generator=None)
scripts/cogvideox_fun/train.py:281
Function
generate_timestep_with_lognorm
(low, high, shape, device="cpu", generator=None)
scripts/cogvideox_fun/train_warped_noise.py:282
Function
get_config
()
config/default_omnimatte.py:4
Function
get_config
()
config/quadmask_cogvideox.py:4
Function
get_config
()
config/default_cogvideox.py:4
Method
get_image_features
( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[
videox_fun/reward/MPS/trainer/models/clip_model.py:65
Method
get_joint_chain
Get the chain of joints between two bones for IK Args: start_bone: Starting bone name end_bone: Ending bone n
data_generation/human_model/human_model.py:192
Method
get_text_features
( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tenso
videox_fun/reward/MPS/trainer/models/clip_model.py:25
Function
get_texture_categories
Get the possible texture categories for an object. Args: obj_name: Name of the object Returns: list: List of texture ca
data_generation/object_texture_mapping.py:364
Method
get_trainable_params
(self)
videox_fun/utils/lora_utils.py:312
Function
get_video
( input_video_path, sample_size, max_video_length=49, temporal_window_size=49,
videox_fun/utils/utils.py:450
Function
get_video_reader_batch
(video_reader, batch_index)
videox_fun/data/dataset_image_video.py:338
Function
get_video_reader_batch
(video_reader, batch_index)
videox_fun/data/dataset_image_video_warped.py:338
Function
get_video_reader_batch
(video_reader, batch_index)
videox_fun/data/dataset_video.py:75
Function
get_width_and_height_from_image_and_base_resolution
(image, base_resolution)
videox_fun/utils/utils.py:29
Method
guidance_scale
(self)
videox_fun/pipeline/pipeline_cogvideox_fun.py:576
Method
guidance_scale
(self)
videox_fun/pipeline/pipeline_wan_fun.py:366
Method
guidance_scale
(self)
videox_fun/pipeline/pipeline_cogvideox_fun_inpaint.py:713
Function
infer_forward_api
(_: gr.Blocks, app: FastAPI, controller)
videox_fun/api/api.py:108
Method
interrupt
(self)
videox_fun/pipeline/pipeline_cogvideox_fun.py:588
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