Functions — github.com/Francis-Rings/MotionFollower

Method__init__

( self, conditioning_embedding_channels: int, conditioning_channels: int = 3,

src/models/pose_guider.py:13

Method__init__

( self, sample_size: Optional[int] = None, in_channels: int = 4, out_chann

src/models/estimator.py:32

Method__init__

( self, channels, use_conv=False, out_channels=None, padding=1, name="conv" )

src/models/resnet.py:87

Method__init__

( self, *, in_channels, out_channels=None, conv_shortcut=False,

src/models/resnet.py:117

Method__init__

( self, in_channels, num_attention_heads=8, num_transformer_block=2, a

src/models/motion_module.py:44

Method__init__

( self, in_channels, num_attention_heads, attention_head_dim, num_laye

src/models/motion_module.py:94

Method__init__

( self, dim, num_attention_heads, attention_head_dim, attention_block_

src/models/motion_module.py:185

Method__init__

( self, attention_mode=None, cross_frame_attention_mode=None, temporal_positio

src/models/motion_module.py:280

Method_encode_prompt

( self, prompt, device, num_videos_per_prompt,

src/pipelines/motion_editor.py:182

Method_encode_prompt

( self, prompt, device, num_videos_per_prompt, do_classifier_free

src/pipelines/pipeline_pose2vid_collection.py:176

Method_execution_device

(self)

src/pipelines/motion_editor.py:98

Method_execution_device

(self)

src/pipelines/pipeline_pose2vid_collection.py:92

Method_set_gradient_checkpointing

(self, module, value=False)

src/models/transformer_3d.py:120

Method_set_gradient_checkpointing

(self, module, value=False)

src/models/unet_3d.py:353

Method_set_gradient_checkpointing

(self, module, value=False)

src/models/estimator.py:354

Methodattn_processors

r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with

src/models/unet_3d.py:253

Methodattn_processors

r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with

src/models/estimator.py:254

Methodcreate_custom_forward

(module, return_dict=None)

src/models/unet_3d_blocks.py:437

Methodcreate_custom_forward

(module)

src/models/unet_3d_blocks.py:589

Methodcreate_custom_forward

(module, return_dict=None)

src/models/unet_3d_blocks.py:758

Methodcreate_custom_forward

(module)

src/models/unet_3d_blocks.py:899

Methodcustom_forward

(*inputs)

src/models/unet_3d_blocks.py:438

Methodcustom_forward

(*inputs)

src/models/unet_3d_blocks.py:590

Methodcustom_forward

(*inputs)

src/models/unet_3d_blocks.py:759

Methodcustom_forward

(*inputs)

src/models/unet_3d_blocks.py:900

Functiondelete_additional_ckpt

(base_path, num_keep)

src/utils/util.py:35

Methoddisable_vae_slicing

(self)

src/pipelines/motion_editor.py:82

Methoddisable_vae_slicing

(self)

src/pipelines/pipeline_pose2vid_collection.py:76

Methodenable_sequential_cpu_offload

(self, gpu_id=0)

src/pipelines/motion_editor.py:85

Methodenable_sequential_cpu_offload

(self, gpu_id=0)

src/pipelines/pipeline_pose2vid_collection.py:79

Methodenable_vae_slicing

(self)

src/pipelines/motion_editor.py:79

Methodenable_vae_slicing

(self)

src/pipelines/pipeline_pose2vid_collection.py:73

Methodextra_repr

(self)

src/models/motion_module.py:305

Methodfn_recursive_add_processors

( name: str, module: torch.nn.Module, processors: Dict[str, AttentionProce

src/models/unet_3d.py:262

Methodfn_recursive_add_processors

( name: str, module: torch.nn.Module, processors: Dict[str, AttentionPr

src/models/estimator.py:263

Methodfn_recursive_attn_processor

(name: str, module: torch.nn.Module, processor)

src/models/unet_3d.py:381

Methodfn_recursive_attn_processor

(name: str, module: torch.nn.Module, processor)

src/models/estimator.py:382

Methodfn_recursive_retrieve_slicable_dims

(module: torch.nn.Module)

src/models/unet_3d.py:298

Methodfn_recursive_retrieve_slicable_dims

(module: torch.nn.Module)

src/models/estimator.py:299

Methodfn_recursive_set_attention_slice

( module: torch.nn.Module, slice_size: List[int] )

src/models/unet_3d.py:340

Methodfn_recursive_set_attention_slice

( module: torch.nn.Module, slice_size: List[int] )

src/models/estimator.py:341

Methodforward

(self, x)

src/models/appearance_net.py:50

Methodforward

(self, x)

src/models/appearance_net.py:86

Methodforward

(self, x)

src/models/appearance_net.py:117

Methodforward

(self, x, c)

src/models/transformer_3d.py:41

Methodforward

( self, hidden_states, encoder_hidden_states=None, timestep=None, retu

src/models/transformer_3d.py:124

Methodforward

r""" Args: sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor timestep (`torch.FloatTen

src/models/unet_3d.py:397

Methodforward

r""" The forward method of the `Attention` class. Args: hidden_states (`torch.Tensor`): The hidden s

src/models/attn_process_diffuser.py:485

Methodforward

(self, f: torch.FloatTensor, zq: torch.FloatTensor)

src/models/attn_process_diffuser.py:1692

Methodforward

( self, hidden_states, encoder_hidden_states=None, timestep=No

src/models/attention.py:106

Methodforward

( self, hidden_states, temb=None, encoder_hidden_states=None, attentio

src/models/unet_3d_blocks.py:276

Methodforward

( self, hidden_states, temb=None, encoder_hidden_states=None, attentio

src/models/unet_3d_blocks.py:416

Methodforward

(self, hidden_states, temb=None, encoder_hidden_states=None, appearance_feature=None)

src/models/unet_3d_blocks.py:582

Methodforward

( self, hidden_states, res_hidden_states_tuple, temb=None, encoder_hid

src/models/unet_3d_blocks.py:734

Methodforward

( self, hidden_states, res_hidden_states_tuple, temb=None, upsample_si

src/models/unet_3d_blocks.py:882

Methodforward

(self, conditioning)

src/models/pose_guider.py:47

Methodforward

r""" Args: sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor timestep (`torch.Float

src/models/estimator.py:397

Methodforward

(self, x)

src/models/resnet.py:8

Methodforward

(self, x)

src/models/resnet.py:19

Methodforward

(self, hidden_states, output_size=None)

src/models/resnet.py:51

Methodforward

(self, hidden_states)

src/models/resnet.py:105

Methodforward

(self, input_tensor, temb)

src/models/resnet.py:210

Methodforward

( self, input_tensor, temb, encoder_hidden_states, attention_mask=None

src/models/motion_module.py:76

Methodforward

(self, hidden_states, encoder_hidden_states=None, attention_mask=None)

src/models/motion_module.py:145

Methodforward

( self, hidden_states, encoder_hidden_states=None, attention_mask=None,

src/models/motion_module.py:235

Methodforward

(self, x)

src/models/motion_module.py:274

Methodforward

( self, hidden_states, encoder_hidden_states=None, attention_mask=None,

src/models/motion_module.py:350

Methodfrom_pretrained_2d

( cls, pretrained_model_path: PathLike, motion_module_path: PathLike, subfolde

src/models/unet_3d.py:605

Functionget_context_scheduler

(name: str)

src/pipelines/context.py:44

Functionget_fps

(video_path)

src/utils/util.py:137

Methodget_processor

r""" Get the attention processor in use. Args: return_deprecated_lora (`bool`, *optional*, defaults to `False`):

src/models/attn_process_diffuser.py:395

Functionget_tensor_interpolation_method

()

src/pipelines/utils.py:6

Functionget_total_steps

( scheduler, timesteps: List[int], num_steps: Optional[int] = None, num_frames: int = ...,

src/pipelines/context.py:51

Methodimage2latent

(self, image)

src/pipelines/null_text_optimization.py:83

Functionimport_filename

(filename)

src/utils/util.py:27

Methodinit_prompt

(self, prompt: str)

src/pipelines/null_text_optimization.py:97

Methodinvert

(self, ddim_latents, clip_emb_im=None, ref_images_pil=None, pose_cond_fea=None)

src/utils/inversion.py:53

Methodinvert

(self, latents: torch.Tensor, ref_images_list=None, ref_pose_list=None, null_inner_steps=10, early_stop_epsilo

src/pipelines/null_text_optimization.py:215

Methodlatent2image

(self, latents, return_type='np')

src/pipelines/null_text_optimization.py:73

Functionlinear

(v1, v2, t)

src/pipelines/utils.py:15

Functionmodulate

(x, shift, scale)

src/models/attention.py:18

Methodprepare_latents

( self, batch_size, num_channels_latents, width,

src/pipelines/motion_editor.py:146

Functionread_frames

(video_path)

src/utils/util.py:120

Functionsave_videos_as_frames

(videos: torch.Tensor, path: str, rescale=False)

src/utils/util.py:106

Methodscheduler

(self)

src/pipelines/null_text_optimization.py:172

Methodset_attention_slice

r""" Set the slice size for attention computation. Args: slice_size (`int`): The slice size for atte

src/models/attn_process_diffuser.py:332

Methodset_attn_processor

r""" Sets the attention processor to use to compute attention. Parameters: processor (`dict` of `AttentionProcessor` or o

src/models/unet_3d.py:358

Functionset_tensor_interpolation_method

(is_slerp)

src/pipelines/utils.py:10

Methodset_use_memory_efficient_attention_xformers

r""" Set whether to use memory efficient attention from `xformers` or not. Args: use_memory_efficient_attention_xform

src/models/attn_process_diffuser.py:198

Methodset_use_memory_efficient_attention_xformers

( self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable

src/models/motion_module.py:308

Functionslerp

( v0: torch.Tensor, v1: torch.Tensor, t: float, DOT_THRESHOLD: float = 0.9995 )

src/pipelines/utils.py:19

Functionuniform

( step: int = ..., num_steps: Optional[int] = None, num_frames: int = ..., context_size: Optio

src/pipelines/context.py:14

Functions192 in github.com/Francis-Rings/MotionFollower