Functions — github.com/Tencent-Hunyuan/HunyuanPortrait

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Functions229 in github.com/Tencent-Hunyuan/HunyuanPortrait

⨍ Functions 229◇ Types & classes 61

↓ 19 callersMethod__init__

(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1)

src/models/condition/unet_3d_blocks.py:96

↓ 5 callersMethod__init__

(self)

src/models/condition/coarse_motion.py:173

↓ 4 callersMethod_make_layer

(self, block, planes, num_blocks, stride)

src/models/condition/coarse_motion.py:84

↓ 4 callersMethod_make_layer

(self, block, planes, blocks, stride=1, groups=1, base_width=64)

src/models/condition/coarse_motion.py:121

↓ 4 callersMethodprepare_tokens_with_masks

(self, x, masks=None)

src/models/dinov2/models/vision_transformer.py:242

↓ 3 callersMethod__init__

( self, dim: int, num_heads: int, mlp_ratio: float = 4.0, qkv_bias: bo

src/models/dinov2/layers/block.py:148

↓ 3 callersMethod__init__

(self, embedding_dim: int, intensity_embedding_dim: Optional[int] = None)

src/models/condition/refine_motion.py:10

↓ 3 callersFunctionbox_area

(box)

src/dataset/test_preprocess.py:138

↓ 3 callersFunctionsave_videos_from_pil

(pil_images, path, fps=8)

src/dataset/utils.py:283

↓ 3 callersFunctionseed_everything

(seed)

src/dataset/utils.py:277

↓ 2 callersMethod__init__

(self)

src/models/condition/attention_processor.py:103

↓ 2 callersFunction_filter2d

(input, kernel)

src/pipelines/hunyuan_svd_pipeline.py:705

↓ 2 callersFunction_gaussian

(window_size: int, sigma)

src/pipelines/hunyuan_svd_pipeline.py:728

↓ 2 callersFunctionbox_area

(box)

src/dataset/utils.py:36

↓ 2 callersFunctioncreate_soft_mask

create a soft mask with edge blurring for smooth blending.

gradio_app.py:130

↓ 2 callersFunctioncreate_soft_mask

create a soft mask with edge blurring for smooth blending. size: (width, height)

inference.py:24

↓ 2 callersFunctioncrop_resize_img

(img, bbox, image_size)

src/dataset/test_preprocess.py:108

↓ 2 callersFunctiondrop_add_residual_stochastic_depth

( x: Tensor, residual_func: Callable[[Tensor], Tensor], sample_drop_ratio: float = 0.0, )

src/models/dinov2/layers/block.py:233

↓ 2 callersFunctiondrop_add_residual_stochastic_depth_list

( x_list: List[Tensor], residual_func: Callable[[Tensor, Any], Tensor], sample_drop_ratio: float =

src/models/dinov2/layers/block.py:304

↓ 2 callersMethodforward_features

(self, x, masks=None, text_features=None)

src/models/dinov2/models/vision_transformer.py:283

↓ 2 callersFunctionget_attn_bias_and_cat

this will perform the index select, cat the tensors, and provide the attn_bias from cache

src/models/dinov2/layers/block.py:280

↓ 2 callersFunctionget_head_exp_motion_bucketid

(lmks, nose_index=2, max_value=127)

src/dataset/utils.py:325

↓ 2 callersFunctioninit_ip_adapters

(unet, num_adapter_embeds=[], scale=1.0)

src/models/condition/unet_3d_svd_condition_ip.py:501

↓ 2 callersMethodinterpolate_pos_encoding

(self, x, w, h)

src/models/dinov2/models/vision_transformer.py:208

↓ 2 callersMethodload_weights

(self, weights_path, strict=True)

src/models/dinov2/models/vision_transformer.py:35

↓ 2 callersFunctionmake_2tuple

(x)

src/models/dinov2/layers/patch_embed.py:16

↓ 2 callersFunctionpreprocess

(image_path, video_path, limit=100, image_size=512, area=1.25, det_path=None)

src/dataset/test_preprocess.py:143

↓ 2 callersFunctionprocess_bbox

raw_vid_path: bbox: format: x1, y1, x2, y2 radio: expand radio against bbox size height,width: source image height and width

src/dataset/test_preprocess.py:49

↓ 2 callersFunctionsave_videos_grid

(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=8)

src/dataset/utils.py:308

↓ 2 callersMethodset_attn_processor

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

src/models/condition/unet_3d_svd_condition_ip.py:276

↓ 2 callersFunctionvit_large

(patch_size=16, num_register_tokens=0, **kwargs)

src/models/dinov2/models/vision_transformer.py:428

↓ 2 callersMethodwriteframe

(self, frame)

src/dataset/utils.py:271

↓ 1 callersMethod__init__

( self, in_features: int, hidden_features: Optional[int] = None, out_features:

src/models/dinov2/layers/swiglu_ffn.py:15

↓ 1 callersMethod__init__

(self, in_features, out_features, dtype=torch.float32)

src/models/dinov2/models/vision_transformer.py:27

↓ 1 callersFunction_build_mlp

(nlayers, in_dim, bottleneck_dim, hidden_dim=None, use_bn=False, bias=True)

src/models/dinov2/layers/dino_head.py:44

↓ 1 callersFunction_compute_padding

Compute padding tuple.

src/pipelines/hunyuan_svd_pipeline.py:682

↓ 1 callersMethod_encode_image

(self, image, arcface_embeddings, prompts, uncond_prompts, num_frames, device, num_videos_per_prompt, do_class

src/pipelines/hunyuan_svd_pipeline.py:115

↓ 1 callersMethod_encode_vae_image

( self, image: torch.Tensor, device, num_videos_per_prompt, do_classif

src/pipelines/hunyuan_svd_pipeline.py:139

↓ 1 callersFunction_gaussian_blur2d

(input, kernel_size, sigma)

src/pipelines/hunyuan_svd_pipeline.py:744

↓ 1 callersMethod_get_add_time_ids

( self, fps, motion_bucket_id, noise_aug_strength, dtype, batc

src/pipelines/hunyuan_svd_pipeline.py:157

↓ 1 callersMethod_get_intermediate_layers_chunked

(self, x, n=1)

src/models/dinov2/models/vision_transformer.py:336

↓ 1 callersMethod_get_intermediate_layers_not_chunked

(self, x, n=1)

src/models/dinov2/models/vision_transformer.py:324

↓ 1 callersMethod_initialize_weights

(self)

src/models/dinov2/layers/block.py:113

↓ 1 callersMethodadd_noise

( self, original_samples: torch.Tensor, noise: torch.Tensor, timesteps: torch.

src/schedulers/scheduling_euler_discrete.py:45

↓ 1 callersFunctionadd_residual

(x, brange, residual, residual_scale_factor, scaling_vector=None)

src/models/dinov2/layers/block.py:265

↓ 1 callersFunctionalign_face

(image, landmark, output_shape=(112, 112))

src/dataset/test_preprocess.py:12

↓ 1 callersMethodattention

(self, q: torch.Tensor, kv: torch.Tensor)

src/models/condition/refine_motion.py:58

↓ 1 callersFunctionbox_iou

Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. Arguments:

src/dataset/utils.py:23

↓ 1 callersFunctionbuild_model

(args, only_teacher=False, img_size=224)

src/models/dinov2/models/__init__.py:14

↓ 1 callersFunctioncenter_crop

(img_driven, face_bbox, scale=1.0)

src/dataset/test_preprocess.py:28

↓ 1 callersMethodcheck_inputs

(self, image, height, width)

src/pipelines/hunyuan_svd_pipeline.py:212

↓ 1 callersFunctioncrop_face_motion

(image, landmark, motion_transform, bbox, scale=0.45)

src/dataset/test_preprocess.py:116

↓ 1 callersMethoddecode_latents

(self, latents, num_frames, decode_chunk_size=14)

src/pipelines/hunyuan_svd_pipeline.py:185

↓ 1 callersFunctiondrop_path

(x, drop_prob: float = 0.0, training: bool = False)

src/models/dinov2/layers/drop_path.py:14

↓ 1 callersFunctionencode_image_with_id_features

img: B, 3, 224, 224 id_features: B, 1, 512

src/pipelines/hunyuan_svd_pipeline.py:17

↓ 1 callersFunctionexpand

(bbox, ratio, height, width)

src/dataset/test_preprocess.py:57

↓ 1 callersMethodforward

(self, x: Tensor, id_features = None)

src/models/dinov2/layers/block.py:199

↓ 1 callersMethodforward

(self, x: Tensor)

src/models/dinov2/layers/attention.py:56

↓ 1 callersMethodforward

(self, conditioning)

src/models/condition/pose_guider.py:62

↓ 1 callersMethodforward_features_list

(self, x_list, masks_list)

src/models/dinov2/models/vision_transformer.py:263

↓ 1 callersMethodforward_nested

x_list contains a list of tensors to nest together and run

src/models/dinov2/layers/block.py:328

↓ 1 callersMethodget_alpha

(self, image_only_indicator: torch.Tensor, ndims: int)

src/models/condition/unet_3d_blocks.py:146

↓ 1 callersFunctionget_branges_scales

(x, sample_drop_ratio=0.0)

src/models/dinov2/layers/block.py:257

↓ 1 callersFunctionget_down_block

( down_block_type: str, num_layers: int, in_channels: int, out_channels: int, temb_channel

src/models/condition/unet_3d_blocks.py:416

↓ 1 callersFunctionget_dwpose

(image)

src/dataset/test_preprocess.py:132

↓ 1 callersFunctionget_timestep_embedding

This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings. Args timesteps (

src/models/condition/unet_3d_blocks.py:40

↓ 1 callersMethodget_timesteps

(self, num_inference_steps, strength, device)

src/pipelines/hunyuan_svd_pipeline.py:267

↓ 1 callersFunctionget_up_block

( up_block_type: str, num_layers: int, in_channels: int, out_channels: int, prev_output_ch

src/models/condition/unet_3d_blocks.py:544

↓ 1 callersMethodinit_weights

(self)

src/models/dinov2/models/vision_transformer.py:201

↓ 1 callersFunctionmain

(cfg, args)

inference.py:82

↓ 1 callersFunctionnamed_apply

(fn: Callable, module: nn.Module, name="", depth_first=True, include_root=False)

src/models/dinov2/models/vision_transformer.py:40

↓ 1 callersFunctionnon_max_suppression_face

Performs Non-Maximum Suppression (NMS) on inference results Returns: detections with shape: nx6 (x1, y1, x2, y2, conf, cls)

src/dataset/utils.py:49

↓ 1 callersFunctionpaste_back_frame

paste the generated cropped frame back to the original image

gradio_app.py:146

↓ 1 callersFunctionpaste_back_frame

paste the generated cropped frame back to the original image original_img: original image numpy array generated_crop: generated 512x512 f

inference.py:49

↓ 1 callersMethodprepare_latents

( self, batch_size, num_frames, num_channels_latents, height,

src/pipelines/hunyuan_svd_pipeline.py:226

↓ 1 callersMethodstep

Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned m

src/schedulers/scheduling_euler_discrete.py:78

↓ 1 callersFunctionto_square

(bbox_src, bbox_expend, height, width)

src/dataset/test_preprocess.py:69

↓ 1 callersFunctionxywh2xyxy

(x)

src/dataset/utils.py:13

↓ 1 callersFunctionzero_module

(module)

src/models/condition/pose_guider.py:9

Method__call__

r""" The call function to the pipeline for generation. Args: image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torc

src/pipelines/hunyuan_svd_pipeline.py:296

Method__call__

( self, attn: Attention, hidden_states: torch.Tensor, encoder_hidden_states: O

src/models/condition/attention_processor.py:32

Method__call__

( self, attn: Attention, hidden_states: torch.Tensor, encoder_hidden_states: O

src/models/condition/attention_processor.py:108

Method__call__

( self, attn: Attention, hidden_states: torch.Tensor, encoder_hidden_states: O

src/models/condition/attention_processor.py:225

Method__call__

( self, attn: Attention, hidden_states: torch.Tensor, encoder_hidden_states: O

src/models/condition/attention_processor.py:428

Method__init__

(self, pt_path='checkpoints/yolov5m-face.pt', confThreshold=0.5, nmsThreshold=0.45, device='cuda')

src/dataset/utils.py:123

Method__init__

(self, video_path=None, output_video_path=None, bit_rate='origin', fps=25)

src/dataset/utils.py:188

Method__init__

( self, vae, image_encoder, image_proj, unet, pose_guider,

src/pipelines/hunyuan_svd_pipeline.py:83

Method__init__

( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end:

src/schedulers/scheduling_euler_discrete.py:19

Method__init__

(self, drop_prob=None)

src/models/dinov2/layers/drop_path.py:29

Method__init__

(self, in_channels: int, out_channels: int, **kwargs: Any)

src/models/dinov2/layers/block.py:45

Method__init__

(self, image_dim, id_dim, embed_dim, num_heads, dropout=0.1)

src/models/dinov2/layers/block.py:57

Method__init__

( self, fc_in_channels: int, in_channels: int, ch1x1: int, ch3x3red: i

src/models/dinov2/layers/block.py:80

Method__init__

( self, in_dim, out_dim, use_bn=False, nlayers=3, hidden_dim=2

src/models/dinov2/layers/dino_head.py:13

Method__init__

( self, dim: int, init_values: Union[float, Tensor] = 1e-5, inplace: bool = Fa

src/models/dinov2/layers/layer_scale.py:16

Method__init__

( self, dim: int, num_heads: int = 8, qkv_bias: bool = False, proj_bia

src/models/dinov2/layers/attention.py:37

Method__init__

( self, in_features: int, hidden_features: Optional[int] = None, out_features:

src/models/dinov2/layers/mlp.py:17

Method__init__

( self, in_features: int, hidden_features: Optional[int] = None, out_features:

src/models/dinov2/layers/swiglu_ffn.py:55

Method__init__

( self, img_size: Union[int, Tuple[int, int]] = 224, patch_size: Union[int, Tuple[int,

src/models/dinov2/layers/patch_embed.py:37

Method__init__

Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number

src/models/dinov2/models/vision_transformer.py:60

Method__init__

(self, in_planes, planes, stride=1, expansion = 1)

src/models/condition/coarse_motion.py:7

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