models/utils.py

import cv2
import math
import numpy as np
from PIL import Image

import torch
from torchvision.transforms import InterpolationMode
from torchvision.transforms.functional import normalize, resize
from transformers import T5EncoderModel, T5Tokenizer
from typing import List, Optional, Tuple, Union
from diffusers.models.embeddings import get_3d_rotary_pos_embed
from diffusers.pipelines.cogvideo.pipeline_cogvideox import get_resize_crop_region_for_grid


def tensor_to_pil(src_img_tensor):
    img = src_img_tensor.clone().detach()
    if img.dtype == torch.bfloat16:
        img = img.to(torch.float32)
    img = img.cpu().numpy()
    img = np.transpose(img, (1, 2, 0))
    img = img.astype(np.uint8)
    pil_image = Image.fromarray(img)
    return pil_image
    

def _get_t5_prompt_embeds(
    tokenizer: T5Tokenizer,
    text_encoder: T5EncoderModel,
    prompt: Union[str, List[str]],
    num_videos_per_prompt: int = 1,
    max_sequence_length: int = 226,
    device: Optional[torch.device] = None,
    dtype: Optional[torch.dtype] = None,
    text_input_ids=None,
):
    prompt = [prompt] if isinstance(prompt, str) else prompt
    batch_size = len(prompt)

    if tokenizer is not None:
        text_inputs = tokenizer(
            prompt,
            padding="max_length",
            max_length=max_sequence_length,
            truncation=True,
            add_special_tokens=True,
            return_tensors="pt",
        )
        text_input_ids = text_inputs.input_ids
    else:
        if text_input_ids is None:
            raise ValueError("`text_input_ids` must be provided when the tokenizer is not specified.")

    prompt_embeds = text_encoder(text_input_ids.to(device))[0]
    prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)

    # duplicate text embeddings for each generation per prompt, using mps friendly method
    _, seq_len, _ = prompt_embeds.shape
    prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
    prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)

    return prompt_embeds


def encode_prompt(
    tokenizer: T5Tokenizer,
    text_encoder: T5EncoderModel,
    prompt: Union[str, List[str]],
    num_videos_per_prompt: int = 1,
    max_sequence_length: int = 226,
    device: Optional[torch.device] = None,
    dtype: Optional[torch.dtype] = None,
    text_input_ids=None,
):
    prompt = [prompt] if isinstance(prompt, str) else prompt
    prompt_embeds = _get_t5_prompt_embeds(
        tokenizer,
        text_encoder,
        prompt=prompt,
        num_videos_per_prompt=num_videos_per_prompt,
        max_sequence_length=max_sequence_length,
        device=device,
        dtype=dtype,
        text_input_ids=text_input_ids,
    )
    return prompt_embeds


def compute_prompt_embeddings(
    tokenizer, text_encoder, prompt, max_sequence_length, device, dtype, requires_grad: bool = False
):
    if requires_grad:
        prompt_embeds = encode_prompt(
            tokenizer,
            text_encoder,
            prompt,
            num_videos_per_prompt=1,
            max_sequence_length=max_sequence_length,
            device=device,
            dtype=dtype,
        )
    else:
        with torch.no_grad():
            prompt_embeds = encode_prompt(
                tokenizer,
                text_encoder,
                prompt,
                num_videos_per_prompt=1,
                max_sequence_length=max_sequence_length,
                device=device,
                dtype=dtype,
            )
    return prompt_embeds


def prepare_rotary_positional_embeddings(
    height: int,
    width: int,
    num_frames: int,
    vae_scale_factor_spatial: int = 8,
    patch_size: int = 2,
    attention_head_dim: int = 64,
    device: Optional[torch.device] = None,
    base_height: int = 480,
    base_width: int = 720,
) -> Tuple[torch.Tensor, torch.Tensor]:
    grid_height = height // (vae_scale_factor_spatial * patch_size)
    grid_width = width // (vae_scale_factor_spatial * patch_size)
    base_size_width = base_width // (vae_scale_factor_spatial * patch_size)
    base_size_height = base_height // (vae_scale_factor_spatial * patch_size)

    grid_crops_coords = get_resize_crop_region_for_grid((grid_height, grid_width), base_size_width, base_size_height)
    freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
        embed_dim=attention_head_dim,
        crops_coords=grid_crops_coords,
        grid_size=(grid_height, grid_width),
        temporal_size=num_frames,
    )

    freqs_cos = freqs_cos.to(device=device)
    freqs_sin = freqs_sin.to(device=device)
    return freqs_cos, freqs_sin


def img2tensor(imgs, bgr2rgb=True, float32=True):
    """Numpy array to tensor.

    Args:
        imgs (list[ndarray] | ndarray): Input images.
        bgr2rgb (bool): Whether to change bgr to rgb.
        float32 (bool): Whether to change to float32.

    Returns:
        list[tensor] | tensor: Tensor images. If returned results only have
            one element, just return tensor.
    """

    def _totensor(img, bgr2rgb, float32):
        if img.shape[2] == 3 and bgr2rgb:
            if img.dtype == 'float64':
                img = img.astype('float32')
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img = torch.from_numpy(img.transpose(2, 0, 1))
        if float32:
            img = img.float()
        return img

    if isinstance(imgs, list):
        return [_totensor(img, bgr2rgb, float32) for img in imgs]
    return _totensor(imgs, bgr2rgb, float32)


def to_gray(img):
    x = 0.299 * img[:, 0:1] + 0.587 * img[:, 1:2] + 0.114 * img[:, 2:3]
    x = x.repeat(1, 3, 1, 1)
    return x


def draw_kps(image_pil, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,255,0), (255,0,255)]):
    stickwidth = 4
    limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])
    kps = np.array(kps)

    w, h = image_pil.size
    out_img = np.zeros([h, w, 3])

    for i in range(len(limbSeq)):
        index = limbSeq[i]
        color = color_list[index[0]]

        x = kps[index][:, 0]
        y = kps[index][:, 1]
        length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5
        angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))
        polygon = cv2.ellipse2Poly((int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
        out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color)
    out_img = (out_img * 0.6).astype(np.uint8)

    for idx_kp, kp in enumerate(kps):
        color = color_list[idx_kp]
        x, y = kp
        out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1)

    out_img_pil = Image.fromarray(out_img.astype(np.uint8))
    return out_img_pil


def process_face_embeddings(face_helper, clip_vision_model, handler_ante, eva_transform_mean, eva_transform_std, app, device, weight_dtype, image, original_id_image=None, is_align_face=True, cal_uncond=False):
    """
    Args:
        image: numpy rgb image, range [0, 255]
    """
    face_helper.clean_all()
    image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)  # (724, 502, 3)
    # get antelopev2 embedding
    face_info = app.get(image_bgr)
    if len(face_info) > 0:
        face_info = sorted(face_info, key=lambda x: (x['bbox'][2] - x['bbox'][0]) * (x['bbox'][3] - x['bbox'][1]))[
            -1
        ]  # only use the maximum face
        id_ante_embedding = face_info['embedding']  # (512,)
        face_kps = face_info['kps']
    else:
        id_ante_embedding = None
        face_kps = None

    # using facexlib to detect and align face
    face_helper.read_image(image_bgr)
    face_helper.get_face_landmarks_5(only_center_face=True)
    if face_kps is None:
        face_kps = face_helper.all_landmarks_5[0]
    face_helper.align_warp_face()
    if len(face_helper.cropped_faces) == 0:
        raise RuntimeError('facexlib align face fail')
    align_face = face_helper.cropped_faces[0]  # (512, 512, 3)  # RGB

    # incase insightface didn't detect face
    if id_ante_embedding is None:
        print('fail to detect face using insightface, extract embedding on align face')
        id_ante_embedding = handler_ante.get_feat(align_face)

    id_ante_embedding = torch.from_numpy(id_ante_embedding).to(device, weight_dtype)  # torch.Size([512])
    if id_ante_embedding.ndim == 1:
        id_ante_embedding = id_ante_embedding.unsqueeze(0)  # torch.Size([1, 512])

    # parsing
    if is_align_face:
        input = img2tensor(align_face, bgr2rgb=True).unsqueeze(0) / 255.0  # torch.Size([1, 3, 512, 512])
        input = input.to(device)
        parsing_out = face_helper.face_parse(normalize(input, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]))[0]
        parsing_out = parsing_out.argmax(dim=1, keepdim=True)  # torch.Size([1, 1, 512, 512])
        bg_label = [0, 16, 18, 7, 8, 9, 14, 15]
        bg = sum(parsing_out == i for i in bg_label).bool()
        white_image = torch.ones_like(input)  # torch.Size([1, 3, 512, 512])
        # only keep the face features
        return_face_features_image = torch.where(bg, white_image, to_gray(input))  # torch.Size([1, 3, 512, 512])
        return_face_features_image_2 = torch.where(bg, white_image, input)  # torch.Size([1, 3, 512, 512])
    else:
        original_image_bgr = cv2.cvtColor(original_id_image, cv2.COLOR_RGB2BGR)
        input = img2tensor(original_image_bgr, bgr2rgb=True).unsqueeze(0) / 255.0  # torch.Size([1, 3, 512, 512])
        input = input.to(device)
        return_face_features_image = return_face_features_image_2 = input

    # transform img before sending to eva-clip-vit
    face_features_image = resize(return_face_features_image, clip_vision_model.image_size,
                                 InterpolationMode.BICUBIC)  # torch.Size([1, 3, 336, 336])
    face_features_image = normalize(face_features_image, eva_transform_mean, eva_transform_std)
    id_cond_vit, id_vit_hidden = clip_vision_model(face_features_image.to(weight_dtype), return_all_features=False, return_hidden=True, shuffle=False)  # torch.Size([1, 768]),  list(torch.Size([1, 577, 1024]))
    id_cond_vit_norm = torch.norm(id_cond_vit, 2, 1, True)
    id_cond_vit = torch.div(id_cond_vit, id_cond_vit_norm)

    id_cond = torch.cat([id_ante_embedding, id_cond_vit], dim=-1)  # torch.Size([1, 512]), torch.Size([1, 768])  ->  torch.Size([1, 1280])

    return id_cond, id_vit_hidden, return_face_features_image_2, face_kps    # torch.Size([1, 1280]), list(torch.Size([1, 577, 1024]))