mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 06:15:17 +00:00
81 lines
2.9 KiB
Python
81 lines
2.9 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import numpy as np
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import torch
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from einops import rearrange
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from PIL import Image
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def convert_to_numpy(image):
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if isinstance(image, Image.Image):
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image = np.array(image)
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elif isinstance(image, torch.Tensor):
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image = image.detach().cpu().numpy()
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elif isinstance(image, np.ndarray):
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image = image.copy()
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else:
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raise f'Unsurpport datatype{type(image)}, only surpport np.ndarray, torch.Tensor, Pillow Image.'
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return image
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class DepthV2Annotator:
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def __init__(self, cfg, device=None):
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from .dpt import DepthAnythingV2
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# Model configurations for different variants
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self.model_configs = {
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'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
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'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
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'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
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'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
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}
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# Get model variant from config, default to 'vitl' if not specified
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model_variant = cfg.get('MODEL_VARIANT', 'vitl')
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if model_variant not in self.model_configs:
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raise ValueError(f"Invalid model variant '{model_variant}'. Must be one of: {list(self.model_configs.keys())}")
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pretrained_model = cfg['PRETRAINED_MODEL']
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None else device
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# Get configuration for the selected model variant
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config = self.model_configs[model_variant]
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# Initialize model with the appropriate configuration
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self.model = DepthAnythingV2(
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encoder=config['encoder'],
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features=config['features'],
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out_channels=config['out_channels']
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).to(self.device)
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self.model.load_state_dict(
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torch.load(
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pretrained_model,
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map_location=self.device,
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weights_only=True
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)
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)
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self.model.eval()
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@torch.inference_mode()
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@torch.autocast('cuda', enabled=False)
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def forward(self, image):
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image = convert_to_numpy(image)
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depth = self.model.infer_image(image)
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depth_pt = depth.copy()
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depth_pt -= np.min(depth_pt)
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depth_pt /= np.max(depth_pt)
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depth_image = (depth_pt * 255.0).clip(0, 255).astype(np.uint8)
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depth_image = depth_image[..., np.newaxis]
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depth_image = np.repeat(depth_image, 3, axis=2)
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return depth_image
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class DepthV2VideoAnnotator(DepthV2Annotator):
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def forward(self, frames):
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ret_frames = []
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for frame in frames:
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anno_frame = super().forward(np.array(frame))
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ret_frames.append(anno_frame)
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return ret_frames |