mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 14:16:57 +00:00
Multi Vace controlnets and multithreaded preprocessing
This commit is contained in:
DeepBeepMeep
parent
a790dd8432
commit
28fe3528e8
10
README.md
10
README.md
@ -20,6 +20,16 @@ WanGP supports the Wan (and derived models), Hunyuan Video and LTV Video models
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**Follow DeepBeepMeep on Twitter/X to get the Latest News**: https://x.com/deepbeepmeep
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## 🔥 Latest Updates
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### June 23 2025: WanGP v6.3, Vace Unleashed. Thought we couldnt squeeze Vace even more ?
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- Multithreaded preprocessing when possible for faster generations
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- Multithreaded frames Lanczos Upsampling as a bonus
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- A new Vace preprocessor : *Flow* to extract fluid motion
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- Multi Vace Controlnets: you can now transfer several properties at the same time. This opens new possibilities to explore, for instance if you transfer *Human Movement* and *Shapes* at the same time for some reasons the lighting of your character will take into account much more the environment of your character.
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- Injected Frames Outpainting, in case you missed it in WanGP 6.21
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Don't know how to use all of the Vace features ? Check the Vace Guide embedded in WanGP as it has also been updated.
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### June 19 2025: WanGP v6.2, Vace even more Powercharged
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👋 Have I told you that I am a big fan of Vace ? Here are more goodies to unleash its power:
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- If you ever wanted to watch Star Wars in 4:3, just use the new *Outpainting* feature and it will add the missing bits of image at the top and the bottom of the screen. The best thing is *Outpainting* can be combined with all the other Vace modifications, for instance you can change the main character of your favorite movie at the same time
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@ -20,9 +20,33 @@ def convert_to_numpy(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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self.model = DepthAnythingV2(encoder='vitl', features=256, out_channels=[256, 512, 1024, 1024]).to(self.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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@ -2,13 +2,15 @@
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import os
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import cv2
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import torch
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import numpy as np
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from . import util
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from .wholebody import Wholebody, HWC3, resize_image
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from PIL import Image
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import onnxruntime as ort
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from concurrent.futures import ThreadPoolExecutor
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import threading
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os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
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@ -23,8 +25,6 @@ def convert_to_numpy(image):
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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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def draw_pose(pose, H, W, use_hand=False, use_body=False, use_face=False):
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bodies = pose['bodies']
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faces = pose['faces']
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@ -43,6 +43,56 @@ def draw_pose(pose, H, W, use_hand=False, use_body=False, use_face=False):
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return canvas
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class OptimizedWholebody:
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"""Optimized version of Wholebody for faster serial processing"""
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def __init__(self, onnx_det, onnx_pose, device='cuda:0'):
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providers = ['CPUExecutionProvider'] if device == 'cpu' else ['CUDAExecutionProvider']
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self.session_det = ort.InferenceSession(path_or_bytes=onnx_det, providers=providers)
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self.session_pose = ort.InferenceSession(path_or_bytes=onnx_pose, providers=providers)
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self.device = device
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# Pre-allocate session options for better performance
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self.session_det.set_providers(providers)
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self.session_pose.set_providers(providers)
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# Get input names once to avoid repeated lookups
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self.det_input_name = self.session_det.get_inputs()[0].name
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self.pose_input_name = self.session_pose.get_inputs()[0].name
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self.pose_output_names = [out.name for out in self.session_pose.get_outputs()]
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def __call__(self, ori_img):
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from .onnxdet import inference_detector
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from .onnxpose import inference_pose
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det_result = inference_detector(self.session_det, ori_img)
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keypoints, scores = inference_pose(self.session_pose, det_result, ori_img)
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keypoints_info = np.concatenate(
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(keypoints, scores[..., None]), axis=-1)
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# compute neck joint
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neck = np.mean(keypoints_info[:, [5, 6]], axis=1)
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# neck score when visualizing pred
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neck[:, 2:4] = np.logical_and(
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keypoints_info[:, 5, 2:4] > 0.3,
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keypoints_info[:, 6, 2:4] > 0.3).astype(int)
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new_keypoints_info = np.insert(
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keypoints_info, 17, neck, axis=1)
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mmpose_idx = [
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17, 6, 8, 10, 7, 9, 12, 14, 16, 13, 15, 2, 1, 4, 3
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]
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openpose_idx = [
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1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17
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]
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new_keypoints_info[:, openpose_idx] = \
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new_keypoints_info[:, mmpose_idx]
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keypoints_info = new_keypoints_info
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keypoints, scores = keypoints_info[
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..., :2], keypoints_info[..., 2]
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return keypoints, scores, det_result
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class PoseAnnotator:
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def __init__(self, cfg, device=None):
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onnx_det = cfg['DETECTION_MODEL']
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@ -84,9 +134,7 @@ class PoseAnnotator:
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candidate[un_visible] = -1
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foot = candidate[:, 18:24]
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faces = candidate[:, 24:92]
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hands = candidate[:, 92:113]
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hands = np.vstack([hands, candidate[:, 113:]])
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@ -127,10 +175,24 @@ class PoseAnnotator:
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return ret_data, det_result
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class OptimizedPoseAnnotator(PoseAnnotator):
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"""Optimized version using improved Wholebody class"""
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def __init__(self, cfg, device=None):
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onnx_det = cfg['DETECTION_MODEL']
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onnx_pose = cfg['POSE_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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self.pose_estimation = OptimizedWholebody(onnx_det, onnx_pose, device=self.device)
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self.resize_size = cfg.get("RESIZE_SIZE", 1024)
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self.use_body = cfg.get('USE_BODY', True)
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self.use_face = cfg.get('USE_FACE', True)
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self.use_hand = cfg.get('USE_HAND', True)
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class PoseBodyFaceAnnotator(PoseAnnotator):
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def __init__(self, cfg):
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super().__init__(cfg)
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self.use_body, self.use_face, self.use_hand = True, True, False
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@torch.no_grad()
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@torch.inference_mode
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def forward(self, image):
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@ -138,14 +200,108 @@ class PoseBodyFaceAnnotator(PoseAnnotator):
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return ret_data['detected_map_bodyface']
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class PoseBodyFaceVideoAnnotator(PoseBodyFaceAnnotator):
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class OptimizedPoseBodyFaceVideoAnnotator:
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"""Optimized video annotator with multiple optimization strategies"""
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def __init__(self, cfg, num_workers=5, chunk_size=8):
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self.cfg = cfg
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self.num_workers = num_workers
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self.chunk_size = chunk_size
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self.use_body, self.use_face, self.use_hand = True, True, False
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# Initialize one annotator per worker to avoid ONNX session conflicts
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self.annotators = []
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for _ in range(num_workers):
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annotator = OptimizedPoseAnnotator(cfg)
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annotator.use_body, annotator.use_face, annotator.use_hand = True, True, False
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self.annotators.append(annotator)
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self._current_worker = 0
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self._worker_lock = threading.Lock()
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def _get_annotator(self):
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"""Get next available annotator in round-robin fashion"""
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with self._worker_lock:
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annotator = self.annotators[self._current_worker]
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self._current_worker = (self._current_worker + 1) % len(self.annotators)
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return annotator
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def _process_single_frame(self, frame_data):
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"""Process a single frame with error handling"""
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frame, frame_idx = frame_data
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try:
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annotator = self._get_annotator()
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# Convert frame
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frame = convert_to_numpy(frame)
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input_image = HWC3(frame[..., ::-1])
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resized_image = resize_image(input_image, annotator.resize_size)
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# Process
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ret_data, _ = annotator.process(resized_image, frame.shape[:2])
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if 'detected_map_bodyface' in ret_data:
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return frame_idx, ret_data['detected_map_bodyface']
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else:
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# Create empty frame if no detection
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h, w = frame.shape[:2]
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return frame_idx, np.zeros((h, w, 3), dtype=np.uint8)
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except Exception as e:
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print(f"Error processing frame {frame_idx}: {e}")
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# Return empty frame on error
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h, w = frame.shape[:2] if hasattr(frame, 'shape') else (480, 640)
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return frame_idx, np.zeros((h, w, 3), dtype=np.uint8)
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def forward(self, frames):
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"""Process video frames with optimizations"""
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if len(frames) == 0:
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return []
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# For small number of frames, use serial processing to avoid threading overhead
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if len(frames) <= 4:
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annotator = self.annotators[0]
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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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frame = convert_to_numpy(frame)
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input_image = HWC3(frame[..., ::-1])
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resized_image = resize_image(input_image, annotator.resize_size)
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ret_data, _ = annotator.process(resized_image, frame.shape[:2])
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if 'detected_map_bodyface' in ret_data:
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ret_frames.append(ret_data['detected_map_bodyface'])
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else:
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h, w = frame.shape[:2]
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ret_frames.append(np.zeros((h, w, 3), dtype=np.uint8))
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return ret_frames
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# For larger videos, use parallel processing
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frame_data = [(frame, idx) for idx, frame in enumerate(frames)]
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results = [None] * len(frames)
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# Process in chunks to manage memory
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for chunk_start in range(0, len(frame_data), self.chunk_size * self.num_workers):
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chunk_end = min(chunk_start + self.chunk_size * self.num_workers, len(frame_data))
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chunk_data = frame_data[chunk_start:chunk_end]
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with ThreadPoolExecutor(max_workers=self.num_workers) as executor:
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chunk_results = list(executor.map(self._process_single_frame, chunk_data))
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# Store results in correct order
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for frame_idx, result in chunk_results:
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results[frame_idx] = result
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return results
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# Alias for backward compatibility
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class PoseBodyFaceVideoAnnotator(OptimizedPoseBodyFaceVideoAnnotator):
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"""Backward compatible class name"""
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def __init__(self, cfg, num_workers=2, chunk_size=8):
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# Use optimized version with conservative settings
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super().__init__(cfg, num_workers=num_workers, chunk_size=chunk_size)
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# Keep the existing utility functions
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import imageio
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def save_one_video(file_path, videos, fps=8, quality=8, macro_block_size=None):
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@ -161,11 +317,7 @@ def save_one_video(file_path, videos, fps=8, quality=8, macro_block_size=None):
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def get_frames(video_path):
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frames = []
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# Opens the Video file with CV2
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cap = cv2.VideoCapture(video_path)
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fps = cap.get(cv2.CAP_PROP_FPS)
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print("video fps: " + str(fps))
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i = 0
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@ -175,9 +327,6 @@ def get_frames(video_path):
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break
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frames.append(frame)
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i += 1
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cap.release()
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cv2.destroyAllWindows()
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return frames, fps
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58
preprocessing/flow.py
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58
preprocessing/flow.py
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@ -0,0 +1,58 @@
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# -*- coding: utf-8 -*-
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import torch
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import numpy as np
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import argparse
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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 FlowAnnotator:
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def __init__(self, cfg, device=None):
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from .raft.raft import RAFT
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from .raft.utils.utils import InputPadder
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from .raft.utils import flow_viz
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params = {
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"small": False,
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"mixed_precision": False,
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"alternate_corr": False
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}
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params = argparse.Namespace(**params)
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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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self.model = RAFT(params)
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self.model.load_state_dict({k.replace('module.', ''): v for k, v in torch.load(pretrained_model, map_location="cpu", weights_only=True).items()})
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self.model = self.model.to(self.device).eval()
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self.InputPadder = InputPadder
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self.flow_viz = flow_viz
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def forward(self, frames):
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# frames / RGB
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frames = [torch.from_numpy(convert_to_numpy(frame).astype(np.uint8)).permute(2, 0, 1).float()[None].to(self.device) for frame in frames]
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flow_up_list, flow_up_vis_list = [], []
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with torch.no_grad():
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for i, (image1, image2) in enumerate(zip(frames[:-1], frames[1:])):
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padder = self.InputPadder(image1.shape)
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image1, image2 = padder.pad(image1, image2)
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flow_low, flow_up = self.model(image1, image2, iters=20, test_mode=True)
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flow_up = flow_up[0].permute(1, 2, 0).cpu().numpy()
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flow_up_vis = self.flow_viz.flow_to_image(flow_up)
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flow_up_list.append(flow_up)
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flow_up_vis_list.append(flow_up_vis)
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return flow_up_list, flow_up_vis_list # RGB
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class FlowVisAnnotator(FlowAnnotator):
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def forward(self, frames):
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flow_up_list, flow_up_vis_list = super().forward(frames)
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return flow_up_vis_list[:1] + flow_up_vis_list
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0
preprocessing/raft/__init__.py
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0
preprocessing/raft/__init__.py
Normal file
91
preprocessing/raft/corr.py
Normal file
91
preprocessing/raft/corr.py
Normal file
@ -0,0 +1,91 @@
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import torch
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import torch.nn.functional as F
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from .utils.utils import bilinear_sampler, coords_grid
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try:
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import alt_cuda_corr
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except:
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# alt_cuda_corr is not compiled
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pass
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class CorrBlock:
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def __init__(self, fmap1, fmap2, num_levels=4, radius=4):
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self.num_levels = num_levels
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self.radius = radius
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self.corr_pyramid = []
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# all pairs correlation
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corr = CorrBlock.corr(fmap1, fmap2)
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batch, h1, w1, dim, h2, w2 = corr.shape
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corr = corr.reshape(batch*h1*w1, dim, h2, w2)
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self.corr_pyramid.append(corr)
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for i in range(self.num_levels-1):
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corr = F.avg_pool2d(corr, 2, stride=2)
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self.corr_pyramid.append(corr)
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def __call__(self, coords):
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r = self.radius
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coords = coords.permute(0, 2, 3, 1)
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batch, h1, w1, _ = coords.shape
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out_pyramid = []
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for i in range(self.num_levels):
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corr = self.corr_pyramid[i]
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dx = torch.linspace(-r, r, 2*r+1)
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dy = torch.linspace(-r, r, 2*r+1)
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delta = torch.stack(torch.meshgrid(dy, dx, indexing='ij'), axis=-1).to(coords.device)
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centroid_lvl = coords.reshape(batch*h1*w1, 1, 1, 2) / 2**i
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delta_lvl = delta.view(1, 2*r+1, 2*r+1, 2)
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coords_lvl = centroid_lvl + delta_lvl
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||||
corr = bilinear_sampler(corr, coords_lvl)
|
||||
corr = corr.view(batch, h1, w1, -1)
|
||||
out_pyramid.append(corr)
|
||||
|
||||
out = torch.cat(out_pyramid, dim=-1)
|
||||
return out.permute(0, 3, 1, 2).contiguous().float()
|
||||
|
||||
@staticmethod
|
||||
def corr(fmap1, fmap2):
|
||||
batch, dim, ht, wd = fmap1.shape
|
||||
fmap1 = fmap1.view(batch, dim, ht*wd)
|
||||
fmap2 = fmap2.view(batch, dim, ht*wd)
|
||||
|
||||
corr = torch.matmul(fmap1.transpose(1,2), fmap2)
|
||||
corr = corr.view(batch, ht, wd, 1, ht, wd)
|
||||
return corr / torch.sqrt(torch.tensor(dim).float())
|
||||
|
||||
|
||||
class AlternateCorrBlock:
|
||||
def __init__(self, fmap1, fmap2, num_levels=4, radius=4):
|
||||
self.num_levels = num_levels
|
||||
self.radius = radius
|
||||
|
||||
self.pyramid = [(fmap1, fmap2)]
|
||||
for i in range(self.num_levels):
|
||||
fmap1 = F.avg_pool2d(fmap1, 2, stride=2)
|
||||
fmap2 = F.avg_pool2d(fmap2, 2, stride=2)
|
||||
self.pyramid.append((fmap1, fmap2))
|
||||
|
||||
def __call__(self, coords):
|
||||
coords = coords.permute(0, 2, 3, 1)
|
||||
B, H, W, _ = coords.shape
|
||||
dim = self.pyramid[0][0].shape[1]
|
||||
|
||||
corr_list = []
|
||||
for i in range(self.num_levels):
|
||||
r = self.radius
|
||||
fmap1_i = self.pyramid[0][0].permute(0, 2, 3, 1).contiguous()
|
||||
fmap2_i = self.pyramid[i][1].permute(0, 2, 3, 1).contiguous()
|
||||
|
||||
coords_i = (coords / 2**i).reshape(B, 1, H, W, 2).contiguous()
|
||||
corr, = alt_cuda_corr.forward(fmap1_i, fmap2_i, coords_i, r)
|
||||
corr_list.append(corr.squeeze(1))
|
||||
|
||||
corr = torch.stack(corr_list, dim=1)
|
||||
corr = corr.reshape(B, -1, H, W)
|
||||
return corr / torch.sqrt(torch.tensor(dim).float())
|
||||
235
preprocessing/raft/datasets.py
Normal file
235
preprocessing/raft/datasets.py
Normal file
@ -0,0 +1,235 @@
|
||||
# Data loading based on https://github.com/NVIDIA/flownet2-pytorch
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.utils.data as data
|
||||
import torch.nn.functional as F
|
||||
|
||||
import os
|
||||
import math
|
||||
import random
|
||||
from glob import glob
|
||||
import os.path as osp
|
||||
|
||||
from .utils import frame_utils
|
||||
from .utils.augmentor import FlowAugmentor, SparseFlowAugmentor
|
||||
|
||||
|
||||
class FlowDataset(data.Dataset):
|
||||
def __init__(self, aug_params=None, sparse=False):
|
||||
self.augmentor = None
|
||||
self.sparse = sparse
|
||||
if aug_params is not None:
|
||||
if sparse:
|
||||
self.augmentor = SparseFlowAugmentor(**aug_params)
|
||||
else:
|
||||
self.augmentor = FlowAugmentor(**aug_params)
|
||||
|
||||
self.is_test = False
|
||||
self.init_seed = False
|
||||
self.flow_list = []
|
||||
self.image_list = []
|
||||
self.extra_info = []
|
||||
|
||||
def __getitem__(self, index):
|
||||
|
||||
if self.is_test:
|
||||
img1 = frame_utils.read_gen(self.image_list[index][0])
|
||||
img2 = frame_utils.read_gen(self.image_list[index][1])
|
||||
img1 = np.array(img1).astype(np.uint8)[..., :3]
|
||||
img2 = np.array(img2).astype(np.uint8)[..., :3]
|
||||
img1 = torch.from_numpy(img1).permute(2, 0, 1).float()
|
||||
img2 = torch.from_numpy(img2).permute(2, 0, 1).float()
|
||||
return img1, img2, self.extra_info[index]
|
||||
|
||||
if not self.init_seed:
|
||||
worker_info = torch.utils.data.get_worker_info()
|
||||
if worker_info is not None:
|
||||
torch.manual_seed(worker_info.id)
|
||||
np.random.seed(worker_info.id)
|
||||
random.seed(worker_info.id)
|
||||
self.init_seed = True
|
||||
|
||||
index = index % len(self.image_list)
|
||||
valid = None
|
||||
if self.sparse:
|
||||
flow, valid = frame_utils.readFlowKITTI(self.flow_list[index])
|
||||
else:
|
||||
flow = frame_utils.read_gen(self.flow_list[index])
|
||||
|
||||
img1 = frame_utils.read_gen(self.image_list[index][0])
|
||||
img2 = frame_utils.read_gen(self.image_list[index][1])
|
||||
|
||||
flow = np.array(flow).astype(np.float32)
|
||||
img1 = np.array(img1).astype(np.uint8)
|
||||
img2 = np.array(img2).astype(np.uint8)
|
||||
|
||||
# grayscale images
|
||||
if len(img1.shape) == 2:
|
||||
img1 = np.tile(img1[...,None], (1, 1, 3))
|
||||
img2 = np.tile(img2[...,None], (1, 1, 3))
|
||||
else:
|
||||
img1 = img1[..., :3]
|
||||
img2 = img2[..., :3]
|
||||
|
||||
if self.augmentor is not None:
|
||||
if self.sparse:
|
||||
img1, img2, flow, valid = self.augmentor(img1, img2, flow, valid)
|
||||
else:
|
||||
img1, img2, flow = self.augmentor(img1, img2, flow)
|
||||
|
||||
img1 = torch.from_numpy(img1).permute(2, 0, 1).float()
|
||||
img2 = torch.from_numpy(img2).permute(2, 0, 1).float()
|
||||
flow = torch.from_numpy(flow).permute(2, 0, 1).float()
|
||||
|
||||
if valid is not None:
|
||||
valid = torch.from_numpy(valid)
|
||||
else:
|
||||
valid = (flow[0].abs() < 1000) & (flow[1].abs() < 1000)
|
||||
|
||||
return img1, img2, flow, valid.float()
|
||||
|
||||
|
||||
def __rmul__(self, v):
|
||||
self.flow_list = v * self.flow_list
|
||||
self.image_list = v * self.image_list
|
||||
return self
|
||||
|
||||
def __len__(self):
|
||||
return len(self.image_list)
|
||||
|
||||
|
||||
class MpiSintel(FlowDataset):
|
||||
def __init__(self, aug_params=None, split='training', root='datasets/Sintel', dstype='clean'):
|
||||
super(MpiSintel, self).__init__(aug_params)
|
||||
flow_root = osp.join(root, split, 'flow')
|
||||
image_root = osp.join(root, split, dstype)
|
||||
|
||||
if split == 'test':
|
||||
self.is_test = True
|
||||
|
||||
for scene in os.listdir(image_root):
|
||||
image_list = sorted(glob(osp.join(image_root, scene, '*.png')))
|
||||
for i in range(len(image_list)-1):
|
||||
self.image_list += [ [image_list[i], image_list[i+1]] ]
|
||||
self.extra_info += [ (scene, i) ] # scene and frame_id
|
||||
|
||||
if split != 'test':
|
||||
self.flow_list += sorted(glob(osp.join(flow_root, scene, '*.flo')))
|
||||
|
||||
|
||||
class FlyingChairs(FlowDataset):
|
||||
def __init__(self, aug_params=None, split='train', root='datasets/FlyingChairs_release/data'):
|
||||
super(FlyingChairs, self).__init__(aug_params)
|
||||
|
||||
images = sorted(glob(osp.join(root, '*.ppm')))
|
||||
flows = sorted(glob(osp.join(root, '*.flo')))
|
||||
assert (len(images)//2 == len(flows))
|
||||
|
||||
split_list = np.loadtxt('chairs_split.txt', dtype=np.int32)
|
||||
for i in range(len(flows)):
|
||||
xid = split_list[i]
|
||||
if (split=='training' and xid==1) or (split=='validation' and xid==2):
|
||||
self.flow_list += [ flows[i] ]
|
||||
self.image_list += [ [images[2*i], images[2*i+1]] ]
|
||||
|
||||
|
||||
class FlyingThings3D(FlowDataset):
|
||||
def __init__(self, aug_params=None, root='datasets/FlyingThings3D', dstype='frames_cleanpass'):
|
||||
super(FlyingThings3D, self).__init__(aug_params)
|
||||
|
||||
for cam in ['left']:
|
||||
for direction in ['into_future', 'into_past']:
|
||||
image_dirs = sorted(glob(osp.join(root, dstype, 'TRAIN/*/*')))
|
||||
image_dirs = sorted([osp.join(f, cam) for f in image_dirs])
|
||||
|
||||
flow_dirs = sorted(glob(osp.join(root, 'optical_flow/TRAIN/*/*')))
|
||||
flow_dirs = sorted([osp.join(f, direction, cam) for f in flow_dirs])
|
||||
|
||||
for idir, fdir in zip(image_dirs, flow_dirs):
|
||||
images = sorted(glob(osp.join(idir, '*.png')) )
|
||||
flows = sorted(glob(osp.join(fdir, '*.pfm')) )
|
||||
for i in range(len(flows)-1):
|
||||
if direction == 'into_future':
|
||||
self.image_list += [ [images[i], images[i+1]] ]
|
||||
self.flow_list += [ flows[i] ]
|
||||
elif direction == 'into_past':
|
||||
self.image_list += [ [images[i+1], images[i]] ]
|
||||
self.flow_list += [ flows[i+1] ]
|
||||
|
||||
|
||||
class KITTI(FlowDataset):
|
||||
def __init__(self, aug_params=None, split='training', root='datasets/KITTI'):
|
||||
super(KITTI, self).__init__(aug_params, sparse=True)
|
||||
if split == 'testing':
|
||||
self.is_test = True
|
||||
|
||||
root = osp.join(root, split)
|
||||
images1 = sorted(glob(osp.join(root, 'image_2/*_10.png')))
|
||||
images2 = sorted(glob(osp.join(root, 'image_2/*_11.png')))
|
||||
|
||||
for img1, img2 in zip(images1, images2):
|
||||
frame_id = img1.split('/')[-1]
|
||||
self.extra_info += [ [frame_id] ]
|
||||
self.image_list += [ [img1, img2] ]
|
||||
|
||||
if split == 'training':
|
||||
self.flow_list = sorted(glob(osp.join(root, 'flow_occ/*_10.png')))
|
||||
|
||||
|
||||
class HD1K(FlowDataset):
|
||||
def __init__(self, aug_params=None, root='datasets/HD1k'):
|
||||
super(HD1K, self).__init__(aug_params, sparse=True)
|
||||
|
||||
seq_ix = 0
|
||||
while 1:
|
||||
flows = sorted(glob(os.path.join(root, 'hd1k_flow_gt', 'flow_occ/%06d_*.png' % seq_ix)))
|
||||
images = sorted(glob(os.path.join(root, 'hd1k_input', 'image_2/%06d_*.png' % seq_ix)))
|
||||
|
||||
if len(flows) == 0:
|
||||
break
|
||||
|
||||
for i in range(len(flows)-1):
|
||||
self.flow_list += [flows[i]]
|
||||
self.image_list += [ [images[i], images[i+1]] ]
|
||||
|
||||
seq_ix += 1
|
||||
|
||||
|
||||
def fetch_dataloader(args, TRAIN_DS='C+T+K+S+H'):
|
||||
""" Create the data loader for the corresponding trainign set """
|
||||
|
||||
if args.stage == 'chairs':
|
||||
aug_params = {'crop_size': args.image_size, 'min_scale': -0.1, 'max_scale': 1.0, 'do_flip': True}
|
||||
train_dataset = FlyingChairs(aug_params, split='training')
|
||||
|
||||
elif args.stage == 'things':
|
||||
aug_params = {'crop_size': args.image_size, 'min_scale': -0.4, 'max_scale': 0.8, 'do_flip': True}
|
||||
clean_dataset = FlyingThings3D(aug_params, dstype='frames_cleanpass')
|
||||
final_dataset = FlyingThings3D(aug_params, dstype='frames_finalpass')
|
||||
train_dataset = clean_dataset + final_dataset
|
||||
|
||||
elif args.stage == 'sintel':
|
||||
aug_params = {'crop_size': args.image_size, 'min_scale': -0.2, 'max_scale': 0.6, 'do_flip': True}
|
||||
things = FlyingThings3D(aug_params, dstype='frames_cleanpass')
|
||||
sintel_clean = MpiSintel(aug_params, split='training', dstype='clean')
|
||||
sintel_final = MpiSintel(aug_params, split='training', dstype='final')
|
||||
|
||||
if TRAIN_DS == 'C+T+K+S+H':
|
||||
kitti = KITTI({'crop_size': args.image_size, 'min_scale': -0.3, 'max_scale': 0.5, 'do_flip': True})
|
||||
hd1k = HD1K({'crop_size': args.image_size, 'min_scale': -0.5, 'max_scale': 0.2, 'do_flip': True})
|
||||
train_dataset = 100*sintel_clean + 100*sintel_final + 200*kitti + 5*hd1k + things
|
||||
|
||||
elif TRAIN_DS == 'C+T+K/S':
|
||||
train_dataset = 100*sintel_clean + 100*sintel_final + things
|
||||
|
||||
elif args.stage == 'kitti':
|
||||
aug_params = {'crop_size': args.image_size, 'min_scale': -0.2, 'max_scale': 0.4, 'do_flip': False}
|
||||
train_dataset = KITTI(aug_params, split='training')
|
||||
|
||||
train_loader = data.DataLoader(train_dataset, batch_size=args.batch_size,
|
||||
pin_memory=False, shuffle=True, num_workers=4, drop_last=True)
|
||||
|
||||
print('Training with %d image pairs' % len(train_dataset))
|
||||
return train_loader
|
||||
|
||||
267
preprocessing/raft/extractor.py
Normal file
267
preprocessing/raft/extractor.py
Normal file
@ -0,0 +1,267 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
class ResidualBlock(nn.Module):
|
||||
def __init__(self, in_planes, planes, norm_fn='group', stride=1):
|
||||
super(ResidualBlock, self).__init__()
|
||||
|
||||
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, stride=stride)
|
||||
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
|
||||
num_groups = planes // 8
|
||||
|
||||
if norm_fn == 'group':
|
||||
self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
|
||||
self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
|
||||
if not stride == 1:
|
||||
self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
|
||||
|
||||
elif norm_fn == 'batch':
|
||||
self.norm1 = nn.BatchNorm2d(planes)
|
||||
self.norm2 = nn.BatchNorm2d(planes)
|
||||
if not stride == 1:
|
||||
self.norm3 = nn.BatchNorm2d(planes)
|
||||
|
||||
elif norm_fn == 'instance':
|
||||
self.norm1 = nn.InstanceNorm2d(planes)
|
||||
self.norm2 = nn.InstanceNorm2d(planes)
|
||||
if not stride == 1:
|
||||
self.norm3 = nn.InstanceNorm2d(planes)
|
||||
|
||||
elif norm_fn == 'none':
|
||||
self.norm1 = nn.Sequential()
|
||||
self.norm2 = nn.Sequential()
|
||||
if not stride == 1:
|
||||
self.norm3 = nn.Sequential()
|
||||
|
||||
if stride == 1:
|
||||
self.downsample = None
|
||||
|
||||
else:
|
||||
self.downsample = nn.Sequential(
|
||||
nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3)
|
||||
|
||||
|
||||
def forward(self, x):
|
||||
y = x
|
||||
y = self.relu(self.norm1(self.conv1(y)))
|
||||
y = self.relu(self.norm2(self.conv2(y)))
|
||||
|
||||
if self.downsample is not None:
|
||||
x = self.downsample(x)
|
||||
|
||||
return self.relu(x+y)
|
||||
|
||||
|
||||
|
||||
class BottleneckBlock(nn.Module):
|
||||
def __init__(self, in_planes, planes, norm_fn='group', stride=1):
|
||||
super(BottleneckBlock, self).__init__()
|
||||
|
||||
self.conv1 = nn.Conv2d(in_planes, planes//4, kernel_size=1, padding=0)
|
||||
self.conv2 = nn.Conv2d(planes//4, planes//4, kernel_size=3, padding=1, stride=stride)
|
||||
self.conv3 = nn.Conv2d(planes//4, planes, kernel_size=1, padding=0)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
|
||||
num_groups = planes // 8
|
||||
|
||||
if norm_fn == 'group':
|
||||
self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes//4)
|
||||
self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes//4)
|
||||
self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
|
||||
if not stride == 1:
|
||||
self.norm4 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
|
||||
|
||||
elif norm_fn == 'batch':
|
||||
self.norm1 = nn.BatchNorm2d(planes//4)
|
||||
self.norm2 = nn.BatchNorm2d(planes//4)
|
||||
self.norm3 = nn.BatchNorm2d(planes)
|
||||
if not stride == 1:
|
||||
self.norm4 = nn.BatchNorm2d(planes)
|
||||
|
||||
elif norm_fn == 'instance':
|
||||
self.norm1 = nn.InstanceNorm2d(planes//4)
|
||||
self.norm2 = nn.InstanceNorm2d(planes//4)
|
||||
self.norm3 = nn.InstanceNorm2d(planes)
|
||||
if not stride == 1:
|
||||
self.norm4 = nn.InstanceNorm2d(planes)
|
||||
|
||||
elif norm_fn == 'none':
|
||||
self.norm1 = nn.Sequential()
|
||||
self.norm2 = nn.Sequential()
|
||||
self.norm3 = nn.Sequential()
|
||||
if not stride == 1:
|
||||
self.norm4 = nn.Sequential()
|
||||
|
||||
if stride == 1:
|
||||
self.downsample = None
|
||||
|
||||
else:
|
||||
self.downsample = nn.Sequential(
|
||||
nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm4)
|
||||
|
||||
|
||||
def forward(self, x):
|
||||
y = x
|
||||
y = self.relu(self.norm1(self.conv1(y)))
|
||||
y = self.relu(self.norm2(self.conv2(y)))
|
||||
y = self.relu(self.norm3(self.conv3(y)))
|
||||
|
||||
if self.downsample is not None:
|
||||
x = self.downsample(x)
|
||||
|
||||
return self.relu(x+y)
|
||||
|
||||
class BasicEncoder(nn.Module):
|
||||
def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0):
|
||||
super(BasicEncoder, self).__init__()
|
||||
self.norm_fn = norm_fn
|
||||
|
||||
if self.norm_fn == 'group':
|
||||
self.norm1 = nn.GroupNorm(num_groups=8, num_channels=64)
|
||||
|
||||
elif self.norm_fn == 'batch':
|
||||
self.norm1 = nn.BatchNorm2d(64)
|
||||
|
||||
elif self.norm_fn == 'instance':
|
||||
self.norm1 = nn.InstanceNorm2d(64)
|
||||
|
||||
elif self.norm_fn == 'none':
|
||||
self.norm1 = nn.Sequential()
|
||||
|
||||
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
|
||||
self.relu1 = nn.ReLU(inplace=True)
|
||||
|
||||
self.in_planes = 64
|
||||
self.layer1 = self._make_layer(64, stride=1)
|
||||
self.layer2 = self._make_layer(96, stride=2)
|
||||
self.layer3 = self._make_layer(128, stride=2)
|
||||
|
||||
# output convolution
|
||||
self.conv2 = nn.Conv2d(128, output_dim, kernel_size=1)
|
||||
|
||||
self.dropout = None
|
||||
if dropout > 0:
|
||||
self.dropout = nn.Dropout2d(p=dropout)
|
||||
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.Conv2d):
|
||||
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
|
||||
elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
|
||||
if m.weight is not None:
|
||||
nn.init.constant_(m.weight, 1)
|
||||
if m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
|
||||
def _make_layer(self, dim, stride=1):
|
||||
layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride)
|
||||
layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1)
|
||||
layers = (layer1, layer2)
|
||||
|
||||
self.in_planes = dim
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
|
||||
def forward(self, x):
|
||||
|
||||
# if input is list, combine batch dimension
|
||||
is_list = isinstance(x, tuple) or isinstance(x, list)
|
||||
if is_list:
|
||||
batch_dim = x[0].shape[0]
|
||||
x = torch.cat(x, dim=0)
|
||||
|
||||
x = self.conv1(x)
|
||||
x = self.norm1(x)
|
||||
x = self.relu1(x)
|
||||
|
||||
x = self.layer1(x)
|
||||
x = self.layer2(x)
|
||||
x = self.layer3(x)
|
||||
|
||||
x = self.conv2(x)
|
||||
|
||||
if self.training and self.dropout is not None:
|
||||
x = self.dropout(x)
|
||||
|
||||
if is_list:
|
||||
x = torch.split(x, [batch_dim, batch_dim], dim=0)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class SmallEncoder(nn.Module):
|
||||
def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0):
|
||||
super(SmallEncoder, self).__init__()
|
||||
self.norm_fn = norm_fn
|
||||
|
||||
if self.norm_fn == 'group':
|
||||
self.norm1 = nn.GroupNorm(num_groups=8, num_channels=32)
|
||||
|
||||
elif self.norm_fn == 'batch':
|
||||
self.norm1 = nn.BatchNorm2d(32)
|
||||
|
||||
elif self.norm_fn == 'instance':
|
||||
self.norm1 = nn.InstanceNorm2d(32)
|
||||
|
||||
elif self.norm_fn == 'none':
|
||||
self.norm1 = nn.Sequential()
|
||||
|
||||
self.conv1 = nn.Conv2d(3, 32, kernel_size=7, stride=2, padding=3)
|
||||
self.relu1 = nn.ReLU(inplace=True)
|
||||
|
||||
self.in_planes = 32
|
||||
self.layer1 = self._make_layer(32, stride=1)
|
||||
self.layer2 = self._make_layer(64, stride=2)
|
||||
self.layer3 = self._make_layer(96, stride=2)
|
||||
|
||||
self.dropout = None
|
||||
if dropout > 0:
|
||||
self.dropout = nn.Dropout2d(p=dropout)
|
||||
|
||||
self.conv2 = nn.Conv2d(96, output_dim, kernel_size=1)
|
||||
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.Conv2d):
|
||||
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
|
||||
elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
|
||||
if m.weight is not None:
|
||||
nn.init.constant_(m.weight, 1)
|
||||
if m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
|
||||
def _make_layer(self, dim, stride=1):
|
||||
layer1 = BottleneckBlock(self.in_planes, dim, self.norm_fn, stride=stride)
|
||||
layer2 = BottleneckBlock(dim, dim, self.norm_fn, stride=1)
|
||||
layers = (layer1, layer2)
|
||||
|
||||
self.in_planes = dim
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
|
||||
def forward(self, x):
|
||||
|
||||
# if input is list, combine batch dimension
|
||||
is_list = isinstance(x, tuple) or isinstance(x, list)
|
||||
if is_list:
|
||||
batch_dim = x[0].shape[0]
|
||||
x = torch.cat(x, dim=0)
|
||||
|
||||
x = self.conv1(x)
|
||||
x = self.norm1(x)
|
||||
x = self.relu1(x)
|
||||
|
||||
x = self.layer1(x)
|
||||
x = self.layer2(x)
|
||||
x = self.layer3(x)
|
||||
x = self.conv2(x)
|
||||
|
||||
if self.training and self.dropout is not None:
|
||||
x = self.dropout(x)
|
||||
|
||||
if is_list:
|
||||
x = torch.split(x, [batch_dim, batch_dim], dim=0)
|
||||
|
||||
return x
|
||||
144
preprocessing/raft/raft.py
Normal file
144
preprocessing/raft/raft.py
Normal file
@ -0,0 +1,144 @@
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from .update import BasicUpdateBlock, SmallUpdateBlock
|
||||
from .extractor import BasicEncoder, SmallEncoder
|
||||
from .corr import CorrBlock, AlternateCorrBlock
|
||||
from .utils.utils import bilinear_sampler, coords_grid, upflow8
|
||||
|
||||
try:
|
||||
autocast = torch.amp.autocast
|
||||
except:
|
||||
# dummy autocast for PyTorch < 1.6
|
||||
class autocast:
|
||||
def __init__(self, enabled):
|
||||
pass
|
||||
def __enter__(self):
|
||||
pass
|
||||
def __exit__(self, *args):
|
||||
pass
|
||||
|
||||
|
||||
class RAFT(nn.Module):
|
||||
def __init__(self, args):
|
||||
super(RAFT, self).__init__()
|
||||
self.args = args
|
||||
|
||||
if args.small:
|
||||
self.hidden_dim = hdim = 96
|
||||
self.context_dim = cdim = 64
|
||||
args.corr_levels = 4
|
||||
args.corr_radius = 3
|
||||
|
||||
else:
|
||||
self.hidden_dim = hdim = 128
|
||||
self.context_dim = cdim = 128
|
||||
args.corr_levels = 4
|
||||
args.corr_radius = 4
|
||||
|
||||
if 'dropout' not in self.args:
|
||||
self.args.dropout = 0
|
||||
|
||||
if 'alternate_corr' not in self.args:
|
||||
self.args.alternate_corr = False
|
||||
|
||||
# feature network, context network, and update block
|
||||
if args.small:
|
||||
self.fnet = SmallEncoder(output_dim=128, norm_fn='instance', dropout=args.dropout)
|
||||
self.cnet = SmallEncoder(output_dim=hdim+cdim, norm_fn='none', dropout=args.dropout)
|
||||
self.update_block = SmallUpdateBlock(self.args, hidden_dim=hdim)
|
||||
|
||||
else:
|
||||
self.fnet = BasicEncoder(output_dim=256, norm_fn='instance', dropout=args.dropout)
|
||||
self.cnet = BasicEncoder(output_dim=hdim+cdim, norm_fn='batch', dropout=args.dropout)
|
||||
self.update_block = BasicUpdateBlock(self.args, hidden_dim=hdim)
|
||||
|
||||
def freeze_bn(self):
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.BatchNorm2d):
|
||||
m.eval()
|
||||
|
||||
def initialize_flow(self, img):
|
||||
""" Flow is represented as difference between two coordinate grids flow = coords1 - coords0"""
|
||||
N, C, H, W = img.shape
|
||||
coords0 = coords_grid(N, H//8, W//8).to(img.device)
|
||||
coords1 = coords_grid(N, H//8, W//8).to(img.device)
|
||||
|
||||
# optical flow computed as difference: flow = coords1 - coords0
|
||||
return coords0, coords1
|
||||
|
||||
def upsample_flow(self, flow, mask):
|
||||
""" Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination """
|
||||
N, _, H, W = flow.shape
|
||||
mask = mask.view(N, 1, 9, 8, 8, H, W)
|
||||
mask = torch.softmax(mask, dim=2)
|
||||
|
||||
up_flow = F.unfold(8 * flow, [3,3], padding=1)
|
||||
up_flow = up_flow.view(N, 2, 9, 1, 1, H, W)
|
||||
|
||||
up_flow = torch.sum(mask * up_flow, dim=2)
|
||||
up_flow = up_flow.permute(0, 1, 4, 2, 5, 3)
|
||||
return up_flow.reshape(N, 2, 8*H, 8*W)
|
||||
|
||||
|
||||
def forward(self, image1, image2, iters=12, flow_init=None, upsample=True, test_mode=False):
|
||||
""" Estimate optical flow between pair of frames """
|
||||
|
||||
image1 = 2 * (image1 / 255.0) - 1.0
|
||||
image2 = 2 * (image2 / 255.0) - 1.0
|
||||
|
||||
image1 = image1.contiguous()
|
||||
image2 = image2.contiguous()
|
||||
|
||||
hdim = self.hidden_dim
|
||||
cdim = self.context_dim
|
||||
|
||||
# run the feature network
|
||||
with autocast('cuda', enabled=self.args.mixed_precision):
|
||||
fmap1, fmap2 = self.fnet([image1, image2])
|
||||
|
||||
fmap1 = fmap1.float()
|
||||
fmap2 = fmap2.float()
|
||||
if self.args.alternate_corr:
|
||||
corr_fn = AlternateCorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
|
||||
else:
|
||||
corr_fn = CorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
|
||||
|
||||
# run the context network
|
||||
with autocast('cuda', enabled=self.args.mixed_precision):
|
||||
cnet = self.cnet(image1)
|
||||
net, inp = torch.split(cnet, [hdim, cdim], dim=1)
|
||||
net = torch.tanh(net)
|
||||
inp = torch.relu(inp)
|
||||
|
||||
coords0, coords1 = self.initialize_flow(image1)
|
||||
|
||||
if flow_init is not None:
|
||||
coords1 = coords1 + flow_init
|
||||
|
||||
flow_predictions = []
|
||||
for itr in range(iters):
|
||||
coords1 = coords1.detach()
|
||||
corr = corr_fn(coords1) # index correlation volume
|
||||
|
||||
flow = coords1 - coords0
|
||||
with autocast('cuda', enabled=self.args.mixed_precision):
|
||||
net, up_mask, delta_flow = self.update_block(net, inp, corr, flow)
|
||||
|
||||
# F(t+1) = F(t) + \Delta(t)
|
||||
coords1 = coords1 + delta_flow
|
||||
|
||||
# upsample predictions
|
||||
if up_mask is None:
|
||||
flow_up = upflow8(coords1 - coords0)
|
||||
else:
|
||||
flow_up = self.upsample_flow(coords1 - coords0, up_mask)
|
||||
|
||||
flow_predictions.append(flow_up)
|
||||
|
||||
if test_mode:
|
||||
return coords1 - coords0, flow_up
|
||||
|
||||
return flow_predictions
|
||||
139
preprocessing/raft/update.py
Normal file
139
preprocessing/raft/update.py
Normal file
@ -0,0 +1,139 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
class FlowHead(nn.Module):
|
||||
def __init__(self, input_dim=128, hidden_dim=256):
|
||||
super(FlowHead, self).__init__()
|
||||
self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
|
||||
self.conv2 = nn.Conv2d(hidden_dim, 2, 3, padding=1)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
|
||||
def forward(self, x):
|
||||
return self.conv2(self.relu(self.conv1(x)))
|
||||
|
||||
class ConvGRU(nn.Module):
|
||||
def __init__(self, hidden_dim=128, input_dim=192+128):
|
||||
super(ConvGRU, self).__init__()
|
||||
self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)
|
||||
self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)
|
||||
self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)
|
||||
|
||||
def forward(self, h, x):
|
||||
hx = torch.cat([h, x], dim=1)
|
||||
|
||||
z = torch.sigmoid(self.convz(hx))
|
||||
r = torch.sigmoid(self.convr(hx))
|
||||
q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1)))
|
||||
|
||||
h = (1-z) * h + z * q
|
||||
return h
|
||||
|
||||
class SepConvGRU(nn.Module):
|
||||
def __init__(self, hidden_dim=128, input_dim=192+128):
|
||||
super(SepConvGRU, self).__init__()
|
||||
self.convz1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
|
||||
self.convr1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
|
||||
self.convq1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
|
||||
|
||||
self.convz2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
|
||||
self.convr2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
|
||||
self.convq2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
|
||||
|
||||
|
||||
def forward(self, h, x):
|
||||
# horizontal
|
||||
hx = torch.cat([h, x], dim=1)
|
||||
z = torch.sigmoid(self.convz1(hx))
|
||||
r = torch.sigmoid(self.convr1(hx))
|
||||
q = torch.tanh(self.convq1(torch.cat([r*h, x], dim=1)))
|
||||
h = (1-z) * h + z * q
|
||||
|
||||
# vertical
|
||||
hx = torch.cat([h, x], dim=1)
|
||||
z = torch.sigmoid(self.convz2(hx))
|
||||
r = torch.sigmoid(self.convr2(hx))
|
||||
q = torch.tanh(self.convq2(torch.cat([r*h, x], dim=1)))
|
||||
h = (1-z) * h + z * q
|
||||
|
||||
return h
|
||||
|
||||
class SmallMotionEncoder(nn.Module):
|
||||
def __init__(self, args):
|
||||
super(SmallMotionEncoder, self).__init__()
|
||||
cor_planes = args.corr_levels * (2*args.corr_radius + 1)**2
|
||||
self.convc1 = nn.Conv2d(cor_planes, 96, 1, padding=0)
|
||||
self.convf1 = nn.Conv2d(2, 64, 7, padding=3)
|
||||
self.convf2 = nn.Conv2d(64, 32, 3, padding=1)
|
||||
self.conv = nn.Conv2d(128, 80, 3, padding=1)
|
||||
|
||||
def forward(self, flow, corr):
|
||||
cor = F.relu(self.convc1(corr))
|
||||
flo = F.relu(self.convf1(flow))
|
||||
flo = F.relu(self.convf2(flo))
|
||||
cor_flo = torch.cat([cor, flo], dim=1)
|
||||
out = F.relu(self.conv(cor_flo))
|
||||
return torch.cat([out, flow], dim=1)
|
||||
|
||||
class BasicMotionEncoder(nn.Module):
|
||||
def __init__(self, args):
|
||||
super(BasicMotionEncoder, self).__init__()
|
||||
cor_planes = args.corr_levels * (2*args.corr_radius + 1)**2
|
||||
self.convc1 = nn.Conv2d(cor_planes, 256, 1, padding=0)
|
||||
self.convc2 = nn.Conv2d(256, 192, 3, padding=1)
|
||||
self.convf1 = nn.Conv2d(2, 128, 7, padding=3)
|
||||
self.convf2 = nn.Conv2d(128, 64, 3, padding=1)
|
||||
self.conv = nn.Conv2d(64+192, 128-2, 3, padding=1)
|
||||
|
||||
def forward(self, flow, corr):
|
||||
cor = F.relu(self.convc1(corr))
|
||||
cor = F.relu(self.convc2(cor))
|
||||
flo = F.relu(self.convf1(flow))
|
||||
flo = F.relu(self.convf2(flo))
|
||||
|
||||
cor_flo = torch.cat([cor, flo], dim=1)
|
||||
out = F.relu(self.conv(cor_flo))
|
||||
return torch.cat([out, flow], dim=1)
|
||||
|
||||
class SmallUpdateBlock(nn.Module):
|
||||
def __init__(self, args, hidden_dim=96):
|
||||
super(SmallUpdateBlock, self).__init__()
|
||||
self.encoder = SmallMotionEncoder(args)
|
||||
self.gru = ConvGRU(hidden_dim=hidden_dim, input_dim=82+64)
|
||||
self.flow_head = FlowHead(hidden_dim, hidden_dim=128)
|
||||
|
||||
def forward(self, net, inp, corr, flow):
|
||||
motion_features = self.encoder(flow, corr)
|
||||
inp = torch.cat([inp, motion_features], dim=1)
|
||||
net = self.gru(net, inp)
|
||||
delta_flow = self.flow_head(net)
|
||||
|
||||
return net, None, delta_flow
|
||||
|
||||
class BasicUpdateBlock(nn.Module):
|
||||
def __init__(self, args, hidden_dim=128, input_dim=128):
|
||||
super(BasicUpdateBlock, self).__init__()
|
||||
self.args = args
|
||||
self.encoder = BasicMotionEncoder(args)
|
||||
self.gru = SepConvGRU(hidden_dim=hidden_dim, input_dim=128+hidden_dim)
|
||||
self.flow_head = FlowHead(hidden_dim, hidden_dim=256)
|
||||
|
||||
self.mask = nn.Sequential(
|
||||
nn.Conv2d(128, 256, 3, padding=1),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(256, 64*9, 1, padding=0))
|
||||
|
||||
def forward(self, net, inp, corr, flow, upsample=True):
|
||||
motion_features = self.encoder(flow, corr)
|
||||
inp = torch.cat([inp, motion_features], dim=1)
|
||||
|
||||
net = self.gru(net, inp)
|
||||
delta_flow = self.flow_head(net)
|
||||
|
||||
# scale mask to balence gradients
|
||||
mask = .25 * self.mask(net)
|
||||
return net, mask, delta_flow
|
||||
|
||||
|
||||
|
||||
0
preprocessing/raft/utils/__init__.py
Normal file
0
preprocessing/raft/utils/__init__.py
Normal file
246
preprocessing/raft/utils/augmentor.py
Normal file
246
preprocessing/raft/utils/augmentor.py
Normal file
@ -0,0 +1,246 @@
|
||||
import numpy as np
|
||||
import random
|
||||
import math
|
||||
from PIL import Image
|
||||
|
||||
import cv2
|
||||
cv2.setNumThreads(0)
|
||||
cv2.ocl.setUseOpenCL(False)
|
||||
|
||||
import torch
|
||||
from torchvision.transforms import ColorJitter
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
class FlowAugmentor:
|
||||
def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=True):
|
||||
|
||||
# spatial augmentation params
|
||||
self.crop_size = crop_size
|
||||
self.min_scale = min_scale
|
||||
self.max_scale = max_scale
|
||||
self.spatial_aug_prob = 0.8
|
||||
self.stretch_prob = 0.8
|
||||
self.max_stretch = 0.2
|
||||
|
||||
# flip augmentation params
|
||||
self.do_flip = do_flip
|
||||
self.h_flip_prob = 0.5
|
||||
self.v_flip_prob = 0.1
|
||||
|
||||
# photometric augmentation params
|
||||
self.photo_aug = ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.5/3.14)
|
||||
self.asymmetric_color_aug_prob = 0.2
|
||||
self.eraser_aug_prob = 0.5
|
||||
|
||||
def color_transform(self, img1, img2):
|
||||
""" Photometric augmentation """
|
||||
|
||||
# asymmetric
|
||||
if np.random.rand() < self.asymmetric_color_aug_prob:
|
||||
img1 = np.array(self.photo_aug(Image.fromarray(img1)), dtype=np.uint8)
|
||||
img2 = np.array(self.photo_aug(Image.fromarray(img2)), dtype=np.uint8)
|
||||
|
||||
# symmetric
|
||||
else:
|
||||
image_stack = np.concatenate([img1, img2], axis=0)
|
||||
image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8)
|
||||
img1, img2 = np.split(image_stack, 2, axis=0)
|
||||
|
||||
return img1, img2
|
||||
|
||||
def eraser_transform(self, img1, img2, bounds=[50, 100]):
|
||||
""" Occlusion augmentation """
|
||||
|
||||
ht, wd = img1.shape[:2]
|
||||
if np.random.rand() < self.eraser_aug_prob:
|
||||
mean_color = np.mean(img2.reshape(-1, 3), axis=0)
|
||||
for _ in range(np.random.randint(1, 3)):
|
||||
x0 = np.random.randint(0, wd)
|
||||
y0 = np.random.randint(0, ht)
|
||||
dx = np.random.randint(bounds[0], bounds[1])
|
||||
dy = np.random.randint(bounds[0], bounds[1])
|
||||
img2[y0:y0+dy, x0:x0+dx, :] = mean_color
|
||||
|
||||
return img1, img2
|
||||
|
||||
def spatial_transform(self, img1, img2, flow):
|
||||
# randomly sample scale
|
||||
ht, wd = img1.shape[:2]
|
||||
min_scale = np.maximum(
|
||||
(self.crop_size[0] + 8) / float(ht),
|
||||
(self.crop_size[1] + 8) / float(wd))
|
||||
|
||||
scale = 2 ** np.random.uniform(self.min_scale, self.max_scale)
|
||||
scale_x = scale
|
||||
scale_y = scale
|
||||
if np.random.rand() < self.stretch_prob:
|
||||
scale_x *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch)
|
||||
scale_y *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch)
|
||||
|
||||
scale_x = np.clip(scale_x, min_scale, None)
|
||||
scale_y = np.clip(scale_y, min_scale, None)
|
||||
|
||||
if np.random.rand() < self.spatial_aug_prob:
|
||||
# rescale the images
|
||||
img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
|
||||
img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
|
||||
flow = cv2.resize(flow, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
|
||||
flow = flow * [scale_x, scale_y]
|
||||
|
||||
if self.do_flip:
|
||||
if np.random.rand() < self.h_flip_prob: # h-flip
|
||||
img1 = img1[:, ::-1]
|
||||
img2 = img2[:, ::-1]
|
||||
flow = flow[:, ::-1] * [-1.0, 1.0]
|
||||
|
||||
if np.random.rand() < self.v_flip_prob: # v-flip
|
||||
img1 = img1[::-1, :]
|
||||
img2 = img2[::-1, :]
|
||||
flow = flow[::-1, :] * [1.0, -1.0]
|
||||
|
||||
y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0])
|
||||
x0 = np.random.randint(0, img1.shape[1] - self.crop_size[1])
|
||||
|
||||
img1 = img1[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
|
||||
img2 = img2[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
|
||||
flow = flow[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
|
||||
|
||||
return img1, img2, flow
|
||||
|
||||
def __call__(self, img1, img2, flow):
|
||||
img1, img2 = self.color_transform(img1, img2)
|
||||
img1, img2 = self.eraser_transform(img1, img2)
|
||||
img1, img2, flow = self.spatial_transform(img1, img2, flow)
|
||||
|
||||
img1 = np.ascontiguousarray(img1)
|
||||
img2 = np.ascontiguousarray(img2)
|
||||
flow = np.ascontiguousarray(flow)
|
||||
|
||||
return img1, img2, flow
|
||||
|
||||
class SparseFlowAugmentor:
|
||||
def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=False):
|
||||
# spatial augmentation params
|
||||
self.crop_size = crop_size
|
||||
self.min_scale = min_scale
|
||||
self.max_scale = max_scale
|
||||
self.spatial_aug_prob = 0.8
|
||||
self.stretch_prob = 0.8
|
||||
self.max_stretch = 0.2
|
||||
|
||||
# flip augmentation params
|
||||
self.do_flip = do_flip
|
||||
self.h_flip_prob = 0.5
|
||||
self.v_flip_prob = 0.1
|
||||
|
||||
# photometric augmentation params
|
||||
self.photo_aug = ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3, hue=0.3/3.14)
|
||||
self.asymmetric_color_aug_prob = 0.2
|
||||
self.eraser_aug_prob = 0.5
|
||||
|
||||
def color_transform(self, img1, img2):
|
||||
image_stack = np.concatenate([img1, img2], axis=0)
|
||||
image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8)
|
||||
img1, img2 = np.split(image_stack, 2, axis=0)
|
||||
return img1, img2
|
||||
|
||||
def eraser_transform(self, img1, img2):
|
||||
ht, wd = img1.shape[:2]
|
||||
if np.random.rand() < self.eraser_aug_prob:
|
||||
mean_color = np.mean(img2.reshape(-1, 3), axis=0)
|
||||
for _ in range(np.random.randint(1, 3)):
|
||||
x0 = np.random.randint(0, wd)
|
||||
y0 = np.random.randint(0, ht)
|
||||
dx = np.random.randint(50, 100)
|
||||
dy = np.random.randint(50, 100)
|
||||
img2[y0:y0+dy, x0:x0+dx, :] = mean_color
|
||||
|
||||
return img1, img2
|
||||
|
||||
def resize_sparse_flow_map(self, flow, valid, fx=1.0, fy=1.0):
|
||||
ht, wd = flow.shape[:2]
|
||||
coords = np.meshgrid(np.arange(wd), np.arange(ht), indexing='ij')
|
||||
coords = np.stack(coords, axis=-1)
|
||||
|
||||
coords = coords.reshape(-1, 2).astype(np.float32)
|
||||
flow = flow.reshape(-1, 2).astype(np.float32)
|
||||
valid = valid.reshape(-1).astype(np.float32)
|
||||
|
||||
coords0 = coords[valid>=1]
|
||||
flow0 = flow[valid>=1]
|
||||
|
||||
ht1 = int(round(ht * fy))
|
||||
wd1 = int(round(wd * fx))
|
||||
|
||||
coords1 = coords0 * [fx, fy]
|
||||
flow1 = flow0 * [fx, fy]
|
||||
|
||||
xx = np.round(coords1[:,0]).astype(np.int32)
|
||||
yy = np.round(coords1[:,1]).astype(np.int32)
|
||||
|
||||
v = (xx > 0) & (xx < wd1) & (yy > 0) & (yy < ht1)
|
||||
xx = xx[v]
|
||||
yy = yy[v]
|
||||
flow1 = flow1[v]
|
||||
|
||||
flow_img = np.zeros([ht1, wd1, 2], dtype=np.float32)
|
||||
valid_img = np.zeros([ht1, wd1], dtype=np.int32)
|
||||
|
||||
flow_img[yy, xx] = flow1
|
||||
valid_img[yy, xx] = 1
|
||||
|
||||
return flow_img, valid_img
|
||||
|
||||
def spatial_transform(self, img1, img2, flow, valid):
|
||||
# randomly sample scale
|
||||
|
||||
ht, wd = img1.shape[:2]
|
||||
min_scale = np.maximum(
|
||||
(self.crop_size[0] + 1) / float(ht),
|
||||
(self.crop_size[1] + 1) / float(wd))
|
||||
|
||||
scale = 2 ** np.random.uniform(self.min_scale, self.max_scale)
|
||||
scale_x = np.clip(scale, min_scale, None)
|
||||
scale_y = np.clip(scale, min_scale, None)
|
||||
|
||||
if np.random.rand() < self.spatial_aug_prob:
|
||||
# rescale the images
|
||||
img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
|
||||
img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
|
||||
flow, valid = self.resize_sparse_flow_map(flow, valid, fx=scale_x, fy=scale_y)
|
||||
|
||||
if self.do_flip:
|
||||
if np.random.rand() < 0.5: # h-flip
|
||||
img1 = img1[:, ::-1]
|
||||
img2 = img2[:, ::-1]
|
||||
flow = flow[:, ::-1] * [-1.0, 1.0]
|
||||
valid = valid[:, ::-1]
|
||||
|
||||
margin_y = 20
|
||||
margin_x = 50
|
||||
|
||||
y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0] + margin_y)
|
||||
x0 = np.random.randint(-margin_x, img1.shape[1] - self.crop_size[1] + margin_x)
|
||||
|
||||
y0 = np.clip(y0, 0, img1.shape[0] - self.crop_size[0])
|
||||
x0 = np.clip(x0, 0, img1.shape[1] - self.crop_size[1])
|
||||
|
||||
img1 = img1[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
|
||||
img2 = img2[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
|
||||
flow = flow[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
|
||||
valid = valid[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
|
||||
return img1, img2, flow, valid
|
||||
|
||||
|
||||
def __call__(self, img1, img2, flow, valid):
|
||||
img1, img2 = self.color_transform(img1, img2)
|
||||
img1, img2 = self.eraser_transform(img1, img2)
|
||||
img1, img2, flow, valid = self.spatial_transform(img1, img2, flow, valid)
|
||||
|
||||
img1 = np.ascontiguousarray(img1)
|
||||
img2 = np.ascontiguousarray(img2)
|
||||
flow = np.ascontiguousarray(flow)
|
||||
valid = np.ascontiguousarray(valid)
|
||||
|
||||
return img1, img2, flow, valid
|
||||
132
preprocessing/raft/utils/flow_viz.py
Normal file
132
preprocessing/raft/utils/flow_viz.py
Normal file
@ -0,0 +1,132 @@
|
||||
# Flow visualization code used from https://github.com/tomrunia/OpticalFlow_Visualization
|
||||
|
||||
|
||||
# MIT License
|
||||
#
|
||||
# Copyright (c) 2018 Tom Runia
|
||||
#
|
||||
# Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
# of this software and associated documentation files (the "Software"), to deal
|
||||
# in the Software without restriction, including without limitation the rights
|
||||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
# copies of the Software, and to permit persons to whom the Software is
|
||||
# furnished to do so, subject to conditions.
|
||||
#
|
||||
# Author: Tom Runia
|
||||
# Date Created: 2018-08-03
|
||||
|
||||
import numpy as np
|
||||
|
||||
def make_colorwheel():
|
||||
"""
|
||||
Generates a color wheel for optical flow visualization as presented in:
|
||||
Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007)
|
||||
URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf
|
||||
|
||||
Code follows the original C++ source code of Daniel Scharstein.
|
||||
Code follows the the Matlab source code of Deqing Sun.
|
||||
|
||||
Returns:
|
||||
np.ndarray: Color wheel
|
||||
"""
|
||||
|
||||
RY = 15
|
||||
YG = 6
|
||||
GC = 4
|
||||
CB = 11
|
||||
BM = 13
|
||||
MR = 6
|
||||
|
||||
ncols = RY + YG + GC + CB + BM + MR
|
||||
colorwheel = np.zeros((ncols, 3))
|
||||
col = 0
|
||||
|
||||
# RY
|
||||
colorwheel[0:RY, 0] = 255
|
||||
colorwheel[0:RY, 1] = np.floor(255*np.arange(0,RY)/RY)
|
||||
col = col+RY
|
||||
# YG
|
||||
colorwheel[col:col+YG, 0] = 255 - np.floor(255*np.arange(0,YG)/YG)
|
||||
colorwheel[col:col+YG, 1] = 255
|
||||
col = col+YG
|
||||
# GC
|
||||
colorwheel[col:col+GC, 1] = 255
|
||||
colorwheel[col:col+GC, 2] = np.floor(255*np.arange(0,GC)/GC)
|
||||
col = col+GC
|
||||
# CB
|
||||
colorwheel[col:col+CB, 1] = 255 - np.floor(255*np.arange(CB)/CB)
|
||||
colorwheel[col:col+CB, 2] = 255
|
||||
col = col+CB
|
||||
# BM
|
||||
colorwheel[col:col+BM, 2] = 255
|
||||
colorwheel[col:col+BM, 0] = np.floor(255*np.arange(0,BM)/BM)
|
||||
col = col+BM
|
||||
# MR
|
||||
colorwheel[col:col+MR, 2] = 255 - np.floor(255*np.arange(MR)/MR)
|
||||
colorwheel[col:col+MR, 0] = 255
|
||||
return colorwheel
|
||||
|
||||
|
||||
def flow_uv_to_colors(u, v, convert_to_bgr=False):
|
||||
"""
|
||||
Applies the flow color wheel to (possibly clipped) flow components u and v.
|
||||
|
||||
According to the C++ source code of Daniel Scharstein
|
||||
According to the Matlab source code of Deqing Sun
|
||||
|
||||
Args:
|
||||
u (np.ndarray): Input horizontal flow of shape [H,W]
|
||||
v (np.ndarray): Input vertical flow of shape [H,W]
|
||||
convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.
|
||||
|
||||
Returns:
|
||||
np.ndarray: Flow visualization image of shape [H,W,3]
|
||||
"""
|
||||
flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8)
|
||||
colorwheel = make_colorwheel() # shape [55x3]
|
||||
ncols = colorwheel.shape[0]
|
||||
rad = np.sqrt(np.square(u) + np.square(v))
|
||||
a = np.arctan2(-v, -u)/np.pi
|
||||
fk = (a+1) / 2*(ncols-1)
|
||||
k0 = np.floor(fk).astype(np.int32)
|
||||
k1 = k0 + 1
|
||||
k1[k1 == ncols] = 0
|
||||
f = fk - k0
|
||||
for i in range(colorwheel.shape[1]):
|
||||
tmp = colorwheel[:,i]
|
||||
col0 = tmp[k0] / 255.0
|
||||
col1 = tmp[k1] / 255.0
|
||||
col = (1-f)*col0 + f*col1
|
||||
idx = (rad <= 1)
|
||||
col[idx] = 1 - rad[idx] * (1-col[idx])
|
||||
col[~idx] = col[~idx] * 0.75 # out of range
|
||||
# Note the 2-i => BGR instead of RGB
|
||||
ch_idx = 2-i if convert_to_bgr else i
|
||||
flow_image[:,:,ch_idx] = np.floor(255 * col)
|
||||
return flow_image
|
||||
|
||||
|
||||
def flow_to_image(flow_uv, clip_flow=None, convert_to_bgr=False):
|
||||
"""
|
||||
Expects a two dimensional flow image of shape.
|
||||
|
||||
Args:
|
||||
flow_uv (np.ndarray): Flow UV image of shape [H,W,2]
|
||||
clip_flow (float, optional): Clip maximum of flow values. Defaults to None.
|
||||
convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.
|
||||
|
||||
Returns:
|
||||
np.ndarray: Flow visualization image of shape [H,W,3]
|
||||
"""
|
||||
assert flow_uv.ndim == 3, 'input flow must have three dimensions'
|
||||
assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]'
|
||||
if clip_flow is not None:
|
||||
flow_uv = np.clip(flow_uv, 0, clip_flow)
|
||||
u = flow_uv[:,:,0]
|
||||
v = flow_uv[:,:,1]
|
||||
rad = np.sqrt(np.square(u) + np.square(v))
|
||||
rad_max = np.max(rad)
|
||||
epsilon = 1e-5
|
||||
u = u / (rad_max + epsilon)
|
||||
v = v / (rad_max + epsilon)
|
||||
return flow_uv_to_colors(u, v, convert_to_bgr)
|
||||
137
preprocessing/raft/utils/frame_utils.py
Normal file
137
preprocessing/raft/utils/frame_utils.py
Normal file
@ -0,0 +1,137 @@
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
from os.path import *
|
||||
import re
|
||||
|
||||
import cv2
|
||||
cv2.setNumThreads(0)
|
||||
cv2.ocl.setUseOpenCL(False)
|
||||
|
||||
TAG_CHAR = np.array([202021.25], np.float32)
|
||||
|
||||
def readFlow(fn):
|
||||
""" Read .flo file in Middlebury format"""
|
||||
# Code adapted from:
|
||||
# http://stackoverflow.com/questions/28013200/reading-middlebury-flow-files-with-python-bytes-array-numpy
|
||||
|
||||
# WARNING: this will work on little-endian architectures (eg Intel x86) only!
|
||||
# print 'fn = %s'%(fn)
|
||||
with open(fn, 'rb') as f:
|
||||
magic = np.fromfile(f, np.float32, count=1)
|
||||
if 202021.25 != magic:
|
||||
print('Magic number incorrect. Invalid .flo file')
|
||||
return None
|
||||
else:
|
||||
w = np.fromfile(f, np.int32, count=1)
|
||||
h = np.fromfile(f, np.int32, count=1)
|
||||
# print 'Reading %d x %d flo file\n' % (w, h)
|
||||
data = np.fromfile(f, np.float32, count=2*int(w)*int(h))
|
||||
# Reshape data into 3D array (columns, rows, bands)
|
||||
# The reshape here is for visualization, the original code is (w,h,2)
|
||||
return np.resize(data, (int(h), int(w), 2))
|
||||
|
||||
def readPFM(file):
|
||||
file = open(file, 'rb')
|
||||
|
||||
color = None
|
||||
width = None
|
||||
height = None
|
||||
scale = None
|
||||
endian = None
|
||||
|
||||
header = file.readline().rstrip()
|
||||
if header == b'PF':
|
||||
color = True
|
||||
elif header == b'Pf':
|
||||
color = False
|
||||
else:
|
||||
raise Exception('Not a PFM file.')
|
||||
|
||||
dim_match = re.match(rb'^(\d+)\s(\d+)\s$', file.readline())
|
||||
if dim_match:
|
||||
width, height = map(int, dim_match.groups())
|
||||
else:
|
||||
raise Exception('Malformed PFM header.')
|
||||
|
||||
scale = float(file.readline().rstrip())
|
||||
if scale < 0: # little-endian
|
||||
endian = '<'
|
||||
scale = -scale
|
||||
else:
|
||||
endian = '>' # big-endian
|
||||
|
||||
data = np.fromfile(file, endian + 'f')
|
||||
shape = (height, width, 3) if color else (height, width)
|
||||
|
||||
data = np.reshape(data, shape)
|
||||
data = np.flipud(data)
|
||||
return data
|
||||
|
||||
def writeFlow(filename,uv,v=None):
|
||||
""" Write optical flow to file.
|
||||
|
||||
If v is None, uv is assumed to contain both u and v channels,
|
||||
stacked in depth.
|
||||
Original code by Deqing Sun, adapted from Daniel Scharstein.
|
||||
"""
|
||||
nBands = 2
|
||||
|
||||
if v is None:
|
||||
assert(uv.ndim == 3)
|
||||
assert(uv.shape[2] == 2)
|
||||
u = uv[:,:,0]
|
||||
v = uv[:,:,1]
|
||||
else:
|
||||
u = uv
|
||||
|
||||
assert(u.shape == v.shape)
|
||||
height,width = u.shape
|
||||
f = open(filename,'wb')
|
||||
# write the header
|
||||
f.write(TAG_CHAR)
|
||||
np.array(width).astype(np.int32).tofile(f)
|
||||
np.array(height).astype(np.int32).tofile(f)
|
||||
# arrange into matrix form
|
||||
tmp = np.zeros((height, width*nBands))
|
||||
tmp[:,np.arange(width)*2] = u
|
||||
tmp[:,np.arange(width)*2 + 1] = v
|
||||
tmp.astype(np.float32).tofile(f)
|
||||
f.close()
|
||||
|
||||
|
||||
def readFlowKITTI(filename):
|
||||
flow = cv2.imread(filename, cv2.IMREAD_ANYDEPTH|cv2.IMREAD_COLOR)
|
||||
flow = flow[:,:,::-1].astype(np.float32)
|
||||
flow, valid = flow[:, :, :2], flow[:, :, 2]
|
||||
flow = (flow - 2**15) / 64.0
|
||||
return flow, valid
|
||||
|
||||
def readDispKITTI(filename):
|
||||
disp = cv2.imread(filename, cv2.IMREAD_ANYDEPTH) / 256.0
|
||||
valid = disp > 0.0
|
||||
flow = np.stack([-disp, np.zeros_like(disp)], -1)
|
||||
return flow, valid
|
||||
|
||||
|
||||
def writeFlowKITTI(filename, uv):
|
||||
uv = 64.0 * uv + 2**15
|
||||
valid = np.ones([uv.shape[0], uv.shape[1], 1])
|
||||
uv = np.concatenate([uv, valid], axis=-1).astype(np.uint16)
|
||||
cv2.imwrite(filename, uv[..., ::-1])
|
||||
|
||||
|
||||
def read_gen(file_name, pil=False):
|
||||
ext = splitext(file_name)[-1]
|
||||
if ext == '.png' or ext == '.jpeg' or ext == '.ppm' or ext == '.jpg':
|
||||
return Image.open(file_name)
|
||||
elif ext == '.bin' or ext == '.raw':
|
||||
return np.load(file_name)
|
||||
elif ext == '.flo':
|
||||
return readFlow(file_name).astype(np.float32)
|
||||
elif ext == '.pfm':
|
||||
flow = readPFM(file_name).astype(np.float32)
|
||||
if len(flow.shape) == 2:
|
||||
return flow
|
||||
else:
|
||||
return flow[:, :, :-1]
|
||||
return []
|
||||
82
preprocessing/raft/utils/utils.py
Normal file
82
preprocessing/raft/utils/utils.py
Normal file
@ -0,0 +1,82 @@
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import numpy as np
|
||||
from scipy import interpolate
|
||||
|
||||
|
||||
class InputPadder:
|
||||
""" Pads images such that dimensions are divisible by 8 """
|
||||
def __init__(self, dims, mode='sintel'):
|
||||
self.ht, self.wd = dims[-2:]
|
||||
pad_ht = (((self.ht // 8) + 1) * 8 - self.ht) % 8
|
||||
pad_wd = (((self.wd // 8) + 1) * 8 - self.wd) % 8
|
||||
if mode == 'sintel':
|
||||
self._pad = [pad_wd//2, pad_wd - pad_wd//2, pad_ht//2, pad_ht - pad_ht//2]
|
||||
else:
|
||||
self._pad = [pad_wd//2, pad_wd - pad_wd//2, 0, pad_ht]
|
||||
|
||||
def pad(self, *inputs):
|
||||
return [F.pad(x, self._pad, mode='replicate') for x in inputs]
|
||||
|
||||
def unpad(self,x):
|
||||
ht, wd = x.shape[-2:]
|
||||
c = [self._pad[2], ht-self._pad[3], self._pad[0], wd-self._pad[1]]
|
||||
return x[..., c[0]:c[1], c[2]:c[3]]
|
||||
|
||||
def forward_interpolate(flow):
|
||||
flow = flow.detach().cpu().numpy()
|
||||
dx, dy = flow[0], flow[1]
|
||||
|
||||
ht, wd = dx.shape
|
||||
x0, y0 = np.meshgrid(np.arange(wd), np.arange(ht), indexing='ij')
|
||||
|
||||
x1 = x0 + dx
|
||||
y1 = y0 + dy
|
||||
|
||||
x1 = x1.reshape(-1)
|
||||
y1 = y1.reshape(-1)
|
||||
dx = dx.reshape(-1)
|
||||
dy = dy.reshape(-1)
|
||||
|
||||
valid = (x1 > 0) & (x1 < wd) & (y1 > 0) & (y1 < ht)
|
||||
x1 = x1[valid]
|
||||
y1 = y1[valid]
|
||||
dx = dx[valid]
|
||||
dy = dy[valid]
|
||||
|
||||
flow_x = interpolate.griddata(
|
||||
(x1, y1), dx, (x0, y0), method='nearest', fill_value=0)
|
||||
|
||||
flow_y = interpolate.griddata(
|
||||
(x1, y1), dy, (x0, y0), method='nearest', fill_value=0)
|
||||
|
||||
flow = np.stack([flow_x, flow_y], axis=0)
|
||||
return torch.from_numpy(flow).float()
|
||||
|
||||
|
||||
def bilinear_sampler(img, coords, mode='bilinear', mask=False):
|
||||
""" Wrapper for grid_sample, uses pixel coordinates """
|
||||
H, W = img.shape[-2:]
|
||||
xgrid, ygrid = coords.split([1,1], dim=-1)
|
||||
xgrid = 2*xgrid/(W-1) - 1
|
||||
ygrid = 2*ygrid/(H-1) - 1
|
||||
|
||||
grid = torch.cat([xgrid, ygrid], dim=-1)
|
||||
img = F.grid_sample(img, grid, align_corners=True)
|
||||
|
||||
if mask:
|
||||
mask = (xgrid > -1) & (ygrid > -1) & (xgrid < 1) & (ygrid < 1)
|
||||
return img, mask.float()
|
||||
|
||||
return img
|
||||
|
||||
|
||||
def coords_grid(batch, ht, wd):
|
||||
coords = torch.meshgrid(torch.arange(ht), torch.arange(wd), indexing='ij')
|
||||
coords = torch.stack(coords[::-1], dim=0).float()
|
||||
return coords[None].repeat(batch, 1, 1, 1)
|
||||
|
||||
|
||||
def upflow8(flow, mode='bilinear'):
|
||||
new_size = (8 * flow.shape[2], 8 * flow.shape[3])
|
||||
return 8 * F.interpolate(flow, size=new_size, mode=mode, align_corners=True)
|
||||
@ -417,7 +417,7 @@ class WanAttentionBlock(nn.Module):
|
||||
freqs,
|
||||
context,
|
||||
hints= None,
|
||||
context_scale=1.0,
|
||||
context_scale=[1.0],
|
||||
cam_emb= None,
|
||||
block_mask = None,
|
||||
audio_proj= None,
|
||||
@ -431,7 +431,7 @@ class WanAttentionBlock(nn.Module):
|
||||
grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
|
||||
freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
|
||||
"""
|
||||
hint = None
|
||||
hints_processed = None
|
||||
attention_dtype = self.self_attn.q.weight.dtype
|
||||
dtype = x.dtype
|
||||
|
||||
@ -442,10 +442,13 @@ class WanAttentionBlock(nn.Module):
|
||||
"context" : context,
|
||||
"e" : e,
|
||||
}
|
||||
if self.block_id == 0:
|
||||
hint = self.vace(hints, x, **kwargs)
|
||||
hints_processed= []
|
||||
for scale, hint in zip(context_scale, hints):
|
||||
if scale == 0:
|
||||
hints_processed.append(None)
|
||||
else:
|
||||
hint = self.vace(hints, None, **kwargs)
|
||||
hints_processed.append(self.vace(hint, x, **kwargs) if self.block_id == 0 else self.vace(hint, None, **kwargs))
|
||||
|
||||
latent_frames = e.shape[0]
|
||||
e = (self.modulation + e).chunk(6, dim=1)
|
||||
# self-attention
|
||||
@ -506,11 +509,13 @@ class WanAttentionBlock(nn.Module):
|
||||
x.addcmul_(y, e[5])
|
||||
x, y = reshape_latent(x , 1), reshape_latent(y , 1)
|
||||
|
||||
if hint is not None:
|
||||
if context_scale == 1:
|
||||
if hints_processed is not None:
|
||||
for hint, scale in zip(hints_processed, context_scale):
|
||||
if scale != 0:
|
||||
if scale == 1:
|
||||
x.add_(hint)
|
||||
else:
|
||||
x.add_(hint, alpha= context_scale)
|
||||
x.add_(hint, alpha= scale)
|
||||
return x
|
||||
|
||||
|
||||
@ -605,8 +610,8 @@ class MLPProj(torch.nn.Module):
|
||||
|
||||
class WanModel(ModelMixin, ConfigMixin):
|
||||
def setup_chipmunk(self):
|
||||
from chipmunk.util import LayerCounter
|
||||
from chipmunk.modules import SparseDiffMlp, SparseDiffAttn
|
||||
# from chipmunk.util import LayerCounter
|
||||
# from chipmunk.modules import SparseDiffMlp, SparseDiffAttn
|
||||
seq_shape = (21, 45, 80)
|
||||
chipmunk_layers =[]
|
||||
for i in range(self.num_layers):
|
||||
@ -941,7 +946,7 @@ class WanModel(ModelMixin, ConfigMixin):
|
||||
t,
|
||||
context,
|
||||
vace_context = None,
|
||||
vace_context_scale=1.0,
|
||||
vace_context_scale=[1.0],
|
||||
clip_fea=None,
|
||||
y=None,
|
||||
freqs = None,
|
||||
@ -972,7 +977,7 @@ class WanModel(ModelMixin, ConfigMixin):
|
||||
|
||||
chipmunk = offload.shared_state.get("_chipmunk", False)
|
||||
if chipmunk:
|
||||
from chipmunk.ops.voxel import voxel_chunk_no_padding, reverse_voxel_chunk_no_padding
|
||||
# from chipmunk.ops.voxel import voxel_chunk_no_padding, reverse_voxel_chunk_no_padding
|
||||
voxel_shape = (4, 6, 8)
|
||||
|
||||
x_list = x
|
||||
@ -1065,10 +1070,10 @@ class WanModel(ModelMixin, ConfigMixin):
|
||||
# Vace embeddings
|
||||
c = [self.vace_patch_embedding(u.to(self.vace_patch_embedding.weight.dtype).unsqueeze(0)) for u in vace_context]
|
||||
c = [u.flatten(2).transpose(1, 2) for u in c]
|
||||
c = c[0]
|
||||
|
||||
# c = c[0]
|
||||
c = [ [sub_c] for sub_c in c]
|
||||
kwargs['context_scale'] = vace_context_scale
|
||||
hints_list = [ [c] for _ in range(len(x_list)) ]
|
||||
hints_list = [ c ]* len(x_list)
|
||||
del c
|
||||
|
||||
should_calc = True
|
||||
|
||||
@ -132,7 +132,7 @@ class WanT2V:
|
||||
inactive = [i * (1 - m) + 0 * m for i, m in zip(frames, masks)]
|
||||
reactive = [i * m + 0 * (1 - m) for i, m in zip(frames, masks)]
|
||||
inactive = self.vae.encode(inactive, tile_size = tile_size)
|
||||
self.toto = inactive[0].clone()
|
||||
|
||||
if overlapped_latents != None :
|
||||
# inactive[0][:, 0:1] = self.vae.encode([frames[0][:, 0:1]], tile_size = tile_size)[0] # redundant
|
||||
inactive[0][:, 1:overlapped_latents.shape[1] + 1] = overlapped_latents
|
||||
@ -220,7 +220,7 @@ class WanT2V:
|
||||
ref_img = white_canvas
|
||||
return ref_img.to(device)
|
||||
|
||||
def prepare_source(self, src_video, src_mask, src_ref_images, total_frames, image_size, device, original_video = False, keep_frames= [], start_frame = 0, fit_into_canvas = None, pre_src_video = None, inject_frames = [], outpainting_dims = None):
|
||||
def prepare_source(self, src_video, src_mask, src_ref_images, total_frames, image_size, device, keep_frames= [], start_frame = 0, fit_into_canvas = None, pre_src_video = None, inject_frames = [], outpainting_dims = None):
|
||||
image_sizes = []
|
||||
trim_video = len(keep_frames)
|
||||
def conv_tensor(t, device):
|
||||
@ -254,7 +254,7 @@ class WanT2V:
|
||||
image_sizes.append(image_size)
|
||||
else:
|
||||
src_video[i] = conv_tensor(sub_src_video[:num_frames], device)
|
||||
src_mask[i] = torch.zeros_like(src_video[i], device=device) if original_video else torch.ones_like(src_video[i], device=device)
|
||||
src_mask[i] = torch.ones_like(src_video[i], device=device)
|
||||
if prepend_count > 0:
|
||||
src_video[i] = torch.cat( [sub_pre_src_video, src_video[i]], dim=1)
|
||||
src_mask[i] = torch.cat( [torch.zeros_like(sub_pre_src_video), src_mask[i]] ,1)
|
||||
@ -278,15 +278,15 @@ class WanT2V:
|
||||
if ref_images is not None:
|
||||
image_size = image_sizes[i]
|
||||
for j, ref_img in enumerate(ref_images):
|
||||
if ref_img is not None:
|
||||
if ref_img is not None and not torch.is_tensor(ref_img):
|
||||
src_ref_images[i][j] = self.fit_image_into_canvas(ref_img, image_size, 1, device)
|
||||
return src_video, src_mask, src_ref_images
|
||||
|
||||
def decode_latent(self, zs, ref_images=None, tile_size= 0 ):
|
||||
if ref_images is None:
|
||||
ref_images = [None] * len(zs)
|
||||
else:
|
||||
assert len(zs) == len(ref_images)
|
||||
# else:
|
||||
# assert len(zs) == len(ref_images)
|
||||
|
||||
trimed_zs = []
|
||||
for z, refs in zip(zs, ref_images):
|
||||
@ -312,7 +312,7 @@ class WanT2V:
|
||||
input_ref_images = None,
|
||||
input_video=None,
|
||||
target_camera=None,
|
||||
context_scale=1.0,
|
||||
context_scale=None,
|
||||
width = 1280,
|
||||
height = 720,
|
||||
fit_into_canvas = True,
|
||||
@ -478,6 +478,7 @@ class WanT2V:
|
||||
|
||||
if vace:
|
||||
ref_images_count = len(input_ref_images[0]) if input_ref_images != None and input_ref_images[0] != None else 0
|
||||
context_scale = context_scale if context_scale != None else [1.0] * len(z)
|
||||
kwargs.update({'vace_context' : z, 'vace_context_scale' : context_scale})
|
||||
if overlapped_latents != None :
|
||||
overlapped_latents_size = overlapped_latents.shape[1] + 1
|
||||
|
||||
267
wgp.py
267
wgp.py
@ -45,7 +45,7 @@ AUTOSAVE_FILENAME = "queue.zip"
|
||||
PROMPT_VARS_MAX = 10
|
||||
|
||||
target_mmgp_version = "3.4.9"
|
||||
WanGP_version = "6.21"
|
||||
WanGP_version = "6.3"
|
||||
settings_version = 2
|
||||
prompt_enhancer_image_caption_model, prompt_enhancer_image_caption_processor, prompt_enhancer_llm_model, prompt_enhancer_llm_tokenizer = None, None, None, None
|
||||
|
||||
@ -429,8 +429,8 @@ def process_prompt_and_add_tasks(state, model_choice):
|
||||
return update_queue_data(queue)
|
||||
|
||||
def get_preview_images(inputs):
|
||||
inputs_to_query = ["image_start", "image_end", "video_guide", "image_refs","video_mask", "video_source"]
|
||||
labels = ["Start Image", "End Image", "Video Guide", "Image Reference","Video Mask", "Video Source"]
|
||||
inputs_to_query = ["image_start", "image_end", "video_source", "video_guide", "video_mask", "image_refs" ]
|
||||
labels = ["Start Image", "End Image", "Video Source", "Video Guide", "Video Mask", "Image Reference"]
|
||||
start_image_data = None
|
||||
start_image_labels = []
|
||||
end_image_data = None
|
||||
@ -1768,7 +1768,7 @@ def fix_settings(model_type, ui_defaults):
|
||||
return
|
||||
|
||||
video_prompt_type = ui_defaults.get("video_prompt_type", "")
|
||||
if model_type in ["hunyuan_custom", "hunyuan_custom_edit", "hunyuan_custom_audio", "hunyuan_avatar", "phantom", "phantom_1.3B"]:
|
||||
if model_type in ["hunyuan_custom", "hunyuan_custom_edit", "hunyuan_custom_audio", "hunyuan_avatar", "phantom_14B", "phantom_1.3B"]:
|
||||
if not "I" in video_prompt_type: # workaround for settings corruption
|
||||
video_prompt_type += "I"
|
||||
if model_type in ["hunyuan"]:
|
||||
@ -2081,8 +2081,8 @@ def download_models(model_filename, model_type):
|
||||
|
||||
shared_def = {
|
||||
"repoId" : "DeepBeepMeep/Wan2.1",
|
||||
"sourceFolderList" : [ "pose", "scribble", "depth", "mask", "wav2vec", "" ],
|
||||
"fileList" : [ ["dw-ll_ucoco_384.onnx", "yolox_l.onnx"],["netG_A_latest.pth"], ["depth_anything_v2_vitl.pth"], ["sam_vit_h_4b8939_fp16.safetensors"], ["config.json", "feature_extractor_config.json", "model.safetensors", "preprocessor_config.json", "special_tokens_map.json", "tokenizer_config.json", "vocab.json"],
|
||||
"sourceFolderList" : [ "pose", "scribble", "flow", "depth", "mask", "wav2vec", "" ],
|
||||
"fileList" : [ ["dw-ll_ucoco_384.onnx", "yolox_l.onnx"],["netG_A_latest.pth"], ["raft-things.pth"], ["depth_anything_v2_vitl.pth","depth_anything_v2_vitb.pth"], ["sam_vit_h_4b8939_fp16.safetensors"], ["config.json", "feature_extractor_config.json", "model.safetensors", "preprocessor_config.json", "special_tokens_map.json", "tokenizer_config.json", "vocab.json"],
|
||||
[ "flownet.pkl" ] ]
|
||||
}
|
||||
process_files_def(**shared_def)
|
||||
@ -2512,6 +2512,7 @@ def apply_changes( state,
|
||||
enhancer_enabled_choice = 0,
|
||||
fit_canvas_choice = 0,
|
||||
preload_in_VRAM_choice = 0,
|
||||
depth_anything_v2_variant_choice = "vitl",
|
||||
notification_sound_enabled_choice = 1,
|
||||
notification_sound_volume_choice = 50
|
||||
):
|
||||
@ -2540,6 +2541,7 @@ def apply_changes( state,
|
||||
"fit_canvas": fit_canvas_choice,
|
||||
"enhancer_enabled" : enhancer_enabled_choice,
|
||||
"preload_in_VRAM" : preload_in_VRAM_choice,
|
||||
"depth_anything_v2_variant": depth_anything_v2_variant_choice,
|
||||
"notification_sound_enabled" : notification_sound_enabled_choice,
|
||||
"notification_sound_volume" : notification_sound_volume_choice
|
||||
}
|
||||
@ -2577,7 +2579,7 @@ def apply_changes( state,
|
||||
transformer_types = server_config["transformer_types"]
|
||||
model_filename = get_model_filename(transformer_type, transformer_quantization, transformer_dtype_policy)
|
||||
state["model_filename"] = model_filename
|
||||
if all(change in ["attention_mode", "vae_config", "boost", "save_path", "metadata_type", "clear_file_list", "fit_canvas", "notification_sound_enabled", "notification_sound_volume"] for change in changes ):
|
||||
if all(change in ["attention_mode", "vae_config", "boost", "save_path", "metadata_type", "clear_file_list", "fit_canvas", "depth_anything_v2_variant", "notification_sound_enabled", "notification_sound_volume"] for change in changes ):
|
||||
model_choice = gr.Dropdown()
|
||||
else:
|
||||
reload_needed = True
|
||||
@ -2818,7 +2820,7 @@ def get_preprocessor(process_type, inpaint_color):
|
||||
"POSE_MODEL": "ckpts/pose/dw-ll_ucoco_384.onnx",
|
||||
"RESIZE_SIZE": 1024
|
||||
}
|
||||
anno_ins = lambda img: PoseBodyFaceVideoAnnotator(cfg_dict).forward(img)[0]
|
||||
anno_ins = lambda img: PoseBodyFaceVideoAnnotator(cfg_dict).forward(img)
|
||||
elif process_type=="depth":
|
||||
# from preprocessing.midas.depth import DepthVideoAnnotator
|
||||
# cfg_dict = {
|
||||
@ -2827,30 +2829,72 @@ def get_preprocessor(process_type, inpaint_color):
|
||||
# anno_ins = lambda img: DepthVideoAnnotator(cfg_dict).forward(img)[0]
|
||||
|
||||
from preprocessing.depth_anything_v2.depth import DepthV2VideoAnnotator
|
||||
cfg_dict = {
|
||||
"PRETRAINED_MODEL": "ckpts/depth/depth_anything_v2_vitl.pth"
|
||||
# "PRETRAINED_MODEL": "ckpts/depth/depth_anything_vitb14.pth"
|
||||
|
||||
if server_config.get("depth_anything_v2_variant", "vitl") == "vitl":
|
||||
cfg_dict = {
|
||||
"PRETRAINED_MODEL": "ckpts/depth/depth_anything_v2_vitl.pth",
|
||||
'MODEL_VARIANT': 'vitl'
|
||||
}
|
||||
anno_ins = lambda img: DepthV2VideoAnnotator(cfg_dict).forward(img)[0]
|
||||
else:
|
||||
cfg_dict = {
|
||||
"PRETRAINED_MODEL": "ckpts/depth/depth_anything_v2_vitb.pth",
|
||||
'MODEL_VARIANT': 'vitb',
|
||||
}
|
||||
|
||||
anno_ins = lambda img: DepthV2VideoAnnotator(cfg_dict).forward(img)
|
||||
elif process_type=="gray":
|
||||
from preprocessing.gray import GrayVideoAnnotator
|
||||
cfg_dict = {}
|
||||
anno_ins = lambda img: GrayVideoAnnotator(cfg_dict).forward(img)[0]
|
||||
anno_ins = lambda img: GrayVideoAnnotator(cfg_dict).forward(img)
|
||||
elif process_type=="scribble":
|
||||
from preprocessing.scribble import ScribbleVideoAnnotator
|
||||
cfg_dict = {
|
||||
"PRETRAINED_MODEL": "ckpts/scribble/netG_A_latest.pth"
|
||||
}
|
||||
anno_ins = lambda img: ScribbleVideoAnnotator(cfg_dict).forward(img)[0]
|
||||
anno_ins = lambda img: ScribbleVideoAnnotator(cfg_dict).forward(img)
|
||||
elif process_type=="flow":
|
||||
from preprocessing.flow import FlowVisAnnotator
|
||||
cfg_dict = {
|
||||
"PRETRAINED_MODEL": "ckpts/flow/raft-things.pth"
|
||||
}
|
||||
anno_ins = lambda img: FlowVisAnnotator(cfg_dict).forward(img)
|
||||
elif process_type=="inpaint":
|
||||
anno_ins = lambda img : inpaint_color
|
||||
# anno_ins = lambda img : np.full_like(img, inpaint_color)
|
||||
anno_ins = lambda img : len(img) * [inpaint_color]
|
||||
elif process_type == None or process_type in ["vace", "identity"]:
|
||||
anno_ins = lambda img : img
|
||||
else:
|
||||
anno_ins = lambda img : img[0]
|
||||
raise Exception(f"process type '{process_type}' non supported")
|
||||
return anno_ins
|
||||
|
||||
def preprocess_video_with_mask(input_video_path, input_mask_path, height, width, max_frames, start_frame=0, fit_canvas = False, target_fps = 16, block_size= 16, expand_scale = 2, process_type = "inpaint", to_bbox = False, RGB_Mask = False, negate_mask = False, process_outside_mask = None, inpaint_color = 127, outpainting_dims = None):
|
||||
|
||||
def process_images_multithread(image_processor, items, process_type, wrap_in_list = True, max_workers: int = os.cpu_count()/ 2) :
|
||||
if not items:
|
||||
return []
|
||||
|
||||
import concurrent.futures
|
||||
start_time = time.time()
|
||||
print(f"Preprocessus:{process_type} started")
|
||||
if process_type in ["prephase", "upsample"]:
|
||||
if wrap_in_list :
|
||||
items = [ [img] for img in items]
|
||||
with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
|
||||
futures = {executor.submit(image_processor, img): idx for idx, img in enumerate(items)}
|
||||
results = [None] * len(items)
|
||||
for future in concurrent.futures.as_completed(futures):
|
||||
idx = futures[future]
|
||||
results[idx] = future.result()
|
||||
|
||||
if wrap_in_list:
|
||||
results = [ img[0] for img in results]
|
||||
else:
|
||||
results= image_processor(items)
|
||||
|
||||
end_time = time.time()
|
||||
print(f"duration:{end_time-start_time:.1f}")
|
||||
|
||||
return results
|
||||
|
||||
def preprocess_video_with_mask(input_video_path, input_mask_path, height, width, max_frames, start_frame=0, fit_canvas = False, target_fps = 16, block_size= 16, expand_scale = 2, process_type = "inpaint", process_type2 = None, to_bbox = False, RGB_Mask = False, negate_mask = False, process_outside_mask = None, inpaint_color = 127, outpainting_dims = None, proc_no = 1):
|
||||
from wan.utils.utils import calculate_new_dimensions, get_outpainting_frame_location, get_outpainting_full_area_dimensions
|
||||
|
||||
def mask_to_xyxy_box(mask):
|
||||
@ -2871,14 +2915,21 @@ def preprocess_video_with_mask(input_video_path, input_mask_path, height, width,
|
||||
return None, None
|
||||
any_mask = input_mask_path != None
|
||||
pose_special = "pose" in process_type
|
||||
if process_type == "pose_depth": process_type = "pose"
|
||||
any_identity_mask = False
|
||||
if process_type == "identity":
|
||||
any_identity_mask = True
|
||||
negate_mask = False
|
||||
process_outside_mask = None
|
||||
preproc = get_preprocessor(process_type, inpaint_color)
|
||||
preproc2 = get_preprocessor(process_outside_mask, inpaint_color) if process_type != process_outside_mask else preproc
|
||||
preproc2 = None
|
||||
if process_type2 != None:
|
||||
preproc2 = get_preprocessor(process_type2, inpaint_color) if process_type != process_type2 else preproc
|
||||
if process_outside_mask == process_type :
|
||||
preproc_outside = preproc
|
||||
elif preproc2 != None and process_outside_mask == process_type2 :
|
||||
preproc_outside = preproc2
|
||||
else:
|
||||
preproc_outside = get_preprocessor(process_outside_mask, inpaint_color)
|
||||
video = get_resampled_video(input_video_path, start_frame, max_frames, target_fps)
|
||||
if any_mask:
|
||||
mask_video = get_resampled_video(input_mask_path, start_frame, max_frames, target_fps)
|
||||
@ -2905,12 +2956,16 @@ def preprocess_video_with_mask(input_video_path, input_mask_path, height, width,
|
||||
num_frames = min(len(video), len(mask_video))
|
||||
else:
|
||||
num_frames = len(video)
|
||||
masked_frames = []
|
||||
masks = []
|
||||
|
||||
if any_identity_mask:
|
||||
any_mask = True
|
||||
|
||||
for frame_idx in range(num_frames):
|
||||
proc_list =[]
|
||||
proc_list_outside =[]
|
||||
proc_mask = []
|
||||
|
||||
# for frame_idx in range(num_frames):
|
||||
def prep_prephase(frame_idx):
|
||||
frame = Image.fromarray(video[frame_idx].cpu().numpy()) #.asnumpy()
|
||||
frame = frame.resize((width, height), resample=Image.Resampling.LANCZOS)
|
||||
frame = np.array(frame)
|
||||
@ -2946,17 +3001,33 @@ def preprocess_video_with_mask(input_video_path, input_mask_path, height, width,
|
||||
else:
|
||||
target_frame = frame
|
||||
|
||||
processed_img = preproc([target_frame])
|
||||
|
||||
if any_mask:
|
||||
if preproc2 != None:
|
||||
frame = preproc2([frame])
|
||||
masked_frame = np.where(mask[..., None], processed_img, frame)
|
||||
return (target_frame, frame, mask)
|
||||
else:
|
||||
masked_frame = processed_img
|
||||
return (target_frame, None, None)
|
||||
|
||||
proc_lists = process_images_multithread(prep_prephase, [frame_idx for frame_idx in range(num_frames)], "prephase", wrap_in_list= False)
|
||||
proc_list, proc_list_outside, proc_mask = [None] * len(proc_lists), [None] * len(proc_lists), [None] * len(proc_lists)
|
||||
for frame_idx, frame_group in enumerate(proc_lists):
|
||||
proc_list[frame_idx], proc_list_outside[frame_idx], proc_mask[frame_idx] = frame_group
|
||||
prep_prephase = None
|
||||
video = None
|
||||
mask_video = None
|
||||
|
||||
if preproc2 != None:
|
||||
proc_list2 = process_images_multithread(preproc2, proc_list, process_type2)
|
||||
#### to be finished ...or not
|
||||
proc_list = process_images_multithread(preproc, proc_list, process_type)
|
||||
if any_mask:
|
||||
proc_list_outside = process_images_multithread(preproc_outside, proc_list_outside, process_outside_mask)
|
||||
else:
|
||||
proc_list_outside = proc_mask = len(proc_list) * [None]
|
||||
|
||||
masked_frames = []
|
||||
masks = []
|
||||
for frame_no, (processed_img, processed_img_outside, mask) in enumerate(zip(proc_list, proc_list_outside, proc_mask)):
|
||||
if any_mask :
|
||||
masked_frame = np.where(mask[..., None], processed_img, processed_img_outside)
|
||||
if process_outside_mask != None:
|
||||
mask = np.full_like(mask, 255)
|
||||
mask = torch.from_numpy(mask)
|
||||
@ -2967,6 +3038,8 @@ def preprocess_video_with_mask(input_video_path, input_mask_path, height, width,
|
||||
full_frame[margin_top:margin_top+height, margin_left:margin_left+width] = mask
|
||||
mask = full_frame
|
||||
masks.append(mask)
|
||||
else:
|
||||
masked_frame = processed_img
|
||||
|
||||
if isinstance(masked_frame, int):
|
||||
masked_frame= np.full( (height, width, 3), inpaint_color, dtype= np.uint8)
|
||||
@ -2981,15 +3054,18 @@ def preprocess_video_with_mask(input_video_path, input_mask_path, height, width,
|
||||
masked_frame = full_frame
|
||||
|
||||
masked_frames.append(masked_frame)
|
||||
proc_list[frame_no] = proc_list_outside[frame_no] = proc_mask[frame_no] = None
|
||||
|
||||
|
||||
if args.save_masks:
|
||||
from preprocessing.dwpose.pose import save_one_video
|
||||
saved_masked_frames = [mask.cpu().numpy() for mask in masked_frames ]
|
||||
save_one_video("masked_frames.mp4", saved_masked_frames, fps=target_fps, quality=8, macro_block_size=None)
|
||||
save_one_video(f"masked_frames{'' if proc_no==1 else str(proc_no)}.mp4", saved_masked_frames, fps=target_fps, quality=8, macro_block_size=None)
|
||||
if any_mask:
|
||||
saved_masks = [mask.cpu().numpy() for mask in masks ]
|
||||
save_one_video("masks.mp4", saved_masks, fps=target_fps, quality=8, macro_block_size=None)
|
||||
preproc = None
|
||||
preproc2 = None
|
||||
preproc_outside = None
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
@ -3066,11 +3142,11 @@ def parse_keep_frames_video_guide(keep_frames, video_length):
|
||||
for i in range(start_range, end_range + 1):
|
||||
frames[i] = True
|
||||
else:
|
||||
if not is_integer(section):
|
||||
if not is_integer(section) or int(section) == 0:
|
||||
error =f"Invalid integer {section}"
|
||||
break
|
||||
index = absolute(int(section))
|
||||
frames[index] = True
|
||||
frames[index-1] = True
|
||||
|
||||
if len(error ) > 0:
|
||||
return [], error
|
||||
@ -3113,6 +3189,8 @@ def generate_video(
|
||||
keep_frames_video_guide,
|
||||
video_guide_outpainting,
|
||||
video_mask,
|
||||
control_net_weight,
|
||||
control_net_weight2,
|
||||
mask_expand,
|
||||
audio_guide,
|
||||
sliding_window_size,
|
||||
@ -3397,6 +3475,7 @@ def generate_video(
|
||||
frames_already_processed = None
|
||||
pre_video_guide = None
|
||||
overlapped_latents = None
|
||||
context_scale = None
|
||||
window_no = 0
|
||||
extra_windows = 0
|
||||
guide_start_frame = 0
|
||||
@ -3496,60 +3575,48 @@ def generate_video(
|
||||
if len(error) > 0:
|
||||
raise gr.Error(f"invalid keep frames {keep_frames_video_guide}")
|
||||
keep_frames_parsed = keep_frames_parsed[guide_start_frame: guide_start_frame + current_video_length]
|
||||
|
||||
context_scale = [ control_net_weight]
|
||||
if "V" in video_prompt_type:
|
||||
extra_label = ""
|
||||
if "X" in video_prompt_type:
|
||||
process_outside_mask = "inpaint"
|
||||
elif "Y" in video_prompt_type:
|
||||
process_outside_mask = "depth"
|
||||
extra_label = " and Depth"
|
||||
elif "W" in video_prompt_type:
|
||||
process_outside_mask = "scribble"
|
||||
extra_label = " and Shapes"
|
||||
process_map = { "Y" : "depth", "W": "scribble", "X": "inpaint", "Z": "flow"}
|
||||
process_outside_mask = process_map.get(filter_letters(video_prompt_type, "YWX"), None)
|
||||
preprocess_type2 = preprocess_type = None
|
||||
process_map = { "D" : "depth", "P": "pose", "S": "scribble", "F": "flow", "C": "gray", "M": "inpaint", "U": "identity"}
|
||||
for process_num, process_letter in enumerate( filter_letters(video_prompt_type, "PDSFCMU")):
|
||||
if process_num == 0:
|
||||
preprocess_type = process_map.get(process_letter, "vace")
|
||||
else:
|
||||
process_outside_mask = None
|
||||
preprocess_type = None
|
||||
if "P" in video_prompt_type :
|
||||
progress_args = [0, get_latest_status(state,f"Extracting Open Pose{extra_label} Information")]
|
||||
preprocess_type = "pose"
|
||||
elif "D" in video_prompt_type :
|
||||
progress_args = [0, get_latest_status(state,"Extracting Depth Information")]
|
||||
preprocess_type = "depth"
|
||||
elif "S" in video_prompt_type :
|
||||
progress_args = [0, get_latest_status(state,"Extracting Shapes Information")]
|
||||
preprocess_type = "scribble"
|
||||
elif "C" in video_prompt_type :
|
||||
progress_args = [0, get_latest_status(state,f"Extracting Gray Level{extra_label} Information")]
|
||||
preprocess_type = "gray"
|
||||
elif "M" in video_prompt_type :
|
||||
progress_args = [0, get_latest_status(state,f"Creating Inpainting{extra_label} Mask")]
|
||||
preprocess_type = "inpaint"
|
||||
elif "U" in video_prompt_type :
|
||||
progress_args = [0, get_latest_status(state,f"Creating Identity{extra_label} Mask")]
|
||||
preprocess_type = "identity"
|
||||
else:
|
||||
progress_args = [0, get_latest_status(state,f"Creating Vace Generic{extra_label} Mask")]
|
||||
preprocess_type = "vace"
|
||||
send_cmd("progress", progress_args)
|
||||
video_guide_copy, video_mask_copy = preprocess_video_with_mask(video_guide, video_mask, height=image_size[0], width = image_size[1], max_frames= len(keep_frames_parsed) if guide_start_frame == 0 else len(keep_frames_parsed) - reuse_frames, start_frame = guide_start_frame, fit_canvas = sample_fit_canvas, target_fps = fps, process_type = preprocess_type, expand_scale = mask_expand, RGB_Mask = True, negate_mask = "N" in video_prompt_type, process_outside_mask = process_outside_mask, outpainting_dims = outpainting_dims )
|
||||
preprocess_type2 = process_map.get(process_letter, None)
|
||||
process_names = { "pose": "Open Pose", "depth": "Depth Mask", "scribble" : "Shapes", "flow" : "Flow Map", "gray" : "Gray Levels", "inpaint" : "Inpaint Mask", "U": "Identity Mask", "vace" : "Vace Data"}
|
||||
status_info = "Extracting " + process_names[preprocess_type]
|
||||
extra_process_list = ([] if preprocess_type2==None else [preprocess_type2]) + ([] if process_outside_mask==None or process_outside_mask == preprocess_type else [process_outside_mask])
|
||||
if len(extra_process_list) == 1:
|
||||
status_info += " and " + process_names[extra_process_list[0]]
|
||||
elif len(extra_process_list) == 2:
|
||||
status_info += ", " + process_names[extra_process_list[0]] + " and " + process_names[extra_process_list[1]]
|
||||
send_cmd("progress", [0, get_latest_status(state, status_info)])
|
||||
video_guide_copy, video_mask_copy = preprocess_video_with_mask(video_guide, video_mask, height=image_size[0], width = image_size[1], max_frames= len(keep_frames_parsed) if guide_start_frame == 0 else len(keep_frames_parsed) - reuse_frames, start_frame = guide_start_frame, fit_canvas = sample_fit_canvas, target_fps = fps, process_type = preprocess_type, expand_scale = mask_expand, RGB_Mask = True, negate_mask = "N" in video_prompt_type, process_outside_mask = process_outside_mask, outpainting_dims = outpainting_dims, proc_no =1 )
|
||||
video_guide_copy2 = video_mask_copy2 = None
|
||||
if preprocess_type2 != None:
|
||||
video_guide_copy2, video_mask_copy2 = preprocess_video_with_mask(video_guide, video_mask, height=image_size[0], width = image_size[1], max_frames= len(keep_frames_parsed) if guide_start_frame == 0 else len(keep_frames_parsed) - reuse_frames, start_frame = guide_start_frame, fit_canvas = sample_fit_canvas, target_fps = fps, process_type = preprocess_type2, expand_scale = mask_expand, RGB_Mask = True, negate_mask = "N" in video_prompt_type, process_outside_mask = process_outside_mask, outpainting_dims = outpainting_dims, proc_no =2 )
|
||||
|
||||
if video_guide_copy != None:
|
||||
if sample_fit_canvas != None:
|
||||
image_size = video_guide_copy.shape[-3: -1]
|
||||
sample_fit_canvas = None
|
||||
refresh_preview["video_guide"] = Image.fromarray(video_guide_copy[0].cpu().numpy())
|
||||
if video_guide_copy2 != None:
|
||||
refresh_preview["video_guide"] = [refresh_preview["video_guide"], Image.fromarray(video_guide_copy2[0].cpu().numpy())]
|
||||
if video_mask_copy != None:
|
||||
refresh_preview["video_mask"] = Image.fromarray(video_mask_copy[0].cpu().numpy())
|
||||
frames_to_inject_parsed = frames_to_inject[guide_start_frame: guide_start_frame + current_video_length]
|
||||
|
||||
src_video, src_mask, src_ref_images = wan_model.prepare_source([video_guide_copy],
|
||||
[video_mask_copy],
|
||||
[image_refs_copy],
|
||||
src_video, src_mask, src_ref_images = wan_model.prepare_source([video_guide_copy] if video_guide_copy2 == None else [video_guide_copy, video_guide_copy2],
|
||||
[video_mask_copy] if video_guide_copy2 == None else [video_mask_copy, video_mask_copy2],
|
||||
[image_refs_copy] if video_guide_copy2 == None else [image_refs_copy, image_refs_copy],
|
||||
current_video_length, image_size = image_size, device ="cpu",
|
||||
original_video= "O" in video_prompt_type,
|
||||
keep_frames=keep_frames_parsed,
|
||||
start_frame = guide_start_frame,
|
||||
pre_src_video = [pre_video_guide],
|
||||
pre_src_video = [pre_video_guide] if video_guide_copy2 == None else [pre_video_guide, pre_video_guide],
|
||||
fit_into_canvas = sample_fit_canvas,
|
||||
inject_frames= frames_to_inject_parsed,
|
||||
outpainting_dims = outpainting_dims,
|
||||
@ -3632,6 +3699,7 @@ def generate_video(
|
||||
audio_proj= audio_proj_split,
|
||||
audio_scale= audio_scale,
|
||||
audio_context_lens= audio_context_lens,
|
||||
context_scale = context_scale,
|
||||
ar_step = model_mode, #5
|
||||
causal_block_size = 5,
|
||||
causal_attention = True,
|
||||
@ -3768,15 +3836,12 @@ def generate_video(
|
||||
w *= scale
|
||||
h = int(h)
|
||||
w = int(w)
|
||||
new_frames =[]
|
||||
for i in range( sample.shape[1] ):
|
||||
frame = sample[:, i]
|
||||
frame = resize_lanczos(frame, h, w)
|
||||
frame = frame.unsqueeze(1)
|
||||
new_frames.append(frame)
|
||||
sample = torch.cat(new_frames, dim=1)
|
||||
new_frames = None
|
||||
sample = sample * 2 - 1
|
||||
frames_to_upsample = [sample[:, i] for i in range( sample.shape[1]) ]
|
||||
def upsample_frames(frame):
|
||||
return resize_lanczos(frame, h, w).unsqueeze(1)
|
||||
sample = torch.cat(process_images_multithread(upsample_frames, frames_to_upsample, "upsample", wrap_in_list = False), dim=1)
|
||||
frames_to_upsample = None
|
||||
sample.mul_(2).sub_(1)
|
||||
|
||||
if sliding_window :
|
||||
if frames_already_processed == None:
|
||||
@ -4764,6 +4829,8 @@ def save_inputs(
|
||||
video_guide,
|
||||
keep_frames_video_guide,
|
||||
video_mask,
|
||||
control_net_weight,
|
||||
control_net_weight2,
|
||||
mask_expand,
|
||||
audio_guide,
|
||||
sliding_window_size,
|
||||
@ -4955,13 +5022,13 @@ def refresh_video_prompt_type_image_refs(state, video_prompt_type, video_prompt_
|
||||
return video_prompt_type, gr.update(visible = visible),gr.update(visible = visible), gr.update(visible = visible and "F" in video_prompt_type_image_refs), gr.update(visible= ("F" in video_prompt_type_image_refs or "V" in video_prompt_type) and vace )
|
||||
|
||||
def refresh_video_prompt_type_video_mask(video_prompt_type, video_prompt_type_video_mask):
|
||||
video_prompt_type = del_in_sequence(video_prompt_type, "XWYNA")
|
||||
video_prompt_type = del_in_sequence(video_prompt_type, "XYZWNA")
|
||||
video_prompt_type = add_to_sequence(video_prompt_type, video_prompt_type_video_mask)
|
||||
visible= "A" in video_prompt_type
|
||||
return video_prompt_type, gr.update(visible= visible), gr.update(visible= visible )
|
||||
|
||||
def refresh_video_prompt_type_video_guide(state, video_prompt_type, video_prompt_type_video_guide):
|
||||
video_prompt_type = del_in_sequence(video_prompt_type, "DSPCMUV")
|
||||
video_prompt_type = del_in_sequence(video_prompt_type, "PDSFCMUV")
|
||||
video_prompt_type = add_to_sequence(video_prompt_type, video_prompt_type_video_guide)
|
||||
visible = "V" in video_prompt_type
|
||||
mask_visible = visible and "A" in video_prompt_type and not "U" in video_prompt_type
|
||||
@ -5242,12 +5309,19 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
|
||||
("Transfer Human Motion", "PV"),
|
||||
("Transfer Depth", "DV"),
|
||||
("Transfer Shapes", "SV"),
|
||||
("Transfer Flow", "FV"),
|
||||
("Recolorize", "CV"),
|
||||
("Inpainting", "MV"),
|
||||
("Vace raw format", "V"),
|
||||
("Perform Inpainting", "MV"),
|
||||
("Use Vace raw format", "V"),
|
||||
("Keep Unchanged", "UV"),
|
||||
("Transfer Human Motion & Depth", "PDV"),
|
||||
("Transfer Human Motion & Shape", "PSV"),
|
||||
("Transfer Human Motion & Flow", "PFV"),
|
||||
("Transfer Depth & Shape", "DSV"),
|
||||
("Transfer Depth & Flow", "DFV"),
|
||||
("Transfer Shapes & Flow", "SFV"),
|
||||
],
|
||||
value=filter_letters(video_prompt_type_value, "DSPCMUV"),
|
||||
value=filter_letters(video_prompt_type_value, "PDSFCMUV"),
|
||||
label="Control Video Process", scale = 2, visible= True
|
||||
)
|
||||
elif hunyuan_video_custom_edit:
|
||||
@ -5256,7 +5330,7 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
|
||||
("Inpaint Control Video", "MV"),
|
||||
("Transfer Human Motion", "PMV"),
|
||||
],
|
||||
value=filter_letters(video_prompt_type_value, "DSPCMUV"),
|
||||
value=filter_letters(video_prompt_type_value, "PDSFCMUV"),
|
||||
label="Video to Video", scale = 3, visible= True
|
||||
)
|
||||
else:
|
||||
@ -5286,8 +5360,10 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
|
||||
("Non Masked Area, rest Depth", "YNA"),
|
||||
("Masked Area, rest Shapes", "WA"),
|
||||
("Non Masked Area, rest Shapes", "WNA"),
|
||||
("Masked Area, rest Flow", "ZA"),
|
||||
("Non Masked Area, rest Flow", "ZNA"),
|
||||
],
|
||||
value= filter_letters(video_prompt_type_value, "XYWNA"),
|
||||
value= filter_letters(video_prompt_type_value, "XYZWNA"),
|
||||
visible= "V" in video_prompt_type_value and not "U" in video_prompt_type_value and not hunyuan_video_custom,
|
||||
label="Area Processed", scale = 2
|
||||
)
|
||||
@ -5466,6 +5542,9 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
|
||||
audio_guidance_scale = gr.Slider(1.0, 20.0, value=ui_defaults.get("audio_guidance_scale",5), step=0.5, label="Audio Guidance", visible=fantasy)
|
||||
embedded_guidance_scale = gr.Slider(1.0, 20.0, value=6.0, step=0.5, label="Embedded Guidance Scale", visible=(hunyuan_t2v or hunyuan_i2v))
|
||||
flow_shift = gr.Slider(0.0, 25.0, value=ui_defaults.get("flow_shift",3), step=0.1, label="Shift Scale")
|
||||
with gr.Row(visible = vace):
|
||||
control_net_weight = gr.Slider(0.0, 2.0, value=ui_defaults.get("control_net_weight",1), step=0.1, label="Control Net Weight #1", visible=vace)
|
||||
control_net_weight2 = gr.Slider(0.0, 2.0, value=ui_defaults.get("control_net_weight2",1), step=0.1, label="Control Net Weight #2", visible=vace)
|
||||
with gr.Row():
|
||||
negative_prompt = gr.Textbox(label="Negative Prompt", value=ui_defaults.get("negative_prompt", "") )
|
||||
with gr.Tab("Loras"):
|
||||
@ -6144,6 +6223,15 @@ def generate_configuration_tab(state, blocks, header, model_choice, prompt_enhan
|
||||
interactive= not lock_ui_compile
|
||||
)
|
||||
|
||||
depth_anything_v2_variant_choice = gr.Dropdown(
|
||||
choices=[
|
||||
("Large (more precise but 2x slower)", "vitl"),
|
||||
("Big (less precise, less VRAM needed but faster)", "vitb"),
|
||||
],
|
||||
value= server_config.get("depth_anything_v2_variant", "vitl"),
|
||||
label="Depth Anything v2 Vace Preprocessor Model type",
|
||||
)
|
||||
|
||||
vae_config_choice = gr.Dropdown(
|
||||
choices=[
|
||||
("Auto", 0),
|
||||
@ -6224,6 +6312,7 @@ def generate_configuration_tab(state, blocks, header, model_choice, prompt_enhan
|
||||
enhancer_enabled_choice,
|
||||
fit_canvas_choice,
|
||||
preload_in_VRAM_choice,
|
||||
depth_anything_v2_variant_choice,
|
||||
notification_sound_enabled_choice,
|
||||
notification_sound_volume_choice
|
||||
],
|
||||
|
||||
Loading…
Reference in New Issue
Block a user