Marco
793402f93f
This commit introduces several new performance-oriented custom nodes and utilities: - Added LTXV Multi Concat and LTXV Multi Concat (beta) for faster frame injection using latent inpainting logic. - Refactored LTXV index resolution logic into ltxv_utils.py. - Updated ImageListSampler to anchor edge frames properly and support output index normalization scaling via target_frames. - Added metadata-based prompt extraction image loaders (LoadImageAndExtractPrompt, FolderImageAndExtractPrompt, and FolderImageMetadataByName). - Included animation tracking and camera switcher nodes.
106 lines
4.0 KiB
Python
106 lines
4.0 KiB
Python
import random
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import torch
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class AnotherCameraSwitcher:
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"""
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Multi-camera director node. Receives up to 8 video inputs (IMAGE batches)
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and alternates between them in equal segments, simulating camera cuts.
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All input videos must have the same resolution and at least as many frames
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as the longest cut plan requires.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"video_1": ("IMAGE",),
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"switch_every_seconds": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 300.0, "step": 0.1,
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"tooltip": "Cut to a different camera every N seconds"}),
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"fps": ("FLOAT", {"default": 24.0, "min": 1.0, "max": 120.0, "step": 0.1,
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"tooltip": "Frames per second of the input videos"}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFF, "step": 1}),
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"avoid_repeat": ("BOOLEAN", {"default": True,
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"tooltip": "Avoid picking the same camera twice in a row"}),
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},
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"optional": {
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"video_2": ("IMAGE",),
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"video_3": ("IMAGE",),
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"video_4": ("IMAGE",),
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"video_5": ("IMAGE",),
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"video_6": ("IMAGE",),
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"video_7": ("IMAGE",),
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"video_8": ("IMAGE",),
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}
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}
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RETURN_TYPES = ("IMAGE", "STRING",)
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RETURN_NAMES = ("video", "cut_log",)
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FUNCTION = "execute"
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CATEGORY = "AnotherUtils/video"
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def execute(self, video_1, switch_every_seconds, fps, seed, avoid_repeat,
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video_2=None, video_3=None, video_4=None,
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video_5=None, video_6=None, video_7=None, video_8=None):
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# Collect all connected videos
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videos = [video_1]
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for v in [video_2, video_3, video_4, video_5, video_6, video_7, video_8]:
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if v is not None:
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videos.append(v)
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num_cameras = len(videos)
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total_frames = video_1.shape[0]
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frames_per_cut = max(1, round(switch_every_seconds * fps))
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# Validate all videos have compatible dimensions
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_, h, w, c = video_1.shape
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for i, v in enumerate(videos):
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if v.shape[1] != h or v.shape[2] != w:
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raise ValueError(
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f"video_{i+1} has resolution {v.shape[2]}x{v.shape[1]}, "
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f"but video_1 is {w}x{h}. All videos must match."
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)
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rng = random.Random(seed)
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output_frames = []
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cut_log_lines = []
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frame_idx = 0
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last_camera = -1
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while frame_idx < total_frames:
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seg_end = min(frame_idx + frames_per_cut, total_frames)
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# Pick camera
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if num_cameras == 1:
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cam = 0
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elif avoid_repeat and num_cameras > 1:
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choices = [c for c in range(num_cameras) if c != last_camera]
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cam = rng.choice(choices)
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else:
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cam = rng.randint(0, num_cameras - 1)
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last_camera = cam
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src_video = videos[cam]
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# If source video is shorter than needed, wrap around
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seg_len = seg_end - frame_idx
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src_total = src_video.shape[0]
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indices = [(frame_idx + j) % src_total for j in range(seg_len)]
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segment = src_video[indices]
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output_frames.append(segment)
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time_start = frame_idx / fps
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time_end = seg_end / fps
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cut_log_lines.append(f"[{time_start:6.2f}s - {time_end:6.2f}s] camera {cam+1} (frames {frame_idx}-{seg_end-1})")
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frame_idx = seg_end
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output = torch.cat(output_frames, dim=0)
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cut_log = f"Camera Switcher | {num_cameras} cameras | {frames_per_cut} frames/cut ({switch_every_seconds}s @ {fps}fps)\n"
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cut_log += f"Total: {total_frames} frames ({total_frames/fps:.1f}s)\n"
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cut_log += "-" * 60 + "\n"
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cut_log += "\n".join(cut_log_lines)
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return (output, cut_log,)
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