88 lines
3.1 KiB
Python
88 lines
3.1 KiB
Python
import torch
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class RS_Last_Frame:
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SIZE = (200, 40)
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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_frames": ("*",),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "get_last_frame"
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CATEGORY = "🦊 RaykoStudio"
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def get_last_frame(self, video_frames):
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frames = None
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if isinstance(video_frames, torch.Tensor):
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frames = video_frames
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print(f"[RS_Last_Frame] ✅ Received ready IMAGE tensor: {frames.shape}")
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elif hasattr(video_frames, 'get_components'):
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try:
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comp = video_frames.get_components()
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print(f"[RS_Last_Frame] 🔍 get_components returned type: {type(comp)}")
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if isinstance(comp, (tuple, list)) and len(comp) > 0:
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frames = comp[0]
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print(f"[RS_Last_Frame] 📦 Extracted from tuple[0]: {type(frames)}")
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elif isinstance(comp, dict):
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frames = comp.get('video') or comp.get('frames') or comp.get('images')
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print(f"[RS_Last_Frame] 📦 Extracted from dict: {type(frames)}")
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elif hasattr(comp, 'video'):
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frames = comp.video
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elif hasattr(comp, 'frames'):
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frames = comp.frames
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elif hasattr(comp, 'images'):
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frames = comp.images
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elif hasattr(comp, 'data'):
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frames = comp.data
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else:
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frames = comp
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print(f"[RS_Last_Frame] 📦 Direct assignment: {type(frames)}")
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if frames is not None:
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print(f"[RS_Last_Frame] ✅ Successfully extracted frames: {type(frames)}, shape={getattr(frames, 'shape', 'N/A')}")
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except Exception as e:
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print(f"[RS_Last_Frame] ❌ get_components error: {e}")
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frames = None
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elif isinstance(video_frames, dict):
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frames = video_frames.get('frames') or video_frames.get('images') or video_frames.get('video')
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print(f"[RS_Last_Frame] 📦 Processed input dictionary")
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if frames is None:
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raise ValueError(
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f"❌ Failed to extract frames.\n"
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f"Input type: {type(video_frames)}\n"
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f"Available methods: {[m for m in dir(video_frames) if not m.startswith('_')]}"
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)
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if not isinstance(frames, torch.Tensor):
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raise ValueError(f"Expected torch.Tensor, got {type(frames)}")
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if frames.dim() == 5:
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frames = frames[0]
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elif frames.dim() == 4 and frames.shape[1] <= 4:
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frames = frames.permute(0, 2, 3, 1)
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elif frames.dim() != 4:
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raise ValueError(f"Unsupported tensor dimension: {frames.shape}")
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last_frame = frames[-1].unsqueeze(0)
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print(f"[RS_Last_Frame] 🖼️ Returned last frame: {last_frame.shape}")
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return (last_frame,)
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NODE_CLASS_MAPPINGS = {
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"RS_Last_Frame": RS_Last_Frame
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"RS_Last_Frame": "🦊 RS Last Frame"
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} |