Meisoftcoltd
73581ac582
- Delete deprecated modules: `sequence.py`, `paths.py`, `batch.py`, `debug.py`. - Clean `image.py`, retaining only SessionImageReceiver, SessionImageSender, and auxiliary function. - Clean `video.py`, retaining only IncrementalVideoStitcher and WanFrameValidator. - Clean `loop.py`, retaining only SequentialLoopStart and SequentialLoopTrigger. - Rewrite `__init__.py` mapping explicitly to the 6 core components and bump version to 1.0.0.
104 lines
3.4 KiB
Python
104 lines
3.4 KiB
Python
import os
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import random
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import numpy as np
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import torch
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from PIL import Image
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import folder_paths
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from . import register_node
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# Variable global para almacenar el último fotograma de la sesión
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global_session_image = None
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def tensor_to_temp_image(tensor_image, prefix="session_img"):
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"""Convierte un tensor de ComfyUI [1, H, W, C] a PNG temporal para la UI."""
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temp_dir = folder_paths.get_temp_directory()
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filename = f"{prefix}_{random.randint(10000, 99999)}.png"
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filepath = os.path.join(temp_dir, filename)
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# Extraemos el primer (y único) frame del tensor y lo convertimos
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img_array = 255. * tensor_image[0].cpu().numpy()
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img = Image.fromarray(np.clip(img_array, 0, 255).astype(np.uint8))
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img.save(filepath)
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return {"filename": filename, "subfolder": "", "type": "temp"}
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@register_node
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class SessionImageReceiver:
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"""Proporciona la imagen inicial o la última generada, detectando el inicio automáticamente."""
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"initial_image": ("IMAGE",),
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"current_loop_index": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("current_image",)
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OUTPUT_NODE = True
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FUNCTION = "get_image"
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CATEGORY = "🔁 Sequential Batcher/Image"
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@classmethod
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def IS_CHANGED(cls, **kwargs):
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return float("NaN")
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def get_image(self, initial_image, current_loop_index):
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global global_session_image
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# Extracción segura por si en el futuro se usa INPUT_IS_LIST
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loop_idx = current_loop_index[0] if isinstance(current_loop_index, list) else current_loop_index
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# LÓGICA NATIVA: Si es el primer ciclo (index 0), reiniciamos
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is_first_cycle = (loop_idx == 0)
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if is_first_cycle or global_session_image is None:
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global_session_image = initial_image
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print(f"[Sequential Batcher] Receiver: Ciclo {loop_idx}. Iniciando sesión con la imagen original.")
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else:
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print(f"[Sequential Batcher] Receiver: Ciclo {loop_idx}. Usando el último fotograma del ciclo anterior.")
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ui_image = tensor_to_temp_image(global_session_image, "receiver")
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return {"ui": {"images": [ui_image]}, "result": (global_session_image, )}
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@register_node
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class SessionImageSender:
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"""Extrae, guarda en memoria y MUESTRA el último fotograma de un lote de vídeo."""
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"generated_images": ("IMAGE",),
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},
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}
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RETURN_TYPES = ()
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RETURN_NAMES = ()
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OUTPUT_NODE = True
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FUNCTION = "set_image"
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CATEGORY = "🔁 Sequential Batcher/Image"
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@classmethod
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def IS_CHANGED(cls, **kwargs):
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return float("NaN")
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def set_image(self, generated_images):
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global global_session_image
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# Extraemos solo el último elemento preservando la dimensión del lote [1, H, W, C]
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last_frame = generated_images[-1:].clone()
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global_session_image = last_frame
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print(f"[Sequential Batcher] Sender: Último fotograma capturado con éxito.")
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# Generar vista previa para la UI
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ui_image = tensor_to_temp_image(last_frame, "sender")
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# Solo actualizamos la UI, no hay salida de cables ("result" no es necesario si RETURN_TYPES es vacío)
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return {"ui": {"images": [ui_image]}}
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