Meisoftcoltd
e87cad300c
- `loop.py`: Added check for `trigger_dependency` in `SequentialLoopTrigger.trigger_next`. - `image.py`: Added checks for `generated_images` and `current_loop_index` in `SessionImageSender.set_image`. - `video.py`: Added list-aware checks for `trigger` and `current_loop_index` in `IncrementalVideoStitcher.stitch_incremental` due to `INPUT_IS_LIST = True`. All validations throw a `ValueError` with clear Spanish instructions when inputs are missing to provide immediate user feedback on the ComfyUI canvas.
118 lines
4.0 KiB
Python
118 lines
4.0 KiB
Python
import os
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import time
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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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global_session_image = None
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def tensor_to_temp_image(tensor_image, prefix="session_img"):
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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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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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@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}),
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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 time.time()
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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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loop_idx = current_loop_index[0] if isinstance(current_loop_index, list) else current_loop_index
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is_first = (loop_idx == 0)
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print(f"\n{'='*50}")
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print(f"📥 [DEBUG] NODO: Image Receiver")
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print(f" -> Ciclo actual detectado: {loop_idx}")
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if is_first or global_session_image is None:
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global_session_image = initial_image.clone().cpu()
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print(f" -> 🆕 Iniciando sesión con la imagen ORIGINAL.")
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selected = initial_image
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else:
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print(f" -> ♻️ Usando el fotograma rescatado de la RAM.")
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selected = global_session_image
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print(f" -> 🖼️ Tensor shape: {selected.shape}")
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print(f"{'='*50}\n")
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ui_image = tensor_to_temp_image(selected, "receiver")
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return {"ui": {"images": [ui_image]}, "result": (selected, )}
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@register_node
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class SessionImageSender:
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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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"current_loop_index": ("INT", {"default": 0, "min": 0, "max": 10000}),
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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 time.time()
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def set_image(self, generated_images, current_loop_index):
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if generated_images is None:
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raise ValueError("❌ ERROR CRÍTICO: El nodo 'Session Image Sender' no está recibiendo imágenes. Conecta la salida de tu VAE Decode o Sampler.")
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if current_loop_index is None:
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raise ValueError("❌ ERROR CRÍTICO: El nodo 'Session Image Sender' necesita el cable de 'current_loop_index' desde el Loop Start para nombrar los archivos.")
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global global_session_image
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loop_idx = current_loop_index[0] if isinstance(current_loop_index, list) else current_loop_index
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print(f"\n{'='*50}")
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print(f"📤 [DEBUG] NODO: Image Sender")
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print(f" -> Ciclo actual: {loop_idx} | Frames recibidos: {generated_images.shape[0]}")
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last_frame = generated_images[-1:].clone().cpu()
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global_session_image = last_frame
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# Guardar disco progresivo
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out_dir = folder_paths.get_output_directory()
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filename = f"keyframe_{loop_idx:03d}.png"
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filepath = os.path.join(out_dir, filename)
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img_array = 255. * last_frame[0].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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print(f" -> 💾 Keyframe guardado físicamente: {filename}")
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print(f" -> 🧠 RAM asegurada para el próximo ciclo.")
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print(f"{'='*50}\n")
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ui_image = tensor_to_temp_image(last_frame, "sender")
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return {"ui": {"images": [ui_image]}}
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