248 lines
9.1 KiB
Python
248 lines
9.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# Copyright 2025-2026 Raykosan (RaykoStudio)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import json
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import numpy as np
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import re
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import time
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from PIL import Image
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from PIL.PngImagePlugin import PngInfo
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import folder_paths
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import torch
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from server import PromptServer
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from aiohttp import web
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class RS_VAE_Decode_Save:
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def __init__(self):
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self.output_dir = folder_paths.get_output_directory()
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self.last_temp_file = None
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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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"samples": ("LATENT",),
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"vae": ("VAE",),
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"save_path": ("STRING", {"default": ""}),
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"file_prefix": ("STRING", {"default": "img"}),
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"format": (["png", "jpg", "webp"], {"default": "png"}),
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"node_data": ("STRING", {"default": "{}", "multiline": False}),
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},
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"hidden": {
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"prompt": "PROMPT",
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"extra_pnginfo": "EXTRA_PNGINFO",
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},
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image",)
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FUNCTION = "decode_and_save"
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OUTPUT_NODE = True
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CATEGORY = "🦊 RaykoStudio"
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DESCRIPTION = "Combines native VAE Decode and Save Image into a single node."
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PNG_COMPRESSION = 1
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JPG_QUALITY = 90
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WEBP_QUALITY = 90
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EMBED_WORKFLOW = True
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@staticmethod
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def _sanitize_path_component(component: str) -> str:
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if not isinstance(component, str):
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component = str(component)
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component = component.replace("\\", "/")
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while "../" in component or "./" in component:
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component = component.replace("../", "").replace("./", "")
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component = re.sub(r'[<>:"|?*]', '_', component)
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return component.strip()
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@staticmethod
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def _normalize_images(images):
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images = images.squeeze()
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if images.dim() == 2:
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images = images.unsqueeze(0).unsqueeze(-1)
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if images.dim() == 3:
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if images.shape[-1] in [1, 3, 4]:
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images = images.unsqueeze(0)
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else:
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images = images.unsqueeze(-1)
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if images.dim() == 4:
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b, h, w, c = images.shape
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if c not in [1, 3, 4]:
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if images.shape[1] in [1, 3, 4]:
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images = images.permute(0, 2, 3, 1)
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if images.dim() == 5:
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if images.shape[1] == 1:
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images = images.squeeze(1)
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elif images.shape[0] == 1:
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images = images.squeeze(0)
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else:
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images = images.reshape(images.shape[0], -1, images.shape[-2], images.shape[-1])
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if images.dim() != 4:
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try:
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images = images.view(images.shape[0], images.shape[-2], images.shape[-1], -1)
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except:
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raise ValueError(f"[RS] Cannot normalize tensor with shape: {images.shape}")
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if images.shape[-1] == 1:
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images = images.repeat(1, 1, 1, 3)
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if images.dtype != torch.float32:
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images = images.float()
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images = torch.clamp(images, 0.0, 1.0)
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return images
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def _get_next_counter(self, directory: str, prefix: str, extension: str) -> int:
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try:
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pattern = re.compile(rf'^{re.escape(prefix)}_(\d{{5}})\.{re.escape(extension)}$')
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max_num = 0
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if os.path.exists(directory):
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for f in os.listdir(directory):
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match = pattern.match(f)
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if match:
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num = int(match.group(1))
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if num > max_num:
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max_num = num
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return max_num + 1
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except Exception:
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return 1
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def _cleanup_temp(self):
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try:
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temp_dir = folder_paths.get_temp_directory()
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if not os.path.exists(temp_dir):
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return
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now = time.time()
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cutoff = now - 7200
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for filename in os.listdir(temp_dir):
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if filename.startswith("rs_prev_"):
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filepath = os.path.join(temp_dir, filename)
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try:
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if os.path.getmtime(filepath) < cutoff:
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os.remove(filepath)
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except:
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pass
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except Exception as e:
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print(f"[RS] Cleanup error: {e}")
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def decode_and_save(self, samples, vae, save_path, file_prefix, format, node_data,
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prompt=None, extra_pnginfo=None):
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images = vae.decode(samples["samples"])
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images = self._normalize_images(images)
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self._cleanup_temp()
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clean_path = save_path.strip()
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is_absolute = os.path.isabs(clean_path) or (len(clean_path) > 1 and clean_path[1] == ':')
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if is_absolute:
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target_dir = os.path.normpath(clean_path)
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else:
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safe_path = self._sanitize_path_component(clean_path)
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target_dir = os.path.join(self.output_dir, safe_path) if safe_path else self.output_dir
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try:
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os.makedirs(target_dir, exist_ok=True)
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except Exception as e:
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raise PermissionError(f"[RS] Cannot create directory: {target_dir}. Error: {e}")
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safe_prefix = self._sanitize_path_component(file_prefix)
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if not safe_prefix:
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safe_prefix = "img"
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batch_size = images.shape[0]
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ext_map = {"png": "png", "jpg": "jpg", "webp": "webp"}
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extension = ext_map.get(format, "png")
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start_counter = self._get_next_counter(target_dir, safe_prefix, extension)
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saved_files = []
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temp_dir = folder_paths.get_temp_directory()
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if self.last_temp_file and os.path.exists(os.path.join(temp_dir, self.last_temp_file)):
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try: os.remove(os.path.join(temp_dir, self.last_temp_file))
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except: pass
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for i in range(batch_size):
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current_counter = start_counter + i
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filename_main = f"{safe_prefix}_{current_counter:05}.{extension}"
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filepath_main = os.path.join(target_dir, filename_main)
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img_array = np.clip(255.0 * images[i].cpu().numpy(), 0, 255).astype(np.uint8)
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img = Image.fromarray(img_array)
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save_kwargs = {}
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if format == "png":
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save_kwargs["compress_level"] = self.PNG_COMPRESSION
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if self.EMBED_WORKFLOW and prompt:
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metadata = PngInfo()
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metadata.add_text("prompt", json.dumps(prompt))
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if extra_pnginfo:
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for key, value in extra_pnginfo.items():
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try: metadata.add_text(key, json.dumps(value))
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except: pass
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save_kwargs["pnginfo"] = metadata
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elif format == "jpg":
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save_kwargs["quality"] = self.JPG_QUALITY
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save_kwargs["optimize"] = True
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save_kwargs["progressive"] = True
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elif format == "webp":
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save_kwargs["quality"] = self.WEBP_QUALITY
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save_kwargs["method"] = 4
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img.save(filepath_main, **save_kwargs)
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temp_name = f"rs_prev_{int(time.time() * 1000)}_{i}.png"
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filepath_temp = os.path.join(temp_dir, temp_name)
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img.save(filepath_temp, compress_level=1)
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if i == batch_size - 1:
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self.last_temp_file = temp_name
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saved_files.append({
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"filename": temp_name,
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"subfolder": "",
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"type": "temp"
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})
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return {"ui": {"images": saved_files}, "result": (images,)}
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@PromptServer.instance.routes.get("/rs_folders")
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async def get_output_folders(request):
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try:
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output_dir = folder_paths.get_output_directory()
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subfolders = []
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if os.path.exists(output_dir):
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for item in os.listdir(output_dir):
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full_path = os.path.join(output_dir, item)
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if os.path.isdir(full_path):
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real_path = os.path.realpath(full_path)
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real_output = os.path.realpath(output_dir)
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if real_path.startswith(real_output + os.sep) or real_path == real_output:
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subfolders.append(item)
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return web.json_response({"subfolders": sorted(subfolders)})
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except Exception as e:
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return web.json_response({"subfolders": [], "error": str(e)}, status=500)
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NODE_CLASS_MAPPINGS = {
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"RS_VAE_Decode_Save": RS_VAE_Decode_Save,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"RS_VAE_Decode_Save": "🦊 RS Decode Save Image",
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} |