100 lines
3.7 KiB
Python
100 lines
3.7 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 folder_paths
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import json
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import hashlib
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from nodes import LoraLoader
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class RSLoRAbatch:
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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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"model": ("MODEL", {"tooltip": "Input model to apply LoRA"}),
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"use_clip": ("BOOLEAN", {
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"default": True,
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"label": "Apply LoRA to CLIP",
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"tooltip": "Toggle CLIP application via UI button"
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}),
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"lora_data": ("STRING", {
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"default": "[]",
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"multiline": False,
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"forceInput": False,
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"tooltip": "JSON array of LoRA configs (managed by JS widget)"
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}),
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},
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"optional": {
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"clip": ("CLIP", {"tooltip": "Input CLIP to apply LoRA (optional)"}),
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}
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}
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RETURN_TYPES = ("MODEL", "CLIP")
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RETURN_NAMES = ("MODEL", "CLIP")
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FUNCTION = "load_lora"
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CATEGORY = "🦊 RaykoStudio"
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DESCRIPTION = "Batch LoRA loader with CLIP toggle and queue-based batch mode."
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@classmethod
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def IS_CHANGED(cls, lora_data="[]", **kwargs):
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if lora_data is None:
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lora_data = "[]"
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return hashlib.md5(lora_data.encode()).hexdigest()
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def load_lora(self, model, use_clip, lora_data="[]", clip=None):
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if not lora_data:
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lora_data = "[]"
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try:
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loras = json.loads(lora_data) if isinstance(lora_data, str) else lora_data
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if not isinstance(loras, list):
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loras = []
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except Exception:
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loras = []
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lora_loader = LoraLoader()
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result_clip = clip
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for lora in loras:
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if not isinstance(lora, dict):
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continue
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name = lora.get("name", "")
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strength_model = float(lora.get("strength_model", 1.0))
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strength_clip = float(lora.get("strength_clip", strength_model))
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enabled = lora.get("enabled", True)
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if name and name != "None" and enabled:
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lora_relative_path = name.replace("\\", "/")
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lora_full_path = folder_paths.get_full_path("loras", lora_relative_path)
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if lora_full_path:
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try:
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if use_clip and clip is not None:
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model, result_clip = lora_loader.load_lora(
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model, result_clip, lora_relative_path,
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strength_model, strength_clip
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)
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else:
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model, _ = lora_loader.load_lora(
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model, None, lora_relative_path,
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strength_model, strength_clip
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)
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except Exception as e:
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print(f"[RS LoRA Batch] Error loading '{name}': {e}")
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return (model, result_clip)
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NODE_CLASS_MAPPINGS = {"RSLoRAbatch": RSLoRAbatch}
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NODE_DISPLAY_NAME_MAPPINGS = {"RSLoRAbatch": "🦊 RS LoRA Tester"} |