500 lines
22 KiB
Python
500 lines
22 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 comfy.sd
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import torch
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import json
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import os
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import struct
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import hashlib
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import re
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from nodes import UNETLoader, CLIPLoader, VAELoader, LoraLoader, DualCLIPLoader
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from server import PromptServer
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import aiohttp
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CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
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PRESETS_DIR = os.path.join(CURRENT_DIR, "preset_models")
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LORA_PRESETS_DIR = os.path.join(PRESETS_DIR, "loras")
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RAYKO_LORA_DATA_DIR = os.path.join(CURRENT_DIR, "rayko_lora_data")
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os.makedirs(PRESETS_DIR, exist_ok=True)
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os.makedirs(LORA_PRESETS_DIR, exist_ok=True)
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os.makedirs(RAYKO_LORA_DATA_DIR, exist_ok=True)
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# --- Helpers for LoRA Info ---
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def clean_html(text):
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if not text:
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return ""
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clean = re.sub(r'<[^>]+>', ' ', str(text))
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clean = re.sub(r'\s+', ' ', clean).strip()
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return clean
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def compute_sha256(file_path):
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sha256_hash = hashlib.sha256()
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with open(file_path, "rb") as f:
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for chunk in iter(lambda: f.read(8192), b""):
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sha256_hash.update(chunk)
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return sha256_hash.hexdigest()
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def save_to_rayko_db(file_hash, data, source, overwrite=False):
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db_path = os.path.join(RAYKO_LORA_DATA_DIR, f"{file_hash}.json")
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if not overwrite and os.path.exists(db_path):
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return False
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db_data = {
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"hash": file_hash,
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"name": data.get("full_name", ""),
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"trained_words": data.get("trained_words", []),
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"description": data.get("description", ""),
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"source": source,
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"imported_at": __import__('datetime').datetime.now().isoformat()
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}
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with open(db_path, 'w', encoding='utf-8') as f:
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json.dump(db_data, f, indent=2, ensure_ascii=False)
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return True
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def extract_metadata_from_safetensors(lora_full_path):
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try:
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with open(lora_full_path, "rb") as f:
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header_size = struct.unpack("<Q", f.read(8))[0]
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header_bytes = f.read(header_size)
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header = json.loads(header_bytes.decode("utf-8"))
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if "__metadata__" in header:
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meta = header["__metadata__"]
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return {
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"full_name": meta.get("modelspec.title", ""),
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"trained_words": [t.strip() for t in meta.get("modelspec.tags", "").split(",") if t.strip()] if "modelspec.tags" in meta else [],
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"description": clean_html(meta.get("modelspec.description", "") or meta.get("ss_training_comment", ""))
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}
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except Exception:
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pass
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return None
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# --- Node ---
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class RaykoModelsLoader:
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@classmethod
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def INPUT_TYPES(cls):
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unet_files = folder_paths.get_filename_list("unet")
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clip_files = folder_paths.get_filename_list("clip")
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vae_files = folder_paths.get_filename_list("vae")
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vae_files_with_pixel = vae_files + ["pixel_space"]
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weight_dtype_opts = ["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"]
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clip_type_opts = [
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"stable_diffusion", "stable_cascade", "sd3", "flux", "flux2", "lumina2", "ideogram4", "qwen_image", "boogu", "krea2", "ltxv", "wan", "ace", "hunyuan_image", "hidream", "chroma", "mochi", "cosmos", "pixart", "kolors", "ultrapix", "omnigen2", "ovis", "longcat_image", "cogvideox", "lens", "pixeldit",
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]
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device_opts = ["default", "cpu", "cuda"]
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required = {
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"unet_name": (unet_files, {"tooltip": "Diffusion model (UNET)"}),
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"weight_dtype": (weight_dtype_opts, {"default": "default"}),
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"use_clip2": ("BOOLEAN", {"default": False, "label": "Enable second CLIP"}),
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"clip_name": (clip_files, {"tooltip": "First CLIP model (or primary CLIP for dual mode)"}),
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"clip_name2": (clip_files, {"tooltip": "Second CLIP model (for dual-clip models like Flux, SD3)"}),
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"clip_type": (clip_type_opts, {"default": "stable_diffusion"}),
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"clip_device": (device_opts, {"default": "default"}),
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"vae_name": (vae_files_with_pixel, {"tooltip": "model VAE (includes pixel_space for direct pixel manipulation)"}),
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"lora_data": ("STRING", {"default": "[]", "multiline": False, "forceInput": False}),
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}
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return {"required": required}
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RETURN_TYPES = ("MODEL", "CLIP", "VAE")
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RETURN_NAMES = ("MODEL", "CLIP", "VAE")
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FUNCTION = "load_models"
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CATEGORY = "🦊 RaykoStudio"
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DESCRIPTION = "The node combines the loaders of the model, clip, vae and lore. Presets allow quick switching between model configurations (UNET/CLIP/VAE) without affecting LoRA settings."
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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: lora_data = "[]"
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return hashlib.md5(lora_data.encode()).hexdigest()
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def load_models(self, unet_name, weight_dtype, use_clip2, clip_name, clip_name2, clip_type, clip_device, vae_name, lora_data="[]"):
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if not lora_data: lora_data = "[]"
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wd = None if weight_dtype == "default" else weight_dtype
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model = UNETLoader().load_unet(unet_name=unet_name, weight_dtype=wd)[0]
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dev = None if clip_device == "default" else clip_device
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if use_clip2 and clip_name2 and clip_name2 != "None":
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clip = DualCLIPLoader().load_clip(clip_name1=clip_name, clip_name2=clip_name2, type=clip_type, device=dev)[0]
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else:
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clip = CLIPLoader().load_clip(clip_name=clip_name, type=clip_type, device=dev)[0]
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if vae_name == "pixel_space":
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sd = {"pixel_space_vae": torch.tensor(1.0)}
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vae = comfy.sd.VAE(sd=sd, metadata=None)
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vae.throw_exception_if_invalid()
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else:
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vae = VAELoader().load_vae(vae_name=vae_name)[0]
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try:
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loras = json.loads(lora_data) if lora_data else []
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if not isinstance(loras, list): loras = []
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except Exception:
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loras = []
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lora_loader = LoraLoader()
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for lora in loras:
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if not isinstance(lora, dict): 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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model, clip = lora_loader.load_lora(model, clip, lora_relative_path, strength_model, strength_clip)
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except Exception as e:
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print(f"[Rayko] ✗ LoRA Error: {e}")
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return (model, clip, vae)
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NODE_CLASS_MAPPINGS = {"RaykoModelsLoader": RaykoModelsLoader}
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NODE_DISPLAY_NAME_MAPPINGS = {"RaykoModelsLoader": "🦊 RS Models Loader"}
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# --- General Endpoints ---
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@PromptServer.instance.routes.get("/rayko/get_loras")
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async def get_loras(request):
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return aiohttp.web.json_response(sorted(folder_paths.get_filename_list("loras"), key=lambda x: x.lower()))
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@PromptServer.instance.routes.post("/rayko/get_lora_info")
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async def rayko_get_lora_info(request):
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try:
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data = await request.json()
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lora_name = data.get("name", "")
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if not lora_name:
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return aiohttp.web.Response(status=400, text="No name provided")
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lora_relative_path = lora_name.replace("\\", "/")
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lora_full_path = folder_paths.get_full_path("loras", lora_relative_path)
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if not lora_full_path or not os.path.exists(lora_full_path):
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return aiohttp.web.json_response({"error": "File not found"})
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file_hash = compute_sha256(lora_full_path)
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db_path = os.path.join(RAYKO_LORA_DATA_DIR, f"{file_hash}.json")
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if os.path.exists(db_path):
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try:
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with open(db_path, 'r', encoding='utf-8') as f:
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db_data = json.load(f)
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return aiohttp.web.json_response({
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"full_name": db_data.get("name", lora_name),
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"trained_words": db_data.get("trained_words", []),
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"description": clean_html(db_data.get("description", "")),
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"source": db_data.get("source", "rayko_db")
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})
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except Exception:
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pass
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safetensors_meta = extract_metadata_from_safetensors(lora_full_path)
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if safetensors_meta and (safetensors_meta["trained_words"] or safetensors_meta["description"]):
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save_to_rayko_db(file_hash, safetensors_meta, "safetensors")
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return aiohttp.web.json_response({
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"full_name": safetensors_meta["full_name"] or lora_name,
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"trained_words": safetensors_meta["trained_words"],
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"description": safetensors_meta["description"],
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"source": "safetensors"
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})
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return aiohttp.web.json_response({
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"full_name": lora_name,
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"trained_words": [],
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"description": "",
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"source": "none"
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})
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except Exception as e:
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return aiohttp.web.Response(status=500, text=str(e))
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@PromptServer.instance.routes.post("/rayko/fetch_civitai_info")
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async def rayko_fetch_civitai_info(request):
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try:
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data = await request.json()
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lora_name = data.get("name", "")
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if not lora_name:
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return aiohttp.web.Response(status=400, text="No name provided")
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lora_relative_path = lora_name.replace("\\", "/")
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lora_full_path = folder_paths.get_full_path("loras", lora_relative_path)
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if not lora_full_path or not os.path.exists(lora_full_path):
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return aiohttp.web.json_response({"error": "File not found"})
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file_hash = compute_sha256(lora_full_path)
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civitai_url = f"https://civitai.com/api/v1/model-versions/by-hash/{file_hash}"
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async with aiohttp.ClientSession() as session:
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async with session.get(civitai_url, headers={"User-Agent": "ComfyUI-RaykoStudio/1.0"}) as response:
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if response.status == 404:
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return aiohttp.web.json_response({"error": "not_found", "message": "Not found on Civitai."})
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elif response.status == 429:
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return aiohttp.web.json_response({"error": "rate_limit", "message": "Rate limit reached."})
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elif response.status != 200:
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return aiohttp.web.json_response({"error": "api_error", "message": f"API status {response.status}"})
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civitai_data = await response.json()
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model_id = civitai_data.get("modelId")
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model_tags = []
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if model_id:
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try:
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model_url = f"https://civitai.com/api/v1/models/{model_id}"
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async with session.get(model_url, headers={"User-Agent": "ComfyUI-RaykoStudio/1.0"}) as model_response:
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if model_response.status == 200:
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model_data = await model_response.json()
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model_tags = model_data.get("tags", [])
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except Exception as e:
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print(f"[Rayko] Error fetching full model details: {e}")
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raw_trained = civitai_data.get("trainedWords", [])
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trained_words = []
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if isinstance(raw_trained, list):
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trained_words.extend([str(t).strip() for t in raw_trained if str(t).strip()])
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if isinstance(model_tags, list):
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trained_words.extend([str(t).strip() for t in model_tags if str(t).strip()])
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trained_words = list(dict.fromkeys(trained_words))
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raw_description = civitai_data.get("description", "")
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clean_description = clean_html(raw_description)
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model_name = civitai_data.get("model", {}).get("name", "")
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version_name = civitai_data.get("name", "")
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if model_name:
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if version_name and version_name.lower() not in ["v1", "v1.0", "default", "latest", ""]:
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full_name = f"{model_name} ({version_name})"
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else:
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full_name = model_name
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else:
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full_name = lora_name
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cache_data = {
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"full_name": full_name,
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"trained_words": trained_words,
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"description": clean_description
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}
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save_to_rayko_db(file_hash, cache_data, "civitai", overwrite=True)
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return aiohttp.web.json_response({
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"full_name": full_name,
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"trained_words": trained_words,
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"description": clean_description,
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"source": "civitai"
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})
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except aiohttp.ClientError as e:
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return aiohttp.web.json_response({"error": "network", "message": f"Network error: {str(e)}"})
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except Exception as e:
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return aiohttp.web.Response(status=500, text=str(e))
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# --- MODEL PRESETS ---
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@PromptServer.instance.routes.post("/rayko_models/save_preset")
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async def rayko_models_save_preset(request):
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try:
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data = await request.json()
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name = "".join(c for c in data.get("name", "").strip() if c.isalnum() or c in " _-").strip()
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if not name: return aiohttp.web.Response(status=400, text="Invalid name")
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filepath = os.path.join(PRESETS_DIR, f"{name}.json")
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with open(filepath, 'w', encoding='utf-8') as f:
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json.dump({k: data.get(k, "") for k in ["unet_name", "weight_dtype", "use_clip2", "clip_name", "clip_name2", "clip_type", "clip_device", "vae_name"]}, f, indent=2)
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return aiohttp.web.Response(status=200, text="OK")
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except Exception as e:
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return aiohttp.web.Response(status=500, text=str(e))
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@PromptServer.instance.routes.post("/rayko_models/list_presets")
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async def rayko_models_list_presets(request):
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try:
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presets = [f[:-5] for f in os.listdir(PRESETS_DIR) if f.endswith('.json')] if os.path.exists(PRESETS_DIR) else []
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return aiohttp.web.json_response(sorted(presets, key=lambda x: x.lower()))
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except Exception as e:
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return aiohttp.web.Response(status=500, text=str(e))
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@PromptServer.instance.routes.post("/rayko_models/load_preset")
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async def rayko_models_load_preset(request):
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try:
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name = (await request.json()).get("name")
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filepath = os.path.join(PRESETS_DIR, f"{name}.json")
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if os.path.exists(filepath):
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with open(filepath, 'r', encoding='utf-8') as f:
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return aiohttp.web.json_response(json.load(f))
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return aiohttp.web.Response(status=404, text="Preset not found")
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except Exception as e:
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return aiohttp.web.Response(status=500, text=str(e))
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@PromptServer.instance.routes.post("/rayko_models/delete_preset")
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async def rayko_models_delete_preset(request):
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try:
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name = (await request.json()).get("name")
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filepath = os.path.join(PRESETS_DIR, f"{name}.json")
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if os.path.exists(filepath):
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os.remove(filepath)
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return aiohttp.web.Response(status=200, text="OK")
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return aiohttp.web.Response(status=404, text="Preset not found")
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except Exception as e:
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return aiohttp.web.Response(status=500, text=str(e))
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# --- LORA PRESETS ---
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@PromptServer.instance.routes.post("/rayko_loras/save_preset")
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async def rayko_loras_save_preset(request):
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try:
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data = await request.json()
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name = "".join(c for c in data.get("name", "").strip() if c.isalnum() or c in " _-").strip()
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if not name: return aiohttp.web.Response(status=400, text="Invalid name")
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filepath = os.path.join(LORA_PRESETS_DIR, f"{name}.json")
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with open(filepath, 'w', encoding='utf-8') as f:
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json.dump({"lora_rows": data.get("lora_rows", [])}, f, indent=2)
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return aiohttp.web.Response(status=200, text="OK")
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except Exception as e:
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return aiohttp.web.Response(status=500, text=str(e))
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@PromptServer.instance.routes.post("/rayko_loras/list_presets")
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async def rayko_loras_list_presets(request):
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try:
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presets = [f[:-5] for f in os.listdir(LORA_PRESETS_DIR) if f.endswith('.json')] if os.path.exists(LORA_PRESETS_DIR) else []
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return aiohttp.web.json_response(sorted(presets, key=lambda x: x.lower()))
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except Exception as e:
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return aiohttp.web.Response(status=500, text=str(e))
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@PromptServer.instance.routes.post("/rayko_loras/load_preset")
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async def rayko_loras_load_preset(request):
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try:
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name = (await request.json()).get("name")
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filepath = os.path.join(LORA_PRESETS_DIR, f"{name}.json")
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if os.path.exists(filepath):
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with open(filepath, 'r', encoding='utf-8') as f:
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return aiohttp.web.json_response(json.load(f))
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return aiohttp.web.Response(status=404, text="Preset not found")
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except Exception as e:
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return aiohttp.web.Response(status=500, text=str(e))
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@PromptServer.instance.routes.post("/rayko_loras/delete_preset")
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async def rayko_loras_delete_preset(request):
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try:
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name = (await request.json()).get("name")
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filepath = os.path.join(LORA_PRESETS_DIR, f"{name}.json")
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if os.path.exists(filepath):
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os.remove(filepath)
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return aiohttp.web.Response(status=200, text="OK")
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return aiohttp.web.Response(status=404, text="Preset not found")
|
|
except Exception as e:
|
|
return aiohttp.web.Response(status=500, text=str(e))
|
|
|
|
@PromptServer.instance.routes.post("/rayko/add_tags")
|
|
async def rayko_add_tags(request):
|
|
try:
|
|
data = await request.json()
|
|
lora_name = data.get("name", "")
|
|
new_tags = data.get("tags", [])
|
|
|
|
if not lora_name:
|
|
return aiohttp.web.Response(status=400, text="No name provided")
|
|
if not isinstance(new_tags, list) or not new_tags:
|
|
return aiohttp.web.json_response({"error": "No tags provided"})
|
|
|
|
lora_relative_path = lora_name.replace("\\", "/")
|
|
lora_full_path = folder_paths.get_full_path("loras", lora_relative_path)
|
|
|
|
if not lora_full_path or not os.path.exists(lora_full_path):
|
|
return aiohttp.web.json_response({"error": "File not found"})
|
|
|
|
file_hash = compute_sha256(lora_full_path)
|
|
db_path = os.path.join(RAYKO_LORA_DATA_DIR, f"{file_hash}.json")
|
|
|
|
existing_data = {}
|
|
if os.path.exists(db_path):
|
|
try:
|
|
with open(db_path, 'r', encoding='utf-8') as f:
|
|
existing_data = json.load(f)
|
|
except Exception:
|
|
pass
|
|
|
|
existing_tags = existing_data.get("trained_words", [])
|
|
if not isinstance(existing_tags, list):
|
|
existing_tags = []
|
|
|
|
combined_tags = existing_tags.copy()
|
|
for tag in new_tags:
|
|
tag = str(tag).strip()
|
|
if tag and tag not in combined_tags:
|
|
combined_tags.append(tag)
|
|
|
|
save_data = {
|
|
"full_name": existing_data.get("name", ""),
|
|
"trained_words": combined_tags,
|
|
"description": existing_data.get("description", "")
|
|
}
|
|
|
|
save_to_rayko_db(file_hash, save_data, "manual", overwrite=True)
|
|
|
|
return aiohttp.web.json_response({
|
|
"trained_words": combined_tags,
|
|
"added": len(combined_tags) - len(existing_tags)
|
|
})
|
|
|
|
except Exception as e:
|
|
return aiohttp.web.Response(status=500, text=str(e))
|
|
|
|
@PromptServer.instance.routes.post("/rayko/remove_tag")
|
|
async def rayko_remove_tag(request):
|
|
try:
|
|
data = await request.json()
|
|
lora_name = data.get("name", "")
|
|
tag_to_remove = data.get("tag", "")
|
|
|
|
if not lora_name or not tag_to_remove:
|
|
return aiohttp.web.Response(status=400, text="Missing parameters")
|
|
|
|
lora_relative_path = lora_name.replace("\\", "/")
|
|
lora_full_path = folder_paths.get_full_path("loras", lora_relative_path)
|
|
|
|
if not lora_full_path or not os.path.exists(lora_full_path):
|
|
return aiohttp.web.json_response({"error": "File not found"})
|
|
|
|
file_hash = compute_sha256(lora_full_path)
|
|
db_path = os.path.join(RAYKO_LORA_DATA_DIR, f"{file_hash}.json")
|
|
|
|
if not os.path.exists(db_path):
|
|
return aiohttp.web.json_response({"error": "No data found"})
|
|
|
|
with open(db_path, 'r', encoding='utf-8') as f:
|
|
existing_data = json.load(f)
|
|
|
|
existing_tags = existing_data.get("trained_words", [])
|
|
if tag_to_remove in existing_tags:
|
|
existing_tags.remove(tag_to_remove)
|
|
|
|
save_data = {
|
|
"full_name": existing_data.get("name", ""),
|
|
"trained_words": existing_tags,
|
|
"description": existing_data.get("description", "")
|
|
}
|
|
|
|
save_to_rayko_db(file_hash, save_data, existing_data.get("source", "manual"), overwrite=True)
|
|
|
|
return aiohttp.web.json_response({
|
|
"trained_words": existing_tags,
|
|
"removed": tag_to_remove
|
|
})
|
|
|
|
except Exception as e:
|
|
return aiohttp.web.Response(status=500, text=str(e)) |