107 lines
4.5 KiB
Python
107 lines
4.5 KiB
Python
import folder_paths
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import comfy.sd
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import json
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import os
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from nodes import UNETLoader, CLIPLoader, VAELoader, LoraLoader
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from server import PromptServer
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import aiohttp
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print("\033[93m🦊\033[0m \033[93mRaykoStudio - RS Models Loader \033[92mLOADED\033[0m")
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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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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", "qwen_image", "ltxv", "wan", "ace", "hunyuan_image", "hidream", "chroma", "mochi", "cosmos", "pixart", "kolors", "ultrapix",
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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": "Модель диффузии (UNET)"}),
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"weight_dtype": (weight_dtype_opts, {"default": "default"}),
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"clip_name": (clip_files, {"tooltip": "CLIP модель"}),
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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, {"tooltip": "VAE модель"}),
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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 = "RS Loaders"
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@classmethod
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def IS_CHANGED(cls, lora_data="[]", **kwargs):
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import hashlib
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return hashlib.md5(lora_data.encode()).hexdigest()
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def load_models(self, unet_name, weight_dtype, clip_name, clip_type, clip_device, vae_name, lora_data="[]"):
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print(f"\n[Rayko] === ЗАПУСК load_models ===")
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print(f"[Rayko] lora_data RAW: {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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print(f"[Rayko] UNET: {unet_name}")
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dev = None if clip_device == "default" else clip_device
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clip = CLIPLoader().load_clip(clip_name=clip_name, type=clip_type, device=dev)[0]
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print(f"[Rayko] CLIP: {clip_name}")
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vae = VAELoader().load_vae(vae_name=vae_name)[0]
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print(f"[Rayko] VAE: {vae_name}")
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try:
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loras = json.loads(lora_data) if lora_data else []
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print(f"[Rayko] LoRA count: {len(loras)}")
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except Exception as e:
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loras = []
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print(f"[Rayko] Parse error: {e}")
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lora_loader = LoraLoader()
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for i, lora in enumerate(loras):
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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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print(f"[Rayko] LoRA[{i}]: {name} | strength={strength_model} | enabled={enabled}")
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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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print(f"[Rayko] Applying LoRA: {lora_relative_path} (strength={strength_model})")
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model, clip = lora_loader.load_lora(
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model, clip,
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lora_relative_path,
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strength_model,
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strength_clip
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)
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print(f"[Rayko] ✓ LoRA applied successfully")
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except Exception as e:
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print(f"[Rayko] ✗ Error: {e}")
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else:
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print(f"[Rayko] ✗ LoRA file not found: {lora_relative_path}")
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print(f"[Rayko] === ЗАВЕРШЕНО ===\n")
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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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@PromptServer.instance.routes.get("/rayko/get_loras")
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async def get_loras(request):
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loras = folder_paths.get_filename_list("loras")
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return aiohttp.web.json_response(sorted(loras, key=lambda x: x.lower())) |