109 lines
5.7 KiB
Python
109 lines
5.7 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 comfy.sd
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import torch
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import json
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import hashlib
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from nodes import UNETLoader, CLIPLoader, VAELoader, LoraLoader, DualCLIPLoader
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class RaykoModelsLoaderPro:
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@classmethod
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def INPUT_TYPES(cls):
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unet_files = ["None"] + folder_paths.get_filename_list("unet")
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clip_files = ["None"] + folder_paths.get_filename_list("clip")
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vae_files = ["None"] + folder_paths.get_filename_list("vae") + ["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", "stable_audio", "sd3", "flux", "flux2", "flux3", "lumina2", "ideogram4", "qwen_image", "boogu", "krea2", "moss_image", "ltxv", "wan", "minimax", "yue2", "joyimage", "mage", "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, {"default": "None", "tooltip": "Primary Diffusion model"}),
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"unet_name2": (unet_files, {"default": "None", "tooltip": "Secondary Diffusion model (optional, e.g., Refiner)"}),
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"weight_dtype": (weight_dtype_opts, {"default": "default"}),
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"clip_name": (clip_files, {"default": "None", "tooltip": "Primary CLIP model"}),
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"clip_name2": (clip_files, {"default": "None", "tooltip": "Secondary CLIP model (for dual-clip 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, {"default": "None", "tooltip": "Primary VAE model"}),
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"vae_name2": (vae_files, {"default": "None", "tooltip": "Secondary VAE model (optional)"}),
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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", "MODEL", "CLIP", "VAE", "VAE")
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RETURN_NAMES = ("MODEL", "MODEL 2", "CLIP", "VAE", "VAE 2")
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FUNCTION = "load_models"
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CATEGORY = "🦊 RaykoStudio"
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DESCRIPTION = "Pro version: Combines loaders for up to 2 models, dual-clip, 2 VAEs and LoRAs. Leave fields as 'None' if not needed."
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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, unet_name2, weight_dtype, clip_name, clip_name2, clip_type, clip_device, vae_name, vae_name2, 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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dev = None if clip_device == "default" else clip_device
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model1 = UNETLoader().load_unet(unet_name=unet_name, weight_dtype=wd)[0] if unet_name != "None" else None
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model2 = UNETLoader().load_unet(unet_name=unet_name2, weight_dtype=wd)[0] if unet_name2 != "None" else None
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if clip_name2 != "None" and clip_name != "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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elif clip_name != "None":
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clip = CLIPLoader().load_clip(clip_name=clip_name, type=clip_type, device=dev)[0]
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elif clip_name2 != "None":
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clip = CLIPLoader().load_clip(clip_name=clip_name2, type=clip_type, device=dev)[0]
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else:
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clip = CLIPLoader().load_clip(clip_name=clip_files[1], type=clip_type, device=dev)[0]
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vae1 = VAELoader().load_vae(vae_name=vae_name)[0] if vae_name != "None" else None
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vae2 = VAELoader().load_vae(vae_name=vae_name2)[0] if vae_name2 != "None" else None
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if model1 is not None and clip is not None:
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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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model1, clip = lora_loader.load_lora(model1, clip, lora_relative_path, strength_model, strength_clip)
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except Exception as e:
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print(f"[Rayko Pro] ✗ LoRA Error: {e}")
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return (model1, model2, clip, vae1, vae2)
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NODE_CLASS_MAPPINGS = {"RaykoModelsLoaderPro": RaykoModelsLoaderPro}
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NODE_DISPLAY_NAME_MAPPINGS = {"RaykoModelsLoaderPro": "🦊 RS Models Loader Pro"} |