Added RS Models Loader Pro node
This commit is contained in:
Raykosan
+3
-397
@@ -17,73 +17,8 @@ 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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@@ -95,7 +30,7 @@ class RaykoModelsLoader:
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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", "joyimage", "mage", "ace", "hunyuan_image", "hidream", "chroma", "mochi", "cosmos", "pixart", "kolors", "ultrapix", "omnigen2", "ovis", "longcat_image", "cogvideox", "lens", "pixeldit",
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"stable_diffusion", "stable_cascade", "sd3", "flux", "flux2", "lumina2", "ideogram4", "qwen_image", "boogu", "krea2", "ltxv", "wan", "minimax", "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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@@ -130,7 +65,7 @@ class RaykoModelsLoader:
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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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if use_clip2 and clip_name2 and clip_name2 != "None" and clip_name2 != "":
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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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@@ -168,333 +103,4 @@ class RaykoModelsLoader:
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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")
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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/add_tags")
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async def rayko_add_tags(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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new_tags = data.get("tags", [])
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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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if not isinstance(new_tags, list) or not new_tags:
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return aiohttp.web.json_response({"error": "No tags 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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existing_data = {}
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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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existing_data = json.load(f)
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except Exception:
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pass
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existing_tags = existing_data.get("trained_words", [])
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if not isinstance(existing_tags, list):
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existing_tags = []
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combined_tags = existing_tags.copy()
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for tag in new_tags:
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tag = str(tag).strip()
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if tag and tag not in combined_tags:
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combined_tags.append(tag)
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save_data = {
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"full_name": existing_data.get("name", ""),
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"trained_words": combined_tags,
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"description": existing_data.get("description", "")
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}
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save_to_rayko_db(file_hash, save_data, "manual", overwrite=True)
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return aiohttp.web.json_response({
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"trained_words": combined_tags,
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"added": len(combined_tags) - len(existing_tags)
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||||
})
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||||
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||||
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))
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {"RaykoModelsLoader": "🦊 RS Models Loader"}
|
||||
@@ -0,0 +1,109 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# Copyright 2025-2026 Raykosan (RaykoStudio)
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import folder_paths
|
||||
import comfy.sd
|
||||
import torch
|
||||
import json
|
||||
import hashlib
|
||||
from nodes import UNETLoader, CLIPLoader, VAELoader, LoraLoader, DualCLIPLoader
|
||||
|
||||
class RaykoModelsLoaderPro:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
unet_files = ["None"] + folder_paths.get_filename_list("unet")
|
||||
clip_files = ["None"] + folder_paths.get_filename_list("clip")
|
||||
vae_files = ["None"] + folder_paths.get_filename_list("vae") + ["pixel_space"]
|
||||
|
||||
weight_dtype_opts = ["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"]
|
||||
clip_type_opts = [
|
||||
"stable_diffusion", "stable_cascade", "sd3", "flux", "flux2", "lumina2", "ideogram4", "qwen_image", "boogu", "krea2", "ltxv", "wan", "minimax", "joyimage", "mage", "ace", "hunyuan_image", "hidream", "chroma", "mochi", "cosmos", "pixart", "kolors", "ultrapix", "omnigen2", "ovis", "longcat_image", "cogvideox", "lens", "pixeldit",
|
||||
]
|
||||
device_opts = ["default", "cpu", "cuda"]
|
||||
|
||||
required = {
|
||||
"unet_name": (unet_files, {"default": "None", "tooltip": "Primary Diffusion model"}),
|
||||
"unet_name2": (unet_files, {"default": "None", "tooltip": "Secondary Diffusion model (optional, e.g., Refiner)"}),
|
||||
"weight_dtype": (weight_dtype_opts, {"default": "default"}),
|
||||
"clip_name": (clip_files, {"default": "None", "tooltip": "Primary CLIP model"}),
|
||||
"clip_name2": (clip_files, {"default": "None", "tooltip": "Secondary CLIP model (for dual-clip like Flux/SD3)"}),
|
||||
"clip_type": (clip_type_opts, {"default": "stable_diffusion"}),
|
||||
"clip_device": (device_opts, {"default": "default"}),
|
||||
"vae_name": (vae_files, {"default": "None", "tooltip": "Primary VAE model"}),
|
||||
"vae_name2": (vae_files, {"default": "None", "tooltip": "Secondary VAE model (optional)"}),
|
||||
"lora_data": ("STRING", {"default": "[]", "multiline": False, "forceInput": False}),
|
||||
}
|
||||
return {"required": required}
|
||||
|
||||
RETURN_TYPES = ("MODEL", "MODEL", "CLIP", "VAE", "VAE")
|
||||
RETURN_NAMES = ("MODEL", "MODEL 2", "CLIP", "VAE", "VAE 2")
|
||||
FUNCTION = "load_models"
|
||||
CATEGORY = "🦊 RaykoStudio"
|
||||
DESCRIPTION = "Pro version: Combines loaders for up to 2 models, dual-clip, 2 VAEs and LoRAs. Leave fields as 'None' if not needed."
|
||||
|
||||
@classmethod
|
||||
def IS_CHANGED(cls, lora_data="[]", **kwargs):
|
||||
if lora_data is None: lora_data = "[]"
|
||||
return hashlib.md5(lora_data.encode()).hexdigest()
|
||||
|
||||
def load_models(self, unet_name, unet_name2, weight_dtype, clip_name, clip_name2, clip_type, clip_device, vae_name, vae_name2, lora_data="[]"):
|
||||
if not lora_data: lora_data = "[]"
|
||||
|
||||
wd = None if weight_dtype == "default" else weight_dtype
|
||||
dev = None if clip_device == "default" else clip_device
|
||||
|
||||
model1 = UNETLoader().load_unet(unet_name=unet_name, weight_dtype=wd)[0] if unet_name != "None" else None
|
||||
model2 = UNETLoader().load_unet(unet_name=unet_name2, weight_dtype=wd)[0] if unet_name2 != "None" else None
|
||||
|
||||
if clip_name2 != "None" and clip_name != "None":
|
||||
clip = DualCLIPLoader().load_clip(clip_name1=clip_name, clip_name2=clip_name2, type=clip_type, device=dev)[0]
|
||||
elif clip_name != "None":
|
||||
clip = CLIPLoader().load_clip(clip_name=clip_name, type=clip_type, device=dev)[0]
|
||||
elif clip_name2 != "None":
|
||||
clip = CLIPLoader().load_clip(clip_name=clip_name2, type=clip_type, device=dev)[0]
|
||||
else:
|
||||
clip = CLIPLoader().load_clip(clip_name=clip_files[1], type=clip_type, device=dev)[0]
|
||||
|
||||
vae1 = VAELoader().load_vae(vae_name=vae_name)[0] if vae_name != "None" else None
|
||||
vae2 = VAELoader().load_vae(vae_name=vae_name2)[0] if vae_name2 != "None" else None
|
||||
|
||||
if model1 is not None and clip is not None:
|
||||
try:
|
||||
loras = json.loads(lora_data) if lora_data else []
|
||||
if not isinstance(loras, list): loras = []
|
||||
except Exception:
|
||||
loras = []
|
||||
|
||||
lora_loader = LoraLoader()
|
||||
for lora in loras:
|
||||
if not isinstance(lora, dict): continue
|
||||
name = lora.get("name", "")
|
||||
strength_model = float(lora.get("strength_model", 1.0))
|
||||
strength_clip = float(lora.get("strength_clip", strength_model))
|
||||
enabled = lora.get("enabled", True)
|
||||
|
||||
if name and name != "None" and enabled:
|
||||
lora_relative_path = name.replace("\\", "/")
|
||||
lora_full_path = folder_paths.get_full_path("loras", lora_relative_path)
|
||||
if lora_full_path:
|
||||
try:
|
||||
model1, clip = lora_loader.load_lora(model1, clip, lora_relative_path, strength_model, strength_clip)
|
||||
except Exception as e:
|
||||
print(f"[Rayko Pro] ✗ LoRA Error: {e}")
|
||||
|
||||
return (model1, model2, clip, vae1, vae2)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {"RaykoModelsLoaderPro": RaykoModelsLoaderPro}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {"RaykoModelsLoaderPro": "🦊 RS Models Loader Pro"}
|
||||
+1
-1
@@ -29,7 +29,7 @@ IGNORED_MODULES: Set[str] = {'__init__.py'}
|
||||
REQUIRED_MODULES: Set[str] = set()
|
||||
|
||||
PACKAGE_DISPLAY_NAME = "ComfyUI_RaykoStudio"
|
||||
__version__ = "0.39.0"
|
||||
__version__ = "0.40.0"
|
||||
|
||||
def load_modules():
|
||||
print(f"🦊 {PACKAGE_DISPLAY_NAME}: Version: {__version__}")
|
||||
|
||||
+3
-1
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "ComfyUI_RaykoStudio",
|
||||
"version": "0.39.0",
|
||||
"version": "0.40.0",
|
||||
"description": "Set of custom nodes for ComfyUI providing additional image processing capabilities",
|
||||
"author": "Raykosan",
|
||||
"license": "Apache License 2.0",
|
||||
@@ -30,6 +30,7 @@
|
||||
"Rayko_LoRA_Batch.py"
|
||||
"Rayko_LoRA_Loader.py"
|
||||
"Rayko_Models_Loader.py"
|
||||
"Rayko_Models_Loader_Pro.py"
|
||||
"Rayko_MultiLatent.py"
|
||||
"Rayko_Outpaint.py"
|
||||
"Rayko_Prompts.py"
|
||||
@@ -43,6 +44,7 @@
|
||||
"Rayko_Text_Overlay_Pro.py"
|
||||
"Rayko_Upscaler.py"
|
||||
"Rayko_VAE_Save_Image.py"
|
||||
"rayko_api.py"
|
||||
"settings_manager_api.py"
|
||||
]
|
||||
}
|
||||
|
||||
+1
-1
@@ -1,7 +1,7 @@
|
||||
[project]
|
||||
name = "ComfyUI_RaykoStudio"
|
||||
description = "Set of custom nodes for ComfyUI providing additional image processing capabilities"
|
||||
version = "0.39.0"
|
||||
version = "0.40.0"
|
||||
license = { file = "LICENSE" }
|
||||
dependencies = ["torch", "numpy", "Pillow>=10.0.0", "freetype-py>=2.5.1", "pycairo>=1.29.0", "scipy>=1.10.0"]
|
||||
|
||||
|
||||
+406
@@ -0,0 +1,406 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# Copyright 2025-2026 Raykosan (RaykoStudio)
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import folder_paths
|
||||
import json
|
||||
import os
|
||||
import struct
|
||||
import hashlib
|
||||
import re
|
||||
from server import PromptServer
|
||||
import aiohttp
|
||||
|
||||
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
PRESETS_DIR = os.path.join(CURRENT_DIR, "preset_models")
|
||||
LORA_PRESETS_DIR = os.path.join(PRESETS_DIR, "loras")
|
||||
RAYKO_LORA_DATA_DIR = os.path.join(CURRENT_DIR, "rayko_lora_data")
|
||||
|
||||
os.makedirs(PRESETS_DIR, exist_ok=True)
|
||||
os.makedirs(LORA_PRESETS_DIR, exist_ok=True)
|
||||
os.makedirs(RAYKO_LORA_DATA_DIR, exist_ok=True)
|
||||
|
||||
def clean_html(text):
|
||||
if not text:
|
||||
return ""
|
||||
clean = re.sub(r'<[^>]+>', ' ', str(text))
|
||||
clean = re.sub(r'\s+', ' ', clean).strip()
|
||||
return clean
|
||||
|
||||
def compute_sha256(file_path):
|
||||
sha256_hash = hashlib.sha256()
|
||||
with open(file_path, "rb") as f:
|
||||
for chunk in iter(lambda: f.read(8192), b""):
|
||||
sha256_hash.update(chunk)
|
||||
return sha256_hash.hexdigest()
|
||||
|
||||
def save_to_rayko_db(file_hash, data, source, overwrite=False):
|
||||
db_path = os.path.join(RAYKO_LORA_DATA_DIR, f"{file_hash}.json")
|
||||
if not overwrite and os.path.exists(db_path):
|
||||
return False
|
||||
db_data = {
|
||||
"hash": file_hash,
|
||||
"name": data.get("full_name", ""),
|
||||
"trained_words": data.get("trained_words", []),
|
||||
"description": data.get("description", ""),
|
||||
"source": source,
|
||||
"imported_at": __import__('datetime').datetime.now().isoformat()
|
||||
}
|
||||
with open(db_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(db_data, f, indent=2, ensure_ascii=False)
|
||||
return True
|
||||
|
||||
def extract_metadata_from_safetensors(lora_full_path):
|
||||
try:
|
||||
with open(lora_full_path, "rb") as f:
|
||||
header_size = struct.unpack("<Q", f.read(8))[0]
|
||||
header_bytes = f.read(header_size)
|
||||
header = json.loads(header_bytes.decode("utf-8"))
|
||||
if "__metadata__" in header:
|
||||
meta = header["__metadata__"]
|
||||
return {
|
||||
"full_name": meta.get("modelspec.title", ""),
|
||||
"trained_words": [t.strip() for t in meta.get("modelspec.tags", "").split(",") if t.strip()] if "modelspec.tags" in meta else [],
|
||||
"description": clean_html(meta.get("modelspec.description", "") or meta.get("ss_training_comment", ""))
|
||||
}
|
||||
except Exception:
|
||||
pass
|
||||
return None
|
||||
|
||||
@PromptServer.instance.routes.get("/rayko/get_loras")
|
||||
async def get_loras(request):
|
||||
return aiohttp.web.json_response(sorted(folder_paths.get_filename_list("loras"), key=lambda x: x.lower()))
|
||||
|
||||
@PromptServer.instance.routes.post("/rayko/get_lora_info")
|
||||
async def rayko_get_lora_info(request):
|
||||
try:
|
||||
data = await request.json()
|
||||
lora_name = data.get("name", "")
|
||||
if not lora_name:
|
||||
return aiohttp.web.Response(status=400, text="No name 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")
|
||||
if os.path.exists(db_path):
|
||||
try:
|
||||
with open(db_path, 'r', encoding='utf-8') as f:
|
||||
db_data = json.load(f)
|
||||
return aiohttp.web.json_response({
|
||||
"full_name": db_data.get("name", lora_name),
|
||||
"trained_words": db_data.get("trained_words", []),
|
||||
"description": clean_html(db_data.get("description", "")),
|
||||
"source": db_data.get("source", "rayko_db")
|
||||
})
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
safetensors_meta = extract_metadata_from_safetensors(lora_full_path)
|
||||
if safetensors_meta and (safetensors_meta["trained_words"] or safetensors_meta["description"]):
|
||||
save_to_rayko_db(file_hash, safetensors_meta, "safetensors")
|
||||
return aiohttp.web.json_response({
|
||||
"full_name": safetensors_meta["full_name"] or lora_name,
|
||||
"trained_words": safetensors_meta["trained_words"],
|
||||
"description": safetensors_meta["description"],
|
||||
"source": "safetensors"
|
||||
})
|
||||
|
||||
return aiohttp.web.json_response({
|
||||
"full_name": lora_name,
|
||||
"trained_words": [],
|
||||
"description": "",
|
||||
"source": "none"
|
||||
})
|
||||
except Exception as e:
|
||||
return aiohttp.web.Response(status=500, text=str(e))
|
||||
|
||||
@PromptServer.instance.routes.post("/rayko/fetch_civitai_info")
|
||||
async def rayko_fetch_civitai_info(request):
|
||||
try:
|
||||
data = await request.json()
|
||||
lora_name = data.get("name", "")
|
||||
if not lora_name:
|
||||
return aiohttp.web.Response(status=400, text="No name 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)
|
||||
civitai_url = f"https://civitai.com/api/v1/model-versions/by-hash/{file_hash}"
|
||||
|
||||
async with aiohttp.ClientSession() as session:
|
||||
async with session.get(civitai_url, headers={"User-Agent": "ComfyUI-RaykoStudio/1.0"}) as response:
|
||||
if response.status == 404:
|
||||
return aiohttp.web.json_response({"error": "not_found", "message": "Not found on Civitai."})
|
||||
elif response.status == 429:
|
||||
return aiohttp.web.json_response({"error": "rate_limit", "message": "Rate limit reached."})
|
||||
elif response.status != 200:
|
||||
return aiohttp.web.json_response({"error": "api_error", "message": f"API status {response.status}"})
|
||||
|
||||
civitai_data = await response.json()
|
||||
|
||||
model_id = civitai_data.get("modelId")
|
||||
model_tags = []
|
||||
if model_id:
|
||||
try:
|
||||
model_url = f"https://civitai.com/api/v1/models/{model_id}"
|
||||
async with session.get(model_url, headers={"User-Agent": "ComfyUI-RaykoStudio/1.0"}) as model_response:
|
||||
if model_response.status == 200:
|
||||
model_data = await model_response.json()
|
||||
model_tags = model_data.get("tags", [])
|
||||
except Exception as e:
|
||||
print(f"[Rayko] Error fetching full model details: {e}")
|
||||
|
||||
raw_trained = civitai_data.get("trainedWords", [])
|
||||
trained_words = []
|
||||
if isinstance(raw_trained, list):
|
||||
trained_words.extend([str(t).strip() for t in raw_trained if str(t).strip()])
|
||||
if isinstance(model_tags, list):
|
||||
trained_words.extend([str(t).strip() for t in model_tags if str(t).strip()])
|
||||
|
||||
trained_words = list(dict.fromkeys(trained_words))
|
||||
|
||||
raw_description = civitai_data.get("description", "")
|
||||
clean_description = clean_html(raw_description)
|
||||
|
||||
model_name = civitai_data.get("model", {}).get("name", "")
|
||||
version_name = civitai_data.get("name", "")
|
||||
|
||||
if model_name:
|
||||
if version_name and version_name.lower() not in ["v1", "v1.0", "default", "latest", ""]:
|
||||
full_name = f"{model_name} ({version_name})"
|
||||
else:
|
||||
full_name = model_name
|
||||
else:
|
||||
full_name = lora_name
|
||||
|
||||
cache_data = {
|
||||
"full_name": full_name,
|
||||
"trained_words": trained_words,
|
||||
"description": clean_description
|
||||
}
|
||||
|
||||
save_to_rayko_db(file_hash, cache_data, "civitai", overwrite=True)
|
||||
|
||||
return aiohttp.web.json_response({
|
||||
"full_name": full_name,
|
||||
"trained_words": trained_words,
|
||||
"description": clean_description,
|
||||
"source": "civitai"
|
||||
})
|
||||
except aiohttp.ClientError as e:
|
||||
return aiohttp.web.json_response({"error": "network", "message": f"Network error: {str(e)}"})
|
||||
except Exception as e:
|
||||
return aiohttp.web.Response(status=500, text=str(e))
|
||||
|
||||
@PromptServer.instance.routes.post("/rayko_models/save_preset")
|
||||
async def rayko_models_save_preset(request):
|
||||
try:
|
||||
data = await request.json()
|
||||
name = "".join(c for c in data.get("name", "").strip() if c.isalnum() or c in " _-").strip()
|
||||
if not name: return aiohttp.web.Response(status=400, text="Invalid name")
|
||||
filepath = os.path.join(PRESETS_DIR, f"{name}.json")
|
||||
preset_data = {k: v for k, v in data.items() if k != "name"}
|
||||
with open(filepath, 'w', encoding='utf-8') as f:
|
||||
json.dump(preset_data, f, indent=2)
|
||||
return aiohttp.web.Response(status=200, text="OK")
|
||||
except Exception as e:
|
||||
return aiohttp.web.Response(status=500, text=str(e))
|
||||
|
||||
@PromptServer.instance.routes.post("/rayko_models/list_presets")
|
||||
async def rayko_models_list_presets(request):
|
||||
try:
|
||||
presets = [f[:-5] for f in os.listdir(PRESETS_DIR) if f.endswith('.json')] if os.path.exists(PRESETS_DIR) else []
|
||||
return aiohttp.web.json_response(sorted(presets, key=lambda x: x.lower()))
|
||||
except Exception as e:
|
||||
return aiohttp.web.Response(status=500, text=str(e))
|
||||
|
||||
@PromptServer.instance.routes.post("/rayko_models/load_preset")
|
||||
async def rayko_models_load_preset(request):
|
||||
try:
|
||||
name = (await request.json()).get("name")
|
||||
filepath = os.path.join(PRESETS_DIR, f"{name}.json")
|
||||
if os.path.exists(filepath):
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
return aiohttp.web.json_response(json.load(f))
|
||||
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_models/delete_preset")
|
||||
async def rayko_models_delete_preset(request):
|
||||
try:
|
||||
name = (await request.json()).get("name")
|
||||
filepath = os.path.join(PRESETS_DIR, f"{name}.json")
|
||||
if os.path.exists(filepath):
|
||||
os.remove(filepath)
|
||||
return aiohttp.web.Response(status=200, text="OK")
|
||||
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_loras/save_preset")
|
||||
async def rayko_loras_save_preset(request):
|
||||
try:
|
||||
data = await request.json()
|
||||
name = "".join(c for c in data.get("name", "").strip() if c.isalnum() or c in " _-").strip()
|
||||
if not name: return aiohttp.web.Response(status=400, text="Invalid name")
|
||||
filepath = os.path.join(LORA_PRESETS_DIR, f"{name}.json")
|
||||
lora_rows = data.get("lora_rows", [])
|
||||
with open(filepath, 'w', encoding='utf-8') as f:
|
||||
json.dump({"lora_rows": lora_rows}, f, indent=2)
|
||||
return aiohttp.web.Response(status=200, text="OK")
|
||||
except Exception as e:
|
||||
return aiohttp.web.Response(status=500, text=str(e))
|
||||
|
||||
@PromptServer.instance.routes.post("/rayko_loras/list_presets")
|
||||
async def rayko_loras_list_presets(request):
|
||||
try:
|
||||
presets = [f[:-5] for f in os.listdir(LORA_PRESETS_DIR) if f.endswith('.json')] if os.path.exists(LORA_PRESETS_DIR) else []
|
||||
return aiohttp.web.json_response(sorted(presets, key=lambda x: x.lower()))
|
||||
except Exception as e:
|
||||
return aiohttp.web.Response(status=500, text=str(e))
|
||||
|
||||
@PromptServer.instance.routes.post("/rayko_loras/load_preset")
|
||||
async def rayko_loras_load_preset(request):
|
||||
try:
|
||||
name = (await request.json()).get("name")
|
||||
filepath = os.path.join(LORA_PRESETS_DIR, f"{name}.json")
|
||||
if os.path.exists(filepath):
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
return aiohttp.web.json_response(json.load(f))
|
||||
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_loras/delete_preset")
|
||||
async def rayko_loras_delete_preset(request):
|
||||
try:
|
||||
name = (await request.json()).get("name")
|
||||
filepath = os.path.join(LORA_PRESETS_DIR, f"{name}.json")
|
||||
if os.path.exists(filepath):
|
||||
os.remove(filepath)
|
||||
return aiohttp.web.Response(status=200, text="OK")
|
||||
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))
|
||||
@@ -152,8 +152,6 @@ app.registerExtension({
|
||||
}
|
||||
};
|
||||
|
||||
// Если use_clip2 выключен и clip_name2 пустой или невалидный,
|
||||
// подставляем первый доступный клип для корректного отображения
|
||||
let safeClip2 = data.clip_name2;
|
||||
if (!data.use_clip2 && (!safeClip2 || safeClip2 === "")) {
|
||||
const clip1Value = data.clip_name || "";
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user