mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-08-06 22:10:14 -03:00
feat(SaveImageLM): upgrade metadata output to A1111/Civitai-compatible format
- Replace plain-text Lora hashes with Hashes JSON dict matching A1111 convention - Add Civitai resources JSON array with AIR URNs for direct model version linking - Add Clip skip, Version: ComfyUI fields to generation params line - Build AIR strings from local scanner cache (no API calls needed) - Add complete sampler name mapping (CIVITAI_SAMPLER_MAP) and base model → AIR slug mapping (BASE_MODEL_AIR_SLUG) sourced from civitai ecosystem constants - Remove lora text prepending from prompt line; LoRA info now in structured JSON sections
This commit is contained in:
Will Miao
@@ -16,6 +16,156 @@ from PIL import Image, PngImagePlugin
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import piexif
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import logging
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# Civitai-compatible sampler name mapping: ComfyUI internal → A1111 display name
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CIVITAI_SAMPLER_MAP = {
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"euler": "Euler",
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"euler_ancestral": "Euler a",
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"lms": "LMS",
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"heun": "Heun",
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"dpm_2": "DPM2",
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"dpm_2_ancestral": "DPM2 a",
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"dpmpp_2s_ancestral": "DPM++ 2S a",
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"dpmpp_2m": "DPM++ 2M",
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"dpmpp_sde": "DPM++ SDE",
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"dpmpp_sde_gpu": "DPM++ SDE",
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"dpmpp_2m_sde": "DPM++ 2M SDE",
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"dpmpp_2m_sde_gpu": "DPM++ 2M SDE",
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"dpmpp_3m_sde": "DPM++ 3M SDE",
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"dpm_fast": "DPM fast",
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"dpm_adaptive": "DPM adaptive",
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"ddim": "DDIM",
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"plms": "PLMS",
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"uni_pc_bh2": "UniPC",
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"uni_pc": "UniPC",
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"lcm": "LCM",
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}
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# Base model display name → AIR URN slug
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# Sourced from civitai source: src/shared/constants/basemodel.constants.ts
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BASE_MODEL_AIR_SLUG = {
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# Stable Diffusion family
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"SD 1.4": "sd1",
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"SD 1.5": "sd1",
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"SD 1.5 LCM": "sd1",
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"SD 1.5 Hyper": "sd1",
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"SD 2.0": "sd2",
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"SD 2.0 768": "sd2",
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"SD 2.1": "sd2",
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"SD 2.1 768": "sd2",
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"SD 2.1 Unclip": "sd2",
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"SD 3.0": "sd3",
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"SD 3.5": "sd35",
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"SD 3.5 Large": "sd35",
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"SD 3.5 Large Turbo": "sd35",
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"SD 3.5 Medium": "sd35",
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"SDXL 0.9": "sdxl",
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"SDXL 1.0": "sdxl",
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"SDXL 1.0 LCM": "sdxl",
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"SDXL Lightning": "sdxl",
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"SDXL Hyper": "sdxl",
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"SDXL Turbo": "sdxl",
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"SDXL Distilled": "sdxldistilled",
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"Stable Cascade": "scascade",
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"Stable Video Diffusion": "svd",
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"SVD": "svd",
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"SVD XT": "svdxt",
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# SDXL community fine-tunes
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"Pony": "pony",
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"Pony Diffusion": "pony",
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"Illustrious": "illustrious",
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"NoobAI": "noobai",
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"Animagine": "illustrious",
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# Flux family
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"Flux.1": "flux1",
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"Flux.1 D": "flux1",
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"Flux.1 S": "flux1",
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"Flux.1 Krea": "fluxkrea",
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"Flux.1 Kontext": "flux1kontext",
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"Flux.2": "flux2",
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"Flux.2 D": "flux2",
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"Flux.2 Klein 9B": "flux2klein_9b",
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"Flux.2 Klein 9B Base": "flux2klein_9b_base",
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"Flux.2 Klein 4B": "flux2klein_4b",
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"Flux.2 Klein 4B Base": "flux2klein_4b_base",
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# Other image models (sorted alphabetically)
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"AuraFlow": "auraflow",
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"Chroma": "chroma",
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"HiDream": "hidream",
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"HiDream-O1": "hidream-o1",
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"Hunyuan DiT": "hydit1",
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"Hunyuan Video": "hyv1",
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"Kolors": "kolors",
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"Lumina": "lumina",
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"Mochi": "mochi",
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"ODOR": "odor",
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"PixArt Alpha": "pixarta",
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"PixArt Sigma": "pixarte",
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"Playground v2": "playgroundv2",
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"Playground v2.5": "playgroundv2",
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"Pony Diffusion V7": "ponyv7",
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# Video models
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"CogVideoX": "cogvideox",
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"LTX Video": "ltxv",
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"LTX Video 2": "ltxv2",
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"LTX Video 2.3": "ltxv23",
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"Wan Video": "wanvideo",
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"Wan Video 1.3B T2V": "wanvideo_13b_t2v",
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"Wan Video 14B T2V": "wanvideo_14b_t2v",
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"Wan Video 14B I2V 480p": "wanvideo_14b_i2v_480p",
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"Wan Video 14B I2V 720p": "wanvideo_14b_i2v_720p",
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# Third-party / proprietary image models
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"Boogu": "boogu",
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"Ernie": "ernie",
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"Grok": "grok",
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"HappyHorse": "happyhorse",
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"Ideogram": "ideogram",
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"Ideogram 4.0": "ideogram",
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"Imagen": "imagen4",
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"Imagen 4": "imagen4",
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"Krea": "krea2",
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"Krea 2": "krea2",
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"Lens": "lens",
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"MAI": "mai",
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"Nano Banana": "nanobanana",
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"OpenAI": "openai",
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"Reve": "reve",
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"Reve 2": "reve",
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"Reve 2.1": "reve",
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"Seedream": "seedream",
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"Sora": "sora2",
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"Sora 2": "sora2",
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"Veo": "veo3",
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"Veo 2": "veo3",
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"Veo 3": "veo3",
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"ZImageTurbo": "zimageturbo",
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"ZImageBase": "zimagebase",
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"ZImage": "zimagebase",
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# Third-party video models
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"Hailuo by MiniMax": "minimax",
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"Haiper": "haiper",
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"Kling": "kling",
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"Lightricks": "lightricks",
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"Seedance": "seedance",
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"Vidu": "vidu",
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# Qwen family
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"Qwen": "qwen",
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"Qwen 2": "qwen2",
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# Anima
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"Anima": "anima",
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# Special
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"Upscaler": "upscaler",
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"Other": "other",
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}
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logger = logging.getLogger(__name__)
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@@ -142,148 +292,181 @@ class SaveImageLM:
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return None
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def format_metadata(self, metadata_dict):
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"""Format metadata in the requested format similar to userComment example"""
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if not metadata_dict:
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return ""
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def _resolve_model_cache_entry(self, scanner_type: str, name: str):
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"""Resolve model hash, civitai metadata, and base_model from scanner cache.
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Returns (hash_str, civitai_dict, base_model_str). All values are empty defaults when not found."""
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scanner = ServiceRegistry.get_service_sync(scanner_type)
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if scanner is None or not name:
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return "", {}, ""
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# Helper function to only add parameter if value is not None
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def add_param_if_not_none(param_list, label, value):
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if value is not None:
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param_list.append(f"{label}: {value}")
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entry = self._get_cached_model_by_name(scanner, name)
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if entry is None:
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basename = os.path.splitext(os.path.basename(name))[0]
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hash_val = scanner.get_hash_by_filename(basename)
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return (hash_val or "").lower(), {}, ""
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hash_val = (entry.get("sha256") or "").lower()
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civitai = entry.get("civitai") or {}
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base_model = entry.get("base_model") or ""
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return hash_val, civitai, base_model
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@staticmethod
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def _get_civitai_sampler_name(sampler_name: str, scheduler: str) -> str:
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if sampler_name in CIVITAI_SAMPLER_MAP:
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civitai_name = CIVITAI_SAMPLER_MAP[sampler_name]
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if scheduler == "karras":
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civitai_name += " Karras"
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elif scheduler == "exponential":
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civitai_name += " Exponential"
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return civitai_name
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else:
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if scheduler and scheduler != "normal":
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return f"{sampler_name}_{scheduler}"
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return sampler_name
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@staticmethod
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def _build_air_string(base_model: str, model_type: str, model_id: int, version_id: int) -> str:
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slug = BASE_MODEL_AIR_SLUG.get(base_model, "other")
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type_lower = model_type.lower() if model_type else "other"
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return f"urn:air:{slug}:{type_lower}:civitai:{model_id}@{version_id}"
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def format_metadata(self, metadata_dict: dict) -> str:
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"""Format metadata as A1111-compatible parameters string with Hashes JSON and Civitai resources."""
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if not metadata_dict: return ""
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# Extract the prompt and negative prompt
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prompt = metadata_dict.get("prompt", "")
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negative_prompt = metadata_dict.get("negative_prompt", "")
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# Extract loras from the prompt if present
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steps = metadata_dict.get("steps")
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cfg = metadata_dict.get("guidance")
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if cfg is None:
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cfg = metadata_dict.get("cfg_scale")
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if cfg is None:
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cfg = metadata_dict.get("cfg")
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seed = metadata_dict.get("seed")
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size = metadata_dict.get("size")
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sampler = metadata_dict.get("sampler") or ""
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scheduler = metadata_dict.get("scheduler") or "normal"
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checkpoint = metadata_dict.get("checkpoint") or ""
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loras_text = metadata_dict.get("loras", "")
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lora_hashes = {}
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clip_skip = metadata_dict.get("clip_skip")
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# If loras are found, add them on a new line after the prompt
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# Parse LoRA entries from <lora:name:strength> format
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lora_entries: list[tuple[str, float]] = []
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if loras_text:
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prompt_with_loras = f"{prompt}\n{loras_text}"
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for match in re.findall(r"<lora:([^:]+):([^>]+)>", loras_text):
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lora_name, strength_str = match
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try:
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strength = float(strength_str)
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except (ValueError, TypeError):
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strength = 1.0
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lora_entries.append((lora_name, strength))
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# Extract lora names from the format <lora:name:strength>
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lora_matches = re.findall(r"<lora:([^:]+):([^>]+)>", loras_text)
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# Resolve checkpoint hash and Civitai data from local cache
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ckpt_hash, ckpt_civitai, ckpt_base_model = "", {}, ""
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ckpt_display_name = ""
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if checkpoint:
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ckpt_hash, ckpt_civitai, ckpt_base_model = self._resolve_model_cache_entry(
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"checkpoint_scanner", checkpoint
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)
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ckpt_display_name = os.path.splitext(os.path.basename(checkpoint))[0]
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# Get hash for each lora
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for lora_name, strength in lora_matches:
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hash_value = self.get_lora_hash(lora_name)
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if hash_value:
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lora_hashes[lora_name] = hash_value
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else:
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prompt_with_loras = prompt
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# Resolve LoRA hash and Civitai data from local cache
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loras_data: list[dict] = []
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for lora_name, strength in lora_entries:
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lora_hash, lora_civitai, lora_base_model = self._resolve_model_cache_entry(
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"lora_scanner", lora_name
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)
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loras_data.append({
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"name": lora_name,
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"strength": strength,
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"hash": lora_hash,
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"civitai": lora_civitai,
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"base_model": lora_base_model,
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})
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# Format the first part (prompt and loras)
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metadata_parts = [prompt_with_loras]
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# Build Hashes JSON (A1111 / Civitai standard format)
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hashes: dict[str, str] = {}
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if ckpt_hash:
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hashes["model"] = ckpt_hash[:10].upper()
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for lora in loras_data:
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if lora["hash"]:
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hashes[f"LORA:{lora['name']}"] = lora["hash"][:10].upper()
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# Add negative prompt
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# Build Civitai resources JSON array
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civitai_resources: list[dict] = []
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if ckpt_civitai.get("id", 0) > 0:
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ckpt_resource: dict = {}
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ckpt_type = (ckpt_civitai.get("model") or {}).get("type", "Checkpoint")
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model_id = ckpt_civitai.get("modelId", 0)
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version_id = ckpt_civitai.get("id", 0)
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if model_id and version_id:
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ckpt_resource["air"] = self._build_air_string(
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ckpt_base_model, ckpt_type, int(model_id), int(version_id)
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)
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elif version_id:
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ckpt_resource["modelVersionId"] = int(version_id)
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if ckpt_civitai.get("name"):
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ckpt_resource["versionName"] = ckpt_civitai["name"]
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if ckpt_resource:
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civitai_resources.append(ckpt_resource)
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for lora in loras_data:
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lora_civitai = lora["civitai"]
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if not lora_civitai or lora_civitai.get("id", 0) <= 0:
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continue
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lora_resource: dict = {"weight": lora["strength"]}
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lora_type = (lora_civitai.get("model") or {}).get("type", "LORA")
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model_id = lora_civitai.get("modelId", 0)
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version_id = lora_civitai.get("id", 0)
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if model_id and version_id:
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lora_resource["air"] = self._build_air_string(
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lora["base_model"], lora_type, int(model_id), int(version_id)
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)
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elif version_id:
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lora_resource["modelVersionId"] = int(version_id)
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if lora_civitai.get("name"):
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lora_resource["versionName"] = lora_civitai["name"]
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civitai_resources.append(lora_resource)
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sampler_display = self._get_civitai_sampler_name(sampler, scheduler)
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# Build output lines
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lines = [prompt] if prompt else [""]
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if negative_prompt:
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metadata_parts.append(f"Negative prompt: {negative_prompt}")
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lines.append(f"Negative prompt: {negative_prompt}")
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# Format the second part (generation parameters)
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params = []
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params: list[str] = []
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if steps is not None:
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params.append(f"Steps: {steps}")
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if sampler_display:
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params.append(f"Sampler: {sampler_display}")
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if cfg is not None:
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params.append(f"CFG scale: {cfg}")
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if seed is not None:
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params.append(f"Seed: {seed}")
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if size:
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params.append(f"Size: {size}")
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if clip_skip:
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try:
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cs = int(clip_skip)
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if cs != 0:
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params.append(f"Clip skip: {abs(cs)}")
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except (ValueError, TypeError):
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pass
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if ckpt_hash:
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params.append(f"Model hash: {ckpt_hash[:10].upper()}")
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if ckpt_display_name:
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params.append(f"Model: {ckpt_display_name}")
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if hashes:
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params.append(f"Hashes: {json.dumps(hashes, separators=(',', ':'))}")
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params.append("Version: ComfyUI")
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if civitai_resources:
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params.append(
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f"Civitai resources: {json.dumps(civitai_resources, separators=(',', ':'))}"
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)
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# Add standard parameters in the correct order
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if "steps" in metadata_dict:
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add_param_if_not_none(params, "Steps", metadata_dict.get("steps"))
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# Combine sampler and scheduler information
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sampler_name = None
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scheduler_name = None
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if "sampler" in metadata_dict:
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sampler = metadata_dict.get("sampler")
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# Convert ComfyUI sampler names to user-friendly names
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sampler_mapping = {
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"euler": "Euler",
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"euler_ancestral": "Euler a",
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"dpm_2": "DPM2",
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"dpm_2_ancestral": "DPM2 a",
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"heun": "Heun",
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"dpm_fast": "DPM fast",
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"dpm_adaptive": "DPM adaptive",
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"lms": "LMS",
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"dpmpp_2s_ancestral": "DPM++ 2S a",
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"dpmpp_sde": "DPM++ SDE",
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"dpmpp_sde_gpu": "DPM++ SDE",
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"dpmpp_2m": "DPM++ 2M",
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"dpmpp_2m_sde": "DPM++ 2M SDE",
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"dpmpp_2m_sde_gpu": "DPM++ 2M SDE",
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"ddim": "DDIM",
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}
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sampler_name = sampler_mapping.get(sampler, sampler)
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if "scheduler" in metadata_dict:
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scheduler = metadata_dict.get("scheduler")
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scheduler_mapping = {
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"normal": "Simple",
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"karras": "Karras",
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"exponential": "Exponential",
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"sgm_uniform": "SGM Uniform",
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"sgm_quadratic": "SGM Quadratic",
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}
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scheduler_name = scheduler_mapping.get(scheduler, scheduler)
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# Add combined sampler and scheduler information
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if sampler_name:
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if scheduler_name:
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params.append(f"Sampler: {sampler_name} {scheduler_name}")
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else:
|
||||
params.append(f"Sampler: {sampler_name}")
|
||||
|
||||
# CFG scale (Use guidance if available, otherwise fall back to cfg_scale or cfg)
|
||||
if "guidance" in metadata_dict:
|
||||
add_param_if_not_none(params, "CFG scale", metadata_dict.get("guidance"))
|
||||
elif "cfg_scale" in metadata_dict:
|
||||
add_param_if_not_none(params, "CFG scale", metadata_dict.get("cfg_scale"))
|
||||
elif "cfg" in metadata_dict:
|
||||
add_param_if_not_none(params, "CFG scale", metadata_dict.get("cfg"))
|
||||
|
||||
# Seed
|
||||
if "seed" in metadata_dict:
|
||||
add_param_if_not_none(params, "Seed", metadata_dict.get("seed"))
|
||||
|
||||
# Size
|
||||
if "size" in metadata_dict:
|
||||
add_param_if_not_none(params, "Size", metadata_dict.get("size"))
|
||||
|
||||
# Model info
|
||||
if "checkpoint" in metadata_dict:
|
||||
# Ensure checkpoint is a string before processing
|
||||
checkpoint = metadata_dict.get("checkpoint")
|
||||
if checkpoint is not None:
|
||||
# Get model hash
|
||||
model_hash = self.get_checkpoint_hash(checkpoint)
|
||||
|
||||
# Extract basename without path
|
||||
checkpoint_name = os.path.basename(checkpoint)
|
||||
# Remove extension if present
|
||||
checkpoint_name = os.path.splitext(checkpoint_name)[0]
|
||||
|
||||
# Add model hash if available
|
||||
if model_hash:
|
||||
params.append(
|
||||
f"Model hash: {model_hash[:10]}, Model: {checkpoint_name}"
|
||||
)
|
||||
else:
|
||||
params.append(f"Model: {checkpoint_name}")
|
||||
|
||||
# Add LoRA hashes if available
|
||||
if lora_hashes:
|
||||
lora_hash_parts = []
|
||||
for lora_name, hash_value in lora_hashes.items():
|
||||
lora_hash_parts.append(f"{lora_name}: {hash_value[:10]}")
|
||||
|
||||
if lora_hash_parts:
|
||||
params.append(f'Lora hashes: "{", ".join(lora_hash_parts)}"')
|
||||
|
||||
# Combine all parameters with commas
|
||||
metadata_parts.append(", ".join(params))
|
||||
|
||||
# Join all parts with a new line
|
||||
return "\n".join(metadata_parts)
|
||||
lines.append(", ".join(params))
|
||||
return "\n".join(lines)
|
||||
|
||||
# credit to nkchocoai
|
||||
# Add format_filename method to handle pattern substitution
|
||||
|
||||
Reference in New Issue
Block a user