waveshaker/loras_tools

Note: This repository hosts management scripts/tools only. No model weights are included.

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Tools for:

🖼️ waveshaker/loras_gallery

🧊 waveshaker/loras

📊 waveshaker/dataset_collections

📦 waveshaker/comfy_models

🧠 waveshaker/universal-lora-studio

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• models_manager_v*.py is used for centralized local management of waveshaker/loras_gallery | waveshaker/loras | waveshaker/dataset_collection: (Click to expand)

  Features:

  1. JSON Management: Pull/upload waveshaker/loras_gallery/models.json, maintain card metadata (Title, Trigger, Dataset URL, Showcase URL, Update Time in Unix timestamp), and globally manage custom KV fields. Supports both full-prefix JSON (for local editing) and clean relative-path JSON (for minimal cloud uploads).
  2. Model Upload: Auto-clean local images and convert them to WebP, then upload the .safetensors and .webp files to the waveshaker/loras Model repo. Automatically appends new model entries with an update_time timestamp and updates the minimal cloud waveshaker/loras_gallery/models.json.
  3. Dataset Upload: Automated pipeline (TXT cleanup, PNG to JPG, Parquet packaging) and push to the waveshaker/dataset_collections Dataset repo.
  4. Showcase Upload: Process local PNGs (extract metadata, convert to WebP), generate/update gallery.json, and push to the waveshaker/loras/comfyui_showcase folder in the Models repo. Supports both local-only generation and incremental upload.
• base_model_uploader_gui.py is a GUI tool for batch uploading base models and cloning remote repos to waveshaker/comfy_models (Click to expand)

  Features:

  • Streamlines batch uploading of large .safetensors files. It supports multi-threaded transfers, auto-resumption, and status tracking to skip completed files.
  • Enables instant server-side duplication of remote HuggingFace models via URL without downloading.
• comfy_dataset_prep_wf_v**.json: A ComfyUI workflow for LoRA dataset prep. (Click to expand)

  Integrates Krea2, Qwen Image 2.1, Flux 2 Klein 9B, and SQVLM for high-res(1-1.56MP) upscaling and auto-captioning of faces and bodies.

  my LLM settings in system_prompts_user.json in ComfyUI\custom_nodes\ComfyUI_Simple_Qwen3-VL-gguf(aka SQVLM):

          "📄🖼️🎬Qwen3.8-27B-Uncensored-HauhauCS-Aggressive.IQ3_XS": {
              "model_path": "G:\\StableDiffusion\\MODELS\\LLM\\GGUF\\Qwen3.8-27B-Uncensored-HauhauCS-Aggressive.IQ3_XS.gguf",
              "mmproj_path": "G:\\StableDiffusion\\MODELS\\LLM\\GGUF\\Qwen3.8-27B-Uncensored-HauhauCS-Aggressive.mmproj-BF16.gguf",
              "max_tokens": 4096,
              "max_frames": 81,
              "image_min_tokens": 128,
              "image_max_tokens": 1024,
              "n_ctx": 102400,
              "n_batch": 2048,
              "n_ubatch": 512,
              "enable_thinking": false,
              "n_keep": 512,
              "n_gpu_layers": -1,
              "n_threads": 8,
              "use_mmap": true,
              "split_mode": 0,
              "temperature": 0.8,
              "top_p": 0.95,
              "min_p": 0.05,
              "repeat_penalty": 1.1,
              "presence_penalty": 0.0,
              "top_k": 40,
              "chat_handler": "qwen35",
              "script": "qwen3vl_run.py",
              "debug": true,
              "verbose": false
          },
  

• showcase_tempalte_v**.json: A ComfyUI workflow designed to generate PNG images with embedded workflow metadata. (Click to expand)

  The accompanying Python tool models_manager_v*.py parses these images to extract the exact Prompt, generation parameters, and LoRA stacks, automatically compiling them into a structured gallery.json for the web gallery's "ComfyUI Showcase" viewer.

  Wildcards used in workflow:

  • SFW Wildcards 1 DL
  • SFw Wildcards 2 DL

• filter_nsfw.py: A script for rough NSFW filtering(based on NudeNet). It provides a quick initial screen, but manual review is still necessary for accurate results.

• batch_preview_generator/: A 5-steps batch pipeline for auto-generating and deploying showcase preview images. (Click to expand)

  It follows a structured 5-step workflow:

  1. Uses 0_batch_template_*.json to batch generate random preview images.
  2. Runs 1_batch_regroup.py to automatically reorganize all output images into their respective subfolders.
  3. Runs 2_batch_process_meta.py to extract the meta data from the png files and convert the images into webp format.
  4. Runs 3_batch_upload_showcase.py to batch upload the processed showcase folders to Hugging Face.
  5. Runs 4_batch_modify_json.py to automatically update the models.json data by regex-matching the showcase items for each entry, leaving you ready for a final manual upload.

  The batch_preview_generator/ is primarily designed for adapting older models like Pony and FLUX.1 - Dev, with the main goal of rapid batch generation. For newer models, use showcase_tempalte_v**.json to generate images, and then upload them using models_manager_v*.py.

• hf_space_cap_checker.py: Compares and calculates the total storage size of models defined in waveshaker/comfy_models/studio_config.json and waveshaker/universal-lora-studio/model_config.py. Filters duplicates via the HF API to ensure the total size does not exceed Hugging Face's 150 GB capacity limit.

• checkpoint_splitter/: Utilities to split AIO checkpoints into VAE, TE, and UNET, including UNET int8 quantization and checkpoint re-merging. (Click to expand)

  • check_keys.py: Inspects the keys of split safetensors files to verify and adjust split logic.
  • checkpoint_int8_convrot.py: Uses Kijai's quant_int8_convrot.py to quantize the UNET of an AIO checkpoint to int8 convrot, without altering the Text Encoder (TE) or VAE.
  • split_checkpoint_Illustrious.py: Extracts the VAE, TE, and UNET from Illustrious checkpoints.
  • split_checkpoint_pony.py: Extracts the VAE, TE, and UNET from Pony checkpoints.
  • split_checkpoint_sd15.py: Extracts the VAE, TE, and UNET from SD 1.5 checkpoints.
  • split_checkpoint_sdxl.py: Extracts the VAE, TE, and UNET from SDXL checkpoints.

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