| --- |
| license: mit |
| tags: |
| - lora |
| - tool |
| - python |
| - gui |
| pipeline_tag: text-to-image |
| --- |
| |
| > **Note:** This repository hosts management scripts/tools only. No model weights are included. |
|
|
| --- |
|
|
| ### Tools for: |
|
|
| 🖼️ <a href="https://huggingface.co/spaces/waveshaker/loras_gallery" target="_blank">**waveshaker/loras_gallery**</a> |
| |
| 🧊 <a href="https://huggingface.co/waveshaker/loras" target="_blank">**waveshaker/loras**</a> |
| |
| 📊 <a href="https://huggingface.co/datasets/waveshaker/dataset_collections" target="_blank">**waveshaker/dataset_collections**</a> |
|
|
| 📦 <a href="https://huggingface.co/waveshaker/comfy_models" target="_blank">**waveshaker/comfy_models**</a> |
| |
| 🧠 <a href="https://huggingface.co/spaces/waveshaker/universal-lora-studio" target="_blank">**waveshaker/universal-lora-studio**</a> |
| |
| --- |
| |
| - <details> |
| <summary><code>models_manager_v*.py</code> is used for centralized local management of <code>waveshaker/loras_gallery</code> | <code>waveshaker/loras</code> | <code>waveshaker/dataset_collection</code>: (Click to expand)</summary> |
| |
| 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. |
|  |
| |
| </details> |
| |
| - <details> |
| <summary><code>base_model_uploader_gui.py</code> is a GUI tool for batch uploading base models and cloning remote repos to <code>waveshaker/comfy_models</code> (Click to expand)</summary> |
| |
| 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. |
| |
| </details> |
| |
| - <details> |
| <summary><code>comfy_dataset_prep_wf_v**.json</code>: A ComfyUI workflow for LoRA dataset prep. (Click to expand)</summary> |
| |
| Integrates Krea2, Qwen Image 2.1, Flux 2 Klein 9B, and [SQVLM](https://github.com/KLL535/ComfyUI_Simple_Qwen3-VL-gguf/tree/main) 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 |
| }, |
| ``` |
| </details> |
| |
| - <details> |
| <summary><code>showcase_tempalte_v**.json</code>: A ComfyUI workflow designed to generate PNG images with embedded workflow metadata. (Click to expand)</summary> |
| |
| 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](https://civitai.com/models/2231696) |
| - [SFw Wildcards 2 DL](https://civitai.com/models/2765483/bunnys-sfw-wildcards-or-lora-friendly-or-16582-prompts-or-565m-tokens) |
| |
| </details> |
| |
| - `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. |
| |
| - <details> |
| <summary><code>batch_preview_generator/</code>: A 5-steps batch pipeline for auto-generating and deploying showcase preview images. (Click to expand)</summary> |
| |
| 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. |
| |
| </details> |
| <p style="font-size: 0.85em;"><em>The <code style="font-size: 0.85em;">batch_preview_generator/</code> is primarily designed for adapting older models like <strong style="font-size: 0.85em;">Pony</strong> and <strong style="font-size: 0.85em;">FLUX.1 - Dev</strong>, with the main goal of rapid batch generation. For newer models, use <code style="font-size: 0.85em;">showcase_tempalte_v**.json</code> to generate images, and then upload them using <code style="font-size: 0.75em;">models_manager_v*.py</code>.</em></p> |
| |
| - `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. |
| |
| - <details> |
| <summary><code>checkpoint_splitter/</code>: Utilities to split AIO checkpoints into VAE, TE, and UNET, including UNET int8 quantization and checkpoint re-merging. (Click to expand)</summary> |
| |
| - `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. |
| |
| </details> |
| --- |
| |
