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πŸš€ ComfyUI Workflows by wizdroid

License: MIT ComfyUI Flux Nunchaku

High-quality, production-ready ComfyUI workflows focused on character consistency, fast generation, and automated dataset creation for LoRA training.

These workflows power consistent character generation, multi-reference image-to-image pipelines, lightning-fast turbo inference, state-of-the-art upscaling, and intelligent dataset preparation.

✨ Highlights

  • Flux2-Klein Series β€” Premium quality with Nunchaku acceleration for blazing fast inference
  • Multi-Reference i2i (1/2/3 images) β€” Industry-leading character identity preservation using Reference Latent injection
  • Qwen & Z-Image-Turbo β€” Versatile text-to-image and fast generation options
  • SeedVR2 Upscaler β€” One of the best image upscalers available (DiT + VAE with advanced tiling & color correction)
  • WizdroidLoRADataset β€” Custom node for automated vision-LLM captioning, dataset structuring, and validation β€” perfect companion for training consistent characters
  • LoRA-ready β€” Built-in toggleable LoRA loading across most pipelines
  • Square 1:1 + Flexible Resolutions β€” Optimized for character reference work

Pro Tip: Pair these workflows with the Consistent Character Reference Prompts (PROMPTS.md) (40+ square 1:1 variations covering angles, expressions, beauty, upper/full body, and profiles) for unbeatable training data.

πŸ“ Workflows

Workflow Type Size Key Features Best For
flux2-klein-t2i.json Text-to-Image ~23 MB Nunchaku Flux2-Klein 9B, advanced Flux sampler, LoRA support High-quality prompt-driven generation
flux2-klein-1i2i.json 1-Image Ref ~29 MB Single reference image + Reference Latent Strong character consistency from one photo
flux2-klein-2i2i.json 2-Image Ref ~35 MB Dual reference images Even stronger identity with two angles/expressions
flux2-klein-3i2i.json 3-Image Ref ~41 MB Triple reference images + advanced conditioning Maximum character fidelity (recommended for LoRA training)
qwen-t2i.json Text-to-Image ~14 MB Qwen-based, flexible resolution, LoRA Alternative aesthetic / artistic control
qwen-aio-t2i.json Text-to-Image ~12 MB Qwen All-in-One variant Quick Qwen generations
qwen-aio-1i2i.json 1-Image Ref ~14 MB Qwen image reference Qwen-powered character consistency
qwen-aio-2i2i.json 2-Image Ref ~15 MB Dual reference Multi-ref Qwen
qwen-aio-3i2i.json 3-Image Ref ~16 MB Triple reference Maximum Qwen consistency
z-image-turbo-t2i.json Fast T2I ~18 MB Z-Image-Turbo (very few steps), Nunchaku Lightning-fast prototyping & iteration
seedvr2-i2i.json Upscaler ~13 MB SeedVR2 DiT + VAE upscaler, tiling, color correction Best-in-class 2×–4Γ— upscaling & detail recovery
dataset-generator.json Dataset Tool ~3 MB WizdroidLoRADataset + Ollama vision (Moondream) Automated LoRA training dataset creation with captions

πŸ› οΈ Installation & Setup

1. ComfyUI

Make sure you have a recent ComfyUI installation.

2. Required Custom Nodes

Install these via ComfyUI Manager (recommended) or git clone into custom_nodes/:

  • ComfyUI-nunchaku β€” For accelerated Flux inference
  • ComfyUI-SeedVR2_VideoUpscaler (ainvfx) β€” For the SeedVR2 upscaler workflow
  • Wizdroid Character β€” Custom nodes for character prompting, multi-angle generation, animation adapters, I2V latent patches, and the WizdroidLoRADataset for automated captioning & dataset creation
    git clone https://github.com/wizdroid/wizdroid-character custom_nodes/wizdroid-character

Restart ComfyUI after installing custom nodes.

3. Models

Place models in the standard ComfyUI folders (or update paths inside workflows):

Flux2-Klein (main quality engine):

  • flux/2/flux2-klein-9B.safetensors
  • flux/2/vae.safetensors

Qwen:

  • qwen/qwen-image-edit-sfw.safetensors
  • qwen/qwen3-8B.safetensors (or 4B variant)
  • qwen/qwen3-4B.safetensors

Z-Image Turbo:

  • z-image/z-image-turbo.safetensors
  • z-image/vae.safetensors
  • z-image/turbo/4nup4m4.safetensors (example turbo LoRA)

SeedVR2:

  • seedvr2_ema_7b_sharp_fp16.safetensors (DiT)
  • ema_vae_fp16.safetensors (VAE)

CLIP / Other:

  • Various Qwen CLIP models as referenced in the workflows

4. Ollama (for Dataset Generator)

The dataset-generator.json workflow uses a local vision model via Ollama:

# Install Ollama and pull a good vision model (Moondream recommended in the workflow)
ollama pull moondream:latest

Start Ollama (ollama serve) before running the dataset workflow. You can change the model/URL inside the node.

πŸš€ How to Use

  1. Load a workflow: Drag & drop any .json file directly onto your ComfyUI canvas, or use Load β†’ select the file.
  2. Queue Prompt as usual.
  3. Most workflows expose clean proxy widgets on the right for:
    • Prompt / negative prompt
    • Seed, steps, CFG, sampler
    • Width / Height
    • LoRA toggle + strength
    • Reference images (for i2i workflows)

Character Consistency Workflow Recommendations

  • Use flux2-klein-3i2i.json (or 2i2i) with 2–3 high-quality, varied reference photos of your character.
  • Generate using the same character across many angles/expressions using the reference prompts collection.
  • Feed the resulting images into dataset-generator.json to auto-caption and structure a ready-to-train dataset (with your character trigger tag).

πŸ’‘ Tips for Best Results

  • Reference Images: Clean, well-lit, minimal makeup/jewelry/background. Square 1:1 references work great.
  • Prompting: Start simple ("Photo of a woman", "beautiful detailed face") and let the reference images do the heavy lifting for identity.
  • LoRAs: The LoRA switch is disabled by default in many workflows. Enable it and point to character or style LoRAs.
  • Speed vs Quality: Use z-image-turbo-t2i.json for rapid iteration, then switch to Flux2-Klein for final outputs.
  • Upscaling: Always run important generations through seedvr2-i2i.json for that extra polish.
  • Dataset Quality: Use the dataset generator + good reference images + Ollama captions, then manually review the validation_report.

οΏ½ Full Guide: Mastering Consistent Characters

For the complete step-by-step tutorial (problem β†’ solution β†’ exact pipeline using these workflows + PROMPTS.md + the WizdroidLoRADataset node β†’ pro tips + visual examples), see the dedicated CivitAI article:

β†’ Mastering Consistent Characters for LoRA Training

The article covers:

  • Why single-reference methods fail for modern models (Flux, SD3, Aurora, etc.)
  • How Reference Latent injection + clean square 1:1 prompts solve identity drift
  • Generating 40–100+ varied training images using the included PROMPTS.md
  • Using the WizdroidLoRADataset node for perfect auto-captions, validation reports, and trigger word injection
  • Training recommendations, best practices, and real-world results

This README is the technical reference. The CivitAI guide is the teaching + visual companion. Use both.

οΏ½πŸ“œ License

MIT License β€” feel free to use, modify, and share these workflows.

🀝 Contributing

Pull requests and workflow improvements are welcome! If you create amazing results with these, tag @wizdroid β€” I'd love to see what you build.

Made with ❀️ for the ComfyUI community by wizdroid

Consistent characters start with great references and great tools.

Additional Resources

For HF users: The HF_README_UPDATED.md file in this folder is a clean, concise version you can copy directly into the repo root as README.md if you prefer a shorter model card. The current README is the rich version recommended for the repo.

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