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comfyui-workflows

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🚀 ComfyUI Workflows by wizdroid

License: MIT

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

📁 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×–4× upscaling & detail recovery
`dataset-generator.json`	Dataset Tool	~3 MB	WizdroidLoRADataset + Ollama vision (Moondream)	Automated LoRA training dataset creation with captions

🛠️ 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 — For accelerated Flux inference
ComfyUI-SeedVR2_VideoUpscaler (ainvfx) — For the SeedVR2 upscaler workflow
Wizdroid Character — 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:

bash

# 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.

🚀 How to Use

Load a workflow: Drag & drop any .json file directly onto your ComfyUI canvas, or use Load → select the file.
Queue Prompt as usual.
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).

💡 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.

� Full Guide: Mastering Consistent Characters

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

→ 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.

�📜 License

MIT License — 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 — 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

PROMPTS.md — 40 Consistent Character Reference Prompts
CivitAI Guide: Mastering Consistent Characters for LoRA Training (update this link after posting the article)
Full workflows and updates in this repository31442/mastering-consistent-characters-for-lora-training)

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.

wizdroid/comfyui-workflows