Ideogram 4 GGUF Workflow for ComfyUI

A simple workflow for running Ideogram 4 in GGUF quantized format on ComfyUI, optimized for systems with limited VRAM.

Tested on RTX 3060 12GB with 16GB RAM

⚠️ Don't forget to update your ComfyUI first!

Available Versions:

- v1.0 - Standard (2 UNET GGUF models) - Default traditional workflow

- v2.0 - TurboTime (Single UNET GGUF) - ⚡ Faster, better quality, super low VRAM

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🆕 What's New in v2.0

- ⚡ Optimized workflow: Removed redundant nodes (Dual Model CFG Guider, CFG Override)

- 🚀 TurboTime LoRA support: 2-step generation with CFG=0.0

- 💾 Low VRAM mode: Works on 8GB+ GPUs with Q4_0 quantization

- 🎯 Cleaner structure: Added BasicGuider for proper CFG handling

- 📝 Faster generation: ~1-2 seconds per image on modern GPUs

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📦 Required Models

1. UNET Models → ComfyUI/models/unet/

Download unet files from HuggingFace (Recommended):

- Repository: https://huggingface.co/molbal/ideogram-4-gguf

- Main Model (choose desired quantized version):

- ideogram4-transformer-q4_0.gguf (5.64 GB) - ⭐ Best for low VRAM

- ideogram4-transformer-q4_1.gguf (6.21 GB)

- ideogram4-transformer-q5_0.gguf (6.77 GB)

- ideogram4-transformer-q5_1.gguf (7.33 GB)

- ideogram4-transformer-q8_0.gguf (10.1 GB) - Best quality

- Unconditional Model (not needed in workflow v2.0):

- ideogram4-unconditional_transformer-q4_0.gguf (5.64 GB)

- ideogram4-unconditional_transformer-q4_1.gguf (6.21 GB)

- ideogram4-unconditional_transformer-q5_0.gguf (6.77 GB)

- ideogram4-unconditional_transformer-q5_1.gguf (7.33 GB)

- ideogram4-unconditional_transformer-q8_0.gguf (10.1 GB)

💡 Note: The repository includes inference speed and memory usage charts to help you choose the best quantization for your system.

Alternative link for unet files (Civitai - Q4_0 only):

- Download from: https://civitai.com/models/2681714/ideogram-4-gguf

- Note: File names may differ slightly from HuggingFace

2. Text Encoder → ComfyUI/models/clip/

- Qwen3-8B-Q4_K_M.gguf

- Source: https://huggingface.co/Qwen/Qwen3-8B-GGUF

3. Prompt Enhancer (optional) → ComfyUI/models/clip/

- gemma4_e4b_it_fp8_scaled.safetensors

- Source: https://huggingface.co/Comfy-Org/gemma-4

- Automatically converts natural language to JSON format

4. VAE → ComfyUI/models/vae/

- flux2-vae.safetensors

- Download: https://huggingface.co/Comfy-Org/flux2-dev/resolve/main/split_files/vae/flux2-vae.safetensors

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⚙️ Custom Node Required

Single GGUF custom node by molbal (for both UNET and CLIP)

- Repository: [molbal/ComfyUI-GGUF](https://github.com/molbal/ComfyUI-GGUF)

- Install: git clone https://github.com/molbal/ComfyUI-GGUF.git

- Nodes: UnetLoaderGGUF + GGUFCLIPLoader

⚠️ Important: This is a fork of city96/ComfyUI-GGUF with Ideogram 4 support. If you have city96 version installed, remove it first (same folder name causes conflicts).

Installation steps:

1. Stop ComfyUI

2. Delete the entire ComfyUI-GGUF folder in custom_nodes (not just the files inside it)

3. Clone: git clone https://github.com/molbal/ComfyUI-GGUF.git

4. Restart ComfyUI

💡 Optional: If you encounter any import errors, install requirements:

pip install -r ComfyUI-GGUF/requirements.txt

✅ That's it! This single extension handles both UNET and CLIP loading.

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📊 Choosing the Right Quantization

The HuggingFace repository includes helpful charts for:

- Inference Speed: How fast each quant generates images

- Memory Usage: How much VRAM each quant requires

General Guidelines:

- Q4_0: Lowest VRAM (~5.5 GB), fastest, good quality

- Q5_0/Q5_1: Balanced VRAM (~6.8-7.3 GB), better quality

- Q8_0: Highest VRAM (~10 GB), best quality, slower

💡 Check the charts in the repository for detailed comparisons!

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🗒️ JSON Prompting (Optional)

Ideogram 4 works best with structured JSON prompts. This workflow uses Gemma 4 to automatically convert your natural language prompts into JSON format for:

- ✅ Better text rendering and typography

- ✅ More accurate composition control

- ✅ Consistent results across generations

Just write a simple prompt like "A poster for a coffee shop" and let Gemma 4 handle the rest!

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💡 Quick Tips

- For TurboTime LoRA (v2.0): Use only ideogram4-transformer-q4_0.gguf (no unconditional model needed)
- TurboTime LoRA: Download from: https://huggingface.co/ostris/ideogram_4_turbotime_lora

- For standard mode (v1.0): Use both main and unconditional models

- CFG Settings: 0.0 for TurboTime, 7.0 for standard mode

- Scheduler: mu=0.5, std=1.75 for TurboTime | mu=0.0, std=1.5 for standard

- Quantization: Q4_0 for lowest VRAM, Q8_0 for best quality

🙏 Credits

- Ideogram 4 Model: [Ideogram AI] https://ideogram.ai/

- GGUF Conversion & Custom Node: [molbal] https://github.com/molbal/ComfyUI-GGUF

- TurboTime LoRA: [ostris] https://huggingface.co/ostris/ideogram_4_turbotime_lora

- Workflow: [dvdufo] https://civitai.com/user/dvdufo