<h1 id="ideogram-4-gguf-workflow-for-comfyui">Ideogram 4 GGUF Workflow for ComfyUI</h1>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---🆕 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---📦 Required Models1. UNET Models → ComfyUI/models/unet/Download unet files from HuggingFace (Recommended):- Repository: <a target="_blank" rel="ugc" href="https://huggingface.co/molbal/ideogram-4-gguf">https://huggingface.co/molbal/ideogram-4-gguf</a>- Main Model (choose desired quantized version):- <code>ideogram4-transformer-q4_0.gguf</code> (5.64 GB) - ⭐ Best for low VRAM- <code>ideogram4-transformer-q4_1.gguf</code> (6.21 GB)- <code>ideogram4-transformer-q5_0.gguf</code> (6.77 GB)- <code>ideogram4-transformer-q5_1.gguf</code> (7.33 GB)- <code>ideogram4-transformer-q8_0.gguf</code> (10.1 GB) - Best quality- Unconditional Model (not needed in workflow v2.0):- <code>ideogram4-unconditional_transformer-q4_0.gguf</code> (5.64 GB)- <code>ideogram4-unconditional_transformer-q4_1.gguf</code> (6.21 GB)- <code>ideogram4-unconditional_transformer-q5_0.gguf</code> (6.77 GB)- <code>ideogram4-unconditional_transformer-q5_1.gguf</code> (7.33 GB)- <code>ideogram4-unconditional_transformer-q8_0.gguf</code> (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: <a target="_blank" rel="ugc" href="https://civarchive.com/models/2681714/ideogram-4-gguf">https://civarchive.com/models/2681714/ideogram-4-gguf</a>- Note: File names may differ slightly from HuggingFace2. Text Encoder → ComfyUI/models/clip/- Qwen3-8B-Q4_K_M.gguf- Source: <a target="_blank" rel="ugc" href="https://huggingface.co/Qwen/Qwen3-8B-GGUF">https://huggingface.co/Qwen/Qwen3-8B-GGUF</a>3. Prompt Enhancer (optional) → ComfyUI/models/clip/- gemma4_e4b_it_fp8_scaled.safetensors- Source: <a target="_blank" rel="ugc" href="https://huggingface.co/Comfy-Org/gemma-4">https://huggingface.co/Comfy-Org/gemma-4</a>- Automatically converts natural language to JSON format4. VAE → ComfyUI/models/vae/- flux2-vae.safetensors- Download: <a target="_blank" rel="ugc" href="https://huggingface.co/Comfy-Org/flux2-dev/resolve/main/split_files/vae/flux2-vae.safetensors">https://huggingface.co/Comfy-Org/flux2-dev/resolve/main/split_files/vae/flux2-vae.safetensors</a>---⚙️ Custom Node RequiredSingle GGUF custom node by <code>molbal</code> (for both UNET and CLIP)- Repository: [molbal/ComfyUI-GGUF](<a target="_blank" rel="ugc" href="https://github.com/molbal/ComfyUI-GGUF">https://github.com/molbal/ComfyUI-GGUF</a>)- Install: <code>git clone https://github.com/molbal/ComfyUI-GGUF.git</code>- 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 ComfyUI2. Delete the entire <code>ComfyUI-GGUF</code> folder in custom_nodes (not just the files inside it)3. Clone: <code>git clone https://github.com/molbal/ComfyUI-GGUF.git</code>4. Restart ComfyUI💡 Optional: If you encounter any import errors, install requirements:<pre><code>pip install -r ComfyUI-GGUF/requirements.txt</code></pre>✅ That's it! This single extension handles both UNET and CLIP loading.---📊 Choosing the Right QuantizationThe HuggingFace repository includes helpful charts for:- Inference Speed: How fast each quant generates images- Memory Usage: How much VRAM each quant requiresGeneral 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!---🗒️ 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 generationsJust write a simple prompt like "A poster for a coffee shop" and let Gemma 4 handle the rest!---💡 Quick Tips- For TurboTime LoRA (v2.0): Use only <code>ideogram4-transformer-q4_0.gguf</code> (no unconditional model needed) - TurboTime LoRA: Download from: <a target="_blank" rel="ugc" href="https://huggingface.co/ostris/ideogram_4_turbotime_lora">https://huggingface.co/ostris/ideogram_4_turbotime_lora</a>- 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] <a target="_blank" rel="ugc" href="https://ideogram.ai/">https://ideogram.ai/</a>- GGUF Conversion &amp; Custom Node: [molbal] <a target="_blank" rel="ugc" href="https://github.com/molbal/ComfyUI-GGUF">https://github.com/molbal/ComfyUI-GGUF</a>- TurboTime LoRA: [ostris] <a target="_blank" rel="ugc" href="https://huggingface.co/ostris/ideogram_4_turbotime_lora">https://huggingface.co/ostris/ideogram_4_turbotime_lora</a>- Workflow: [dvdufo] <a target="_blank" rel="ugc" href="https://civarchive.com/user/dvdufo">https://civarchive.com/user/dvdufo</a>

Ideogram 4 GGUF Workflow

## v1.0 - Initial GGUF WorkflowA simple workflow to test Ideogram 4 with GGUF quantized models on ComfyUI.Features:- Works with GGUF models (Q4_0 quantization)- Tested on RTX 3060 12GB with 16GB RAM- Uses Dual Model CFG Guider for proper conditioning- Includes automatic JSON prompting via Gemma 4Requirements:- ComfyUI (updated version)- GGUF models: ideogram4-Q4_0.gguf + ideogram4_uncond-Q4_0.gguf- Text encoder: Qwen3-8B-Q4_K_M.gguf- Custom node: molbalIf you want faster generation with TurboTime LoRA, check out v2.0! 😊

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