Ideogram 4 GGUF Workflow

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 (and 32GB Virtual 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 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 --- ⚙️ 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. --- 📊 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! --- 🗒️ 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! --- 💡 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 --- 💻 System Requirements Minimum: - GPU: 8GB+ VRAM (12GB recommended) - RAM: 16GB+ (32GB recommended) - Virtual Memory (Pagefile): 32GB+ ⚠️ Important: If you experience crashes during VAE decode, make sure your Windows Virtual Memory (Pagefile) is set to at least 32GB: - Settings → System → About → Advanced system settings → Performance → Settings → Advanced → Virtual memory - Set to 32GB or more This is especially important for systems with 16GB RAM running GGUF models! --- 💡 TurboTime LoRA Tips Steps Guide (with TurboTime Lora): - 2 steps: Fastest (~15s), good for quick tests and drafts - 4-6 steps: Good balance (~30s), decent quality for iterations - 12-20 steps: ⭐ Recommended! Best quality/speed ratio (~1.5 min) - 30-50 steps: Maximum quality (~3-4 min), all fine details rendered - 50+ steps: Marginal improvement over 30, not worth the extra time Unlike other Turbo LoRAs (Flux/SDXL), increasing steps does NOT overcook the image with TurboTime Lora! You can safely use 20+ steps for better anatomy and details. Pro Tip: If your prompt includes fine details (flowers, jewelry, text), use at least 20 steps for them to appear correctly. --- 🙏 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