README.md

---
license: other
license_name: ideogram-4-non-commercial
license_link: https://huggingface.co/ideogram-ai/ideogram-4-fp8/blob/main/LICENSE.md
base_model:
- ideogram-ai/ideogram-4-fp8
pipeline_tag: text-to-image
tags:
  - text-to-image
  - image-generation
  - diffusion
  - flow-matching
  - dit
  - ideogram
  - quantization
  - gguf
  - comfyui
---

# Ideogram 4 GGUF

Quantized GGUF diffusion transformer weights for [Ideogram 4](https://huggingface.co/ideogram-ai/ideogram-4-fp8), converted from the original FP8 release for use with ComfyUI GGUF loader nodes.

This repository contains GGUF files for the two Ideogram 4 diffusion components:

- `ideogram4-transformer-*.gguf`: the main text-guided diffusion transformer.
- `ideogram4-unconditional_transformer-*.gguf`: the unconditional transformer used by CFG workflows.

These files are not a complete standalone Ideogram 4 package. Your workflow still needs the other runtime assets expected by Ideogram 4 in ComfyUI, such as the text or multimodal encoder components and VAE.

## ComfyUI Support

Use these models with the ComfyUI nodes from [molbal/ComfyUI-GGUF](https://github.com/molbal/ComfyUI-GGUF). Install that custom node repository into your ComfyUI `custom_nodes` folder, then restart ComfyUI.

Place the downloaded `.gguf` files in one of ComfyUI's diffusion model folders:

```text
ComfyUI/models/diffusion_models/
ComfyUI/models/unet/
```

Load the files with `Unet Loader (GGUF)` or `Unet Loader (GGUF/Advanced)` in an Ideogram 4 workflow that accepts separate main and unconditional diffusion models.

## Files

Each quant level is published for both the main transformer and the unconditional transformer.

| Quant | Main transformer | Unconditional transformer | Approx. size per file |
| --- | --- | --- | --- |
| Q4_0 | `ideogram4-transformer-q4_0.gguf` | `ideogram4-unconditional_transformer-q4_0.gguf` | 5.64 GB |
| Q4_1 | `ideogram4-transformer-q4_1.gguf` | `ideogram4-unconditional_transformer-q4_1.gguf` | 6.21 GB |
| Q5_0 | `ideogram4-transformer-q5_0.gguf` | `ideogram4-unconditional_transformer-q5_0.gguf` | 6.77 GB |
| Q5_1 | `ideogram4-transformer-q5_1.gguf` | `ideogram4-unconditional_transformer-q5_1.gguf` | 7.33 GB |
| Q8_0 | `ideogram4-transformer-q8_0.gguf` | `ideogram4-unconditional_transformer-q8_0.gguf` | 10.14 GB |

The main and unconditional models do not need to use the same quant level.

## Suggested Pairings

| Main transformer | Unconditional transformer | Notes |
| --- | --- | --- |
| `q8_0` | `q8_0` | Highest precision GGUF pair in this repo. |
| `q8_0` | `q5_1` | Keeps the main transformer high precision while reducing memory on the unconditional side. |
| `q8_0` | `q4_1` | Larger quality bias toward the main transformer with lower CFG-side memory. |
| `q5_1` | `q4_1` | Balanced quality and size. |
| `q5_0` | `q4_1` | Lower memory starting point. |
| `q4_0` | `q4_0` | Smallest available GGUF pair. |

## Inference Measurements

### Peak Memory

The chart below shows peak RAM and VRAM measured during Ideogram 4 inference with different main/unconditional quant pairings, including an NVFP4 baseline for comparison.

![Peak memory usage of GGUF quants and NVFP4](./FTu9T-peak-memory-usage-of-gguf-quants-and-nvfp4-.png)

### Relative Inference Speed

The chart below compares relative per-iteration inference speed across quant pairings, with the NVFP4 + NVFP4 run used as the 100% reference in the chart.

![Ideogram 4 inference speed among various quantization combinations](./XbvQQ-ideogram-4-inference-speed-among-various-quantization-combinations-.png)

Measurements were taken by `u/molbal` on Windows 11 with an AMD Ryzen 7 6800H CPU, 48 GB RAM, and an RTX 3080 Laptop GPU with 8 GB VRAM. Exact memory use and speed will vary by workflow, image size, sampler settings, ComfyUI version, and loaded auxiliary models.

## Download

Download the two files you want to pair from the Files tab, or use the Hugging Face CLI. For example:

```bash
huggingface-cli download molbal/ideogram-4-gguf ideogram4-transformer-q5_1.gguf ideogram4-unconditional_transformer-q4_1.gguf --local-dir ComfyUI/models/diffusion_models
```

## Compatibility Notes

These are non-K GGUF quantizations intended for PyTorch dequantization in ComfyUI. K-quants are not included because this ComfyUI loading path does not use fused quantized linear kernels.

If a workflow fails to load these files, update [molbal/ComfyUI-GGUF](https://github.com/molbal/ComfyUI-GGUF) and confirm that both the main and unconditional transformer files are present in a ComfyUI diffusion model folder.

## License

These files are derived from [ideogram-ai/ideogram-4-fp8](https://huggingface.co/ideogram-ai/ideogram-4-fp8) and follow the [Ideogram 4 non-commercial license](https://huggingface.co/ideogram-ai/ideogram-4-fp8/blob/main/LICENSE.md).