If you want to run Ideogram 4 locally, you may have seen terms such as Ideogram 4 GGUF, Q4, Q5, Q8, ComfyUI, FP8, and NF4.
The important thing to know is that these are not all the same thing.
Ideogram officially released Ideogram 4 as an open-weight text-to-image model with FP8 and NF4 weights. Community developers have also converted the model to GGUF for local tools such as ComfyUI and stable-diffusion.cpp.
This guide explains where to download Ideogram 4 GGUF, what Q4/Q5/Q8 mean, how to use GGUF with ComfyUI, how stable-diffusion.cpp fits into the picture, what hardware you need, and which format may be best for your setup.
Quick Answer
Ideogram 4 GGUF is a community-converted version of the official Ideogram 4 model.
Ideogram's official repositories provide FP8 and NF4 versions. GGUF files are third-party conversions intended for compatible local software.
For ComfyUI, one current option is the Ideogram 4 GGUF repository on Hugging Face, which provides separate main and unconditional transformer files in Q4_0, Q4_1, Q5_0, Q5_1 and Q8_0. For stable-diffusion.cpp, the leejet/ideogram-4-GGUF repository provides GGUF files converted for use with stable-diffusion.cpp.
What Is Ideogram 4?
Ideogram 4 is Ideogram's first open-weight text-to-image model.
The official model card describes it as a 9.3-billion-parameter foundation model trained from scratch rather than a fine-tune of an existing model. It introduces structured JSON prompting, multilingual text rendering, layout controls, color-palette controls and native 2K image generation.
The official models are available through Hugging Face:
The official model repositories are gated, so users need to accept the applicable terms before accessing the model files.
What Is Ideogram 4 GGUF?
GGUF is a model file format used for distributing quantized AI models.
Quantization reduces the precision used to store model weights. Lower-bit versions generally require less storage and can reduce memory requirements, although actual runtime memory depends on the complete workflow.
For Ideogram 4, GGUF is not an official Ideogram model format.
The distinction is simple:
- Ideogram 4 = the original model
- FP8/NF4 = official Ideogram weight formats
- GGUF = community-converted format for compatible local software
This distinction matters when downloading files.
You should always check the repository, base model, conversion method, required components and license before using a GGUF model.
Where to Download Ideogram 4 GGUF
There are several Ideogram 4 GGUF repositories on Hugging Face, so it is important to choose one designed for your software.
Ideogram 4 GGUF for ComfyUI
The molbal/ideogram-4-gguf repository provides quantized GGUF diffusion transformer weights converted from the original Ideogram 4 FP8 release.
It contains two types of files:
- ideogram4-transformer-*.gguf — the main diffusion transformer
- ideogram4-unconditional_transformer-*.gguf — the unconditional transformer used by CFG workflows
The repository specifically documents these files for use with ComfyUI GGUF loader nodes.
Ideogram 4 GGUF for stable-diffusion.cpp
Another useful repository is leejet/ideogram-4-GGUF on Hugging Face.
These files were converted using stable-diffusion.cpp, and the repository provides an example using sd-cli. The Ideogram 4 stable-diffusion.cpp guide also explains the required diffusion models, VAE and Qwen3-VL text/multimodal model.
Do not assume that a GGUF file made for one workflow automatically works with another.
Ideogram 4 GGUF Q4 vs Q5 vs Q8
The main reason users choose GGUF is the availability of different quantization levels.
In simple terms:
- Q4 = smaller files and lower precision
- Q5 = middle ground between size and precision
- Q8 = larger files with higher precision
The current ComfyUI-focused molbal/ideogram-4-gguf repository lists these approximate sizes for each transformer file:
| Quantization | Approx. size per file | General use |
|---|---|---|
| Q4_0 | 5.64 GB | Smallest option |
| Q4_1 | 6.21 GB | Lower-memory option |
| Q5_0 | 6.77 GB | Mid-range option |
| Q5_1 | 7.33 GB | Balanced option |
| Q8_0 | 10.14 GB | Highest precision in this repository |
These are individual file sizes, not total VRAM requirements.
For example, a workflow that loads both a main transformer and an unconditional transformer requires more storage and memory than one file alone.
The repository documents several pairings, including:
- Q4_0 + Q4_0
- Q5_0 + Q4_1
- Q5_1 + Q4_1
- Q8_0 + Q4_1
- Q8_0 + Q5_1
- Q8_0 + Q8_0
The repository describes Q5_1 + Q4_1 as a balanced quality-and-size option.
Which Ideogram 4 GGUF should you choose?
If storage and memory are your biggest limitations, start with a Q4 configuration.
If you want a practical balance, Q5_1 + Q4_1 is one documented option.
If you have substantially more memory and want higher precision, consider a Q8 pairing.
These recommendations apply to the current molbal repository. Other GGUF repositories may use different quantization names, pairings or runtime requirements.
What Do You Need to Run Ideogram 4 GGUF?
A GGUF transformer file is not a complete Ideogram 4 installation.
Depending on the workflow, you may need:
- A compatible GPU
- Sufficient VRAM
- System RAM
- SSD storage
- ComfyUI or stable-diffusion.cpp
- GGUF-compatible loader/support
- Main Ideogram 4 transformer
- Unconditional transformer
- Text or multimodal encoder
- VAE
- Compatible workflow or command
The current ComfyUI GGUF repository explicitly says its GGUF files are not a complete standalone Ideogram 4 package and that additional runtime assets, including encoder components and a VAE, are required.
The stable-diffusion.cpp documentation similarly lists an Ideogram 4 model, unconditional model, VAE and Qwen3-VL-8B-Instruct model as part of the setup.
Can Ideogram 4 GGUF Run on 8GB VRAM?
Some configurations can run on 8GB GPUs, but 8GB should not be treated as a universal minimum requirement.
The current ComfyUI GGUF repository reports measurements made using an RTX 3080 Laptop GPU with 8GB VRAM and 48GB system RAM. It also warns that actual memory usage and speed vary according to the workflow, image size, sampler settings, ComfyUI version and other loaded models.
A ComfyUI GGUF development report also documents Ideogram GGUF testing on an RTX 3080 Laptop GPU with 8GB VRAM.
So, if you have 8GB VRAM, Ideogram 4 GGUF may be possible, but your complete system matters.
A smaller quantization, CPU/RAM offloading and a carefully configured workflow can make a major difference.
Do not confuse the size of a GGUF file with the total VRAM required to generate an image.
How to Use Ideogram 4 GGUF in ComfyUI
The exact setup depends on the repository you choose, but the general process is:
1. Install or update ComfyUI
Start with a current ComfyUI installation.
For the official model route, ComfyUI also provides a repackaged Ideogram 4 model repository with the required model components. You can find it at Comfy-Org/Ideogram-4 on Hugging Face.
2. Install GGUF support
For the community GGUF route, install a compatible ComfyUI GGUF implementation.
The current ComfyUI-GGUF repository supports Ideogram GGUF models, and the Ideogram GGUF model card instructs users to install the corresponding custom nodes.
3. Download the GGUF files
For the molbal repository, choose a main transformer and an unconditional transformer.
For example:
and
ideogram4-unconditional_transformer-q4_1.gguf
The repository documents the available combinations.
4. Put the models in the correct folder
The current repository recommends placing the GGUF files in a ComfyUI diffusion-model directory such as:
It also documents ComfyUI/models/unet/ as another supported location.
5. Install the supporting models
You may also need the appropriate encoder and VAE.
Do not assume that the two GGUF transformer files contain everything required.
6. Load a compatible workflow
Use a workflow designed specifically for Ideogram 4 GGUF.
A normal Stable Diffusion workflow is not automatically compatible with Ideogram 4.
7. Generate your image
Load the models, enter your prompt and run the workflow.
If the workflow uses Ideogram 4's structured prompting system, the prompt may be converted into a structured JSON caption before image generation.
Ideogram 4 GGUF Workflow Explained
A simplified ComfyUI workflow looks like this:
↓
Text / Multimodal Encoder
↓
Ideogram 4 Main Transformer + Unconditional Transformer
↓
Sampling
↓
VAE
↓
Generated Image
The actual workflow can contain additional nodes and settings.
Ideogram 4's official model card describes a structured JSON prompting interface with controls for layout, colors and other image properties.
The official model also uses a prompt-rewriting or “Magic Prompt” approach when a normal text prompt is supplied through its CLI workflow.
Ideogram 4 GGUF with stable-diffusion.cpp
If you do not want to use ComfyUI, stable-diffusion.cpp is another local option.
The project added Ideogram 4 support in June 2026 and supports GGUF model files.
The official Ideogram 4 stable-diffusion.cpp guide documents the required components and example commands.
The leejet/ideogram-4-GGUF repository also provides Q4_0 main and unconditional GGUF files specifically for this workflow.
A typical setup includes:
- Ideogram 4 diffusion GGUF
- Ideogram 4 unconditional GGUF
- Qwen3-VL-8B-Instruct GGUF
- VAE
- sd-cli
This makes stable-diffusion.cpp a different route from ComfyUI rather than simply another GGUF loader.
Ideogram 4 GGUF vs FP8 vs NF4
Here is the simplest comparison:
| Format | Official Ideogram format? | Main purpose |
|---|---|---|
| FP8 | Yes | Official Ideogram 4 weights |
| NF4 | Yes | Official lower-precision format |
| GGUF Q4 | No | Community local conversion |
| GGUF Q5 | No | Community balance of size and precision |
| GGUF Q8 | No | Higher-precision community conversion |
The official Ideogram model card lists both FP8 and NF4 as 9.3B versions. It lists NF4 with CUDA and Diffusers support and FP8 with broader hardware support.
If you want the official model route, start with FP8 or NF4.
If your workflow specifically requires GGUF, use a compatible community conversion.
Official Ideogram 4 vs Community GGUF
There are three useful local routes to understand.
Official Ideogram route
Use the official Ideogram 4 FP8 or NF4 model.
ComfyUI GGUF route
Use a community GGUF conversion together with compatible ComfyUI GGUF nodes.
stable-diffusion.cpp route
Use the leejet GGUF conversion with stable-diffusion.cpp.
These routes should not be mixed without checking compatibility.
Common Ideogram 4 GGUF Problems
GGUF does not appear in ComfyUI
Check the model directory and make sure the GGUF custom nodes are installed correctly.
The unconditional model is missing
Some Ideogram 4 workflows require both the main and unconditional transformer.
Download the matching pair recommended by your repository.
ComfyUI runs out of memory
Try a smaller quantization or a lower-memory pairing.
Remember that total runtime memory is larger than the file size of a single GGUF model.
Encoder or VAE is missing
Download the additional components specified by the workflow.
The model loads but generation fails
Check that your ComfyUI version, GGUF nodes, workflow and model conversion are compatible.
stable-diffusion.cpp reports a model error
Check that the Qwen3-VL text/multimodal model, VAE and both Ideogram diffusion models are present.
Also verify downloaded files if you suspect an incomplete or corrupted model download. The stable-diffusion.cpp documentation and issue tracker contain examples of setup problems involving missing or invalid text-encoder files.
Is Ideogram 4 GGUF Worth Using?
For users interested in local AI image generation, yes, Ideogram 4 GGUF is worth exploring.
The main advantages are:
- Local image generation
- Multiple quantization choices
- ComfyUI support
- stable-diffusion.cpp support
- More control over the generation workflow
- Ability to choose a model configuration that fits your hardware
There are also disadvantages:
- Setup is more technical
- Model files are large
- Multiple components may be required
- Hardware limitations matter
- Community conversions require compatibility checks
- Troubleshooting may be necessary
If you are new to local AI, the official Ideogram 4 route may be easier to understand.
If you specifically want GGUF, ComfyUI or stable-diffusion.cpp, community conversions provide useful alternatives.
Frequently Asked Questions
Is Ideogram 4 GGUF official?
No. Ideogram officially provides Ideogram 4 in FP8 and NF4. GGUF files are community conversions of the model.
Where can I download Ideogram 4 GGUF?
For ComfyUI, see the molbal Ideogram 4 GGUF repository
For stable-diffusion.cpp, see the leejet Ideogram 4 GGUF repository
What does Q4 mean in Ideogram 4 GGUF?
Q4 generally means a lower-bit quantized version. It produces smaller model files than Q5 or Q8 versions.
What is the difference between Ideogram 4 Q4 and Q8?
Q4 files are smaller and generally more memory-friendly. Q8 files are substantially larger and retain higher precision.
Can Ideogram 4 GGUF run on 8GB VRAM?
Some community configurations have been tested on 8GB GPUs, but 8GB is not a universal minimum or guarantee. Actual requirements depend on the complete workflow, quantization, image size, system RAM and other loaded models.
Can I use Ideogram 4 GGUF with ComfyUI?
Yes. Community GGUF repositories provide Ideogram 4 files for ComfyUI, and compatible GGUF custom nodes are available.
Can I use Ideogram 4 GGUF with stable-diffusion.cpp?
Yes. stable-diffusion.cpp added Ideogram 4 support and documents the required model components and commands.
Is Ideogram 4 open source?
Ideogram describes Ideogram 4 as its first open-weight text-to-image model. Do not automatically describe it as an unrestricted open-source model because its model license contains specific restrictions.
Is Ideogram 4 GGUF free for commercial use?
Do not assume so.
The official Ideogram 4 license is a Non-Commercial Model Agreement. The agreement defines permitted non-commercial purposes and places restrictions on commercial use. Community GGUF repositories state that their files follow the original model's license. Read the current license before using the model or its derivatives commercially.
Final Takeaway
Ideogram 4 GGUF is a practical option for users who want to experiment with local Ideogram 4 image generation.
The most important distinction is between the original model and its community conversions.
Ideogram officially provides FP8 and NF4 weights. GGUF versions are community conversions designed for compatible local tools.
For ComfyUI, the current molbal/ideogram-4-gguf repository provides Q4_0, Q4_1, Q5_0, Q5_1 and Q8_0 versions for the main and unconditional transformers.
For stable-diffusion.cpp, the leejet/ideogram-4-GGUF repository provides GGUF files converted for that software.
Before downloading anything, check:
- Which software you are using
- Which GGUF repository you are using
- Which Q4/Q5/Q8 quantization fits your hardware
- Whether you need both main and unconditional models
- Which encoder and VAE are required
- Whether your GPU and system RAM are sufficient
- What the current license allows
If you want the official route, start with Ideogram's FP8 or NF4 models.
If you specifically want a GGUF workflow, choose the conversion designed for your software and follow its documentation rather than mixing files from unrelated repositories.
That approach gives you the best chance of getting Ideogram 4 running locally without unnecessary trial and error.
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