You must have been keeping up with the image/video generation models, you have probably noticed the explosion of different model variants.
No matter what Stable Diffusion WebUI (ForgeUI, ComfyUI, Automatic1111, Fooocus etc) you are using sometimes, it is getting a bit confusing in selecting which one to choose. Between these model formats, good old GGUF, and various quantization options.
We have been digging, researched and sharing the experiences to help you clear things up.
Table of Contents:
Different Diffusion based Model formats
There are multiple formats out there in the open source market. But you can get the best suitable setup for your art generation.
(a) The base model
(b) FP16 (Full Precision)
Think of FP16 as the "no compromises" option. It is the full 16-bit floating point precision that serves as our gold standard for quality. This is suitable for professionals who do not want their result to be effected by even minimal.
The catch is that it's hungry for VRAM. You will typically need 8GB or more to run these comfortably. But if you have got the hardware, many users swear this is still the way to go.
(c) GGUF (GPT-Generated Unified Format)
GGUF started in the LLM world but has become a staple for Stable Diffusion too. What's good thing about GGUF is its flexibility.
Means you can choose your level of compression (Q4_K, Q5_K, Q8_0, etc.) depending on your hardware constraints that suits most. It's the Swiss Army knife of model formats with broad compatibility across different setups.
Many of the developers like City96, Kijai share their quantized models on their github and Hugging Face repositories. You can find by accessing their public repositories.
The range generally goes from Q2 to Q8. Q8 will give you more precision with detailed result but also consumes more VRAM whereas Q2 generates with lower quality, faster and the VRAM consumption is comparatively lower.
(d) FP8 (8-bit Floating Point)
Generally you will observe these model with Fux. FP8 format is making waves by cutting precision in half compared to the FP16, but with some clever optimizations that preserve quality surprisingly well.
If you are running newer NVIDIA GPUs like 4000 series, this might be your sweet spot between quality and efficiency.
(e) NF4 (4-bit Normal Float)
For those of you who trying to squeeze impressive images out of modest hardware, NF4 is the great option. This 4-bit quantization reduces memory requirements to its lowest.
The images won't win any pixel-peeping contests, but they are totally usable for many purposes if its for general use cases. NF4 is part of the BitsAndBytes (BNB) quantization approach and excels at making large models run on limited hardware.
Comparison Table- GGUF, FP8, FP16, NF4
| Feature | FP16 | FP8 | GGUF Q8_0 | GGUF Q5_K | NF4 |
|---|---|---|---|---|---|
| Bit Precision | 16-bit | 8-bit | 8-bit | 5-bit | 4-bit |
| VRAM Usage | Highest | Medium | Medium-High | Low-Medium | Lowest |
| Image Quality | Reference (Highest) | Very High (95-98% of FP16) | High (90-95% of FP16) | Good (85-90% of FP16) | Acceptable (75-85% of FP16) |
| Generation Speed | Fast on high-end GPUs | Fast on newer GPUs | Medium | Medium-Fast | Variable (hardware dependent) |
| Recommended VRAM (Minimum) | 8GB+ | 6GB+ | 6GB+ | 4GB+ | 3GB+ |
| Best For | Final renders, Quality-critical work | Balance of quality and efficiency | General purpose | Limited VRAM scenarios | Highly constrained hardware |
| CLIP Encoder Speed | Standard | Optimized (Flux) | Standard | Standard | Standard |
| Hardware Optimization | RTX 3000/4000 series | RTX 3000/4000 series | Broad compatibility | Broad compatibility | Specialized |
| File Size | Largest | Medium | Medium | Smaller | Smallest |
| XL Model Support on 8GB VRAM | No | Limited | Limited | Yes | Yes |
| Quality Degradation | None | Minimal | Slight | Moderate | Noticeable but usable |
| Community Adoption | High | Growing | High | High | Medium |
